METHODS AND COMPOSITIONS FOR THE DIAGNOSIS, PROGNOSIS AND TREATMENT OF ACUTE MYELOID LEUKEMIA

Information

  • Patent Application
  • 20150031641
  • Publication Number
    20150031641
  • Date Filed
    March 11, 2013
    11 years ago
  • Date Published
    January 29, 2015
    10 years ago
Abstract
Gene mutations are associated with the progression of acute myeloid leukemia (AML). The invention relates to methods and systems for evaluating the progression of AML based on these gene mutations. The present invention also relates to methods and compositions for treating AML patients by modulating the expression or activity of certain genes involved in AML progression and/or their encoded proteins. The invention further relates to methods and compositions for determining the responsiveness of an AML patient to induction chemotherapy therapy.
Description
SEQUENCE LISTING

The instant application contains a Sequence Listing, in computer readable form that is hereby incorporated by reference in its entirety into the present disclosure. The sequence listing file, created on Mar. 7, 2013 and updated on Sep. 10, 2014 is named 3314022A_SequenceListing.txt and is 73.6 KB in size.


FIELD OF INVENTION

The invention described herein relates to methods useful in the diagnosis, treatment and management of cancers. The field of the present invention is molecular biology, genetics, oncology, clinical diagnostics, bioinformatics. In particular, the field of the present invention relates to the diagnosis, prognosis and treatment of blood cancer.


BACKGROUND OF THE INVENTION

The following description of the background of the invention is provided simply as an aid in understanding the invention and is not admitted to describe or constitute prior art to the invention.


After cardiovascular disease, cancer is the leading cause of death in the developed world. In the United States alone, over one million people are diagnosed with cancer each year, and over 500,000 people die each year as a result of it. It is estimated that 1 in 3 Americans will develop cancer during their lifetime, and one in five will die from cancer. Further, it is predicted that cancer may surpass cardiovascular diseases as the number one cause of death within 5 years. As such, considerable efforts are directed at improving treatment and diagnosis of this disease.


Most cancer patients are not killed by their primary tumor. They succumb instead to metastases: multiple widespread tumor colonies established by malignant cells that detach themselves from the original tumor and travel through the body, often to distant sites. In the case of blood cancers, there are four types depending upon the origin of the affected cells and the course of the disease. The latter criterion classifies the types into either acute or chronic. The former criterion further divides the types as lymphoblastic or lymphocytic leukemias and myeloid or myelogenous leukemias. These malignancies have varying prognoses, depending on the patient and the specifics of the condition.


Blood primarily consists of red blood cells (RBC), white blood cells (WBC) and platelets. The red blood cells' function is to carry oxygen to the body, the white blood cells protect our body, and platelets help clot the blood after injury. Irrespective of the types of the disease, any abnormality in these cell types leads to blood cancer. The main categories of blood cancer include Acute Lymphocytic or Lymphoblastic Leukemias (ALL), Chronic Lymphocytic or Lymphoblastic Leukemias (CLL), Acute Myelogenous or Myeloid Leukemias (AML), and Chronic Myelogenous or Myeloid Leukemias (CML).


In the case of leukemia, the bone marrow and the blood itself are attacked, such that the cancer interferes with the body's ability to make blood. In the patient, this most commonly manifests itself in the form of fatigue, anemia, weakness, and bone pain. It is diagnosed with a blood test in which specific types of blood cells are counted. Treatment for leukemia usually includes chemotherapy and radiation to kill the cancer, and measures like stem cell transplants are sometimes required. As outlined above, there are several different types of leukemia, with myeloid leukemia being usually subdivided into two groups: Acute Myeloid Leukemia (AML) and Chronic Myeloid Leukemia (CML).


AML is characterized by an increase in the number of myeloid cells in the marrow and an arrest in their maturation, frequently resulting in hematopoietic insufficiency. In the United States, the annual incidence of AML is approximately 2.4 per 100,000 and it increases progressively with age to a peak of 12.6 per 100,000 adults 65 years of age or older. Despite improved therapeutic approaches, prognosis of AML is very poor around the globe. Even in the United States, the five-year survival rate among patients who are less than 65 years of age is less than 40%. During approximately the last decade this value was 15. Similarly, the prognosis of CML is also very poor in spite of advancement of clinical medicine.


Acute myeloid leukemia (AML) is a heterogeneous disorder that includes many entities with diverse genetic abnormalities and clinical features. The pathogenesis has only been fully delineated for relatively few types of leukemia. Patients with intermediate and poor risk cytogenetics represent the majority of AML; chemotherapy based regimens fail to cure most of these patients, and stem cell transplantation is frequently the treatment choice. Since allogeneic stem cell transplantation is not an option for many patients with high risk leukemia, there is a need to improve our understanding of the biology of these leukemias and to develop improved therapies.


Since not enough is known of the etiology, cell physiology and molecular genetics of acute myeloid leukemia, the development of effective new agents and novel treatment and/or prognostic methods against myeloid leukemia, and in particular acute myeloid leukemia, is a major focal point today in translational oncology research. However, there are inherent difficulties in the diagnosis and treatment of cancer including, among other things, the existence of many different subgroups of cancer and the concomitant variation in appropriate treatment strategies to maximize the likelihood of positive patient outcome.


One relatively new approach is to investigate the genetic profile of cancer, an effort aimed at identifying perturbations in genes that lead to the malignant phenotype. These gene profiles, including gene expression and mutations, provide valuable information about biological processes in normal and disease cells. However, cancers differ widely in their genetic “signature,” leading to difficulty in diagnosis and treatment, as well as in the development of effective therapeutics.


Increasingly, genetic signatures are being identified and exploited as tools for disease detection as well as for prognosis and prospective assessment of therapeutic success. Genetic profiling of cancers, including leukemias, may provide a more effective approach to cancer management and/or treatment. In the context of the present invention, specific genes and gene products, and groups of genes and their gene products, involved in progression of meyoloblasts into a malignant phenotype is still largely unknown. As such, there is a great need in the art to better understand the genetic profile of acute myeloid leukemia, in an effort to provide improved therapeutics, and tools for the treatment, therapy and diagnosis of acute myeloid leukemia and other cancers of the blood. There is a great need for improved methods for diagnosing acute myeloid leukemia and for determining the prognosis of patients afflicted by this disease.


SUMMARY OF THE INVENTION

One aspect of the present disclosure is a method of predicting survival of a patient with acute myeloid leukemia, said method comprising: analyzing a genetic sample isolated from the patient for the presence of cytogenetic abnormalities and a mutation in at least one of FLT3, NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, MLL-PTD, ASXL1, PHF6, KRAS, PTEN, P53, HRAS, and EZH2 genes; and (i) predicting poor survival of the patient if a mutation is present in at least one of FLT3, MLL-PTD, ASXL1 and PHF6 genes, or (ii) predicting favorable survival of the patient if a mutation is present in IDH2R140 and/or a mutation is present in CEBPA. In one embodiment, the method further comprises, predicting intermediate survival of the patient with cytogenetically-defined intermediate risk AML if: (i) no mutation is present in any of FLT3-ITD, TET2, MLL-PTD, DNMT3A, ASXL1 or PHF6 genes, (ii) a mutation in CEBPA is present in the presence of a FLT3-ITD mutation, or (iii) a mutation is present in FLT3-ITD but trisomy 8 is absent. In another embodiment, the method further comprises predicting unfavorable survival of the patient if (i) a mutation in TET2, ASXL1, or PHF6 or an MLL-PTD is present in a patient without the FLT3-ITD mutation, or (ii) the patient has a FLT3-ITD mutation and a mutation in TET2, DNMT3A, MLL-PTD or trisomy 8.


Unless context demands otherwise, in this and any other aspect of the invention, the mutation may be any one of those described in the Table below entitled “Specific somatic mutations identified in the sequencing of 18 genes in AML patients, and the nature of these mutations”.


In one embodiment, the sample is DNA and it is extracted from bone marrow or blood from the patient. The extraction may be historical, and in all embodiments herein the sample may be utilized in the invention as a previously provided sample i.e. the extraction or isolation is not part of the method per se. In a related embodiment, the genetic sample is DNA isolated from mononuclear cells (MNC) from the patient. In one embodiment, poor or unfavorable survival of the patient is survival of less than or equal to about 10 months. In another embodiment, intermediate survival the patient is survival of about 18 months to about 30 months. In another embodiment, favorable survival of the patient is survival of about 32 months or more.


In one aspect, the present disclosure is a method of predicting survival of a patient with acute myeloid leukemia, said method comprising, assaying a genetic sample from the patient's blood or bone marrow for the presence of a mutation in at least one of genes FLT3, NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, MLL-PTD, ASXL1, PHF6, KRAS, PTEN, P53, HRAS, and EZH2 in said sample; and predicting a poor survival of the patient if a mutation is present in at least one of genes FLT3-ITD, MLL-PTD, ASXL1, PHF6; or predicting a favorable survival of the patient if a mutation is present in CEBPA or a mutation is present in IDH2 at R140. In one embodiment, the patient is characterized as intermediate-risk on the basis of cytogenetic analysis.


In one embodiment, amongst patients with cytogenetically-defined intermediate-risk acute myeloid leukemia who have FLT3-ITD mutation, at least one of the following: trisomy 8 or a mutation in TET2, DNMT3A, or the MLL-PTD are associated with an adverse outcome and poor overall survival of the patient. In another embodiment, amongst patients with cytogenetically-defined intermediate-risk acute myeloid leukemia who have a mutation in FLT3-ITD gene, a mutation in CEBPA gene is associated with improved outcome and overall survival of the patient. In one embodiment, in a cytogenetically-defined intermediate risk AML patient with both IDH1/IDH2 and NPM1 mutations, the overall survival is improved compared to NPM1-mutant patients wild-type for both IDH1 and IDH2. In one embodiment, amongst patients acute myeloid leukemia, IDH2R140 mutations are associated with improved overall survival. Poor or unfavorable survival (adverse risk) of the patient, in one example, is survival of less than or equal to about 10 months. Favorable survival of the patient, in one example, is survival of about 32 months or more.


One aspect of the present disclosure is a method of predicting survival of a patient with acute myeloid leukemia, said method comprising assaying a genetic sample from the patient's blood or bone marrow for the presence of a mutation in genes ASXL1 and WT1; and determining the patient has or will develop primary refractory acute myeloid leukemia if mutated ASXL1 and WT1 genes are detected.


Another aspect of the present disclosure is a method of determining responsiveness of a patient with acute myeloid leukemia to high dose therapy, said method comprising analyzing a genetic sample isolated from the patient for the presence of a mutation in genes DNMT3A, and NPM1, and for the presence of a MLL translocation; and (i) identifying the patient as one who will respond to high dose therapy if a mutation in DNMT3A or NPM1 or an MLL translocation are present, or (ii) identifying the patient as one who will not respond to high dose therapy in the absence of mutations in DNMT3A or NPM1 or an MLL translocation.


In one embodiment, the therapy comprises the administration of anthracycline. In one example, the anthracycline is selected from the group consisting of Daunorubicin, Doxorubicin, Epirubicin, Idarubicin, Mitoxantrone, and Adriamycin. In a particular example, the anthracycline is Daunorubicin. In one embodiment, the high dose administration is Daunorubicin administered at 60 mg per square meter of body-surface area (60 mg/m2), or higher, daily for three days. In a particular embodiment, the high dose administration is Daunorubicin administered at about 90 mg per square meter of body-surface area (90 mg/m2), daily for three days. In one embodiment, the high dose daunorubicin is administered at about 70 mg/m2 to about 140 mg/m2. In a particular embodiment, the high dose daunorubicin is administered at about 70 mg/m2 to about 120 mg/m2. In a related embodiment, this high dose administration is given each day for three days, that is for example a total of about 300 mg/m2 over the three days (3×100 mg/m2). In another example, this high dose is administered daily for 2-6 days. In other clinical situations, an intermediate daunorubicin dose is administered. In one embodiment, the intermediate dose daunorubicin is administered at about 60 mg/m2. In one embodiment, the intermediate dose daunorubicin is administered at about 30 mg/m2 to about 70 mg/m2. Additionally, the related anthracycline idarubicin, in one embodiment, is administered at from about 4 mg/m2 to about 25 mg/m2. In one embodiment, the high dose idarubicin is administered at about 10 mg/m2 to 20 mg/m2. In one embodiment, the intermediate dose idarubicin is administered at about 6 mg/m2 to about 10 mg/m2. In a particular embodiment, idarubicin is administered at a dose of about 8 mg/m2 daily for five days. In another example, this intermediate dose is administered daily for 2-10 days.


In one aspect, the present disclosure is a method of predicting whether a patient suffering from acute myeloid leukemia will respond better to high dose chemotherapy than to standard dose chemotherapy, the method comprising: obtaining a DNA sample obtained from the patient's blood or bone marrow; determining the mutational status of genes DNMT3A and NPM1, and the presence of a MLL translocation; and predicting that the subject will be more responsive to high dose chemotherapy than standard dose chemotherapy where the sample is positive for a mutation in DNMT3A or NPM1 or an MLL translocation, or predicting that the subject will be non-responsive to high dose chemotherapy compared to standard dose chemotherapy where the sample is wild type with no mutations in DNMT3a or NPM1 genes and no translocation in MLL.


One aspect of the present disclosure is a method of screening a patient with acute myeloid leukemia for responsiveness to treatment with high dose of Daunorubicin or a pharmaceutically acceptable salt, solvate, or hydrate thereof, comprising: obtaining a genetic sample comprising an acute myeloid leukemic cell from said individual; and assaying the sample and detecting the presence of a mutation in DNMT3A or NPM1 or an MLL translocation; and correlating a finding of a mutation in DNMT3A or NPM1 or an MLL translocation, as compared to wild type controls where there is no mutation, with said acute myeloid leukemia patient being more sensitive to high dose treatment with Daunorubicin or a pharmaceutically acceptable salt, solvate, or hydrate thereof. In one embodiment, the method further comprises predicting the patient is at a lower risk of relapse of acute myeloid leukemia following chemotherapy if a mutation in DNMT3A or NPM1 or an MLL translocation is detected.


Another aspect of the present disclosure is a method of determining whether a human has an increased genetic risk for developing or developing a relapse of acute myeloid leukemia, comprising, analyzing a genetic sample isolated from the human's blood or bone marrow for the presence of a mutation in at least one gene from FLT3, NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, MLL-PTD, ASXL1, PHF6, KRAS, PTEN, P53, HRAS, and EZH2; and determining the individual with cytogenetically-defined intermediate risk AML has an increased genetic risk for developing or developing a relapse of acute myeloid leukemia, relative to a control human with no such gene mutations in said genes, when: (i) a mutation in at least one of TET2, MLL-PTD, ASXL1 and PHF6 genes is detected when the patient has no FLT3-ITD mutation, or (ii) a mutation in at least one of TET2, MLL-PTD, and DNMT3A genes or trisomy 8 is detected when the patient has a FLT3-ITD mutation.


In one aspect, the present disclosure is a method for preparing a personalized genomics profile for a patient with acute myeloid leukemia, comprising: subjecting mononuclear cells extracted from a bone marrow aspirate or blood sample from the patient to gene mutational analysis; assaying the sample and detecting the presence of a cytoegentic abnormality and one or more mutations in a gene selected from the group consisting of FLT3, NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, MLL-PTD, ASXL1, PHF6, KRAS, PTEN, P53, HRAS, and EZH2 in said cells; and generating a report of the data obtained by the gene mutation analysis, wherein the report comprises a prediction of the likelihood of survival of the patient or a response to therapy.


In one aspect, the disclosure is a kit for determining treatment of a patient with AML, the kit comprising means for detecting a mutation in at least one gene selected from the group consisting of ASXL1, DNMT3A, NPM1, PHF6, WT1, TP53, EZH2, CEBPA, TET2, RUNX1, PTEN, FLT3, HRAS, KRAS, NRAS, KIT, IDH1, and IDH2; and instructions for recommended treatment based on the presence of a mutation in one or more of said genes. In one example, the instructions for recommended treatment for the patient based on the presence of a DNMT3A or NPM1 mutation or MLL translocation indicate high-dose daunorubicin as the recommended treatment.


One aspect of the present disclosure is a method of treating, preventing or managing acute myeloid leukemia in a patient, comprising, analyzing a genetic sample isolated from the patient for the presence of a mutation in genes DNMT3A, and NPM1, and for the presence of a MLL translocation; identifying the patient as one who will respond to high dose chemotherapy better than standard dose chemotherapy if a mutation in DNMT3A or NPM1 or a MLL translocation are present; and administering high dose therapy to the patient. The patient, in one example, is characterized as intermediate-risk on the basis of cytogenetic analysis. In one example, the therapy comprises the administration of anthracycline. In a related embodiment, administering high dose therapy comprises administering one or more high dose anthracycline antibiotics selected from the group consisting of Daunorubicin, Doxorubicin, Epirubicin, Idarubicin, Mitoxantrone, and Adriamycin.


One aspect of the present disclosure is directed to a method of predicting survival of a patient with acute myeloid leukemia, comprising: (a) analyzing a sample isolated from the patient for the presence of (i) a mutation in at least one of FLT3, MLL-PTD, ASXL1, and PHF6 genes, plus optionally one or more of NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, KRAS, PTEN, P53, HRAS, and EZH2 genes; or (ii) a mutation in IDH2 and/or CEBPA genes, plus optionally one or more of FLT3, MLL-PTD, ASXL1, PHF6, NPM1, DNMT3A, NRAS, TET2, WT1, IDH1, KIT, RUNX1, KRAS, PTEN, P53, HRAS, and EZH2 genes; and (b) (i) predicting poor survival of the patient if a mutation is present in at least one of FLT3, MLL-PTD, ASXL1 and PHF6 genes, or (ii) predicting favorable survival of the patient if a mutation is present in IDH2R140 and/or a mutation is present in CEBPA. The method further comprises analyzing the sample for the presence of cytogenetic abnormalities. The method further comprises predicting favorable survival of the patient if the following mutation is present: IDH2R140Q.


Other aspects of the present disclosure include the chemotherapeutics for use in the methods described herein, or use of those in the preparation of a medicament when used in the methods described herein.





BRIEF DESCRIPTION OF THE DRAWINGS

The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawing(s) will be provided by the Office upon request and payment of the necessary fee.



FIG. 1 shows the mutational complexity of AML. Circos diagram depicting relative frequency and pairwise co-occurrence of mutations in de novo AML patients enrolled in the ECOG protocol E1900 (Panel A). The arc length corresponds to the frequency mutations in the first gene and the ribbon width corresponds to the percentage of patients that also have a mutation in the second gene. Pairwise co-occurrence of mutations is denoted only once, beginning with the first gene in the clockwise direction. Since only pairwise mutations are encoded for clarity, the arc length was adjusted to maintain the relative size of the arc and the correct proportion of patients with a single mutant allele is represented by the empty space within each mutational subset. Panel A also contains the mutational frequency in the test cohort. Panels B and C show the mutational events in DNMT3A and FLT3 mutant patients respectively.



FIG. 2 shows multivariate risk classification of intermediate-risk AML. Kaplan-Meier estimates of overall survival (OS) are shown for the risk stratification of intermediate-risk AML (p-values represent a comparison of all curves). For FLT3-ITD negative, intermediate-risk AML (Panel A) there are three genotypes: poor defined by mutant TET2 or ASXL1 or PHF6 or MLL-PTD, good defined by mutant IDH1 or IDH2 and mutant NPM1, and intermediate defined by all other genotypes. For FLT3-ITD positive, intermediate-risk AML (Panel B), there is the mutant CEBPA genotype, poor defined by mutant TET2 or DNMT3A or MLL-PTD or trisomy 8, and all other genotypes.



FIG. 3 shows revised AML risk stratification based on integrated genetic analysis. FIG. 3A shows a revised risk stratification based on integrated cytogenetic and mutational analysis. Final overall risk groups are on the right. FIG. 3B shows the impact of integrated mutational analysis on risk stratification in the test cohort of AML patients (p-values represent a comparison of all curves). The black curves show the patients in the cytogenetic risk groups that remained unchanged. The green curve shows patients that were reclassifed from intermediate-risk to favorable-risk. The red curve shows patients that were reclassified from intermediate-risk to unfavorable-risk. FIG. 3C confirms the reproducibility of the genetic prognostic schema in an independent cohort of 104 samples from the E1900 trial (p-values represent a comparison of all curves).



FIG. 4 shows the molecular determinants of response to high-dose Daunorubicin induction chemotherapy. Kaplan-Meier estimates of OS in the entire cohort according to DNMT3A mutational status (Panel A) and DNMT3A status in patients receiving high-dose or standard-dose daunorubicin (Panel B). OS in patients according to treatment arm is shown in patients with DNMT3A or NPM1 mutations or MLL translocations (Panel C) and patients lacking DNMT3A or NPM1 mutations or MLL translocations (Panel D).



FIG. 5 shows comprehensive mutational profiling improves risk-stratification and clinical management of patients with acute myeloid leukemia (AML). Use of mutational profiling delineates subsets of cytogenetically defined intermediate-risk patients with markedly different prognoses and reallocates a substantial proportion of patients to favorable or unfavorable-risk categories (A). In addition, mutational profiling identifies genetically defined subsets of AML patients with improved outcome with high-dose anthracycline induction chemotherapy (B).



FIG. 6 shows Circos diagrams for each gene.



FIG. 7 shows Circos diagrams for all genes and some relevant cytogenetic abnormalities in patients within cytogenetically-defined favorablerisk (Panel A), intermediate-risk (Panel B), and unfavorable-risk (Panel C) subgroups. The percentage of patients in each cytogenetic risk category with >2 mutations is displayed in Panel D. The proportion of intermediate risk patients with 2 or more somatic mutations was significantly higher than of patients in the other 2 cytogenetic subgroups



FIG. 8 is a Circos diagram, showing the mutual exclusivity of IDH1, IDH2, TET2, and WT1 mutations.



FIG. 9 shows Kaplan-Meier estimates of OS according to mutational status: data are shown for OS in the entire cohort according to the mutational status of PHF6 (Panel A) and ASXL1 (Panel B).



FIG. 10 shows Kaplan-Meier survival estimates shown for IDH2 (Panel A), IDH2 R140 (Panel B), IDH1 (Panel C) and the IDH2 R172 allele (Panel D) in the entire cohort. Panel E shows both IDH2 alleles while Panel F shows all three IDH alleles (pvalue represents comparison of all curves). These data show that the IDH2 R140 allele is the only IDH allele to have prognostic relevance in the entire cohort.



FIG. 11 shows Kaplan-Meier estimates of OS in patients from the test cohort with core-binding factor alterations with mutations in KIT versus those wildtype for KIT. KIT mutations were not associated with a difference in OS when patients with any corebinding factor alteration (i.e. patients with t(8;21), inv(16), or t(16;16)) were studied (A). In contrast, KIT mutations were associated with a significant decrease in OS in patients bearing t(8;21) specifically (B). KIT mutations were not associated with adverse OS in patients with inv(16) or t(16;16) (C).



FIG. 12 shows Kaplan-Meier survival estimates for TET2 in cytogenetically defined intermediate-risk patients in the cohort.



FIG. 13 shows Kaplan-Meier survival estimates for NPM1-mutant patients with cytogenetically-defined intermediate-risk in the cohort. Only those with concomitant IDH mutations have improved survival.



FIG. 14 shows the risk classification schema for FLT3-ITD widltype (A) and mutant (B) intermediate-risk AML shown in FIG. 3 is shown here for normal-karyotype patients only.



FIG. 15 shows that the mutational prognostic schema predicts outcome regardless of post-remission therapy with no transplantation (A), autologous transplantation (B), and allogeneic transplantation (C) (p-value represents comparison of all curves). Note, curves represent overall risk categories integrating cytogenetic and mutational analysis (as shown in final column in FIG. 3A).



FIG. 16 shows Kaplan-Meier estimates of OS in the entire cohort according to DNMT3A mutational status (Panel A and B), MLL translocation status (Panel C and D) or NPM1 mutational status in patients receiving high-dose or standard-dose daunorubicin (Panels E and F). OS in patients according to treatment arm is shown in DNMT3A mutant (Panel A) and wild-type (Panel B) patients. Panel C shows OS in MLL translocated patients receiving high-dose or standard-dose daunorubicin while Panel D shows OS in non-MLL translocated patients depending on daunorubicin dose. OS in patients according to treatment arm is shown in NPM1 mutant (Panel E) and wild-type (Panel F) patients as well.





DETAILED DESCRIPTION OF THE INVENTION

To facilitate understanding of the invention, the following definitions are provided. It is to be understood that, in general, terms not otherwise defined are to be given their meaning or meanings as generally accepted in the art. The terminology used herein is for the purpose of describing particular embodiments only and is not intended to limit the scope of the present invention which will be limited only by the appended claims.


In practicing the present invention, many conventional techniques in molecular biology are used. These techniques are described in greater detail in, for example, Molecular Cloning: a Laboratory Manual 3rd edition, J. F. Sambrook and D. W. Russell, ed. Cold Spring Harbor Laboratory Press 2001 and DNA Microarrays: A Molecular Cloning Manual. D. Bowtell and J. Sambrook, eds. Cold Spring Harbor Laboratory Press 2002. Additionally, standard protocols, known to and used by those of skill in the art in mutational analysis of mammalian cells, including manufacturers' instruction manuals for preparation of samples and use of microarray platforms are hereby incorporated by reference.


In the description that follows, a number of terms are used extensively. The following definitions are provided to facilitate understanding of the invention. Unless otherwise specified, “a,” “an,” “the,” and “at least one” are used interchangeably and mean one or more than one.


The terms “cancer”, “cancerous”, or “malignant” refer to or describe the physiological condition in mammals that is typically characterized by unregulated growth of tumor cells. Examples of a blood cancer include but are not limited to acute myeloid leukemia.


The term “diagnose” as used herein refers to the act or process of identifying or determining a disease or condition in a mammal or the cause of a disease or condition by the evaluation of the signs and symptoms of the disease or disorder. Usually, a diagnosis of a disease or disorder is based on the evaluation of one or more factors and/or symptoms that are indicative of the disease. That is, a diagnosis can be made based on the presence, absence or amount of a factor which is indicative of presence or absence of the disease or condition. Each factor or symptom that is considered to be indicative for the diagnosis of a particular disease does not need be exclusively related to said particular disease; i.e. there may be differential diagnoses that can be inferred from a diagnostic factor or symptom. Likewise, there may be instances where a factor or symptom that is indicative of a particular disease is present in an individual that does not have the particular disease.


“Expression profile” as used herein may mean a genomic expression profile. Profiles may be generated by any convenient means for determining a level of a nucleic acid sequence e.g. quantitative hybridization of microRNA, labeled microRNA, amplified microRNA, cRNA, etc., quantitative PCR, ELISA for quantitation, and the like, and allow the analysis of differential gene expression between two samples. A subject or patient tumor sample, e.g., cells or collections thereof, e.g., tissues, is assayed. Samples are collected by any convenient method, as known in the art.


“Gene” as used herein may be a natural (e.g., genomic) gene comprising transcriptional and/or translational regulatory sequences and/or a coding region and/or non-translated sequences (e.g., introns, 5′- and 3′-untranslated sequences). The coding region of a gene may be a nucleotide sequence coding for an amino acid sequence or a functional RNA, such as tRNA, rRNA, catalytic RNA, siRNA, miRNA or antisense RNA. The term “gene” has its meaning as understood in the art. However, it will be appreciated by those of ordinary skill in the art that the term “gene” has a variety of meanings in the art, some of which include gene regulatory sequences (e.g., promoters, enhancers, etc.) and/or intron sequences, and others of which are limited to coding sequences. It will further be appreciated that definitions of “gene” include references to nucleic acids that do not encode proteins but rather encode functional RNA molecules such as tRNAs. For the purpose of clarity we note that, as used in the present application, the term “gene” generally refers to a portion of a nucleic acid that encodes a protein; the term may optionally encompass regulatory sequences. This definition is not intended to exclude application of the term “gene” to non-protein coding expression units but rather to clarify that, in most cases, the term as used in this document refers to a protein coding nucleic acid.


“Mammal” for purposes of treatment or therapy refers to any animal classified as a mammal, including humans, domestic and farm animals, and zoo, sports, or pet animals, such as dogs, horses, cats, cows, etc. Preferably, the mammal is human.


“Microarray” refers to an ordered arrangement of hybridizable array elements, preferably polynucleotide probes, on a substrate.


Therapeutic agents for practicing a method of the present invention include, but are not limited to, inhibitors of the expression or activity of genes identified and disclosed herein, or protein translation thereof. An “inhibitor” is any substance which retards or prevents a chemical or physiological reaction or response. Common inhibitors include but are not limited to antisense molecules, antibodies, and antagonists.


The term “poor” as used herein may be used interchangeably with “unfavorable.” The term “good” as used herein may be referred to as “favorable.” The term “poor responder” as used herein refers to an individual whose cancer grows during or shortly thereafter standard therapy, for example radiation-chemotherapy, or who experiences a clinically evident decline attributable to the cancer. The term “respond to therapy” as used herein refers to an individual whose tumor or cancer either remains stable or becomes smaller/reduced during or shortly thereafter standard therapy, for example radiation-chemotherapy.


“Probes” may be derived from naturally occurring or recombinant single- or double-stranded nucleic acids or may be chemically synthesized. They are useful in detecting the presence of identical or similar sequences. Such probes may be labeled with reporter molecules using nick translation, Klenow fill-in reaction, PCR or other methods well known in the art. Nucleic acid probes may be used in southern, northern or in situ hybridizations to determine whether DNA or RNA encoding a certain protein is present in a cell type, tissue, or organ.


“Prognosis” as used herein refers to a forecast as to the probable outcome of cancer, including the prospect of recovery from the cancer. As used herein the terms prognostic information and predictive information are used interchangeably to refer to any information that may be used to foretell any aspect of the course of a disease or condition either in the absence or presence of treatment. Such information may include, but is not limited to, the average life expectancy of a patient, the likelihood that a patient will survive for a given amount of time (e.g., 6 months, 1 year, 5 years, etc.), the likelihood that a patient will be cured of a disease, the likelihood that a patient's disease will respond to a particular therapy (wherein response may be defined in any of a variety of ways). Prognostic and predictive information are included within the broad category of diagnostic information.


The term “prognosis” as used herein refers to a prediction of the probable course and outcome of a clinical condition or disease. A prognosis of a patient is usually made by evaluating factors or symptoms of a disease that are indicative of a favorable or unfavorable course or outcome of the disease. The phrase “determining the prognosis” as used herein refers to the process by which the skilled artisan can predict the course or outcome of a condition in a patient. The term “prognosis” does not refer to the ability to predict the course or outcome of a condition with 100% accuracy. Instead, the skilled artisan will understand that the term “prognosis” refers to an increased probability that a certain course or outcome will occur; that is, that a course or outcome is more likely to occur in a patient exhibiting a given condition, when compared to those individuals not exhibiting the condition. A prognosis may be expressed as the amount of time a patient can be expected to survive. Alternatively, a prognosis may refer to the likelihood that the disease goes into remission or to the amount of time the disease can be expected to remain in remission. Prognosis can be expressed in various ways; for example prognosis can be expressed as a percent chance that a patient will survive after one year, five years, ten years or the like. Alternatively prognosis may be expressed as the number of months, on average, that a patient can expect to survive as a result of a condition or disease. The prognosis of a patient may be considered as an expression of relativism, with many factors effecting the ultimate outcome. For example, for patients with certain conditions, prognosis can be appropriately expressed as the likelihood that a condition may be treatable or curable, or the likelihood that a disease will go into remission, whereas for patients with more severe conditions prognosis may be more appropriately expressed as likelihood of survival for a specified period of time.


The terms “favorable prognosis” and “positive prognosis,” or “unfavorable prognosis” and “negative prognosis” as used herein are relative terms for the prediction of the probable course and/or likely outcome of a condition or a disease. A favorable or positive prognosis predicts a better outcome for a condition than an unfavorable or negative or adverse prognosis. In a general sense a “favorable prognosis” is an outcome that is relatively better than many other possible prognoses that could be associated with a particular condition, whereas an “unfavorable prognosis” predicts an outcome that is relatively worse than many other possible prognoses that could be associated with a particular condition. Typical examples of a favorable or positive prognosis include a better than average cure rate, a lower propensity for metastasis, a longer than expected life expectancy, differentiation of a benign process from a cancerous process, and the like. For example, if a prognosis is that a patient has a 50% probability of being cured of a particular cancer after treatment, while the average patient with the same cancer has only a 25% probability of being cured, then that patient exhibits a positive prognosis. A positive prognosis may be diagnosis of a benign tumor if it is distinguished over a cancerous tumor.


The term “relapse” or “recurrence” as used in the context of cancer in the present application refers to the return of signs and symptoms of cancer after a period of remission or improvement.


As used herein a “response” to treatment may refer to any beneficial alteration in a subject's condition that occurs as a result of treatment. Such alteration may include stabilization of the condition (e.g., prevention of deterioration that would have taken place in the absence of the treatment), amelioration of symptoms of the condition, improvement in the prospects for cure of the condition. One may refer to a subject's response or to a tumor's response. In general these concepts are used interchangeably herein.


“Treatment” or “therapy” refer to both therapeutic treatment and prophylactic or preventative measures. The term “therapeutically effective amount” refers to an amount of a drug effective to treat a disease or disorder in a mammal. In the case of cancer, the therapeutically effective amount of the drug may reduce the number of cancer cells; reduce the tumor size; inhibit (i.e., slow to some extent and preferably stop) cancer cell infiltration into peripheral organs; inhibit (i.e., slow to some extent and preferably stop) tumor metastasis; inhibit, to some extent, tumor growth; and/or relieve to some extent one or more of the symptoms associated with the disorder.


For the recitation of numeric ranges herein, each intervening number there between with the same degree of precision is explicitly contemplated. For example, for the range of 2-5, the numbers 3 and 4 are contemplated in addition to 2 and 5, and for the range 2.0-3.0, the number 2.0, 2.1, 2.2, 2.3, 2.4, 2.5, 2.6, 2.7, 2.8, 2.9 and 3.0 are explicitly contemplated. As used herein, the term “about” X or “approximately” X refers to +/−10% of the stated value of X.


Inherent difficulties in the diagnosis and treatment of cancer include among other things, the existence of many different subgroups of cancer and the concomitant variation in appropriate treatment strategies to maximize the likelihood of positive patient outcome. Current methods of cancer treatment are relatively non-selective. Typically, surgery is used to remove diseased tissue; radiotherapy is used to shrink solid tumors; and chemotherapy is used to kill rapidly dividing cells.


In the case of blood cancers, it is worthy to begin by noting that blood primarily consists of red blood cells (RBC), white blood cells (WBC) and platelets. Red blood cells carry oxygen to the body, the white blood cells police and protect the body, and platelets help clot the blood when there is injury. Abnormalities in these cell types can lead to blood cancer. The main categories of blood cancer are Acute Lymphocytic or Lymphoblastic Leukemias (ALL), Chronic Lymphocytic or Lymphoblastic Leukemias (CLL), Acute Myelogenous or Myeloid Leukemias (AML), and Chronic Myelogenous or Myeloid Leukemias (CML).


Both leukemia and lymphoma are hematologic malignancies (cancers) of the blood and bone marrow. In the case of leukemia, the cancer is characterized by abnormal proliferation of leukocytes and is one of the four major types of cancer. The cancer interferes with the body's ability to make blood, and the cancer attacks the bone marrow and the blood itself, causing fatigue, anemia, weakness, and bone pain. Leukemia is diagnosed with a blood test in which specific types of blood cells are counted; it accounts for about 29,000 adults and 2,000 children diagnosed each year in the United States. Treatment for leukemia typically includes chemotherapy and radiation to kill the cancer, and may involve bone marrow transplantation in some cases.


Leukemias are classified according to the type of leukocyte most prominently involved. Acute leukemias are predominantly undifferentiated cell populations and chronic leukemias have more mature cell forms. The acute leukemias are divided into lymphoblastic (ALL) and non-lymphoblastic (ANLL) types, with ALL being predominantly a childhood disease while ANLL, also known as acute myeloid leukemia (AML), being a more common acute leukemia among adults.


AML is characterized by an increase in the number of myeloid cells in the marrow and an arrest in their maturation, frequently resulting in hematopoietic insufficiency. In the United States, the annual incidence of AML is approximately 2.4 per 100,000 and it increases progressively with age to a peak of 12.6 per 100,000 adults 65 years of age or older. Despite improved therapeutic approaches, prognosis of AML is very poor around the globe. Even in the United States, five-year survival rate among patients who are less than 65 years of age is less than 40%.


Acute myeloid leukemia (AML) is a heterogeneous disorder that includes many entities with diverse genetic abnormalities and clinical features. The pathogenesis is known for relatively few types of leukemia. Patients with intermediate and poor risk cytogenetics represent the majority of AML; chemotherapy based regimens fail to cure most of these patients and stem cell transplantation is frequently the treatment choice. Since allogeneic stem cell transplantation is not an option for many patients with high risk leukemia, there is a need to improve our understanding of the biology of these leukemias and to develop improved therapies. Despite considerable advances, not enough is known of the etiology, cell physiology and molecular genetics of acute myeloid leukemia. As such, the development of effective new agents and novel treatment and/or prognostic methods against myeloid leukemia, and in particular acute myeloid leukemia, remains a focal point today in translational oncology research.


Significant progress has been made in understanding risk factors, including genetic factors, that may contribute to AML, but the relevance of these factors to clinical outcome remains unclear. In addition, the expression level and antibody staining pattern of several proteins have been shown to be predictive of outcome and of the likelihood of response to therapy. However, the clinical outcome of individual patients remains uncertain, and the ability to predict which patients are likely to benefit from a particular type of therapy (e.g., a certain drug or class of drug) remains elusive.


In the present disclosure, leukemic samples from patients with diagnosed AML were obtained. Bone marrow or peripheral blood samples were collected, prepared by Ficoll-Hypaque (Nygaard) gradient centrifugation. Cytogenetic analyses of the samples were performed at presentation, as previously described (Bloomfield; Leukemia 1992; 6:65-67. 21). The criteria used to describe a cytogenetic clone and karyotype followed the recommendations of the International System for Human Cytogenetic Nomenclature. DNA was extracted from diagnostic bone marrow aspirate samples or peripheral blood samples using method described previously (Zuo et al. Mod Pathol. 2009; 22, 1023-1031).


The present disclosure is based on mutational analysis of 18 genes in 398 patients with AML younger than 60 years of age randomized to receive induction therapy including high-dose or standard dose daunorubicin. Prognostic findings were further validated in an independent set of 104 patients.


The inventors of the instant application have identified ≧1 somatic alteration in 97.3% of patients. These Applicants discovered (1) that FLT3-ITD (p=0.001), MLL-PTD (p=0.009), ASXL1 (p=0.05), and PHF6 (p=0.006) mutations are associated with reduced overall survival (“OS”); and (2) that CEBPA (p=0.05) and IDH2R140Q (p=0.01) mutations were associated with improved OS.


Accordingly, in one aspect of the present disclosure is a method of predicting survival of a patient with acute myeloid leukemia, said method comprising: analyzing a genetic sample isolated from the patient for the presence of cytogenetic abnormalities and a mutation in at least one of FLT3, NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, MLL-PTD, ASXL1, PHF6, KRAS, PTEN, P53, HRAS, and EZH2 genes; and (i) predicting poor survival of the patient if a mutation is present in at least one of FLT3, MLL-PTD, ASXL1 and PHF6 genes, or (ii) predicting favorable survival of the patient if a mutation is present in IDH2R140 (e.g. IDH2R140Q) and/or a mutation is present in CEBPA. In one embodiment, the method further comprises, predicting intermediate survival of the patient with cytogenetically-defined intermediate risk AML if: (i) no mutation is present in any of FLT3-ITD, TET2, MLL-PTD, DNMT3A, ASXL1 or PHF6 genes, (ii) a mutation in CEBPA is and the FLT3-ITD is present, or (iii) a mutation is present in FLT3-ITD but trisomy 8 is absent. In another embodiment, the method further comprises predicting unfavorable survival of the patient if (i) a mutation in TET2, ASXL1, or PHF6 or an MLL-PTD is present in a patient without the FLT3-ITD mutation, or (ii) the patient has a FLT3-ITD mutation and a mutation in TET2, DNMT3A, MLL-PTD or trisomy 8.


The genetic sample may be obtained from a bone marrow aspirate or the patient's blood. Once the sample is obtained, in one example, the mononuclear cells are isolated according to known techniques including Ficoll separation and their DNA is extracted. In a particular embodiment, poor survival or adverse risk of the patient is survival of less than or equal to about 10 months. Whereas, in one embodiment, intermediate survival the patient is survival of about 18 months to about 30 months. In another embodiment, favorable survival of the patient is survival of about 32 months or more.


In another aspect, the present disclosure teaches a method of predicting survival of a patient with acute myeloid leukemia, said method comprising, assaying a genetic sample from the patient's blood or bone marrow for the presence of a mutation in at least one of genes FLT3, NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, MLL-PTD, ASXL1, PHF6, KRAS, PTEN, P53, HRAS, and EZH2 in said sample; and predicting a poor survival of the patient if a mutation is present in at least one of genes FLT3-ITD, MLL-PTD, ASXL1, PHF6; or predicting a favorable survival of the patient if a mutation is present in CEBPA or a mutation is present in IDH2 at R140. In one embodiment, the patient is characterized as intermediate-risk on the basis of cytogenetic analysis.


In one embodiment, amongst patients with cytogenetically-defined intermediate-risk acute myeloid leukemia who have FLT3-ITD mutation, at least one of the following: trisomy 8 or a mutation in TET2, DNMT3A, or the MLL-PTD are associated with an adverse outcome and poor overall survival of the patient. In another embodiment, amongst patients with cytogenetically-defined intermediate-risk acute myeloid leukemia who have a mutation in FLT3-ITD gene, a mutation in CEBPA gene is associated with improved outcome and overall survival of the patient. In one embodiment, in a cytogenetically-defined intermediate risk AML patient with both IDH1/IDH2 and NPM1 mutations, the overall survival is improved compared to NPM1-mutant patients wild-type for both IDH1 and IDH2. In one embodiment, amongst patients with acute myeloid leukemia, IDH2R140 mutations are associated with improved overall survival. Poor or unfavorable survival (adverse risk) of the patient, in one example, is survival of less than or equal to about 10 months. Favorable survival of the patient, in one example, is survival of about 32 months or more.


In one embodiment, the favorable impact of NPM1 mutations was restricted to patients with co-occurring IDH1/IDH2 and NPM1 mutations. Further, Applicants identified genetic predictors of outcome that improved risk stratification in AML independent of age, WBC count, induction dose, and post-remission therapy and validated their significance in an independent cohort. Applicants discovered that high-dose daunorubicin improved survival in patients with DNMT3A or NPM1 mutations or MLL translocations (p=0.001) relative to treatment with standard dose daunorubicin, but not in patients wild-type for these alterations (p=0.67).


These data provide clinical implications of genetic alterations in AML by delineating mutations that predict outcome in AML and improve AML risk stratification. Applicants have herein discovered and demonstrated the utility of mutational profiling to improve prognostic and therapeutic decisions in AML, and in particular, have shown that DNMT3A or NPM1 mutations or MLL translocations predict for improved outcome with high-dose induction chemotherapy.


Previous studies have highlighted the clinical and biologic heterogeneity of acute myeloid leukemia (AML). However, a relatively small number of cytogenetic and molecular lesions have sufficient relevance to influence clinical practice. The prognostic relevance of cytogenetic abnormalities has led to the widespread adoption of risk stratification into three cytogenetically-defined risk groups with significant differences in OS. Although progress has been made in defining prognostic markers for AML, a significant proportion of patients lack a specific abnormality of prognostic significance. Additionally, there is significant heterogeneity in outcome for individual patients in each risk group.


Recent studies have identified a number of recurrent somatic mutations in patients with AML, however, to date, whether mutational profiling of a larger set of genes would improve prognostication in AML has not been investigated in a clinical trial cohort. Here, Applicants conceived that integrated mutational analysis of all known molecular alterations occurring in >5% of AML patients would allow for the identification of novel molecular markers of outcome in AML and allow for the identification of molecularly defined subsets of patients who benefit from dose-intensified induction chemotherapy.


High-Throughput Mutational Profiling in AML: Comprehensive Genetic Analysis


Clinical studies have demonstrated that acute myeloid leukemia (AML) is heterogeneous with respect to presentation and to clinical outcome, and studies have shown that cytogenetics can be used to improve prognostication and to guide therapeutic decisions. More recently, genetic studies have improved our understanding of the genetic basis of AML. Applicants recognized genetic lesions represent prognostic markers which can be used to risk stratify AML patients and guide therapeutic decisions. However, although a number of gene mutations occur at significant frequency in AML, their prognostic value is not known in large phase III clinical trial cohorts.


Applicants report for the first time in a uniformly treated clinical cohort, the mutational status of all genes known to be significantly (>5%) mutated in AML as well as the effect of mutations in these genes on outcome and response to therapy. Applicants used a high throughput re-sequencing platform to perform full length resequencing of the coding regions of FLT3, NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, MLL-PTD, ASXL1, PHF6, KRAS, PTEN, P53, HRAS, and EZH2 in pre-treatment genomic DNA from 398 patients with de novo AML enrolled in the ECOG E1900 Study.


Including both mutations and cytogenetic abnormalities, Applicants were able to identify a clonal alteration in 91.2% of all patients in the E1900 cohort; 42% had 1 somatic alteration, 36.4% had 2 alterations, 11.3% had 3 alterations and 1.5% had 4 alterations. Mutational data from each patient was correlated with overall survival, disease-free survival, and with treatment assignment (standard dose or high dose daunorubicin). Applicants discovered somatic mutations in FLT3 (37% total; 30% ITD, 7% TKD), DNMT3A (23%), NPM1 (14%), CEBPA (10%), TET2 (10%), NRAS (10%), WT1 (10%), KIT (9%), IDH2 (8%), IDH1 (6%), RUNX1 (6%), ASXL1 (4%), PHF6 (3%), KRAS (2.5%), TP53 (2%), PTEN (1.5%); the only genes without mutations in Applicants' screen were HRAS and EZH2.


Applicants next used correlation analysis to assess whether mutations were positively or negatively correlated (FIG. 1). In addition to identified mutational correlations (FLT3 and NPM1, KIT and core binding factor leukemia), Applicants found that FLT3 and ASXL1 mutations were mutually exclusive in this large cohort (p=0.0008). Further, Applicants found that IDH1/IDH2 mutations were mutually exclusive of both TET2 (p=0.02), and WT1 (p=0.01) mutations, suggesting these mutations have overlapping roles in AML pathogenesis.


Applicants next set out to investigate if any mutations were associated with lack of response to chemotherapy; notably mutations in ASXL1 (p=0.0002) and WT1 (p=0.03) were enriched in patients with primary refractory-AML. Integration of mutational data with outcome in the ECOG E1900 trial revealed significant effects that mutations in FLT3 (p=0.0005), ASXL1 (p=0.005), and PHF6 (p=0.02) were associated with reduced overall survival. In addition, Applicants found that mutations in CEBPA (p=0.04) and in IDH2 (p=0.003) were associated with improved overall survival; the favorable impact of IDH1 mutations on outcome was exclusively seen in patients with IDH2R140 mutations.


This data represents a comprehensive mutational analysis of 18 genes in a uniformly-treated de novo AML cohort, which allowed Applicants to delineate the mutational frequency of this gene set in de novo AML, the pattern of mutational cooperativity in AML and the clinical effects of gene mutations on survival and response to therapy in AML. In one embodiment, Applicants identified mutations in ASXL1 and WT1 as being significantly enriched in patients who failed to respond to standard induction chemotherapy in AML. This data provides important clinical implications of genetic alterations in AML and provides insight into the multistep pathogenesis of adult AML. In one embodiment, the acute myeloid leukemia is selected from newly diagnosed, relapsed or refractory acute myeloid leukemia.


Accordingly, one aspect of the present disclosure is a method of predicting survival of a patient with acute myeloid leukemia, said method comprising assaying a genetic sample from the patient's blood or bone marrow for the presence of a mutation in genes ASXL1 and WT1; and determining the patient has or will develop primary refractory acute myeloid leukemia if mutated ASXL1 and WT1 genes are detected. The sample can be a bone marrow aspirate or the patient's blood. Further, in one embodiment, the mononuclear cells are isolated for use in the assay.


Applicants have developed a mutational classifier which can be used to predict risk of relapse in adults with acute myeloid leukemia by combining standard analysis of cytogenetics with full length sequencing of FLT3, NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, MLL-PTD, ASXL1, PHF6, KRAS, PTEN, P53, HRAS, and EZH2. The teachings of the instant application allow for the development of an integrated mutation classifier which can more accurately predict outcome and relapse risk than currently available techniques. In one embodiment, poor survival is survival of less than or equal to about ten months. In another embodiment, intermediate survival of the patient is survival of about 18 months to about 30 months. In a related embodiment, favorable survival of the patient is survival of about 32 months or more.


In one embodiment, in patients with FLT3-ITD wild-type intermediate-risk acute myeloid leukemia, TET2, ASXL1, PHF6, and MLL-PTD gene mutations were independently shown to be associated with adverse outcome and poor overall survival of the patient. In another embodiment, in patients with FLT3-ITD mutant intermediate-risk acute myeloid leukemia, CEBPA gene mutations were associated with improved outcome and overall survival of the patient. In yet another embodiment, in cytogenetically-defined intermediate risk AML patients with FLT3-ITD mutant intermediate-risk acute myeloid leukemia, trisomy 8 and TET2, DNMT3A, and MLL-PTD mutations were associated with an adverse outcome and poor overall survival of the patient. In one embodiment, cytogenetically-defined intermediate risk AML patients with both IDH1/IDH2 and NPM1 mutations have an improved overall survival compared to NPM1-mutant patients wild-type for both IDH1 and IDH2. In a related embodiment, IDH2 R140Q mutations are associated with improved overall survival in the overall cohort of AML patients.


One aspect of the present disclosure is directed to a method of predicting survival of a patient with acute myeloid leukemia, comprising: (a) analyzing a sample isolated from the patient for the presence of (i) a mutation in at least one of FLT3, MLL-PTD, ASXL1, and PHF6 genes, plus optionally one or more of NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, KRAS, PTEN, P53, HRAS, and EZH2 genes; or (ii) a mutation in IDH2 and/or CEBPA genes, plus optionally one or more of FLT3, MLL-PTD, ASXL1, PHF6, NPM1, DNMT3A, NRAS, TET2, WT1, IDH1, KIT, RUNX1, KRAS, PTEN, P53, HRAS, and EZH2 genes; and (b) (i) predicting poor survival of the patient if a mutation is present in at least one of FLT3, MLL-PTD, ASXL1 and PHF6 genes, or (ii) predicting favorable survival of the patient if a mutation is present in IDH2R140 and/or a mutation is present in CEBPA. The method may further comprise analyzing the sample for the presence of cytogenetic abnormalities. The method may further comprise predicting favorable survival of the patient if the following mutation is present: IDH2R140Q.


Furthermore, Applicants have discovered that DNMT3A mutations, NPM1 mutations or MLL fusions predict for improved outcome with high dose chemotherapy, which includes dose-intensified induction therapy. The teachings of the instant application provide for accurate risk stratification of AML patients and the ability to decide which patients need more agreessive therapy given high risk, and identification of low risk patients less in need of intensive post remission therapy. Moreover, it is possible to identify genotypically defined subsets of patients who would benefit from induction with dose-intensified anthracyclines, for example, daunorubicin. The present disclosure provides for more accurate assessment in risk classification. Presently, there is no effective way to determine which patients suffering from AML benefit from high dose daunorubicin. In one embodiment, the present disclosure provides for a novel classifier as well as a predictor of response.


Accordingly, one aspect of the present disclosure is a method of determining responsiveness of a patient with acute myeloid leukemia to high dose therapy, said method comprising analyzing a genetic sample isolated from the patient for the presence of a mutation in genes DNMT3A, and NPM1, and for the presence of a MLL translocation; and (i) identifying the patient as one who will respond to high dose therapy if a mutation in DNMT3A or NPM1 or an MLL translocation are present, or (ii) identifying the patient as one who will not respond to high dose therapy in the absence of mutations in DNMT3A or NPM1 or an MLL translocation. In one embodiment, the sample is DNA extracted from bone marrow or blood from the patient. The genetic sample may be DNA isolated from mononuclear cells (MNC) from blood or bone marrow of the patient. In one embodiment, the therapy comprises the administration of anthracycline. Examples of anthracyclines include Daunorubicin, Doxorubicin, Epirubicin, Idarubicin, Mitoxantrone, and Adriamycin. In a particular example, the anthracycline is Daunorubicin.


The method may be used to predict a patient's response to therapy before beginning therapy, during therapy, or after therapy is completed. For example, by predicting a patient's response to therapy before beginning therapy, the information may be used in determining the best therapy option for the patient.


One embodiment of the present invention is directed to methods to screen a patient for the prognosis for acute myeloid leukemia. The invention may provide information concerning the survival rate of a patient, the predicted life span of the patient, and/or the predicted likelihood of survival for the patient. In one embodiment, poor survival is referred generally as survival of about 10 months or less, and good prognosis or long-term survival is considered to be more than about 36 months or longer. In one embodiment, poor survival is considered as about one to 16 months, whereas good, favorable or long-term survival is considered to be range of about 30 to 42 months, more than about 46 months, or more than about 60 months. In one embodiment, good survival is considered to be about 30 months or longer.


In any aspect of the invention, unless context demands otherwise, the following combinations of genes and\or cytogenetic defects may be analyzed or assayed: FLT3 and CEBPA; FLT3 and trisomy 8; FLT3 and TET2; FLT3 and DNMT3A; FLT3 and MLL; FLT3, MLL, ASXL1 and PHF6, optionally with TET2 or DNMT3A; IDH2 and CEBPA; IDH1, IDH2 and NPM1; IDH2, ASXL1 and WT1; DNMT3A, NPM1 and MLL. Any of these combinations may be combined with any one or more other genes shown in the Table entitled ‘Genes analyzed for somatic mutations in genomic DNA of patients with AML and their clinical associations’. Optionally at least 2, 3, 4, 5, 6, 7, 8, 9, 10, 11, 12, 13, 14, 15, 16, 17, 18 or 19 genes are analyzed or assayed, which genes are listed in said table.


The present invention is also directed to a method for determining if an individual will respond to one or more therapies for acute myeloid leukemia. The therapy may be of any kind, but in specific embodiments it comprises chemotherapy, such as one or more anthracycline antibiotic agents. In one embodiment, the chemotherapy comprises the antimetabolite cytarabine in combination with an anthracycline.


In certain embodiments of the invention the therapy is chemotherapy, immunotherapy, antibody-based therapy, radiation therapy, or supportive therapy (essentially any implemented for leukemia). In a particular embodiment, the therapy comprises the administration of a chemotherapeutic agent comprising anthracycline antibiotics. Examples of such anthracycline antibiotics include, but are not limited to, Daunorubicin, Doxorubicin, Epirubicin, Idarubicin, Mitoxantrone, and Adriamycin. In some embodiments, the chemotherapy is Gleevac or idarubicin and ara-C. In a particular embodiment, daunorubicin is used.


Often, diagnostic assays are directed by a medical practitioner treating a patient, the diagnostic assays are performed by a technician who reports the results of the assay to the medical practitioner, and the medical practitioner uses the values from the assays as criteria for diagnosing the patient. Accordingly, the component steps of the method of the present invention may be performed by more than one person.


Prognosis may be a prediction of the likelihood that a patient will survive for a particular period of time, or said prognosis is a prediction of how long a patient may live, or the prognosis is the likelihood that a patent will recover from a disease or disorder. There are many ways that prognosis can be expressed. For example prognosis can be expressed in terms of complete remission rates (CR), overall survival (OS) which is the amount of time from entry to death, disease-free survival (DFS) which is the amount of time from CR to relapse or death. In one embodiment, favorable likelihood of survival, or overall survival, of the patient includes survival of the patient for about eighteen months or more.


A prognosis is often determined by examining one or more prognostic factors or indicators. These are markers, the presence or amount of which in a patient (or a sample obtained from the patient) signal a probability that a given course or outcome will occur. The skilled artisan will understand that associating a prognostic indicator with a predisposition to an adverse outcome may involve statistical analysis. Additionally, a change in factor concentration from a baseline level may be reflective of a patient prognosis, and the degree of change in marker level may be related to the severity of adverse events. Statistical significance is often determined by comparing two or more populations, and determining a confidence interval and/or a p value. See, e.g., Dowdy and Wearden, Statistics for Research, John Wiley & Sons, New York, 1983. In one embodiment, confidence intervals of the invention are 90%, 95%, 97.5%, 98%, 99%, 99.5%, 99.9% and 99.99%, while preferred p values are 0.1, 0.05, 0.025, 0.02, 0.01, 0.005, 0.001, and 0.0001. Exemplary statistical tests for associating a prognostic indicator with a predisposition to an adverse outcome are described.


One approach to the study of cancer is genetic profiling, an effort aimed at identifying perturbations in gene expression and/or mutation that lead to the malignant phenotype. These gene expression profiles and mutational status provide valuable information about biological processes in normal and disease cells. However, cancers differ widely in their genetic signature, leading to difficulty in diagnosis and treatment, as well as in the development of effective therapeutics. Increasingly, gene mutations are being identified and exploited as tools for disease detection as well as for prognosis and prospective assessment of therapeutic success.


The inventors of the instant application hypothesized that genetic profiling of acute myeloid leukemia would provide a more effective approach to cancer management and/or treatment. The inventors have herein identified that mutations of a panel of genes lead to the malignant phenotype.


The present inventors have used a molecular approach to the problem and have identified a set of gene mutations in acute myeloid leukemia correlates significantly with overall survival. Accordingly, the present invention relates to gene mutation profiles useful in assessing prognosis and/or predicting the recurrence of acute myeloid leukemia. In one aspect, the present invention relates to a set of genes, the mutation of which in bone marrow or blood cells, in particular mononuclear cells, of a patient correlates with the likelihood of poor survival. The present invention relates to the prognosis and/or therapy response outcome of a patient with acute myeloid leukemia. The present invention provides several genes, the mutation of which, alone or in combination, has prognostic value, specifically with respect to survival.


In one example, the disclosure is a method of determining whether a human has an increased genetic risk for developing or developing a relapse of acute myeloid leukemia, comprising, analyzing a genetic sample isolated from the human's blood or bone marrow for the presence of a mutation in at least one gene from FLT3, NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, MLL-PTD, ASXL1, PHF6, KRAS, PTEN, P53, HRAS, and EZH2; and determining the individual with cytogenetically-defined intermediate risk AML has an increased genetic risk for developing or developing a relapse of acute myeloid leukemia, relative to a control human with no such gene mutations in said genes, when: (i) a mutation in at least one of TET2, MLL-PTD, ASXL1 and PHF6 genes is detected when the patient has no FLT3-ITD mutation, or (ii) a mutation in at least one of TET2, MLL-PTD, and DNMT3A genes or trisomy 8 is detected when the patient has a FLT3-ITD mutation.


To date, no test exists that predicts outcome in acute myeloid leukemia, where one can stratify AML patients into good versus poor responders, and in particular, identify patients who would respond better to high dose chemotherapy. As a consequence, some individuals may be overtreated, in that they unnecessarily receive treatment that has minimal effect. Alternatively, some individuals may be undertreated, in that additional agents added to standard therapy may improve outcome for these patients who would be refractory to standard treatment alone. As such, it is desirable to prospectively distinguish responders from non-responders to standard therapy prior to the initiation of therapy in order to optimize therapy for individual patients.


Accordingly, one aspect of the present disclosure is a method of predicting whether a patient suffering from acute myeloid leukemia will respond better to high dose chemotherapy than to standard dose chemotherapy, the method comprising, obtaining a DNA sample obtained from the patient's blood or bone marrow; determining the mutational status of genes DNMT3A and NPM1, and the presence of a MLL translocation; and predicting that the subject will be more responsive to high dose chemotherapy than standard dose chemotherapy where the sample is positive for a mutation in DNMT3A or NPM1 or an MLL translocation, or predicting that the subject will be non-responsive to high dose chemotherapy compared to standard dose chemotherapy where the sample is wild type with no mutations in DNMT3A or NPM1 genes and no translocation in MLL.


In one embodiment, the invention provides a clinical test that is useful to predict outcome in acute myeloid leukemia. The mutational status and/or expression of one or more specific genes is measured in the sample. Individuals are stratified into those who are likely to respond well to therapy vs. those who will not. The information from the results of the test is used to help determine the best therapy for the patient in need of therapy. Patients are stratified into those who are likely to have a poor prognosis vs. those who will have a good prognosis with standard therapy. A health care provider uses the results of the test to help determine the course of action, for example the best therapy, for the patient in need of therapy.


Because certain markers from a patient relate to the prognosis of a patient in a continuous fashion, the determination of prognosis can be performed using statistical analyses to relate the determined marker status to the prognosis of the patient. A skilled artisan is capable of designing appropriate statistical methods. For example the methods of the present invention may employ the chi-squared test, the Kaplan-Meier method, the log-rank test, multivariate logistic regression analysis, Cox's proportional-hazard model and the like in determining the prognosis. Computers and computer software programs may be used in organizing data and performing statistical analyses.


In one embodiment, a test is provided whereby a sample, for example a bone marrow or blood sample, is profiled for a gene set and, from the mutation profile results, an estimate of the likelihood of response to standard acute myeloid leukemia therapy is determined. In another embodiment, the invention concerns a method of predicting the prognosis and/or likelihood of response to standard and/or high dose chemotherapy, following treatment, in an individual with acute myeloid leukemia, comprising determining the mutational status of one or more genes, in particular one to DNMT3A or NPM1 genes, or a MLL translocation, in a genetic sample obtained from the patient, normalized against a control gene or genes. A total value is computed for each individual from the mutational status of the individual genes in this gene set.


The present invention relates to the diagnosis, prognosis and treatment of blood cancer, including predicting the response to therapy and stratifying patients for therapy. The present disclosure teaches the mutational frequency, prognostic significance, and therapeutic relevance of integrated mutation profiling in 398 patients from the ECOG E1900 phase III clinical trial and validates these data in an independent cohort of 104 patients from the same trial. Previous studies have suggested that mutational analysis of CEBPA, NPM1, and FLT3-ITD can be used to risk stratify intermediate-risk AML patients. By performing comprehensive mutational analysis on a large cohort of patients treated on a single clinical trial, Applicants demonstrate that more extensive mutational analysis can better discriminate AML patients into relevant prognostic groups (FIG. 3). For example, FLT3-ITD-negative NPM1/IDH mutant patients represent a favorable risk AML subset defined by a specific mutational genotype, whereas FLT3-ITD-negative NPM1-mutant patients without concurrent IDH mutations had a much less favorable outcome, particularly in patients with concurrent poor-risk mutations.


Furthermore, Applicants discovered that TET2, ASXL1, MLL-PTD, PHF6, and DNMT3A mutations can be used to define patients with adverse outcome in cytogenetically-defined intermediate-risk AML patients without the FLT3-ITD. Taken together, these data demonstrate that mutational analysis of a larger set of genetic alterations can be used to discriminate AML patients into more precise subsets with favorable, intermediate, or unfavorable risk with marked differences in overall outcome. This approach can be used to define an additional set of patients with mutationally defined favorable outcome with induction and consolidation therapy alone, and a set of patients with mutationally defined unfavorable risk who are candidates for allogeneic stem cell transplantation or clinical trials given their poor outcome with standard AML therapy (FIG. 5A).


The two recent randomized trials examining the benefits of anthracycline dose-intensification in AML demonstrated that more intensive induction chemotherapy improves outcomes in AML. (Fernandez et al., N Engl J Med, 2009, 361, 1249-59; Lowenberg et al., N Engl J Med, 2009, 361, 1235-48). Notably, re-evaluation of the original E1900 trial using our 502 patient cohort revealed that there was an even distribution of patients within each genetic risk category in both treatment arms of the original trial (p=0.41, Pearson's Chi-squared test). However, the initial reports of these studies did not identify whether dose-intensified induction therapy improved outcomes in different AML subgroups.


Applicants have discovered that anthracycline dose-intensification markedly improves outcomes in patients with mutations in DNMT3A or NPM1 or MLL translocations, suggesting mutational profiling can be used to determine which patients benefit from dose-intensive induction therapy (FIG. 5B).


Applicants also discovered mutational combinations that commonly occur in AML patients and those that rarely, if ever, co-occur consistent with the existence of additional mutational complementation groups. For example, the observation that TET2 and IDH mutations are mutually exclusive in this AML cohort led to functional studies linking IDH mutations and loss-of-function TET2 mutations in a shared mechanism of hematopoietic transformation.


As is true in the case of many treatment regimens, some patients respond to treatment with chemotherapy, for example an anthracycline antibiotic, daunorubicin, and others do not. Prescribing the treatment to a patient who is unlikely to respond to it is not desirable. Thus, it would be useful to know how a patient could be expected to respond to such treatment before a drug is administered so that non-responders would not be unnecessarily treated and so that those with the best chance of benefiting from the drug are properly treated and monitored. Further, of those who respond to treatment, there may be varying degrees of response. Treatment with therapeutics other than anthracycline or treatment with therapeutics in addition to the anthracycline daunorubicin may be beneficial for those patients who would not respond to a particular chemotherapy or in whom response to the particular chemotherapy, e.g. daunorubicin, or a similar anthracycline antibiotic, alone is less than desired.


The present disclosure demonstrates the ability of integrated mutational profiling of a clinical trial cohort to advance our understanding of AML biology, improve current prognostic models, and inform therapeutic decisions. In particular, these data indicate that more detailed genetic analysis can lead to improved risk stratification and identification of patients who benefit from more intensive induction chemotherapy.


In a specific aspect, the present disclosure is a method of screening a patient with acute myeloid leukemia for responsiveness to treatment with high dose of Daunorubicin or a pharmaceutically acceptable salt, solvate, or hydrate thereof, comprising: obtaining a genetic sample comprising an acute myeloid leukemic cell from said individual; and assaying the sample and detecting the presence of a mutation in DNMT3A or NPM1 or an MLL translocation; and correlating a finding of a mutation in DNMT3A or NPM1 or an MLL translocation, as compared to wild type controls where there is no mutation, with said acute myeloid leukemia patient being more sensitive to high dose treatment with Daunorubicin or a pharmaceutically acceptable salt, solvate, or hydrate thereof. In one embodiment, the method further comprises predicting the patient is at a lower risk of relapse of acute myeloid leukemia following chemotherapy if a mutation in DNMT3A or NPM1 or an MLL translocation is detected. In one embodiment, the method further comprises predicting the patient is at a lower risk of relapse of acute myeloid leukemia following chemotherapy if either DNMT3A or NPM1 mutations or an MLL translocation are detected.


Stratification of patient populations to predict therapeutic response is becoming increasingly valuable in the clinical management of cancer patients. For example, companion diagnostics are required for the stratification of patients being treated with targeted therapies such as trastuzumab (Herceptin, Genentech) in metastatic breast cancer, and cetuximab (Erbitux, Merck) in colorectal cancer. Predictive biomarkers are also being utilized for imatinib (Gleevec, Novartis) in gastrointestinal stromal tumors, and for gefitinib (Iressa, Astra-Zeneca) in lung cancer. Currently there is no method available to predict response to an anthracycline antibiotic in acute myeloid leukemia. To identify genes that are associated with greater sensitivity to an anthracycline antibiotic, and in particular to daunorubicine, Applicants assayed for the presence of mutations in certain genes as described above.


Genes Analyzed for Somatic Mutations in Genomic DNA of Patients with AML and their Clinical Associations, as Presently Disclosed













GENE
CLINICAL ASSOCIATION IN AML







FLT3
Internal tandem duplications or mutations in the tyrosine kinase



domain of the receptor tyrosine kinase FLT3 are important for



predicting survival in the overall cohort of AML patients as well as



those with cytogenetically-defined intermediate-risk AML.


DNMT3A
Mutations in DNMT3A were relevant for (a) predicting for adverse



overall survival in the presence of the FLT3-ITD in patients with



cytogenetically-defined intermediate-risk AML and (b) predicting



for responsiveness to high-dose induction chemotherapy with daunorubicin



and cytarabine.


NPM1
Mutations in NPM1 were relevant for (a) predicting for improved



overall survival when they co-occurred with IDH1/2 mutations in



cytogenetically-defined intermediate-risk AML and (b) predicting



for responsiveness to high-dose induction chemotherapy with



daunorubicin and cytarabine.


NRAS
Activating mutations in NRAS were seen in 10% of AML patients studied



here.


CEBPA
Mutations in CEBPA were relevant for (a) predicting for improved



overall survival in the overall cohort of AML patients regardless of



cytogenetic risk (b) predicting for intermediate overall risk in



patients with cytogenetically-defined intermediate-risk AML and



the presence of the FLT3ITD.


TET2
Mutations in TET2 were relevant for predicting for worsened



overall risk in patients with cytogenetically-defined intermediate-



risk AML regardless of the presence of the FLT3ITD.


WT1
Mutations in WT1 were present in 8% of AML patients here overall



but were enriched amongst patients who were refractory to initial induction



chemotherapy.


IDH2
Mutations in IDH2 were relevant for (a) predicting for improved



overall survival in the overall cohort of AML patients regardless of



cytogenetic risk specifically when mutations were present at



Arginine 140; (b) predicting for favorable overall risk in patients



with cytogenetically-defined intermediate-risk AML and no



FLT3ITD when accompanied by an NPM1 mutation.


IDH1
Mutations in IDH1 were relevant for predicting for favorable



overall risk in patients with cytogenetically-defined intermediate-



risk AML and no FLT3ITD when accompanied by an NPM1



mutation.


KIT
Mutations in KIT were seen in 6% of AML patients overall but



were enriched in patients with core-binding factor translocations. In



the presence of a mutation in KIT, patients with t(8;16) had an



worsened overall survival compared to t(8;16) AML patients who



were KIT wildtype.


RUNX1
Mutations in RUNX1 were present in 5% of AML patients here.


MLL
Partial tandem duplications in MLL were relevant for (a) predicting



for improved overall survival in patients receiving high-dose



induction chemotherapy and (b) predicting for adverse overall



survival in patients with cytogenetically-defined intermediate-risk



AML regardless of mutations in FLT3.


ASXL1
Mutations in ASXL1 were relevant for (a) predicting for adverse



overall survival in the entire cohort of AML patients (b) predicting



for adverse overall survival in cytogenetically-defined



intermediate-risk AML patients who did not have the FLT3ITD and



(c) were enriched amongst patients who failed to respond to initial



induction chemotherapy.


PHF6
Mutations in ASXL1 were relevant for (a) predicting for adverse



overall survival in the entire cohort of AML patients and (b)



predicting for adverse overall survival in cytogenetically-defined



intermediate-risk AML patients who did not have the FLT3ITD.


KRAS
Mutations in KRAS were present in 2% of AML patients studied here.


PTEN
Mutations in PTEN were present in 2% of AML patients studied here.


TP53
Mutations in TP53 were present in 2% of AML patients studied here.


HRAS
Mutations in HRAS were found in none of the AML patients studied here.


EZH2
Mutations in EZH2 were found in none of the AML patients studied here.









Specific Somatic Mutations Identified in the Sequencing of 18 Genes in AML Patients, and the Nature of these Mutations














NATURE AND TYPE OF SOMATIC MUTATIONS


GENE
IDENTIFIED







FLT3
Numerous somatic internal tandem duplications in FLT3 were identified.



These have been shown to result in constitutive activation of FLT3



signaling and are listed below. In addition, mutations in the tyrosine



kinase domain of FLT3 were also identified and also shown to result in



hyperactive signaling of FLT3.



The specific internal tandem duplication mutations identified were as



followed, though any in-frame insertion of nucleotides in the



juxtamembrane domain of FLT3 is scored as an internal tandem



duplication.



FLT3 p.Q580_V581ins12; FLT3 p.D586_N587ins15; FLT3



p.F590_Y591ins14; FLT3 p.Y591_V592ins23; FLT3



p.D593_F594ins12; FLT3 p.F594_R595ins14; FLT3 p.R595_E596ins12;



FLT3 p.Y597_E598ins17; FLT3 p.E598_Y599ins14; FLT3



p.Y599_D600ins14; FLT3 p.D600_L601ins21; FLT3



p.K602_W603ins14; FLT3 p.E604_F605ins15; FLT3 p.L610_E611ins11;



FLT3 p.F612_G613ins30



Tyrosine kinase domain mutations identified:



FLT3 D835Y; FLT3 D835E; FLT3 D835H; FLT3 D835V


DNMT3A
Mutations in DNMT3A were found as (1) out-of-frame insertion/deletions



predicted to result in loss-of-function of the protein, (2) somatic nonsense



mutations also predicted to result in loss-of-function of the protein, and



(3) somatic missense mutations. Any out-of-frame insertion/deletion or



somatic nonsense mutation would be scored as a mutation in the



algorithm.



Insertions/Deletions:



FS at amino acid (AA) 296; FS at AA 458; FS at AA 492; FS at AA



537; FS at AA 571; FS at AA 592; FS at AA 639; FS at AA 695; FS at



AA 706; FS at AA 731; FS at AA 765; FS at AA 772; FS at AA 804; FS



at AA 902.



Nonsense mutations:



DNMT3A W581C; DNMT3A W581R; DNMT3A Y660X; DNMT3A



Q696X; DNMT3A W753X; DNMT3A Q816X; DNMT3A Q886X;



DNMT3A S892X.



Missense mutations:



DNMT3A E30A; DNMT3A P76Q; DNMT3A S105N; DNMT3A L125V;



DNMT3A W297S; DNMT3A G298W; DNMT3A V328F; DNMT3A



G511E; DNMT3A C537Y; DNMT3A W581C; DNMT3A W581R;



DNMT3A R635W; DNMT3A V636L; DNMT3A S663P; DNMT3A



E664K; DNMT3A R676W; DNMT3A I681T; DNMT3A G699S;



DNMT3A S714C; DNMT3A V716I; DNMT3A T727A; DNMT3A F734L;



DNMT3A T862N; DNMT3A R882C; DNMT3A R882H; DNMT3A



R882S;


NPM1
Insertion/deletion mutations in NPM1 which disrupt the N-terminal



nucleolar localization signal of nueleophosmin and generate a nuclear



export signal in its place were identified.



NPM1 p.W288fs*12; NPM1 p.W288fs*16; NPM1 p.W290fs*8; NPM1



p.W290fs*10; NPM1 p.W290_K292>CFSK


NRAS
Activating mutations in NRAS were identified.



NRas G12A; NRas G12D; NRas G12S, NRas G13D; NRas G13R; NRas



Q61R; NRas Q61E; NRas Q61H; NRas Q61K; NRas Q61R; NRas



Q64D


CEBPA
Mutations in CEBPA were identified as (1) out-of-frame



insertions/deletions (2) nonsense mutations and (3) somatic missense



mutations. All of these mutations have been previously identified as



somatic mutations and were shown to either result in a predicted shorter



protein product with altered function or to affect dimerization of CEBPA.



Insertions/deletions:



CEBPA FS at AA 13; CEBPA FS at AA 15; CEBPA FS at AA 20;



CEBPA FS at AA 28; CEBPA FS at AA 35; CEBPA FS at AA 50;



CEBPA FS at AA 93; CEBPA FS at AA 190; CEBPA FS at AA 195;



CEBPA FS at AA 197; CEBPA FS at AA301; CEBPA FS at AA 303;



CEBPA FS at AA 305; CEBPA FS at AA 308; CEBPA FS at AA 309;



CEBPA FS at AA 311; CEBPA FS at AA 312; CEBPA FS at AA 313;



CEBPA FS at AA 315.



Nonsense mutations:



CEBPA K275X; CEBPA E329X



Somatic missense mutations:



CEBPA R291C; CEBPA R300H; CEBPA L335R; CEBPA R339P.


TET2
Mutations in TET2 were found as out-of-frame insertions/deletions



predicted to result in loss of functional protein, nonsense mutations also



predicted to result in loss of functional protein, and somatic missense



mutations. Any out-of-frame insertion/deletion or somatic nonsense



mutation would be scored as a mutation in our algorithm.



Insertions/deletions:



TET2 FS at AA 270; TET2 FS at AA 586; TET2 FS at AA 912; TET2 FS



at AA 921; TET2 FS at AA 958; TET2 FS at AA 966; TET2 FS at AA



1034; TET2 FS at AA 1114; TET2 FS at AA 1118; TET2 FS at AA



1299; TET2 FS at AA 1322; TET2 FS at AA 1395; TET2 FS at AA



1439; TET2 FS at AA1448; TET2 FS at AA 1893; TET2 FS at AA1960.



Nonsense mutations:



TET2 S327X; TET2 K433X; TET2 R544X; TET2 R550X; TET2 Q622X;



TET2 Q891X; TET2 Q916X; TET2 W1003X; TET2 E1405X; TET2



S1486X; TET2 Q1524X; TET2 Y1902X



Missense mutations:



TET2 P426L; TET2 E452A; TET2 F868L; TET2 Q1021R; TET2



Q1084P; TET2 E1141K; TET2 H1219Y; TET2 N1260K; TET2 R1261C;



TET2 G1283D; TET2 W1292R; TET2 R1365H; TET2 G1369V; TET2



R1572W; TET2 H1817N; TET2 E1851K; TET2 I1873T; TET2 R1896M;



TET2 S1898F; TET2 P1962L


WT1
Mutations in WT1 were identified as out-of-frame insertion/deletions as



well as somatic nonsense mutations all of which are predicted to disrupt



function of WT1. Somatic missense mutations were also identified.



Insertions/Deletions:



WT1 FS at AA 95; WT1 FS at AA 123; WT1 FS at AA 303; WT1 FS at



AA 368; WT1 FS at AA 369; WT1 FS at AA 370; WT1 FS at AA 371;



WT1 FS at AA 377; WT1 FS at AA 380; WT1 FS at AA 381; WT1 FS at



AA 390; WT1 FS at AA 395; WT1 FS at AA 409; WT1 FS at AA 420;



WT1 FS at AA 471.



Nonsense mutations:



WT1 E302X; WT1 C350X; WT1 S381X; WT1 K459X



Missense mutations:



WT1 G60R; WT1 M250T; WT1 C350R; WT1 T337R.


IDH2
Gain-of-function point mutations in IDH2 were found.



IDH2 R140Q, IDH2 R172K


IDH1
Gain-of-function point mutations in IDH1 were found.



IDH1 R132C, IDH1 R132G, IDH1 R132H, IDH1 R132S.


KIT
Somatic missense mutations in KIT which result in hyperactivation of



KIT signaling were identified. These are found as missense mutations at



amino acid 816 or in-frame deletions in exon 8.



In-frame deletions:



KIT FS at AA 418; KIT FS at AA 530.



Somatic missense mutations:



KIT D816Y; KIT D816V.


RUNX1
Mutations in RUNX1 were found as somatic out-of-frame



insertion/deletion mutations and nonsense mutations which are all



predicted to result in loss-of-function. Somatic missense mutations were



also found. Any out-of-frame insertion/deletion or somatic nonsense



mutation would be scored as a mutation in the algorithm.



Somatic insertions/deletions:



RUNX1 FS at AA 135.; RUNX1 FS at AA 147; RUNX1 FS at AA 183;



RUNX1 FS at AA 185; RUNX1 FS at AA 220; RUNX1 FS at AA 236;



RUNX1 FS at AA 321; RUNX1 FS at AA 340; RUNX1 FS at AA 415.



Somatic nonsense mutations:



RUNX1 Y140X; RUNX1 R204X; RUNX1 Q272X; RUNX1 E316X;



RUNX1 Y414X.



Somatic missense mutations:



RUNX1 E8Q; RUNX1 G24A; RUNX1 V31A; RUNX1 L56S; RUNX1



W106C; RUNX1 F158S; RUNX1 D160A; RUNX1 D160E; RUNX1



R166G; RUNX1 S167T; RUNX1 G168E; RUNX1 D198N; RUNX1



R232W.


MLL
Somatic insertions which result in partial tandem duplications in MLL



were identified.


ASXL1
Mutations in ASXL1 were found as somatic out-of-frame



insertion/deletion mutations and nonsense mutations which are all



predicted to result in loss-of-function. Somatic missense mutations were



also found. Any out-of-frame insertion/deletion or somatic nonsense



mutation would be scored as a mutation in the algorithm.



ASXL1 FS at AA 590; ASXL1 FS at AA 630; ASXL1 FS at AA 633;



ASXL1 FS at AA 634; ASXL1 FS at AA 640; ASXL1 FS at AA 685;



ASXL1 FS at AA 890.



Somatic nonsense mutations:



ASXL1 C594X; ASXL1 R693X; ASXL1 R1068X



Somatic missense mutations:



ASXL1 E348Q; ASXL1 M1050V.


PHF6
Somatic out-of-frame insertion/deletion mutations, missense mutations,



and nonsense mutations were seen in PHF6, all of which are predicted



to result in a loss-of-function. Any out-of-frame insertion/deletion or



somatic nonsense mutation would be scored as a mutation in the



algorithm.



Insertion/deletions:



PHF6 FS at AA 176.



Nonsense mutations:



PHF6 R274X; PHF6 G291X; PHF6 Y301X.



Somatic missense mutations:



PHF6 I115K; PHF6 I314T; PHF6 H329L; PHF6 L362P.


KRAS
Activating mutations in KRAS were seen.



KRas G12D; KRas G12S; KRas G12V; KRas G13D; KRas I36M; KRas



Q61H.


PTEN
Somatic missense mutations in PTEN were identified which result in



loss-of-function of PTEN. Any out-of-frame insertion/deletion or



somatic nonsense mutation would be scored as a mutation in the



algorithm.



PTEN H75L; PTEN N82Y; PTEN R142W; PTEN R308H; PTEN



P339S; PTEN S380C; PTEND386G


TP53
Mutations in TP53 were found as somatic out-of-frame



insertion/deletions, nonsense mutations, and missense mutations all of



which are predicted to result in loss of TP53 function. Any out-of-frame



insertion/deletion or somatic nonsense mutation would be scored as a



mutation in our algorithm.



Insertion/Deletions:



TP53 FS at AA 30; TP53 FS at AA 31; TP53 FS at AA 45; TP53 FS at



AA 93; TP53 FS at AA 337.



Nonsense mutations:



TP53 R213X



Misense mutations:



TP53 S20L; TP53 F54L; TP53 H193R; TP53 R196Q; TP53 C242Y;



TP53 R267Q); TP53 R273H; TP53 T284P; TP53 G356R.









Based on the present studies, a revised risk stratification for AML patients was devised. First, patients with internal tandem duplications in FLT3, partial tandem duplications in MLL, or mutations in ASXL1 or PHF6 are considered to have adverse overall survival regardless of cytogenetic characteristics. In contrast, patients with mutations in IDH2 at R140 or mutations in CEBPA are predicted to have favorable overall risk. For patients who do not have any of the above molecular alterations, cytogenetic status is then considered in order to determine overall risk. Cytogenetic status is defined in this prediction algorithm based on the study by Slovak, M et al. Blood 2000; 96:4075-83. In this cytogenetic classification, patients with cytogenetic alterations denoted as predicting for favorable cytogenetic risk (t(8;21), inv(16), or t(16;16)) or adverse cytogenetic risk (del(5q)/25, 27/del(7q), abn 3q, 9q, 11q, 20q, 21q, 17p, t(6;9), t(9;22) and complex karyotypes (≧3 unrelated abn)) are predicted to have an overall favorable risk or an overall adverse risk respectively. Patients which do not have any of the aforementioned favorable or adverse cytogenetic alterations, are then considered to have cytogenetically defined intermediate-risk AML. Such patients with cytogenetically defined intermediate-risk AML are further subdivided based on the presence or absence of the FLT3ITD mutation to determine overall risk. Patients with cytogenetically-defined intermediate risk AML and no FLT3ITD mutation are expected to have (1) a favorable overall risk if they have mutations in both NPM1 and IDH1/2, (2) an unfavorable overall risk if they have mutations in any one of TET2, ASXL1, PHF6, or have the MLL-PTD mutation, (3) an intermediate overall risk if they have no mutations in TET2, ASXL1, PHF6, and no MLL-PTD mutation and no NPM1 mutation in the presence of an IDH1 or IDH2 mutation. In contrast, patients with cytogenetically-defined intermediate risk AML and the presence of the FLT3ITD mutation are expected to have (1) an intermediate overall risk if they have a CEBPA mutation as well, (2) an unfavorable overall risk if they have a mutation in TET2 or DNMT3A, or have the MLL-PTD mutation or trisomy 8, (3) an intermediate overall risk if they have no mutations in TET2, DNMT3A, and no MLL-PTD mutation and no trisomy 8. In addition to the above algorithm which serves to predict overall risk at the time of diagnosis of AML patients, the present study also identified molecular predictors for response to high-dose induction chemotherapy for AML. In this part of the study, patients with mutations in any one of DNMT3A or NPM1 or an MLL-translocation/rearrangement were found to have an improved overall survival after induction chemotherapy compared with patients with no mutations in DNMT3A or NPM1 and no MLL-translocation/rearrangement.


In one embodiment, expression of nucleic acid markers is used to select clinical treatment paradigms for acute myeloid leukemia. Treatment options, as described herein, may include but are not limited to chemotherapy, radiotherapy, adjuvant therapy, or any combination of the aforementioned methods. Aspects of treatment that may vary include, but are not limited to: dosages, timing of administration, or duration or therapy; and may or may not be combined with other treatments, which may also vary in dosage, timing, or duration.


One of ordinary skill in the medical arts may determine an appropriate treatment paradigm based on evaluation of differential mutational profile of one or more nucleic acid targets identified. In one embodiment, cancers that express markers that are indicative of acute myeloid leukemia and poor prognosis may be treated with more aggressive therapies, as taught above. In another embodiment, where the gene mutations that are indicative of being a poor responder to one or more therapies may be treated with one or more alternative therapies.


In one embodiment, the sample is obtained from blood by phlebotomy or by any suitable means in the art, for example, by fine needle aspirated cells, e.g. cells from the bone marrow. The sample may comprise one or more mononuclear cells. A sample size required for analysis may range from 1, 100, 500, 1000, 5000, 10,000, to 50,000, 10,000,000 or more cells. The appropriate sample size may be determined based on the cellular composition and condition of the sample and the standard preparative steps for this determination and subsequent isolation of the nucleic acid and/or protein for use in the invention are well known to one of ordinary skill in the art.


Without limiting the scope of the present invention, any number of techniques known in the art can be employed for profiling of acute myeloid leukemia. In one embodiment, the determining step(s) comprises use of a detection assay including, but not limited to, sequencing assays, polymerase chain reaction assays, hybridization assays, hybridization assay employing a probe complementary to a mutation, fluorescent in situ hybridization (FISH), nucleic acid array assays, bead array assays, primer extension assays, enzyme mismatch cleavage assays, branched hybridization assays, NASBA assays, molecular beacon assays, cycling probe assays, ligase chain reaction assays, invasive cleavage structure assays, ARMS assays, and sandwich hybridization assays. In some embodiments, the detecting step is carried out using cell lysates. In some embodiments, the methods may comprise detecting a second nucleic acid target. In one embodiment, the second nucleic acid target is RNA. In one embodiment, the determining step comprises polymerase chain reaction, microarray analysis, immunoassay, or a combination thereof.


In one embodiment of the presently claimed method, mutations in one or more of the FLT3-ITD, DNMT3A, NPM1, IDH1, TET2, KIT, MLL-PTD, ASXL1, WT1, PHF6, CEBPA, IDH2 genes provides information about survival and/or response to therapy, wherein mutations in one or more of said genes is associated with a change in overall survival. One embodiment of the present invention further comprises detecting the mutational status of one or more genes selected from the group consisting of TET2, ASXL1, DNMT3A, PHF6, WT1, TP53, EZH2, RUNX1, PTEN, FLT3, CEBPA, MLL, HRAS, KRAS, NRAS, KIT, IDH1, and IDH2.


Identification of predictors that precisely distinguish individuals who will and will not experience a durable response to standard acute myeloid leukemia therapy is needed. The inventors of the present application identified a need for a consensus gene profile that is reproducibly associated with patient outcome for acute myeloid leukemia. In particular, the inventors of the present application have discovered certain mutations of genes in patients with acute myeloid leukemia correlate with poor survival and patient outcome. In one embodiment, the method is screening an individual for acute myeloid leukemia prognosis. In another embodiment, the method is screening an individual for response to acute myeloid leukemia therapy.


In one embodiment, the coding regions of one or more of the genes from the group consisting of TET2, ASXL1, DNMT3A, PHF6, WT1, TP53, EZH2, NPM1, CEBPA, RUNX1, and PTEN, and coding exons of one or more of the genes from the group consisting of FLT3, HRAS, KRAS, NRAS, KIT, IDH1, and IDH2 were assayed to detect the presence of mutations. In a particular embodiment, the mutational status of one or more of the FLT3-ITD, MLL-PTD, ASXL1, PHF6, DNMT3A, IDH2, and NPM1 genes provides information about survival and/or response to therapy. The acute myeloid leukemia can be newly diagnosed, relapsed or refractory acute myeloid leukemia.


One embodiment of the present invention is directed to a kit for determining treatment of a patient with AML, the kit comprising means for detecting a mutation in at least one gene selected from the group consisting of ASXL1, DNMT3A, NPM 1, PHF6, WT1, TP53, EZH2, CEBPA, TET2, RUNX1, PTEN, FLT3, HRAS, KRAS, NRAS, KIT, IDH1, and IDH2; and instructions for recommended treatment based on the presence of a mutation in one or more of said genes. In one example, the instructions for recommended treatment for the patient based on the presence of a DNMT3A or NPM1 mutation or MLL translocation indicate high-dose daunorubicin as the recommended treatment.


Kits of the invention may comprise any suitable reagents to practice at least part of a method of the invention, and the kit and reagents are housed in one or more suitable containers. For example, the kit may comprise an apparatus for obtaining a sample from an individual, such as a needle, syringe, and/or scalpel. The kit may include other reagents, for example, reagents suitable for polymerase chain reaction, such as nucleotides, thermophilic polymerase, buffer, and/or salt. The kit may comprise a substrate comprising polynucleotides, such as a microarray, wherein the microarray comprises one or more of the genes ASXL1, DNMT3A, PHF6, NPM1, CEBPA, TET2, WT1, TP53, EZH2, RUNX1, PTEN, FLT3, HRAS, KRAS, NRAS, KIT, IDH1, and IDH2.


In another embodiment, an array comprises polynucleotides hybridizing to at least 2, or at least 3, or at least 5, or at least 8, or at least 11, or at least 18 of the genes: TET2, ASXL1, DNMT3A, PHF6, WT1, TP53, EZH2, RUNX1, PTEN, FLT3, HRAS, KRAS, NRAS, NPM1, CEPA, KIT, IDH1, and IDH2. In one embodiment, the arrays comprise polynucleotides hybridizing to all of the listed genes.


As noted, the drugs of the instant invention can be therapeutics directed to gene therapy or antisense therapy. Oligonucleotides with sequences complementary to an mRNA sequence can be introduced into cells to block the translation of the mRNA, thus blocking the function of the gene encoding the mRNA. The use of oligonucleotides to block gene expression is described, for example, in, Strachan and Read, Human Molecular Genetics, 1996. These antisense molecules may be DNA, stable derivatives of DNA such as phosphorothioates or methylphosphonates, RNA, stable derivatives of RNA such as 2′-O-alkylRNA, or other antisense oligonucleotide mimetics. Antisense molecules may be introduced into cells by microinjection, liposome encapsulation or by expression from vectors harboring the antisense sequence.


One aspect of the present disclosure is a method of treating, preventing or managing acute myeloid leukemia in a patient, comprising, analyzing a genetic sample isolated from the patient for the presence of a mutation in genes DNMT3A, and NPM1, and for the presence of a MLL translocation; identifying the patient as one who will respond to high dose chemotherapy better than standard dose chemotherapy if a mutation in DNMT3A or NPM1 or a MLL translocation are present; and administering high dose therapy to the patient. The patient, in one example, is characterized as intermediate-risk on the basis of cytogenetic analysis. In one example, the therapy comprises the administration of anthracycline. In a related embodiment, administering high dose therapy comprises administering one or more high dose anthracycline antibiotics selected from the group consisting of Daunorubicin, Doxorubicin, Epirubicin, Idarubicin, Mitoxantrone, and Adriamycin. In one embodiment, Daunorubicin, Idarubicin and/or Mitoxantrone is used.


In one embodiment, the high dose administration is Daunorubicin administered at 60 mg per square meter of body-surface area (60 mg/m2), or higher, daily for three days. In a particular embodiment, the high dose administration is Daunorubicin administered at about 90 mg per square meter of body-surface area (90 mg/m2), daily for three days. In one embodiment, the high dose daunorubicin is administered at about 70 mg/m2 to about 140 mg/m2. In a particular embodiment, the high dose daunorubicin is administered at about 70 mg/m2 to about 120 mg/m2. In a related embodiment, this high dose administration is given each day for three days, that is for example a total of about 300 mg/m2 over the three days (3×100 mg/m2). In another example, this high dose is administered daily for 2-6 days. In other clinical situations, an intermediate daunorubicin dose is administered. In one embodiment, the intermediate dose daunorubicin is administered at about 60 mg/m2. In one embodiment, the intermediate dose daunorubicin is administered at about 30 mg/m2 to about 70 mg/m2. Additionally, the related anthracycline idarubicin, in one embodiment, is administered at from about 4 mg/m2 to about 25 mg/m2. In one embodiment, the high dose idarubicin is administered at about 10 mg/m2 to 20 mg/m2. In one embodiment, the intermediate dose idarubicin is administered at about 6 mg/m2 to about 10 mg/m2. In a particular embodiment, idarubicin is administered at a dose of about 8 mg/m2 daily for five days. In another example, this intermediate dose is administered daily for 2-10 days.


In another aspect, the present disclosure is a method for preparing a personalized genomics profile for a patient with acute myeloid leukemia, comprising: subjecting mononuclear cells extracted from a bone marrow aspirate or blood sample from the patient to gene mutational analysis; assaying the sample and detecting the presence of trisomy 8 and one or more mutations in a gene selected from the group consisting of FLT3ITD, NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, MLL-PTD, ASXL1, PHF6, KRAS, PTEN, P53, HRAS, and EZH2 in said cells; and generating a report of the data obtained by the gene mutation analysis, wherein the report comprises a prediction of the likelihood of survival of the patient or a response to therapy.


Methods of monitoring gene expression by monitoring RNA or protein levels are known in the art. RNA levels can be measured by any methods known to those of skill in the art such as, for example, differential screening, subtractive hybridization, differential display, and microarrays. A variety of protocols for detecting and measuring the expression of proteins, using either polyclonal or monoclonal antibodies specific for the proteins, are known in the art. Examples include Western blotting, enzyme-linked immunosorbent assay (ELISA), radioimmunoassay (RIA), and fluorescence activated cell sorting (FACS).


Examples

The invention, having been generally described, may be more readily understood by reference to the following examples, which are included merely for purposes of illustration of certain aspects and embodiments of the present invention, and are not intended to limit the invention in any way.


Each of the applications and patents cited in this text, as well as each document or reference cited in each of the applications and patents (“application cited documents”), and each of the PCT and foreign applications or patents corresponding to and/or paragraphing priority from any of these applications and patents, and each of the documents cited or referenced in each of the application cited documents, are hereby expressly incorporated herein by reference. More generally, documents or references are cited in this text, either in a Reference List or in the text itself; and, each of these documents or references (“herein-cited references”), as well as each document or reference cited in each of the herein-cited references (including any manufacturer's specifications, instructions, etc.), is hereby expressly incorporated herein by reference.


Patients


Mutational analysis was performed on diagnostic patient samples from the ECOG E1900 trial in the test (n=398) and validation (n=104) cohorts. The test cohort comprised of all E1900 patients for whom viably frozen cells were available for DNA extraction and mutational profiling. The validation cohort comprised of a second set of patients for whom samples were banked in Trizol, which was used to extract DNA for mutational studies.


Clinical characteristics of the patients studied compared to the complete E1900 trial cohort are in Table 1. The median follow-up time of patients included for analysis was 47.4 months from induction randomization. Cytogenetic analysis, fluorescent in situ hybridization, and RT-PCR for recurrent cytogenetic lesions was performed as described initially by Slovak et al. and utilized previously with central review by the ECOG Cytogenetics Committee (see ref. 16 and 17).


Mutational Analysis


Source of the DNA was bone marrow for 55.2% (277/502) and peripheral blood for 44.8% (225/502) of the samples. Applicants sequenced the entire coding regions of TET2, ASXL1, DNMT3A, CEBPA, PHF6, WT1, TP53, EZH2, RUNX1, and PTEN and the regions of previously described mutations for FLT3, NPM1, HRAS, KRAS, NRAS, KIT, IDH1, and IDH2.


The genomic coordinates and sequences of all primers utilized in the instant disclosure are provided for in Table 2. Paired remission DNA was available from 241 of the 398 samples in the initially analyzed cohort and 65 of the 104 in the validation cohort. Variants that could not be validated as bona fide somatic mutations due to unavailable remission DNA and their absence from the published literature of somatic mutations were censored with respect to mutational status for that specific gene. Further details of the sequencing methodology are provided infra.


Statistical Analysis


Mutual exclusivity of pairs of mutations was evaluated by fourfold contingency tables and Fisher's exact test. The association between mutations and cytogenetic risk classification was tested using the chi-square test. Hierarchical clustering was performed using the Lance-Williams dissimilarity formula and complete linkage.


Survival time was measured from date of randomization to date of death for those who died and date of last follow-up for those who were alive at the time of analysis. Survival probabilities were estimated using the Kaplan-Meier method and compared across mutant and wild-type patients using the log-rank test. Multivariate analyses were conducted using the Cox model with forward selection. Proportional hazards assumption was checked by testing for a non-zero slope in a regression of the scaled Schoenfeld residuals on functions of time (Table 3).


When necessary, such as the analyses performed in various subsets, results of the univariate analyses were used to select the variables to be included in the forward variable search. Final multivariate models informed the development of novel risk classification rules. When indicated, p-values were adjusted to control the family wise error rate (FWER) using the complete null distribution approximated by resampling obtained through PROC MULTTEST in SAS or the multtest library in R19. These adjustements were performed to adjust for the probability of making one or more false discoveries given that multiple pairwise tests were being performed. The only exception is adjustment for tests regarding effect of mutations on response to induction dose where a step-down Holm procedure was used to correct for multiple testing. All analyses were performed using SAS 9.2 (www.sas.com) and R 2.12 (www.r-project.org).


Supplementary Methods


Diagnostic Samples from ECOG 1900 Clinical Trial: DNA was isolated from pretreatment bone marrow samples of 398 patients enrolled in the ECOG E1900 trial; DNA was isolated from mononuclear cells after Ficoll purification. IRB approval was obtained at Weill Cornell Medical College and Memorial Sloan Kettering Cancer Center. All genomic DNA samples were whole genome amplified using 029 polymerase. Remission DNA was available from 241 patients who achieved complete remission after induction chemotherapy. Cytogenetic, fluorescent in situ hybridization, and RT-PCR for recurrent cytogenetic lesions was performed as described previously (Bullinger et al., N Engl J Med 2004, 350, 1605-1616) with central review by the ECOG Cytogenetics Committee.


Integrated Mutational Analysis:


Mutational analysis of the entire coding regions of TET2, ASXL1, DNMT3A, PHF6, WT1, TP53, NPM1, CEBPA, EZH2, RUNX1, and PTEN and of coding exons of FLT3, HRAS, KRAS, NRAS, KIT, IDH1, and IDH2 with known somatic mutations was performed using PCR amplification and bidirectional Sanger sequencing as previously described. 13 Primer sequences and PCR conditions are provided in Table 1.


Target regions in individual patient samples were PCR amplified using standard techniques and sequenced using conventional Sanger sequencing, yielding 93.3% of all trimmed reads with an average quality score of 20 or more. All traces were reviewed manually using Mutation Surveyor (SoftGenetics, State College, Pa.). All variants were validated by repeat PCR amplification and Sanger resequencing of unamplified diagnostic DNA. All mutations which were not previously reported to be either somatic or germline were analyzed in matched remission DNA, when available, to determine somatic status. All patients with variants whose somatic status could not be determined were censored with regard to mutational status for the specific gene.


NPM1/CEBPA Next-Generation Sequencing Analysis:


A mononucleotide tract near the canonical frameshift mutations in NPM1 and the high GC content of the CEBPA gene limited Applicants' ability to obtain sufficiently high quality Sanger sequence traces for primary mutation calling. Applicants therefore performed pooled amplicon resequencing of NPM1 and CEBPA using the SOLiD 4 system. We performed PCR amplification followed by barcoding (20 pools each with 20 samples) and SOLiD sequencing. The data was processed through the Bioscope pipeline: all variants not present in reference sequence were manually inspected and validated by repeat PCR amplification and Sanger sequencing.


Mutational Cooperativity Matrix:


Applicants adapted the Circos graphical algorithm to visualize co-occuring mutations in AML patients. The arc length corresponds to the proportion of patient with mutations in the first gene and the ribbon corresponds to the percentage of patients with a coincident mutation in the second gene. Pairwise cooccurrence of mutations is denoted only once, beginning with the first gene in the clockwise direction. Since only pairwise mutations are encoded for clarity, the arc length was adjusted to maintain the relative size of the arc and the correct proportion of patients with a single mutant allele is represented by the empty space within each mutational subset.


Statistical Analysis:


Mutual exclusivitity of pairs of mutations were evaluated by fourfold contingency tables and Fisher's exact test. The association between mutations and cytogenetic risk classification was tested using the chi-square test. Hierarchical clustering was performed using the Lance-Williams dissimilarity formula and complete linkage. Survival time was measured from date of randomization to date of death for those who died and date of last follow-up for those who were alive at the time of analysis. Survival probabilities were estimated using the Kaplan-Meier method and compared across mutant and wildtype patients using the log-rank test. Multivariate analyses were conducted using the Cox model. Proportional hazards assumption was checked by testing for a non-zero slope in a regression of the scaled Schoenfeld residuals on functions of time. Many of the statistical analyses conducted in this study use Cox regression which depends on the assumption of proportional hazards.


Table 3 shows the results of the checks which were conducted for each mutation to determine whether the resultant survival curves (one curve for mutant and one curve for wildtype for each mutation) satisfy this assumption. A significant p-value indicates a departure from the proposal hazard assumption. Out of the 27 mutations included in this study, only a single one significantly deviated from proportional hazards (MLL-PTD, p=0.04). Considering the possible multiple testing problem, one would have expected 1-2 significances in this table by chance only hence Applicants conclude that it is acceptable to use the Cox regression model for all mutations. Forward model selection was employed. When necessary, such as the analyses performed in various subsets, results of the univariate analyses were used to select the variables to be included in the forward variable search. Final multivariate models informed the development of novel risk classification rules. All analyses were performed using SAS 9.2 (www.sas.com) and R 2.12 (www.r-project.org).


Frequency of Genetic Alterations in De Novo AML.


Somatic alterations were identified in 97.3% of patients. FIGS. 1A-C show the frequency of somatic mutations in the entire cohort and the interrelationships between the various mutations visually represented using a Circos plot. Data for all molecular subsets are provided in FIGS. 6 and 7 and Tables 4 and 5. In particular, mutational heterogeneity in patients with intermediate risk AML was higher than in patients with favorable or unfavorable risk AML (p=0.01; FIG. 7D).


Mutational Complementation Groups in AML.


Integrated mutational analysis allowed Applicants to identify frequently co-occurring mutations and mutations that were mutually exclusive in the E1900 patient cohort (Table 6). In addition to noting a frequent co-occurrence between KIT mutations and core-binding factor alterations t(8;21) and inv(16)/t(16;16) (p<0.001), Applicants found significant co-occurrence of IDH1 or IDH2 mutations with NPM1 mutations (p<0.001), and DNMT3A mutations with NPM1, FLT3, and IDH1 alleles (p<0.001 for all) (Table 7). Applicants previously reported IDH1 and IDH2 mutations were mutually exclusive with TET2 mutations; detailed mutational analysis revealed that IDH1/2 mutations were also exclusive with WT1 mutations (p<0.001; FIG. 8 and Table 8). Applicants also observed that DNMT3A mutations and MLL-translocations were mutually exclusive (p<0.01).


Molecular Determinants of Overall Survival in AML.


Univariate analysis revealed that FLT3 internal tandem duplication (FLT3-ITD) (p=0.001) and MLL partial tandem duplication (MLL-PTD) (p=0.009) mutations were associated with adverse OS (Table 9), while CEBPA (p=0.05) mutations and patients with core-binding factor alterations t(8;21) and inv(16)/t(16;16) (p<0.001) were associated with improved OS.2,23 In addition, PHF6 (p=0.006) and ASXL1 (p=0.05) mutations were associated with reduced OS (FIG. 9). IDH2 mutations were associated with improved OS in the entire cohort (FIG. 10) (p=0.01; 3 year OS=66%). The favorable impact of IDH2 mutations was exclusive to patients with IDH2 R140Q mutations (p=0.009; FIG. 10). All findings in univariate analysis were also statistically significant in multivariate analysis (adjusted p<0.05) (taking into account age, white blood cell count, transplantation and cytogenetics) (Table 9) with the exception of MLL-PTD, PHF6 and ASXL1 mutations. KIT mutations were associated with reduced OS in t(8;21)-positive AML (p=0.006) but not in patients with inv(16)/t(16;16) (p=0.19) (FIG. 11).


Prognostic Value of Molecular Alterations in Intermediate-Risk AML.


Amongst patients with cytogenetically-defined intermediate-risk AML (Table 10), FLT3-ITD mutations were associated with reduced OS (p=0.008). Similar to their effect on the entire cohort, ASXL1 and PHF6 mutations were associated with reduced survival and IDH2 R140Q mutations were associated with improved survival (Table 10). In addition, Applicants found that TET2 mutations were associated with reduced OS in patients with intermediate-risk AML (p=0.007; FIG. 12).


Multivariate statistical analysis revealed that FLT3-ITD mutations represented the primary predictor of outcome in patients with intermediate-risk AML (adjusted p<0.001). Applicants then performed multivariate analysis to identify mutations that affected outcome in patients with FLT3-ITD wild-type and mutant intermediate-risk AML, respectively. In patients with FLT3-ITD wild-type intermediate-risk AML, TET2, ASXL1, PHF6, and MLL-PTD mutations were independently associated with adverse outcome. Importantly, patients with both IDH1/IDH2 and NPM1 mutations (3 year OS=89%) but not NPM1-mutant patients wild-type for both IDH1 and IDH2 (3 year OS=31%), had improved OS within this subset of patients (p<0.001, FIG. 13). We then classified patients with FLT3-ITD wild-type intermediate-risk AML into three categories with marked differences in OS (adjusted p<0.001, FIG. 2A): patients with IDH1/IDH2 and NPM1 mutations (3 year OS=89%), patients with either TET2, ASXL1, PHF6, or MLL-PTD mutations (3 year OS=6.3%), and patients wild-type for TET2, ASXL1, PHF6, and MLL-PTD without co-occurring IDH1/NPM1 mutations (3 year OS=46.2%). Similar results were obtained when analysis was restricted to patients with a normal karyotype (FIG. 14A).


In patients with FLT3-ITD mutant, intermediate-risk AML, Applicants found that CEBPA mutations were associated with improved outcome and that trisomy 8 and TET2, DNMT3A, and MLL-PTD mutations were associated with adverse outcome. We used these data to classify patients with FLT3-ITD mutant intermediate-risk AML into three categories. The first category included patients with trisomy 8 or TET2, DNMT3A, or MLL-PTD mutations, which were associated with adverse outcome (3 year OS=14.5%) significantly worse than for patients wild-type for CEBPA, TET2, DNMT3A, and MLL-PTD (3 year OS=35.2%; p<0.001) or for patients with CEBPA mutations (3 year OS=42%; p<0.001, FIG. 2B). The survival of patients with FLT3-ITD mutant intermediate-risk AML who were wild-type for CEBPA, TET2, DNMT3A, and MLL-PTD did not differ from patients with CEBPA-mutant/FLT3-ITD mutant AML (p=0.34), suggesting that the presence of poor risk mutations more precisely identifies FLT3-ITD mutant AML patients with adverse outcome than the absence of CEBPA mutations alone. These same three risk groups also had significant prognostic value in FLT3-ITD mutant, normal karyotype AML (FIG. 14B).


Prognostic Schema Using Integrated Mutational and Cytogenetic Profiling.


These results allowed us to develop a prognostic schema integrating our findings from comprehensive mutational analysis with cytogenetic data into 3 risk groups with favorable (median: not reached, 3-year: 64%), intermediate (25.4 months, 42%), and adverse risk (10.1 months, 12%) (FIGS. 3A and 3B, Table 11). The mutational prognostic schema predicted for outcome independent age, WBC count, induction dose, and transplantation status in multivariate analysis (adjusted p<0.001). Our classification held true regardless of post-remission therapy with autologous, allogeneic, or consolidation chemotherapy alone (FIG. 15). Given the number of variables on our prognostic classification, we tested the reproducibility of this predictor in an independent cohort of 104 patients from the ECOG E1900 trial. Importantly, mutational analysis of the validation cohort confirmed the reproducibility of our prognostic schema to predict outcome in AML (adjusted p<0.001; FIG. 3C). The mutational prognostic schema was independent of treatment-related mortality (death within 30 days) or lack of response to induction chemotherapy (inability to achieve complete remission) in the test cohort and in the combined test/validation cohorts (Table 12).


Genetic Predictors of Response to Induction Chemotherapy.


Recent studies noted that DNMT3A-mutant AML is associated with adverse outcome. However, Applicants here found that DNMT3A mutations were not associated with adverse outcome in the ECOG 1900 cohort (FIG. 4A; p=0.15). The ECOG 1900 trial randomized patients to induction therapy with cytarabine plus 45 or 90 mg/m2 daunorubicin (Fernandez et al. N Eng J Med 2009, 361: 1249-1259). Applicants therefore conceived that high dose daunorubicin improved outcomes in AML patients with DNMT3A mutations. Indeed Applicants found that DNMT3A mutational status had a significant impact on the outcome with dose-intensive chemotherapy (FIG. 4B; p=0.02).


Applicants then assessed the effects of DNMT3A mutational status on outcome according to treatment arm, and found that high-dose daunorubicin was associated with improved survival in DNMT3A mutant patients (FIG. 16A; p=0.04) but not in patients wild-type for DNMT3A (FIG. 16B; p=0.15). In addition to DNMT3A mutations, univariate analysis revealed that dose-intensified induction therapy improved outcome in AML patients with MLL translocations (FIGS. 16C and 11D; p=0.01; p-value adjusted for multiple-testing=0.06) and NPM1 mutations (FIGS. 16E and 11F; p=0.01; p-value adjusted for multiple-testing=0.1; Table 13).


Applicants then separated the patients in our cohort into two groups: patients with mutations in DNMT3A or NPM1 or MLL translocations, and patients wild-type for these 3 genetic abnormalities. Dose-intensive induction therapy was associated with a marked improvement in survival in DNMT3A/NPM1/MLL translocation-positive patients (FIG. 4C; p=0.001) but not in patients wild-type for DNMT3A, NPM1, and MLL translocations (FIG. 4D; p=0.67). This finding was independent of the clinical co-variates of age, WBC count, transplantation status, treatment-related mortality, and chemotherapy resistance (adjusted p=0.008 and p=0.34 for mutant and wild-type patients respectively), suggesting that high-dose anthracycline chemotherapy offers benefit to genetically defined AML subgroups.


All publications, patents, and patent applications mentioned herein are hereby incorporated by reference in their entirety as if each individual publication or patent was specifically and individually indicated to be incorporated by reference. In case of conflict, the present application, including any definitions herein, will control. While several aspects of the present invention have been described and depicted herein, alternative aspects may be effected by those skilled in the art to accomplish the same objectives. Those skilled in the art will recognize, or be able to ascertain using no more than routine experimentation, many equivalents to the specific embodiments of the invention described herein. Accordingly, it is intended by the appended claims to cover all such alternative aspects as fall within the true spirit and scope of the invention.












TABLE 1







Validation




Test cohort
cohort
Entire cohort


Variable
(N = 398)
(N = 104)
(N = 657)







Age





Group - no (%)


<50 yr
227 (57.0)
42 (40.8)
360 (54.8)


≧50 yr
171 (43.0)
61 (59.2)
297 (45.2)


Median - yr
46.5
53
48.0


Range - yr
18-60
18-60
17-60


Sex - no. (%)


Male
207 (52.0)
51 (49.5)
335 (51.0)


Female
191 (48.0)
52 (50.5)
322 (49.0)


Peripheral blood


white-cell count


Level - no. (%)


<10,000/mm3
123 (30.9)
84 (81.6)
306 (46.6)


≧10,000/mm3
275 (69.1)
18 (17.5)
350 (53.3)


Missing data
0 (0) 
1 (1)  
 1 (0.2)


Median - cells/
19.9
2.5
12.3


mm3 × 1000


Range - cells/
 1-213
 1-117
 1-366


mm3 × 1000


Hemoglobin


Level - no. (%)


<10 g/dl
276 (69.3)
77 (74.8)
464 (70.6)


≧10 g/dl
121 (30.4)
25 (24.3)
191 (29.1)


Missing data
 1 (0.3)
1 (1)  
 2 (0.3)


Median - g/dl
9.2
9.2
9.2


Range - g/dl
 5-30
 5-14
 5-30


Peripheral-blood


platelet count


Level - no. (%)


<50,000/mm3
194 (48.7)
43 (41.7)
305 (46.4)


≧50,000/mm3
204 (51.3)
59 (57.3)
351 (53.4)


Missing data
0 (0) 
1 (1)  
 1 (0.2)


Median - g/dl
50.0
61
50.0


Range - g/dl
 1-650
 6-995
 1-995


Blasts


Peripheral blood


Median %
47.5
8
31


Range %
 0-98
 0-99
 0-99


Bone Marrow


Median %
68.5
49
64.0


Range %
 3-100
 17-100
 3-100


Leukemia


Classification - no


(%)


Not reviewed
0 (0) 
0
21 (3.2)


AML Minimally
20 (5.0)
5 (4.9)
29 (4.4)


Differentiated


AML w/o Maturation
 96 (24.1)
22 (21.4)
155 (23.6)


AML w/ Maturation
 61 (15.3)
27 (26.2)
112 (17.0)


Acute myelomonocytic
 52 (13.1)
7 (6.8)
63 (9.6)


Leukemia


Acute monocytic/
27 (6.8)
3 (2.9)
40 (6.1)


monoblastic Leukemia


Acute erythroid
 8 (2.0)
6 (5.8)
29 (4.4)


Leukemia


Acute
0 (0) 
2 (1.9)
 3 (0.5)


megakaryoblastic


Leukemia


Cytogenetic profile -


no. (%)


Favorable
 67 (16.8)
10 (9.7) 
 89 (13.5)


Indeterminate
 85 (21.4)
22 (21.4)
176 (26.8)


Intermediate
180 (45.2)
42 (40.8)
267 (40.6)


Normal karyotype
163 (41.0)
42 (40.4)
244 (37.1)


Unfavorable
 65 (16.3)
29 (28.2)
122 (18.6)


Patients with
11/398 (2.8)   
4 (3.9)
22/657 (3.3)   


secondary AML


Survival (days)


Median
535.2
650.9
621
















TABLE 2







Genomic DNA primer sequences utilized for comprehensive genetic analysis.


All primer sequences are displayed with Ml3F2/M13R2 tags












Gene
Ganomic
Forward Orimer Sequence
SEQ ID NO.
Reverse Primer Sequence
SEQ ID NO.















ASXL1
chr20:30410194-30410296
GTAAAACGACGGCCAGTGGTCCTGTCTCAGTCCCTCA
1
CAGGAAACAGCTATGACCTCTTAAAGGAAGATGGCCCC
166



chr20:30417847-30417930
GTAAAACGACGGCCAGTCCAGCGGTACCTCATAGCAT
2
CAGGAAACAGCTATGACCGCGTTAGGCACAATAGAGGC
167



chr20:30420478-30420587
GTAAAACGACGGCCAGTTGGATTTCGGGTATCACATAA
3
CAGGAAACAGCTATGACCtccaagaatcaCTGCACCAA
168



chr20:30479591-30479712
GTAAAACGACGGCCAGTTCCCTCTTTTTCAAAAGCATACA
4
CAGGAAACAGCTATGACCACCCATCCATTAAAGGGTCC
169



chr20:30479788-30479886
GTAAAACGACGGCCAGTTTGCTGTCACAGAAGGATGC
5
CAGGAAACAGCTATGACCTGTCATCATTCATCCTCCCA
170



chr20:30480801-30480895
GTAAAACGACGGCCAGTAATGATGCTTGGCACAGTGA
6
CAGGAAACAGCTATGACCCAGAGCCCAGCACTAGAACC
171



chr20:30481364-30481517
GTAAAACGACGGCCAGTGGTTCTAGTGCTGGGCTCTG
7
CAGGAAACAGCTATGACCAAAATAGAGGGCCACCCAAG
172



chr20:30482784-30482948
GTAAAACGACGGCCAGTGCTTTGTGGAGCCTGTTCTC
8
CAGGAAACAGCTATGACCAGAAGGATCAAGGGGGAAAA
173



chr20:30483046-30483143
GTAAAACGACGGCCAGTGTCAAATGAAGCGCAACAGA
9
CAGGAAACAGCTATGACCGGAGACATGCAACACCACAC
174



chr20:30484343-30484449
GTAAAACGACGGCCAGTCAAGGAGTTGCTTGGTCTCA
10
CAGGAAACAGCTATGACCCACGTTCTGCTGCAATGACT
175



chr20:30484747-30485127
GTAAAACGACGGCCAGTCGACAGGAAATGGAGAAGGA
11
CAGGAAACAGCTATGACCTTCTGATCCTTGGGTTCCTG
176



chr20:30485128-30485381
GTAAAACGACGGCCAGTAAAAGTGGCTTGTGTGTCCC
12
CAGGAAACAGCTATGACCGGCTGTCTCAAGCAAACCTC
177



chr20:30485895-30486275
GTAAAACGACGGCCAGTGAGGTTTGCTTGAGACAGCC
13
CAGGAAACAGCTATGACCGAAGGCAGGTCCTCTCTCCT
178



chr20:30486276-30486655
GTAAAACGACGGCCAGTGGACCCTCGCAGACATTAAA
14
CAGGAAACAGCTATGACCTGTTCTGCAGGCAATCAGTC
179



chr20:30486656-30487035
GTAAAACGACGGCCAGTGCCATGTCCAGAGCTAGGAG
15
CAGGAAACAGCTATGACCTGGCACAGTCCAGAGTGAAG
180



chr20:30487036-30487415
GTAAAACGACGGCCAGTCTTGAAAACCAAGGCTCTCG
16
CAGGAAACAGCTATGACCCACAAGTGGGTTAGTGGCCT
181



chr20:30487416-30487795
GTAAAACGACGGCCAGTCAAGGTGAATGGTGACATGC
17
CAGGAAACAGCTATGACCCTGGATGGAGGGAGTCAAAA
182



chr20:30487796-30488175
GTAAAACGACGGCCAGTCTGAGTACCAGCCAAGAGCC
18
CAGGAAACAGCTATGACCAAGTGACCCACCAGTTCCAG
183



chr20:30488176-30488555
GTAAAACGACGGCCAGTTTTTGACTCCCTCCATCCAG
19
CAGGAAACAGCTATGACCACACTGGAGCGAGATGCTTT
184



chr20:30488556-30488935
GTAAAACGACGGCCAGTCTGGAACTGGTGGGTCACTT
20
CAGGAAACAGCTATGACCTATACCCAGGAAACCCCTCC
185





CEBPa
chr19:38483156-38483535
GTAAAACGACGGCCAGTGCAAGTATCCGAGCAAAACC
21
CAGGAAACAGCTATGACCGAGGAGGGGAGAATTCTTGG
186



chr19:38483156-38483535
GTAAAACGACGGCCAGTCCGACGGAGAGTCTCATTTT
22
CAGGAAACAGCTATGACCCCTGCTATAGGCTGGGCTTC
187



chr19:38483156-38483535
GTAAAACGACGGCCAGTGGAGAGGCGTGGAACTAGAG
23
CAGGAAACAGCTATGACCCTTGGTGCGTCTAAGATGAGG
188



chr19:38483536-38483915
GTAAAACGACGGCCAGTTCATAACTCCGGTCCCTCTG
24
CAGGAAACAGCTATGACCCTGGAGCTGACCAGTGACAA
189



chr19:38483916-38484295
GTAAAACGACGGCCAGTCATTTCCAAGGCACAAGGTT
25
CAGGAAACAGCTATGACCTGGACAAGAACAGCAACGAG
190



chr19:38484296-38484675
GTAAAACGACGGCCAGTTTGTCACTGGTCAGCTCCAG
26
CAGGAAACAGCTATGACCCCTTCAACGACGAGTTCCTG
181



chr19:38484296-38484675
GTAAAACGACGGCCAGTTTGTCACTGGTCAGCTCCAG
27
CAGGAAACAGCTATGACCCACCTGCAGTTCCAGATCG
182



chr19:38484296-38484675
GTAAAACGACGGCCAGTCAGGTGCATGGTGGTCTG
28
CAGGAAACAGCTATGACCATCGACATCAGCGCCTACAT
193



chr19:38484676-38485055
GTAAAACGACGGCCAGTCTCGTTGCTGTTCTTGTCCA
29
CAGGAAACAGCTATGACCCGGGAGAACTCTAACTCCCC
194



chr19:38484676-38485055
GTAAAACGACGGCCAGTCTCGTTGCTGTTCTTGTCCA
30
CAGGAAACAGCTATGACCCAGGCTGGAGCCCCTGTA
195



chr19:38484676-38485055
GTAAAACGACGGCCAGTGCTTGGCTTCATCCTCCTC
31
CAGGAAACAGCTATGACCTCGGCCGACTTCTACGAG
196



chr19:38485056-38485160
GTAAAACGACGGCCAGTATGTAGGCGCTGATGTCGAT
32
CAGGAAACAGCTATGACCCGGGAGAACTCTAACTCCCC
197





DNMT3a
chr2:25310489-25310793
GTAAAACGACGGCCAGTCCTCTCTCCCACCTTTCCTC
33
CAGGAAACAGCTATGACCCTGAGTGCCGGGTTGTTTAT




chr2:25312079-25312198
GTAAAACGACGGCCAGTGGAAAACAAGTCAGGTGGGA
34
CAGGAAACAGCTATGACCTGGATCTAAGATTGGCCAGG
199



chr2:25313308-25313378
GTAAAACGACGGCCAGTccacactagctggagaagca
35
CAGGAAACAGCTATGACCggggctcttaccctgtgaac
200



chr2:25315502-25315588
GTAAAACGACGGCCAGTcatggcagagcagctagtca
36
CAGGAAACAGCTATGACCtgtgtggctcctgagagaga
201



chr2:25316674-25316823
GTAAAACGACGGCCAGTAATACCCAACCCCAGGAGTC
37
CAGGAAACAGCTATGACCCTTCCTGTCTGCCTCTGTCC
202



chr2:25317012-25317103
GTAAAACGACGGCCAGTGAAGCCATTAGTGAGCTGGC
38
CAGGAAACAGCTATGACCCAACTTGGTCCCGTTCTTGT
203



chr2:25317934-25318080
GTAAAACGACGGCCAGTTTGCCAAAAGTATTGGGAGG
39
CAGGAAACAGCTATGACCCCAGTTGGATCCAGAAAGGA
204



chr2:25320270-25320355
GTAAAACGACGGCCAGTaagcttcccctttgggataa
40
CAGGAAACAGCTATGACCcagggtgtgtgggtctagga
205



chr2:25320527-25320711
GTAAAACGACGGCCAGTAGGGTCCTAAGCAGTGAGCA
41
CAGGAAACAGCTATGACCCGGTCTTTCCATTCCAGGTA
206



chr2:25320912-25321025
GTAAAACGACGGCCAGTaggtgtgctacctggaatgg
42
CAGGAAACAGCTATGACCcagggcttaggctctgtgag
207



chr2:25321625-25321705
GTAAAACGACGGCCAGTATCTGGGGACTAAAATGGGG
43
CAGGAAACAGCTATGACCCCTGGACTCTTTTCTGGCTG
208



chr2:25322392-25322437
GTAAAACGACGGCCAGTAGCAAAGGTGAAAGGCTGAA
44
CAGGAAACAGCTATGACCAGCCCAAGGTCAAGGAGATT
209



chr2:25322532-25322682
GTAAAACGACGGCCAGTTCCCAGGCAACAAACTTACC
45
CAGGAAACAGCTATGACCGAACAAGTTGGAGACCAGGC
210



chr2:25322992-25323149
GTAAAACGACGGCCAGTTCTTCTGGAGGAGGAAAGCA
46
CAGGAAACAGCTATGACCCCTGTGCCACCCTCACTACT
211



chr2:25323423-25323531
GTAAAACGACGGCCAGTAGTAGTGAGGGTGGCACAGG
47
CAGGAAACAGCTATGACCCTCCTCTTTGCATCGGGTAA
212



chr2:25323963-25324122
GTAAAACGACGGCCAGTCTTACACTTGCAAGCACCCA
48
CAGGAAACAGCTATGACCGCCTCGTGACCACTGTGTAA
213



chr2:25324409-25324625
GTAAAACGACGGCCAGTCATCCACCAAGACACAATGC
49
CAGGAAACAGCTATGACCCTGTCACTGTTCCGGGTTTT
214



chr2:25326029-25326097
GTAAAACGACGGCCAGTTCTTCTCCACAATTCCCCTG
50
CAGGAAACAGCTATGACCAGGGCCGTGTTTCCTAGATT
215



chr2:25328566-25328684
GTAAAACGACGGCCAGTCACTCTTTTCAAACCCGGAG
51
CAGGAAACAGCTATGACCgcgcTAATCTCTTCCAGAGC
216



chr2:25351313-25351460
GTAAAACGACGGCCAGTactgaggcccatcacttctg
52
CAGGAAACAGCTATGACCcattgtgtttgaggcgagtg
217



chr2:25351872-25351916
GTAAAACGACGGCCAGTCTTCCCACAGAGGGATGTGT
53
CAGGAAACAGCTATGACCgaaCAGCTAAACGGCCAGAG
218



chr2:25358585-25358964
GTAAAACGACGGCCAGTTACAATCACCCAGCCCTCTC
54
CAGGAAACAGCTATGACCAGCGGTCAATGATCCAAAAC
219



chr2:25358965-25359084
GTAAAACGACGGCCAGTAGCCAAGTCCCTGACTCTCA
55
CAGGAAACAGCTATGACCAGCGGTCAATGATCCAAAAC
220



chr2:25376511-25376616
GTAAAACGACGGCCAGTTTGAAGAATGGGGTACCTGC
56
CAGGAAACAGCTATGACCGGTGGGGGCATATTACACAG
221



chr2:25390285-25390534
GTAAAACGACGGCCAGTtgcggtcatgcaCTCAGTAT
57
CAGGAAACAGCTATGACCGATCCTCTTCTCTCCCCCAC
222





EZH2
chr7:148135407-148135731
GTAAAACGACGGCCAGTcttccacatattcacaggcagt
59
CAGGAAACAGCTATGACCcttcagcaggctttgttgtg
223



chr7:148137095-148137180
GTAAAACGACGGCCAGTGCGGCATGATATGAGAAGGT
59
CAGGAAACAGCTATGACCCGCAAGGGTAACAAAATTCG
224



chr7:148137334-148137415
GTAAAACGACGGCCAGTtggtgtcagtgagcatgaaga
60
CAGGAAACAGCTATGACCttttagattttgtggtggatgc
225



chr7:148138357-148138439
GTAAAACGACGGCCAGTCACAAGAGGTGAGGTGAGCA
61
CAGGAAACAGCTATGACCGTGACCCTTTTTGTTGCGTT
226



chr7:148139649-148139745
GTAAAACGACGGCCAGTAGCATGCAAATCCACAAACA
62
CAGGAAACAGCTATGACCGTGTGCCCAATTACTGCCTT
227



chr7:148141983-148142162
GTAAAACGACGGCCAGTTTTGCCCCAGCTAAATCATC
63
CAGGAAACAGCTATGACCgtacagcccttgccacgtaT
228



chr7:148142938-148143064
GTAAAACGACGGCCAGTCCTGCCTCACACACACAGAC
64
CAGGAAACAGCTATGACCCTTGGGGGTGGGAGAGTATT
229



chr7:148143530-148143571
GTAAAACGACGGCCAGTCGGCTACATCTCAGTCCCAT
65
CAGGAAACAGCTATGACCATTTGTAGCTTCCCGCAGAA
230



chr7:148144708-148144803
GTAAAACGACGGCCAGTCCAACAACAGCCCTTAGGAA
66
CAGGAAACAGCTATGACCCCCAGCATCTAGCAGTGTCA
231



chr7:148145246-148145416
GTAAAACGACGGCCAGTTGACACTGCTAGATGCTGGG
67
CAGGAAACAGCTATGACCGCCGATTGGATTTGAGTTGT
232



chr7:148145901-148146142
GTAAAACGACGGCCAGTACAACTCAAATCCAATCGGC
68
CAGGAAACAGCTATGACCTGCCCTGATGTTGACATTTT
233



chr7:148147620-148147712
GTAAAACGACGGCCAGTGAGAGGGGCTTGGGATCTAC
69
CAGGAAACAGCTATGACCTGCGCATCAGTTTTACTTGC
234



chr7:148154478-148154657
GTAAAACGACGGCCAGTTCAGAGCAATCCTCAAGCAA
70
CAGGAAACAGCTATGACCTTCTTGATAACACCATGCACAA
235



chr7:148155188-148155291
GTAAAACGACGGCCAGTAAGTGTAGTGGCTCATCCGC
71
CAGGAAACAGCTATGACCttctgcttcccagtgctctT
236



chr7:148156764-148156905
GTAAAACGACGGCCAGTccaccctacctggccATAAT
72
CAGGAAACAGCTATGACCTGCTTCCTTTGCCTAACACC
237



chr7:148157752-148157873
GTAAAACGACGGCCAGTGAGCCCCTATATGCCACAGA
73
CAGGAAACAGCTATGACCTGCTTATTGGTGAGAGGGGT
238



chr7:148160658-148160775
GTAAAACGACGGCCAGTctgtcttgattcaccttgacaat
74
CAGGAAACAGCTATGACCggctacagcttaaggttgtcct
239



chr7:148174494-148174623
GTAAAACGACGGCCAGTGGTCAATGATTTCCTCCCAA
75
CAGGAAACAGCTATGACCATGGCAATCGTTTCCTGTTC
240



chr7:148175206-148175330
CAGGAAACAGCTATGACCATGGCAATCGTTTCCTGTTC
76
CAGGAAACAGCTATGACCgcagcacaaatgagcacct
241





FLT3
chr13:27490603-27490726
GTAAAACGACGGCCAGTCCTGAAGCTGCAGAAAAACC
77
CAGGAAACAGCTATGACCTCCATCACCGGTACCTCCTA
242



chr13:27490603-27490726
GTAAAACGACGGCCAGTGTTGACACCCCAATCCACTC
78
CAGGAAACAGCTATGACCGTGACCGGCTCCTCAGATAA
243



chr13:27506218-27506351
GTAAAACGACGGCCAGTTTTCCAAAAGCACCTGATCC
79
CAGGAAACAGCTATGACCTCATTGTCGTTTTAACCCTGC
244





HRAS
chr11:523765-523944
GTAAAACGACGGCCAGTGATCTGCTCCCTGAGAGGTG
80
CAGGAAACAGCTATGACCAGAGGCTGGCTGTGTGAACT
245



chr11:523765-523944
GTAAAACGACGGCCAGTCTCCCTGGTACCTCTCATGC
81
CAGGAAACAGCTATGACCGTGGGTTTGCCCTTCAGAT
246





IDH1
chr2:208821337-208821629
GTAAAACGACGGCCAGTTGTGTTGAGATGGACGCCTA
82
CAGGAAACAGCTATGACCGGTGTACTCAGAGCCTTCGC
247





IDH2
chr15:88432822-88432983
GTAAAACGACGGCCAGTCTGCCTCTTTGTGGCCTAAG
83
CAGGAAACAGCTATGACCATTCTGGTTGAAAGATGGCG
248





JAK2
chr9:5063697-5063785
GTAAAACGACGGCCAGTGGGTTTCCTCAGAACGTTGA
84
CAGGAAACAGCTATGACCCTGACACCTAGCTGTGATCCTG
249





KIT
chr4:55284506-55284621
GTAAAACGACGGCCAGTTTCTGCCCTTTGAACTTGCT
85
CAGGAAACAGCTATGACCAAAGCCACATGGCTAGAAAA
250



chr4:55288338-55288465
GTAAAACGACGGCCAGTCCACACCCTGTTCACTCCTT
86
CAGGAAACAGCTATGACCTGGCAAACCTATCAAAAGGG
251



chr4:55293992-55294115
GTAAAACGACGGCCAGTTGTGAACATCATTCAAGGCG
87
CAGGAAACAGCTATGACCTGTTCAGCATACCATGCAAA
252





KRas
chr12:25271434-25271613
GTAAAACGACGGCCAGTTGCATGGCATTAGCAAAGAC
88
CAGGAAACAGCTATGACCGGTGCTTAGTGGCCATTTGT
253



chr12:25289474-25289596
GTAAAACGACGGCCAGTCCAAGGAAAGTAAAGTTCCCA
89
CAGGAAACAGCTATGACCCGTCTGCAGTCAACTGGAAT
254





NPM1
chr5:170770135-170770493
GTAAAACGACGGCCAGTCTCGGGAGATGAAGTTGGAA
90
CAGGAAACAGCTATGACCactccagcctaggggaAAAA
255





NRas
chr1:115057943-115058122
GTAAAACGACGGCCAGTGTGGTAACCTCATTTCCCCA
91
CAGGAAACAGCTATGACCGGGACAAACCAGATAGGCAG
256



chr1:115060193-115060321
GTAAAACGACGGCCAGTCAGGTTTTAGAAACTTCAGCAGC
92
CAGGAAACAGCTATGACCATTAATCCGGTGTTTTTGCG
257





PHF6
chrX:133339267-133339451
GTAAAACGACGGCCAGTggggcttagagtggcttaattt
93
CAGGAAACAGCTATGACCgtctctgttgctgccggtat
258



chrX:133339700-133339802
GTAAAACGACGGCCAGTTCTGAAAACCAGAAGGTGGC
94
CAGGAAACAGCTATGACCGGATTTTGCTGGCTCAGAGA
259



chrX:133355196-133355330
GTAAAACGACGGCCAGTACCAATTTGTTTTCCTTGACAGA
95
CAGGAAACAGCTATGACCCGAGCAGTACACTTCACCCA
260



chrX:133355604-133355648
GTAAAACGACGGCCAGTACCACTGTGCATTGCATGAT
96
CAGGAAACAGCTATGACCTGAAAAGTGGCTGAAACGTG
261



chrX:133375183-133375353
GTAAAACGACGGCCAGTCTGAAACATTGGGTGGCTTT
97
CAGGAAACAGCTATGACCTTGGGCTTTAGATCACAGGG
262



chrX:133375518-133375662
GTAAAACGACGGCCAGTATGAACATGAACTGGAGCCC
98
CAGGAAACAGCTATGACCTTGGGCTTTAGATCACAGGG
263



chrX:133376711-133376987
GTAAAACGACGGCCAGTTTAATCTTGGCTCCACACTGG
99
CAGGAAACAGCTATGACCGCTTGCAAATGCCTTGAAAT
264



chrX:133378864-133379244
GTAAAACGACGGCCAGTtttcttgaaatacggcttacga
100
CAGGAAACAGCTATGACCccggcccagtgtatgtagtt
265



chrX:133386896-133387276
GTAAAACGACGGCCAGTCCCATGTTTTAAATGGGCAC
101
CAGGAAACAGCTATGACCATGATGCTTGAGGGGAACAC
266





PTEN
chr10:89614098-89614406
GTAAAACGACGGCCAGTatcagctaagccaagtcc
102
CAGGAAACAGCTATGACCgcaacctgaccagggttaaa
267



chr10:89643761-89643846
GTAAAACGACGGCCAGTCTCCAGCTATAGTGGGGAAA
103
CAGGAAACAGCTATGACCCTGTATCCCCCTGAAGTCCA
268



chr10:89675249-89675294
GTAAAACGACGGCCAGTCCATAGAAGGGGTATTTGTTGG
104
CAGGAAACAGCTATGACCTGCCAACAATGTTTTACCTCA
269



chr10:89680782-89680826
GTAAAACGACGGCCAGTAAAGATTCAGGCAATGTTTGTT
105
CAGGAAACAGCTATGACCTCTCACTCGATAATCTGGATGAC
270



chr10:89682749-89682988
GTAAAACGACGGCCAGTGGAATCCAGTGTTTCTTTTAAATACC
106
CAGGAAACAGCTATGACCGAAACCCAAAATCTGTTTTCCA
271



chr10:89701854-89701996
GTAAAACGACGGCCAGTGGCTACGACCCAGTTACCAT
107
CAGGAAACAGCTATGACCTAAAACCCATTGCTTTTGGC
272



chr10:89707589-89707756
GTAAAACGACGGCCAGTTGCTTGAGATCAAGATTGCAG
108
CAGGAAACAGCTATGACCGCCATAAGGCCTTTTCCTTC
273



chr10:89710630-89710855
GTAAAACGACGGCCAGTGCAACAGATAACTCAGATTGCC
109
CAGGAAACAGCTATGACCTTTTGACGCTGTGTACATTGG
274



chr10:89715023-89715403
GTAAAACGACGGCCAGTTGTTCATCTGCAAAATGGAAT
110
CAGGAAACAGCTATGACCTAAAACGGGAAAGTGCCATC
275





RUNX1
chr21:35086148-35086527
GTAAAACGACGGCCAGTCTTCCTGTTTGCTTTCCAGC
111
CAGGAAACAGCTATGACCCACGCGCTACCACACCTAC
276



chr21:35086528-35086777
GTAAAACGACGGCCAGTACCACGTCGCTCTGGTTC
112
CAGGAAACAGCTATGACCATCCTCGTCCTCTTGGGAGT
277



chr21:35093467-35093629
GTAAAACGACGGCCAGTAAGAAAATCAGTGCATGGGC
113
CAGGAAACAGCTATGACCACCCTGGTACATAGGCCACA
278



chr21:35115824-35115863
GTAAAACGACGGCCAGTTGTTACGACGGTTTGCAGAG
114
CAGGAAACAGCTATGACCGGAAGGGAAGGGAAATCTTG
279



chr21:35128576-35128768
GTAAAACGACGGCCAGTAGTTGGTCTGGGAAGGTGTG
115
CAGGAAACAGCTATGACCGGAAAGACAAGAAAAGCCCC
280



chr21:35153640-35153745
GTAAAACGACGGCCAGTGCAACTTTTTGGCTTTACGG
116
CAGGAAACAGCTATGACCGGTAACTTGTGCTGAAGGGC
281



chr21:35174723-35174880
GTAAAACGACGGCCAGTCCGAGTTTCTAGGGATTCCA
117
CAGGAAACAGCTATGACCCATTGCTATTCCTCTGCAACC
282



chr21:35181009-35181389
GTAAAACGACGGCCAGTAGAAAGCTGAGACGAGTGCC
118
CAGGAAACAGCTATGACCGCAGAACCAGAACGTTTTCC
283



chr21:35187091-35187130
GTAAAACGACGGCCAGTGGAATCAGCAGAAACAGCCT
119
CAGGAAACAGCTATGACCAACCACGTGCATAAGGAACA
284



chr21:35343008-35343388
GTAAAACGACGGCCAGTGGTGAAACAAGCTGCCATTT
120
CAGGAAACAGCTATGACCTTTGGGCCTCATAAACAACC
285





TET2
chr4:106374502-106374882
GTAAAACGACGGCCAGTCACCCTTGTTCTCCATGACC
121
CAGGAAACAGCTATGACCTGGTTGACTGCTTTCACCTG
286



chr4:106374883-106375262
GTAAAACGACGGCCAGTAAATGGAGACACCAAGTGGC
122
CAGGAAACAGCTATGACCGAGGTATGCGATGGGTGAGT
287



chr4:106375263-106375642
GTAAAACGACGGCCAGTATGAGCAGGAGGGGAAAAGT
123
CAGGAAACAGCTATGACCTGGTGTGGTAGTGGCAGAAA
288



chr4:106375643-106376022
GTAAAACGACGGCCAGTACTCACCCATCGCATACCTC
124
CAGGAAACAGCTATGACCAGATAGTGCTGTGTTGGGGG
289



chr4:106376023-106376402
GTAAAACGACGGCCAGTTTCCACAGGTTCCTCAGCTT
125
CAGGAAACAGCTATGACCGAGAAGTGCACCTGGTGTGA
290



chr4:106376783-106377162
GTAAAACGACGGCCAGTAAGGCAAGCTTACACCCAGA
126
CAGGAAACAGCTATGACCGGTTCCACCTTAATTGGCCT
291



chr4:106377163-106377542
GTAAAACGACGGCCAGTAATGTCCAAATGGGACTGGA
127
CAGGAAACAGCTATGACCACTGGCCCTGACATTTCAAC
292



chr4:106377543-106377922
GTAAAACGACGGCCAGTCCCCAGAAGGACACTCAAAA
128
CAGGAAACAGCTATGACCCAAATTGCTGCCAGACTCAA
293



chr4:106377923-106378302
GTAAAACGACGGCCAGTACTTGATAGCCACACCCCAG
129
CAGGAAACAGCTATGACCTTCCCCCAACTCATGAAGAC
294



chr4:106381723-106382102
GTAAAACGACGGCCAGTtgcacaaaaggtagaatgcaa
130
CAGGAAACAGCTATGACCacgtgggatttcacacaaca
295



chr4:106383436-106383533
GTAAAACGACGGCCAGTTTTCCCATTTTCACCCACAT
131
CAGGAAACAGCTATGACCACCCAATTCTCAGGGTCAGA
296



chr4:106384175-106384384
GTAAAACGACGGCCAGTAGGGTCAAAGCCCACTTTTT
132
CAGGAAACAGCTATGACCTGAGGCCATGTGGTTACAGA
297



chr4:106400224-106400375
GTAAAACGACGGCCAGTGTGTGGTTATGCCACAGCTT
133
CAGGAAACAGCTATGACCCCAAAGAGGAAGTTTTTGTTGC
298



chr4:106402364-106402454
GTAAAACGACGGCCAGTACCATACGGCTTAATTCCCC
134
CAGGAAACAGCTATGACCTGTTACAATTGCTGCCAATGA
299



chr4:106410215-106410353
GTAAAACGACGGCCAGTTGTCATTCCATTTTGTTTCTGG
135
CAGGAAACAGCTATGACCCTGCTAAGCTGTCCTCAGCC
300



chr4:106413169-106413524
GTAAAACGACGGCCAGTTCTGGATCAACTAGGCCACC
136
CAGGAAACAGCTATGACCGGGGGCAAAACCAAAATAAT
301



chr4:106415653-106416033
GTAAAACGACGGCCAGTTCAAGCAGAGGCATGTTCAG
137
CAGGAAACAGCTATGACCTATTTCCAAACCTTGGCTGG
302



chr4:106416034-106416413
GTAAAACGACGGCCAGTAATCCCATGAACCCTTACCC
138
CAGGAAACAGCTATGACCACCAGACCTCATCGTTGTCC
303



chr4:106416414-106416793
GTAAAACGACGGCCAGTATCAGTGGACAACTGCTCCC
139
CAGGAAACAGCTATGACCATGAAACGCAGGTAAGTGGG
304



chr4:106416794-106417173
GTAAAACGACGGCCAGTATTGGCACTAGTCCAGGGTG
140
CAGGAAACAGCTATGACCACTGTGACCTTTCCCCACTG
305





TP53
chr17:7505821-7506057
GTAAAACGACGGCCAGTCGGAACTCCTGAGCTGAAAG
141
CAGGAAACAGCTATGACCGCAGGAGAGTTGCTTGAACC
306



chr17:7510128-7510287
GTAAAACGACGGCCAGTGTGCTGTGTGCTGGGATTAC
142
CAGGAAACAGCTATGACCGTGCCAGGAGCTGTTCTAGG
307



chr17:7513585-7513733
GTAAAACGACGGCCAGTCCACAACAAAACACCAGTGC
143
CAGGAAACAGCTATGACCAAAGCATTGGTCAGGGAAAA
308



chr17:7514651-7514758
GTAAAACGACGGCCAGTTCAACCGGAGGAAGACTAAAAA
144
CAGGAAACAGCTATGACCATCAGCCAAGATTGCACCAT
309



chr17:7517249-7517309
GTAAAACGACGGCCAGTaagcaggctaggctaagctatg
145
CAGGAAACAGCTATGACCaaggaccagaccagctttca
310



chr17:7517577-7517651
GTAAAACGACGGCCAGTTGTCTTTGAGGCATCACTGC
146
CAGGAAACAGCTATGACCGCGCACAGAGGAAGAGAATC
311



chr17:7517743-7517880
GTAAAACGACGGCCAGTGTGGTTTCTTCTTTGGCTGG
147
CAGGAAACAGCTATGACCCAAGGGTGGTTGGGAGTAGA
312



chr17:7518223-7518333
GTAAAACGACGGCCAGTtggaagaaatcggtaagaggtg
148
CAGGAAACAGCTATGACCctgcttgccacaggtctcc
313



chr17:7518901-7519014
GTAAAACGACGGCCAGTTTGCACATCTCATGGGGTTA
149
CAGGAAACAGCTATGACCAGTCACAGCACATGACGGAG
314



chr17:7519095-7519475
GTAAAACGACGGCCAGTTTACCTGCAATTGGGGCATT
150
CAGGAAACAGCTATGACCGCAGGCTAGGCTAAGCTATGATG
315



chr17:7520036-7520315
GTAAAACGACGGCCAGTGCCAAAGGGTGAAGAGGAAT
151
CAGGAAACAGCTATGACCGTAAGGACAAGGGTTGGGCT
316



chr17:7520424-7520446
GTAAAACGACGGCCAGTTCATCTGGACCTGGGTCTTC
152
CAGGAAACAGCTATGACCCCCCTCTGAGTCAGGAAACA
317



chr11:7520563-7520665
GTAAAACGACGGCCAGTAGCCCAACCCTTGTCCTTAC
153
CAGGAAACAGCTATGACCCAGCCATTCTTTTCCTGCTC
318





WT1
chr11:32367041-32367301
GTAAAACGACGGCCAGTGGGGACATGATCAGCTATGG
154
CAGGAAACAGCTATGACCTCCTTAAAGCCCCAAGAGGT
319



chr11:32370093-32370186
CAGGAAACAGCTATGACCGCCACGCACTATTCCTTCTC
155
GTAAAACGACGGCCAGTGGGAAATCTAAGGGTGAGGC
320



chr11:32370787-32370877
CAGGAAACAGCTATGACCTGTGGGGTGTTTCCTTTTCT
156
GTAAAACGACGGCCAGTGTTGGGGATCATCCTACCCT
321



chr11:32374378-32374529
CAGGAAACAGCTATGACCTAGCAGTGTGAGAGCCTGGA
157
GTAAAACGACGGCCAGTGGAGTGTGAATGGGAGTGGT
322



chr11:32378069-32378166
CAGGAAACAGCTATGACCTAAGGAACTAAAGGGCCGGT
158
GTAAAACGACGGCCAGTCCATCATTCCCTCCTGATTG
323



chr11:32394611-32394662
CAGGAAACAGCTATGACCGAATAAGAAGAGGTGGGGGC
159
GTAAAACGACGGCCAGTGGCTTTTCACTGGATTCTGG
324



chr11:32395698-32395776
CAGGAAACAGCTATGACCACCAACTAGGGGAAGGAGGA
160
GTAAAACGACGGCCAGTCTGTGCAGAGATCAGTGGGA
325



chr11:32406077-32406180
GTAAAACGACGGCCAGTCAGAGACCAGGGAGATCAGC
161
GTAAAACGACGGCCAGTGACTGCTAGGGGAATGCAAA
326



chr11:32406618-32406741
GTAAAACGACGGCCAGTTGCCATTGGGGTAATGATTT
162
CAGGAAACAGCTATGACCCAAGGTCACATCCAGGGACT
327



chr11:32408651-32408935
GTAAAACGACGGCCAGTAGTGAAGGCCGAATTTCTGA
163
CAGGAAACAGCTATGACCTCCAAGGCCTGTACAAGGAG
328



chr11:32412821-32413201
GTAAAACGACGGCCAGTGGTAAGAGCTGCGGTCAAAA
164
CAGGAAACAGCTATGACCCTACAGCAGCCAGAGCAGC
329



chr11:32413202-32413581
GTAAAACGACGGCCAGTGGCTCCTGTTTGATGAAGGA
165
CAGGAAACAGCTATGACCGTAAGGAGTTCAAGGCAGCG
330



















TABLE 3







Gene
p-value



















DNMT3A
0.17



IDH1
0.24



IDH2
0.59



IDH2R140Q
0.61



IDH2R172K
0.13



TET2
0.92



ASXL1
0.16



FLT3
0.6



NPM1
0.23



PHF6
0.09



KIT
0.24



CEBPA
0.23



WT1
0.68



KRas
0.45



NRas
0.49



P53
0.85



PTEN
0.95



RUNX1
0.09



CBF
0.67



Del(5q)
0.66



EVI
0.9



MLL-PTD
0.04



Split MLL
0.21



Monosomy 7
0.97



t(6;9)
0.36



Trisomy 8
0.89



AML1-ETO
0.08





















TABLE 4






Overall
Favorable
Intermediate
Unfavorable


Gene
Frequency (%)
Risk
Risk
Risk







FLT3 (ITD,
37 (30, 7)
8 (3, 5)
52 (42, 7)*
36 (35, 1)


TKD)1


NPM1
29
4
49*
12


DNMT3A
23
4
33*
15


NRAS
10
12 
5
2


CEBPA
9
5
12 
5


TET2
8
5
8
10


WT1
8
1
12*
5


IDH2
8
3
9
9


IDH1
7
3
9
3


KIT
6
28*
1
0


RUNX1
5
3
6
6


MLL-PTD2
5
0
5
8


ASXL1
3
0
4
2


PHF6
3
1
2
3


KRAS
2
7
5
3


PTEN
2
1
2
1


TP53
2
0
1
6


HRAS
0
0
0
0


EZH2
0
0
0
0






1ITD—internal tandem duplication; TKD—tyrosine kinase domain mutation.




2PTD—partial tandem duplication.



*denotes mutations which were significantly enriched in a specific cytogenetic risk group compared to the entire cohort (p < 0.01 for all).

























TABLE 5








DNMT3a
IDH1
IDH2
TET2
ASXL1
FLT3
NPM1
CEBPA
WT1
KRas
NRas
PHF6





DNMT3a

 3.3%
1.5%  
1.5%  
0%
13.3%  
14.3%  
1.75%  
0.75%  
0.75%  
2.5%  
0%




(13/398) 
(6/398)
(6/398)
(0/398)
(53/398) 
(57/398) 
(7/398)
(3/398)
(3/398)
(10/398) 
(0/398)


IDH1
 3.3%

0%
0%
0.25%  
1%
1.5%  
0.25%  
0%
0.25%  
0.75%  
0.5%  



(13/398) 

(0/398)
(0/398)
(1/398)
(4/398)
(6/398)
(1/398)
(0/398)
(1/398)
(3/398)
(2/398)


IDH2
 1.5%
  0%

0%
0.5%  
2%
2%
0%
0%
0%
0.75%  
0%



(6/398)
(0/398)

(0/398)
(2/398)
(8/398)
(8/398)
(0/398)
(0/398)
(0/398)
(3/398)
(0/398)


TET2
 1.5%
  0%
0%

0.75%  
3%
1.5%  
0.5%  
0.5%  
0%
1%
0.25%  



(6/398)
(0/398)
(0/398)

(3/398)
(12/398) 
(6/398)
(2/398)
(2/398)
(0/398)
(4/398)
(1/398)


ASXL1
  0%
0.25%
0.5%  
0.75%  

0%
0.25%  
0.5%  
0%
0%
0.25%  
0.25%  



(0/398)
(1/398)
(2/398)
(3/398)

(0/398)
(1/398)
(2/398)
(0/398)
(0/398)
(1/398)
(1/398)


FLT3
13.3%
  1%
2%
3%
0%

6.8%  
3.5%  
5%
0.25%  
0.5%  
1%



(53/398) 
(4/398)
(8/398)
(12/398) 
(0/398)

(27/398) 
(14/398) 
(20/398) 
(1/398)
(2/398)
(4/398)


NPM1
14.3%
 1.5%
2%
1.5%  
0.25%  
6.8%  

0.5%  
0.25%  
0.5%  
1.3%  
0%



(57/398) 
(6/398)
(8/398  
(6/398)
(1/398)
(27/398) 

(2/398)
(1/398)
(2/398)
(5/398)
(0/398)


CEBPA
1.75%
0.25%
0%
0.5%  
0.5%  
3.5%  
0.5%  

1.3%  
0%
0.5%  
0.5%  



(7/398)
(1/398)
(0/398)
(2/398)
(2/398)
(14/398) 
(2/398)

(5/398)
(0/398)
(2/398)
(2/398)


WT1
0.75%
  0%
0%
0.5%  
0%
5%
0.25%  
1.3%  

0%
0.75%  
0%



(3/398)
(0/398)
(0/398)
(2/398)
(0/398)
(20/398) 
(1/398)
(5/398)

(0/398)
(3/398)
(0/398)


KRas
0.75%
0.25%
0%
0%
0%
0.25%  
0.5%  
0%
0%

0%
0%



(3/398)
(1/398)
(0/398)
(0/398)
(0/398)
(1/398)
(2/398)
(0/398)
(0/398)

(0/398)
(0/398)


NRas
 2.5%
0.75%
0.75%  
1%
0.25%  
0.5%  
1.3%  
0.5%  
0.75%  
0%

0%



(10/398)
(3/398)
(3/398)
(4/398)
(1/398)
(2/398)
(5/398)
(2/398)
(3/398)
(0/398)

(0/398)


PHF6
  0%
 0.5%
0%
0.25%  
0.25%  
1%
0%
0.5%  
0%
0%
0%




(0/398)
(2/398)
(0/398)
(1/398)
(1/398)
(4/398)
(0/398)
(2/398)
(0/398)
(0/398)
(0/398)



KIT
 0.5%
0.25%
0%
0%
0%
0%
0.25%  
0.5%  
0%
0%
0.25%  
0%



(2/398)
(1/398)
(0/398)
(0/398)
(0/398)
(0/398)
(1/398)
(2/398)
(0/398)
(0/398)
(1/398)
(0/398)


TP53
0.25%
  0%
0%
0.25%  
0%
0.25%  
0%
0%
0%
0%
0%
0%



(1/398)
(0/398)
(0/398)
(1/398)
(0/398)
(1/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)


PTEN
0.75%
 0.5%
0%
0%
0%
0.5%  
0.5%  
0%
0%
0%
0.5%  
0%



(3/398)
(2/398)
(0/398)
(0/398)
(0/398)
(2/398)
(2/398)
(0/398)
(0/398)
(0/398)
(2/398)
(0/398)


RUNX1
0.75%
0.25%
0.75%  
0.25%  
1%
1.5%  
0.5%  
0%
0.75%  
0.25%  
0.5%  
0%



(3/398)
(1/398)
(3/398)
(1/398)
(4/398)
(6/398)
(2/398)
(0/398)
(3/398 
(1/398)
(2/398)
(0/398)


CBF
0.25%
0.25%
0%
1.3%  
1.3%  
1.5%  
0%
1%
1%
0.5%  
3%
0.25%  



(1/398)
(1/398)
(0/398)
(5/398)
(5/398)
(6/398)
(0/398)
(4/398)
(4/398)
(2/398)
(12/398) 
(1/398)


Del (5q)
  0%
  0%
0%
0.25%  
0%
0.25%  
0%
0%
0%
0%
0%
0.25%  



(0/398)
(0/398)
(0/398)
(1/398)
(0/398)
(1/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(1/398)


EVI1
  0%
  0%
0%
0.25%  
0.25%  
0.25%  
0%
0%
0%
0%
0.25%  
0.25%  



(0/398)
(0/398)
(0/398)
(1/398)
(1/398)
(1/398)
(0/398)
(0/398)
(0/398)
(0/398)
(1/398)
(1/398)


MLL-PTD
  1%
 0.5%
0.75%  
0%
0.5%  
2.5%
0%
0.5%  
0.5%  
0%
0%
0.25%  



(4/398)
(2/398)
(3/398)
(0/398)
(2/398)
(10/398) 
(0/398)
(2/398)
(2/398)
(0/398)
(0/398)
(1/398)


Split MLL
0.25%
0.25%
0.5%  
0%
0.25%  
0.5%  
0%
0%
0%
0.25%  
0.75%  
0%



(1/398)
(1/398)
(2/398)
(0/398)
(1/398)
(2/398)
(0/398)
(0/398)
(0/398)
(1/398)
(3/398)
(0/398)


Monosomy (7/7q)
0.25%
0.25%
0.25%  
0.25%  
0%
0%
0%
0.25%  
0%
0%
0%
0%



(1/398)
(1/398)
(1/398)
(1/398)
(0/398)
(0/398)
(0/398)
(1/398)
(0/398)
(0/398)
(0/398)
(0/398)


t(6; 9)
  0%
  0%
0%
0%
0.25%  
0.25%  
0%
0%
0.25%  
0%
0%
0%



(0/398)
(0/398)
(0/398)
(0/398)
(1/398)
(1/398)
(0/398)
(0/398)
(1/398)
(0/398)
(0/398)
(0/398)


Tri(8)
 1.5%
 0.5%
0%
0.25%  
0.25%  
2.26%  
0.25%  
0.25%  
0%
0%
0%
0%



(6/398)
(2/398)
(0/398)
(1/398)
(1/398)
(9/398)
(1/398)
(1/398)
(0/398)
(0/398)
(0/398)
(0/398)


AML1-ETO
  0%
  0%
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%



(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)





























Del


Split
Monosomy






KIT
TP53
PTEN
RUNX1
CBF
(5q)
EVI1
MLL- PTD
MLL
(7/7q)
t(6; 9)
Tri(8)
AML1-ETO





DNMT3a
0.5%  
0.25%  
0.75%  
0.75%  
0.25%  
0%
0%
1%
0.25%  
0.25%  
0%
1.5%  
0%



(2/398)
(1/398)
(3/398)
(3/398)
(1/398)
(0/398)
(0/398)
(4/398)
(1/398)
(1/398)
(0/398)
(6/398)
(0/398)


IDH1
0.25%  
0%
0.5%  
0.25%  
0.25%  
0%
0%
0.5%  
0.25%  
0.25%  
0%
0.5%  
0%



(1/398)
(0/398)
(2/398)
(1/398)
(1/398)
(0/398)
(0/398)
(2/398)
(1/398)
(1/398)
(0/398)
(2/398)
(0/398)


IDH2
0%
0%
0%
0.75%  
0%
0%
0%
0.75%  
0.5%  
0.25%  
0%
0%
0%



(0/398)
(0/398)
(0/398)
(3/398)
(0/398)
(0/398)
(0/398)
(3/398)
(2/398)
(1/398)
(0/398)
(0/398)
(0/398)


TET2
0%
0.25%  
0%
0.25%  
1.3%  
0.25%  
0.25%  
0%
0%
0.25%  
0%
0.25%  
0%



(0/398)
(1/398)
(0/398)
(1/398)
(5/398)
(1/398)
(1/398)
(0/398)
(0/398)
(1/398)
(0/398)
(1/398)
(0/398)


ASXL1
0%
0%
0%
1%
1.3%  
0%
0.25%  
0.5%  
0.25%  
0%
0.25%
0.25%  
0%



(0/398)
(0/398)
(0/398)
(4/398)
(5/398)
(0/398)
(1/398)
(2/398)
(1/398)
(0/398)
(1/398)
(1/398)
(0/398)


FLT3
0%
0.25%  
0.5%  
1.5%  
1.5%  
0.25%  
0.25%  
2.5%  
0.5%  
0%
0.25%  
2.26%  
0%



(0/398)
(1/398)
(2/398)
(6/398)
(6/398)
(1/398)
(1/398)
(10/398) 
(2/398)
(0/398)
(1/398)
(9/398)
(0/398)


NPM1
0.25%  
0%
0.5%  
0.5%  
0%
0%
0%
0%
0%
0%
0%
0.25%  
0%



(1/398)
(0/398)
(2/398)
(2/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(1/398)
(0/398)


CEBPA
0.5%  
0%
0%
0%
1%
0%
0%
0.5%  
0%
0.25%  
0%
0.25%  
0%



(2/398)
(0/398)
(0/398)
(0/398)
(4/398)
(0/398)
(0/398)
(2/398)
(0/398)
(1/398)
(0/398)
(1/398)
(0/398)


WT1
0%
0%
0%
0.75%  
1%
0%
0%
0.5%  
0%
0%
0.25%  
0%
0%



(0/398)
(0/398)
(0/398)
(3/398)
(4/398)
(0/398)
(0/398)
(2/398)
(0/398)
(0/398)
(1/398)
(0/398)
(0/398)


KRas
0%
0%
0%
0.25%  
0.5%  
0%
0%
0%
0.25%  
0%
0%
0%
0%



(0/398)
(0/398)
(0/398)
(1/398)
(2/398)
(0/398)
(0/398)
(0/398)
(1/398)
(0/398)
(0/398)
(0/398)
(0/398)


NRas
0.25%  
0%
0.5%  
0.5%  
3%
0%
0.25%  
0%
0.75%  
0%
0%
0%
0%



(1/398)
(0/398)
(2/398)
(2/398)
(12/398) 
(0/398)
(1/398)
(0/398)
(3/398)
(0/398)
(0/398)
(0/398)
(0/398)


PHF6
0%
0%
0%
0%
0.25%  
0.25%  
0.25%  
0.25%  
0%
0%
0%
0%
0%



(0/398)
(0/398)
(0/398)
(0/398)
(1/398)
(1/398)
(1/398)
(1/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)


KIT

0%
0%
0%
5.3%  
0%
0%
0%
0%
0%
0%
0%
0%




(0/398)
(0/398)
(0/398)
(21/398) 
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)


TP53
0%

0.25%  
0.25%  
0%
0.25%  
0%
0.25%  
0%
0%
0%
0%
0%



(0/398)

(1/398)
(1/398)
(0/398)
(1/398)
(0/398)
(1/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)


PTEN
0%
0.25%  

0%
0.25%  
0%
0%
0%
0%
0%
0%
0%
0%



(0/398)
(1/398)

(0/398)
(1/395)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)


RUNX1
0%
0.25%  
0%

0.5%  
0.75%  
0%
1%
0%
0.25%  
0%
0%
0%



(0/398)
(1/398)
(0/398)

(2/398)
(3/398)
(0/398)
(4/398)
(0/398)
(1/398)
(0/398)
(0/398)
(0/398)


CBF
5.3%  
0%
0.25%  
0.5%  

0%
0%
0%
0%
0%
0%
0%
0.25%  



(21/398) 
(0/398)
(1/398)
(2/398)

(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(1/398)


Del (5q)
0%
0.25%  
0%
0.75%  
0%

0%
1%
0%
0%
0%
0%
0%



(0/398)
(1/398)
(0/398)
(3/398)
(0/398)

(0/398)
(4/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)


EVI1
0%
0%
0%
0%
0%
0%

0%
0%
0%
0%
0%
0%



(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)

(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)


MLL-PTD
0%
0.25%  
0%
1%
0%
1%
0%

0.5%  
0.25%  
0%
0.25%  
0%



(0/398)
(1/398)
(0/398)
(4/398)
(0/398)
(4/398)
(0/398)

(2/398)
(1/398)
(0/398)
(1/398)
(0/398)


Split MLL
0%
0%
0%
0%
0%
0%
0%
0%

0%
0%
0%
0%



(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)

(0/398)
(0/398)
(0/398)
(0/398)


Monosomy (7/7q)
0%
0%
0%
0.25%  
0%
0%
0%
0.25%  
0%

0%
0%
0%



(0/398)
(0/398)
(0/398)
(1/398)
(0/398)
(0/398)
(0/398)
(1/398)
(0/398)

(0/398)
(0/398)
(0/398)


t(6; 9)
0%
0%
0%
0%
0%
0%
0%
0%
0%
0%

0%
0%



(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)

(0/398)
(0/398)


Tri(8)
0%
0%
0%
0%
0%
0%
0%
0.25%  
0%
0%
0%

0%



(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(1/398)
(0/398)
(0/398)
(0/398)

(0/398)


AML1-ETO
0%
0%
0%
0%
0.25%  
0%
0%
0%
0%
0%
0%
0%




(0/398)
(0/398)
(0/398)
(0/398)
(1/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)
(0/398)






















TABLE 6






Abnormality #1
Abnormality #2
M/M2
WT/M3
M/W4
WT/WT5





















1
DNMT3A
IDH
19
32
70
262


2
DNMT3A
IDH1
13
9
76
286


3
DNMT3A
IDH2
6
23
83
272


4
DNMT3A
IDH2_R140Q
3
18
86
277


5
DNMT3A
IDH2_R172K
3
5
86
290


6
DNMT3A
TET2
6
26
83
266


7
DNMT3A
ASXL1
0
10
88
285


8
DNMT3A
FLT3
52
92
37
204


9
DNMT3A
NPM1
57
57
32
239


10
DNMT3A
PHF6
0
9
88
284


11
DNMT3A
KIT
2
21
87
275


12
DNMT3A
CEBPa
6
26
82
267


13
DNMT3A
WT1
3
26
86
264


14
DNMT3A
KRAS
2
6
87
288


15
DNMT3A
NRAS
10
28
79
267


16
DNMT3A
TP53
1
7
86
283


17
DNMT3A
PTEN
3
2
86
293


18
DNMT3A
RUNX1
3
16
85
267


19
DNMT3A
CBF
1
71
88
225


20
DNMT3A
del5q
1
5
88
291


21
DNMT3A
EVI1pos
0
5
89
291


22
DNMT3A
MLLPTD or split
4
13
85
283




MLLPTD






23
DNMT3A
splitMLLPTD or
1
21
88
275




split MLL






24
DNMT3A
MLLPTD or split
5
32
84
264




MLL






25
DNMT3A
Monosomy7
1
2
88
294


26
DNMT3A
t(6; 9)
0
2
89
294


27
DNMT3A
trisomy 8
6
9
83
287


28
DNMT3A
AML1ETO
0
1
89
295


29
DNMT3A_R882
IDH
13
38
50
282


30
DNMT3A_R882
IDH1
9
13
54
308


31
DNMT3A_R882
IDH2
4
25
59
296


32
DNMT3A_R882
IDH2_R140Q
2
19
61
302


33
DNMT3A_R882
IDH2_R172K
2
6
61
315


34
DNMT3A_R882
IDH1_IDH2_R172K
11
19
52
302


35
DNMT3A_R882
TET2
4
28
59
290


36
DNMT3A_R882
ASXL1
0
10
62
311


37
DNMT3A_R882
FLT3
41
103
22
219


38
DNMT3A_R882
NPM1
43
71
20
251


39
DNMT3A_R882
PHF6
0
9
62
310


40
DNMT3A_R882
KIT
2
21
61
301


41
DNMT3A_R882
CEBPa
4
28
58
291


42
DNMT3A_R882
WT1
0
29
63
287


43
DNMT3A_R882
KRAS
2
6
61
314


44
DNMT3A_R882
NRAS
5
33
58
288


45
DNMT3A_R882
TP53
1
7
60
309


46
DNMT3A_R882
PTEN
2
3
61
318


47
DNMT3A_R882
RUNX1
2
17
61
291


48
DNMT3A_R882
CBF
0
72
63
250


49
DNMT3A_R882
del5q
1
5
62
317


50
DNMT3A_R882
EVI1pos
0
5
63
317


51
DNMT3A_R882
MLLPTD or split
3
14
60
308




MLLPTD






52
DNMT3A_R882
splitMLLPTD or
0
22
63
300




split MLL






53
DNMT3A_R882
MLLPTD or split
3
34
60
288




MLL






54
DNMT3A_R882
Monosomy7
0
3
63
319


55
DNMT3A_R882
t(6: 9)
0
2
63
320


56
DNMT3A_R882
trisomy 8
5
10
58
312


57
DNMT3A_R882
AML1ETO
0
1
63
321


58
DNMT3A_other
IDH
6
45
22
310


59
DNMT3A_other
IDH1
4
18
24
338


60
DNMT3A_other
IDH2
2
27
26
329


61
DNMT3A_other
IDH2_R140Q
1
20
27
336


62
DNMT3A_other
IDH2_R172K
1
7
27
349


63
DNMT3A_other
IDH1_IDH2_R172K
5
25
23
331


64
DNMT3A_other
TET2
2
30
26
323


65
DNMT3A_other
ASXL1
0
10
28
345


66
DNMT3A_other
FLT3
12
132
16
225


67
DNMT3A_other
NPM1
15
99
13
258


68
DNMT3A_other
PHF6
0
9
28
344


69
DNMT3A_other
KIT
0
23
28
334


70
DNMT3A_other
CEBPa
2
30
26
323


71
DNMT3A_other
WT1
3
26
25
325


72
DNMT3A_other
KRAS
0
8
28
347


73
DNMT3A_other
NRAS
6
32
22
324


74
DNMT3A_other
TP53
0
8
28
341


75
DNMT3A_other
PTEN
1
4
27
352


76
DNMT3A_other
RUNX1
2
17
25
327


77
DNMT3A_other
CBF
1
71
27
286


78
DNMT3A_other
del5q
1
5
27
352


79
DNMT3A_other
EVI1pos
0
5
28
352


80
DNMT3A_other
MLLPTD or split
1
16
27
341




MLLPTD






81
DNMT3A_other
splitMLLPTD or
1
21
27
336




split MLL






82
DNMT3A_other
MLLPTD or split
2
35
26
322




MLL






83
DNMT3A_other
Monosomy7
1
2
27
355


84
DNMT3A_other
t(6; 9)
0
2
28
355


85
DNMT3A_other
trisomy 8
1
14
27
343


86
DNMT3A_other
AML1ETO
0
1
28
356


87
IDH
TET2
0
33
56
301


88
IDH
ASXL1
3
7
54
329


89
IDH
FLT3
13
133
44
205


90
IDH
NPM1
31
87
26
251


91
IDH
PHF6
2
7
54
328


92
IDH
KIT
1
22
56
316


93
IDH
CEBPa
1
33
56
302


94
IDH
WT1
0
30
56
303


95
IDH
KRAS
1
7
56
329


96
IDH
NRAS
6
34
51
303


97
IDH
TP53
0
8
57
323


98
IDH
PTEN
2
4
55
333


99
IDH
RUNX1
4
16
52
308


100
IDH
CBF
1
71
56
267


101
IDH
del5q
0
6
57
332


102
IDH
EVI1pos
0
5
57
333


103
IDH
MLLPTD or split
5
13
52
325




MLLPTD






104
IDH
splitMLLPTD or
2
19
55
319




split MLL






105
IDH
MLLPTD or split
6
31
51
307




MLL






106
IDH
Monosomy7
2
2
55
336


107
IDH
t(6; 9)
0
2
57
336


108
IDH
trisomy 8
2
13
55
325


109
IDH
AML1ETO
0
1
57
337


110
IDH1
IDH2
0
33
24
338


111
IDH1
IDH2_R140Q
0
24
24
347


112
IDH1
IDH2_R172K
0
9
24
362


113
IDH1
TET2
0
33
24
334


114
IDH1
ASXL1
1
9
23
361


115
IDH1
FLT3
4
142
20
230


116
IDH1
NPM1
14
104
10
268


117
IDH1
PHF6
2
7
21
362


118
IDH1
KIT
1
22
23
350


119
IDH1
CEBPa
1
33
23
336


120
IDH1
WT1
0
30
23
337


121
IDH1
KRAS
1
7
23
363


122
IDH1
NRAS
3
37
21
334


123
IDH1
TP53
0
8
24
356


124
IDH1
PTEN
2
4
22
367


125
IDH1
RUNX1
1
19
22
339


126
IDH1
CBF
1
71
23
301


127
IDH1
del5q
0
6
24
366


128
IDH1
EVI1pos
0
5
24
367


129
IDH1
MLLPTD or split
2
16
22
356




MLLPTD






130
IDH1
splitMLLPTD or
0
21
24
351




split MLL






131
IDH1
MLLPTD or split
2
35
22
337




MLL






132
IDH1
Monosomy7
1
3
23
369


133
IDH1
t(6; 9)
0
2
24
370


134
IDH1
trisomy 8
2
13
22
359


135
IDH1
AML1ETO
0
1
24
371


136
IDH2
ASXL1
2
8
31
353


137
IDH2
FLT3
9
138
24
225


138
IDH2
NPM1
17
101
16
262


139
IDH2
PHF6
0
9
33
350


140
IDH2
KIT
0
23
33
340


141
IDH2
CEBPa
0
34
33
325


142
IDH2
WT1
0
30
33
327


143
IDH2
KRAS
0
8
33
353


144
IDH2
NRAS
3
37
30
325


145
IDH2
TP53
0
8
33
348


146
IDH2
PTEN
0
6
33
356


147
IDH2
RUNX1
3
17
30
331


148
IDH2
CBF
0
72
33
291


149
IDH2
del5q
0
6
33
357


150
IDH2
EVI1pos
0
5
33
358


151
IDH2
MLLPTD or split
3
15
30
348




MLLPTD






152
IDH2
splitMLLPTD or
2
20
31
343




split MLL






153
IDH2
MLLPTD or split
4
34
29
329




MLL






154
IDH2
Monosomy7
1
3
32
360


155
IDH2
t(6; 9)
0
2
33
361


156
IDH2
Trisomy 8
0
15
33
348


157
IDH2
AML1ETO
0
1
33
362


158
IDH2_R140Q
IDH2_R172K
0
9
24
363


159
IDH2_R140Q
TET2
0
33
23
335


160
IDH2_R140Q
ASXL1
1
9
23
361


161
IDH2_R140Q
FLT3
8
139
16
233


162
IDH2_R140Q
NPM1
16
102
8
270


163
IDH2_R140Q
PHF6
0
9
24
359


164
IDH2_R140Q
KIT
0
23
24
349


165
IDH2_R140Q
CEBPa
0
34
24
334


166
IDH2_R140Q
WT1
0
30
24
336


167
IDH2_R140Q
KRAS
0
8
24
362


168
IDH2_R140Q
NRAS
3
37
21
334


169
IDH2_R140Q
TP53
0
8
24
357


170
IDH2_R140Q
PTEN
0
6
24
365


171
IDH2_R140Q
RUNX1
2
18
22
339


172
IDH2_R140Q
CBF
0
72
24
300


173
IDH2_R140Q
del5q
0
6
24
366


174
IDH2_R140Q
EVI1pos
0
5
24
367


175
IDH2_R140Q
MLLPTD or split
1
17
23
355




MLLPTD






176
IDH2_R140Q
splitMLLPTD or
2
20
22
352




split MLL






177
IDH2_R140Q
MLLPTD or split
2
36
22
336




MLL






178
IDH2_R140Q
Monosomy7
1
3
23
369


179
IDH2_R140Q
t(6; 9)
0
2
24
370


180
IDH2_R140Q
trisomy 8
0
15
24
357


181
IDH2_R140Q
AML1ETO
0
1
24
371


182
IDH2_R172K
TET2
0
33
9
349


183
IDH2_R172K
ASXL1
1
9
8
376


184
IDH2_R172K
FLT3
1
146
8
241


185
IDH2_R172K
NPM1
1
117
8
270


186
IDH2_R172K
PHF6
0
9
9
374


187
IDH2_R172K
KIT
0
23
9
364


188
IDH2_R172K
CEBPa
0
34
9
349


189
IDH2_R172K
WT1
0
30
9
351


190
IDH2_R172K
KRAS
0
8
9
377


191
IDH2_R172K
NRAS
0
40
9
346


192
IDH2_R172K
TP53
0
8
9
372


193
IDH2_R172K
PTEN
0
6
9
380


194
IDH2_R172K
RUNX1
1
19
8
353


195
IDH2_R172K
CBF
0
72
9
315


196
IDH2_R172K
del5q
0
6
9
381


197
IDH2_R172K
EVI1pos
0
6
9
382


198
IDH2_R172K
MLLPTD or split
2
16
7
371




MLLPTD






199
IDH2_R172K
splitMLLPTD or
0
22
9
365




split MLL






200
IDH2 R172K
MLLPTD or split
2
36
7
351




MLL






201
IDH2_R172K
Monosomy7
0
4
9
383


202
IDH2_R172K
t(6; 9)
0
2
9
385


203
IDH2_R172K
Trisomy 8
0
15
9
372


204
IDH2_R172K
AML1ETO
0
1
9
386


205
TET2
ASXL1
4
6
29
351


206
TET2
FLT3
12
134
21
225


207
TET2
NPM1
10
106
23
253


208
TET2
PHF6
2
7
31
348


209
TET2
KIT
1
22
32
337


210
TET2
CEBPa
2
31
30
325


211
TET2
WT1
3
27
30
326


212
TET2
KRAS
0
8
33
349


213
TET2
NRAS
4
34
29
325


214
TET2
TP53
1
7
32
344


215
TET2
PTEN
1
5
32
353


216
TET2
RUNX1
3
15
29
330


217
TET2
CBF
4
67
29
292


218
TET2
del5q
0
6
33
353


219
TET2
EVI1pos
1
4
32
355


220
TET2
MLLPTD or split
0
18
33
341




MLLPTD






221
TET2
splitMLLPTD or
1
21
32
338




split MLL






222
TET2
MLLPTD or split
1
37
32
322




MLL






223
TET2
Monosomy7
1
2
32
357


224
TET2
t(6; 9)
0
2
33
357


225
TET2
Trisomy 8
1
14
32
345


226
TET2
AML1ETO
0
1
33
358


227
ASXL1
FLT3
0
146
10
239


228
ASXL1
NPM1
1
117
9
268


229
ASXL1
PHF6
1
8
9
373


230
ASXL1
KIT
0
22
10
363


231
ASXL1
CEBPa
2
32
8
349


232
ASXL1
WT1
0
30
10
349


233
ASXL1
KRAS
0
8
10
375


234
ASXL1
NRAS
1
38
9
346


235
ASXL1
TP53
0
8
9
370


236
ASXL1
PTEN
0
6
10
378


237
ASXL1
RUNX1
5
15
4
356


238
ASXL1
CBF
0
71
10
314


239
ASXL1
del5q
0
6
10
379


240
ASXL1
EVI1pos
0
5
10
380


241
ASXL1
MLLPTD or split
0
17
10
368




MLLPTD






242
ASXL1
splitMLLPTD or
0
22
10
363




split MLL






243
ASXL1
MLLPTD or split
0
37
10
348




MLL






244
ASXL1
Monosomy7
0
4
10
381


245
ASXL1
t(6; 9)
0
2
10
383


246
ASXL1
Trisomy 8
0
15
10
370


247
ASXL1
AML1ETO
0
1
10
384


248
FLT3
NPM1
63
55
84
195


249
FLT3
PHF6
3
6
143
241


250
FLT3
KIT
0
23
147
227


251
FLT3
CEBPa
13
21
131
228


252
FLT3
WT1
18
12
127
234


253
FLT3
KRAS
1
7
146
241


254
FLT3
NRAS
3
37
144
212


255
FLT3
TP53
1
7
144
237


256
FLT3
PTEN
2
4
144
246


257
FLT3
RUNX1
6
14
139
223


258
FLT3
CBF
6
66
141
184


259
FLT3
del5q
1
5
146
245


260
FLT3
EVI1pos
1
4
146
246


261
FLT3
MLLPTD or split
10
8
137
242




MLLPTD






262
FLT3
splitMLLPTD or
2
20
145
230




split MLL






263
FLT3
MLLPTD or split
11
27
136
223




MLL






264
FLT3
Monosomy7
0
4
147
246


265
FLT3
t(6; 9)
1
1
146
249


266
FLT3
Trisomy 8
9
6
138
244


267
FLT3
AML1ETO
0
1
147
249


268
NPM1
PHF6
0
9
118
266


269
NPM1
KIT
2
21
116
258


270
NPM1
CEBPa
3
31
113
246


271
NPM1
WT1
6
24
111
250


272
NPM1
KRAS
3
5
115
272


273
NPM1
NRAS
14
26
103
253


274
NPM1
TP53
1
7
115
266


275
NPM1
PTEN
3
3
115
275


276
NPM1
RUNX1
4
16
114
248


277
NPM1
CBF
0
72
118
207


278
NPM1
del5q
0
6
118
273


279
NPM1
EVI1pos
0
5
118
274


280
NPM1
MLLPTD or split
0
18
118
261




MLLPTD






281
NPM1
splitMLLPTD or
0
22
118
257




split MLL






282
NPM1
MLLPTD or split
0
38
118
241




MLL






283
NPM1
Monosomy7
0
4
118
275


284
NPM1
t(6; 9)
0
2
118
277


285
NPM1
Trisomy 8
2
13
116
268


286
NPM1
AML1ETO
0
1
118
278


287
PHF6
KIT
0
23
9
361


288
PHF6
CEBPa
2
32
7
348


289
PHF6
WT1
0
30
9
348


290
PHF6
KRAS
0
8
9
374


291
PHF6
NRAS
0
39
9
344


292
PHF6
TP53
0
8
9
368


293
PHF6
PTEN
0
6
9
377


294
PHF6
RUNX1
1
19
8
350


295
PHF6
CBF
1
70
8
314


296
PHF6
del5q
1
5
8
379


297
PHF6
EVI1pos
1
4
8
380


298
PHF6
MLLPTD or split
1
17
8
367




MLLPTD






299
PHF6
splitMLLPTD or
0
22
9
362




split MLL






300
PHF6
MLLPTD or split
1
37
8
347




MLL






301
PHF6
Monosomy7
0
4
9
380


302
PHF6
t(6; 9)
0
2
9
382


303
PHF6
Trisomy 8
1
13
8
371


304
PHF6
AML1ETO
0
1
9
383


305
KIT
CEBPa
2
32
21
338


306
KIT
WT1
0
30
22
339


307
KIT
KRAS
0
8
22
365


308
KIT
NRAS
2
38
21
335


309
KIT
TP53
0
8
23
356


310
KIT
PTEN
0
6
23
367


311
KIT
RUNX1
0
20
22
340


312
KIT
CBF
21
51
2
323


313
KIT
del5q
0
6
23
368


314
KIT
EVI1pos
0
5
23
369


315
KIT
MLLPTD or split
0
18
23
356




MLLPTD






316
KIT
splitMLLPTD or
0
22
23
352




split MLL






317
KIT
MLLPTD or split
0
38
23
336




MLL






318
KIT
Monosomy7
0
4
23
370


319
KIT
t(6; 9)
0
2
23
372


320
KIT
Trisomy 8
0
15
23
359


321
KIT
AML1ETO
0
1
23
373


322
CEBPa
WT1
4
26
28
329


323
CEBPa
KRAS
0
8
34
349


324
CEBPa
NRAS
2
38
32
320


325
CEBPa
TP53
0
8
34
343


326
CEBPa
PTEN
0
6
34
352


327
CEBPs
RUNX1
0
20
33
326


328
CEBPa
CBF
4
68
30
291


329
CEBPa
del5q
0
6
34
353


330
CEBPa
EVI1pos
1
4
33
355


331
CEBPa
MLLPTD or split
2
16
32
343




MLLPTD






332
CEBPa
splitMLLPTD or
0
21
34
336




split MLL






333
CEBPa
MLLPTD or split
2
35
32
324




MLL






334
CEBPa
Monosomy7
0
3
34
356


335
CEBPa
t(6; 9)
0
2
34
357


336
CEBPa
Trisomy 8
1
14
33
345


337
CEBPa
AML1ETO
0
1
34
358


338
WT1
KRAS
0
8
30
351


339
WT1
NRAS
3
37
27
323


340
WT1
TP53
0
8
30
345


341
WT1
PTEN
0
6
30
354


342
WT1
RUNX1
3
17
26
330


343
WT1
CBF
1
69
29
292


344
WT1
del5q
0
6
30
355


345
WT1
EVI1pos
0
4
30
357


346
WT1
MLLPTD or split
2
16
28
345




MLLPTD






347
WT1
splitMLLPTD or
0
22
30
339




split MLL






348
WT1
MLLPTD or split
2
36
28
325




MLL






349
WT1
Monosomy7
0
4
30
357


350
WT1
t(6; 9)
1
1
29
360


351
WT1
Trisomy 8
1
14
29
347


352
WT1
AML1ETO
0
1
30
360


353
KRAS
NRAS
0
40
8
346


354
KRAS
TP53
0
8
8
371


355
KRAS
PTEN
0
6
8
380


356
KRAS
RUNX1
1
19
7
353


357
KRAS
CBF
2
68
6
319


358
KRAS
del5q
0
6
8
381


359
KRAS
EVI1pos
0
5
8
382


360
KRAS
MLLPTD or split
0
18
8
369




MLLPTD






361
KRAS
splitMLLPTD or
1
21
7
366




split MLL






362
KRAS
MLLPTD or split
1
37
7
350




MLL






363
KRAS
Monosomy7
0
4
8
383


364
KRAS
t(6; 9)
0
2
8
385


365
KRAS
Trisomy 8
0
15
8
372


366
KRAS
AML1ETO
0
1
8
386


367
NRAS
TP53
0
8
39
341


368
NRAS
PTEN
2
4
38
351


369
NRAS
RUNX1
2
18
35
326


370
NRAS
CBF
12
60
28
296


371
NRAS
del5q
0
6
40
350


372
NRAS
EVI1pos
1
4
39
352


373
NRAS
MLLPTD or split
0
18
40
338




MLLPTD






374
NRAS
splitMLLPTD or
2
20
38
336




split MLL






375
NRAS
MLLPTD or split
2
36
38
320




MLL






376
NRAS
Monosomy7
0
4
40
352


377
NRAS
t(6; 9)
0
2
40
354


378
NRAS
Trisomy 8
0
15
40
341


379
NRAS
AML1ETO
0
1
40
365


380
TP53
PTEN
1
5
7
375


381
TP53
RUNX1
1
19
7
348


382
TP53
CBF
0
72
8
309


383
TP53
del5q
1
5
7
376


384
TP53
EVI1pos
0
5
8
376


385
TP53
MLLPTD or split
0
17
8
364




MLLPTD






386
TP53
splitMLLPTD or
0
22
8
359




split MLL






387
TP53
MLLPTD or split
0
37
8
344




MLL






388
TP53
Monosomy7
0
4
8
377


389
TP53
t(6; 9)
0
2
8
379


390
TP53
trisomy 8
0
15
8
366


391
TP53
AML1ETO
0
1
8
380


392
PTEN
RUNX1
0
20
6
355


393
PTEN
CBF
1
71
5
319


394
PTEN
del5q
0
6
6
384


395
PTEN
EVI1pos
0
5
6
385


396
PTEN
MLLPTD or split
0
18
6
372




MLLPTD






397
PTEN
splitMLLPTD or
0
22
6
368




split MLL






398
PTEN
MLLPTD or split
0
38
6
352




MLL






399
PTEN
Monosomy7
0
4
6
386


400
PTEN
t(6; 9)
0
2
6
388


401
PTEN
trisomy 8
0
15
6
375


402
PTEN
AML1ETO
0
1
6
389


403
RUNX1
CBF
2
65
18
296


404
RUNX1
del5q
3
3
17
359


405
RUNX1
EVI1pos
0
4
20
358


406
RUNX1
MLLPTD or split
3
15
17
347




MLLPTD






407
RUNX1
splitMLLPTD or
0
19
20
343




split MLL






408
RUNX1
MLLPTD or split
3
32
17
330




MLL






409
RUNX1
Monosomy7
1
2
19
360


410
RUNX1
t(6; 9)
0
2
20
360


411
RUNX1
trisomy 8
0
14
20
348


412
RUNX1
AML1ETO
0
1
20
361


413
CBF
del5q
0
6
72
319


414
CBF
EVI1pos
0
5
72
320


415
CBF
MLLPTD or split
0
18
72
307




MLLPTD






416
CBF
splitMLLPTD or
0
22
72
303




split MLL






417
CBF
MLLPTD or split
0
38
72
287




MLL






418
CBF
Monosomy7
0
4
72
321


419
CBF
t(6; 9)
0
2
72
323


420
CBF
trisomy 8
0
15
72
310


421
CBF
AML1ETO
1
0
71
325


422
del5q
EVI1pos
0
5
6
386


423
del5q
MLLPTD or split
0
18
6
373




MLLPTD






424
del5q
splitMLLPTD or
0
22
6
369




split MLL






425
del5q
MLLPTD or split
0
38
6
353




MLL






426
del5q
Monosomy7
0
4
6
387


427
del5q
t(6; 9)
0
2
6
389


428
del5q
trisomy 8
0
15
6
376


429
del5q
AML1ETO
0
1
6
390


430
EVI1pos
MLLPTD or split
0
18
5
374




MLLPTD






431
EVI1pos
splitMLLPTD or
0
22
5
370




split MLL






432
EVI1pos
MLLPTD or split
0
38
5
354




MLL






433
EVI1pos
Monosomy7
0
4
5
388


434
EVI1pos
t(6; 9)
0
2
5
390


435
EVI1pos
trisomy 8
0
15
5
377


436
EVI1pos
AML1ETO
0
1
5
391


437
MLLPTD or split
Monosomy7
1
3
17
376



MLLPTD







438
MLLPTD or split
t(6; 9)
0
2
18
377



MLLPTD







439
MLLPTD or split
trisomy 8
0
15
18
364



MLLPTD







440
MLLPTD or split
AML1ETO
0
1
18
378



MLLPTD







441
splitMLLPTD or
Monosomy7
0
4
22
371



split MLL







442
splitMLLPTD or
t(6; 9)
0
2
22
373



split MLL







443
splitMLLPTD or
trisomy 8
0
15
22
360



split MLL







444
splitMLLPTD or
AML1ETO
0
1
22
374



split MLL







445
MLLPTD or split
Monosomy7
1
3
37
356



MLL







446
MLLPTD or split
t(6; 9)
0
2
38
357



MLL







447
MLLPTD or split
trisomy 8
0
15
38
344



MLL







448
MLLPTD or split
AML1ETO
0
1
38
358



MLL







449
Monosomy7
t(6; 9)
0
2
4
391


450
Monosomy7
trisomy 8
0
15
4
378


451
Monosomy7
AML1ETO
0
1
4
392


452
t(6; 9)
trisomy 8
0
15
2
380


453
t(6; 9)
AML1ETO
0
1
2
394


454
Trisomy 8
AML1ETO
0
1
15
381





1) Single nucleotide variants which could not be verified as bona fide somatic mutations were censored from analysis, therefore sample number does not add up to 398 in all instances.



2Number of patients mutated for both gene #1 and gene #2.




3Number of patients wildtype for gene #1 but mutant for gene #2.




4Number of patients mutated for gene #1 and wildtype for gene #2.




5Number of patients wildtype for both genes.














TABLE 7








text missing or illegible when filed

















Mutated
Mutated


%


%

Adjusted


Gene #1
Gene #2
M/M2
WT/M3
M/M4
M/WT5
WT/WT6
M/WT7
p-value8
p-value9



















ASXL1
RUNX1
5
15
25.0
4
356
1.1
<0.001
<0.001


DNMT3A
NPM1
57
57
50.0
32
239
11.8
<0.001
<0.001


DNMT3A
FLT3
52
92
36.1
37
204
15.4
<0.001
<0.001



ITD










DNMT3A
IDH1
13
9
59.1
76
286
21.0
<0.001
0.008


DNMT3A
IDH1 or
19
32
37.3
70
262
21.1
0.02
0.91



IDH2










FLT3 ITD
NPM1
63
55
53.4
84
195
30.1
<0.001
<0.001


FLT3 ITD
WT1
18
12
60.0
127
234
35.2
0.01
0.94


IDH1 or
NPM1
31
87
26.3
26
251
9.4
<0.001
0.002


IDH2











IDH1
NPM1
14
104
11.9
10
268
3.6
0.004
0.38


IDH1
PTEN
2
4
33.3
22
367
5.7
0.05
0.69


IDH2
NPM1
17
101
14.4
16
262
5.8
0.01
0.67


IDH2
NPM1
16
102
13.6
8
270
2.9
<0.001
0.01


R140Q











KIT
CBF
21
51
29.2
2
323
0.6
<0.001
<0.001


NRAS
CBF
12
60
16.7
28
296
8.6
0.05
0.1


RUNX1
Del 5q
3
3
50.0
17
359
4.5
0.002
1.0


TET2
ASXL1
4
6
40.0
29
351
7.6
0.006
0.03





1) Single nucleotide variants which could not be verified as bona fide somatic mutations were censored from analysis, therefore sample number does not sum up to 398 in all instances.



2Number of patients mutated for both gene #1 and gene #2.




3Number of patients wildtype for gene #1 but mutant for gene #2.




4Percentage of patients mutant for gene #1 and gene #2 over all patients mutated for either gene.




5Number of patients mutated for gene #1 and wildtype for gene #2.




6Number of patients wildtype for both genes.




7Percentage of patients mutant for either gene over all patients wildtype for either gene.




8P-value by Fisher's exact test.




9P-value adjusted for multiple comparisons.




text missing or illegible when filed indicates data missing or illegible when filed














TABLE 8








text missing or illegible when filed

















Mutated
Mutated


%


%

Adjusted


Gene #1
Gene #2
M/M2
WT/M3
M/M4
M/WT5
WT/WT6
M/WT7
p-value8
p-value9



















ASXL1
FLT3
0
146
0
10
239
4.0
0.02
0.94


CBF
MLL
0
38
0
72
287
20.1
<0.001
0.99



abnormalities










CBF
Split MLL
0
22
0
72
303
19.2
0.02
1.0


CBF
MLL PTD
0
18
0
72
307
19.0
0.05
1.0


DNMT3A
CBF
1
71
1.4
88
225
28.1
<0.001
0.11


DNMT3A
Split MLL
1
21
4.6
88
275
24.2
0.04
0.97


DNMT3A
WT1
0
29
0
63
287
18.0
0.01
0.92


R882











FLT3
CBF
6
66
8.3
141
184
43.4
<0.001
0.02


FLT3
NRAS
3
37
7.5
144
212
40.5
<0.001
0.008


FLT3
KIT
0
23
0
147
227
39.3
<0.001
0.04


FLT3
Splt MLL
2
20
9.1
145
230
38.7
0.005
0.39


IDH1 or
CBF
1
71
1.4
56
267
17.3
<0.001
0.63


IDH2











IDH1 or
TET2
0
33
0
56
301
15.7
0.008
0.97


IDH2











IDH1 or
WT1
0
30
0
56
303
15.6
0.01
0.98


IDH2











IDH1 or
FLT3
13
133
8.9
44
205
17.7
0.02
1.0


IDH2











IDH1 or
CEBPA
1
33
2.9
56
302
15.6
0.04
0.99


IDH2











IDH1
FLT3
4
142
2.7
20
230
8.0
0.04
1.0


IDH2
CBF
0
72
0
33
291
10.2
0.002
0.99


NPM1
CBF
0
72
0
118
207
36.3
<0.001
0.001


NPM1
MLL
0
38
0
118
241
32.9
<0.001
0.02



abnormalities










NPM1
Split MLL
0
22
0
118
257
31.5
<0.001
0.59


NPM1
MLL PTD
0
18
0
118
261
31.1
0.002
0.59


NPM1
CEBPA
3
31
8.2
113
246
31.5
0.005
0.34


NPM1
KIT
2
21
8.7
116
258
31.0
0.03
0.99


WT1
CBF
1
69
1.4
29
292
9.0
0.03
1.0





1) Single nucleotide variants which could not be verified as bona fide somatic mutations were censored from analysis, therefore sample number does not sum up to 398 in all instances.



2Number of patients mutated for both gene #1 and gene #2




3Number of patients wildtype for gene #1 but mutant for gene #2




4Percentage of patients mutant for gene #1 and gene #2 over all patients mutated for either gene




5Number of patients mutated for gene #1 and wildtype for gene #2




6Number of patients wildtype for both genes




7Percentage of patients mutant for either genes over all patients wildtype for either gene




8P-value by Fisher's exact test.




9P-value adjusted for multiple comparisons




text missing or illegible when filed indicates data missing or illegible when filed



















TABLE 9










MV


Gene/


Median
UV
analysis


Cytogenetic
Mutational
Number of
Survival
analysis
p-


Abnormality
Status
patients
(months)
p-value2
value3




















DNMT3A
Mutant
88
14.1
0.19
0.29



Wildtype
296
21.3


DNMT3A
R882 Mutant
63
14.1
0.14
0.26



Wildtype
321
21.3


DNMT3A
Non-R882
27
18.2
0.90
0.91



Mutant



Wildtype
357
18.0


IDH1/2
Mutant for
56
42.4
0.009
0.001



IDH1 or



IDH2



Wildtype
358
16.2


IDH1
Mutant
23
38.7
0.42
0.59



Wildtype
372
17.0


IDH2
Mutant
33
49.4
0.01
0.001



Wildtype
362
16.3


IDH2
R140Q
24

0.009
0.001



Mutant



Wildtype
371
16.6


IDH2
R172K
9
41.3
0.58
0.46



Mutant



Wildtype
386
16.9


TET2
Mutant
33
13.2
0.16
0.61



Wildtype
358
18.0


ASXL1
Mutant
10
10.3
0.05
0.22



Wildtype
384
17.7


FLT3
Mutant
148
13.8
0.006
0.003



Wildtype
248
22.0


NPM1
Mutant
118
22.3
0.07
0.005



Wildtype
278
16.5


PHF6
Mutant
9
 4.3
0.006
0.08



Wildtype
383
17.7


KIT
Mutant
23
57.9
0.08
0.6



Wildtype
373
16.6


CEBPa
Mutant
34
31.7
0.05
0.03



Wildtype
358
16.9


WT1
Mutant
30
12.2
0.23
0.19



Wildtype
360
17.7


KRAS
Mutant
8

0.17
0.19



Wildtype
386
16.9


NRAS
Mutant
40
21.3
0.13
0.19



Wildtype
355
16.9


TP53
Mutant
8
12.4
0.14
0.83



Wildtype
380
18.2


PTEN
Mutant
6
15.2
0.68
0.68



Wildtype
389
17.9


RUNX1
Mutant
20
16.9
0.90
0.63



Wildtype
361
16.9


CBF
Present
43

0.001
0.47


translocations
Absent
353
16.2


Del 5q
Present
12
 7.0
0.001
0.46



Absent
384
18.0


EVI positive
Present
8
 2.8
<0.001
0.02



Absent
388
18.0


MLL PTD
Present
19
12.6
0.009
0.19



Absent
377
18.0


Split MLL
Present
25
11.7
0.05
0.44



Absent
371
18.2


Any MLL
Present
39
10.9
<0.001
0.33


abnormalities
Absent
357
19.7


Monosomy 7
Present
9
 3.5
<0.001
0.18



Absent
387
18.0


t(6;9)
Present
2
15.8
0.42
0.81



Absent
394
17.5


Trisomy 8
Present
19
10.2
0.06
0.03



Absent
377
18.0


t(8;21)
Present
29
47.1
0.02
0.37



Absent
367
16.5





1) Absence of value under column for overall survival indicates that deaths were not observed.



2Univariate (UV) analysis p-value (calculated by Log-rank test).




3Multivariate (MV) analysis p-value taking into account WBC count, age, transplantation, and cytogenetics.


















TABLE 10








Median



Gene/Cytogenetic

Number of
Survival


Abnormality
Mutational Status
patients
(months)
p-value2



















DNMT3A
Mutant
75
14.08
0.17



Wildtype
151
22.83


DNMT3A
R882 Mutant
56
14.08
0.07



Wildtype
170
22.83


DNMT3A
Non-R882 Mutant
21
23.52
0.57



Wildtype
205
17.96


IDH1/2
Mutant for IDH1 or
46

0.001



IDH2



Wildtype
188
15.53


IDH1
Mutant
21
38.65
0.49



Wildtype
213
17.53


IDH2
Mutant
25

0.001



Wildtype
209
16.15


IDH2
R140Q Mutant
18

0.001



Wildtype
216
16.91


IDH2
R172K Mutant
7
37.96
0.44



Wildtype
227
16.94


TET2
Mutant
17
 8.82
0.008



Wildtype
214
19.08


ASXL1
Mutant
6
24.42
0.48



Wildtype
227
17.66


FLT3
Mutant
120
13.52
0.001



Wildtype
114
34.31


NPM1
Mutant
110
23.52
0.04



Wildtype
124
16.15


PHF6
Mutant
3
 2.53
<0.0001



Wildtype
229
17.96


KIT
Mutant
2

0.98



Wildtype
232
17.66


CEBPa
Mutant
26
31.68
0.14



Wildtype
207
16.91


WT1
Mutant
26
10.94
0.12



Wildtype
205
18.26


KRAS
Mutant
5

0.09



Wildtype
229
17.53


NRAS
Mutant
20

0.10



Wildtype
213
16.94


TP53
Mutant
2

0.57



Wildtype
229
17.89


PTEN
Mutant
4

0.99



Wildtype
229
17.89


RUNX1
Mutant
13
16.91
0.54



Wildtype
215
17.89


EVI positive
Present
2
 1.25
<0.0001



Absent
232
17.89


MLL PTD
Present
12
16.54
0.04



Absent
222
18.26


Split MLL
Present
7
21.71
0.96



Absent
227
17.77


Any MLL
Present
17
16.15
0.08


abnormalitiy
Absent
217
18.95


Trisomy 8
Present
19
10.16
0.04



Absent
215
18.25





1) Absence of value under column for overall survival indicates that deaths were not observed.



2P-value calculated by Log-rank test.


















TABLE 11a





Cytogenetic

Test
Validation



Classi-

cohort
cohort
Overall


fication
Mutations
(% (N))
(% (N))
Risk







Inversion
Any
19.7%
15.5%
Favor-


(16), t(8;21)

(71)
(13)
able












Normal
FLT3-ITD
NPM1 and
 5.8%
 7.1%



Karyotype or
negative
IDH1/2 mutant
(21)
 (6)


Intermediate
FLT3-ITD
ASXL1, MLL-
35.5%
27.4%
Inter-


Risk
negative
PTD, PHF6
(129) 
(23)
mediate


Cytogenetic

and TET2-


Lesions

wildtype



FLT3-ITD
CEBPA



negative
mutant



or positive



FLT3-ITD
MLL-PTD,



positive
TET2, and




DNMT3A




wild-type, and




trisomy 8




negative



FLT3-ITD
TET2, MLL-
20.9%
21.4%
Unfa-



negative
PTD, ASXL1,
(76)
(18)
vorable




or PHF6




mutant



FLT3-ITD
TET2, MLL-



positive
PTD,




DNMT3A




mutant or




trisomy 8











Unfavorable
Any
18.2%
28.6%





(66)
(24)




















TABLE 12







Hazard Ratio
Confidence Interval
p-value
















Test cohort (n = 398)












Favorable
Reference

<0.001



Intermediate
1.88
1.15-3.05



Unfavorable
6.16
3.83-9.88







Entire cohort (n = 502)












Favorable
Reference

<0.001



Intermediate
1.83
1.18-2.85



Unfavorable
5.76
3.76-8.82








1Treatment-related mortality defined as death within 30 days after beginning induction chemotherapy.





2Chemotherapy resistance defined as failure to enter complete remission despite not incurring treatment-related morality, or relapse.

















TABLE 13





Gene/Cytogenetic





Abnormality
Mutational Status
p-value1
Adjusted p-value2


















DNMT3A
Mutant
0.01
0.10



Wildtype
0.14
0.28


IDH1
Mutant
0.62




Wildtype
0.01



IDH2
Mutant
0.33




Wildtype
0.05



IDH2 R140Q
R140Q Mutant
0.15
1.0 



Wildtype
0.05
0.22


IDH2 R172K
R172K Mutant
0.73




Wildtype
0.02



TET2
Mutant
0.45
1.0 



Wildtype
0.006
0.04


ASXL1
Mutant
0.08
0.50



Wildtype
0.009
0.05


FLT3
Mutant
0.14
0.71



Wildtype
0.10
0.30


NPM1
Mutant
0.01
0.11



Wildtype
0.20
0.20


PHF6
Mutant
0.19
0.77



Wildtype
0.005
0.04


KIT
Mutant
0.12




Wildtype
0.004



CEBPa
Mutant
0.56
0.56



Wildtype
0.003
0.03


WT1
Mutant
0.2




Wildtype
0.02



KRAS
Mutant
0.62




Wildtype
0.01



NRAS
Mutant
0.15




Wildtype
0.04



TP53
Mutant
0.75




Wildtype
0.01



PTEN
Mutant
0.78




Wildtype
0.02



RUNX1
Mutant
0.47




Wildtype
0.01



EVI positive
Present
0.90




Absent
0.03



MLL PTD
Present
0.27




Absent
0.01



Split MLL
Present
0.007
0.07



Absent
0.06
0.25






1P-value calculated by Log-rank test.




2P-value adjusted for multiple testing by a step-down Holm procedure (see Supplementary Methods), “—” indicates adjusted p-value not performed.






Claims
  • 1. A method of predicting survival of a patient with acute myeloid leukemia (AML), said method comprising: (a) analyzing a genetic sample isolated from the patient for the presence of cytogenetic abnormalities and a mutation in at least one of FLT3, NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, MLL-PTD, ASXL1, PHF6, KRAS, PTEN, P53, HRAS, and EZH2 genes; and(b) (i) predicting poor survival of the patient if a mutation is present in at least one of FLT3, MLL-PTD, ASXL1 and PHF6 genes, or (ii) predicting favorable survival of the patient if a mutation is present in IDH2R140 and/or a mutation is present in CEBPA.
  • 2. The method of claim 1, further comprising, predicting intermediate survival of the patient with cytogenetically-defined intermediate risk AML if: (i) no mutation is present in any of FLT3-ITD, TET2, MLL-PTD, DNMT3A, ASXL1 or PHF6 genes,(ii) a mutation in CEBPA and the FLT3-ITD is present, or(iii) a mutation is present in FLT3-ITD but trisomy 8 is absent.
  • 3. The method of claim 1, further comprising: predicting unfavorable survival of the patient with cytogenetically-defined intermediate-risk AML if (i) a mutation in TET2, ASXL1, or PHF6 or an MLL-PTD is present in a patient without the FLT3-ITD mutation, or(ii) the patient has a FLT3-ITD mutation and a mutation in TET2, DNMT3A, MLL-PTD or trisomy 8.
  • 4. The method of claim 2, wherein intermediate survival the patient is survival of about 18 months to about 30 months.
  • 5. A method of predicting survival of a patient with acute myeloid leukemia, said method comprising: (a) assaying a genetic sample from the patient's blood or bone marrow for the presence of a mutation in at least one of genes FLT3, NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, MLL-PTD, ASXL1, PHF6, KRAS, PTEN, P53, HRAS, and EZH2 in said sample; and(b) predicting a poor survival of the patient if a mutation is present in at least one of genes FLT3-ITD, MLL-PTD, ASXL1, PHF6; or predicting a favorable survival of the patient if a mutation is present in CEBPA or a mutation is present in IDH2 at R140.
  • 6. The method of claim 5, wherein amongst patients with cytogenetically-defined intermediate-risk acute myeloid leukemia who have FLT3-ITD mutation, at least one of the following: trisomy 8 or a mutation in TET2, DNMT3A, or the MLL-PTD are associated with an adverse outcome and poor overall survival of the patient.
  • 7. The method of claim 5, wherein amongst patients with cytogenetically-defined intermediate-risk acute myeloid leukemia who have a mutation in FLT3-ITD gene, a mutation in CEBPA gene is associated with improved outcome and overall survival of the patient.
  • 8. The method of claim 5, wherein in a cytogenetically-defined intermediate risk AML patient with both IDH1/IDH2 and NPM1 mutations, the overall survival is improved compared to NPM1-mutant patients wild-type for both IDH1 and IDH2.
  • 9. The method of claim 5, wherein amongst patients with acute myeloid leukemia, IDH2R140 mutations are associated with improved overall survival.
  • 10. The method of claim 1, wherein poor or unfavorable survival (adverse risk) of the patient is survival of less than or equal to about 10 months.
  • 11. The method of claim 1, wherein favorable survival of the patient is survival of about 32 months or more.
  • 12. A method of predicting survival of a patient with acute myeloid leukemia, said method comprising: (a) assaying a genetic sample from the patient's blood or bone marrow for the presence of a mutation in genes ASXL1 and WT1; and(b) determining the patient has or will develop primary refractory acute myeloid leukemia if mutated ASXL1 and WT1 genes are detected.
  • 13. A method of determining responsiveness of a patient with acute myeloid leukemia to high dose therapy, said method comprising: (a) analyzing a genetic sample isolated from the patient for the presence of a mutation in genes DNMT3A, and NPM1, and for the presence of a MLL translocation; and(b) (i) identifying the patient as one who will respond to high dose therapy if a mutation in DNMT3A or NPM1 or an MLL translocation are present; or (ii) identifying the patient as one who will not respond to high dose therapy in the absence of mutations in DNMT3A or NPM1 or an MLL translocation.
  • 14. A method of predicting whether a patient suffering from acute myeloid leukemia will respond better to high dose chemotherapy than to standard dose chemotherapy, the method comprising: (a) obtaining a DNA sample obtained from the patient's blood or bone marrow;(b) determining the mutational status of genes DNMT3A and NPM1, and the presence of a MLL translocation; and(c) predicting that the subject will be more responsive to high dose chemotherapy than standard dose chemotherapy where the sample is positive for a mutation in DNMT3A or NPM1 or an MLL translocation; or predicting that the subject will be non-responsive to high dose chemotherapy compared to standard dose chemotherapy where the sample is wild type with no mutations in DNMT3a or NPM1 genes and no translocation in MLL.
  • 15. A method of screening a patient with acute myeloid leukemia for responsiveness to treatment with high dose of Daunorubicin or a pharmaceutically acceptable salt, solvate, or hydrate thereof, comprising: obtaining a genetic sample comprising an acute myeloid leukemic cell from said individual; and assaying the sample and detecting the presence of a mutation in DNMT3A or NPM1 or an MLL translocation; and correlating a finding of a mutation in DNMT3A or NPM1 or an MLL translocation, as compared to wild type controls where there is no mutation, with said acute myeloid leukemia patient being more sensitive to high dose treatment with Daunorubicin or a pharmaceutically acceptable salt, solvate, or hydrate thereof.
  • 16. The method of claim 15, wherein the method further comprises predicting the patient is at a lower risk of relapse of acute myeloid leukemia following chemotherapy if a mutation in DNMT3A or NPM1 or an MLL translocation is detected.
  • 17. A method of determining whether a human has an increased genetic risk for developing or developing a relapse of acute myeloid leukemia, comprising: (a) analyzing a genetic sample isolated from the human's blood or bone marrow for the presence of a mutation in at least one gene from FLT3, NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, MLL-PTD, ASXL1, PHF6, KRAS, PTEN, P53, HRAS, and EZH2; and(b) determining the individual with cytogenetically-defined intermediate risk AML has an increased genetic risk for developing or developing a relapse of acute myeloid leukemia, relative to a control human with no such gene mutations in said genes, when: (i) a mutation in at least one of TET2, MLL-PTD, ASXL1 and PHF6 genes is detected when the patient has no FLT3-ITD mutation, or (ii) a mutation in at least one of TET2, MLL-PTD, and DNMT3A genes or trisomy 8 is detected when the patient has a FLT3-ITD mutation.
  • 18. A method for preparing a personalized genomics profile for a patient with acute myeloid leukemia, comprising: (a) subjecting mononuclear cells extracted from a bone marrow aspirate or blood sample from the patient to gene mutational analysis;(b) assaying the sample and detecting the presence of a cytogenetic abnormality and one or more mutations in a gene selected from the group consisting of FLT3, NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, MLL-PTD, ASXL1, PHF6, KRAS, PTEN, P53, HRAS, and EZH2 in said cells; and(c) generating a report of the data obtained by the gene mutation analysis, wherein the report comprises a prediction of the likelihood of survival of the patient or a response to therapy.
  • 19. A kit for determining treatment of a patient with AML, the kit comprising means for detecting a mutation in at least one gene selected from the group consisting of ASXL1, DNMT3A, NPM1, PHF6, WT1, TP53, EZH2, CEBPA, TET2, RUNX1, PTEN, FLT3, HRAS, KRAS, NRAS, KIT, IDH1, and IDH2; and instructions for recommended treatment based on the presence of a mutation in one or more of said genes.
  • 20. The kit of claim 31, wherein the instructions for recommended treatment for the patient based on the presence of a DNMT3A or NPM1 mutation or MLL translocation indicate high-dose daunorubicin as the recommended treatment.
  • 21. A method of treating, preventing or managing acute myeloid leukemia in a patient, comprising: (a) analyzing a genetic sample isolated from the patient for the presence of a mutation in genes DNMT3A, and NPM1, and for the presence of a MLL translocation;(b) identifying the patient as one who will respond to high dose chemotherapy better than standard dose chemotherapy if a mutation in DNMT3A or NPM1 or a MLL translocation are present; and(c) administering high dose therapy to the patient.
  • 22. The method of claim 5, wherein the patient is characterized as intermediate-risk on the basis of cytogenetic analysis.
  • 23. The method of claim 14, wherein the therapy comprises the administration of anthracycline.
  • 24. The method of claim 14 or claim 21, wherein administering high dose therapy comprises administering one or more high dose anthracycline antibiotics selected from the group consisting of Daunorubicin, Doxorubicin, Epirubicin, Idarubicin, Mitoxantrone, and Adriamycin.
  • 25. The method of claim 13, wherein the sample is DNA extracted from bone marrow or blood from the patient.
  • 26. The method of claim 13, wherein the genetic sample is DNA isolated from mononuclear cells (MNC) from the patient.
  • 27. The method of claim 21, wherein the high dose administration is Daunorubicin administered at from about 70 mg/m2 to about 140 mg/m2, or Idarubicin administered at from about 10 mg/m2 to about 20 mg/m2.
  • 28-33. (canceled)
  • 34. A method of predicting survival of a patient with acute myeloid leukemia, comprising: (a) analyzing a sample isolated from the patient for the presence of (i) a mutation in at least one of FLT3, MLL-PTD, ASXL1, and PHF6 genes, plus optionally one or more of NPM1, DNMT3A, NRAS, CEBPA, TET2, WT1, IDH1, IDH2, KIT, RUNX1, KRAS, PTEN, P53, HRAS, and EZH2 genes; or(ii) a mutation in IDH2 and/or CEBPA genes, plus optionally one or more of FLT3, MLL-PTD, ASXL1, PHF6, NPM1, DNMT3A, NRAS, TET2, WT1, IDH1, KIT, RUNX1, KRAS, PTEN, P53, HRAS, and EZH2 genes; and(b) (i) predicting poor survival of the patient if a mutation is present in at least one of FLT3, MLL-PTD, ASXL1 and PHF6 genes, or (ii) predicting favorable survival of the patient if a mutation is present in IDH2R140 and/or a mutation is present in CEBPA.
  • 35. The method of claim 34, further comprising analyzing the sample for the presence of cytogenetic abnormalities.
  • 36. The method of claim 34, further comprising (ii) predicting favorable survival of the patient if the following mutation is present: IDH2R140Q.
CROSS REFERENCE TO RELATED APPLICATION

This application is a national phase filing under 35 U.S.C. §371 of PCT International Application PCT/US2013/030208, filed Mar. 11, 2013, and published under PCT Article 21(2) in English as WO 2013/138237 A1 on Sep. 19, 2013. This application also claims priority to U.S. provisional patent application No. 61/609,723 filed Mar. 12, 2012; the entire contents of these applications are incorporated by reference.

FEDERALLY-SPONSORED RESEARCH OR DEVELOPMENT

This invention was made with Government support under contract U54CA143798-01 awarded by the National Cancer Institute Physical Sciences Oncology Center. The U.S. Government has certain rights in this invention.

PCT Information
Filing Document Filing Date Country Kind
PCT/US13/30208 3/11/2013 WO 00
Provisional Applications (1)
Number Date Country
61609723 Mar 2012 US