Patent Application
20250003008
Since the 2011 approval of the prototypical ICI, the anti-CTLA-4 antibody ipilimumab, for the treatment of advanced melanoma, immune checkpoint inhibition has become a standard treatment option across solid tumors. As of early 2022, ICIs have been approved by the United States Food and Drug Administration (FDA) for the treatment of 17 distinct solid tumor histologies in addition to two tumor-agnostic indications for microsatellite instability-high tumors. PD-L1 expression and tumor mutation burden have been shown to in-part predict clinical responses to immune checkpoint blockade. Nevertheless, with the exception of mismatch repair deficient tumors, TMB has failed to consistently demonstrate clinical utility in predicting responses to cancer immunotherapy. Efforts to separate subsets of alterations that may predominantly drive an effective anti-tumor immune response have yet to reveal a universal genomic predictive biomarker.
Malignant pleural mesothelioma (MPM) affects more than 30,000 people worldwide each year and is fatal in nearly all cases1. Exposure to asbestos and consequent chronic inflammation in the pleural cavity is responsible for the majority of cases with a typical disease latency of 30-40 years, especially in the context of co-occurring defects in DNA repair and germline cancer predisposition syndromes2-4. For over 15 years, cisplatin and pemetrexed combination chemotherapy was the only approved systemic therapy; this approval was based on a phase 3 study that showed an improvement in survival from 9.3 months with cisplatin alone to 12.1 months with the combination5. With the exception of bevacizumab (which has not achieved regulatory approval in the United States), adding novel agents to platinum doublet chemotherapy has not improved survival6-9. Recently, several phase 2 studies have reported on the efficacy of single agent PD-1 inhibitors in chemotherapy-pretreated MPM10-13.
We now provide new methods for treating subject suffering from a cancer, including a subject having one or more solid tumors.
In one aspect, methods are provided for assessing, monitoring, and/or treating mammals (e.g., humans) having, or suspected of having, cancer including solid tumors.
In a particular aspect, a sample (e.g., a blood sample) obtained from a mammal having one or more tumors can be assessed to determine.
Subjects having one or more tumors and that do not exhibit such high number of mutations in polyploid regions of the genome may not therapeutically benefit, or at may benefit less from such chemotherapeutic and/or immunotherapeutic treatment.
A high number of mutations in the haploid and/or polyploid regions of the genome may include a 3, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90 or 100 percent or more increased number of mutations in such regions relative to a subject that would not be responsive to chemotherapeutic and/or immunotherapeutic treatment.
In one aspect, methods are provided for assessing and/or treating a subject diagnosed with a tumor, comprising: 1) obtaining a biological sample from the subject; 2) identifying germline or somatic mutations in the subject's genome; 3) analyzing T cell receptor (TCR) clonotypes; 4) determining the number of mutations in haploid and/or polyploid regions of the subject's genome; and 5) selecting the subject as being responsive to chemotherapy, immunotherapy or combined chemotherapy and immunotherapy treatment when the subject is determined to have a high number of mutations in haploid and/or polyploid regions of the subject's genome.
In certain aspects, a subject is identified as having mutations in tumor suppressor genes, DNA repair genes, chromatin regulating genes and/or defects in homologous recombination are predictive of responsiveness to chemotherapeutic and/or immunotherapeutic treatment.
In particular aspects, subjects are identified as having a high number of mutations in haploid regions of the genome and those identified subjects are identified or selected or assessed as being favorably responsive to chemotherapeutic and/or immunotherapeutic treatment.
In additional particular aspects, subjects are identified as having a high number of mutations in polyploid regions of the genome and those identified subjects are identified or selected or assessed as being favorably responsive to chemotherapeutic and/or immunotherapeutic treatment.
In further particular aspects, subjects are identified as having a high number of mutations in haploid and polyploid regions of the genome and those identified subjects are identified or selected or assessed as being favorably responsive to chemotherapeutic and/or immunotherapeutic treatment.
Again, a high number of mutations in the haploid and/or polyploid regions of the genome may include a 3, 5, 10, 15, 20, 25, 30, 35, 40, 45, 50, 60, 70, 80, 90 or 100 percent or more increased number of mutations in such regions relative to a subject that would not be responsive to chemotherapeutic and/or immunotherapeutic treatment.
In certain embodiments, the tumor comprises a low tumor mutation burden (TMB-L), a medium tumor mutation burden (TMB-M) or a high tumor mutation burden (TMB-H). Suitably, a high tumor mutational burden comprises 10 or more non-synonymous somatic mutations per megabase; a medium tumor mutational burden comprises between 2 to less than 10 non-synonymous somatic mutations per megabase; and a low tumor mutation burden comprises less than 2 non-synonymous somatic mutations per megabase.
In certain aspects, the tumor comprises a low tumor mutation burden.
The nonsynonymous sequence alterations suitably encode one or more immunogenic neoantigens, for example HLA class I and HLA class II restricted neoantigens.
In certain aspects, detection of an enrichment of HLA class I and HLA class II restricted neoantigens as compared to a control, is predictive of the subject's responsiveness to the immunotherapy.
In certain aspects, detection of a less clonal T cell receptor repertoire is predictive of the subject's responsiveness to the immunotherapy.
In certain aspects, one or more germline mutations are detected in one or more cancer susceptibility genes. For example, the one or more cancer susceptibility genes may comprise BAP1, MLH1, MLH3, BRCA1/2, BLM or combinations thereof.
In certain aspects, the one or more tumor suppressor genes comprise BAP1, CDKN2A. NF2, SETD2, PBRM1, TP53 or combinations thereof.
In certain aspects, the one or more chromatin genes comprise mutations in members of a SWI/SNF chromatin remodeling complex. A preferred SWI/SNF chromatin remodeling complex may comprise for example ARID1A and ARID1B genes.
In certain aspects, the present methods may further comprise detecting mutations in genes KDM3B and KDM4C encoding histone demethylases and in KMT2C gene encoding methyltransferases.
In certain aspects, subjects identified as having a high degree of tumor aneuploidy and genome-wide copy number breakpoints are predictive of responsiveness to combined chemotherapeutic and/or immunotherapeutic treatment.
In certain aspects, subjects identified as having defective homologous recombination are predictive of responsiveness to combined chemotherapy and immunotherapy.
In certain aspects, detection of mutation signatures is indicative of an apolipoprotein B mRNA editing catalytic polypeptide-like (APOBEC) mutagenesis are predictive of non-responsiveness to combined chemotherapy and immunotherapy.
In certain aspects, the subject has cancer including without limitation, colorectal cancer, as well as, for example, leukemias, e.g., acute myelogenous leukemia (AML), chronic myelogenous leukemia (CML), acute lymphocytic leukemia (ALL), and chronic lymphocytic leukemia, AIDS related cancers such as Kaposi's sarcoma; breast cancers; bone cancers such as Osteosarcoma, Chondrosarcomas, Ewing's sarcoma, Fibrosarcomas, Giant cell tumors, Adamantinomas, and Chordomas; Brain cancers such as Meningiomas, Glioblastomas, Lower-Grade Astrocytomas, Oligodendrocytomas, Pituitary Tumors, Schwannomas, and Metastatic brain cancers; cancers of the head and neck including various lymphomas such as mantle cell lymphoma, non-Hodgkins lymphoma, adenoma, squamous cell carcinoma, laryngeal carcinoma, gallbladder and bile duct cancers, cancers of the retina such as retinoblastoma, cancers of the esophagus, gastric cancers, multiple myeloma, ovarian cancer, uterine cancer, thyroid cancer, testicular cancer, endometrial cancer, melanoma, lung cancer, bladder cancer, prostate cancer, lung cancer (including non-small cell lung carcinoma), pancreatic cancer, sarcomas, Wilms' tumor, cervical cancer, head and neck cancer, skin cancers, nasopharyngeal carcinoma, liposarcoma, epithelial carcinoma, renal cell carcinoma, gallbladder adeno carcinoma, parotid adenocarcinoma, endometrial sarcoma, multidrug resistant cancers. In certain aspects, the subject has a solid tumor. In additional aspects, the subject has non-small cell lung cancer. In further aspects, the subject has head and neck cancer. In further aspects, the subject has melanoma. In certain aspects, the subject has a mesothelioma, such as malignant pleural mesothelioma (MPM). In yet further aspects, the subject has an epithelioid MPM.
Any of a variety of chemotherapeutic agents may be administered to a subject in accordance with the present methods including for example: cisplatin (CDDP), carboplatin, bevacizumab, procarbazine, mechlorethamine, cyclophosphamide, camptothecin, ifosfamide, melphalan, imatinib mesylate, chlorambucil, busulfan, nitrosurea, dactinomycin, daunorubicin, doxorubicin, bleomycin, plicomycin, mitomycin, etoposide (VP16), tamoxifen, raloxifene, estrogen receptor binding agents, taxol, gemcitabien, navelbine, famesyl-protein transferase inhibitors, transplatinum, 5-fluorouracil, vincristine, vinblastine, methotrexate, temazolomide, platinum or any analog or derivative variants or combinations thereof.
Similarly, any of a variety of immunotherapeutic agents may be administered to a subject in accordance with the present methods including for example: a checkpoint inhibitor, cytokines, antibodies, adoptive cell therapy, co-stimulatory receptor agonist, a stimulator of innate immune cells, an activator of innate immunity, chimeric antigen receptor T cells (CAR-T), CAR-NK cells, a toll like receptor (TLR) agonist and combinations thereof. In certain aspects the administered immunotherapeutic agent may comprise: durvalumab, nivolumab, or atezolizuma and combinations thereof.
In certain aspects, a method of identifying and distinguishing single-copy, multi-copy and persistent tumor mutations (pTMB) in a biological sample comprises performing a genome-wide analysis of sequence coverage distribution and b-allele frequency of heterozygous single nucleotide polymorphisms (SNPs) for determining purity and segmental tumor and normal copy numbers from either whole genome, whole exome or targeted panel next-generation sequencing; calculating the expected variant allele fraction for a mutation at a cellular fraction with mutant copies per cancer cell and calculating mutation clonality for each cancer cell in the biological sample; assigning minor and major copy numbers to mutated loci and classifying mutations as single-copy, multi-copy or persistent tumor mutations; and identifying and distinguishing single-copy, multi-copy and persistent tumor mutations. In certain embodiments, wherein mutation multiplicity (number of mutated copies per cell) and cancer cell fraction (proportion of cancer cells harboring the mutation) are calculated based on the mutant read count, total coverage, tumor purity, and major and minor allele-specific copy number in the tumor and normal counterpart for each mutation. In certain embodiment, mutations in regions of the genome with a single copy are classified as single copy mutations. In certain embodiments, mutations present in more than one copy per cancer cell are classified as multi-copy mutations. In certain embodiments, the persistent tumor mutations are defined as the number of mutations classified as either single-copy or multi-copy mutations. In certain aspects, a method of predicting response to therapy, e.g. immunotherapy, chemotherapy, comprises computing persistent tumor mutation burden by whole exome or targeted next generation sequencing.
In certain aspects, a method of assessing differential potential of persistent mutations in predicting cancer outcome compared to tumor mutation burden (TMB), comprises defining a number of loss-prone mutations in each tumor sample. In certain embodiments, the number of loss-prone mutations in each tumor sample is defined as the difference between the total number of mutations assessed and the number of persistent mutations.
In further aspects, methods and systems are provided that include assessing the number of persistent mutations in tumors of a subject to predict clinical response from immunotherapy containing regimens, in tumors with high tumor mutation burden for which tumor mutation burden failed to do so.
In yet further aspects, methods and systems are provided that include assessing the number of persistent mutations in tumors of subject to identify tumors that can be prospectively selected and treated with immunotherapy containing regimens, including in the setting of an interventional biomarker-directed clinical trial.
Unless otherwise defined, all terms (including technical and scientific terms) used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this invention belongs. It will be further understood that terms, such as those defined in commonly used dictionaries, should be interpreted as having a meaning that is consistent with their meaning in the context of the relevant art and will not be interpreted in an idealized or overly formal sense unless expressly so defined herein.
As used herein, the singular forms “a”, “an” and “the” are intended to include the plural forms as well, unless the context clearly indicates otherwise. Furthermore, to the extent that the terms “including”, “includes”, “having”, “has”, “with”, or variants thereof are used in either the detailed description and/or the claims, such terms are intended to be inclusive in a manner similar to the term “comprising.”
The term “about” or “approximately” means within an acceptable error range for the particular value as determined by one of ordinary skill in the art, which will depend in part on how the value is measured or determined, i.e., the limitations of the measurement system. For example, “about” can mean within 1 or more than 1 standard deviation, per the practice in the art. Alternatively, “about” can mean a range of up to 20%, up to 10%, up to 5%, or up to 1% of a given value or range. Alternatively, particularly with respect to biological systems or processes, the term can mean within an order of magnitude within S-fold, and also within 2-fold, of a value. Where particular values are described in the application and claims, unless otherwise stated the term “about” meaning within an acceptable error range for the particular value should be assumed.
The term “cancer” as used herein is meant, a disease, condition, trait, genotype or phenotype characterized by unregulated cell growth or replication as is known in the art; including lung cancer (including non-small cell lung carcinoma), gastric cancer, colorectal cancer, as well as, for example, leukemias, e.g., acute myelogenous leukemia (AML), chronic myelogenous leukemia (CML), acute lymphocytic leukemia (ALL), and chronic lymphocytic leukemia, AIDS related cancers such as Kaposi's sarcoma; breast cancers; bone cancers such as Osteosarcoma, Chondrosarcomas, Ewing's sarcoma, Fibrosarcomas, Giant cell tumors, Adamantinomas, and Chordomas; Brain cancers such as Meningiomas, Glioblastomas, Lower-Grade Astrocytomas, Oligodendrocytomas, Pituitary Tumors, Schwannomas, and Metastatic brain cancers; cancers of the head and neck including various lymphomas such as mantle cell lymphoma, non-Hodgkins lymphoma, adenoma, squamous cell carcinoma, laryngeal carcinoma, gallbladder and bile duct cancers, cancers of the retina such as retinoblastoma, cancers of the esophagus, gastric cancers, multiple myeloma, ovarian cancer, uterine cancer, thyroid cancer, testicular cancer, endometrial cancer, melanoma, bladder cancer, prostate cancer, pancreatic cancer, sarcomas, Wilms' tumor, cervical cancer, head and neck cancer, skin cancers, nasopharyngeal carcinoma, liposarcoma, epithelial carcinoma, renal cell carcinoma, gallbladder adeno carcinoma, parotid adenocarcinoma, endometrial sarcoma, and multidrug resistant cancers.
As used herein, the terms “comprising,” “comprise” or “comprised,” and variations thereof, in reference to defined or described elements of an item, composition, apparatus, method, process, system, etc. are meant to be inclusive or open ended, permitting additional elements, thereby indicating that the defined or described item, composition, apparatus, method, process, system, etc. includes those specified elements—or, as appropriate, equivalents thereof—and that other elements can be included and still fall within the scope/definition of the defined item, composition, apparatus, method, process, system, etc.
“Diagnostic” or “diagnosed” means identifying the presence or nature of a pathologic condition. Diagnostic methods differ in their sensitivity and specificity. The “sensitivity” of a diagnostic assay is the percentage of diseased individuals who test positive (percent of “true positives”). Diseased individuals not detected by the assay are “false negatives.” Subjects who are not diseased and who test negative in the assay, are termed “true negatives.” The “specificity” of a diagnostic assay is 1 minus the false positive rate, where the “false positive” rate is defined as the proportion of those without the disease who test positive. While a particular diagnostic method may not provide a definitive diagnosis of a condition, it suffices if the method provides a positive indication that aids in diagnosis.
An “effective amount” as used herein, means an amount which provides a therapeutic or prophylactic benefit.
“Optional” or “optionally” means that the subsequently described event or circumstance can or cannot occur, and that the description includes instances where the event or circumstance occurs and instances where it does not.
As used in this specification and the appended claims, the term “or” is generally employed in its sense including “and/or” unless the content clearly dictates otherwise.
“Parenteral” administration of an immunogenic composition includes, e.g., subcutaneous (s.c.), intravenous (i.v.), intramuscular (i.m.), or intrasternal injection, or infusion techniques.
The terms “patient” or “individual” or “subject” are used interchangeably herein, and refers to a mammalian subject to be treated, with human patients being preferred. In some embodiments, the methods of the invention find use in experimental animals, in veterinary application, and in the development of animal models for disease, including, but not limited to, rodents including mice, rats, and hamsters, and primates.
The terms “sample,” “patient sample,” “biological sample,” and the like, encompass a variety of sample types obtained from a patient, individual, or subject and can be used in a diagnostic, prognostic and/or monitoring assay. The patient sample may be obtained from a healthy subject, a diseased patient, or a patient with lung cancer. In certain embodiments, a sample that is “provided” can be obtained by the person (or machine) conducting the assay, or it can have been obtained by another, and transferred to the person (or machine) carrying out the assay. Moreover, a sample obtained from a patient can be divided and only a portion may be used for diagnosis. Further, the sample, or a portion thereof, can be stored under conditions to maintain sample for later analysis. The definition specifically encompasses blood and other liquid samples of biological origin (including, but not limited to, peripheral blood, serum, plasma, cord blood, amniotic fluid, cerebrospinal fluid, urine, saliva, stool and synovial fluid), solid tissue samples such as a biopsy specimen or tissue cultures or cells derived therefrom and the progeny thereof. In certain embodiment, a sample comprises cerebrospinal fluid. In a specific embodiment, a sample comprises a blood sample. In another embodiment, a sample comprises a plasma sample. In yet another embodiment, a serum sample is used. The definition of “sample” also includes samples that have been manipulated in any way after their procurement, such as by centrifugation, filtration, precipitation, dialysis, chromatography, treatment with reagents, washed, or enriched for certain cell populations. The terms further encompass a clinical sample, and also include cells in culture, cell supernatants, tissue samples, organs, and the like. Samples may also comprise fresh-frozen and/or formalin-fixed, paraffin-embedded tissue blocks, such as blocks prepared from clinical or pathological biopsies, prepared for pathological analysis or study by immunohistochemistry.
As defined herein, a “therapeutically effective” amount of a compound or agent (i.e., an effective dosage) means an amount sufficient to produce a therapeutically (e.g., clinically) desirable result. The compositions can be administered from one or more times per day to one or more times per week; including once every other day. The skilled artisan will appreciate that certain factors can influence the dosage and timing required to effectively treat a subject, including but not limited to the severity of the disease or disorder, previous treatments, the general health and/or age of the subject, and other diseases present. Moreover, treatment of a subject with a therapeutically effective amount of the compounds of the invention can include a single treatment or a series of treatments.
As used herein, the terms “treat,” treating,” “treatment,” and the like refer to reducing or ameliorating a disorder and/or symptoms associated therewith. It will be appreciated that, although not precluded, treating a disorder or condition does not require that the disorder, condition or symptoms associated therewith be completely eliminated.
As used herein, the term “tumor” means a mass of transformed cells that are characterized by neoplastic uncontrolled cell multiplication and at least in part, by containing angiogenic vasculature. The abnormal neoplastic cell growth is rapid and continues even after the stimuli that initiated the new growth has ceased. The term “tumor” is used broadly to include the tumor parenchymal cells as well as the supporting stroma, including the angiogenic blood vessels that infiltrate the tumor parenchymal cell mass. Although a tumor generally is a malignant tumor, i.e., a cancer having the ability to metastasize (i.e. a metastatic tumor), a tumor also can be nonmalignant (i.e. non-metastatic tumor). Tumors are hallmarks of cancer, a neoplastic disease the natural course of which is fatal. Cancer cells exhibit the properties of invasion and metastasis and are highly anaplastic.
Ranges: throughout this disclosure, various aspects of the invention can be presented in a range format. It should be understood that the description in range format is merely for convenience and brevity and should not be construed as an inflexible limitation on the scope of the invention. Accordingly, the description of a range should be considered to have specifically disclosed all the possible subranges as well as individual numerical values within that range. For example, description of a range such as from 1 to 6 should be considered to have specifically disclosed subranges such as from 1 to 3, from 1 to 4, from 1 to 5, from 2 to 4, from 2 to 6, from 3 to 6 etc., as well as individual numbers within that range, for example, 1, 2, 2.7, 3, 4, 5, 5.3, and 6. This applies regardless of the breadth of the range.
The patent or application file contains at least one drawing executed in color. Copies of this patent or patent application publication with color drawings will be provided by the Office upon request and payment of the necessary fee.
Mesothelioma is a rare and fatal cancer with limited therapeutic options until the recent approval of combination immune checkpoint blockade. Disclosed herein are results of the phase 2 PrE0505 trial (NCT02899195) of the anti-PD-L1 antibody, durvalumab, plus platinum-pemetrexed chemotherapy for patients with previously untreated unresectable pleural mesothelioma. The primary endpoint was overall survival compared to historic control with cisplatin and pemetrexed chemotherapy while secondary and exploratory endpoints included safety, progression-free survival, and biomarkers of response. The combination of durvalumab with chemotherapy met the pre-specified primary endpoint reaching a median survival of 20.4 months vs. 12.1 months with historical control. Treatment-emergent adverse events were consistent with known side effects of chemotherapy and all adverse events due to immunotherapy were grade ≤2. Integrated genomic and immune cell repertoire analyses revealed that a higher immunogenic mutation burden coupled with a more diverse T cell repertoire were linked with favorable clinical outcome. Structural genome-wide analyses demonstrated a higher degree of genomic instability in responding tumors of epithelioid histology. Patients with germline alterations in cancer predisposing genes, especially those involved in DNA repair, were more likely to attain long term survival. Our findings indicate that concurrent durvalumab with platinum-based chemotherapy has promising clinical activity and that responses are driven by the complex genomic background of malignant pleural mesothelioma.
Following the non-small cell lung cancer paradigm, where the combination of first line chemotherapy with PD-1 pathway blockade has become a standard approach for advanced disease14, chemo-immunotherapy is currently being explored in MPM. In the first-line setting, the phase 2 DREAM study of durvalumab with chemotherapy achieved its primary endpoint of progression-free survival at 6 months and showed the regimen to be tolerable and active in this setting15. Furthermore, the combination of the anti-CTLA-4 antibody, ipilimumab, with the anti-PD-1 antibody, nivolumab, has been shown to improve survival for previous untreated patients when compared to chemotherapy with robust efficacy being limited to non-epithelioid histology16.
While several studies have expanded our understanding of the genomic landscape of MPM and identified putative actionable alterations, these have not been translated to therapeutic progress17-19. More than 50% of MPMs carry germline or somatic mutations in genes involved in DNA repair and homologous recombination3,20. BAP1, a nuclear ubiquitin carboxyterminal hydrolase has been reported to be frequently mutated in the germline and tumor cells of patients with MPM17,18,20. Heterozygous germline BAP1 alterations predispose to mesothelioma especially in the context of asbestos exposure21 and similarly, germline BLM mutations may increase susceptibility to asbestos carcinogenesis and emergence of mesothelioma22. Inactivation of tumor suppressor genes such as BAP1, NF2, CDKN2A, TP53 and SETD2 by sequence or structural alterations is thought to be the predominant oncogenic mechanism for MPM17. Notably, MPM harbors a low tumor mutation burden (TMB) of less than 2 nonsynonymous mutations per megabase17,18 and has therefore been considered a tumor with low neoantigen-driven immunogenicity. Nevertheless, the promising clinical efficacy of immune checkpoint blockade for MPM calls for in-depth genomic and functional analyses to investigate the mechanisms of therapeutic response and resistance. As further disclosed below, the combination of durvalumab with platinum-based chemotherapy in a phase 2 clinical trial was investigated including to establish safety and efficacy and explore genomic and immunologic features of response in patients with unresectable MPM.
In certain embodiments, the methods embodied herein, identifying a mammal as having cancer.
1) obtaining a biological sample from the subject: 2) identifying germline or somatic mutations in the subject's genome; 3) identifying copy number profiles across the genome; 4) analyzing T cell receptor (TCR) clonotypes; and 5) identifying subjects responsive to chemotherapy, immunotherapy or combined chemotherapy and immunotherapy. In certain aspects, the identified subjects may be administered a chemotherapeutic, an immunotherapeutic or a chemotherapeutic and an immunotherapeutic, and, thereby treating the subject.
In certain embodiments, a subject is diagnosed as having cancer, e.g. early stage cancer. In certain embodiments, the type of cancer is identified and the cancer is treated by various therapeutics, including therapeutics specific for the type of cancer, including chemotherapy, immunotherapy or combined chemotherapy and immunotherapy.
The cancer treatment may include surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or any combinations thereof.
The method also can include administering to the mammal a cancer treatment (e.g., surgery, adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy, targeted therapy, or any combinations thereof).
The mammal can be monitored for the presence of cancer after administration of the cancer treatment.
Cancer therapies in general also include a variety of combination therapies with both chemical and radiation based treatments. Combination chemotherapies include, for example, cisplatin (CDDP), carboplatin, procarbazine, mechlorethamine, cyclophosphamide, camptothecin, ifosfamide, melphalan, chlorambucil, busulfan, nitrosurea, dactinomycin, daunorubicin, doxorubicin, bleomycin, plicomycin, mitomycin, etoposide (VP16), tamoxifen, raloxifene, estrogen receptor binding agents, taxol, gemcitabien, navelbine, famesyl-protein transferase inhibitors, transplatinum, 5-fluorouracil, vincristine, vinblastine and methotrexate, Temazolomide (an aqueous form of DTIC), or any analog or derivative variant of the foregoing. The combination of chemotherapy with biological therapy is known as biochemotherapy. The chemotherapy may also be administered at low, continuous doses which is known as metronomic chemotherapy.
Yet further combination chemotherapies include, for example, alkylating agents such as thiotepa and cyclosphosphamide; alkyl sulfonates such as busulfan, improsulfan and piposulfan; aziridines such as benzodopa, carboquone, meturedopa, and uredopa; ethylenimines and methylamelamines including altretamine, triethylenemelamine, trietylenephosphoramide, triethiylenethiophosphoramide and trimethylolomelamine; acetogenins (especially bullatacin and bullatacinone); a camptothecin (including the synthetic analogue topotecan); bryostatin; callystatin; CC-1065 (including its adozelesin, carzelesin and bizelesin synthetic analogues); cryptophycins (particularly cryptophycin 1 and cryptophycin 8); dolastatin; duocarmycin (including the synthetic analogues, KW-2189 and CB1-TMI); eleutherobin; pancratistatin; a sarcodictyin; spongistatin; nitrogen mustards such as chlorambucil, chlornaphazine, cholophosphamide, estramustine, ifosfamide, mechlorethamine, mechlorethamine oxide hydrochloride, melphalan, novembichin, phenesterine, prednimustine, trofosfamide, uracil mustard; nitrosureas such as carmustine, chlorozotocin, fotemustine, lomustine, nimustine, and ranimnustine; antibiotics such as the enediyne antibiotics (e.g., calicheamicin, especially calicheamicin gammal1 and calicheamicin omegal1; dynemicin, including dynemicin A; bisphosphonates, such as clodronate; an esperamicin; as well as neocarzinostatin chromophore and related chromoprotein enediyne antiobiotic chromophores, aclacinomysins, actinomycin, authrarnycin, azaserine, bleomycins, cactinomycin, carabicin, carminomycin, carzinophilin, chromomycinis, dactinomycin, daunorubicin, detorubicin, 6-diazo-5-oxo-L-norleucine, doxorubicin (including morpholino-doxorubicin, cyanomorpholino-doxorubicin, 2-pyrrolino-doxorubicin and deoxydoxorubicin), epirubicin, esorubicin, idarubicin, marcellomycin, mitomycins such as mitomycin C, mycophenolic acid, nogalarnycin, olivomycins, peplomycin, potfiromycin, puromycin, quelamycin, rodorubicin, streptonigrin, streptozocin, tubercidin, ubenimex, zinostatin, zorubicin; anti-metabolites such as methotrexate and 5-fluorouracil (5-FU); folic acid analogues such as denopterin, pteropterin, trimetrexate; purine analogs such as fludarabine, 6-mercaptopurine, thiamiprine, thioguanine; pyrimidine analogs such as ancitabine, azacitidine, 6-azauridine, carmofur, cytarabine, dideoxyuridine, doxifluridine, enocitabine, floxuridine; androgens such as calusterone, dromostanolone propionate, epitiostanol, mepitiostane, testolactone, anti-adrenals such as mitotane, trilostane; folic acid replenisher such as frolinic acid; aceglatone; aldophosphamide glycoside; aminolevulinic acid; eniluracil, amsacrine; bestrabucil; bisantrene; edatraxate; defofamine; demecolcine; diaziquone; elformithine; elliptinium acetate; an epothilone; etoglucid; gallium nitrate; hydroxyurea; lentinan; lonidainine, maytansinoids such as maytansine and ansamitocins; mitoguazone; mitoxantrone; mopidanmol; nitraerine; pentostatin; phenamet, pirarubicin; losoxantrone; podophyllinic acid; 2-ethylhydrazide; procarbazine; PSK polysaccharide complex, razoxane; rhizoxin; sizofiran; spirogermanium; tenuazonic acid; triaziquone; 2,2′,2″-trichlorotriethylamine; trichothecenes (especially T-2 toxin, verracurin A, roridin A and anguidine); urethan; vindesine; dacarbazine; mannomustine; mitobronitol; mitolactol; pipobroman; gacytosine; arabinoside (“Ara-C”); cyclophosphamide; taxoids, e.g., paclitaxel and docetaxel gemcitabine; 6-thioguanine; mercaptopurine; platinum coordination complexes such as cisplatin, oxaliplatin and carboplatin; vinblastine; platinum; etoposide (VP-16); ifosfamide; mitoxantrone; vincristine; vinorelbine; novantrone; teniposide; edatrexate; daunomycin; aminopterin; xeloda; ibandronate; irinotecan (e.g., CPT-11); topoisomerase inhibitor RFS 2000; difluorometlhylornithine (DMFO); retinoids such as retinoic acid; capecitabine; carboplatin, procarbazine, plicomycin, gemcitabien, navelbine, farnesyl-protein transferase inhibitors, transplatinum; and pharmaceutically acceptable salts, acids or derivatives of any of the above.
Immunotherapeutics, generally, rely on the use of immune effector cells and molecules to target and destroy cancer cells. The immune effector may be, for example, an antibody specific for some marker on the surface of a tumor cell. The antibody alone may serve as an effector of therapy or it may recruit other cells to actually effect cell killing. The antibody also may be conjugated to a drug or toxin (chemotherapeutic, radionuclide, ricin A chain, cholera toxin, pertussis toxin, etc.) and serve merely as a targeting agent. Alternatively, the effector may be a lymphocyte carrying a surface molecule that interacts, either directly or indirectly, with a tumor cell target. Various effector cells include cytotoxic T cells and NK cells as well as genetically engineered variants of these cell types modified to express chimeric antigen receptors.
The immunotherapy may comprise suppression of T regulatory cells (Tregs), myeloid derived suppressor cells (MDSCs) and cancer associated fibroblasts (CAFs). In some embodiments, the immunotherapy is a tumor vaccine (e.g., whole tumor cell vaccines, peptides, and recombinant tumor associated antigen vaccines), or adoptive cellular therapies (ACT) (e.g., T cells, natural killer cells, TILs, and LAK cells). The T cells may be engineered with chimeric antigen receptors (CARs) or T cell receptors (TCRs) to specific tumor antigens. As used herein, a chimeric antigen receptor (or CAR) may refer to any engineered receptor specific for an antigen of interest that, when expressed in a T cell, confers the specificity of the CAR onto the T cell. Once created using standard molecular techniques, a T cell expressing a chimeric antigen receptor may be introduced into a patient, as with a technique such as adoptive cell transfer. In some aspects, the T cells are activated CD4 and/or CD8 T cells in the individual which are characterized by γ-IFN-producing CD4 and/or CD8 T cells and/or enhanced cytolytic activity relative to prior to the administration of the combination. The CD4 and/or CD8 T cells may exhibit increased release of cytokines selected from the group consisting of IFN-γ, TNF-α and interleukins. The CD4 and/or CD8 T cells can be effector memory T cells. In certain embodiments, the CD4 and/or CD8 effector memory T cells are characterized by having the expression of CD44high CD62Llow.
The immunotherapy may be a cancer vaccine comprising one or more cancer antigens, in particular a protein or an immunogenic fragment thereof, DNA or RNA encoding said cancer antigen, in particular a protein or an immunogenic fragment thereof, cancer cell lysates, and/or protein preparations from tumor cells. As used herein, a cancer antigen is an antigenic substance present in cancer cells. In principle, any protein produced in a cancer cell that has an abnormal structure due to mutation can act as a cancer antigen. In principle, cancer antigens can be products of mutated Oncogenes and tumor suppressor genes, products of other mutated genes, overexpressed or aberrantly expressed cellular proteins, cancer antigens produced by oncogenic viruses, oncofetal antigens, altered cell surface glycolipids and glycoproteins, or cell type-specific differentiation antigens. Examples of cancer antigens include the abnormal products of ras and p53 genes. Other examples include tissue differentiation antigens, mutant protein antigens, oncogenic viral antigens, cancer-testis antigens and vascular or stromal specific antigens Tissue differentiation antigens are those that are specific to a certain type of tissue. Mutant protein antigens are likely to be much more specific to cancer cells because normal cells shouldn't contain these proteins. Normal cells will display the normal protein antigen on their MHC molecules, whereas cancer cells will display the mutant version Some viral proteins are implicated in forming cancer, and some viral antigens are also cancer antigens Cancer-testis antigens are antigens expressed primarily in the germ cells of the testes, but also in fetal ovaries and the trophoblast. Some cancer cells aberrantly express these proteins and therefore present these antigens, allowing attack by T-cells specific to these antigens. Exemplary antigens of this type are CTAG1 B and MAGEA1 as well as Rindopepimut, a 14-mer intradermal injectable peptide vaccine targeted against epidermal growth factor receptor (EGFR) vll1 variant. Rindopepimut is particularly suitable for treating glioblastoma when used in combination with an inhibitor of the CD95/CD95L signaling system as described herein. Also, proteins that are normally produced in very low quantities, but whose production is dramatically increased in cancer cells, may trigger an immune response. An example of such a protein is the enzyme tyrosinase, which is required for melanin production. Normally tyrosinase is produced in minute quantities but its levels are very much elevated in melanoma cells. Oncofetal antigens are another important class of cancer antigens. Examples are alphafetoprotein (AFP) and carcinoembryonic antigen (CEA). These proteins are normally produced in the early stages of embryonic development and disappear by the time the immune system is fully developed. Thus self-tolerance does not develop against these antigens. Abnormal proteins are also produced by cells infected with oncoviruses, e.g. EBV and HPV. Cells infected by these viruses contain latent viral DNA which is transcribed and the resulting protein produces an immune response. A cancer vaccine may include a peptide cancer vaccine, which in some embodiments is a personalized peptide vaccine. In some embodiments, the peptide cancer vaccine is a multivalent long peptide vaccine, a multi-peptide vaccine, a peptide cocktail vaccine, a hybrid peptide vaccine, or a peptide-pulsed dendritic cell vaccine
The immunotherapy may be an antibody, such as part of a polyclonal antibody preparation, or may be a monoclonal antibody. The antibody may be a humanized antibody, a chimeric antibody, an antibody fragment, a bispecific antibody or a single chain antibody. An antibody as disclosed herein includes an antibody fragment, such as, but not limited to, Fab, Fab′ and F (ab′) 2, Fd, single-chain Fvs (scFv), single-chain antibodies, disulfide-linked Fvs (sdfv) and fragments including either a VL or VH domain. In some aspects, the antibody or fragment thereof specifically binds epidermal growth factor receptor (EGFR1, Erb-B1), HER2/neu (Erb-B2), CD20, Vascular endothelial growth factor (VEGF), insulin-like growth factor receptor (IGF-1R), TRAIL-receptor, epithelial cell adhesion molecule, carcino-embryonic antigen, Prostate-specific membrane antigen, Mucin-1, CD30, CD33, or CD40.
Examples of monoclonal antibodies include, without limitation, trastuzumab (anti-HER2/neu antibody); Pertuzumab (anti-HER2 mAb); cetuximab (chimeric monoclonal antibody to epidermal growth factor receptor EGFR); panitumumab (anti-EGFR antibody); nimotuzumab (anti-EGFR antibody); Zalutumumab (anti-EGFR mAb); Necitumumab (anti-EGFR mAb); MDX-210 (humanized anti-HER-2 bispecific antibody); MDX-210 (humanized anti-HER-2 bispecific antibody); MDX-447 (humanized anti-EGF receptor bispecific antibody); Rituximab (chimeric murine/human anti-CD20 mAb); Obinutuzumab (anti-CD20 mAb); Ofatumumab (anti-CD20 mAb); Tositumumab-1131 (anti-CD20 mAb); Ibritumomab tiuxetan (anti-CD20 mAb); Bevacizumab (anti-VEGF mAb); Ramucirumab (anti-VEGFR2 mAb); Ranibizumab (anti-VEGF mAb); Aflibercept (extracellular domains of VEGFR1 and VEGFR2 fused to IgG1 Fc); AMG386 (angiopoietin-1 and -2 binding peptide fused to IgG1 Fc); Dalotuzumab (anti-IGF-1R mAb); Gemtuzumab ozogamicin (anti-CD33 mAb); Alemtuzumab (anti-Campath-1/CD52 mAb); Brentuximab vedotin (anti-CD30 mAb); Catumaxomab (bispecific mAb that targets epithelial cell adhesion molecule and CD3); Naptumomab (anti-ST4 mAb); Girentuximab (anti-Carbonic anhydrase ix); or Farletuzumab (anti-folate receptor). Other examples include antibodies such as Panorex™ (17-1A) (murine monoclonal antibody); Panorex (MAb17-1A) (chimeric murine monoclonal antibody); BEC2 (ami-idiotypic mAb, mimics the GD epitope) (with BCG); Oncolym (Lym-1 monoclonal antibody); SMART M195 Ab, humanized 13′ 1 LYM-1 (Oncolym), Ovarex (B43.13, anti-idiotypic mouse mAb); 3622W94 mAb that binds to EGP40 (17-1A) pancarcinoma antigen on adenocarcinomas; Zenapax (SMART Anti-Tac (IL-2 receptor); SMART M195 Ab, humanized Ab, humanized); NovoMAb-G2 (pancarcinoma specific Ab); TNT (chimeric mAb to histone antigens); TNT (chimeric mAb to histone antigens); Gliomab-H (Monoclonals-Humanized Abs); GNI-250 Mab; EMD-72000 (chimeric-EGF antagonist); LymphoCide (humanized IL.L.2 antibody); and MDX-260 bispecific, targets GD-2, ANA Ab, SMART IDIO Ab, SMART ABL 364 Ab or ImmuRAIT-CEA. Further examples of antibodies include Zanulimumab (anti-CD4 mAb), Keliximab (anti-CD4 mAb); Ipilimumab (MDX-101; anti-CTLA-4 mAb); Tremilimumab (anti-CTLA-4 mAb); (Daclizumab (anti-CD25/IL-2R mAb); Basiliximab (anti-CD25/IL-2R mAb); MDX-1106 (anti-PD1 mAb); antibody to GITR, GC1008 (anti-TGF-β antibody); metelimumab/CAT-192 (anti-TGF-β antibody); lerdelimumab/CAT-152 (anti-TGF-B antibody); ID11 (anti-TGF-β antibody); Denosumab (anti-RANKL mAb); BMS-663513 (humanized anti-4-1BB mAb); SGN-40 (humanized anti-CD40 mAb); CP870,893 (human anti-CD40 mAb); Infliximab (chimeric anti-TNF mAb, Adalimumab (human anti-TNF mAb); Certolizumab (humanized Fab anti-TNF); Golimumab (anti-TNF); Etanercept (Extracellular domain of TNFR fused to IgG1 Fc); Belatacept (Extracellular domain of CTLA-4 fused to Fc); Abatacept (Extracellular domain of CTLA-4 fused to Fc); Belimumab (anti-B Lymphocyte stimulator); Muromonab-CD3 (anti-CD3 mAb); Otelixizumab (anti-CD3 mAb); Teplizumab (anti-CD3 mAb); Tocilizumab (anti-IL6R mAb); REGN88 (anti-IL6R mAb); Ustekinumab (anti-IL-12/23 mAb); Briakinumab (anti-IL-12/23 mAb); Natalizumab (anti-α4 integrin); Vedolizumab (anti-α4 β7 integrin mAb); Tl h (anti-CD6 mAb); Epratuzumab (anti-CD22 mAb); Efalizumab (anti-CD11a mAb); and Atacicept (extracellular domain of transmembrane activator and calcium-modulating ligand interactor fused with Fc).
As discussed above, methods are provided for assessing, monitoring, and/or treating mammals (e.g., humans) having, or suspected of having, cancer. For example, a sample (e.g., a blood sample) obtained from a mammal can be assessed to determine:
Following such identifying, one or more cancer treatments can be administered to the mammal to treat the mammal.
In some cases, during or after the course of a cancer treatment (e.g., any of the cancer treatments described herein), a mammal can undergo monitoring (or be selected for increased monitoring) and/or further diagnostic testing
Any appropriate mammal can be assessed, monitored, and/or treated as described herein. A mammal can be a mammal having cancer. A mammal can be a mammal suspected of having cancer. Examples of mammals that can be assessed, monitored, and/or treated as described herein include, without limitation, humans, primates such as monkeys, dogs, cats, horses, cows, pigs, sheep, mice, and rats.
Any appropriate sample from a mammal can be assessed as described herein. In some cases, a sample can be fluid sample (e.g., a liquid biopsy). Examples of samples include, without limitation, blood (e.g., whole blood, serum, or plasma), amnion, tissue, urine, cerebrospinal fluid, saliva, sputum, broncho-alveolar lavage, bile, lymphatic fluid, cyst fluid, stool, ascites, pap smears, breast milk, and exhaled breath condensate.
As discussed above, when treating a mammal having, or suspected of having, cancer as described herein, the mammal can be administered one or more cancer treatments. A cancer treatment can be any appropriate cancer treatment. One or more cancer treatments described herein can be administered to a mammal at any appropriate frequency (e.g., once or multiple times over a period of time ranging from days to weeks). Examples of cancer treatments include, without limitation adjuvant chemotherapy, neoadjuvant chemotherapy, radiation therapy, hormone therapy, cytotoxic therapy, immunotherapy, adoptive T cell therapy (e.g., chimeric antigen receptors and/or T cells having wild-type or modified T cell receptors), targeted therapy such as administration of kinase inhibitors (e.g., kinase inhibitors that target a particular genetic lesion, such as a translocation or mutation), (e.g. a kinase inhibitor, an antibody, a bispecific antibody), signal transduction inhibitors, bispecific antibodies or antibody fragments (e.g., BiTEs), monoclonal antibodies, immune checkpoint inhibitors, surgery (e.g., surgical resection), or any combination of the above. In some cases, a cancer treatment can reduce the severity of the cancer, reduce a symptom of the cancer, and/or to reduce the number of cancer cells present within the mammal.
In some cases, a cancer treatment can include an immune checkpoint inhibitor. Non-limiting examples of immune checkpoint inhibitors include nivolumab (Opdivo), pembrolizumab (Keytruda), atezolizumab (tecentriq), avelumab (bavencio), dorvalumab (imfinzi), ipilimumab (yervoy). See, e.g., Pardoll (2012) Nat. Rev Cancer 12: 252-264; San et al. (2017) Eor Rev Med Pharmacol Sci 21(6): 1198-1205; Hamanisbi et al. (2015) J. Clin. Oncol 33(34): 4015-22; Brahmer et al. (2012) N Engl J Med 366(26): 2455-65; Ricciuti et al. (2017) J. Thorac Oncol. 12(5) e51-e55; Ellis et al. (2017) Clio Lung Cancer pii: S1525-7304(17)30043-8; Zou and Awad (2017) Ann Oncol 28(4): 685-687; Sorscher (2017) N Engl J Med 376(10: 996-7; Hui et al. (2017) Ann Oncol 28(4): 874-881, Vansteenkiste et al. (2017) Expert Opin Biol Ther 17(6): 781-789; Hellmann et al. (2017) Lancet Oncol. 18(1): 31-41; Chen (2017) J. Chin Med Assoc 80(1): 7-14.
In some cases, a cancer treatment can be an adoptive T cell therapy (e.g., chimeric antigen receptors and/or T cells having wild-type or modified T cell receptors). See, e.g., Rosenberg and Restifo (2015) Science 348(6230): 62-68; Chang and Chen (2017) Trends Mol Med 23(5): 430-450; Yee and Lizee (2016) Cancer J. 23(2): 144-148, Chen et al. (2016) Oncoimmunology 6(2): e1273302, US 2016/0194404; US 2014/0050788; US 2014/0271635; U.S. Pat. No. 9,233,125; incorporated by reference in their entirety herein.
As discussed above, in some cases, a cancer treatment can be a chemotherapeutic agent
PrE0505, a phase 2 single arm multicenter study, enrolled human patients with previously untreated unresectable MPM (NCT02899195,
The primary endpoint of the study was overall survival (OS), defined as time from study registration to death from any cause. Patients last known to be alive were censored at their date of last follow-up. We assumed a null hypothesis that the median OS with chemo-immunotherapy would be equal to the OS of 12 months with pemetrexed/cisplatin alone (historical control). The total planned enrollment of 55 patients (50 eligible) with 32 events, allowed for 90% power to detect a 37% reduction in the OS hazard rate of 0.058 to 0.037 based on Wald test for the log failure rate parameter using one-sided type I error rate of 10%. This would correspond to a 58% improvement in the median OS from 12 to 19 months4. Secondary endpoints included progression-free survival (PFS), best objective response, and toxicity (Methods). PFS was defined as the time from study registration to documented disease progression or death from any cause, whichever occurred first. Patients who did not experience an event of interest were censored at the date they were last known to be alive and progression-free. Exploratory endpoints included investigating the genomic and immunologic underpinning of response to chemo-immunotherapy; to this end we performed whole exome sequencing (WES), coupled with genome-wide focal and large-scale copy number aberration analysis and sequence deconvolution (Methods). In parallel, we evaluated the intra-tumoral T cell repertoire and functional neoantigen-specific T cell responses (Methods below and
PrE0505 enrolled 55 patients at 15 academic and community cancer centers in the United States. Eligible patients were 18 years of age or older and had histologically unselected MPM that was deemed to be surgically unresectable, an Eastern Cooperative Oncology Group performance-status score of 0 or 1, adequate organ function including GFR of ≥45 mL/min, and measurable disease by RECIST 1.1 modified for pleural mesothelioma23,24. Key exclusion criteria were immunodeficiency, ongoing systemic immunosuppressive therapy, active autoimmune or infectious disease, and clinically significant concurrent cancer. Demographics and disease characteristics are summarized in Table 1; median age was 68 (range 35-83), the majority of patients were male (82%) and 75% of tumors were of epithelioid histology. Patients who had continued clinical benefit by investigator assessment (n=20) were allowed to continue on treatment past radiographic progression.
All patients were included in the eligible population for efficacy analyses. The median follow-up was 24.2 months at the time of this analysis, with 33 death events. The median OS for all patients enrolled was 20.4 months (95% CI: 13.0 to 28.5, 80% CI: 15.1, 27.9) and was significantly longer than the historic control of 12 months (one-sided p=0.0014;
The study enrolled the full planned cohort of 55 patients after initial safety analysis; the most commonly reported treatment-emergent adverse events (TEAEs) were mostly of low grade and included fatigue (67%), nausea (56%) and anemia (56%;
As previously shown17, MPMs in this cohort harbored a low tumor mutation burden, with some tumors harboring a higher than expected TMB in the setting of mutations in DNA damage repair genes (
We then evaluated nonsynonymous sequence alterations associated with putatively immunogenic neoantigens (immunogenic mutations-IMMs, Methods) and found an enrichment of high MHC class I IMM burden (p=0.064) as well as a higher MHC class II IMM burden (p=0.023) in responsive tumors (
Mesothelioma may arise in the context of germline mutations in cancer susceptibility genes, including BAP1, MLH1, MLH3, BRCA1/2 and BLM20,22, however the potential impact of germline MPM-predisposing mutations on response to chemo-immunotherapy has not been previously evaluated. Patients with pathogenic germline loss-of-function mutations in MPM susceptibility genes (Methods), predominantly those involved in DNA damage repair, bad a significantly prolonged survival (log rank p-0.05 and p=0.032 for all patients and epithelioid MPM respectively,
Genomic instability and particularly large scale copy number losses, are hallmarks of MPM18,29 and our large-scale copy number analyses revealed recurrent chromosomal arm losses or loss of heterozygosity (LOH) of 4p, 4q, 69, 9p, 10q, 13q, 14q, 18q and 22q as well as LOH/deletion of 3p21.1, where BAP1 lies (
As reported previously15, we did not observe any association between radiographic responses, PFS or OS and PD-L1 expression on tumor cells. In looking at the tumor microenvironment of MPM, the composition of the pre-existing intra-tumoral TCR repertoire tied into the genomic footprint of MPM has not been previously investigated in the context of chemo-immunotherapy. Baseline tumors of patients with an OS ≥12 months harbored a more diverse TCR repertoire in contrast to tumors of patients with shorter overall survival, which showed a higher TCR repertoire clonality and a higher proportion of high frequency TCR clones (p=0.018 and p=0.006 respectively, FIG. Sa-c. We investigated the reshaping of the intra-tumoral T cell repertoire for three patients (295, 459 and 926) who had long term therapeutic responses but eventually developed acquired resistance, by serially sampling tumors prior to therapy and at the time of acquired resistance. Interestingly, at the time of acquired resistance, significant reshaping signified by TCR clonotypic expansions and regressions was noted such that the tumor infiltrating lymphocyte (TIL) repertoire from all 3 cases was significantly more clonal (
The PrE0505 trial demonstrated promising rates of response, progression-free and overall survival for patients who received durvalumab with standard chemotherapy as first-line therapy for unresectable MPM. Treatment was well tolerated and there were low rates of immune-mediated toxicity. The median overall survival of 20.4 months in the PrE0505 trial is encouraging in the context of several recent phase 2 and 3 clinical trials that enrolled a similarly representative population of treatment-naïve patients7-9,16. Allied to recent results from the DREAM study, these data launched the ongoing phase 3 PrE0506/DREAM3R trial (NCT04334759) which compares durvalumab with chemotherapy to chemotherapy alone15. Of note, the survival for patients with epithelioid MPM in the PrE0505 trial exceeded two years and several patients with epithelioid MPM continue to be free from tumor progression at the time of this publication. This potential benefit from chemo-immunotherapy for epithelioid MPM is in contrast to the recent CheckMate-743 trial that reported a striking survival advantage favoring ipilimumab-nivolumab over chemotherapy for patients with non-epithelioid histology (18.1 versus 8.8 months); however no significant survival difference between the two treatment arms for patients with epithelioid MPM (18.7 versus 16.5 months)16. Given the known chemosensitivity of epithelioid MPM and relative chemo-resistance of non-epithelioid MPM it is possible that chemo-immunotherapy may confer a synergistic advantage particularly for patients with epithelioid MPM. As both DREAM and PrE0505 trials mandated that patients conclude durvalumab treatment after one full year of treatment, it is conceivable that some patients would derive additional benefit from maintenance therapy until disease progression, although data on this point are conflicting across tumor types30. The investigational arm of the ongoing phase 3 PrE0506/DREAM3R trial includes treatment with maintenance durvalumab until confirmed disease progression thus addressing this potential concern.
The clinical efficacy of chemo-immunotherapy demonstrated in the PrE0505 trial challenged the common paradigm of immunotherapy responsive tumors, as MPM harbors a low nonsynonymous TMB that conceptually may limit the number of presented immunogenic neoantigens. While tumors with TMB in the lower end of the spectrum are historically thought to have TMB-independent mechanisms of response to immunotherapy19, we discovered that a higher immunogenic mutation load distinguished responding tumors, particularly in the epithelioid MPM group. Importantly, these findings were not corroborated in MPM treated with standard-of-care therapies, suggesting an association with durvalumab. Clonal TMB represents a dominant tumor-intrinsic determinant of clinical response to immunotherapy31, which is consistent with our findings of clonal TMB predicting radiographic response in epithelioid MPM. Similarly, a high subclonal mutation burden, in part mediated by abnormal activity of the APOBEC enzymes; may enable tumor immune escape32,33. We indeed discovered an inverse association between an APOBEC mutational signature and response to chemo-immunotherapy in epithelioid MPM in the PrE0505 cohort. To substantiate these findings, we pulsed autologous T cells with peptides derived from immunogenic mutations and identified neoantigen-specific TCR expansions in vitro, suggesting that robust neoantigen-specific responses were linked with favorable clinical outcome.
Consistent with the notion that MPM is driven by inactivating mutations in tumor suppressor genes17,18,29,34,35, we identified recurring inactivating largely non-overlapping genomic alterations in BAP1, CDKN2A, NF2, SETD2, PBRM1 and TP53 independent of therapeutic response. While we did not find an enrichment in alterations of any single gene in tumors from patients with differential responses to chemo-immunotherapy, a trend towards an enrichment in somatic mutations in chromatin regulating genes was noted in tumors from patients achieving an overall survival ≥12 months; these alterations may mediate transcriptional changes of genes involved in immune related signaling pathways36 or may be linked with a genomic instability phenotype that can predispose to response to immunotherapy37.
Germline genetic susceptibility has been established as a seminal event in MPM tumorigenesis, mostly involving tumor suppressor genes in DNA repair mechanisms20,22,29,38 The frequency of 32% for BAP1 genomic alterations in the PrE0505 cohort is in line with these previously reported analyses17,29,39 Presence of germline BAP1 mutations has been linked with a longer 5-year survival, suggesting a less aggressive phenotype of MPM arising in the context of a BAP1 cancer syndrome28,39,40. The underlying etiology of this phenomenon remains unclear, with one potential explanation being that the tumor microenvironment of BAP1-null tumors is more inflammatory27. While we did not find prolonged survival for patients with somatic BAP1 mutations, BAP/mutant tumors were found to have a higher degree of CD8+ T cell infiltration. Importantly, patients harboring deleterious germline mutations in MPM predisposing genes, including but not limited to genes involved in DNA homologous recombination, achieved significantly longer progression-free and overall survival with chemo-immunotherapy. Inherited defects in homologous recombination repair, resulting in microdeletions and DNA breaks, may be linked with longer overall survival following platinum chemotherapy20,28 as well as affect adaptive and innate immunity, ultimately potentiating response to immune checkpoint blockade41,42. Our findings suggest that germline genotypes may impact clinical outcomes after chemo-immunotherapy and germline testing should be considered for clinical decision making for patients with mesothelioma.
Importantly, we found that DNA repair deficiency and defective homologous recombination in particular, determined by both sequence mutational spectra as well as by genome-wide copy number analyses was a hallmark of responding tumors, especially for epithelioid MPM. Overall, responding epithelioid tumors harbored a higher content of genome-wide copy number breakpoints, suggesting that genomic instability impacts therapeutic efficacy for chemo-immunotherapy. While we did not detect any evidence of oscillating copy number changes within any given chromosome indicative of chromothripsis in the PrE0505 cohort, there were 3 cases with extensive genome-wide loss of heterozygosity, a phenomenon called genome near-haploidization (GNH) which has been previously reported in five MPM cases17. Interestingly, two of the tumors with GNH in our cohort harbored BAP1 mutations and all patients achieved an overall survival longer than 12 months. As GNH harboring MPM may comprise a novel molecular subtype of MPM with distinctive clinical behavior17, our findings suggest that these unique genomic features may be linked with favorable response to chemo-immunotherapy. Conceptually, immunogenic mutations residing in genomic loci that undergo haploidization cannot be lost under the selective pressure of immunotherapy43, and therefore may drive a sustained anti-tumor immune response. Consistent with this hypothesis, we discovered that tumors that harbored a higher number of sequence alterations in single copy regions of the genome responded to combined chemo-immunotherapy.
The density of the CD8+ T cell infiltrate has been associated with effective anti-tumor immune responses44,45, however in the PrE0505 cohort, neither CD8+ T cell infiltration nor PD-L1 protein expression predicted response to chemo-immunotherapy. Notably, the tumor microenvironment of responding tumors contained a less clonal T cell receptor repertoire that become more polarized at the time of acquired resistance. In contrast to the notion that anti-tumor immune responses in the context of immunotherapy are driven by a clonal T cell repertoire in TMB-high tumors such as melanoma44 or non-small cell lung cancer46, our findings suggest that maximal immune cell repertoire diversity is required to mount an effective anti-tumor immune response in MPM. Consistent with this notion, a higher TCR diversity has been previously shown to correlate with improved outcome in bladder cancer, colorectal cancer, hepatocellular carcinoma and uterine cancer47.
Our study was limited by its small sample size and absence of a non-durvalumab control arm. In order to interpret our molecular findings with respect to response to chemo-immunotherapy compared to standard-of-care therapy alone, we performed genomic analyses of an independent cohort of 82 mesotheliomas obtained from the TCGA registry. This cohort did not include patients treated with chemo-immunotherapy or immunotherapy and analyses of the TCGA mesothelioma cohort suggested that the genomic features of response in the PrE0505 trial were driven by durvalumab. Importantly, definitively assessing the predictive versus prognostic nature of our findings is needed and planned within the ongoing phase 3 randomized DREAM3R/PrE0506 trial. Furthermore, while the control survival was chosen based on the historical control that led to the approval of pemetrexed with cisplatin5, recent randomized data have shown both shorter and longer survival for the pemetrexed-cisplatin combination8,9, it is therefore possible that control assumptions may have underestimated the expected survival with chemotherapy alone.
In summary, we report the favorable clinical efficacy of the PrE0505 study of chemo-immunotherapy for unresectable MPM with in depth molecular and functional analyses, which provide an understanding of the complex genomic and immune cell features of response to combined chemo-immunotherapy with potential broad clinical implications.
The first patient enrollment date was: Jun. 12, 2017 and the last patient enrollment date was: Jun. 21, 2018 and a detailed outline of the numbers of patients available for analyses. Due to biospecimen availability and biospecimen quality, germline genomic data were evaluable for 44 patients and somatic genomic data were evaluable for 40 patients. A Data Safety Monitoring Board reviewed the study twice a year and patient consent was provided for sample collection.
The full list of the inclusion and exclusion criteria is shown below:
Imaging was performed every 6 weeks during the concurrent phase of treatment and every 9 weeks during maintenance durvalumab. Best objective response was evaluated by RECIST Version 1.1 criteria modified for mesothelioma. Toxicity was determined using the CTCAE Version 4.03 criteria.
Secondary objectives included safety and tolerability of durvalumab and durvalumab in combination with chemotherapy in subjects with malignant pleural mesothelioma, percentage of patients progression-free at 24 weeks from the time of registration (response coded based on modified RECIST 1.1 criteria for mesothelioma), progression-free survival-measured from the time of study registration until radiologic progression, clinical progression or death, best objective response rate with evaluation continued up to 1 year (response coded based on modified RECIST Version 1.1 criteria for mesothelioma). Exploratory objectives included assessment of tumor baseline PD-L1 expression, the genomic and neoantigen landscape of tumors, dynamics of circulating cell free tumor DNA and other blood-based biomarkers.
We obtained matched tumor-normal exome sequencing data from 82 patients with MPM in TCGA (cancergenome.nih.gov), as outlined in the TCGA publication guidelines cancergenome. nih.gov/publications/publicationguidelines. WES-derived somatic mutation calls from the TCGA PanCancer Atlas MC3 project were retrieved from the NCI Genomic Data Commons (gdc.cancer.gov/about-data/publications/mc3-2017). The MC3 mutation call set is the result of application of a uniform analysis pipeline including a standardized set of six mutation callers and an array of automated filters to all the entire TCGA exome data48. Tumor mutation burden was calculated as the number of nonsynonymous mutations detected by whole exome sequencing.
Formalin fixed paraffin embedded tumor tissue and matched peripheral blood were collected prior to therapy initiation. DNA was extracted from patients' tumors and matched peripheral blood using the Qiagen DNA kit (Qiagen, CA). Fragmented genomic DNA from tumor and normal samples was used for Illumina TruSeq library construction (Illumina, San Diego, CA) and exonic regions were captured in solution using the Agilent SureSelect v.4 kit (Agilent, Santa Clara, CA) according to the manufacturers' instructions as previously described43,49,50. Paired-end sequencing, resulting in 100 bases from each end of the fragments for the exome libraries was performed using Illumina HiSeq 2000/2500 instrumentation (Illumina, San Diego, CA). The mean depth of total coverage for the pre-treatment tumors and matched normal DNA samples was 220× (166× distinct) and 105× (90× distinct) respectively. On average, 94% of the bases in the target region had a minimum coverage of 10×; four tumor samples (329, 351, 629, and 923) were determined to be of low purity by mutation and copy number analyses and were excluded from all WES-based analyses of somatic alterations, while their matched normal DNA samples were included in the germline analyses.
Somatic mutations, consisting of point mutations, insertions, and deletions across the whole exome were identified using the VariantDx custom software for identifying mutations in matched tumor and normal samples as previously described43,49. Mutations were annotated with the number of tumor samples harboring identical amino acid changes in cosmic database (v91). MHC class I and II neoantigens were derived from nonsynonymous single base substitutions using MHCnuggets51. Ranks of neopeptides were determined based on their MHC binding affinity compared to 10,000 human proteome peptides per peptide length per binding MHC allele. Sequence alterations resulting in neopeptides ranking in the 1st percentile were considered putatively immunogenic mutations.
A set of cancer susceptibility genes with alterations contributing to germline predisposition to mesothelioma was compiled from the literature3. Nonsynonymous germline alterations in the above set were identified by applying Strelka 2.9.252 and the candidate mutation set was first filtered to include positions where the genotype was of sufficient quality and could be resolved in both normal and tumor samples of each patient. Variants were subsequently annotated using OpenCravat53. Confirmed pathogenic variants-hereafter termed germline deleterious mutations-including nonsense, frameshift, splice site, and missense variants, in genes with known cancer susceptibility potential were identified based on annotation in the Clin Var database and published evidence of a damaging effect on protein function.
Mutation signatures were derived based on the fraction of coding point mutations in each of 96 trinucleotide contexts and estimated the contribution of each signature to each tumor sample using the deconstructSigs R package (v1.8.0) with the default “signatures. nature2013” settings54,55.
OptiType v1.2. was used to determine HLA class I haplotypes56, xHLA was used to determine HLA class II haplotypes for HLA-DPB1, HLA-DQB1, HLA-DRB157, and SOAP-HLA was used to determined class II haplotypes for HLA-DPA1 and HLA-DQA158. A separate bioinformatic analysis using POLYSOLVER59 was utilized to detect and annotate the somatic mutations in class I HLA genes. We determined HLA class I loss in the tumor by applying LOHHLA60. We evaluated somatic loss of HLA class II genes by review of allele-specific copy number of these loci, where minor copy number of zero indicated loss of heterozygosity. The number of unique tumor HLA class I and II alleles was calculated by subtracting the number of heterozygous alleles with somatic LOH from the total number of unique germline alleles. We subsequently computed an HLA Evolutionary Divergence (HED) score by using Grantham distances between protein sequences of allele pairs for each HLA-A, HLA-B and HLA-C locus25. HLA class I allele protein sequences are obtained from the ImMunoGeneTics/HLA database61. A cumulative HED score for each sample was also computed as the arithmetic mean of the three individual divergences, assuming equal contribution from each locus.
We utilized FACETS 0.6.1 to estimate the purity of each tumor sample, the integer allele-specific copy number profile across the genome, and the cellular fraction associated with each aberrant somatic copy number alteration62. The estimated allele-specific copy number profiles were reviewed to ensure quality of fit. In four cases with very low tumor purity (329, 351, 629, and 923), the copy number states and ploidy could not be resolved; these cases were excluded from subsequent copy number based analyses. Furthermore, we investigated potential associations between copy number-derived tumor purity and tumor mutation and immunogenic mutation load; these analyses revealed a weak association between tumor purity and tumor mutation burden derived features when all patients were considered, but no statistically significant association between these features in epithelioid mesotheliomas. Three cases (225, 926 and 922) harbored extensive loss of heterozygosity across the genome with evidence of genome near-haploidization (
For each somatic sequence alteration, the observed mutant and total read counts, the tumor purity and the tumor copy number at the mutated locus were integrated using SCHISM63 and as previously described50 to determine the clonality, i.e. the fraction of cancer cells that harbor the alteration.
Several metrics characterizing the degree of genome aneuploidy were calculated including the fraction of genome with loss of heterozygosity (LOH), the fraction of genome with allelic imbalance, the number of copy number breakpoints, and the entropy of the multinomial probability distribution corresponding to the genome representation of different copy number levels50. The number of copy number breakpoints was used as a proxy measure for the extent of somatic structural alterations in each tumor.
To assess the extent of homologous recombination deficiency in tumors, three individual and one combined metric were determined based on the allele-specific copy number profiles by applying the R package scar-HRD 0.1.164: telomeric allelic imbalance (HRD-TAI score; the count of CN segments with allelic imbalance that extend of telomeres), loss of heterozygosity profiles (HRD-LOH score; the number of segments with a minimum size of 15 Mb which do not span the entire chromosome), and large-scale state transitions (HRD-LST score; the number of breakpoints between segments with minimum size of 10 Mb where the gap between the segments does not exceed 3 Mb). A combined metric for homologous recombination deficiency, HRD-sum, was defined as the sum of the three individual metrics.
To better characterize the background rate of loss in regions of the genome with a single copy per cell (haploid) versus euploid regions (2 copies per cell, no loss of heterozygosity), we analyzed somatic copy number profiles of 1086 mesothelioma and non-small cell lung cancer tumors from TCGA. In each tumor, we first determined the chromosome arms where at least 75% of the arm length was covered by the copy number state of interest. The set of tumor samples was then narrowed down to those with at least one arm in haploid state (n=544). Next, across all chromosome arms of a given state, the rate of loss was determined as follows: in the haploid arms, the loss rate was defined as the total number of bases with somatic copy number of 0 within these arms, divided by the total length of arms in haploid state. For the diploid arms, the loss rate was defined as the total number of bases with somatic copy number of 0 within these arms multiplied by 2 added to the number of bases with somatic copy number of 1, and then divided by the total length of arms in euploid state.
Intra-tumoral TCR clones were evaluated by next generation sequencing of the baseline tumor as well as matched baseline-resistant tumors for cases 295, 459 and 926. TCR-13 CDR3 regions were amplified using the survey ImmunoSeq assay in a multiplex PCR method using 45 forward primers specific to TCR V13 gene segments and 13 reverse primers specific to TCR J13 gene segments (Adaptive Biotechnologies)65. Productive TCR sequences were further analyzed and clone counts were based on CDR3 amino acid sequences. Dominant TCR clones were assessed by estimating the proportion of TCR repertoire constituted by the top 5% of unique clones, for these analyses TCR repertoires were filtered for clones representing at least 0.01% of the repertoire. For each sample, a clonality metric was estimated in order to quantitate the extent of mono- or oligo-clonal expansion by measuring the shape of the clone frequency distribution. For differential abundance analysis between baseline and on-therapy tumors, we selected the most expanded and most regressed TCR clonotypes, corresponding to fold changes in productive frequency of TCR clones with an FDR<0.0101 (Fisher's Exact test) and requiring at least 0.01% relative repertoire abundance at baseline or resistance time-points.
Total RNA was extracted from 10 μm FFPE sections with the RNeasy FFPE kit (Qiagen). The quality of total RNA was assessed by calculating the DV200 index measured with the RNA 6000 Pico Kit (Agilent Technologies). RNAseq libraries were generated by ribosomal depletion (Illumina Ribo-Zero Gold IRNA removal kit) followed by reverse transcription into strand-specific cDNA libraries (NEBNext Ultra directional RNA library kit for Illumina). Paired-end sequencing, resulting in 150 bases from each end of the fragments, was performed using Illumina NovaSeq 6000 S4 generating an average of 200M total reads per library. RNA-seq data was then mapped to the human transcriptome using STAR66 followed by RSEM for isoform and gene-level quantification67. Transcripts associated with RNA genes, mitochondrial genes, and ribosomal proteins were masked Normalization of raw transcript counts and differential expression analysis was performed using DESeq268.
Immunohistochemistry for CD8/PD-L1 dual detection was performed on formalin-fixed, paraffin embedded sections on a Ventana Discovery Ultra autostainer (Roche Diagnostics) utilizing a primary mouse anti-human CD8 antibody, (1:100 dilution, clone m7103, Dako) and a rabbit anti-human anti-PD-L1 antibody (1:100 dilution; EIL3N clone, Cell Signaling Technologies) as previously described. A minimum of 100 tumor cells were evaluated per specimen and a PD-L1 tumor proportion score (TPS) was calculated based on the percentage of tumor cells with PD-L1 positive staining. CD8-positive lymphocyte density was evaluated by the average number of CD8+ cells in 10 representative high power fields (40× objective, 400× magnification).
To identify immunogenic mutation-derived neopeptide-specific, HLA class I restricted T cell clones in the peripheral blood, we applied the high throughput TCRseq-based platform MANAFEST (Mutation Associated NeoAntigen Functional Expansion of Specific T-cells) as previously described43,69. Briefly, putative neopeptides identified above (jpt Peptide Technologies) were each used to stimulate 250,000 T cells in vitro for 10 days. On day 0, T cells were isolated from peripheral blood mononuclear cells (PBMC) by negative selection (EasySep; STEMCELL Technologies). The T cell-negative fraction was co-cultured with an equal number of selected T cells in culture medium (IMDM/5% human serum with 50 μg/mL gentamicin) with 1 μg/mL relevant neoantigenic peptide, 1 μg/mL of an MHC class I-restricted CMV, EBV, and flu peptide epitope pool (CEFX, jpt Peptide Technologies), 1 μg/mL of pools representing the HIV-1 Gag protein (ipt Peptide Technologies) and no peptide. On day 3, half the medium was replaced with fresh medium containing cytokines for a final concentration of 50 IU/ml IL-2 (Chiron), 25 ng/ml IL-7 (Miltenyi), and 25 ng/ml IL-15 (PeproTech). On day 7, half the medium was replaced with fresh culture medium containing cytokines for a final concentration of 100 IU/mL IL-2 and 25 ng/ml IL-7 and IL-15. On day 10, cells were harvested, washed twice with PBS, and the CD8+ fraction was isolated using a CD8+ negative enrichment kit (EasySep; STEMCELL Technologies). DNA was extracted from each CD8-enriched culture condition. TCR Vβ CDR3 sequencing was performed by the SKCCC FEST and TCR Immunogenomics Core (FTIC) on genomic DNA from each T cell condition using the Oncomine TCR Beta short-read assay (Illumina, Inc). DNA libraries were pooled and sequenced on an Illumina iSeq 100 using unique dual indexes to prevent index hopping, with an estimated recovery of ˜50,000 reads per sample. Data pre-processing was performed to eliminate non-productive TCR sequences (sequences that did not translate into a productive protein) and to align and trim the nucleotide sequences to obtain only the CDR3 region. Additionally, for inclusion in our analyses, CDR3 sequences needed to begin with “C”, end with “F” or “W”, and have at least 7 amino acids in the CDR3, which are universally accepted parameters for delineating the CDR3 region70. Productive clonality of each sample and productive frequency of each clone was calculated to reflect the processed data. Resultant processed data files were uploaded to our publicly-available MANAFEST analysis web app (www.stat-apps.one.jhmi.edu) to bioinformatically identify neoantigen-specific T cell clonotypes. To be considered antigen-specific, a T-cell clonotype must have met the following criteria: 1) significant expansion (Fisher's exact test with Benjamini-Hochberg correction for FDR, p<0.05) compared to T cells cultured without peptide, 2) significant expansion compared to every other peptide-stimulated culture (FDR<0.05), 3) an odds ratio >5 compared to all other conditions, 4) at least 30 reads in the “positive” well, and 4) at least 2× higher frequency than background clonotypic expansions as detected in the HIV negative control condition
OS and PFS distributions were estimated using the Kaplan-Meier method, and Cox proportional hazards models were used to estimate the hazard ratios among subgroups. The confidence intervals of objective response rate (defined as the percentage of patients achieving complete or partial response) we calculated based on an exact binomial distribution. Objective response rates were compared between subgroups using Fisher's exact tests. Differences in genomic and molecular features between tumors of responding and non-responding patients were evaluated using chi-squared or Fisher's exact test for categorical variables and the Mann-Whitney test for continuous variables. The Pearson correlation coefficient (R) was used to assess correlations between continuous variables and the Spearman rho coefficient was calculated for non-parametric correlations. We investigated potential correlations between the genomic features described and other than the expected co-linearity between non-synonymous mutation burden and MHC class I and II mutation associated neoantigens we did not identify any potential confounding relationships among features. The median point estimate and 95% CI for progression-free and overall survival were estimated by the Kaplan-Meier method and survival curves were compared by using the nonparametric log rank test. For the survival analyses of the TCGA mesothelioma cohort, progression-free interval was defined as the time interval from diagnosis to progression of disease, local recurrence, distant metastasis or death, whichever was applicable. Statistical analyses were done using the SPSS software program (version 25.0.0 for Windows, IBM, Armonk, NY), SAS (version 9.4) and R version 3.2 and higher (cran.r-project.org).
All raw sequencing data, utilized to generate
We have recently shown that a higher number of sequence alterations contained in single copy regions of the genome differentiate responding from non-responding tumors in the context of immune checkpoint blockade (Forde et al., Durvalumab with Platinum-Pemetrexed for Unresectable Pleural Mesothelioma: Survival, Genomic and Immunologic Analyses from the phase 2 PrE0505 trial Nature medicine (2021)). These findings suggested that mutations and associated neoantigens contained in regions of the genome with in a single copy per cancer cell cannot be eliminated under the selective pressure of therapy and therefore mediate sustained neoantigen-driven immune responses and long-term clinical benefit.
To further support these findings in a pan-cancer manner, we investigated the background rate of loss in regions of the genome with a single copy per cell (haploid) versus euploid regions (2 copies per cell) and analyzed somatic copy number profiles of 5,279 tumors, including immunotherapy responsive cancers such as melanoma, non-small cell lung cancer and mesothelioma. These analyses revealed that the rate of loss in haploid regions was consistently lower than that of euploid regions (
Conceptually, mutations contained in single or multiple copies of the tumor genome cannot be lost under the selective pressure of immunotherapy (Anagnostou, V. et al. Evolution of Neoantigen Landscape during Immune Checkpoint Blockade in Non-Small Cell Lung Cancer. Cancer Discov 7, 264-276, doi:10.1158/2159-8290.CD-16-0828 (2017)), and therefore may drive a sustained anti-tumor immune response. This persistent mutation burden essentially functions as an intrinsic vaccine that fuels adaptive immune responses in the tumor microenvironment and cannot be by passed by neoantigen loss via chromosomal deletions and loss of heterozygosity. Consistent with this hypothesis, patients with tumors with a higher number of sequence alterations in single or multiple copy regions of the cancer genome (persistent mutation burden) had a longer overall survival, in analyses of the TCGA non-small cell lung cancer and melanoma sub-cohorts (
Importantly, we discovered that tumors with a high persistent mutation burden were more responsive to immune checkpoint blockade compare to TMB-high tumors in a series of cohorts spanning non-small cell lung cancer, melanoma and mesothelioma and various immune checkpoint inhibitors (
The current working hypothesis for tumor-intrinsic features that determine the magnitude of anti-tumor immune responses relies on the assumption that each mutation contributes equally to a composite measure of tumor foreignness, reflected in the number of sequence alterations per coding DNA sequence or tumor mutation burden (TMB). However, with the exception of mismatch repair deficient tumors, TMB has failed to consistently demonstrate clinical utility in predicting responses to cancer immunotherapy. Efforts to separate subsets of alterations that may predominantly drive an effective anti-tumor immune response have yet to reveal a universal genomic predictive biomarker1,2.
It was hypothesized, herein, that tumors with a higher frequency of sequence alterations in either haploid regions or in multiple copies would have a fitness disadvantage in the context of immunotherapy, as these alterations would continuously render them visible to the immune system, resulting in sustained tumor immunologic tumor control. Deletions of single copy alleles through chromosomal loss are typically not tolerated in cancer cells unless they are relatively small homozygous deletions5, as larger chromosomal deletions would contain essential genes in linkage with the mutation and would be lethal. Similarly, mutation loss by chromosomal deletions and loss of heterozygosity3 is evolutionary unlikely when mutations are contained in multiple copies. Therefore, these “persistent” mutations (which we hereafter refer to as pTMB) may function as an intrinsic driver of tumor rejection in the tumor microenvironment (
To investigate these hypotheses in a pan-cancer manner, we first evaluated the rate of loss in regions of the genome with a single copy per cell (haploid) versus euploid regions (2 copies per cell) using copy number profiles of 5,244 tumors across 31 tumor types from The Cancer Genome Atlas (TCGA), including immunotherapy responsive cancers such as melanoma, non-small cell lung cancer (NSCLC) and mesothelioma. These analyses revealed that the rate of loss in haploid regions was consistently lower than that in euploid regions (
Next, we explored the relationship between persistent mutation content and TMB in seven published ICB cohorts across three tumor types (n=485; melanoma6-8, NSCLC2,9 and mesothelioma10) and a new cohort of patients with HPV negative (HPV−) head and neck cancer (HNSCC) who received ICB (n=39). Similar to the TCGA analyses, we did not detect a significant enrichment for a higher persistent mutation fraction in tumors harboring a higher TMB in the HNSCC (Spearman ρ=−0.083, p=0.61), melanoma (Spearman ρ=0.066, p=0.35) and mesothelioma cohorts (Spearman ρ=0.065, p=0.69), while a weak correlation between TMB and persistent mutation fraction was observed in the NSCLC cohorts (NSCLC-Anagnostou: Spearman ρ=0.26, p=0.03, NSCLC-Shim: Spearman ρ=0.41, p=2.3e-08;
We theorized that the impact of pTMB would be exemplified in the context of treatment with immune checkpoint blockade (ICB), where inherent anti-tumor immune responses would be enhanced and sustained in the presence of continued persistent mutation-associated neoantigen (pMANA) stimulation. As our analyses pointed towards subsets of mutations within TMB that may carry differential weights in demarcating tumor foreignness, we evaluated persistent mutations in comparison to mutations that are more likely to be lost in the context of tumor evolution. We refer to the latter as “loss-prone” mutations and these account for the majority of coding alterations that constitute a tumor's TMB. We next asked the question whether there are differential clonal compositions between the persistent and loss-prone mutation subsets. In the HNSCC, mesothelioma, and NSCLC cohorts, we did not detect a difference in the fraction of clonal alterations between persistent mutations and loss-prone mutations, while in the melanoma cohort, persistent mutations tended to be more clonal (
Conceptually, persistent mutations—that by definition reside in aneuploid regions of the genome—are integrally linked with tumor aneuploidy and next, we assessed the relationship between persistent mutations and fraction of the genome with allelic imbalance. In the ICB cohorts, a moderate degree of correlation was observed between the extent of tumor aneuploidy and pTMB (Spearman ρ range: 0.39-0.60;
We investigated potential bias related to different timing of acquisition of persistent mutations, background mutation rates and accuracy of mutation calls in these loci and evaluated the distribution of sequence properties such as GC content and replication timing as well as mutation call quality in persistent versus loss-prone mutations utilizing exome data from 9,242 tumors from TCGA. (
We then evaluated whether a higher pTMB was linked with clinical outcome in patients with previously untreated tumors from the TCGA (Methods). Our analyses showed that the association between persistent mutation load and clinical outcome was context-dependent; whereby a significant association with prolonged overall survival was noted for lung squamous cell carcinoma (pTMB: 56.27 vs 43.86 months, log-rank p=0.085; clonal pTMB: 60.48 vs 35.32 months, log-rank p=0.028), melanoma (pTMB: 65.83 vs 23.69 months, log-rank p=0.036; clonal pTMB: 65.83 vs 23.69 months, log-rank p=0.013), and uterine carcinosarcoma (pTMB: 27.53 vs 17.15 months, log-rank p=0.021; clonal pTMB: 50.13 vs 14.68 months, log-rank p=2.66e-03) but not for any other cancer type studied (
Importantly, we hypothesized that tumors with a high persistent mutation content would be the most visible” to the immune system and would therefore regress in the context of immunotherapy, a phenomenon that would be reflected in sustained clinical responses to therapy. To this end, we evaluated the potential of pTMB, multi-copy and only-copy mutations in predicting response to immune checkpoint blockade in 542 patients with melanoma, NSCLC, mesothelioma and HNSCC (Methods). We discovered that tumors with a high pTMB attained higher rates of therapeutic response with ICB, while TMB alone or the number of loss-prone mutations less optimally distinguished responding from non-responding tumors (
Next, we evaluated the effect size of persistent mutations, loss-prone mutations, and TMB on clinical outcome. In the melanoma, HNSCC and mesothelioma cohorts, the effect size for pTMB was larger than TMB or loss-prone mutations (HNSCC: pTMB Cohen's d=−0.96, TMB d=−0.64, loss-prone d=−0.61; melanoma: pTMB d=−0.57, TMB d=−0.44, loss-prone d=−0.35; mesothelioma: pTMB d=−0.74, TMB d=−0.51, loss-prone d=−0.58), highlighting the importance of pTMB in informing therapeutic response to ICB. In the NSCLC cohorts, the effect size for pTMB, while very close to that of TMB, clearly exceeded the effect size of loss-prone mutations (NSCLC-Anagnostou: pTMB d=−0.89, TMB d=−0.93, loss-prone d=−0.58; NSCLC-Shim: pTMB d=−0.53, TMB d=−0.54, loss-prone d=−0.44). These findings suggest that the power of TMB to distinguish between responding and non-responding tumors in the context of ICB is largely driven by the persistent mutation content. Notably, in the NSCLC cohort, clonal pTMB more optimally distinguished responding from non-responding tumors (Mann Whitney U-test p=1.03e-04 and p=1.60e-03 for NSCLC-Anagnostou and NSCLC-Shim respectively). In the melanoma cohort, the number of multi-copy mutations was tightly correlated with therapeutic response (Mann Whitney U-test p=5.42e-07), while in the mesothelioma cohort, the number of only-copy mutations better distinguished responding and non-responding tumors (Mann Whitney U-test p=3.15e-02;
To further explore the immediate clinical utility of pTMB, we evaluated the feasibility of estimating pTMB from gene panel targeted next-generation sequencing, that is widely used in clinical cancer care. Using the genomic intervals from a widely-adopted clinical targeted NGS gene panel (309 genes; Methods), we performed in silico simulations utilizing whole exome sequence data from the melanoma and NSCLC ICB cohorts and computed TMB and pTMB in each tumor as captured by the region of interest of the targeted NGS panel. pTMB more accurately differentiated responding from non-responding tumors (melanoma, n=202, p=1.37E-07 for pTMB and p=1.22E-05 for TMB; NSCLC-Shim, n=169, p=6.7E-04 for pTMB and p=0.014 for TMB; NSCLC-Anagnostou, n=74, p=0.02 for pTMB and p=2.0E-03 for TMB; Mann Whitney U-test;
We previously demonstrated the association between tumor aneuploidy and persistent mutations and as tumor aneuploidy has been associated with inferior outcomes to ICB, likely in the context of an immune excluded tumor microenvironment13, we investigated whether pTMB has an incremental value over tumor aneuploidy and whole genome doubling (WGD) events in predicting therapeutic response. Tumor aneuploidy (Mann Whitney U-test p=0.35, p=0.73, p=0.35, p=0.07, p=0.50 for the NSCLC-Anagnostou, NSCLC-Shim, melanoma, mesothelioma and HNSCC cohorts respectively) or occurrence of WGD alone (Fisher's exact p=0.43, p=0.73, p=0.11, p=0.23, p-0.48 for the NSCLC-Anagnostou, NSCLC-Shim, melanoma, mesothelioma and HNSCC cohorts respectively) failed to predict response to ICB in all cohorts assessed (
To establish the biological plausibility of persistent mutations in the context of tumor evolution, we performed serial whole exome sequencing analyses of longitudinal tumor samples before and after ICB treatment. We hypothesized that clonal persistent mutations would not be eliminated in the context of tumor evolution under the selective pressure of immunotherapy, as they are unlikely to undergo subclonal elimination in the context of therapy and also unlikely to be lost by chromosomal deletions (potentially lethal in the case of mutations residing in single copy regions and biologically implausible in the multiple copy regions). Consistent with our hypothesis, in analyzing pre-treatment and post-acquired resistance tumor samples from 8 patients with NSCLC treated with ICB (Methods), we discovered a marked difference in the frequency of loss between clonal persistent and loss-prone mutation sets. Across 16 serially biopsied tumors from 8 patients, a total of 363 out of 2836 clonal mutations that were detected in the baseline tumor were lost in the descendent tumor. Of these, the vast majority were clonal loss-prone mutations (358 out of 363, 98.6%). In 6 out of 8 patients analyzed, no clonal persistent mutation was lost in the descendent tumor, and of the two remaining patients, each had two clonal multi-copy mutations that were not detected in the descendent tumor, suggesting an extremely low rate of loss in this mutation category (clonal multi-copy mutations: 4 out of 1031 lost, 0.4% loss frequency, clonal only-copy mutations: 1 out of 117 lost, 0.9% loss frequency,
Shifting our focus from the tumor to the tumor microenvironment (TME), we explored transcriptomic profiles in the TME of IC-treated tumors and postulated that a high pTMB would generate an un-interrupted feed of neoantigens that would in turn trigger interferon-γ signaling and adaptive immunity cascades that may be enhanced with ICB. To this end, serial RNA sequencing analyses of ICB-treated melanomas14 (Methods) revealed a marked enrichment in interferon-γ and inflammatory response related gene sets prior to therapy (
We further dissected integrated whole exome and RNA sequencing data in the TCGA and ICB melanoma14 cohorts to investigate the relationship of pTMB, tumor aneuploidy and TME immune phenotypes. To this end, we compared the expression of genes representing cytolytic activity between tumors in the top and bottom tertile of TMB, pTMB, and aneuploidy (Methods). We found a higher expression of cytolytic markers in pTMB-high compared to TMB-high or aneuploidy-low tumors in both the TCGA (TMB p >0.05 for all genes; pTMB p=0.02 for GZMK, IFNG, and PRF1. p=0.04 for NKG7, aneuploidy p>0.05 for all genes,
Taken together, our analyses suggested that a high persistent mutation burden, which comprises a biologically relevant measure of tumor foreignness within the overall TMB, would represent an “uneditable” target set for adaptive immune responses (
Since the first reports recognizing TMB as a predictor of clinical response to immune checkpoint blockade in melanoma and non-small cell lung cancer16,17, it has become clear that TMB as a numeric value or binarized feature can only partially predict response to immune checkpoint blockade. Our findings suggest that a high pTMB, a biologically relevant measure of tumor foreignness within the overall TMB, represents an “uneditable” target set for adaptive immune responses and may function as an intrinsic driver of sustained immunologic tumor control that cannot be readily bypassed by neoantigen loss via chromosomal deletions during cancer evolution.
Similar to TMB, that is linked with response to immunotherapy in a dose-dependent and cancer lineage-specific manner18, pTMB has to be considered in the context of the background aneuploidy rate within a specific tumor type. What we have learned from the increasing number of studies evaluating the overall TMB in predicting therapeutic response with ICB is that using a fixed pan-cancer threshold for a biomarker with different distributions and dynamic ranges depending on cancer lineage19 can be challenging and may miss up to 25% of ICB-responsive tumors20. In our current work and in order to avoid these challenges, we evaluated both persistent mutations and TMB as continuous variables in the context of response to immune checkpoint blockade, thus our findings are less susceptible to artefactual associations resulting from application of a threshold. To expand our analyses in supporting a biologically distinct role for pTMB that is reflected in therapeutic response difference compared to overall TMB-based classifications, we evaluated the number of tumors with differential pTMB/TMB classification within the melanoma ICB cohort and found a higher response rate among tumors reclassified by their pTMB content. Notably, the relative contribution of multi- and only-copy mutation components to the overall pTMB varies across cancer lineages. This pattern appears to be driven by the dominant copy number state of the tumor; suggesting that the dominant copy number state has to be considered together with the sequence alteration load affecting these genomic regions.
The premise of pTMB relies on the potential of pMANAs to mediate sustained neoantigen-driven immune responses. Overall MANA burden has failed to demonstrate an incremental value over TMB in predicting clinical outcomes with immune checkpoint blockade. Nevertheless, it is the neoantigen quality not the quantity that may be most informative in predicting therapeutic response21-23. Another feature that may determine the role of mutation-associated neoantigens in anti-tumor immune responses is expression, as expressed single base substitution-derived neopeptides have been shown to more accurately predict response to ICB compared to TMB14. In line with this notion, the significance of mutant protein abundance in driving T cell responses may further support the importance of multi-copy persistent mutations, as their presence at higher number of copies per cell likely correlates with higher expression of mutant mRNAs and proteins. We indeed found a marginal improvement in outcome prognostication of pMANA burden compared to pTMB in ICB treated NSCLC. Importantly, by testing pMANA-specific TCR clonotypic expansions in vitro, we provide proof that pMANAs can elicit memory T cell responses that are likely to drive tumor elimination.
Placing pTMB in context of other genomic features that have been associated with response to ICB1, we assessed the clonal architecture of persistent mutations and considered the potential confounding effect of tumor clonal heterogeneity. Overall in the TCGA and ICB cohorts, the cellular fractions of persistent mutations did not differ from loss-prone mutations; notably, persistent mutations in the multi-copy category tended to be more clonal in a cancer lineage-dependent manner, suggesting that these may have been acquired earlier in tumor evolution prior to the copy number gain event. Persistent mutations more optimally distinguished responding from non-responding tumors compared to clonal TMB in all ICB cohorts analyzed. Importantly, clonal persistent mutations were more significantly associated with response in the ICB-treated NSCLC cohorts. These findings suggest that persistent mutation content is distinct from a tumor's clonal heterogeneity and considering these features together may be most informative in predicting response to immunotherapy.
While pTMB is related to tumor aneuploidy and a higher degree of large-scale chromosomal changes has been reported in ICB non-responsive tumors13, in the ICB cohorts analyzed tumor aneuploidy alone failed to differentiate responding from non-responsive tumors. While our analyses did not show a strong association between tumor aneuploidy or whole-genome doubling and response to ICB as individual predictive biomarkers, the number of persistent mutations was correlated with tumor aneuploidy, and we found an enrichment for multi-copy persistent mutations in tumors with whole-genome doubling. Our findings highlight the importance of measuring mutational burden in regions of the genome with structural changes rather than considering overall TMB or tumor aneuploidy independently.
Importantly, we studied the evolution of persistent mutations in the evolutionary trajectories shaped by selective pressure of ICB. We hypothesized that multi-copy mutations would inherently be more difficult to lose, as the process of loss would require multiple distinct genomic events and similarly, single-copy mutations are unlikely to be lost by chromosomal deletions as these may be detrimental to the cancer cell; rendering persistent mutation loss not biologically plausible. Consistent with this notion, we discovered that persistent mutations are retained in the context of tumor evolution while losses predominantly affect loss-prone mutations. While approximately 99% of mutations lost in serial analyses of NSCLC during ICB were loss-prone mutations, four clonal multi-copy persistent mutations were not detected in comparative analyses of baseline/ICB resistant tumors, which may be explained by presence of the multiple copies of a mutation in tandem or in close proximity on a common chromosomal segment, thus the loss of multiple copies could be achieved by a single genomic event.
Taken together our findings suggest that mutations located in single-copy regions or these present in multiple copies in the cancer genome are unlikely to be lost under the selective pressure of immunotherapy due to the intrinsic fitness cost to the tumor and therefore serve as a key driver of sustained immunologic tumor control.
We evaluated 10,742 tumor samples from TCGA and 485 non-small cell lung cancer, melanoma and mesothelioma tumor samples from published cohorts of patients that received immune checkpoint blockade2,4,6-9,24. Patients with melanoma across 4 source studies6-8,22 were combined to generate an aggregated melanoma cohort (n=202). Clinical outcomes were retrieved from the original publications. We further performed whole exome sequencing analyses for a cohort of 39 patients with HPV negative (HPV−) HNSCC who received ICB at University of Chicago (HNSCC Cohort). We assessed serially sampled NSCLC tumors from 8 patients from a published study3 as well as from 4 patients with NSCLC who received ICB at the Nederlands Kanker Instituut (NKI set). The studies were conducted in accordance with the Declaration of Helsinki and were approved by the Institutional Review Board (IRB), and patients provided written informed consent for sample acquisition for research purposes.
DNA extraction and genomic library preparation were performed as previously described2. The coding sequences were captured in solution using the SureSelect XT Human All Exon V6 kit in the HNSCC cohort, and using the SureSelect Human All Exon V4 kit in the NKI cohort. Whole exome sequencing derived multi-center mutation calls from the TCGA pan-cancer atlas25 were retrieved from the NCI Genomic Data Commons (https://gdc.cancer gov/about-data/publications/mc3-2017) and filtered to keep nonsynonymous alterations. For the ovarian cancer (OV) tumor type, indels were excluded from all downstream analyses to minimize technical artifacts26. Somatic copy number profiles including estimates of tumor purity and ploidy, as well as allele-specific copy number states27 were acquired via the pan-cancer atlas (https://gdc.cancer.gov/about-data/publications/pancanatlas). Clinical annotations of tumors were accessed using the TCGA clinical data resource28. For the HNSCC subset in TCGA, HPV status was retrieved from eBioPortal (https://www.ebioportal.org/study/summary?id=hnsc_tega_pan_can_atlas_2018) Analyses of copy number profiles to establish the background rate of genomic loss were performed on 10,742 tumor samples where the segmental allele-specific copy numbers were available. For a subset of 9,242 tumor samples from the above, both somatic mutation calls and copy-number profiles were available, enabling assessment of persistent mutations. For a subset of 8,925 tumors where clinical data annotations including overall survival and tumor stage assessment were available, survival analyses evaluating the contribution of persistent mutations were performed.
For the publicly available published ICB cohorts, we retrieved allele-specific copy number profile, tumor purity and ploidy estimates, as well as somatic mutation calls, raw gene expression counts, and clinical annotations of response to treatment from the original publications. Furthermore, for the Riaz et al., melanoma cohort6 allele-specific somatic copy number profile, and tumor purity and ploidy estimates were generated by application of FACETS to tumor and matched normal sequence data29. For the Liu et al. melanoma cohort7 the short read archive files were accessioned from SRA and this sample set was filtered to only keep tumors with no prior anti-CTLA4 treatment. Adaptor sequences were detected and trimmed using FASTP30. Sequenced reads were aligned to the reference genome assembly hg19 using bowtie231, and duplicate reads marked by sambamba32. Tumor purity and ploidy estimates, as well as somatic copy number profiles were derived by application of FACETS29 to tumor and matched normal pairs. For the Hugo et al. melanoma cohort8, fastq files were obtained from the SRA. Sequencing read processing and alignment were performed as described for Liu et al. cohort, and copy number profiles were similarly obtained by application of FACETS. For the Shim et al., NSCLC cohort9, somatic mutations were narrowed down to those with mutant allele fraction greater than or equal to 10% to minimize sequencing artifacts. Tumor purity and ploidy estimates, and somatic copy number profiles were generated by application of FACETS29 to tumor and matched normal pairs. For the HNSCC cohort, somatic mutations were identified using the Strelka mutation calling pipeline33. Mutations in common SNP locations (dbSNP v138) and greater than one BLAT34 hit were filtered out. The final set of mutations were obtained after filtering for tumor mutant allele fraction >=10%, normal mutant allele fraction <=3% and matched normal coverage >=11×. For samples from the NKI set, sequence read processing and alignment were performed as previously described2. Tumor purity and ploidy estimates and somatic copy number profiles were derived by application of FACETS. While we did not have uniform documentation of MSI in the IO cohorts analyzed, the very low background prevalence of MSI-high tumors in NSCLC (<1%), melanoma (<1%), mesothelioma (˜2%), and HNSCC (<1%)35 renders MSI an unlikely confounder in this study.
Mutation cellular fractions were estimated as previously described3,36. Considering the tumor sample purity α, tumor copy number nT, and normal copy number nN, the expected variant allele fraction Vexp for a mutation at cellular fraction C with multiplicity m (i.e. m mutant copies per cancer cell) can be calculated as
The purity and segmental tumor and normal copy numbers were determined via genome-wide analysis of sequencing coverage distribution and b-allele frequency of heterozygous SNPs in each cohort. Assuming a binomial distribution for the number of reads harboring the mutant allele, a 95% confidence interval (CI) is constructed for Vexp using the distinct total coverage and mutant read counts for each mutation (i.e. coverage and read counts after exclusion of reads marked as duplicates). Since estimates for α, nT, and nN are available, this yields a 95% CI for the product of mutation cellular fraction C and multiplicity m. By application of the following rules, one can derive estimates for C and m: (1) If the confidence interval for m C contains an integer, the mutation is deemed clonal and that value is assigned to the multiplicity. (2) If the entire CI is below 1, multiplicity is assumed to be 1 and the mutation is subclonal except cases where it is within a tolerance threshold of 1 (C>0.75). (3) For a CI that is entirely above 1 and does not include any integer, m is greater than one and is assigned such that the CI falls within the expected range [0, 1]. Mutation clonality can now be calculated using the rule in (2).
The nonsynonymous somatic mutations in each tumor were intersected with the segmental integer copy number profile to assign minor and major copy number states to the mutated loci. Mutation multiplicity (number of mutated copies per cell) and cancer cell fraction (proportion of cancer cells harboring the mutation) were estimated based on the mutant read count, total coverage, tumor purity, and the major and minor allele-specific copy number in the tumor and normal compartments for each mutation. Mutations present in more than one copies per cancer cell constituted the multi-copy category. Those present in regions of the genome with a single copy (total copy number=1) were included in the single-copy category. The persistent tumor mutation burden was defined as the number of mutations in either multi-copy or single-copy category. For mesotheliomas, given the predominance of copy number losses4,37, the persistent mutation burden was limited to mutations within single-copy regions of the genome. Furthermore, to assess the differential potential of persistent mutation in predicting outcome compared to TMB, we defined the number of loss-prone mutations in each tumor sample as the difference between the total number of mutations assessed (excluding mutations on sex chromosome or those at loci without copy-number assignment) and the number of persistent mutations. Finally, to achieve harmonized comparisons, mutations on sex chromosomes or on loci lacking allele-specific copy number assignment were excluded from analyses.
Aneuploidy metrics were calculated for tumor samples from TCGA and ICB cohorts, and their relationship with persistent mutation burden was characterized. Furthermore, in IO-treated cohorts, aneuploidy metrics were also considered as independent predictors of outcome. The fraction of genome with allelic imbalance (AI) was calculated as a broad metric summarizing the aneuploidy level across the autosomes. We also considered the fraction of genome with single copies (total copy number of 1) and the fraction of genome with multiple copies (i.e. major allele-specific copy number greater than 1) given their direct link to persistent mutation burden. Tumor samples with more that 50% of the autosomal length at major allele-specific copy number of 2 or above were marked as having undergone whole-genome doubling (WGD)38.
To evaluate the background rate of genomic loss, we analyzed the somatic copy number profiles of 10,742 samples from TCGA. In each tumor sample, the chromosome arms in diploid state were defined as those where 75% of the length of segments covering the arm was copy neutral (total copy number of 2) and did not harbor loss of heterozygosity (LOH). The chromosome arms in haploid state had 75% of their length covered by segments with a total copy number of 1. The rate of loss in diploid regions of the genome was defined as RD
Where Ipindicates the total length of segments in arms of diploid state, lDHD is the total length of the segments in homozygous deletion in diploid arms, and lDHM is the total length of the segments with single copy loss in diploid arms. Similarly, the rate of loss in haploid regions of the genome was defined as RH
Where lH is the total length of segments in haploid arms and lHHD is the total length of segments with homozygous deletion in those arms. Comparison of the background rates of genomic loss was performed on subset of the TCGA tumor samples where at least one chromosome arm was found in each of diploid and haploid states (n=5,244).
Evaluation of pTMB Quantification by Gene Panel Targeted NGS
To determine the feasibility of using clinical targeted NGS to estimate persistent tumor mutation burden, we performed in silico simulations as follows. Given the inherent limitation of targeted NGS in identification of mutations in tumor types with low TMB, we performed a focused analysis in melanoma and NSCLC cohorts. We assumed that allele-specific copy number estimates could be derived by targeted NGS as previously shown29,39. Therefore, we focused our analysis on the subset of mutations that would be captured by the genomic intervals contained in FoundationOne CDx, which is a widely used clinical targeted NGS panel. The list of 309 genes with their full coding sequence included in FoundationOne CDx panel was retrieved from the FDA website at https://www.accessdata.fda.gov/cdrh_docs/pdf17/P170019S006C.pdf. The RefSeq Select transcript set was used to determine the genomic coordinates of the coding exons for each gene. Mutations in each tumor sample were intersected with panel coordinates to determine simulated estimates of TMB and pTMB as captured by the panel.
Expression counts from RNA sequencing of pre-treatment melanoma tumors from the CM038 melanoma cohort were retrieved from the original publiation24. Differential expression testing was performed using DESeq240 and the resulting p-values were corrected for multiple testing using the Benjamini-Hochberg procedure. For the TCGA tumor type SKCM, the TCGAbiolinks R package41 was used to download harmonized raw RNAseq counts data from the NCI Genomic Data Commons within the target cancer type. This sample set was then narrowed down to the set of samples with persistent mutation estimates and available overall survival data, and comparisons were performed between samples within the top tertile of PTMB/TMB-informed risk (high risk) vs the remaining set (low risk). For gene set enrichment analysis, each gene which passed the count threshold was ranked by −log(p)*sign(fc) where p is p-value and fc is fold-change, resulting to ranking where the genes on each flank represent the mostly statistically significantly up- or down-regulated genes and the genes in the middle are the least significant. Gene set enrichment analysis (gsea) was then performed using the fgsea R package42 with a curated list of gene sets from the Molecular Signatures Database related to immune responses and cancer hallmarks. Tumors were classified into high or low groups for TMB and pTMB using the 2nd tertile value. The complete list of gene sets contains the gsea results for comparisons based on persistent mutation burden (pTMB) and TMB in baseline and on-treatment samples. The p-values for gsea were corrected for multiple testing with the Benjamini-Hochberg procedure. Quantile-quantile plots were made to provide a visual comparison of the ranks of pathway genes to a set of ranks sampled from the background distribution.
Gene level expression values (in Count Per Million) were used from the CM038 IO melanoma cohort24 and TCGA melanoma cohort. In each cohort, expression levels for a selected set of gene markers of cytolytic activity were compared between tumors in the top and bottom tertiles of a number of key variables of interest using Mann Whitney U-test. Furthermore, a multivariable linear regression model defined the combined contribution of a mutation based marker (i.e. pTMB, TMB, etc) and aneuploidy (as measured by the fraction of genome with allelic imbalance) to cytolytic activity. Briefly, both mutation based marker values and gene expression levels were pseudo log transformed to control the right skew in the distribution. Next, each variable was scaled to have zero mean and unit variance over the analyzed cohort. In each regression model, predictor coefficients and the associated p-values were recorded. In the IO melanoma cohort, multivariable logistic regression was used to model the contribution of mutation based markers and aneuploidy. In addition, estimates for the relative abundance of 22 immune cell subpopulations derived by CIBERSORT v1.06 were retrieved from the earlier publication. For the TCGA melanoma tumors, the relative abundance of CD8 T cells were retrieved from the genomic data commons43.
For the 8 NSCLC patients with serially biopsied tumor samples, tumor samples were acquired prior to ICB and at the time of acquired resistance; for all cases a minimum of 6 months lapsed between ICB initiation and re-biopsy in the setting of acquired resistance. For each patient, the set of mutations identified in the baseline sample was annotated with distinct total coverage, distinct mutant read count, and minor and major allele-specific copy numbers. These annotations were combined with the estimated purity of the tumor sample to yield estimates of mutation cancer cell fraction and multiplicity. The combination of copy number assignment and multiplicity estimate for each mutation in the baseline sample enabled identification of only-copy, multi-copy, and persistent mutations, as well as those prone to loss (loss-prone). Mutations identified in the baseline sample with mutant allele fraction of zero at the time of progression were deemed lost.
An integrated analysis of persistent mutation associated neoantigens was performed in the ICB NSCLC2 and melanoma24 cohorts. Briefly, MANA predictions by ImmunoSelect-R pipeline (Personal Genome Diagnostics, Baltimore, MD) were retrieved from the original studies. The set of predicted peptides were restricted to those with predicted MHC class I binding affinity (IC50) less than 500 nM, and mutations with at least one associated peptide were marked as MANA-encoding. Mutations in genes with non-zero median expression in the respective TCGA tumor type were marked as expressed.
We compared persistent and loss-prone mutations with regards to the replication timing of the mutated loci in melanoma (TCGA-SKCM) and NSCLC (TCGA-LUAD, TCGA-LUSC) tumors from TCGA. We retrieved replication timing scores for NHEK (skin) and IMR90 (lung) cell lines, which were measured by Repli-Seq methodology as part of the ENCODE project, using the UCSC table browser. In each cell line, we used scores from the “wavlet-smoothed signal” track, which is the result of application of a wavelet smoothing transformation to the weighted average of the percentage-normalized signals in 1 kb intervals across the genome, where higher values indicate earlier replication timing. Genomic intervals were marked based on their quintile membership, and the frequency of persistent and loss-prone mutations across the quintiles were visualized. Replication timing of persistent and loss-prone mutations were compared by evaluating Cohen's d effect size.
To assess the possibility of technical artifacts preferentially impacting the somatic mutation calls in our pan-cancer analysis of 31 tumor types, we determined the prevalence of persistent and loss-prone mutations identified in regions of the genome susceptible to limitations of NGS analysis. The UCSC Table Browser was used to retrieve the “Problematic Regions” track, including regions marked by ENCODE11, Genome-In-A-Bottle12 and NCBI GeT-RM44.
The MANAFEST (Mutation-Associated NeoAntigen Functional Expansion of Specific T Cells) assay3 was employed determine MANA-specific T cell clonotypic expansions in the peripheral blood of a patient with NSCLC that attained long progression-free and overall survival with immune checkpoint blockade (CGLU310). Briefly, candidate neopeptides (JPT Peptide Technologies) were synthesized and each used to stimulate and co-culture T cells in vitro as previously described. On day 10, cells were harvested and the CD8+ fraction was isolated using a CD8+-negative enrichment kit (EasySep, STEMCELL Technologies), followed by DNA extraction from each CD8-enriched culture condition. TCR VB CDR3 sequencing was performed by the SKCCC FEST and TCR Immunogenomics Core (FTIC) on genomic DNA from each T cell condition using the Oncomine TCR Beta short-read assay (Illumina) as previously described10. Following data pre-processing, alignment and trimming, productive frequencies of TCR clonotypes were calculated. To be considered antigen-specific, a T cell clonotype must have met the following criteria: (1) significant expansion (Fisher's exact test with Benjamini-Hochberg correction for FDR, P<0.05) compared to T cells cultured without peptide; (2) significant expansion compared to every other peptide-stimulated culture (FDR<0.05); (3) an odds ratio greater than 5 compared to all other conditions; (4) at least 30 reads in the ‘positive’ well; and (5) at least 2× higher frequency than background clonotypic expansions as detected in the HIV-negative control condition.
Evaluation of Differentially Classified Tumors by TMB and pTMB
Re-classification rates based on pTMB vs TMB were computed as follows: in each tumor type and for each variable (TMB and pTMB), a series of quantile values ranging from 5% to 95% in 5% increments were applied to define samples with high and low values for that variable. Next, at each quantile value, we calculated the rate at which sample classification differed by the two metrics (i.e. the combined prevalence of samples that were pTMB-low, TMB-high and samples that were pTMB-high, TMB-low within that tumor type for that quantile threshold) to derive the re-classification rate. In the ICB cohorts, we determined the cases with differential PTMB/TMB classification and compared the therapeutic response rates between pTMB-low/TMB-high and pTMB-high/TMB-low tumors. For each predictor variable (pTMB or pTMB), the second tertile was used to determine tumor samples with high and low values. Given the current cohort sizes, sufficient sample size for this comparison was only available in the combined melanoma cohort where 23 samples were labeled as TMB-high/pTMB-low and another 23 samples were marked as TMB-low/pTMB-high.
The relationship of pTMB and TMB with overall survival was assessed in 8,925 patients from 31 cohorts in TOGA. Tumor types with more than 50 informative samples were analyzed and overall survival was selected as an endpoint. Briefly, in each tumor type and stage combination, Cox proportional-hazards (CoxPH) models were constructed for each one of the following features as independent continuous predictors: TMB, persistent mutations-pTMB, clonal pTMB, multi-copy mutations, clonal multi-copy mutations, only-copy mutations, clonal only-copy mutations (continuous CoxPH model). In cases where an increase in the predictor variable was associated with better outcome (longer survival), a second CoxPH model is used to assess the difference in overall survival between tumors in the top third and bottom two thirds of predicted risk (categorical CoxPH model).
For the TCGA cohort, in each tumor type, a cox proportional hazard's model was used to evaluate the contribution of TMB and pTMB to overall survival. The predicted risk values from these models were then used to stratify the tumors into low and high-risk groups using the second tertile of the predicted risk as the threshold. For the immunotherapy cohorts, clinical response assessments were retrieved from the original publications. The Mann-Whitney U test was used to evaluate the difference of continuous variables between groups, including the differences of predictive variables between responding and non-responding tumors, and the difference of background rate of loss between haploid and diploid regions of the genome. Cohen's d statistic was used to quantify the effect size of each predictor variable in the ICB cohorts. Fisher's exact test was used to assess the association of dichotomous variables (such as whole-genome doubling) with therapeutic response. The association of the fraction of mutations in single and multi-copy regions with TMB ranks was evaluated by the Pearson correlation coefficient, while non-parametric correlations were evaluated by the Spearman correlation coefficient. The Kaplan-Meier method was used to estimate the survival function and the survival curves were compared using the non-parametric log-rank test. All p values were based on two-sided testing and differences were considered significant at p<0.05. Statistical analyses were done using R version 3.6 and higher, http://www.R-project.org/)
Source data for the TCGA tumor samples were retrieved from http://cancergenome.nih.gov. WES-derived somatic mutation calls from the TCGA PanCancer Atlas MC3 project were retrieved from the NCI Genomic Data Commons (https://gdc.cancer.gov/about-data/publications/mc3-2017). Previously published genomic data, re-analyzed here, were obtained from the material of the original publications and from dbGaP under accession code phs000452.v3.p17, and Sequence Read Archive (SRA) under accession codes SRP0958096, SRP0679388 and SRP0902948. WES sequence data for the HNSCC and NKI cohorts from patients who consented to data deposition can be retrieved from the European Genome-phenome Archive (EGA accession number EGAS00001006660).
While the invention has been described in conjunction with the detailed description thereof, the foregoing description is intended to illustrate and not limit the scope of the invention, which is defined by the scope of the appended claims. Other aspects, advantages, and modifications are within the scope of the following claims.
The patent and scientific literature referred to herein establishes the knowledge that is available to those with skill in the art. All United States patents and published or unpublished United States patent applications cited herein are incorporated by reference. All published foreign patents and patent applications cited herein are hereby incorporated by reference. All other published references, documents, manuscripts and scientific literature cited herein are hereby incorporated by reference.
The present application claims the benefit of U.S. provisional application 63/276,525 filed Nov. 5, 2021, which is incorporated by referenced herein.
This invention was made with government support under grant W81XWH-20-1-0638 awarded by the U.S. Army. The government has certain rights in the invention.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2022/079403 | 11/7/2022 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 63276525 | Nov 2021 | US |
