Patent Application
20220275460
Embodiments of the present invention relate to methods of predicting a radiotherapy success in a method of treating a lung cancer of a patient, the use of specific markers for predicting a radiotherapy success in a method of treating a lung cancer of a patient, a database comprising the markers, and a computer program product for use in such a method.
The overwhelming majority (˜70%) of cancer patients needs a radiation therapy at some point in the course of its treatment. There are basically three forms of radiation therapy in cancer:
External beam radiation therapy (EBRT), in particular x-ray and particle therapy
Brachytherapy (internal radiation therapy), in particular low-dose rate (LDR) and high-dose rate (HDR) brachytherapy
Targeted radiation therapy
These can be combined among each other or with adjuvant therapies, such as chemotherapy, immunotherapy, and surgical therapy. Patients respond differently to radiation therapy, depending on cancer type, cancer stage, and form of therapy; with a response rate of ˜50% for conventional radiotherapy. This is due in particular to the tumor tissue's inherent sensitivity or resistance to radiation. Furthermore, (irreversible) side effects can occur, depending on the radiosensitivity inherent to the healthy tissue that is located adjacent to the tumor.
To avoid ineffective therapies and (irreversible) side effects, there is a clinical need for a solution that can predict radiotherapy success. This is particularly interesting from a health economics point of view, since such a solution would reduce the patient's average length of stay in the hospital and, consequently, the associated cost. Moreover, the avoidance of inefficacious treatments with potentially adverse side effects would positively influence overall outcome and prognosis. In addition, any solution that speeds up the time of clinical decision support and that provides transparency into the decision-marking process to make it comprehensible to the clinical is desirable.
Existing products and products under development differ particularly in the following features:
Prediction of radiosensitivity/radioresistance in tumor tissue versus radiosensitivity/radiotoxicity in healthy tissue
Universal (pan-cancer) versus tissue-specific (e.g. squamous cell lung carcinoma) prediction/application
Technologies used and measured biological substance
Medical knowledge and clinical guidelines integrated
Several platforms exist or are in development to determine radiosensitivity/radioresistance in tumor tissue.
Cvergenx' pGRT (precision genomic radiation therapy) platform (under development) is based on measuring the activity of ten genes (via RNA) to calculate a tumor radiosensitivity index (RSI, Eschrich et al., 2009, Int J Radiat Oncol Biol Phys, DOI: 10.1016/j.ijrobp.2009.06.014) and its integration into a model to adapt radiation dose (genomic-adjusted radiation dose, GARD, Scott et al., 2017, Lancet Oncol, DOI: 10.1016/S1470-2045(16)30648-9) in chemoradiation therapy.
PFS Genomics' RadiotypeDX test (under development) is based on measuring the activity of 51 genes (via RNA) in breast cancer tissue and predicts a locoregional recurrence after adjuvant radiotherapy (Speers et al., 2015, Clin Cancer Res, DOI: 10.1158/1078-0432.CCR-14-2898).
Genomic Health's OncotypeDx test is based on measuring the activity of 21 genes (via RNA) in breast cancer tissue (in particular ductal carcinoma in situ, DCIS) and predicts radiotherapy success as well as locoregional recurrence after radiotherapy.
GenomeDx' Decipher test is based on measuring the activity of 22 and 24 genes (via RNA) in prostate cancer tissue and predicts the development of distal metastases after radical prostatectomy and post-operative radiotherapy (post-operative radiation therapy outcomes score, PORTOS, Zhao et al., 2016, Lancet Oncol, under development, DOI: 10.1016/S1470-2045(16)30491-0), respectively.
OncoRadiomics' RadiomiX platform is based on calculating general, quantitative tumor features from medical CT images and predicts radiotherapy success, among other things (Aerts et al, 2014, Nat Commun, DOI: 10.1038/ncomms5006).
Also, several approaches exist regarding the determination of radiotoxicity in healthy tissue.
NovaGray's Breast test is based on measuring apoptotic T-lymphocytes, induced by in vitro radiation of a blood sample (radiation-induced T-lymphocyte apoptosis, RILA), and identifies patients who likely respond hypersensitive to ionizing radiation and, therefore, are at risk of developing irreversible side effects, in particular fibrosis, in healthy breast tissue.
DiaCarta's RadTox test (under development) is based on measuring circulating, cell-free DNA (cfDNA) in blood, particularly the repetitive Alu sequence, as a measure of radiotherapy-induced tissue damage, and monitors potentially toxic side effects in healthy tissue, located adjacent to the tumor, after radiotherapy.
However, there remains a need for efficient markers for predicting a radiotherapy success in a method of treating a lung cancer of a patient.
The inventors have found efficient markers for predicting radiotherapy success in patients suffering from a lung cancer.
In a first aspect, embodiments of the present invention relate to a method of predicting a radiotherapy success in a method of treating a lung cancer of a patient, comprising:
obtaining or providing a sample of the patient, and
determining a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene selected from MLH1, EP300, BRCA2, FLT1, GRINA, HEATR1, MUTYH, NUDT1, PARP10, PRPF19, KCNJ12, MGMT, PMS2P9, DDX11, EPHA2, GEN1, SETD2, and ZNF208, and/or a gene mutational burden in at least one gene selected from the group consisting of MLH1, DDX11, APOLD1, GEN1, GPER1, POLB, PRPF19, ANKRD30A, AR, BTK, GTF2H4, HEATR1, IKBKE, KAT6A, NUDT19, PARP1, PTPRT, TAF3, TDG, MAP3K1, and SLC9A4, and/or a tumor mutational burden (TMB).
Further disclosed is in a second aspect the use of a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene selected from MLH1, EP300, BRCA2, FLT1, GRINA, HEATR1, MUTYH, NUDT1, PARP10, PRPF19, KCNJ12, MGMT, PMS2P9, DDX11, EPHA2, GEN1, SETD2, and ZNF208, and/or a gene mutational burden in at least one gene selected from the group consisting of MLH1, DDX11, APOLD1, GEN1, GPER1, POLB, PRPF19, ANKRD30A, AR, BTK, GTF2H4, HEATR1, IKBKE, KAT6A, NUDT19, PARP1, PTPRT, TAF3, TDG, MAP3K1, and SLC9A4, and/or a tumor mutational burden (TMB), particularly a change in the nucleotide sequence chosen from rs1801406, rs1046456, rs3754334, rs7993418, rs16981869, rs72407975, rs67714660, rs56261297, rs2275685, rs2275687, rs1885533, rs1885534, rs1006456, rs2794763, rs1799977, rs3219472, rs1062492, rs11136344, rs11136345, rs2240045, rs4082155, rs10425763, rs20551, rs72846670, rs73979902, rs16906255, and/or rs73144285, further particularly rs1799977 and/or rs20551, as disclosed in the Single Nucleotide Polymorphism Database dbSNP of the National Center for Biotechnology Information (NCBI), as a marker for predicting a radiotherapy success in a method of treating a lung cancer of a patient.
Embodiments of the present invention furthermore is directed in a third aspect to a database comprising the above markers of the second aspect.
A fourth aspect relates to a method of predicting a radiotherapy success in a method of treating a lung cancer of a patient, comprising:
obtaining nucleotide sequence data and/or epigenomics profile data and/or expression profile data and/or copy number data of a sample of a patient, and
determining the presence of at least one marker of the second aspect.
A fifth aspect is directed to a computer program product comprising computer executable instructions which, when executed, perform the method of the fourth aspect.
In a sixth aspect, a method of predicting a radiotherapy success in a method of treating a lung cancer of a patient is disclosed, comprising:
obtaining nucleotide sequence data and/or epigenomics profile data and/or expression profile data and/or copy number data of a sample of a patient, and analyzing the nucleotide sequence data using the computer program product the fifth aspect.
Further aspects and embodiments of the present invention are dis-closed in the dependent claims and can be taken from the following description, figures and examples, without being limited thereto.
The enclosed drawings should illustrate embodiments of the present invention and convey a further understanding thereof. In connection with the description they serve as explanation of concepts and principles of the present invention. Other embodiments and many of the stated advantages can be derived in relation to the drawings. The elements of the drawings are not necessarily to scale towards each other. Identical, functionally equivalent and acting equal features and components are denoted in the figures of the drawings with the same reference numbers, unless noted otherwise.
In
Unless defined otherwise, technical and scientific terms used herein have the same meaning as commonly understood by one of ordinary skill in the art to which this embodiments of the present invention belong.
In general, a gene is a sequence of nucleotides (nucleotide sequence) in DNA—which can be copied to RNA, e.g. mRNA—or RNA, i.e. nucleic acid molecules, coding for a molecule that has a function in an organism. The term “nucleic acid molecule” refers to a polynucleotide molecule having a defined sequence. It comprises DNA molecules, RNA molecules, nucleotide analog molecules and combinations and derivatives thereof, such as DNA molecules or RNA molecules with incorporated nucleotide analogs or cDNA.
With regard to embodiments of the present invention, the following genes have the following sequence ID No. as reference gene, as per the attached sequence protocol:
A change in the nucleotide sequence relates to a variation in the sequence as compared to a reference sequence. A change in the nucleotide sequence is for example a deletion of one or multiple nucleotides, an insertion of one or multiple nucleotides, a substitution of one or multiple nucleotides, a duplication of one or a sequence of multiple nucleotides, a translocation of one or a sequence of multiple nucleotides, etc. Thus, it also encompasses single-nucleotide variants (SNVs) and multi-nucleotide variants (MNVs).
An epigenomics profile corresponds to the multitude of all epigenomic modifications, i.e. DNA methylation, DNA hydroxymethylation, histone modification, etc., that can occur in a patient.
An expression profile corresponds to the quantity and/or activity of all molecules that are expressed or realized from a gene, i.e. mRNAs, proteins, etc., including the multitude of its modifications, i.e. RNA methylation, protein phosphorylation, etc., that can occur in a patient.
A copy number corresponds to the number of copies of any defined DNA region within the genome. In the diploid human genome, e.g., the characteristic copy number of any gene is two. Deviations from this copy number can occur in a patient as a result of structural sequence variations. These deviations are termed copy number variations (CNVs) and are herein also termed change in copy number.
In the context of embodiments of the present invention, a “sample” is a sample which comprises at least nucleotide sequence and/or epigenetic profile and/or expression profile and/or copy number information of a patient. Examples for samples are cells, tissue, biopsy specimens, body fluids, blood, urine, saliva, sputum, plasma, serum, cell culture supernatant, swab sample and others, e.g. tumor tissue.
According to certain embodiments the sample is a blood sample. In this regard it was found that the mutations particularly are not somatic, so that also blood samples, e.g. peripheral blood, are possible as samples. Peripheral blood in this regard refers to the circulating pool of blood within the patient. According to certain embodiments the sample is a tissue or a biopsy specimen which can be fixated or not, wherein a fixation can be e.g. carried out by freezing or usual fixation methods like for formalin-fixed paraffin-embedded (FFPE) tissue.
According to certain embodiments, the patient in the present methods is a vertebrate, more preferably a mammal and most preferred a human.
A vertebrate within embodiments of the present invention refers to animals having a vertebrae, which includes mammals—including humans, birds, reptiles, amphibians and fishes. Embodiments of the present invention thus are not only suitable for human medicine, but also for veterinary medicine.
The gene mutational burden (GMB) is a biomarker that measures the mutational burden in a gene by counting the number of considered mutations and e.g. provides a proxy for the remaining functionality of the gene. This metric can e.g. be used to classify genes into classes with low, medium, and high mutational burdens. The mutational burden is either a natural number (including 0) or a positive rational number (e.g. relative to 1 kbp). The number of the GMB can be entered into calculations for evaluating the probability of a radiotherapy success in a method of treating a lung cancer of a patient. The gene mutational burden in one gene can e.g. differ between patients that do and patients that do not respond to radiotherapy. It is not excluded that patients that do respond differ from patients that do not respond to radiotherapy by one or more specific mutations in a single gene, but do not show a difference in the overall mutational burden of the gene.
The tumor mutational burden (TMB) is a biomarker that measures the mutational burden in all or a subset of all genes by counting the number of considered mutations and e.g. approximates the number of mutations present in a tumor of a cancer patient. This metric can e.g. be used to classify tumors into classes with low, medium, and high mutational burdens. The tumor mutational burden is either a natural number (including 0) or a positive rational number (e.g. relative to 1 kbp). The number of the TMB can be entered into calculations for evaluating the probability of a radiotherapy success in a method of treating a lung cancer of a patient. The tumor mutational burden can e.g. differ between patients that do and patients that do not respond to radiotherapy.
Radiotherapy success can be based on a combination of somatic features/mutations (e.g. radiosensitivity/radioresistance of cancerous tissue) and germline features/mutations (innate radiosensitivity/radioresistance).
According to certain embodiments, a success in radiotherapy with adjuvant therapies, particularly chemotherapy, immunotherapy, and/or surgical therapy, is predicted in the present methods of treating a lung cancer of a patient.
In embodiments of the present invention the radiotherapy method is not particularly restricted. According to certain embodiments, the radiotherapy is external beam radiation therapy (EBRT), in particular x-ray and particle therapy, brachytherapy (internal radiation therapy), in particular low-dose rate (LDR) and high-dose rate (HDR) brachytherapy, or targeted radiation therapy. It is also not excluded that adjuvant therapies are included. According to certain embodiments the radiotherapy method thus also can be radiochemotherapy.
According to certain embodiments, the prediction of radiotherapy success includes a prediction of locoregional response and/or recurrence.
Gene coordinates can be obtained from the following resource:
ftp://ftp.ncbi.nlm.nih.gov/genomes/all/GCF/000/001/405/GCF_000001405.33_GRCh38.p7/GCF_000001405.33_GRCh38.p7_feature table.txt.gz.
Before embodiments of the present invention are described in exemplary detail, it is to be understood that the present invention is not limited to the particular component parts of the process steps of the methods described herein as such methods may vary. It is also to be understood that the terminology used herein is for purposes of describing particular embodiments only, and is not intended to be limiting. It must be noted that, as used in the specification and the appended claims, the singular forms “a,” “an” and “the” include singular and/or plural referents unless the context clearly dictates otherwise. For example, the term “a” as used herein can be understood as one single entity or in the meaning of “one or more” entities. It is also to be understood that plural forms include singular and/or plural referents unless the context clearly dictates otherwise. It is moreover to be understood that, in case parameter ranges are given which are delimited by numeric values, the ranges are deemed to include these limitation values.
In a first aspect, at least one embodiment of the present invention relate to a method of predicting a radiotherapy success in a method of treating a lung cancer of a patient, comprising:
obtaining or providing a sample of the patient, and
determining a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene selected from MLH1, EP300, BRCA2, FLT1, GRINA, HEATR1, MUTYH, NUDT1, PARP10, PRPF19, KCNJ12, MGMT, PMS2P9, DDX11, EPHA2, GEN1, SETD2, and ZNF208, and/or a gene mutational burden in at least one gene selected from the group consisting of MLH1, DDX11, APOLD1, GEN1, GPER1, POLB, PRPF19, ANKRD30A, AR, BTK, GTF2H4, HEATR1, IKBKE, KAT6A, NUDT19, PARP1, PTPRT, TAF3, TDG, MAP3K1, and SLC9A4, and/or a tumor mutational burden (TMB).
According to certain embodiments, the present invention relates to a method of predicting a radiotherapy success in a method of treating a lung cancer of a patient, comprising:
obtaining or providing a sample of the patient, and
determining a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene selected from MLH1, EP300, HEATR1, NUDT1, and MGMT, and/or a gene mutational burden in at least one gene selected from the group consisting of MLH1, DDX11, PRPF19, BTK, GTF2H4, KAT6A, TDG, MAP3K1, and SLC9A4, and/or a tumor mutational burden (TMB), particularly a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene selected from MLH1 and EP300, and/or a gene mutational burden in MLH1, and/or a tumor mutational burden (TMB).
The present method is especially suitable for predicting a radiotherapy success in a method of treating a lung cancer of a patient, particularly in a method of treating a non-small cell lung cancer (NSCLC), and further particularly in a method of treating lung squamous cell carcinoma (LUSC) and/or lung adenocarcinoma (LUAD).
In the present method the obtaining or providing of a sample of the patient is not particularly restricted but is preferably non-invasive, e.g. the sample can be taken from a stock or a storage, be obtained in vitro, etc.
Also the determining of a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene selected from MLH1, EP300, BRCA2, FLT1, GRINA, HEATR1, MUTYH, NUDT1, PARP10, PRPF19, KCNJ12, MGMT, PMS2P9, DDX11, EPHA2, GEN1, SETD2, and ZNF208, and/or a gene mutational burden in at least one gene selected from the group consisting of MLH1, DDX11, APOLD1, GEN1, GPER1, POLB, PRPF19, ANKRD30A, AR, BTK, GTF2H4, HEATR1, IKBKE, KAT6A, NUDT19, PARP1, PTPRT, TAF3, TDG, MAP3K1, and SLC9A4, and/or a tumor mutational burden (TMB) is not particularly restricted.
For the determining of a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number, it is e.g. possible to obtain nucleotide sequence information of one or more nucleotide sequences of MLH1, EP300, BRCA2, FLT1, GRINA, HEATR1, MUTYH, NUDT1, PARP10, PRPF19, KCNJ12, MGMT, PMS2P9, DDX11, EPHA2, GEN1, SETD2, and ZNF208, and/or an epigenomics profile for one or more genes of MLH1, EP300, BRCA2, FLT1, GRINA, HEATR1, MUTYH, NUDT1, PARP10, PRPF19, KCNJ12, MGMT, PMS2P9, DDX11, EPHA2, GEN1, SETD2, and ZNF208, and/or an expression profile for one or more genes of MLH1, EP300, BRCA2, FLT1, GRINA, HEATR1, MUTYH, NUDT1, PARP10, PRPF19, KCNJ12, MGMT, PMS2P9, DDX11, EPHA2, GEN1, SETD2, and ZNF208, and/or determine a copy number of one or more genes of MLH1, EP300, BRCA2, FLT1, GRINA, HEATR1, MUTYH, NUDT1, PARP10, PRPF19, KCNJ12, MGMT, PMS2P9, DDX11, EPHA2, GEN1, SETD2, and ZNF208, in the sample by a suitable method, which is not particularly restricted.
For example, nucleotide sequence information can be obtained by DNA sequencing methods that are not particularly restricted, e.g. Sanger sequencing methods, shotgun sequencing methods, bridge PCR methods, and next-generation sequencing methods.
The methods of sequencing nucleic acids referred to as next-generation sequencing have opened the possibility of large-scale genomic analysis. The term “next-generation sequencing” or “high-throughput sequencing” refers to high-throughput sequencing technologies that parallelize the sequencing process, producing thousands or millions of sequences at once. Examples include Massively Parallel Signature Sequencing (MPSS), Polony sequencing, 454 pyrosequencing, Illumina (Solexa) sequencing, SOLiD sequencing, Ion Torrent semiconductor sequencing, DNA nanoball sequencing, Helioscope™ single molecule sequencing, Single Molecule Real Time (SMRT) sequencing, Nanopore DNA sequencing, and RNA Polymerase (RNAP) sequencing, each with or without prior employment of target enrichment techniques like hybridization capture.
An epigenomics profile can be obtained based on specific epigenetic modifications that are not specifically restricted, e.g. histone modification assays like ChIP-Chip and ChIP-Seq and DNA methylation assays based on microarray or bead array, bisulfite sequencing, or mass spectrometry.
The expression profile of genes can either be determined on the level of RNAs, e.g. using quantitative real-time PCR, DNA microarray, or RNA sequencing methods, or on the level of proteins, e.g. using quantitative proteomics methods.
The copy number and copy number variations/changes can e.g. be determined by comparative genomic hybridizations to microarrays or next-generation sequencing methods.
After the determination, the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number of the one or more gene can then be compared to the respective reference, i.e. the reference sequence, reference epigenomics profile, reference expression profile and reference copy number. By comparison, a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number can then be determined.
However, it is not excluded that the respective information about the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number of the one or more gene is already provided together with the sample of the patient, so that e.g. only the comparing to a reference has to be carried out.
In embodiments of the present invention a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene selected from MLH1, EP300, BRCA2, FLT1, GRINA, HEATR1, MUTYH, NUDT1, PARP10, PRPF19, KCNJ12, MGMT, PMS2P9, DDX11, EPHA2, GEN1, SETD2, and ZNF208, and/or a gene mutational burden in at least one gene selected from the group consisting of MLH1, DDX11, APOLD1, GEN1, GPER1, POLB, PRPF19, ANKRD30A, AR, BTK, GTF2H4, HEATR1, IKBKE, KAT6A, NUDT19, PARP1, PTPRT, TAF3, TDG, MAP3K1, and SLC9A4, and/or a tumor mutational burden (TMB) can be indicative of an enhanced or a reduced radiotherapy success in a method of treating a lung cancer of a patient. This means that a certain change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of one gene selected from MLH1, EP300, BRCA2, FLT1, GRINA, HEATR1, MUTYH, NUDT1, PARP10, PRPF19, KCNJ12, MGMT, PMS2P9, DDX11, EPHA2, GEN1, SETD2, and ZNF208, and/or a gene mutational burden in one gene selected from the group consisting of MLH1, DDX11, APOLD1, GEN1, GPER1, POLB, PRPF19, ANKRD30A, AR, BTK, GTF2H4, HEATR1, IKBKE, KAT6A, NUDT19, PARP1, PTPRT, TAF3, TDG, MAP3K1, and SLC9A4, and/or a tumor mutational burden (TMB) can be indicative of an enhanced radiotherapy success in a method of treating a lung cancer of a patient, while another change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of a different gene selected from MLH1, EP300, BRCA2, FLT1, GRINA, HEATR1, MUTYH, NUDT1, PARP10, PRPF19, KCNJ12, MGMT, PMS2P9, DDX11, EPHA2, GEN1, SETD2, and ZNF208, and/or a gene mutational burden in a different gene selected from the group consisting of MLH1, DDX11, APOLD1, GEN1, GPER1, POLB, PRPF19, ANKRD30A, AR, BTK, GTF2H4, HEATR1, IKBKE, KAT6A, NUDT19, PARP1, PTPRT, TAF3, TDG, MAP3K1, and SLC9A4, can be indicative of a reduced radiotherapy success in a method of treating a lung cancer of a patient.
For example, it was found that LUSC patients with higher TMB show a better local response to radiotherapy compared to LUSC patients with lower TMB. However, better results are obtained when combinations of features, particularly with the TMB as one feature, are considered, i.e. at least one of the changes in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of the genes mentioned for the present methods, and/or at least one GMB for the genes mentioned for the present methods.
According to certain embodiments the lung cancer is a non-small cell lung cancer, wherein the change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number, particularly a change in the nucleotide sequence compared to a reference sequence, is determined in at least one gene selected from the group consisting of BRCA2, DDX11, EPHA2, FLT1, GEN1, GRINA, HEATR1, MLH1, MUTYH, NUDT1, PARP10, PRPF19, SETD2, ZNF208, EP300, KCNJ12, MGMT, and PMS2P9, particularly MLH1 and/or EP300, and/or wherein the gene mutational burden is determined in at least one gene selected from the group consisting of APOLD1, DDX11, GEN1, GPER1, MLH1, POLB, PRPF19, ANKRD30A, AR, BTK, GTF2H4, HEATR1, IKBKE, KAT6A, MAP3K1, NUDT19, PARP1, PTPRT, SLC9A4, TAF3, and TDG, particularly MLH1, and/or wherein the tumor mutational burden is determined, preferably wherein a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number, particularly a change in the nucleotide sequence compared to a reference sequence, of at least one gene selected from MLH1, EP300, HEATR1, NUDT1, and MGMT, and/or a gene mutational burden in at least one gene selected from the group consisting of MLH1, DDX11, PRPF19, BTK, GTF2H4, KAT6A, TDG, MAP3K1, and SLC9A4, and/or a tumor mutational burden (TMB) is determined, further preferably wherein a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number, particularly a change in the nucleotide sequence compared to a reference sequence, of at least one gene selected from MLH1 and EP300, and/or a gene mutational burden in MLH1, and/or a tumor mutational burden (TMB) is determined.
According to certain embodiments at least one change in the nucleotide sequence is determined chosen from rs1801406, rs1046456, rs3754334, rs7993418, rs16981869, rs72407975, rs67714660, rs56261297, rs2275685, rs2275687, rs1885533, rs1885534, rs1006456, rs2794763, rs1799977, rs3219472, rs1062492, rs11136344, rs11136345, rs2240045, rs4082155, rs10425763, rs20551, rs72846670, rs73979902, rs16906255, and rs73144285, as disclosed in the Single Nucleotide Polymorphism Database dbSNP of the National Center for Biotechnology Information (NCBI), and/or NC_000007.14:g.77040016_77040017delinsCA, as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature, preferably rs1885534, rs2794763, rs1799977, rs1062492, rs20551, rs16906255, particularly rs1799977 and/or rs20551, as disclosed in the Single Nucleotide Polymorphism Database dbSNP of the National Center for Biotechnology Information (NCBI).
The changes as disclosed in the Single Nucleotide Polymorphism Database dbSNP of the National Center for Biotechnology Information (NCBI) are thereby as follows (for Homo sapiens):
rs1801406: position: chr13:32337751 (GRCh38.p12, obtained from NCBI) (position 22272 in SEQ ID No. 3, BRCA2); alleles: A>C (missense variant), A>G (synonymous variant); variation Type: Single Nucleotide Variation (SNV); gene: BRCA2
rs1046456: position: chr12:31101061 (GRCh38.p12) (position 27488 in SEQ ID No. 14, DDX11); alleles: C>T; variation Type: SNV; gene: DDX11, synonymous or missense variant
rs3754334: position: chr1:16125272 (GRCh38.p12) (position 936 in SEQ ID No. 14, EPHA2); alleles: G>A; variation Type: SNV; gene: EPHA2, synonymous variant
rs7993418: position: chr13:28308924 (GRCh38.p12) (position 8579 in SEQ ID No. 4, FLT1); alleles: G>A; variation Type: SNV; gene: FLT1, synonymous variant
rs16981869: position: chr2:17764976 (GRCh38.p12) (position 11290 in SEQ ID No. 16, GEN1); alleles: A>G; variation Type: SNV; gene: GEN1, missense variant
rs72407975: position: chr2:17768673:17768675 (GRCh38.p7) (positions 14987:14989 in SEQ ID No. 16, GEN1); alleles: TAA/−; variation Type: Insertion and Deletion (Indel); gene: GEN1, intron variant
rs67714660: chr8:143991836 (GRCh38.p12) (position 1779 in SEQ ID No. 5, GRINA); alleles: C>A, C>G; variation Type: SNV; gene: GRINA, intron variant
rs56261297: chr8:143992685 (GRCh38.p12) (position 2628 in SEQ ID No. 5, GRINA); alleles: C>T; variation Type: SNV; gene: GRINA, intron variant
rs2275685: chr1:236553659 (GRCh38.p12) (position 4655 in SEQ ID No. 6, HEATR1); alleles: C>T; variation Type: SNV; gene: HEATR1, synonymous variant
rs2275687: chr1:236554626 (GRCh38.p12) (position 5622 in SEQ ID No. 6, HEATR1); alleles: T>C; variation Type: SNV; gene: HEATR1, missense variant
rs1885533: chr1:236555893 (GRCh38.p12) (position 6889 in SEQ ID No. 6, HEATR1); alleles: A>C, A>G, A>T; variation Type: SNV; gene: HEATR1, missense variant
rs1885534: chr1:236555959 (GRCh38.p12) (position 6955 in SEQ ID No. 6, HEATR1); alleles: G>A; variation Type: SNV; gene: HEATR1, intron variant
rs1006456: chr1:236585205 (GRCh38.p12) (position 36201 in SEQ ID No. 6, HEATR1); alleles: T>C; variation Type: SNV; gene: HEATR1, synonymous variant
rs2794763: chr1:236586349 (GRCh38.p12) (position 37345 in SEQ ID No. 6, HEATR1); alleles: T>C; variation Type: SNV; gene: HEATR1, missense variant
rs1799977: chr3:37012077 (GRCh38.p12) (position 18728 in SEQ ID No. 1, MLH1); alleles: A>C, A>G, A>T; variation Type: SNV; gene: MLH1, missense variant
rs3219472: chr1:45338378 (GRCh38.p12) (position 9137 in SEQ ID No. 7, MUTYH); alleles: C>T; variation Type: SNV; gene: MUTYH, intron variant
rs1062492: chr7:2251050 (GRCh38.p12) (position 8829 in SEQ ID No. 8, NUDT1); alleles: C>T; variation Type: SNV; gene: NUDT1, 3′UTR variant
rs11136344: chr8:143985257 (GRCh38.p12) (position 8106 in SEQ ID No. 9, PARP10); alleles: T>C; variation Type: SNV; gene: PARP10, missense variant
rs11136345: chr8:143985944 (GRCh38.p12) (position 8793 in SEQ ID No. 9, PARP10); alleles: G>A (synonymous variant), G>C (missense variant); variation Type: SNV; gene: PARP10
rs2240045: chr11:60903374 (GRCh38.p12) (position 12828 in SEQ ID No. 10, PRPF19); alleles: C>T; variation Type: SNV; gene: PRPF19, intron variant
rs4082155: chr3:47083895 (GRCh38.p12) (position 67488 in SEQ ID No. 17, SETD2); alleles: G>A, G>T; variation Type: SNV; gene: SETD2, missense variant
rs10425763: chr19:21973116 (GRCh38.p12) (position 7022 in SEQ ID No. 18, ZNF208); alleles: T>C; variation Type: SNV; gene: ZNF208, missense variant
rs20551: chr22:41152004 (GRCh38.p12) (position 59395 in SEQ ID No. 2, EP300); alleles: A>G; variation Type: SNV; gene: EP300, missense variant
rs72846670: chr17:21416533 (GRCh38.p12) (position 40147 in SEQ ID No. 11, KCNJ12); alleles: C>G (missense variant), C>T (synonymous variant); variation Type: SNV; gene: KCNJ12
rs73979902: chr17:21416556 (GRCh38.p12) (position 40170 in SEQ ID No. 11, KCNJ12); alleles: G>A, G>T; variation Type: SNV; gene: KCNJ12, missense variant
rs16906255: chr10:129467401 (GRCh38.p12) (position 218 in SEQ ID No. 12, MGMT); alleles: T>G; variation Type: SNV; gene: MGMT, intron variant
rs73144285: chr7:77040017 (GRCh38.p12) (NC_000007.14:g.77040016_77040017delinsCA, position 77040016 in chromosome 7 corresponds to position 537 in SEQ ID No. 13, PMS2P9; alleles: G>A, G>T; variation Type: SNV; gene: PMS2P9, non-coding transcript variant
According to certain embodiments, the present invention relates to a method of predicting a radiotherapy success in a method of treating a lung cancer of a patient, comprising:
obtaining or providing a sample of the patient, and
determining at least one change in the nucleotide sequence chosen from rs1801406, rs1046456, rs3754334, rs7993418, rs16981869, rs72407975, rs67714660, rs56261297, rs2275685, rs2275687, rs1885533, rs1885534, rs1006456, rs2794763, rs1799977, rs3219472, rs1062492, rs11136344, rs11136345, rs2240045, rs4082155, rs10425763, rs20551, rs72846670, rs73979902, rs16906255, and rs73144285, preferably rs1885534, rs2794763, rs1799977, rs1062492, rs20551, rs16906255, particularly rs1799977 and/or rs20551, as disclosed in the Single Nucleotide Polymorphism Database dbSNP of the National Center for Biotechnology Information (NCBI), and/or a gene mutational burden in at least one gene selected from the group consisting of MLH1, DDX11, APOLD1, GEN1, GPER1, POLB, PRPF19, ANKRD30A, AR, BTK, GTF2H4, HEATR1, IKBKE, KAT6A, NUDT19, PARP1, PTPRT, TAF3, TDG, MAP3K1, and SLC9A4, and/or a tumor mutational burden (TMB).
According to certain embodiments, the present invention relates to a method of predicting a radiotherapy success in a method of treating a lung cancer of a patient, comprising:
obtaining or providing a sample of the patient, and
determining at least one change in the nucleotide sequence chosen from rs1801406, rs1046456, rs3754334, rs7993418, rs16981869, rs72407975, rs67714660, rs56261297, rs2275685, rs2275687, rs1885533, rs1885534, rs1006456, rs2794763, rs1799977, rs3219472, rs1062492, rs11136344, rs11136345, rs2240045, rs4082155, rs10425763, rs20551, rs72846670, rs73979902, rs16906255, and rs73144285, preferably rs1885534, rs2794763, rs1799977, rs1062492, rs20551, rs16906255, particularly rs1799977 and/or rs20551, as disclosed in the Single Nucleotide Polymorphism Database dbSNP of the National Center for Biotechnology Information (NCBI), and/or a gene mutational burden in at least one gene selected from the group consisting of MLH1, DDX11, PRPF19, BTK, GTF2H4, KAT6A, TDG, MAP3K1, and SLC9A4, particularly in MLH1, and/or a tumor mutational burden (TMB).
According to certain embodiments, the at least one change in the nucleotide sequence determined is preferably chosen from NC_000013.11:g.32337751A>G, NC_000012.12:g.31101061C>T, NC_000001.11:g.16125272G>A, NC_000013.11:g.28308924G>A, NC_000002.12:g.17764976A>G, NC_000002.12:g.17768673_17768675delTAA, NC_000008.11:g.143991836C>G, NC_000008.11:g.143992685C>T, NC_000001.11:g.236553659C>T, NC_000001.11:g.2365546261>C, NC_000001.11:g.236555893A>G, NC_000001.11:g.236555959G>A, NC_000001.11:g.2365852051>C, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000001.11:g.45338378C>T, NC_000007.14:g.2251050C>T, NC_000008.11:g.1439852571>C, NC_000008.11:g.143985944G>A, NC_000011.10:g.60903374C>T, NC_000003.12:g.47083895G>A, NC_000019.10:g.219731161>C, NC_000022.11:g.41152004A>G, NC_000017.11:g.21416533C>T, NC_000017.11:g.21416556G>A, NC_000010.11:g.1294674011>G, and NC_000007.14:g.77040016_77040017delinsCA, further preferably chosen from NC_000001.11:g.236555959G>A, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000007.14:g.2251050C>T, NC_000022.11:g.41152004A>G, NC_000010.11:g.129467401T>G, particularly NC_000003.12:g.37012077A>G or NC_000022.11:g.41152004A>G, as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature.
According to certain embodiments, the present invention relates to a method of predicting a radiotherapy success in a method of treating a lung cancer of a patient, comprising:
obtaining or providing a sample of the patient, and
determining at least one change in the nucleotide sequence chosen from NC_000013.11:g.32337751A>G, NC_000012.12:g.31101061C>T, NC_000001.11:g.16125272G>A, NC_000013.11:g.28308924G>A, NC_000002.12:g.17764976A>G, NC_000002.12:g.17768673_17768675delTAA, NC_000008.11:g.143991836C>G, NC_000008.11:g.143992685C>T, NC_000001.11:g.236553659C>T, NC_000001.11:g.2365546261>C, NC_000001.11:g.236555893A>G, NC_000001.11:g.236555959G>A, NC_000001.11:g.2365852051>C, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000001.11:g.45338378C>T, NC_000007.14:g.2251050C>T, NC_000008.11:g.1439852571>C, NC_000008.11:g.143985944G>A, NC_000011.10:g.60903374C>T, NC_000003.12:g.47083895G>A, NC_000019.10:g.219731161>C, NC_000022.11:g.41152004A>G, NC_000017.11:g.21416533C>T, NC_000017.11:g.21416556G>A, NC_000010.11:g.1294674011>G, and NC_000007.14:g.77040016_77040017delinsCA, further preferably chosen from NC_000001.11:g.236555959G>A, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000007.14:g.2251050C>T, NC_000022.11:g.41152004A>G, NC_000010.11:g.129467401T>G, particularly NC_000003.12:g.37012077A>G or NC_000022.11:g.41152004A>G, as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature, and/or a gene mutational burden in at least one gene selected from the group consisting of MLH1, DDX11, APOLD1, GEN1, GPER1, POLB, PRPF19, ANKRD30A, AR, BTK, GTF2H4, HEATR1, IKBKE, KAT6A, NUDT19, PARP1, PTPRT, TAF3, TDG, MAP3K1, and SLC9A4, and/or a tumor mutational burden (TMB).
According to certain embodiments, the present invention relates to a method of predicting a radiotherapy success in a method of treating a lung cancer of a patient, comprising:
obtaining or providing a sample of the patient, and
determining at least one change in the nucleotide sequence chosen from NC_000013.11:g.32337751A>G, NC_000012.12:g.31101061C>T, NC_000001.11:g.16125272G>A, NC_000013.11:g.28308924G>A, NC_000002.12:g.17764976A>G, NC_000002.12:g.17768673_17768675delTAA, NC_000008.11:g.143991836C>G, NC_000008.11:g.143992685C>T, NC_000001.11:g.236553659C>T, NC_000001.11:g.2365546261>C, NC_000001.11:g.236555893A>G, NC_000001.11:g.236555959G>A, NC_000001.11:g.2365852051>C, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000001.11:g.45338378C>T, NC_000007.14:g.2251050C>T, NC_000008.11:g.1439852571>C, NC_000008.11:g.143985944G>A, NC_000011.10:g.60903374C>T, NC_000003.12:g.47083895G>A, NC_000019.10:g.219731161>C, NC_000022.11:g.41152004A>G, NC_000017.11:g.21416533C>T, NC_000017.11:g.21416556G>A, NC_000010.11:g.1294674011>G, and NC_000007.14:g.77040016_77040017delinsCA, further preferably chosen from NC_000001.11:g.236555959G>A, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000007.14:g.2251050C>T, NC_000022.11:g.41152004A>G, NC_000010.11:g.129467401T>G, particularly NC_000003.12:g.37012077A>G or NC_000022.11:g.41152004A>G, as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature, and/or a gene mutational burden in at least one gene selected from the group consisting of MLH1, DDX11, PRPF19, BTK, GTF2H4, KAT6A, TDG, MAP3K1, and SLC9A4, particularly in MLH1, and/or a tumor mutational burden (TMB).
According to certain embodiments, a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene selected from MLH1, EP300, BRCA2, EPHA2,FLT1, GRINA, HEATR1, NUDT1, PARP10, PRPF19, KCNJ12, SETD2, and ZNF208, preferably HEATR1, MLH1, EP300, and NUDT1, particularly preferably MLH1 and/or EP300, is indicative of an enhanced radiotherapy success in a method of treating a lung cancer of a patient.
According to certain embodiments, a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene selected from DDX11, GEN1, MUTYH, MGMT, and PMS2P9, preferably MGMT, is indicative of a reduced radiotherapy success in a method of treating a lung cancer of a patient.
According to certain embodiments, a change in the nucleotide sequence chosen from rs1801406, rs3754334, rs7993418, rs67714660, rs56261297, rs2275685, rs2275687, rs1885533, rs1885534, rs1006456, rs2794763, rs1799977, rs1062492, rs11136344, rs11136345, rs2240045, rs4082155, rs10425763, rs20551, rs72846670, and/or rs73979902, preferably rs1885534, rs2794763, rs1799977, rs1062492, and/or rs20551, further preferably rs1799977 and/or rs20551, is indicative of an enhanced radiotherapy success in a method of treating a lung cancer of a patient, and/or a change in the nucleotide sequence chosen from rs1046456, rs16981869, rs72407975, rs3219472, rs16906255, and/or rs73144285, preferably rs16906255, is indicative of a reduced radiotherapy success in a method of treating a lung cancer of a patient.
According to certain embodiments, a change in the nucleotide sequence chosen from NC_000013.11:g.32337751A>G, NC_000001.11:g.16125272G>A, NC_000013.11:g.28308924G>A, NC_000008.11:g.143991836C>G, NC_000008.11:g.143992685C>T, NC_000001.11:g.236553659C>T, NC_000001.11:g.2365546261>C, NC_000001.11:g.236555893A>G, NC_000001.11:g.236555959G>A, NC_000001.11:g.2365852051>C, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000007.14:g.2251050C>T, NC_000008.11:g.1439852571>C, NC_000008.11:g.143985944G>A, NC_000011.10:g.60903374C>T, NC_000003.12:g.47083895G>A, NC_000019.10:g.219731161>C, NC_000022.11:g.41152004A>G, NC_000017.11:g.21416533C>T, and/or NC_000017.11:g.21416556G>A, preferably NC_000001.11:g.236555959G>A, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000007.14:g.2251050C>T, and/or NC_000022.11:g.41152004A>G, further preferably NC_000003.12:g.37012077A>G and/or NC_000022.11:g.41152004A>G, as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature, is indicative of an enhanced radiotherapy success in a method of treating a lung cancer of a patient, and/or a change in the nucleotide sequence chosen from NC_000012.12:g.31101061C>T, NC_000002.12:g.17764976A>G, NC_000002.12:g.17768673_17768675delTAA, NC_000001.11:g.45338378C>T, NC_000010.11:g.1294674011>G, and/or NC_000007.14:g.77040016_77040017delinsCA, preferably NC_000010.11:g.129467401T>G, as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature, is indicative of a reduced radiotherapy success in a method of treating a lung cancer of a patient.
According to certain embodiments the cancer is a lung squamous cell carcinoma, wherein the change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number, particularly a change in the nucleotide sequence compared to a reference sequence, is determined in at least one gene selected from the group consisting of BRCA2, DDX11, EPHA2, FLT1, GEN1, GRINA, HEATR1, MLH1, MUTYH, NUDT1, PARP10, PRPF19, SETD2, and ZNF208, particularly MLH1, and/or wherein the gene mutational burden is determined in at least one gene selected from the group consisting of APOLD1, DDX11, GEN1, GPER1, MLH1, POLB, and PRPF19, preferably DDX11, MLH1, and PRPF19, particularly MLH1, and/or wherein the tumor mutational burden is determined.
According to certain embodiments the cancer is a lung squamous cell carcinoma, wherein at least one change in the nucleotide sequence is determined chosen from rs1801406, rs1046456, rs3754334, rs7993418, rs16981869, rs72407975, rs67714660, rs56261297, rs2275685, rs2275687, rs1885533, rs1885534, rs1006456, rs2794763, rs1799977, rs3219472, rs1062492, rs11136344, rs11136345, rs2240045, rs4082155, and rs10425763, preferably rs1885534, rs2794763, rs1799977, rs1062492, particularly rs1799977, as disclosed in the Single Nucleotide Polymorphism Database dbSNP of the National Center for Biotechnology Information (NCBI), and/or wherein the gene mutational burden is determined in at least one gene selected from the group consisting of APOLD1, DDX11, GEN1, GPER1, MLH1, POLB, and PRPF19, preferably DDX11, MLH1, and PRPF19, particularly MLH1, and/or wherein the tumor mutational burden is determined.
According to certain embodiments the cancer is a lung squamous cell carcinoma, wherein at least one change in the nucleotide sequence is determined chosen from NC_000013.11:g.32337751A>G, NC_000012.12:g.31101061C>T, NC_000001.11:g.16125272G>A, NC_000013.11:g.28308924G>A, NC_000002.12:g.17764976A>G, NC_000002.12:g.17768673_17768675delTAA, NC_000008.11:g.143991836C>G, NC_000008.11:g.143992685C>T, NC_000001.11:g.236553659C>T, NC_000001.11:g.2365546261>C, NC_000001.11:g.236555893A>G, NC_000001.11:g.236555959G>A, NC_000001.11:g.2365852051>C, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000001.11:g.45338378C>T, NC_000007.14:g.2251050C>T, NC_000008.11:g.1439852571>C, NC_000008.11:g.143985944G>A, NC_000011.10:g.60903374C>T, NC_000003.12:g.47083895G>A, NC_000019.10:g.219731161>C, preferably NC_000001.11:g.236555959G>A, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000007.14:g.2251050C>T, particularly NC_000003.12:g.37012077A>G, as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature, and/or wherein the gene mutational burden is determined in at least one gene selected from the group consisting of APOLD1, DDX11, GEN1, GPER1, MLH1, POLB, and PRPF19, preferably DDX11, MLH1, and PRPF19, particularly MLH1, and/or wherein the tumor mutational burden is determined.
According to certain embodiments, a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene selected from MLH1, BRCA2, EPHA2, FLT1, GRINA, HEATR1, NUDT1, PARP10, PRPF19, SETD2, and ZNF208, preferably HEATR1, MLH1, and NUDT1, particularly preferably MLH1, is indicative of an enhanced radiotherapy success in a method of treating a lung squamous cell carcinoma of a patient.
According to certain embodiments, a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene selected from DDX11, GEN1, and MUTYH, is indicative of a reduced radiotherapy success in a method of treating a lung squamous cell carcinoma of a patient.
According to certain embodiments a change in the nucleotide sequence chosen from rs1801406, rs3754334, rs7993418, rs67714660, rs56261297, rs2275685, rs2275687, rs1885533, rs1885534, rs1006456, rs2794763, rs1799977, rs1062492, rs11136344, rs11136345, rs2240045, rs4082155, and/or rs10425763, preferably rs1885534, rs2794763, rs1799977, and/or rs1062492, further preferably rs1799977, is indicative of an enhanced radiotherapy success in a method of treating a lung squamous cell carcinoma of a patient, and/or a change in the nucleotide sequence chosen from rs1046456, rs16981869, rs72407975, and/or rs3219472 is indicative of a reduced radiotherapy success in a method of treating a lung squamous cell carcinoma of a patient.
According to certain embodiments a change in the nucleotide sequence chosen from NC_000013.11:g.32337751A>G, NC_000001.11:g.16125272G>A, NC_000013.11:g.28308924G>A, NC_000008.11:g.143991836C>G, NC_000008.11:g.143992685C>T, NC_000001.11:g.236553659C>T, NC_000001.11:g.2365546261>C, NC_000001.11:g.236555893A>G, NC_000001.11:g.236555959G>A, NC_000001.11:g.2365852051>C, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000007.14:g.2251050C>T, NC_000008.11:g.1439852571>C, NC_000008.11:g.143985944G>A, NC_000011.10:g.60903374C>T, NC_000003.12:g.47083895G>A, and/or NC_000019.10:g.21973116T>C, preferably NC_000001.11:g.236555959G>A, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, and/or NC_000007.14:g.2251050C>T, particularly NC_000003.12:g.37012077A>G, as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature, is indicative of an enhanced radiotherapy success in a method of treating a lung squamous cell carcinoma of a patient, and/or a change in the nucleotide sequence chosen from NC_000012.12:g.31101061C>T, NC_000002.12:g.17764976A>G, NC_000002.12:g.17768673_17768675delTAA, and/or NC_000001.11:g.45338378C>T, as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature, is indicative of a reduced radiotherapy success in a method of treating a lung squamous cell carcinoma of a patient.
According to certain embodiments a combination of at least two changes in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number within one gene or in at least two genes, particularly at least two changes in the nucleotide sequences above, and/or a combination of gene mutational burdens in at least two genes, and/or a combination of at least one change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene, particularly at least one change in the nucleotide sequence above, and a gene mutational burden in at least one gene that is the same or different thereof, and/or at least one change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene, particularly at least one change in the nucleotide sequence above, and the tumor mutational burden, and/or a gene mutational burden in at least one gene and the tumor mutational burden, particularly at least one change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene, particularly at least one change in the nucleotide sequence above, and the tumor mutational burden, are determined.
A second aspect of embodiments of the present invention is directed to a use of a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene selected from MLH1, EP300, BRCA2, FLT1, GRINA, HEATR1, MUTYH, NUDT1, PARP10, PRPF19, KCNJ12, MGMT, PMS2P9, DDX11, EPHA2, GEN1, SETD2, and ZNF208, preferably MLH1, EP300, HEATR1, NUDT1, and MGMT, further preferably MLH1 and EP300, particularly a change in the nucleotide sequence chosen from rs1801406, rs1046456, rs3754334, rs7993418, rs16981869, rs72407975, rs67714660, rs56261297, rs2275685, rs2275687, rs1885533, rs1885534, rs1006456, rs2794763, rs1799977, rs3219472, rs1062492, rs11136344, rs11136345, rs2240045, rs4082155, rs10425763, rs20551, rs72846670, rs73979902, rs16906255, and/or rs73144285, as disclosed in the Single Nucleotide Polymorphism Database dbSNP of the National Center for Biotechnology Information (NCBI), and/or NC_000007.14:g.77040016_77040017delinsCA, as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature, preferably rs1885534, rs2794763, rs1799977, rs1062492, rs20551, rs16906255, particularly rs1799977 and/or rs20551, as disclosed in the Single Nucleotide Polymorphism Database dbSNP of the National Center for Biotechnology Information (NCBI), further particularly at least one change in the nucleotide sequence chosen from NC_000013.11:g.32337751A>G, NC_000012.12:g.31101061C>T, NC_000001.11:g.16125272G>A, NC_000013.11:g.28308924G>A, NC_000002.12:g.17764976A>G, NC_000002.12:g.17768673_17768675delTAA, NC_000008.11:g.143991836C>G, NC_000008.11:g.143992685C>T, NC_000001.11:g.236553659C>T, NC_000001.11:g.2365546261>C, NC_000001.11:g.236555893A>G, NC_000001.11:g.236555959G>A, NC_000001.11:g.2365852051>C, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000001.11:g.45338378C>T, NC_000007.14:g.2251050C>T, NC_000008.11:g.1439852571>C, NC_000008.11:g.143985944G>A, NC_000011.10:g.60903374C>T, NC_000003.12:g.47083895G>A, NC_000019.10:g.219731161>C, NC_000022.11:g.41152004A>G, NC_000017.11:g.21416533C>T, NC_000017.11:g.21416556G>A, NC_000010.11:g.1294674011>G, and NC_000007.14:g.77040016_77040017delinsCA, further preferably chosen from NC_000001.11:g.236555959G>A, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000007.14:g.2251050C>T, NC_000022.11:g.41152004A>G, NC_000010.11:g.129467401T>G, particularly NC_000003.12:g.37012077A>G or NC_000022.11:g.41152004A>G, as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature, and/or a gene mutational burden in at least one gene selected from the group consisting of MLH1, DDX11, APOLD1, GEN1, GPER1, POLB, PRPF19, ANKRD30A, AR, BTK, GTF2H4, HEATR1, IKBKE, KAT6A, NUDT19, PARP1, PTPRT, TAF3, TDG, MAP3K1, and SLC9A4, preferably MLH1, DDX11, PRPF19, BTK, GTF2H4, KAT6A, TDG, MAP3K1, and SLC9A4, further preferably MLH1, and/or a tumor mutational burden (TMB), as a marker for predicting a radiotherapy success in a method of treating a lung cancer of a patient.
According to certain embodiments the second aspect is directed to the use of a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of at least one gene selected from MLH1, EP300, BRCA2, FLT1, GRINA, HEATR1, MUTYH, NUDT1, PARP10, PRPF19, KCNJ12, MGMT, PMS2P9, DDX11, EPHA2, GEN1, SETD2, and ZNF208, preferably MLH1, EP300, HEATR1, NUDT1, and MGMT, further preferably MLH1 and EP300, particularly a change in the nucleotide sequence chosen from rs1801406, rs1046456, rs3754334, rs7993418, rs16981869, rs72407975, rs67714660, rs56261297, rs2275685, rs2275687, rs1885533, rs1885534, rs1006456, rs2794763, rs1799977, rs3219472, rs1062492, rs11136344, rs11136345, rs2240045, rs4082155, rs10425763, rs20551, rs72846670, rs73979902, rs16906255, and/or rs73144285, as disclosed in the Single Nucleotide Polymorphism Database dbSNP of the National Center for Biotechnology Information (NCBI), and/or NC_000007.14:g.77040016_77040017delinsCA as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature, preferably rs1885534, rs2794763, rs1799977, rs1062492, rs20551, rs16906255, particularly rs1799977 and/or rs20551, as disclosed in the Single Nucleotide Polymorphism Database dbSNP of the National Center for Biotechnology Information (NCBI), further particularly at least one change in the nucleotide sequence chosen from NC_000013.11:g.32337751A>G, NC_000012.12:g.31101061C>T, NC_000001.11:g.16125272G>A, NC_000013.11:g.28308924G>A, NC_000002.12:g.17764976A>G, NC_000002.12:g.17768673_17768675delTAA, NC_000008.11:g.143991836C>G, NC_000008.11:g.143992685C>T, NC_000001.11:g.236553659C>T, NC_000001.11:g.2365546261>C, NC_000001.11:g.236555893A>G, NC_000001.11:g.236555959G>A, NC_000001.11:g.2365852051>C, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000001.11:g.45338378C>T, NC_000007.14:g.2251050C>T, NC_000008.11:g.1439852571>C, NC_000008.11:g.143985944G>A, NC_000011.10:g.60903374C>T, NC_000003.12:g.47083895G>A, NC_000019.10:g.219731161>C, NC_000022.11:g.41152004A>G, NC_000017.11:g.21416533C>T, NC_000017.11:g.21416556G>A, NC_000010.11:g.1294674011>G, and NC_000007.14:g.77040016_77040017delinsCA, further preferably chosen from NC_000001.11:g.236555959G>A, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000007.14:g.2251050C>T, NC_000022.11:g.41152004A>G, NC_000010.11:g.129467401T>G, particularly NC_000003.12:g.37012077A>G or NC_000022.11:g.41152004A>G, as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature, as a marker for predicting a radiotherapy success in a method of treating a lung cancer of a patient.
For the use of the second aspect also the differentiations regarding the specific genes and sequences with regard to treating LUSC and/or with regard to enhanced/reduced radiotherapy success, etc., given with regard to the first aspect apply.
A third aspect of embodiments of the present invention relates to a database comprising the markers disclosed in the second aspect. The database particularly comprises at least one change each in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number of the genes MLH1 and EP300, preferably MLH1, EP300, HEATR1, NUDT1, and MGMT, further preferably of the genes MLH1, EP300, BRCA2, FLT1, GRINA, HEATR1, MUTYH, NUDT1, PARP10, PRPF19, KCNJ12, MGMT, PMS2P9, DDX11, EPHA2, GEN1, SETD2, and ZNF208. According to certain embodiments, the database of the third aspect particularly comprises the changes in the nucleotide sequence rs1799977 and rs20551, as disclosed in the Single Nucleotide Polymorphism Database dbSNP of the National Center for Biotechnology Information (NCBI), preferably rs1885534, rs2794763, rs1799977, rs1062492, rs20551, and rs16906255, further preferably rs1801406, rs1046456, rs3754334, rs7993418, rs16981869, rs72407975, rs67714660, rs56261297, rs2275685, rs2275687, rs1885533, rs1885534, rs1006456, rs2794763, rs1799977, rs3219472, rs1062492, rs11136344, rs11136345, rs2240045, rs4082155, rs10425763, rs20551, rs72846670, rs73979902, rs16906255, and rs73144285, as disclosed in the Single Nucleotide Polymorphism Database dbSNP of the National Center for Biotechnology Information (NCBI), and NC_000007.14:g.77040016_77040017delinsCA, as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature, as markers. According to certain embodiments, the database of the third aspect further particularly comprises the changes in the nucleotide sequence NC_000003.12:g.37012077A>G and NC_000022.11:g.41152004A>G, preferably NC_000001.11:g.236555959G>A, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000007.14:g.2251050C>T, NC_000022.11:g.41152004A>G, NC_000010.11:g.129467401T>G, further preferably NC_000013.11:g.32337751A>G, NC_000012.12:g.31101061C>T, NC_000001.11:g.16125272G>A, NC_000013.11:g.28308924G>A, NC_000002.12:g.17764976A>G, NC_000002.12:g.17768673_17768675delTAA, NC_000008.11:g.143991836C>G, NC_000008.11:g.143992685C>T, NC_000001.11:g.236553659C>T, NC_000001.11:g.2365546261>C, NC_000001.11:g.236555893A>G, NC_000001.11:g.236555959G>A, NC_000001.11:g.2365852051>C, NC_000001.11:g.2365863491>C, NC_000003.12:g.37012077A>G, NC_000001.11:g.45338378C>T, NC_000007.14:g.2251050C>T, NC_000008.11:g.1439852571>C, NC_000008.11:g.143985944G>A, NC_000011.10:g.60903374C>T, NC_000003.12:g.47083895G>A, NC_000019.10:g.219731161>C, NC_000022.11:g.41152004A>G, NC_000017.11:g.21416533C>T, NC_000017.11:g.21416556G>A, NC_000010.11:g.1294674011>G, and NC_000007.14:g.77040016_77040017delinsCA, as complied to the Human Genome Variation Society (HGVS; https://www.hgvs.org/) variant nomenclature, as markers. In addition, the database of the third aspect comprises according to certain embodiments a gene mutational burden in the gene MLH1, preferably in the genes MLH1, DDX11, PRPF19, BTK, GTF2H4, KAT6A, TDG, and MAP3K1, further preferably in the genes MLH1, DDX11, APOLD1, GEN1, GPER1, POLB, PRPF19, ANKRD30A, AR, BTK, GTF2H4, HEATR1, IKBKE, KAT6A, NUDT19, PARP1, PTPRT, TAF3, TDG, MAP3K1, and SLC9A4, as markers. In addition, the database of the third aspect comprises according to certain embodiments at least one tumor mutational burden (TMB) as a marker.
Apart from containing the markers, the database is not particularly restricted, and can be e.g. machine readable, etc.
A fourth aspect of embodiments of the present invention relates to a method of predicting a radiotherapy success in a method of treating a lung cancer of a patient, comprising:
obtaining nucleotide sequence data and/or epigenomics profile data and/or expression profile data and/or copy number data of a sample of a patient, and
determining the presence of at least one marker as disclosed in the second aspect.
In the method of the fourth aspect the step of obtaining nucleotide sequence data and/or epigenomics profile data and/or expression profile data and/or copy number data of a sample of a patient is not particularly restricted, and the respective data can be obtained by any suitable method, particularly in electronic form, e.g. based on an evaluation of a sample of a patient.
Also, the step of determining the presence of at least one marker as disclosed in the second aspect is not particularly restricted, and it can be carried out in any suitable manner, e.g. using sufficient tools for automatically evaluating the nucleotide sequence data and/or epigenomics profile data and/or expression profile data and/or copy number data of the sample of the patient for the presence of the respective marker(s). For example, the evaluation can be carried out with the help of or exclusively using a computer program product, which is not particularly restricted.
In
In a fifth aspect a computer program product is disclosed, comprising computer executable instructions which, when executed, perform a method according to the fourth aspect.
The computer program product is thereby not particularly restricted as long as it comprises computer executable instructions which, when executed, perform the method according to the fourth aspect. According to certain embodiments the computer program product is one on which program commands or program codes of a computer program for executing said method are stored. According to certain embodiments the computer program product is or comprises a storage medium.
A sixth aspect of embodiments of the present invention relates to a method of predicting a radiotherapy success in a method of treating a lung cancer of a patient, comprising:
obtaining nucleotide sequence data and/or epigenomics profile data and/or expression profile data and/or copy number data of a sample of a patient, and
analyzing the nucleotide sequence data using the computer program product of the fifth aspect.
Again, the step of obtaining nucleotide sequence data and/or epigenomics profile data and/or expression profile data and/or copy number data of a sample of a patient is not particularly restricted, as in the fourth aspect.
Also, the step of analyzing the nucleotide sequence data using the computer program product of the fifth aspect is not particularly restricted and can be carried out e.g. automatically, but also with the settings of suitable parameters by a user.
The inventors have also particularly found that a change in the nucleotide sequence and/or epigenomics profile and/or expression profile and/or copy number compared to a reference sequence and/or epigenomics profile and/or expression profile and/or copy number in a DNA damage repair gene can be predictive of a radiotherapy success in a method of treating a cancer of a patient, particularly a lung cancer.
Particularly this was found for the following DNA damage repair genes:
UNG, SMUG1, MBD4, TDG, OGG1, MUTYH, NTHL1, MPG, NEIL1, NEIL2, NEIL3, APEX1, APEX2, LIG3, XRCC1, PNKP, APLF, PARP1, PARP2, PARP3, MGMT, ALKBH2, ALKBH3, TDP1, TDP2, MSH2, MSH3, MSH6, MLH1, PMS2, MSH4, MSH5, MLH3, PMS1, PMS2P3, XPC, RAD23B, CETN2, RAD23A, XPA, DDB1, DDB2, RPA1, RPA2, RPA3, ERCC3, ERCC2, GTF2H1, GTF2H2, GTF2H3, GTF2H4, GTF2H5, CDK7, CCNH, MNAT1, ERCC5, ERCC1, ERCC4, LIG1, ERCC8, ERCC6, UVSSA, XAB2, MMS19, RAD51, RAD51B, RAD51D, DMC1, XRCC2, XRCC3, RAD52, RAD54L, RAD54B, BRCA1, SHFM1, RAD50, MRE11A, NBN, RBBP8, MUS81, EME1, EME2, SLX1A, SLX1B, GEN1, FANCA, FANCB, FANCC, BRCA2, FANCD2, FANCE, FANCF, FANCG, FANCI, BRIP1, FANCL, FANCM, PALB2, RAD51C, SLX4, FAAP20, FAAP24, XRCC6, XRCC5, PRKDC, LIG4, XRCC4, DCLRE1C, NHEJ1, NUDT1, DUT, RRM2B, POLB, POLG, POLD1, POLE, PCNA, REV3L, MAD2L2, REV1, POLH, POLI, POLQ, POLK, POLL, POLM, POLN, FEN1, FAN1, TREX1, TREX2, EXO1, APTX, SP011, ENDOV, UBE2A, UBE2B, RAD18, SHPRH, HLTF, RNF168, SPRTN, RNF8, UBE2V2, UBE2N, H2AFX, CHAF1A, SETMAR, BLM, WRN, RECQL4, ATM, MPLKIP, DCLRE1A, DCLRE1B, RPA4, PRPF19, RECQL, RECQL5, HELQ, RDM1, NABP2, ATR, ATRIP, MDC1, RAD1, RAD9A, HUS1, RAD17, CHEK1, CHEK2, TP53, TP53BP1, RIF1, TOPBP1, CLK2, PER1, and RNF4.
The DNA damage repair genes therein can be e.g. grouped as follows:
Base excision repair (BER) DNA glycosylases
Other BER and strand break joining factors
Poly(ADP-ribose) polymerase (PARP) enzymes that bind to DNA
Repair of DNA-topoisomerase crosslinks
Mismatch excision repair (MMR)
Nucleotide excision repair (NER)
TFIIH (Transcription factor II H)
Homologous recombination
Fanconi anemia
Non-homologous end-joining
Modulation of nucleotide pools
DNA polymerases (catalytic subunits)
Editing and processing nucleases
Ubiquitination and modification
Chromatin structure and modification
Genes defective in diseases associated with sensitivity to DNA damaging agents
Other identified genes with known or suspected DNA repair function
Other conserved DNA damage response genes
The above embodiments can be combined arbitrarily, if appropriate. Further possible embodiments and implementations of embodiments of the present invention comprise also combinations of features not explicitly mentioned in the foregoing or in the following with regard to the Examples of the present invention. Particularly, a person skilled in the art will also add individual aspects as improvements or additions to the respective basic form of the present invention.
Embodiments of the present invention will now be described in detail with reference to several examples thereof. However, these examples are illustrative and do not limit the scope of the present invention.
A cohort of 84 patients with non-small cell lung cancer (NSCLC) and different response rates to chemoradiotherapy was evaluated in a cross-validated evaluation. The therapy comprised neoadjuvant radiochemotherapy (RCT).
The samples of the cohort were fresh frozen tumor tissue. NSCLC samples were classified as either lung adenocarcinoma (LUAD), lung squamous cell carcinoma (LUSC), NSCLC neuroendocrine differentiation (NE), or NSCLC not otherwise specified (NOS). Patient samples were classified as either complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD), dependent on locoregional response to chemoradiation. LUSC samples were classified as either complete response (CR), partial response (PR), stable disease (SD), or progressive disease (PD), dependent on locoregional response to chemoradiation.
The exome of extracted sample DNA was enriched using Agilent's SureSelect Human All Exon V6+COSMIC hybridization capture kit and sequenced on an Illumina NextSeq 500 instrument.
Further information regarding the patients is given in Tables 1 (cancer stage), 2 (cancer subtype) and 3 (pathological response).
Small insertions, deletions, single nucleotide variants (SNVs), multiple nucleotide variants (MNVs), and replacements (in the following termed variants) were called using the Low Frequency Variant Detection tool from the Identify and Annotate Variants (WES)-Workflow of QIAGEN's
Biomedical Genomics Workbench 5.0.1.
The parameter setting was as follows:
In the following, the methods used to build models predicting radiotherapy response are described.
Three feature levels were included:
1. Small insertions, small deletions, single nucleotide variants (SNVs), multiple nucleotide variants (MNVs), and replacements (in the following termed variants)
2. Mutational burden in single genes (GMB)
3. Mutational burden in multiple genes (TMB)
Only informative GMB features (standard deviation across samples >0.5) and frequently occurring variants (variants occurring in at least 50% of samples in either response group) were considered.
Features with a p-value <0.05 calculated using either Fisher's exact tests (variants) or Wilcoxon rank-sum tests (GMB features) were selected. In the process, a bootstrapping procedure was applied to primarily select stable/recurrent features, i.e. features that repeatedly emerge in bootstrapping samples (features with p-value <0.05 in at least 80% of 50 bootstrap samples containing a random sample of 80% of all samples).
For the predictive models, both linear—in particular elastic-net regularized logistic regression (R package glmnet 2.0-16; alpha=0.99)—and non-linear—in particular random forest (R package randomForest 4.6-14; ntree=1000)—models were calculated predicting radiotherapy response. To evaluate the performance of predictive models, a 100-fold iterated 4-fold cross-validation procedure was applied.
The variants described in the following were found.
The variants are uniquely characterized by their Human Genome Variation Society (HGVS) nomenclature, Single Nucleotide Polymorphism Database (dbSNP) identifier, ClinVar identifier and/or Universal Protein Knowledgebase (UniProtKB) identifier (as of Apr. 25, 2019)
The following variants are specifically predictive for radiotherapy response in lung squamous cell carcinoma (LUSC) patients. The number and fraction of patients carrying the respective variant are split into those that do respond (CR and PR) and those that do not respond (SD and PD) to locoregional chemoradiation.
The following variants are predictive of radiotherapy response in the 84 non-small cell lung cancer (NSCLC) patients. The number and fraction of patients carrying the respective variant are split into those that do respond (CR and PR) and those that do not respond (SD and PD) to locoregional chemoradiation.
The mutational burden in a gene (GMB) has been calculated as the sum over all variants in that gene using the gene coordinates from Ensembl release 91. The following GMBs were found as predictive.
The GMB of the following genes is specifically predictive for radiotherapy response in LUSC patients:
The statistical significance of association with radiotherapy response is higher for the GMB of these genes.
The GMB of the following genes is specifically predictive for radiotherapy response in NSCLC—including LUSC—patients:
The statistical significance of association with radiotherapy response is higher for the GMB of these genes.
iii) TMB
The tumor mutational burden (TMB) has been calculated as the sum over all somatic variants in one of the following genomic territories
1. All genomic positions that are covered by the SureSelect Human All Exon V6+COSMIC r2 kit (Design ID: S07604715) of Agilent (65,724,874 bases)
2. All genomic positions that are covered by the NEOplus v2 RUO panel of NEO New Oncology (1,150,757 bases)
3. All genomic positions that are covered by both above kits (1,118,543 bases)
Somatic variants were defined as those that could not be found in the human reference sequence (build 38 patch release 7), the Common dbSNP database (build 150), the 1000 Genomes Project (all phase 3 individuals) or the HapMap Project (phase 3 populations ASW, CEU, CHB, CHD, GIH, HCB, JPT, LWK, MEX, MKK, TSI, YRI) using the Filter Somatic Variants (WES)-Workflow of QIAGEN's Biomedical Genomics Workbench 5.0.1.
Hence, the TMB is a natural number (including zero). To make a TMB value better comparable to a TMB value that has been calculated on a genomic territory of a different size, the TMB value can also be specified as the average number of somatic variants per mega base (1 MB) by dividing the TMB value by the size of the genomic territory and multiplying the resulting ratio by 1,000,000. In this case, the TMB is a positive rational number.
While this procedure exemplifies the calculation of the TMB, the TMB can also be calculated differently, e.g. considering only non-synonymous variants or using genomic territories different to those stated above.
The TMB value of each LUSC sample is overlaid (and randomly scattered along the x-axis for better separation) and colored according to treatment response. The TMB value has been calculated within the genomic territory described in above 3. To separate responding from non-responding samples, a cutoff value can be used. In the box plot a possible TMB cutoff value of 21.45648 per MB is drawn as dashed line. This cutoff results in a prediction accuracy
As an exemplary value, also a radiotherapy success prediction has been made for a combination of the above SNV in MLH1 combined with TMB.
The TMB cutoff value of 21.45648 per MB is drawn as dashed line to make the two figures easier to compare. The accuracy is
when using the presence or absence of the MLH1 variant as predictor. The accuracy increases to
when combining (OR logical operator) the presence or absence of the MLH1 variant with the TMB cutoff value. While this calculation exemplifies the combination of two features, the combination can also be done using a linear—e.g. logistic regression—or non-linear—e.g. Random Forest—statistical/machine learning method.
The following paragraphs contain the description of an exemplary further idea, which should not be interpreted in any way limiting the inventive concept:
Generally, so-called “omics technologies” can allow measuring the following parameters on a genomic scale:
variations in DNA (e.g. SNVs, MNVs, InDels, copy number, etc.),
changes in epigenomic profiles (e.g. DNA methylations, DNA hydroxymethylations, histone modifications, etc.),
changes in expression levels of RNAs and/or proteins, and the multitude of their modifications (e.g. RNA methylations, protein phosphorylations, etc.)
These variations, and features calculated from combinations of few and/or many variations, such as the tumor mutational burden (TMB), can be—positively or negatively—associated with radiotherapy success, and thus, may be leveraged in algorithms for predicting radiotherapy success. One challenge can be to prioritize those variations with respect to their impact on radiotherapy success and select those variations that are most relevant for a predictor of radiotherapy success.
Those variations could be preselected from the pool of variations in DNA and/or changes in epigenomic profiles and/or changes in RNA and/or protein expression levels (in the following termed biomolecule features) that reside in biomolecules that are known to be involved (as active component or its regulator) in the biological process of either DNA damage repair (e.g. MLH1, a tumor suppressor gene, taking a physiological role in DNA mismatch repair) and/or angiogenesis (e.g. EP300, a transcriptional coactivator, taking a physiological role in the stimulation of hypoxia-induced genes, e.g. VEGF).
Therefore, the biomolecule features can be selected in a way that
they are, separately or (linearly or nonlinearly) combined, predictive of therapy success and/or
they are also predictive of therapy success when using other treatment regimens of radiation and/or adjuvant therapy and/or
they are also predictive of other subtypes of lung cancer, other cancer types (e.g. breast cancer) and different tumor stages).
The (pre)selection of the biomolecule features that are known to be involved in either DNA damage response or angiogenesis, which are biological processes that are fundamental to the tissue's response to ionizing radiation, may enable the extraction of the most relevant biomolecule features required for predicting radiotherapy success and/or locoregional recurrence.
The biomolecule features
can be obtained from/measured in different biospecimens, such as tumor tissue, blood, urine, sputum, etc. and/or
can be measured by any technology (e.g. PCR, sequencing, hybridization-based) and/or
can be combined irrespective of being somatic (radiosensitivity/radioresistance of cancerous tissue) or germline (innate radiosensitivity).
While focusing on a defined set of biomolecules can reduce computation analysis time, ignoring features in biomolecules that are involved in biological processes not relevant to the tissue's response to ionizing radiation could facilitate to uncover relevant but otherwise not statistically significant features.
Since in this exemplary idea, the number of biomolecules to inspect may be reduced, clinicians may be able to interpret results faster. Furthermore, clinicians may be able to appraise the impact of relevant variations on radiotherapy success in the context of other biomolecules from the same biological process (e.g. by visualizing variations in the corresponding biological pathway).
| Number | Date | Country | Kind |
|---|---|---|---|
| 19192006.5 | Aug 2019 | EP | regional |
This application is the National Phase under 35 U.S.C. § 371 of International Application No. PCT/EP2020/071658, which has an international filing date of Jul. 31, 2020, and which designated the United States of America, and which claims priority to European Application No. EP 19192006.5, filed Aug. 16, 2019, the entire contents of each of which are incorporated herein by reference.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/EP2020/071658 | 7/31/2020 | WO |
