Patent Application
20230103419
The invention is directed to methods of using biomarkers present in a cancer patient's germline genome to predict the cancer patient's response to radiation.
Many patients experience a toxic response to anti-cancer radiation therapies, resulting in both short and long-term side effects from this therapy; however, it is difficult to predict whether or not a patient will have a toxic response to a therapy before administration.
According to the National Institutes of Health (NIH), the term “biomarker” is defined as “a characteristic that is objectively measured and evaluated as an indicator of normal biologic or pathogenic processes or pharmacological response to a therapeutic intervention” (Biomarkers Definitions Working Group, 2001, Clin. Pharmacol. Ther. 69:89-95)
The development of improved diagnostics based on the discovery of biomarkers has the potential to avoid toxicities resulting from radiation generally or from particular radiation regimens by allowing those likely to develop a toxic response to pursue another therapy or to take steps to prepare for the toxic side effects.
Technologies such as genomics, proteomics, and molecular imaging currently enable rapid, sensitive and reliable detection of specific gene mutations, expression levels of particular genes, and other molecular biomarkers. However, the clinical utilization of cancer biomarkers to predict response or toxicity remains largely unrealized because few cancer biomarkers have been discovered. For example, a recent review article states:
Further, there is a need in the art to identify biomarkers that have the ability to predict whether or not a patient is likely to have a toxic response, and a good or poor systemic response, to a given radiation regimen so that medical professionals can determine the best course of treatment prior to administration and patients can avoid toxic responses considering systemic response to such therapies. For example, there is a need in the art to identify biomarkers that will assist in predicting the toxicity of a given radiation regimen in a patient. Even if a patient would respond to such a therapy, if the therapy would be toxic to that patient, it would be helpful for doctors to know this in advance and take the likely toxicity response into consideration when determining whether a given radiation regimen is appropriate for a patient.
Thus, there is a particular need in the art to identify biomarkers that predict whether a patient is likely to experience toxic side effects, and response, to radiation therapy.
The invention is based, in part, on the discovery that cancer patients carrying one or more specified mutations in their genome may have a toxic response, or an altered systemic response (e.g., an increased risk of mortality, distant metastasis or biochemical relapse), to radiation therapy as compared to other cancer patients, for example, patients homozygous for the wild-type allele who do not experience a toxic response or an altered systemic response. The invention is also based, in part, on the discovery that cancer patients carrying one or more specified mutations in their genome may not experience a toxic response or an altered systemic response to radiation therapy as compared to other patients, for example, patients homozygous for the wild-type allele, who do experience a toxic response or an altered systemic response.
In certain aspects, the disclosure relates to a method of treating cancer comprising administering a radiation treatment to a patient identified as carrying or not carrying one or more mutations selected from:
In certain embodiments, the radiation treatment comprises hypofractionated or traditional radiation, and the patient is identified as carrying or not carrying one or more mutations selected from:
In certain embodiments, the radiation treatment comprises hypofractionated radiation, and the patient is identified as carrying or not carrying one or more mutations selected from:
In certain embodiments, the radiation treatment comprises conventionally fractionated (traditional) radiation, and the patient is identified as carrying or not carrying one or more mutations selected from:
In certain embodiments, the cancer is selected from adrenal cancer, anal cancer, bile duct cancer, bladder cancer, bone cancer, brain/CNS, basal cell skin cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, eye cancer, gallbladder cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumor (GIST), gastric cancer, glioma, glioblastoma, head and neck cancer, Hodgkin disease, Kaposi sarcoma, kidney cancer, laryngeal and hypopharyngeal cancer, leukemia, liver cancer, lymphoma, malignant mesothelioma, merkel cell carcinoma, metastatic urothelial carcinoma, multiple myeloma, myeloma, myelodysplastic syndrome, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroendocrine cancer, neuroblastoma, non-Hodgkin lymphoma, oral cavity and oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile cancer, pituitary tumors, prostate cancer, renal cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, squamous cell skin cancer, small intestine cancer, stomach cancer, testicular cancer, thymus cancer, thyroid cancer, uterine cancer, or vaginal cancer.
In certain embodiments, the cancer is sarcoma. In certain embodiments, the toxicity is wound toxicity. In certain embodiments, the patient has a tumor on a lower extremity.
In certain embodiments, radiation treatment comprises hypofractionated radiation or conventionally fractionated (traditional) radiation, and the patient is identified as carrying or not carrying one or more mutations selected from:
In certain embodiments, the radiation treatment comprises hypofractionated radiation, and the patient is identified as carrying or not carrying one or more mutations selected from:
In certain embodiments, the cancer is selected from adrenal cancer, anal cancer, bile duct cancer, bladder cancer, bone cancer, brain/CNS, basal cell skin cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, eye cancer, gallbladder cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumor (GIST), gastric cancer, glioma, glioblastoma, head and neck cancer, Hodgkin disease, Kaposi sarcoma, kidney cancer, laryngeal and hypopharyngeal cancer, leukemia, liver cancer, lymphoma, malignant mesothelioma, merkel cell carcinoma, metastatic urothelial carcinoma, multiple myeloma, myeloma, myelodysplastic syndrome, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroendocrine cancer, neuroblastoma, non-Hodgkin lymphoma, oral cavity and oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile cancer, pituitary tumors, prostate cancer, renal cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, squamous cell skin cancer, small intestine cancer, stomach cancer, testicular cancer, thymus cancer, thyroid cancer, uterine cancer, or vaginal cancer.
In certain embodiments, the cancer is prostate cancer.
In certain embodiments, the hypofractionated radiation regimen comprises administering from about 20 Gy to about 35 Gy radiation over about 5 to about 8 fractions. In certain embodiments, the hypofractionated radiation regimen comprises administering stereotactic body radiation therapy.
In certain embodiments, the radiation treatment comprises conventionally fractionated (traditional) radiation, and the patient is identified as carrying or not carrying one or more mutations selected from:
In certain embodiments, the patient is treated with conventionally fractionated (traditional) radiation, and the patient is identified as carrying or not carrying one or more mutations selected from:
In certain embodiments, the cancer is selected from adrenal cancer, anal cancer, bile duct cancer, bladder cancer, bone cancer, brain/CNS, basal cell skin cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, eye cancer, gallbladder cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumor (GIST), gastric cancer, glioma, glioblastoma, head and neck cancer, Hodgkin disease, Kaposi sarcoma, kidney cancer, laryngeal and hypopharyngeal cancer, leukemia, liver cancer, lymphoma, malignant mesothelioma, merkel cell carcinoma, metastatic urothelial carcinoma, multiple myeloma, myeloma, myelodysplastic syndrome, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroendocrine cancer, neuroblastoma, non-Hodgkin lymphoma, oral cavity and oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile cancer, pituitary tumors, prostate cancer, renal cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, squamous cell skin cancer, small intestine cancer, stomach cancer, testicular cancer, thymus cancer, thyroid cancer, uterine cancer, or vaginal cancer.
In certain embodiments, the cancer is prostate cancer.
In certain embodiments, the method is a reduced toxicity method and the toxicity is acute or long-term toxicity. In certain embodiments, the toxicity is GI and/or GU toxicity. In certain embodiments, the method results in less toxicity than a hypofractionated radiation regimen. In certain embodiments, the traditional radiation regimen comprises administering from about 40 Gy to about 60 Gy radiation over a period of about 5 to about 6 weeks. In certain embodiments, the traditional radiation regimen comprises administering from about 40 Gy to about 60 Gy radiation over about 15 to about 30 fractions. In certain embodiments, the radiation therapy is external beam radiation therapy.
In another aspect, the disclosure relates to a method of treating cancer comprising administering a radiation treatment to a cancer patient identified as carrying or not carrying one or more mutations in a SNP selected from: TREX1_rs11797, LIN28A_rs9438623, miR.34b.c.promoter_rs4938723, FCGR2A_rs1801274, CD274_rs4742098, IL.6_rs12700386, SPI1_rs2071304, ERCC4_rs4781562, IL10_rs3024496, IL.6_rs2069840, rs2187668, CD274_rs4143815, KRAS_rs61764370, SHC4_rs1062124, HAMP_rs1882694, rs922075, ERCC1_rs11615, EXO1_rs4150021, STAT4_rs7574070, and SOS1_rs1059313; wherein the presence or the absence of the SNP is associated with a reduced risk of distant failure in response to radiation therapy.
In certain embodiments, the patient is identified as carrying or not carrying one or more mutations in a SNP selected from:
In certain embodiments, the radiation therapy is hypofractionated radiation. In certain embodiments, the cancer is sarcoma.
In another aspect, the disclosure relates to a method of treating cancer in a cancer patient comprising administering a radiation treatment to a patient identified as carrying or not carrying one or more mutations in a SNP selected from: BIRC5_rs2239680, SMAD1_rs11724777, IL12A_rs568408, and IL13_rs20541, wherein the presence or the absence of the SNP is not associated with an increased risk of mortality.
In certain embodiments, the patient is identified as carrying or not carrying one or more mutations in a SNP selected from:
In certain embodiments, the radiation therapy is conventionally fractionated (traditional) radiation.
In another aspect, the disclosure relates to a method of treating cancer in a cancer patient comprising (i) administering a radiation treatment and a different cancer treatment or (ii) administering a different cancer treatment instead of radiation to a patient identified as carrying or not carrying one or more mutations in a SNP selected from: BIRC5_rs2239680, SMAD1_rs11724777, IL12A_rs568408, and IL13_rs20541, wherein the presence or the absence of the SNP is associated with an increased risk of mortality.
In certain embodiments, the patient is identified as carrying or not carrying one or more mutations in a SNP selected from:
In certain embodiments, the radiation therapy is conventionally fractionated (traditional) radiation. In certain embodiments, the different cancer treatment comprises brachytherapy, chemotherapy, androgen deprivation therapy, immunotherapy, high intensity focused ultrasound, cryotherapy, laser ablation, photodynamic therapy, or surgery.
In certain embodiments, the cancer is prostate cancer.
In another aspect, the disclosure relates to a method of treating cancer in a cancer patient comprising administering a radiation treatment and/or a different cancer treatment to a patient identified as carrying or not carrying one or more mutations in a SNP selected from: BMP2_rs3178250, SMAD1_rs11724777, IL10RB_rs2834167, CD274_rs822339, CD274_rs2282055, IL12A_rs568408, PARP1_rs8679, CD274_rs1411262, BMP2_rs1979855, and P2RX7_rs3751143, wherein the presence or the absence of the SNP is associated with an increased risk of (decreased time to) biochemical relapse or is associated with a reduced risk of biochemical relapse following radiation therapy.
In certain embodiments, if the patient is identified as carrying or not carrying one or more mutations in a SNP associated with increased risk of biochemical relapse and selected from:
In certain embodiments, if the patient is identified as carrying or not carrying one or more mutations in a SNP associated with decreased risk of biochemical relapse and selected from:
In certain embodiments, the radiation therapy is conventionally fractionated (traditional) radiation. In certain embodiments, the additional treatment or the different treatment is hypofractionated radiation, brachytherapy, chemotherapy, androgen deprivation therapy, immunotherapy, high intensity focused ultrasound, cryotherapy, laser ablation, photodynamic therapy, or surgery.
In certain embodiments, the cancer is prostate cancer.
In another aspect, the disclosure relates to a method of treating cancer in a cancer patient comprising administering a radiation treatment and/or a different cancer treatment to a patient identified as carrying or not carrying one or more mutations in a SNP selected from: IL8_rs4073, IL10_rs3024496_P1, BIRC5_rs2239680, RAC1_rs9374, IL10_rs3024496_P1.P2, BMP2_rs3178250, and NBN_rs1805794, wherein the presence or the absence of the SNP is associated with an increased risk of distant metastasis or is associated with a reduced risk of distant metastasis.
In certain embodiments, if the patient is identified as carrying or not carrying one or more of the following mutations in a SNP associated with increased risk of distant metastasis:
In certain embodiments, if the patient is identified as carrying or not carrying one or more of the following mutations in a SNP associated with decreased risk of distant metastasis:
In certain embodiments, the radiation therapy is conventionally fractionated (traditional) radiation. In certain embodiments, the additional treatment or the different treatment is hypofractionated radiation, brachytherapy, chemotherapy, androgen deprivation therapy, immunotherapy, high intensity focused ultrasound, cryotherapy, laser ablation, photodynamic therapy, or surgery.
In certain embodiments, the cancer is prostate cancer.
In another aspect, the disclosure relates to a method of treating prostate cancer in a patient comprising administering a radiation treatment to a patient identified as carrying or not carrying one or more mutations in a SNP selected from: HAMP_rs10421768, XRCC1_rs25487, IL.6_rs2069840, XRCC4_rs1040363, and IL19_rs2243158, wherein the presence or the absence of the SNP is not associated with a reduced risk of impotency.
In certain embodiments, the patient is identified as carrying or not carrying one or more mutations in a SNP selected from:
In certain embodiments, the radiation therapy is conventionally fractionated (traditional) radiation.
In another aspect, the disclosure relates to a method for determining the toxicity of a radiation treatment in a cancer patient comprising determining whether the patient carries one or more of the following mutations:
In certain embodiments, the method comprises determining whether the patient carries one or more of the following mutations:
In certain embodiments, the patient has a decreased likelihood of having a toxic response to the radiation treatment if the patient is carrying or not carrying one or more mutations selected from:
In certain embodiments, the patient has an increased likelihood of having a toxic response to the radiation treatment if the patient carries or does not carry one or more mutations selected from:
In certain embodiments, the cancer is selected from adrenal cancer, anal cancer, bile duct cancer, bladder cancer, bone cancer, brain/CNS, basal cell skin cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, eye cancer, gallbladder cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumor (GIST), gastric cancer, glioma, glioblastoma, head and neck cancer, Hodgkin disease, Kaposi sarcoma, kidney cancer, laryngeal and hypopharyngeal cancer, leukemia, liver cancer, lymphoma, malignant mesothelioma, merkel cell carcinoma, metastatic urothelial carcinoma, multiple myeloma, myeloma, myelodysplastic syndrome, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroendocrine cancer, neuroblastoma, non-Hodgkin lymphoma, oral cavity and oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile cancer, pituitary tumors, prostate cancer, renal cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, squamous cell skin cancer, small intestine cancer, stomach cancer, testicular cancer, thymus cancer, thyroid cancer, uterine cancer, or vaginal cancer. In certain embodiments, the cancer is sarcoma.
In certain embodiments, the radiation treatment comprises hypofractionated radiation. In certain embodiments, the toxicity comprises wound toxicity. In certain embodiments, the patient has a tumor on a lower extremity.
In certain embodiments, the method comprising determining whether the patient carries one or more mutations selected from:
In certain embodiments, the patient has a decreased likelihood of having a toxic response if the patient carries or does not carry one or more mutations selected from:
In certain embodiments, the patient has an increased likelihood of having a toxic response if the patient carries or does not carry one or more mutations selected from:
In certain embodiments, the radiation treatment is a hypofractionated radiation regimen. In certain embodiments, the hypofractionated radiation regimen comprises administering from about 20 Gy to about 35 Gy radiation over about 5 to about 8 fractions. In certain embodiments, the hypofractionated radiation regimen comprises administering stereotactic body radiation therapy.
In certain embodiments, the cancer is selected from adrenal cancer, anal cancer, bile duct cancer, bladder cancer, bone cancer, brain/CNS, basal cell skin cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, eye cancer, gallbladder cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumor (GIST), gastric cancer, glioma, glioblastoma, head and neck cancer, Hodgkin disease, Kaposi sarcoma, kidney cancer, laryngeal and hypopharyngeal cancer, leukemia, liver cancer, lymphoma, malignant mesothelioma, merkel cell carcinoma, metastatic urothelial carcinoma, multiple myeloma, myeloma, myelodysplastic syndrome, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroendocrine cancer, neuroblastoma, non-Hodgkin lymphoma, oral cavity and oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile cancer, pituitary tumors, prostate cancer, renal cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, squamous cell skin cancer, small intestine cancer, stomach cancer, testicular cancer, thymus cancer, thyroid cancer, uterine cancer, or vaginal cancer. In certain embodiments, the cancer is prostate cancer.
In certain embodiments, the patient has an decreased likelihood of having a toxic response if the patient carries or does not carry one or more mutations selected from:
In certain embodiments, the patient has an increased likelihood of having a toxic response if the patient carries or does not carry one or more mutations selected from:
In certain embodiments, the cancer is selected from adrenal cancer, anal cancer, bile duct cancer, bladder cancer, bone cancer, brain/CNS, basal cell skin cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, eye cancer, gallbladder cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumor (GIST), gastric cancer, glioma, glioblastoma, head and neck cancer, Hodgkin disease, Kaposi sarcoma, kidney cancer, laryngeal and hypopharyngeal cancer, leukemia, liver cancer, lymphoma, malignant mesothelioma, merkel cell carcinoma, metastatic urothelial carcinoma, multiple myeloma, myeloma, myelodysplastic syndrome, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroendocrine cancer, neuroblastoma, non-Hodgkin lymphoma, oral cavity and oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile cancer, pituitary tumors, prostate cancer, renal cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, squamous cell skin cancer, small intestine cancer, stomach cancer, testicular cancer, thymus cancer, thyroid cancer, uterine cancer, or vaginal cancer. In certain embodiments, the cancer is prostate cancer.
In certain embodiments, the toxicity is acute or long-term toxicity. In certain embodiments, the toxicity is GI and/or GU toxicity.
In certain embodiments, the radiation treatment is a conventionally fractionated (traditional) radiation regimen. In certain embodiments, the traditional radiation regimen comprises administering from about 40 Gy to about 60 Gy radiation over a period of about 5 to about 6 weeks. In certain embodiments, the traditional radiation regimen comprises administering from about 40 Gy to about 60 Gy radiation over about 15 to about 30 fractions. In certain embodiments, the radiation treatment is external beam radiation therapy.
In another aspect, the disclosure relates to a method for determining whether a cancer patient is likely to experience distant failure in response to radiation, the method comprising determining whether the patient carries one or more mutations selected from: TREX1_rs11797, LIN28A_rs9438623, miR.34b.c.promoter_rs4938723, FCGR2A_rs1801274, CD274_rs4742098, IL.6_rs12700386, SPI1_rs2071304, ERCC4_rs4781562, IL10_rs3024496, IL.6_rs2069840, rs2187668, CD274_rs4143815, KRAS_rs61764370, SHC4_rs1062124, HAMP_rs1882694, rs922075, ERCC1_rs11615, EXO1_rs4150021, STAT4_rs7574070, and SOS1_rs1059313.
In certain embodiments, the method comprises determining whether a patient is carrying or not carrying one or more mutations in a SNP selected from:
In certain embodiments, the radiation therapy is conventionally fractionated (traditional) radiation. In certain embodiments, the cancer is prostate cancer.
In another aspect, the disclosure relates to a method for determining whether a cancer patient has an increased or decreased risk of mortality (e.g., prostate cancer specific mortality) following a radiation treatment, the method comprising determining whether the patient carries one or more mutations selected from: BIRC5_rs2239680, SMAD1_rs11724777, IL12A_rs568408, and IL13_rs20541, and, wherein the presence or the absence of the SNP is associated with an increased or decreased risk of mortality.
In certain embodiments, the patient is identified as carrying or not carrying one or more mutations in a SNP selected from:
wherein the presence or the absence of the SNP is associated with a decreased risk of mortality.
In certain embodiments, the patient is identified as carrying or not carrying one or more mutations in a SNP selected from:
wherein the presence or the absence of the SNP is associated with an increased risk of mortality.
In certain embodiments, the radiation treatment is conventionally fractionated (traditional) radiation. In certain embodiments, the cancer is prostate cancer.
In another aspect, the disclosure relates to a method for determining whether a cancer patient has an increased or decreased risk of biochemical relapse following a radiation treatment, the method comprising determining whether the patient carries one or more mutations selected from: BMP2_rs3178250, SMAD1_rs11724777, IL10RB_rs2834167, CD274_rs822339, CD274_rs2282055, IL12A_rs568408, PARP1_rs8679, CD274_rs1411262, BMP2_rs1979855, and P2RX7_rs3751143.
In certain embodiments, the patient has an increased risk of biochemical relapse following a radiation treatment if the patient is identified as carrying or not carrying one or more mutations in a SNP selected from:
In certain embodiments, the patient has a decreased risk of biochemical relapse following a radiation treatment if the patient is identified as carrying or not carrying one or more of the following mutations in a SNP:
In certain embodiments, the radiation therapy is conventionally fractionated (traditional) radiation. In certain embodiments, the cancer is prostate cancer.
In another aspect, the disclosure relates to a method for determining whether a cancer patient has an increased or decreased risk of distant metastasis following a radiation treatment, the method comprising determining whether the patient is carrying or not carrying one or more mutations in a SNP selected from: IL8_rs4073, IL10_rs3024496_P1, BIRC5_rs2239680, RAC1_rs9374, IL10_rs3024496_P1.P2, BMP2_rs3178250, and NBN_rs1805794, wherein the presence or the absence of the SNP is associated with an increased or decreased risk of distant metastasis.
In certain embodiments, the patient has an increased risk of distant metastasis following a radiation treatment if the patient is identified as carrying or not carrying one or more mutations in a SNP selected from:
In certain embodiments, the patient has a decreased risk of distant metastasis following a radiation treatment if the patient is identified as carrying or not carrying one or more of the following mutations in a SNP:
In certain embodiments, the radiation therapy is conventionally fractionated (traditional) radiation. In certain embodiments, the cancer is prostate cancer.
In another aspect, the disclosure relates to a method for determining whether a prostate cancer patient has an increased or decreased risk of impotency following treatment with radiation, the method comprising determining whether the patient is carrying or not carrying one or more mutations in a SNP selected from: HAMP_rs10421768, XRCC1_rs25487, IL.6_rs2069840, XRCC4_rs1040363, and IL19_rs2243158.
In certain embodiments, the patient is identified as carrying or not carrying one or more mutations in a SNP associated with a increased risk of impotency following treatment with radiation and selected from:
In certain embodiments, the patient is identified as carrying or not carrying one or more mutations in a SNP associated with a decreased risk of impotency following treatment with radiation and selected from:
In certain embodiments, the radiation treatment is conventionally fractionated (traditional) radiation.
In another aspect, the disclosure relates to a method for determining the toxicity of a radiation treatment in a cancer patient, wherein the is patient identified as carrying or not carrying a G nucleotide at a position corresponding to position 101 of SEQ ID NO: 19 (IL.6_rs2069840).
In certain embodiments, the patient is further identified as carrying or not carrying an A nucleotide at a position corresponding to position 101 of SEQ ID NO: 69 (CD6_rs76677607). In certain embodiments, the patient is further identified as carrying or not carrying a C nucleotide at a position corresponding to position 101 of SEQ ID NO: 27 (CD274_rs4143815).
In certain embodiments, the patient is further identified as carrying or not carrying a G nucleotide at a position corresponding to position 101 of SEQ ID NO: 5 (SHC4_rs1062124). In certain embodiments, the patient is further identified as carrying or not carrying a C nucleotide at a position corresponding to position 101 of SEQ ID NO: 15 (ERCC1_rs3212948). In certain embodiments, the patient is further identified as carrying or not carrying an A nucleotide at a position corresponding to position 101 of SEQ ID NO: 117 (RAC1_rs9374). In certain embodiments, the method further comprises determining whether the patient has a lower extremity tumor. In certain embodiments, the patient is further identified as carrying or not carrying a T nucleotide at a position corresponding to position 101 of SEQ ID NO: 37 (UNGC.41.IL1RAP). In certain embodiments, the patient is further identified as carrying or not carrying a C nucleotide at a position corresponding to position 101 of SEQ ID NO: 27 (CD274_rs4143815). In certain embodiments, the patient is further identified as carrying or not carrying a C nucleotide at a position corresponding to position 101 of SEQ ID NO: 1 (miR.99a.promoter). In certain embodiments, the patient is further identified as carrying or not carrying an A nucleotide at a position corresponding to position 101 of SEQ ID NO: 13 (IL1A_rs1800587).
In certain embodiments, the method further comprises administering a reduced toxicity radiation treatment to the patient.
In certain embodiments, the cancer is selected from adrenal cancer, anal cancer, bile duct cancer, bladder cancer, bone cancer, brain/CNS, basal cell skin cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, eye cancer, gallbladder cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumor (GIST), gastric cancer, glioma, glioblastoma, head and neck cancer, Hodgkin disease, Kaposi sarcoma, kidney cancer, laryngeal and hypopharyngeal cancer, leukemia, liver cancer, lymphoma, malignant mesothelioma, merkel cell carcinoma, metastatic urothelial carcinoma, multiple myeloma, myeloma, myelodysplastic syndrome, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroendocrine cancer, neuroblastoma, non-Hodgkin lymphoma, oral cavity and oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile cancer, pituitary tumors, prostate cancer, renal cancer, retinoblastoma, rhabdomyosarcoma, salivary gland cancer, sarcoma, squamous cell skin cancer, small intestine cancer, stomach cancer, testicular cancer, thymus cancer, thyroid cancer, uterine cancer, or vaginal cancer. In certain embodiments, the cancer is sarcoma.
In certain embodiments, the toxicity is wound toxicity.
In certain embodiments, the radiation treatment comprises a hypofractionated radiation regimen. In certain embodiments, the hypofractionated radiation regimen comprises administering from about 20 Gy to about 35 Gy radiation over about 5 to about 8 fractions. In certain embodiments, the hypofractionated radiation regimen comprises administering stereotactic body radiation therapy.
In certain embodiments, the radiation treatment comprises a conventionally fractionated (traditional) radiation regimen. In certain embodiments, the traditional radiation regimen comprises administering from about 40 Gy to about 60 Gy radiation over a period of about 5 to about 6 weeks. In certain embodiments, the traditional radiation regimen comprises administering from about 40 Gy to about 60 Gy radiation over about 15 to about 30 fractions.
The invention is based, in part, on the discovery that a cancer patient carrying one or more specified mutations in their genome may exhibit increased or decreased toxicity to radiation therapy (e.g., hypofractionated radiation) as compared to other patients, e.g., wild-type patients. The mutations may commonly be referred to as single nucleotide polymorphisms or “SNPs,” and certain mutations disclosed herein are functional mutations that are present in the germline. The mutations are generally to a single nucleotide, for example, substitution of a nucleotide or deletion of a nucleotide, although in specific instances the mutations described herein relate to the deletion of more than one nucleotide.
In certain embodiments, the increased or decreased toxicity may comprise short-term (acute) toxicity. In certain embodiments, the increased or decreased toxicity may comprise long-term (late) toxicity. The terms short-term toxicity and acute toxicity are used interchangeably herein. The terms long-term and late or late-term toxicity are used interchangeably herein.
In certain embodiments, for example, when the cancer is sarcoma, the toxicity comprises wound toxicity. In certain embodiments, for example, when the cancer is prostate cancer, the toxicity comprises gastrointestinal (GI) and/or genitourinary (GU) toxicity.
The invention is based, in part, on the discovery that a cancer patient carrying one or more specified mutations in their genome may exhibit increased likelihood of experiencing mortality, biochemical relapse, or distant failure (i.e., distant metastasis) despite undergoing treatment with radiation therapy.
The mutations referred to herein include functional mutations that disrupt microRNA pathways, and include microRNA binding site mutations. A microRNA (miRNA) is a small non-coding RNA molecule containing about 22 nucleotides found in plants, animals, and some viruses that functions in RNA silencing and post-transcription regulation of gene expression. These functions are integral to miRNAs' role as critical stress response mediators, including mediating the immune and inflammatory response. DNA damage is also known to cause changes in the global profile of miRNA expression (Weidhaas et al. (2007) C
As described in the examples herein, several mutations have been identified as being pertinent to determining a cancer patient's likelihood of exhibiting a toxic response to a radiation treatment or to having an increased risk for mortality, distant failure or metastasis, or impotency. These mutations (also referred to herein as “markers,” “biomarkers,” or “variants”) are shown in SEQ ID NOS: 1, 3, 5, 7, 9, 11, 13, 15, 17, 19, 21, 23, 25, 27, 29, 31, 33, 35, 37, 39, 41, 43, 45, 47, 49, 51, 53, 55, 57, 59, 61, 63, 65, 67, 69, 71, 73, 75, 77, 79, 81, 83, 85, 87, 89, 91, 93, 95, 97, 99, 101, 103, 105, 107, 109, 111, 113, 115, 117, 119, 121, 123, 125, 127, 129, 131, 135, 137, 139, 141, 143, 145, 147, 149, 151, 153, 155, 157, 159, 161, 163, 165, 167, 169, 171, 173, 175, and 177 of
As the skilled artisan recognizes, double-stranded DNA comprises two strands of DNA, wherein each nucleotide of one strand is paired with its complementary nucleotide on the opposite strand. According to Watson-Crick base pairing for DNA, Adenine (A) pairs with Thymine (T), Thymine (T) pairs with Adenine (A), Guanine (G) pairs with Cytosine (C), and Cytosine (C) pairs with Guanine (G). DNA sequences are typically represented with an upper strand sequence oriented in a 5′ to 3′ direction, and a complementary lower strand oriented in a 3′ to 5′ direction. The 5′ to 3′ sequence of a complementary strand of DNA (e.g., a lower strand sequence) can be determined by reversing the direction of the sequence (e.g., an upper strand sequence) and interchanging each nucleotide with its complementary nucleotide, i.e., generating the reverse complement. Accordingly, in the following embodiments, it is understood that where a patient is identified as carrying or not carrying a mutation by detecting a variant allele occurring at position 101 of a given SEQ ID NO., that variant allele can equally be detected by detecting the complementary nucleotide of the variant allele on the opposite strand, i.e., the complementary nucleotide in the reverse complement sequence of that SEQ ID NO.
For example, where a C nucleotide at a position corresponding to position 101 of SEQ ID NO: 1 (miR.99a.promoter) can be detected, a G nucleotide at a corresponding position of the reverse complement sequence of SEQ ID NO: 1 can alternatively be detected. Stated another way, a C nucleotide at a position corresponding to position 101 of SEQ ID NO: 1 can be detected by detecting a G nucleotide at a corresponding position of the reverse complement sequence of SEQ ID NO: 1. As is understood by the skilled person, for each variant provided herein, the variant can alternatively be detected by detecting the complementary nucleotide at a corresponding position of the reverse complement sequence in which the variant is present.
The SNPs described herein can be detected to determine whether a cancer patient is likely to have a toxic response to radiation, or to having an increased risk for mortality, distant failure or metastasis, or impotency. Accordingly, the disclosure relates in part to a method for determining whether a cancer patient is likely to have a toxic response to radiation, or to having an increased risk for mortality, distant failure or metastasis, or impotency, the method comprising detecting a SNPs described herein, wherein the detection of a mutant (variant) or wild-type allele of a given SNP associated with a given outcome (e.g., a toxic response to radiation, or to having an increased risk for mortality, distant failure or metastasis, or impotency) is indicative of the patient's likelihood of having a toxic response to radiation, increased risk of mortality, increased risk of distant failure or metastasis, or increased risk of impotency.
The disclosure further relates to a method (e.g., a reduced toxicity method) of treating a cancer patient with radiation, the method comprising administering radiation to a patient, wherein the patient has been determined to carry one or more copies of a mutant (variant) or wild-type allele of a SNP described herein, wherein mutant (variant) or wild-type allele is associated with a lower risk of a toxic response, mortality, distant failure or metastasis, or impotency. In certain embodiments, the radiation is hypofractionated radiation, stereotactic body radiation treatment (SBRT), or traditional radiation. In certain embodiments, the cancer is sarcoma. In certain embodiments, the toxic response is wound toxicity. In certain embodiments, the cancer is prostate cancer. In certain embodiments, the toxic response is acute toxicity, late toxicity, gastrointestinal (GI) toxicity, or genitourinary toxicity (GU).
A biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is found in the promoter region of the human miR99a gene (“miR.99a.promoter”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs4848306 SNP found in the promoter region of the human interleukin 1beta (IL1B) gene (“IL1.B_rs4848306”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs1062124 SNP found in the 3′ UTR region of the human SHC adaptor protein 4 gene (“SHC4_rs1062124”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs17599026 SNP found in an intronic region of the human lysine demethylase 3B (KDM3B) gene (“rs17599026”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation, biochemical relapse and/or distant metastasis is the rs9374 SNP found in the 3′ UTR of the human Rac family small GTPase 1 gene (“RAC1_rs9374”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs1150258 SNP found in the human interleukin 24 (IL24) gene (“IL24_rs1150258”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs1800587 SNP found in the promoter region of the human interleukin 1 alpha (IL1A) gene (“IL1A_rs1800587”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs3212948 SNP found in an intronic region of the human ERCC excision repair 1, endonuclease non-catalytic subunit gene (“ERCC1_rs3212948”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation or exhibiting distant failure is the rs12700386 SNP found near the human interleukin-6 (IL-6) gene (“IL.6_rs12700386”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation, exhibiting distant failure, or is the rs2069840 SNP found near the human interleukin-6 (IL-6) gene (“IL.6_rs2069840”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs2306327 SNP found in the human calcium/calmodulin dependent protein kinase II gamma (CAMK2G) gene (“CAMK2G_rs2306327”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs1980499 SNP found upstream of the human bone morphogenetic protein 2 (BMP2) gene (“BMP2_rs1980499”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs1460008 SNP found in the 3′ UTR region of the human Epiregulin (EREG) gene (“EREG_rs1460008”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation or exhibiting distant failure is the rs4143815 SNP found in the human CD274 gene (“CD274_rs4143815”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs7787 SNP found in the 3′ UTR of the human interleukin 17D (IL17D) gene (“IL17D_rs7787”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation or biochemical relapse following a radiation treatment is the rs3744483SNP found in the 3′ UTR of the human signal transducer and activator of transcription 3 (STAT3) gene (“STAT3_rs3744483”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs17563 SNP found in the human bone morphogenetic protein 4 (BMP4) gene (“BMP4_rs17563”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs10488631 SNP found in the interferon regulatory factor 5 (IRF5) gene (“IRF5_rs10488631”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the UNGC.41.IL1RAP SNP found in the non-coding region of the human ILRAP gene (“UNGC.41.IL1RAP”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs1798 SNP found in the 3′ UTR of the human interleukin-19 (IL19) gene (“IL19_rs1798”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs8240 SNP found in the human RAD23 homolog A, nucleotide excision repair protein gene (“RAD23A_rs8240”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs15869 SNP found in the 3′ UTR of the human BRCA2 DNA repair associated gene (“BRCA2_rs15869”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation or to experiencing a shorter time to biochemical relapse is the rs2282055 SNP found in an intronic region of the human CD274 gene (“CD274_rs2282055”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation or likelihood of biochemical relapse following a radiation treatment is the rs3178250 SNP found in the 3′ UTR of the human bone morphogenetic protein (BMP2) gene (“BMP2_rs3178250”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation and likelihood of biochemical relapse following radiation treatment is the rs1411262 SNP found in an intronic region of the human CD274 gene (“CD274_rs1411262”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation and likelihood of biochemical relapse following radiation treatment is the rs2297136 SNP found in an intronic region of the human CD274 gene (“CD274_rs2297136”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation or likelihood of biochemical relapse is the rs1979855 SNP found upstream of the human bone morphogenetic protein (BMP2) gene (“BMP2_rs1979855”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs1893217 SNP found upstream of the human protein tyrosine phosphatase non-receptor type 2 (PTPN2) gene (“PTPN2_rs1893217”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation and likelihood of biochemical relapse following a radiation treatment is the rs822339 SNP found in an intronic region of the human CD274 gene (“CD274_rs822339”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs6695772 SNP found upstream of the human basic leucine zipper ATF-like transcription factor 3 (BATF3) gene (“BATF3_rs6695772”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs11465660 SNP found in the 3′ UTR of the interleukin-18 receptor 1 (IL18R1) gene (“IL18R1_rs11465660”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs11615 SNP found in the 3′ UTR of the ERCC excision repair 1, endonuclease non-catalytic subunit (ERCC1) gene (“ERCC1_rs11615”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs11991 SNP found in the 3′ UTR of the ABL proto-oncogene 1, non-receptor tyrosine kinase (ABL1) gene (“ABL1_rs11991”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs189037 SNP found in the noncoding transcript of the human ATM gene (“ATM_rs189037”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs76677607 SNP found in the 3′ UTR of the human CD6 gene (“CD6_rs76677607” or “CD6_rs76677607”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs17561 SNP, which causes a missense mutation in the coding sequence of the human interleukin 1 alpha (IL1A) gene (“IL1A_rs17561”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs7334543 SNP found in the 3′ UTR of the human BRCA2 gene (“BRCA2_rs7334543”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs118142475 SNP found in the intron region of the human ILF3 gene (“ILF3_rs118142475”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs2239680 SNP found in the non-coding region of the human BIRC5 gene (“BIRC5_rs2239680”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs1805794 SNP found in the coding region of the human NBN gene (“NBN_rs1805794”). In
A biomarker relevant to determining a cancer patient's likelihood of experiencing distant failure despite undergoing radiation therapy (e.g., hypofractionated radiation) is the rs11797 SNP found in the coding region of the human TREX gene (“TREX_rs11797”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs4742098 SNP found in the intron region of the human CD274 gene (“CD274_rs4742098”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs4781562 SNP found in the 3′ UTR region of the human ERCC4 gene (“ERCC4_rs4781562”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs2303428 SNP found in the intron region of the human MSH2 gene (“MSH2_rs2303428”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs1040363 SNP found in the intron region of the human XRCC4 gene (“XRCC4_rs1040363”). In
A biomarker relevant to determining a cancer patient's likelihood of having biochemical relapse despite undergoing radiation therapy (e.g., conventionally-fractionated radiation) is the rs2834167 SNP found in the coding region of the human IL10RB gene (“IL10RB_rs2834167”). In
A biomarker relevant to determining a cancer patient's likelihood of mortality or having a biochemical relapse despite undergoing radiation therapy (e.g., hypofractionated radiation) is the rs11724777 SNP found upstream of the human SMAD1 gene (“SMAD1_rs11724777”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs10421768 SNP found upstream of the human HAMP gene (“HAMP_rs10421768”). In
A biomarker relevant to determining a cancer patient's likelihood of experiencing impotency after undergoing radiation therapy (e.g., hypofractionated radiation) is the rs25487 SNP found in the coding region of the human XRCC1 gene (“XRCC1_rs25487”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs2243158 SNP found in the intron region of the human IL19 gene (“IL19_rs2243158”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs2280883 SNP found in the intron region of the human FOXP3 gene (“FOXP3_rs2280883”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs3024496 SNP found in the 3′UTR region of the human IL10 gene (“IL10_rs3024496_P1.P2”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs2232643 SNP found in the 3′ UTR region of the human LIG4 gene (“LIG4_rs2232643”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs2476491 SNP found in the intron region of the human IL2RA gene (“IL2RA_rs2476491”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation, likelihood of mortality, or likelihood of developing impotency following radiation treatment is the rs568408 SNP found in the 3′ UTR region of the human IL12A gene (“IL12A_rs568408”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs2037547 SNP found in the 3′ UTR region of the human GSK3B gene (“GSK3B_rs2037547”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs1800797 SNP found in the intron region of the human IL6 gene (“IL6_rs1800797”). In
A biomarker relevant to determining a cancer patient's likelihood of experiencing distant failure despite undergoing radiation therapy (e.g., hypofractionated radiation) is the rs1801274 SNP found in the coding region of the human FCGR2A gene (“FCGR2A_rs1801274”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs373759 SNP found in the intron region of the human ATM gene (“ATM_rs373759”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs8230 SNP found in the 3′ UTR region of the human CETN2 gene (“CETN2_rs8230”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs861539 SNP found in the coding region of the human XRCC3 gene (“XRCC3_rs861539”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs4938723 SNP found within the human mir34.b.c. promoter (“miR.34b.c.promoter_rs4938723”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs9438623 SNP found within the 3′ UTR region of the human LIN28A gene (“LIN28A_rs9438623”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs9673 SNP found within the 3′ UTR region of the human FANCC gene (“FANCC_rs9673”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs465646 SNP found within the promoter region of the human REV3L gene (“REV3L_rs465646”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs1800795 SNP found within the intron region of the human IL6 gene (“IL6_rs1800795”). In
A biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation or experiencing distant failure despite undergoing radiation therapy (e.g., hypofractionated radiation) is the rs2071304 SNP found within the intron region of the human SPI1 gene (“SPI1_rs2071304”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation or experiencing distant failure or metastasis is the rs3024496 SNP found within the 3′ UTR region of the human IL10 gene (“IL10_rs3024496”). In
Another biomarker relevant to determining a cancer patient's likelihood of experiencing distant failure despite undergoing radiation therapy (e.g., hypofractionated radiation) is the rs4150021 SNP found within the 3′ UTR region of the human EXO1 gene (“EXO1_rs4150021”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs2232365 SNP found within the intron region of the human FOXP3 gene (“FOXP3_rs2232365”). In
Another biomarker relevant to determining a cancer patient's likelihood of experiencing distant failure despite undergoing radiation therapy (e.g., hypofractionated radiation) is the rs1882694 SNP found within the 5′ upstream region of the human HAMP gene (“HAMP_rs1882694”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs1800872 SNP found within the 5′ upstream region of the human IL10 gene (“IL10_rs1800872”). In
Another biomarker relevant to determining a cancer patient's risk of biochemical relapse despite undergoing radiation therapy (e.g., hypofractionated radiation) is the rs11556218 SNP found within the coding region of the human IL16 gene (“IL16_rs11556218”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation or biochemical relapse following radiation treatment is the rs11256497 SNP found within the intron region of the human IL2RA gene (“IL2RA_rs11256497”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs228942 SNP found within the coding region of the human IL2RB gene (“IL2RB_rs228942”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs17084733 SNP found within the 3′ UTR region of the human KIT gene (“KIT_rs17084733”). In
Another biomarker relevant to determining a cancer patient's likelihood of experiencing distant failure despite undergoing radiation therapy (e.g., hypofractionated radiation) is the rs61764370 SNP found within the 3′ UTR region of the human KRAS gene (“KRAS_rs61764370”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs3093772 SNP found within the 3′ UTR region of the human LIG4 gene (“LIG4_rs3093772_P1.P2”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs769412 SNP found within the coding region of the human MDM2 gene (“MDM2_rs769412”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs3751143 SNP found within the coding region of the human P2RX7 gene (“P2RX7_rs3751143”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation or experiencing distant failure is the rs2187668 SNP found within the intron region of the human HLA-DQA1 gene (“rs2187668”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs3024505 SNP found within the IL10 gene (“IL10_rs3024505”). In
Another biomarker relevant to determining a cancer patient's likelihood of experiencing distant failure despite undergoing radiation therapy (e.g., hypofractionated radiation) is the rs922075 SNP found within the intron region of the human ATRIP gene and upstream of the CCDC51 gene (“rs922075”). In
Another biomarker relevant to determining a cancer patient's likelihood of experiencing distant failure despite undergoing radiation therapy (e.g., hypofractionated radiation) is the rs1059313 SNP found within the 3′ UTR region of the human SOS1 gene (“SOS1_rs1059313”). In
Another biomarker relevant to determining a cancer patient's likelihood of experiencing distant failure despite undergoing radiation therapy (e.g., hypofractionated radiation) is the rs7574070 SNP found within the intron region of the human STAT4 gene (“STAT4_rs7574070”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the UNGC.96 SNP found within the human TGFB2 gene (“UNGC.96.TGFB2_NA”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs41282644 SNP found within the 3′ UTR region of the human VEGFA gene (“VEGFA_rs41282644”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs1800469 SNP found within the upstream region of the human transforming growth factor beta 1 TGFB1 gene (“TGFB1_rs1800469”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs884225 SNP found within the 3′ UTR region of the human epidermal growth factor (EGFR) gene (“EGFR_rs884225”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs17388569 SNP found in sequence GRCh37.p13 on human chromosome 2 (“rs17388569” also referred to as “rs1347682”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs4986790 SNP, which is a missense variant of the toll like receptor 4 (TLR4) (“TRL4_rs4986790”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs1800057 SNP, which is a missense variant of the ATM serine/threonine kinase (“ATM_rs1800057”). In
Another biomarker relevant to determining a cancer patient's likelihood of having a toxic response to radiation is the rs79383051 SNP, which is a noncoding transcript variant of the interleukin 1 receptor accessory protein (plus strand) (“ILRAP_rs79383051”). In
Another biomarker relevant to determining a cancer patient's time to mortality in response to radiation is the rs20541 SNP, which is a missense variant of the interleukin 13 protein (plus strand) (“IL3_rs20541”). In
Another biomarker relevant to determining a cancer patient's time to biochemical relapse in response to radiation is the rs8679 SNP, which is a 3′ UTR variant of the poly(ADP-ribose) polymerase 1 (minus strand) (“PARP1_rs8679”). In
Another biomarker relevant to determining a cancer patient's time to distant metastasis in response to radiation is the rs4073 SNP, which is an upstream variant of the interleukin-8 (IL-8) gene (also called C-X-C motif chemokine ligand 8 (CXCL8)) (plus strand) (“IL8_rs4073”). In
Each of the biomarkers disclosed herein as being predictive of toxicity may be used either alone or in combination with one or more of the other markers disclosed herein as predictive of toxicity in order to predict whether or not a patient will have a toxic response to radiation therapy. For example, one may use one, two, three, four, five, six, seven, eight, nine, or ten of the biomarkers in determining a patient's predicted toxicity to a radiation therapy. In particular, determining whether a patient is homozygous or heterozygous for a particular marker associated with toxicity to radiation (or does not carry the marker, i.e., the patient is wild-type) can be useful in determining the patient's predicted toxicity to a radiation therapy. For example, one may determine the zygosity of a patient with respect to one, two, three, four, five, six, seven, eight, nine or ten of the biomarkers disclosed herein as predictive of toxicity in order to determine the patient's predicted toxicity to a radiation therapy. For example, one may determine the zygosity of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or at least nine of the aforementioned markers associated with toxicity.
In one embodiment of the invention, determining whether a patient is likely to have a toxic or non-toxic response to a radiation treatment, includes identifying whether the patient carries or does not carry one or more of the following mutations:
In certain aspects, the disclosure relates to a method of treating a patient with a sarcoma, wherein said sarcoma is preferably treated by hypofractionated or conventionally fractionated radiation treatment (CF-RT), wherein if said patient is carrying or not carrying one or more mutations from group (i), said patient is treated with hypofractionated radiation, and if said patient is carrying or not carrying one or more mutations from group (ii), said patient is treated with a conventionally fractionated radiation treatment (CF-RT), wherein group (i) is:
and group (ii) is:
In certain embodiments, the toxicity is wound toxicity. In certain embodiments, the sarcoma is present on a lower extremity.
In certain embodiments, the hypofractionated radiation is administration of radiation from about 20 Gy to about 35 Gy radiation over about 5 to about 8 fractions.
In certain embodiments, the CF-RT is administration of radiation from about 40 Gy to about 60 Gy radiation over a period of about 5 to about 6 weeks. In certain embodiments, the CF-RT regimen comprises administering from about 40 Gy to about 60 Gy radiation over about 15 to about 30 fractions.
The disclosure further relates to a method of treating a patient with a prostate cancer, wherein said prostate cancer is preferably treated by hypofractionated radiation, CF-RT, or a different treatment, wherein if said patient is identified as carrying or not carrying one or more mutations from group (i), said patient is treated with hypofractionated radiation, and if said patient is identified as carrying or not carrying one or more mutations from group (ii), said patient is treated with a conventionally fractionated radiation treatment (CF-RT) radiation, and wherein if said patient is identified as carrying or not carrying one or more mutations from group (iii), said patient is treated with a different treatment, wherein group (i) is:
group (ii) is:
and group (iii) is:
In certain embodiments, the hypofractionated radiation regimen comprises administering from about 20 Gy to about 35 Gy radiation over about 5 to about 8 fractions. In certain embodiments, the hypofractionated radiation regimen comprises administering stereotactic body radiation therapy. In certain embodiments, the method is a reduced toxicity method and the toxicity comprises acute or long-term toxicity. In certain embodiments, the toxicity comprises GI and/or GU toxicity.
In certain embodiments, the CF-RT regimen comprises administering from about 40 Gy to about 60 Gy radiation over a period of about 5 to about 6 weeks. In certain embodiments, the CF-RT regimen comprises administering from about 40 Gy to about 60 Gy radiation over about 15 to about 30 fractions.
In certain embodiments, the radiation therapy comprises external beam radiation therapy.
In certain embodiments, the different treatment comprises brachytherapy, chemotherapy, androgen deprivation therapy, immunotherapy, high intensity focused ultrasound, cryotherapy, laser ablation, photodynamic therapy, or surgery.
In another aspect, the disclosure relates to a method of treating a patient with a sarcoma, wherein said sarcoma is preferably treated by hypofractionated radiation, CF-RT, or a different treatment, wherein if said patient is identified as carrying or not carrying one or more mutations from group (i), said patient is treated with hypofractionated radiation, and if said patient is identified as carrying or not carrying one or more mutations from group (ii), said patient is treated with a conventionally fractionated radiation treatment (CF-RT) radiation and/or a different treatment, thereby to avoid distant failure, wherein group (i) is:
and group (ii) is:
In another aspect, the disclosure relates to a method of treating a patient with a prostate cancer, wherein said prostate cancer is preferably treated by conventionally fractionated radiation treatment (CF-RT) or a different treatment, wherein if said patient is identified as carrying or not carrying one or more mutations from group (i), said patient is treated with a conventionally fractionated radiation treatment (CF-RT) radiation, whereas if said patient is identified as carrying or not carrying one or more mutations from group (ii), said patient is treated with a different treatment, thereby to avoid an increased risk of mortality, wherein group (i) is:
and group (ii) is:
In another aspect, the disclosure relates to a method of treating a patient with a prostate cancer, wherein said prostate cancer is preferably treated by conventionally fractionated radiation treatment (CF-RT) or a different treatment, wherein if said patient is identified as carrying or not carrying one or more mutations from group (i), said patient is treated with a conventionally fractionated radiation treatment (CF-RT) radiation, whereas if said patient is identified as carrying or not carrying one or more mutations from group (ii), said patient is treated with a different treatment, thereby to avoid an increased risk of biochemical relapse, wherein group (i) is:
and group (ii) is:
In another aspect, the disclosure relates to a method of treating a patient with a prostate cancer, wherein said prostate cancer is preferably treated by conventionally fractionated radiation treatment (CF-RT) or a different treatment, wherein if said patient is identified as carrying or not carrying one or more mutations from group (i), said patient is treated with a conventionally fractionated radiation treatment (CF-RT) radiation, whereas if said patient is identified as carrying or not carrying one or more mutations from group (ii), said patient is treated with a different treatment and optionally CF-RT, thereby to avoid an increased risk of distant metastasis, wherein group (i) is:
and group (ii) is:
In another aspect, the disclosure relates to a method of treating a patient with a prostate cancer, wherein said prostate cancer is preferably treated by conventionally fractionated radiation treatment (CF-RT) or a different treatment, wherein if said patient is identified as carrying or not carrying one or more mutations from group (i), said patient is treated with a conventionally fractionated radiation treatment (CF-RT) radiation, whereas if said patient is identified as carrying or not carrying one or more mutations from group (ii), said patient is treated with a different treatment, thereby to avoid an increased risk of impotency, wherein group (i) is:
and group (ii) is:
In certain embodiments, the CF-RT regimen comprises administering from about 40 Gy to about 60 Gy radiation over a period of about 5 to about 6 weeks. In certain embodiments, the CF-RT regimen comprises administering from about 40 Gy to about 60 Gy radiation over about 15 to about 30 fractions.
In certain embodiments, the radiation therapy comprises external beam radiation therapy.
In certain embodiments, the different treatment comprises hypofractionated radiation, brachytherapy, chemotherapy, androgen deprivation therapy, immunotherapy, high intensity focused ultrasound, cryotherapy, laser ablation, photodynamic therapy, or surgery.
In certain embodiments, the hypofractionated radiation regimen comprises administering from about 20 Gy to about 35 Gy radiation over about 5 to about 8 fractions.
In certain embodiments, the hypofractionated radiation regimen comprises administering stereotactic body radiation therapy.
In another aspect, the disclosure relates to a method for determining whether a cancer patient with a sarcoma has a decreased likelihood of having a toxic response to a hypofractionated radiation treatment, the method comprising determining if the patient is carrying or not carrying one or more of the following mutations:
In another aspect, the disclosure relates to a method for determining whether a cancer patient with a sarcoma has an increased likelihood of having a toxic response to a hypofractionated radiation treatment, the method comprising determining if the patient is carrying or not carrying one or more of the following mutations:
In certain embodiments, the toxic response comprises wound toxicity. In certain embodiments, the patient has a tumor on a lower extremity.
In another aspect, the disclosure relates to a method for determining whether a prostate cancer patient has a decreased likelihood of having a toxic response to a hypofractionated radiation treatment, the method comprising determining if the patient is carrying or not carrying one or more of the following mutations:
In another aspect, the disclosure relates to a method for determining whether a prostate cancer patient has an increased likelihood of having a toxic response to a hypofractionated radiation treatment, the method comprising determining if the patient is carrying or not carrying one or more mutations selected from:
In certain embodiments, the hypofractionated radiation regimen comprises administering from about 20 Gy to about 35 Gy radiation over about 5 to about 8 fractions. In certain embodiments, the hypofractionated radiation regimen comprises administering stereotactic body radiation therapy.
In another aspect, the disclosure relates to a method for determining whether a prostate cancer patient has an increased likelihood of having a toxic response to CF-RT, the method comprising determining if the patient is carrying or not carrying one or more mutations selected from:
In certain embodiments, the toxicity is acute or long-term toxicity. In certain embodiments, the toxicity is GI and/or GU toxicity.
In certain embodiments, the CF-RT regimen comprises administering from about 40 Gy to about 60 Gy radiation over a period of about 5 to about 6 weeks. In certain embodiments, the CF-RT regimen comprises administering from about 40 Gy to about 60 Gy radiation over about 15 to about 30 fractions.
In certain embodiments, the radiation treatment is external beam radiation therapy.
In another aspect, the disclosure relates to a method for determining whether a cancer patient having a sarcoma has a decreased likelihood of distant failure in response to hypofractionated radiation, the method comprising determining whether the patient is carrying or not carrying one or more mutations in the following SNPs:
In certain embodiments, the hypofractionated radiation regimen comprises administering from about 20 Gy to about 35 Gy radiation over about 5 to about 8 fractions.
In another aspect, the disclosure relates to a method for determining whether a prostate cancer patient has an increased risk of mortality following CF-RT, the method comprising identifying the patient as carrying or not carrying one or more mutations in a SNP selected from:
In another aspect, the disclosure relates to a method for determining whether a prostate cancer patient has an increased risk of biochemical relapse following CF-RT, the method comprising identifying the patient as carrying or not carrying one or more mutations in a SNP selected from:
In another aspect, the disclosure relates to a method for determining whether a prostate cancer patient has an increased risk of distant metastasis following CF-RT, the method comprising identifying the patient as carrying or not carrying one or more of the following mutations in a SNP:
In another aspect, the disclosure relates to a method for determining whether a prostate cancer patient has a decreased risk of impotency following treatment with radiation, the method comprising identifying the patient as carrying or not carrying one or more mutations in a SNP selected from:
In certain embodiments, a determining step includes reviewing a medical record of the patient containing the results of a SNP genotyping test. In certain embodiments, the determining step includes performing a SNP genotyping method as described herein. The assessment of the patient's likelihood of a toxic response to a radiation therapy may be based on determining the presence or absence of only one, only two, only three, only four, only five, only six, only seven, only eight, only nine or all ten of these markers, but does not necessarily require assessment of all ten markers. For example, the assessment may be based on determining the presence or absence of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or at least nine of the aforementioned markers associated with toxicity.
If the patient carries or does not carry one or more of these mutations, the patient is predicted as having a decreased probability of having a toxic response to the agent. In one embodiment, it is determined whether the patient carries one, two, three, four or all five of the aforementioned biomarkers. For example, the determination of whether a patient has a decreased probability of a toxic response to the agent may be based on whether the patient carries or does not carry only one, only two, only three, only four, or all five biomarkers, but may not require assessment of all five markers. For example, the assessment may be based on determining the presence or absence of at least one, at least two, at least three, or at least four of the aforementioned markers associated with toxicity.
In certain embodiments, the disclosure provides a method for determining a patient's likelihood of developing toxicity based on a clinical variable (presence of lower extremity tumor) and 9 SNPs—IL6_rs2069840, SHC4_rs1062124, ERCC1_rs3212948, RAC1_rs9374, UNGC.41.IL1RAP, CD274_rs4143815, miR99a promoter, IL1A_rs1800587, and CD6_rs76677607. Accordingly, in certain embodiments, the disclosure relates to a method of administering radiation to a cancer patient, wherein the cancer patient is identified as having a lower extremity tumor and/or is carrying or not carrying one or more mutations selected from a G nucleotide at a position corresponding to position 101 of SEQ ID NO: 19 (IL.6_rs2069840), a G nucleotide at a position corresponding to position 101 of SEQ ID NO: 5 (SHC4_rs1062124), a C nucleotide at a position corresponding to position 101 of SEQ ID NO: 15 (ERCC1_rs3212948), an A nucleotide at a position corresponding to position 101 of SEQ ID NO: 117 (RAC1_rs9374), a T nucleotide at a position corresponding to position 101 of SEQ ID NO: 37 (UNGC.41.IL1RAP), a C nucleotide at a position corresponding to position 101 of SEQ ID NO: 27 (CD274_rs4143815), a C nucleotide at a position corresponding to position 101 of SEQ ID NO: 1 (miR.99a.promoter), an A nucleotide at a position corresponding to position 101 of SEQ ID NO: 13 (IL1A_rs1800587), and an A nucleotide at a position corresponding to position 101 of SEQ ID NO: 69 (CD6_rs76677607).
In certain embodiments, if a patient is identified as not homozygous wild-type for IL6_rs2069840, the marker CD6_rs76677607 is considered. In certain embodiments, a patient who is identified as homozygous wild-type (0) for CD6_rs76677607 is not predicted to have a toxic response to radiation. If the patient is identified as not homozygous wild-type (0) for CD6_rs76677607, the marker CD274_rs4143815 is considered. If the patient is identified as homozygous wild-type (0) for CD274_rs4143815, the patient is not predicted to have a toxic response. If the patient is identified as not homozygous wild-type (0) for CD274_rs4143815, the patient is predicted to have a toxic response.
If the patient is identified as homozygous wild-type (0) for the first variable of predictive value, IL6_rs2069840, the marker SHC4_rs1062124 is considered. If the patient is identified as homozygous wild-type (0) for SHC4_rs1062124, the marker ERCC1_rs3212948 is considered. If the patient is identified as homozygous wild-type (0) for ERCC1_rs3212948, the patient is not predicted to have a toxic response. If the patient is identified as not homozygous wild-type (0) for ERCC1_rs3212948, the marker RAC1_rs9374 should be considered. If the patient is identified as homozygous wild-type (0) for RAC1_rs9374, the patient is not predicted to have a toxic response. If the patient is identified as not homozygous wild-type (0) for RAC1_rs9374, the patient is predicted to have a toxic response.
If the patient is identified as not homozygous wild-type (0) for SHC4_rs1062124, the presence of a lower extremity tumor should be considered. If the patient does not have a tumor on a lower extremity, the patient is not predicted to have a toxic response. If the patient does have a tumor on a lower extremity, the marker UNGC.41.IL1RAP is considered. If the patient is identified as not homozygous wild-type (0) for UNGC.41.IL1RAP, the patient is not predicted to have a toxic response. If the patient is identified as homozygous wild-type (0) for UNGC.41.IL1RAP, the marker CD274_rs4143815 should be considered. If the patient is identified as not homozygous wild-type (0) or heterozygous (1) for CD274_rs4143815, the patient is not predicted to have a toxic response. If the patient is identified as homozygous wild-type (0) or heterozygous (1) for CD274_rs4143815, the marker miR.99a.promoter is considered. If the patient is identified as homozygous wild-type (0) for miR.99a.promoter, the patient is predicted to have a toxic response. If the patient is identified as not homozygous wild-type (0) for miR.99a.promoter, the marker IL1A_rs1800587 is considered. If the patient is heterozygous (1) for IL1A_rs1800587, the patient is not predicted to have a toxic response. If the patient is identified as not heterozygous (1) for IL1A_rs1800587, the patient is predicted to have a toxic response.
In certain embodiments, if the patient is predicted not to have a toxic response, the method further comprises administering radiation to the patient. In certain embodiments, if the patient is predicted to have a toxic response, the method further comprises administering a different treatment to the patient, for example, a different form of radiation or brachytherapy, chemotherapy, androgen deprivation therapy, immunotherapy, high intensity focused ultrasound, cryotherapy, laser ablation, photodynamic therapy, or surgery. In certain embodiments, the method further comprises determining whether the patient is homozygous wild type or heterozygous for a recited SNP.
Each of the biomarkers disclosed herein as being predictive of increased risk for mortality, distant failure or metastasis, or impotency may be used either alone or in combination with one or more of the other markers disclosed herein as predictive of increased risk for mortality, distant failure or metastasis, or impotency in order to predict whether or not a patient has an increased risk for mortality, distant failure or metastasis, or impotency regardless of whether radiation therapy is administered. For example, one may use one, two, three, four, five, six, seven, eight, nine, or ten of the biomarkers in determining a patient's predicted risk for mortality, distant failure or metastasis, or impotency. In particular, determining whether a patient is homozygous or heterozygous for a particular marker associated with increased risk for mortality, distant failure or metastasis, or impotency (or does not carry the marker, i.e., the patient is wild-type) can be useful in determining the patient's predicted risk for mortality, distant failure or metastasis, or impotency following radiation treatment. For example, one may determine the zygosity of a patient with respect to one, two, three, four, five, six, seven, eight, nine or ten of the biomarkers disclosed herein as predictive of increased risk for mortality, distant failure or metastasis, or impotency in order to determine the patient's predicted increased risk for mortality, distant failure or metastasis, or impotency following radiation therapy. For example, one may determine the zygosity of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or at least nine of the aforementioned markers associated with increased risk for mortality, distant failure or metastasis, or impotency.
In one embodiment of the invention, determining whether a patient has an increased risk of distant failure following a radiation treatment includes determining whether the patient carries or does not carry one or more of the following mutations:
In one embodiment of the invention, determining whether a patient has an increased risk of mortality following a radiation treatment includes determining whether the patient carries or does not carry one or more of the following mutations:
In one embodiment of the invention, determining whether a patient has an increased risk of distant metastasis following a radiation treatment includes determining whether the patient carries or does not carry one or more of the following mutations:
In one embodiment of the invention, determining whether a patient has an increased risk of impotency following a radiation treatment includes determining whether the patient carries or does not carry one or more of the following mutations:
In certain embodiments, a determining step includes reviewing a medical record of the patient containing the results of a SNP genotyping test. In certain embodiments, the determining step includes performing a SNP genotyping method as described herein. The assessment of the patient's likelihood of experiencing morality, distant failure or metastasis, or impotency following a radiation therapy may be based on determining the presence or absence of only one, only two, only three, only four, only five, only six, only seven, only eight, only nine or all ten of these markers, but does not necessarily require assessment of all ten markers. For example, the assessment may be based on determining the presence or absence of at least one, at least two, at least three, at least four, at least five, at least six, at least seven, at least eight, or at least nine of the aforementioned markers associated with morality, distant failure or metastasis, or impotency.
If the patient carries or does not carry one or more of these mutations, the patient is predicted as having a decreased probability of experiencing morality, distant failure or metastasis, or impotency following a radiation therapy. In one embodiment, it is determined whether the patient carries one, two, three, four or all five of the aforementioned biomarkers. For example, the determination of whether a patient has a decreased probability of experiencing morality, distant failure or metastasis, or impotency following a radiation therapy may be based on whether the patient does not carry only one, only two, only three, only four, or all five biomarkers, but may not require assessment of all five markers. For example, the assessment may be based on determining the presence or absence of at least one, at least two, at least three, or at least four of the aforementioned markers associated with morality, distant failure or metastasis, or impotency.
As used herein, the terms “treat,” “treating,” or “treatment” in the context of cancer refer to (a) slowing of growth of a tumor, (b) cessation of growth of a tumor, (c) regression, or (d) improvement in one or more the patient's symptoms. According to one embodiment of the invention, “treating” or “treat” may refer to patient outcomes where a patient receiving radiation therapy exhibits a response to that therapy. The terms “radiation treatment” and “radiation therapy” are used interchangeably herein.
As used herein, the term “toxicity” or “toxic response” refers to the occurrence of one or more adverse reactions a patient may experience in response to a cancer therapy, for example, radiation. In certain embodiments, the Radiation Therapy Oncology Group (RTOG) morbidity grading system is used to assess toxicity. (See, (Cox et al. (1995) Int J Radiat Oncol Biol Phys 31(5):1341-6.) In certain embodiments, the toxicity is short term toxicity. In certain embodiments, the toxicity is long term toxicity (i.e., toxicities occurring at least three months after radiation treatment). In certain embodiments, the toxicity is wound toxicity. In certain embodiments, the toxicity is gastrointestinal (GI) toxicity (e.g., nausea, diarrhea, vomiting, bleeding). In certain embodiments, the toxicity is genitourinary (GU) toxicity (e.g., macrohematuria, frequent urination, and urinary retention). In certain embodiments, the toxicity is skin toxicity (e.g., contact dermatitis, photosensitive dermatitis, contact urticaria, chemical-induced acne, pigmentary disturbance, drug rash (cutaneous reaction), hair disturbance, and nail disturbance). In certain embodiments, the toxicity is fibrosis. In certain embodiments, the toxicity is dysphagia. In certain embodiments, the toxicity is impotency. In certain embodiments, long term (late) toxicity is determined using the RTOG/EORTC Late Radiation Morbidity Scoring Schema as shown in TABLE 1. In certain embodiments, short term (acute) toxicity is determined using the RTOG Scoring Schema for Cooperative Group Common Toxicity Criteria as shown in TABLE 2.
The term “increased probability/likelihood” or “reduced probability/likelihood” in the context of the present invention, relates to the increased or reduced probability that an event will occur over a specific time period, and can mean a subject's “absolute” probability or “relative” probability. Absolute probability can be measured with reference to either actual observation post-measurement for the relevant time cohort, or with reference to index values developed from statistically valid historical cohorts that have been followed for the relevant time period. Relative probability refers to the ratio of absolute probabilities of a subject compared either to the absolute probabilities of low probability cohorts or an average population probability, which can vary by how clinical probabilities are assessed. Odds ratios, the proportion of positive events to negative events for a given test result, are also commonly used (odds are according to the formula p/(1−p) where p is the probability of event and (1−p) is the probability of no event) to no-conversion.
A patient's “increased likelihood, risk, or probability” of having a toxic response to a therapy or may be based on a comparison to the rate of toxic response (or predicted rate of toxic response) of a cohort of patients having a different genotype than the patient's genotype with respect to a particular biomarker or biomarkers. An “increased likelihood, risk, or probability” of having a toxic response to a therapy may also be based on a comparison to the rate of toxic response (or predicted rate of response) for a cohort of patients without taking the marker (or markers) into consideration.
A patient's “decreased likelihood, risk, or probability” of having a toxic response to a therapy may be based on a comparison to the rate of toxic response (or predicted rate of toxic response) of a cohort of patients having a different genotype than the patient's genotype with respect to a particular biomarker or biomarkers. A “decreased likelihood, risk, or probability” of having a toxic response to a therapy may also be based on a comparison to the rate of toxic response (or predicted rate of response) for a cohort of patients without taking the marker (or markers) into consideration.
A patient's “increased likelihood, risk, or probability” of experiencing mortality, distant failure or metastasis, or impotency following a therapy or may be based on a comparison to the rate or timing of (e.g. a shorter time to) mortality, distant failure or metastasis, or impotency experienced by a cohort of patients having a different genotype than the patient's genotype with respect to a particular biomarker or biomarkers. An “increased likelihood, risk, or probability” of experiencing mortality, distant failure or metastasis, or impotency following a therapy may also be based on a comparison to the mortality, distant failure or metastasis, or impotency experienced by a cohort of patients without taking the marker (or markers) into consideration.
A patient's “decreased likelihood, risk, or probability” of experiencing mortality, distant failure or metastasis, or impotency following a therapy or may be based on a comparison to the rate or timing of (e.g. a longer time to) mortality, distant failure or metastasis, or impotency experienced by a cohort of patients having a different genotype than the patient's genotype with respect to a particular biomarker or biomarkers. An “decreased likelihood, risk, or probability” of experiencing mortality, distant failure or metastasis, or impotency following a therapy may also be based on a comparison to the mortality, distant failure or metastasis, or impotency experienced by a cohort of patients without taking the marker (or markers) into consideration.
As used herein, the terms “mutant” or “wildtype” refer to the allele status of the SNP, as determined by The Single Nucleotide Polymorphism Database (National Center for Biotechnology Information (NCBI)).
As used herein, the term “mortality” refers to death. Accordingly, a patient carrying a mutant or wild type SNP associated with a shortened time to mortality is more likely to experience death sooner than a patient who does not carry that mutant or wild type SNP, following a radiation treatment (e.g., a traditional radiation treatment).
As used herein, the term “biochemical relapse” refers to elevated blood levels of Prostate Specific Antigen (PSA). Accordingly, a patient carrying a mutant or wild type SNP associated with a shortened time to biochemical relapse is more likely to experience elevated blood levels sooner than a patient who does not carry that mutant or wild type SNP, following a radiation treatment (e.g., a traditional radiation treatment).
As used herein, the terms “distant failure” or “distant metastasis” refer to the recurrence of cancer, i.e., the presence of cancer cells and/or a tumor at a site that is distant from a primary tumor, and are used interchangeably.
As used herein, the term “potency” refers to the ability to develop or sustain a penile erection sufficient to conclude coitus. The term “impotency” refers to the inability to develop or sustain a penile erection sufficient to conclude coitus. In certain embodiments, potency and/or impotency is assessed by interview of a patient by the treating physician.
Linkage disequilibrium (LD) refers to the co-inheritance of alleles (e.g., alternative nucleotides) at two or more different SNP sites at frequencies greater than would be expected from the separate frequencies of occurrence of each allele in a given population. The expected frequency of co-occurrence of two alleles that are inherited independently is the frequency of the first allele multiplied by the frequency of the second allele. Alleles that co-occur at expected frequencies are said to be in “linkage equilibrium.” In contrast, LD refers to any non-random genetic association between allele(s) at two or more different SNP sites, which is generally due to the physical proximity of the two loci along a chromosome. LD can occur when two or more SNP sites are in close physical proximity to each other on a given chromosome and therefore alleles at these SNP sites will tend to remain unseparated for multiple generations with the consequence that a particular nucleotide (allele) at one SNP site will show a non-random association with a particular nucleotide (allele) at a different SNP site located nearby. Hence, genotyping one of the SNP sites will give almost the same information as genotyping the other SNP site that is in LD.
For screening individuals for genetic disorders (e.g. prognostic or risk) purposes, if a particular SNP site is found to be useful for screening a disorder, then the skilled artisan would recognize that other SNP sites which are in LD with this SNP site would also be useful for screening the condition. Accordingly, in certain embodiments, the detection of one or more of the SNPs described herein (“the recited SNP”) also includes detecting a SNP in LD with the recited SNP.
Various degrees of LD can be encountered between two or more SNPs with the result being that some SNPs are more closely associated (i.e., in stronger LD) than others. Furthermore, the physical distance over which LD extends along a chromosome differs between different regions of the genome, and therefore the degree of physical separation between two or more SNP sites necessary for LD to occur can differ between different regions of the genome.
For screening applications, polymorphisms (e.g., SNPs and/or haplotypes) that are not the actual disease-causing (causative) polymorphisms, but are in LD with such causative polymorphisms, are also useful. In such instances, the genotype of the polymorphism(s) that is/are in LD with the causative polymorphism is predictive of the genotype of the causative polymorphism and, consequently, predictive of the phenotype (e.g., disease) that is influenced by the causative SNP(s). Thus, polymorphic markers that are in LD with causative polymorphisms are useful as markers, and are particularly useful when the actual causative polymorphism(s) is/are unknown.
Linkage disequilibrium in the human genome is reviewed in: Wall et al. (2003) NAT REV GENET. 4(8):587-97; Gamer et al. (2003) GENET EPIDEMIOL. 24 (1):57-67; Ardlie et al. (2002) NAT REV GENET. 3(4):299-309 (erratum in (2002) NAT REV GENET 3(7):566); and Remm et al. (2002) CURR OPIN CHEM BIOL. 6(1):24-30.
The screening techniques of the present invention may employ a variety of methodologies to determine whether a test subject has a SNP or a SNP pattern associated with an increased or decreased risk of developing a detectable trait or whether the individual suffers from a detectable trait as a result of a particular polymorphism/mutation, including, for example, methods which enable the analysis of individual chromosomes for haplotyping, family studies, single sperm DNA analysis, or somatic hybrids. The trait analyzed using the diagnostics of the invention may be any detectable trait that is commonly observed in pathologies and disorders.
The process of determining which specific nucleotide (i.e., allele) is present at each of one or more SNP positions is referred to as SNP genotyping. The present invention provides methods of SNP genotyping in order to determine whether a patient has a particular genotype with respect to the mutations disclosed herein as useful biomarkers in predicting a patient's toxicity response to radiation.
Nucleic acid samples can be genotyped to determine which allele(s) is/are present at any given genetic region (e.g., SNP position) of interest by methods well known in the art. The neighboring sequence can be used to design SNP detection reagents such as oligonucleotide probes, which may optionally be implemented in a kit format. Exemplary SNP genotyping methods are described in Chen et al. (2003) P
SNP genotyping can include the steps of, for example, collecting a biological sample from a human subject (e.g., sample of tissues, cells, fluids, secretions, etc.), isolating nucleic acids (e.g., genomic DNA, mRNA or both) from the cells of the sample, contacting the nucleic acids with one or more primers which specifically hybridize to a region of the isolated nucleic acid containing a target SNP under conditions such that hybridization and amplification of the target nucleic acid region occurs, and determining the nucleotide present at the SNP position of interest, or, in some assays, detecting the presence or absence of an amplification product (assays can be designed so that hybridization and/or amplification will only occur if a particular SNP allele is present or absent). In some assays, the size of the amplification product is detected and compared to the length of a control sample; for example, deletions and insertions can be detected by a change in size of the amplified product compared to a normal genotype.
A biological sample for determination of the presence or absence of any of the mutations disclosed herein can be any tissue or fluid from the patient that contains nucleic acids. Various embodiments include paraffin imbedded tissue, frozen tissue, surgical fine needle aspirations, and cells of various tissues of the subject, such as blood cells or a cheek swab.
In one embodiment, determining whether a patient is a carrier of a particular germline mutation, or has a particular genotype or zygosity with respect to that mutation, is based on genetically evaluating normal cells (as opposed to tumor cells) from the patient, for example, blood cells or cells from a cheek swab.
In one embodiment, determining whether a patient is a carrier of a particular germline mutation, or has a particular genotype or zygosity with respect to that mutation, includes reviewing a medical record of the patient containing the results of a SNP genotyping test.
The disclosure relates in part to the discovery of biomarkers that are predictive of a patient's likelihood of developing toxicity to a hypofractionated radiation treatment. A hypofractionated radiation treatment generally provides the same amount of radiation as a traditional radiation treatment, but in a shorter amount of time. Hypofractionated radiation may be more effective and/or more convenient for the patient, but a subset of patients experience toxicity to hypofractionated radiation. The biomarkers described herein can be used to evaluate a patient to determine whether they are likely to develop toxicity to a hypofractionated radiation treatment. If a patient carries certain mutations associated with toxicity, or does not carry certain other mutations that indicate the patient is unlikely to develop toxicity, the patient may be administered a traditional radiation treatment. Likewise, if a patient carries certain mutations that indicate the patient is unlikely to develop toxicity, or does not carry mutations that indicate the patient is likely to develop toxicity, the patient may be administered a hypofractionated radiation treatment.
The disclosure relates in part to the discovery of biomarkers that are predictive of a patient's likelihood of developing toxicity to a stereotactic body radiation treatment (SBRT). An SBRT radiation treatment generally provides the same amount of radiation as a traditional radiation treatment, but in a shorter amount of time. SBRT may be more effective and/or more convenient for the patient, but a subset of patients experience toxicity to hypofractionated radiation. The biomarkers described herein can be used to evaluate a patient to determine whether they are likely to develop toxicity to a SBRT radiation treatment. If a patient carries certain mutations associated with toxicity, or does not carry certain other mutations that indicate the patient is unlikely to develop toxicity, the patient may be administered a traditional radiation treatment. Likewise, if a patient carries certain mutations that indicate the patient is unlikely to develop toxicity, or does not carry mutations that indicate the patient is likely to develop toxicity, the patient may be administered a SBRT radiation treatment.
The disclosure further relates, in part, to the discovery of biomarkers that are predictive of a patient's likelihood of developing toxicity to a standard (conventional) fractionated radiation treatment (CF-RT). The biomarkers described herein can be used to evaluate a patient to determine whether they are likely to develop toxicity to a standard fractionated radiation treatment. If a patient carries certain mutations associated with toxicity, or does not carry certain other mutations that indicate the patient is unlikely to develop toxicity, the patient may be administered a hypofractionated radiation treatment or a cancer treatment that does not include standard fractionated radiation (a different cancer treatment). Likewise, if a patient carries certain mutations that indicate the patient is unlikely to develop toxicity, or does not carry mutations that indicate the patient is likely to develop toxicity, the patient may be administered a standard fractionated radiation treatment.
The disclosure also relates in part to the discovery of biomarkers that are predictive of a patient's likelihood of developing impotency, and likelihood of exhibiting a shortened time to biochemical relapse, distant metastasis and mortality following a cancer treatment (e.g., a standard fractionated radiation treatment as shown in Example 3). The biomarkers described herein can be used to evaluate a patient to determine whether they are likely to experience impotency and/or to experience a shorter time to biochemical relapse, distant metastasis and/or mortality following a cancer treatment. If a patient carries certain mutations associated with impotency, biochemical relapse, distant metastasis and mortality, or does not carry certain other mutations that indicate the patient is unlikely to experience impotency or experience a shorter time to biochemical relapse, distant metastasis and mortality, the patient may be administered a different cancer treatment (e.g., a chemotherapeutic treatment or hypofractionated radiation treatment). In certain embodiments, the different cancer treatment includes brachytherapy, hypofractionated radiation, chemotherapy, androgen deprivation therapy, immunotherapy, high intensity focused ultrasound, cryotherapy, laser ablation, photodynamic therapy, and surgery. Likewise, if a patient carries certain mutations that indicate the patient is unlikely to experience impotency or a shortened time to biochemical relapse, distant metastasis and mortality, or does not carry mutations that indicate the patient is likely to experience impotency or a shorter time to biochemical relapse, distant metastasis and mortality, the patient may be administered the cancer treatment (e.g., a standard fractionation radiation treatment).
A traditional radiation treatment is, in general, administered over a longer period of time than a hypofractionated radiation treatment. In certain embodiments, a traditional radiation treatment comprises administering from about 35 Gy to about 90 Gy radiation over a period of about 3 to about 8 weeks and/or over about 10 to about 30 fractions. In certain embodiments, the dose per fraction is about 2 Gy.
For example, a traditional radiation treatment can comprise administering from about 40 to about 90 Gy radiation, from about 45 to about 90 Gy radiation, from about 50 to about 90 Gy radiation, from about 55 to about 90 Gy radiation, from about 60 to about 90 Gy radiation, from about 65 to about 90 Gy radiation, from about 70 to about 90 Gy radiation, from about 75 to about 90 Gy radiation, from about 80 to about 90 Gy radiation, from about 85 to about 90 Gy radiation, from about 35 to about 85 Gy radiation, from about 40 to about 85 Gy radiation, from about 45 to about 85 Gy radiation, from about 50 to about 85 Gy radiation, from about 55 to about 85 Gy radiation, from about 60 to about 85 Gy radiation, from about 65 to about 85 Gy radiation, from about 70 to about 85 Gy radiation, from about 75 to about 85 Gy radiation, from about 80 to about 85 Gy radiation, from about 35 to about 80 Gy radiation, from about 40 to about 80 Gy radiation, from about 45 to about 80 Gy radiation, from about 50 to about 80 Gy radiation, from about 55 to about 80 Gy radiation, from about 60 to about 80 Gy radiation, from about 65 to about 80 Gy radiation, from about 70 to about 80 Gy radiation, from about 75 to about 80 Gy radiation, from about 35 to about 75 Gy radiation, from about 40 to about 75 Gy radiation, from about 45 to about 75 Gy radiation, from about 50 to about 75 Gy radiation, from about 55 to about 75 Gy radiation, from about 60 to about 75 Gy radiation, from about 65 to about 75 Gy radiation, from about 70 to about 75 Gy radiation, from about 35 to about 70 Gy radiation, from about 40 to about 70 Gy radiation, from about 45 to about 70 Gy radiation, from about 50 to about 70 Gy radiation, from about 55 to about 70 Gy radiation, from about 60 to about 70 Gy radiation, from about 65 to about 70 Gy radiation, from about 35 to about 65 Gy radiation, from about 40 to about 65 Gy radiation, from about 45 to about 65 Gy radiation, from about 50 to about 65 Gy radiation, from about 55 to about 65 Gy radiation, from about 60 to about 65 Gy radiation, from about 35 to about 60 Gy radiation, from about 40 to about 60 Gy radiation, from about 45 to about 60 Gy radiation, from about 50 to about 60 Gy radiation, from about 55 to about 60 Gy radiation, from about 35 to about 55 Gy radiation, from about 40 to about 55 Gy radiation, from about 45 to about 55 Gy radiation, from about 50 to about 55 Gy radiation, from about 35 to about 50 Gy radiation, from about 40 to about 50 Gy radiation, from about 45 to about 50 Gy radiation, from about 35 to about 45 Gy radiation, from about 40 to about 45, or from about 35 to 40 Gy radiation.
The radiation can be administered over a period of from about 3 to about 7 weeks, from about 4 to about 7 weeks, from about 5 to about 7 weeks, from about 6 to about 7 weeks, from about 3 to about 6 weeks, from about 4 to about 6 weeks, from about 5 to about 6 weeks, from about 3 to about 5 weeks, from about 4 to about 5 weeks, or from about 3 to about 4 weeks. In certain embodiments, the radiation is administered over a period of about 3 weeks, 4 weeks, 5 weeks, 6 weeks, or 7 weeks.
The radiation can be administered over about 10 to about 25 fractions, about 10 to about 20 fractions, about 10 to about 15 fractions, about 15 to about 30 fractions, about 15 to about 25 fractions, about 15 to about 20 fractions, about 20 to about 30 fractions, about 20 to about 25 fractions, or about 25 to about 20 fractions. In certain embodiments, the radiation is administered over about 10 fractions, 11 fractions, 12 fractions, 13 fractions, 14 fractions, 15 fractions, 16 fractions, 17 fractions, 18 fractions, 19 fractions, 20 fractions, 21 fractions, 22 fractions, 23 fractions, 24 fractions, 25 fractions, 26 fractions, 27 fractions, 28 fractions, 29 fractions or 30 fractions.
In certain embodiments, a hypofractionated radiation regimen comprises administering from about 20 Gy to about 50 Gy radiation over about 1 week to about 5 weeks, and/or over about 5 to about 15 fractions. In certain embodiments, the dose per fraction is greater than about 2 Gy (e.g., 2, 2%, 3, 3.2, 3.3 or greater).
In certain embodiments, a hypofractionated radiation regimen comprises administering from about 25 to about 50 Gy radiation, from about 35 to about 50 Gy radiation, from about 40 to about 50 Gy radiation, from about 20 to about 45 Gy radiation, from about 25 to about 45 Gy radiation, from about 30 to about 45 Gy radiation, from about 35 to about 45 Gy radiation, from about 40 to about 45 Gy radiation, from about 20 to about 40 Gy radiation, from about 25 to about 40 Gy radiation, from about 30 to about 40 Gy radiation, from about 35 to about 40 Gy radiation, from about 20 to about 35 Gy radiation, from about 25 to about 35 Gy radiation, from about 30 to about 35 Gy radiation, from about 20 to about 30 Gy radiation, from about 25 to about 30 Gy radiation, or from about 20 to about 25 Gy radiation.
In certain embodiments, a hypofractionated radiation regimen is administered over a period of from about 2 to about 5 weeks, about 3 to 5 weeks, about 4 to 5 weeks, about 1 to 4 weeks, about 2 to 4 weeks, about 3 to 4 weeks, about 1 to 3 weeks, about 2 to 3 weeks, or about 1 to 2 weeks. In certain embodiments, the radiation is administered over a period of about 1, about 2, about 3, about 4 or about 5 weeks.
In certain embodiments, a hypofractionated radiation regimen is administered over about 5 to about 15 fractions, about 5 to about 10 fractions, or about 10 to about 15 fractions. In certain embodiments, the radiation is administered over about 5 fractions, about 6 fractions, about 7 fractions, about 8 fractions, about 9 fractions, about 10 fractions, about 11 fractions, about 12 fractions, about 13 fractions, about 14 fractions, or about 15 fractions.
The radiation may be external-beam radiation therapy such as photon beams of x-rays or gamma rays, electron beams, or proton therapy. The radiation may be internal radiation therapy (brachytherapy) where radiation is delivered from a radiation source placed inside or on the body, even inside the tumor tissue. The radiation source may be a radioactive isotope in the form of a seed or pellet implanted in or placed on the patient.
Cancers that may be treated according to the methods of the invention and cancers for which a patient's responsiveness to a treatment therapy can be determined according to the methods of the invention include sarcoma, prostate cancer, adrenal cancer, anal cancer, bile duct cancer, bladder cancer, bone cancer, cancer of the brain or central nervous system, basal cell skin cancer, breast cancer, cervical cancer, colorectal cancer, endometrial cancer, esophageal cancer, eye cancer, gallbladder cancer, gastrointestinal carcinoid tumors, gastrointestinal stromal tumor (GIST), gastric cancer, glioma, glioblastoma, head and neck cancer (including head and neck squamous cell carcinoma), Hodgkin disease, Classical Hodgkin Lymphoma, diffuse large B cell lymphoma, follicular lymphoma, Kaposi sarcoma, kidney cancer, laryngeal and hypopharyngeal cancer, leukemia (including acute myeloid leukemia), liver cancer (including hepatocellular carcinoma), lung cancer (including non-small cell lung cancer and metastatic non-small cell lung cancer), lymphoma, malignant mesothelioma, melanoma (including unresectable or metastatic melanoma), merkel cell carcinoma, metastatic urothelial carcinoma, multiple myeloma, myeloma, myelodysplastic syndrome, nasal cavity and paranasal sinus cancer, nasopharyngeal cancer, neuroendocrine cancer, neuroblastoma, non-Hodgkin lymphoma, oral cavity and oropharyngeal cancer, osteosarcoma, ovarian cancer, pancreatic cancer, penile cancer, pituitary tumors, renal cancer (including renal cell carcinoma), retinoblastoma, rhabdomyosarcoma, salivary gland cancer, squamous cell skin cancer, small intestine cancer, stomach cancer, testicular cancer, thymus cancer, thyroid cancer, uterine cancer, or vaginal cancer.
Patients with histologically confirmed STS of the extremity or trunk with planned neoadjuvant RT and surgery were eligible. All patients were 18 or older and had Eastern Cooperative Oncology Group (ECOG) performance status 0 to 2. Exclusion criteria included evidence of distant metastases, planned neoadjuvant or adjuvant chemotherapy; prior RT to the area to be irradiated; and active treatment of a second malignancy.
Patients were assigned to receive neoadjuvant RT followed by surgery 2 to 6 weeks later. Radiation CT and/or MRI simulation was performed with custom immobilization. The gross, clinical and planning target volumes were defined according to RTOG 06306. A dose of 6 Gy×5 fractions (30 Gy) was delivered to at least 95% of the PTV. Intensity modulated (IMRT), 3D conformal, or electron planning techniques were used. Radiation plans were deemed acceptable if they met dosimetric parameters outlined in TABLE 3. All patients underwent daily image guidance except two patients receiving electron RT. All surgeries were performed by one of four dedicated sarcoma surgeons.
The primary endpoint of this study was the rate of late grade ≥2 radiation morbidity (fibrosis, lymphedema or joint stiffness) at median two-year follow-up (minimum one year). Fibrosis and joint stiffness were graded based on Radiation Therapy Oncology Group/European Organisation for Research and Treatment of Cancer (RTOG/EORTC) criteria, and lymphedema was graded by Stern's scale. Other secondary endpoints included acute toxicities as assessed by the frequency and severity of adverse events (AEs) using CTCAE v4.0 toxicity criteria, the rate of major wound complications, pathologic treatment effect, and the rate of local and distant recurrences. Major wound complications were defined as per established criteria from prospective clinical trials of extremity soft tissue sarcoma. Pathologic treatment effect was defined as the percentage of surgical tissue with hyalinization or necrosis relative to pre-treatment biopsy. Time-to-event end points were measured from enrollment.
Patients were seen after the completion of radiation and prior to surgery by the radiation oncologist and/or the sarcoma surgeon. Patients were followed closely in the postoperative setting. The patients' status was reviewed 3 months after surgery, and then at least every 6 months thereafter. The patients were followed both clinically and radiographically after treatment with CT or MRI of the primary site and CT of the chest at least every 6 months for the first two years and then at least annually in the third year. Patients with myxoid liposarcoma were also evaluated with CT of the abdomen and pelvis.
A subset of patients were consented to a parallel imaging study under IRB approval from May 2016 to June 2018. Diffusion-weighted images (DWI) were acquired up to four times for each patient using a 0.35T MR-guided radiotherapy machine (ViewRay, MRIdian, Mountain View, Calif.) including before the first fraction of treatment and at least 14 days after RT but prior to surgical resection.
The study was designed to evaluate the rate of grade ≥2 radiation morbidity (subcutaneous tissue fibrosis, joint stiffness, or edema at 2 years to mirror the initial design of RTOG-06306 with a target absolute improvement of 20% in the rate of grade ≥2 late radiation-associated toxicity compared to the historical neoadjuvant RT arm of the CAN-NCIC-SR2 study from 37% to 17%.
Between May 2016 and May 2018, 52 patients with localized high-risk STS of the extremity or trunk were enrolled. Of these, 50 patients ultimately underwent neoadjuvant RT and surgery (
The relationship between major wound complications and relevant clinical variables, including tumor size, tumor site, tumor depth, time interval from radiation to surgery, and two radiation dosimetric variables using univariate logistic regression was examined. Dosimetric variables included the maximum radiation dose (Gy) to the skin (minimum 0.5 cc volume), and volume of the skin (cc) receiving 12 Gy. Likelihood ratio test (LRT) was used to assess the significance of categorical variables. Pathologic outcomes were reported with descriptive statistics. Differences in pre- and posttreatment tumor volumes by diffusion-weighted MRI were assessed by paired t-test. Comparisons of average distance traveled and volume of patients between the two years prior to study enrollment and the study period were made using unpaired t-tests.
Genomic DNA from blood or saliva was analyzed for single nucleotide polymorphisms (SNPs) disrupting miRNA binding sites, promoter regions or coding sequences as previously identified. Biomarkers in binding sites in genes involved in the immune system and DNA damage response, as well as promoters and coding sequences of miRNAs that regulated key genes known to be critical in the DNA damage or immune response were enriched in the analysis. As a final step in the evaluation for candidate variants to test, variants were limited to those predicted to be found in between 0.5% to 25% of the population, to find biomarkers that are likely to be detected in reasonably small cohorts of patients. This list was reduced to ˜116 variants by fitting the above defined priority parameters for miRNA pathway variants. Panels were run using the Sequenom platform. Each panel was run with internal controls that used Taqman Genotyping as the gold standard. Any biomarker with less than a 90% call rate or more than 1% error found by controls was excluded from further analysis. To insure sufficient marginal variation in the final panel, any biomarker with an observed rate of mutation less than 12.5% in the training sample was excluded from the analysis.
The relationship of this set of 116 SNPs with the incidence of major wound complications was examined. Each SNP was defined as a categorical variable. Lower extremity tumor site was also included as a categorical variable as it was the only clinical variable associated with major wound complications. The association between this panel of potential germline biomarkers and tumor site with wound toxicity was assessed using four classifiers on the set of 50 sarcoma patients, with wound toxicity rate 32%. Trained classifiers, whose hyperparameters were selected to optimize the F1 score with leave-one-out cross-validation (LOOCV), included classification trees (CT) (Breiman et al: Classification and Regression Trees. Boca Raton, Fla., CRC Press, 2017), random forests (RF) (Breiman L: Random Forests. Machine Learning 45:5-32, 2001), boosted trees (BT) (Chen T, Guestrin C: XGBoost: A Scalable Tree Boosting System, KDD. San Francisco, Calif., 2016) and LASSO-regularized logistic regression (LASSO-LR) (Tibshirani R: Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society Series B (Methodological):267-88, 1996). The CT were tuned on minimum split and minimum observations in any terminal node, RF were tuned on number of trees and variables considered at each split, BT were tuned on the learning parameter eta, tree depth, and the number of rounds, and LASSO-LR models were tuned on the regularization parameter lambda. The subjects with toxicity were up-weighted through oversampling method. The final performance measures, accuracy, specificity, sensitivity, negative predictive value, positive predictive value, area under the curve (AUC), and F1 score were reported using stratified 10-fold cross-validation. The threshold of number of predictors to include in the model was determined as the use of top k predictors allowed with the highest AUC among k=5, 10, 15, . . . , 50. Importance measures via filter method with R package FSelector (Romanski P, Kotthoff L: FSelector: Selecting Attributes, (ed R package version 0.31), 2018) were then used to select top k predictors to train the classifiers and this was determined by mean rank from 1000 sample sets of their respective value. The four important measures are entropy-based information gain between predictors and response, variable importance based on ranger impurity importance, the entropy-based gain ratio between predictors and response and the univariate model score. Via 1000 oversampling sets, the order of significance of these k predictors was determined according to the obtained variable importance measure of mean decrease in the Gini impurity from the trained random forest classifier for its best prediction performance among 4 classifiers. These top k predictors to wound toxicity with their order of significance were reported. CT, RF, BT, and LASSO-LR classifiers were fit in R (version 3.6.0) (R Development Core Team: Stats package (power.prop.test( ) function) in R: A language and environment for statistical computing. Vienna, Austria: R Foundation for Statistical Computing, 2018) with mlr (Bischl et al.: mlr: Machine Learning in R, (ed R package version 2.13), 2018) calling rpart (Therneau T AB, and Ripley B.: rpart: Recursive Partitioning and Regression Trees, (ed R package version 4.1-13), 2018), ranger (Marvin N. Wright S W, Philipp Probst: ranger: A Fast Implementation of Random Forests, (ed R package version 0.11.2), 2019), xgboost (Chen T, He T: xgboost: eXtreme Gradient Boosting, (ed R package version 0.82.1), 2019), and glmnet (Jerome Friedman et al.: glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models, (ed R package version 2.0-16), 2018), respectively, and the tree plot was generated with package reprtree (Banerjee M, Ding Y, Noone A M: Identifying representative trees from ensembles. Stat Med 31:1601-16, 2012).
The final cross validated tuning parameters for the reported classifiers are as follows: minimum split of 5 and minimum observations of 5 in any terminal node for classification trees (CT), three variables considered at each split with 15 trees for random forests (RF), learning parameter eta of 0.367, max depth of 2, and 13 rounds for boosted trees (BT) and regularization parameter lambda equal to 0.001 for LASSO-regularized logistic regression (LASSO-LR). For each classifier, all remaining hyperparameters were assigned their default values as defined through their associated R packages.
The study enrolled patients across a broad age spectrum, including five patients between age 80 and 90, three of whom had ECOG performance status of 2. With the exception of one tumor, all were intermediate- or high-grade. Tumor size among enrolled patients was heterogeneous, ranging from 1.2 to 28 cm, and twelve patients had tumors greater than 10 cm. Twelve patients (24%) received neoadjuvant RT prior to re-resection for gross (n=9, 18%) or microscopic (n=3, 6%) residual disease. The median time between completion of RT and surgery was 28 days (range 14-55).
The five-day neoadjuvant RT regimen was well-tolerated without grade 3 or higher acute or subacute RT-associated toxicities. The most severe radiation dermatitis was grade 2 and occurred in four patients (8%); other grade 2 toxicities were pain flare (n=3, 6%) and nausea (n=1, 2%). Radiation-associated late toxicities (fibrosis, joint stiffness or lymphedema) as measured by RTOG/EORTC criteria are summarized in TABLE 4.
No grade 3 or higher late toxicities were observed. Overall, 7 of 44 evaluable patients (16%) developed at least one grade 2 or higher radiation-associated late toxicity, which met the primary endpoint. Grade 2 fibrosis (11%) and joint stiffness (11%) were more frequent than grade 2 lymphedema (4%). Of evaluable patients, 34 patients had minimum two years follow-up; five (14.7%) developed grade ≥2 fibrosis, lymphedema and/or joint stiffness. A non-significant trend was observed toward increased grade ≥2 radiation-associated toxicities in patients with tumors larger than the median size of 6.5 cm (p=0.101, χ2 test), while no association was observed between RT modality (IMRT, 3D, or electron) and toxicities, though the limited number of patients treated with 3D-conformal and electron RT limits this comparison (data not shown).
Major wound complications were observed in 16 of 50 patients (32%). This rate is on par with rates of major wound complications observed in prospective studies of neoadjuvant RT (35% in the neoadjuvant RT arm of the NCIC Phase 3 study; 30.5% in multi-institutional phase 2 study of image guided IMRT, and 36.6% in RTOG 0630; O'Sullivan et al (2002) L
Wound complications were not associated with time interval from radiation to surgery, tumor depth, tumor size or either of two parameters for radiation dose to the skin.
Association of Germline Biomarkers with Wound Complications
Given the paucity of clinical factors that predict for wound complications, it was hypothesized that inherent patient radiosensitivity may contribute to the risk of wound complications after neoadjuvant RT. In exploratory analysis, among lower extremity tumor site and a panel of 116 annotated SNPs in miRNA binding sites, tumor site and 19 SNPs were identified as the top 20 predictors for major wound complication rate (TABLE 6).
The prediction performance for four proposed classifiers (classification tree, random forest, boosted tree, and LASSO-LR) using these 20 predictors jointly were fairly similar (TABLE 7), with random forests performing the best, with an accuracy of 0.855, a specificity of 0.792, sensitivity of 0.917, AUC of 0.952 and F1 of 0.868.
A representative decision tree based on the random forest classifier is shown in
As shown in
If the patient is homozygous wild-type (0) for the first variable of predictive value, CD6_rs76677607, by following “Y” and travelling left on the tree, the marker SHC4_rs1062124 should be considered. If the patient is homozygous wild-type (0) for SHC4_rs1062124, by following “Y” and travelling left on the tree, the marker ERCC1_rs3212948 should be considered. If the patient is homozygous wild-type (0) for ERCC1_rs3212948, by following “Y” and travelling left on the tree, the patient is not predicted to have a toxic response. If the patient is not homozygous wild-type (0) for ERCC1_rs3212948, by following “N” and travelling right on the tree, the marker RAC1_rs9374 should be considered. If the patient is homozygous wild-type (0) for RAC1_rs9374, by following “Y” and travelling left on the tree, the patient is not predicted to have a toxic response. If the patient is not homozygous wild-type (0) for RAC1_rs9374, by following “N” and travelling right on the tree, the patient is predicted to have a toxic response.
If the patient is not homozygous wild-type (0) for SHC4_rs1062124, by following “N” and travelling right on the tree, the presence of a lower extremity tumor should be considered. If the patient does not have a tumor on a lower extremity, by following “N” and travelling right on the tree, the patient is not predicted to have a toxic response. If the patient does have a tumor on a lower extremity, by following “Y” and travelling left on the tree, the marker UNGC.41.IL1RAP should be considered. If the patient is not homozygous wild-type (0) for UNGC.41.IL1RAP, by following “N” and travelling right on the tree, the patient is not predicted to have a toxic response. If the patient is homozygous wild-type (0) for UNGC.41.IL1RAP, by following “Y” and travelling left on the tree, the marker CD274_rs4143815 should be considered. If the patient is not homozygous wild-type (0) or heterozygous (1) for CD274_rs4143815, by following “N” and travelling right on the tree, the patient is not predicted to have a toxic response. If the patient is homozygous wild-type (0) or heterozygous (1) for CD274_rs4143815, by following “Y” and travelling left on the tree, the marker miR.99a.promoter should be considered. If the patient is homozygous wild-type (0) for miR.99a.promoter, by following “Y” and travelling left on the tree, the patient is predicted to have a toxic response. If the patient is not homozygous wild-type (0) for miR.99a.promoter, by following “N” and travelling right on the tree, the marker IL1A_rs1800587 should be considered. If the patient is heterozygous (1) for IL1A_rs1800587, by following “Y” and travelling left on the tree, the patient is not predicted to have a toxic response. If the patient is not heterozygous (1) for IL1A_rs1800587, by following “N” and travelling right on the tree, the patient is predicted to have a toxic response.
While clinical or dosimetric predictors other than lower extremity tumor location were not associated with wound toxicity, an exploratory analysis of germline SNPs in miRNA binding sites suggests a role for patient-intrinsic biology as a factor in the development of wound complications after neoadjuvant RT. There are indeed germline differences in radiosensitivity of normal tissues between individuals. (See, e.g., Grossberg et al. (2018) JAMA Oncol 4:1751-1757, West et al. (2010) Radiother Oncol 94:117-8, Chang-Claude et al. (2009) Br J Cancer 100:1680-6, Isomura et al. (2008) Clin Cancer Res 14:6683-9, Giotopoulos et al. (2007) Br J Cancer 96:1001-7, Damaraju et al. (2006) Clin Cancer Res 12:2545-54, Andreassen et al. (2006) Int J Radiat Biol 82:577-86, Andreassen et al. (2003) Radiother Oncol 69:127-35, Quarmby et al. (2003) Int J Radiat Biol 79:137-43, Angele et al. (2003) Cancer Res 63:8717-25.) The majority of existing evidence on this topic centers on late skin and tissue response to radiotherapy of the breast.
The above analysis identified a set of 19 germline alterations in microRNA binding sites in genes with roles in immune and DNA damage response that, in combination with lower extremity tumor location, are a predictor of major wound complications. These data are limited by the sample size of the phase 2 study. Nonetheless, these data provide for the identification of patients who are at risk of major wound complications in advance of treatment. For these at-risk patients, more aggressive dosimetric constraints, consideration of adjuvant radiotherapy, alternative surgical approaches or changes in post-surgical wound care may be warranted. An increase in the number of patients treated with neoadjuvant RT and the distance traveled by patients to the high-volume center that coincided with study initiation was observed, suggesting five-day neoadjuvant RT would increase the utilization of neoadjuvant RT and access to care at high-volume sarcoma centers.
Fifty (50) sarcoma patients from Example 1 with distant failure status (response variable, 1 when positive, 0 when negative) were studied. One hundred sixteen (116) genotyping data and tumor site, as categorical variable, for each patient were collected.
The association between a panel of potential germline biomarkers and distant failure, disease-free survival, was assessed using four classifiers on a set of 50 sarcoma patients, with distant failure rate 20%, Trained classifiers, whose hyperparameters were selected to optimize the F1 score with leave-one-out cross-validation (LOOCV), included classification trees (Breiman L, Friedman J H, Olshen R A, Stone C J, eds. Classification and regression trees.: Boca Raton, Fla.: CRC Press.; 2017), random forests (Breiman L. Random forests. Machine Learning 2001; 45:5-32, boosted trees (XGBoost: A Scalable Tree Boosting System. 2016. at https://arxiv.org/abs/1603.02754), and LASSO-regularized logistic regression (LASSO-LR) (Tibshirani R. Regression Shrinkage and Selection via the Lasso. Journal of the Royal Statistical Society 1996; Series B (Methodological):267-88).
The Classification Trees were tuned on minimum split and minimum observations in any terminal node, Random Forests were tuned on number of trees and variables considered at each split, Boosted Trees were tuned on the learning parameter eta, tree depth, and the number of rounds, and LASSO-LR models were tuned on the regularization parameter lambda. The subjects with toxicity were up-weighted through oversampling method for training set in each iteration of cross-validation. The final performance measures, accuracy, specificity, sensitivity, negative predictive value, positive predictive value, area under the curve (AUC), and F1 score were reported using LOOCV for 100 over sample sets. The threshold of number of predictors to include in the model was determined as the use of top k predictors allowed highest AUC among k=5, 10, 15, . . . , 50. Importance measures via filter method (Piotr Romanski L. K. (2018). FSelector: Selecting Attributes. R package version 0.31) were used to select top k predictors, as determined by mean rank from 1000 sample sets of their respective value and whose pvalue from a chi-squared test was less or equal to 0.3, to train the classifier. The four important measures are entropy-based information gain between predictors and response, variable importance based on ranger impurity importance, the entropy-based gain ratio between predictors and response and the univariate model score.
Classification Trees, Random Forests, Boosted Trees, and LASSO-LR classifiers were fit in R (version 3.6.0) (R Core Team (2019). R: A language and environment for statistical computing. R Foundation for Statistical Computing, Vienna, Austria. URL http://www.R-project.org/) with mlr (Bischl B, et al. (2018). mlr: Machine Learning in R. R package version 2.13) calling rpart (Therneau T, Atkinson B, and Ripley B. (2018). rpart: Recursive Partitioning and Regression Trees. R package version 4.1-13), ranger (Wright M N, Wager S, and Probst P. (2019). ranger: A Fast Implementation of Random Forests. R package version 0.11.2), xgboost (Chen T, et al. (2019). xgboost: Extreme Gradient Boosting. R package version 0.82.1), glmnet (Friedman J, et al. (2018). glmnet: Lasso and Elastic-Net Regularized Generalized Linear Models. R package version 2.0-16), respectively, and the tree plot was generated with the package reprtree (Banerjee, Mousumi & Ding, Ying & Noone, Anne-Michelle. (2012). Identifying representative trees from ensembles. Statistics in medicine. 31. 1601-16. 10.1002/sim.4492). Via 100 over sampling sets, the order of significance of these k predictors was determined according to the obtained variable importance measure of mean decrease in the Gini impurity from the trained random forest classifier for its best prediction performance of PPV among 4 classifiers.
The classification strategies described are compared in TABLE 8 for predicting distant failure. Sensitivity is intended as the probability of correctly predicting distant failure; specificity is intended as the probability of correctly predicting no distant failure. PPV is intended as the probability of correctly classifying a patient as having distant failure, and NPV is intended as the probability of correctly classifying a patient as not exhibiting distant failure. The F1 Score is an evaluation of test accuracy determined using the PPV and Sensitivity measures.
TABLE 9 shows rank order of marginal associations between distant failure and biomarkers. P values as shown are based on independent Pearson's Chi-squared Test for Count Data. As shown, the top predictors of distant failure are TREX1_rs11797, LIN28A_rs9438623, rs2187668, miR.34b.c.promoter_rs4938723, IL.6_rs12700386, CD274_rs4742098, FCGR2A_rs1801274, IL.6_rs2069840, CD274_rs4143815, ERCC4_rs4781562, STAT4_rs7574070, EXO1_rs4150021, HAMP_rs1882694, IL10_rs3024496, KRAS_rs61764370, rs922075, ERCC1_rs11615, SOS1_rs1059313, SPI1_rs2071304, and, SHC4_rs1062124.
Given the high effectiveness of definitive treatments for localized prostate cancer, quality of life following treatment is a paramount factor in patient-physician shared decision-making. After definitive radiotherapy, a major quality of life limiting toxicity is late genitourinary (GU) toxicity, which could manifest as increased urinary frequency, retention, pain, and bleeding. The 5-year late grade ≥2 GU toxicity rates following modern radiotherapy ranges from 12-15% with an insidious increase over time. Overall, these rates appear to be similar whether a patient is treated with conventionally-fractionated radiotherapy (CF-RT; 1.8-2.0 Gy per fraction over 39-45 treatment sessions) or stereotactic body radiotherapy (SBRT; >7 Gy per fraction over 5 or fewer sessions). While the overall toxicity rates also are similar, an unanswered question is whether variability in intrinsic radiosensitivity, which should rationally differ based on radiation fraction size delivered, may predict whether individual men are more likely to experience significant toxicity after SBRT versus CF-RT.
The following Example identifies mirSNPs that can help to aid in the prediction of toxicity following CF-RT versus SBRT in a fractionation-dependent manner.
In this study, 93 prostate cancer patients from the University of California, Los Angeles (NCT01059513 [n=63] and NCT02296229 [n=30]) were administered hypofractionated stereotactic body radiation therapy (SBRT) with 40 Gy in 5 fractions (8 Gy per fraction) for a period not exceeding 14 consecutive days. SBRT plans were delivered by volumetric modulated arc therapy, with a planning margin of 5 mm around the prostate, reduced to 3-5 mm posteriorly. Inter- and intrafractional motion management relied on alignment to intraprostatic fiducial markers. Gastrointestinal (GI) and genitourinary (GU) toxicity was assessed.
In addition, gastrointestinal (GI) and genitourinary (GU) toxicity was assessed for prostate cancer patients receiving the conventionally-fractionated radiation treatment (CF-RT; i.e., standard fractionation treatment), using data from the OSLO study. The OSLO cohort dataset is described in, e.g., Hayman et al. (2019) Urol. Oncol. 37(4):289.e19-289.e26 and Lilleby et al. (2013) Prostate 73(10):1038-1047. Briefly, patients in the CF-RT cohort received 74 Gy in 37 fractions to the prostate and 50 Gy in 25 fractions to the pelvic lymph nodes as described previously. The initial portion was delivered with intensity modulated radiotherapy techniques, with expansions on the prostate ranging from 13-15 mm and imaging guidance predominantly consisting of alignment to bony markers.
Genomic DNA from peripheral blood mononuclear cells, whole blood, or tumor tissue was isolated using standard techniques and analyzed in a Clinical Laboratory Improvement Amendments—certified laboratory as previously described. (Weidhaas et al. (2017) “The KRAS-Variant and Cetuximab Response in Head and Neck Squamous Cell Cancer: A Secondary Analysis of a Randomized Clinical Trial,” JAMA Oncology 3:483-91. Biomarkers were identified from a pool of miRNA-based biomarkers discovered and determined to be functional previously through sequencing and bioinformatic approaches. Chen et al. (2015) “Targeted resequencing of the microRNAome and 3′UTRome reveals functional germline DNA variants with altered prevalence in epithelial ovarian cancer,” Oncogene 34:2125-37. Mutations in DNA damage repair and response genes and immune response genes, in the key gene targets of miRNAs known to be critical in the DNA damage or immune response, and in the promoters of miRNAs known to be important in these responses were prioritized. Therefore, for this analysis, panels were run using the Sequenome platform, an analysis which included approximately 500 single nucleotide polymorphisms or deletions. Each panel was run with internal controls that used Taqman Genotyping as the gold standard. To compare the genetic variation between the CF-RT and SBRT cohorts, we calculated the fixation index for each analyzed mirSNP. Holsinger et al. (2009) “Genetics in geographically structured populations: defining, estimating and interpreting F(ST),” Nat Rev Genet. 10:639-50.
Statistical models and analyses were conducted in R (version 4.0.0). All mirSNPs with variance close to zero (nearZeroVar::caret version 6.0-84)27 or that had an almost perfect correlation (r2≥0.99) were removed. Fisher's exact test was used to test the pairwise independence between each mirSNP and the outcome of experiencing a late grade ≥2 GU toxicity event as scored on the Radiation Therapy Oncology Group (RTOG) scale. (Cox et al. (1995) “Toxicity criteria of the Radiation Therapy Oncology Group (RTOG) and the European Organization for Research and Treatment of Cancer (EORTC),” International journal of radiation oncology, biology, physics. 31:1341-6.
This scoring system was developed in 1985 and grades the severity of radiation-induced reactions from 0 to 5, with grade 2 toxicity being considered moderate. For the initial assessment of pairwise independence, the p-values were only used for model selection and thus no adjustment for multiplicity of testing was performed. Due to the unequal distribution of patients with and without toxicity, up-sampling was used (upSample::caret version 6.0-84) to create balanced populations. Random forest (randomForest::randomForest version 4.6-14), boosted trees (gbm::gbm version 2.1.5), and elastic net (glmnet::glmnet version 3.0-2) models were then generated to predict toxicity in each patient for both the CF-RT and the SBRT cohorts. (Liaw et al. Classification and Regression by Random Forest. R News. 2002; 2:18-22; Greenwell et al. (2019) “Generalized Boosted Regression Models,” R Foundation for Statistical Computation; Kuhn M. caret: Classification and Regression. R Foundation for Statistical Computing; 2019; Friedman et al. (2010) Regularization Paths for Generalized Linear Models via Coordinate Descent,” Journal of Statistical Software 33:1-22.) The p-value threshold for inclusion in the models were 0.3 except for the boosted tree model for the SBRT cohort, which had a p-value threshold of 0.15. Each model was run using various parameters, and model performance was assessed for sensitivity, specificity, negative predictive value (NPV), positive predictive value (PPV), F1 score, and AUC (AUC::cvAUC version 1.1.0), using held-out samples. (LeDell E P, M.; van der Laan, M. cvAUC: Cross-Validated Area Under the ROC Curve Confidence Intervals. R Foundation for Statistical Computing; 2014.) To reduce selection bias and overfitting, these metrics were calculated using leave-one-out-cross validation.
TABLE 10 shows toxicity events by grade for the 93 prostate cancer patients in the SBRT analysis set.
Toxicity events at or above grade 2 are considered high grade toxicity. No patients experienced acute GI toxicity at or above grade 2, only 3 patients experienced acute GU high grade toxicity. In the long term, 5 patients experienced GI high grade toxicity (grade 2 and 3), and 14 patients experienced long term GU high grade toxicity (grade 2 and 3).
GI and GU toxicities appear uncorrelated within the short and long term. Low grade acute GU toxicity is mildly correlated with long term low grade GU toxicity (X-squared=25.9, p.val=0.04), but no correlation is detected between high grade toxicity in the short and long terms.
Long term higher grade toxicity is defined as GU toxicity of grade 2 or higher. The analysis is based on a sample of 93 prostate cancer patients, stratified adaptively using 13 biomarkers.
TABLE 11 shows statistically significant (Fisher test p.val_0.05) marginal association between late term GU toxicity (grade 2 or higher) and genetic mutations (homozygous and heterozygous combined).
These results indicate that patients carrying the BMP2_rs1979855 mutation, PTPN2_rs1893217 mutation or BRCA2_rs15869 are more likely to develop toxicity than patients who are homozygous wild type for the respective markers. The results further indicate that patients who are homozygous wild-type for the ABL1_rs11991 or ERCC1_rs11615 SNPs are more likely to develop toxicity than patients who carry the mutation for the respective markers.
Several classification strategies are compared in TABLE 12 for predicting acute and late term genitourinary (GU) toxicity, at or above grade 2. The following parameters were used for the determination of late term toxicity: Random Forest classification p-value threshold of 0.3 and mtry=1; Elastic Net classification p-value threshold of 0.3, α=0.1, and λ=0.0395; and Boosted Trees classification p-value threshold of 0.15, int_depth=1, and num_trees=100. Variable importance is evaluated using a “filter” methodology. Sensitivity is intended as the probability of correctly predicting a toxic response; specificity is intended as the probability of correctly predicting no toxic response. PPV is intended as the probability of correctly classifying a patient as exhibiting a toxic response, and NPV is intended as the probability of correctly classifying a patient as not exhibiting a toxic response. The F1 Score is an evaluation of test accuracy determined using the PPV and Sensitivity measures.
TABLE 13 shows descriptive summaries of Acute and Late-term GU Toxicity in the CF-RT data set, as they relate to basic clinical and physiological information. Marginal associations were observed between acute and late term GU toxicity (at or above grade 2) and several clinical and physiological variables. Patients experiencing acute toxicity are, on average, 3 years older (p.val=0.025). Patients experiencing late term toxicity tend to be exposed to longer androgen deprivation therapy (ADT) duration (p.val=0.007).
TABLE 14 shows marginal associations between acute toxicity (at or above grade 2) and biomarkers for the CF-RT data set. P values are based on independent Pearson's Chi-squared Test for Count Data. As shown, the top predictors of acute toxicity at or above grade 2 are ATM_rs189037, CD6_rs76677607, IL1A_rs1800587, IL1A_rs17561, BRCA2_rs7334543, ILF3.58_rs118142475, and BIRC5_rs2239680.
TABLE 15 shows marginal associations between late toxicity (at or above grade 2) and biomarkers for the CF-RT data set. P values are based on independent Pearson's Chi-squared Test for Count Data. As shown, the top predictors of late toxicity at or above grade 2 are CD6_rs76677607, CD274_rs2297136, BRCA2_rs7334543, NBN_rs1805794, ILI.1_rs4848306, CD274_rs4742098, ERCC4_rs4781562, and MSH2_rs2303428.
Several classification strategies are compared in TABLE 16 for predicting acute and late term genitourinary (GU) toxicity, at or above grade 2. All models were fit using variables that had a p-value of 0.3 or less when conducting a chi-squared test for independence. The following parameters were used for the determination of acute term toxicity: Random Forest classification p-value threshold of 0.3 and mtry=1; Elastic Net classification p-value threshold of 0.3, α=0.1, and λ=0.0404; and Boosted Trees classification p-value threshold of 0.15, int_depth=3, and num_trees=100. The following parameters were used for the determination of late term toxicity: Random Forest classification p-value threshold of 0.3 and mtry=3; Elastic Net classification p-value threshold of 0.3, α=0.1, and λ=0.00404; and Boosted Trees classification p-value threshold of 0.3, int_depth=2, and num_trees=150. Variable importance is evaluated using a “filter” methodology. Sensitivity is intended as the probability of correctly predicting a toxic response; specificity is intended as the probability of correctly predicting no toxic response. PPV is intended as the probability of correctly classifying a patient as exhibiting a toxic response, and NPV is intended as the probability of correctly classifying a patient as not exhibiting a toxic response. The F1 Score is an evaluation of test accuracy determined using the PPV and Sensitivity measures.
SNPs Predictive of Prostate Cancer Mortality, Biochemical Relapse, Distant Metastasis and Potency were also determined.
Survival data was available for the OSLO patients, including time until Prostate Cancer Specific Mortality, time until Distant Metastasis, and time until Biochemical Relapse. Due to the duration of the study, there is right censoring, meaning that the event had not happened in some patients yet. In this analysis, a log-rank test (survival version 2.44-1.1) was conducted to determine a SNPs marginal association with the time until the event of interest. TABLES 17, 18, and 19 contain the unadjusted P-values for the log-rank test for each SNP.
TABLE 17 shows P-values for the Log-Rank test using Prostate Cancer Specific Mortality as the outcome. A significant p-value indicates that the survival curve for a patient with the given gene mutation is significantly different than the survival curve if the patient did not have the gene mutation. This test only considers one gene at a time. As shown, the top predictors of prostate cancer specific mortality are BIRC5_rs2239680, CD6_rs76677607, XRCC4_rs1040363, and SMAD1_rs11724777.
TABLE 18 shows P-values for the Log-Rank test using Biochemical Relapse (elevated blood levels of Prostate Specific Antigen, PSA) as the outcome. A significant p-value indicates that the survival curve for a patient with the given gene mutation is significantly different than the survival curve if the patient did not have the gene mutation. This test only considers one gene at a time. As shown, the top predictors of biochemical relapse are IL10RB_rs2834167, CD274_rs1411262, CD274_rs822339, SMAD1_rs11724777, IL18R1_rs11465660, and IL16_rs11556218.
TABLE 19 shows P-values for the Log-Rank test using distant metastasis as the outcome. A significant p-value indicates that the survival curve for a patient with the given gene mutation is significantly different than the survival curve if the patient did not have the gene mutation. This test only considers one gene at a time. As shown, the top predictors of distant metastasis are IL10_rs3024496, FOXP3_rs2280883, BIRC5_rs2239680, IL10_rs3024496_P1.P2, LIG4_rs2232643, RAC1_rs9374, and time.
Data from the OSLO cohort was also analyzed for potency, using the “Potency column” (Column J). As shown in TABLE 20, the top predictors of potency are HAMP_rs10421768, XRCC1_rs25487, IL.6_rs2069840, XRCC4_rs1040363, and IL19_rs2243158.
Several classification strategies are compared in TABLE 21 for predicting potency. The following parameters were used for the determination of potency: Random Forest classification p-value threshold of 0.2 and mtry=1; Elastic Net classification p-value threshold of 0.2, α=1.0, and λ=0.00395; and Boosted Trees classification p-value threshold of 0.2, int_depth=3, and num_trees=150. PPV is intended as the probability of correctly classifying a patient as exhibiting impotency, and NPV is intended as the probability of correctly classifying a patient as exhibiting potency. The F1 Score is an evaluation of test accuracy determined using the PPV and Sensitivity measures.
The following analysis was conducted on two cohorts of men (OSLO and SBRT, as described in Example 3), each cohort receiving a different treatment regimen for advanced prostate cancer. The programming language used to analyze the data set was R (Version 3.6.1). The goal of this analysis was to use microRNA-based germline biomarkers to help identify patients that will experience Long-Term (Late) and Acute Genitourinary Toxicity. Toxicity was measured using the RTOG morbidity grading system (Cox et al. (1995) Int J Radiat Oncol Biol Phys 31(5):1341-6, and within the analysis, it was converted to a binary variable of those with low-grade toxicity (RTOG score ≤1) versus patients with high-grade toxicity (RTOG score ≥2). TABLE 22 shows the distribution of the patients based off of type of toxicity and treatment.
No analysis was conducted on Acute Genitourinary Toxicity for SBRT because only 3 patients experienced toxicity.
TABLE 22 shows toxicity grades by type of toxicity. In this study, a toxicity of 2 or higher is considered to be high grade toxicity.
Biomarkers with variance close to zero (caret::nearZeroVar version 6.0-84) and biomarkers that had almost perfect correlation r2xi, xj≥0.99) were removed. The Fisher's Exact Test for Count Data was then conducted to test the pairwise independence of the response (Toxicity) and each predictor (microRNA-based germline biomarkers). For each analysis, a p-value threshold was used to determine which predictors would be used in the models. Since the p-values were only used as a selection criteria, they are not adjusted to correct for the multiplicity of testing. Therefore, the p-values obtained should not be used for any inferential purposes. TABLES 23, 24, and 25 contain the selected variables, as well as the percentages of people who are toxic with the mutation and without the mutation.
Due to the imbalance in the responses, up-sampling was used (caret::upSample version 6.0-84) to ensure the same number of Toxic patients as Non-Toxic patients. After up-sampling, Random Forest, Boosted Trees, and Elastic Net were fit to predict toxicity in each patient. Each model was run using various model parameters in order to find the optimal parameter. The metrics used for model performance were Sensitivity, Specificity, Negative Predicted Value (NPV), Positive Predicted Value (PPV), and F1 Score (Defined Below). To reduce selection bias and overfitting, each of these metrics were calculated using leave-one-out cross-validation. TABLES 26 and 27 contain the evaluation metrics for SBRT and OSLO patients. TABLES 28 and 29 contain evaluation metrics for predicting patient toxicity in one group while being trained on patients in the other group. The poor performance of the models suggests that the underlying mechanisms that determine Late GU Toxicity are different in each treatment regimen.
To determine variable importance, the F1 score was used as the main metric. To calculate the variable importance for a single SNP (Si), the F1 score was calculated using all of the SNPs in the model (F1*), and then the F1 score was calculated when removing the SNP (Si) from the model (F˜1). The F1 scores were all calculated using leave-one-out cross-validation. The variable importance score was then calculated as F1*−F˜1. Thus, a simple interpretation of the variable importance score would be the additional F1 score added by including that SNP. This method of calculating variable importance scores allows for negative scores as well. The plots of the variable importance were made using ggplot2 (version 3.2.1).
TABLE 23 shows marginal associations between acute toxicity (at or above grade 2) and genetic mutations. All tests of association are based on Pearson's Chi-squared test.
TABLE 24 shows marginal associations between late toxicity (at or above grade 2) and genetic mutations. All tests of association are based on Pearson's Chi-squared test.
TABLE 25 shows marginal associations between late toxicity (at or above grade 2) and genetic mutations. All tests of association are based on Pearson's Chi-squared test.
TABLE 26 shows measures of performance for each model for OSLO patients. Parameters were as follows:
Acute Toxicity
Random Forest: P-val thresh: 0.3, mtry=1, nodesize=85, ntree=100
Elastic Net: P-val thresh: 0.3, α=0.1, λ=0.0404
Boosted Trees: P-val thresh: 0.3, int_depth=3, num_trees=100
Late Toxicity
Random Forest: P-val thresh: 0.3, mtry=3, nodesize=85, ntree=100
Elastic Net: P-val thresh: 0.3, α=0.1, λ=0.0404
Boosted Trees: P-val thresh: 0.3, int_depth=2, num_trees=150
TABLE 27 shows measures of performance for each model for SBRT patients. Parameters were as follows:
Random Forest: P-val thresh: 0.3, mtry=1, nodesize=85, ntree=100
Elastic Net: P-val thresh: 0.3, α=0.1, λ=0.0395
Boosted Trees: P-val thresh: 0.15, int_depth=1, num_trees=100
TABLE 28 shows measures of performed created by training a model on the OSLO patients and predicting toxicity in SBRT patients.
TABLE 29 shows measures of performed created by training a model on the SBRT patients and predicting toxicity in OSLO patients.
Further analysis of mortality, biochemical relapse, and distant metastases was conducted as follows. In this analysis, a log-rank test (survival version 2.44-1.1) was conducted to determine a SNPs marginal association with the time until the event of interest. The log-rank test compares the estimated hazard functions of patient with the mutation of a SNP to those without a mutation at each observed event time. The hazard ratio for each SNP was estimated using a Cox proportional hazards regression model (coxph::survival version 2.44-1.1). Due to the duration of the study, right censoring occurred, meaning that the event had not happened in some patients yet. TABLES 30, 31, 32, and 33 contain the unadjusted P-values for the log-rank test, as well as the hazard ratio for each SNP.
Further analysis of mortality, biochemical relapse, and distant metastases was conducted as follows. In this analysis, a log-rank test (survival version 2.44-1.1) was conducted to determine a SNPs marginal association with the time until the event of interest. The log-rank test compares the estimated hazard functions of patient with the mutation of a SNP to those without a mutation at each observed event time. The hazard ratio for each SNP was estimated using a Cox proportional hazards regression model (coxph::survival version 2.44-1.1). Due to the duration of the study, right censoring occurred, meaning that the event had not happened in some patients yet. In this analysis, we adjusted for the Age, Gleason grade, T stage, and the initial PSA of the patient. TABLES 34, 35, 36, and 37 contain the P-values and the hazard ratio estimated by the Cox regression for each SNP.
TABLE 37 provides Marginal Association for each predictor and Potency. To find the p-value for marginal association between Testosterone level, Age, Time on ADT treatment, and Time since ending ADT treatment and Potency, we created a simple logistic regression and evaluated the Wald statistic. We used the same three models used in modeling Toxicity, and used Leave-one-out Cross-validation to obtain our measures of performance. Variable Importance was also obtained in the same fashion that it was obtained in modeling Toxicity.
The tests for marginal association between the SNPS and Potency are based on Pearson's Chi-squared test. The test for marginal association between Testosterone, Age, Time since ending ADT treatment, and Time on ADT treatment and Potency were conducted using a Wald statistic.
Potency
Random Forest: P-val thresh: 0.3, mtry=1, nodesize=85, ntree=100
Elastic Net: P-val thresh: 0.2, α=0.1, λ=0.00395
Boosted Trees: P-val thresh: 0.2, int_depth=2, num_trees=50
The entire disclosure of each of the patent and scientific documents referred to herein is incorporated by reference for all purposes.
The invention may be embodied in other specific forms without departing from the spirit or essential characteristics thereof. The foregoing embodiments are therefore to be considered in all respects illustrative rather than limiting on the invention described herein. Scope of the invention is thus indicated by the appended claims rather than by the foregoing description, and all changes that come within the meaning and range of equivalency of the claims are intended to be embraced therein.
This application claims priority to and the benefit of U.S. Provisional Patent Application No. 62/975,650, filed on Feb. 12, 2020, which is incorporated herein by reference in its entirety.
| Filing Document | Filing Date | Country | Kind |
|---|---|---|---|
| PCT/US2021/018016 | 2/12/2021 | WO |
| Number | Date | Country | |
|---|---|---|---|
| 62975650 | Feb 2020 | US |
