Predetermined genomic adjusted radiation dose (GARD) values for nasopharyngeal and oropharyngeal cancers

Abstract

Disclosed herein are systems and methods for personalized treatment of individual patient tumor. In one embodiment, a computer software configured to integrate with a radiation therapy treatment planning system can be configured to assign a radiation sensitivity index (RSI) of a subject's tumor based at least in part on expression levels of one or more signature genes in the tumor, calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or 46.1 to 65 for oropharyngeal cancer, and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

Description

PREDETERMINED GENOMIC ADJUSTED RADIATION DOSE (GARD) VALUES FOR NASOPHARYNGEAL AND OROPHARYNGEAL CANCERSCROSS-REFERENCE TO RELATED APPLICATIONS

[0001] This application claims the benefit of, and priority under 35 U.S.C. §119(e) to, U.S. Provisional Patent Application No. 63 / 578,115, filed on August 22, 2023, entitled “Predetermined Genomic Adjusted Radiation Dose (GARD) Values for Nasopharyngeal and Oropharyngeal Cancers,” the contents of which are incorporated by reference herein in their entirety.

[0002] This application is related to U.S. Patent Application No. 18 / 538,477, filed December 13, 2023, which is a continuation of U.S. Patent Application No. 18 / 148,502, filed December 30, 2022, which is a continuation of U.S. Patent Application No. 16 / 658,961, filed October 21 , 2019, now US Patent No. 11,547,871, which claims priority to U.S. Provisional Patent Application No. 62 / 747,861, filed on October 19, 2018, entitled “Systems and Methods for Personalized Radiation Therapy,” each of which are incorporated by reference in their entireties.SUMMARY[00023 Disclosed herein are systems and methods for personalized treatment of individual patient tumor. In one embodiment, a computer software configured to integrate with a radiation therapy treatment planning system can be configured to assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor, calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about 41.9 for oropharyngeal cancer, and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

[0004] In an additional embodiment, a computer-implemented method for minimizing the risk of radiation therapy can include obtaining a radiation sensitivity index (RSI) of a subject’s tumor from expression levels of one or more signature genes in the tumor, calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about 41.9 for oropharyngeal cancer, andproviding a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

[0005] In a further embodiment, a method of calculating a personalized radiation therapy dosage for a subject can include determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample, and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer about 41.9 for oropharyngeal cancer.

[0006] In a further embodiment, a method of treating a subject can include determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample, calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about. 41.9 for oropharyngeal cancer, and administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for nasopharyngeal cancer.

[0007] In a further embodiment, a system for developing a personalized radiation therapy treatment plan for a subject having a tumor can include one or more processors and a memory' operably coupled to the one or more processors. The memory can include computer-executable instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to: determine a radiation sensitivity index (RSI) of the tumor from expression levels of one or more signature genes in the tumor, calculate a personalized radiation dosage (RxRSI) for the subject based at least in part on a pre-determined genomic adjusted radiation dose (GARD) value and the RSI, calculate normal tissue toxicity of the personalized radiation dosage, calculate dosimetric parameters for normal tissues of the subject for a plurality of potential RxRSI values, calculate relative risk for potential RxRSI values of the plurality of potential RxRSI values, select the RxRSI value of the subject from the plurality of potential RxRSI values based at least in part on the relative risk, and provide the personalized radiation therapy treatment plan for the subject.

[0008] In a further embodiment, a method of developing a personalized radiation treatment plan can include assigning a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor, calculating a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on apre-determined genomic adjusted radiation dose (GARD) value and the RSI, and providing the recommended RxRSI as a radiation therapy dose for a radiation plan.

[0009] In a further embodiment, a computer software configured to integrate with a radiation therapy treatment planning system can be configured to assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor, calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer, and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

[0010] In a further embodiment, a computer-implemented method for minimizing the risk of radiation therapy can include obtaining a radiation sensitivity index (RSI) of a subject’s tumor from expression levels of one or more signature genes in the tumor, calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer, and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

[0011] In a further embodiment, a method of calculating a personalized radiation therapy dosage for a subject can include determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample, and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer.

[0012] In a further embodiment, a method of treating a subject can include determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample, calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer, and administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for nasopharyngeal cancer.

[0013] In a further embodiment, a computer software configured to integrate with a radiation therapy treatment planning system can be configured to assign a radiation sensitivity index(RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor, calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 41.9 for oropharyngeal cancer, and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

[0014] In a further embodiment, a computer-implemented method for minimizing the risk of radiation therapy can include obtaining a radiation sensitivity index (RSI) of a subject's tumor from expression levels of one or more signature genes in the tumor, calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 41.9 for oropharyngeal cancer, and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

[0015] In a further embodiment, a method of calculating a personalized radiation therapy dosage for a subject can include determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample, and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 41.9 for oropharyngeal cancer.

[0016] In a further embodiment, a method of treating a subject can include determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample, calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 41.9 for oropharyngeal cancer, and administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for nasopharyngeal cancer.

[0017] In a further embodiment, a computer software configured to integrate with a radiation therapy treatment planning system can be configured to assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor, calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 46. 1 to 65 for oropharyngealcancer, and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

[0018] In a further embodiment, a computer-implemented method for minimizing the risk of radiation therapy can include obtaining a radiation sensitivity index (RSI) of a subject's tumor from expression levels of one or more signature genes in the tumor, calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about. 46.1 to 65 for oropharyngeal cancer, and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

[0019] In a further embodiment, a method of calculating a personalized radiation therapy dosage for a subject can include determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample, and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 46.1 to 65 for oropharyngeal cancer.

[0020] In a further embodiment, a method of treating a subject can include determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample, calculating a personalized radiation dosage (RxRSI) for the subject based at least in part, on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 46.1 to 65 for oropharyngeal cancer, and administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for nasopharyngeal cancer.

[0021] In a further embodiment, a computer software configured to integrate with a radiation therapy treatment planning system can be configured to assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor, calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 59.5 for nasopharyngeal cancer, and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

[0022] In a further embodiment, a computer-implemented method for minimizing the risk of radiation therapy can include obtaining a radiation sensitivity index (RSI) of a subject’s tumorfrom expression levels of one or more signature genes in the tumor, calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 59.5 for nasopharyngeal cancer, and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

[0023] In a further embodiment, a method of calculating a personalized radiation therapy dosage for a subject can include determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample, and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 59.5 for nasopharyngeal cancer.

[0024] In a further embodiment, a method of treating a subject can include determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject’s tumor sample, calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 59.5 for nasopharyngeal cancer, and administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for nasopharyngeal cancer.BRIEF DESCRIPTION OF THE FIGURES

[0025] The file of this patent contains at least one drawl ng / photograph executed in color. Copies of this patent with color drawing(s) / photograph(s) will be provided by the Office upon request and payment of the necessary fee.

[0026] FIG. 1A shows GARD plotted against EQD2 (Gy), according to an exemplary embodiment of the present disclosure.

[0027] FIG. IB shows GARD plotted against EQD2 (Gy), according to an exemplary embodiment of the present disclosure.

[0028] FIG. 2A shows log relative hazard plotted against GARD, according to an exemplary embodiment of the present disclosure.

[0029] FIG. 2B shows log relative hazard plotted against GARD, according to an exemplar}' embodiment of the present disclosure.

[0030] FIG. 3A shows a plot of survival probability over time, according to an exemplary' embodiment of the present disclosure.

[0031] FIG. 3B shows a plot of survival probability over time, according to an exemplary embodiment of the present disclosure.

[0032] FIG. 3C shows a plot of survival probability over time, according to an exemplary' embodiment of the present disclosure.

[0033] FIG. 3D shows a plot of survival probability over time, according to an exemplary embodiment of the present disclosure.

[0034] FIG. 4 shows a nomogram incorporating GARD, TNM8, and a 3 -cluster prognostic model, according to an exemplary embodiment of the present disclosure.

[0035] FIG. 5 shows a plot of sensitivity versus specificity, according to an exemplary embodimen t of the present disclosure.

[0036] FIGs. 6A-6B show plots of percent of patients free of local reoccurrence versus time, according to an exemplary embodiment of the present disclosure.

[0037] FIGs. 7A-7F show' plots of survival over time for radiosensitive and radioresistant subjects, according to an exemplary embodiment of the present disclosure.

[0038] FIGs. 8A-8F show plots of survival over time for radiosensitive and radioresistant subjects, according to an exemplary embodiment of the present disclosure.

[0039] FIGs. 9A-9B show's plots of frequencies of RSI, according to an exemplary' embodiment of the present disclosure.

[0040] FIG. 10 illustrates a block diagram of an illustrative data processing system according to an embodiment.

[0041] FIGs. 1 1A-1 1B show plots of survival probability over time, according to an exemplary' embodiment of the present disclosure.

[0042] FIG. I2A shows a plot of physical dose versus RxRSI, according to an exemplary embodiment of the present disclosure.

[0043] FIG. 12B shows plot of percentage of patients achieving high GARD versus dose, according to an exemplary embodiment of the present disclosure.

[0044] FIG. 13 A shows a plot of RNA degradation, according to an exemplary' embodiment of the present disclosure.

[0045] FIG. 13B shows a plot of relative log expression values, according to an exemplary' embodiment of the present disclosure.

[0046] FIG. 13C shows a normalized unsealed standard errors plot, according to an exemplary' embodiment of the present disclosure.

[0047] FIG-. 13D shows a histogram plot of raw' NPC0501 data, according to an exemplar}' embodiment of the present disclosure.

[0048] FIG. 14A shows a plot of RMA processed data, according to an exemplary embodiment of the present disclosure.

[0049] FIG. 14B shows a plot of local failure free rate versus time, according to an exemplary' embodiment of the present disclosure.

[0050] FIG. I 5A show's a plot of distant metastasis free rate versus time, according to an exemplary' embodiment of the present disclosure.

[0051] FIG. 15B show's a plot of progress free survival versus time, according to an exemplary embodiment of the present disclosure.

[0052] FIG. 16 show's a box-plot of RSI score from three independent advanced NPC cohorts, according to an exemplary embodiment of the present disclosure.

[0053] FIG. 17A shows a plot of physical dose versus RxRSI, according to an exemplary' embodiment, of the present disclosure.

[0054] FIG. 17B show's plot of percentage of patients achieving high GARD versus dose, according to an exemplary' embodiment of the present disclosure.

[0055] FIG. 18 provides a flowchart showing subject selection, inclusion, and exclusion criteria, according to an exemplary embodiment of the present disclosure.DETAILED DESCRIPTION

[0056] Various aspects now will be described more fully hereinafter. Such aspects may, however, be embodied in many different forms and should not be construed as limited to the embodiments set forth herein; rather, these embodiments are provided so that this disclosure will be thorough and complete, and will fully convey its scope to those skilled in the art.

[0057] Where a range of values is provided, it is intended that, each intervening value between the upper and lower limit of that range and any other stated or intervening value in that, stated range is encompassed within the disclosure. For example, if a range of 1 gm to 8 pm is stated, it is intended that 2 pm, 3 pm, 4 pm, 5 pm, 6 pm, and 7 pm are also explicitly disclosed, as well as the range of values greater than or equal to 1 pm and the range of values less than or equal to 8 pm.

[0058] The singular forms “a,” “an,” and “the” include plural referents unless the context clearly dictates otherwise. Unless defined otherwise, all technical and scientific terms used herein have the same meanings as commonly understood by one of ordinary skill in the art. Although any methods and materials similar or equivalent to those described herein can beused in the practice or testing of embodiments disclosed, the preferred methods, devices, and materials are now described.

[0059] The transitional term “comprising,” which is synonymous with “including,” “containing,” or “characterized by,” is inclusive or open-ended and does not exclude additional, unrecited elements or method steps. By contrast, the transitional phrase “consisting of' excludes any element, step, or ingredient not specified in the claim. The transitional phrase “consisting essentially of’ limits the scope of a claim to the specified materials or steps “and those that do not materially affect the basic and novel character! stic(s)” of the claimed invention. In embodiments or claims where the term comprising is used as the transition phrase, such embodiments can also be envisioned with replacement of the term “comprising” with the terms “consisting of’ or “consisting essentially of.”

[0069] The term “patient” and “subject” are interchangeable and may be taken to mean any living organism which may be treated with compounds of the present invention. As such, the terms “patient” and “subject” may include, but are not limited to, any non-human mammal, primate or human. In some embodiments, the “patient” or “subject” is a mammal, such as mice, rats, other rodents, rabbits, dogs, cats, swine, cattle, sheep, horses, primates, or humans. In some embodiments, the patient or subject is an adult, child or infant. In some embodiments, the patient or subject is a human.

[0061] The term “treating” is used herein, for instance, in reference to methods of treating a nasopharyngeal and / or oropharyngeal disorder or a systemic condition, and generally includes the administration of a compound or composition or a therapy regimen which reduces the frequency of, or delays the onset of, symptoms of a medical condition. This can include reversing, reducing, or arresting the symptoms, clinical signs, and underlying pathology of a condition in a manner to improve or stabilize a subject’s condition.

[0062] As is outlined in greater detail below, when reference is made to “about” a certain GARD value, it is understood that “about” means within a range of + / - 10%.

[0063] Radiation therapy (RT) is the medical use of radiation to treat malignant cells, such as cancer cells. This radiation can have an electromagnetic form, such as a high-energy photon, or a particulate form, such as an electron, proton, neutron, or alpha particle. By far, the most common form of radiation used in practice today is high-energy photons. Photon absorption in human tissue is determined by the energy of the radiation, as well as the atomic structure of the tissue in question. The basic unit of energy used in radiation oncology is the electron volt (eV); 103 eV=T keV, 106 eV=l MeV. At therapeutic energies, the three major interactions between photons and tissue are the photoelectric effect, Compton effect, and pair production.

[0064] Due to biological heterogeneity, radiation therapy (RT) does not uniformly work on all tissue samples, and a uniform “one-size fits all” RT dose for a given cancer-type may not be ideal. Therefore, there remains a need for personalized radiation dose planning methods and systems.

[0065] Disclosed herein are systems and methods for personalized treatment of individual patient tumor. In one embodiment, a computer software configured to integrate with a radiation therapy treatment planning system can be configured to assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in pail on expression levels of one or more signature genes in the tumor, calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about 41.9 for oropharyngeal cancer, and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan. In an alternative embodiment, the pre-determined GARD value can be about 46.1 to 65 for oropharyngeal cancer. In an alternative embodiment, the pre-determined GARD value can be about. 59.5 for nasopharyngeal cancer.

[0066] The GARD value can be about 45 for nasopharyngeal cancer or about 41.9 for oropharyngeal cancer. In an alternative embodiment, the GARD value can be about 45. 1 to 65 for oropharyngeal cancer. In an alternative embodiment, the pre-determined GARD value can be about 59.5 for nasopharyngeal cancer.

[0067] The computer software can be further configured to calculate the recommended RxRSI based in part on normal tissue toxicity for a treatment plan using the recommended RxRSI.

[0068] The computer software can be further configured to calculate the normal tissue toxicity based at least in part on risks to a plurality of tissue sites.

[0069] The computer software can be further configured to receive, from the radiation therapy treatment planning system, a plurality of radiation plans each using the recommended RxRSI, calculate normal tissue toxicity for each radiation plan of the plurality of radiation plans, penalize each radiation plan of the plurality of radiation plans based on the normal tissue toxicity of the radiation treatment plan, and provide, to the radiation therapy treatment planning system, at least one recommended radiation plan that is least penalized of the plurality of radiation plans.

[0070] The computer software can be further configured to calculate the recommended RxRSI based in part on a predefined standard of care dose range.

[0071] The computer software can be further configured to calculate a proposed RxRSI based for the subject based at least in part on the pre-determined GARD value and the RSI, compare the proposed RxRSI to a predefined standard of care dose range, assign the recommended RxRSI a value within the predefined standard of care dose range when the proposed RxRSI is within or below the predefined standard of care dose range, and recommend consideration of the subject for clinical trial if the proposed RxRSI is above the predefined standard of care dose range.

[0072] The computer software can be further configured to apply a linear regression model to the expression levels of the one or more signature genes in the tumor and assign the RSI based at least in part on the linear regression model.

[0073] The pre-determined GARD value can be based at least in part on a plurality of GARD values for subjects in a cohort.

[0074] In an additional embodiment, a computer-implemented method for minimizing the risk of radiation therapy can include obtaining a radiation sensitivity index (RSI) of a subject’s tumor from expression levels of one or more signature genes in the tumor, calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about. 41.9 for oropharyngeal cancer, and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI. In an alternative embodiment, the pre-determined GARD value is about 46.1 to 65 for oropharyngeal cancer. In an alternative embodiment, the pre-determined GARD value can be about 59.5 for nasopharyngeal cancer.

[0075] The computer-implemented method can further include calculating normal tissue toxicity of the personalized radiation dosage and providing the personalized radiation therapy treatment plan based at least in part on the normal tissue toxicity. In an embodiment, the normal tissue toxicity is based at least in part on risks to a plurality of tissue sites, such as for example and not limitation, esophagus, lung, heart, small bowel, brain, rectum, bladder, spinal cord, kidney, or skin.

[0076] The computer-implemented method can provide a plurality of radiation treatment plans, which can be penalized based on tissue toxicity to arrive at an optimal radiation treatment plan that has the least tissue toxicity.

[0077] In an additional embodiment, a method of developing a personalized radiation treatment plan can include assigning a radiation sensitivity index (RSI) of a subject’s tumor based at least in part, on expression levels of one or more signature genes in the tumor,calculating a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on a pre-d etennined genomic adjusted radiation dose (GARD) value and the RSI, and providing the recommended RxRSI as a radiation therapy dose for a radiation plan,

[0078] The method can further include calculating the recommended RxRSI based in part on normal tissue toxicity for a treatment plan using the recommended RxRSI.

[0079] The method can further include calculating the normal tissue toxicity based at least in part on risks to a plurality of tissue sites.

[0080] The method can further include calculating a respective normal tissue toxicity for each radiation plan of a plurality of radiation plans each utilizing the recommended RxRSI, penalizing each radiation plan of the plurality of radiation plans based on the normal tissue toxicity of the radiation treatment plan, and providing at least one recommended radiation plan that is least penalized of the plurality of radiation plans.

[0081] The method can further include determining whether the recommended RxRSI is within a predefined standard of care dose range.

[0082] The method can further include calculating a proposed RxRSI based for the subject based at least in part on the pre-determined GARD value and the RSI, comparing the proposed RxRSI to a predefined standard of care dose range, assigning the recommended RxRSI a value within the predefined standard of care dose range when the proposed RxRSI is within or below the predefined standard of care dose range, and recommending consideration of the subject for clinical trial if the proposed RxRSI is above the predefined standard of care dose range.

[0083] The method can further include applying a linear regression model to the expression levels of the one or more signature genes in the tumor and assigning the RSI based at least in part on the linear regression model.

[0084] The pre-determined GARD value is based at least in part on a plurality of GARD values for subjects in a cohort.

[0085] In a further embodiment, a method of calculating a personalized radiation therapy dosage for a subject can include determining expression level s of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample, and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about 41.9 for oropharyngeal cancer. In an alternative embodiment, the pre-determined GARD value is about 46. 1 to 65 for oropharyngealcancer. In an alternative embodiment, the pre-determined GARD value can be about 59.5 for nasopharyngeal cancer.

[0086] In a further embodiment, a method of treating a subject can include determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample, calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about 41.9 for oropharyngeal cancer, and administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for nasopharyngeal cancer. In an alternative embodiment, the pre-determined GARD value is about 46. 1 to 65 for oropharyngeal cancer. In an alternative embodiment, the pre-determined GARD value can be about 59.5 for nasopharyngeal cancer.

[0087] In some embodiments, any method known in the art may be used for obtaining a tumor sample from a subject. The tumor sample may comprise at least one living cell (preferably a plurality of cells), e.g., a cell from a tumor (e.g., from a biopsy), a normal cell, or a cultured cell. Commonly used methods to obtain tumor cells include surgical (the use of tissue taken from the tumor after removal of all or part of the tumor) and needle biopsies. The samples should be treated in any way that preserves intact the expression levels of the living cells as much as possible, e.g., flash freezing or chemical fixation, e.g., formalin fixation. Any method known in the art can be used to extract material, e.g., protein or nucleic acid (e.g., mRNA) from the sample. For example, mechanical or enzymatic cell disruption can be used, followed by a solid phase method (e.g., using a column) or phenol -chloroform extraction, e.g., guanidinium thiocyanate-phenol-chloroform extraction of the RNA. A number of kits are commercially available for use in isolating mRNA. Purification can also be used if desired.

[0088] In some embodiments, the tumor is a cancer tumor selected from nasopharyngeal cancer. In some embodiments, the tumor is a cancer tumor selected from oropharyngeal cancer. In some embodiments, the tumor is a cancer tumor selected from colorectal cancer, breast cancer, ovarian cancer, pancreatic cancer, head and neck cancer, bladder cancer, liver cancer, renal cancer, melanoma, gastrointestinal cancer, prostate cancer, small cell lung cancer, nonsmall cell lung cancer, sarcoma, glioblastoma, T- cell lymphoma, B-cell lymphoma, endometrial cancer, and cervical cancer.

[0089] In some embodiments, any method known in the art may be used to determine the expression levels in a tumor sample. Gene expression levels can be determined in manydifferent ways including the quantification of fluorescence of hybridized mRNA on glass slides, Northern blot analysis, real-time reverse transcription PCR (RT-PCR), microarray or other measures of gene expression abundance.

[0090] In some embodiments, the methods include determining expression levels of signature genes in one or more cells of a tumor. In some embodiments, the methods include determining the expression levels of a plurality of signature genes, e.g., two, three, four, five, six, seven, eight, nine, or all ten signature genes, as follows: androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STATI); protein kinase C, beta (PKCb); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUMO1 ); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); and interferon regulatory factor 1 (IRF1).

[0091] In some embodiments, the methods include determining expression levels of signature genes in one or more cells of a tumor, and determining a radiation sensitivity index (RSI) of the tumor based on the expression levels of the signature genes. To determine the RSI, the methods described herein may use a rank-based linear algorithm.

[0092] In some embodiments, determining a radiation sensitivity index of a tumor comprises applying a linear regression model to the gene expression levels, e.g., a rank-based linear regression model. In some embodiments, the expression levels of the plurality of signature genes are weighted. .A linear regression model useful in the methods described herein includes gene expression levels and coefficients, or weights, for combining expression levels. The coefficients can be calculated using a least-squares fit of the proposed model to a measure of cellular radiation sensitivity. The functional form of the algorithm is given below, where each of the kj coefficients will be determined by fitting expression level s to a particular RSI measure:

[0093] Further methods and embodiments for determining radiation sensitivity index (RSI) are described in U.S. Patent Nos. 8,660,801; 9,846,762; and 8,655,598, which are incorporated herein by reference.

[0094] As described herein, RSI provides an indication of whether radiation therapy is likely to be effective in treating the subject’s tumor. RSI has a value approximately between 0 and 1 (Eschrich et al., Systems biology modeling of the radiosensitivity network: a biomarker discovery platform, Int. J. Radiat. Oncol. Biol. Phys. (2009)). It should be understood that assigning RSI according to the linear regression model of gene expression levels described in U.S. Pat. Nos. 8,660,801; 9,846,762; and 8,655,598, is provided only as an example and thatother known techniques for assigning radiation sensitivity can optionally be used with the systems and methods described herein.

[0095] In some embodiments, the method further comprises calculating the Genomic Adjusted Radiation Dose (GARD) for each tumor sample, and is described in U.S. Patent Application No. 15 / 571,617, granted as U.S. Patent No. 10,697,023, which incorporated herein by reference. GARD is derived using the linear quadratic (LQ) model, the individual RSI and the radiation dose and fractionation schedule for each patient as follows:

[0096] The LQ model in its simplest form is represented by:where n is the number of fractions of radiation, d is the dose per fraction, and a and represent the linear and quadratic radiosensitivity parameters, respectively.

[0097] Since RSI is a molecular estimate of SF2 in cell lines (survival fraction at 2 Gy), a patient-specific a is derived by substituting RSI for Survival (S) in the equation above, where dose (d) is 2 Gy, n=l and p is a constant (0.05 / Gy2). GARD is calculated using the classic equation for biologic effect shown by equation E=nd(a+pd), the patient-specific a and the radiation dose and fractionation received by each patient. Additionally, the GARD value can be predictive of tumor recurrence in the subject after treatment.

[0098] In some embodiments, the method comprises calculating a personalized radiation dosage (RxRSI) for each individual tumor or subject based on a pre-determined GARD value. The personalized radiation dose or RxRSI is the physical dose required to achieve a pre- determined GARD value. The RxRSI is calculated using the formula below:RxRSI :::: GARD target value / (α + βd), where alpha is calculated based on the patient’s RSI as described above and beta is a constant (0.05 / Gy2).

[0099] In some embodiments, a pre-determined GARD value may be calculated based on improved outcome in a particular cancer type. In other embodiments, a pre-determined GARD value may be calculated based on empiric values for a cancer type.

[0100] The pre-determined GARD value may vary depending on the cancer type. For example, the pre-determined GARD value for a subject suffering from nasopharyngeal cancer may be about 45. For example, the pre-determined GARD value for a subject suffering from oropharyngeal cancer may be between about 41.9 for oropharyngeal cancer. In an alternative embodiment, the pre-determined GARD value for a subject suffering from oropharyngeal cancer may be about 46.1 to 65 for oropharyngeal cancer. In an alternative embodiment, thepre-determined GARD value can be about 59.5 for nasopharyngeal cancer. In some embodiments, the pre-determined GARD value for other cancers may be more or less than 33, such as any number between 2 and 150.

[0101] The empirical radiation dose for a solid epithelial tumor ranges from 60 to 80 Gy, while lymphomas are treated with 20 to 40 Gy. For example, lung cancers are treated between 60 and 74 Gy, prostate cancers are generally treated between 37.25 to 80 Gy, esophageal cancers are treated between 44 to 70 Gy, oropharyngeal cancers are treated between 60 to 70 Gy, and nasopharyngeal cancers are treated between 66 to 70 Gy. It is possible that the empirical dose that the patients receive is lower or higher than what they need. A personalized radiation dose would be ideal to achieve an improved outcome.

[0102] In some embodiments, the personalized radiation dose that is calculated may be 5% less than the empirical dosing value, may be 10% less than the empirical dosing value, may be 15% less than the empirical dosing value, may be 20% less than the empirical dosing value, may be 25% less than the empirical dosing value, may be 30% less than the empirical dosing value, may be 35% less than the empirical dosing value, may be 40% less than the empirical dosing value, may be 50% less than the empirical dosing value, or may be 60% less than the empirical dosing value.

[0103] In some embodiments, the personalized radiation dose that is calculated may be 5% more than the empirical dosing value, may be 10% more than the empirical dosing value, may be 15% more than the empirical dosing value, may be 20% more than the empirical dosing value, may be 25% more than the empirical dosing value, may be 30% more than the empirical dosing value, may be 35% more than the empirical dosing value, may be 40% more than the empirical dosing value, may be 50% more than the empirical dosing value, or may be 60% more than the empirical dosing value.

[0104] In some embodiments, radiation is administered in at least about 1 Gray (Gy) fraction at least once every other day to a treatment volume. In some embodiments, radiation is administered in at least about 2 Gy fractions at least once per day to a treatment volume. In some embodiments, radiation is administered in at least about 2 Gy fractions at least once per day to a treatment volume for five consecutive days per week. In another embodiment, radiation is administered in 3 Gy fractions every other day, three times per week to a treatment volume. In yet another embodiment, a total of at least about 20 Gy, about 30 Gy, about 40 Gy, about 50 Gy, about 60 Gy, about 70 Gy, about 80 Gy, about 90 Gy, or about 100 Gy of radiation is administered to a subject in need thereof.

[0105] The methods disclosed herein may be practiced in an adjuvant setting. “Adjuvant setting” refers to a clinical setting in which an individual has a history of a proliferative disease, particularly cancer, and generally (but not necessarily) has been treated with therapy, which includes, but is not limited to, surgery and / or chemotherapy. However, because of a history’ of the proliferative disease, these individuals are considered at risk of developing that disease or may harbor detectable and / or microscopic disease. Treatment or administration in the “adjuvant setting” refers to a subsequent mode of treatment.

[0106] The methods provided herein may also be practiced in a “neoadjuvant setting,” that is, the method may be carried out before the primary / defmitive therapy. In some aspects, the individual has previously been treated. In other aspects, the individual has not previously been treated. In some aspects, the treatment is a first line therapy.

[0107] In some embodiments, any of the methods of treatment of RT described herein can be administered in combination with one or more additional therapies to the individual, such as surgery and / or chemotherapy. In some embodiments, various classes of chemotherapeutic agents can be administered in combination with RT. Non-limiting examples include: alkylating agents (e.g. cisplatin, carboplatin, or oxaliplatin), antimetabolites (e.g., azathioprine or mercaptopurine), anthracyclines, plant alkaloids (including, e.g. vinca alkaloids (such as, vincristine, vinblastine, vinorelbine, or vindesine) and taxanes (such as, paclitaxel, taxol, or docetaxel)), topoisomerase inhibitors (e.g., camptothecins, irinotecan, topotecan, amsacrine, etoposide, etoposide phosphate, or teniposide), podophyllotoxin (and derivatives thereof, such as etoposide and teniposide), and other antineoplastics (e.g., dactinomycin, doxorubicin, epirubicin, bleomycin, mechlorethamine, cyclophosphamide, chlorambucil, or ifosfamide).

[0108] In some embodiments, a radiation therapy treatment disclosed herein may be combined with other targeted therapies, such as immunoconjugates or antibodies coupled to cytotoxic agents. Non-limiting cytotoxic agents that can be coupled to an antibody include a chemotherapeutic agent, a drug, a growth inhibitory agent, a toxin (e.g., an enzymatically active toxin of bacterial, fungal, plant, or animal origin, or fragments thereof), or a radioactive isotope (i.e., a radioconjugate).

[0109] Also disclosed herein are systems and methods for developing a personalized radiation therapy treatment plan for a subject having a tumor. In some embodiments, the system can include a one or more processors and a memory’ operably coupled to the one or more processors. The memory can include computer-executable instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to determine a radiation sensitivity index (RSI) of the tumor from expression levels of one or more signaturegenes in the tumor, determine a genomic adjusted radiation dose (GARD) value based on RSI, radiation dose and fractionation schedule of the patient, calculate a personalized radiation dosage (RxRSI) for the subject based on a pre-determined GARD value, calculate the normal tissue toxicity of the personalized radiation dosage, and provide the personalized radiation therapy treatment plan for the subject.

[0110] In some embodiments, the system can include a one or more processors and a memory operably coupled to the one or more processors, the memory having computer-executable instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to provide a personalized radiation therapy treatment plan for the subject. The personalized radiation therapy treatment plan for the subject can be based on one or more of the following input:

[0111] a radiation sensitivity index (RSI) of the tumor from expression levels of one or more signature genes in the tumor, a genomic adjusted radiation dose (GARD) value based on RSI, radiation dose and fractionation schedule of the patient, a personalized radiation dosage (RxRSI) for the subject based on a pre-determined GARD value, and the normal tissue toxicity of the personalized radiation dosage.

[0112] In some embodiments, the system can include a one or more processors and a memory operably coupled to the one or more processors, the memory having computer-executable instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to calculate a personalized radiation dosage (RxRSI) for the subject based on a pre-determined GARD value, calculate the normal tissue toxicity of the personalized radiation dosage, and provide the personalized radiation therapy treatment plan for the subject.

[0113] In some embodiments, the system can include one or more processors and a memory' operably coupled to the one or more processors, the memory having computer-executable instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to provide the personalized radiation therapy treatment plan for the subject based on personalized radiation dosage (RxRSI) for the subject and the normal tissue toxicity of the personalized radiation dosage.

[0114] In some embodiments, the method includes integrating the prescribed RT dosage into a commercially available radiation treatment planning system that generates personalized treatment plan based on the patient’s RSI, GARD and RxRSI values. The methods disclosed herein can be implemented in digital electronic circuitry, or in computer software, firmware, or hardware, or in combinations of them.

[0115] Accordingly, as discussed herein, various embodiments may include non-transitory computer-readable media for analyzing health information. In particular, some embodiments may have a health / diagnosis analysis system configured to analyze, examine, search, investigate, consider, evaluate, and / or otherwise process health information and to generate various medical assessments based on the health information. Non-limiting examples of medical assessments include medical diagnoses, medical orders, and / or risk assessments. Health information, as used herein, may include any type of information associated with the health or physical characteristics of a patient, including, but not limited to, name, address, age, gender, demographic information, weight, height, medications, surgeries and other medical procedures (e.g., diagnostic tests, diagnostic imaging tests, or the like), occupation(s), past and current medical conditions, family history-, patient description of health condition, healthcare professional description of health condition, and / or symptoms.

[0116] In some embodiments, the analysis process may involve accessing health information associated with a patient and providing a medical assessment based on various analyses of the health information. In some embodiments, the health information analysis system may receive input, from a healthcare provider concerning the accuracy, completeness, correctness, or other measure of a medical assessment for use in determining future medical assessments.

[0117] The systems and devices described herein provide multiple technological advantages on current processes and techniques. One non-limiting technological advantage is that the health information analysis system may provide medical assessments to healthcare professionals based on a patient’s full medical history, including across healthcare providers and information platforms. Such analyses are generally not possible using conventional processes and technology because, for instance, they would require a great deal of time to be effective and practical when providing healthcare to patients.

[0118] Another non-limiting technological advantage may be that the health information analysis system is capable of dynamically adapting its analysis processes based on healthcare professional feedback, updated information, or the like. A further non-limiting technological advantage is that the health information analysis system may present timely and dynamically updated information to medical professionals in a format that is readily comprehensible to provide a timely analysis, including in real-time or substantially real-time. The presentation of health information according to some embodiments allows medical professionals to provide more efficient and effective healthcare to patients compared with conventional techniques and processes that are generally paper-based or use limited graphical user interfaces (GUI) that arenot capable of providing a comprehensive and meaningful picture of a patient’s health information.

[0119] In some embodiments, the information, or data, acquired by the system may generally include all information collected or generated prior to the medical procedure. Thus, for example, information about the patient may be acquired from a patient intake form or electronic medical record (EMR). Examples of patient information that may be collected include, without limitation, patient demographics, diagnoses, medical histories, progress notes, vital signs, medical history information, allergies, and lab results. The data may also include images related to the patient’s area of interest. It should be understood, that the images may be captured using any known or future medical imaging device, for example, Magnetic Resonance Imaging (MRI), Computed Tomography (CT), X-ray, ultrasound, or any other modality known in the art. The data may also comprise quality of life data captured from the patient. For example, in one embodiment, a patient may use a software application (“app”) to answer one or more questionnaires regarding their current quality of life. In a further embodiment, the health information may include demographic, anthropometric, cultural, or other specific traits about, a patient that can coincide with activity levels and specific patient, activities to customize the surgical plan to the patient. For example, certain cultures or demographics may be more likely to perform a repetitive physical task or be exposed to a particular set of environmental factors.

[0120] In a further embodiment, the computer system may refine or improve the diagnosis by adjusting weighted factors and / or modifying one or more determination factors based on outcome data. For example, an embodiment may utilize, a closed loop algorithm to perform statistical and machine learning modeling. In certain implementations, the outcome data may include overall survival information, progression-free survival information, response rate to a specific drug, and / or other similar outcome data.

[0121] For example, a procedure for refining weights can involve testing a variety of statistical and machine learning modeling techniques and selecting the one that performs best. For a given set of medical procedures, multiple models may be trained to predict the outcomes. The best model can be selected, or a combination and / or averaging of the best models may be newly generated. In certain implementations, rules can be in place to determine what alterations are made to the system.

[0122] Accordingly, the algorithm / system as described herein may include machine learning and / or other similar statistical-based modeling techniques. For example, the algorithm used may depend on an expected outcome. For example, a processing device can be configured touse a first process or algorithm to calculate refinements to a derived diagnosis based upon a first set of outcome data while also using a second or different algorithm to calculate refinements. Different methods and algorithms may be used to calculate the refined weights in concert or substantially simultaneously. The output of each of the different methods and algorithms can then be compared / further analyzed to determine which output is highest rated, or the output of each method and algorithm can be combined into a combinational metric.

[0123] In some embodiments, the personalized radiation dosage (RxRSI) may be calculated using a computer program product. The computer program product may include a computer readable storage medium (or media) having computer readable program instructions thereon for causing one or more processors to carry out aspects of the present invention.

[0124] The computer readable storage medium can be a non-transitory tangible device that can retain and store instructions for use by an instruction execution device (e.g., one or more processors). The computer readable storage medium may be, for example, but is not limited to, an electronic, magnetic, optical, electromagnetic, semiconductor storage device, or any suitable combination of the foregoing. A non-exhaustive list of more specific examples of the computer readable storage medium includes the following: a portable computer diskette, a head disk, random access memory (RAM), read-only memory (ROM), erasable programmable read-only memory (EPROM or Flash memory), static random access memory (SRAM), a compact disc read-only memory (CD-ROM), a digital versatile disk (DVD), a memory / stick, a floppy disk, a mechanically encoded device such as punch-card(s) or raised structures in a groove having instructions recorded thereon, and / or any suitable combination of the foregoing.

[0125] A computer readable storage medium, as used herein, is not to be construed as being transitory signals per se, such as radio waves or other freely propagating electromagnetic- waves, electromagnetic waves propagating through a waveguide or other transmission media (e.g., light pulses passing through a fiber-optic cable), or electrical signals transmitted through a ware.

[0126] Computer readable program instructions described herein may be downloaded to respective computing / processing devices from a computer readable storage medium or to an external computer or external storage device via a network, for example, the Internet, a local area network (LAN), a wide area network (WAN), and / or a wireless network. The network may comprise conductive transmission cables (e.g., copper cables), optical transmission fibers, wireless transmission, routers, firewalls, switches, gateway computers, and / or edge servers. A network adapter card or network interface in each computing / processing device receives computer readable program instructions from the network and forwards the computer readableprogram instructions for storage in a computer readable storage medium within the respective computing / processing device.

[0127] The one or more processors can process instructions for execution within the computing device, including instructions stored in the memory'. The one or more processors can also include separate analog and digital processors. The one or more processors can provide, for example, coordination of the other components of the device, such as the user interface, applications, and wireless communication,

[0128] The one or more processors may communicate with a user through a control interface and / or a display interface coupled to a display. The display can be, for example, a TFT LCD display, an OLED display, or other appropriate display technology. The display interface can comprise appropriate circuitry' for driving the display to present graphical and other information to a user. The control interface can receive commands from a user and convert them for submission to the one or more processors. In addition, an external interface can be in communication with processor, so as to enable near area communication of device with other devices.

[0129] In some embodiments, the system includes computer software that, integrates information for each individual patient including imaging, genomic and clinical data (i.e. clinical prescription). The system generates a conventional standard of care (SoC) treatment plan as well as a personalized treatment plan that incorporates the individual patient RSI, GARD, RxRSI, and normal tissue toxicity. The physician can then evaluate both plans and choose which one to use for the patient based on standard dose-volume histogram (DVH) metrics of normal tissue and tumor coverage.

[0130] In some embodiments, a computer-implemented method for minimizing the risk of radiation therapy is provided. The method can include obtaining a radiation sensitivity index (RSI) of a subject’s tumor from expression levels of one or more signature genes in the tumor, determining a genomic adjusted radiation dose (GARD) value based on RSI, radiation dose and fractionation schedule of the subject, calculating a personalized radiation dosage (RxRSI) for the subject based on a pre-determined GARD value, calculating normal tissue toxicity of the personalized radiation dosage, and providing a personalized radiation therapy treatment plan for the subject.

[0131] FIG. 10 illustrates a block diagram of an illustrative data processing system 700 in which aspects of the illustrative embodiments are implemented. The data processing system 700 is an example of a computer, such as a server or client, in which computer usable code or instructions implementing the process for illustrative embodiments of the present invention arelocated. In some embodiments, the data processing system 700 may be a server computing device. For example, data processing system 700 can be implemented in a server or another similar computing device operably connected to a surgical system. The data processing system 700 can be configured to, for example, transmit and receive information related to a patient and / or a related surgical plan with the surgical system.

[0132] In the depicted example, data processing system 700 can employ a hub architecture including a north bridge and memory controller hub (NB / MCH) 701 and south bridge and input / output (I / O) controller hub (SB / ICH) 702. Processing unit 703, main memory 704, and graphics processor 705 can be connected to the NB / MCH 701. Graphics processor 705 can be connected to the NB / MCH 701 through, for example, an accelerated graphics port. (AGP).

[0133] In the depicted example, a network adapter 706 connects to the SB / ICH 702. An audio adapter 707, keyboard and mouse adapter 708, modem 709, read only memory (ROM) 710, hard disk drive (HDD) 711, optical drive (e.g., CD or DVD) 712, universal serial bus (USB) ports and other communication ports 713, and PClZPCIe devices 714 may connect to the SB / ICH 702 through bus system 716. PCI / PCIe devices 714 may include Ethernet adapters, add-in cards, and PC cards for notebook computers. ROM 710 may be, for example, a flash basic input / output system (BIOS). The HDD 711 and optical drive 712 can use an integrated drive electronics (IDE) or serial advanced technology attachment (SATA) interface. A super I / O (SIO) device 715 can be connected to the SB / ICH 702.

[0134] An operating system can run on the processing unit 703. The operating system can coordinate and provide control of various components within the data processing sy stem 700. As a client, the operating system can be a commercially available operating system. An object- oriented programming system, such as the JavaTM programming system, may run in conjunction with the operating system and provide calls to the operating system from the object-oriented programs or applications executing on the data processing system 700. As a server, the data processing system 700 can be an IBM® eServerTM System® running the Advanced Interactive Executive operating system or the Linux operating system. The data processing system 700 can be a symmetric multiprocessor (SMP) system that can include a plurality of processors in the processing unit 703. Alternatively, a single processor system may be employed.

[0135] Instructions for the operating system, the object-oriented programming system, and applications or programs are located on storage devices, such as the HDD 711, and are ioaded into the main memory' 704 for execution by the processing unit 703. The processes for embodiments described herein can be performed by the processing unit 703 using computerusable program code, which can be located in a memory such as, for example, main memory 704, ROM 710, or in one or more peripheral devices.

[0136] A bus system 716 can be comprised of one or more busses. The bus system 716 can be implemented using any type of communication fabric or architecture that can provide for a transfer of data between different components or devices attached to the fabric or architecture. A communication unit such as the modem 709 or the network adapter 706 can include one or more devices that can be used to transmit and receive data.

[0137] Those of ordinary skill in the art will appreciate that the hardware depicted in FIG. 10 may vary depending on the implementation. Other internal hardware or peripheral devices, such as flash memory, equivalent non-volatile memory, or optical disk drives may be used in addition to or in place of the hardware depicted. Moreover, the data processing system 700 can take the form of any of a number of different data processing systems, including but not limited to, client computing devices, server computing devices, tablet computers, laptop computers, telephone or other communication devices, personal digital assistants, and the like. Essentially, data processing system 700 can be any known or later developed data processing system without architectural limitation.

[0138] Throughout this disclosure, the following abbreviations may be used: ECOG, Eastern Cooperative Oncology Group; MRI, magnetic resonance imaging, CT: computed tomography; AJCC / UICC: The American Joint Committee on Cancer / The Union for International Cancer Control; LDH: Lactate Dehydrogenase; 2D RT: 2-dimensional radiotherapy; IMRT: intensity modulated radiotherapy, PFS, progression-free survival; UVA, univariable analysis, MVA, multivariable analysis; RT, radiotherapy; RSI, radio-sensitivity index; RR, radio-resistant; RS, radio-sensitive; HR, hazard ratio; CI, confidence interval, OS, overall survival.

[0139] Examples

[0140] Example 1; Personalizing Radiotherapy Prescription Dose Using Genomic Markers of Radiosensitivity

[0141] The empiric basis of radiation therapy (RT), the most commonly utilized therapeutic agent in clinical oncology, has gone unmodified for over 70 years. RT is prescribed based on a uniform, one-size fits all approach, delivering small daily doses of RT over several weeks (i.e. fractionation). This fractionation approach is based on studies performed in rams and rabbits by Regaud, Schinz and Slotopolsky over 100 years ago. And the standard total doses for control of sub-clinical, microscopic and macroscopic disease (50, 60 and 70 Gy) were established in the 1960s based on tumor control probability models for head and neck cancer patients.

[0142] The linear quadratic (LQ) model has been a stalwart in the field that has informed RT dose and fractionation since originally proposed by Catcheside and Lea in the 1940s. The LQ proposes that radiation response is a two parameter function of dose delivered (one parameter, alpha, is linear in dose, and the other, beta, is quadratic). Of note, it has been utilized to calculate equivalent dose and fractionation regimens that have been shown to be safe and effective in clinical trials. However, a fundamental limitation of the LQ model is that it assumes that tumor biology is homogenous and that all individuals in a population have a similar opportunity to benefit from RT, with differences in response being related to probabilistic events. Thus, the LQ model predicted that uniform RT dose escalation would result in significant clinical gains across multiple disease sites. Unfortunately, multiple prospective Phase 3 randomized trials have recently disproven this prediction.

[0143] The development of “omic” technologies has revealed that cancer is the most heterogeneous and complex disease that affects humans. The era of precision medicine is focused on the identification of parameters that drive biological heterogeneity. Rather than a single disease with a uniform treatment, the complexity and diversity of cancer requires many treatment options that are matched and optimized based on the patient’s individual tumor biology.

[0144] Although RT remains a critical curative agent for cancer, it has yet to adapt a biological basis in the clinic. It was previously proposed that the gene expression-based radiosensitivity index (RSI), a surrogate for intrinsic cellular radiosensitivity, and the genomic- adjusted radiation dose (GARD), an individualized quantitative metric of the clinical effect of RT, could serve as the first approach to biology-based RT. Both RSI and GARD have been validated in multiple clinical cohorts and disease sites as a predictor of clinical outcome in patients treated with RT. Importantly, the Lancet Oncology commission identified GARD as a research priority in the field of radiation oncology. In addition, a recent independent study from Lund University provides corroborative evidence that RSI is predictive of RT benefit in breast cancer, a predictive biomarker.

[0145] Radiosensitivity Index (RSI)

[0146] RSI is calculated generally with the equation:and specifically herein using the following equation with constants ki-ky, substituted:RSI =-0 0098009*AR + 0 0128283*cJun + 0.0254552*STATl - 0.0017589*PKC -• 0- 0038171 *RelA + 0- 1070213 *cABL - 0.0002509* SUMO 1 - 0 0092431*PAK2 - 0- 0204469 *HD AC 1 - O O441683*1RFL

[0147] Genomic Adjusted Radiation Dose (GARD)

[0148] GARD is derived using the LQ model, the individual RSI and the radiation dose / fractionation schedule for each patient. First, a patient-specific ex is derived by substituting RSI for Survival (S) in the LQ equation below where dose (d) is 2Gy, n = 1 and p is a constant (0.05 / Gy2):

[0149] GARD is calculated using the classic equation for biologic effect, GARD = nd (ex + pd), and using the patient-specific a is calculated as stated above, and the number of fractions (n) and dose per fraction (d) received by each patient.

[0150] Biologically-Optimized Personalized RT dose (RxRSI)

[0151] RxRSI is the physical dose required to achieve a previously identified GARD threshold, RxRSI is calculated using the following formula:where alpha is calculated based on the patient’s RSI as described above and beta is a constant (0.05 / Gy2).

[0152] Treatment decision-making in oropharyngeal squamous cell carcinoma (OPSCC) includes clinical stage, HPV status, and smoking history'. Despite improvements in staging with separation of HPV positive and negative OPSCC in AJCC Sth edition (AJCC8), patients are largely treated with a uniform approach, with recent efforts on de-intensification in low-risk patients. GARD is shown that it can be used to predict overall survival (OS) in HPV-positive OPSCC patients treated with radiotherapy (RT).

[0153] Technical Variability of RSI and GARD

[0154] As described above, GARD is a personalized metric designed to quantify the expected biological effect of RT based on an individual’s genomic profile. GARD integrates the patientspecific RSI with the LQ model, creating a tailored approach to radiation dosing. However, there can be variability in the GARD calculation, largely due to the processes involved in determining RSI.

[0155] RSI is derived from the expression levels of specific genes, introducing variability from both biological sources - such as tumor heterogeneity and genetic polymorphisms - andtechnical factors, including the efficiency of RNA extraction and the accuracy of gene expression profiling. This variability is reflected in the standard deviation of RSI, which has been observed to be approximately 10%. Furthermore, reproducibility studies conducted under Clinical Laboratory Improvement Amendments (CLIA) conditions demonstrated a median RSI difference of 0.06 (6.47% of range) across samples, with some samples showing variability up to 15%. This variability directly affects the calculation of the patient-specific radiosensitivity parameter (a) used in the LQ model, thereby impacting the precision of GARD.

[0156] Additionally, the steps involved in processing the sample, isolating RNA, and radiating the chip each contribute to the overall uncertainty in RSI, which subsequently influences GARD. The observed variability in RSI, as well as in other gene expression signatures, underscores the range of possible GARD values rather than a single, precise figure. [0.157] This inherent variability justifies the need to interpret GARD within a range when considering its application. Therefore, when reference is made to “about” a certain GARD value, it is understood to mean within a range of + / - 10%, providing the necessary flexibility to account for these technical variations. The flexibility allows the GARD values to be interpreted with a margin that accommodates not only the median variability observed but also the higher variability seen in certain samples, which can extend beyond the 6% range to as much as 15% in some cases.

[0158] Biological Variability of RSI and GARD

[0159] The distribution of both the RSI and the GARD varies significantly across different disease sites, reflecting inherent biological variability. For example, cancers such as gliomas, soft tissue sarcomas, and melanomas tend to exhibit more radioresistant profiles, with higher mean RSI values. In contrast, oropharyngeal and nasopharyngeal cancers typically display more radiosensitive distributions, characterized by lower mean RSI values. This variability underscores the necessity of disease specific calibration of GARD, as the underlying radiosensitivity and therapeutic responses to RT differ markedly across cancer types.

[0160] Furthermore, GARD, which quantifies the therapeutic benefit of RT, must, account for these inter-disease differences. The therapeutic efficacy of RT is not uniform across all cancers; rather it is highly dependent on the biological characteristics of each disease site. For instance, in oropharyngeal and nasopharyngeal cancers, RT is often employed as the primary curative modality, necessitating precise calibration of GARD to reflect its therapeutic role. This contrast with its application in breast cancer, where RT serves primarily as an adjuvant treatment post-surgery, with a different therapeutic objective. The decision to assess nasopharyngeal and oropharyngeal cancers in the below examples is thus based on their distinctradiosensitivity profiles and the pivotal role of RT in their treatment, which requires tailored GARD calibration to ensure accurate therapeutic predictions.

[0161] Example 2: Evaluation of HPV-Positive Oranharvugeal Cancer Using GARD: Analysis of RT Outcomes and Potential for Treatment De-escalation

[0162] Methods

[0163] Gene expression profiles (Affymetrix Clariom D) were analyzed for 234 formalin- fixed paraffin-embedded samples from HPV-positive OPSCC patients within an international, multi-institutional, prospective / retrospective observational study. 11 1 patients were stage I, 64 were stage II, and 59 were stage III. GARD, a measure of the biological effect of RT, was calculated for each patient as previously described. In total, 191 patients received definitive treatment (chemoradiation or RT alone), and 31 patients received post-operative RT. Two RT dose fractionations were utilized for definitive cases (70 Gy in 35 fractions or 69.96 Gy in 33 fractions). Median RT dose was 70 Gy (mean 50.88-74) for definitive cases and 66 Gy (range 44-70) for post-operative cases. 29 patients received surgery and adjuvant chemoradiation, 14 received surgery and adjuvant RT, and 19 received RT alone. The median follow up \vas 46.2 months (95% CI, 33.5-63.1). Cox proportional hazards analyses were performed with GARD as a continuous variable and ROC analyses compared the performance of GARD with AJCC8.

[0164] Patient Cohort

[0165] Patients in this analysis were part of the Big Data to Decide project (BD2Decide, NCT028322102), a collaboration of seven European centers to develop a clinico-genomic database of head and neck cancer patients. The details of this project have been previously described.

[0166] Briefly, BD2Decide enrolled a total of 1,537 patients (1,086 retrospectively and 451 prospectively) with loco-regional advanced head and neck cancer (Stage III-IVa, IVb) treated with curative intent, including 377 patients with HPV+ oropharyngeal cancer. After excluding patients with inadequate tissue sample (n=85), poor RNA quality (n=6), HPV DNA-negative (n 14). patients that underwent single-modality treatment (a 37) and one patient treated with surgery’ alone, a final study population of 234 patients remained. The study was approved by institutional review boards of each of the participating institutions and when possible, patients consented to enrollment or a waiver for consent was approved. Patients w'ere treated in a nine- year window, with follow-up closed two years thereafter. HPV testing was performed with p 16 immunohistochemistry and confirmed by HPV DNA testing following positive staining. In total, 191 patients received definitive RT primary’ treatment (chemoradiation (n=172) or RT alone (n=l 9)), and 43 patients received post-operative RT (post-op chemoradiation (n 29) orpost-op RT alone (n==14)). Two RT dose fractionations were utilized for definitive RT cases (70 Gy in 35 fractions or 69.96 Gy in 33 fractions). Median RT dose was 70 Gy (mean 50.88- 74) for definitive cases and 66 Gy (range 44-70) for post-operative cases. The median follow up was 46.2 months (IQR 33.5-63.1).

[0167] Bioinformatie and Statistical Analysis

[0168] All patient tumors undement gene expression profiling using Affymetrix Oariom D on their formalin fixed samples and RSI values were generated using a 10-gene signature. A patient specific genomic parameter, ag, was subsequently calculated using the linear-quadratic model to estimate patient radiosensitivity using the relation:where dose <7 is 2 Gy, n is 1. The assumption is made that p is a constant at 0.05 / Gy2This genomic cu is then used together with each patient’s specific radiation dosing to calculate their clinical GARD value (GARDc)where ncis the number of fractions and dcis the dose per fraction per the clinically delivered radiation plan to each patient. These values were calculated without information about clinical outcome. Cox proportional hazards regression was used to assess the association between GARD as a continuous variable and overall survival (OS). OS was defined as the time between primary diagnosis and death or last follow-up. While the continuous analysis is statistically the most rigorous, to make clinical translation simpler, a series of discrete analyses was performed to mimic the discrete dose levels preferred by clinicians. Discrete analyses were performed with median GARD and also, for hypothesis generation, using an algorithm to minimize chi-squared to derive presumed optimal groups in three dose levels.

[0171] In siiico simulation of HN005

[0172] An in silico trial was modeled after HN005 (NCT03952585), a prospective randomized phase 3 clinical trial testing the non-inferiority of RT de-intensification (from 70Gy to 60Gy) in early stage HPV-positive oropharyngeal cancers treated with concurrent cisplatin-based chemoradiation. Each virtual patient was randomly selected from the RSI distribution of the complete cohort. The virtual patients were randomized to receive 70Gy in 35 fractions or 60Gy in 30 fractions. The estimated enrollment for HN005 was 590, so 200 patients per arm were used. GARD was calculated for each virtual patient, and an OS curve was predicted based on the GARD level achieved, using the optimized three dose GARD level approach (GARD < 46.1 = low, 46.1< GARD < 65 = intermediate, GARD > 65== high). Each of these survivalcurves was modeled with a Weibull curve fit to the Kaplan-Meier estimate of that GARD level . The weighted average of individual patients’ survival curves represented the overall survival estimate for each trial arm. This entire process was repeated a total of 20 times to replicate the variability between different groups of patients.

[0173] To determine whether GARD could identify a successful de-i ntensifi cation strategy, a variation of this trial with selective de-intensifi cation based on GARD was also performed. In this approach, rather than de-intensifying all patients, only patients who would remain in either GARD dose level high (GARD > 65= high) or intermediate (46.1< GARD < 65 = intermediate) with either 60 or 70 Gy were assigned to 60 Gy. All patients that achieve GARD dose level low (GARD < 46.1 = low) were excluded from selective de-escalation. In addition, patients who drop from GARD high to GARD intermediate when de-escalated were also excluded from selective de-escalation.

[0174] Nomogram

[0175] After performing standard analyses to determine the association of GARD with outcome, it is sought to incorporate GARD into a model using previously validated variables. Subsequently, a nomogram incorporating GARD, AJCC8 staging, the three-cluster gene expression model, and other clinical variables was constructed following standard methods. The nomogram was validated by comparison of receiver operating characteristic (ROC) curve analysis.

[0176] Results

[0177] Despite uniform radiation dose utilization, GARD showed significant heterogeneity (range 30-110), reflecting the underlying genomic differences in the cohort. On multivariate analysis, each unit increase in GARD was associated with an improvement in OS (HR = 0.9539, (0.9140, 0.9955), p = 0.0306) compared to AJCC8 (HR = 2.3150, (0.9260, 5.7875), p = 0.0726). ROC analysis for GARD at 36 months yielded an AUC of 81.6 (70.8, 92.4) compared with an AUC of 65.0 (48.9, 81.0) for the NRG clinical nomogram. GARD>64.2 was associated with improved OS (HR = 0.280 (0.100, 0.781), p = 0.015). In a virtual trial, GARD predicts that uniform RT dose de-escalation results in overall inferior OS but proposes two separate genomic strategies where selective RT dose de-escalation in GARD-selected populations results in clinical equipoise.

[0178] As shown in FIG. 1A, GARD ranged from 31.7 to 108.9 (IQR56.4-71.9) with a median for the whole cohort of 63.5. Plotted along the edges of the joint plot between GARD and EQD2 are kernel density estimates for the entire cohort, revealing wide heterogeneity in GARD (std = 13.8) in the setting of near homogeneity in RT dosing (std = 3.1). The differencebetween GARD and EQD2 is best exemplified by the patients treated with ‘standard’ fractionation - with EQD2 measures between 69-71, see FIG. IB. The range of GARD for those patients ranged was 33.7-108.9 (IQR 56.9-72.9) even though they ail were treated to (approximately) the same RT dose (EQD2), highlighting the wide differential in the predicted effect of uniform clinical dosing strategies, GARD is continuously associated with OS in RT- treated HPV-positive OPSCC patients. Previously, it has been demonstrated that GARD was associated with overall survival, recurrence risk and was predictive of RT benefit in a pooled pan-cancer analysis including 1615 patients. RT therapeutic benefit is a critical factor impacting clinical out-come in HPV+ patients. It is shown herein that GARD would be associated with clinical outcome in this analysis of HPV+ oropharyngeal squamous cell carcinoma patients collected through the B2DECIDE project.

[0179] As seen in FIGs. I A-1B, GARD exhibits large underlying genomic heterogeneity in radiation effect compared to radiation dose alone. In FIG. 1A, EQD2 (median 70.0, std 3.1) is plotted against associated GARD (median 63.5, std 13.8) for each patient in the whole cohort. Kernel density estimates are plotted on each edge to show7the distributions of the individual variables. As seen in FIG. IB, plotting patients who received an EQD2 of 69-71 Gy only (standard dosing) highlights GARD’ s ability to stratify patients by their genomic heterogeneity. Data points are overlaid with a box-whisker plot with box representing quartiles and whiskers extending to 1 .5 times IQR.

[0180] EQD2 survival analysis

[0181] GARD was the only variable statistically associated with OS both as a. continuous (HR = 0.954 (0.914,0.996), p = 0.031) and discrete variable (HR 0.291 (0.103,0.818), p = 0.019) (Table 1). Smoking (pack-years) was associated with OS as a continuous variable but not as a discrete variable. To further evaluate GARD’s prognostic ability a discrete analysis using the GARD median as a cut-point (GARD = 64.2) was performed for the patients treated with primary definitive RT. It was found that GARD high vs. low significantly associated with OS (p::::0.01) (FIG. 3A)): GARD-high patients had a 99.0% and 94.6% 3-year and 5 year-OS rate, whereas GARD-low patients achieved a 89.2% and 79.2% 3 year and 5-year OS rate, respectively. This dichotomization resulted in a HR of 0.280 (0.100, 0.781), p = 0.015.

[0182] Table 1 . Multivariate analy sis of definitive RT patients

[0183] Having shown in previous analyses that GARD was predictive of outcome in pooled cohorts, it is now to be demonstrated that it would be continuously associated with OS in this, the largest cohort of OPHNSCC with genomics, details on radiation treatment and clinical outcome gathered to date.

[0184] To test this, a Cox proportional hazards analysis of GARD and OS is performed in both the entire cohort, and also the subset that were treated with RT alone. As shown in FIGs. 2A-2B, GARD is associated with OS as a continuous variable in both groups. For each unit increase in GARD there is an improvement in OS (HR = 0.967 (0.937, 0.998) per unit GARD, p = 0.038) in the entire cohort. This association of GARD with OS was statistically stronger when including only patients treated with definitive RT (HR = 0.955 (0.915, 0,996) per unit GARD, p = 0.038). This association of GARD with OS was statistically stronger when including only patients treated with definitive RT (HR::::0.955 (0.915, 0.996) per unit GARD, p = 0.030). Examining only patients who received the standard-of-care range 69-71 Gy EQD2 within this group, the HR was 0.940 (0.899, 0.983), p = 0.007. While significant differences throughout the entire cohort were found, in order to keep the cohort population uniform, the remainder of the analysis focused on definitive primary' RT patients.

[0185] As seen in FIGs 2A-2B, GARD is a continuous predictor of OS in radiation treated patients with HPV+ OPHNSCC. Performing a Cox regression analysis for GARD as a continuous variable revealed statistically significant associations with OS for the entire cohort (FIG. 2A), and an even stronger signal for patients treated with RT alone (FIG. 2B). FIG. 2A shows Cox proportional hazards analysis demonstrates significant continuous association between GARD and OS for the entire cohort (p = 0.038, HR = 0.967 (0.937, 0.998) per unitGARD). FIG. 213 shows Cox proportional hazards analysis demonstrates significant continuous association between GARD and OS for the subset of patients treated with RT alone (p = 0.030, HR == 0.955 (0.915, 0.996) per unit GARD).

[0186] The data in FIG. 2A are stratified by median GARD, while the data in FIG. 2B are organized in optimal tertiles.

[0187] To further evaluate GARD’s prognostic ability, a discrete analysis using the GARD median as a cut-point. (GARD=:64.2) is performed in the patients treated with definitive RT. FIG. 3A shows that GARD-high patients have an improved OS when compared with GARD- low patients (p = 0.01). GARD-high patients have a 99% and 94.6% 3year and 5 year-OS rate whereas GARD-low patients achieve a 89.2% and 79.2% 3 year and 5-year OS rate, respectively. This dichotomization results in a HR of 0.28 (0. 10, 0.78) with p = 0.015.

[0188] FIGs. 3A-3B show discrete GARD cutpoints display survival differences between groups. FIG. 3 A shows stratifying patients by median GARD shows a significant difference in OS at 5 years by the log-rank rest. To generate hypotheses regarding poorest performing groups, the cohorts are stratified by two cutpoints by minimizing the chi-squared statistic and calculating the log-rank statistic as seen in FIG. 3B.

[0189] To read this nomogram shown in FIG. 4, tally points from the first row for a patient’ s corresponding GARD, AJCC8 stage, and molecular cluster group. The total points corresponds to a 3 -year survival estimate.

[0190] To determine whether GARD could identify a group of patients with poor prognosis, an exploratory discrete analysis based on an optimized two cut-point analysis is performed. Minimizing the chi-square at two discrete values reveals three groups with maximally different outcomes, as shown in FIG. 3B. This analysis revealed two cutpoints at GARD 65 and 46.1. Patients that achieve the highest GARD (GARD > 65) have a 3year OS of 100% compared with 91.3% (85.3, 97.8) for the GARD intermediate group (46.1< GARD < 65) and 62.5% (33.6, 100) for the group that achieves the lowest GARD (GARD < 46). These differences are statistically significant with p < 0.001, though this statistic be interpreted carefully as the groups were chosen by maximizing differences post-hoc.

[0191] GARD predicts that empiric dose de-escalation would result in inferior clinical outcome. Recently, NRG announced that the dose de-escalation arm in HN-005 (chemo-RT to 60 Gy) had failed to achieve the non-inferiority threshold defined by the statistical analysis plan. One possible explanation for these results is that empiric dose de-escalation results in a small number of patients falling from the GARD intermediate cohort (46.1< GARD < 65) to the GARD low cohort (GARD < 46) leading to an inferior result for empiric dose de-escalation.To test this, an in silico clinical trial was performed to evaluate GARD-based predictions of clinical outcome for empiric dose de-escalation to 60 Gy (with concurrent chemotherapy) as HN-005. It was found that GARD predicts that empiric dose de-escalation would result in an inferior clinical outcome. The predicted 3-year OS for patients modeled at 70 Gy is 94.2% compared with 90.2% for patients modeled at 60 Gy FIG. 6A. Empiric, unselected dose deintensification is predicted to increase the proportion of patients in the GARD low group while decreasing the proportion of patients in the GARD high group. The 70Gy in silico aim had 13, 90, and 97 patients in the low, intermediate, and high GARD groups, while the 60Gy in silico arm had 36, 123, and 41 patients in those groups.

[0192] To further evaluate the ability of GARD to identify subpopulations at higher risk of failure, a re-analysis of the GARD cutpoint was conducted. Although HPV-positive patients generally have an excellent prognosis, recent interim analyses of trials such as HN005 have highlighted the necessity of more precise tools to identify patient subsets who may not benefit from uniform treatment de-escalation. It was hypothesized that GARD could serve this purpose. Given these considerations, a discrete analysis was performed to determine an optimized GARD cutpoint, which indicated 41 .97 as a critical threshold for stratifying patients based on overall survival outcomes.

[0193] FIGs. 3C-3D depict the Kaplan-Meier survival curves for both RSI (FIG. 3C) and GARD (FIG. 3D) using this updated GARD cutpoint of 41.97. In the RSI plot, the “rsi optimalMc” line represents patients with RSI values below the threshold, indicating lower radiosensitivity, while the “'rsi__optimal;=high” line represents those above the threshold, indicating higher radiosensitivity. The GARD plot shows that patients with GARD values greater than 41.97 (“gard_optimal=high”) have markedly better survival outcomes compared to those with lower GARD values (“gard optimafolow”). The separation between the curves, particularly in the GARD plot (p :::: 0.0045), underscores the utility of GARD in stratifying patient risk more effectively than RSI alone. These findings further support the use of GARD in identifying patients who may not be suitable candidates for treatment de-escalation and in guiding personalized RT decisions in this patient population.

[0194] Next, it was determined that GARD can be used to develop a clinical trial strategy that predicts equivalent outcome at 70 or 60 Gy. In one approach, GARD can identify patients that would remain above the GARD-high cutpoint (65) at 70 or 60 Gy. This approach selects patients with RSI< 0.1 15 which compromise 18% of the total HPV+ population. In another approach, GARD-high and intermediate patients at 70 Gy that remain in the same group at 60 Gy, would also be predicted to achieve equipoise with selective dose de-escalation.Approximately 55% of HPV patients would be eligible for this approach. It should be noted that both approaches exclude GARD-low patients (as these patients are predicted to require dose intensification) and patients that fall from GARD-high to GARD-intermediate or GARD- intermediate to GARD-low at 60Gy. The predicted OS curve for the second approach to de- escalation is shown in FIG. 6B.

[0195] GARD outperforms AJCC TNM version 8 as a prognostic factor in HPV-positive oropharyngeal patients The Cox analysis suggested that GARD may outperform TNM8 as a prognostic factor in this group of patients. To further test this, ROC analysis comparing GARD and TNM8 as prognostic factors for overall survival (3-year) is performed. FIG. 5 shows that GARD achieves an AUC = 81.6 compared to an AUC = 65.0 for TNM8, consistent with GARD being more accurate model for OS than standard clinical stage.

[0196] A clinical nomogram including staging and genomics significantly outperforms AJCC8 To develop a more accurate model of individualized risk, a clinico-genomic nomogram was constructed incorporating GARD, TNM8, and the previously developed 3 -cluster prognostic model FIG. 5. The nomogram awards points based on each individual patient’s GARD, clinical stage and cluster prognostic group. A vertical line is drawn from the total points calculated to the predicted 3-year OS for the patient. When the cohort was assessed for prognosis via the nomogram the total points calculated ranged from 8.1-184.7 which corresponded to predicted 3-year OS from 70.04-99.79. The performance for the nomogram is shown in Table 2.

[0197] Table 2[0.198] Both GARD and the 3-cluster prognostic model outperform TNM8 as a single parameter model (GARD vs. 3-cluster vs. TNM8 AUC (3 year OS) 81.6 vs 72.8 vs. 65.0). The combination of GARD / 3-cluster / TNM8 achieved the best performance (AUC 84.0). FIGs, 6A- 6B - GARD predicts the results of treatment de-intensifi cation as in HN005. FIG. 6A shows that uniform de-intensifi cation produces decreased OS. FIG. 6B shows that selective deintensification produces similar OS even when approximately two ■thirds of patients in the second arm receive the lower RT dose.

[0199] Discussion

[0200] The development of prognostic models to more accurately classify cancer is a central goal of personalized medicine. It is shown herein that G ARD, a previously generated model of the treatment effect of RT, is associated with overall survival in HPV+ oropharyngeal cancer patients treated with RT both as a continuous and dichotomous variable. Furthermore, using time-dependent ROC analysis, it is shown that GARD outperforms the current NRG clinical nomogram for prediction of overall survival of these patients. Finally, it is shown that GARD predicts that uniform RT dose de-escalation would result in an inferior overall survival over standard RT dose, similar to the results recently announced for HN005. However, GARD proposes two different clinical strategies to selective RT dose de-escalation that it predicts would achieve clinical equipoise.

[0201] Since its confirmation as a biomarker of outcome in HNC, HPV status has been incorporated into the diagnostic algorithm of the disease. Ang et al. demonstrated that HPV+ HNC patients had 58% reduction in the risk of death when compared with HPV- patients. Thi s effect translated into a 25-point absolute difference in 3 year OS distinguished by HPV status (82.4% vs. 57.1%). In this analysis, it is demonstrated that GARD identifies a 72% reduction in the risk of death in GARD-high HPV+ patients. This translates into an absolute 10% and 16% difference in 3 and 5 year OS between GARD-high and GARD-low patients. In addition GARD outperformed the established NRG nomogram for overall survival. Finally, a model integrating GARD and the NRG nomogram achieved the highest prognostic ability. Thus, GARD can resolve prognosis for HPV+ patients with the same magnitude that HPV did for head and neck cancer patients.

[0292] A central clinical question for HPV+ oropharyngeal cancer patients is whether their treatment can be deintensified while preserving their excellent prognosis. While there had been significant enthusiasm for this approach, it was dampened by the recent NRG announcement that the dose de-escalation arm in HN-005 did not meet the pre-defined statistical threshold for non-inferiority. Thus, clinical factors alone are not enough to identify patients where radiation dose de-intensifi cation can be performed without clinical outcome detriment.

[0203] While it is demonstrated that GARD outperforms standard clinical variables as a prognostic factor in HPV+ oropharyngeal cancer, GARD can further improve the ability to define appropriate subpopulations for treatment de-intensification. For example, in an exploratory analysis, it is shown that GARD identifies a small group of HPV+ patients with poor prognosis (GARD < 46. 1) who achieve a 3-year OS of 62.5%. In addition, GARD is also an actionable model that can provide guidance on RT dosing for genomi cal ly -definedsubpopulations. This can inform the design of the next generation of clinical trials for these patients. Proof of principle is demonstrated for GARD-based clinical trial design by showing that GARD predicts that empiric dose de-escalation as performed in HN-005 would result in an inferior 3-year OS for the patients treated to the lower dose. Finally, at least two designs (of many possible) are proposed that, show that GARD-based modeling predicts would result in clinical equipoise between 70 and 60 Gy with appropriately chosen patients for de-escalation. In the first design, only patients in the top 18% of the GARD distribution would be eligible, while in the second design approximately 55% of HPV+ patients would be eligible. A key observation is that a small subset of patients may need dose intensification and should not be eligible for these trials. Further work on chemotherapy sensitivity could also inform future iterations of these trials.

[0204] In conclusion, it is demonstrated that GARD outperforms the NRG clinical nomogram of outcome as a prognostic biomarker in HPV+ OPSCC patients and defines prognostic groups that can inform clinical trial design. While HPV is a classic biomarker in that its result is fixed and cannot be changed, the GARD value for a patient can be optimized by adjusting the RT dose. This supports the hypothesis that GARD could be used to optimize clinical outcome for HPV+ oropharyngeal SCC patients by the personalization of RT dose. Even without the use for dose personalization, however, the strong improvement (quantitatively equivalent to the seminal findings of HPV positivity itself) in outcome prognostication suggests that obtaining GARD should be considered for HPV+ oropharyngeal cancer patients.

[0205] Example 3; Validation of a Radiosensitivity Signature (RSI) and Genomic- ad justed Radiation Dose (GARD) in Nasopharyngeal Cancer

[0206] Gene-expression-based radiation-sensitivity index (RSI) and concept of genomic- adjusted radiation dose (GARD) have been proposed and validated in multiple disease sites. RSI and GARD in radiotherapy (RT)-treated nasopharyngeal cancer (NPC) patients were tested.

[0207] Materials and Methods

[0208] RSI and GARD were tested in 92 locally advanced NPC patients (stage III to stage IVB) treated in randomized phase III trial of this group (NPC-0501 trial). Patients were dichotomized into radiosensitive (RS) and radio-resistant (RR) cohorts by using 25thpercentile of RSI as cut-off point (RSI cut-off 0.18, range: 0.14 to 0.42). GARD was derived based on RSI and physical radiation dose prescribed. The RS / RR variable was compared with prognosticvariables and survival rates. Log rank test was used to determine difference between survival curves and cox proportional hazard models were used to obtain hazard ratio (HR).

[0209] Results

[0210] Patients predicted to be radiosensitive (RS) had better 5-year failure-free rate (FFR) (82.4% vs. 64.8%, p:::0.07), better 5-year overall survival (OS) rate (89.5% vs. 67.9%, p=0.03), and 5-year progression-free survival (PFS) rate (78.3% vs. 59.4%, p=0.04). There was no significant difference in 5-year serious (> grade 3) late-toxicity free rate between 2 groups (RS: 81.8% vs. RR: 67.7%, p=0.38). Multivariable analyses showed that RSI was significant for PFS (hazard ratio, HR: 2.41 for RR vs. RS, p=0.04) and OS (HR: 3.93 for RR vs. RS, p=0.04). There was a wide range of GARD value (range: 31.1 to 67.9) despite the homogenous physical radiation dose delivered (median: 70Gy range: 66 to 76Gy), patients achieved GARD threshold of 59.5 (75thpercentile) corresponded to 5-year OS and PFS rates of 87% and 78% respectively. A subset of patients (21.7%, 20 / 92) with RSI of 0.14 to 0.18 was identified to require the radiation dose of <70Gy (range: 61 to 69Gy) to achieve the GARD threshold.

[0211] RSI is an independent predictor of survival, but not the toxicity, in radiotherapy- treated NPC patients. The GARD-based model disclosed herein provided a framework for individualized dose prescription. Further validation in large-scale prospective cohort is warranted.

[0212] Table. 3 shows comparison of survival outcomes of Radiosensitive (RS) vs. Radioresistant (RR) patients.

[0213] Table 3

[0214] Table 4 shows clinical Characteristics of Pati ents (N 92)

[0215] Table 4

[0216] FIGs. 7A-7F shows comparison of Radiosensitive (RS) vs. Radio-resistant (RR) patients. FIG. 7A shows overall survival (OS). FIG. 7B shows progression-free survival (PF'S). FIG. 7C shows failure-free rate (FFR). FIG. 7D shows local failure-free rate (LFFR). FIG. 7E shows loco-regional recurrence-free survival (LRRFS). FIG. 7F shows distant metastasis-free survival (DMFS).

[0217] Table 5 shows the data represented in FIGs. 7A-7F.

[0218] Table 5

[0219] Table 6 shows Univariable and Multivariable Cox regression analyses of progression free survival (N=92).

[0220] Table 6

[0221] Note that variables in the UVA with a P<0.1 will be considered in the MVA.

[0222] Table 7 shows Univariable and Multivariable Cox regression analyses of Overall survival S (N=92).

[0223] Table 7

[0224] Table 8 shows incidence of major late toxicities (> Grade 3) in Radiosensitive (RS) versus Radio-resistant (RR) patients.

[0225] Table 8

[0226] FIGs. 8A-8F show comparison of Low GARD score vs. high GARD score patients in overall survival (FIG. 8A), progression-free survival (FIG. 8B), failure-free rate (FIG. 8C), local failure-free rate (FIG. 8D), loco-regional recurrence-free survival (FIG. 8E), distant metastasis-free survival (FIG. 8F).

[0227] Table 9 shows the data represented in FIGs 8A-8F.

[0228] Table 9

[0229] FIG. 9A shows distribution of RSI in 92 NPC patients. FIG. 9B shows calculating the RxRSI (the physical dose to achieve a biological outcome) for each patient ( X 92 ). NB: Define GARD threshold of > 45 (correspond to 5-year OS rate: 87% and 5-year PFS rate: 78.3%) as desired biological outcomes.

[0230] While significant interest has been focused on the development of better therapeutic agents including targeted agents and immunotherapy, RT remains a fundamental curative treatment for the majority of patients with cancer. It has been estimated that 40% of all cancer cures are due to RT. In contrast, to date, no targeted agent or immunotherapy has shown similar curative potential in solid tumors. Shifting to a biology-based system will provide a new direction for radiation oncology with multiple opportunities to improve clinical outcome. And that opportunity is not small. Approximately, 50% of all cancer patients receive RT which translates to about 850,000 patients in the US. A moderate improvement in RT-based cures of 5% would represent an additional 42,500 patients potentially being cured. According to the American Cancer Society, this is approximately the same number of patients that die from breast cancer every year in the US.

[0231] In conclusion, radiation oncology has employed an empiric uniform approach to prescribe RT that is based on models developed and published over 70 years ago. It isdemonstrated that this one-size fits all approach is biologically inaccurate for the majority of patients, and results in significant detriment of clinical outcome for patients treated with RT. A new paradigm is proposed, where the field updates its assumptions by acknowledging the biologically heterogeneity of tumors and moves towards the delivery' of biological optimal doses of RT.

[0232] Example 4: Using the Genomic Ad justed Radiation Dose (GARD) to Personalize the Radiation Dose in Nasopharyngeal Cancer

[0233] Overview

[0234] Locally advanced nasopharyngeal cancer (NPC) patients undergoing radiotherapy are at risk of treatment failure, particularly locoregional recurrence. To optimize the individual radiation dose, genomically adjusted radiation dose (GARD) can be used to correlate with locoregional control and survival.

[0235] A total of 92 patients with American Joint Committee on Cancer / International Union Against Cancer stage III to stage IVB recruited in a randomized phase HI trial were assessed. Patients were treated with concurrent chemo-radiotherapy plus (neo) adjuvant chemotherapy.

[0236] Despite the homogenous physical radiation dose prescribed (Median: 70Gy, range 66-76Gy), there was a wide range of GARD values (median: 50.7, range 31.1-67.8) in this cohort. In multivariable analysis, a GARD threshold (GARDT) of 45 was independently associated with locoregional failure-free rate (p==:0.008) and overall survival (p:==0.04). By evaluating the physical dose required to achieve the GARDT (RxRSI), three distinct clinical subgroups were identified: (a) radiosensitive tumors that require an RxRSI at dose < 66Gy (N=59, 64.1%) (b) moderately radiosensitive tumors that require an RxRSI dose within the current standard of care range (66-74Gy) (N=20, 21.7%), (c) radioresistant tumors that need a significant dose escalation above the current standard of care (>74Gy) (N=13, 14.1%). Only 39% of patients were optimized with a uniform dose of 60Gy, compared to 83% of patients who were optimized with 70Gy. The distribution of RxRSI w'as validated in an independent cohort.

[0237] It was found that GARD is independently associated with locoregional control and overall sunrival in radiotherapy-treated NPC patients. These results did not support uniform dose escalation or de-escalation, and GARD may be a potential framework to personalize radiotherapy dose.

[0238] GARD is an independent factor in predicting locoregional control and overall survival in locally advanced NPC patients treated in a phase III trial. Over 60% of patients might achieve desirable clinical outcomes at < 66Gy, while 15% of patients w'ould never be optimized unlessdose escalation > 80Gy. These results have been validated in external cohorts. GARD provides a framework for treatment escalation or de-escalation in RT -treated NPC patients.

[0239] Introduction

[0240] Advanced RT techniques such as intensity-modulated radiotherapy (WIRT) have improved the anatomical precision of local treatment for primary' NPC, Despite this, around 20-30% of patients with radio-resistant tumors develop local recurrence and / or distant metastases. Additionally, a significant proportion of patients in remission experience RT- induced late toxi cities. Current radiation dosing is uniformly standardized at 66-70Gy for gross disease and 54-60Gy for elective nodal regions, despite substantial heterogeneity in treatment response. Hence, a genomic approach to individualize RT dose has yet to be utilized.

[0241] Although NPC is recognized as a radiosensitive tumor, substantial biological heterogeneity exists, and the current one-size-fits-all approach of uniform dose prescription may underdose and / or overdose a proportion of patients, thereby adversely impacting clinical outcomes. Therefore, the effect of GARD on clinical outcomes in patients with locally advanced NPC treated is examined in a randomized phase III trial and the physical dose required to achieve desirable clinical outcomes is calculated.

[0242] Materials and Methods

[0243] Patient population

[0244] A total of 803 patients with locally advanced NPC were enrolled in a randomized phase III trial. Eligible patients had histologically confirmed NPC, stage III to stage IVB disease as per the 6thedition of the American Joint Committee on Cancer / International Union Against Cancer. The patients were recruited from seven oncology centers in Hong Kong between September 2006 and September 2012 and were treated with concurrent chemoradiotherapy plus (neo)-adjuvant chemotherapy. The median follow-up time for all patients was 8.4 years. Formalin-fixed paraffin-embedded (FFPE) samples from the nasopharyngeal were prospectively archived, and a total of 92 available samples with sufficient quality were identified for analysis (see FIG. 18).

[0245] External validation of RSI

[0246] The gene expression profiles (GSE12452 and GSE 13597) downloaded from Gene Expression Omnibus (GEO) datasets were used for external validation of RSI. A total of 56 patients were included in the validation set, and their characteristics were detailed in table 10.

[0247] Table 10. Baseline demographics and tumor characteristics of patients in GSE12452 and GSE13597 cohort.0248] RNA preparation and gene expression profiSing

[0249] The details of tissue processing, RNA isolation, and quality assurance have been published previously. RNA was extracted from FFPE samples using the QIAGEN FFPE RNeasy kit (QIAGEN GmbH, Hilden, Germany). AU paraffin was removed from freshly cut FFPE tissue before RNA purification, and the sample was briefly heated in RNeasy lysis bugger. The extracted RNA was analyzed using a Nanodrop 2000 spectrophotometer (ThermoFisher Scientific, Waltham, MA, USA), and RNA integrity numbers were determined to evaluate RNA integration using an Agilent Bioanalyzer 2100 (Agilent Technologies, Santa Clara, CA, USA). Total RNA was amplified using an Ovation FFPE WTA System (NuGEN, San Carlos, CA, USA), and an Encore Biotin Module (NuGEN) was used for fragmentation and labeling. The Affymetrix Human U133 GeneChip (Affymetrix, Santa Clara, CA, USA) was used for microarray gene expression analysis, and 54675 probes were detected from the FFPE samples and summarized into 20174 genes. The maximum expression signal was kept among the same gene probes. The microarray data were pre-processed with the RMA algorithm of the affy package in R. Evaluation of quality control metrics demonstrated that the samples had good RNA yield and quality (see FIGs. 13A-14A).

[0259] Radiosensitivity Index (RSI)

[0251] The 10-gene assay was conducted on tissue samples based on gene expression. Each of the 10 genes in the assay was ranked according to gene expression, with the highest expressed gene ranked as 10 and the lowest expressed gene ranked as I . The RSI was determined using the previously published ranked-based linear algorithm:

[0252] RSI - -0.0098009 x AR + 0.0128283 xcJun + 0.0254552 x STAT1 - 0.0017589 x PKC - 0.0038171 x RelA + 0.1070213 x cABL - 0.0002509 x SUMO1 - 0.0092431 x CDK1 - 0.0204469 x HDAC1 - 0.0441683 x IRF1

[0253] Genomic Adjusted Radiation Dose (GARD)

[0254] The calculation for GARD is similar to the biological effective dose (BED), except the patient-specific a is derived by replacing the RSI for survival (S) S = e-nd (off Pd), where dose (d) is 2 Gy, n = 1, and β is a constant (0.05 / Gy). A higher GARD value predicts a stronger radiation therapeutic effect.

[0255] GARD threshold (GARDT) was identified by minimizing the p-value of the log-rank test in comparing the locoregional failure-free rate of patient cohorts by iterating through possible cut-off points for GARD (Table 11).

[0256] Table 11 : Comparison of various cut-off value GARD versus clinical endpoints

[0257] The cut-off of GARDT of 45 was identified to dichotomize patients into two groups with high GARD (GARD >45) and low GARD (GARD < 45). Patients with high GARD had a locoregional failure-free rate of 85% and overall survival of 75%.

[0258] Biologically Optimized Personalized RT Dose (RxRSI)

[0259] RxRsI is the physical dose (in Gy) needed to achieve a GARD threshold (GARDT) for a desired clinical outcome, as defined previously. RxRSI is calculated using the following formula: GARDT / (a+pd), where a is calculated based on each individual’s RSI and P is a constant (0.05 / Gy). When comparing RxRSI and the actual physical dose received by the NPC patients, it is defined that RxRSI and the empirical dose matched if they were within 10% of one another.

[0260] Statistical Analysis

[0261] Statistical analyses were conducted using R (version 3.6.1). The Kruskal-Wallis and Pearsons tests were used, when appropriate, to test the differences between groups. The locoregional failure-free rate (LRFFR) and overall survival (OS) were calculated using the Kaplan-Meier (KM) method, with the log-rank test used to test differences. The LRFFR was defined as the time from randomization to failure within the head and neck region and censoring death without failure. The OS was defined as the time from randomization to death from any cause. Other endpoints reported include progression-free survival (PFS), local failure- free rate (LFFR), and distant metastasis failure-free rate (DMFFR). The Cox proportional hazard model analyses were used to conduct univariable analysis (UVA) and multivariable (MVA) analysis. Variables that showed significant effects on UVA (p < 0.1) were included in the MVA.

[0262] Results

[0263] Patient Characteristics

[0264] Out of the 92 patients with NPC included in the analysis, 74 patients were classified as high GARD (GARD >45), and 18 patients were classified as low GARD (GARD <45). There were no significant differences in demographics, tumor staging, and treatment received between the 2 groups, except for a slightly higher proportion of patients with ECOG performance status 1 in the low GARD group (37.5% vs. 9.5%, p==0.087) (Table 12). The median dose to the primary tumor was 70Gy (range: 66-76Gy). The RSI was significantly lower in the high GARD group (median: 0,22 vs, 0.32, p<0.001).

[0265] Table 12: Clinical Characteristics of Patients (Ar= 92)

[0267] With a median follow-up of 7.1 years (range: 0.8 to 11.9 years), 37 patients relapsed, with 14 patients having loco-regional failures, 15 having distant metastases, and 8 having both loco-regional and distant failures. Twenty-seven deaths were recorded, with 26 being due to disease progression and 1 being due to a brain abscess.

[0268] FIGs. 11A-11B show comparisons of clinical outcomes between patients with high GARD 45 vs. low GARD 45. FIG. 11 A shows loco-regional failure free rate (LRFFR). FIG.1 IB Overall survival (OS). Table 13 relates to the results shown in FIGs. 11 A-l IB.

[0269] Table 13[0270J The high GARD group was associated with significantly better 3-year and 5-year LRFFR than the low GARD group (3-year: 84.4% vs. 54.5%, 5-year: 84.4% vs. 36.4%, p=0.001) (FIG. HA). MVA showed that the GARD score was the only independent factor in predicting the LRFFR rate (hazard ratio [HR]: 0.27, 95% CI, 0.10-0.71, p=0.008) (Table 14). Similarly, the 3-year and 5-year local control rates were better in patients with high GARD (p=0.028) (FIG. 14B).

[0271] To understand the combined contributions of tumor and excess normal tissue effects on outcomes, penalized local control (pLC) was calculated, which includes local recurrence and events related to RT-related toxicity, but does not account for death due to disease progression or other causes. The penalized local control curve can be modelled as S(t)= CiSi(t) + C2S2(t), where Ci = fraction of patients with GARD greater than the predetermined GARD value and C2:=:fraction of patients with GARD less than the predetermined GARD value. Finally, the survival curve for the 70 Gy group was adjusted for the predicted hazard ratios in order to compare pLC for the two groups:whereare the risks for each adverse outcome due to the increased dose from 60 to 70 Gy (naso- and oro-, respectively).

[0272] The 3-year OS and 5-year OS rates of the high GARD group were significantly better than those of the low GARD group (3-year: 86.3% vs. 77.8%, 5-year: 74.0% vs. 61.1%, p=0.036) (FIG. 1 IB). MVA showed that the GARD score was an independent factor (HR: 0.38, 95% CI: 0.15-0.97, pH).043) in predicting OS (Table 14). There was a trend suggesting high GARD score was associated with better progression-free survival (p=0.036) and distant metastasis-free survival (p 0.094) (FIGs. 15A-15B).[0273| Table 14: Univariable and Multivariable Cox regression analyses of LRFFR and OS(X 92)

[0274] Variables in the UVA with a P<0. 1 wall be considered in the MVA.

[0275] RxRSI

[0276] As GARD is significantly predictive for locoregional control and OS and is an actionable metric, GARD is used to calculate the individual radiotherapy dose needed to achieve the desired clinical effect (desirable GARD). FIG. 9 A showed the distribution of RSI of 92 patients, which demonstrated a wide heterogeneity in radiation sensitivity in NPC (RSI range: 0.14 - 0,42). FIG. 12A - calculating the RxRSI (the physical dose to achieve an optimized biological outcome) for each patient (N 02) that identify three groups of patients: (1) Patients who require less than standard dose (<66Gy) (N:::59, 64.1%) [Green], (2) Patients who require standard dose (66-74Gy) (N=20, 21.7%) [Orange], and (3) patients who require more than the standard dose (>74Gy) (N:::13, 14.1%) [Red],

[0277] FIG. 12B show's a model for the percentage of patients in NPC-0501 cohort (N=92) achieving high GARD with the doses between 40Gy to 74Gy. The RxRSI (the physical dosepredicted to optimize the biological outcome) was calculated and identified three distinct clinical subgroups: (a) radiosensitive tumors that achieve RxRSI at dose < 66Gy (current standard of care) (N=59, 64.1%) (b) moderately radiosensitive tumors that achieve RxRSI at the current standard of care dose (66-74Gy) (N=20, 21.7%), and (c) radioresistant tumors that require significant dose escalation above the current standard of care (>74Gy) (N::::13, 14.1%) (FIG. 12A). A similar pattern of RSI and RxRSI was observed in the external validation set: radiosensitive (60,8%), moderately radiosensitive (18.8%), and radioresistant (20.3%) (FIGs. 16- 17 A).

[0278] The present model predicted that, at uniform dosing of 50Gy and 60Gy, the GARD of 11% and 39% of patients, respectively, would be optimized. The number of patients rose sharply to 83% at a dose of 70Gy (FIG. 12B). A similar pattern was observed in the external expression profile (FIG. 17B).

[0279] FIGs. 13A-13C show the quality check plots of NPC-0501 cohort. FIG. 13A - RNA degradation plot shows though there are some degradations in 5’ RNA sequences, but still high concentration in 3’ RNA, the slope of fluorescence curves mostly are over 0. Together with the relative log expression values (RLE) plot shown in FIG. 13B, which shown the normalized gene expression logged data are distributed around 0; and the normalized unsealed standard errors (NUSE) plot (FIG 13C) shown the NUSE value near 1, all these plots proved that, the quality of FFPE samples sequencing data are good enough for further analy sis.

[0280] The histogram plots of FIG. 13D raw NPC0501 data and FIG. 14A RAIA processed data. Each line represents one sample. Most samples have shown consistency after pre- processing.

[0281] FIGs. 14B-15B show comparison of clinical outcomes between patients with high GARD 45 vs. low GARD 45 (FIG. 14B - local failure free rate (LFFR), FIG. 15A - distant metastasis free rate (DMFR), and FIG. I 5B progression-free survival (PFS)).

[0282] Table 15

[0283] FIG. 16 shows a boxplot of RSI score from three independent advanced NPC cohorts.The calculated RSI values have similar distribution and range among independent datasets.

[0284] RxRSI was calculated from two NPC gene expression profile (GSE12452 and GSE13597) (N=69) and found that (1) 60.8% of patients required less than standard dose (<66Gy), (2) 18.8% of patients who require standard dose (66-74Gy), and (3) 20.3% of patients who require more than the standard dose (>74Gy) (FIG. 17 A).

[0285] Patients in two NPC gene expression profile (GSEI2452 and GSE13597) (N::::69) achieved high GARD with dose between 40Gy to 74Gy (FIG. 17B).

[0286] Impact of GARD in different treatment sequences of NPC-0501 trial

[0287] In the NPC-0501 trial, 68 patients received induction-concurrent chemoradiation (induction-CRT), with 52 patients (76.5%) having a high GARD value, and 16 (23.5%) having a low GARD value. Additionally, 24 patients received CRT-adjuvant, with 22 (91.7%) having high GARD values and 2 (8.3%) having low GARD values. There was no significant difference in LRFFR (5-year: 79.2% vs. 72.3%, p-0.663) and OS (5-year: 69.1% vs. 78.4%, p= 0.426) between patients receiving induction-CRT and CRT-adjuvant (Table 16). However, patients with high GARD had significantly better clinical outcomes than those with low GARD value in either the induction-CRT or CRT-adjuvant group, while there were no differences in clinical outcomes of patients in the same GARD group irrespective of treatment sequences (Table 16).

[0288] Table 16: Impact of GARD on clinical outcomes in patients with different treatment sequences.

[0290] Among the 92 evaluated patients, 20 (21.7%) developed severe (> grade 3) late toxicities. The most common late toxicities were peripheral neuropathy (n=9, 9.8%), ear- related (deafness / otitis) (n 9, 9.8%), and soft tissue / bone damages (n 3. 3.3%) were among. There was no significant difference in the incidence of grade 3 or above late toxicities (p=0.491) and serious late-toxicity-free rates (p=0.532) between patients with high and low GARD values (Table 17).

[0291] Table 17. Incidence of major late toxicities (> Grade 3) in high GARD versus lowGARD patients

[0292] Discussion

[0293] This disclosure represents the first evaluation of the impact of previously validated radiation metric, GARD, on clinical outcomes in patients with NPC. The present findings demonstrated that GARD is an independent predictor of locoregional control and overall survival in patients with NPC who undergo radiotherapy. In addition, is has been shown that GARD can be used to personalize radiotherapy based on the biology of tumor, with over 60% of patients potentially benefiting from dose de-escalation to achieve GARDT of 45. However, approximately 15% of patients with radio-resistant tumors are unlikely to achieve desirable outcomes with the current standard of care dose of 70Gy. The present pattern of radiosensitivity and RxRSI were independently validated in the external cohorts.

[0294] Regarding the prescription dose of definitive radiotherapy for NPC, most international guidelines recommend a dose of 70Gy to the gross disease and 50-60Gy to elective regions. The present disclosure supports these recommendations, as only around 40% of patients achieved optimized GARD by receiving a uniform prescription of 60Gy, and this percentage increased to around 85% of patients with a dose increase to 70Gy. However, the marginal benefit of further dose escalation beyond 70Gy is minimal, with only 5% of patients potentially benefiting from an increase to 80Gy, and the associated treatment-related complications may not be justified. Notably, around 15% of patients were unable to achieve desirable GARD with the current standard of care dose prescription, which is consistent with evidence indicating that around 10-15% of patients with locally advanced disease develop loco- regional failures.

[0295] The metric of GARD has been extensively studied in multiple large cohorts of over 20 disease sites and has been found to be predictive of overall survival in radiotherapy-treated patients in a pooled analysis. These findings have led the European Organization for Research and Treatment of Cancer to comment that RSI and GARD represent ‘near level one evidence’ for their application in clinical practice. In this disclosure, the GARD is validated in NPCpatients using long-term clinical data and prospective archived tissue samples from a phase III randomized trial (NPC-0501).

[0296] Several gene expression prognostic signatures have been proposed in patients with NPC, but their clinical applicability remains limited. In contrast, the GARD metric is a clinically actionable framework that allows for the personalization of the radiotherapy prescription dose based on the biological heterogeneity of the tumor. This approach predicts the optimal radiotherapy dose for individual patients, which provides the first opportunity to depart from the currently ‘one-size-fits-all’ approach to radiotherapy dose prescription in NPC patients. The GARD metric is distinct from gene expression prognostic signatures in this regard.

[0297] The application of intensity-modulated radiotherapy (IMRT) has greatly reduced the toxicity, but late complications such as xerostomia, temporal lobe necrosis, and dysphagia still significantly affect patients’ quality of life. These complications are largely attributed to the total dose of 70Gy near critical structures. Currently, there is no prospective trial on dose de- escalation in the adult population, and data from retrospective series are conflicting. For example, a propensity score matching series of T1 -T3 NPC patients showed that 32 patients with incomplete radiation received a median dose of 63.7Gy and had comparable outcomes to those of a full dose of 70Gy, while a prospective observational study showed that 103 patients with early-stage NPC received 68Gy in 30 fractions to the gross tumor, and only I patient developed local failure. However, a dose to the primary' tumor < 66.5Gy compromises locoregional control. Given the heterogenous biological effect of homogenous physical doses of radiation for NPC tumors, dose de-escalation in unselected populations is not supported, as only 40% of patients achieved desirable GARD at the prescription dose of 60Gy. However, it is shown that that 60% of patients with radiosensitive tumors (RSI: 0.14 - 0.25) might be overdosed and suffer from unnecessary toxicities under current practice. Therefore, the present data provide a rationale to conduct a genomically-guided radiation dose de-escalation trial (60Gy vs. 70Gy) in this group of patients with favorable RSI.

[0298] For a small subset of NPC patients (around 15%) with radio-resistant tumors, radiation dose escalation may not be feasible by the current technology or may not be tolerable for adjacent normal tissue. In such situations, patients may require more potent systemic therapy or alternative radiation techniques such as functional image-guided dose painting to sub-volumes of radio-resistant regions or proton and heavy particle therapy.

[0299] Personalizing radiotherapy is incomplete without addressing normal tissue toxicity. However, the present data showed that GARD, based on tumor molecular profiling, did not correlate with treatment-related late toxicity.

[0300] In contrary' to previous analyses on RSI based on fresh tumor samples, the present analyses were conducted on FFPE samples. Robust quality control analyses were performed to ensure the validity of the present findings. Fresh frozen tissue is typically preferred for detecting gene mutational profiles due to its superior preservation of RNA, but it is not always available due to cost and technical difficulties. The feasibility of using FFPE tissues in RSI analysis will facilitate future research in the field of genomically-guided radiation.

[0301] Conclusion

[0302] GARD was independently associated with loco-regional control and overall survival of radiotherapy-treated NPC patients. The present results did not support uniform dose escalation or de-escalation, and GARD may be a potential framework for personalizing radiotherapy doses.

[0303] CLAUSES

[0304] Examples of the present disclosure can be implemented by any of the following numbered clauses:

[0305] Clause 1 : A computer software configured to integrate with a radiation therapy treatment planning system, the computer software being configured to: assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor; calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about 41.9 for oropharyngeal cancer; and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

[0306] Clause 2: The computer software of Clause I, further being configured to: calculate the RxRSI based in part on normal tissue toxicity for a treatment plan using the recommended RxRSI.

[0307] Clause 3: The computer software of Clause 1 or Clause 2, further being configured to: calculate the normal tissue toxicity based at least in part on risks to a plurality of tissue sites.

[0308] Clause 4: The computer software of any one of Clauses 1 to 3, further being configured to: receive, from the radiation therapy treatment planning system, a plurality of radiation plans each using the RxRSI; calculate normal tissue toxicity for each radiation planof the plurality of radiation plans; penalize each radiation plan of the plurality of radiation plans based on the normal tissue toxicity of the radiation treatment plan; and provide, to the radiation therapy treatment planning system, at least one recommended radiation plan that is least penalized of the plurality of radiation plans.

[0309] Clause 5: The computer software of any one of Clauses 1 to 4, further being configured to: calculate the recommended RxRSI based in part on a predefined standard of care dose range.

[0310] Clause 6: The computer software of any one of Clauses 1 to 5, further being configured to: calculate a proposed RxRSI based for the subject based at least in part on the pre-determined GARD value and the RSI; compare the proposed RxRSI to a predefined standard of care dose range; assign the recommended RxRSI a value within the predefined standard of care dose range when the proposed RxRSI is within or below the predefined standard of care dose range; and recommend consideration of the subject for clinical trial if the proposed RxRSI is above the predefined standard of care dose range.

[0311] Clause 7: The computer software of any one of Clauses 1 to 6, further being configured to: apply a linear regression model to the expression levels of the one or more signature genes in the tumor; and assign the RSI based at least in part on the linear regression model.

[0312] Clause 8: A computer-implemented method for minimizing risk of radiation therapy comprising: obtaining a radiation sensitivity index (RSI) of a subject's tumor from expression levels of one or more signature genes in the tumor; calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about 41.9 for oropharyngeal cancer; and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

[0313] Clause 9: The computer-implemented method of Clause 8, further comprising: calculating normal tissue toxicity of the personalized radiation dosage; and providing the personalized radiation therapy treatment plan based at least in part on the normal tissue toxicity.

[0314] Clause 10: The computer-implemented method of Clause 8 or Clause 9, further comprising: calculating relative risk for potential RxRSI values; and selecting the RxRSI based at least in part on the relative risk.

[0315] Clause 1 1 : A method of calculating a personalized radiation therapy dosage for a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample; applying a linear regression model to the expression levels andassigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about 41.9 for oropharyngeal cancer.

[0316] Clause 12: The method of Clause 11, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

[0317] Clause 13: The method of Clause 11 or Clause 12, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUMO1); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor 1 (IRTT); and combinations thereof.

[0318] Clause 14: The method of any one of Clauses 11 to 13, further comprising: determining a dose limiting structure of the normal tissues based at least in the RxRSI.

[0319] Clause 15: A method of treating a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample; applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about 41.9 for oropharyngeal cancer.

[0320] Clause 16: The method of Clause 15, further comprising: administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for nasopharyngeal cancer.

[0321] Clause 17: The method of Clause 15 or Clause 16, further comprising: calculating relative risk for the RxRSI, and selecting the RxRSI value of the subject based at least in part on the relative risk.

[0322] Clause 18: The method of any one of Clauses 15 to 17, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (ST ATI); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-likemodifier 1 (SUMO1); p21 activated kinase-2 (PAK2), histone deacetylase 1 (HDAC1); interferon regulatory' factor 1 (IRF1); and combinations thereof.

[0323] Clause 19: A system for developing a personalized radiation therapy treatment plan for a subject having a tumor, comprising: one or more processors; and a memory / operably coupled to the one or more processors, the memory / having computer-executable instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to: determine a radiation sensitivity index (RSI) of the tumor from expression levels of one or more signature genes in the tumor; calculate a personalized radiation dosage (RxRSI) for the subject based at least in part on a pre-determined genomic adjusted radiation dose (GARD) value and the RSI; calculate normal tissue toxicity of the personalized radiation dosage; calculate dosimetric parameters for normal tissues of the subject for a plurality of potential RxRSI values; calculate relative risk for potential RxRSI values of the plurality of potential RxRSI values; select the RxRSI value of the subject from the plurality of potential RxRSI values based at least in part, on the relative risk, and provide the personalized radiation therapy treatment plan for the subject.

[0324] Clause 20: The system of Clause 19, wherein determining the radiation sensitivity index (RSI) of the tumor comprises: determining expression levels of one or more signature genes from the subject's tumor; and applying a linear regression model to the expression levels and determining the radiation sensitivity index (RSI) of the tumor.

[0325] Clause 21 : The system of Clause 19 or Clause 20, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (ST ATI); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUMO1); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory / factor 1 (IRF1); and combinations thereof.

[0326] Clause 22: A method of developing a personalized radiation treatment plan, the method comprising: assigning a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor; calculating a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on a pre-determined genomic adjusted radiation dose (GARD) value and the RSI; and providing the recommended RxRSI as a radiation therapy dose for a radiation plan.

[0327] Clause 23: The method of Clause 22, further comprising: calculating the recommended RxRSI based in part on normal tissue toxicity for a treatment plan using the recommended RxRSI.

[0328] Clause 24: The method of Clause 22 or Clause 23, further comprising: calculating the normal tissue toxicity based at least in part on risks to a plurality of tissue sites.

[0329] Clause 25 : The method of any one of Clauses 22 to 24, further comprising: calculating a respective normal tissue toxicity for each radiation plan of a plurality of radiation plans each utilizing the recommended RxRSI; penalizing each radiation plan of the plurality of radiation plans based on the normal tissue toxicity of the radiation treatment plan; and providing at least one recommended radiation plan that is least penalized of the plurality of radiation plans.

[0330] Clause 26: The method of any one of Clauses 22 to 25, further comprising: determining whether the recommended RxRSI is within a predefined standard of care dose range.

[0330] Clause 27 : The method of any one of Clauses 22 to 26, further comprising: calculating a proposed RxRSI based for the subject based at least in part on the pre-determined GARD value and the RSI, comparing the proposed RxRSI to a predefined standard of care dose range, assigning the recommended RxRSI a value within the predefined standard of care dose range when the proposed RxRSI is within or below the predefined standard of care dose range; and recommending consideration of the subject for clinical trial if the proposed RxRSI is above the predefined standard of care dose range.

[0332] Clause 28: The method of any one of Clauses 22 to 27, further comprising: applying a linear regression model to the expression levels of the one or more signature genes in the tumor; and assigning the RSI based at least in part on the linear regression model.

[0333] Clause 29: The method of any one of Clauses 22 to 28, wherein the pre-determined GARD value is based at least in part on a plurality of GARD values for subjects in a cohort.

[0334] Clause 30: A computer software configured to integrate with a radiation therapy treatment planning system, the computer software being configured to: assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor; calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer; and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

[0335] Clause 31 : The computer software of Clause 30, further being configured to: calculate the RxRSI based in part on normal tissue toxicity for a treatment plan using the recommended RxRSI.

[0336] Clause 32: The computer software of Clause 30 or Clause 31, further being configured to: calculate the normal tissue toxicity based at least in part on risks to a plurality of tissue sites.

[0337] Clause 33: The computer software of any one of Clauses 30 to 32, further being configured to: receive, from the radiation therapy treatment planning system, a plurality of radiation plans each using the RxRSI; calculate normal tissue toxicity for each radiation plan of the plurality of radiation plans; penalize each radiation plan of the plurality of radiation plans based on the normal tissue toxicity of the radiation treatment plan; and provide, to the radiation therapy treatment planning system, at least one recommended radiation plan that is least penalized of the plurality of radiation plans.

[0338] Clause 34: The computer software of any one of Clauses 30 to 33, further being configured to: calculate the recommended RxRSI based in part on a predefined standard of care dose range.

[0339] Clause 35: The computer software of any one of Clauses 30 to 34, further being configured to: calculate a proposed RxRSI based for the subject based at least in part on the pre-determined GARD value and the RSI; compare the proposed RxRSI to a predefined standard of care dose range; assign the recommended RxRSI a value within the predefined standard of care dose range when the proposed RxRSI is within or below the predefined standard of care dose range; and recommend consideration of the subject for clinical trial if the proposed RxRSI is above the predefined standard of care dose range.

[0340] Clause 36: The computer software of any one of Clauses 30 to 35, further being configured to: apply a linear regression model to the expression levels of the one or more signature genes in the tumor, and assign the RSI based at least in part on the linear regression model.

[0341] Clause 37: A computer-implemented method for minimizing risk of radiation therapy comprising: obtaining a radiation sensitivity index (RSI) of a subject's tumor from expression levels of one or more signature genes in the tumor; calculating a personalized radiation dosage (RxRSI) for the subject based at least in part, on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer; and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

[0342] Clause 38: The computer-implemented method of Clause 37, further comprising: calculating normal tissue toxicity of the personalized radiation dosage; and providing the personalized radiation therapy treatment plan based at least in part on the normal tissue toxicity,

[0343] Clause 39: The computer-implemented method of Clause 37 or Clause 38, further comprising: calculating relative risk for potential RxRSI values, and selecting the RxRSI based at least in part on the relative risk.

[0344] Clause 40: A method of calculating a personalized radiation therapy dosage for a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample; applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer.

[0345] Clause 41 : The method of Clause 40, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

[0346] Clause 42: The method of Clause 40 or Clause 41, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUMO1); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor 1 (IRF1 ); and combinations thereof.

[0347] Clause 43: The method of any one of Clauses 40 to 42, further comprising: determining a dose limiting structure of the normal tissues based at least in the RxRSI.

[0348] Clause 44: A method of treating a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample; applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer.

[0349] Clause 45 : The method of Clause 44, further comprising: administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for nasopharyngeal cancer.

[0350] Clause 46: The method of Clause 44 or Clause 45, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

[0351] Clause 47: The method of any one of Clauses 44 to 46, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC), V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUMO1); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor I (IRF1 ); and combinations thereof.

[0352] Clause 48: A computer software configured to integrate with a radiation therapy treatment planning system, the computer software being configured to: assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levelsof one or more signature genes in the tumor; calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 41.9 for oropharyngeal cancer; and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

[0353] Clause 49: The computer software of Clause 48, further being configured to: calculate the RxRSI based in part on normal tissue toxicity for a treatment plan using the recommended RxRSI.

[0354] Clause 50: The computer software of Clause 48 or Clause 49, further being configured to: calculate the normal tissue toxicity based at least in part on risks to a plurality of tissue sites.

[0355] Clause 51 : The computer software of any one of Clauses 48 to 50, further being configured to: receive, from the radiation therapy treatment planning system, a plurality of radiation plans each using the RxRSI; calculate normal tissue toxicity for each radiation plan of the plurality of radiation plans; penalize each radiation plan of the plurality of radiation plans based on the normal tissue toxicity of the radiation treatment plan; and provide, to the radiation therapy treatment planning system, at least one recommended radiation plan that is least penalized of the plurality of radiation plans.

[0356] Clause 52: The computer software of any one of Clauses 48 to 51, further being configured to: calculate the recommended RxRSI based in part on a predefined standard of care dose range.

[0357] Clause 53: The computer software of any one of Clauses 48 to 52, further being configured to: calculate a proposed RxRSI based for the subject based at least in part on the pre-determined GARD value and the RSI; compare the proposed RxRSI to a predefined standard of care dose range; assign the recommended RxRSI a value within the predefined standard of care dose range when the proposed RxRSI is within or below the predefined standard of care dose range; and recommend consideration of the subject for clinical trial if the proposed RxRSI is above the predefined standard of care dose range.

[0358] Clause 54: The computer software of any one of Clauses 48 to 53, further being configured to: apply a linear regression model to the expression levels of the one or more signature genes in the tumor; and assign the RSI based at least in part on the linear regression model.

[0359] Clause 55: A computer-implemented method for minimizing risk of radiation therapy comprising: obtaining a radiation sensitivity index (RSI) of a subject's tumor from expression levels of one or more signature genes in the tumor; calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 41.9 for oropharyngeal cancer; and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

[0360] Clause 56: The computer-implemented method of Clause 55, further comprising: calculating normal tissue toxicity of the personalized radiation dosage; and providing the personalized radiation therapy treatment plan based at least in part on the normal tissue toxicity .

[0361] Clause 57: The computer-implemented method of Clause 55 or Clause 56, further comprising: calculating relative risk for potential RxRSI values; and selecting the RxRSI based at least in part on the relative risk.

[0362] Clause 58: A method of calculating a personalized radiation therapy dosage for a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample; applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 41.9 for oropharyngeal cancer.

[0363] Clause 59: The method of Clause 58, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

[0364] Clause 60: The method of Clause 58 or Clause 59, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR). jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (R.ELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier I (SUMO1); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor 1 (IRF1); and combinations thereof.

[0365] Clause 61 : The method of any one of Clauses 58 to 60, further comprising: determining a dose limiting structure of the normal tissues based at least in the RxRSI.

[0366] Clause 62: A method of treating a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample; applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 41.9 for oropharyngeal cancer.

[0367] Clause 63 : The method of Clause 62, further comprising: administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for oropharyngeal cancer.

[0368] Clause 64: The method of Clause 62 or Clause 63, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

[0369] Clause 65: The method of any one of Clauses 62 to 64, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor GAR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC), V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier I (SUMO1); p21 activated kinase-2 (PAK.2); histone deacetylase 1 (HDAC1); interferon regulatory' factor 1 (IRF1 ); and combinations thereof.

[0370] Clause 66: A computer software configured to integrate with a radiation therapy treatment planning system, the computer software being configured to: assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor; calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 46.1 to 65 fororopharyngeal cancer, and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

[0371] Clause 67 : The computer software of Clause 66, further being configured to: calculate the RxRSI based in part on normal tissue toxicity for a treatment plan using the recommended RxRSI.

[0372] Clause 68: The computer software of Clause 66 or Clause 67, further being configured to: calculate the normal tissue toxicity based at least in part on risks to a plurality of tissue sites.

[0373] Clause 69: The computer software of any one of Clauses 66 to 68, further being configured to: receive, from the radiation therapy treatment planning system, a plurality of radiation plans each using the RxRSI; calculate normal tissue toxicity for each radiation plan of the plurality of radiation plans; penalize each radiation plan of the plurality of radiation plans based on the normal tissue toxicity of the radiation treatment plan; and provide, to the radiation therapy treatment planning system, at least one recommended radiation plan that is least penalized of the plurality of radiation plans.

[0374] Clause 70: The computer software of any one of Clauses 66 to 69, further being configured to: calcul ate the recommended RxRSI based in part on a predefined standard of care dose range.

[0375] Clause 71 : The computer software of any one of Clauses 66 to 70, further being configured to: calculate a proposed RxRSI based for the subject based at least in part on the pre-determined GARD value and the RSI; compare the proposed RxRSI to a predefined standard of care dose range; assign the recommended RxRSI a value within the predefined standard of care dose range when the proposed RxRSI is within or below the predefined standard of care dose range, and recommend consideration of the subject for clinical trial if the proposed RxRSI is above the predefined standard of care dose range.

[0376] Clause 72: The computer software of any one of Clauses 66 to 71, further being configured to: apply a linear regression model to the expression levels of the one or more signature genes in the tumor; and assign the RSI based at least in part on the linear regression model.

[0377] Clause 73 : A computer-implemented method for minimizing risk of radiation therapy comprising: obtaining a radiation sensitivity index (RSI) of a subject's tumor from expression levels of one or more signature genes in the tumor; calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 46.1 to 65 fororopharyngeal cancer; and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

[0378] Clause 74: The computer-implemented method of Clause 73, further comprising: calculating normal tissue toxicity of the personalized radiation dosage; and providing the personalized radiation therapy treatment plan based at least in part on the normal tissue toxicity.

[0379] Clause 75: The computer-implemented method of Clause 73 or Clause 74, further comprising: calculating relative risk for potential RxRSI values; and selecting the RxRSI based at least in part on the relative risk.

[0380] Clause 76: A method of calculating a personalized radiation therapy dosage for a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample; applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 46.1 to 65 for oropharyngeal cancer.

[0381] Clause 77: The method of Clause 76, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

[0382] Clause 78: The method of Clause 76 or Clause 77, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR). jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUMO1); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor 1 (IRF1), and combinations thereof.

[0383] Clause 79: The method of any one of Clauses 76 to 78, further comprising: determining a dose limiting structure of the normal ti ssues based at least in the RxR SI.

[0384] Clause 80: A method of treating a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample; applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 46.1 to 65 for oropharyngeal cancer.

[0385] Clause 81 : The method of Clause 80, further comprising: administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for oropharyngeal cancer.

[0386] Clause 82: The method of Clause 80 or Clause 81, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

[0387] Clause 83: The method of any one of Clauses 80 to 82, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor ( AR): jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene I (c-Abl); small ubiquitin-like modifier 1 (SUMO1); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor 1 (IRF1); and combinations thereof.

[0388] Clause 84: A computer software configured to integrate with a radiation therapy treatment planning system, the computer software being configured to: assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor; calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 59.5 for nasopharyngeal cancer; and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

[0389] Clause 85 : The computer software of Clause 84, further being configured to: calculate the RxRSI based in part on normal tissue toxicity for a treatment plan using the recommended RxRSI.

[0390] Clause 86: The computer software of Clause 84 or Clause 85, further being configured to: calculate the normal tissue toxicity based at least in part on risks to a plurality of tissue sites.

[0391] Clause 87: The computer software of any one of Clauses 84 to 86, further being configured to: receive, from the radiation therapy treatment planning system, a plurality of radiation plans each using the RxRSI; calculate normal tissue toxicity for each radiation plan of the plurality of radiation plans; penalize each radiation plan of the plurality of radiation plans based on the normal tissue toxicity of the radiation treatment plan; and provide, to the radiation therapy treatment planning system, at least one recommended radiation plan that is least penalized of the plurality of radiation plans.

[0392] Clause 88: The computer software of any one of Clauses 84 to 87, further being configured to: calculate the recommended RxRSI based in part on a predefined standard of care dose range.

[0393] Clause 89: The computer software of any one of Clauses 84 to 88, further being configured to: calculate a proposed RxRSI based for the subject based at least in part on the pre-determined GARD value and the RSI; compare the proposed RxRSI to a predefined standard of care dose range; assign the recommended RxRSI a value within the predefined standard of care dose range when the proposed RxRSI is within or below the predefined standard of care dose range; and recommend consideration of the subject for clinical trial if the proposed RxRSI is above the predefined standard of care dose range.

[0394] Clause 90: The computer software of any one of Clauses 84 to 89, further being configured to: apply a linear regression model to the expression levels of the one or more signature genes in the tumor; and assign the RSI based at least in part on the linear regression model.

[0395] Clause 91 : A computer-implemented method for minimizing risk of radiation therapy comprising: obtaining a radiation sensitivity index (RSI) of a subject's tumor from expression levels of one or more signature genes in the tumor; calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 59.5 for nasopharyngeal cancer; and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

[0396] Clause 92: The computer-implemented method of Clause 91, further comprising: calculating normal tissue toxicity of the personalized radiation dosage; and providing the personalized radiation therapy treatment plan based at least in part on the normal tissue toxicity.

[0397] Clause 93: The computer-implemented method of Clause 91 or Clause 92, further comprising: calculating relative risk for potential RxRSI values; and selecting the RxRSI based at least in part on the relative risk.

[0398] Clause 94: A method of calculating a personalized radiation therapy dosage for a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample; applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 59.5 for nasopharyngeal cancer.

[0399] Clause 95: The method of Clause 94, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

[0400] Clause 96: The method of Clause 94 or Clause 95, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUMO1); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor 1 (IRF1 ); and combinations thereof.

[0401] Clause 97: The method of any one of Clauses 94 to 96, further comprising: determining a dose limiting structure of the normal tissues based at least in the RxRSI

[0402] Clause 98: A method of treating a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample; applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample, and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 59.5 for nasopharyngeal cancer.

[0403] Clause 99: The method of Clause 98, further comprising: administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for nasopharyngeal cancer.

[0404] Clause 100: The method of Clause 98 or Clause 99, further comprising: calculating relative risk for the RxRSI, and selecting the RxRSI value of the subject based at least in part on the relative risk.

[0405] Clause 101 : The method of any one of Clauses 98 to 100, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUMO1); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory / factor 1 (IRF1); and combinations thereof.

Claims

CLAIMSWhat Is C laimed Is:

1. A computer software configured to integrate with a radiation therapy treatment planning system, the computer software being configured to: assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor; calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about 41.9 for oropharyngeal cancer; and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

2. The computer software of claim 1, further being configured to: calculate the RxRSI based in part on normal tissue toxicity for a treatment plan using the recommended RxRSI.

3. The computer software of claim 1, further being configured to: calculate the normal tissue toxicity based at least in part on risks to a plurality of tissue sites.

4. The computer software of claim 1, further being configured to: receive, from the radiation therapy treatment planning system, a plurality of radiation plans each using the RxRSI, calculate normal tissue toxicity for each radiation plan of the plurality of radiation plans; penalize each radiation plan of the plurality of radiation plans based on the normal tissue toxicity of the radiation treatment plan; and provide, to the radiation therapy treatment planning system, at least one recommended radiation plan that is least penalized of the plurality of radiation plans.

5. The computer software of claim 1, further being configured to:calculate the recommended RxRSI based in part on a predefined standard of care dose range.

6. The computer software of claim 1, further being configured to: calculate a proposed RxRSI based for the subject based at least in part on the predetermined GARD value and the RSI; compare the proposed RxRSI to a predefined standard of care dose range; assign the recommended RxRSI a value within the predefined standard of care dose range when the proposed RxRSI is within or below the predefined standard of care dose range; and recommend consideration of the subject for clinical trial if the proposed RxRSI is above the predefined standard of care dose range.

7. The computer software of claim 1, further being configured to: apply a linear regression model to the expression levels of the one or more signature genes in the tumor; and assign the RSI based at least in part on the linear regression model.

8. computer-implemented method for minimizing risk of radiation therapy comprising: obtaining a radiation sensitivity index (RSI) of a subject’s tumor from expression levels of one or more signature genes in the tumor; calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about. 41.9 for oropharyngeal cancer; and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

9. The computer-implemented method of claim 8, further comprising: calculating normal tissue toxicity of the personalized radiation dosage; and providing the personalized radiation therapy treatment plan based at least in part on the normal tissue toxicity.

10. The computer-implemented method of claim 8, further comprising: calculating relative risk for potential RxRSI values; and selecting the RxRSI based at least in part on the relative risk.

11. A method of calculating a personalized radiation therapy dosage for a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample; applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject’s tumor sample, and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about 41.9 for oropharyngeal cancer.

12. The method of claim 1 1, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

13. The method of claim 11, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1(ST ATI); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUMO1); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor 1 (IRF1); and combinations thereof.

14. The method of claim 11, further comprising: determining a dose limiting structure of the normal tissues based at least in the RxRSI.

15. ,A method of treating a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample;applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSl) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer or about 41.9 for oropharyngeal cancer.

16. The method of claim 15, further comprising: administering the calculated personalized radiation dosage (RxRSl) to the subject as a treatment for nasopharyngeal cancer.

17. The method of claim 15, further comprising: calculating relative risk for the RxRSl; and selecting the RxRSl value of the subject based at least in part on the relative risk.

18. The method of claim 15, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1(ST ATI); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUM01); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor 1 (IRF1); and combinations thereof.

19. A system for developing a personalized radiation therapy treatment plan for a subject having a tumor, comprising: one or more processors; and a memory' operably coupled to the one or more processors, the memory having computer-executable instructions stored thereon that, when executed by the one or more processors, cause the one or more processors to: determine a radiation sensitivity index (RSI) of the tumor from expression levels of one or more signature genes in the tumor; calculate a personalized radiation dosage (RxRSl) for the subject based at least in part on a pre-determined genomic adjusted radiation dose (GARD) value and the RSI; calculate normal tissue toxicity of the personalized radiation dosage;calculate dosimetric parameters for normal tissues of the subject for a plurality of potential RxRSI values; calculate relative risk for potential RxRSI values of the plurality of potential RxRSI values; select the RxRSI value of the subject from the plurality of potential RxRSI values based at least in part on the relative risk; and provide the personalized radiation therapy treatment plan for the subject.

20. The system of claim 19, wherein determining the radiation sensitivity index (RSI) of the tumor comprises: determining expression levels of one or more signature genes from the subject’s tumor; and applying a linear regression model to the expression levels and determining the radiation sensitivity index (RSI) of the tumor.21 . The system of claim 19, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1(ST ATI); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUMO1); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor 1 (IRF1); and combinations thereof.

22. A method of developing a personalized radiation treatment plan, the method comprising: assigning a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor; calculating a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on a pre-determined genomic adjusted radiation dose (GARD) value and the RSI; and providing the recommended RxRSI as a radiation therapy dose for a radiation plan.

23. The method of claim 22, further comprising:calculating the recommended RxRSI based in part on normal tissue toxicity for a treatment plan using the recommended RxRSI.

24. The method of claim 22, further comprising: calculating the normal tissue toxicity based at least in part on risks to a plurality of tissue sites.

25. The method of claim 22, further comprising: calculating a respective normal tissue toxicity for each radiation plan of a plurality of radiation plans each utilizing the recommended RxRSI; penalizing each radiation plan of the plurality of radiation plans based on the normal tissue toxicity of the radiation treatment plan; and providing at least one recommended radiation plan that is least penalized of the plurality of radiation plans.

26. The method of claim 22, further comprising: determining whether the recommended RxRSI is within a predefined standard of care dose range.

27. The method of claim 22, further comprising: calculating a proposed RxRSI based for the subject based at least in part on the predetermined GARD value and the RSI; comparing the proposed RxRSI to a predefined standard of care dose range; assigning the recommended RxRSI a value within the predefined standard of care dose range when the proposed RxRSI is within or below the predefined standard of care dose range; and recommending consideration of the subject for clinical trial if the proposed RxRSI is above the predefined standard of care dose range.

28. The method of claim 22, further comprising: applying a linear regression model to the expression levels of the one or more signature genes in the tumor; and assigning the RSI based at least in part on the linear regression model.

29. The method of claim 22, wherein the pre-determined GARD value is based at least in part on a plurality of GARD values for subjects in a cohort.

30. A computer software configured to integrate with a radiation therapy treatment planning system, the computer software being configured to: assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor; calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer; and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

31. The computer software of claim 30, further being configured to: calculate the RxRSI based in part on normal tissue toxicity for a treatment plan using the recommended RxRSI.

32. The computer software of claim 30, further being configured to: calculate the normal tissue toxicity based at least in part on risks to a plurality of tissue sites.

33. The computer software of claim 30, further being configured to: receive, from the radiation therapy treatment planning system, a plurality of radiation plans each using the RxRSI; calculate normal ti ssue toxicity for each radiation plan of the plurality of radiation plans; penalize each radiation plan of the plurality of radiati on plans based on the normal tissue toxicity of the radiation treatment plan; and provide, to the radiation therapy treatment planning system, at least one recommended radiation plan that is least penalized of the plurality of radiation plans.

34. The computer software of claim 30, further being configured to: calculate the recommended RxRSI based in part on a predefined standard of care dose range.

35. The computer software of claim 30, further being configured to: calculate a proposed RxRSI based for the subject based at least in part on the predetermined GARD value and the RSI; compare the proposed RxRSI to a predefined standard of care dose range; assign the recommended RxRSI a value within the predefined standard of care dose range when the proposed RxRSI is within or below the predefined standard of care dose range; and recommend consideration of the subject for clinical trial if the proposed RxRSI is above the predefined standard of care dose range.

36. The computer software of claim 30, further being configured to: apply a linear regression model to the expression levels of the one or more signature genes in the tumor; and assign the RSI based at least in part on the linear regression model.

37. A computer-implemented method for minimizing risk of radiation therapy comprising: obtaining a radiation sensitivity index (RSI) of a subject's tumor from expression levels of one or more signature genes in the tumor; calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer; and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

38. The computer-implemented method of claim 37, further comprising: calculating normal tissue toxicity of the personalized radiation dosage; and providing the personalized radiation therapy treatment plan based at least in part on the normal tissue toxicity.

39. The computer-implemented method of claim 37, further comprising: calculating relative risk for potential RxRSI values; and selecting the RxRSI based at least in part on the relative risk.

40. A method of calculating a personalized radiation therapy dosage for a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer.41 . The method of claim 40, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

42. The method of claim 40, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65), c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUMO1); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor 1 (IRF1); and combinations thereof.

43. The method of claim 40, further comprising: determining a dose limiting structure of the normal tissues based at least in theRxRSI.

44. A method of treating a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample; applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; andcalculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 45 for nasopharyngeal cancer.

45. The method of claim 44, further comprising: administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for nasopharyngeal cancer.

46. The method of claim 44, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

47. The method of claim 44, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUM01); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor 1 (IRF1); and combinations thereof.

48. A computer software configured to integrate with a radiation therapy treatment planning system, the computer software being configured to: assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor; calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 41.9 for oropharyngeal cancer; and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

49. The computer software of claim 48, further being configured to: calculate the RxRSI based in part on normal tissue toxicity for a treatment plan using the recommended RxRSI.

50. The computer software of claim 48, further being configured to: calculate the normal tissue toxicity based at least in part on risks to a plurality of tissue sites.

51. The computer software of claim 48, further being configured to: receive, from the radiation therapy treatment planning system, a plurality of radiation plans each using the RxRSI; calculate normal tissue toxicity for each radiation plan of the plurality of radiation plans; penalize each radiation plan of the plurality of radiation plans based on the normal tissue toxicity of the radiation treatment plan; and provide, to the radiation therapy treatment planning system, at least one recommended radiation plan that is least penalized of the plurality of radiation plans.

52. The computer software of claim 48, further being configured to: calculate the recommended RxRSI based in part on a predefined standard of care dose range.

53. The computer software of claim 48, further being configured to: calculate a proposed RxRSI based for the subject based at least in part on the predetermined GARD value and the RSI; compare the proposed RxRSI to a predefined standard of care dose range; assign the recommended RxRSI a value within the predefined standard of care dose range when the proposed RxRSI is within or below the predefined standard of care dose range; and recommend consideration of the subject for clinical trial if the proposed RxRSI is above the predefined standard of care dose range.

54. The computer software of claim 48, further being configured to: apply a linear regression model to the expression levels of the one or more signature genes in the tumor; and assign the RSI based at least in part on the linear regression model.55, ,A computer-implemented method for minimizing risk of radiation therapy comprising: obtaining a radiation sensitivity index (RSI) of a subject’s tumor from expression levels of one or more signature genes in the tumor; calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 41.9 for oropharyngeal cancer, and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

56. The computer-implemented method of claim 55, further comprising: calculating normal tissue toxicity of the personalized radiation dosage; and providing the personalized radiation therapy treatment plan based at least in part on the normal tissue toxicity.

57. The computer-implemented method of claim 55, further comprising: calculating relative risk for potential RxRSI values; and selecting the RxRSI based at least in part on the relative risk.

58. A method of calculating a personalized radiation therapy dosage for a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 41.9 for oropharyngeal cancer.

59. The method of claim 58, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

60. The method of claim 58, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier I (SUMO1); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory' factor 1 (IRF I); and combinations thereof61. The method of claim 58, further comprising: determining a dose limiting structure of the normal tissues based at least in the RxRSI.

62. A method of treating a subject, the method comprising: determining expression levels of one or more signature genes from a subject’s tumor sample; applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 41.9 for oropharyngeal cancer.

63. The method of claim 62, further comprising: administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for nasopharyngeal cancer.

64. The method of claim 62, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

65. The method of claim 62, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65), c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUMO1); p21activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor 1 (IRF1); and combinations thereof.

66. A computer software configured to integrate with a radiation therapy treatment planning system, the computer software being configured to: assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor; calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 46.1 to 65 for oropharyngeal cancer; and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

67. The computer software of claim 66, further being configured to: calculate the RxRSI based in part on normal tissue toxicity for a treatment plan using the recommended RxRSI.

68. The computer software of claim 66, further being configured to: calculate the normal tissue toxicity based at least in part on risks to a plurality of tissue sites.

69. The computer software of claim 66, further being configured to: receive, from the radiation therapy treatment planning system, a plurality of radiation plans each using the RxRSI; calculate normal ti ssue toxicity for each radiation plan of the plurality of radiation plans; penalize each radiation plan of the plurality of radiati on plans based on the normal tissue toxicity of the radiation treatment plan; and provide, to the radiation therapy treatment planning system, at least one recommended radiation plan that is least penalized of the plurality of radiation plans.

70. The computer software of claim 66, further being configured to: calculate the recommended RxRSI based in part on a predefined standard of care dose range.

71. The computer software of claim 66, further being configured to: calculate a proposed RxRSI based for the subject based at least in part on the predetermined GARD value and the RSI; compare the proposed RxRSI to a predefined standard of care dose range; assign the recommended RxRSI a value within the predefined standard of care dose range when the proposed RxRSI is within or below the predefined standard of care dose range; and recommend consideration of the subject for clinical trial if the proposed RxRSI is above the predefined standard of care dose range.

72. The computer software of claim 66, further being configured to: apply a linear regression model to the expression levels of the one or more signature genes in the tumor; and assign the RSI based at least in part on the linear regression model.

73. A computer-implemented method for minimizing risk of radiation therapy comprising: obtaining a radiation sensitivity index (RSI) of a subject's tumor from expression levels of one or more signature genes in the tumor; calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 46.1 to 65 for oropharyngeal cancer; and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

74. The computer-implemented method of claim 73, further comprising: calculating normal tissue toxicity of the personalized radiation dosage; and providing the personalized radiation therapy treatment plan based at least in part on the normal tissue toxicity.

75. The computer-implemented method of claim 73, further comprising: calculating relative risk for potential RxRSI values; and selecting the RxRSI based at least in part on the relative risk.

76. A method of calculating a personalized radiation therapy dosage for a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 46.1 to 65 for oropharyngeal cancer.

77. The method of claim 76, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

78. The method of claim 76, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1(ST ATI); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abi oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUM01); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor 1 (IRF1); and combinations thereof.

79. The method of claim 76, further comprising: determining a dose limiting structure of the normal tissues based at least in the RxRSI.

80. A method of treating a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample; applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject’s tumor sample, andcalculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 46.1 to 65 for oropharyngeal cancer.

81. The method of claim 80, further comprising: administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for nasopharyngeal cancer.

82. The method of claim 80, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

83. The method of claim 80, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT 1); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUMO1); p2I activated kinase-2 (PAK2), histone deacetylase 1 (HDAC1), interferon regulatory factor 1 (IRF1); and combinations thereof.

84. A computer software configured to integrate with a radiation therapy treatment planning system, the computer software being configured to: assign a radiation sensitivity index (RSI) of a subject’s tumor based at least in part on expression levels of one or more signature genes in the tumor; calculate a recommended personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 59.5 for nasopharyngeal cancer; and provide, to the radiation therapy treatment planning system, the recommended RxRSI as a radiation therapy dose for a radiation plan.

85. The computer software of claim 84, further being configured to: calculate the RxRSI based in part on normal tissue toxicity for a treatment plan using the recommended RxRSI.86, The computer software of claim 84, further being configured to: calculate the normal tissue toxicity based at least in part on risks to a plurality of tissue sites.87, The computer software of claim 84, further being configured to: receive, from the radiation therapy treatment planning system, a plurality of radiation plans each using the RxRSI, calculate normal tissue toxicity for each radiation plan of the plurality of radiation plans; penalize each radiation plan of the plurality of radiation plans based on the normal tissue toxicity of the radiation treatment plan; and provide, to the radiation therapy treatment planning system, at least one recommended radiation plan that is least penalized of the plurality of radiation plans.88, The computer software of claim 84, further being configured to: calculate the recommended RxRSI based in part on a predefined standard of care dose range.89, The computer software of claim 84, further being configured to: calculate a proposed RxRSI based for the subject based at least in part on the predetermined GARD value and the RSI; compare the proposed RxRSI to a predefined standard of care dose range; assign the recommended RxRSI a value within the predefined standard of care dose range when the proposed RxRSI is within or below the predefined standard of care dose range; and recommend consideration of the subject for clinical trial if the proposed RxRSI is above the predefined standard of care dose range.90, The computer software of claim 84, further being configured to: apply a linear regression model to the expression levels of the one or more signature genes in the tumor; and assign the RSI based at least in part, on the linear regression model.

91. ,A computer-implemented method for minimizing risk of radiation therapy comprising: obtaining a radiation sensitivity index (RSI) of a subject’s tumor from expression levels of one or more signature genes in the tumor; calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 59.5 for nasopharyngeal cancer; and providing a personalized radiation therapy treatment plan for the subject based at least in part on the RxRSI.

92. The computer-implemented method of claim 91, further comprising: calculating normal tissue toxicity of the personalized radiation dosage; and providing the personalized radiation therapy treatment plan based at least in part on the normal tissue toxicity.

93. The computer-implemented method of claim 91, further comprising: calculating relative risk for potential RxRSI values; and selecting the RxRSI based at least in part on the relative risk.

94. A method of calculating a personalized radiation therapy dosage for a subject, the method comprising: determining expression levels of one or more signature genes from a subject's tumor sample, applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 59.5 for nasopharyngeal cancer.

95. The method of claim 94, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.

96. The method of claim 94, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65); c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier I (SUM01); p21 activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory' factor 1 (IRF I); and combinations thereof97. The method of claim 94, further comprising: determining a dose limiting structure of the normal tissues based at least in the RxRSI.

98. A method of treating a subject, the method comprising: determining expression levels of one or more signature genes from a subject’s tumor sample; applying a linear regression model to the expression levels and assigning a radiation sensitivity index (RSI) to the subject's tumor sample; and calculating a personalized radiation dosage (RxRSI) for the subject based at least in part on the RSI and a pre-determined genomic adjusted radiation dose (GARD) value, the pre-determined GARD value being about 59.5 for nasopharyngeal cancer.

99. The method of claim 98, further comprising: administering the calculated personalized radiation dosage (RxRSI) to the subject as a treatment for nasopharyngeal cancer.

100. The method of claim 98, further comprising: calculating relative risk for the RxRSI; and selecting the RxRSI value of the subject based at least in part on the relative risk.101 . The method of claim 98, wherein determining the expression levels of one or more signature genes comprises determining the expression levels of genes selected from androgen receptor (AR); jun oncogene (c-Jun); signal transducer and activator of transcription 1 (STAT1); protein kinase C, beta (PKC); V-rel reticuloendotheliosis viral oncogene homolog A (RELA or p65), c-Abl oncogene 1 (c-Abl); small ubiquitin-like modifier 1 (SUMO1); p21activated kinase-2 (PAK2); histone deacetylase 1 (HDAC1); interferon regulatory factor 1(IRF1); and combinations thereof

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