Intricacies of Human-AI Interaction in Dynamic Decision-Making for Precision Oncology: A Case Study in Response-Adaptive Radiotherapy.

Background: Adaptive treatment strategies that can dynamically react to individual cancer progression can provide effective personalized care. Longitudinal multi-omics information, paired with an artificially intelligent clinical decision support system (AI-CDSS) can assist clinicians in determining optimal therapeutic options and treatment adaptations. However, AI-CDSS is not perfectly accurate, as such, clinicians' over/under reliance on AI may lead to unintended consequences, ultimately failing to develop optimal strategies. To investigate such collaborative decision-making process, we conducted a Human-AI interaction case study on response-adaptive radiotherapy (RT). Methods: We designed and conducted a two-phase study for two disease sites and two treatment modalities-adaptive RT for non-small cell lung cancer (NSCLC) and adaptive stereotactic body RT for hepatocellular carcinoma (HCC)-in which clinicians were asked to consider mid-treatment modification of the dose per fraction for a number of retrospective cancer patients without AI-support (Unassisted Phase) and with AI-assistance (AI-assisted Phase). The AI-CDSS graphically presented trade-offs in tumor control and the likelihood of toxicity to organs at risk, provided an optimal recommendation, and associated model uncertainties. In addition, we asked for clinicians' decision confidence level and trust level in individual AI recommendations and encouraged them to provide written remarks. We enrolled 13 evaluators (radiation oncology physicians and residents) from two medical institutions located in two different states, out of which, 4 evaluators volunteered in both NSCLC and HCC studies, resulting in a total of 17 completed evaluations (9 NSCLC, and 8 HCC). To limit the evaluation time to under an hour, we selected 8 treated patients for NSCLC and 9 for HCC, resulting in a total of 144 sets of evaluations (72 from NSCLC and 72 from HCC). Evaluation for each patient consisted of 8 required inputs and 2 optional remarks, resulting in up to a total of 1440 data points. Results: AI-assistance did not homogeneously influence all experts and clinical decisions. From NSCLC cohort, 41 (57%) decisions and from HCC cohort, 34 (47%) decisions were adjusted after AI assistance. Two evaluations (12%) from the NSCLC cohort had zero decision adjustments, while the remaining 15 (88%) evaluations resulted in at least two decision adjustments. Decision adjustment level positively correlated with dissimilarity in decision-making with AI [NSCLC: ρ = 0.53 ( p < 0.001); HCC: ρ = 0.60 ( p < 0.001)] indicating that evaluators adjusted their decision closer towards AI recommendation. Agreement with AI-recommendation positively correlated with AI Trust Level [NSCLC: ρ = 0.59 ( p < 0.001); HCC: ρ = 0.7 ( p < 0.001)] indicating that evaluators followed AI's recommendation if they agreed with that recommendation. The correlation between decision confidence changes and decision adjustment level showed an opposite trend [NSCLC: ρ = -0.24 ( p = 0.045), HCC: ρ = 0.28 ( p = 0.017)] reflecting the difference in behavior due to underlying differences in disease type and treatment modality. Decision confidence positively correlated with the closeness of decisions to the standard of care (NSCLC: 2 Gy/fx; HCC: 10 Gy/fx) indicating that evaluators were generally more confident in prescribing dose fractionations more similar to those used in standard clinical practice. Inter-evaluator agreement increased with AI-assistance indicating that AI-assistance can decrease inter-physician variability. The majority of decisions were adjusted to achieve higher tumor control in NSCLC and lower normal tissue complications in HCC. Analysis of evaluators' remarks indicated concerns for organs at risk and RT outcome estimates as important decision-making factors. Conclusions: Human-AI interaction depends on the complex interrelationship between expert's prior knowledge and preferences, patient's state, disease site, treatment modality, model transparency, and AI's learned behavior and biases. The collaborative decision-making process can be summarized as follows: (i) some clinicians may not believe in an AI system, completely disregarding its recommendation, (ii) some clinicians may believe in the AI system but will critically analyze its recommendations on a case-by-case basis; (iii) when a clinician finds that the AI recommendation indicates the possibility for better outcomes they will adjust their decisions accordingly; and (iv) When a clinician finds that the AI recommendation indicate a worse possible outcome they will disregard it and seek their own alternative approach.

Using the genomic adjusted radiation dose (GARD) to personalize the radiation dose in nasopharyngeal cancer.

BACKGROUND: Locally advanced nasopharyngeal cancer (NPC) patients undergoing radiotherapy are at risk of treatment failure, particularly locoregional recurrence. To optimize the individual radiation dose, we hypothesize that the genomic adjusted radiation dose (GARD) can be used to correlate with locoregional control. METHODS: 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 III trial were assessed (NPC-0501) (NCT00379262). Patients were treated with concurrent chemo-radiotherapy plus (neo) adjuvant chemotherapy. The primary endpoint is locoregional failure free rate (LRFFR). RESULTS: Despite the homogenous physical radiation dose prescribed (Median: 70 Gy, range 66-76 Gy), 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 LRFFR (p = 0.008). By evaluating the physical dose required to achieve the GARDT (RxRSI), three distinct clinical subgroups were identified: (1) radiosensitive tumors that RxRSI at dose < 66 Gy (N = 59, 64.1 %) (b) moderately radiosensitive tumors that RxRSI dose within the current standard of care range (66-74 Gy) (N = 20, 21.7 %), (c) radioresistant tumors that need a significant dose escalation above the current standard of care (>74 Gy) (N = 13, 14.1 %). CONCLUSION: GARD is independently associated with locoregional control in radiotherapy-treated NPC patients from a Phase 3 clinical trial. GARD may be a potential framework to personalize radiotherapy dose for NPC patients.

Trimodal Therapy Using an MR-guided Radiation Therapy Partial Bladder Tumor Boost in Muscle Invasive Bladder Cancer.

Purpose: Bladder preservation with trimodal therapy (TMT; maximal tumor resection followed by chemoradiation) is an effective paradigm for select patients with muscle invasive bladder cancer. We report our institutional experience of a TMT protocol using nonadaptive magnetic resonance imaging-guided radiation therapy (MRgRT) for partial bladder boost (PBB). Methods and Materials: A retrospective analysis was performed on consecutive patients with nonmetastatic muscle invasive bladder cancer who were treated with TMT using MRgRT between 2019 and 2022. Patients underwent intensity modulated RT-based nonadaptive MRgRT PBB contoured on True fast imaging with steady state precession (FISP) images (full bladder) followed sequentially by computed tomography-based RT to the whole empty bladder and pelvic lymph nodes with concurrent chemotherapy. MRgRT treatment time, table shifts, and dosimetric parameters of target coverage and normal tissue exposure were described. Prospectively assessed acute and late genitourinary and gastrointestinal (GI) toxicity were reported. Two-year local control was assessed with Kaplan-Meier methods. Results: Seventeen patients were identified for analysis. PBB planning target volume margins were ≤8 mm in 94% (n = 16) of cases. Dosimetric target coverage parameters were favorable and all normal tissue dose constraints were met. For MRgRT PBB fractions, median table shifts were 0.4 cm (range, 0-3.15), 0.45 cm (0-2.65), and 0.75 cm (0-4.8) in the X, Y, and Z planes, respectively. Median treatment time for MRgRT PBB fractions was 9 minutes (range, 6.9-17.4). We identified 32 out of 100 total MRgRT fractions that may have benefitted from online adaptation based on changes in organ position relative to planning target volume, predominantly because of small bowel (13/32, 41%) or rectum (8/32, 25%). Two patients discontinued RT prematurely. The incidence of highest-grade acute genitourinary toxicity was 1 to 2 (69%) and 3 (6%), whereas the incidence of acute GI toxicity was 1 to 2 (81%) and 3 (6%). There were no late grade 3 events; 17.6% had late grade 2 cystitis and none had late GI toxicity. With median follow-up of 18.2 months (95% CI, 12.4-22.5), the local control rate was 92%, and no patient has required salvage cystectomy. Conclusions: Nonadaptive MRgRT PBB is feasible with favorable dosimetry and low resource utilization. Larger studies are needed to evaluate for potential benefits in toxicity and local control associated with this approach in comparison to standard treatment techniques.

A clinicogenomic model including GARD predicts outcome for radiation treated patients with HPV+ oropharyngeal squamous cell carcinoma.

Background: 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 8th edition (AJCC8), patients are largely treated with a uniform approach, with recent efforts focused on de-intensification in low-risk patients. We have previously shown, in a pooled analysis, that the genomic adjusted radiation dose (GARD) is predictive of radiation treatment benefit and can be used to guide RT dose selection. We hypothesize that GARD can be used to predict overall survival (OS) in HPV-positive OPSCC patients treated with radiotherapy (RT). Methods: 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 including patients with AJCC 7th edition stage III-IVb. GARD, a measure of the treatment effect of RT, was calculated for each patient as previously described. In total, 191 patients received primary RT definitive treatment (chemoradiation or RT alone, and 43 patients received post-operative RT. Two RT dose fractionations were utilized for primary RT cases (70 Gy in 35 fractions or 69.96 Gy in 33 fractions). Median RT dose was 70 Gy (range 50.88-74) for primary RT definitive cases and 66 Gy (range 44-70) for post-operative RT cases. The median follow up was 46.2 months (95% CI, 33.5-63.1). Cox proportional hazards analyses were performed with GARD as both a continuous and dichotomous variable and time-dependent ROC analyses compared the performance of GARD with the NRG clinical nomogram for overall survival. Results: Despite uniform radiation dose utilization, GARD showed significant heterogeneity (range 30-110), reflecting the underlying genomic differences in the cohort. On multivariable analysis, each unit increase in GARD was associated with an improvement in OS (HR = 0.951 (0.911, 0.993), p = 0.023) compared to AJCC8 (HR = 1.999 (0.791, 5.047)), p = 0.143). ROC analysis for GARD at 36 months yielded an AUC of 80.6 (69.4, 91.9) compared with an AUC of 73.6 (55.4, 91.7) 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. Conclusions: In this multi-institutional cohort of patients with HPV-positive OPSCC, GARD predicts OS as a continuous variable, outperforms the NRG nomogram and provides a novel genomic strategy to modern clinical trial design. We propose that GARD, which provides the first opportunity for genomic guided personalization of radiation dose, should be incorporated in the diagnostic workup of HPV-positive OPSCC patients.

Towards Data Driven RT Prescription: Integrating Genomics into RT Clinical Practice.

The genomic era has significantly changed the practice of clinical oncology. The use of genomic-based molecular diagnostics including prognostic genomic signatures and new-generation sequencing has become routine for clinical decisions regarding cytotoxic chemotherapy, targeted agents and immunotherapy. In contrast, clinical decisions regarding radiation therapy (RT) remain uninformed about the genomic heterogeneity of tumors. In this review, we discuss the clinical opportunity to utilize genomics to optimize RT dose. Although from the technical perspective, RT has been moving towards a data-driven approach, RT prescription dose is still based on a one-size-fits all approach, with most RT dose based on cancer diagnosis and stage. This approach is in direct conflict with the realization that tumors are biologically heterogeneous, and that cancer is not a single disease. Here, we discuss how genomics can be integrated into RT prescription dose, the clinical potential for this approach and how genomic-optimization of RT dose could lead to new understanding of the clinical benefit of RT.

The Radiosensitivity Index Gene Signature Identifies Distinct Tumor Immune Microenvironment Characteristics Associated With Susceptibility to Radiation Therapy.

PURPOSE: Radiation therapy (RT) is a mainstay of cancer care, and accumulating evidence suggests the potential for synergism with components of the immune response. However, few data describe the tumor immune contexture in relation to RT sensitivity. To address this challenge, we used the radiation sensitivity index (RSI) gene signature to estimate the RT sensitivity of >10,000 primary tumors and characterized their immune microenvironments in relation to the RSI. METHODS AND MATERIALS: We analyzed gene expression profiles of 10,469 primary tumors (31 types) within a prospective tissue collection protocol. The RT sensitivity of each tumor was estimated by the RSI and respective distributions were characterized. The tumor biology measured by the RSI was evaluated by differentially expressed genes combined with single sample gene set enrichment analysis. Differences in the expression of immune regulatory molecules were assessed and deconvolution algorithms were used to estimate immune cell infiltrates in relation to the RSI. A subset (n = 2368) of tumors underwent DNA sequencing for mutational frequency characterization. RESULTS: We identified a wide range of RSI values within and across various tumor types, with several demonstrating nonunimodal distributions (eg, colon, renal, lung, prostate, esophagus, pancreas, and PAM50 breast subtypes; P < .05). Across all tumor types, stratifying RSI at a tumor type-specific median identified 7148 differentially expressed genes, of which 146 were coordinate in direction. Network topology analysis demonstrates RSI measures a coordinated STAT1, IRF1, and CCL4/MIP-1ß transcriptional network. Tumors with an estimated high sensitivity to RT demonstrated distinct enrichment of interferon-associated signaling pathways and immune cell infiltrates (eg, CD8+ T cells, activated natural killer cells, M1-macrophages; q < 0.05), which was in the context of diverse expression patterns of various immunoregulatory molecules. CONCLUSIONS: This analysis describes the immune microenvironments of patient tumors in relation to the RSI gene expression signature.

Changing Radiotherapy Paradigms in Penile Cancer.

Radiation therapy (RT) has not been prominent in the treatment of penile cancer because of poorly reproducible results when used in the adjuvant setting. A genomic signature has recently been described that assays radiosensitivity of tumors and informs radiotherapy doses in these cases. Clinical validation in more than 1600 patients demonstrated associations with both overall survival and time to first recurrence. In addition, the signature predicted and quantified the therapeutic benefit of RT for each individual patient. Since penile cancer patients were not part of this analysis, we applied the model to patients with primary and nodal penile cancer tissue and clinical outcomes. Patient summary : Radiotherapy has not been widely used for treatment of penile cancer. New genetic data suggest that radiation doses commonly used to treat penile cancer are too low. This would explain prior poor results using radiation in this disease.

Radiation therapy in the management of the inguinal region in penile cancer: What's the evidence?

Due to its rarity and lack of prospective studies, clinical evidence for the management of the inguinal lymphatic nodal basin with radiation therapy in penile cancer (PeCa) has been limited. In this report, we review the current literature and further investigated the landscape of radiation sensitivity in nodal metastases of PeCa utilizing our well-established genome-based radiosensitivity index (RSI) platform. We hypothesized that optimal therapeutic gain could be achieved in PeCa stratified by the combination of clinicopathological parameters, genomic heterogeneity, and RSI-based radiation dose prescription (RxRSI). Similar to primary PeCa lesions, we found that the majority of PeCa nodal metastases are genomically radioresistant with significant heterogeneity. RxRSI should be considered to inform and optimize the radiation therapy dose prescription to the individual tumor biology.

Tumor-immune ecosystem dynamics define an individual Radiation Immune Score to predict pan-cancer radiocurability.

Radiotherapy efficacy is the result of radiation-mediated cytotoxicity coupled with stimulation of antitumor immune responses. We develop an in silico 3-dimensional agent-based model of diverse tumor-immune ecosystems (TIES) represented as anti- or pro-tumor immune phenotypes. We validate the model in 10,469 patients across 31 tumor types by demonstrating that clinically detected tumors have pro-tumor TIES. We then quantify the likelihood radiation induces antitumor TIES shifts toward immune-mediated tumor elimination by developing the individual Radiation Immune Score (iRIS). We show iRIS distribution across 31 tumor types is consistent with the clinical effectiveness of radiotherapy, and in combination with a molecular radiosensitivity index (RSI) combines to predict pan-cancer radiocurability. We show that iRIS correlates with local control and survival in a separate cohort of 59 lung cancer patients treated with radiation. In combination, iRIS and RSI predict radiation-induced TIES shifts in individual patients and identify candidates for radiation de-escalation and treatment escalation. This is the first clinically and biologically validated computational model to simulate and predict pan-cancer response and outcomes via the perturbation of the TIES by radiotherapy.

Pan-cancer prediction of radiotherapy benefit using genomic-adjusted radiation dose (GARD): a cohort-based pooled analysis.

BACKGROUND: Despite advances in cancer genomics, radiotherapy is still prescribed on the basis of an empirical one-size-fits-all paradigm. Previously, we proposed a novel algorithm using the genomic-adjusted radiation dose (GARD) model to personalise prescription of radiation dose on the basis of the biological effect of a given physical dose of radiation, calculated using individual tumour genomics. We hypothesise that GARD will reveal interpatient heterogeneity associated with opportunities to improve outcomes compared with physical dose of radiotherapy alone. We aimed to test this hypothesis and investigate the GARD-based radiotherapy dosing paradigm. METHODS: We did a pooled, pan-cancer analysis of 11 previously published clinical cohorts of unique patients with seven different types of cancer, which are all available cohorts with the data required to calculate GARD, together with clinical outcome. The included cancers were breast cancer, head and neck cancer, non-small-cell lung cancer, pancreatic cancer, endometrial cancer, melanoma, and glioma. Our dataset comprised 1615 unique patients, of whom 1298 (982 with radiotherapy, 316 without radiotherapy) were assessed for time to first recurrence and 677 patients (424 with radiotherapy and 253 without radiotherapy) were assessed for overall survival. We analysed two clinical outcomes of interest: time to first recurrence and overall survival. We used Cox regression, stratified by cohort, to test the association between GARD and outcome with separate models using dose of radiation and sham-GARD (ie, patients treated without radiotherapy, but modelled as having a standard-of-care dose of radiotherapy) for comparison. We did interaction tests between GARD and treatment (with or without radiotherapy) using the Wald statistic. FINDINGS: Pooled analysis of all available data showed that GARD as a continuous variable is associated with time to first recurrence (hazard ratio [HR] 0·98 [95% CI 0·97-0·99]; p=0·0017) and overall survival (0·97 [0·95-0·99]; p=0·0007). The interaction test showed the effect of GARD on overall survival depends on whether or not that patient received radiotherapy (Wald statistic p=0·011). The interaction test for GARD and radiotherapy was not significant for time to first recurrence (Wald statistic p=0·22). The HR for physical dose of radiation was 0·99 (95% CI 0·97-1·01; p=0·53) for time to first recurrence and 1·00 (0·96-1·04; p=0·95) for overall survival. The HR for sham-GARD was 1·00 (0·97-1·03; p=1·00) for time to first recurrence and 1·00 (0·98-1·02; p=0·87) for overall survival. INTERPRETATION: The biological effect of radiotherapy, as quantified by GARD, is significantly associated with time to first recurrence and overall survival for patients with cancer treated with radiation. It is predictive of radiotherapy benefit, and physical dose of radiation is not. We propose integration of genomics into radiation dosing decisions, using a GARD-based framework, as the new paradigm for personalising radiotherapy prescription dose. FUNDING: None. VIDEO ABSTRACT.

Genomically Guided Breast Radiation Therapy: A Review of the Current Data and Future Directions.

PURPOSE: To highlight the current evidence and the limitations in data to support a personalized approach in breast oncology radiation therapy management and define steps needed for clinical implementation. METHODS AND MATERIALS: A critical review of the current literature on the use of genomics in breast radiation therapy was undertaken by a group of breast radiation oncologists to discuss current data, future directions, and challenges. RESULTS: A summary of the existing data, ongoing clinical trials, and future directions is provided. The authors note many groups have developed radiation-specific genomic assays, which demonstrate promise in prediction of local control and benefit from radiation therapy; however, prospective validation of their utility is needed. Limitations continue to exist in our understanding of tumor biology and how it can be integrated into clinical practice. CONCLUSIONS: Given the relative ubiquity of breast radiation therapy, the variety of dose and fractionation approaches, and the current data to support a personalized approach, it is our belief that the delivery of breast radiation therapy is uniquely poised for a genomically personalized radiation therapy approach. Prospective clinical trials implementing genomic signatures are needed at this time to advance the field.

Genomic identification of sarcoma radiosensitivity and the clinical implications for radiation dose personalization.

BACKGROUND: Soft-tissue sarcomas (STS) are heterogeneous with variable response to radiation therapy (RT). Utilizing the radiosensitivity index (RSI) we estimated the radiobiologic ratio of lethal to sublethal damage (α/ß), genomic-adjusted radiation dose(GARD), and in-turn a biological effective radiation dose (BED). METHODS: Two independent cohorts of patients with soft-tissue sarcoma were identified. The first cohort included 217 genomically-profiled samples from our institutional prospective tissue collection protocol; RSI was calculated for these samples, which were then used to dichotomize the population as either highly radioresistant (HRR) or conventionally radioresistant (CRR). In addition, RSI was used to calculate α/ß ratio and GARD, providing ideal dosing based on sarcoma genomic radiosensitivity. A second cohort comprising 399 non-metastatic-STS patients treated with neoadjuvant RT and surgery was used to validate our findings. RESULTS: Based on the RSI of the sample cohort, 84% would historically be considered radioresistant. We identified a HRR subset that had a significant difference in the RSI, and clinically a lower tumor response to radiation (2.4% vs. 19.4%), 5-year locoregional-control (76.5% vs. 90.8%), and lower estimated α/ß (3.29 vs. 5.98), when compared to CRR sarcoma. Using GARD, the dose required to optimize outcome in the HRR subset is a BEDα/ß=3.29 of 97 Gy. CONCLUSIONS: We demonstrate that on a genomic scale, that although STS is radioresistant overall, they are heterogeneous in terms of radiosensitivity. We validated this clinically and estimated an α/ß ratio and dosing that would optimize outcome, personalizing dose.

Proof-of-principle Phase I results of combining nivolumab with brachytherapy and external beam radiation therapy for Grade Group 5 prostate cancer: safety, feasibility, and exploratory analysis.

BACKGROUND: To determine whether combining brachytherapy with immunotherapy is safe in prostate cancer (PCa) and provides synergistic effects, we performed a Phase I/II trial on the feasibility, safety, and benefit of concurrent delivery of anti-PD-1 (nivolumab) with high-dose-rate (HDR) brachytherapy and androgen deprivation therapy (ADT) in patients with Grade Group 5 (GG5) PCa. METHODS: Eligible patients were aged 18 years or older with diagnosis of GG5 PCa. Patients received ADT, nivolumab every two weeks for four cycles, with two cycles prior to first HDR, and two more cycles prior to second HDR, followed by external beam radiotherapy. The primary endpoint was to determine safety and feasibility. This Phase I/II trial is registered with ClinicalTrials.gov (NCT03543189). RESULTS: Between September 2018 and June 2019, six patients were enrolled for the Phase I safety lead-in with a minimum observation period of 3 months after nivolumab administration. Overall, nivolumab was well tolerated in combination with ADT and HDR treatment. One patient experienced a grade 3 dose-limiting toxicity (elevated Alanine aminotransferase and Aspartate aminotransferase) after the second cycle of nivolumab. Three patients (50%) demonstrated early response with no residual tumor detected in ≥4 of 6 cores on biopsy post-nivolumab (4 cycles) and 1-month post-HDR. Increase in CD8+ and FOXP3+/CD4+ T cells in tissues, and CD4+ effector T cells in peripheral blood were observed in early responders. CONCLUSION: Combination of nivolumab with ADT and HDR is well tolerated and associated with evidence of increased immune infiltration and antitumor activity.

Personalizing Radiotherapy Prescription Dose Using Genomic Markers of Radiosensitivity and Normal Tissue Toxicity in NSCLC.

INTRODUCTION: Cancer sequencing efforts have revealed that cancer is the most complex and heterogeneous disease that affects humans. However, radiation therapy (RT), one of the most common cancer treatments, is prescribed on the basis of an empirical one-size-fits-all approach. We propose that the field of radiation oncology is operating under an outdated null hypothesis: that all patients are biologically similar and should uniformly respond to the same dose of radiation. METHODS: We have previously developed the genomic-adjusted radiation dose, a method that accounts for biological heterogeneity and can be used to predict optimal RT dose for an individual patient. In this article, we use genomic-adjusted radiation dose to characterize the biological imprecision of one-size-fits-all RT dosing schemes that result in both over- and under-dosing for most patients treated with RT. To elucidate this inefficiency, and therefore the opportunity for improvement using a personalized dosing scheme, we develop a patient-specific competing hazards style mathematical model combining the canonical equations for tumor control probability and normal tissue complication probability. This model simultaneously optimizes tumor control and toxicity by personalizing RT dose using patient-specific genomics. RESULTS: Using data from two prospectively collected cohorts of patients with NSCLC, we validate the competing hazards model by revealing that it predicts the results of RTOG 0617. We report how the failure of RTOG 0617 can be explained by the biological imprecision of empirical uniform dose escalation which results in 80% of patients being overexposed to normal tissue toxicity without potential tumor control benefit. CONCLUSIONS: Our data reveal a tapestry of radiosensitivity heterogeneity, provide a biological framework that explains the failure of empirical RT dose escalation, and quantify the opportunity to improve clinical outcomes in lung cancer by incorporating genomics into RT.

Modeling precision genomic-based radiation dose response in rectal cancer.

Aim: Genomic-based risk stratification to personalize radiation dose in rectal cancer. Patients & methods: We modeled genomic-based radiation dose response using the previously validated radiosensitivity index (RSI) and the clinically actionable genomic-adjusted radiation dose. Results: RSI of rectal cancer ranged from 0.19 to 0.81 in a bimodal distribution. A pathologic complete response rate of 21% was achieved in tumors with an RSI <0.31 at a minimal genomic-adjusted radiation dose of 29.76 when modeling RxRSI to the commonly prescribed physical dose of 50 Gy. RxRSI-based dose escalation to 55 Gy in tumors with an RSI of 0.31-0.34 could increase pathologic complete response by 10%. Conclusion: This study provides a theoretical platform for development of an RxRSI-based prospective trial in rectal cancer.

Using the Radiosensitivity Index (RSI) to Predict Pelvic Failure in Endometrial Cancer Treated With Adjuvant Radiation Therapy.

PURPOSE: Variability exists in the adjuvant treatment for endometrial cancer (EC) based on surgical pathology and institutional preference. The radiosensitivity index (RSI) is a previously validated multigene expression index that estimates tumor radiosensitivity. We evaluate RSI as a genomic predictor for pelvic failure (PF) in EC patients treated with adjuvant radiation therapy (RT). METHODS AND MATERIALS: Using our institutional tissue biorepository, we identified EC patients treated between January 1999 and April 2011 with primarily endometrioid histology (n = 176; 86%) who received various adjuvant therapies. The RSI 10-gene signature was calculated for each sample using the previously published algorithm. Radiophenotype was determined using the previously identified cutpoint where RSI ≥ 0.375 denotes radioresistance (RR) and RSI < 0.375 describes radiosensitivity. RESULTS: A total of 204 patients were identified, of which 83 (41%) were treated with adjuvant RT. Median follow-up was 38.5 months. All patients underwent hysterectomy with bilateral salpingo-oophorectomy with the majority undergoing lymph node dissection (n = 181; 88%). In patients treated with radiation, RR tumors were more likely to experience PF (3-year pelvic control 84% vs 100%; P = .02) with worse PF-free survival (PFFS) (3-year PFFS 65% vs 89%; P = .04). Furthermore, in the patients who did not receive RT, there was no difference in PF (P = .87) or PFFS (P = .57) between the RR/radiosensitive tumors. On multivariable analysis, factors that continued to predict for PF included the RR phenotype (hazard ratio [HR], 12.2; P = .003), lymph node involvement (HR, 4.4; P = .02), and serosal or adnexal involvement (HR, 5.3; P = .01). CONCLUSIONS: On multivariable analysis, RSI was found to be a significant predictor of PF in patients treated with adjuvant RT. We propose using RSI to predict which patients are at higher risk for failing in the pelvis and may be candidates for treatment escalation in the adjuvant setting.

Intrinsic radiosensitivity, genomic-based radiation dose and patterns of failure of penile cancer in response to adjuvant radiation therapy.

PURPOSE: Optimal postoperative radiation therapy (PORT) dose is unclear in penile squamous cell carcinoma (PeSCC). Herein, we characterized the radiosensitivity index (RSI) and genomic-adjusted radiation dose (GARD) profiles in a cohort of patients with PeSCC, and assessed the application of GARD to personalize PORT. METHODS: A total of 25 PeSCC samples were identified for transcriptomic profiling. The RSI score and GARD were derived for each sample. A cohort of 34 patients was reviewed for clinical correlation. RESULTS: The median RSI for PeSCC was 0.482 (range 0.215-0.682). The majority (n = 21; 84%) of cases were classified as radioresistant. PeSCC GARD ranged from 9.56 to 38.39 (median 18.25), suggesting variable therapeutic effects from PORT. We further determined the optimal GARD-based RT doses to improve locoregional control. We found that therapeutic benefit was only achieved in 52% of PeSCC lesions with PORT of 50 Gy, in contrast to 84% benefit from GARD-modeled PORT of 66 Gy. In the clinical cohort, the majority of patients presented with pathological N2 or N3 disease (n = 31; 91%) and was treated with adjuvant concurrent platinum-based chemoradiotherapy (CRT, n = 30; 88%). Fourteen of the 34 patients (41%) had locoregional recurrence (LRR), of which half had LRR within six months of completion of PORT. CONCLUSIONS: The majority of PeSCC are intrinsically radioresistant with a low GARD-based therapeutic effect from PORT dose of 50 Gy, consistent with the observed high rate of LRR in the clinical cohort. A GARD-based strategy will allow personalizing PORT dose prescription to individual tumor biology and improve outcomes.