Although altered protocols that challenge conventional radiation fractionation have been tested in prospective clinical trials, we still have limited understanding of how to select the most ...appropriate fractionation schedule for individual patients. Currently, the prescription of definitive radiotherapy is based on the primary site and stage, without regard to patient-specific tumor or host factors that may influence outcome. We hypothesize that the proportion of radiosensitive proliferating cells is dependent on the saturation of the tumor carrying capacity. This may serve as a prognostic factor for personalized radiotherapy (RT) fractionation.
We introduce a proliferation saturation index (PSI), which is defined as the ratio of tumor volume to the host-influenced tumor carrying capacity. Carrying capacity is as a conceptual measure of the maximum volume that can be supported by the current tumor environment including oxygen and nutrient availability, immune surveillance and acidity. PSI is estimated from two temporally separated routine pre-radiotherapy computed tomography scans and a deterministic logistic tumor growth model. We introduce the patient-specific pre-treatment PSI into a model of tumor growth and radiotherapy response, and fit the model to retrospective data of four non-small cell lung cancer patients treated exclusively with standard fractionation. We then simulate both a clinical trial hyperfractionation protocol and daily fractionations, with equal biologically effective dose, to compare tumor volume reduction as a function of pretreatment PSI.
With tumor doubling time and radiosensitivity assumed constant across patients, a patient-specific pretreatment PSI is sufficient to fit individual patient response data (R(2) = 0.98). PSI varies greatly between patients (coefficient of variation >128 %) and correlates inversely with radiotherapy response. For this study, our simulations suggest that only patients with intermediate PSI (0.45-0.9) are likely to truly benefit from hyperfractionation. For up to 20 % uncertainties in tumor growth rate, radiosensitivity, and noise in radiological data, the absolute estimation error of pretreatment PSI is <10 % for more than 75 % of patients.
Routine radiological images can be used to calculate individual PSI, which may serve as a prognostic factor for radiation response. This provides a new paradigm and rationale to select personalized RT dose-fractionation.
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.
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.
•Soft tissue sarcomas have traditionally been treated with a one-size fits all approach, despite a wide range of histologies and clinical outcomes.•The radiosensitivity index has demonstrated that ...soft tissue sarcomas are in general radioresistant, however exhibit a wide range of radiosensitivity.•These differences in radiosensitivity are associated with decreased locoregional control in patients with radioresistant histologies.•Using the radiosensitivity index we identify specific histologies of soft tissue sarcoma that may be more radioresistant, and suggest a genomic-based radiation dosing framework.
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/β), genomic-adjusted radiation dose(GARD), and in-turn a biological effective radiation dose (BED).
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/β 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.
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/β (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/β=3.29 of 97 Gy.
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/β ratio and dosing that would optimize outcome, personalizing dose.
We have previously developed a multigene expression model of tumor radiosensitivity (RSI) with clinical validation in multiple cohorts and disease sites. We hypothesized RSI would identify ...glioblastoma patients who would respond to radiation and predict treatment outcomes. Clinical and array based gene expression (Affymetrix HT Human Genome U133 Array Plate Set) level 2 data was downloaded from the cancer genome atlas (TCGA). A total of 270 patients were identified for the analysis: 214 who underwent radiotherapy and temozolomide and 56 who did not undergo radiotherapy. Median follow-up for the entire cohort was 9.1 months (range: 0.04-92.2 months). Patients who did not receive radiotherapy were more likely to be older (p < 0.001) and of poorer performance status (p < 0.001). On multivariate analysis, RSI is an independent predictor of OS (HR = 1.64, 95% CI 1.08-2.5; p = 0.02). Furthermore, on subset analysis, radiosensitive patients had significantly improved OS in the patients with high MGMT expression (unmethylated MGMT), 1 year OS 84.1% vs. 53.7% (p = 0.005). This observation held on MVA (HR = 1.94, 95% CI 1.19-3.31; p = 0.008), suggesting that RT has a larger therapeutic impact in these patients. In conclusion, RSI predicts for OS in glioblastoma. These data further confirm the value of RSI as a disease-site independent biomarker.
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).
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.
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).
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.
•GARD is associated with locoregional control in NPC and may serve as a potential framework to personalize radiotherapy dose.•Radiosensitive tumors for which GARD-optimized doses were estimated at ...less than the current standard (<66 Gy) (64.1 %).•Moderately radiosensitive tumors for which GARD-optmized doses were similar to current standard (66–74 Gy) (21.7 %).•Radioresistant tumors that GARD proposes may require dose escalation above the current standard (>74 Gy) (14.1 %).
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.
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).
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 %).
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.
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