Summary Radiotherapy has long been the mainstay of treatment for patients with head and neck cancer and has traditionally involved a stage-dependent strategy whereby all patients with the same TNM ...stage receive the same therapy. We believe there is a substantial opportunity to improve radiotherapy delivery beyond just technological and anatomical precision. In this Series paper, we explore several new ideas that could improve understanding of the phenotypic and genotypic differences that exist between patients and their tumours. We discuss how exploiting these differences and taking advantage of precision medicine tools—such as genomics, radiomics, and mathematical modelling—could open new doors to personalised radiotherapy adaptation and treatment. We propose a new treatment shift that moves away from an era of empirical dosing and fractionation to an era focused on the development of evidence to guide personalisation and biological adaptation of radiotherapy. We believe these approaches offer the potential to improve outcomes and reduce toxicity.
Summary Background Despite its common use in cancer treatment, radiotherapy has not yet entered the era of precision medicine, and there have been no approaches to adjust dose based on biological ...differences between or within tumours. We aimed to assess whether a patient-specific molecular signature of radiation sensitivity could be used to identify the optimum radiotherapy dose. Methods We used the gene-expression-based radiation-sensitivity index and the linear quadratic model to derive the genomic-adjusted radiation dose (GARD). A high GARD value predicts for high therapeutic effect for radiotherapy; which we postulate would relate to clinical outcome. Using data from the prospective, observational Total Cancer Care (TCC) protocol, we calculated GARD for primary tumours from 20 disease sites treated using standard radiotherapy doses for each disease type. We also used multivariable Cox modelling to assess whether GARD was independently associated with clinical outcome in five clinical cohorts: Erasmus Breast Cancer Cohort (n=263); Karolinska Breast Cancer Cohort (n=77); Moffitt Lung Cancer Cohort (n=60); Moffitt Pancreas Cancer Cohort (n=40); and The Cancer Genome Atlas Glioblastoma Patient Cohort (n=98). Findings We calculated GARD for 8271 tissue samples from the TCC cohort. There was a wide range of GARD values (range 1·66–172·4) across the TCC cohort despite assignment of uniform radiotherapy doses within disease types. Median GARD values were lowest for gliomas and sarcomas and highest for cervical cancer and oropharyngeal head and neck cancer. There was a wide range of GARD values within tumour type groups. GARD independently predicted clinical outcome in breast cancer, lung cancer, glioblastoma, and pancreatic cancer. In the Erasmus Breast Cancer Cohort, 5-year distant-metastasis-free survival was longer in patients with high GARD values than in those with low GARD values (hazard ratio 2·11, 95% 1·13–3·94, p=0·018). Interpretation A GARD-based clinical model could allow the individualisation of radiotherapy dose to tumour radiosensitivity and could provide a framework to design genomically-guided clinical trials in radiation oncology. Funding None.
Development of a radiosensitivity predictive assay is a central goal of radiation oncology. We reasoned a gene expression model could be developed to predict intrinsic radiosensitivity and treatment ...response in patients.
Radiosensitivity (determined by survival fraction at 2 Gy) was modeled as a function of gene expression, tissue of origin, ras status (mut/wt), and p53 status (mut/wt) in 48 human cancer cell lines. Ten genes were identified and used to build a rank-based linear regression algorithm to predict an intrinsic radiosensitivity index (RSI, high index = radioresistance). This model was applied to three independent cohorts treated with concurrent chemoradiation: head-and-neck cancer (HNC, n = 92); rectal cancer (n = 14); and esophageal cancer (n = 12).
Predicted RSI was significantly different in responders (R) vs. nonresponders (NR) in the rectal (RSI R vs. NR 0.32 vs. 0.46, p = 0.03), esophageal (RSI R vs. NR 0.37 vs. 0.50, p = 0.05) and combined rectal/esophageal (RSI R vs. NR 0.34 vs. 0.48, p = 0.001511) cohorts. Using a threshold RSI of 0.46, the model has a sensitivity of 80%, specificity of 82%, and positive predictive value of 86%. Finally, we evaluated the model as a prognostic marker in HNC. There was an improved 2-year locoregional control (LRC) in the predicted radiosensitive group (2-year LRC 86% vs. 61%, p = 0.05).
We validate a robust multigene expression model of intrinsic tumor radiosensitivity in three independent cohorts totaling 118 patients. To our knowledge, this is the first time that a systems biology-based radiosensitivity model is validated in multiple independent clinical datasets.
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.
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.
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.
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.
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Previously, we developed a radiosensitivity molecular signature radiosensitivity index (RSI) that was clinically validated in 3 independent datasets (rectal, esophageal, and head and neck) in 118 ...patients. Here, we test RSI in radiotherapy (RT)-treated breast cancer patients.
RSI was tested in 2 previously published breast cancer datasets. Patients were treated at the Karolinska University Hospital (n = 159) and Erasmus Medical Center (n = 344). RSI was applied as previously described.
We tested RSI in RT-treated patients (Karolinska). Patients predicted to be radiosensitive (RS) had an improved 5-year relapse-free survival when compared with radioresistant (RR) patients (95% vs. 75%, P = 0.0212), but there was no difference between RS/RR patients treated without RT (71% vs. 77%, P = 0.6744), consistent with RSI being RT-specific (interaction term RSI × RT, P = 0.05). Similarly, in the Erasmus dataset, RT-treated RS patients had an improved 5-year distant metastasis-free survival over RR patients (77% vs. 64%, P = 0.0409), but no difference was observed in patients treated without RT (RS vs. RR, 80% vs. 81%, P = 0.9425). Multivariable analysis showed RSI is the strongest variable in RT-treated patients (Karolinska, HR = 5.53, P = 0.0987, Erasmus, HR = 1.64, P = 0.0758) and in backward selection (removal α of 0.10), RSI was the only variable remaining in the final model. Finally, RSI is an independent predictor of outcome in RT-treated ER(+) patients (Erasmus, multivariable analysis, HR = 2.64, P = 0.0085).
RSI is validated in 2 independent breast cancer datasets totaling 503 patients. Including prior data, RSI is validated in 5 independent cohorts (621 patients) and represents, to our knowledge, the most extensively validated molecular signature in radiation oncology.
Evidence from the management of oligometastases with stereotactic body radiation therapy (SBRT) reveals differences in outcomes based on primary histology. We have previously identified a multigene ...expression index for tumor radiosensitivity (RSI) with validation in multiple independent cohorts. In this study, we assessed RSI in liver metastases and assessed our clinical outcomes after SBRT based on primary histology.
Patients were identified from our prospective, observational protocol. The previously tested RSI 10 gene assay was run on samples and calculated using the published algorithm. An independent cohort of 33 patients with 38 liver metastases treated with SBRT was used for clinical correlation.
A total of 372 unique metastatic liver lesions were identified for inclusion from our prospective, institutional metadata pool. The most common primary histologies for liver metastases were colorectal adenocarcinoma (n=314, 84.4%), breast adenocarcinoma (n=12, 3.2%), and pancreas neuroendocrine (n=11, 3%). There were significant differences in RSI of liver metastases based on histology. The median RSIs for liver metastases in descending order of radioresistance were gastrointestinal stromal tumor (0.57), melanoma (0.53), colorectal neuroendocrine (0.46), pancreas neuroendocrine (0.44), colorectal adenocarcinoma (0.43), breast adenocarcinoma (0.35), lung adenocarcinoma (0.31), pancreas adenocarcinoma (0.27), anal squamous cell cancer (0.22), and small intestine neuroendocrine (0.21) (P<.0001). The 12-month and 24-month Kaplan-Meier rates of local control (LC) for colorectal lesions from the independent clinical cohort were 79% and 59%, compared with 100% for noncolorectal lesions (P=.019), respectively.
In this analysis, we found significant differences based on primary histology. This study suggests that primary histology may be an important factor to consider in SBRT radiation dose selection.
Recently, we developed radiosensitivity (RSI), a clinically validated molecular signature that estimates tumor radiosensitivity. In the present study, we tested whether integrating RSI with the ...molecular subtype refines the classification of local recurrence (LR) risk in breast cancer.
RSI and molecular subtype were evaluated in 343 patients treated with breast-conserving therapy that included whole-breast radiation therapy with or without a tumor bed boost (dose range 45-72 Gy). The follow-up period for patients without recurrence was 10 years. The clinical endpoint was LR-free survival.
Although RSI did not uniformly predict for LR across the entire cohort, combining RSI and the molecular subtype identified a subpopulation with an increased risk of LR: triple negative (TN) and radioresistant (reference TN-radioresistant, hazard ratio HR 0.37, 95% confidence interval CI 0.15-0.92, P=.02). TN patients who were RSI-sensitive/intermediate had LR rates similar to those of luminal (LUM) patients (HR 0.86, 95% CI 0.47-1.57, P=.63). On multivariate analysis, combined RSI and molecular subtype (P=.004) and age (P=.001) were the most significant predictors of LR. In contrast, integrating RSI into the LUM subtype did not identify additional risk groups. We hypothesized that radiation dose escalation was affecting radioresistance in the LUM subtype and serving as a confounder. An increased radiation dose decreased LR only in the luminal-resistant (LUM-R) subset (HR 0.23, 95% CI 0.05-0.98, P=.03). On multivariate analysis, the radiation dose was an independent variable only in the LUMA/B-RR subset (HR 0.025, 95% CI 0.001-0.946, P=.046), along with age (P=.008), T stage (P=.004), and chemotherapy (P=.008).
The combined molecular subtype-RSI identified a novel molecular subpopulation (TN and radioresistant) with an increased risk of LR after breast-conserving therapy. We propose that the combination of RSI and molecular subtype could be useful in guiding radiation therapy-based decisions in breast cancer.
The discovery of effective biomarkers is a fundamental goal of molecular medicine. Developing a systems-biology understanding of radiosensitivity can enhance our ability of identifying ...radiation-specific biomarkers.
Radiosensitivity, as represented by the survival fraction at 2 Gy was modeled in 48 human cancer cell lines. We applied a linear regression algorithm that integrates gene expression with biological variables, including ras status (mut/wt), tissue of origin and p53 status (mut/wt).
The biomarker discovery platform is a network representation of the top 500 genes identified by linear regression analysis. This network was reduced to a 10-hub network that includes c-Jun, HDAC1, RELA (p65 subunit of NFKB), PKC-beta, SUMO-1, c-Abl, STAT1, AR, CDK1, and IRF1. Nine targets associated with radiosensitization drugs are linked to the network, demonstrating clinical relevance. Furthermore, the model identified four significant radiosensitivity clusters of terms and genes. Ras was a dominant variable in the analysis, as was the tissue of origin, and their interaction with gene expression but not p53. Overrepresented biological pathways differed between clusters but included DNA repair, cell cycle, apoptosis, and metabolism. The c-Jun network hub was validated using a knockdown approach in 8 human cell lines representing lung, colon, and breast cancers.
We have developed a novel radiation-biomarker discovery platform using a systems biology modeling approach. We believe this platform will play a central role in the integration of biology into clinical radiation oncology practice.
We assessed the radiosensitivity of lung metastases on the basis of primary histologic type by using a validated gene signature and model lung metastases for the gnomically adjusted radiation dose ...(GARD).
Tissue samples were identified from our prospective observational protocol. The radiosensitivity index (RSI) 10-gene assay was run on samples and calculated alongside the GARD by using the previously published algorithms. A cohort of 105 patients with 137 lung metastases treated with stereotactic body radiation therapy (SBRT) at our institution was used for clinical correlation.
A total of 138 unique metastatic lung lesions from our institution’s tissue biorepository were identified for inclusion. There were significant differences in the RSI of lung metastases on the basis of histology. In order of decreasing radioresistance, the median RSIs for the various histologic types of cancer were endometrial adenocarcinoma (0.49), soft-tissue sarcoma (0.47), melanoma (0.44), rectal adenocarcinoma (0.43), renal cell carcinoma (0.33), head and neck squamous cell cancer (0.33), colon adenocarcinoma (0.32), and breast adenocarcinoma (0.29) (p = 0.002). We modeled the GARD for these samples and identified the biologically effective dose necessary to optimize local control. The 12- and 24-month Kaplan-Meier rates of local control for radioresistant versus radiosensitive histologic types from our clinical correlation cohort after lung SBRT were 92%/87% and 100%, respectively (p = 0.02).
In this analysis, we have noted significant differences in radiosensitivity on the basis of primary histologic type of lung metastases and have modeled the biologically effective dose necessary to optimize local control. This study suggests that primary histologic type may be an additional factor to consider in selection of SBRT dose to the lung and that dose personalization may be feasible.
We previously developed a multigene expression model of tumor radiation sensitivity index (RSI) with clinical validation in multiple independent cohorts (breast, rectal, esophageal, and head and neck ...patients). The purpose of this study was to assess differences between RSI scores in primary colon cancer and metastases.
Patients were identified from our institutional review board-approved prospective observational protocol. A total of 704 metastatic and 1362 primary lesions were obtained from a de-identified metadata pool. RSI was calculated using the previously published rank-based algorithm. An independent cohort of 29 lung or liver colon metastases treated with 60 Gy in 5 fractions stereotactic body radiation therapy (SBRT) was used for validation.
The most common sites of metastases included liver (n=374; 53%), lung (n=116; 17%), and lymph nodes (n=40; 6%). Sixty percent of metastatic tumors, compared with 54% of primaries, were in the RSI radiation-resistant peak, suggesting metastatic tumors may be slightly more radiation resistant than primaries (P=.01). In contrast, when we analyzed metastases based on anatomical site, we uncovered large differences in RSI. The median RSIs for metastases in descending order of radiation resistance were ovary (0.48), abdomen (0.47), liver (0.43), brain (0.42), lung (0.32), and lymph nodes (0.31) (P<.0001). These findings were confirmed when the analysis was restricted to lesions from the same patient (n=139). In our independent cohort of treated lung and liver metastases, lung metastases had an improved local control rate compared to that in patients with liver metastases (2-year local control rate of 100% vs 73.0%, respectively; P=.026).
Assessment of radiation sensitivity between primary and metastatic tissues of colon cancer histology revealed significant differences based on anatomical location of metastases. These initial results warrant validation in a larger clinical cohort.
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