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] 0middot98 [95% CI 0middot97-0middot99]; p=0middot0017) and overall survival (0middot97 [0middot95-0middot99]; p=0middot0007). The interaction test showed the effect of GARD on overall survival depends on whether or not that patient received radiotherapy (Wald statistic p=0middot011). The interaction test for GARD and radiotherapy was not significant for time to first recurrence (Wald statistic p=0middot22). The HR for physical dose of radiation was 0middot99 (95% CI 0middot97-1middot01; p=0middot53) for time to first recurrence and 1middot00 (0middot96-1middot04; p=0middot95) for overall survival. The HR for sham-GARD was 1middot00 (0middot97-1middot03; p=1middot00) for time to first recurrence and 1middot00 (0middot98-1middot02; p=0middot87) 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. Copyright (c) 2021 Elsevier Ltd. All rights reserved.

Automated summary

A comprehensive analysis of 11 clinical cohorts of patients with different types of cancer revealed that the genomic-adjusted radiation dose (GARD) model is significantly associated with time to first recurrence and overall survival. GARD helps optimize the benefits of radiotherapy and serves as an important framework for personalized prescription of radiation therapy dose.