Background: Given the lack of biomarkers to predict a pathologic complete response (pCR) after neoadjuvant chemoradiation (NACRT) for rectal cancer, selection for non-operative management (NOM) mandates complete clinical response. We have previously developed/validated a model to assess genomic-based tumor radiosensitivity: the radiosensitivity index (RSI), which formulates a clinically actionable model to calculate genomic-adjusted radiation dose (GARD). We determined the profiles of RSI and GARD for rectal cancer and correlated these findings with the pathologic response patterns. Methods: One hundred seventeen rectal cancer patients treated from 2009 to 2018 with NACRT were assessed for the tumor regression grade (TRG) (0 = pCR; 1 = moderate response; 2 = partial response; 3 = poor response). RSI was analyzed in an independent tissue cohort of 113 resected rectal cancer samples. GARD was derived as described before, which shows a high GARD value indicated a superior therapeutic effect of radiation. Results: Median follow-up from completion of NACRT was 26 months. The primary tumor stages were 7% T2, 84% T3, and 9% T4. The majority of patients (82%) received concurrent 5-FU or Capecitabine and (83%) received RT dose of 50.4 Gy (range 45-56 Gy). Median time from end of NACRT to surgery was 61 days (range 36-105 days). The patterns of pathological response were TRG 0 (n = 24; 21%), 1 (n = 62; 53%), 2 (n = 25; 21%), and 3 (n = 6; 5%), suggesting heterogeneous sensitivity to treatment with similar tumor stage and treatment regimens. The median RSI for the tissue cohort was 0.46 (range 0.19-0.81) with 37% of the samples considered radiosensitive based on prior data. GARD values ranged from 5.17 to 41.79 (median 19.24), suggesting heterogeneous RT therapeutic effects. Conclusions: The findings from the clinical cohort were consistent with the tissue cohort showing significant heterogeneity in the individual tumor radiosensitivity and GARD-based RT therapeutic effects. With the development of GARD-based prospective trials, we anticipate more biology-based customized RT dosing which could optimize patient selection for NOM and individualize the most appropriate dose for each patient.