Radiation and Cancer Biology

MO 19 - Biology 7 - Normal Tissues

1236 - The Radiogenomic Atlas: Systematic Charting of Genetic Variants That Determine Sensitivity to Radiation

Wednesday, September 18
8:45 AM - 8:50 AM
Location: Room W192

The Radiogenomic Atlas: Systematic Charting of Genetic Variants That Determine Sensitivity to Radiation
B. Yard1, A. Petty2, J. Castrillon2, and M. Abazeed3; 1Cleveland Clinic Lerner Research Institute, Cleveland, OH, 2Cleveland Clinic, Cleveland, OH, 3Department of Radiation Oncology, Taussig Cancer Institute, Cleveland Clinic, Cleveland, OH

Purpose/Objective(s): The impact of the preponderance of common or rare cancer genetic alterations on sensitivity of cells to ionizing radiation remain unknown. We conducted a systematic, large-scale profiling effort to identify genetic variants that alter cellular sensitivity to radiation.

Materials/Methods: Candidate variants from more than 1600 genes were prioritized on the basis of their location within conserved protein domains and association with radiation sensitivity using a previously completed large-scale radiation cancer cell line profiling effort. In addition, commonly mutated genes (CMG) and randomly selected variants (RSV) were profiled. We conducted high-content mutagenesis in three steps: PCR, in vitro recombination and transformation. The discontinuity at the mutation site was repaired by endogenous bacterial repair mechanism. The ORFs were transferred into lentiviral vectors for stable expression under heterologous promoters in SV40 immortalized cell (BEAS-2B). For a subset of variants that were predicted to confer a loss-of-function phenotype, expression of the endogenous gene was repressed by CRISPRi. The effect of individual variants on radiation response was assessed using our previously benchmarked high-throughput platform at doses of 2, 4 and 6 Gy. The platform was calibrated against vector control (negcon) and the canonical, cyto- and radio-protective NFE2L2 E79K gain-of-function mutation (poscon) and results were validated by colony formation assays.

Results: Over 1600 replicates have been tested to date, comprising 91 genes and 510 alleles. Alleles nominated by our in silico computational enrichment methods were significantly more likely to result in a validated allele that conferred changes in the vulnerability of cells to radiation (P<0.001). The vast majority of CMG alleles did not alter the radio-phenotype. We discovered novel radioresistant alleles involved in several cellular functional categories including cellular signaling, cytoskeleton, cell cycle, apoptosis and DNA methylation. Rare cancer variants (<1% frequency in pan-cancer studies) also significantly altered radiation sensitivity, suggesting that comprehensive genomic information is likely to be required to predict sensitivity to radiation.

Conclusion: Determining the functional impact of cancer genetic alterations remains a major obstacle in the implementation of personalized radiotherapy. Here, we report on a large-scale profiling effort to identify and classify mutant alleles that govern radiation survival. Our results reveal new insights into the mechanisms of cellular survival to radiation and genome maintenance.

Author Disclosure: B. Yard: None. A. Petty: None. J. Castrillon: None. M. Abazeed: Research Grant; Bayer, Siemens Healthcare. Honoraria; Bayer. Travel Expenses; Bayer.

Mohamed Abazeed, MD, PhD

Cleveland Clinic Taussig Cancer Institute

Disclosure:
Employment
Cleveland Clinic: Assistant Professor: Employee

Compensation
Bayer: Honoraria: Research Grants: Travel Expenses;
Siemens Healthcare: Research Grants

Biography:
My laboratory seeks to leverage the ongoing development of comprehensive cancer diagnostic tests that represent a critical step toward individualized cancer care by helping physicians recommend treatments based on the molecular and imaging profiles of individual tumors. Every project in the laboratory is committed to more “precise” cancer therapies as greater precision translates to improved responses in patients and less treatment-related toxicity. We demonstrate this commitment though projects that seek to enhance our understanding of targetable genetic alterations in the genome (functional genomics), the appreciation and delineation of inter- and intra-tumoral heterogeneity and the development of imaging deep learning/AI frameworks that individualize anti-cancer treatments.

Presentation(s):

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