P70.03 Computational Omics Biology Model (CBM) Identifies Amplifications of Chromosome 6p to Predict Chemotherapy Resistance

Abstract

Gemcitabine and carboplatin/cisplatin (“platinum”)-based combinations are used to treat a wide variety of malignancies including gynecologic, breast, lung, and occult primary cancers. In Non-Small Cell Lung Cancer (NSCLC), these combinations led to a substantial improvement in overall survival. Nevertheless, a large proportion of patients do not respond. An optimal cytotoxic strategy for managing NSCLC and the discovery of predictive biomarkers for cytotoxic chemotherapy to guide treatment selection remain unmet needs in the clinic. The Cellworks Computational Omics Biology Model (CBM) platform identified a unique chromosomal signature which permits a stratification of patients that are most likely to respond to gemcitabine and platinum treatments. Twenty patients treated with gemcitabine and platinum were identified from a TCGA dataset and analyzed. The mutation and copy number aberrations from individual cases served as input into the CBM to generate a patient-specific protein network map from PubMed and other online resources. Disease-biomarkers unique to each patient were identified within patient-specific protein network maps. Digital drug biosimulations were conducted by measuring the effect of gemcitabine and platinum on a cell growth score comprised of a composite of cell proliferation, viability, apoptosis, metastasis, and other cancer hallmarks. Drug biosimulations were conducted by mapping the drug combination to the patient genome along with a rational mechanism of action and validated based on the patient’s genomic profile and biological consequences. Of the 20 patients treated with gemcitabine and platinum, 12 had clinical responses while 8 were non-responders. The CBM correctly predicted response in 17/20 patients with 85% accuracy, 63% specificity and 100% sensitivity. The CBM identified that novel amplified segments of Chromosome 6p were associated with non-responsiveness to gemcitabine and platinum therapy. Key genes on these segments include E2F3, MDC1, TAP1 and TNF. Amplification of E2F3 leads to activation of MSH2/6, which enhances mismatch repair thereby causing resistance. Amplification of MDC1 leads to activation of CHECK2, BRCA1, ATM, and NBN_RAD51_MRE1 Complex which stimulates homologous recombination repair. Amplification of TAP1 reduces gemcitabine transport. Besides 6p amplification, PRMT7 deletion was also associated with gemcitabine resistance. Notably, BRCA2-del, RB1-del, NPM1-del, LIG4-del, XRCC4-del, RAD50-del, ATRX-Del, RBBP8-del, XRCC6-del, and FBXW7-del were also prevalent among gemcitabine non-responders. Interestingly, these aberrations also happen to be key criteria for predicting response to etoposide. Therefore, etoposide and platinum combinations might have provided better disease control for these patients. Amplification of chromosome 6p appears to be an important cause of treatment failure for patients receiving gemcitabine-platinum combinations. In this small patient group, the Cellworks CBM was especially useful for identifying non-responders. Biosimulation can identify novel patient subgroups for therapy response prediction and has promise to help select more effective therapies.