Cellworks Omics Biology Model (CBM) to predict therapy response and identify novel biomarkers for 5FU-based combination therapy in gastric cancer patients.
Authors: Michael Castro, Ansu Kumar, Himanshu Grover, Vivek Patil, Ashish Agrawal, Anuj Tyagi, Humera Azam, Karthik Sundara Raju, Poornachandra G, Yuvavani Velkuru, Nirjhar Mundkur, Vishwas Joseph, Pallavi Kumari, Vijayashree P S, Naga Ganesh, Yashaswini Ullal, Yugandhara Narvekar, Annapoorna Prakash, Adity Ghosh
Journal: Journal of Clinical Oncology (2021)

e16091 Background: Using a 5FU backbone, a variety of combination chemotherapy regimens have been adopted for gastric cancer. However, the genetic heterogeneity of the disease suggests certain drugs would lead to better outcomes for specific patients. In principle, the incorporation of molecular profiling information into treatment selection can generate superior survival for patients. Therefore, we conducted a pilot study using the CBM to identify novel genomic biomarkers of response and resistance to several 5FU-based regimens. Methods: 12 gastric cancer patients treated with 5FU-cisplatin (N = 4), 5FU-VP16 (N = 5) and 5FU-cisplatin-docetaxel (N = 3) were selected from the TCGA database. Mutation and copy number aberrations from each case served as input for CBM to generate patient-specific protein network maps generated from PubMed and other resources. Disease-biomarkers unique to each patient were identified within protein network maps. Drug impact on the disease network was digitally simulated to determine treatment efficacy by measuring effect of chemotherapy on the cell growth score, i.e., a composite of cell proliferation, viability, apoptosis, metastasis, DNA damage and other cancer hallmarks. Effectively, the mechanism of action of each drug was mapped to each patient’s genome and the biological consequences of genomic abnormalities were correlated with response. Results: Of the 12 patients in the study, 10 patients responded to 5FU-based regimens and 2 were non-responders. CBM correctly predicted the therapy response in each therapy segment. Key response criteria were: 5FU-cisplatin - ARID1A (LOF), RAD51 (LOF), MBD1 (LOF), DUT (LOF). 5FU-VP16 - FBXW7 (DEL), RAD51D (DEL), RAD51C (DEL), XRCC2 (DEL), XRCC3 (DEL). 5FU-cisplatin-docetaxel - ARID1A (DEL), CHEK1 (DEL), REV3L (DEL), XRCC3(DEL) and TUBB (AMP). Resistance biomarkers were also identified for these drugs. FBXW7 loss caused cisplatin resistance, but also predicted sensitivity to VP16. Interestingly, all five 5FU-VP16 responders harbored FBXW7 loss. Similarly, in two 5FU-cisplatin-docetaxel non-responders, KIFC1 (GOF), YBX1(AMP), CDC20 (AMP) were key biomarkers for docetaxel resistance. In one of these patients, ATM (DEL), RBBP8 (DEL) and GSTP1 (DEL) predicted response to VP16. Conclusions: The efficacy of individual cytotoxic agents varies in the population. Mutations and copy number changes in DNA checkpoint, DNA repair, and tumor suppressor genes impact responsive to chemotherapy and should be routinely assessed. Notably, 5FU-VP16 combination could have delivered better antitumor efficacy for several non-responders or 25% of the patients in this small cohort. CBM is able to identify both the most active as well inefficacious components of the treatment upfront. We suggest that this approach should be prospectively evaluated in a larger cohort.