Ciznadija, D. | Search bioRxiv

bioRxiv2022

Pharmaco-Pheno-Multiomic Integration Reveals Biomarker Profiles and Therapeutic Response Prediction Models in Leukemia and Ovarian Cancer

Despite considerable progress made in improving therapeutic strategies, the overall survival for patients diagnosed with various cancer types remains low. Further, patients often cycle through multiple therapeutic options before finding an effective regimen for the specific malignancy being treated. A focus on building enhanced computational models, which prioritize therapeutic regimens based on a tumors complete molecular profile, will improve the patient experience and augment initial outcomes. In this study, we present an integrative analysis of multiple omic datasets coupled with phenotypic and therapeutic response profiles of Cytarabine from a cohort of primary AML tumors, and Olaparib from a cohort of Patient-Derived Xenograft (PDX) models of ovarian cancer. These analyses, termed Pharmaco-Pheno-Multiomic (PPMO) Integration, established novel complex biomarker profiles that were used to accurately predict prospective therapeutic response profiles in cohorts of newly profiled AML and ovarian tumors. Results from the computational analyses also provide new insights into disease etiology and the mechanisms of therapeutic resistance. Collectively, this study provides proof-of-concept in the use of PPMO to establish highly accurate predictive models of therapeutic response, and the power of leveraging this method to unveil cancer disease mechanisms.

cancer biology↗

bioRxiv2020

A tyrosine kinase protein interaction map reveals targetable EGFR network oncogenesis in lung cancer

SUMMARYSignaling networks balance the activities of many physically interacting proteins and perturbations to this network influence downstream signaling, potentially leading to oncogenic states. Using affinity purification-mass spectrometry we defined this network for all 90 human tyrosine kinases revealing 1,463 mostly novel interactions between these key cancer proteins and diverse molecular complexes. Modulation of interactor levels altered growth phenotypes associated with corresponding tyrosine kinase partners suggesting that tumors may alter the stoichiometries of interactors to maximize oncogenic signaling. We show that the levels of EGFR interactors delineates this form of network oncogenesis in 19% of EGFR wild-type lung cancer patients which were mostly otherwise oncogene negative, predicting sensitivity to EGFR inhibitors in vitro and in vivo. EGFR network oncogenesis occurs through mechanistically distinct network alleles often in cooperation with weak oncogenes in the MAPK pathway. Network oncogenesis may be a common and targetable convergent mechanism of oncogenic pathway activation in cancer.HIGHLIGHTSA human tyrosine kinome protein interaction map reveals novel physical and functional associations.Dependence on oncogenic tyrosine kinases is modulated through perturbation of their interactors.EGFR network oncogenesis in up to 19% of EGFR wild-type lung cancers is targetable.EGFR network oncogenesis cooperates with weak oncogenes in the MAPK pathway.Competing Interest StatementS.L.W. is an employee of Pfizer. D.C., I.S., E.M., are employees of Champions oncology. S.B. has received funding from Clovis Oncology, Pfizer, Revolution Medicines and Ideaya Biosciences.View Full Text

cancer biology↗