Vedell, P. T. | Search bioRxiv

bioRxiv2022

Integration of multi-omics data shows downregulation of mismatch repair, purin, and tublin pathways in AR-negative triple-negative chemotherapy-resistant breast tumors

Triple negative breast cancer (TNBC) patients who fail to achieve a pathological complete response to neoadjuvant chemotherapy (NAC) will likely experience recurrence of the disease within 3-4 years. Prognostic assessment of early recurrence could facilitate clinical decisions and impact disease survival. This study investigated pre- and post-NAC multi-omics data from both an in-house and a public clinical study to identify biomarkers of NAC response. We observed significant transcriptional differences associated with response in the residual disease (post-NAC biopsies), which did not exist in the pre-NAC biopsies. We further refined the post-NAC transcriptional changes to a 17-gene signature and machine learning models were applied to evaluate the signatures diagnostic potential (area under the curve [≥] 0.8). Interestingly, the signature was enriched with down-regulated immune genes and several of these genes demonstrated prognostic potential in basal TNBC tumors. Our 17-gene signature provides additional insight into TNBC recurrence, which warrants further investigation.

cancer biology↗

bioRxiv2017

PANOPLY: A computational method for identification of promising drugs for a patient based on multidimensional data

PurposeThe majority of cancer patients receive treatments that are minimally informed by omics data. We propose a precision medicine computational framework (PANOPLY: Precision cancer genomic report: single sample inventory) to identify and prioritize drug targets and cancer therapy regimens.\n\nMethodsThe PANOPLY approach integrates clinical data with germline and somatic features obtained from multi-omics platforms, and apply machine learning, and network analysis approaches in the context of the individual patient and matched controls. The PANOPLY workflow employs four steps (i) selection of matched controls to the case of interest (ii) identification of case-specific genomic events (iii) identification of suitable drugs using the driver-gene network and random forest analyses and (iv) provide an integrated multi-omics case report of the patient with prioritization of anti-cancer drugs.\n\nResultsThe PANOPLY workflow can be executed on a stand-alone virtual machine and is also available for download as an R package. We applied the method to an institutional breast cancer neoadjuvant chemotherapy study which collected clinical and genomic data as well as patient-derived xenografts (PDXs) to investigate the prioritization offered by PANOPLY. In a chemotherapy-resistant PDX model, we found that that the prioritized drug, olaparib, was more effective than placebo in treating the tumor (P < 0.05). We also applied PANOPLY to in-house and publicly accessible multi-omics tumor datasets with therapeutic response or survival data available.\n\nConclusionPANOPLY shows promise as a means to prioritize drugs based on clinical and multi-omics data for an individual cancer patient. Additional studies are needed to confirm this approach.

bioinformatics↗