Abstract B073: Understanding treatment response in pancreatic cancer: NetraAI provides genetic differentiation in FOLFIRINOX and gemcitabine response

Abstract

Abstract Pancreatic cancer is known as a “silent killer” due to vague symptoms, late diagnosis, rapid progression, and poor response to therapy. Due to the highly malignant nature of this disease, prolonging progression-free survival (PFS) and overall survival (OS) for patients is critical. Standard of care involves combination chemotherapy using FOLFIRINOX (FFX) or Gemcitabine with nab-paclitaxel (GnP). The COMPASS trial performed whole genome sequencing (WGS) and RNA sequencing (RNASeq) on pancreatic ductal adenocarcinoma (PDAC) patients prior to FFX or GnP first-line combination therapy, finding molecular heterogeneity between patients and differing response to chemotherapy. Using the COMPASS trial dataset consisting of only 208 patients and NetraAI, a novel machine learning (ML) platform using a novel causal feature set discovery methodology, we explored generated hypotheses to differentiate causal factors that affect patient response to FFX and GnP. Differentiating FFX and GnP treatment response in stable disease patients revealed 3 distinct subpopulations with genetic differentiation related to neuron death and regulation, neurotrophin signaling, and neuron apoptotic processes. NetraAI identified genes driving patient subpopulations, such as LRRC8E, PTPRH, and UTG1A13p. Further investigation revealed that LRRC29 and CLEC19A distinguished a group of patients that responded well to FFX. LRRC29 relates to HOOK1, which negatively regulates epithelial-to-mesenchymal transition and has been reported to be decreased in PDAC. Low HOOK1 and SHP2 expression in non-small cell lung cancer is associated with a good response to GnP, suggesting that chemotherapy response depends on the interaction of the chemotherapeutic agent and genetic factors involved in disease progression as opposed to the tumor type. These results suggest that LRRC29 expression can act as a biomarker to choose between GnP and FFX treatment. In this way, NetraAI's ability to generate explainable hypotheses around patient subpopulations can help oncologists personalize treatments and better understand clinical trial patient populations. By identifying distinct genetic signatures within patient datasets. By identifying unique molecular heterogeneity that explains differing responses to therapy, NetraAI is a powerful tool that can extract insights from multi-dimensional and highly heterogenous patient population datasets that can help to inform clinical trial enrichment decisions to improve the success of pivotal trials. This work has a significant impact on precision medicine and clinical trial de-risking, while identifying specific genes and pathways involved in patient response to therapy. Citation Format: Bessi Qorri, Mike Tsay, Josh Spiegel, Douglas Cook, Joseph Geraci. Understanding treatment response in pancreatic cancer: NetraAI provides genetic differentiation in FOLFIRINOX and gemcitabine response [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Pancreatic Cancer; 2023 Sep 27-30; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(2 Suppl):Abstract nr B073.