A PBMC and machine learning based biomarker signature to predict second line chemotherapy success in advanced PDAC: Translational data of the PREDICT trial—A prospective, multicenter, trial of the AIO Pancreatic Cancer Group (AIO-PAK-0216).

Abstract

4187 Background: The PREDICT trial, a recent phase IIIb/IV study, aimed to address the critical need for improved personalized treatment strategies in advanced pancreatic cancer. This translational investigation examined the predictive value of 1 st line chemotherapy (CTX) response on the efficacy of subsequent 2 nd line treatment by using liquid biomarkers combined with machine learning (ML). Methods: Pts. were stratified into two cohorts based on short or long 2 nd line CTX time to treatment failure (S-/L-TTF2; n = 10 per group, 80/20% quantiles). Treatment-naïve PDAC tissue specimens underwent laser microdissection for tumor cell enrichment, followed by RNA profiling using NanoString™ PanCancer IO360. Selected differentially regulated genes from the omics results were utilized to screen peripheral blood mononuclear cells (PBMCs) from 2 nd line treatment-naïve patients at protein (flow cytometry, FC) or RNA (RT-qPCR) levels. FC data was analyzed using R-based single-cell clustering (x-Shift, FlowSOM, T-REX) to generate HyperGates (HGs), with subsequent ML-based back-gating (HyperFinder algorithm) of the most differential clusters. Additionally, classical ManualGates (MGs) were generated. Feature selection employed the Weka-based WrapperSubsetEval (WSE) algorithm with eight different classifiers (NB, KLR, LR, SMO, IBk, RT, RF, J48). The classifier/subset with optimal performance was utilized for binary classification (S-TTF2 vs. L-TTF2) of training (80%, n = 66) and validation (20%, n = 16) datasets. Results: Transcriptome analysis of L- vs. S-TTF2 tumor tissues revealed increased inflammation (upregulated 18-gene signature), immune cell activation/infiltration (e.g. CD4/CD8 T cells), and immune exhaustion (upregulation of e.g. PDCD1, LAG3, CTLA4, TIGIT) in L-TTF tumors. Further, the favorable Bailey immunogenic subtype was enriched in the L-TTF2 group. Analysis of eight FC panels (19 candidates) revealed 1198 differential clusters with HGs and 881 classical MGs. Feature selection, combining FC data with RT-qPCR results and clinical parameters, identified a best performing signature of 5 HGs and 2 MGs for 7 protein markers (CXCR4, CD8, CD4, CD62P, CD307b, CD45, CD121b). ML using a kernel logistic regression successfully predicted S- and L-TTF2 binary groups prior to 2 nd line CTX with nal-IRI/5-FU/LV (ROC-AUC > 0.90 for training and validation). Conclusions: We identified a favorable tumor immune microenvironment in L-TTF aPDAC patients, characterized by CD8 T cell-inflamed ("hot") tumor tissues prior to 2 nd line CTX. A 7-marker liquid biomarker panel, comprising 7 flow cytometry PBMC population gates, was developed for early prediction of 2 nd line nal-IRI/5-FU/LV CTX success. These findings aim to advance personalized treatment strategies. Clinical trial information: NCT03468335 .