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).
Year: 2025
Type: conference-abstract
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 .
Source: Journal of Clinical Oncology
Authors: Anton Lahusen, Manfred P. Lutz, Meinolf Karthaus, Martina Kirchner, Klaus Kluck, Sarah Wisser, Phyllis Fung-Yi Cheung, Nikolaus Ansorge, Christof Burkart, Thomas Decker, Ludwig Fischer von Weikersthal, Thomas Geer, Anke Gerhardt, Jan Budczies, Jens T. Siveke, Andrea Tannapfel, Thomas Seufferlein, Albrecht Stenzinger, Tim Eiseler, Thomas Jens Ettrich
Institutions: Universität Ulm, University Hospital Ulm, cts CaritasKlinikum Saarbrücken, München Klinik Neuperlach, University Hospital Heidelberg, Ruhr University Bochum, Essen University Hospital, Schwarzwald-Baar Klinikum, Klinikum St. Marien Amberg, German Cancer Research Center, National Center for Tumor Diseases
Language: English
Subfield: Oncology
Field: Medicine
Domain: Health Sciences
Open Access status: bronze
Funder: Servier
OpenAlex ID: W4410817063