Abstract
Although the combination of chemotherapy with immunotherapy has led to significant improvements in the treatment of some solid tumors, metastatic pancreatic ductal adenocarcinoma (mPDAC) prognosis has remained largely unaffected by such approaches. Recently, PRINCE, a randomized phase 2 clinical study, reported significantly improved 1-year overall survival (OS) for mPDAC patients treated with nivolumab and chemotherapy (nivo/chemo) compared to historical control but not for sotigalimab and chemotherapy (sotiga/chemo) or a combination of the three. Interestingly, the study identified potential improvement to treatment outcome with patient stratification. Here, we report an unbiased mass spectrometry (MS)-based proteomics profiling of a subset of plasma samples from the PRINCE trial (n = 211, 62 individuals from nivo/chemo and sotiga/chemo longitudinal samples). Briefly, plasma samples were depleted, digested to tryptic peptides, measured by MS/MS and quantified using Spectronaut (Biognosys). Data was investigated for both protein and peptide biomarker with an emphasis on baseline biomarkers associated with clinical outcomes. Plasma profiling identified 1,662 proteins and 17,451 modified peptides across the cohorts. First, we developed a model that identified the pharmacodynamic effects of treatment in individual patients. In accordance with the PRINCE study and among the major model contributors, sotiga/chemo increased proteins associated with innate immunity and chemokines (including CCL15) while proteins aiding in T cell activation and immune cell migration increased with nivo/chemo (including CXCL7 and CD115). Second, we looked for markers in the pretreatment plasma samples that could predict OS. Overall, we found 25 predictive markers (p < 0.05), with only six shared among the two arms, including attractin and CD58. Among the seven predictive biomarkers specific to nivo/chemo, we found MEGF10 and GALNT1, which are suggested to play a role in neoantigen generation. For sotiga/chemo, we found 12 predictive biomarkers including IGF2, CD304, and periostin (known to support immune responses). Third, we expanded our predictive biomarker search to the identified peptides, an analysis that is currently only possible using an unbiased mass spectrometry-based approach. Using such an approach we identified predictive peptides, likely cleavage products, as well as predictive post-translational modifications. Herein we demonstrate the value of both an unbiased approach, as well as the use of peptide level data for novel biomarker identification. We identify numerous proteins and peptides that have the potential to be used for better patient treatment stratification in the case of mPDAC and immunotherapy.
Citation Format: Nigel Beaton, Marco Tognetti, Kamil Sklodowski, Roland Bruderer, Lukas Reiter. Mass spectrometry-based protein biomarker analysis in chemoimmunotherapy combinations identifies unique immune signatures in pancreatic cancer. [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5300.
RSS Feeds