Abstract 6915: Integrated proteogenomic analysis for the NCI patient-derived cancer model repository

Abstract

Abstract Background: Studying protein expression involved in cancer from patient-derived xenografts (PDX) enhances understanding of tumorigenesis and may shed light on patient therapeutic decisions. Recently, mass spectrometry-based proteomics sequencing techniques have enabled the acquisition of high-throughput PDX protein/phosphoprotein expression profiles and paved a way for investigating oncogenic signaling pathways and their interactions in different tumor histologies. Integrated analysis of these proteomic data with existing biomarkers derived from whole exome and RNAseq data will provide the community with a rich resource for translational research. Materials and methods: iTRAQ-based proteomics/phospho-proteomics data, with 11 Tandem Mass Tag (TMT) channels, were processed and median-normalized at gene level. The gene expression data were derived from tximport and DESeq2 from of RNA-Seq. In addition, whole exome sequencing (WES) data were used to compute mutations and copy number profiles. For data visualization, the R Bioconductor package ComplexHeatmap was used extensively. Results: PDX proteomics/phospho-proteomics data were analyzed alongside with gene expression, copy number and mutational profiles for several cancer types, revealing vivid molecular regulation for many cancer-related pathways. A moderate yet significant Spearman correlation coefficient of over 0.45 and 0.5 was observed between proteomics and RNA-Seq data at gene and pathway level, respectively. The PDX protein and gene expression pattern from common cancer regulatory pathways of bladder (BLCA, n=32 models), colon (COAD, n=82), lung (NSCLC, n=32), pancreatic (PAAD, n=52), metastatic breast cancer (CSNOS) (n=16) and other histologies were aggregated and discussed at the model level. EGFR, MET and cell-cycle genes CDKN2A and CDKN2B exhibited significant positive correlation among copy number, gene expression and protein expression profiles. Protein and gene expression profiles of PAM50 genes were shown for CSNOS samples, to further sub-classify or correct misclassification. Further analysis using proteomics/phospho-proteomics data with CNA identified several trans-regulatory events. Lastly, we identified RTN1 as a potential biomarker, exhibiting lower expression in sensitive rare-tumor PDX models prior to Axitinib and Vandetanib treatment. Conclusions: With the multi-omics datasets comprising proteomics/phospho-proteomics, RNA-Seq and WES datasets from the NCI Patient Derived Models Repository cohort, we were able to query some important cancer biological processes at a higher resolution. In addition, we revealed gene- and pathway-level regulatory differences from various histologies. Overall, the multi-omics data from PDX models showed promising recapitulation of original tumor activity and should continue to serve as an amenable and scalable drug screening platform for pre-clinical trials. Citation Format: Peter I. Wu, Li Chen, Yuri Kotliarov, Jianwen Fang, Yingdong Zhao, Yvonne Evrard, Lijun Chen, Shahanawaz Jiwani, Biswajit Das, Chris A. Karlovich, Hui Zhang, Lisa McShane, Melinda G. Hollingshead, Mickey Williams, James H. Doroshow. Integrated proteogenomic analysis for the NCI patient-derived cancer model repository [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6915.