Molecular Subtyping Combined with Biological Pathway Analyses to Study Regorafenib Response in Clinically Relevant Mouse Models of Colorectal Cancer

Abstract

Purpose: Regorafenib (REG) is approved for the treatment of metastatic colorectal cancer, but has modest survival benefit and associated toxicities. Robust predictive/early response biomarkers to aid patient stratification are outstanding. We have exploited biological pathway analyses in a patient-derived xenograft (PDX) trial to study REG response mechanisms and elucidate putative biomarkers. Experimental Design: Molecularly subtyped PDXs were annotated for REG response. Subtyping was based on gene expression (CMS, consensus molecular subtype) and copy-number alteration (CNA). Baseline tumor vascularization, apoptosis, and proliferation signatures were studied to identify predictive biomarkers within subtypes. Phospho-proteomic analysis was used to identify novel classifiers. Supervised RNA sequencing analysis was performed on PDXs that progressed, or did not progress, following REG treatment. Results: Improved REG response was observed in CMS4, although intra-subtype response was variable. Tumor vascularity did not correlate with outcome. In CMS4 tumors, reduced proliferation and higher sensitivity to apoptosis at baseline correlated with response. Reverse phase protein array (RPPA) analysis revealed 4 phospho-proteomic clusters, one of which was enriched with non-progressor models. A classification decision tree trained on RPPA- and CMS-based assignments discriminated non-progressors from progressors with 92% overall accuracy (97% sensitivity, 67% specificity). Supervised RNA sequencing revealed that higher basal EPHA2 expression is associated with REG resistance. Conclusions: Subtype classification systems represent canonical “termini a quo” (starting points) to support REG biomarker identification, and provide a platform to identify resistance mechanisms and novel contexts of vulnerability. Incorporating functional characterization of biological systems may optimize the biomarker identification process for multitargeted kinase inhibitors.

Proteomics as a promising biomarker in food authentication, quality and safety: A review

“…The two most common protein identification methods include Peptide Fragment Fingerprinting (PFF) and peptide mass fingerprinting (PMF) that are both involved in enzymatic digestion of the identified proteins. In a process named top‐down proteomics (Lafferty et al, 2021), tandem mass spectrometry (MS/MS) data from the intact protein can be used alternatively. All of these procedures include the sequence information present in the database of the homologous or respective protein.…”

“…fingerprinting (PMF) that are both involved in enzymatic digestion of the identified proteins. In a process named top-down proteomics(Lafferty et al, 2021), tandem mass spectrometry (MS/MS) data from the intact protein can be used alternatively. All of these procedures include the sequence information present in the database of the homologous or respective protein.Protein databases currently available, such as UniProtKB (Uniprot Consortium, 2019) or NCBI (National Center for Biotechnology Information), U.S. National Library of Medicine, Bethesda, MD, USA, mostly do not provide accurate protein sequence data for the multitude of proteins found in food products of various plant, animal, or fungal origins.…”

PAGER-scFGA: unveiling cell functions and molecular mechanisms in cell trajectories through single-cell functional genomics analysis

“…Functional genomics analysis serves as a pivotal tool for unraveling the intricate interplay of genes, proteins, and small molecules, orchestrating specific functions in response to both extrinsic and intrinsic factors, such as small molecules or secreted proteins. Functional genomics has witnessed notable success in advancing disease diagnosis ( Zhang and Chen, 2010 ; Drier et al, 2013 ; Livshits et al, 2015 ; Bock and Ortea, 2020 ; Pian et al, 2021 ), cancer subtyping ( Zhang and Chen, 2013 ; Mallavarapu et al, 2020 ; Lafferty et al, 2021 ), and personalized medicine ( Chen et al, 2007 ; Hamburg and Collins, 2010 ; Raghavan et al, 2017 ). To gain a profound understanding of the molecular mechanisms underpinning diseases or contributing to various disorders, comprehensive functional genome analysis has emerged, encompassing genomics, epigenomics, proteomics, and interactomics.…”

DEMA: a distance-bounded energy-field minimization algorithm to model and layout biomolecular networks with quantitative features

“…Ideal network layouts are expected to not only present a global view of items with more space-filling and fewer overlapping nodes but also highlight specific parts of the views coupling with biological annotations ( Kobourov, 2012 ; Pinto et al , 2010 ; You et al , 2010 ). Therefore, the mined critical biomarkers and models can further assist disease diagnosis ( Bock and Ortea, 2020 ; Drier et al , 2013 ; Liu et al , 2016 ; Livshits et al , 2015 ; Pian et al , 2021 ; Zhang and Chen, 2010 ), cancer subtyping ( Lafferty et al , 2021 ; Mallavarapu et al , 2020 ; Zhang and Chen, 2013 ) and personalized medicine ( Chen et al , 2007 ; Hamburg and Collins, 2010 ; Raghavan et al , 2017 ). Various analytical strategies have been applied to biological network analysis, such as sub-network analysis in MCODE ( Bader and Hogue, 2003 ), hub gene identification and geneset enrichment analysis, to extract critical genomic information ( Khan et al , 2020 ).…”