Abstract 4627: Integrated multi-layer analysis reveals novel insights into the molecular landscape of colorectal cancer (CRC)

Abstract

Abstract Metabolic reprogramming, rewired signalling cascades, altered microbiota composition and aberrations in the tumor micro-environment have been implicated in CRC progression and response to treatment. In this study, we integrated proteomics, metabolomics, gut microbiota and predictions from network-analysis in tumor and non-cancerous tissue to build a comprehensive atlas to decipher the associations among alterations that occur in the different molecular layers. We performed our molecular fingerprinting in fresh frozen tissue samples from both tumor (centre and/or junction) and matched mucosa (5 and/or 10 cm distance) prospectively collected from a cohort of n=53 stage 0-IV CRC patients. We quantified the protein expression of a panel of 86 cancer-related targets involved in DNA damage/repair mechanisms, cell cycle regulation, growth/angiogenesis signalling, apoptosis and bioenergetics by Reverse Phase Protein Array (RPPA) in n=157 samples (n=48 from tumor and n=109 from mucosa) from n=53 patients. RPPA profiling on both tumor and matched normal tissue was available for 34 out of 53 patients. We performed High Resolution Magic-Angle Spinning Nuclear Magnetic Resonance Spectroscopy (1H HR-MAS NMR) to profile metabolites in n=332 samples from n=52 patients (n=161 and n=171 from tumor and normal tissue, respectively). We characterized gut microbiota features from 16S rRNA sequencing in n=54 samples from n=18 patients. We assessed bioenergetic fitness using an ordinary-differential equations based model of core carbon metabolism that we are developing. The model can simulate the network dynamic and response to fuels (such as glucose, pyruvate and lactate) from extracellular milieu and their utilization and conversion via reactions in the cytoplasm and mitochondrial compartments. We used RPPA-based protein expression for HKII, TIGAR, PGYM, LDHA, MCT4, ATP5A as a proxy for enzymatic activities as case-specific inputs to the model. We identified distinct phenotypic differences when comparing tumors with normal tissue at each data type. Tumors showed higher protein levels for CHK1, GSK3B and LDHA; were enriched with fusobacteria; had increased metabolic levels of glycerphosphocholine, isoglutamine, phosphocholine, taurine and lactate; and were predicted by the mathematical model to have a more glycolytic phenotype. Normal mucosa had higher protein levels for GAB1 and elevated isobutyrate and lipids content. We further analysed the NMR spectra and using a genetic algorithm we identified 10 features (ppm levels) that could classify the tissue type with 88% cross-validated accuracy. We presented a unified analysis connecting dysregulations affecting the proteome, metabolome and microbiome. We provided insights into the heterogeneity of CRC which may lead to more specific molecular classifications and ultimately more targeted treatments. Citation Format: Manuela Salvucci, Liam Poynter, Reza Minerzami, Steven Carberry, Robert O'Byrne, Mattia Cremona, Bryan T. Hennessy, Kirill Veselkov, James Kinross, Jochen H. Prehn. Integrated multi-layer analysis reveals novel insights into the molecular landscape of colorectal cancer (CRC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 4627.