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Apoptosis is essential for chemotherapy responses. We previously developed the mathematical models APOPTO-CELL and DR_MOMP to estimate the magnitude of tumor cells resilience to undergo and execute apoptosis in response to chemotherapy, and demonstrated that these models are prognostic markers of clinical responses in stage 3 colorectal cancer patients [Salvucci M et al., Clin Cancer Res 2017; Lindner AU et al., Gut 2018]. However, the heterogeneity of colorectal cancer tissue represents a hurdle that we have yet to overcome on our way to entirely understand differences in response and clinical outcome in patients. Quantification of protein levels as input for systems studies routinely reflects a local cell population average rather than protein levels in individual cells. In this study we aim to gain a better understanding of apoptosis resilience in tumors with respect to tumor cell heterogeneity and cell composition and local aspects of the tumor microenvironment. Two to three core slices of formalin-fixed and paraffin-embedded primary colorectal cancer tissue were collected from 165 stage II-IV patients from a previously used cohort [Salvucci M et al. 2017; Lindner AU et al. 2018]. The levels of 25 protein markers for cell type and immune status, apoptosis, metabolism and cell proliferation, as well as cell segmentation markers, were measured on TMA slides using a multiplexed tissue immunofluorescence imaging platform (Cell DIVE [Gerdes et al., Proc Natl Acad Sci USA 2013]). Batch correction between slides was performed using linear regression (using markers for regulatory T cell in patient tissues as housekeeping cells. Formalin-fixed gene deficient cells were used in parallel for antibody validation. The layout allowed us to create protein profiles for on average 13,079 (± 2,282 SD) cells per core per patient, and to categorize these as epithelial-like cells, regulatory T cells, T helper cells, cytotoxic T cells, other leukocytes and other cells. The data will be used to identify cells contributing most to the signal measured with traditional methods (such as reverse protein phase arrays) that were used to predict patients clinical response to therapy in our previous studies. Further, we will assess spatial differences in distribution and frequency of different cells and their contribution to clinical outcome in colorectal cancer patients. Specifically, we are investigating metrics to characterize the spatial features and layout found in each core in terms of textures, degree of lacunarity, neighboring cells composition and patterns of protein expression (diffuse vs. patchy) and co-expression among cell types. Further, we will characterize the degree of intra- and inter-patient spatial heterogeneity and will examine the association between the identified spatial motifs with macroscopical characteristics of the tumor and outcome. Citation Format: Andreas Urlich Lindner, Manuela Salvucci, Xanthi Stachtea, Steven Carberry, Philip D. Dunne, Anup Sood, Elizabeth McDonough, Sanghee Cho, Pierre Laurent-Puig, Sandra Van Schaeybroeck, Manuel Salto-Tellez, John F. Graf, Markus Rehm, Mark Lawler, Daniel B. Longley, Fiona Ginty Fiona, Jochen H. Prehn. Exploratory multiplex tissue image analysis of the impact of heterogeneity in the microenvironment of primary colorectal cancer on apoptosis markers in patients [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-088.

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.

Colorectal cancer (CRC) is the third and second most commonly diagnosed cancer in males and females, and the second most common cause of cancer-related deaths in the developed world. Identifying the importance of epidermal growth factor (EGF) signalling pathways for the survival of CRC cells (Van Cutsem, Kohne et al. 2011) resulted in development of therapies that target EGF-receptors (EGFR). Late stage CRC patients are mainly treated with monoclonal anti-EGFR antibodies such as cetuximab. Although anti-EGFR therapies have significantly improved survival in CRC patients (Van Cutsem, Kohne et al. 2009), they are not effective in patients with activating KRAS, BRAF, NRAS and PI3KCA mutations (De Roock, Claes et al. 2010). Nevertheless, between 50-60% of patients will not benefit from the additional anti-EGFR treatment, even when these have a quadruple wild-type status (De Roock, De Vriendt et al. 2011), suggesting that novel biomarkers and targeted therapies are required. In this study, we investigate potential biomarkers for cetuximab response in a panel of 93 quadruple wild-type, liver metastatic CRC samples from patient derived xenografts (PDX) with known responses to cetuximab. Matching PDXs were used to measure changes in tumour volumes post cetuximab treatment (Bertotti, Papp et al. 2015) and were later categorised as regressing, progressing or stabilised. Protein expression levels were measured in matched PDXs, using a reverse phase protein array (RPPA) with a panel of 72 antibodies targeting key cancer related proteins as previously described). Statistical analysis of measured values showed significant correlation between high protein expression levels, such as Chk-1, PARP, HIAP-2 (cIAP-1), and PDX progression post cetuximab treatment. These proteins were significantly expressed lower in both regressing and stable PDXs. Interestingly, we found a high cross correlation between expression levels of these proteins across all the samples, giving them a great potential to act as predictive biomarkers for cetuximab response. Bioinformatics pathway enrichment of these proteins showed a significant enrichment of intrinsic apoptotic and cell cycle signalling pathways. These results have also been investigated in publically available patient data with cetuximab response and together will be used to construct a deterministic mathematical cell cycle model that will help to predict the outcome of cetuximab therapy in future. Citation Format: Naser Monsefi, Robert O’Byrne, Steven Carberry, Eugenia R. Zanella, Ana Barat, Mattia Cremona, Clare Morgan, Bryan T. Hennessy, Andrea Bertotti, Livio Trusolino, Jochen H.M. Prehn. Investigating potential molecular biomarkers for cetuximab response in metastatic colorectal cancer tissue using reverse phase protein array. [abstract]. In: Proceedings of the 107th Annual Meeting of the American Association for Cancer Research; 2016 Apr 16-20; New Orleans, LA. Philadelphia (PA): AACR; Cancer Res 2016;76(14 Suppl):Abstract nr LB-354.