Molecular classification of colorectal cancer

Colorectal cancer (CRC) is a heterogeneous disease with several clinical, pathological, and molecular presentations. A comprehensive and unifying molecular classification would be useful for genotypephenotype correlations, to better understand disease progression, and to predict responses to treatment. Such a classification would be helpful for quickly and efficiently translating results from the laboratory to the clinic and closing the gap between research breakthroughs and actually implementing them clinically. In November 2015, an international consortium consisting of six expert groups published the first consensus on molecular subtypes of colorectal cancer, by bringing together six previously published CRC classifications.peer-reviewe

Acquired and Intrinsic Resistance to Colorectal Cancer Treatment

First line therapy for colorectal cancer (CRC) is usually fluoropyrimidine monotherapy and oxaliplatin, or irinotecan-based therapy. Additionally, targeted therapies such as bevacizumab, aflibercept, ramucirumab, regorafenib, cetuximab and panitumumab are indicated in combination with chemotherapy in metastatic CRC. Resistance of CRC to treatment is the principal rationale for treatment failure. Resistance can be intrinsic (primary resistance) or acquired (secondary resistance). Here, we discuss the classical model of resistance, which focuses primarily on mechanisms involving alterations in drug metabolism, increased drug efflux, secondary mutations in drug targets, inactivation of apoptotic pathways, p53 and DNA damage repair. Other resistance mechanisms, including the Warburg effect, cancer stem cells, intra-tumor heterogeneity and pharmacoepigenomic mechanisms will also be discussed. We conclude the chapter with a systems medicine approach to predict response to treatment for the discovery and validation of predictive biomarkers that are urgently needed

The landscape of genomic copy number alterations in colorectal cancer and their consequences on gene expression levels and disease outcome

This work has been supported by the Instituto de Salud Carlos III and co-funded by the European Regional Development Fund (ERDF) [CP13/00160, PI14/00783, PI17/01304 to JC]; the Agència de Gestió d'Ajuts Universitaris i de Recerca, Generalitat de Catalunya [2017 SGR 1035]; PERIS Generalitat de Catalunya [SLT002/16/00398 to JC]; Fundación Científica de la Asociación Española Contra el Cáncer [GCB13131592CAST]; the intramural program of the National Institutes of Health. CIBEREHD is funded by the Instituto de Salud Carlos III. This article is based upon work from COST Action [CA17118], supported by COST (European Cooperation in Science and Technology). RB is supported by a REACH HIGH Scholars Programme-PostDoctoral Grants. The grant is part-financed by the EU, Operational Programme II-Cohesion Policy 2014–2020 investing in human capital to create more opportunities and promote the wellbeing of society-European Social Fund.Aneuploidy, the unbalanced state of the chromosome content, represents a hallmark of most solid tumors, including colorectal cancer. Such aneuploidies result in tumor specific genomic imbalances, which emerge in premalignant precursor lesions. Moreover, increasing levels of chromosomal instability have been observed in adenocarcinomas and are maintained in distant metastases. A number of studies have systematically integrated copy number alterations with gene expression changes in primary carcinomas, cell lines, and experimental models of aneuploidy. In fact, chromosomal aneuploidies target a number of genes conferring a selective advantage for the metabolism of the cancer cell. Copy number alterations not only have a positive correlation with expression changes of the majority of genes on the altered genomic segment, but also have effects on the transcriptional levels of genes genome-wide. Finally, copy number alterations have been associated with disease outcome; nevertheless, the translational applicability in clinical practice requires further studies. Here, we (i) review the spectrum of genetic alterations that lead to colorectal cancer, (ii) describe the most frequent copy number alterations at different stages of colorectal carcinogenesis, (iii) exemplify their positive correlation with gene expression levels, and (iv) discuss copy number alterations that are potentially involved in disease outcome of individual patients.Publisher PDFPeer reviewe

Multiscale genomic, transcriptomic and proteomic analysis of colorectal cancer cell lines to identify novel biomarkers

Introduction: Resistance to colorectal cancer (CRC) therapies is a significant cause of treatment failure. We used an in vitro model to identify novel therapeutic targets, explain mechanisms of carcinogenesis and resistance to therapy, and ultimately aid patient stratification for therapy. Methods: A panel of 15 CRC cell lines was profiled by comparative genomic hybridisation, gene expression profiling, reverse phase protein array analysis, and chemosensitivity assays with respect to 5fluorouracil, oxaliplatin, and BEZ235. As proof of concept, fluorescence in situ hybridization and automated quantitative protein analysis were employed to investigate a candidate biomarker in a CRC patient cohort (n=n8).peer-reviewe

Multi-scale genomic, transcriptomic and proteomic analysis of colorectal cancer cell lines to identify novel biomarkers

This work was partially funded by the Strategic Educational Pathways Scholarship (Malta). The scholarship is part-financed by the European Union – European Social Fund (ESF) under Operational Programme II – Cohesion Policy 2007-2013, “Empowering People for More Jobs and a Better Quality of Life”. This project was additionally funded by Medical Research Scotland.Selecting colorectal cancer (CRC) patients likely to respond to therapy remains a clinical challenge. The objectives of this study were to establish which genes were differentially expressed with respect to treatment sensitivity and relate this to copy number in a panel of 15 CRC cell lines. Copy number variations of the identified genes were assessed in a cohort of CRCs. IC50’s were measured for 5-fluorouracil, oxaliplatin, and BEZ-235, a PI3K/mTOR inhibitor. Cell lines were profiled using array comparative genomic hybridisation, Illumina gene expression analysis, reverse phase protein arrays, and targeted sequencing of KRAS hotspot mutations. Frequent gains were observed at 2p, 3q, 5p, 7p, 7q, 8q, 12p, 13q, 14q, and 17q and losses at 2q, 3p, 5q, 8p, 9p, 9q, 14q, 18q, and 20p. Frequently gained regions contained EGFR, PIK3CA, MYC, SMO, TRIB1, FZD1, and BRCA2, while frequently lost regions contained FHIT and MACROD2. TRIB1 was selected for further study. Gene enrichment analysis showed that differentially expressed genes with respect to treatment response were involved in Wnt signalling, EGF receptor signalling, apoptosis, cell cycle, and angiogenesis. Stepwise integration of copy number and gene expression data yielded 47 candidate genes that were significantly correlated. PDCD6 was differentially expressed in all three treatment responses. Tissue microarrays were constructed for a cohort of 118 CRC patients and TRIB1 and MYC amplifications were measured using fluorescence in situ hybridisation. TRIB1 and MYC were amplified in 14.5% and 7.4% of the cohort, respectively, and these amplifications were significantly correlated (p≤0.0001). TRIB1 protein expression in the patient cohort was significantly correlated with pERK, Akt, and Caspase 3 expression. In conclusion, a set of candidate predictive biomarkers for 5-fluorouracil, oxaliplatin, and BEZ235 are described that warrant further study. Amplification of the putative oncogene TRIB1 has been described for the first time in a cohort of CRC patients.Publisher PDFPeer reviewe

Towards functional multiscale analysis of colorectal cancer

Background: The five year overall survival rate for colorectal cancer (CRC) patients varies between 38.8% and 59.9%. Selecting patients who are likely to respond to therapy remains a clinical and pathological challenge, hence the need for predictive and prognostic biomarkers. The objectives of this study were: 1) to establish which genes were differentially expressed with respect to sensitivity to treatment, 2) to integrate the list of differentially expressed genes with copy number to systematically identify predictive biomarkers, and 3) to establish which genes are commonly gained in the panel of CRC cell lines. As proof of concept of the approach the copy number variations of the identified genes were assessed in a cohort of Dukes’ A and B cancers, in order to analyse the likelihood of these genes acting as useful biomarkers. Methods: Cell viability assays were carried out on a panel 15 CRC cell lines. IC50s were measured for 5-fluoruracil (5-FU), oxaliplatin (L-OHP), and BEZ-235, a PI3K/mTOR inhibitor. We carried out a systematic array-based survey of gene expression and copy number variation in CRC cell lines, and compared these to responses to different treatments. Cell lines were profiled using array comparative genomic hybridisation (aCGH; NimbleGen 135k), Illumina gene expression analysis, reverse phase protein arrays (RPPA), and targeted sequencing of KRAS hotspot mutations. The associations between the biological variables and drug sensitivity were assessed using correlation coefficients, chi-square analysis, and the Mann Whitney-U test. Tissue microarrays (TMA) were constructed for a cohort of CRC patients (n=118) and TRIB1 and MYC amplifications were measured using fluorescence in situ hybridisation (FISH). The protein expression for trib1 and 14 associated biomarkers were investigated using Automated Quantitative Analysis (AQUA) and analysed using the Pearson’s correlation coefficient. Results: Twenty-three regions were frequently gained, and fourteen regions were lost across the cell line panel. Gains were observed at 2p, 3q, 5p, 7p, 7q, 8q, 12p, 13q, 14q, and 17q, and losses at 2q, 3p, 5q, 8p, 9p, 9q, 14q, 18q, and 20p. Frequently gained regions contained EGFR, PIK3CA, MYC, SMO, TRIB1, FZD1, and BRCA2, while frequently lost regions contained FHIT and MACROD2. Gene enrichment analysis showed that differentially expressed genes with respect to treatment response were involved in Wnt signalling, EGF receptor signalling, apoptosis, cell cycle, and angiogenesis. Stepwise integration of copy number and gene expression data yielded 47 candidate genes that were significantly correlated (corrected p-value ≤0.05). Differentially expressed genes common to all three treatment responses included AEBP2, DDX56, MRPL32, MRPS17, MYC, NSMCE2, and TBRG4. TRIB1 (n=76) and MYC (n=81) were amplified (FISH score ≥1.8) in 14.5% and 7.4% of the CRC cohort, respectively. TRIB1 and MYC amplifications were significantly correlated (corrected p-value ≤ 0.0001). Trib1 protein expression in the patient cohort was significantly correlated (corrected p-value ≤ 0.01) with protein expression of pErk, Akt, and Caspase 3. Conclusions: The CRC in-vitro model was used effectively in this study for discovery of both predictive and prognostic biomarkers. A set of candidate predictive biomarkers for 5-FU, L-OHP, and BEZ235 have been described, worthy of further study. Amplification of the putative oncogene TRIB1 has been assessed for the first time in a cohort of CRC patients. Inhibition of TRIB1 may be a synthetic lethal approach when MYC amplifications are present, which requires further clinical and experimental validation

Hierarchical clustering using the genomic segmentations of the 15 CRC cell lines.

<p>Hierarchical clustering using the genomic segmentations of the 15 CRC cell lines.</p

Unsupervised hierarchical clustering for the 47 candidate genes annotated according to response to therapy.

<p>Unsupervised hierarchical clustering for the 47 candidate genes annotated according to response to therapy.</p

Unsupervised hierarchical clustering of RPPA protein expression data using Euclidian distance with average linkage.

<p>Unsupervised hierarchical clustering of RPPA protein expression data using Euclidian distance with average linkage.</p

Spearman’s correlation network using Bonferroni Correction (p = 0.05) and circular network layout (http://www.tmanavigator.org/).

<p>Abbreviations: N—nucleus, C—cytoplasm.</p