Abnormal DNA-methylation is well known to play an important role in cancer onset and development, and colon cancer is no exception to this rule. Recent years have seen the increased use of large-scale technologies, (such as methylation microarray assays or specific sequencing of methylated DNA), to determine whole genome profiles of CpG island methylation in tissue samples. Comprehensive study of methylation array data from transcriptome high-throughput platforms permits determination of gene methylation markers, important for cancer profiling. Here, three large-scale methylation datasets for colon cancer have been compared to determine locus-specific methylation agreement. These data are from the GEO database, where colon cancer and apparently healthy adjacent tissues are represented by sample sizes 125 and 29 respectively in the first dataset, 24 of each in the second and 118 of each in the third. Several data analysis techniques have been employed, including Clustering, Discriminant Principal Component Analysis, Discriminant Analysis and ROC curves, in order (i) to obtain a better insight on the locus-specific concomitant methylation structures for these diverse data and (ii) to determine a robust potential marker set for indicative screening, drawn from all data taken together. The extent of the agreement between the analysed datasets is reported. Further, potential screening methylation markers, for which methylation profiles are consistent across tissue samples and several datasets, are highlighted and discussed.

Recently, considerable attention has been paid to gene expression-based classifications of colorectal cancers (CRC) and their association with patient prognosis. In addition to changes in gene expression, abnormal DNA-methylation is known to play an important role in cancer onset and development, and colon cancer is no exception to this rule. Large-scale technologies, such as methylation microarray assays and specific sequencing of methylated DNA, have been used to determine whole genome profiles of CpG island methylation in tissue samples. In this article, publicly available microarray-based gene expression and methylation data sets are used to characterize expression subtypes with respect to locus-specific methylation. A major objective was to determine whether integration of these data types improves previously characterized subtypes, or provides evidence for additional subtypes. We used unsupervised clustering techniques to determine methylation-based subgroups, which are subsequently annotated with three published expression-based classifications, comprising from three to six subtypes. Our results showed that, while methylation profiles provide a further basis for segregation of certain (Inflammatory and Goblet-like) finer-grained expression-based subtypes, they also suggest that other finer-grained subtypes are not distinctive and can be considered as a single subtype.

3580 Background: Somatic copy number alterations (SCNA) are genomic alterations evident in cancers including mCRC. These alterations support biomarker discovery, allowing identification of variants that can be used to predict response to therapy. Herein, we studied the impact of SCNAs on mCRC patient response to bevacizumab (BVZ). Methods: SCNA data was assembled from mCRC tumors in the TCGA cohort (n = 676), from the CAIRO 2 study [n = 143] and from the ANGIOPREDICT cohort (n = 258) [Betge et al. Digestion 2016 94(3):129-137]. GISTIC v2.0 was used to identify the most frequent and overrepresented chromosomal aberrations. A region was considered deleted if the logR value was < 0.1 and amplified when the logR was > 0.1. A cutoff q-value of 0.25 was used to select significantly overrepresented SCNAs. To further explore the impact of new ANGIOPREDICT clusters and consensus molecular subtypes (CMS), [Guinney J et al Nat Med. 2015 21(11):1350-6], a panel of seven xenografts representing each CMS subtype was treated with FOLFOX (40mg/kg 5-FU, 13.4mg/kg folinic acid & 2.4mg/kg oxaliplatin) + B20 antibody (mouse avastin, 10mg/kg) for 4 weeks. Results: Unsupervised hierarchical clustering classified all 1077 tumors into 3 consensus SCNA subgroups termed ‘ANGIOPREDICT’ clusters 1-3. Concordance between CMS and ANGIOPREDICT clusters was evident: CMS1 - Cluster 1 78%, CMS3 - Cluster 1 50%. CMS2 - Cluster 2/3 92% and CMS4 - Cluster 2/3 84%. Tumors with intermediate or high copy number instability (ANGIOPREDICT cluster 2 & 3) showed improved progression free survival (PFS): 369d vs. 227d p = 1.36e^-7) whereas tumors with low copy number alterations displayed a poor response to BVZ therapy (PFS: 147d vs 152d p = 0.906). Xenografts corresponding to CMS 2&4/ ANGIOPREDICT cluster 2-3 showed improved response to FOLFOX + B20 therapy and increased PFS (p < 0.001) vs. CMS1&3/ ANGIOPREDICT cluster 1. Conclusions: For the first time, in a large test and validation cohort including retrospectively collected and randomized controlled trial patient samples, we have identified a predictive genomic biomarker for BVZ therapy in mCRC. We have further demonstrated the utility of CMS subtyping to stratify patients to BVZ .

Recurrent chromosomal translocations are found in many blood and solid cancers. Balanced translocations, frequent in lymphoid malignancies, lead to the formation of two aberrant derivative (der) chromosomes. This event often leads to overexpression of an oncogene. In many cases, the expression of an oncogene is not enough to produce a malignant phenotype; however, most part of the studies focus on the events involving the chromosome where the oncogene is located, but rarely the other der chromosome where other oncogenic alterations may potentially arise. Mantle cell lymphoma (MCL), an aggressive B-cell non-Hodgkin lymphoma, is a perfect example of this. In 85% of the cases, it is characterized by the translocation t(11;14), which leads to the overexpression of cyclin D1 (CCND1) gene which results juxtaposed to the immunoglobulin heavy chain (IGH) gene on the der14 chromosome. This feature alone is not sufficient to induce oncogenesis. Here we focused on the der11 chromosome. We demonstrated that expression of 88 genes located in a 15mb region close to the translocation breakpoint on the der11 was deregulated both in the GRANTA-519 MCL cell line and in B-cells from MCL patients. We found that a large segment of der11containing deregulated genes was relocated from its normal position in the nuclear periphery towards the center of the nucleus in close proximity to the nucleolus where the abundant nucleolar protein nucleolin binds a subset of genes located close to the breakpoint and activates their expression. This finding allowed to identify new potential oncogenes involved in MCL and the mechanisms of their upregulation.