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Item does not contain fulltext, BACKGROUND: Here we present updated survival of the CAIRO2 trial and assessed whether the addition of anti-EGFR to anti-VEGF therapy could still be an effective treatment option for patients with extended RAS/BRAF wildtype and left-sided metastatic colorectal cancer (mCRC). MATERIALS AND METHODS: Retrospective updated survival and extended RAS and BRAF V600E mutational analysis were performed in the CAIRO2 trial, a multicenter, randomized phase III trial on the effect of adding cetuximab to a combination of capecitabine, oxaliplatin (CAPOX), and bevacizumab in mCRC. RESULTS: Updated survival analysis confirmed that the addition of cetuximab did not provide a benefit on either progression free (PFS) or overall survival (OS) in the intention-to-treat population. With the extended mutational analyses 31 KRAS, 31 NRAS and 12 BRAF V600E additional mutations were found. No benefit of the addition of cetuximab was observed within the extended wildtype group, even when selecting only left-sided tumors (PFS HR 0.96, p = 0.7775). However, compared to the original trial an increase of 6.5 months was seen for patients with both extended wildtype and left-sided tumors (median OS 28.6 months). CONCLUSION: Adding cetuximab to CAPOX and bevacizumab does not provide clinical benefit in patients with mCRC, even in the extended wildtype group with left-sided tumors. However, in the extended wildtype group we did observe clinically relevant higher survival compared to the initial trial report, indicating that it is important to analyze a broader panel of RAS and BRAF variants using more recent sequencing techniques when assessing survival benefit after anti-EGFR therapy.

Contains fulltext : 283537.pdf (Publisher’s version ) (Open Access), Previously, colorectal cancer (CRC) has been classified into four distinct molecular subtypes based on transcriptome data. These consensus molecular subtypes (CMSs) have implications for our understanding of tumor heterogeneity and the prognosis of patients. So far, this classification has been based on the use of messenger RNAs (mRNAs), although microRNAs (miRNAs) have also been shown to play a role in tumor heterogeneity and biological differences between CMSs. In contrast to mRNAs, miRNAs have a smaller size and increased stability, facilitating their detection. Therefore, we built a miRNA-based CMS classifier by converting the existing mRNA-based CMS classification using machine learning (training dataset of n = 271). The performance of this miRNA-assigned CMS classifier (CMS-miRaCl) was evaluated in several datasets, achieving an overall accuracy of ~ 0.72 (0.6329-0.7987) in the largest dataset (n = 158). To gain insight into the biological relevance of CMS-miRaCl, we evaluated the most important features in the classifier. We found that miRNAs previously reported to be relevant in microsatellite-instable CRCs or Wnt signaling were important features for CMS-miRaCl. Following further studies to validate its robustness, this miRNA-based alternative might simplify the implementation of CMS classification in clinical workflows.