Decisiones y sus consecuencias

The present study aimed to characterize the landscape of somatic mutations and copy number variations of MCL to identify driver genes and molecular mechanisms that are involved in development and progression of MCL. Identification of new driver genes and pathways can lead to the development of better stratifications and prognosis markers and can introduce novel targets of therapies in MCL. By detecting novel molecular mechanism and targets of genetic alterations, we might be able to suggest potential biomarker and novel targets for therapies. In this study, we used frontline bioinformatics tools and datasets to detected somatic alterations in 67 MCL cases and to analyze the functional impact of

the mutations on the protein to distinguish passenger mutations from drivers. We merged a new cohort of 24 MCL patients with two previously published cohorts. By using a cohesive set of algorithms for analysing the larger samples size of exome sequencing data, we discovered novel driver mutations and genes that were missing from the previous exome sequencing studies possibly because of the low frequency of these mutations in MCL, low sample size of the other studies and un-appreciation for noncoding and silent mutations. Our integrated analysis of somatic SNVs, CNVs and altered pathways in MCL, revealed the significant rate of mutations in pathways that regulate malignant B-cell interactions with TME and four key cellular functions namely immune surveillance, cell migration and invasion, transcription regulation, inflammatory responses and cell survival. One of the most significant findings to emerge from this study is a high number of mutations in pathways with roles in the regulation of cell adhesion, invasion, and migrations such as focal adhesion, ECM-receptor interactions, Rho GTPase, GPCR, phospholipid metabolism in MCL. We suggest that these novel driver pathways are the underlying causes of the widespread dissemination of MCL and the aggressive behavior of this disease. Furthermore, the present study is the first to identify the activating impact of the noncoding and silent hotspot mutations of MAP3K14 and HNRNPH1 in MCL and other types of lymphoma. In addition, our findings suggest several driver genes as valuable candidates for the development of diagnostic and prognostic biomarkers in MCL and other types of lymphoma. The findings of this research provided insights for a better understanding of the genetic alterations, driver genes and driver pathways that are the underlying mechanisms of progression, invasion, and metastasis of MCL. Taken together, these findings have significant implications for the development of new therapeutic approaches and prognosis biomarkers. Moreover, the scope of this study was limited in terms of access to the WES data for CNV calling for the cohort-Z and cohort-B. Because of the low number of available cases (24) for CNV analysis, employing statistical approaches for detection of targets of recurrent CNVs was not the optimal approach. The available algorithms require minimum sample sizes to generate reliable results that were larger than our sample size. Lack of expression data was another weakness of our study. Gene expression data is a valuable source of information that can facilitate accurate

identification of targets of recurrent CNVs. However, integrated analysis of recurrent SNVs, CNVs and mutated pathways, remarkably increased the accuracy of analysis and produced results that were in line with findings of gene-expression based studies and lead to the discovery of novel genes as well as most of the previously known targets of CNVs in MCL. Another source of weakness in this study was using bioinformatics algorithms that were limited to the identification of significant genes based truncating mutations. Our research showed that a large number of mutated genes in our cases of MCL were harboring noncoding and silent mutations close to the splicing regions. This finding implicates that driver genes are remaining unnoticed because of lack of appreciation of noncoding and silent mutations. One of the strengths of this study was using multiple approaches and bioinformatics algorithms for analyzing CNVs and SNVs, and integrating SNV and CNV data for detecting the driver genes. The second strength of this project was combining three WES datasets that were prepared in different conditions. Using a larger sample size helped us to detect mutations that occur with lower frequency. In addition, by using three different datasets and comparing the list of variants obtained from each cohort, we could distinguish false positives and technical artifacts that were specific to individual cohorts. Another strength of the study was using publically available datasets of somatic variants to collect mutations that were detected previously in our genes of interest in other cancers. This approach assists analysis of mutations and their functions by extending the list of mutations in the gene of interest. Using this method, we discovered oncogenic driver mutations (G33G, G53G) of MAP3K14 that were detected by previous sequencing-based studies but their significance and recurrence are being overlooked because of the predicted lack of effect on the protein. In addition, the focus of sequencing-based studies is mainly on coding and non-synonymous mutations the challenges in predicting the function of silent and noncoding mutations. As a result, most of the data available in the cancer mutation databases such as COSMIC are lacking synonymous and noncoding mutations (Parry et al. 2013; Jenny Zhang et al. 2014). For instance, we found G33G and G53G mutations in five patients, four of which were already studied by Zhang et al. (2014) and Bea et al. (2013); However, neither of the studies reported silent and noncoding mutations (Beà et al. 2013; Jenny Zhang et al. 2014) and we are the first study that identified the significance of

recurrent G33G and G53G mutations and the recurrence of mutations (G33G, G53G, A67G and A84G) in the first exon of MAP3K14. Therefore, it is likely that the silent mutations of G33G and G53G are more frequent in lymphoma than what was estimated in the present study and mutations might be occurring in a wider range of B-cell lymphomas. In addition, the sequencing read depth for MAP3K14 was low in our dataset and an additional novel MCL cohort available in Morin lab. For this reason, a further targeted sequencing study could assess the occurrence of MAP3K14 mutations in MCL and other types of lymphoma associated with activation of NF-kB signaling such as ABC-DLBCL. MM, CLL, SMZL, and WM have similar biology to MCL, and constitutive activation of NF-kB signaling is one the key events in the biology of these lymphomas. It would also be necessary to assess the impact of silent and not-synonymous mutations of MCL on regulatory motifs. Based on our results, we expect a large number of driver genes to be identified in MCL by this analysis. Further investigation and experimentation into the gene expression profile of the candidate genes are recommended.

Finally, functional studies of the novel driver mutations, genes and pathways are necessary to confirm the function of mutations in the protein and the driving role of the genes in the development of lymphoma. The impact of clusters of mutations of MAP3K14 on splicing can be confirmed by RNA-sequencing of the affected cases. It is also interesting to study the impact of the four clusters of mutations of MAP3K14 in living cells and animal models. In the discussion, we hypothesized that mutations of G53G and K54N might have a different mechanism of function than the rest of hotspot mutations of MAP3K14. The mutational hotspots in MAP3K14 might have a great value in the development of diagnostic and prognostic biomarkers for the leukemic subtype of MCL with poor outcome. Further research on these mutations would improve the understanding of NIK functions and might result in the identification of the unknown partners and novel motifs involved in regulation of NIK stability. Finally, our results showed a translocation in the breakpoint between recurrent gains and deletions of 10p. We hypothesize that analyzing the breakpoints of recurrent CNVs and recurrent noncoding mutations, will result in the identification of more structural variants in MCL.

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