CRISPR/dCAS9-mediated DNA demethylation screen identifies functional epigenetic determinants of colorectal cancer.

BACKGROUND: Promoter hypermethylation of tumour suppressor genes is frequently observed during the malignant transformation of colorectal cancer (CRC). However, whether this epigenetic mechanism is functional in cancer or is a mere consequence of the carcinogenic process remains to be elucidated. RESULTS: In this work, we performed an integrative multi-omic approach to identify gene candidates with strong correlations between DNA methylation and gene expression in human CRC samples and a set of 8 colon cancer cell lines. As a proof of concept, we combined recent CRISPR-Cas9 epigenome editing tools (dCas9-TET1, dCas9-TET-IM) with a customized arrayed gRNA library to modulate the DNA methylation status of 56 promoters previously linked with strong epigenetic repression in CRC, and we monitored the potential functional consequences of this DNA methylation loss by means of a high-content cell proliferation screen. Overall, the epigenetic modulation of most of these DNA methylated regions had a mild impact on the reactivation of gene expression and on the viability of cancer cells. Interestingly, we found that epigenetic reactivation of RSPO2 in the tumour context was associated with a significant impairment in cell proliferation in p53-/- cancer cell lines, and further validation with human samples demonstrated that the epigenetic silencing of RSPO2 is a mid-late event in the adenoma to carcinoma sequence. CONCLUSIONS: These results highlight the potential role of DNA methylation as a driver mechanism of CRC and paves the way for the identification of novel therapeutic windows based on the epigenetic reactivation of certain tumour suppressor genes.

RFcaller: a machine learning approach combined with read-level features to detect somatic mutations.

The cost reduction in sequencing and the extensive genomic characterization of a wide variety of cancers are expanding tumor sequencing to a wide number of research groups and the clinical practice. Although specific pipelines have been generated for the identification of somatic mutations, their results usually differ considerably, and a common approach is to use several callers to achieve a more reliable set of mutations. This procedure is computationally expensive and time-consuming, and it suffers from the same limitations in sensitivity and specificity as other approaches. Expert revision of mutant calls is therefore required to verify calls that might be used for clinical diagnosis. This step could take advantage of machine learning techniques, as they provide a useful approach to incorporate expert-reviewed information for the identification of somatic mutations. Here we present RFcaller, a pipeline based on machine learning algorithms, for the detection of somatic mutations in tumor-normal paired samples that does not require large computing resources. RFcaller shows high accuracy for the detection of substitutions and insertions/deletions from whole genome or exome data. It allows the detection of mutations in driver genes missed by other approaches, and has been validated by comparison to deep and Sanger sequencing.

The Oncogenic FOXL2 C134W Mutation Is a Key Driver of Granulosa Cell Tumors.

Adult-type granulosa cell tumors (AGCT) are the most common type of malignant ovarian sex cord-stromal tumors. Most AGCTs carry the somatic variant c.402C>G (p.C134W) affecting the transcription factor FOXL2. Germline dominant variants in FOXL2 are responsible for blepharophimosis syndrome, which is characterized by underdevelopment of the eyelid. In this work, we generated a mouse model harboring the C134W variant of FOXL2 to evaluate in vivo the poorly understood oncogenic role of FOXL2. The mutation was dominant regarding eyelid hypoplasia, reminiscent of blepharophimosis syndrome. Interestingly, Foxl2+/C134W female mice had reduced fertility and developed AGCTs through a progression from abnormal ovaries with aberrant granulosa cells to ovaries with stromal hyperplasia and atypia and on to tumors in adut mice. The genes dysregulated in mouse AGCTs exhibited the hallmarks of cancer and were consistent with a gain-of-function of the mutated allele affecting TGFß signaling. A comparison of these data with previous results on human AGCTs indicated similar deregulated pathways. Finally, a mutational analysis of mouse AGCT transcriptomic data suggested the absence of additional driver mutations apart from FOXL2-C134W. These results provide a clear in vivo example in which a single mutational hit triggers tumor development associated with profound transcriptomic alterations. SIGNIFICANCE: A newly generated mouse model carrying a FOXL2 mutation characteristic of adult-type granulosa cell tumors shows that FOXL2 C134W shifts the transcriptome towards a signature of granulosa cell cancer and drives tumorigenesis.

Molecular map of chronic lymphocytic leukemia and its impact on outcome.

Recent advances in cancer characterization have consistently revealed marked heterogeneity, impeding the completion of integrated molecular and clinical maps for each malignancy. Here, we focus on chronic lymphocytic leukemia (CLL), a B cell neoplasm with variable natural history that is conventionally categorized into two subtypes distinguished by extent of somatic mutations in the heavy-chain variable region of immunoglobulin genes (IGHV). To build the 'CLL map,' we integrated genomic, transcriptomic and epigenomic data from 1,148 patients. We identified 202 candidate genetic drivers of CLL (109 new) and refined the characterization of IGHV subtypes, which revealed distinct genomic landscapes and leukemogenic trajectories. Discovery of new gene expression subtypes further subcategorized this neoplasm and proved to be independent prognostic factors. Clinical outcomes were associated with a combination of genetic, epigenetic and gene expression features, further advancing our prognostic paradigm. Overall, this work reveals fresh insights into CLL oncogenesis and prognostication.

PanCancer analysis of somatic mutations in repetitive regions reveals recurrent mutations in snRNA U2.

Current somatic mutation callers are biased against repetitive regions, preventing the identification of potential driver alterations in these loci. We developed a mutation caller for repetitive regions, and applied it to study repetitive non protein-coding genes in more than 2200 whole-genome cases. We identified a recurrent mutation at position c.28 in the gene encoding the snRNA U2. This mutation is present in B-cell derived tumors, as well as in prostate and pancreatic cancer, suggesting U2 c.28 constitutes a driver candidate associated with worse prognosis. We showed that the GRCh37 reference genome is incomplete, lacking the U2 cluster in chromosome 17, preventing the identification of mutations in this gene. Furthermore, the 5'-flanking region of WDR74, previously described as frequently mutated in cancer, constitutes a functional copy of U2. These data reinforce the relevance of non-coding mutations in cancer, and highlight current challenges of cancer genomic research in characterizing mutations affecting repetitive genes.

Integrative methylome-transcriptome analysis unravels cancer cell vulnerabilities in infant MLL-rearranged B cell acute lymphoblastic leukemia.

B cell acute lymphoblastic leukemia (B-ALL) is the most common childhood cancer. As predicted by its prenatal origin, infant B-ALL (iB-ALL) shows an exceptionally silent DNA mutational landscape, suggesting that alternative epigenetic mechanisms may substantially contribute to its leukemogenesis. Here, we have integrated genome-wide DNA methylome and transcriptome data from 69 patients with de novo MLL-rearranged leukemia (MLLr) and non-MLLr iB-ALL leukemia uniformly treated according to the Interfant-99/06 protocol. iB-ALL methylome signatures display a plethora of common and specific alterations associated with chromatin states related to enhancer and transcriptional control in normal hematopoietic cells. DNA methylation, gene expression, and gene coexpression network analyses segregated MLLr away from non-MLLr iB-ALL and identified a coordinated and enriched expression of the AP-1 complex members FOS and JUN and RUNX factors in MLLr iB-ALL, consistent with the significant enrichment of hypomethylated CpGs in these genes. Integrative methylome-transcriptome analysis identified consistent cancer cell vulnerabilities, revealed a robust iB-ALL-specific gene expression-correlating dmCpG signature, and confirmed an epigenetic control of AP-1 and RUNX members in reshaping the molecular network of MLLr iB-ALL. Finally, pharmacological inhibition or functional ablation of AP-1 dramatically impaired MLLr-leukemic growth in vitro and in vivo using MLLr-iB-ALL patient-derived xenografts, providing rationale for new therapeutic avenues in MLLr-iB-ALL.