Less is more in the hunt for driver mutations

Unified views on variant impact across many diseases

Genomic studies of human disorders are often performed by distinct research communities (i.e., focused on rare diseases, common diseases, or cancer). Despite underlying differences in the mechanistic origin of different disease categories, these studies share the goal of identifying causal genomic events that are critical for the clinical manifestation of the disease phenotype. Moreover, these studies face common challenges, including understanding the complex genetic architecture of the disease, deciphering the impact of variants on multiple scales, and interpreting noncoding mutations. Here, we highlight these challenges in depth and argue that properly addressing them will require a more unified vocabulary and approach across disease communities. Toward this goal, we present a unified perspective on relating variant impact to various genomic disorders.

Reliability of BRAF mutation detection using plasma sample: A systematic review and meta-analysis

BACKGROUND

Testing of B-Raf proto-oncogene (BRAF) mutation in tumor is necessary before targeted therapies are given. When tumor samples are not available, plasma samples are commonly used for the testing of BRAF mutation. The aim of this study was to investigate the diagnostic accuracy of BRAF mutation testing using plasma sample of cancer patients.

METHODS

Databases of Pubmed, Embase, and Cochrane Library were searched for eligible studies investigating BRAF mutation in paired tissue and plasma samples of cancer patients. A total of 798 publications were identified after database searching. After removing 229 duplicated publications, 569 studies were screened using the following exclusion criteria: (1) BRAF mutation not measured in plasma or in tumor sample; (2) lacking BRAF-wildtype or BRAF-mutated samples; (3) tissue and plasma samples not paired; (4) lacking tumor or plasma samples; (5) not plasma sample; (6) not cancer; (7) un-interpretable data. Accuracy data and relevant information were extracted from each eligible study by 2 independent researchers and analyzed using statistical software.

RESULTS

After pooling the accuracy data from 3943 patients of the 53 eligible studies, the pooled sensitivity, specificity, and diagnostic odds ratio of BRAF mutation testing using plasma sample were 69%, 98%, and 55.78, respectively. Area under curve of summary receiver operating characteristic curve was 0.9435. Subgroup analysis indicated that BRAF mutation testing using plasma had overall higher accuracy (diagnostic odds ratio of 89.17) in colorectal cancer, compared to melanoma and thyroid carcinoma. In addition, next-generation sequencing had an overall higher accuracy in detecting BRAF mutation using plasma sample (diagnostic odds ratio of 63.90), compared to digital polymerase chain reaction (PCR) and conventional PCR, while digital PCR showed the highest sensitivity (74%) among the 3 techniques.

CONCLUSION

BRAF testing using plasma sample showed an overall high accuracy compared to paired tumor tissue sample, which could be used for cancer genotyping when tissue sample is not available. Large prospective studies are needed to further investigate the accuracy of BRAF mutation testing in plasma sample.

Passenger Mutations in More Than 2,500 Cancer Genomes: Overall Molecular Functional Impact and Consequences

The dichotomous model of "drivers" and "passengers" in cancer posits that only a few mutations in a tumor strongly affect its progression, with the remaining ones being inconsequential. Here, we leveraged the comprehensive variant dataset from the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) project to demonstrate that-in addition to the dichotomy of high- and low-impact variants-there is a third group of medium-impact putative passengers. Moreover, we also found that molecular impact correlates with subclonal architecture (i.e., early versus late mutations), and different signatures encode for mutations with divergent impact. Furthermore, we adapted an additive-effects model from complex-trait studies to show that the aggregated effect of putative passengers, including undetected weak drivers, provides significant additional power (∼12% additive variance) for predicting cancerous phenotypes, beyond PCAWG-identified driver mutations. Finally, this framework allowed us to estimate the frequency of potential weak-driver mutations in PCAWG samples lacking any well-characterized driver alterations.

Combined burden and functional impact tests for cancer driver discovery using DriverPower

The discovery of driver mutations is one of the key motivations for cancer genome sequencing. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumour types, we describe DriverPower, a software package that uses mutational burden and functional impact evidence to identify driver mutations in coding and non-coding sites within cancer whole genomes. Using a total of 1373 genomic features derived from public sources, DriverPower's background mutation model explains up to 93% of the regional variance in the mutation rate across multiple tumour types. By incorporating functional impact scores, we are able to further increase the accuracy of driver discovery. Testing across a collection of 2583 cancer genomes from the PCAWG project, DriverPower identifies 217 coding and 95 non-coding driver candidates. Comparing to six published methods used by the PCAWG Drivers and Functional Interpretation Working Group, DriverPower has the highest F1 score for both coding and non-coding driver discovery. This demonstrates that DriverPower is an effective framework for computational driver discovery.

Identification of Cancer Drivers at CTCF Insulators in 1,962 Whole Genomes

Recent studies have shown that mutations at non-coding elements, such as promoters and enhancers, can act as cancer drivers. However, an important class of non-coding elements, namely CTCF insulators, has been overlooked in the previous driver analyses. We used insulator annotations from CTCF and cohesin ChIA-PET and analyzed somatic mutations in 1,962 whole genomes from 21 cancer types. Using the heterogeneous patterns of transcription-factor-motif disruption, functional impact, and recurrence of mutations, we developed a computational method that revealed 21 insulators showing signals of positive selection. In particular, mutations in an insulator in multiple cancer types, including 16% of melanoma samples, are associated with TGFB1 up-regulation. Using CRISPR-Cas9, we find that alterations at two of the most frequently mutated regions in this insulator increase cell growth by 40%-50%, supporting the role of this boundary element as a cancer driver. Thus, our study reveals several CTCF insulators as putative cancer drivers.

Enhancer Remodeling and MicroRNA Alterations Are Associated with Acquired Resistance to ALK Inhibitors

Anaplastic lymphoma kinase (ALK) inhibitors are highly effective in patients with ALK fusion-positive lung cancer, but acquired resistance invariably emerges. Identification of secondary mutations has received considerable attention, but most cases cannot be explained by genetic causes alone, raising the possibility of epigenetic mechanisms in acquired drug resistance. Here, we investigated the dynamic changes in the transcriptome and enhancer landscape during development of acquired resistance to ALK inhibitors. Histone H3 lysine 27 acetylation (H3K27ac) was profoundly altered during acquisition of resistance, and enhancer remodeling induced expression changes in both miRNAs and mRNAs. Decreased H3K27ac levels and reduced miR-34a expression associated with the activation of target genes such as AXL. Panobinostat, a pan-histone deacetylase inhibitor, altered the H3K27ac profile and activated tumor-suppressor miRNAs such as miR-449, another member of the miR-34 family, and synergistically induced antiproliferative effects with ALK inhibitors on resistant cells, xenografts, and EML4-ALK transgenic mice. Paired analysis of patient samples before and after treatment with ALK inhibitors revealed that repression of miR-34a or miR-449a and activation of AXL were mutually exclusive of secondary mutations in ALK. Our findings indicate that enhancer remodeling and altered expression of miRNAs play key roles in cancer drug resistance and suggest that strategies targeting epigenetic pathways represent a potentially effective method for overcoming acquired resistance to cancer therapy.Significance: Epigenetic deregulation drives acquired resistance to ALK inhibitors in ALK-positive lung cancer. Cancer Res; 78(12); 3350-62. ©2018 AACR.