Discovery of biclonal origin and a novel oncogene SLC12A5 in colon cancer by single-cell sequencing

Single-cell sequencing is a powerful tool for delineating clonal relationship and identifying key driver genes for personalized cancer management. Here we performed single-cell sequencing analysis of a case of colon cancer. Population genetics analyses identified two independent clones in tumor cell population. The major tumor clone harbored APC and TP53 mutations as early oncogenic events, whereas the minor clone contained preponderant CDC27 and PABPC1 mutations. The absence of APC and TP53 mutations in the minor clone supports that these two clones were derived from two cellular origins. Examination of somatic mutation allele frequency spectra of additional 21 whole-tissue exome-sequenced cases revealed the heterogeneity of clonal origins in colon cancer. Next, we identified a mutated gene SLC12A5 that showed a high frequency of mutation at the single-cell level but exhibited low prevalence at the population level. Functional characterization of mutant SLC12A5 revealed its potential oncogenic effect in colon cancer. Our study provides the first exome-wide evidence at single-cell level supporting that colon cancer could be of a biclonal origin, and suggests that low-prevalence mutations in a cohort may also play important protumorigenic roles at the individual level.     

The Mutational Landscape of Acute Promyelocytic Leukemia Reveals an Interacting Network of Co-Occurrences and Recurrent Mutations

Preliminary Acute Promyelocytic Leukemia (APL) whole exome sequencing (WES) studies have identified a huge number of somatic mutations affecting more than a hundred different genes mainly in a non-recurrent manner, suggesting that APL is a heterogeneous disease with secondary relevant changes not yet defined. To extend our knowledge of subtle genetic alterations involved in APL that might cooperate with PML/RARA in the leukemogenic process, we performed a comprehensive analysis of somatic mutations in APL combining WES with sequencing of a custom panel of targeted genes by next-generation sequencing. To select a reduced subset of high confidence candidate driver genes, further in silico analysis were carried out. After prioritization and network analysis we found recurrent deleterious mutations in 8 individual genes (STAG2, U2AF1, SMC1A, USP9X, IKZF1, LYN, MYCBP2 and PTPN11) with a strong potential of being involved in APL pathogenesis. Our network analysis of multiple mutations provides a reliable approach to prioritize genes for additional analysis, improving our knowledge of the leukemogenesis interactome. Additionally, we have defined a functional module in the interactome of APL. The hypothesis is that the number, or the specific combinations, of mutations harbored in each patient might not be as important as the disturbance caused in biological key functions, triggered by several not necessarily recurrent mutations.     

Distinct mutational backgrounds and clonal architectures implicated prognostic discrepancies in small-cell carcinomas of the esophagus and lung

Small-cell carcinoma of the esophagus (SCCE) is a rare and aggressive cancer. Although several consistent genomic changes were observed previously between SCCE and small-cell lung cancer (SCLC), detailed mutational landscapes revealing discrepancies in genetic underpinnings of tumorigenesis between these two cancers are scarce, and little attention has been paid to answer whether these genetic alterations were related to the prognosis. Herein by performing whole-exome sequencing of 48 SCCE and 64 SCLC tumor samples, respectively we have shown that the number of driver mutations in SCCE was significantly lower than in SCLC (p = 0.0042). In SCCE, 46% of recurrent driver mutations were clonal, which occurred at an early stage during tumorigenesis, while 16 driver mutations were found clonal in SCLC. NOTCH1/3, PIK3CA, and ATM were specifically clonal in SCCE, while TP53 was clonal in SCLC. The total number of clonal mutations differed between two cancers and presented lower in SCCE compared to SCLC (p = 0.0036). Moreover, overall survival (OS) was shorter in patients with higher numbers of clonal mutations for both cancers. In summary, SCCE showed distinct mutational background and clonal architecture compared with SCLC. Organ-specific clonal events revealed different molecular mechanisms underlying tumorigenesis, tumor development, patients’ prognosis, and possible variations in therapeutic outcomes to candidate treatments.Subject terms: Oesophageal cancer, Small-cell lung cancer     

Identification of Druggable Cancer Driver Genes Amplified across TCGA Datasets

The Cancer Genome Atlas (TCGA) projects have advanced our understanding of the driver mutations, genetic backgrounds, and key pathways activated across cancer types. Analysis of TCGA datasets have mostly focused on somatic mutations and translocations, with less emphasis placed on gene amplifications. Here we describe a bioinformatics screening strategy to identify putative cancer driver genes amplified across TCGA datasets. We carried out GISTIC2 analysis of TCGA datasets spanning 14 cancer subtypes and identified 461 genes that were amplified in two or more datasets. The list was narrowed to 73 cancer-associated genes with potential “druggable” properties. The majority of the genes were localized to 14 amplicons spread across the genome. To identify potential cancer driver genes, we analyzed gene copy number and mRNA expression data from individual patient samples and identified 40 putative cancer driver genes linked to diverse oncogenic processes. Oncogenic activity was further validated by siRNA/shRNA knockdown and by referencing the Project Achilles datasets. The amplified genes represented a number of gene families, including epigenetic regulators, cell cycle-associated genes, DNA damage response/repair genes, metabolic regulators, and genes linked to the Wnt, Notch, Hedgehog, JAK/STAT, NF-KB and MAPK signaling pathways. Among the 40 putative driver genes were known driver genes, such as EGFR, ERBB2 and PIK3CA. Wild-type KRAS was amplified in several cancer types, and KRAS-amplified cancer cell lines were most sensitive to KRAS shRNA, suggesting that KRAS amplification was an independent oncogenic event. A number of MAP kinase adapters were co-amplified with their receptor tyrosine kinases, such as the FGFR adapter FRS2 and the EGFR family adapter GRB7. The ubiquitin-like ligase DCUN1D1 and the histone methyltransferase NSD3 were also identified as novel putative cancer driver genes. We discuss the patient tailoring implications for existing cancer drug targets and we further discuss potential novel opportunities for drug discovery efforts.     

Distinguishing between driver and passenger mutations in individual cancer genomes by network enrichment analysis

Background

In somatic cancer genomes, delineating genuine driver mutations against a background of multiple passenger events is a challenging task. The difficulty of determining function from sequence data and the low frequency of mutations are increasingly hindering the search for novel, less common cancer drivers. The accumulation of extensive amounts of data on somatic point and copy number alterations necessitates the development of systematic methods for driver mutation analysis.

Results

We introduce a framework for detecting driver mutations via functional network analysis, which is applied to individual genomes and does not require pooling multiple samples. It probabilistically evaluates 1) functional network links between different mutations in the same genome and 2) links between individual mutations and known cancer pathways. In addition, it can employ correlations of mutation patterns in pairs of genes. The method was used to analyze genomic alterations in two TCGA datasets, one for glioblastoma multiforme and another for ovarian carcinoma, which were generated using different approaches to mutation profiling. The proportions of drivers among the reported de novo point mutations in these cancers were estimated to be 57.8% and 16.8%, respectively. The both sets also included extended chromosomal regions with synchronous duplications or losses of multiple genes. We identified putative copy number driver events within many such segments. Finally, we summarized seemingly disparate mutations and discovered a functional network of collagen modifications in the glioblastoma. In order to select the most efficient network for use with this method, we used a novel, ROC curve-based procedure for benchmarking different network versions by their ability to recover pathway membership.

Conclusions

The results of our network-based procedure were in good agreement with published gold standard sets of cancer genes and were shown to complement and expand frequency-based driver analyses. On the other hand, three sequence-based methods applied to the same data yielded poor agreement with each other and with our results. We review the difference in driver proportions discovered by different sequencing approaches and discuss the functional roles of novel driver mutations. The software used in this work and the global network of functional couplings are publicly available at http://research.scilifelab.se/andrej_alexeyenko/downloads.html.

Electronic supplementary material

The online version of this article (doi:10.1186/1471-2105-15-308) contains supplementary material, which is available to authorized users.     

Probing altered enzyme activity in the biochemical characterization of cancer

Enzymes have evolved to catalyze their precise reactions at the necessary rates, locations, and time to facilitate our development, to respond to a variety of insults and challenges, and to maintain a healthy, balanced state. Enzymes achieve this extraordinary feat through their unique kinetic parameters, myriad regulatory strategies, and their sensitivity to their surroundings, including substrate concentration and pH. The Cancer Genome Atlas (TCGA) highlights the extraordinary number of ways in which the finely tuned activities of enzymes can be disrupted, contributing to cancer development and progression often due to somatic and/or inherited genetic alterations. Rather than being limited to the domain of enzymologists, kinetic constants such as k_cat, K_m, and k_cat/K_m are highly informative parameters that can impact a cancer patient in tangible ways—these parameters can be used to sort tumor driver mutations from passenger mutations, to establish the pathways that cancer cells rely on to drive patients’ tumors, to evaluate the selectivity and efficacy of anti-cancer drugs, to identify mechanisms of resistance to treatment, and more. In this review, we will discuss how changes in enzyme activity, primarily through somatic mutation, can lead to altered kinetic parameters, new activities, or changes in conformation and oligomerization. We will also address how changes in the tumor microenvironment can affect enzymatic activity, and briefly describe how enzymology, when combined with additional powerful tools, and can provide us with tremendous insight into the chemical and molecular mechanisms of cancer.     

Integrative Analysis of Somatic Mutations Altering MicroRNA Targeting in Cancer Genomes

Determining the functional impact of somatic mutations is crucial to understanding tumorigenesis and metastasis. Recent sequences of several cancers have provided comprehensive lists of somatic mutations across entire genomes, enabling investigation of the functional impact of somatic mutations in non-coding regions. Here, we study somatic mutations in 3′UTRs of genes that have been identified in four cancers and computationally predict how they may alter miRNA targeting, potentially resulting in dysregulation of the expression of the genes harboring these mutations. We find that somatic mutations create or disrupt putative miRNA target sites in the 3′UTRs of many genes, including several genes, such as MITF, EPHA3, TAL1, SCG3, and GSDMA, which have been previously associated with cancer. We also integrate the somatic mutations with germline mutations and results of association studies. Specifically, we identify putative miRNA target sites in the 3′UTRs of BMPR1B, KLK3, and SPRY4 that are disrupted by both somatic and germline mutations and, also, are in linkage disequilibrium blocks with high scoring markers from cancer association studies. The somatic mutation in BMPR1B is located in a target site of miR-125b; germline mutations in this target site have previously been both shown to disrupt regulation of BMPR1B by miR-125b and linked with cancer.     

Chromothripsis is a common mechanism driving genomic rearrangements in primary and metastatic colorectal cancer

Background

Structural rearrangements form a major class of somatic variation in cancer genomes. Local chromosome shattering, termed chromothripsis, is a mechanism proposed to be the cause of clustered chromosomal rearrangements and was recently described to occur in a small percentage of tumors. The significance of these clusters for tumor development or metastatic spread is largely unclear.

Results

We used genome-wide long mate-pair sequencing and SNP array profiling to reveal that chromothripsis is a widespread phenomenon in primary colorectal cancer and metastases. We find large and small chromothripsis events in nearly every colorectal tumor sample and show that several breakpoints of chromothripsis clusters and isolated rearrangements affect cancer genes, including NOTCH2, EXO1 and MLL3. We complemented the structural variation studies by sequencing the coding regions of a cancer exome in all colorectal tumor samples and found somatic mutations in 24 genes, including APC, KRAS, SMAD4 and PIK3CA. A pairwise comparison of somatic variations in primary and metastatic samples indicated that many chromothripsis clusters, isolated rearrangements and point mutations are exclusively present in either the primary tumor or the metastasis and may affect cancer genes in a lesion-specific manner.

Conclusions

We conclude that chromothripsis is a prevalent mechanism driving structural rearrangements in colorectal cancer and show that a complex interplay between point mutations, simple copy number changes and chromothripsis events drive colorectal tumor development and metastasis.     

Comprehensive characterization of somatic variants associated with intronic polyadenylation in human cancers

Somatic single nucleotide variants (SNVs) in cancer genome affect gene expression through various mechanisms depending on their genomic location. While somatic SNVs near canonical splice sites have been reported to cause abnormal splicing of cancer-related genes, whether these SNVs can affect gene expression through other mechanisms remains an open question. Here, we analyzed RNA sequencing and exome data from 4,998 cancer patients covering ten cancer types and identified 152 somatic SNVs near splice sites that were associated with abnormal intronic polyadenylation (IPA). IPA-associated somatic variants favored the localization near the donor splice sites compared to the acceptor splice sites. A proportion of SNV-associated IPA events overlapped with premature cleavage and polyadenylation events triggered by U1 small nuclear ribonucleoproteins (snRNP) inhibition. GC content, intron length and polyadenylation signal were three genomic features that differentiated between SNV-associated IPA and intron retention. Notably, IPA-associated SNVs were enriched in tumor suppressor genes (TSGs), including the well-known TSGs such as PTEN and CDH1 with recurrent SNV-associated IPA events. Minigene assay confirmed that SNVs from PTEN, CDH1, VEGFA, GRHL2, CUL3 and WWC2 could lead to IPA. This work reveals that IPA acts as a novel mechanism explaining the functional consequence of somatic SNVs in human cancer.     

Germline Mutations in MAP3K6 Are Associated with Familial Gastric Cancer

Gastric cancer is among the leading causes of cancer-related deaths worldwide. While heritable forms of gastric cancer are relatively rare, identifying the genes responsible for such cases can inform diagnosis and treatment for both hereditary and sporadic cases of gastric cancer. Mutations in the E-cadherin gene, CDH1, account for 40% of the most common form of familial gastric cancer (FGC), hereditary diffuse gastric cancer (HDGC). The genes responsible for the remaining forms of FGC are currently unknown. Here we examined a large family from Maritime Canada with FGC without CDH1 mutations, and identified a germline coding variant (p.P946L) in mitogen-activated protein kinase kinase kinase 6 (MAP3K6). Based on conservation, predicted pathogenicity and a known role of the gene in cancer predisposition, MAP3K6 was considered a strong candidate and was investigated further. Screening of an additional 115 unrelated individuals with non-CDH1 FGC identified the p.P946L MAP3K6 variant, as well as four additional coding variants in MAP3K6 (p.F849Sfs*142, p.P958T, p.D200Y and p.V207G). A somatic second-hit variant (p.H506Y) was present in DNA obtained from one of the tumor specimens, and evidence of DNA hypermethylation within the MAP3K6 gene was observed in DNA from the tumor of another affected individual. These findings, together with previous evidence from mouse models that MAP3K6 acts as a tumor suppressor, and studies showing the presence of somatic mutations in MAP3K6 in non-hereditary gastric cancers and gastric cancer cell lines, point towards MAP3K6 variants as a predisposing factor for FGC.     

The “next-generation” knowledge of papillary thyroid carcinoma

The application of Next-Generation Sequencing for studying the genetics of papillary thyroid carcinomas (PTC) has recently revealed new somatic mutations and gene fusions as potential new tumor-initiating events in patients without any known driver lesion. Gene and miRNA expression analyses defined clinically relevant subclasses correlated to tumor progression. In addition, it has been shown that tumor driver mutations in BRAF, and RET rearrangements - altogether termed “BRAF-like” carcinomas - have a very similar expression pattern and constitute a distinct category. Conversely, “RAS-like” carcinomas have a different genomic, epigenomic, and proteomic profile. These findings justify the need to reconsider PTC classification schemes.     

Genomic profile analysis of diffuse-type gastric cancers

Background

Stomach cancer is the third deadliest among all cancers worldwide. Although incidence of the intestinal-type gastric cancer has decreased, the incidence of diffuse-type is still increasing and its progression is notoriously aggressive. There is insufficient information on genome variations of diffuse-type gastric cancer because its cells are usually mixed with normal cells, and this low cellularity has made it difficult to analyze the genome.

Results

We analyze whole genomes and corresponding exomes of diffuse-type gastric cancer, using matched tumor and normal samples from 14 diffuse-type and five intestinal-type gastric cancer patients. Somatic variations found in the diffuse-type gastric cancer are compared to those of the intestinal-type and to previously reported variants. We determine the average exonic somatic mutation rate of the two types. We find associated candidate driver genes, and identify seven novel somatic mutations in CDH1, which is a well-known gastric cancer-associated gene. Three-dimensional structure analysis of the mutated E-cadherin protein suggests that these new somatic mutations could cause significant functional perturbations of critical calcium-binding sites in the EC1-2 junction. Chromosomal instability analysis shows that the MDM2 gene is amplified. After thorough structural analysis, a novel fusion gene TSC2-RNF216 is identified, which may simultaneously disrupt tumor-suppressive pathways and activate tumorigenesis.

Conclusions

We report the genomic profile of diffuse-type gastric cancers including new somatic variations, a novel fusion gene, and amplification and deletion of certain chromosomal regions that contain oncogenes and tumor suppressors.     

β-Catenin Serves as a Clutch between Low and High Intercellular E-Cadherin Bond Strengths

A wide range of invasive pathological outcomes originate from the loss of epithelial phenotype and involve either loss of function or downregulation of transmembrane adhesive receptor complexes, including Ecadherin (Ecad) and binding partners β-catenin and α-catenin at adherens junctions. Cellular pathways regulating wild-type β-catenin level, or direct mutations in β-catenin that affect the turnover of the protein have been shown to contribute to cancer development, through induction of uncontrolled proliferation of transformed tumor cells, particularly in colon cancer. Using single-molecule force spectroscopy, we show that depletion of β-catenin or the prominent cancer-related S45 deletion mutation in β-catenin present in human colon cancers both weaken tumor intercellular Ecad/Ecad bond strength and diminishes the capacity of specific extracellular matrix proteins—including collagen I, collagen IV, and laminin V—to modulate intercellular Ecad/Ecad bond strength through α-catenin and the kinase activity of glycogen synthase kinase 3 (GSK-3β). Thus, in addition to regulating tumor cell proliferation, cancer-related mutations in β-catenin can influence tumor progression by weakening the adhesion of tumor cells to one another through reduced individual Ecad/Ecad bond strength and cellular adhesion to specific components of the extracellular matrix and the basement membrane.     

Early-Stage Induction of SWI/SNF Mutations during Esophageal Squamous Cell Carcinogenesis

The SWI/SNF chromatin remodeling complex is frequently inactivated by somatic mutations of its various components in various types of cancers, and also by aberrant DNA methylation. However, its somatic mutations and aberrant methylation in esophageal squamous cell carcinomas (ESCCs) have not been fully analyzed. In this study, we aimed to clarify in ESCC, what components of the SWI/SNF complex have somatic mutations and aberrant methylation, and when somatic mutations of the SWI/SNF complex occur. Deep sequencing of components of the SWI/SNF complex using a bench-top next generation sequencer revealed that eight of 92 ESCCs (8.7%) had 11 somatic mutations of 7 genes, ARID1A, ARID2, ATRX, PBRM1, SMARCA4, SMARCAL1, and SMARCC1. The SMARCA4 mutations were located in the Forkhead (85Ser>Leu) and SNF2 family N-terminal (882Glu>Lys) domains. The PBRM1 mutations were located in a bromodomain (80Asn>Ser) and an HMG-box domain (1,377Glu>Lys). For most mutations, their mutant allele frequency was 31–77% (mean 61%) of the fraction of cancer cells in the same samples, indicating that most of the cancer cells in individual ESCC samples had the SWI/SNF mutations on one allele, when present. In addition, a BeadChip array analysis revealed that a component of the SWI/SNF complex, ACTL6B, had aberrant methylation at its promoter CpG island in 18 of 52 ESCCs (34.6%). These results showed that genetic and epigenetic alterations of the SWI/SNF complex are present in ESCCs, and suggested that genetic alterations are induced at an early stage of esophageal squamous cell carcinogenesis.     

Predicting oncogene mutations of lung cancer using deep learning and histopathologic features on whole-slide images

Lung cancer is a leading cause of death in both men and women globally. The recent development of tumor molecular profiling has opened opportunities for targeted therapies for lung adenocarcinoma (LUAD) patients. However, the lack of access to molecular profiling or cost and turnaround time associated with it could hinder oncologists' willingness to order frequent molecular tests, limiting potential benefits from precision medicine. In this study, we developed a weakly supervised deep learning model for predicting somatic mutations of LUAD patients based on formalin-fixed paraffin-embedded (FFPE) whole-slide images (WSIs) using LUAD subtypes-related histological features and recent advances in computer vision. Our study was performed on a total of 747 hematoxylin and eosin (H&E) stained FFPE LUAD WSIs and the genetic mutation data of 232 patients who were treated at Dartmouth-Hitchcock Medical Center (DHMC). We developed our convolutional neural network-based models to analyze whole slides and predict five major genetic mutations, i.e., BRAF, EGFR, KRAS, STK11, and TP53. We additionally used 111 cases from the LUAD dataset of the CPTAC-3 study for external validation. Our model achieved an AUROC of 0.799 (95% CI: 0.686–0.904) and 0.686 (95% CI: 0.620–0.752) for predicting EGFR genetic mutations on the DHMC and CPTAC-3 test sets, respectively. Predicting TP53 genetic mutations also showed promising outcomes. Our results demonstrated that H&E stained FFPE LUAD whole slides could be utilized to predict oncogene mutations, such as EGFR, indicating that somatic mutations could present subtle morphological characteristics in histology slides, where deep learning-based feature extractors can learn such latent information.     

Integrated RNA and DNA sequencing improves mutation detection in low purity tumors

Identifying somatic mutations is critical for cancer genome characterization and for prioritizing patient treatment. DNA whole exome sequencing (DNA-WES) is currently the most popular technology; however, this yields low sensitivity in low purity tumors. RNA sequencing (RNA-seq) covers the expressed exome with depth proportional to expression. We hypothesized that integrating DNA-WES and RNA-seq would enable superior mutation detection versus DNA-WES alone. We developed a first-of-its-kind method, called UNCeqR, that detects somatic mutations by integrating patient-matched RNA-seq and DNA-WES. In simulation, the integrated DNA and RNA model outperformed the DNA-WES only model. Validation by patient-matched whole genome sequencing demonstrated superior performance of the integrated model over DNA-WES only models, including a published method and published mutation profiles. Genome-wide mutational analysis of breast and lung cancer cohorts (n = 871) revealed remarkable tumor genomics properties. Low purity tumors experienced the largest gains in mutation detection by integrating RNA-seq and DNA-WES. RNA provided greater mutation signal than DNA in expressed mutations. Compared to earlier studies on this cohort, UNCeqR increased mutation rates of driver and therapeutically targeted genes (e.g. PIK3CA, ERBB2 and FGFR2). In summary, integrating RNA-seq with DNA-WES increases mutation detection performance, especially for low purity tumors.