Cancer Driver Research Articles

Oncogenic fusion of CD63-BCAR4 contributes cancer stem cell-like properties via ALDH1 activity.

Gene fusions are common somatic alterations in cancers, and fusions with tumorigenic features have been identified as novel drivers of cancer and therapeutic targets. Few studies have determined whether the oncogenic ability of fusion genes is related to the induction of stemness in cells. Cancer stem cells (CSCs) are a subset of cells that contribute to cancer progression, metastasis, and recurrence, and are critical components of the aggressive features of cancer. Here, we investigated the CSC-like properties induced by CD63-BCAR4 fusion gene, previously reported as an oncogenic fusion, and its potential contribution for the enhanced metastasis as a notable characteristic of CD63-BCAR4. CD63-BCAR4 overexpression facilitates sphere formation in immortalized bronchial epithelial cells. The significantly enhanced sphere-forming activity observed in tumor-derived cells from xenografted mice of CD63-BCAR4 overexpressing cells was suppressed by silencing of BCAR4. RNA microarray analysis revealed that ALDH1A1 was upregulated in the BCAR4 fusion-overexpressing cells. Increased activity and expression of ALDH1A1 were observed in the spheres of CD63-BCAR4 overexpressing cells compared with those of the empty vector. CD133 and CD44 levels were also elevated in BCAR4 fusion-overexpressing cells. Increased NANOG, SOX2, and OCT-3/4 protein levels were observed in metastatic tumor cells derived from mice injected with CD63-BCAR4 overexpressing cells. Moreover, DEAB, an ALDH1A1 inhibitor, reduced the migration activity induced by CD63-BCAR4 as well as the sphere-forming activity. Our findings suggest that CD63-BCAR4 fusion induces CSC-like properties by upregulating ALDH1A1, which contributes to its metastatic features.

Efficacy assessment of a novel pan-RAS inhibitor in KRAS-mutant and wild type colorectal 3D bioprinted organoid tumor tissue.

91 Background: Colorectal cancer (CRC) is the third leading type of cancer worldwide, with ~150,000 new cases in the US annually and a grim 14% 5-year survival for patients diagnosed at a late stage. A lack of treatment options leads to persistently poor prognosis for patients with advanced stage disease. KRAS mutations are well known drivers of CRC and other GI cancers. Multiple KRAS mutations occur in CRC, including G12D (34%), G12V (21%), G13D (20%), G12C (8%), and others (18%). Existing KRAS-targeted therapies have limited use in CRC, underscoring the need for pan-RAS inhibitors in treating CRC and other RAS driven cancers. Objective: Assess activity of ADT-007, our pan-RAS inhibitor, on wild-type (WT) and KRAS-mutant 3D bioprinted organoid tumor (BOT) tissue using our high-throughput ex vivo platform. Methods: Using previously established bioprinting protocols, WT and mutant BOTs were printed with HT29 and HCT116 cells, respectively. HT29 is an established human WT CRC cell line with known sensitivity to proteosome and survivin inhibitors. HCT116 is a KRASG13D mutant human CRC cell line. 3 sets of BOTs were generated and acclimated for 24h. One set was treated for 72h with proteosome inhibitor Bortezomib, another with survivin inhibitor YM155, and the third with our novel pan-RAS inhibitor ADT-007. Dose response curves were generated from both conventional ATP luminescence readouts and high-content imaging. Results: BOT tissue microarchitecture was validated and >200 µm diffusion in BOTs was confirmed using high-content imaging. Differential response was quantified using Cell TiterGlo endpoint assay as well as advanced image processing of high-content live/dead nuclear stained images captured at multiple z-plains. ADT-007 IC50 was found to be substantially lower for mutant HCT116 compared to that for WT HT29 cell line BOTs, which was consistent with separately conducted in vitro and in vivo studies. Conclusions: A pan-RAS inhibitor, such as ADT-007 with high selectivity for cancer cells with activated RAS that is not limited to a specific KRAS mutant allele or RAS isozyme, could have broader use for CRC and other RAS-driven cancers. Further, due to their potential to replicate biophysical characteristics of a tumor and its microenvironment, BOT based precision and personalized medicine platforms can provide more accurate drug efficacy readout compared to in vitro cancer models.

Proteostasis perturbation of N-Myc leveraging HSP70 mediated protein turnover improves treatment of neuroendocrine prostate cancer

N-Myc is a key driver of neuroblastoma and neuroendocrine prostate cancer (NEPC). One potential way to circumvent the challenge of undruggable N-Myc is to target the protein homeostasis (proteostasis) system that maintains N-Myc levels. Here, we identify heat shock protein 70 (HSP70) as a top partner of N-Myc, which binds a conserved “SELILKR” motif and prevents the access of E3 ubiquitin ligase, STIP1 homology and U-box containing protein 1 (STUB1), possibly through steric hindrance. When HSP70’s dwell time on N-Myc is increased by treatment with the HSP70 allosteric inhibitor, STUB1 is in close proximity with N-Myc and becomes functional to promote N-Myc ubiquitination on the K416 and K419 sites and forms polyubiquitination chains linked by the K11 and K63 sites. Notably, HSP70 inhibition significantly suppressed NEPC tumor growth, increased the efficacy of aurora kinase A (AURKA) inhibitors, and limited the expression of neuroendocrine-related pathways.

Genetic counselling legislation and practice in cancer in EU Member States.

Somatic and germline genetic alterations are significant drivers of cancer. Increasing integration of new technologies which profile these alterations requires timely, equitable and high-quality genetic counselling to facilitate accurate diagnoses and informed decision-making by patients and their families in preventive and clinical settings. This article aims to provide an overview of genetic counselling legislation and practice across European Union (EU) Member States to serve as a foundation for future European recommendations and action. National legislative databases of all 27 Member States were searched using terms relevant to genetic counselling, translated as appropriate. Interviews with relevant experts from each Member State were conducted to validate legislative search results and provide detailed insights into genetic counselling practice in each country. Genetic counselling is included in national legislative documents of 22 of 27 Member States, with substantial variation in legal mechanisms and prescribed details (i.e. the 'who, what, when and where' of counselling). Practice is similarly varied. Workforce capacity (25 of 27 Member States) and genetic literacy (all Member States) were common reported barriers. Recognition and/or better integration of genetic counsellors and updated legislation and were most commonly noted as the 'most important change' which would improve practice. This review highlights substantial variability in genetic counselling across EU Member States, as well as common barriers notwithstanding this variation. Future recommendations and action should focus on addressing literacy and capacity challenges through legislative, regulatory and/or strategic approaches at EU, national, regional and/or local levels.

Machine learning and deep learning tools for the automated capture of cancer surveillance data.

The National Cancer Institute and the Department of Energy strategic partnership applies advanced computing and predictive machine learning and deep learning models to automate the capture of information from unstructured clinical text for inclusion in cancer registries. Applications include extraction of key data elements from pathology reports, determination of whether a pathology or radiology report is related to cancer, extraction of relevant biomarker information, and identification of recurrence. With the growing complexity of cancer diagnosis and treatment, capturing essential information with purely manual methods is increasingly difficult. These new methods for applying advanced computational capabilities to automate data extraction represent an opportunity to close critical information gaps and create a nimble, flexible platform on which new information sources, such as genomics, can be added. This will ultimately provide a deeper understanding of the drivers of cancer and outcomes in the population and increase the timeliness of reporting. These advances will enable better understanding of how real-world patients are treated and the outcomes associated with those treatments in the context of our complex medical and social environment.

ECD-CDGI: An efficient energy-constrained diffusion model for cancer driver gene identification.

The identification of cancer driver genes (CDGs) poses challenges due to the intricate interdependencies among genes and the influence of measurement errors and noise. We propose a novel energy-constrained diffusion (ECD)-based model for identifying CDGs, termed ECD-CDGI. This model is the first to design an ECD-Attention encoder by combining the ECD technique with an attention mechanism. ECD-Attention encoder excels at generating robust gene representations that reveal the complex interdependencies among genes while reducing the impact of data noise. We concatenate topological embedding extracted from gene-gene networks through graph transformers to these gene representations. We conduct extensive experiments across three testing scenarios. Extensive experiments show that the ECD-CDGI model possesses the ability to not only be proficient in identifying known CDGs but also efficiently uncover unknown potential CDGs. Furthermore, compared to the GNN-based approach, the ECD-CDGI model exhibits fewer constraints by existing gene-gene networks, thereby enhancing its capability to identify CDGs. Additionally, ECD-CDGI is open-source and freely available. We have also launched the model as a complimentary online tool specifically crafted to expedite research efforts focused on CDGs identification.

Single nucleotide and copy number variants of cancer driver genes inform drug response in multiple cancers.

Due to the heterogeneity of cancer, precision medicine has been a major challenge for cancer treatment. Determining medication regimens based on patient genotypes has become a research hotspot in cancer genomics. In this study, we aim to identify key biomarkers for targeted therapies based on single nucleotide variants (SNVs) and copy number variants (CNVs) of genes. The experiment is carried out on 7 cancers on the Encyclopedia of Cancer Cell Lines (CCLE) dataset. Considering the high mutability of driver genes which result in abundant mutated samples, the effect of data sparsity can be eliminated to a large extent. Therefore, we focus on discovering the relationship between driver mutation patterns and three measures of drug response, namely area under the curve (AUC), half maximal effective concentration (EC50), and log2-fold change (LFC). First, multiple statistical methods are applied to assess the significance of difference in drug response between sample groups. Next, for each driver gene, we analyze the extent to which its mutations can affect drug response. Based on the results of multiple hypothesis tests and correlation analyses, our main findings include the validation of several known drug response biomarkers such as BRAF, NRAS, MAP2K1, MAP2K2, and CDKN2A, as well as genes with huge potential to infer drug responses. It is worth emphasizing that we identify a list of genes including SALL4, B2M, BAP1, CCDC6, ERBB4, FOXA1, GRIN2A, and PTPRT, whose impact on drug response spans multiple cancers and should be prioritized as key biomarkers for targeted therapies. Furthermore, based on the statistical p-values and correlation coefficients, we construct gene-drug sensitivity maps for cancer drug recommendation. In this work, we show that driver mutation patterns could be used to tailor therapeutics for precision medicine.

Efficacy of immune checkpoint inhibitors in advanced non-small cell lung cancer patients with rare KRAS mutations: a real-world retrospective study.

Kirsten rat sarcoma homolog (KRAS) mutations are one of the key drivers in non-small cell lung cancer (NSCLC) and FDA-approved specific inhibitors of KRAS-G12C mutation are available clinically. However, inhibitors of certain KRAS mutation subtypes remain unavailable, especially rare KRAS mutations including G13C, G13D, and Q61H. In this study, we retrospectively investigated the outcomes of NSCLC patients with rare KRAS-mutation to determine if they may benefit from immune checkpoint inhibitors (ICIs). Our retrospective study involved 240 advanced NSCLC patients with KRAS mutations, who visited Shanghai Chest Hospital from July 2018 to July 2021. Complete clinical and pathological data were recorded and progression-free survival (PFS) and overall survival (OS) were adopted as primary endpoints. The median follow-up time was 36.5 months (range, 30.8-42.1 months) and the median OS was 9.7 months (range, 7.6-11.8 months). Of the 240 patients evaluated, 130 (54.2%) received chemotherapy and 110 (45.8%) received ICI-based treatment. Among the patients who received chemotherapy, patients with rare KRAS-mutations presented worse survival outcomes (median PFS, 3.4 vs. 4.1 months, P=0.047; median OS, 5.2 vs. 7.1 months, P=0.02) than conventional KRAS-mutant patients. PFS and OS of rare KRAS-mutation patients were prolonged after immunotherapy (median PFS 7.3 vs. 3.4 months, P<0.001; median OS, 13.3 vs. 5.2 months, P<0.001) and had no significant difference compared with conventional KRAS-mutant patients, in part of them whose programmed death-ligand 1 (PD-L1) expression data before immunotherapy were available (n=72), patients with a higher rate of PD-L1 positive tumor cells (≥50%) presented elevated PFS and OS. Despite having potential survival disadvantage compared with other NSCLC patients, rare KRAS-mutant patients (other than G12A, C, D, V) could benefit specifically from ICI-based therapy and survival outcomes are correlated with PD-L1 expression.

Drivers of breast cancer and cervical cancer screening among women of reproductive age: insights from the Ghana Demographic and Health Survey

BackgroundThe two major causes of cancer-related deaths among women in Ghana are breast cancer (BC) and cervical cancer (CC). These types of cancers typically do not show any symptoms until they have progressed. Therefore, it is important to screen for early detection. This research aimed to investigate the rate of breast cancer and cervical cancer screening, as well as the factors associated with it, among women of reproductive age in Ghana.MethodsThis study analysed data from the 2022 Ghana Demographic and Health Survey. A total of 15,014 women aged 15 to 49 years were included in the analysis. Descriptive statistics and binary logistic regression were employed to analyse the data with the aid of STATA/SE, version 17.ResultsIt was found that 18.4% and 5.0% of the women had screened for BC and CC, respectively. Women aged 45–49 years were about three times more likely (aOR = 2.83, 95% CI: 1.88–4.24) to screen for BC compared to those aged 15–19 years. Women who had tested for HIV had increased odds (aOR = 1.88, 95% CI: 1.56–2.25) of screening for BC compared to their counterparts. Women within the richest wealth index (aOR = 1.95, 95% CI: 1.40–2.72) had increased odds of screening for BC compared to those in the poorest wealth index. Regarding CC screening, women with higher education (aOR = 2.56, 95% CI: 1.53–4.29) were two times more likely to screen for CC compared to those with no formal education. Women who did not use tobacco (aOR = 0.45, 95% CI: 0.21–0.96) had decreased odds of CC screening compared to their counterparts.ConclusionsThis study showed that the uptake of BC and CC screening services among women in Ghana was very low. The drivers of BC and CC screening included enabling, predisposing, and need factors. Stakeholders can leverage the mass media to raise awareness and educate women in reproductive age about the importance of BC and CC screening. This study provides relevant information that can inform BC and CC policies and programmes in Ghana.

Bioinformatics tools and resources for cancer and application.

Tumor bioinformatics plays an important role in cancer research and precision medicine. The primary focus of traditional cancer research has been molecular and clinical studies of a number of fundamental pathways and genes. In recent years, driven by breakthroughs in high-throughput technologies, large-scale cancer omics data have accumulated rapidly. How to effectively utilize and share these data is particularly important. To address this crucial task, many computational tools and databases have been developed over the past few years. To help researchers quickly learn and understand the functions of these tools, in this review, we summarize publicly available bioinformatics tools and resources for pan-cancer multi-omics analysis, regulatory analysis of tumorigenesis, tumor treatment and prognosis, immune infiltration analysis, immune repertoire analysis, cancer driver gene and driver mutation analysis, and cancer single-cell analysis, which may further help researchers find more suitable tools for their research.

Development of in vitro and in vivo c-Met targeted dual-modal nanoprobes for NIR II fluorescent bioimaging and magnetic resonance imaging of breast carcinoma metastasis

Because of the conventional bioimaging methods, including ultrasound and X-ray examinations often lack accuracy and sensitivity in the early diagnosis, as a heterogeneous subtype of breast cancer, it remains an urgent need to develop efficaciously and sensitively diagnostic drivers of triple negative breast cancer (TNBC). The overexpression of c-mesenchymal epithelial transition-factor (c-Met) is associated with the basal subtype of breast carcinoma and linked with decreased survival rate of TNBC patients. Here, we have synthesized a dual-modal nanoprobe with luminescence imaging in the bio-window of near infrared II region (NIR II, 1000–1700) and magnetic resonance imaging (MRI) performances. After c-Met targeting binding protein decoration, this Fe3O4@mSiO2-ICG/cMBP had outstanding size stability and preeminent biocompatibility. Meaningfully, it presented effective TNBC cells recognition in vitro. Besides, after caudal-vein injection of this nanoprobe, both NIR II luminescence imaging and MRI demonstrated that it can more efficiently concentrated in metastatic TNBC tumors in comparison with Fe3O4@mSiO2-ICG and clinical used Gd-DTPA. Meanwhile, the core–shell nanoprobes also showed negligible toxicity in vivo. All results suggested that our cMBP modified nanocomposite could provide a fascinating and efficient non-invasive diagnostic tool for TNBC detection in clinic.

Transcription factor DDIT3 is a potential driver in pancreatic cancer.

Pancreatic ductal adenocarcinoma (PDAC) is a highly lethal and aggressive tumor that affects the digestive tract, leading to high mortality and poor survival rates. The purpose of the present study was to evaluate the expression levels of DNA damage-inducible transcript 3 (DDIT3) in pancreatic cancer and to investigate its effects in in vitro and in vivo experiments. Bioinformatics analysis indicated that DDIT3 expression was higher in pancreatic cancer tumor tissues and associated with a poor prognosis. Positive or strong positive DDIT3 expression was observed in PDAC, and no or weak expression was observed in normal pancreatic tissues. It was also highly expressed in PDAC cells, while being expressed at lower levels in normal pancreatic ductal epithelial cells. Transfection of short hairpin RNA targeting the DDIT3 gene reduced the proliferation, migration and invasion of PANC-1 cells. In vivo, in an in situ implantation tumor model with Pan02 cells, the size and weight of the tumors were reduced in the DDIT3 knockdown Pan02 cell-implanted group. These data suggested that DDIT3 represents a novel predictive biomarker for the potential treatment of patients presenting with PDAC.

USP11 promotes prostate cancer progression by up-regulating AR and c-Myc activity

Androgen receptor (AR) is a main driver for castration-resistant prostate cancer (CRPC). c-Myc is an oncogene underlying prostate tumorigenesis. Here, we find that the deubiquitinase USP11 targets both AR and c-Myc in prostate cancer (PCa). USP11 expression was up-regulated in metastatic PCa and CRPC. USP11 knockdown (KD) significantly inhibited PCa cell growth. Our RNA-seq studies revealed AR and c-Myc as the top transcription factors altered after USP11 KD. ChIP-seq analysis showed that either USP11 KD or replacement of endogenous USP11 with a catalytic-inactive USP11 mutant significantly decreased chromatin binding by AR and c-Myc. We find that USP11 employs two mechanisms to up-regulate AR and c-Myc levels: namely, deubiquitination of AR and c-Myc proteins to increase their stability and deubiquitination of H2A-K119Ub, a repressive histone mark, on promoters of AR and c-Myc genes to increase their transcription. AR and c-Myc reexpression in USP11-KD PCa cells partly rescued cell growth defects. Thus, our studies reveal a tumor-promoting role for USP11 in aggressive PCa through upregulation of AR and c-Myc activities and support USP11 as a potential target against PCa.

Unveiling hidden connections in omics data via pyPARAGON: an integrative hybrid approach for disease network construction.

Network inference or reconstruction algorithms play an integral role in successfully analyzing and identifying causal relationships between omics hits for detecting dysregulated and altered signaling components in various contexts, encompassing disease states and drug perturbations. However, accurate representation of signaling networks and identification of context-specific interactions within sparse omics datasets in complex interactomes pose significant challenges in integrative approaches. To address these challenges, we present pyPARAGON (PAgeRAnk-flux on Graphlet-guided network for multi-Omic data integratioN), a novel tool that combines network propagation with graphlets. pyPARAGON enhances accuracy and minimizes the inclusion of nonspecific interactions in signaling networks by utilizing network rather than relying on pairwise connections among proteins. Through comprehensive evaluations on benchmark signaling pathways, we demonstrate that pyPARAGON outperforms state-of-the-art approaches in node propagation and edge inference. Furthermore, pyPARAGON exhibits promising performance in discovering cancer driver networks. Notably, we demonstrate its utility in network-based stratification of patient tumors by integrating phosphoproteomic data from 105 breast cancer tumors with the interactome and demonstrating tumor-specific signaling pathways. Overall, pyPARAGON is a novel tool for analyzing and integrating multi-omic data in the context of signaling networks. pyPARAGON is available at https://github.com/netlab-ku/pyPARAGON.

Real-Time Measurement of a Weak Interaction of a Transcription Factor Motif with a Protein Hub at Single-Molecule Precision.

Transcription factors often interact with other protein cofactors, regulating gene expression. Direct detection of these brief events using existing technologies remains challenging due to their transient nature. In addition, intrinsically disordered domains, intranuclear location, and lack of cofactor-dependent active sites of transcription factors further complicate the quantitative analysis of these critical processes. Here, we create a genetically encoded label-free sensor to identify the interaction between a motif of the MYC transcription factor, a primary cancer driver, and WDR5, a chromatin-associated protein hub. Using an engineered nanopore equipped with this motif, WDR5 is probed through reversible captures and releases in a one-by-one and time-resolved fashion. Our single-molecule kinetic measurements indicate a weak-affinity interaction arising from a relatively slow complex association and a fast dissociation of WDR5 from the tethered motif. Further, we validate this subtle interaction by determinations in an ensemble using single nanodisc-wrapped nanopores immobilized on a biolayer interferometry sensor. This study also provides the proof-of-concept for a sensor that reveals unique recognition signatures of different protein binding sites. Our foundational work may be further developed to produce sensing elements for analytical proteomics and cancer nanomedicine.

PARP4 interacts with hnRNPM to regulate splicing during lung cancer progression

BackgroundThe identification of cancer driver genes from sequencing data has been crucial in deepening our understanding of tumor biology and expanding targeted therapy options. However, apart from the most commonly altered genes, the mechanisms underlying the contribution of other mutations to cancer acquisition remain understudied. Leveraging on our whole-exome sequencing of the largest Asian lung adenocarcinoma (LUAD) cohort (n = 302), we now functionally assess the mechanistic role of a novel driver, PARP4.MethodsIn vitro and in vivo tumorigenicity assays were used to study the functional effects of PARP4 loss and mutation in multiple lung cancer cell lines. Interactomics analysis by quantitative mass spectrometry was conducted to identify PARP4’s interaction partners. Transcriptomic data from cell lines and patient tumors were used to investigate splicing alterations.ResultsPARP4 depletion or mutation (I1039T) promotes the tumorigenicity of KRAS- or EGFR-driven lung cancer cells. Disruption of the vault complex, with which PARP4 is commonly associated, did not alter tumorigenicity, indicating that PARP4’s tumor suppressive activity is mediated independently. The splicing regulator hnRNPM is a potentially novel PARP4 interaction partner, the loss of which likewise promotes tumor formation. hnRNPM loss results in splicing perturbations, with a propensity for dysregulated intronic splicing that was similarly observed in PARP4 knockdown cells and in LUAD cohort patients with PARP4 copy number loss.ConclusionsPARP4 is a novel modulator of lung adenocarcinoma, where its tumor suppressive activity is mediated not through the vault complex—unlike conventionally thought, but in association with its novel interaction partner hnRNPM, thus suggesting a role for splicing dysregulation in LUAD tumorigenesis.

Discovery of the Natural Bibenzyl Compound Erianin in Dendrobium Inhibiting the Growth and EMT of Gastric Cancer through Downregulating the LKB1-SIK2/3-PARD3 Pathway.

Erianin, a bibenzyl compound found in dendrobium extract, has demonstrated broad anticancer activity. However, its mechanism of action in gastric cancer (GC) remains poorly understood. LKB1 is a tumor-suppressor gene, and its mutation is an important driver of various cancers. Yet some studies have reported contradictory findings. In this study, we combined bioinformatics and in vitro and in vivo experiments to investigate the effect and potential mechanism of Erianin in the treatment of GC. The results show that LKB1 was highly expressed in patients' tumor tissues and GC cells, and it was associated with poor patient prognosis. Erianin could promote GC cell apoptosis and inhibit the scratch repair, migration, invasion, and epithelial-mesenchymal transition (EMT) characteristics. Erianin dose-dependently inhibited the expression of LKB1, SIK2, SIK3, and PARD3 but had no significant effect on SIK1. Erianin also inhibited tumor growth in CDX mice model. Unexpectedly, 5-FU also exhibited a certain inhibitory effect on LKB1. The combination of Erianin and 5-FU significantly improved the anti-tumor efficacy of 5-FU in the growth of GC cells and xenograft mouse models. In summary, Erianin is a potential anti-GC compound that can inhibit GC growth and EMT properties by targeting the LKB1-SIK2/3-PARD3-signaling axis. The synergistic effect of Erianin and 5-FU suggests a promising therapeutic strategy for GC treatment.

Copy number losses of oncogenes and gains of tumor suppressor genes generate common driver mutations

Cancer driver genes can undergo positive selection for various types of genetic alterations, including gain-of-function or loss-of-function mutations and copy number alterations (CNA). We investigated the landscape of different types of alterations affecting driver genes in 17,644 cancer exomes and genomes. We find that oncogenes may simultaneously exhibit signatures of positive selection and also negative selection in different gene segments, suggesting a method to identify additional tumor types where an oncogene is a driver or a vulnerability. Next, we characterize the landscape of CNA-dependent selection effects, revealing a general trend of increased positive selection on oncogene mutations not only upon CNA gains but also upon CNA deletions. Similarly, we observe a positive interaction between mutations and CNA gains in tumor suppressor genes. Thus, two-hit events involving point mutations and CNA are universally observed regardless of the type of CNA and may signal new therapeutic opportunities. An analysis with focus on the somatic CNA two-hit events can help identify additional driver genes relevant to a tumor type. By a global inference of point mutation and CNA selection signatures and interactions thereof across genes and tissues, we identify 9 evolutionary archetypes of driver genes, representing different mechanisms of (in)activation by genetic alterations.

Hotspot Exon 15 Mutations in BRAF Are Uncommon in Feline Tumours.

BRAF is one of multiple RAF proteins responsible for the activation of the MAPK cell signalling cascade involved in cell growth, differentiation, and survival. A hotspot BRAFV600E mutation, in exon 15, was determined to be a driver in 100% hairy cell leukaemias, 50%-60% of human melanomas, 30%-50% of human thyroid carcinomas and 10%-20% of human colorectal carcinomas. The orthologous BRAFV595E mutation was seen in 67% and 80% of canine bladder transitional cell carcinomas and prostatic adenocarcinomas, respectively. Since veterinary and human cancers exploit similar pathways and BRAF is highly conserved across species, BRAF can be expected to be a driver in a feline cancer. Primers were developed to amplify exon 15 of feline BRAF. One hundred ninety-six feline tumours were analysed. Sanger sequencing of the 211 bp PCR amplicon was done. A BRAF mutation was found in one tumour, a cutaneous melanoma. The mutation was a BRAFV597E mutation, orthologous to the canine and human hotspot mutations. A common synonymous variant, BRAFT586T, was seen in 23% (47/196) of tumours. This variant was suspected to be a single nucleotide polymorphism. BRAF was not frequently mutated in common feline tumours or in tumour types that frequently harbour BRAF mutations in human and canine cancers. As is seen in canine cancer genomics, the mutational profile in feline tumours may not parallel the histologic equivalent in human oncology.

Co-expression in tissue-specific gene networks links genes in cancer-susceptibility loci to known somatic driver genes

BackgroundThe genetic background of cancer remains complex and challenging to integrate. Many somatic mutations within genes are known to cause and drive cancer, while genome-wide association studies (GWAS) of cancer have revealed many germline risk factors associated with cancer. However, the overlap between known somatic driver genes and positional candidate genes from GWAS loci is surprisingly small. We hypothesised that genes from multiple independent cancer GWAS loci should show tissue-specific co-regulation patterns that converge on cancer-specific driver genes.ResultsWe studied recent well-powered GWAS of breast, prostate, colorectal and skin cancer by estimating co-expression between genes and subsequently prioritising genes that show significant co-expression with genes mapping within susceptibility loci from cancer GWAS. We observed that the prioritised genes were strongly enriched for cancer drivers defined by COSMIC, IntOGen and Dietlein et al. The enrichment of known cancer driver genes was most significant when using co-expression networks derived from non-cancer samples of the relevant tissue of origin.ConclusionWe show how genes within risk loci identified by cancer GWAS can be linked to known cancer driver genes through tissue-specific co-expression networks. This provides an important explanation for why seemingly unrelated sets of genes that harbour either germline risk factors or somatic mutations can eventually cause the same type of disease.