Secondary Somatic Mutations in G-Protein-Related Pathways and Mutation Signatures in Uveal Melanoma

BACKGROUND

Uveal melanoma (UM), a rare cancer of the eye, is characterized by initiating mutations in the genes G-protein subunit alpha Q (GNAQ), G-protein subunit alpha 11 (GNA11), cysteinyl leukotriene receptor 2 (CYSLTR2), and phospholipase C beta 4 (PLCB4) and by metastasis-promoting mutations in the genes splicing factor 3B1 (SF3B1), serine and arginine rich splicing factor 2 (SRSF2), and BRCA1-associated protein 1 (BAP1). Here, we tested the hypothesis that additional mutations, though occurring in only a few cases ("secondary drivers"), might influence tumor development.

METHODS

We analyzed all the 4125 mutations detected in exome sequencing datasets, comprising a total of 139 Ums, and tested the enrichment of secondary drivers in Kyoto Encyclopedia of Genes and Genomes (KEGG) pathways that also contained the initiating mutations. We searched for additional mutations in the putative secondary driver gene protein tyrosine kinase 2 beta (PTK2B) and we developed new mutational signatures that explain the mutational pattern observed in UM.

RESULTS

Secondary drivers were significantly enriched in KEGG pathways that also contained GNAQ and GNA11, such as the calcium-signaling pathway. Many of the secondary drivers were known cancer driver genes and were strongly associated with metastasis and survival. We identified additional mutations in PTK2B. Sparse dictionary learning allowed for the identification of mutational signatures specific for UM.

CONCLUSIONS

A considerable part of rare mutations that occur in addition to known driver mutations are likely to affect tumor development and progression.

ncdDetect2: improved models of the site-specific mutation rate in cancer and driver detection with robust significance evaluation

Motivation

Understanding the mutational processes that act during cancer development is a key topic of cancer biology. Nevertheless, much remains to be learned, as a complex interplay of processes with dependencies on a range of genomic features creates highly heterogeneous cancer genomes. Accurate driver detection relies on unbiased models of the mutation rate that also capture rate variation from uncharacterized sources.

Results

Here, we analyse patterns of observed-to-expected mutation counts across 505 whole cancer genomes, and find that genomic features missing from our mutation-rate model likely operate on a megabase length scale. We extend our site-specific model of the mutation rate to include the additional variance from these sources, which leads to robust significance evaluation of candidate cancer drivers. We thus present ncdDetect v.2, with greatly improved cancer driver detection specificity. Finally, we show that ranking candidates by their posterior mean value of their effect sizes offers an equivalent and more computationally efficient alternative to ranking by their P-values.

Availability and implementation

ncdDetect v.2 is implemented as an R-package and is freely available at http://github.com/TobiasMadsen/ncdDetect2

Supplementary information

Supplementary data are available at Bioinformatics online.

Assessment of IFN-γ and granzyme-B production by in "sitro" technology

Tumor neantigens (TNAs) and tumor-associated antigens (TAAs) are crucial triggers of anticancer immune responses. Through major histocompatibility complex, such antigens activate T cells, which, by releasing interferon gamma (IFN-γ) and granzyme B (GRZB), act as crucial effectors against tumor onset and progression. However, in response to immune pressure, cancer cells use different strategies to favor the establishment of an immunosuppressive tumor microenvironment (TME). Elucidating the dynamics of tumor-host co-evolution provides novel opportunities for personalized cancer immunotherapies. The in sitro (in vitro+in situ) technology is an experimental approach involving the preparation of heterocellular cell suspensions from fresh tumors and their use in vitro. The in sitro experimental setup offers the possibility to (1) analyze immune-related parameters (e.g., quantification of cytokines released in the TME), (2) reveal the mechanism of action of drugs, and (3) unveil crucial cell-intrinsic and cell-extrinsic processes boosting anticancer immune responses. Nonetheless, the in sitro technology does not fully recapitulate the complexity of the tissue "in situ" nor models the patterns of infiltrating immune cell localization, and hence parallel experimentation should be scheduled. In this chapter we discuss in sitro technology to analyze and quantify IFN-γ and GRZB production by T cells either co-cultured with cancer cells in the presence of exogenous adjuvant stimuli (i.e., an antibody targeting the immune checkpoint programmed cell death protein 1, and recombinant calreticulin) and boosting with TAAs (i.e., the model SIINFEKL ovalbumin antigen). Specifically, we describe IFN-γ and GRZB quantification by flow cytometry, ELISA and ELISpot technologies.

Nucleation of antagonistic organisms and cellular competitions on curved, inflating substrates

We consider the dynamics of spatially distributed, diffusing populations of organisms with antagonistic interactions. These interactions are found on many length scales, ranging from kilometer-scale animal range dynamics with selection against hybrids to micron-scale interactions between poison-secreting microbial populations. We find that the dynamical line tension at the interface between antagonistic organisms suppresses survival probabilities of small clonal clusters: the line tension introduces a critical cluster size that an organism with a selective advantage must achieve before deterministically spreading through the population. We calculate the survival probability as a function of selective advantage δ and antagonistic interaction strength σ. Unlike a simple Darwinian selective advantage, the survival probability depends strongly on the spatial diffusion constant D_{s} of the strains when σ>0, with suppressed survival when both species are more motile. Finally, we study the survival probability of a single mutant cell at the frontier of a growing spherical cluster of cells, such as the surface of an avascular spherical tumor. Both the inflation and curvature of the frontier significantly enhance the survival probability by changing the critical size of the nucleating cell cluster.

Interrogating Mutant Allele Expression via Customized Reference Genomes to Define Influential Cancer Mutations

Genetic alterations are essential for cancer initiation and progression. However, differentiating mutations that drive the tumor phenotype from mutations that do not affect tumor fitness remains a fundamental challenge in cancer biology. To better understand the impact of a given mutation within cancer, RNA-sequencing data was used to categorize mutations based on their allelic expression. For this purpose, we developed the MAXX (Mutation Allelic Expression Extractor) software, which is highly effective at delineating the allelic expression of both single nucleotide variants and small insertions and deletions. Results from MAXX demonstrated that mutations can be separated into three groups based on their expression of the mutant allele, lack of expression from both alleles, or expression of only the wild-type allele. By taking into consideration the allelic expression patterns of genes that are mutated in PDAC, it was possible to increase the sensitivity of widely used driver mutation detection methods, as well as identify subtypes that have prognostic significance and are associated with sensitivity to select classes of therapeutic agents in cell culture. Thus, differentiating mutations based on their mutant allele expression via MAXX represents a means to parse somatic variants in tumor genomes, helping to elucidate a gene’s respective role in cancer.

Dark-matter matters: Discriminating subtle blood cancers using the darkest DNA

The confluence of deep sequencing and powerful machine learning is providing an unprecedented peek at the darkest of the dark genomic matter, the non-coding genomic regions lacking any functional annotation. While deep sequencing uncovers rare tumor variants, the heterogeneity of the disease confounds the best of machine learning (ML) algorithms. Here we set out to answer if the dark-matter of the genome encompass signals that can distinguish the fine subtypes of disease that are otherwise genomically indistinguishable. We introduce a novel stochastic regularization, ReVeaL, that empowers ML to discriminate subtle cancer subtypes even from the same ‘cell of origin’. Analogous to heritability, implicitly defined on whole genome, we use predictability (F₁score) definable on portions of the genome. In an effort to distinguish cancer subtypes using dark-matter DNA, we applied ReVeaL to a new WGS dataset from 727 patient samples with seven forms of hematological cancers and assessed the predictivity over several genomic regions including genic, non-dark, non-coding, non-genic, and dark. ReVeaL enabled improved discrimination of cancer subtypes for all segments of the genome. The non-genic, non-coding and dark-matter had the highest F₁ scores, with dark-matter having the highest level of predictability. Based on ReVeaL’s predictability of different genomic regions, dark-matter contains enough signal to significantly discriminate fine subtypes of disease. Hence, the agglomeration of rare variants, even in the hitherto unannotated and ill-understood regions of the genome, may play a substantial role in the disease etiology and deserve much more attention.

Many subtypes of cancer are unable to be distinguished based on their genomic profiles. With the ever-increasing use of sequencing, we now have the ability to look deeper into the genome and pick up on hidden signals in areas typically considered irrelevant to disease. To overcome the issue of rare variants and the vast amount of heterogeneity found in these non-coding sectors, we introduce a new algorithm capable of correcting for both challenges, ReVeaL. Using this approach, we are able to demonstrate that the non-coding regions of the genome have more signal for distinguishing subtle subtypes of disease compared to all the coding regions. Specifically, we show that the darkest unexplored genomic regions, the non-coding genome with no functional annotation whatsoever in the literature, have the strongest signal. Thus dark-matter does indeed matter and should not be ignored but rather considered for the continued pressing task of finding biomarkers of disease to adequately treat our patients.

CRISPR/Cas9 as a tool to dissect cancer mutations

The CRISPR/Cas9 system is transforming many biomedical disciplines, including cancer research. Through its flexible programmability and efficiency to induce DNA double strand breaks it has become straightforward to introduce cancer mutations into cells in vitro and/or in vivo. However, not all mutations contribute equally to tumorigenesis and distinguishing essential mutations for tumor growth and survival from biologically inert mutations is cumbersome. Here we present a method to screen for the functional relevance of mutations in high throughput in established cancer cell lines. We employ the CRISPR/Cas9 system to probe cancer vulnerabilities in a colorectal carcinoma cell line in an attempt to identify novel cancer driver mutations. We designed 100 high quality sgRNAs that are able to specifically cleave mutations present in the colorectal carcinoma cell line RKO. An all-in-one lentiviral library harboring these sgRNAs was then generated and used in a pooled screen to probe possible growth dependencies on these mutations. Genomic DNA at different time points were collected, the sgRNA cassettes were PCR amplified, purified and sgRNA counts were quantified by means of deep sequencing. The analysis revealed two sgRNAs targeting the same mutation (UTP14A: S99delS) to be depleted over time in RKO cells. Validation and characterization confirmed that the inactivation of this mutation impairs cell growth, nominating UTP14A: S99delS as a putative driver mutation in RKO cells. Overall, our approach demonstrates that the CRISPR/Cas9 system is a powerful tool to functionally dissect cancer mutations at large-scale.

A driving test for oncogenic mutations

Activating mutations in protein kinases are a frequent cause of cancer, and selecting drugs that act on these oncogenic kinases can lead to effective therapies. Targeted or whole-genome sequencing of tumor samples can readily reveal the presence of mutations, but discerning previously uncharacterized activating "driver" mutations that will respond to drug treatment from much more abundant but inconsequential "passenger" mutations is problematic. Chakroborty et al. apply a screening approach that leverages error-prone PCR and a proliferating cell model to identify such gain-of-function mutants in the epidermal growth factor receptor (EGFR) kinase. The screen is validated by the identification of known cancer-promoting mutations and reveals a previously unappreciated oncogenic EGFR mutation, A702V, demonstrating its power for discovery of driver mutations.

Functional Characterization of Colon Cancer-Associated Mutations in ADAM17: Modifications in the Pro-Domain Interfere with Trafficking and Maturation

Colorectal cancer is one of the most commonly diagnosed malignancies in the Western world and is associated with elevated expression and activity of epidermal growth factor receptors (EGF-R). The metalloproteinase ADAM17 is involved in EGF-R activation by processing EGF-R ligands from membrane-bound pro-ligands. Underlining the link between colon cancer and ADAM17, genetic intestinal cancer models in ADAM17-deficient mice show a reduced tumor burden. In this study, we characterize point mutations within the ADAM17 gene found in the tissue of colon cancer patients. In order to shed light on the role of ADAM17 in cancer development, as well as into the mechanisms that regulate maturation and cellular trafficking of ADAM17, we here perform overexpression studies of four ADAM17 variants located in the pro-, membrane-proximal- and cytoplasmic-domain of the ADAM17 protein in ADAM10/17-deficient HEK cells. Interestingly, we found a cancer-associated point mutation within the pro-domain of ADAM17 (R177C) to be most impaired in its proteolytic activity and trafficking to the cell membrane. By comparing this variant to an ADAM17 construct lacking the entire pro-domain, we discovered similar functional limitations and propose a crucial role of the pro-domain for ADAM17 maturation, cellular trafficking and thus proteolytic activity.

Opportunities and challenges of implementing Pharmacogenomics in cancer drug development

Cancer drug development is a time and resources consuming process. Around 90% of drugs entering clinical trials fail due to lack of efficacy and/or safety issues, more often after conspicuous research and economic efforts. Part of the discarded drugs might be beneficial only in a subgroup of the study patients, and some adverse events might be prevented by identifying those patients more vulnerable to toxicities. The implementation of pharmacogenomic biomarkers allows the categorization of patients, to predict efficacy and toxicity and to optimize the drug development process. Around seventy FDA approved drugs currently present one or more genetic biomarker to keep in consideration, and with the progress of Precision Medicine tailoring therapies on individuals' genomic landscape promises to become a new standard of cancer care. In the current article we review the role of pharmacogenomics in cancer drug development, underlying the advantages and challenges of their implementation.

Recurrent Noncoding Mutations in Skin Cancers: UV Damage Susceptibility or Repair Inhibition as Primary Driver?

Somatic mutations arising in human skin cancers are heterogeneously distributed across the genome, meaning that certain genomic regions (e.g., heterochromatin or transcription factor binding sites) have much higher mutation densities than others. Regional variations in mutation rates are typically not a consequence of selection, as the vast majority of somatic mutations in skin cancers are passenger mutations that do not promote cell growth or transformation. Instead, variations in DNA repair activity, due to chromatin organization and transcription factor binding, have been proposed to be a primary driver of mutational heterogeneity in melanoma. However, as discussed in this review here, recent studies indicate that chromatin organization and transcription factor binding also significantly modulate the rate at which UV lesions form in DNA. The authors propose that local variations in lesion susceptibility may be an important driver of mutational hotspots in melanoma and other skin cancers, particularly at binding sites for ETS transcription factors.

Mutational and Antigenic Landscape in Tumor Progression and Cancer Immunotherapy

Evolving neoplasms accumulate non-synonymous mutations at a high rate, potentially enabling the expression of antigenic epitopes that can be recognized by the immune system. Since they are not covered by central tolerance, such tumor neoantigens (TNAs) should be under robust immune control as they surge. However, genetic defects that impair cancer cell eradication by the immune system coupled with the establishment of local immunosuppression can enable TNA accumulation, which is generally associated with improved clinical sensitivity to various immunotherapies. Here, we explore how tumor-intrinsic factors and immunological processes shape the mutational and antigenic landscape of evolving neoplasms to influence clinical responses to immunotherapy, and propose strategies to achieve robust immunological control of the disease despite disabled immunosurveillance.

Untouchable genes in the human genome: Identifying ideal targets for cancer treatment

BACKGROUND

Usually, genes with a higher-than-expected number of somatic mutations in tumor samples are assumed to be cancer related. We identified genes with a fewer-than-expected number of somatic mutations - "untouchable genes".

METHODS

To predict the expected number of somatic mutations, we used a linear regression model with the number of mutations in the gene as an outcome, and gene characteristics, including gene size, nucleotide composition, level of evolutionary conservation, expression level and others, as predictors. Analysis of residuals from the regression model was used to compare the observed and predicted number of mutations.

RESULTS

We have identified 19 genes with a less-than-expected number of loss-off-function (nonsense, frameshift or pathogenic missense) mutations - i.e., untouchable genes. The number of silent or neutral missense mutations in untouchable genes was equal or higher than the expected number. Many mucins, including MUC16, MUC17, MUC6, MUC5AC, MUC5B, and MUC12, are untouchable. We hypothesized that untouchable mucins help tumor cells to avoid immune response by providing a protective coat that prevents direct contact between effector immune cells, e.g., cytotoxic T-cells, and tumor cells. Survival analysis of available TCGA data demonstrated that overall survival of patients with low (below the median) expression of untouchable mucins was better compared to patients with high expression of untouchable mucins. Aside from mucins, we have identified a number of other untouchable genes.

CONCLUSIONS

Untouchable genes may be ideal targets for cancer treatment since suppression of untouchable genes is expected to inhibit survival of tumor cells.

Mutation tracking of a patient with EGFR-mutant lung cancer harboring de novo MET amplification: Successful treatment with gefitinib and crizotinib

OBJECTIVE

De novo mesenchymal-epithelial transition (MET) amplification is believed to promote primary resistance to epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors in the non-squamous non-small cell lung cancer (NSCLC). We sought to seek the treatment of a patient with EGFR-mutant NSCLC harboring de novo MET amplification.

MATERIALS AND METHODS

After clinical diagnosis, tissue and plasma samples were obtained from the patient and subjected to next-generation sequencing to identify and dynamic monitor the mutations.

RESULTS

The patient was treated with gefitinib monotherapy in the beginning and experienced primary resistance to gefitinib but achieved a good response to the combination therapy of gefitinib and crizotinib. He achieved a 16.8-month progress free survival with the combination therapy. NGS of plasma circulating cell-free tumor DNA shown that L858R mutation was no longer detectable and the copy number of MET dropped when the patient got remission.

CONCLUSIONS

The combination of EGFR- and MET- tyrosine kinase inhibitors may be an effective treatment for the rare mutations.

Analyzing Biomolecular Ensembles

Several techniques are available to generate conformational ensembles of proteins and other biomolecules either experimentally or computationally. These methods produce a large amount of data that need to be analyzed to identify structure-dynamics-function relationship. In this chapter, we will cover different tools to unveil the information hidden in conformational ensemble data and to guide toward the rationalization of the data. We included routinely used approaches such as dimensionality reduction, as well as new methods inspired by high-order statistics and graph theory.

Understanding Obesity as a Risk Factor for Uterine Tumors Using Drosophila

Multiple large-scale epidemiological studies have identified obesity as an important risk factor for a variety of human cancers, particularly cancers of the uterus, gallbladder, kidney, liver, colon, and ovary, but there is much uncertainty regarding how obesity increases the cancer risks. Given that obesity has been consistently identified as a major risk factor for uterine tumors, the most common malignancies of the female reproductive system, we use uterine tumors as a pathological context to survey the relevant literature and propose a novel hypothesis: chronic downregulation of the cyclin-dependent kinase 8 (CDK8) module, composed of CDK8 (or its paralog CDK19), Cyclin C, MED12 (or MED12L), and MED13 (or MED13L), by elevated insulin or insulin-like growth factor signaling in obese women may increase the chances to dysregulate the activities of transcription factors regulated by the CDK8 module, thereby increasing the risk of uterine tumors. Although we focus on endometrial cancer and uterine leiomyomas (or fibroids), two major forms of uterine tumors, our model may offer additional insights into how obesity increases the risk of other types of cancers and diseases. To illustrate the power of model organisms for studying human diseases, here we place more emphasis on the findings obtained from Drosophila melanogaster.

Age-related diseases as vicious cycles

The mortality rates of age-related diseases (ARDs) increase exponentially with age. Processes described by the exponential growth function typically involve a branching chain reaction or, more generally, a positive feedback loop. Here I propose that each ARD is mediated by one or several positive feedback loops (vicious cycles). I then identify critical vicious cycles in five major ARDs: atherosclerosis, hypertension, diabetes, Alzheimer's and Parkinson's. I also propose that the progression of ARDs can be halted by selectively interrupting the vicious cycles and suggest the most promising targets.

Potential application of cell reprogramming techniques for cancer research

The ability to control the transition from an undifferentiated stem cell to a specific cell fate is one of the key techniques that are required for the application of interventional technologies to regenerative medicine and the treatment of tumors and metastases and of neurodegenerative diseases. Reprogramming technologies, which include somatic cell nuclear transfer, induced pluripotent stem cells, and the direct reprogramming of specific cell lineages, have the potential to alter cell plasticity in translational medicine for cancer treatment. The characterization of cancer stem cells (CSCs), the identification of oncogene and tumor suppressor genes for CSCs, and the epigenetic study of CSCs and their microenvironments are important topics. This review summarizes the application of cell reprogramming technologies to cancer modeling and treatment and discusses possible obstacles, such as genetic and epigenetic alterations in cancer cells, as well as the strategies that can be used to overcome these obstacles to cancer research.

Integrating tumor genomics into studies of the microbiome in colorectal cancer

Although the gut microbiome has been linked to colorectal cancer (CRC) development, associations of microbial taxa with CRC status are often inconsistent across studies. We have recently shown that tumor genomics, a factor that is rarely incorporated in analyses of the CRC microbiome, has a strong effect on the composition of the microbiota. Here, we discuss these results in the wider context of studies characterizing interaction between host genetics and the microbiome, and describe the implications of our findings for understanding the role of the microbiome in CRC.

Gene characteristics predicting missense, nonsense and frameshift mutations in tumor samples

BACKGROUND

Because driver mutations provide selective advantage to the mutant clone, they tend to occur at a higher frequency in tumor samples compared to selectively neutral (passenger) mutations. However, mutation frequency alone is insufficient to identify cancer genes because mutability is influenced by many gene characteristics, such as size, nucleotide composition, etc. The goal of this study was to identify gene characteristics associated with the frequency of somatic mutations in the gene in tumor samples.

RESULTS

We used data on somatic mutations detected by genome wide screens from the Catalog of Somatic Mutations in Cancer (COSMIC). Gene size, nucleotide composition, expression level of the gene, relative replication time in the cell cycle, level of evolutionary conservation and other gene characteristics (totaling 11) were used as predictors of the number of somatic mutations. We applied stepwise multiple linear regression to predict the number of mutations per gene. Because missense, nonsense, and frameshift mutations are associated with different sets of gene characteristics, they were modeled separately. Gene characteristics explain 88% of the variation in the number of missense, 40% of nonsense, and 23% of frameshift mutations. Comparisons of the observed and expected numbers of mutations identified genes with a higher than expected number of mutations- positive outliers. Many of these are known driver genes. A number of novel candidate driver genes was also identified.

CONCLUSIONS

By comparing the observed and predicted number of mutations in a gene, we have identified known cancer-associated genes as well as 111 novel cancer associated genes. We also showed that adding the number of silent mutations per gene reported by genome/exome wide screens across all cancer type (COSMIC data) as a predictor substantially exceeds predicting accuracy of the most popular cancer gene predicting tool - MutsigCV.

Challenges in the introduction of next-generation sequencing (NGS) for diagnostics of myeloid malignancies into clinical routine use

Given the vast phenotypic and genetic heterogeneity of acute and chronic myeloid malignancies, hematologists have eagerly awaited the introduction of next-generation sequencing (NGS) into the routine diagnostic armamentarium to enable a more differentiated disease classification, risk stratification, and improved therapeutic decisions. At present, an increasing number of hematologic laboratories are in the process of integrating NGS procedures into the diagnostic algorithms of patients with acute myeloid leukemia (AML), myelodysplastic syndromes (MDS), and myeloproliferative neoplasms (MPNs). Inevitably accompanying such developments, physicians and molecular biologists are facing unexpected challenges regarding the interpretation and implementation of molecular genetic results derived from NGS in myeloid malignancies. This article summarizes typical challenges that may arise in the context of NGS-based analyses at diagnosis and during follow-up of myeloid malignancies.

Deciphering molecular properties of hypermutated gastrointestinal cancer

Great mutational heterogeneity is observed both across cancer types (>1000-fold) and within a given cancer type, with a fraction harboring >10 mutations per million bases, thus termed hypermutation. We determined the genome-wide effects of high mutation load on the transcriptome and methylome of two cancer types; namely, colorectal cancer (CRC) and stomach adenocarcinoma (STAD). Briefly, hierarchical clustering of the expression and methylation profiles showed that the majority of CRC and STAD hypermutated samples were mixed and separated from their respective non-hypermutated samples, exceeding the boundary of tissue specificity. Further in-detailed exploration uncovered that the underlying molecular mechanism may be related to the perturbation of chromatin remodeling genes.

Studying how genetic variants affect mechanism in biological systems

Genetic variants are currently a major component of system-wide investigations into biological function or disease. Approaches to select variants (often out of thousands of candidates) that are responsible for a particular phenomenon have many clinical applications and can help illuminate differences between individuals. Selecting meaningful variants is greatly aided by integration with information about molecular mechanism, whether known from protein structures or interactions or biological pathways. In this review we discuss the nature of genetic variants, and recent studies highlighting what is currently known about the relationship between genetic variation, biomolecular function, and disease.

A pan-cancer atlas of cancer hallmark-associated candidate driver lncRNAs

Substantial cancer genome sequencing efforts have discovered many important driver genes contributing to tumorigenesis. However, very little is known about the genetic alterations of long non‐coding RNAs (lncRNAs) in cancer. Thus, there is a need for systematic surveys of driver lncRNAs. Through integrative analysis of 5918 tumors across 11 cancer types, we revealed that lncRNAs have undergone dramatic genomic alterations, many of which are mutually exclusive with well‐known cancer genes. Using the hypothesis of functional redundancy of mutual exclusivity, we developed a computational framework to identify driver lncRNAs associated with different cancer hallmarks. Applying it to pan‐cancer data, we identified 378 candidate driver lncRNAs whose genomic features highly resemble the known cancer driver genes (e.g. high conservation and early replication). We further validated the candidate driver lncRNAs involved in ‘Tissue Invasion and Metastasis’ in lung adenocarcinoma and breast cancer, and also highlighted their potential roles in improving clinical outcomes. In summary, we have generated a comprehensive landscape of cancer candidate driver lncRNAs that could act as a starting point for future functional explorations, as well as the identification of biomarkers and lncRNA‐based target therapy.

Metastatic tumor cells - genotypes and phenotypes

BACKGROUND

Metastasis is the primary cause of mortality in cancer patients. Therefore, elucidating the genetics and epigenetics of metastatic tumor cells and the mechanisms by which tumor cells acquire metastatic properties constitute significant challenges in cancer research.

OBJECTIVE

To summarize the current understandings of the specific genotype and phenotype of the metastatic tumor cells.

METHOD and RESULT

In-depth genetic analysis of tumor cells, especially with advances in the next-generation sequencing, have revealed insights of the genotypes of metastatic tumor cells. Also, studies have shown that the cancer stem cell (CSC) and epithelial to mesenchymal transition (EMT) phenotypes are associated with the metastatic cascade.

CONCLUSION

In this review, we will discuss recent advances in the field by focusing on the genomic instability and phenotypic dynamics of metastatic tumor cells.

Inherent and toxicant-provoked reduction in DNA repair capacity: A key mechanism for personalized risk assessment, cancer prevention and intervention, and response to therapy

Genomic investigations reveal novel evidence which indicates that genetic predisposition and inherent drug response are key factors for development of cancer and for poor response to therapy. However, mechanisms for these outcomes and interactions with environmental factors have not been well-characterized. Therefore, cancer risk, prevention, intervention and prognosis determinations have still mainly been based on population, rather than on individualized, evaluations. The objective of this review was to demonstrate that a key mechanism which contributes to the determination is inherent and/or toxicant-provoked reduction in DNA repair capacity. In addition, functional and quantitative determination of DNA repair capacity on an individual basis would dramatically change the evaluation and management of health problems from a population to a personalized basis. In this review, justifications for the scenario were delineated. Topics to be presented include assays for detection of functional DNA repair deficiency, mechanisms for DNA repair defects, toxicant-perturbed DNA repair capacity, epigenetic mechanisms (methylation and miRNA expression) for alteration of DNA repair function, and bioinformatics approach to analyze large amount of genomic data. Information from these topics has recently been and will be used for better understanding of cancer causation and of response to therapeutic interventions. Consequently, innovative genomic- and mechanism-based evidence can be increasingly used to develop more precise cancer risk assessment, and target-specific and personalized medicine.

Cancer-mutation network and the number and specificity of driver mutations

Cancer genomics has produced extensive information on cancer-associated genes, but the number and specificity of cancer-driver mutations remains a matter of debate. We constructed a bipartite network in which 7,665 tumors from 30 cancer types are connected via shared mutations in 198 previously identified cancer genes. We show that about 27% of the tumors can be assigned to statistically supported modules, most of which encompass one or two cancer types. The rest of the tumors belong to a diffuse network component suggesting lower gene specificity of driver mutations. Linear regression of the mutational loads in cancer genes was used to estimate the number of drivers required for the onset of different cancers. The mean number of drivers in known cancer genes is approximately two, with a range of one to five. Cancers that are associated with modules had more drivers than those from the diffuse network component, suggesting that unidentified and/or interchangeable drivers exist in the latter.

Characterization of potential driver mutations involved in human breast cancer by computational approaches

Breast cancer is the second most frequently occurring form of cancer and is also the second most lethal cancer in women worldwide. A genetic mutation is one of the key factors that alter multiple cellular regulatory pathways and drive breast cancer initiation and progression yet nature of these cancer drivers remains elusive. In this article, we have reviewed various computational perspectives and algorithms for exploring breast cancer driver mutation genes. Using both frequency based and mutational exclusivity based approaches, we identified 195 driver genes and shortlisted 63 of them as candidate drivers for breast cancer using various computational approaches. Finally, we conducted network and pathway analysis to explore their functions in breast tumorigenesis including tumor initiation, progression, and metastasis.

Tumorigenic Cell Reprogramming and Cancer Plasticity: Interplay between Signaling, Microenvironment, and Epigenetics

Accumulating evidences indicate that many tumors rely on subpopulations of cancer stem cells (CSCs) with the ability to propagate malignant clones indefinitely and to produce an overt cancer. Of importance, CSCs seem to be more resistant to the conventional cytotoxic treatments, driving tumor growth and contributing to relapse. CSCs can originate from normal committed cells which undergo tumor-reprogramming processes and reacquire a stem cell-like phenotype. Increasing evidences also show how tumor homeostasis and progression strongly rely on the capacity of nontumorigenic cancer cells to dedifferentiate to CSCs. Both tumor microenvironment and epigenetic reprogramming drive such dynamic mechanisms, favoring cancer cell plasticity and tumor heterogeneity. Here, we report new developments which led to an advancement in the CSC field, elucidating the concepts of cancer cell of origin and CSC plasticity in solid tumor initiation and maintenance. We further discuss the main signaling pathways which, under the influence of extrinsic environmental factors, play a critical role in the formation and maintenance of CSCs. Moreover, we propose a review of the main epigenetic mechanisms whose deregulation can favor the onset of CSC features both in tumor initiation and tumor maintenance. Finally, we provide an update of the main strategies that could be applied to target CSCs and cancer cell plasticity.

Validating the concept of mutational signatures with isogenic cell models

The diversity of somatic mutations in human cancers can be decomposed into individual mutational signatures, patterns of mutagenesis that arise because of DNA damage and DNA repair processes that have occurred in cells as they evolved towards malignancy. Correlations between mutational signatures and environmental exposures, enzymatic activities and genetic defects have been described, but human cancers are not ideal experimental systems-the exposures to different mutational processes in a patient's lifetime are uncontrolled and any relationships observed can only be described as an association. Here, we demonstrate the proof-of-principle that it is possible to recreate cancer mutational signatures in vitro using CRISPR-Cas9-based gene-editing experiments in an isogenic human-cell system. We provide experimental and algorithmic methods to discover mutational signatures generated under highly experimentally-controlled conditions. Our in vitro findings strikingly recapitulate in vivo observations of cancer data, fundamentally validating the concept of (particularly) endogenously-arising mutational signatures.

Association mining of mutated cancer genes in different clinical stages across 11 cancer types

Many studies have demonstrated that some genes (e.g. APC, BRAF, KRAS, PTEN, TP53) are frequently mutated in cancer, however, underlying mechanism that contributes to their high mutation frequency remains unclear. Here we used Apriori algorithm to find the frequent mutational gene sets (FMGSs) from 4,904 tumors across 11 cancer types as part of the TCGA Pan-Cancer effort and then mined the hidden association rules (ARs) within these FMGSs. Intriguingly, we found that well-known cancer driver genes such as BRAF, KRAS, PTEN, and TP53 were often co-occurred with other driver genes and FMGSs size peaked at an itemset size of 3∼4 genes. Besides, the number and constitution of FMGS and ARs differed greatly among different cancers and stages. In addition, FMGS and ARs were rare in endocrine-related cancers such as breast carcinoma, ovarian cystadenocarcinoma, and thyroid carcinoma, but abundant in cancers contact directly with external environments such as skin melanoma and stomach adenocarcinoma. Furthermore, we observed more rules in stage IV than in other stages, indicating that distant metastasis needed more sophisticated gene regulatory network.

Practical aspects of NGS-based pathways analysis for personalized cancer science and medicine

Nowadays, the personalized approach to health care and cancer care in particular is becoming more and more popular and is taking an important place in the translational medicine paradigm. In some cases, detection of the patient-specific individual mutations that point to a targeted therapy has already become a routine practice for clinical oncologists. Wider panels of genetic markers are also on the market which cover a greater number of possible oncogenes including those with lower reliability of resulting medical conclusions. In light of the large availability of high-throughput technologies, it is very tempting to use complete patient-specific New Generation Sequencing (NGS) or other "omics" data for cancer treatment guidance. However, there are still no gold standard methods and protocols to evaluate them. Here we will discuss the clinical utility of each of the data types and describe a systems biology approach adapted for single patient measurements. We will try to summarize the current state of the field focusing on the clinically relevant case-studies and practical aspects of data processing.

QuaDMutEx: quadratic driver mutation explorer

BACKGROUND

Somatic mutations accumulate in human cells throughout life. Some may have no adverse consequences, but some of them may lead to cancer. A cancer genome is typically unstable, and thus more mutations can accumulate in the DNA of cancer cells. An ongoing problem is to figure out which mutations are drivers - play a role in oncogenesis, and which are passengers - do not play a role. One way of addressing this question is through inspection of somatic mutations in DNA of cancer samples from a cohort of patients and detection of patterns that differentiate driver from passenger mutations.

RESULTS

We propose QuaDMutEx, a method that incorporates three novel elements: a new gene set penalty that includes non-linear penalization of multiple mutations in putative sets of driver genes, an ability to adjust the method to handle slow- and fast-evolving tumors, and a computationally efficient method for finding gene sets that minimize the penalty, through a combination of heuristic Monte Carlo optimization and exact binary quadratic programming. Compared to existing methods, the proposed algorithm finds sets of putative driver genes that show higher coverage and lower excess coverage in eight sets of cancer samples coming from brain, ovarian, lung, and breast tumors.

CONCLUSIONS

Superior ability to improve on both coverage and excess coverage on different types of cancer shows that QuaDMutEx is a tool that should be part of a state-of-the-art toolbox in the driver gene discovery pipeline. It can detect genes harboring rare driver mutations that may be missed by existing methods. QuaDMutEx is available for download from https://github.com/bokhariy/QuaDMutEx under the GNU GPLv3 license.

The number of key carcinogenic events can be predicted from cancer incidence

The widely accepted multiple-hit hypothesis of carcinogenesis states that cancers arise after several successive events. However, no consensus has been reached on the quantity and nature of these events, although “driver” mutations or epimutations are considered the most probable candidates. By using the largest publicly available cancer incidence statistics (20 million cases), I show that incidence of 20 most prevalent cancer types in relation to patients’ age closely follows the Erlang probability distribution (R² = 0.9734–0.9999). The Erlang distribution describes the probability y of k independent random events occurring by the time x, but not earlier or later, with events happening on average every b time intervals. This fits well with the multiple-hit hypothesis and potentially allows to predict the number k of key carcinogenic events and the average time interval b between them, for each cancer type. Moreover, the amplitude parameter A likely predicts the maximal populational susceptibility to a given type of cancer. These parameters are estimated for 20 most common cancer types and provide numerical reference points for experimental research on cancer development.

Identification of driver copy number alterations in diverse cancer types and application in drug repositioning

Results from numerous studies suggest an important role for somatic copy number alterations (SCNAs) in cancer progression. Our work aimed to identify the drivers (oncogenes or tumor suppressor genes) that reside in recurrently aberrant genomic regions, including a large number of genes or non-coding genes, which remain a challenge for decoding the SCNAs involved in carcinogenesis. Here, we propose a new approach to comprehensively identify drivers, using 8740 cancer samples involving 18 cancer types from The Cancer Genome Atlas (TCGA). On average, 84 drivers were revealed for each cancer type, including protein-coding genes, long non-coding RNAs (lncRNA) and microRNAs (miRNAs). We demonstrated that the drivers showed significant attributes of cancer genes, and significantly overlapped with known cancer genes, including MYC, CCND1 and ERBB2 in breast cancer, and the lncRNA PVT1 in multiple cancer types. Pan-cancer analyses of drivers revealed specificity and commonality across cancer types, and the non-coding drivers showed a higher cancer-type specificity than that of coding drivers. Some cancer types from different tissue origins were found to converge to a high similarity because of the significant overlap of drivers, such as head and neck squamous cell carcinoma (HNSC) and lung squamous cell carcinoma (LUSC). The lncRNA SOX2-OT, a common driver of HNSC and LUSC, showed significant expression correlation with the oncogene SOX2. In addition, because some drivers are common in multiple cancer types and have been targeted by known drugs, we found that some drugs could be successfully repositioned, as validated by the datasets of drug response assays in cell lines. Our work reported a new method to comprehensively identify drivers in SCNAs across diverse cancer types, providing a feasible strategy for cancer drug repositioning as well as novel findings regarding cancer-associated non-coding RNA discovery.

Modeling the dynamics of chromosomal alteration progression in cervical cancer: A computational model

Computational modeling has been applied to simulate the heterogeneity of cancer behavior. The development of Cervical Cancer (CC) is a process in which the cell acquires dynamic behavior from non-deleterious and deleterious mutations, exhibiting chromosomal alterations as a manifestation of this dynamic. To further determine the progression of chromosomal alterations in precursor lesions and CC, we introduce a computational model to study the dynamics of deleterious and non-deleterious mutations as an outcome of tumor progression. The analysis of chromosomal alterations mediated by our model reveals that multiple deleterious mutations are more frequent in precursor lesions than in CC. Cells with lethal deleterious mutations would be eliminated, which would mitigate cancer progression; on the other hand, cells with non-deleterious mutations would become dominant, which could predispose them to cancer progression. The study of somatic alterations through computer simulations of cancer progression provides a feasible pathway for insights into the transformation of cell mechanisms in humans. During cancer progression, tumors may acquire new phenotype traits, such as the ability to invade and metastasize or to become clinically important when they develop drug resistance. Non-deleterious chromosomal alterations contribute to this progression.

Precision medicine for hepatocellular carcinoma: driver mutations and targeted therapy

Hepatocellular carcinoma (HCC) is the third most frequent cause of tumor-related mortality and there are an estimated approximately 850,000 new cases annually. Most HCC patients are diagnosed at middle or advanced stage, losing the opportunity of surgery. The development of HCC is promoted by accumulated diverse genetic mutations, which confer selective growth advantages to tumor cells and are called "driver mutations". The discovery of driver mutations provides a novel precision medicine strategy for late stage HCC, called targeted therapy. In this review, we summarized currently discovered driver mutations and corresponding signaling pathways, made an overview of identification methods of driver mutations and genes, and classified targeted drugs for HCC. The knowledge of mutational landscape deepen our understanding of carcinogenesis and promise future precision medicine for HCC patients.

Cancer cell reprogramming to identify the genes competent for generating liver cancer stem cells

The cancer stem cell (CSC) hypothesis postulates that cancer originates from the malignant transformation of stem/progenitor cells and is considered to apply to many cancers, including liver cancer. Identification that CSCs are responsible for drug resistance, metastasis, and secondary tumor appearance suggests that these populations are novel obligatory targets for the treatment of cancer. Here, we describe our new method for identifying potential CSC candidates. The reprogramming of cancer cells via induced pluripotent stem cell (iPSC) technology is a novel therapy for the treatment and for the study of CSC-related genes. This technology has advantages for studying the interactions between CSC-related genes and the cancer niche microenvironment. This technology may also provide a useful platform for studying the genes involved in the generation of CSCs before and after reprogramming, and for elucidating the mechanisms underlying cancer initiation and progression. The present review summarizes the current understanding of transcription factors involved in the generation of liver CSCs from liver cancer cell-derived iPSCs and how these contribute to oncogenesis, and discusses the modeling of liver cancer development.

Perspectives on Gene Regulatory Network Evolution

Animal development proceeds through the activity of genes and their cis-regulatory modules (CRMs) working together in sets of gene regulatory networks (GRNs). The emergence of species-specific traits and novel structures results from evolutionary changes in GRNs. Recent work in a wide variety of animal models, and particularly in insects, has started to reveal the modes and mechanisms of GRN evolution. I discuss here various aspects of GRN evolution and argue that developmental system drift (DSD), in which conserved phenotype is nevertheless a result of changed genetic interactions, should regularly be viewed from the perspective of GRN evolution. Advances in methods to discover related CRMs in diverse insect species, a critical requirement for detailed GRN characterization, are also described.

Molecular subtypes of colorectal cancers determined by PCR-based analysis

Tumor tissue consists of a heterogeneous cell population. The allelic imbalance (AI) ratio, determined in isolated tumor glands, is a good index of tumor heterogeneity. However, associations of the patterns of AI and microsatellite instability (MSI) development, observed in most cases of colorectal cancer (CRC), with tumor progression have not been reported previously. In this study, we examined whether CRC genetic profiles stratified by a combination of the AI ratio and MSI facilitate categorization of CRC, and whether these genetic profiles are associated with specific molecular alterations in CRC. A crypt isolation method was used to isolate DNA from tumors and normal glands obtained from 147 sporadic CRCs. AI and MSI statuses were determined using PCR‐based microsatellite analysis and stratified based on AI ratio and MSI status. DNA methylation status (high methylation, intermediate methylation and low methylation status and mutations in KRAS,BRAF, and TP53 were examined. In addition, mucin markers were immunostained. Based on this analysis, four subgroups were categorized. Subgroup 1 was characterized by a high MSI status and BRAF mutation; subgroup 2 was closely associated with a high AI ratio, which accumulated during the early phases of colorectal carcinogenesis, and TP53 mutation; subgroup 3 was associated with a low AI ratio, seen during the later phases of colorectal carcinogenesis, and KRAS mutation; and subgroup 4 was defined as a minor subgroup. These results confirmed that classification of distinct molecular profiles provides important insights into colorectal carcinogenesis.

High number of kinome-mutations in non-small cell lung cancer is associated with reduced immune response and poor relapse-free survival

Lung cancer is the leading cause of cancer related death, and the past years’ improved insight into underlying molecular events has significantly improved outcome for specific subsets of patients. In particular, several new therapies that target protein kinases have been implemented, and many more are becoming available. We have investigated lung cancer specimens for somatic mutations in a targeted panel of 612 human genes, the majority being protein kinases. The somatic mutation profiles were correlated to profiles of immune cell infiltration as well as relapse‐free survival. Targeted deep sequencing was performed on 117 tumour/normal pairs using the SureSelect Human Kinome kit (Agilent Technologies), with capture probes targeting 3.2 Mb of the human genome, including exons and untranslated regions of all known kinases, kinase receptors and selected cancer‐related genes (612 genes in total). CD8 staining was determined using Ventana Benchmark. Survival analyses were performed using SPSS. The number of mutations per sample ranged from 0 to 50 (within the 612 genes tested), with a median of nine. The prognosis was worse for patients with more than the median number of mutations. A significant correlation was found between mutations in one of selected DNA‐repair genes and the total number of mutations in that tumour (p < 0.001). There was a significant inverse correlation between the number of infiltrating stromal CD8+ lymphocytes and the presence of EGFR mutations.

What's new?

Lung carcinomas are among the tumours with highest mutation frequency. Here, the authors performed mutational analyses of 612 genes–including all known kinases and kinase receptors–in 117 non‐small cell lung cancer (NSCLC) tumours. They also investigated the relationship of mutation rate to number of infiltrating lymphocytes and to the clinical course of the disease. The number of mutations per sample varied, and the relapse‐free survival was worse for patients with more than the median number of mutations. Also, there was a significant inverse correlation between the number of infiltrating stromal CD8+ lymphocytes and the presence of EGFR mutations.

Genetic landscape of sporadic vestibular schwannoma

OBJECTIVE Vestibular schwannoma (VS) is a benign tumor with associated morbidities and reduced quality of life. Except for mutations in NF2, the genetic landscape of VS remains to be elucidated. Little is known about the effect of Gamma Knife radiosurgery (GKRS) on the VS genome. The aim of this study was to characterize mutations occurring in this tumor to identify new genes and signaling pathways important for the development of VS. In addition, the authors sought to evaluate whether GKRS resulted in an increase in the number of mutations. METHODS Forty-six sporadic VSs, including 8 GKRS-treated tumors and corresponding blood samples, were subjected to whole-exome sequencing and tumor-specific DNA variants were called. Pathway analysis was performed using the Ingenuity Pathway Analysis software. In addition, multiplex ligation-dependent probe amplification was performed to characterize copy number variations in the NF2 gene, and microsatellite instability testing was done to investigate for DNA replication error. RESULTS With the exception of a single sample with an aggressive phenotype that harbored a large number of mutations, most samples showed a relatively low number of mutations. A median of 14 tumor-specific mutations in each sample were identified. The GKRS-treated tumors harbored no more mutations than the rest of the group. A clustering of mutations in the cancer-related axonal guidance pathway was identified (25 patients), as well as mutations in the CDC27 (5 patients) and USP8 (3 patients) genes. Thirty-five tumors harbored mutations in NF2 and 16 tumors had 2 mutational hits. The samples without detectable NF2 mutations harbored mutations in genes that could be linked to NF2 or to NF2-related functions. None of the tumors showed microsatellite instability. CONCLUSIONS The genetic landscape of VS seems to be quite heterogeneous; however, most samples had mutations in NF2 or in genes that could be linked to NF2. The results of this study do not link GKRS to an increased number of mutations.

Non-coding cancer driver candidates identified with a sample- and position-specific model of the somatic mutation rate

Non-coding mutations may drive cancer development. Statistical detection of non-coding driver regions is challenged by a varying mutation rate and uncertainty of functional impact. Here, we develop a statistically founded non-coding driver-detection method, ncdDetect, which includes sample-specific mutational signatures, long-range mutation rate variation, and position-specific impact measures. Using ncdDetect, we screened non-coding regulatory regions of protein-coding genes across a pan-cancer set of whole-genomes (n = 505), which top-ranked known drivers and identified new candidates. For individual candidates, presence of non-coding mutations associates with altered expression or decreased patient survival across an independent pan-cancer sample set (n = 5454). This includes an antigen-presenting gene (CD1A), where 5'UTR mutations correlate significantly with decreased survival in melanoma. Additionally, mutations in a base-excision-repair gene (SMUG1) correlate with a C-to-T mutational-signature. Overall, we find that a rich model of mutational heterogeneity facilitates non-coding driver identification and integrative analysis points to candidates of potential clinical relevance.

The genomic landscape of breast cancer and its interaction with host immunity

Molecular profiling of thousands of primary breast cancers has uncovered remarkable genomic diversity between breast cancer subtypes, and even within subtypes. Only a few driver genes are recurrently altered at high frequency highlighting great challenges for precision medicine. Considerable evidence also confirms the role of host immunosurveillance in influencing response to therapy and prognosis in HER2+ and triple negative breast cancer. The role of immunosurveillance in ER + disease remains unclear. Advances in both these fields have lead to intensified interest in the interaction between genomic landscapes and host anti-tumour immune responses in breast cancer. In this review, we discuss the potential genomic determinants of host anti-tumour immunity - mutational load, driver alterations, mutational processes and neoantigens - and their relationship with immunity in breast cancer. Significant differences exist in both the genomic and immune characteristics amongst breast cancer subtypes. While ER + disease appears to be less immunogenic than HER2+ and triple negative breast cancer, it displays the greatest degree of heterogeneity. Mutational and neoantigen load appears to incompletely explains immune responses in breast cancer. Driver alterations do not appear to increase immunogenicity. Instead, they could contribute to immune-evasion or an immunosuppressive microenvironment, and therefore represent potential therapeutic targets. Finally, we also discuss the tailoring of immunotherapeutic strategies by genomic alterations, with possible multimodal combination approaches to maximise clinical benefits.

Functional analysis reveals that RBM10 mutations contribute to lung adenocarcinoma pathogenesis by deregulating splicing

RBM10 is an RNA splicing regulator that is frequently mutated in lung adenocarcinoma (LUAD) and has recently been proposed to be a cancer gene. How RBM10 mutations observed in LUAD affect its normal functions, however, remains largely unknown. Here integrative analysis of RBM10 mutation and RNA expression data revealed that LUAD-associated RBM10 mutations exhibit a mutational spectrum similar to that of tumor suppressor genes. In addition, this analysis showed that RBM10 mutations identified in LUAD patients lacking canonical oncogenes are associated with significantly reduced RBM10 expression. To systematically investigate RBM10 mutations, we developed an experimental pipeline for elucidating their functional effects. Among six representative LUAD-associated RBM10 mutations, one nonsense and one frameshift mutation caused loss-of-function as expected, whereas four missense mutations differentially affected RBM10-mediated splicing. Importantly, changes in proliferation rates of LUAD-derived cells caused by these RBM10 missense mutants correlated with alterations in RNA splicing of RBM10 target genes. Together, our data implies that RBM10 mutations contribute to LUAD pathogenesis, at least in large part, by deregulating splicing. The methods described in this study should be useful for analyzing mutations in additional cancer-associated RNA splicing regulators.

Mutated Chromatin Regulatory Factors as Tumor Drivers in Cancer

Genes encoding proteins that regulate chromatin structure and DNA modifications [i.e., chromatin regulatory factors (CRF)] and genes encoding histone proteins harbor recurrent mutations in most human cancers. These mutations lead to modifications in tumor chromatin and DNA structure and an altered epigenetic state that contribute to tumorigenesis. Mutated CRFs have now been identified in most types of cancer and are increasingly regarded as novel therapeutic targets. In this review, we discuss DNA alterations in CRFs and how these influence tumor chromatin structure and function, which in turn leads to tumorigenesis. We also discuss the clinical implications and review concepts of targeted treatments for these mutations. Continued research on CRF mutations will be critical for our future understanding of cancer biology and the development and implementation of novel cancer therapies. Cancer Res; 77(2); 227-33. ©2017 AACR.

A CRISPR/Cas9 Functional Screen Identifies Rare Tumor Suppressors

An enormous amount of tumor sequencing data has been generated through large scale sequencing efforts. The functional consequences of the majority of mutations identified by such projects remain an open, unexplored question. This problem is particularly complicated in the case of rare mutations where frequency of occurrence alone or prediction of functional consequences are insufficient to distinguish driver from passenger or bystander mutations. We combine genome editing technology with a powerful mouse cancer model to uncover previously unsuspected rare oncogenic mutations in Burkitt's lymphoma. We identify two candidate tumor suppressors whose loss cooperate with MYC over-expression to accelerate lymphomagenesis. Our results highlight the utility of in vivo CRISPR/Cas9 screens combined with powerful mouse models to identify and validate rare oncogenic modifier events from tumor mutational data.

The genetic heterogeneity of colorectal cancer predisposition - guidelines for gene discovery

BACKGROUND

Colorectal cancer (CRC) is a cumulative term applied to a clinically and genetically heterogeneous group of neoplasms that occur in the bowel. Based on twin studies, up to 45 % of the CRC cases may involve a heritable component. Yet, only in 5-10 % of these cases high-penetrant germline mutations are found (e.g. mutations in APC and DNA mismatch repair genes) that result in a familial aggregation and/or an early onset of the disease. Genome-wide association studies have revealed that another ~5 % of the CRC cases may be explained by a cumulative effect of low-penetrant risk factors. Recent attempts to identify novel genetic factors using whole exome and whole genome sequencing has proven to be difficult since the remaining, yet to be discovered, high penetrant CRC predisposing genes appear to be rare. In addition, most of the moderately penetrant candidate genes identified so far have not been confirmed in independent cohorts. Based on literature examples, we here discuss how careful patient and cohort selection, candidate gene and variant selection, and corroborative evidence may be employed to facilitate the discovery of novel CRC predisposing genes.

CONCLUSIONS

The picture emerges that the genetic predisposition to CRC is heterogeneous, involving complex interplays between common and rare (inter)genic variants with different penetrances. It is anticipated, however, that the use of large clinically well-defined patient and control datasets, together with improved functional and technical possibilities, will yield enough power to unravel this complex interplay and to generate accurate individualized estimates for the risk to develop CRC.