Pan-cancer analysis of whole genomes

Integrated genomic analysis identifies a genetic mutation model predicting response to immune checkpoint inhibitors in melanoma

Several biomarkers such as tumor mutation burden (TMB), neoantigen load (NAL), programmed cell‐death receptor 1 ligand (PD‐L1) expression, and lactate dehydrogenase (LDH) have been developed for predicting response to immune checkpoint inhibitors (ICIs) in melanoma. However, some limitations including the undefined cut‐off value, poor uniformity of test platform, and weak reliability of prediction have restricted the broad application in clinical practice. In order to identify a clinically actionable biomarker and explore an effective strategy for prediction, we developed a genetic mutation model named as immunotherapy score (ITS) for predicting response to ICIs therapy in melanoma, based on whole‐exome sequencing data from previous studies. We observed that patients with high ITS had better durable clinical benefit and survival outcomes than patients with low ITS in three independent cohorts, as well as in the meta‐cohort. Notably, the prediction capability of ITS was more robust than that of TMB. Remarkably, ITS was not only an independent predictor of ICIs therapy, but also combined with TMB or LDH to better predict response to ICIs than any single biomarker. Moreover, patients with high ITS harbored the immunotherapy‐sensitive characteristics including high TMB and NAL, ultraviolet light damage, impaired DNA damage repair pathway, arrested cell cycle signaling, and frequent mutations in NF1 and SERPINB3/4. Overall, these findings deserve prospective investigation in the future and may help guide clinical decisions on ICIs therapy for patients with melanoma.

Retrospective evaluation of whole exome and genome mutation calls in 746 cancer samples

The Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) curated consensus somatic mutation calls using whole exome sequencing (WES) and whole genome sequencing (WGS), respectively. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2,658 cancers across 38 tumour types, we compare WES and WGS side-by-side from 746 TCGA samples, finding that ~80% of mutations overlap in covered exonic regions. We estimate that low variant allele fraction (VAF < 15%) and clonal heterogeneity contribute up to 68% of private WGS mutations and 71% of private WES mutations. We observe that ~30% of private WGS mutations trace to mutations identified by a single variant caller in WES consensus efforts. WGS captures both ~50% more variation in exonic regions and un-observed mutations in loci with variable GC-content. Together, our analysis highlights technological divergences between two reproducible somatic variant detection efforts.

Genome-Wide Somatic Alterations in Multiple Myeloma Reveal a Superior Outcome Group

PURPOSE

Multiple myeloma (MM) is accompanied by heterogeneous somatic alterations. The overall goal of this study was to describe the genomic landscape of myeloma using deep whole-genome sequencing (WGS) and develop a model that identifies patients with long survival.

METHODS

We analyzed deep WGS data from 183 newly diagnosed patients with MM treated with lenalidomide, bortezomib, and dexamethasone (RVD) alone or RVD + autologous stem cell transplant (ASCT) in the IFM/DFCI 2009 study (ClinicalTrials.gov identifier: NCT01191060). We integrated genomic markers with clinical data.

RESULTS

We report significant variability in mutational load and processes within MM subgroups. The timeline of observed activation of mutational processes provides the basis for 2 distinct models of acquisition of mutational changes detected at the time of diagnosis of myeloma. Virtually all MM subgroups have activated DNA repair-associated signature as a prominent late mutational process, whereas APOBEC signature targeting C>G is activated in the intermediate phase of disease progression in high-risk MM. Importantly, we identify a genomically defined MM subgroup (17% of newly diagnosed patients) with low DNA damage (low genomic scar score with chromosome 9 gain) and a superior outcome (100% overall survival at 69 months), which was validated in a large independent cohort. This subgroup allowed us to distinguish patients with low- and high-risk hyperdiploid MM and identify patients with prolongation of progression-free survival. Genomic characteristics of this subgroup included lower mutational load with significant contribution from age-related mutations as well as frequent NRAS mutation. Surprisingly, their overall survival was independent of International Staging System and minimal residual disease status.

CONCLUSION

This is a comprehensive study identifying genomic markers of a good-risk group with prolonged survival. Identification of this patient subgroup will affect future therapeutic algorithms and research planning.

Tumor suppressor p53: from engaging DNA to target gene regulation

The p53 transcription factor confers its potent tumor suppressor functions primarily through the regulation of a large network of target genes. The recent explosion of next generation sequencing protocols has enabled the study of the p53 gene regulatory network (GRN) and underlying mechanisms at an unprecedented depth and scale, helping us to understand precisely how p53 controls gene regulation. Here, we discuss our current understanding of where and how p53 binds to DNA and chromatin, its pioneer-like role, and how this affects gene regulation. We provide an overview of the p53 GRN and the direct and indirect mechanisms through which p53 affects gene regulation. In particular, we focus on delineating the ubiquitous and cell type-specific network of regulatory elements that p53 engages; reviewing our understanding of how, where, and when p53 binds to DNA and the mechanisms through which these events regulate transcription. Finally, we discuss the evolution of the p53 GRN and how recent work has revealed remarkable differences between vertebrates, which are of particular importance to cancer researchers using mouse models.

Large-Scale Topological Changes Restrain Malignant Progression in Colorectal Cancer

Widespread changes to DNA methylation and chromatin are well documented in cancer, but the fate of higher-order chromosomal structure remains obscure. Here we integrated topological maps for colon tumors and normal colons with epigenetic, transcriptional, and imaging data to characterize alterations to chromatin loops, topologically associated domains, and large-scale compartments. We found that spatial partitioning of the open and closed genome compartments is profoundly compromised in tumors. This reorganization is accompanied by compartment-specific hypomethylation and chromatin changes. Additionally, we identify a compartment at the interface between the canonical A and B compartments that is reorganized in tumors. Remarkably, similar shifts were evident in non-malignant cells that have accumulated excess divisions. Our analyses suggest that these topological changes repress stemness and invasion programs while inducing anti-tumor immunity genes and may therefore restrain malignant progression. Our findings call into question the conventional view that tumor-associated epigenomic alterations are primarily oncogenic.

Using the "Hidden" genome to improve classification of cancer types

It is increasingly common clinically for cancer specimens to be examined using techniques that identify somatic mutations. In principle, these mutational profiles can be used to diagnose the tissue of origin, a critical task for the 3% to 5% of tumors that have an unknown primary site. Diagnosis of primary site is also critical for screening tests that employ circulating DNA. However, most mutations observed in any new tumor are very rarely occurring mutations, and indeed the preponderance of these may never have been observed in any previous recorded tumor. To create a viable diagnostic tool we need to harness the information content in this "hidden genome" of variants for which no direct information is available. To accomplish this we propose a multilevel meta-feature regression to extract the critical information from rare variants in the training data in a way that permits us to also extract diagnostic information from any previously unobserved variants in the new tumor sample. A scalable implementation of the model is obtained by combining a high-dimensional feature screening approach with a group-lasso penalized maximum likelihood approach based on an equivalent mixed-effect representation of the multilevel model. We apply the method to the Cancer Genome Atlas whole-exome sequencing data set including 3702 tumor samples across seven common cancer sites. Results show that our multilevel approach can harness substantial diagnostic information from the hidden genome.

Artificial Intelligence and Machine Learning in Nuclear Medicine: Future Perspectives

Artificial intelligence and machine learning based approaches are increasingly finding their way into various areas of nuclear medicine imaging. With the technical development of new methods and the expansion to new fields of application, this trend is likely to become even more pronounced in future. Possible means of application range from automated image reading and classification to correlation with clinical outcomes and to technological applications in image processing and reconstruction. In the context of tumor imaging, that is, predominantly FDG or PSMA PET imaging but also bone scintigraphy, artificial intelligence approaches can be used to quantify the whole-body tumor volume, for the segmentation and classification of pathological foci or to facilitate the diagnosis of micro-metastases. More advanced applications aim at the correlation of image features that are derived by artificial intelligence with clinical endpoints, for example, whole-body tumor volume with overall survival. In nuclear medicine imaging of benign diseases, artificial intelligence methods are predominantly used for automated and/or facilitated image classification and clinical decision making. Automated feature selection, segmentation and classification of myocardial perfusion scintigraphy can help in identifying patients that would benefit from intervention and to forecast clinical prognosis. Automated reporting of neurodegenerative diseases such as Alzheimer's disease might be extended to early diagnosis-being of special interest, if targeted treatment options might become available. Technological approaches include artificial intelligence-based attenuation correction of PET images, image reconstruction or anatomical landmarking. Attenuation correction is of special interest for avoiding the need of a coregistered CT scan, in the process of image reconstruction artefacts might be reduced, or ultra low-dose PET images might be denoised. The development of accurate ultra low-dose PET imaging might broaden the method's applicability, for example, toward oncologic PET screening. Most artificial intelligence approaches in nuclear medicine imaging are still in early stages of development, further improvements are necessary for broad clinical applications. In this review, we describe the current trends in the context fields of body oncology, cardiac imaging, and neuroimaging while an additional section puts emphasis on technological trends. Our aim is not only to describe currently available methods, but also to place a special focus on the description of possible future developments.

Paired tumor sequencing and germline testing in breast cancer management: An experience of a single academic center

BACKGROUND

Genetic testing for cancer predisposition is recommended to women with breast cancer who meet the criteria for such testing. After the FDA approvals of the poly ADP ribose polymerase (PARP) inhibitors, olaparib and talazoparib, for treatment of metastatic breast cancer, carrying germline mutations in BRCA1 and BRCA2 genes, the genetic testing result has become critical in their care. With the recent FDA approval of alpelisib for the treatment of PIK3CA-mutated hormone-receptor positive metastatic breast cancer, tumor molecular profiling to identify somatic mutations and potential molecularly targeted agents is increasingly utilized in the treatment of advanced breast cancer.

AIM

Combining germline and somatic sequencing (paired testing) offers an advantage over a single technique approach. Our study evaluates the role of paired testing on the management of breast cancer patients.

METHODS AND RESULTS

Forty-three breast cancer patients treated at Rush University Medical Center underwent paired germline and somatic variant testing in 2015 to 2017. A retrospective chart review was conducted with the analysis of demographic, clinical, and genomic data. Three actionable germline variants were found in the CHEK2 (2) and ATM (1) genes. 95% of tumors had somatic mutations. Seventy-seven percent of tumors had genomic alterations targetable with agents approved for breast cancer and 88% had molecular targets for agents approved for other cancers. Clinical examples of such use are described and potential future directions of tumor and paired testing are discussed.

CONCLUSIONS

Germline variants were present in a relatively small patient group not routinely tested for inherited alterations. Potentially targetable somatic alterations were identified in the majority of breast cancers. Paired testing is a feasible and efficient approach that delivers valuable information for the care of breast cancer patients and eliminates serial testing.

Pre- and Post-Zygotic TP53 De Novo Mutations in SHH-Medulloblastoma

Simple Summary

Medulloblastoma is the most common malignant brain tumor in children. In a subset of cases, a causal factor is a constitutive mutation of the TP53 gene, which may be inherited or arise for the first time in a patient (de novo). Using an immunohistochemistry assay as a screening tool, we selected patients suspected of harboring a TP53 mutation and offered genetic counseling and germline testing. Our study, which was the first to investigate the parental origin of TP53 mutations in medulloblastoma, allowed the identification of two additional cases with de novo mutations. Moreover, we demonstrated that in one patient the mutation originated at a post-zygotic stage, resulting in somatic mosaicism. These findings have important implications for genetic counseling since they highlight the occurrence of both pre- and post-zygotic TP53 de novo mutations in medulloblastoma, pointing out that in a specific subgroup of patients genetic testing should be offered regardless of family history.

Abstract

Li-Fraumeni syndrome (LFS) is an autosomal dominant disorder caused by mutations in the TP53 gene, predisposing to a wide spectrum of early-onset cancers, including brain tumors. In medulloblastoma patients, the role of TP53 has been extensively investigated, though the prevalence of de novo mutations has not been addressed. We characterized TP53 mutations in a monocentric cohort of consecutive Sonic Hedgehog (SHH)-activated medulloblastoma patients. Germline testing was offered based on tumor p53 immunostaining positivity. Among 24 patients, three (12.5%) showed tumor p53 overexpression, of whom two consented to undergo germline testing and resulted as carriers of TP53 mutations. In the first case, family history was uneventful and the mutation was not found in either of the parents. The second patient, with a family history suggestive of LFS, unexpectedly resulted as a carrier of the mosaic mutation c.742=/C>T p.(Arg248=/Trp). The allele frequency was 26% in normal tissues and 42–77% in tumor specimens. Loss of heterozygosity (LOH) in the tumor was also confirmed. Notably, the mosaic case has been in complete remission for more than one year, while the first patient, as most TP53-mutated medulloblastoma cases from other cohorts, showed a severe and rapidly progressive disease. Our study reported the first TP53 mosaic mutation in medulloblastoma patients and confirmed the importance of germline testing in p53 overexpressed SHH-medulloblastoma, regardless of family history.

Pathogenic impact of transcript isoform switching in 1,209 cancer samples covering 27 cancer types using an isoform-specific interaction network

Under normal conditions, cells of almost all tissue types express the same predominant canonical transcript isoform at each gene locus. In cancer, however, splicing regulation is often disturbed, leading to cancer-specific switches in the most dominant transcripts (MDT). To address the pathogenic impact of these switches, we have analyzed isoform-specific protein-protein interaction disruptions in 1,209 cancer samples covering 27 different cancer types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) project of the International Cancer Genomics Consortium (ICGC). Our study revealed large variations in the number of cancer-specific MDT (cMDT) with the highest frequency in cancers of female reproductive organs. Interestingly, in contrast to the mutational load, cancers arising from the same primary tissue had a similar number of cMDT. Some cMDT were found in 100% of all samples in a cancer type, making them candidates for diagnostic biomarkers. cMDT tend to be located at densely populated network regions where they disrupted protein interactions in the proximity of pathogenic cancer genes. A gene ontology enrichment analysis showed that these disruptions occurred mostly in protein translation and RNA splicing pathways. Interestingly, samples with mutations in the spliceosomal complex tend to have higher number of cMDT, while other transcript expressions correlated with mutations in non-coding splice-site and promoter regions of their genes. This work demonstrates for the first time the large extent of cancer-specific alterations in alternative splicing for 27 different cancer types. It highlights distinct and common patterns of cMDT and suggests novel pathogenic transcripts and markers that induce large network disruptions in cancers.

A Practical Guide for Structural Variation Detection in the Human Genome

Profiling genetic variants-including single nucleotide variants, small insertions and deletions, copy number variations, and structural variations (SVs)-from both healthy individuals and individuals with disease is a key component of genetic and biomedical research. SVs are large-scale changes in the genome and involve breakage and rejoining of DNA fragments. They may affect thousands to millions of nucleotides and can lead to loss, gain, and reshuffling of genes and regulatory elements. SVs are known to impact gene expression and potentially result in altered phenotypes and diseases. Therefore, identifying SVs from the human genomes is particularly important. In this review, I describe advantages and disadvantages of the available high-throughput assays for the discovery of SVs, which are the most challenging genetic alterations to detect. A practical guide is offered to suggest the most suitable strategies for discovering different types of SVs including common germline, rare, somatic, and complex variants. I also discuss factors to be considered, such as cost and performance, for different strategies when designing experiments. Last, I present several approaches to identify potential SV artifacts caused by samples, experimental procedures, and computational analysis. © 2020 Wiley Periodicals LLC.

The genetic and epigenetic landscape of early-onset colorectal cancer

Colorectal cancer (CRC) in individuals under the age of 50 is a problem that is increasing in USA and around the world. In this review, we discuss the degree to which early-onset (EO)CRC may be due to unsuspected Lynch syndrome or other inherited germline variants that predispose to cancer, describe the known somatic genetic alterations in EO tumors and discuss alterations in DNA methylation. Approximately 20% of EOCRCs can be attributed to identifiable germline mutations in genes that cause familial cancer syndromes. A variety of other genetic/epigenetic alterations have also been reported. We conclude that this is a heterogeneous problem, that requires a comprehensive analysis of genetic/epigenetic signatures to better understand EOCRC. Various subsets of EOCRCs must be analyzed individually for clues regarding the etiologies and possible specific therapies for this disease.

DNA mismatch repair promotes APOBEC3-mediated diffuse hypermutation in human cancers

Certain mutagens, including the APOBEC3 (A3) cytosine deaminase enzymes, can create multiple genetic changes in a single event. Activity of A3s results in striking 'mutation showers' occurring near DNA breakpoints; however, less is known about the mechanisms underlying the majority of A3 mutations. We classified the diverse patterns of clustered mutagenesis in tumor genomes, which identified a new A3 pattern: nonrecurrent, diffuse hypermutation (omikli). This mechanism occurs independently of the known focal hypermutation (kataegis), and is associated with activity of the DNA mismatch-repair pathway, which can provide the single-stranded DNA substrate needed by A3, and contributes to a substantial proportion of A3 mutations genome wide. Because mismatch repair is directed towards early-replicating, gene-rich chromosomal domains, A3 mutagenesis has a high propensity to generate impactful mutations, which exceeds that of other common carcinogens such as tobacco smoke and ultraviolet exposure. Cells direct their DNA repair capacity towards more important genomic regions; thus, carcinogens that subvert DNA repair can be remarkably potent.

Subclonal reconstruction of tumors by using machine learning and population genetics

Most cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction methods based on machine learning aim to separate those subpopulations in a sample and infer their evolutionary history. However, current approaches are entirely data driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in the analysis if evolution is not accounted for, and this is exacerbated with multi-sampling of the same tumor. We present a novel approach for model-based tumor subclonal reconstruction, called MOBSTER, which combines machine learning with theoretical population genetics. Using public whole-genome sequencing data from 2,606 samples from different cohorts, new data and synthetic validation, we show that this method is more robust and accurate than current techniques in single-sample, multiregion and longitudinal data. This approach minimizes the confounding factors of nonevolutionary methods, thus leading to more accurate recovery of the evolutionary history of human cancers.

Genomics and Functional Genomics of Malignant Pleural Mesothelioma

Malignant pleural mesothelioma (MPM) is a rare, aggressive cancer of the mesothelial cells lining the pleural surface of the chest wall and lung. The etiology of MPM is strongly associated with prior exposure to asbestos fibers, and the median survival rate of the diagnosed patients is approximately one year. Despite the latest advancements in surgical techniques and systemic therapies, currently available treatment modalities of MPM fail to provide long-term survival. The increasing incidence of MPM highlights the need for finding effective treatments. Targeted therapies offer personalized treatments in many cancers. However, targeted therapy in MPM is not recommended by clinical guidelines mainly because of poor target definition. A better understanding of the molecular and cellular mechanisms and the predictors of poor clinical outcomes of MPM is required to identify novel targets and develop precise and effective treatments. Recent advances in the genomics and functional genomics fields have provided groundbreaking insights into the genomic and molecular profiles of MPM and enabled the functional characterization of the genetic alterations. This review provides a comprehensive overview of the relevant literature and highlights the potential of state-of-the-art genomics and functional genomics research to facilitate the development of novel diagnostics and therapeutic modalities in MPM.

Extrachromosomal DNA is associated with oncogene amplification and poor outcome across multiple cancers

Extrachromosomal DNA (ecDNA) amplification promotes intratumoral genetic heterogeneity and accelerated tumor evolution1-3; however, its frequency and clinical impact are unclear. Using computational analysis of whole-genome sequencing data from 3,212 cancer patients, we show that ecDNA amplification frequently occurs in most cancer types but not in blood or normal tissue. Oncogenes were highly enriched on amplified ecDNA, and the most common recurrent oncogene amplifications arose on ecDNA. EcDNA amplifications resulted in higher levels of oncogene transcription compared to copy number-matched linear DNA, coupled with enhanced chromatin accessibility, and more frequently resulted in transcript fusions. Patients whose cancers carried ecDNA had significantly shorter survival, even when controlled for tissue type, than patients whose cancers were not driven by ecDNA-based oncogene amplification. The results presented here demonstrate that ecDNA-based oncogene amplification is common in cancer, is different from chromosomal amplification and drives poor outcome for patients across many cancer types.

Sex differences in oncogenic mutational processes

Sex differences have been observed in multiple facets of cancer epidemiology, treatment and biology, and in most cancers outside the sex organs. Efforts to link these clinical differences to specific molecular features have focused on somatic mutations within the coding regions of the genome. Here we report a pan-cancer analysis of sex differences in whole genomes of 1983 tumours of 28 subtypes as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium. We both confirm the results of exome studies, and also uncover previously undescribed sex differences. These include sex-biases in coding and non-coding cancer drivers, mutation prevalence and strikingly, in mutational signatures related to underlying mutational processes. These results underline the pervasiveness of molecular sex differences and strengthen the call for increased consideration of sex in molecular cancer research.

Depiction of tumor stemlike features and underlying relationships with hazard immune infiltrations based on large prostate cancer cohorts

Prostate cancer stemness (PCS) cells have been reported to drive tumor progression, recurrence and drug resistance. However, there is lacking systematical assessment of stemlike indices and associations with immunological properties in prostate adenocarcinoma (PRAD). We thus collected 7 PRAD cohorts with 1465 men and calculated the stemlike indices for each sample using one-class logistic regression machine learning algorithm. We selected the mRNAsi to quantify the stemlike indices that correlated significantly with prognosis and accordingly identified 21 PCS-related CpG loci and 13 pivotal signature. The 13-gene based PCS model possessed high predictive significance for progression-free survival (PFS) that was trained and validated in 7 independent cohorts. Meanwhile, we conducted consensus clustering and classified the total cohorts into 5 PCS clusters with distinct outcomes. Samples in PCScluster5 possessed the highest stemness fractions and suffered from the worst prognosis. Additionally, we implemented the CIBERSORT algorithm to infer the differential abundance across 5 PCS clusters. The activated immune cells (CD8+ T cell and dendritic cells) infiltrated significantly less in PCScluster5 than other clusters, supporting the negative regulations between stemlike indices and anticancer immunity. High mRNAsi was also found to be associated with up-regulation of immunosuppressive checkpoints, like PDL1. Lastly, we used the Connectivity Map (CMap) resource to screen potential compounds for targeting PRAD stemness, including the top hits of cell cycle inhibitor and FOXM1 inhibitor. Taken together, our study comprehensively evaluated the PRAD stemlike indices based on large cohorts and established a 13-gene based classifier for predicting prognosis or potential strategies for stemness treatment.

Shiny-SoSV: A web-based performance calculator for somatic structural variant detection

Somatic structural variants are an important contributor to cancer development and evolution. Accurate detection of these complex variants from whole genome sequencing data is influenced by a multitude of parameters. However, there are currently no tools for guiding study design nor are there applications that could predict the performance of somatic structural variant detection. To address this gap, we developed Shiny-SoSV, a user-friendly web-based calculator for determining the impact of common variables on the sensitivity, precision and F1 score of somatic structural variant detection, including choice of variant detection tool, sequencing depth of coverage, variant allele fraction, and variant breakpoint resolution. Using simulation studies, we determined singular and combinatoric effects of these variables, modelled the results using a generalised additive model, allowing structural variant detection performance to be predicted for any combination of predictors. Shiny-SoSV provides an interactive and visual platform for users to easily compare individual and combined impact of different parameters. It predicts the performance of a proposed study design, on somatic structural variant detection, prior to the commencement of benchwork. Shiny-SoSV is freely available at https://hcpcg.shinyapps.io/Shiny-SoSV with accompanying user’s guide and example use-cases.

Multi-site clonality analysis uncovers pervasive heterogeneity across melanoma metastases

Metastatic melanoma carries a poor prognosis despite modern systemic therapies. Understanding the evolution of the disease could help inform patient management. Through whole-genome sequencing of 13 melanoma metastases sampled at autopsy from a treatment naïve patient and by leveraging the analytical power of multi-sample analyses, we reveal evidence of diversification among metastatic lineages. UV-induced mutations dominate the trunk, whereas APOBEC-associated mutations are found in the branches of the evolutionary tree. Multi-sample analyses from a further seven patients confirmed that lineage diversification was pervasive, representing an important mode of melanoma dissemination. Our analyses demonstrate that joint analysis of cancer cell fraction estimates across multiple metastases can uncover previously unrecognised levels of tumour heterogeneity and highlight the limitations of inferring heterogeneity from a single biopsy.

Mucinous Adenocarcinoma of the Rectum: A Whole Genome Sequencing Study

Introduction

Mucinous adenocarcinoma of the rectum is an infrequently encountered histological subtype that is associated with an impaired response to chemoradiotherapy and a worse overall prognosis. A genomic profile analysis of mucinous rectal tumors has not yet been performed. The aim of this study was to comprehensively describe the burden of somatic mutations and copy number variation as well as perform mutational signature and microbial analysis of an in-house collected cohort of mucinous adenocarcinoma of the rectum.

Methods

Genomic DNA was extracted from 10 cases of mucinous rectal cancer and matched normal tissue. Whole genome sequencing (WGS) was carried out on these 10 cases and a comprehensive bioinformatic analysis was undertaken.

Results

The average number of SNVs, InDels and SVs in the cohort was 16,600, 1,855, and 120, respectively. A single case was MSI-H. KRAS mutations were found in 70% of cases while TP53 was mutated in only 40% of cases. CNA gain was identified on chromosomes 7, 8, 12, 13, and 20 while CNA loss was found on chromosomes 4, 8, 17, and 18 corresponding to oncogenes and tumor suppressor genes, respectively. Overall mucinous rectal cancers are more likely to be MSI-H and to have KRAS, BRAF, and PIK3CA mutations when compared to rectal adenocarcinoma NOS. Microbial analysis demonstrated an abundance of Fusobacterium nucleatum in tumor samples compared to normal tissue.

Conclusion

This study provides a detailed WGS analysis of 10 cases of mucinous rectal cancer. It demonstrates an important lesson in tumor biology in that histologically similar tumors can have extensive differences at the genomic level. This study is relevant as it raises important questions about the relationship between bacteria and malignancy.

Hunting down the dominating subclone of cancer stem cells as a potential new therapeutic target in multiple myeloma: An artificial intelligence perspective

The development of single-cell subclones, which can rapidly switch from dormant to dominant subclones, occur in the natural pathophysiology of multiple myeloma (MM) but is often "pressed" by the standard treatment of MM. These emerging subclones present a challenge, providing reservoirs for chemoresistant mutations. Technological advancement is required to track MM subclonal changes, as understanding MM's mechanism of evolution at the cellular level can prompt the development of new targeted ways of treating this disease. Current methods to study the evolution of subclones in MM rely on technologies capable of phenotypically and genotypically characterizing plasma cells, which include immunohistochemistry, flow cytometry, or cytogenetics. Still, all of these technologies may be limited by the sensitivity for picking up rare events. In contrast, more incisive methods such as RNA sequencing, comparative genomic hybridization, or whole-genome sequencing are not yet commonly used in clinical practice. Here we introduce the epidemiological diagnosis and prognosis of MM and review current methods for evaluating MM subclone evolution, such as minimal residual disease/multiparametric flow cytometry/next-generation sequencing, and their respective advantages and disadvantages. In addition, we propose our new single-cell method of evaluation to understand MM's mechanism of evolution at the molecular and cellular level and to prompt the development of new targeted ways of treating this disease, which has a broad prospect.

Towards multi-omics characterization of tumor heterogeneity: a comprehensive review of statistical and machine learning approaches

The multi-omics molecular characterization of cancer opened a new horizon for our understanding of cancer biology and therapeutic strategies. However, a tumor biopsy comprises diverse types of cells limited not only to cancerous cells but also to tumor microenvironmental cells and adjacent normal cells. This heterogeneity is a major confounding factor that hampers a robust and reproducible bioinformatic analysis for biomarker identification using multi-omics profiles. Besides, the heterogeneity itself has been recognized over the years for its significant prognostic values in some cancer types, thus offering another promising avenue for therapeutic intervention. A number of computational approaches to unravel such heterogeneity from high-throughput molecular profiles of a tumor sample have been proposed, but most of them rely on the data from an individual omics layer. Since the heterogeneity of cells is widely distributed across multi-omics layers, methods based on an individual layer can only partially characterize the heterogeneous admixture of cells. To help facilitate further development of the methodologies that synchronously account for several multi-omics profiles, we wrote a comprehensive review of diverse approaches to characterize tumor heterogeneity based on three different omics layers: genome, epigenome and transcriptome. As a result, this review can be useful for the analysis of multi-omics profiles produced by many large-scale consortia. Contact:sunkim.bioinfo@snu.ac.kr.

The road ahead in genetics and genomics

In celebration of the 20th anniversary of Nature Reviews Genetics, we asked 12 leading researchers to reflect on the key challenges and opportunities faced by the field of genetics and genomics. Keeping their particular research area in mind, they take stock of the current state of play and emphasize the work that remains to be done over the next few years so that, ultimately, the benefits of genetic and genomic research can be felt by everyone.

To celebrate the first 20 years of Nature Reviews Genetics, we asked 12 leading scientists to reflect on the key challenges and opportunities faced by the field of genetics and genomics.

Amy L. McGuire is the Leon Jaworski Professor of Biomedical Ethics and Director of the Center for Medical Ethics and Health Policy at Baylor College of Medicine. She has received numerous teaching awards at Baylor College of Medicine, was recognized by the Texas Executive Women as a Woman on the Move in 2016 and was invited to give a TedMed talk titled “There is No Genome for the Human Spirit” in 2014. In 2020, she was elected as a Hastings Center Fellow. Her research focuses on ethical and policy issues related to emerging technologies, with a particular focus on genomic research, personalized medicine and the clinical integration of novel neurotechnologies.

Stacey Gabriel is the Senior Director of the Genomics Platform at the Broad Institute since 2012 and has led platform development, execution and operation since its founding. She is Chair of Institute Scientists and serves on the institute’s executive leadership team. She is widely recognized as a leader in genomic technology and project execution. She has led the Broad’s contributions to numerous flagship projects in human genetics, including the International HapMap Project, the 1000 Genomes Project, The Cancer Genome Atlas, the National Heart, Lung, and Blood Institute’s Exome Sequencing Project and the TOPMed programme. She is Principal Investigator of the Broad’s All of Us (AoU) Genomics Center and serves on the AoU Program Steering Committee.

Sarah A. Tishkoff is the David and Lyn Silfen University Associate Professor in Genetics and Biology at the University of Pennsylvania, Philadelphia, USA, and holds appointments in the School of Medicine and the School of Arts and Sciences. She is a member of the US National Academy of Sciences and a recipient of an NIH Pioneer Award, a David and Lucile Packard Career Award, a Burroughs/Wellcome Fund Career Award and an American Society of Human Genetics Curt Stern Award. Her work focuses on genomic variation in Africa, human evolutionary history, the genetic basis of adaptation and phenotypic variation in Africa, and the genetic basis of susceptibility to infectious disease in Africa.

Ambroise Wonkam is Professor of Medical Genetics, Director of GeneMAP (Genetic Medicine of African Populations Research Centre) and Deputy Dean Research in the Faculty of Health Sciences, University of Cape Town, South Africa. He has successfully led numerous NIH- and Wellcome Trust-funded projects over the past decade to investigate clinical variability in sickle cell disease, hearing impairment genetics and the return of individual findings in genetic research in Africa. He won the competitive Clinical Genetics Society International Award for 2014 from the British Society of Genetic Medicine. He is president of the African Society of Human Genetics.

Aravinda Chakravarti is Director of the Center for Human Genetics and Genomics, the Muriel G. and George W. Singer Professor of Neuroscience and Physiology, and Professor of Medicine at New York University School of Medicine. He is an elected member of the US National Academy of Sciences, the US National Academy of Medicine and the Indian National Science Academy. He has been a key participant in the Human Genome Project, the International HapMap Project and the 1000 Genomes Project. His research attempts to understand the molecular basis of multifactorial disease. He was awarded the 2013 William Allan Award by the American Society of Human Genetics and the 2018 Chen Award by the Human Genome Organization.

Eileen E. M. Furlong is Head of the Genome Biology Department at the European Molecular Biology Laboratory (EMBL) and a member of the EMBL Directorate. She is an elected member of the European Molecular Biology Organization (EMBO) and the Academia Europaea, and a European Research Council (ERC) advanced investigator. Her group dissects fundamental principles of how the genome is regulated and how it drives cell fate decisions during embryonic development, including how developmental enhancers are organized and function within the 3D nucleus. Her work combines genetics, (single-cell) genomics, imaging and computational approaches to understand these processes. Her research has advanced the development of genomic methods for use in complex multicellular organisms.

Barbara Treutlein is Associate Professor of Quantitative Developmental Biology in the Department of Biosystems Science and Engineering of ETH Zurich in Basel, Switzerland. Her group uses and develops single-cell genomics approaches in combination with stem cell-based 2D and 3D culture systems to study how human organs develop and regenerate and how cell fate is regulated. For her work, Barbara has received multiple awards, including the Friedmund Neumann Prize of the Schering Foundation, the Dr. Susan Lim Award for Outstanding Young Investigator of the International Society of Stem Cell Research and the EMBO Young Investigator Award.

Alexander Meissner is a scientific member of the Max Planck Society and currently Managing Director of the Max Planck Institute (MPI) for Molecular Genetics in Berlin, Germany. He heads the Department of Genome Regulation and is a visiting scientist in the Department of Stem Cell and Regenerative Biology at Harvard University. Before his move to the MPI, he was a tenured professor at Harvard University and a senior associate member of the Broad Institute, where he co-directed the epigenomics programme. In 2018, he was elected as an EMBO member. His laboratory uses genomic tools to study developmental and disease biology with a particular focus on epigenetic regulation.

Howard Y. Chang is the Virginia and D. K. Ludwig Professor of Cancer Genomics at Stanford University and an investigator at the Howard Hughes Medical Institute. He is a physician–scientist who has focused on deciphering the hidden information in the non-coding genome. His laboratory is best known for studies of long non-coding RNAs in gene regulation and development of new epigenomic technologies. He is an elected member of the US National Academy of Sciences, the US National Academy of Medicine, and the American Academy of Arts and Sciences.

Núria López-Bigas is ICREA research Professor at the Institute for Research in Biomedicine and Associate Professor at the University Pompeu Fabra. She obtained an ERC Consolidator Grant in 2015 and was elected as an EMBO member in 2016. Her work has been recognized with the prestigious Banc de Sabadell Award for Research in Biomedicine, the Catalan National Award for Young Research Talent and the Career Development Award from the Human Frontier Science Program. Her research focuses on the identification of cancer driver mutations, genes and pathways across tumour types and in understanding the mutational processes that lead to the accumulation of mutations in cancer cells.

Eran Segal is Professor in the Department of Computer Science and Applied Mathematics at the Weizmann Institute of Science, heading a multidisciplinary laboratory with extensive experience in machine learning, computational biology and analysis of heterogeneous high-throughput genomic data. His research focuses on the microbiome, nutrition and genetics, and their effect on health and disease and aims to develop personalized medicine based on big data from human cohorts. He has published more than 150 publications and received several awards and honours for his work, including the Overton and the Michael Bruno awards. He was recently elected as an EMBO member and as a member of the Israel Young Academy.

Jin-Soo Kim is Director of the Center for Genome Engineering in the Institute for Basic Science in Daejon, South Korea. He has received numerous awards, including the 2017 Asan Award in Medicine, the 2017 Yumin Award in Science and the 2019 Research Excellence Award (Federation of Asian and Oceanian Biochemists and Molecular Biologists). He was featured as one of ten Science Stars of East Asia in Nature (558, 502–510 (2018)) and has been recognized as a highly cited researcher by Clarivate Analytics since 2018. His work focuses on developing tools for genome editing in biomedical research.

Targeting Multiple Signaling Pathways in Cancer: The Rutin Therapeutic Approach

Multiple dysregulated signaling pathways are implicated in the pathogenesis of cancer. The conventional therapies used in cancer prevention/treatment suffer from low efficacy, considerable toxicity, and high cost. Hence, the discovery and development of novel multi-targeted agents to attenuate the dysregulated signaling in cancer is of great importance. In recent decades, phytochemicals from dietary and medicinal plants have been successfully introduced as alternative anticancer agents due to their ability to modulate numerous oncogenic and oncosuppressive signaling pathways. Rutin (also known as rutoside, quercetin-3-O-rutinoside and sophorin) is an active plant-derived flavonoid that is widely distributed in various vegetables, fruits, and medicinal plants, including asparagus, buckwheat, apricots, apples, cherries, grapes, grapefruit, plums, oranges, and tea. Rutin has been shown to target various inflammatory, apoptotic, autophagic, and angiogenic signaling mediators, including nuclear factor-κB, tumor necrosis factor-α, interleukins, light chain 3/Beclin, B cell lymphoma 2 (Bcl-2), Bcl-2 associated X protein, caspases, and vascular endothelial growth factor. A comprehensive and critical analysis of the anticancer potential of rutin and associated molecular targets amongst various cancer types has not been performed previously. Accordingly, the purpose of this review is to present an up-to-date and critical evaluation of multiple cellular and molecular mechanisms through which the anticancer effects of rutin are known to be exerted. The current challenges and limitations as well as future directions of research are also discussed.

Characterization of tumors with ultralow tumor mutational burden in Japanese cancer patients

Tumor mutational burden analysis using whole‐exome sequencing highlights features of tumors with various mutations or known driver alterations. Cancers with few changes in the exon regions have unclear characteristics, even though low‐mutated tumors are often detected in pan‐cancer analysis. In the present study, we analyzed tumors with low tumor mutational burden listed in the Japanese version of The Cancer Genome Atlas, a data set of 5020 primary solid tumors. Our analysis revealed that detection rates of known driver mutations and copy number variation were decreased in samples with tumor mutational burden below 1.0 (ultralow tumor), compared with those in samples with low tumor mutational burden (≤5 mutations/Mb). This trend was also observed in The Cancer Genome Atlas data set. In the ultralow tumor mutational burden tumors, expression analysis showed decreased TP53 inactivation and chromosomal instability. TP53 inactivation frequently correlated with PI3K/mTOR‐related gene expression, implying suppression of the PI3K/mTOR pathway in ultralow tumor mutational burden tumors. In common with mutational burden, the T cell‐inflamed gene expression profiling signature was a potential marker for prediction of an immune checkpoint inhibitor response, and some ultralow tumor mutational burden tumor populations highly expressed this signature. Our analysis focused on how these tumors could provide insight into tumors with low somatic alteration that are difficult to detect solely using whole‐exome sequencing.

Mutational signature SBS8 predominantly arises due to late replication errors in cancer

Although a majority of somatic mutations in cancer are passengers, their mutational signatures provide mechanistic insights into mutagenesis and DNA repair processes. Mutational signature SBS8 is common in most cancers, but its etiology is debated. Incorporating genomic, epigenomic, and cellular process features for multiple cell-types we develop genome-wide composite epigenomic context-maps relevant for mutagenesis and DNA repair. Analyzing somatic mutation data from multiple cancer types in their epigenomic contexts, we show that SBS8 preferentially occurs in gene-poor, lamina-proximal, late replicating heterochromatin domains. While SBS8 is uncommon among mutations in non-malignant tissues, in tumor genomes its proportions increase with replication timing and speed, and checkpoint defects further promote this signature - suggesting that SBS8 probably arises due to uncorrected late replication errors during cancer progression. Our observations offer a potential reconciliation among different perspectives in the debate about the etiology of SBS8 and its relationship with other mutational signatures.

Methodologies for detecting environmentally induced DNA damage and repair

Environmental DNA damaging agents continuously challenge the integrity of the genome by introducing a variety of DNA lesions. The DNA damage caused by environmental factors will lead to mutagenesis and subsequent carcinogenesis if they are not removed efficiently by repair pathways. Methods for detection of DNA damage and repair can be applied to identify, visualize, and quantify the DNA damage formation and repair events, and they enable us to illustrate the molecular mechanisms of DNA damage formation, DNA repair pathways, mutagenesis, and carcinogenesis. Ever since the discovery of the double helical structure of DNA in 1953, a great number of methods have been developed to detect various types of DNA damage and repair. Rapid advances in sequencing technologies have facilitated the emergence of a variety of novel methods for detecting environmentally induced DNA damage and repair at the genome-wide scale during the last decade. In this review, we provide a historical overview of the development of various damage detection methods. We also highlight the current methodologies to detect DNA damage and repair, especially some next generation sequencing-based methods.

Black Lives Matter Worldwide: Retooling Precision Oncology for True Equity of Cancer Care

In a recent issue of Cancer Cell, Carrot-Zhang et al. identified ancestry-specific molecular variants and expression changes among patients from The Cancer Genome Atlas (TCGA).¹ Their study findings and limitations highlight the critical need to diversify populations represented in cancer genomics research.

Targeting the MAPK7/MMP9 axis for metastasis in primary bone cancer

Metastasis is the leading cause of cancer-related death. This multistage process involves contribution from both tumour cells and the tumour stroma to release metastatic cells into the circulation. Circulating tumour cells (CTCs) survive circulatory cytotoxicity, extravasate and colonise secondary sites effecting metastatic outcome. Reprogramming the transcriptomic landscape is a metastatic hallmark, but detecting underlying master regulators that drive pathological gene expression is a key challenge, especially in childhood cancer. Here we used whole tumour plus single-cell RNA-sequencing in primary bone cancer and CTCs to perform weighted gene co-expression network analysis to systematically detect coordinated changes in metastatic transcript expression. This approach with comparisons applied to data collected from cell line models, clinical samples and xenograft mouse models revealed mitogen-activated protein kinase 7/matrix metallopeptidase 9 (MAPK7/MMP9) signalling as a driver for primary bone cancer metastasis. RNA interference knockdown of MAPK7 reduces proliferation, colony formation, migration, tumour growth, macrophage residency/polarisation and lung metastasis. Parallel to these observations were reduction of activated interleukins IL1B, IL6, IL8 plus mesenchymal markers VIM and VEGF in response to MAPK7 loss. Our results implicate a newly discovered, multidimensional MAPK7/MMP9 signalling hub in primary bone cancer metastasis that is clinically actionable.

Cancer, hear my battle CRY

Circadian clocks are cell-autonomous self-sustaining oscillators that allow organisms to anticipate environmental changes throughout the solar day and persist in nearly every cell examined. Environmental or genetic disruption of circadian rhythms increases the risk of several types of cancer, but the underlying mechanisms are not well understood. Here, we discuss evidence connecting circadian rhythms-with emphasis on the cryptochrome proteins (CRY1/2)-to cancer through in vivo models, mechanisms involving known tumor suppressors and oncogenes, chemotherapeutic efficacy, and human cancer risk.

Updates in the field of hereditary nonpolyposis colorectal cancer

INTRODUCTION

Up to one third of colorectal cancers show familial clustering and 5% are hereditary single-gene disorders. Hereditary non-polyposis colorectal cancer comprises DNA mismatch repair-deficient and -proficient subsets, represented by Lynch syndrome (LS) and familial colorectal cancer type X (FCCTX), respectively. Accurate knowledge of molecular etiology and genotype-phenotype correlations are critical for tailored cancer prevention and treatment.

AREAS COVERED

The authors highlight advances in the molecular dissection of hereditary non-polyposis colorectal cancer, based on recent literature retrieved from PubMed. Future possibilities for novel gene discoveries are discussed.

EXPERT COMMENTARY

LS is molecularly well established, but new information is accumulating of the associated clinical and tumor phenotypes. FCCTX remains poorly defined, but several promising candidate genes have been discovered and share some preferential biological pathways. Multi-level characterization of specimens from large patient cohorts representing multiple populations, combined with proper bioinformatic and functional analyses, will be necessary to resolve the outstanding questions.

Functional and genetic determinants of mutation rate variability in regulatory elements of cancer genomes.. [DOI: 10.1101/2020.07.29.226373] [Citation(s) in RCA: 0] [Impact Index Per Article: 0]

Background

Cancer genomes are shaped by mutational processes with complex spatial variation at multiple scales. Entire classes of regulatory elements are affected by local variations in mutation frequency. However, the underlying mutational mechanisms with functional and genetic determinants remain poorly understood.

Results

We characterised the mutational landscape of 1.3 million gene regulatory and chromatin architectural elements in 2,419 whole cancer genomes with transcriptional and pathway activity, functional conservation and recurrent driver events. We developed RM2, a statistical model that quantifies mutational enrichment or depletion in classes of genomic elements through genetic, trinucleotide and megabase-scale effects. We report a map of localised mutational processes affecting CTCF binding sites, transcription start sites (TSS) and tissue-specific open-chromatin regions. We show that increased mutational frequency in TSSs correlates with mRNA abundance in most cancer types, while open-chromatin regions are generally enriched in mutations. We identified ∼10,000 CTCF binding sites with core DNA motifs and constitutive binding in 66 cell types that represent focal points of local mutagenesis. We detected site-specific mutational signatures, such as SBS40 in open-chromatin regions in prostate cancer and SBS17b in CTCF binding sites in gastrointestinal cancers. We also proposed candidate drivers of localised mutagenesis: BRAF mutations associate with mutational enrichments at CTCF binding sites in melanoma, and ARID1A mutations with TSS-specific mutations in pancreatic cancer.

Conclusions

Our method and catalogue of localised mutational processes provide novel perspectives to cancer genome evolution, mutagenesis, DNA repair and driver discovery. Functional and genetic correlates of localised mutagenesis provide mechanistic hypotheses for future studies.

Genetics and Epigenetics of Sex Bias: Insights from Human Cancer and Autoimmunity

High-throughput sequencing and genome-wide association studies have revealed a sex bias in human diseases. The underlying molecular mechanisms remain, however, unknown. Here, we cover recent advances in cancer and autoimmunity focusing on intrinsic genetic and epigenetic differences underlying sex biases in human disease. These studies reveal a central role of genome regulatory mechanisms including genome repair, chromosome folding, and epigenetic regulation in dictating the sex bias. These highlight the importance of considering sex as a variable in both basic science and clinical investigations. Understanding the molecular mechanisms underlying sex bias in human diseases will be instrumental in making a first step forwards into the era of personalized medicine.

Literature Review of BARD1 as a Cancer Predisposing Gene with a Focus on Breast and Ovarian Cancers

Soon after the discovery of BRCA1 and BRCA2 over 20 years ago, it became apparent that not all hereditary breast and/or ovarian cancer syndrome families were explained by germline variants in these cancer predisposing genes, suggesting that other such genes have yet to be discovered. BRCA1-associated ring domain (BARD1), a direct interacting partner of BRCA1, was one of the earliest candidates investigated. Sequencing analyses revealed that potentially pathogenic BARD1 variants likely conferred a low-moderate risk to hereditary breast cancer, but this association is inconsistent. Here, we review studies of BARD1 as a cancer predisposing gene and illustrate the challenge of discovering additional cancer risk genes for hereditary breast and/or ovarian cancer. We selected peer reviewed research articles that focused on three themes: (i) sequence analyses of BARD1 to identify potentially pathogenic germline variants in adult hereditary cancer syndromes; (ii) biological assays of BARD1 variants to assess their effect on protein function; and (iii) association studies of BARD1 variants in family-based and case-control study groups to assess cancer risk. In conclusion, BARD1 is likely to be a low-moderate penetrance breast cancer risk gene.

Non-BRAF Mutant Melanoma: Molecular Features and Therapeutical Implications

Melanoma is one of the most aggressive tumors of the skin, and its incidence is growing worldwide. Historically considered a drug resistant disease, since 2011 the therapeutic landscape of melanoma has radically changed. Indeed, the improved knowledge of the immune system and its interactions with the tumor, and the ever more thorough molecular characterization of the disease, has allowed the development of immunotherapy on the one hand, and molecular target therapies on the other. The increased availability of more performing technologies like Next-Generation Sequencing (NGS), and the availability of increasingly large genetic panels, allows the identification of several potential therapeutic targets. In light of this, numerous clinical and preclinical trials are ongoing, to identify new molecular targets. Here, we review the landscape of mutated non-BRAF skin melanoma, in light of recent data deriving from Whole-Exome Sequencing (WES) or Whole-Genome Sequencing (WGS) studies on melanoma cohorts for which information on the mutation rate of each gene was available, for a total of 10 NGS studies and 992 samples, focusing on available, or in experimentation, targeted therapies beyond those targeting mutated BRAF. Namely, we describe 33 established and candidate driver genes altered with frequency greater than 1.5%, and the current status of targeted therapy for each gene. Only 1.1% of the samples showed no coding mutations, whereas 30% showed at least one mutation in the RAS genes (mostly NRAS) and 70% showed mutations outside of the RAS genes, suggesting potential new roads for targeted therapy. Ongoing clinical trials are available for 33.3% of the most frequently altered genes.

The Metastatic Cascade as the Basis for Liquid Biopsy Development

The metastatic cascade describes the process whereby aggressive cancer cells leave the primary tumor, travel through the bloodstream, and eventually reach distant organs to develop one or several metastases. During the last decade, innovative technologies have exploited the recent biological knowledge to identify new circulating biomarkers for the screening and early detection of cancer, real-time monitoring of treatment response, assessment of tumor relapse risk (prognosis), identification of new therapeutic targets and resistance mechanisms, patient stratification, and therapeutic decision-making. These techniques are broadly described using the term of Liquid Biopsy. This field is in constant progression and is based on the detection of circulating tumor cells, circulating free nucleic acids (e.g., circulating tumor DNA), circulating tumor-derived extracellular vesicles, and tumor-educated platelets. The aim of this review is to describe the biological principles underlying the liquid biopsy concept and to discuss how functional studies can expand the clinical applications of these circulating biomarkers.

A rare variant of African ancestry activates 8q24 lncRNA hub by modulating cancer associated enhancer

Genetic variation at the 8q24 locus is linked with the greater susceptibility to prostate cancer in men of African ancestry. One such African ancestry specific rare variant, rs72725854 (A>G/T) (~6% allele frequency) has been associated with a ~2-fold increase in prostate cancer risk. However, the functional relevance of this variant is unknown. Here we show that the variant rs72725854 is present in a prostate cancer-specific enhancer at 8q24 locus. Chromatin-conformation capture and dCas9 mediated enhancer blocking establish a direct regulatory link between this enhancer and lncRNAs PCAT1, PRNCR1 and PVT1. The risk allele ('T') is associated with higher expression of PCAT1, PVT1 and c-myc in prostate tumors. Further, enhancer with the risk allele gains response to androgen stimulation by recruiting the transcription factor SPDEF whereas, non-risk alleles remain non-responsive. Elevated expression of these lncRNAs and c-myc in risk allele carriers may explain their greater susceptibility to prostate cancer.

Developing Drugs for Tissue-Agnostic Indications: A Paradigm Shift in Leveraging Cancer Biology for Precision Medicine

Targeted therapies have reshaped the landscape of the development of cancer therapeutics. Recent biomarker‐driven, tissue‐agnostic clinical trials represent a significant paradigm shift in precision cancer medicine. Despite their growth in preclinical and clinical studies, to date only a few biomarker‐driven, tissue‐agnostic indications have seen approval by the US Food and Drug Administration (FDA). These approvals include pembrolizumab in microsatellite instability‐high or mismatch repair deficient solid tumors, as well as both larotrectinib and entrectinib in NTRK fusion‐positive tumors. Complex cancer biology, clinical trial design, and identification of resistance mechanisms represent some of the challenges that future tissue‐agnostic therapies have to overcome. In this Review, we present a brief history of the development of tissue‐agnostic therapies, comparing the similarities in the approval of pembrolizumab, larotrectinib, and entrectinib for tissue‐agnostic indications. We also explore the future of tissue‐agnostic cancer therapeutics while identifying important challenges for the future that drugs targeting tissue‐agnostic indications will face.

The Frequency of Ras Mutations in Cancer

Ras is frequently mutated in cancer, however, there is a lack of consensus in the literature regarding the cancer mutation frequency of Ras, with quoted values varying from 10%-30%. This variability is at least in part due to the selective aggregation of data from different databases and the dominant influence of particular cancer types and particular Ras isoforms within these datasets. To provide a more definitive figure for Ras mutation frequency in cancer, we cross-referenced the data in all major publicly accessible cancer mutation databases to determine reliable mutation frequency values for each Ras isoform in all major cancer types. These percentages were then applied to current U.S. cancer incidence statistics to estimate the number of new patients each year that have Ras-mutant cancers. We find that approximately 19% of patients with cancer harbor Ras mutations, equivalent to approximately 3.4 million new cases per year worldwide. We discuss the Ras isoform and mutation-specific trends evident within the datasets that are relevant to current Ras-targeted therapies.

PertInInt: An Integrative, Analytical Approach to Rapidly Uncover Cancer Driver Genes with Perturbed Interactions and Functionalities

A major challenge in cancer genomics is to identify genes with functional roles in cancer and uncover their mechanisms of action. We introduce an integrative framework that identifies cancer-relevant genes by pinpointing those whose interaction or other functional sites are enriched in somatic mutations across tumors. We derive analytical calculations that enable us to avoid time-prohibitive permutation-based significance tests, making it computationally feasible to simultaneously consider multiple measures of protein site functionality. Our accompanying software, PertInInt, combines knowledge about sites participating in interactions with DNA, RNA, peptides, ions, or small molecules with domain, evolutionary conservation, and gene-level mutation data. When applied to 10,037 tumor samples, PertInInt uncovers both known and newly predicted cancer genes, while additionally revealing what types of interactions or other functionalities are disrupted. PertInInt’s analysis demonstrates that somatic mutations are frequently enriched in interaction sites and domains and implicates interaction perturbation as a pervasive cancer-driving event.

A fast, analytical framework called PertInInt enables efficient integration of multiple measures of protein site functionality—including interaction, domain, and evolutionary conservation—with gene-level mutation data in order to rapidly detect cancer driver genes along with their disrupted functionalities.

Role of Telomeres and Telomeric Proteins in Human Malignancies and Their Therapeutic Potential

Telomeres are the ends of linear chromosomes comprised of repetitive nucleotide sequences in humans. Telomeres preserve chromosomal stability and genomic integrity. Telomere length shortens with every cell division in somatic cells, eventually resulting in replicative senescence once telomere length becomes critically short. Telomere shortening can be overcome by telomerase enzyme activity that is undetectable in somatic cells, while being active in germline cells, stem cells, and immune cells. Telomeres are bound by a shelterin complex that regulates telomere lengthening as well as protects them from being identified as DNA damage sites. Telomeres are transcribed by RNA polymerase II, and generate a long noncoding RNA called telomeric repeat-containing RNA (TERRA), which plays a key role in regulating subtelomeric gene expression. Replicative immortality and genome instability are hallmarks of cancer and to attain them cancer cells exploit telomere maintenance and telomere protection mechanisms. Thus, understanding the role of telomeres and their associated proteins in cancer initiation, progression and treatment is very important. The present review highlights the critical role of various telomeric components with recently established functions in cancer. Further, current strategies to target various telomeric components including human telomerase reverse transcriptase (hTERT) as a therapeutic approach in human malignancies are discussed.

A user guide for the online exploration and visualization of PCAWG data

The Pan-Cancer Analysis of Whole Genomes (PCAWG) project generated a vast amount of whole-genome cancer sequencing resource data. Here, as part of the ICGC/TCGA Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium, which aggregated whole genome sequencing data from 2658 cancers across 38 tumor types, we provide a user’s guide to the five publicly available online data exploration and visualization tools introduced in the PCAWG marker paper. These tools are ICGC Data Portal, UCSC Xena, Chromothripsis Explorer, Expression Atlas, and PCAWG-Scout. We detail use cases and analyses for each tool, show how they incorporate outside resources from the larger genomics ecosystem, and demonstrate how the tools can be used together to understand the biology of cancers more deeply. Together, the tools enable researchers to query the complex genomic PCAWG data dynamically and integrate external information, enabling and enhancing interpretation.

The Pan-Cancer Analysis of Whole Genomes project generated a vast array of data. In this article, the authors describe five different online resources to enable readers to explore and visualize the data.

IgCaller for reconstructing immunoglobulin gene rearrangements and oncogenic translocations from whole-genome sequencing in lymphoid neoplasms

Immunoglobulin (Ig) gene rearrangements and oncogenic translocations are routinely assessed during the characterization of B cell neoplasms and stratification of patients with distinct clinical and biological features, with the assessment done using Sanger sequencing, targeted next-generation sequencing, or fluorescence in situ hybridization (FISH). Currently, a complete Ig characterization cannot be extracted from whole-genome sequencing (WGS) data due to the inherent complexity of the Ig loci. Here, we introduce IgCaller, an algorithm designed to fully characterize Ig gene rearrangements and oncogenic translocations from short-read WGS data. Using a cohort of 404 patients comprising different subtypes of B cell neoplasms, we demonstrate that IgCaller identifies both heavy and light chain rearrangements to provide additional information on their functionality, somatic mutational status, class switch recombination, and oncogenic Ig translocations. Our data thus support IgCaller to be a reliable alternative to Sanger sequencing and FISH for studying the genetic properties of the Ig loci.

Immunoglobulin (Ig) rearrangement and translocation information are usually obtained by targeted sequencing of the respective loci. Here, the authors present the IgCaller algorithm, which extracts Ig heavy and light chain genetic properties from short-read whole-genome sequencing results to provide a feasible alternative to direct sequencing.

The Community Oncology and Academic Medical Center Alliance in the Age of Precision Medicine: Cancer Genetics and Genomics Considerations

Recent public policy, governmental regulatory and economic trends have motivated the establishment and deepening of community health and academic medical center alliances. Accordingly, community oncology practices now deliver a significant portion of their oncology care in association with academic cancer centers. In the age of precision medicine, this alliance has acquired critical importance; novel advances in nucleic acid sequencing, the generation and analysis of immense data sets, the changing clinical landscape of hereditary cancer predisposition and ongoing discovery of novel, targeted therapies challenge community-based oncologists to deliver molecularly-informed health care. The active engagement of community oncology practices with academic partners helps with meeting these challenges; community/academic alliances result in improved cancer patient care and provider efficacy. Here, we review the community oncology and academic medical center alliance. We examine how practitioners may leverage academic center precision medicine-based cancer genetics and genomics programs to advance their patients' needs. We highlight a number of project initiatives at the City of Hope Comprehensive Cancer Center that seek to optimize community oncology and academic cancer center precision medicine interactions.

Characterization of colibactin-associated mutational signature in an Asian oral squamous cell carcinoma and in other mucosal tumor types

Mutational signatures can reveal the history of mutagenic processes that cells were exposed to before and during tumorigenesis. We expect that as-yet-undiscovered mutational processes will shed further light on mutagenesis leading to carcinogenesis. With this in mind, we analyzed the mutational spectra of 36 Asian oral squamous cell carcinomas. The mutational spectra of two samples from patients who presented with oral bacterial infections showed novel mutational signatures. One of these novel signatures, SBS_AⁿT, is characterized by a preponderance of thymine mutations, strong transcriptional strand bias, and enrichment for adenines in the 4 bp 5′ of mutation sites. The mutational signature described in this manuscript was shown to be caused by colibactin, a bacterial mutagen produced by E. coli carrying the pks-island. Examination of publicly available sequencing data revealed SBS_AⁿT in 25 tumors from several mucosal tissue types, expanding the list of tissues in which this mutational signature is observed.