Pan-cancer analysis of whole genomes

Tracking Cancer Evolution through the Disease Course

During cancer evolution, constituent tumor cells compete under dynamic selection pressures. Phenotypic variation can be observed as intratumor heterogeneity, which is propagated by genome instability leading to mutations, somatic copy-number alterations, and epigenomic changes. TRACERx was set up in 2014 to observe the relationship between intratumor heterogeneity and patient outcome. By integrating multiregion sequencing of primary tumors with longitudinal sampling of a prospectively recruited patient cohort, cancer evolution can be tracked from early- to late-stage disease and through therapy. Here we review some of the key features of the studies and look to the future of the field. SIGNIFICANCE: Cancers evolve and adapt to environmental challenges such as immune surveillance and treatment pressures. The TRACERx studies track cancer evolution in a clinical setting, through primary disease to recurrence. Through multiregion and longitudinal sampling, evolutionary processes have been detailed in the tumor and the immune microenvironment in non-small cell lung cancer and clear-cell renal cell carcinoma. TRACERx has revealed the potential therapeutic utility of targeting clonal neoantigens and ctDNA detection in the adjuvant setting as a minimal residual disease detection tool primed for translation into clinical trials.

Benchmarking of next and third generation sequencing technologies and their associated algorithms for de novo genome assembly

Genome assemblers are computational tools for de novo genome assembly, based on a plenitude of primary sequencing data. The quality of genome assemblies is estimated by their contiguity and the occurrences of misassemblies (duplications, deletions, translocations or inversions). The rapid development of sequencing technologies has enabled the rise of novel de novo genome assembly strategies. The ultimate goal of such strategies is to utilise the features of each sequencing platform in order to address the existing weaknesses of each sequencing type and compose a complete and correct genome map. In the present study, the hybrid strategy, which is based on Illumina short paired‑end reads and Nanopore long reads, was benchmarked using MaSuRCA and Wengan assemblers. Moreover, the long‑read assembly strategy, which is based on Nanopore reads, was benchmarked using Canu or PacBio HiFi reads were benchmarked using Hifiasm and HiCanu. The assemblies were performed on a computational cluster with limited computational resources. Their outputs were evaluated in terms of accuracy and computational performance. PacBio HiFi assembly strategy outperforms the other ones, while Hi‑C scaffolding, which is based on chromatin 3D structure, is required in order to increase continuity, accuracy and completeness when large and complex genomes, such as the human one, are assembled. The use of Hi‑C data is also necessary while using the hybrid assembly strategy. The results revealed that HiFi sequencing enabled the rise of novel algorithms which require less genome coverage than that of the other strategies making the assembly a less computationally demanding task. Taken together, these developments may lead to the democratisation of genome assembly projects which are now approachable by smaller labs with limited technical and financial resources.

Distinct patterns of somatic genomic alterations and mutational signatures in central and peripheral-type small-cell lung cancer

BACKGROUND

Generally, small-cell lung cancer (SCLC) can be classified into central and peripheral-type. Genomic landscape and genetic heterogeneity between central and peripheral-type SCLCs is scarce.

METHODS

We conducted whole-exome sequencing (WES) of 41 tumor/control pairs of SCLC samples. In all cases, 8,186 genes with non-synonymous mutations were identified, as well as significantly mutated genes (SMGs), tumor mutation burden (TMB), weighted genome instability index (wGII) and somatic copy number alteration (SCNA), the driver recurrent SCNA, and mutational signatures between central and peripheral-type SCLCs.

RESULTS

TMB of peripheral-type SCLCs were higher than central-type. Smoking patients had significantly higher wGII and CNA burden than the non-smokers in SCLCs, these alterations were more obviously in central-types. Furthermore, the driver SCNA regions of central and peripheral-type SCLCs were different. Central and peripheral-type SCLCs had no common recurrent amplification (AMP) cytobands or genes, but had collective recurrent deletion (DEL) including four cytobands and five genes. Interestingly, the AMP 12q24.31 was negative correlated with overall survival (OS) in central but not peripheral-type SCLCs. Moreover, a de novo mutational signature A was found significantly higher in peripheral than central-type SCLCs. In parallel, signature D was predictive of poor outcome, which was mainly in peripheral SCLC patients. COSMIC signatures analysis revealed that the association with signature D and OS was similar to the positive relationship between signature 13 and outcome.

CONCLUSIONS

Central and peripheral-type SCLCs were different in immunotherapy response, genome instability, the driver SCNAs and mutational signatures, which leaded to differences of prognosis.

Identifying high-confidence capture Hi-C interactions using CHiCANE

The ability to identify regulatory interactions that mediate gene expression changes through distal elements, such as risk loci, is transforming our understanding of how genomes are spatially organized and regulated. Capture Hi-C (CHi-C) is a powerful tool to delineate such regulatory interactions. However, primary analysis and downstream interpretation of CHi-C profiles remains challenging and relies on disparate tools with ad-hoc input/output formats and specific assumptions for statistical modeling. Here we present a data processing and interaction calling toolkit (CHiCANE), specialized for the analysis and meaningful interpretation of CHi-C assays. In this protocol, we demonstrate applications of CHiCANE to region capture Hi-C (rCHi-C) and promoter capture Hi-C (pCHi-C) libraries, followed by quality assessment of interaction peaks, as well as downstream analysis specific to rCHi-C and pCHi-C to aid functional interpretation. For a typical rCHi-C/pCHi-C dataset this protocol takes up to 3 d for users with a moderate understanding of R programming and statistical concepts, although this is dependent on dataset size and compute power available. CHiCANE is freely available at https://cran.r-project.org/web/packages/chicane .

Pan-Cancer Drivers Are Recurrent Transcriptional Regulatory Heterogeneities in Early-Stage Luminal Breast Cancer

The heterogeneous composition of solid tumors is known to impact disease progression and response to therapy. Malignant cells coexist in different regulatory states that can be accessed transcriptomically by single-cell RNA sequencing, but these methods have many caveats related to sensitivity, noise, and sample handling. We revised a statistical fluctuation analysis called stochastic profiling to combine with 10-cell RNA sequencing, which was designed for laser-capture microdissection (LCM) and extended here for immuno-LCM. When applied to a cohort of late-onset, early-stage luminal breast cancers, the integrated approach identified thousands of candidate regulatory heterogeneities. Intersecting the candidates from different tumors yielded a relatively stable set of 710 recurrent heterogeneously expressed genes (RHEG), which were significantly variable in >50% of patients. RHEGs were not strongly confounded by dissociation artifacts, cell-cycle oscillations, or driving mutations for breast cancer. Rather, RHEGs were enriched for epithelial-to-mesenchymal transition genes and, unexpectedly, the latest pan-cancer assembly of driver genes across cancer types other than breast. These findings indicate that heterogeneous transcriptional regulation conceivably provides a faster, reversible mechanism for malignant cells to evaluate the effects of potential oncogenes or tumor suppressors on cancer hallmarks. SIGNIFICANCE: Profiling intratumor heterogeneity of luminal breast carcinoma cells identifies a recurrent set of genes, suggesting sporadic activation of pathways known to drive other types of cancer.See related articles by Schaff and colleagues, p. 1853 and Sutcliffe and colleagues, p. 1868.

Inherent mosaicism and extensive mutation of human placentas

Placentas can exhibit chromosomal aberrations that are absent from the fetus1. The basis of this genetic segregation, which is known as confined placental mosaicism, remains unknown. Here we investigated the phylogeny of human placental cells as reconstructed from somatic mutations, using whole-genome sequencing of 86 bulk placental samples (with a median weight of 28 mg) and of 106 microdissections of placental tissue. We found that every bulk placental sample represents a clonal expansion that is genetically distinct, and exhibits a genomic landscape akin to that of childhood cancer in terms of mutation burden and mutational imprints. To our knowledge, unlike any other healthy human tissue studied so far, the placental genomes often contained changes in copy number. We reconstructed phylogenetic relationships between tissues from the same pregnancy, which revealed that developmental bottlenecks genetically isolate placental tissues by separating trophectodermal lineages from lineages derived from the inner cell mass. Notably, there were some cases with full segregation-within a few cell divisions of the zygote-of placental lineages and lineages derived from the inner cell mass. Such early embryonic bottlenecks may enable the normalization of zygotic aneuploidy. We observed direct evidence for this in a case of mosaic trisomic rescue. Our findings reveal extensive mutagenesis in placental tissues and suggest that mosaicism is a typical feature of placental development.

Histone isoforms and the oncohistone code

Recent studies have highlighted the potential for missense mutations in histones to act as oncogenic drivers, leading to the term 'oncohistones'. While histone proteins are highly conserved, they are encoded by multigene families. There is heterogeneity among these genes at the level of the underlying sequence, the amino acid composition of the encoded histone isoform, and the expression levels. One question that arises, therefore, is whether all histone-encoding genes function equally as oncohistones. In this review, we consider this question and explore what this means in terms of the mechanisms by which oncohistones can exert their effects in chromatin.

Clonal expansion in non-cancer tissues

Cancer is a clonal disorder derived from a single ancestor cell and its progenies that are positively selected by acquisition of 'driver mutations'. However, the evolution of positively selected clones does not necessarily imply the presence of cancer. On the contrary, it has become clear that expansion of these clones in phenotypically normal or non-cancer tissues is commonly seen in association with ageing and/or in response to environmental insults and chronic inflammation. Recent studies have reported expansion of clones harbouring mutations in cancer driver genes in the blood, skin, oesophagus, bronchus, liver, endometrium and bladder, where the expansion could be so extensive that tissues undergo remodelling of an almost entire tissue. The presence of common cancer driver mutations in normal tissues suggests a strong link to cancer development, providing an opportunity to understand early carcinogenic processes. Nevertheless, some driver mutations are unique to normal tissues or have a mutation frequency that is much higher in normal tissue than in cancer, indicating that the respective clones may not necessarily be destined for evolution to cancer but even negatively selected for carcinogenesis depending on the mutated gene. Moreover, tissues that are remodelled by genetically altered clones might define functionalities of aged tissues or modified inflammatory processes. In this Review, we provide an overview of major findings on clonal expansion in phenotypically normal or non-cancer tissues and discuss their biological significance not only in cancer development but also in ageing and inflammatory diseases.

A Pilot Study Analyzing the Clinical Utility of Comprehensive Genomic Profiling Using Plasma Cell-Free DNA for Solid Tumor Patients in Japan (PROFILE Study)

BACKGROUND

The clinical utility of plasma cell-free DNA in precision cancer medicine has not been established. A pilot study was conducted to investigate the clinical utility of comprehensive genomic profiling by liquid biopsy in a Japanese population.

METHODS

In this PROFILE study, 102 patients with advanced solid tumors who showed progression with standard systemic therapy underwent liquid biopsy between August 2017 and February 2020. Liquid biopsy was performed using Guardant360.

RESULTS

Of the 102 patients, 56 were women, and the median age was 65 years. Regarding the types of cancer, 31 were hepatobiliary and pancreatic cancer, 17 were gastrointestinal cancer, and 13 were breast cancer. Frequently altered genes were TP53 (53.9%, 46/102), KRAS (25.5%, 26/102), PIK3CA (19.6%, 20/102), and EGFR (17.6%, 18/102). At least one genetic aberration was detected in 92 patients (90.2%). Actionable mutation was discovered in 88 patients (86.3%), and 67 patients (65.7%) were clinical trial candidates. Of the 102 patients, 22 (21.6%) were able to receive biomarker-matched therapy. Their best responses were as follows: 1 complete response, 3 partial responses, 7 stable diseases, and 11 progressive diseases. Additionally, the treated patients were divided on the basis of matching scores (≥ 50% vs. < 50%). The patients were divided into high and low groups. The high group had a higher disease control rate (DCR) of 75% compared with 20% in the low group (P = 0.010).

CONCLUSIONS

The results indicate that liquid biopsy is useful for identifying actionable mutations associated with the clinical response of selected patients.

Advances in bulk and single-cell multi-omics approaches for systems biology and precision medicine

Multi-omics allows the systematic understanding of the information flow across different omics layers, while single omics can mainly reflect one aspect of the biological system. The advancement of bulk and single-cell sequencing technologies and related computational methods for multi-omics largely facilitated the development of system biology and precision medicine. Single-cell approaches have the advantage of dissecting cellular dynamics and heterogeneity, whereas traditional bulk technologies are limited to individual/population-level investigation. In this review, we first summarize the technologies for producing bulk and single-cell multi-omics data. Then, we survey the computational approaches for integrative analysis of bulk and single-cell multimodal data, respectively. Moreover, the databases and data storage for multi-omics, as well as the tools for visualizing multimodal data are summarized. We also outline the integration between bulk and single-cell data, and discuss the applications of multi-omics in precision medicine. Finally, we present the challenges and perspectives for multi-omics development.

Unraveling the genomic landscape of colorectal cancer through mutational signatures

Colorectal cancer, along with most other cancer types, is driven by somatic mutations. Characteristic patterns of somatic mutations, known as mutational signatures, arise as a result of the activities of different mutational processes. Mutational signatures have diverse origins, including exogenous and endogenous sources. In the case of colorectal cancer, the analysis of mutational signatures has elucidated specific signatures for classically associated DNA repair deficiencies, namely mismatch repair (leading to microsatellite instability), base excision repair (due to MUTYH or NTHL1 mutations), and polymerase proofreading (due to POLE and POLD1 exonuclease domain mutations). Additional signatures also play a role in colorectal cancer, including those related to normal aging and those associated with gut microbiota, as well as a number of signatures with unknown etiologies. This chapter provides an overview of the current knowledge of mutational signatures, with a focus on colorectal cancer and on the recently reported signatures in physiologically normal and inflammatory bowel disease-affected somatic colon tissues.

Landmarks of human embryonic development inscribed in somatic mutations

Although cell lineage information is fundamental to understanding organismal development, very little direct information is available for humans. We performed high-depth (250×) whole-genome sequencing of multiple tissues from three individuals to identify hundreds of somatic single-nucleotide variants (sSNVs). Using these variants as "endogenous barcodes" in single cells, we reconstructed early embryonic cell divisions. Targeted sequencing of clonal sSNVs in different organs (about 25,000×) and in more than 1000 cortical single cells, as well as single-nucleus RNA sequencing and single-nucleus assay for transposase-accessible chromatin sequencing of ~100,000 cortical single cells, demonstrated asymmetric contributions of early progenitors to extraembryonic tissues, distinct germ layers, and organs. Our data suggest onset of gastrulation at an effective progenitor pool of about 170 cells and about 50 to 100 founders for the forebrain. Thus, mosaic mutations provide a permanent record of human embryonic development at very high resolution.

The microbiome and human cancer

Microbial roles in cancer formation, diagnosis, prognosis, and treatment have been disputed for centuries. Recent studies have provocatively claimed that bacteria, viruses, and/or fungi are pervasive among cancers, key actors in cancer immunotherapy, and engineerable to treat metastases. Despite these findings, the number of microbes known to directly cause carcinogenesis remains small. Critically evaluating and building frameworks for such evidence in light of modern cancer biology is an important task. In this Review, we delineate between causal and complicit roles of microbes in cancer and trace common themes of their influence through the host's immune system, herein defined as the immuno-oncology-microbiome axis. We further review evidence for intratumoral microbes and approaches that manipulate the host's gut or tumor microbiome while projecting the next phase of experimental discovery.

CDKN2A germline alterations and the relevance of genotype-phenotype associations in cancer predisposition

Although CDKN2A is well-known as a susceptibility gene for melanoma and pancreatic cancer, germline variants have also been anecdotally associated with a broader range of neoplasms including neural system tumors, head and neck squamous cell carcinomas, breast carcinomas, as well as sarcomas. The CDKN2A gene encodes for two distinct tumor suppressor proteins, p16INK4A and p14ARF, however, the independent association of germline alterations affecting these two proteins with cancer is under-appreciated. Here, we reviewed CDKN2A germline alterations reported among individuals and families with cancer in the literature, specifically addressing the cancer phenotypes in relation to the molecular consequence on p16INK4A and p14ARF. While melanoma is observed to associate with variants affecting both p16INK4A and p14ARF transcripts, it is noted that variants affecting p14ARF are more frequently observed with a heterogenous range of cancers. Finally, we reflected on the implications of this inferred genotype-phenotype association in clinical practice and proposed that clinical management of CDKN2A germline variant carriers should involve dedicated cancer genetics services, with multidisciplinary input from various healthcare professionals.

Horizons in Veterinary Precision Oncology: Fundamentals of Cancer Genomics and Applications of Liquid Biopsy for the Detection, Characterization, and Management of Cancer in Dogs

Cancer is the leading cause of death in dogs, in part because many cases are identified at an advanced stage when clinical signs have developed, and prognosis is poor. Increased understanding of cancer as a disease of the genome has led to the introduction of liquid biopsy testing, allowing for detection of genomic alterations in cell-free DNA fragments in blood to facilitate earlier detection, characterization, and management of cancer through non-invasive means. Recent discoveries in the areas of genomics and oncology have provided a deeper understanding of the molecular origins and evolution of cancer, and of the "one health" similarities between humans and dogs that underlie the field of comparative oncology. These discoveries, combined with technological advances in DNA profiling, are shifting the paradigm for cancer diagnosis toward earlier detection with the goal of improving outcomes. Liquid biopsy testing has already revolutionized the way cancer is managed in human medicine - and it is poised to make a similar impact in veterinary medicine. Multiple clinical use cases for liquid biopsy are emerging, including screening, aid in diagnosis, targeted treatment selection, treatment response monitoring, minimal residual disease detection, and recurrence monitoring. This review article highlights key scientific advances in genomics and their relevance for veterinary oncology, with the goal of providing a foundational introduction to this important topic for veterinarians. As these technologies migrate from human medicine into veterinary medicine, improved awareness and understanding will facilitate their rapid adoption, for the benefit of veterinary patients.

Building patient-specific models for receptor tyrosine kinase signaling networks

Cancer progresses due to changes in the dynamic interactions of multidimensional factors associated with gene mutations. Cancer research has actively adopted computational methods, including data-driven and mathematical model-driven approaches, to identify causative factors and regulatory rules that can explain the complexity and diversity of cancers. A data-driven, statistics-based approach revealed correlations between gene alterations and clinical outcomes in many types of cancers. A model-driven mathematical approach has elucidated the dynamic features of cancer networks and identified the mechanisms of drug efficacy and resistance. More recently, machine learning methods have emerged that can be used for mining omics data and classifying patient. However, as the strengths and weaknesses of each method becoming apparent, new analytical tools are emerging to combine and improve the methodologies and maximize their predictive power for classifying cancer subtypes and prognosis. Here, we introduce recent advances in cancer systems biology aimed at personalized medicine, with focus on the receptor tyrosine kinase signaling network.

ActiveDriverDB: Interpreting Genetic Variation in Human and Cancer Genomes Using Post-translational Modification Sites and Signaling Networks (2021 Update)

Deciphering the functional impact of genetic variation is required to understand phenotypic diversity and the molecular mechanisms of inherited disease and cancer. While millions of genetic variants are now mapped in genome sequencing projects, distinguishing functional variants remains a major challenge. Protein-coding variation can be interpreted using post-translational modification (PTM) sites that are core components of cellular signaling networks controlling molecular processes and pathways. ActiveDriverDB is an interactive proteo-genomics database that uses more than 260,000 experimentally detected PTM sites to predict the functional impact of genetic variation in disease, cancer and the human population. Using machine learning tools, we prioritize proteins and pathways with enriched PTM-specific amino acid substitutions that potentially rewire signaling networks via induced or disrupted short linear motifs of kinase binding. We then map these effects to site-specific protein interaction networks and drug targets. In the 2021 update, we increased the PTM datasets by nearly 50%, included glycosylation, sumoylation and succinylation as new types of PTMs, and updated the workflows to interpret inherited disease mutations. We added a recent phosphoproteomics dataset reflecting the cellular response to SARS-CoV-2 to predict the impact of human genetic variation on COVID-19 infection and disease course. Overall, we estimate that 16-21% of known amino acid substitutions affect PTM sites among pathogenic disease mutations, somatic mutations in cancer genomes and germline variants in the human population. These data underline the potential of interpreting genetic variation through the lens of PTMs and signaling networks. The open-source database is freely available at www.ActiveDriverDB.org.

Pan-cancer analysis of non-coding recurrent mutations and their possible involvement in cancer pathogenesis

Cancer-related mutations have been mainly identified in protein-coding regions. Recent studies have demonstrated that mutations in non-coding regions of the genome could also be a risk factor for cancer. However, the non-coding regions comprise 98% of the total length of the human genome and contain a huge number of mutations, making it difficult to interpret their impacts on pathogenesis of cancer. To comprehensively identify cancer-related non-coding mutations, we focused on recurrent mutations in non-coding regions using somatic mutation data from COSMIC and whole-genome sequencing data from The Cancer Genome Atlas (TCGA). We identified 21 574 recurrent mutations in non-coding regions that were shared by at least two different samples from both COSMIC and TCGA databases. Among them, 580 candidate cancer-related non-coding recurrent mutations were identified based on epigenomic and chromatin structure datasets. One of such mutation was located in RREB1 binding site that is thought to interact with TEAD1 promoter. Our results suggest that mutations may disrupt the binding of RREB1 to the candidate enhancer region and increase TEAD1 expression levels. Our findings demonstrate that non-coding recurrent mutations and coding mutations may contribute to the pathogenesis of cancer.

SVExpress: identifying gene features altered recurrently in expression with nearby structural variant breakpoints

Background

Combined whole-genome sequencing (WGS) and RNA sequencing of cancers offer the opportunity to identify genes with altered expression due to genomic rearrangements. Somatic structural variants (SVs), as identified by WGS, can involve altered gene cis-regulation, gene fusions, copy number alterations, or gene disruption. The absence of computational tools to streamline integrative analysis steps may represent a barrier in identifying genes recurrently altered by genomic rearrangement.

Results

Here, we introduce SVExpress, a set of tools for carrying out integrative analysis of SV and gene expression data. SVExpress enables systematic cataloging of genes that consistently show increased or decreased expression in conjunction with the presence of nearby SV breakpoints. SVExpress can evaluate breakpoints in proximity to genes for potential enhancer translocation events or disruption of topologically associated domains, two mechanisms by which SVs may deregulate genes. The output from any commonly used SV calling algorithm may be easily adapted for use with SVExpress. SVExpress can readily analyze genomic datasets involving hundreds of cancer sample profiles. Here, we used SVExpress to analyze SV and expression data across 327 cancer cell lines with combined SV and expression data in the Cancer Cell Line Encyclopedia (CCLE). In the CCLE dataset, hundreds of genes showed altered gene expression in relation to nearby SV breakpoints. Altered genes involved TAD disruption, enhancer hijacking, and gene fusions. When comparing the top set of SV-altered genes from cancer cell lines with the top SV-altered genes previously reported for human tumors from The Cancer Genome Atlas and the Pan-Cancer Analysis of Whole Genomes datasets, a significant number of genes overlapped in the same direction for both cell lines and tumors, while some genes were significant for cell lines but not for human tumors and vice versa.

Conclusion

Our SVExpress tools allow computational biologists with a working knowledge of R to integrate gene expression with SV breakpoint data to identify recurrently altered genes. SVExpress is freely available for academic or commercial use at https://github.com/chadcreighton/SVExpress. SVExpress is implemented as a set of Excel macros and R code. All source code (R and Visual Basic for Applications) is available.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12859-021-04072-0.

Integrative Data Analytic Framework to Enhance Cancer Precision Medicine

With the advancement of high-throughput biotechnologies, we increasingly accumulate biomedical data about diseases, especially cancer. There is a need for computational models and methods to sift through, integrate, and extract new knowledge from the diverse available data, to improve the mechanistic understanding of diseases and patient care. To uncover molecular mechanisms and drug indications for specific cancer types, we develop an integrative framework able to harness a wide range of diverse molecular and pan-cancer data. We show that our approach outperforms the competing methods and can identify new associations. Furthermore, it captures the underlying biology predictive of drug response. Through the joint integration of data sources, our framework can also uncover links between cancer types and molecular entities for which no prior knowledge is available. Our new framework is flexible and can be easily reformulated to study any biomedical problem.

Translational Research in the Era of Precision Medicine: Where We Are and Where We Will Go

The advent of Precision Medicine has globally revolutionized the approach of translational research suggesting a patient-centric vision with therapeutic choices driven by the identification of specific predictive biomarkers of response to avoid ineffective therapies and reduce adverse effects. The spread of "multi-omics" analysis and the use of sensors, together with the ability to acquire clinical, behavioral, and environmental information on a large scale, will allow the digitization of the state of health or disease of each person, and the creation of a global health management system capable of generating real-time knowledge and new opportunities for prevention and therapy in the individual person (high-definition medicine). Real world data-based translational applications represent a promising alternative to the traditional evidence-based medicine (EBM) approaches that are based on the use of randomized clinical trials to test the selected hypothesis. Multi-modality data integration is necessary for example in precision oncology where an Avatar interface allows several simulations in order to define the best therapeutic scheme for each cancer patient.

How Chaotic Is Genome Chaos?

Simple Summary

Cancer genomes can undergo major restructurings involving many chromosomal locations at key stages in tumor development. This restructuring process has been designated “genome chaos” by some authors. In order to examine how chaotic cancer genome restructuring may be, the cell and molecular processes for DNA restructuring are reviewed. Examination of the action of these processes in various cancers reveals a degree of specificity that indicates genome restructuring may be sufficiently reproducible to enable possible therapies that interrupt tumor progression to more lethal forms.

Abstract

Cancer genomes evolve in a punctuated manner during tumor evolution. Abrupt genome restructuring at key steps in this evolution has been called “genome chaos.” To answer whether widespread genome change is truly chaotic, this review (i) summarizes the limited number of cell and molecular systems that execute genome restructuring, (ii) describes the characteristic signatures of DNA changes that result from activity of those systems, and (iii) examines two cases where genome restructuring is determined to a significant degree by cell type or viral infection. The conclusion is that many restructured cancer genomes display sufficiently unchaotic signatures to identify the cellular systems responsible for major oncogenic transitions, thereby identifying possible targets for therapies to inhibit tumor progression to greater aggressiveness.

The Landscape of the Heritable Cancer Genome

Genome-wide association studies (GWAS) have found hundreds of single-nucleotide polymorphisms (SNP) associated with increased risk of cancer. However, the amount of heritable risk explained by SNPs is limited, leaving most of the cancer heritability unexplained. Tumor sequencing projects have shown that causal mutations are enriched in genic regions. We hypothesized that SNPs located in protein coding genes and nearby regulatory regions could explain a significant proportion of the heritable risk of cancer. To perform gene-level heritability analysis, we developed a new method, called Bayesian Gene Heritability Analysis (BAGHERA), to estimate the heritability explained by all genotyped SNPs and by those located in genic regions using GWAS summary statistics. BAGHERA was specifically designed for low heritability traits such as cancer and provides robust heritability estimates under different genetic architectures. BAGHERA-based analysis of 38 cancers reported in the UK Biobank showed that SNPs explain at least 10% of the heritable risk for 14 of them, including late onset malignancies. We then identified 1,146 genes, called cancer heritability genes (CHG), explaining a significant proportion of cancer heritability. CHGs were involved in hallmark processes controlling the transformation from normal to cancerous cells. Importantly, 60 of them also harbored somatic driver mutations, and 27 are tumor suppressors. Our results suggest that germline and somatic mutation information could be exploited to identify subgroups of individuals at higher risk of cancer in the broader population and could prove useful to establish strategies for early detection and cancer surveillance. SIGNIFICANCE: This study describes a new statistical method to identify genes associated with cancer heritability in the broader population, creating a map of the heritable cancer genome with gene-level resolution.See related commentary by Bader, p. 2586.

Signaling Pathways in Cancer: Therapeutic Targets, Combinatorial Treatments, and New Developments

Molecular alterations in cancer genes and associated signaling pathways are used to inform new treatments for precision medicine in cancer. Small molecule inhibitors and monoclonal antibodies directed at relevant cancer-related proteins have been instrumental in delivering successful treatments of some blood malignancies (e.g., imatinib with chronic myelogenous leukemia (CML)) and solid tumors (e.g., tamoxifen with ER positive breast cancer and trastuzumab for HER2-positive breast cancer). However, inherent limitations such as drug toxicity, as well as acquisition of de novo or acquired mechanisms of resistance, still cause treatment failure. Here we provide an up-to-date review of the successes and limitations of current targeted therapies for cancer treatment and highlight how recent technological advances have provided a new level of understanding of the molecular complexity underpinning resistance to cancer therapies. We also raise three basic questions concerning cancer drug discovery based on molecular markers and alterations of selected signaling pathways, and further discuss how combination therapies may become the preferable approach over monotherapy for cancer treatments. Finally, we consider novel therapeutic developments that may complement drug delivery and significantly improve clinical response and outcomes of cancer patients.

Mutational signatures associated with exposure to carcinogenic microplastic compounds bisphenol A and styrene oxide

Microplastic pollutants in oceans and food chains are concerning to public health. Common plasticizing compounds Bisphenol-A (BPA) and Styrene-7,8-Oxide (SO) are now labeled as carcinogens. We show that BPA and SO cause deoxyribonucleic acid damage and mutagenesis in human cells, and analyze the genome-wide point mutation and genomic rearrangement patterns associated with BPA and SO exposure. A subset of the single- and doublet base substitutions shows mutagenesis near or at guanine, consistent with these compounds' preferences to form guanosine adducts. Presence of other mutational signatures suggest additional mutagenesis probably due to complex effects of BPA and SO on diverse cellular processes. Analyzing data for 19 cancer cohorts, we find that tumors of digestive and urinary organs show relatively high similarity in mutational profiles, and the burden of such mutations increases with age. Even within the same cancer type, proportions of corresponding mutational patterns vary among the cohorts from different countries, as does the amount of microplastic waste in ocean waters. BPA and SO are relatively mild mutagens, and other environmental agents can also potentially generate similar, complex mutational patterns in cancer genomes. Nonetheless, our findings call for systematic evaluation of public health consequences of microplastic exposure worldwide.

PNN and KCNQ1OT1 Can Predict the Efficacy of Adjuvant Fluoropyrimidine-Based Chemotherapy in Colorectal Cancer Patients

The benefit of adjuvant chemotherapy in the early stages of colorectal cancer (CRC) is still disappointing and the prediction of treatment outcome quite difficult. Recently, through a transcriptomic approach, we evidenced a role of PNN and KCNQ1OT1 gene expression in predicting response to fluoropyrimidine-based adjuvant chemotherapy in stage III CRC patients. Thus, the aim of this study was to validate in an independent cohort of stages IIIII CRC patients our previous findings. PNN and KCNQ1OT1 mRNA expression levels were evaluated in 74 formalin-fixed paraffin-embedded tumor and matched normal mucosa samples obtained by stages IIIII CRC patients treated with fluoropyrimidine-based adjuvant chemotherapy. PININ, the protein encoded by PNN, was immunohistochemically evaluated in 15 tumor and corresponding normal mucosa samples, selected on the basis of a low, medium, or high mRNA expression tumor/mucosa ratio. PNN and KCNQ1OT1 mRNA mean expression levels were significantly higher in tumor compared with normal tissues. Patients with high PNN or KCNQ1OT1 tumor mRNA levels according to ROC-based cutoffs showed a shorter disease-free survival (DFS) compared with patients with low tumor mRNA gene expression. Also, patients with tumor mRNA expression values of both genes below the identified cutoffs had a significantly longer DFS compared with patients with the expression of one or both genes above the cutoffs. In a representative large cohort of stages IIIII CRC untreated patients retrieved from GEO datasets, no difference in DFS was observed between patients with high and low PNN or KCNQ1OT1 gene expression levels. These data confirm our previous findings and underscore the relevance of PNN and KCNQ1OT1 expression in predicting DFS in early stages of CRC treated with fluoropyrimidine-based adjuvant chemotherapy. If further validated in a prospective case series, both biomarkers could be used to identify patients who benefit from this treatment and to offer alternative chemotherapy regimens to potential unresponsive patients. In relation to the suggested biological role of PNN and KCNQ1OT1 in CRC, they might also be exploited as potential therapeutic targets.

Role of receptor tyrosine kinases mediated signal transduction pathways in tumor growth and angiogenesis-New insight and futuristic vision

In the past two decades, significant progress has been made in the past two decades towards the understanding of the basic mechanisms underlying cancer growth and angiogenesis. In this context, receptor tyrosine kinases (RTKs) play a pivotal role in cell proliferation, differentiation, growth, motility, invasion, and angiogenesis, all of which contribute to tumor growth and progression. Mutations in RTKs lead to abnormal signal transductions in several pathways such as Ras-Raf, MEK-MAPK, PI3K-AKT and mTOR pathways, affecting a wide range of biological functions including cell proliferation, survival, migration and vascular permeability. Increasing evidence demonstrates that multiple kinases are involved in angiogenesis including RTKs such as vascular endothelial growth factor, platelet derived growth factor, epidermal growth factor, insulin-like growth factor-1, macrophage colony-stimulating factor, nerve growth factor, fibroblast growth factor, Hepatocyte Growth factor, Tie 1 & 2, Tek, Flt-3, Flt-4 and Eph receptors. Overactivation of RTKs and its downstream regulation is implicated in tumor initiation and angiogenesis, representing one of the hallmarks of cancer. This review discusses the role of RTKs, PI3K, and mTOR, their involvement, and their implication in pro-oncogenic cellular processes and angiogenesis with effective approaches and newly approved drugs to inhibit their unrestrained action.

Prognostic and Clinicopathological Significance of CCND1/Cyclin D1 Upregulation in Melanomas: A Systematic Review and Comprehensive Meta-Analysis

Our objective was to evaluate the prognostic and clinicopathological significance of cyclin D1 (CD1) overexpression/CCND1 amplification in melanomas. We searched studies published before September 2019 (PubMed, Embase, Web of Science, Scopus). We evaluated the quality of the studies included (QUIPS tool). The impact of CD1 overexpression/CCND1 amplification on overall survival and relevant clinicopathological characteristic were meta-analyzed. We performed heterogeneity, sensitivity, small-study effects, and subgroup analyses. Forty-one studies and 3451 patients met inclusion criteria. Qualitative evaluation demonstrated that not all studies were performed with the same rigor, finding the greatest risk of bias in the study confounding domain. Quantitative evaluation showed that immunohistochemical CD1 overexpression had a statistical association with Breslow thickness (p = 0.007; OR = 2.09,95% CI = 1.23-3.57), significantly higher frequency of CCND1/cyclin D1 abnormalities has been observed in the primary tumor compared to distant metastases (p = 0.004), revealed also by immunohistochemical overexpression of the protein (p < 0.001; OR = 0.53,95% CI = 0.40-0.71), while the CCND1 gene amplification does not show association (p = 0.43); while gene amplification, on the contrary, appeared more frequently in distant metastases (p = 0.04; OR = 1.70,95% CI = 1.01-2.85) and not in the primary tumor. In conclusion, CCND1/cyclin D1 upregulation is a common molecular oncogenic alteration in melanomas that probably favors the growth and expansion of the primary tumor. This upregulation is mainly consequence to the overexpression of the cyclin D1 protein, and not to gene amplification.

Neurofibromatosis in the Era of Precision Medicine: Development of MEK Inhibitors and Recent Successes with Selumetinib

PURPOSE OF REVIEW

Patients with neurofibromatosis type 1 (NF1) are at increased risk for benign and malignant neoplasms. Recently, targeted therapy with the MEK inhibitor class has helped address these needs. We highlight recent successes with selumetinib while acknowledging ongoing challenges for NF1 patients and future directions.

RECENT FINDINGS

MEK inhibitors have demonstrated efficacy for NF1-related conditions, including plexiform neurofibromas and low-grade gliomas, two common causes of NF1-related morbidity. Active investigations for NF1-related neoplasms have benefited from advanced understanding of the genomic and cell signaling alterations in these conditions and development of sound preclinical animal models. Selumetinib has become the first FDA-approved targeted therapy for NF1 following its demonstrated efficacy for inoperable plexiform neurofibroma. Investigations of combination therapy and the development of a representative NF1 swine model hold promise for translating therapies for other NF1-associated pathology.

A Detailed Catalogue of Multi-Omics Methodologies for Identification of Putative Biomarkers and Causal Molecular Networks in Translational Cancer Research

Recent advances in sequencing and biotechnological methodologies have led to the generation of large volumes of molecular data of different omics layers, such as genomics, transcriptomics, proteomics and metabolomics. Integration of these data with clinical information provides new opportunities to discover how perturbations in biological processes lead to disease. Using data-driven approaches for the integration and interpretation of multi-omics data could stably identify links between structural and functional information and propose causal molecular networks with potential impact on cancer pathophysiology. This knowledge can then be used to improve disease diagnosis, prognosis, prevention, and therapy. This review will summarize and categorize the most current computational methodologies and tools for integration of distinct molecular layers in the context of translational cancer research and personalized therapy. Additionally, the bioinformatics tools Multi-Omics Factor Analysis (MOFA) and netDX will be tested using omics data from public cancer resources, to assess their overall robustness, provide reproducible workflows for gaining biological knowledge from multi-omics data, and to comprehensively understand the significantly perturbed biological entities in distinct cancer types. We show that the performed supervised and unsupervised analyses result in meaningful and novel findings.

Comparison of Structural and Short Variants Detected by Linked-Read and Whole-Exome Sequencing in Multiple Myeloma

Linked-read sequencing was developed to aid the detection of large structural variants (SVs) from short-read sequencing efforts. We performed a systematic evaluation to determine if linked-read exome sequencing provides more comprehensive and clinically relevant information than whole-exome sequencing (WES) when applied to the same set of multiple myeloma patient samples. We report that linked-read sequencing detected a higher number of SVs (n = 18,455) than WES (n = 4065). However, linked-read predictions were dominated by inversions (92.4%), leading to poor detection of other types of SVs. In contrast, WES detected 56.3% deletions, 32.6% insertions, 6.7% translocations, 3.3% duplications and 1.2% inversions. Surprisingly, the quantitative performance assessment suggested a higher performance for WES (AUC = 0.791) compared to linked-read sequencing (AUC = 0.766) for detecting clinically validated cytogenetic alterations. We also found that linked-read sequencing detected more short variants (n = 704) compared to WES (n = 109). WES detected somatic mutations in all MM-related genes while linked-read sequencing failed to detect certain mutations. The comparison of somatic mutations detected using linked-read, WES and RNA-seq revealed that WES and RNA-seq detected more mutations than linked-read sequencing. These data indicate that WES outperforms and is more efficient than linked-read sequencing for detecting clinically relevant SVs and MM-specific short variants.

Association Between Sex and Immune-Related Adverse Events During Immune Checkpoint Inhibitor Therapy

BACKGROUND

Accumulated evidence supports the existence of sex-associated differences in immune systems. Understanding the role of sex in immune-related adverse events (irAEs) is important for management of irAE in patients receiving immunotherapy.

METHODS

We performed meta-analysis on published clinical study data and multivariable logistic regression on pharmacovigilance data and applied a propensity algorithm to The Cancer Genome Atlas (TCGA) omics data. We further validated our observations in two independent in-house cohorts of 179 and 767 cancer patients treated with immune checkpoint inhibitors.

RESULTS

A meta-analysis using 13 clinical studies that reported on 1,096 female patients (36.8%, 95% confidence interval [CI] = 35.0%-38.5%) and 1,886 male patients (63.2%, 95% CI = 61.5%-65.0%) demonstrated no statistically significant irAE risk difference between the sexes (odds ratio [OR] = 1.19; 95% CI = 0.91-1.54; 2-sided P = 0.21). Multivariable logistic regression analysis of 12,225 patients from FAERS and 10,979 patients from VigiBase showed no statistically significant difference in irAEs by sex. A propensity score algorithm used on multi-omics data for 6,019 patients from TCGA found no statistically significant difference by sex for irAE-related factors/pathways. The retrospective analysis of two in-house patient cohorts validated these results (OR = 1.55, 95% CI = 0.98-2.47; FDR = 0.13, for cohort 1; OR = 1.16, 95%CI = 0.86-1.57; FDR = 0.39, for cohort 2).

CONCLUSION

We observed minimal sex-associated differences in irAEs among cancer patients who received immune checkpoint inhibitor therapy. It may be unnecessary to consider gender effects for irAE management in clinical practice.

Pathological Features of Tumors of the Nervous System in Hereditary Cancer Predisposition Syndromes: A Review

Hereditary cancer predisposition syndromes (HCS) become more recognizable as the knowledge about them expands, and genetic testing becomes more affordable. In this review, we discussed the known HCS that predispose to central and peripheral nervous system tumors. Different genetic phenomena were highlighted, and the important cellular biological alterations were summarized. Genetic mosaicism and germline mutations are features of HCS, and recently, they were described in normal population and as modifiers for the genetic landscape of sporadic tumors. Description of the tumors arising in these conditions was augmented by representative cases explaining the main pathological findings. Clinical spectrum of the syndromes and diagnostic criteria were tabled to outline their role in defining these disorders. Interestingly, precision medicine has found its way to help these groups of patients by offering targeted preventive measures. Understanding the signaling pathway alteration of mammalian target of rapamycin (mTOR) in tuberous sclerosis helped introducing mTOR inhibitors as a prophylactic treatment in these patients. More research to define the germline genetic alterations and resulting cellular signaling perturbations is needed for effective risk-reducing interventions beyond prophylactic surgeries.

High Expression of LINC01268 is Positively Associated with Hepatocellular Carcinoma Progression via Regulating MAP3K7

Objective

As one of the most common neoplastic diseases, hepatocellular carcinoma (HCC) has a high morbidity and mortality, which seriously threatens human health and places a heavy burden on society and medical care. At present, effective early diagnosis, prognosis and treatment of HCC are limited. Altered gene expression patterns of lncRNA are associated with the occurrence, development and prognosis of various malignancies, including HCC. The aim of this study was to investigate the correlation between the expression of LINC01268 and HCC, and to elucidate the potential underlying molecular mechanism.

Methods

Expression level and localization of LINC01268 in human liver cancer cells and HCC tissues were investigated using RT-qPCR and fluorescent in situ hybridization (FISH), respectively. Correlation of expression levels of LINC01268 and MAP3K7 with differentiation and poor overall patient survival of HCC were analyzed using in house collected and publicly available HCC tissue data. RT-qPCR and Western blot were applied to inspect the effects of depletion and overexpression of LINC01268 on MAP3K7 expression. HCC cell proliferation and apoptosis were also investigated by simultaneous overexpression of LINC01268 and knockdown of MAP3K7, in order to delineate that MAP3K7 is a downstream effector of LINC01268.

Results

In this study, we identified that LINC01268 was highly expressed in HCC cell lines and tissues. High LINC01268 expression level was associated with lower HCC nodule number, moderate/poor differentiation and poor overall survival. Knockdown of LINC01268 inhibited the proliferation of HCC cells, which was enhanced by overexpression of LINC01268. Co-expression analysis implied an interaction between LINC01268 and MAP3K7. Similar to LINC01268, MAP3K7 was highly expressed in HCC cells, and positively correlated with moderate/poor differentiation as well as poor prognosis. Knockdown of LINC01268 in HCC cell lines led to reduction of MAP3K7 at both mRNA and protein levels. Phenotypic effects due to LINC01268 overexpression in HCC cells were reversed by knockdown of MAP3K7.

Conclusion

Taken together, the abnormal high expression of LINC01268 is associated with HCC progression via regulating MAP3K7, suggesting LINC01268 as a novel marker for HCC prognosis and potentially a new therapeutic target.

Whole Genome Interpretation for a Family of Five

Although best practices have emerged on how to analyse and interpret personal genomes, the utility of whole genome screening remains underdeveloped. A large amount of information can be gathered from various types of analyses via whole genome sequencing including pathogenicity screening, genetic risk scoring, fitness, nutrition, and pharmacogenomic analysis. We recognize different levels of confidence when assessing the validity of genetic markers and apply rigorous standards for evaluation of phenotype associations. We illustrate the application of this approach on a family of five. By applying analyses of whole genomes from different methodological perspectives, we are able to build a more comprehensive picture to assist decision making in preventative healthcare and well-being management. Our interpretation and reporting outputs provide input for a clinician to develop a healthcare plan for the individual, based on genetic and other healthcare data.

Antimicrobial peptides against colorectal cancer-a focused review

Despite recent advances in the treatment of colorectal cancer (CRC), low patient survival rate due to emergence of drug resistant cancer cells, metastasis and multiple deleterious side effects of chemotherapy, is a cause of public concern globally. To negate these clinical conundrums, search for effective and harmless novel molecular entities for the treatment of CRC is an urgent necessity. Since antimicrobial peptides (AMPs) are part of innate immunity of living beings, it is quite imperative to look for essential attributes of these peptides which may contribute to their effectiveness against carcinogenesis. Once identified, those characteristics can be suitably modified using several synthetic and computational techniques to further enhance their selectivity and pharmacokinetic profiles. Hence, this review analyses scientific reports describing the antiproliferative action of AMPs derived from several sources, particularly focusing on various colon cancer in vitro/in vivo investigations. On perusal of the literature, it appears that AMPs based therapeutics would definitely find special place in CRC therapy in future either alone or as an adjunct to chemotherapy provided some necessary alterations are made in their natural structures to make them more compatible with modern clinical practice. In this context, further in-depth research is warranted in adequate in vivo models.

Pan-Cancer Analysis of NOS3 Identifies Its Expression and Clinical Relevance in Gastric Cancer

Background:NOS3 (endothelial NOS, eNOS) is a member of the nitric oxide synthase (NOS) enzyme family, mainly participating in nitric oxide (NO) generation. NOS3 has been reported to inhibit apoptosis and promote angiogenesis, proliferation, and invasiveness. However, the expression pattern of NOS3 and its diagnostic and prognostic potential has not been investigated in a pan-cancer perspective.

Methods: Data from the Genotype-Tissue Expression (GTEx), the Cancer Genome Atlas (TCGA), the Cancer Cell Line Encyclopedia (CCLE), and the Cancer Therapeutics Response Portal (CTRP) were employed and NOS3 expression was comprehensively analyzed in normal tissues, cancer tissues, and cell lines. Immunohistochemical staining of tissue sections were used to validate the prognostic role of NOS3 in gastric cancer patients. GSVA and GSEA analyses were performed to investigate signaling pathways related to NOS3 expression.

Results: In normal tissues, NOS3 was expressed highest in the spleen and lowest in the blood. NOS3 expression was increased in stomach adenocarcinoma (STAD) and significantly associated with poor prognosis of patients. Immunohistochemical staining validated that NOS3 was an independent prognostic factor of gastric cancer. Several canonical cancer-related pathways were found to be correlated with NOS3 expression in STAD. The expression of NOS3 was related to the response to QS-11 and brivinib in STAD.

Conclusions:NOS3 was an independent prognostic factor for patients with STAD. Increased expression of NOS3 influenced occurrence and development of STAD through several canonical cancer-related pathways. Drug response analysis reported drugs to suppress NOS3. NOS3 might be a novel target for gastric cancer treatment.

Reconstructing the Lineage Histories and Differentiation Trajectories of Individual Cancer Cells in Myeloproliferative Neoplasms

Some cancers originate from a single mutation event in a single cell. Blood cancers known as myeloproliferative neoplasms (MPNs) are thought to originate when a driver mutation is acquired by a hematopoietic stem cell (HSC). However, when the mutation first occurs in individuals and how it affects the behavior of HSCs in their native context is not known. Here we quantified the effect of the JAK2-V617F mutation on the self-renewal and differentiation dynamics of HSCs in treatment-naive individuals with MPNs and reconstructed lineage histories of individual HSCs using somatic mutation patterns. We found that JAK2-V617F mutations occurred in a single HSC several decades before MPN diagnosis-at age 9 ± 2 years in a 34-year-old individual and at age 19 ± 3 years in a 63-year-old individual-and found that mutant HSCs have a selective advantage in both individuals. These results highlight the potential of harnessing somatic mutations to reconstruct cancer lineages.

The Burden of Post-Translational Modification (PTM)-Disrupting Mutations in the Tumor Matrisome

BACKGROUND

To evaluate the occurrence of mutations affecting post-translational modification (PTM) sites in matrisome genes across different tumor types, in light of their genomic and functional contexts and in comparison with the rest of the genome.

METHODS

This study spans 9075 tumor samples and 32 tumor types from The Cancer Genome Atlas (TCGA) Pan-Cancer cohort and identifies 151,088 non-silent mutations in the coding regions of the matrisome, of which 1811 affecting known sites of hydroxylation, phosphorylation, N- and O-glycosylation, acetylation, ubiquitylation, sumoylation and methylation PTM.

RESULTS

PTM-disruptive mutations (PTMmut) in the matrisome are less frequent than in the rest of the genome, seem independent of cell-of-origin patterns but show dependence on the nature of the matrisome protein affected and the background PTM types it generally harbors. Also, matrisome PTMmut are often found among structural and functional protein regions and in proteins involved in homo- and heterotypic interactions, suggesting potential disruption of matrisome functions.

CONCLUSIONS

Though quantitatively minoritarian in the spectrum of matrisome mutations, PTMmut show distinctive features and damaging potential which might concur to deregulated structural, functional, and signaling networks in the tumor microenvironment.

Structural variant detection in cancer genomes: computational challenges and perspectives for precision oncology

Cancer is generally characterized by acquired genomic aberrations in a broad spectrum of types and sizes, ranging from single nucleotide variants to structural variants (SVs). At least 30% of cancers have a known pathogenic SV used in diagnosis or treatment stratification. However, research into the role of SVs in cancer has been limited due to difficulties in detection. Biological and computational challenges confound SV detection in cancer samples, including intratumor heterogeneity, polyploidy, and distinguishing tumor-specific SVs from germline and somatic variants present in healthy cells. Classification of tumor-specific SVs is challenging due to inconsistencies in detected breakpoints, derived variant types and biological complexity of some rearrangements. Full-spectrum SV detection with high recall and precision requires integration of multiple algorithms and sequencing technologies to rescue variants that are difficult to resolve through individual methods. Here, we explore current strategies for integrating SV callsets and to enable the use of tumor-specific SVs in precision oncology.

Molecular correlates of immune cytolytic subgroups in colorectal cancer by integrated genomics analysis

Although immune checkpoint inhibition (ICI) has shown promising results in metastatic dMMR/MSI-H colorectal cancer (CRC), the majority of pMMR/MSS patients do not respond to such therapies. To systematically evaluate the determinants of immune response in CRC, we explored whether patients with diverse levels of immune cytolytic activity (CYT) have different patterns of chromothripsis and kataegis. Analysis of CRC genomic data from the TCGA, indicated an excess of chromothriptic clusters among CYT-low colon adenocarcinomas, affecting known cancer drivers (APC, KRAS, BRAF, TP53 and FBXW7), immune checkpoints (CD274, PDCD1LG2, IDO1/2 and LAG3) and immune-related genes (ENTPD1, PRF1, NKG7, FAS, GZMA/B/H/K and CD73). CYT-high tumors were characterized by hypermutation, enrichment in APOBEC-associated mutations and kataegis events, as well as APOBEC activation. We also assessed differences in the most prevalent mutational signatures (SBS15, SBS20, SBS54 and DBS2) across cytolytic subgroups. Regarding the composition of immune cells in the tumor milieu, we found enrichment of M1 macrophages, CD8+ T cells and Tregs, as well as higher CD8+ T-cells/Tregs ratio among CYT-high tumors. CYT-high patients had higher immunophenoscores, which is predictive of their responsiveness if they were to be treated with anti-PD-1 alone or in combination with anti-CTLA-4 drugs. These results could have implications for patient responsiveness to immune checkpoint inhibitors.

Chromothripsis drives the evolution of gene amplification in cancer

Focal chromosomal amplification contributes to the initiation of cancer by mediating overexpression of oncogenes1-3, and to the development of cancer therapy resistance by increasing the expression of genes whose action diminishes the efficacy of anti-cancer drugs. Here we used whole-genome sequencing of clonal cell isolates that developed chemotherapeutic resistance to show that chromothripsis is a major driver of circular extrachromosomal DNA (ecDNA) amplification (also known as double minutes) through mechanisms that depend on poly(ADP-ribose) polymerases (PARP) and the catalytic subunit of DNA-dependent protein kinase (DNA-PKcs). Longitudinal analyses revealed that a further increase in drug tolerance is achieved by structural evolution of ecDNAs through additional rounds of chromothripsis. In situ Hi-C sequencing showed that ecDNAs preferentially tether near chromosome ends, where they re-integrate when DNA damage is present. Intrachromosomal amplifications that formed initially under low-level drug selection underwent continuing breakage-fusion-bridge cycles, generating amplicons more than 100 megabases in length that became trapped within interphase bridges and then shattered, thereby producing micronuclei whose encapsulated ecDNAs are substrates for chromothripsis. We identified similar genome rearrangement profiles linked to localized gene amplification in human cancers with acquired drug resistance or oncogene amplifications. We propose that chromothripsis is a primary mechanism that accelerates genomic DNA rearrangement and amplification into ecDNA and enables rapid acquisition of tolerance to altered growth conditions.

The Metabolic Landscape of RAS-Driven Cancers from biology to therapy

Our understanding of how the RAS protein family, and in particular mutant KRAS promote metabolic dysregulation in cancer cells has advanced significantly over the last decade. In this Review, we discuss the metabolic reprogramming mediated by oncogenic RAS in cancer, and elucidating the underlying mechanisms could translate to novel therapeutic opportunities to target metabolic vulnerabilities in RAS-driven cancers.

Adoptive Cellular Therapy for Solid Tumors

Cancer immunotherapy tools include antibodies, vaccines, cytokines, oncolytic viruses, bispecific molecules, and cellular therapies. This review will focus on adoptive cellular therapy, which involves the isolation of a patient's own immune cells followed by their ex vivo expansion and reinfusion. The majority of adoptive cellular therapy strategies utilize T cells isolated from tumor or peripheral blood, but may utilize other immune cell subsets. T-cell therapies in the form of tumor-infiltrating lymphocytes, T-cell receptor T cells, and CAR T cells may act as "living drugs" as these infused cells expand, engraft, and persist in vivo, allowing adaptability over time and enabling durable remissions in subsets of patients. Adoptive cellular therapy has been less successful in the management of solid tumors because of poor homing, proliferation, and survival of transferred cells. Strategies are discussed, including expression of transgenes to address these hurdles. Additionally, advances in gene editing using CRISPR/Cas9 and similar technologies are described, which allow for clinically translatable gene-editing strategies to enhance the antitumor activity and to surmount the hostilities advanced by the host and the tumor. Finally, the common toxicities and approaches to mitigate these are reviewed.

COBRE for Computational Biology of Human Disease at Brown University: Progress and Prospects

We provide a program update on the COBRE Center for the Computational Biology of Human Disease (CBHD) at Brown University and affiliated hospitals. High throughput data from multiple 'omics-level' technologies are fundamental factors in identifying and treating human disease. The acquisition of these data is now straightforward, but the efficient and creative interpretation of these data remains a serious impediment to progress for faculty at all levels in both the basic and translational aspects of biomedical science. The CBHD COBRE seeks to build close collaboration between laboratory scientists working with model systems and data scientists working with computational and bioinformatics tools that can accelerate human disease research implementation. We describe the accomplishments of junior faculty Project Leaders (9) and Pilots Project leaders (8) and the objectives of the CBHD COBRE's core facility: The Computational Biology Core (CBC). To extend the CBHD COBRE's reach in the future, we encourage one and all to visit the CBHD COBRE and bring your data sets and questions. Only by engaging with new people and challenges can the program grow to serve the broader biomedical research community in the State of Rhode Island.

NODAL/TGFβ signalling mediates the self-sustained stemness induced by PIK3CAH1047R homozygosity in pluripotent stem cells

Activating PIK3CA mutations are known 'drivers' of human cancer and developmental overgrowth syndromes. We recently demonstrated that the 'hotspot' PIK3CAH1047R variant exerts unexpected allele dose-dependent effects on stemness in human pluripotent stem cells (hPSCs). In this study, we combine high-depth transcriptomics, total proteomics and reverse-phase protein arrays to reveal potentially disease-related alterations in heterozygous cells, and to assess the contribution of activated TGFβ signalling to the stemness phenotype of homozygous PIK3CAH1047R cells. We demonstrate signalling rewiring as a function of oncogenic PI3K signalling strength, and provide experimental evidence that self-sustained stemness is causally related to enhanced autocrine NODAL/TGFβ signalling. A significant transcriptomic signature of TGFβ pathway activation in heterozygous PIK3CAH1047R was observed but was modest and was not associated with the stemness phenotype seen in homozygous mutants. Notably, the stemness gene expression in homozygous PIK3CAH1047R hPSCs was reversed by pharmacological inhibition of NODAL/TGFβ signalling, but not by pharmacological PI3Kα pathway inhibition. Altogether, this provides the first in-depth analysis of PI3K signalling in hPSCs and directly links strong PI3K activation to developmental NODAL/TGFβ signalling. This work illustrates the importance of allele dosage and expression when artificial systems are used to model human genetic disease caused by activating PIK3CA mutations. This article has an associated First Person interview with the first author of the paper.

Assessing the contribution of tumor mutational phenotypes to cancer progression risk

Cancer occurs via an accumulation of somatic genomic alterations in a process of clonal evolution. There has been intensive study of potential causal mutations driving cancer development and progression. However, much recent evidence suggests that tumor evolution is normally driven by a variety of mechanisms of somatic hypermutability, which act in different combinations or degrees in different cancers. These variations in mutability phenotypes are predictive of progression outcomes independent of the specific mutations they have produced to date. Here we explore the question of how and to what degree these differences in mutational phenotypes act in a cancer to predict its future progression. We develop a computational paradigm using evolutionary tree inference (tumor phylogeny) algorithms to derive features quantifying single-tumor mutational phenotypes, followed by a machine learning framework to identify key features predictive of progression. Analyses of breast invasive carcinoma and lung carcinoma demonstrate that a large fraction of the risk of future clinical outcomes of cancer progression-overall survival and disease-free survival-can be explained solely from mutational phenotype features derived from the phylogenetic analysis. We further show that mutational phenotypes have additional predictive power even after accounting for traditional clinical and driver gene-centric genomic predictors of progression. These results confirm the importance of mutational phenotypes in contributing to cancer progression risk and suggest strategies for enhancing the predictive power of conventional clinical data or driver-centric biomarkers.

Microbiota and cancer: In vitro and in vivo models to evaluate nanomedicines

Nanomedicine implication in cancer treatment and diagnosis studies witness huge attention, especially with the promising results obtained in preclinical studies. Despite this, only few nanomedicines succeeded to pass clinical phase. The human microbiota plays obvious roles in cancer development. Nanoparticles have been successfully used to modulate human microbiota and notably tumor associated microbiota. Taking the microbiota involvement under consideration when testing nanomedicines for cancer treatment might be a way to improve the poor translation from preclinical to clinical trials. Co-culture models of bacteria and cancer cells, as well as animal cancer-microbiota models offer a better representation for the tumor microenvironment and so potentially better platforms to test nanomedicine efficacy in cancer treatment. These models would allow closer representation of human cancer and might smoothen the passage from preclinical to clinical cancer studies for nanomedicine efficacy.