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

Molecular tumor board in patients with metastatic breast cancer

PURPOSE

Comprehensive genomic profiling is becoming increasingly important in the management of patients with metastatic breast cancer (mBC). Real-world clinical outcomes from applying molecular tumor boards (MTBs) recommendations in this context remain limited. Accordingly, we conducted a retrospective, single-institution analysis to evaluate the clinical impact of discussing patients affected by mBC at the MTB.

METHODS

Clinicogenomic data of patients affected by mBCs referred to the European Institute of Oncology MTB between August 2019 and December 2023 were reviewed. Genomic alterations were classified by ESCAT framework. Clinical outcomes of patients showing actionable alterations and receiving molecular-matched therapy (MMT) were compared to those receiving standard therapy (ST).

RESULTS

Ninety-six patients were included. Following MTB discussion, genetic counseling was recommended in 27% (n = 26) of patients, while additional molecular analyses were requested in 25% (n = 24) cases. Fifty-six patients (58%) displayed at least one actionable alteration. For patients with available follow-up (n = 50), 32 (64%) received MMTs and 18 (36%) ST. No differences in real-world progression-free survival (rwPFS) (4.07 months [95% CI 2.14-8.28] vs. 3.12 months [95% CI 1.51-NE], P = 0.8) and 12-month overall survival (OS) (58% [95%CI 43-78] vs. 57% [95%CI 34-97), P = 0.9) were observed between the MMT- and ST-group. Level I ESCAT alterations yielded longer rwPFS (5.82 months [95% CI 3.12-8.41]) compared to ESCAT II (2.14 months [95%CI 1.61-NE]) and ESCAT III (2.10 months [95% CI 2.04-NE]; P = 0.03). Twenty-four percent of patients showed a PFS2/PFS1 ratio > 1.3 from MMT.

CONCLUSION

Molecular tumor boards can provide additional treatment options for patients affected by mBC. Besides treatment recommendations, MTBs also have the utility to assess the validity of discussed genomic reports and to identify alterations worthy of genetic counseling.

The potential clinical utility of Whole Genome Sequencing for patients with cancer: evaluation of a regional implementation of the 100,000 Genomes Project

BACKGROUND

The 100,000 Genomes Project established infrastructure for Whole Genome Sequencing (WGS) in the United Kingdom.

METHODS

A retrospective study of cancer patients recruited to the 100,000 Genomes Project by the West Midlands Genomics Medicine Centre, evaluating clinical relevance of results.

RESULTS

After excluding samples with no sequencing data (1678/4851; 34.6%), 3166 sample sets (germline and somatic) from 3067 participants were sequenced. Results of 1256 participants (41.0%) were interpreted (excluding participants who died (308/3067; 10.0%) or were clinically excluded (1503/3067; 49.0%)). Of these, 323 (25.7%) had no variants in genes which may alter management (Domain 1 genes). Of the remaining 933 participants, 552 (59.2%) had clinical recommendations made (718 recommendations in total). These included therapeutic recommendations (377/933; 40.4%), such as clinical trial, unlicensed or licensed therapies or high TMB recommendations, and germline variants warranting clinical genetics review (85/933; 9.1%). At the last follow up, 20.2% of all recommendations were followed (145/718). However, only a small proportion of therapeutic recommendations were followed (5.1%, 25/491).

CONCLUSIONS

The 100,000 Genomes Project has established infrastructure and regional experience to support personalised cancer care. The majority of those with successful sequencing had actionable variants. Ensuring GTAB recommendations are followed will maximise benefits for patients.

Evaluation of whole genome sequencing utility in identifying driver alterations in cancer genome

In cancer genome analysis, identifying pathogenic alterations and assessing their effects on oncogenic processes is important. Although whole exome sequencing (WES) can effectively detect such changes, driver alterations could not be identified in 27.8% of the cases, according to a previous study. The objectives of the present study were to evaluate the utility of whole genome sequencing (WGS) and clarify its differences with WES in terms of driver alteration detection. For this purpose, WGS analysis was conducted on 177 driverless WES samples, selected from 5,480 fresh frozen samples derived from 5,140 Japanese patients with cancer. These samples were selected as primary tumor, both WES and transcriptome profiling were performed, estimated tumor content of ≥ 30%, and no driver alterations were identified by WES. WGS identified driver and likely driver alterations in 68.4 and 22.6% of the samples, respectively. The most frequent alteration type was oncogene amplification, followed by tumor suppressor gene deletion and small variants located outside the coding region. In the remaining 9.0% of samples, no such signals were identified; therefore, further investigations are required. The current study clearly demonstrated the role and utility of WGS in identifying genomic alterations that contribute to tumorigenesis.

Harmonizing tumor mutational burden analysis: Insights from a multicenter study using in silico reference data sets in clinical whole-exome sequencing (WES)

OBJECTIVES

Tumor mutational burden (TMB) is a significant biomarker for predicting immune checkpoint inhibitor response, but the clinical performance of whole-exome sequencing (WES)-based TMB estimation has received less attention compared to panel-based methods. This study aimed to assess the reliability and comparability of WES-based TMB analysis among laboratories under routine testing conditions.

METHODS

A multicenter study was conducted involving 24 laboratories in China using in silico reference data sets. The accuracy and comparability of TMB estimation were evaluated using matched tumor-normal data sets. Factors such as accuracy of variant calls, limit of detection (LOD) of WES test, size of regions of interest (ROIs) used for TMB calculation, and TMB cutoff points were analyzed.

RESULTS

The laboratories consistently underestimated the expected TMB scores in matched tumor-normal samples, with only 50% falling within the ±30% TMB interval. Samples with low TMB score (<2.5) received the consensus interpretation. Accuracy of variant calls, LOD of the WES test, ROI, and TMB cutoff points were important factors causing interlaboratory deviations.

CONCLUSIONS

This study highlights real-world challenges in WES-based TMB analysis that need to be improved and optimized. This research will aid in the selection of more reasonable analytical procedures to minimize potential methodologic biases in estimating TMB in clinical exome sequencing tests. Harmonizing TMB estimation in clinical testing conditions is crucial for accurately evaluating patients' response to immunotherapy.

[The collaborative project "Personalized medicine for oncology" (PM4Onco) as part of the Medical Informatics Initiative (MII)]

The collaborative project Personalized Medicine for Oncology (PM4Onco) was launched in 2023 as part of the National Decade against Cancer (NKD) and is executed within the Medical Informatics Initiative (MII). Its aim is to establish a sustainable infrastructure for the integration and use of data from clinical and biomedical research and therefore combines the experience and preliminary work of all four consortia of the MII and the leading oncology centers in Germany. The data provided by PM4Onco will be prepared in a suitable form to support decision making in molecular tumor boards. This concept and infrastructure will be extended to 23 participating partner sites and thus improve access to targeted therapies based on clinical information and analysis of molecular genetic alterations in tumors at different stages of the disease. This will help to improve the treatment and prognosis of tumor diseases.Clinical cancer registries are involved in the project to improve data quality through standardized documentation routines. Clinical experts advise on the expansion of the core datasets for personalized medicine (PM). Information on quality of life and treatment outcomes reported by patients in questionnaires, which is rarely collected outside of clinical trials, will make a significant contribution. Patient representatives are involved from the onset to ensure that the important perspective of patients is taken into account in the decision-making process. PM4Onco thus creates an alliance between the MII, oncological centers of excellence, clinical cancer registries, young scientists, patients, and citizens to strengthen and advance PM in cancer therapy.

Implementing Whole Genome Sequencing (WGS) in Clinical Practice: Advantages, Challenges, and Future Perspectives

The integration of whole genome sequencing (WGS) into all aspects of modern medicine represents the next step in the evolution of healthcare. Using this technology, scientists and physicians can observe the entire human genome comprehensively, generating a plethora of new sequencing data. Modern computational analysis entails advanced algorithms for variant detection, as well as complex models for classification. Data science and machine learning play a crucial role in the processing and interpretation of results, using enormous databases and statistics to discover new and support current genotype-phenotype correlations. In clinical practice, this technology has greatly enabled the development of personalized medicine, approaching each patient individually and in accordance with their genetic and biochemical profile. The most propulsive areas include rare disease genomics, oncogenomics, pharmacogenomics, neonatal screening, and infectious disease genomics. Another crucial application of WGS lies in the field of multi-omics, working towards the complete integration of human biomolecular data. Further technological development of sequencing technologies has led to the birth of third and fourth-generation sequencing, which include long-read sequencing, single-cell genomics, and nanopore sequencing. These technologies, alongside their continued implementation into medical research and practice, show great promise for the future of the field of medicine.

Whole genome sequencing in clinical practice

Whole genome sequencing (WGS) is becoming the preferred method for molecular genetic diagnosis of rare and unknown diseases and for identification of actionable cancer drivers. Compared to other molecular genetic methods, WGS captures most genomic variation and eliminates the need for sequential genetic testing. Whereas, the laboratory requirements are similar to conventional molecular genetics, the amount of data is large and WGS requires a comprehensive computational and storage infrastructure in order to facilitate data processing within a clinically relevant timeframe. The output of a single WGS analyses is roughly 5 MIO variants and data interpretation involves specialized staff collaborating with the clinical specialists in order to provide standard of care reports. Although the field is continuously refining the standards for variant classification, there are still unresolved issues associated with the clinical application. The review provides an overview of WGS in clinical practice - describing the technology and current applications as well as challenges connected with data processing, interpretation and clinical reporting.

Therapy-induced APOBEC3A drives evolution of persistent cancer cells

Acquired drug resistance to anticancer targeted therapies remains an unsolved clinical problem. Although many drivers of acquired drug resistance have been identified1-4, the underlying molecular mechanisms shaping tumour evolution during treatment are incompletely understood. Genomic profiling of patient tumours has implicated apolipoprotein B messenger RNA editing catalytic polypeptide-like (APOBEC) cytidine deaminases in tumour evolution; however, their role during therapy and the development of acquired drug resistance is undefined. Here we report that lung cancer targeted therapies commonly used in the clinic can induce cytidine deaminase APOBEC3A (A3A), leading to sustained mutagenesis in drug-tolerant cancer cells persisting during therapy. Therapy-induced A3A promotes the formation of double-strand DNA breaks, increasing genomic instability in drug-tolerant persisters. Deletion of A3A reduces APOBEC mutations and structural variations in persister cells and delays the development of drug resistance. APOBEC mutational signatures are enriched in tumours from patients with lung cancer who progressed after extended responses to targeted therapies. This study shows that induction of A3A in response to targeted therapies drives evolution of drug-tolerant persister cells, suggesting that suppression of A3A expression or activity may represent a potential therapeutic strategy in the prevention or delay of acquired resistance to lung cancer targeted therapy.

Consensus Statements on Precision Oncology in the China Greater Bay Area

BACKGROUND

Next-generation sequencing comprehensive genomic panels (NGS CGPs) have enabled the delivery of tailor-made therapeutic approaches to improve survival outcomes in patients with cancer. Within the China Greater Bay Area (GBA), territorial differences in clinical practices and health care systems and strengthening collaboration warrant a regional consensus to consolidate the development and integration of precision oncology (PO). Therefore, the Precision Oncology Working Group (POWG) formulated standardized principles for the clinical application of molecular profiling, interpretation of genomic alterations, and alignment of actionable mutations with sequence-directed therapy to deliver clinical services of excellence and evidence-based care to patients with cancer in the China GBA.

METHODS

Thirty experts used a modified Delphi method. The evidence extracted to support the statements was graded according to the GRADE system and reported according to the Revised Standards for Quality Improvement Reporting Excellence guidelines, version 2.0.

RESULTS

The POWG reached consensus in six key statements: harmonization of reporting and quality assurance of NGS; molecular tumor board and clinical decision support systems for PO; education and training; research and real-world data collection, patient engagement, regulations, and financial reimbursement of PO treatment strategies; and clinical recommendations and implementation of PO in clinical practice.

CONCLUSION

POWG consensus statements standardize the clinical application of NGS CGPs, streamline the interpretation of clinically significant genomic alterations, and align actionable mutations with sequence-directed therapies. The POWG consensus statements may harmonize the utility and delivery of PO in China's GBA.

Cancer secretome: finding out hidden messages in extracellular secretions

Secretome analysis has gained popularity recently as a very well-designed proteomic approach that is being used to study various interactions and their effects on cellular activity. This analysis is especially helpful while studying the effects of the cells on their microenvironment, paracrine and autocrine processes, their therapeutic purposes, and as a new diagnostic perspective. Cancer is a condition rather than a specific type of disease and is still yet to be fully understood. Cancer secretome is a fairly new concept that is being implemented to examine the interactions taking place in the tumor microenvironment and can help to understand the phenomena like induction of tumorigenesis, stimulation of immune cells, etc. The secretome analysis helps to gain a different perspective on the existing knowledge on cancer and its effects. The recent advances in secretome studies are directed toward secreted components as drug targets, biomarkers, and companion tools for diagnostic and prognostic purposes in cancer. This review aims to find the interactors in different types of cancer and understand the existing unstructured secretome data and its application in prognosis, diagnosis, and in biomarker study.

Integration of CRISPR/Cas9 with artificial intelligence for improved cancer therapeutics

Gene editing has great potential in treating diseases caused by well-characterized molecular alterations. The introduction of clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein 9 (Cas9)–based gene-editing tools has substantially improved the precision and efficiency of gene editing. The CRISPR/Cas9 system offers several advantages over the existing gene-editing approaches, such as its ability to target practically any genomic sequence, enabling the rapid development and deployment of novel CRISPR-mediated knock-out/knock-in methods. CRISPR/Cas9 has been widely used to develop cancer models, validate essential genes as druggable targets, study drug-resistance mechanisms, explore gene non-coding areas, and develop biomarkers. CRISPR gene editing can create more-effective chimeric antigen receptor (CAR)-T cells that are durable, cost-effective, and more readily available. However, further research is needed to define the CRISPR/Cas9 system’s pros and cons, establish best practices, and determine social and ethical implications. This review summarizes recent CRISPR/Cas9 developments, particularly in cancer research and immunotherapy, and the potential of CRISPR/Cas9-based screening in developing cancer precision medicine and engineering models for targeted cancer therapy, highlighting the existing challenges and future directions. Lastly, we highlight the role of artificial intelligence in refining the CRISPR system's on-target and off-target effects, a critical factor for the broader application in cancer therapeutics.

Big data in basic and translational cancer research

Historically, the primary focus of cancer research has been molecular and clinical studies of a few essential pathways and genes. Recent years have seen the rapid accumulation of large-scale cancer omics data catalysed by breakthroughs in high-throughput technologies. This fast data growth has given rise to an evolving concept of 'big data' in cancer, whose analysis demands large computational resources and can potentially bring novel insights into essential questions. Indeed, the combination of big data, bioinformatics and artificial intelligence has led to notable advances in our basic understanding of cancer biology and to translational advancements. Further advances will require a concerted effort among data scientists, clinicians, biologists and policymakers. Here, we review the current state of the art and future challenges for harnessing big data to advance cancer research and treatment.

TMBur: a distributable tumor mutation burden approach for whole genome sequencing

Background

Tumor mutation burden (TMB) is a key characteristic used in a tumor-type agnostic context to inform the use of immune checkpoint inhibitors (ICI). Accurate and consistent measurement of TMB is crucial as it can significantly impact patient selection for therapy and clinical trials, with a threshold of 10 mutations/Mb commonly used as an inclusion criterion. Studies have shown that the most significant contributor to variability in mutation counts in whole genome sequence (WGS) data is differences in analysis methods, even more than differences in extraction or library construction methods. Therefore, tools for improving consistency in whole genome TMB estimation are of clinical importance.

Methods

We developed a distributable TMB analysis suite, TMBur, to address the need for genomic TMB estimate consistency in projects that span jurisdictions. TMBur is implemented in Nextflow and performs all analysis steps to generate TMB estimates directly from fastq files, incorporating somatic variant calling with Manta, Strelka2, and Mutect2, and microsatellite instability profiling with MSISensor. These tools are provided in a Singularity container downloaded by the workflow at runtime, allowing the entire workflow to be run identically on most computing platforms. To test the reproducibility of TMBur TMB estimates, we performed replicate runs on WGS data derived from the COLO829 and COLO829BL cell lines at multiple research centres. The clinical value of derived TMB estimates was then evaluated using a cohort of 90 patients with advanced, metastatic cancer that received ICIs following WGS analysis. Patients were split into groups based on a threshold of 10/Mb, and time to progression from initiation of ICIs was examined using Kaplan–Meier and cox-proportional hazards analyses.

Results

TMBur produced identical TMB estimates across replicates and at multiple analysis centres. The clinical utility of TMBur-derived TMB estimates were validated, with a genomic TMB ≥ 10/Mb demonstrating improved time to progression, even after correcting for differences in tumor type (HR = 0.39, p = 0.012).

Conclusions

TMBur, a shareable workflow, generates consistent whole genome derived TMB estimates predictive of response to ICIs across multiple analysis centres. Reproducible TMB estimates from this approach can improve collaboration and ensure equitable treatment and clinical trial access spanning jurisdictions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12920-022-01348-z.

Mutational Signature and Integrative Genomic Analysis of Human Papillomavirus-Associated Penile Squamous Cell Carcinomas from Latin American Patients

Simple Summary

DNA sequencing has been crucial to comprehending cancer mutational patterns, leading to the identification of driver genes and altered signaling pathways. Thus, identifying new pathogenic variants and their impact on tumor onset, progression, and treatment response has fueled tumor biology research. Here, we present novel findings addressing the first whole-exome sequencing (WES) of human papillomavirus (HPV)-associated penile squamous cell carcinoma (PSCC) from Latin Americans and its association with pathogenesis. We also compared the molecular profile of the tumors to that of three previous studies from populations with different genetic and socioeconomic backgrounds, the majority of which was HPV-negative. We describe the most altered genes and the main pathogenic variants found in the Latin Americans, ten of which are exclusive to our study sample. The data allowed us to identify molecular pathways and druggable targets with potential treatment value for this still-neglected HPV-associated carcinoma.

Abstract

High-throughput DNA sequencing has allowed for the identification of genomic alterations and their impact on tumor development, progression, and therapeutic responses. In PSCC, for which the incidence has progressively increased worldwide, there are still limited data on the molecular mechanisms involved in the disease pathogenesis. In this study, we characterized the mutational signature of 30 human papillomavirus (HPV)-associated PSCC cases from Latin Americans, using whole-exome sequencing. Copy number variations (CNVs) were also identified and compared to previous array-generated data. Enrichment analyses were performed to reveal disrupted pathways and to identify alterations mapped to HPV integration sites (HPVis) and miRNA–mRNA hybridization regions. Among the most frequently mutated genes were NOTCH1, TERT, TTN, FAT1, TP53, CDKN2A, RYR2, CASP8, FBXW7, HMCN2, and ITGA8. Of note, 92% of these altered genes were localized at HPVis. We also found mutations in ten novel genes (KMT2C, SMARCA4, PTPRB, AJUBA, CR1, KMT2D, NBEA, FAM135B, GTF2I, and CIC), thus increasing our understanding of the potential HPV-disrupted pathways. Therefore, our study reveals innovative targets with potential therapeutic benefits for HPV-associated PSCCs. The CNV analysis by sequencing (CNV-seq) revealed five cancer-associated genes as the most frequent with gains (NOTCH1, MYC, NUMA1, PLAG1, and RAD21), while 30% of the tumors showed SMARCA4 with loss. Additionally, four cancer-associated genes (CARD11, CSMD3, KDR, and TLX3) carried untranslated regions (UTRs) variants, which may impact gene regulation by affecting the miRNAs hybridization regions. Altogether, these data contribute to the characterization of the mutational spectrum and its impact on cellular signaling pathways in PSCC, thus reinforcing the pivotal role of HPV infection in the molecular pathogenesis of these tumors.

Branching Copy-Number Evolution and Parallel Immune Profiles across the Regional Tumor Space of Resected Pancreatic Cancer

Pancreatic ductal adenocarcinoma (PDAC) remains a highly lethal disease. The only option for curative treatment is resection of the tumor followed by standard adjuvant chemotherapy. Yet, early relapse due to chemoresistance is almost inevitable. Herein, we delineated the genetic intratumor heterogeneity in resected PDAC, with the aim to identify evolutionary patterns that may be associated with overall survival (OS) following treatment with curative intent. Potential relationships with the adjacent immune microenvironment were also examined. The genetic and immune landscapes of the regional tumor space were analyzed in nine patients with resected PDAC. Targeted deep sequencing and genome wide SNP array were followed by clonal deconvolution and phylogenetic analysis. A mathematical complexity score was developed to calculate the network extent of each phylogeny. Spatial variation in abundancy and tumor nest infiltration of immune cells was analyzed by double IHC staining. Copy-number heterogeneity was denoted as the major contributing factor to the branching architectures of the produced phylogenetic trees. Increased tree complexity was significantly inversely associated with OS, and larger regional maximum aberrations (higher treetops) were associated with increased PD-L1 expression on tumor cells. Contrastingly, an FREM1 gene amplification, found in one patient, coincided with a particularly vigorous immune response. Findings from this limited case series suggest that complex evolutionary patterns may be associated with a shorter survival in surgically treated patients with PDAC. Some hypothesis-generating associations with the surrounding immune microenvironment were also detected.

IMPLICATIONS

Evolutionary copy-number patterns may be associated with survival in patients with resected PDAC.

Guiding the global evolution of cytogenetic testing for hematologic malignancies

Cytogenetics has long represented a critical component in the clinical evaluation of hematologic malignancies. Chromosome banding studies provide a simultaneous snapshot of genome-wide copy number and structural variation, which have been shown to drive tumorigenesis, define diseases, and guide treatment. Technological innovations in sequencing have ushered in our present-day clinical genomics era. With recent publications highlighting novel sequencing technologies as alternatives to conventional cytogenetic approaches, we, an international consortium of laboratory geneticists, pathologists, and oncologists, describe herein the advantages and limitations of both conventional chromosome banding and novel sequencing technologies and share our considerations on crucial next steps to implement these novel technologies in the global clinical setting for a more accurate cytogenetic evaluation, which may provide improved diagnosis and treatment management. Considering the clinical, logistic, technical, and financial implications, we provide points to consider for the global evolution of cytogenetic testing.

Integrating NGS-derived mutational profiling in the diagnosis of multiple lung adenocarcinomas

MICROABSTRACT

Integration of Next Generation Sequencing (NGS) information for use in distinguishing between Multiple Primary Lung Cancer and intrapulmonary metastasis was evaluated. We used a probabilistic model, comprehensive histologic assessment and NGS to classify patients. Integrating NGS data confirmed initial diagnosis (n = 41), revised the diagnosis (n = 12), while resulted in non-informative data (n = 8). Accuracy of diagnosis can be significantly improved with integration of NGS data.

BACKGROUND

Distinguishing between multiple primary lung cancers (MPLC) and intrapulmonary metastases (IPM) is challenging. The goal of this study was to evaluate how Next Generation Sequencing (NGS) information may be integrated in the diagnostic strategy.

PATIENTS AND METHODS

Patients with multiple lung adenocarcinomas were classified using both the comprehensive histologic assessment and NGS. We computed the joint probability of each pair having independent mutations by chance (thus being classified as MPLC). These probabilities were computed using the marginal mutation rates of each mutation, and the known negative dependencies between driver genes and different gene loci. With these NGS-driven data, cases were re-classified as MPLC or IPM.

RESULTS

We analyzed 61 patients with a total of 131 tumors. The most frequent mutation was KRAS (57.3%) which occured at a rate higher than expected (p < 0.001) in lung cancer. No mutation was detected in 25/131 tumors (19.1%). Discordant molecular findings between tumor sites were found in 46 patients (75.4%); 11 patients (18.0%) had concordant molecular findings, and 4 patients (6.6%) had concordant molecular findings at 2 of the 3 sites. After integration of the NGS data, the initial diagnosis was confirmed for 41 patients (67.2%), the diagnosis was revised for 12 patients (19.7%) or was considered as non-informative for 8 patients (13.1%).

CONCLUSION

Integrating the information of NGS data may significantly improve accuracy of diagnosis and staging.

Somatic driver mutation prevalence in 1844 prostate cancers identifies ZNRF3 loss as a predictor of metastatic relapse

Driver gene mutations that are more prevalent in metastatic, castration-resistant prostate cancer (mCRPC) than localized disease represent candidate prognostic biomarkers. We analyze 1,844 localized (1,289) or mCRPC (555) tumors and quantify the prevalence of 113 somatic driver single nucleotide variants (SNVs), copy number aberrations (CNAs), and structural variants (SVs) in each state. One-third are significantly more prevalent in mCRPC than expected while a quarter are less prevalent. Mutations in AR and its enhancer are more prevalent in mCRPC, as are those in TP53, MYC, ZNRF3 and PRKDC. ZNRF3 loss is associated with decreased ZNRF3 mRNA abundance, WNT, cell cycle & PRC1/2 activity, and genomic instability. ZNRF3 loss, RNA downregulation and hypermethylation are prognostic of metastasis and overall survival, independent of clinical and pathologic indices. These data demonstrate a strategy for identifying biomarkers of localized cancer aggression, with ZNRF3 loss as a predictor of metastasis in prostate cancer.

JWES: a new pipeline for whole genome/exome sequence data processing, management, and gene-variant discovery, annotation, prediction, and genotyping

Whole genome and exome sequencing (WGS/WES) are the most popular next‐generation sequencing (NGS) methodologies and are at present often used to detect rare and common genetic variants of clinical significance. We emphasize that automated sequence data processing, management, and visualization should be an indispensable component of modern WGS and WES data analysis for sequence assembly, variant detection (SNPs, SVs), imputation, and resolution of haplotypes. In this manuscript, we present a newly developed findable, accessible, interoperable, and reusable (FAIR) bioinformatics‐genomics pipeline Java based Whole Genome/Exome Sequence Data Processing Pipeline (JWES) for efficient variant discovery and interpretation, and big data modeling and visualization. JWES is a cross‐platform, user‐friendly, product line application, that entails three modules: (a) data processing, (b) storage, and (c) visualization. The data processing module performs a series of different tasks for variant calling, the data storage module efficiently manages high‐volume gene‐variant data, and the data visualization module supports variant data interpretation with Circos graphs. The performance of JWES was tested and validated in‐house with different experiments, using Microsoft Windows, macOS Big Sur, and UNIX operating systems. JWES is an open‐source and freely available pipeline, allowing scientists to take full advantage of all the computing resources available, without requiring much computer science knowledge. We have successfully applied JWES for processing, management, and gene‐variant discovery, annotation, prediction, and genotyping of WGS and WES data to analyze variable complex disorders. In summary, we report the performance of JWES with some reproducible case studies, using open access and in‐house generated, high‐quality datasets.

Data Processing Thresholds for Abundance and Sparsity and Missed Biological Insights in an Untargeted Chemical Analysis of Blood Specimens for Exposomics

Background: An untargeted chemical analysis of bio-fluids provides semi-quantitative data for thousands of chemicals for expanding our understanding about relationships among metabolic pathways, diseases, phenotypes and exposures. During the processing of mass spectral and chromatography data, various signal thresholds are used to control the number of peaks in the final data matrix that is used for statistical analyses. However, commonly used stringent thresholds generate constrained data matrices which may under-represent the detected chemical space, leading to missed biological insights in the exposome research. Methods: We have re-analyzed a liquid chromatography high resolution mass spectrometry data set for a publicly available epidemiology study (n = 499) of human cord blood samples using the MS-DIAL software with minimally possible thresholds during the data processing steps. Peak list for individual files and the data matrix after alignment and gap-filling steps were summarized for different peak height and detection frequency thresholds. Correlations between birth weight and LC/MS peaks in the newly generated data matrix were computed using the spearman correlation coefficient. Results: MS-DIAL software detected on average 23,156 peaks for individual LC/MS file and 63,393 peaks in the aligned peak table. A combination of peak height and detection frequency thresholds that was used in the original publication at the individual file and the peak alignment levels can reject 90% peaks from the untargeted chemical analysis dataset that was generated by MS-DIAL. Correlation analysis for birth weight data suggested that up to 80% of the significantly associated peaks were rejected by the data processing thresholds that were used in the original publication. The re-analysis with minimum possible thresholds recovered metabolic insights about C19 steroids and hydroxy-acyl-carnitines and their relationships with birth weight. Conclusions: Data processing thresholds for peak height and detection frequencies at individual data file and at the alignment level should be used at minimal possible level or completely avoided for mining untargeted chemical analysis data in the exposome research for discovering new biomarkers and mechanisms.

Meta-Analysis and Systematic Review of the Genomics of Mucosal Melanoma

Mucosal melanoma is a rare subtype of melanoma. To date, there has been no comprehensive systematic collation and statistical analysis of the aberrations and aggregated frequency of driver events across multiple studies. Published studies using whole genome, whole exome, targeted gene panel, or individual gene sequencing were identified. Datasets from these studies were collated to summarize mutations, structural variants, and regions of copy-number alteration. Studies using next-generation sequencing were divided into the "main" cohort (n = 173; fresh-frozen samples), "validation" cohort (n = 48; formalin-fixed, paraffin-embedded samples) and a second "validation" cohort comprised 104 tumors sequenced using a targeted panel. Studies assessing mutations in BRAF, KIT, and NRAS were summarized to assess hotspot mutations. Statistical analysis of the main cohort variant data revealed KIT, NF1, BRAF, NRAS, SF3B1, and SPRED1 as significantly mutated genes. ATRX and SF3B1 mutations occurred more commonly in lower anatomy melanomas and CTNNB1 in the upper anatomy. NF1, PTEN, CDKN2A, SPRED1, ATM, CHEK2, and ARID1B were commonly affected by chromosomal copy loss, while TERT, KIT, BRAF, YAP1, CDK4, CCND1, GAB2, MDM2, SKP2, and MITF were commonly amplified. Further notable genomic alterations occurring at lower frequencies indicated commonality of signaling networks in tumorigenesis, including MAPK, PI3K, Notch, Wnt/β-catenin, cell cycle, DNA repair, and telomere maintenance pathways. This analysis identified genomic aberrations that provide some insight to the way in which specific pathways may be disrupted. IMPLICATIONS: Our analysis has shown that mucosal melanomas have a diverse range of genomic alterations in several biological pathways. VISUAL OVERVIEW: http://mcr.aacrjournals.org/content/molcanres/19/6/991/F1.large.jpg.

The impact of recent next generation sequencing and the need for a new classification in gastric cancer

The phenotypical and molecular heterogeneity of gastric cancer has hampered the introduction in clinical practice of a unifying classification of the disease. However, as next generation sequencing (NGS) technologies enhanced the comprehension of the molecular landscape of gastric cancer, novel molecular classification systems have been proposed, allowing the dissection of molecular tumor heterogeneity and paving the way for the development of new targeted therapies. Moreover, the use of NGS analyses in the molecular profiling of formalin-fixed paraffin-embedded (FFPE) specimens will improve patient selection for the enrolment in novel clinical trials. In conclusion, the application of NGS in precision oncology will revolutionize the diagnosis and clinical management in gastric cancer patients.

Pan-cancer analysis of whole genomes

Cancer is driven by genetic change, and the advent of massively parallel sequencing has enabled systematic documentation of this variation at the whole-genome scale1-3. Here we report the integrative analysis of 2,658 whole-cancer genomes and their matching normal tissues across 38 tumour types from the Pan-Cancer Analysis of Whole Genomes (PCAWG) Consortium of the International Cancer Genome Consortium (ICGC) and The Cancer Genome Atlas (TCGA). We describe the generation of the PCAWG resource, facilitated by international data sharing using compute clouds. On average, cancer genomes contained 4-5 driver mutations when combining coding and non-coding genomic elements; however, in around 5% of cases no drivers were identified, suggesting that cancer driver discovery is not yet complete. Chromothripsis, in which many clustered structural variants arise in a single catastrophic event, is frequently an early event in tumour evolution; in acral melanoma, for example, these events precede most somatic point mutations and affect several cancer-associated genes simultaneously. Cancers with abnormal telomere maintenance often originate from tissues with low replicative activity and show several mechanisms of preventing telomere attrition to critical levels. Common and rare germline variants affect patterns of somatic mutation, including point mutations, structural variants and somatic retrotransposition. A collection of papers from the PCAWG Consortium describes non-coding mutations that drive cancer beyond those in the TERT promoter4; identifies new signatures of mutational processes that cause base substitutions, small insertions and deletions and structural variation5,6; analyses timings and patterns of tumour evolution7; describes the diverse transcriptional consequences of somatic mutation on splicing, expression levels, fusion genes and promoter activity8,9; and evaluates a range of more-specialized features of cancer genomes8,10-18.