Comprehensive fundamental somatic variant calling and quality management strategies for human cancer genomes

Neoantigen identification: Technological advances and challenges

Neoantigens have emerged as promising targets for cutting-edge immunotherapies, such as cancer vaccines and adoptive cell therapy. These neoantigens are unique to tumors and arise exclusively from somatic mutations or non-genomic aberrations in tumor proteins. They encompass a wide range of alterations, including genomic mutations, post-transcriptomic variants, and viral oncoproteins. With the advancements in technology, the identification of immunogenic neoantigens has seen rapid progress, raising new opportunities for enhancing their clinical significance. Prediction of neoantigens necessitates the acquisition of high-quality samples and sequencing data, followed by mutation calling. Subsequently, the pipeline involves integrating various tools that can predict the expression, processing, binding, and recognition potential of neoantigens. However, the continuous improvement of computational tools is constrained by the availability of datasets which contain validated immunogenic neoantigens. This review article aims to provide a comprehensive summary of the current knowledge as well as limitations in neoantigen prediction and validation. Additionally, it delves into the origin and biological role of neoantigens, offering a deeper understanding of their significance in the field of cancer immunotherapy. This article thus seeks to contribute to the ongoing efforts to harness neoantigens as powerful weapons in the fight against cancer.

VCFshiny: an R/Shiny application for interactively analyzing and visualizing genetic variants

Summary

Next-generation sequencing generates variants that are typically documented in variant call format (VCF) files. However, comprehensively examining variant information from VCF files can pose a significant challenge for researchers lacking bioinformatics and programming expertise. To address this issue, we introduce VCFshiny, an R package that features a user-friendly web interface enabling interactive annotation, interpretation, and visualization of variant information stored in VCF files. VCFshiny offers two annotation methods, Annovar and VariantAnnotation, to add annotations such as genes or functional impact. Annotated VCF files are deemed acceptable inputs for the purpose of summarizing and visualizing variant information. This includes the total number of variants, overlaps across sample replicates, base alterations of single nucleotides, length distributions of insertions and deletions (indels), high-frequency mutated genes, variant distribution in the genome and of genome features, variants in cancer driver genes, and cancer mutational signatures. VCFshiny serves to enhance the intelligibility of VCF files by offering an interactive web interface for analysis and visualization.

Availability and implementation

The source code is available under an MIT open source license at https://github.com/123xiaochen/VCFshiny with documentation at https://123xiaochen.github.io/VCFshiny.

Genomic profiles of renal cell carcinoma in a small Chinese cohort

Objectives

Our aim was to describe the molecular characteristics of Renal Cell Carcinoma (RCC) and develop a small panel of RCC-associated genes from a large panel of cancer-related genes.

Materials and methods

Clinical data of 55 patients with RCC diagnosed in four hospitals from September 2021 to August 2022 were collected. Among the 55 patients, 38 were diagnosed with clear cell RCC (ccRCC), and the other 17 were diagnosed with non-clear cell RCC (nccRCC), including 10 cases of papillary renal cell carcinoma, 2 cases of hereditary leiomyomatosis and RCC syndrome (HLRCC), 1 eosinophilic papillary RCC, 1 tubular cystic carcinoma, 1 TFE3 gene fusion RCC, and 2 RCC with sarcomatoid differentiation. For each patient, 1123 cancer-related genes and 79 RCC-associated genes were analyzed.

Results

The most frequent mutations in a large panel of 1123 cancer-related genes in the overall population of RCC patients were VHL (51%), PBRM1 (35%), BAP1 (16%), KMT2D (15%), PTPRD (15%), and SETD2 (15%). For ccRCC patients, mutations in VHL, PBRM1, BAP1, and SERD2 can reach 74%, 50%, 24%, and 18%, respectively, while for nccRCC patients, the most frequent mutation was FH (29%), MLH3 (24%), ARID1A (18%), KMT2D (18%), and CREBBP (18%). The germline mutation rate in all 55 patients reached 12.7% (five with FH, one with ATM, and one with RAD50). The small panel containing only 79 RCC-associated genes demonstrated that mutations of VHL, PBRM1, BAP1, and SETD2 in ccRCC patients were 74%, 50%, 24%, and 18% respectively, while for the nccRCC cohort, the most frequent mutations were FH (29%), ARID1A (18%), ATM (12%), MSH6 (12%), BRAF (12%), and KRAS (12%). For ccRCC patients, the spectrum of mutations by large and small panels was almost the same, while for nccRCC patients, the mutation spectrum showed some differences. Even though the most frequent mutations (FH and ARID1A) in nccRCC were both demonstrated by large panels and small panels, other less frequent mutations such as MLH3, KMT2D, and CREBBP were not shown by the small panel.

Conclusion

Our study revealed that nccRCC is more heterogeneous than ccRCC. For nccRCC patients, the small panel shows a more clear profile of genetic characteristics by replacing MLH3, KMT2D, and CREBBP with ATM, MSH6, BRAF, and KRAS, which may help predict prognosis and make clinical decisions.

Molecular Features in Lymphatic Metastases Reflect the Metastasis Mechanism of Lymph Nodes With Non-Small-Cell Lung Cancers

Lymphatic metastasis influences clinical treatment and prognosis of patients with non-small-cell lung cancer (NSCLC). There is an urgency to understand the molecular features and mechanisms of lymph node metastasis. We analyzed the molecular features on pairs of the primary tumor and lymphatic metastasis tissue samples from 15 NSCLC patients using targeted next-generation sequencing. The potential metastasis-related genes were screened from our cohort based on cancer cell fraction. After filtering with gene functions, candidate metastasis-related events were validated in the MSK cohort with Fisher's exact test. The molecular signature and tumor mutational burden were similar in paired samples, and the average mutational concordance was 42.0% ± 28.9%. Its metastatic mechanism is potentially a linear progression based on the metastatic seeding theory. Furthermore, mutated ataxia telangiectasia mutated and Rad3-related (ATR) and tet methylcytosine dioxygenase 2 (TET2) genes were significantly enriched in lymphatic metastases (p ≤ 0.05). Alterations in these two genes could be considered metastasis-related driving events. Mutated ATR and TET2 might play an active role in the metastasis of lymph nodes with NSCLC. More case enrollment and long-term follow-up will further verify the clinical significance of these two genes.

DIVIS: Integrated and Customizable Pipeline for Cancer Genome Sequencing Analysis and Interpretation

Next-generation sequencing (NGS) has drastically enhanced human cancer research, but diverse sequencing strategies, complicated open-source software, and the identification of massive numbers of mutations have limited the clinical application of NGS. Here, we first presented GPyFlow, a lightweight tool that flexibly customizes, executes, and shares workflows. We then introduced DIVIS, a customizable pipeline based on GPyFlow that integrates read preprocessing, alignment, variant detection, and annotation of whole-genome sequencing, whole-exome sequencing, and gene-panel sequencing. By default, DIVIS screens variants from multiple callers and generates a standard variant-detection format list containing caller evidence for each sample, which is compatible with advanced analyses. Lastly, DIVIS generates a statistical report, including command lines, parameters, quality-control indicators, and mutation summary. DIVIS substantially facilitates complex cancer genome sequencing analyses by means of a single powerful and easy-to-use command. The DIVIS code is freely available at https://github.com/niu-lab/DIVIS, and the docker image can be downloaded from https://hub.docker.com/repository/docker/sunshinerain/divis.

Genetic alteration of Chinese patients with rectal mucosal melanoma

Background

Rectal mucosal melanoma (RMM) is a rare and highly aggressive disease with a poor prognosis. Due to the rarity of RMM, there are few studies focusing on its genetic mechanism. This retrospective study aimed to analyze the genetic spectrum and prognosis of RMM in China and lay a foundation for targeted therapy.

Methods

36 patients with primary RMM from Peking University Cancer Hospital were enrolled in this study. The Next-generation sequencing (NGS) data of the tumor samples were fitted into the TruSight™ Oncology 500 (TSO500) Docker pipeline to detect genomic variants. Then, the univariate and multivariate Cox hazard analysis were performed to evaluate the correlations of the variants with the overall survival (OS), along with Kaplan-Meier and log-rank test to determine their significance.

Results

BRAF mutations, NRG1 deletions and mitotic index were significant prognostic factors in the univariate analysis. In multivariable analysis of the OS-related prognostic factors in primary RMM patients, it revealed 2 significant alterations: BRAF mutations [HR 7.732 (95%CI: 1.735–34.456), P = 0.007] and NRG1 deletions [HR 14.976 (95%CI: 2.305–97.300), P = 0.005].

Conclusions

This is the first study to show genetic alterations exclusively to Chinese patients with RMM. We confirmed genetic alterations of RMM differ from cutaneous melanoma (CM). Our study indicates that BRAF and NRG1 were correlated with a poor prognostic of RMM and may be potential therapeutic targets for RMM treatment.

Supplementary Information

The online version contains supplementary material available at 10.1186/s12885-021-08383-6.

Deep Genomic Sequencing of Bladder Urothelial Carcinoma in Southern Chinese Patients: A Single-Center Study

Objective

Bladder urothelial carcinoma (BUC) is a common urological malignancy with molecular heterogeneity. However, the genetic feature of Chinese BUC patients is still not well-identified.

Methods

We performed deep sequencing by a large panel (450 genes) on 22 BUC samples and using matched normal bladder tissue as control. Genomic alterations (GAs), pathways and Tumor Mutation Burden (TMB) were investigated.

Results

The frequencies of GAs (TERT, 54.5%; CREBBP, 27.3%; GATA3, 22.7%; BRAF, 18.2%; TEK, 18.2% and GLI1, 18.2%) were significantly higher in Chinese than Western BUC patients. Other GAs' frequencies were in accordance with previous study (TP53, 50.0%; KDM6A, 31.8%; KMT2D, 22.7%; etc.). Besides, we detected gene amplification in ERBB2, FRS2, FAS, etc. The gene fusion/rearrangement took place in the chromosome 11, 12, 14, 17, 19, 22, and Y. Other than cell cycle and PI3K-AKT-mTOR, mutated genes were more associated with the transcription factor, chromatin modification signaling pathways. Interestingly, the TMB value was significantly higher in the BUC patients at stages T1-T2 than T3-T4 (P = 0.025).

Conclusion

Deep genomic sequencing of BUC can provide new clues on the unique GAs of Chinese patients and assist in therapeutic decision.

Genomic characterisation of diffuse large B-cell lymphoma

Diffuse large B-cell lymphoma (DLBCL) is a genomically heterogenous disease comprised of many subtypes that display significantly different clinical outcomes, in the context of treatment with conventional immunochemotherapy. Poor clinical outcomes in some subtypes, and imperfect identification of high risk individuals in otherwise low risk subgroups, demonstrate there is room for improvement in the subclassification and risk stratification of DLBCL. In addition, more comprehensive profiling may lead to improved molecular testing guided treatment selection. Existing characterisation and risk stratification strategies, such as division of DLBCL into activated B-cell (ABC) and germinal centre B-cell (GCB) subtypes, although prognostically useful, may oversimplify the underlying biology and have proven to be less useful in improving therapy selection. Several groups have proposed more predictive molecular testing based prognostic models with potentially more relevance to therapy choice. These alternative approaches use more resource intensive comprehensive genomic profiling strategies which present practical challenges to implement in diagnostic laboratories. The addition of genomic testing to the subclassification of DLBCL shows promise, but laboratories must identify testing strategies relevant to clinical practice. A consensus on optimal molecular profiling techniques is yet to be achieved. In this article we review various next generation sequencing-based analytical techniques and molecular classification models proposed recently. Emerging therapeutics where molecular profiling may guide patient selection are also reviewed. The potential utility of genomic testing in DLBCL is discussed, in addition to practical considerations when considering introducing genomics into the diagnostic laboratory.