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Next Generation Sequencing Application in Cancer Research

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A special issue of Cancers (ISSN 2072-6694). This special issue belongs to the section "Cancer Informatics and Big Data".

Deadline for manuscript submissions: closed (5 November 2021) | Viewed by 41917

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Dr. Katia Nones

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1. QIMR Berghofer Medical Research Institute, Herston, Brisbane, QLD 4006, Australia
2. Department of Cancer Research, School of Biomedical Sciences, The University of Queensland, Brisbane, QLD 4006, Australia
Interests: genomics; cancer; transcriptomics; DNA methylation

Special Issue Information

Dear Colleagues,

Next-generation sequencing (NGS) has revolutionized our understanding of the mutational landscape of cancers, providing insights into mechanisms underlying the disease. Significant efforts were led by two large consortia, The Cancer Genome Atlas (TCGA) using mainly exome sequencing and the International Cancer Genome Consortium (ICGC) exploring whole genome sequencing. These consortia have generated a massive amount of knowledge about many cancer types, but also have provided methods and tools for analyses of -omics data. These advances in NGS allow us today to research questions that could not be answered previously.

NGS form the basis of technologies such as ATAC-Seq (assay for transposase accessible chromatin with next-generation sequencing), ChiP-Seq (chromatin immunoprecipitation combined with next-generation sequencing), long read sequencing, with RNASeq, single cell sequencing, and spatial transcriptomics that bring new levels of information allowing unprecedented gain in knowledge in cancer biology and treatment. This Special Issue will highlight the use of these technologies to further advance our understanding of cancers.

Dr. Katia Nones
Guest Editor

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Keywords

sequencing genome
transcriptome
epigenome
cancer
targetable mutations

Published Papers (11 papers)

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Editorial

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4 pages, 176 KiB

Open AccessEditorial

The Impact of Next Generation Sequencing in Cancer Research

by Katia Nones and Ann-Marie Patch

Cancers 2020, 12(10), 2928; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers12102928 - 12 Oct 2020

Cited by 7 | Viewed by 2021

Abstract

Next generation sequencing (NGS) describes the technical revolution that enabled massively parallel sequencing of fragmented nucleic acids, thus making possible our current genomic understanding of cancers [...] Full article

(This article belongs to the Special Issue Next Generation Sequencing Application in Cancer Research)

Research

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26 pages, 3778 KiB

Open AccessArticle

Differential Transcriptional Reprogramming by Wild Type and Lymphoma-Associated Mutant MYC Proteins as B-Cells Convert to a Lymphoma Phenotype

by Amir Mahani, Gustav Arvidsson, Laia Sadeghi, Alf Grandien and Anthony P. H. Wright

Cancers 2021, 13(23), 6093; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13236093 - 03 Dec 2021

Viewed by 1561

Abstract

The MYC transcription factor regulates a vast number of genes and is implicated in many human malignancies. In some hematological malignancies, MYC is frequently subject to missense mutations that enhance its transformation activity. Here, we use a novel murine cell system to (i) characterize the transcriptional effects of progressively increasing MYC levels as normal primary B-cells transform to lymphoma cells and (ii) determine how this gene regulation program is modified by lymphoma-associated MYC mutations (T58A and T58I) that enhance its transformation activity. Unlike many previous studies, the cell system exploits primary B-cells that are transduced to allow regulated MYC expression under circumstances where apoptosis and senescence pathways are abrogated by the over-expression of the Bcl-xL and BMI1 proteins. In such cells, transition from a normal to a lymphoma phenotype is directly dependent on the MYC expression level, without a requirement for secondary events that are normally required during MYC-driven oncogenic transformation. A generalized linear model approach allowed an integrated analysis of RNA sequencing data to identify regulated genes in relation to both progressively increasing MYC level and wild type or mutant status. Using this design, a total of 7569 regulated genes were identified, of which the majority (n = 7263) were regulated in response to progressively increased levels of wild type MYC, while a smaller number of genes (n = 917) were differentially regulated, compared to wild type MYC, in T58A MYC- and/or T58I MYC-expressing cells. Unlike most genes that are similarly regulated by both wild type and mutant MYC genes, the set of 917 genes did not significantly overlap with known lipopolysaccharide regulated genes, which represent genes regulated by MYC in normal B cells. The genes that were differently regulated in cells expressing mutant MYC proteins were significantly enriched in DNA replication and G2 phase to mitosis transition genes. Thus, mutants affecting MYC proteins may augment quantitative oncogenic effects on the expression of normal MYC-target genes with qualitative oncogenic effects, by which sets of cell cycle genes are abnormally targeted by MYC as B cells transition into lymphoma cells. The T58A and T58I mutations augment MYC-driven transformation by distinct mechanisms. Full article

(This article belongs to the Special Issue Next Generation Sequencing Application in Cancer Research)

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14 pages, 1796 KiB

Open AccessArticle

Genomic Sub-Classification of Ovarian Clear Cell Carcinoma Revealed by Distinct Mutational Signatures

by Douglas V. N. P. Oliveira, Tine H. Schnack, Tim S. Poulsen, Anne P. Christiansen, Claus K. Høgdall and Estrid V. Høgdall

Cancers 2021, 13(20), 5242; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13205242 - 19 Oct 2021

Cited by 10 | Viewed by 2440

Abstract

Ovarian clear cell carcinoma (OCCC) is characterized by dismal prognosis, partially due to its low sensitivity to standard chemotherapy regimen. It is also well-known for presenting unique molecular features in comparison to other epithelial ovarian cancer subtypes. Here, we aim to identify potential subgroups of patients in order to (1) determine their molecular features and (2) characterize their mutational signature. Furthermore, we sought to perform the investigation based on a potentially clinically relevant setting. To that end, we assessed the mutational profile and genomic instability of 55 patients extracted from the Gynecologic Cancer Database (DGCD) by using a panel comprised of 409 cancer-associated genes and a microsatellite assay, respectively; both are currently used in our routine environment. In accordance with previous findings, ARID1A and PIK3CA were the most prevalent mutations, present in 49.1% and 41.8%, respectively. From those, the co-occurrence of ARID1A and PIK3CA mutations was observed in 36.1% of subjects, indicating that this association might be a common feature of OCCC. The microsatellite instability frequency was low across samples. An unbiased assessment of signatures identified the presence of three subgroups, where “PIK3CA” and “Double hit” (with ARID1A and PIK3CA double mutation) subgroups exhibited unique signatures, whilst “ARID1A” and “Undetermined” (no mutations on ARID1A nor PIK3CA) subgroups showed similar profiles. Those differences were further indicated by COSMIC signatures. Taken together, the current findings suggest that OCCC presents distinct mutational landscapes within its group, which may indicate different therapeutic approaches according to its subgroup. Although encouraging, it is noteworthy that the current results are limited by sample size, and further investigation on a larger group would be crucial to better elucidate them. Full article

(This article belongs to the Special Issue Next Generation Sequencing Application in Cancer Research)

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19 pages, 3824 KiB

Open AccessArticle

Oncomine™ Comprehensive Assay v3 vs. Oncomine™ Comprehensive Assay Plus

by Lau K. Vestergaard, Douglas N. P. Oliveira, Tim S. Poulsen, Claus K. Høgdall and Estrid V. Høgdall

Cancers 2021, 13(20), 5230; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13205230 - 18 Oct 2021

Cited by 20 | Viewed by 4774

Abstract

The usage of next generation sequencing in combination with targeted gene panels has enforced a better understanding of tumor compositions. The identification of key genomic biomarkers underlying a disease are crucial for diagnosis, prognosis, treatment and therapeutic responses. The Oncomine™ Comprehensive Assay v3 (OCAv3) covers 161 cancer-associated genes and is routinely employed to support clinical decision making for a therapeutic course. An improved version, Oncomine™ Comprehensive Assay Plus (OCA-Plus), has been recently developed, covering 501 genes (144 overlapping with OCAv3) in addition to microsatellite instability (MSI) and tumor mutational burden (TMB) assays in one workflow. The validation of MSI and TMB was not addressed in the present study. However, the implementation of new assays must be validated and confirmed across multiple samples before it can be introduced into a clinical setting. Here, we report the comparison of DNA sequencing results from 50 ovarian cancer formalin-fixed, paraffin-embedded samples subjected to OCAv3 and OCA-Plus. A validation assessment of gene mutations identified using OCA-Plus was performed on the 144 overlapping genes and 313,769 intersecting nucleotide positions of the OCAv3 and the OCA-Plus. Our results showed a 91% concordance within variants classified as likely-pathogenic or pathogenic. Moreover, results showed that a region of PTEN is poorly covered by the OCA-Plus assay, hence, we implemented rescue filters for those variants. In conclusion, the OCA-Plus can reflect the mutational profile of genomic variants compared with OCAv3 of 144 overlapping genes, without compromising performance. Full article

(This article belongs to the Special Issue Next Generation Sequencing Application in Cancer Research)

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16 pages, 2311 KiB

Open AccessArticle

Clinical and Molecular Heterogeneity in Patients with Innate Resistance to Anti-PD-1 +/− Anti-CTLA-4 Immunotherapy in Metastatic Melanoma Reveals Distinct Therapeutic Targets

by Tuba N. Gide, Inês Pires da Silva, Camelia Quek, Peter M. Ferguson, Marcel Batten, Ping Shang, Tasnia Ahmed, Alexander M. Menzies, Matteo S. Carlino, Robyn P. M. Saw, John F. Thompson, Richard A. Scolyer, Georgina V. Long and James S. Wilmott

Cancers 2021, 13(13), 3186; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13133186 - 25 Jun 2021

Cited by 11 | Viewed by 3102

Abstract

While immune checkpoint inhibitors targeting the CTLA-4 and PD-1 receptors have significantly improved outcomes of many patients with metastatic melanoma, there remains a group of patients who demonstrate no benefit. In this study, we sought to characterise patients who do not respond to anti-PD-1-based therapies based on their clinical, genetic and immune profiles. Forty patients with metastatic melanoma who did not respond to anti-PD-1 +/− anti-CTLA-4 treatment were identified. Targeted RNA sequencing (n = 37) was performed on pretreatment formalin-fixed paraffin-embedded (FFPE) melanoma specimens. Patients clustered into two groups based on the expression profiles of 26 differentially expressed genes: an immune gene rich group (n = 17) expressing genes associated with immune and T cell signalling, and a second group (n = 20) expressing genes associated with metabolism, signal transduction and neuronal signalling. Multiplex immunohistochemistry validated significantly higher densities of tumour-infiltrating lymphocytes (TILs) and macrophages in the immune gene-rich group. This TIL-high subset of patients also demonstrated higher expression of alternative immune-regulatory drug targets compared to the TIL-low group. Patients were also subdivided into rapid progressors and other progressors (cut-off 2 mo progression-free survival), with significantly lower TILs (p = 0.04) and CD68+ macrophages (p = 0.0091) in the rapid progressors. Furthermore, a trend towards a higher tumour burden was observed in rapid progressors (p = 0.06). These data highlight the need for a personalised and multilayer (clinical and molecular) approach for identifying the most appropriate treatments for anti-PD-1 resistant patients and provides insight into how individual treatment strategies can be achieved. Full article

(This article belongs to the Special Issue Next Generation Sequencing Application in Cancer Research)

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13 pages, 2325 KiB

Open AccessArticle

Epigenomic Analysis of RAD51 ChIP-seq Data Reveals cis-regulatory Elements Associated with Autophagy in Cancer Cell Lines

by Keunsoo Kang, Yoonjung Choi, Hyeonjin Moon, Chaelin You, Minjin Seo, Geunho Kwon, Jahyun Yun, Boram Beck and Kyuho Kang

Cancers 2021, 13(11), 2547; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13112547 - 22 May 2021

Cited by 3 | Viewed by 2900

Abstract

RAD51 is a recombinase that plays a pivotal role in homologous recombination. Although the role of RAD51 in homologous recombination has been extensively studied, it is unclear whether RAD51 can be involved in gene regulation as a co-factor. In this study, we found evidence that RAD51 may contribute to the regulation of genes involved in the autophagy pathway with E-box proteins such as USF1, USF2, and/or MITF in GM12878, HepG2, K562, and MCF-7 cell lines. The canonical USF binding motif (CACGTG) was significantly identified at RAD51-bound cis-regulatory elements in all four cell lines. In addition, genome-wide USF1, USF2, and/or MITF-binding regions significantly coincided with the RAD51-associated cis-regulatory elements in the same cell line. Interestingly, the promoters of genes associated with the autophagy pathway, such as ATG3 and ATG5, were significantly occupied by RAD51 and regulated by RAD51 in HepG2 and MCF-7 cell lines. Taken together, these results unveiled a novel role of RAD51 and provided evidence that RAD51-associated cis-regulatory elements could possibly be involved in regulating autophagy-related genes with E-box binding proteins. Full article

(This article belongs to the Special Issue Next Generation Sequencing Application in Cancer Research)

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9 pages, 486 KiB

Open AccessArticle

Detection of Structural Variants in Circulating Cell-Free DNA from Sarcoma Patients Using Next Generation Sequencing

by Lauren Mc Connell, Jana Gazdova, Katja Beck, Shambhavi Srivastava, Louise Harewood, JP Stewart, Daniel Hübschmann, Albrecht Stenzinger, Hanno Glimm, Christoph E. Heilig, Stefan Fröhling and David Gonzalez

Cancers 2020, 12(12), 3627; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers12123627 - 03 Dec 2020

Cited by 7 | Viewed by 2497

Abstract

Circulating tumour DNA (ctDNA) analysis using next generation sequencing (NGS) is being implemented in clinical practice for treatment stratification and disease monitoring. However, using ctDNA to detect structural variants, a common occurrence in sarcoma, can be challenging. Here, we use a sarcoma-specific targeted NGS panel to identify translocations and copy number variants in a cohort of 12 tissue specimens and matched circulating cell-free DNA (cfDNA) from soft tissue sarcoma patients, including alveolar rhabdomyosarcoma (n = 2), Ewing’s Sarcoma (n = 2), synovial sarcoma (n = 2), extraskeletal myxoid chondrosarcoma (n = 1), clear cell sarcoma (n = 1), undifferentiated round cell sarcoma (n = 1), myxoid liposarcoma (n = 1), alveolar soft part cell sarcoma (n = 1) and dedifferentiated liposarcoma (n = 1). Structural variants were detected in 11/12 (91.6%) and 6/12 (50%) of tissue and plasma samples, respectively. Structural variants were detected in cfDNA at variant allele frequencies >0.2% with an average sequencing depth of 1026×. The results from this cohort show clinical potential for using NGS in ctDNA to aid in the diagnosis and clinical monitoring of sarcomas and warrant additional studies in larger cohorts. Full article

(This article belongs to the Special Issue Next Generation Sequencing Application in Cancer Research)

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Review

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20 pages, 1452 KiB

Open AccessReview

Integrative Analysis of Next-Generation Sequencing for Next-Generation Cancer Research toward Artificial Intelligence

by Youngjun Park, Dominik Heider and Anne-Christin Hauschild

Cancers 2021, 13(13), 3148; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13133148 - 24 Jun 2021

Cited by 13 | Viewed by 8561

Abstract

The rapid improvement of next-generation sequencing (NGS) technologies and their application in large-scale cohorts in cancer research led to common challenges of big data. It opened a new research area incorporating systems biology and machine learning. As large-scale NGS data accumulated, sophisticated data analysis methods became indispensable. In addition, NGS data have been integrated with systems biology to build better predictive models to determine the characteristics of tumors and tumor subtypes. Therefore, various machine learning algorithms were introduced to identify underlying biological mechanisms. In this work, we review novel technologies developed for NGS data analysis, and we describe how these computational methodologies integrate systems biology and omics data. Subsequently, we discuss how deep neural networks outperform other approaches, the potential of graph neural networks (GNN) in systems biology, and the limitations in NGS biomedical research. To reflect on the various challenges and corresponding computational solutions, we will discuss the following three topics: (i) molecular characteristics, (ii) tumor heterogeneity, and (iii) drug discovery. We conclude that machine learning and network-based approaches can add valuable insights and build highly accurate models. However, a well-informed choice of learning algorithm and biological network information is crucial for the success of each specific research question. Full article

(This article belongs to the Special Issue Next Generation Sequencing Application in Cancer Research)

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15 pages, 672 KiB

Open AccessReview

Tumor Mutational Burden as a Potential Biomarker for Immunotherapy in Pancreatic Cancer: Systematic Review and Still-Open Questions

by Rita T. Lawlor, Paola Mattiolo, Andrea Mafficini, Seung-Mo Hong, Maria L. Piredda, Sergio V. Taormina, Giuseppe Malleo, Giovanni Marchegiani, Antonio Pea, Roberto Salvia, Valentyna Kryklyva, Jae Il Shin, Lodewijk A. Brosens, Michele Milella, Aldo Scarpa and Claudio Luchini

Cancers 2021, 13(13), 3119; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13133119 - 22 Jun 2021

Cited by 64 | Viewed by 5743

Abstract

Tumor mutational burden (TMB) is a numeric index that expresses the number of mutations per megabase (muts/Mb) harbored by tumor cells in a neoplasm. TMB can be determined using different approaches based on next-generation sequencing. In the case of high values, it indicates a potential response to immunotherapy. In this systematic review, we assessed the potential predictive role of high-TMB in pancreatic ductal adenocarcinoma (PDAC), as well as the histo-molecular features of high-TMB PDAC. High-TMB appeared as a rare but not-negligible molecular feature in PDAC, being present in about 1.1% of cases. This genetic condition was closely associated with mucinous/colloid and medullary histology (p < 0.01). PDAC with high-TMB frequently harbored other actionable alterations, with microsatellite instability/defective mismatch repair as the most common. Immunotherapy has shown promising results in high-TMB PDAC, but the sample size of high-TMB PDAC treated so far is quite small. This study highlights interesting peculiarities of PDAC harboring high-TMB and may represent a reliable starting point for the assessment of TMB in the clinical management of patients affected by pancreatic cancer. Full article

(This article belongs to the Special Issue Next Generation Sequencing Application in Cancer Research)

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10 pages, 502 KiB

Open AccessReview

Exploiting Clonal Evolution to Improve the Diagnosis and Treatment Efficacy Prediction in Pediatric AML

by Salvatore Nicola Bertuccio, Laura Anselmi, Riccardo Masetti, Annalisa Lonetti, Sara Cerasi, Sara Polidori, Salvatore Serravalle and Andrea Pession

Cancers 2021, 13(9), 1995; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13091995 - 21 Apr 2021

Cited by 5 | Viewed by 1892

Abstract

Despite improvements in therapeutic protocols and in risk stratification, acute myeloid leukemia (AML) remains the leading cause of childhood leukemic mortality. Indeed, the overall survival accounts for ~70% but still ~30% of pediatric patients experience relapse, with poor response to conventional chemotherapy. Thus, there is an urgent need to improve diagnosis and treatment efficacy prediction in the context of this disease. Nowadays, in the era of high throughput techniques, AML has emerged as an extremely heterogeneous disease from a genetic point of view. Different subclones characterized by specific molecular profiles display different degrees of susceptibility to conventional treatments. In this review, we describe in detail this genetic heterogeneity of pediatric AML and how it is linked to relapse in terms of clonal evolution. We highlight some innovative tools to characterize minor subclones that could help to enhance diagnosis and a preclinical model suitable for drugs screening. The final ambition of research is represented by targeted therapy, which could improve the prognosis of pediatric AML patients, as well as to limit the side toxicity of current treatments. Full article

(This article belongs to the Special Issue Next Generation Sequencing Application in Cancer Research)

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18 pages, 1175 KiB

Open AccessReview

Next Generation Sequencing Technology in the Clinic and Its Challenges

by Lau K. Vestergaard, Douglas N. P. Oliveira, Claus K. Høgdall and Estrid V. Høgdall

Cancers 2021, 13(8), 1751; https://0-doi-org.brum.beds.ac.uk/10.3390/cancers13081751 - 07 Apr 2021

Cited by 15 | Viewed by 4874

Abstract

Data analysis has become a crucial aspect in clinical oncology to interpret output from next-generation sequencing-based testing. NGS being able to resolve billions of sequencing reactions in a few days has consequently increased the demand for tools to handle and analyze such large data sets. Many tools have been developed since the advent of NGS, featuring their own peculiarities. Increased awareness when interpreting alterations in the genome is therefore of utmost importance, as the same data using different tools can provide diverse outcomes. Hence, it is crucial to evaluate and validate bioinformatic pipelines in clinical settings. Moreover, personalized medicine implies treatment targeting efficacy of biological drugs for specific genomic alterations. Here, we focused on different sequencing technologies, features underlying the genome complexity, and bioinformatic tools that can impact the final annotation. Additionally, we discuss the clinical demand and design for implementing NGS. Full article

(This article belongs to the Special Issue Next Generation Sequencing Application in Cancer Research)

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