Cancer Genomics Research Research Articles

The Japanese Society of Pathology Practical Guidelines on the handling of pathological tissue samples for cancer genomic medicine.

Clinical cancer genomic testing based on next‐generation sequencing can help select genotype‐matched therapy and provide diagnostic and prognostic information. Pathological tissue from malignant tumors obtained during routine practice are frequently used for genomic testing. This article is aimed to standardize the proper handling of pathological specimens in practice for genomic medicine based on the findings established in “Guidelines on the handling of pathological tissue samples for genomic medicine (in Japanese)” published by The Japanese Society of Pathology (JSP) in 2018. The two‐part practical guidelines are based on empirical data analyses; Part 1 describes the standard preanalytic operating procedures for tissue collection, processing, and storage of formalin‐fixed paraffin‐embedded (FFPE) samples, while Part 2 describes the assessment and selection of FFPE samples appropriate for genomic testing, typically conducted by a pathologist. The guidelines recommend that FFPE sample blocks be used within 3 years from preparation, and the tumor content should be ≥30% (minimum 20%). The empirical data were obtained from clinical studies performed by the JSP in collaboration with leading Japanese cancer genome research projects. The Japanese Ministry of Health, Labour, and Welfare (MHLW) recommended to comply with the JSP practical guidelines in implementing cancer genomic testing under the national health insurance system in over 200 MHLW‐designated core and cooperative cancer genome medicine hospitals in Japan.

Incorporating biological networks into high-dimensional Bayesian survival analysis using an ICM/M algorithm.

The Cox proportional hazards model has been widely used in cancer genomic research that aims to identify genes from high-dimensional gene expression space associated with the survival time of patients. With the increase in expertly curated biological pathways, it is challenging to incorporate such complex networks in fitting a high-dimensional Cox model. This paper considers a Bayesian framework that employs the Ising prior to capturing relations among genes represented by graphs. A spike-and-slab prior is also assigned to each of the coefficients for the purpose of variable selection. The iterated conditional modes/medians (ICM/M) algorithm is proposed for the implementation for Cox models. The ICM/M estimates hyperparameters using conditional modes and obtains coefficients through conditional medians. This procedure produces some coefficients that are exactly zero, making the model more interpretable. Comparisons of the ICM/M and other regularized Cox models were carried out with both simulated and real data. Compared to lasso, adaptive lasso, elastic net, and DegreeCox, the ICM/M yielded more parsimonious models with consistent variable selection. The ICM/M model also provided a smaller number of false positives than the other methods and showed promising results in terms of predictive accuracy. In terms of computing times among the network-aware methods, the ICM/M algorithm is substantially faster than DegreeCox even when incorporating a large complex network. The implementation of the ICM/M algorithm for Cox regression model is provided in R package icmm, available on the Comprehensive R Archive Network (CRAN).

Moving pan-cancer studies from basic research toward the clinic.

Although all cancers share common hallmarks, we have long realized that there is no silver-bullet treatment for the disease. Many clinical oncologists specialize in a single cancer type, based predominantly on the tissue of origin. With advances brought by genetics and cancer genomic research, we now know that cancers are profoundly different, both in origins and in genetic alterations. At the same time, commonalities such as key driver mutations, altered pathways, mutational, immune and microbial signatures and other areas (many revealed by pan-cancer studies) point to the intriguing possibility of targeting common traits across diverse cancer types with the same therapeutic strategies. Studies designed to delineate differences and similarities across cancer types are thus critical in discerning the basic dynamics of oncogenesis, as well as informing diagnoses, prognoses and therapies. We anticipate growing emphases on the development and application of therapies targeting underlying commonalities of different cancer types, while tailoring to the unique tissue environment and intrinsic molecular fingerprints of each cancer type and subtype. Here we summarize the facets of pan-cancer research and how they are pushing progress toward personalized medicine.

Immunogenomics in personalized cancer treatments.

Recent advances in next-generation sequencing technologies have led to significant improvements in cancer genomic research and cancer treatment. Through the use of comprehensive cancer genome data, precision medicine has become more of a reality; albeit, at present, only ~10-15% of patients can benefit from current genomic testing practices. Improvements in cancer genome analyses have contributed to a better understanding of antitumor immunity and have provided solutions for targeting highly cancer-specific neoantigens generated from somatic mutations in individual patients. Since then, numerous studies have demonstrated the importance of neoantigens and neoantigen-reactive T cells in the tumor microenvironment and how their presence influences the beneficial responses associated with various cancer immunotherapies, including immune checkpoint inhibitor therapy. Indeed, cancer immunotherapies that explicitly target neoantigens specific to individual cancer patients would lead to the ultimate form of cancer precision medicine. For this to be realized, several issues would need to be overcome, including the accurate prediction and selection of neoantigens that can induce cytotoxic T cells in individual patients. The precise prediction of target neoantigens will likely accelerate the development of personalized immunotherapy including cancer vaccines and T-cell receptor-engineered T-cell therapy for patients with cancer.

Report of the Premier African Organisation for Research and Training in Cancer Virtual Genomics Conference held from 18-20 February 2021.

The rapidly rising cancer burden and mortality rate in Africa are in contrast to the increase in cancer survivorship in Europe and North America. Genomic medicine has contributed to the rise in survival and has facilitated precision cancer control. However, there is a shortage of African representation in genomic databases, even for cancers that disproportionately affect Africans. To improve this outlook and address research in genomics and genetics relevant to Africa and people of African descent, the African Organisation for Research and Training in Cancer (AORTIC), under the Research Committee’s auspices, organised the Cancer Genomic Conference. The conference aimed to develop a roadmap for cancer genomics research to control the continent’s increasing cancer burden. Presentations at the conference revealed that: (1) Africa is made up of a highly heterogeneous group of people with diverse ethnic groups, (2) Very few African countries have been the focus of cancer genomics research, (3) Cancer exacts a heavy burden on global populations across the African diaspora with obvious genetic variants and cancer disparities and (4) There are differences in the contribution of genetics by race or ancestry and these differences are likely due to evolutionary genetics, contextual factors and genomic architecture. The importance of data security, ethics and integrity of the African genomics data was emphasised. The implementation of the conference highlights will provide the bedrock for pharmacogenomics to guide treatment decisions for cancer in Africa. The conference concluded with the formation of an AORTIC Special Interest Group on cancer genomics. It is the goal of this group to drive the implementation of this Conference’s outcomes.

Gene regulatory networks analysis of muscle-invasive bladder cancer subtypes using differential graphical model

BackgroundRecently, erdafitinib (Balversa), the first targeted therapy drug for genetic alteration, was approved to metastatic urothelial carcinoma. Cancer genomics research has been greatly encouraged. Currently, a large number of gene regulatory networks between different states have been constructed, which can reveal the difference states of genes. However, they have not been applied to the subtypes of Muscle-invasive bladder cancer (MIBC).ResultsIn this paper, we propose a method that construct gene regulatory networks under different molecular subtypes of MIBC, and analyse the regulatory differences between different molecular subtypes. Through differential expression analysis and the differential network analysis of the top 100 differential genes in the network, we find that SERPINI1, NOTUM, FGFR1 and other genes have significant differences in expression and regulatory relationship between MIBC subtypes.ConclusionsFurthermore, pathway enrichment analysis and differential network analysis demonstrate that Neuroactive ligand-receptor interaction and Cytokine-cytokine receptor interaction are significantly enriched pathways, and the genes contained in them are significant diversity in the subtypes of bladder cancer.

Authorship Patterns in Cancer Genomics Publications Across Africa.

PURPOSEAuthorship is a proxy indicator of research capacity. Understanding the research capacity is imperative for developing population-specific cancer control strategies. This is particularly apropos for African nations, where mortality from cancer is projected to surpass that from infectious disease and the populations are critically under-represented in cancer and genomics studies. Here, we present an analysis and discussion of the patterns of authorship in Africa as they pertain to cancer genomics research across African countries.METHODSPubMed metadata of relevant cancer genomics peer-reviewed publications on African populations, published between January 1, 1990, and December 31, 2019, were retrieved and analyzed for patterns of authorship affiliation using R packages, RISmed, and Pubmed.mineR.RESULTSThe data showed that only 0.016% (n = 375) of cancer publications globally were on cancer genomics of African people. More than 50% of the first and last authors of these publications originated from the North African countries of Tunisia, Morocco, Egypt, and Algeria. South Africa (13.6% and 12.7%) and Nigeria (2.2% and 1.9%) were the Sub-Saharan African countries most represented by first and last authorship positions, respectively. The United States contributed 12.6% of first and last authored papers, and nearly 50% of all African countries had no contributing author for the publications we reviewed.CONCLUSIONThis study highlights and brings awareness to the paucity of cancer genomics research on African populations and by African authors and identifies a need for concerted efforts to encourage and enable more research in Africa, needed for achieving global equity in cancer outcomes.

Frontiers in Bladder Cancer Genomic Research.

Most of the etiology studies of bladder cancer focus on genetic changes, mainly including mutation and activation of oncogenes, mutation and inactivation of tumor suppressor genes, and rearrangement or heterozygous deletion of chromosomes. Moreover, bladder cancer is highly heterogeneous mainly due to abnormal changes in the genome and proteome of tumor cells. Surgery is the main treatment for bladder cancer, but because the recurrence rate is high after surgery and most of the muscle-invasive bladder cancer acquires distant metastasis. Therefore, there is a need to combine with chemotherapy to consolidate the treatment effect. However, there are differences in chemosensitivity among patients. In this article, we review the up-to-date genomic researches on bladder cancer occurrence, development, metastasis, and chemosensitivity in patients, in order to provide some theoretical support for the diagnosis and treatment strategy for bladder cancer.

Clinical application of next-generation sequencing in cancer patients.

e15090 Background: Next-generation sequencing (NGS) has been moving rapidly from pure cancer genomics research towards clinical oncology to provide a rigorous personalized approach to treating cancer. Still there is a number of limitations to overcome before we are ready to fully introduce NGS into everyday clinical decision-making. Here we are sharing our experience with using targeted NGS platforms in clinical practice. Methods: We retrospectively evaluated the NGS results and the treatments, provided to the patients after NGS testing between Marсh 2019 and January 2021. The patients’ clinical characteristics, response to treatment and genomic mutation profiles were reviewed. Coprimary endpoints were the percentage of patients with NGS revealed targeted therapy options and the percentage of patients who received mutation-driven therapy. Results: Samples from 95 patients were tested, most frequently GI tumors (n=25, 26%), hepatobiliary tumors (n=11, 12%), breast cancer (n=10, 11%) gynecological tumors (n=10, 11%), central nervous system tumors (n=7, 7%) and others (n=32, 33%). An average of two gene alterations were identified per patient (range 0-15). The 95% (n=90) of samples had at least one detected mutation, and in 42% (n=40) of cases gene mutations happened to be targetable with existing drugs, including off-label prescriptions. Mutation-driven therapy was performed in 9,5% cases (n=10) with clinically meaningful response in 5 patients (2 patients with ovarian cancer, 2 patients with colon cancer and 1 melanoma). Conclusion: Mutational profiling using a targeted NGS panel has identified potentially targetable alterations in a vast majority of advanced cancer patients. The assay has revealed additional therapeutic options in 42% of patients. However, due to the short-term follow-up, just a small number of patients have received mutation-driven therapy. Further study will be required to determine if the mutation-driven therapy will prove to be clinically meaningful in these patients.

Promoting cancer genomics research in Africa: a roadmap

Cancer genomics research in Africa is crucial to understanding the genetic architecture of cancer and tailoring cancer diagnoses and therapies to African populations. Creating this research enterprise in Africa has to be purposeful with a roadmap that incorporates individual scientist-, international collaborator-, university or institution-, and scientific organization-level factors.

Multi-classification and Variable Selection Techniques in Cancer Genomic Data Research

In the past two decades, a huge amount of high-throughput -omics data, such as genomics, transcriptomics, metabolomics, and proteomics, have been generated regarding variations in DNA, RNA, or protein features for many cancers. The tremendous volume and complexity of these data bring significant challenges for biostatisticians, biologists, and clinicians. One of the central goal of analyzing these data is disease classification, which is fundamental for us to explore knowledge, formulate diagnosis, and develop personalized treatment. Here, we review the statistical and machine learning techniques studied in cancer classification and the process or difficulties of categorizing cancer subtypes from their genomic features.

The NCI Genomic Data Commons

The National Cancer Institute (NCI) Genomic Data Commons (GDC) contains more than 2.9 petabytes of genomic and associated clinical data from more than 60 NCI-funded and other contributed cancer genomics research projects. The GDC consists of five applications over a common data model and a common application programming interface.

Systematic Evaluation of Cross Population Polygenic Risk Score on Colorectal Cancer

Abstract The number of findings in cancer genomics research has grown rapidly in the last decade due to the decline in the cost of human sequencing and genotyping. However, the majority of the reported significant marker associated with cancer traits are based on European and East Asian population. Large population such as South Asian and South-East Asian population are under-represented in genomics research. In this study, we explored the possibility of computing a Polygenic Risk Score (PRS) of colorectal cancer on our test sample based on reported significant Single Nucleotide Polymorphism (SNP). The SNPs used to compute the risk score were collected from GWAS Central and GWAS Catalog. Significant SNPs from IC3 study were used as a benchmark. The result shows that calculating colorectal cancer risk score using reported significant marker from different population group is possible. The p-value of our statistic model shows significant differences between case and control group risk score.

Abstract 386: Design and application of an 8-plex multiplex immunofluorescence panel for deep phenotypic profiling of the tumor microenvironment using InSituPlex technology

Abstract Background: The tumor microenvironment is composed of a complicated network of different cell types and activation states, the exact composition of which is important for immuno-oncology research efforts to correlate treatment options and outcome. The number of biomarkers necessary to sufficiently profile the various cellular phenotypes and elucidate contextual relationships in situ is larger than can be accommodated by standard immunohistochemistry (IHC) approaches. Furthermore, conventional IHC requires more sections, potentially exhausting tissue sample availability. To overcome these limitations, Ultivue has developed InSituPlex® technology and UltiMapper™ assays that enable biomarker multiplexing in tissue samples with an automated workflow to support high sample throughput. Methods: An 8-plex immune-profiling panel using the InSituPlex approach was designed and tested to detect and classify T cells, macrophages, and tumor cell populations along with PD-L1 checkpoint expression in multiple tumor types. This assay was designed, developed, and tested using UltiMapper reagents and corresponding protocols. The assay was run on multiple FFPE tissue samples from breast cancer, NSCLC, and colorectal cancer (CRC) cases. Each sample was stained for the following 8 markers: CD3, CD4, CD8, CD68, FoxP3, PD-1, PD-L1, and pan-CK. Staining was performed in a single step using the Leica Biosystems BOND RX autostainer. Whole slide imaging (WSI) was performed in two steps on the ZEISS Axio Scan.Z1 slide scanner, and image analysis was performed using HALO software from Indica Labs. Results: The 8-plex immune-profiling panel demonstrated specific biomarker staining and was verified for staining reproducibility. The 8-plex workflow was compatible across multiple tumor tissue types. The samples were characterized by cell analysis and phenotyped based on their expression of specific biomarkers. The following cell phenotypes were detected: T cells (CD3+), cytotoxic T cells (CD3+/CD8+), regulatory T cells (CD4+/FOXP3+), exhausted T cells (CD3+/PD-1+), macrophages (CD68+), and tumor cells (CK+) with a dynamic range of PD-L1 expression levels. Conclusion: The 8-plex panel revealed important immunophenotypes across multiple sample types. The assay was automated and optimized for a high-throughput workflow. By testing across multiple tissue types, this assay showcases the potential for broad application across additional cancer types. The ability to interrogate an 8-plex I/O panel on a single slide with WSI presents an attractive approach for the next generation of IHC, enabling profiling within the tumor microenvironment and sensitive detection of PD-L1 expression in cancer tissues. Funded by NCI Contract No. 75N910D00024 Author affiliations for CGR staff: Cancer Genomics Research Laboratory, Frederick National Laboratory for Cancer Research, Frederick, MD 21702 Citation Format: Alexis Wong, Mara Koerner, Katir K. Patel, Anna Potier, Stephanie Hennek, Mustapha Abubakar, Mary E. Olanich, Yelena G. Golubeva, Karun Mutreja, Scott Lawrence. Design and application of an 8-plex multiplex immunofluorescence panel for deep phenotypic profiling of the tumor microenvironment using InSituPlex technology [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 386.

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

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

PredCID: prediction of driver frameshift indels in human cancer.

The discrimination of driver from passenger mutations has been a hot topic in the field of cancer biology. Although recent advances have improved the identification of driver mutations in cancer genomic research, there is no computational method specific for the cancer frameshift indels (insertions or/and deletions) yet. In addition, existing pathogenic frameshift indel predictors may suffer from plenty of missing values because of different choices of transcripts during the variant annotation processes. In this study, we proposed a computational model, called PredCID (Predictor for Cancer driver frameshift InDels), for accurately predicting cancer driver frameshift indels. Gene, DNA, transcript and protein level features are combined together and selected for classification with eXtreme Gradient Boosting classifier. Benchmarking results on the cross-validation dataset and independent dataset showed that PredCID achieves better and robust performance compared with existing noncancer-specific methods in distinguishing cancer driver frameshift indels from passengers and is therefore a valuable method for deeper understanding of frameshift indels in human cancer. PredCID is freely available for academic research at http://bioinfo.ahu.edu.cn:8080/PredCID.

Development and validation of a novel EHR-based tumor progression outcome to support biomarker discovery.

e19297 Background: Obtaining clinical outcomes for analysis has historically been a critical barrier to cancer genomics research. EHRs could constitute an important data source to bridge this gap, but EHRs rarely capture structured outcomes such as cancer progression. Novel, robust methods are needed to capture clinically relevant outcomes from EHRs. Methods: Among patients with lung adenocarcinoma whose tumors were sequenced via the Dana Farber Cancer Institute/Brigham and Women’s PROFILE study from 2013-2018, imaging reports following first palliative-intent systemic therapy were annotated using natural language processing (NLP) models trained to capture cancer progression according to the structured “PRISSMM” framework. NLP-based cancer progression and imaging report frequency were jointly modeled using inverse-intensity weighted generalized estimated equations, censored at six months, to explore associations between alterations in lung cancer biomarkers (ALK, EGFR, ROS1, BRAF, KRAS, SMARCA4) and progression. Among patients with KRAS mutations who received immunotherapy, we also analyzed the association between STK11 mutations and progression. The novel outcome generated by the model – imaging report-based progression (iPROG) – corresponded to the difference in the mean log odds of progression per inverse-intensity weighted report associated with a given biomarker; it was reported as adjusted mean probability and in exponentiated form as an odds ratio (OR). Results: Among 690 patients with lung adenocarcinoma, associations between tumor mutations and the iPROG outcome are listed in the Table. Conclusions: A deep NLP model applied to EHR data can capture a novel cancer progression outcome, which is associated with known prognostic markers in lung cancer. Application of this method to large “real world” datasets, with attention to interactions between treatment and genomics, could speed biomarker discovery. [Table: see text]

Oviz-Bio: a web-based platform for interactive cancer genomics data visualization.

Genetics data visualization plays an important role in the sharing of knowledge from cancer genome research. Many types of visualization are widely used, most of which are static and require sufficient coding experience to create. Here, we present Oviz-Bio, a web-based platform that provides interactive and real-time visualizations of cancer genomics data. Researchers can interactively explore visual outputs and export high-quality diagrams. Oviz-Bio supports a diverse range of visualizations on common cancer mutation types, including annotation and signatures of small scale mutations, haplotype view and focal clusters of copy number variations, split-reads alignment and heatmap view of structural variations, transcript junction of fusion genes and genomic hotspot of oncovirus integrations. Furthermore, Oviz-Bio allows landscape view to investigate multi-layered data in samples cohort. All Oviz-Bio visual applications are freely available at https://bio.oviz.org/.

Leveraging methylation to identify the potential causal genes associated with survival in lung adenocarcinoma and lung squamous cell carcinoma.

Understanding the different genetic landscape between lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC) is important for understanding the underlying molecular mechanism, which may facilitate the development of effective and precise treatments. Although previous studies have identified a number of differentially expressed genes (DEGs) responsible for lung cancer, it is unknown which of these genes are causal. The present study integrated DNA methylation, RNA sequencing, clinical characteristics and survival outcomes of patients with LUAD and LUSC from The Cancer Genome Atlas. DEGs were first identified using edgeR by comparing tumor and normal tissue, and differentially methylated probes (DMPs) were assessed using ChAMP. Candidate genes for further time-to-event instrumental variable analysis were selected as the intersecting genes between DEGs and the genes including DMP CpG sites within the transcription start site (TSS1500), with DMPs in TSS1500 region being the instrumental variables. Extensive sensitivity analyses were conducted to assess the robustness of the results. The present study identified 906 DEGs for LUAD, among which 538 also had DMPs in the TSS1500 region. In addition, 1,543 DEGs were identified for LUSC, among which 1,053 also had DMPs in the TSS1500 region. Time-to-event instrumental variable analysis detected eight potential causal genes for LUAD survival, including aryl hydrocarbon receptor nuclear translocator like 2, semaphorin 3G, serum deprivation-response protein, chloride intracellular channel protein 5, LIM zinc finger domain containing 2, epithelial membrane protein 2, carbonic anhydrase 7 and LOC116437. The results also identified that phosphatidylinositol-3,4,5-trisphosphate-dependent Rac exchange factor 2 may be a potential causal gene for LUSC. Therefore, the results of the present study suggested that there was molecular heterogeneity between these two lung cancer subtypes. Such analysis framework can be extended to other cancer genomics research.

The landscape of CD28, CD80, CD86, CTLA4, and ICOS DNA methylation in head and neck squamous cell carcinomas

ABSTRACT CTLA-4 blocking therapeutic antibodies are currently under investigation in head and neck squamous cell carcinoma (HNSCC). A better understanding of the epigenetic regulation of the CD28 superfamily members CD28, CTLA-4, and ICOS and their B7 ligands, CD80 and CD86, could support the development of biomarkers for response prediction to anti-CTLA-4 immunotherapy. We investigated methylation of the encoding genes CD28, CTLA4, ICOS, CD80, and CD86 at single CpG resolution (51 CpG sites) in a cohort of HNSCC (N = 528) and normal adjacent tissue samples (N = 50) provided by The Cancer Genome Research Atlas, in isolated blood leukocytes from healthy individuals (N = 28), and HNSCC cell lines (N = 39). We analysed methylation levels with regard to mRNA expression, overall survival, mutational load, interferon-γ signature, and signatures of immune cell infiltrates. Depending on the location of the CpG sites (promoter, promoter flank, gene body, and intergenic sites), we found significant differences in methylation levels among isolated leukocytes, between leukocytes and HNSCC cell lines, and among HNSCCs. Methylation of all analysed genes correlated inversely or positively with mRNA expression, depending on the CpG site. CD28, CTLA4, and ICOS revealed almost identical correlation patterns. Furthermore, we found significant correlations with survival and features of response to immunotherapy, i.e. interferon-γ signature, signatures of tumour infiltrating immune cells, and mutational load. Our results suggest CD28, CTLA4, ICOS, CD80, and CD86 expression levels are epigenetically co-regulated by DNA methylation. This study provides rationale to test their DNA methylation as potential biomarker for prediction of response to CTLA-4 immune checkpoint inhibitors.