Cancer Genomics Research Research Articles

Equity assessment in innovative cancer genomic interventions: the Can.Heal project

Abstract Background The Can.Heal project (https://canheal.eu/) aims to build a cancer and public health genomic platform. The aim of this study is to assess the extent of integration of the equity perspective in the Can.Heal project. Methods An Equity Guide was developed in the context of the Can.Heal project including guidance and support to implement equity-focused actions in cancer genomics. The Guide consists of a comprehensive set of 18 equity actions organized into three domains: 1) capacity-building, 2) cancer genomic research, 3) cancer genomic services. An equity self-assessment tool was developed to assess the degree of incorporation of the equity perspective in the innovative interventions included in the project, for both the definition and the execution phase. A descriptive analysis was performed. Results Taking into account the 3 domains, the most progress in incorporating equity perspective, in both the definition and execution phase, is observed in the “capacity building” domain (75% and 30%), followed by the “cancer genomic services” domain (43% and 11%), and the “cancer genomic research’ domain (33% and 10%). Equity actions mostly followed in both phases are: “multidisciplinary approach” (80% and 40%), “networking” (70% and 20%), and “ethics, law, and citizen engagement” (80% and 40%). The least performed actions are: “equity evidence assessment” (0% and 0%), “equity in return of genomic results” (29% and 0%), “patients’ experience’ (13% and 0%) and “diverse samples in research studies and clinical trials” (25% and 25%). Conclusions Equity perspective is more considered in the definition phase. More efforts should be done in the execution of equity actions in the Can.Heal project. Key messages • Including a multidisciplinary approach, networking and considering ethics, law, and citizen engagement are the most followed equity actions in Can.Heal project. • There is a need to integrate equity perspective in Can.Heal project, especially in cancer genomic research.

LOCC: a novel visualization and scoring of cutoffs for continuous variables with hepatocellular carcinoma prognosis as an example

BackgroundThe interpretation of large datasets, such as The Cancer Genome Atlas (TCGA), for scientific and research purposes, remains challenging despite their public availability. In this study, we focused on identifying gene expression profiles most relevant to patient prognosis and aimed to develop a method and database to address this issue. To achieve this, we introduced Luo’s Optimization Categorization Curve (LOCC), an innovative tool for visualizing and scoring continuous variables against dichotomous outcomes. To demonstrate the efficacy of LOCC using real-world data, we analyzed gene expression profiles and patient data from TCGA hepatocellular carcinoma samples.ResultsTo showcase LOCC, we demonstrate an optimal cutoff for E2F1 expression in hepatocellular carcinoma, which was subsequently validated in an independent cohort. Compared to ROC curves and their AUC, LOCC offered a superior description of the predictive value of E2F1 expression across various cancer types. The LOCC score, comprised of factors representing significance, range, and impact of the biomarker, facilitated the ranking of all gene expression profiles in hepatocellular carcinoma, aiding in the evaluation and understanding of previously published prognostic gene signatures. We also demonstrate that LOCC does not have the same assumptions required of Cox proportional hazards modeling for accurate analysis. Repeated sampling demonstrated that LOCC scores outperformed ROC’s AUC in discriminating predictors from non-predictors. Additionally, gene set enrichment analysis revealed significant associations between certain genes and prognosis, such as E2F target genes and G2M checkpoint with poor prognosis, and bile acid metabolism and oxidative phosphorylation with good prognosis.ConclusionIn summary, we present LOCC as a novel visualization tool for the analysis of gene expression in cancer, particularly for understanding and selecting cutoffs. Our findings suggest that LOCC scores, which effectively rank genes based on their prognostic potential, represent a more suitable approach than ROC curves and Cox proportional hazard for prognostic modeling and understanding in cancer gene expression analysis. LOCC holds promise as an invaluable tool for advancing precision medicine and furthering biomarker research. Further research regarding multivariable integration and validation will help LOCC reach its full potential and establish its utility across diverse cancer types and clinical settings.Graphical abstract

Abstract A084: Journey to understanding indigenous peoples engagement in cancer genomic research

Abstract Introduction: Tribal Nations across the Southwest have striking disparities in cancer incidence and mortality. Compounded by disproportionately low rates of cancer screening and limited access to healthcare, Native Americans are more often diagnosed at later stages of disease and have the poorest outcomes in all types of cancer when compared to any other racial and ethnic group in the United States. At the University of New Mexico Comprehensive Cancer Center, the Participant Engagement Cancer Genomic Sequencing (PE-CGS) Center is developing culturally appropriate materials and strategies to enhance the engagement and recruitment of Native Americans into genomic research. This effort aims to determine participant knowledges, attitudes, cultural beliefs, and communication preferences regarding clinical genomics testing. Ultimately, targeting improvement of cancer outcomes in Southwestern Tribal communities. Cultural adaptations are crucial with regards to biospecimen collection, storage, and usage, due to historical mistrust of research among Native communities. (i.e., Havasupai Tribe v. the Arizona Board of Regents). A Tribal Advisory Committee, consisting of Tribal leaders, Native physicians, and patient advocates oversee all aspects of the research study. The goal is to identify appropriate optimized health literacy strategies for engaging Native peoples in cancer genomic research. Methods: The team conducted semi-structured discussions with participants to understand their perspectives on motivations to join, cultural safety components, and effectiveness of engagement strategies. With participant consent, the discussions were recorded and transcribed. Analysis included the transcripts and notes taken by the interviewer and followed Grounded Theory principles. All transcripts and notes followed line-by-line analysis by three of the authors (JS, JR and TG) who met regularly to identify and discuss emergent themes. Results: As of May 31, 2024, the PE-CGS study has enrolled 112 participants and completed 39 discussions. Participants emphasized altruistic motives tied to family and community, echoing traditional values, in their decision to participate in the study. This notion emerged during interactions with Native team members, where comfort stemmed from shared experiences (healthcare system, barriers), use of Native language, and adherence to Native social norms. Additional considerations included present autonomy throughout various stages of the study (sample use, data sharing, discussions). Return of genetic testing results improved understanding of treatment options, hereditary risk, and health literacy. Conclusion: Feedback highlights the importance of aligning cultural beliefs with Western medicine through a strengths-based approach. The findings will inform improvements in respectful engagement, communication, information exchange, and recruitment processes with Native American cancer patients and cancer survivors to genomic testing studies. Citation Format: Jessica Sanchez, Koby Blackwater, Joseph Rodman, Tamar Ginossar, Andrew Sussman, Shiraz Mishra, Cindy Blair, Mallery Quetawki, Debra MacKenzie, V. Shane Pankratz, Ursa Brown-Glaberman, Jeffery Trent, Cheryl Willman. Journey to understanding indigenous peoples engagement in cancer genomic research [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A084.

Abstract B093: Facilitating conversations on cancer genomic education: Development of a toolkit for community advisory boards

Abstract Community Advisory Boards are representative groups of individuals that reside in a particular area and serve an important role in the research process. Often CABs are the voice of the community, rather it be key community stakeholders, business, or community organizations. The purpose of this project is to describe the development of a cancer genomic toolkit and initial introduction of the toolkit to local community advisory boards to CAB members interested in cancer genomic topics to increase inclusion and awareness of emerging topics and activities around cancer genomic research. Existing CAB members from the Meharry Medical College/Vanderbilt-Ingram Cancer Center/Tennessee State University Cancer Partnership (MVTCP) and the Research Centers in Minority Institutions (RCMI) at Meharry Medical College were introduced to a cancer genomic toolkit that is in development. CAB members were presented the toolkit during a regularly scheduled CAB meeting for approximately 30 minutes and were allowed 10 minutes to answer questions. After the presentation of the cancer genomic toolkit, CAB members were sent a word document version of the toolkit to provide any feedback for improvement during the development stage. CAB members were allowed up to two months to review the cancer genomic toolkit. Directions for review were to use track changes and leave comments or provide direct feedback via email response. To date, one CAB member has provided individualized feedback. Group feedback included questions and open discussion around historic research participation. Next steps are to introduce the tool to a newly formed genomic community advisory board and begin to reiterate the toolkit for evaluation and implementation for other local CABs. Citation Format: Calandra G. Whitted. Facilitating conversations on cancer genomic education: Development of a toolkit for community advisory boards [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr B093.

Abstract C099: Genetic ancestry specific loss of heterozygosity of essential genes as therapeutic vulnerabilities in cancer

Abstract Barriers in access to clinical care among under-served populations in combination with a vast underrepresentation at a scientific research level perpetuate inequalities in cancer treatment. These disparities are further disseminated due to the lack of understanding underlying genomic differences contributing to cancer outcome among these patients. The contributions of germline and somatic variations in cancer often shape treatment regimens, and these variations are not well investigated in non-European populations. We have described a class of potential therapeutic targets (GEMINI genes) that reflect a combination of germline alterations with somatic loss of heterozygosity (LOH). GEMINI genes are essential genes with frequent germline polymorphisms, whose frequency varies by ancestry. Indeed, because genomes of African ancestry tend to be the most diverse, many GEMINI genes are the most polymorphic in this patient population. When normal cells are heterozygous, LOH leaves cancer cells reliant on the gene products encoded by a single allele, where as healthy cells retain both copies. LOH is a common genetic event, often affecting over 20% of the genome in certain cancers, making this approach widely useful. Targeting GEMINI vulnerabilities opens up possibilities for allele-specific inhibitors or degrader therapeutics that can be catered in an ancestry-dependent manner, to ameliorate inequities in cancer genomics research. One approach to readily target individual alleles is to utilize nucleic acid-based methods, as we’ve previously demonstrated using PRIM1. This therapeutic is expected to be the most specific when targeting polymorphisms that create the greatest differences between alleles. Here, we have prioritized a set of GEMINI targets based upon the extent of differences between alleles, and rates of heterozygosity and LOH across populations. Our initial analysis of TCGA data identified 5664 variants in 1278 essential genes that undergo LOH in cancer. We filtered GEMINI vulnerabilities by means of insertion-deletion (indel) difference of the alternate allele compared to the reference allele, followed by sorting based off maximum allele frequency by genetic ancestry. Here, we identify 31 variants across 23 genes enriched in African ancestry, 7 variants across 7 genes enriched in American ancestry, 12 variants across 12 genes enriched in East Asian ancestry, 4 variants enriched in 4 genes in South Asian ancestry, and 3 variants enriched in 3 genes in European ancestry. In addition, each of these ancestry-specific GEMINI genes exhibit a minimum 10% pan-cancer LOH rate, with the highest LOH frequencies observed in ovarian and kidney chromophobe cancers. As both these cancers exhibit disparities in mortality at a population level, targeting GEMINI genes as a therapeutic strategy may aid in lessening the disproportionate rate of outcome resultant of these cancers. Overall, we identify novel GEMINI genes to assess ancestry-specific genomic variations to serve as targets for precision treatment specific to underserved patient populations. Citation Format: Nicole Peiris, Catherine Hazard, Charlotte E. Farquhar, Andrei Loas, Bradley L. Pentelute, Rameen Beroukhim. Genetic ancestry specific loss of heterozygosity of essential genes as therapeutic vulnerabilities in cancer [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr C099.

Abstract A080: Improving outcomes: Addressing racial and ethnic disparities in colorectal cancer genomic research

Abstract Colorectal cancer (CRC) is the fourth most common cancer diagnosed in the United States (U.S.). This disease presents notable health disparities among racial and ethnic minorities in the U.S. Native Americans (NA) and non-Hispanic Blacks (NHB) exhibit the highest incidence and mortality rates for CRC compared to non-Hispanic Whites (NHW). These disparities are driven by a combination of factors, including socioeconomic inequalities, access to healthcare, lifestyle habits, and genetic susceptibilities. Minority populations often face delays in diagnosis and treatment, leading to advanced disease stages at presentation and poorer outcomes. Addressing these disparities through cancer genomics is essential for developing effective, personalized treatments that can improve outcomes for underrepresented populations. Comprehensive genomic studies can reveal genetic mutations and biomarkers unique to these groups, facilitating targeted therapies and preventive measures tailored to their specific needs. To address these disparities, we conducted an analysis of U.S. population-based colorectal cancer studies within 3 cancer genomic databases: the National Cancer Institute’s Genomic Data Commons, the AACR Project GENIE, and cBioPortal. We identified 13 studies suitable for inclusion, calculating incidence percentages across all racial and ethnic categories. Our analysis encompassed a total of 20,295 patients. Among the analyzed patients, the racial distribution was as follows: NHW (68.5%), Asians (5.30%), NHB (7.7%), Other (5.6%), NA (0.2%), Pacific Islander/Native Hawaiian (0.07%), and not reported (12.7%). Ethnic data was missing for 24.1% (n=4,895) of the patients. Samples were predominantly from individuals identified as Not Hispanic (68.4%), while Hispanic individuals constituted 7.5%. Our results show significant underrepresentation of NHB, NA and Hispanic patients in CRC genomic databases highlighting a disparity between expected and actual representation. These findings underscore the critical need for inclusive research practices. Addressing these disparities in cancer genomics is imperative to develop equitable, effective treatments and improve health outcomes for all populations. Ensuring diverse representation in genomic databases will enhance the understanding of genetic differences across populations, inform targeted therapies, and ultimately contribute to reducing CRC disparities. By addressing these disparities, we can move towards a future where all populations benefit equally from advancements in CRC treatment and prevention, thus improving survival rates and quality of life for minority groups. Citation Format: Gabriela Arroyo Figueroa, Tim F. Greten, Cecilia Monge. Improving outcomes: Addressing racial and ethnic disparities in colorectal cancer genomic research [abstract]. In: Proceedings of the 17th AACR Conference on the Science of Cancer Health Disparities in Racial/Ethnic Minorities and the Medically Underserved; 2024 Sep 21-24; Los Angeles, CA. Philadelphia (PA): AACR; Cancer Epidemiol Biomarkers Prev 2024;33(9 Suppl):Abstract nr A080.

A Comprehensive Review of Bioinformatics Tools for Genomic Biomarker Discovery Driving Precision Oncology.

The rapid advancement of high-throughput technologies, particularly next-generation sequencing (NGS), has revolutionized cancer research by enabling the investigation of genetic variations such as SNPs, copy number variations, gene expression, and protein levels. These technologies have elevated the significance of precision oncology, creating a demand for biomarker identification and validation. This review explores the complex interplay of oncology, cancer biology, and bioinformatics tools, highlighting the challenges in statistical learning, experimental validation, data processing, and quality control that underpin this transformative field. This review outlines the methodologies and applications of bioinformatics tools in cancer genomics research, encompassing tools for data structuring, pathway analysis, network analysis, tools for analyzing biomarker signatures, somatic variant interpretation, genomic data analysis, and visualization tools. Open-source tools and repositories like The Cancer Genome Atlas (TCGA), Genomic Data Commons (GDC), cBioPortal, UCSC Genome Browser, Array Express, and Gene Expression Omnibus (GEO) have emerged to streamline cancer omics data analysis. Bioinformatics has significantly impacted cancer research, uncovering novel biomarkers, driver mutations, oncogenic pathways, and therapeutic targets. Integrating multi-omics data, network analysis, and advanced ML will be pivotal in future biomarker discovery and patient prognosis prediction.

Identifying new cancer genes based on the integration of annotated gene sets via hypergraph neural networks.

Identifying cancer genes remains a significant challenge in cancer genomics research. Annotated gene sets encode functional associations among multiple genes, and cancer genes have been shown to cluster in hallmark signaling pathways and biological processes. The knowledge of annotated gene sets is critical for discovering cancer genes but remains to be fully exploited. Here, we present the DIsease-Specific Hypergraph neural network (DISHyper), a hypergraph-based computational method that integrates the knowledge from multiple types of annotated gene sets to predict cancer genes. First, our benchmark results demonstrate that DISHyper outperforms the existing state-of-the-art methods and highlight the advantages of employing hypergraphs for representing annotated gene sets. Second, we validate the accuracy of DISHyper-predicted cancer genes using functional validation results and multiple independent functional genomics data. Third, our model predicts 44 novel cancer genes, and subsequent analysis shows their significant associations with multiple types of cancers. Overall, our study provides a new perspective for discovering cancer genes and reveals previously undiscovered cancer genes. DISHyper is freely available for download at https://github.com/genemine/DISHyper.

Approaches to engaging participants in a national cancer genomics research network.

11022 Background: Although patient engagement is thought to improve the relevance and acceptability of research across diverse patient populations, a paucity of studies actively evaluate engagement activities to assess their impact. The NCI’s Participant Engagement and Cancer Genome Sequencing (PE-CGS) Network was established to advance cancer genomics for understudied cancers and among underserved populations by engaging patients and survivors as participants in research. We sought to characterize strategies for engaging and evaluating engagement among research centers within PE-CGS, and to describe factors impacting each center’s unique engagement strategies and evaluation methods. Methods: We used a multi-method approach to document centers’ engagement strategies and methods to evaluate engagement.We first completed a document review of centers’ study materials including protocols, specific aims, and publicly available information. We then conducted semi-structured key informant interviews with research center members to validate findings from the document review and to identify factors that influenced engagement strategies. The semi-structured interview guide was developed using the Health Equity Implementation Framework to ensure that we accounted for multi-level factors (societal, contextual, and clinical factors) relevant to engagement. Engagement strategies and evaluation methods were collated and analyzed descriptively. Information about factors that influenced their engagement strategies were analyzed using deductive thematic analysis. Results: Across the five PE-CGS research centers, we identified 9 engagement strategies including: convening at network meetings (n=5), use of advisory boards (n=5), brochures (n=3), newsletters (n=2), community outreach through advocacy organizations (n=1) and in-person events (n=2), and use of branded websites (n=4), social media (n=3), and health technology apps (n=1). Methods used for evaluating engagement included: surveys (n=4), interviews/focus groups (n=4), informal feedback from participants (n=2), randomized experiments (n=1), and N-of-1 studies (n=1). Multi-level factors that influenced engagement included those related to the language and culture of the patient population, availability of validated measures, timing of interactions with participants within their health journey, and patient burden imposed by the experienced cancer. Conclusions: Across a national research network with a priority to advance participant engagement in cancer genomics research, we observed a range of unique engagement strategies and evaluation methods. Notably evaluation approaches were not typically tied to specific engagement strategies, but rather sought to evaluate broader aspects of engagement in the respective research studies, such as their ability to engender inclusivity, collaboration, and trust among participants.

Investigation of RBM10 mutation and its associations with clinical and molecular characteristics in EGFR-mutant and EGFR-wildtype lung adenocarcinoma

BackgroundRBM10 is commonly mutated in lung adenocarcinoma (LUAD). However, its role in the pathogenesis of LUAD remains undefined. EGFR-mutant LUAD represents a distinct subset of non-small cell lung cancer (NSCLC). The function of RBM10 in tumor pathogenesis is supposed to differ between EGFR-mutant and EGFR-wt LUAD. This study aimed to interrogate the prevalence of RBM10 mutation in a large cohort of Chinese patients with LUAD and investigate the association of RBM10 mutation with clinical and molecular characteristics of EGFR-mutant and EGFR-wt LUAD. MethodsTumor sequencing data from 2848 Chinese patients with LUAD were retrospectively reviewed and analyzed. The prevalence of RBM10 was also compared with other three cohorts: OrigMed (n = 1222), MSKCC (n = 1267), and TCGA (n = 566). The associations of RBM10 mutation with clinical and molecular characteristics were assessed. An external cohort of 182 patients with LUAD who received PD-1 inhibitor were used to investigate the association of RBM10 mutation with clinical outcomes upon immunotherapy. ResultsOur cohort showed a higher prevalence of RBM10 in EGFR-mutant LUAD than in EGFR-wt LUAD (14.8 % vs. 6.5 %, p < 0.001). The enrichment of RBM10 mutations in EGFR-mutant LUAD was also seen in another Chinese cohort (OrigMed: 14.9 % vs. 7.8 %, p < 0.001), but not in the two western cohorts (MSKCC: 7.4 % vs. 9.5 %, p = 0.272; TCGA: 8.1 % vs. 6.7 %, p = 0.624). RBM10 mutations co-occurred more frequently with EGFR L858R mutations (23.7 %) than with other types of EGFR mutations (19 del: 7.7 %; other: 7.1 % in others, p < 0.001). In EGFR-mutant LUAD, RBM10 mutations were more commonly found in stage I (18.2 %) and II (21.8 %) vs. stage III (9.4 %) and IV (11.3 %) tumors (p < 0.001). The proportion of PD-L1 positive expression in EGFR-mutant LUAD with concomitant RBM10 mutation was not different from that those without RBM10 mutations (41.8 % vs. 47.9 %, p = 0.566). In contrast, RBM10 mutation occurred more frequently in EGFR-wt LUAD at stage II-IV (stage II: 12.0 %, stage III: 8.7 %, stage IV: 6.6 %) than at stage I (2.8 %). EGFR-wt LUAD with concomitant RBM10 mutations had higher proportions of PD-L1 expression positivity (78.9 % vs. 61.9 %, p = 0.014) and higher tumor mutational load (8.97 vs. 2.99 muts/Mb, p < 0.001) than those without. Patients with EGFR-wt LUAD who also harbored RBM10 loss of function (LOF) mutations had a longer median PFS upon immunotherapy than those with RBM10 non-LOF mutations (7.15 m vs. 2.60 m, HR = 4.83 [1.30–17.94], p = 0.010). ConclusionWe comprehensively investigated RBM10 mutations in a Chinese cohort with LUAD. Compared to western cohorts, a significant enrichment of RBM10 mutations in EGFR-mutant LUAD compared to EGFR-wildtype LUAD in the Chinese population. RBM10 mutation shows different associations with clinical and molecular characteristics between EGFR-mutant and EGFR-wt LUAD, suggesting a divergent mechanism between these two subsets via which RBM10 deficiency contribute to tumor pathogenesis. The findings contribute to our understanding of the molecular landscape of LUAD and highlight the importance of considering population-specific factors in cancer genomics research.

An Effective Method to Identify Cooperation Driver Gene Sets

Background: In cancer genomics research, identifying driver genes is a challenging task. Detecting cancer-driver genes can further our understanding of cancer risk factors and promote the development of personalized treatments. Gene mutations show mutual exclusivity and cooccur, and most of the existing methods focus on identifying driver pathways or driver gene sets through the study of mutual exclusivity, that is functionally redundant gene sets. Moreover, less research on cooperation genes with co-occurring mutations has been conducted. Objective: We propose an effective method that combines the two characteristics of genes, cooccurring mutations and the coordinated regulation of proliferation genes, to explore cooperation driver genes. Methods: This study is divided into three stages: (1) constructing a binary gene mutation matrix; (2) combining mutation co-occurrence characteristics to identify the candidate cooperation gene sets; and (3) constructing a gene regulation network to screen the cooperation gene sets that perform synergistically regulating proliferation. Results: The method performance is evaluated on three TCGA cancer datasets, and the experiments showed that it can detect effective cooperation driver gene sets. In further investigations, it was determined that the discovered set of co-driver genes could be used to generate prognostic classifications, which could be biologically significant and provide complementary information to the cancer genome. Conclusion: Our approach is effective in identifying sets of cancer cooperation driver genes, and the results can be used as clinical markers to stratify patients.

Abstract 6134: The Latino Colorectal Cancer Consortium: A resource for colorectal cancer disparities research

Abstract Purpose: The Latino Colorectal Cancer Consortium (LC3) was established to fill a gap in representation of the diverse Hispanic/Latino/a/x community in cancer genomic research and to enhance understanding of determinants of disparities in the burden of colorectal cancer (CRC). Methods: The LC3 joins together three established studies with deeply-annotated clinical, epidemiologic, and biologic data available for a heterogenous group of Hispanic/Latino/a/x men and women diagnosed with primary colorectal adenocarcinoma: the Hispanic Colorectal Cancer Study (California), the Total Cancer Care Protocol at Moffitt Cancer Center (Florida), and the Puerto Rico Biobank (southern Puerto Rico). Each contributing study collected data on demographics, medical history, family history, and lifestyle factors. Vital status, cause of death, treatment, and clinical characteristics were obtained through medical chart abstraction and/or linkage to cancer registries. Blood, saliva, or normal colonic tissues were used to extract and genotype germline DNA for a subset of participants. Tumor tissues (snap frozen or formalin-fixed paraffin-embedded) were evaluated by pathologists for diagnosis, tissue content, tumor cellularity, necrosis, immune infiltration, and additional histopathologic characteristics. Results: The LC3 has assembled 2,041 participants across the three studies to date, with recruitment ongoing. Median age at diagnosis is 57 (range: 19-93), 54% of participants are male, and 63% are colon cancer cases (vs. rectal cancer). Participants were diagnosed between 1999 and 2022. The target is to generate comprehensive multi-omic data on 600 LC3 participants. To date, 243 have genome-wide germline genotyping, 323 have paired tumor/normal whole exome sequencing, and 257 have T cell receptor immunosequencing (immunoSEQ, Adaptive Biotechnologies). Further, 206 cases are represented on a set of tissue microarray blocks (target N=281). Bulk RNA-seq data are to be generated on 250 tumors. Conclusions: The LC3 fills an important gap in research infrastructure for the scientific community. Key strengths of this unique consortium are its ability to capture a diversity of Latino CRC patients with respect to nativity and cultural heritage and to represent the spectrum of stages from early to metastatic. Ultimately, research from the LC3 will inform development of equitable precision medicine approaches and predictive models to improve the health of the rapidly-growing, heterogeneous Hispanic/Latino/a population. Citation Format: Nicole C. Loroña, Marco Matejcic, Daniel Sobieski, Nathalie T. Nguyen, Hannah J. Hoehn, Diana B. Diaz, Kritika Shankar, Esther Jean-Baptiste, Domenico Coppola, Clifton Fulmer, Ozlen Saglam, Kun Jiang, Teresita Muñoz-Antonia, Idhaliz Flores, Edna Gordian, José A. Oliveras Torres, Seth I. Felder, Julian A. Sanchez, Jason Fleming, Erin M. Siegel, Douglas Cress, Mariana C. Stern, Jamie K. Teer, Stephanie L. Schmit, Jane C. Figueiredo. The Latino Colorectal Cancer Consortium: A resource for colorectal cancer disparities research [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6134.

Abstract 7426: The GenePattern ecosystem for cancer bioinformatics

Abstract As the availability of genomic data and analysis tools from large-scale cancer initiatives continues to increase, with single-cell studies adding new dimensions to the potential scientific insights, the need has become more urgent for a software environment that supports the rapid pace of cancer data science. The GenePattern ecosystem, first introduced in 2004 and updated continually to support the changing needs of cancer genomics research, supports the analytical, computational, and reproducibility needs of the world-wide cancer analysis community.The GenePattern server, available at www.genepattern.org, provides hundreds of analysis methods for scientists at all levels of computational sophistication, with the only requirement for use being a web browser. The GenePattern server offers bulk and single-cell RNA-seq, copy number variation, flow cytometry, network analysis, general machine learning, gene set enrichment analysis, proteomics, and many other modalities. Analysis steps can be linked together into pipelines that can then be edited, shared, and made public. All parameters of an analysis, including the code version, are recorded so that an analytical result can be reproduced at any point in the future.The GenePattern Notebook system, notebook.genepattern.org, is an integration of the Jupyter Notebook environment with the GenePattern server. It combines the research narrative capabilities of Jupyter with the non-programming approach and breadth of analyses available in GenePattern. Scientists using GenePattern Notebook can create documents that include richly-formatted text and multimedia, executable code, and GenePattern analyses. A single GenePattern notebook can therefore comprise a sophisticated analytical workflow that runs on multiple remote servers.We have recently expanded GenePattern Notebook into a new notebook environment, Genomics to Notebook, g2nb.org, which adds the ability to include analyses on any public Galaxy server as well as using the Integrative Genomics Viewer (IGV) and Cytoscape within notebooks. Each analysis or visualization appears a cell within a notebook, preserving the accessibility and ease of use of the notebook metaphor while retaining the essential aspects of each tool’s user interface. When run, the entire analysis appears to execute seamlessly within the notebook. The online workspace also includes a library of featured genomic analysis notebooks, including templates for common analysis tasks as well as cancer-specific research scenarios and compute-intensive methods. Scientists can easily copy these notebooks, use them as is, or adapt them for their research purposes. Citation Format: Michael M. Reich, Thorin Tabor, John Liefeld, Edwin Huang, Forrest Kim, Jill P. Mesirov. The GenePattern ecosystem for cancer bioinformatics [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 7426.

Application of forensic post-mortem techniques in a rapid autopsy to facilitate cancer research - an illustrative case report

ABSTRACT Rapid or ‘warm’ autopsy is an emerging technique in cancer genomic research. The expertise of forensic pathology can fulfil the research requirements for rapid autopsy to procure tumour lesions. This includes managing body transportation, interpretation of computed tomography scans, handling fresh (non-formalin fixed tissue), special dissection techniques, documentation, collection of samples, and subsequent reconstruction. This case report documents our experience in a single patient, with over 90 neuroendocrine tumour metastases, from pre-post-mortem planning, procurement, and subsequent tissue analysis. This case highlights the technical parallels between post-mortem examination and a rapid autopsy for cancer tissue collection. We challenge the forensic pathology community to utilize their expertise and expand their roles in rapid autopsy research, so that forensic pathologists can make a significant contribution to future precision medicine.

An analysis pipeline for understanding 6-thioguanine effects on a mouse tumour genome

Mouse tumour models are extensively used as a pre-clinical research tool in the field of oncology, playing an important role in anticancer drugs discovery. Accordingly, in cancer genomics research, the demand for next-generation sequencing (NGS) is increasing, and consequently, the need for data analysis pipelines is likewise growing. Most NGS data analysis solutions to date do not support mouse data or require highly specific configuration for their use. Here, we present a genome analysis pipeline for mouse tumour NGS data including the whole-genome sequence (WGS) data analysis flow for somatic variant discovery, and the RNA-seq data flow for differential expression, functional analysis and neoantigen prediction. The pipeline is based on standards and best practices and integrates mouse genome references and annotations. In a recent study, the pipeline was applied to demonstrate the efficacy of low dose 6-thioguanine (6TG) treatment on low-mutation melanoma in a pre-clinical mouse model. Here, we further this study and describe in detail the pipeline and the results obtained in terms of tumour mutational burden (TMB) and number of predicted neoantigens, and correlate these with 6TG effects on tumour volume. Our pipeline was expanded to include a neoantigen analysis, resulting in neopeptide prediction and MHC class I antigen presentation evaluation. We observed that the number of predicted neoepitopes were more accurate indicators of tumour immune control than TMB. In conclusion, this study demonstrates the usability of the proposed pipeline, and suggests it could be an essential robust genome analysis platform for future mouse genomic analysis.

Historical evolution of cancer genomics research in Latin America: a comprehensive visual and bibliometric analysis until 2023.

Background: Cancer genomics, as an interdisciplinary research area within the Global Cancer Research agenda, genomics and precision medicine, its important in research and clinical practice in Latin America. To date, there has been no study investigating evolution of this area in this region. The aim of this study was to evaluate for first time, the historical evolution of cancer genomics research in Latin America. Methods: Bibliometric cross-sectional study of documents on cancer genomics published by Latin American authors until 2023 in Scopus was performed. Statistical and visual analysis was performed with R programming language. Results: A total of 1534 documents were obtained. The first document of cancer genomics research was published in 1997, marking the inception of a 26-year evaluation period that extended until 2023. Among the documents, 74.3% (n = 1140) constituted original articles, followed by 22.7% (n = 349) classified as reviews. International collaboration was observed in 6.5% (n = 100) of the articles. Within the compilation of the ten most prolific authors in this region, 90% of them are from Brazil. This observed pattern extends to affiliations as well, wherein the Universidade de São Paulo emerges as the most active institution (n = 255 documents). This arrangement firmly establishes Brazil's prominence as the preeminent country in the region concerning cancer genomics research, showcasing robust collaboration networks both regionally and intercontinentally. An important transition in the studied hot topics over the last 20years was identified, from the exploration of the human genome and the characterization of genomic and proteomic cancer profiles (1997-2010) to an in-depth investigation of cancer stem cells and personalized medicine (2011-2023). Among the array of cancer types under study, predominant attention has been directed towards breast, lung, prostate, and leukemia. Conclusion: Over the course of the past 26years, a favorable and notable evolution has characterized cancer genomics research within Latin America, with Brazil leading the way, which possess a robust network of regional and intercontinental collaboration. Furthermore, the lines of research and hot topics have change in harmony with the region's objectives, strategies, and requisites.

ATACAmp: a tool for detecting ecDNA/HSRs from bulk and single-cell ATAC-seq data

BackgroundHigh oncogene expression in cancer cells is a major cause of rapid tumor progression and drug resistance. Recent cancer genome research has shown that oncogenes as well as regulatory elements can be amplified in the form of extrachromosomal DNA (ecDNA) or subsequently integrated into chromosomes as homogeneously staining regions (HSRs). These genome-level variants lead to the overexpression of the corresponding oncogenes, resulting in poor prognosis. Most existing detection methods identify ecDNA using whole genome sequencing (WGS) data. However, these techniques usually detect many false positive regions owing to chromosomal DNA interference.ResultsIn the present study, an algorithm called “ATACAmp” that can identify ecDNA/HSRs in tumor genomes using ATAC-seq data has been described. High chromatin accessibility, one of the characteristics of ecDNA, makes ATAC-seq naturally enriched in ecDNA and reduces chromosomal DNA interference. The algorithm was validated using ATAC-seq data from cell lines that have been experimentally determined to contain ecDNA regions. ATACAmp accurately identified the majority of validated ecDNA regions. AmpliconArchitect, the widely used ecDNA detecting tool, was used to detect ecDNA regions based on the WGS data of the same cell lines. Additionally, the Circle-finder software, another tool that utilizes ATAC-seq data, was assessed. The results showed that ATACAmp exhibited higher accuracy than AmpliconArchitect and Circle-finder. Moreover, ATACAmp supported the analysis of single-cell ATAC-seq data, which linked ecDNA to specific cells.ConclusionsATACAmp, written in Python, is freely available on GitHub under the MIT license: https://github.com/chsmiss/ATAC-amp. Using ATAC-seq data, ATACAmp offers a novel analytical approach that is distinct from the conventional use of WGS data. Thus, this method has the potential to reduce the cost and technical complexity associated ecDNA analysis.

Explainable Multilayer Graph Neural Network for Cancer Gene Prediction.

The identification of cancer genes is a critical yet challenging problem in cancer genomics research. Existing computational methods, including deep graph neural networks, fail to exploit the multilayered gene-gene interactions or provide limited explanations for their predictions. These methods are restricted to a single biological network, which cannot capture the full complexity of tumorigenesis. Models trained on different biological networks often yield different and even opposite cancer gene predictions, hindering their trustworthy adaptation. Here, we introduce an Explainable Multilayer Graph Neural Network (EMGNN) approach to identify cancer genes by leveraging multiple gene-gene interaction networks and pan-cancer multi-omics data. Unlike conventional graph learning on a single biological network, EMGNN uses a multilayered graph neural network to learn from multiple biological networks for accurate cancer gene prediction. Our method consistently outperforms all existing methods, with an average 7.15% improvement in area under the precision-recall curve (AUPR) over the current state-of-the-art method. Importantly, EMGNN integrated multiple graphs to prioritize newly predicted cancer genes with conflicting predictions from single biological networks. For each prediction, EMGNN provided valuable biological insights via both model-level feature importance explanations and molecular-level gene set enrichment analysis. Overall, EMGNN offers a powerful new paradigm of graph learning through modeling the multilayered topological gene relationships and provides a valuable tool for cancer genomics research. Our code is publicly available at https://github.com/zhanglab-aim/EMGNN.

Omics-based deep learning approaches for lung cancer decision-making and therapeutics development.

Lung cancer has been the most common and the leading cause of cancer deaths globally. Besides clinicopathological observations and traditional molecular tests, the advent of robust and scalable techniques for nucleic acid analysis has revolutionized biological research and medicinal practice in lung cancer treatment. In response to the demands for minimally invasive procedures and technology development over the past decade, many types of multi-omics data at various genome levels have been generated. As omics data grow, artificial intelligence models, particularly deep learning, are prominent in developing more rapid and effective methods to potentially improve lung cancer patient diagnosis, prognosis and treatment strategy. This decade has seen genome-based deep learning models thriving in various lung cancer tasks, including cancer prediction, subtype classification, prognosis estimation, cancer molecular signatures identification, treatment response prediction and biomarker development. In this study, we summarized available data sources for deep-learning-based lung cancer mining and provided an update on recent deep learning models in lung cancer genomics. Subsequently, we reviewed the current issues and discussed future research directions of deep-learning-based lung cancer genomics research.

A Noise-Tolerating Gene Association Network Uncovering an Oncogenic Regulatory Motif in Lymphoma Transcriptomics.

In cancer genomics research, gene expressions provide clues to gene regulations implicating patients' risk of survival. Gene expressions, however, fluctuate due to noises arising internally and externally, making their use to infer gene associations, hence regulation mechanisms, problematic. Here, we develop a new regression approach to model gene association networks while considering uncertain biological noises. In a series of simulation experiments accounting for varying levels of biological noises, the new method was shown to be robust and perform better than conventional regression methods, as judged by a number of statistical measures on unbiasedness, consistency and accuracy. Application to infer gene associations in germinal-center B cells led to the discovery of a three-by-two regulatory motif gene expression and a three-gene prognostic signature for diffuse large B-cell lymphoma.