Genomic Data Commons Research Articles

The effect of cancer-associated mutations on ligand binding and receptor function – A case for the 5-HT2C receptor

The serotonin 5-HT2C receptor is a G protein-coupled receptor (GPCR) mainly expressed in the central nervous system. Besides regulating mood, appetite, and reproductive behavior, it has been identified as a potential target for cancer treatment. In this study, we aimed to investigate the effects of cancer patient-derived 5-HT2C receptor mutations on ligand binding and receptor functionality. By filtering the sequencing data from the Genomic Data Commons data portal (GDC), we selected 12 mutations from multiple cancer types. We found that the affinity of the endogenous agonist serotonin (5-HT) and inverse agonist mesulergine were both drastically decreased by mutations L209HECL2 and F328S6.52, which are located in the orthosteric binding pocket. In the calcium-flux assay, the potency of 5-HT was decreased at F328S6.52, while a trend of increased efficacy was observed. In contrast, 5-HT displayed higher affinity at E306K6.30 and E306A6.30, while a trend of decreased efficacy was observed. These two mutations may disrupt the conserved ionic interaction between E6.30 and R3.50, and thus increase the constitutive activity of the receptor. The inhibitory potency of mesulergine was increased at E306A6.30 but not E306K6.30. Lastly, P365H7.50 decreased the expression level of the receptor by more than ten-fold, which prevented further functional analyses. This study shows that cancer-associated mutations of 5-HT2C receptor have diverse effects on ligand binding and function. Such mutations may affect serotonin-mediated signaling in tumor cells as well as treatment strategies targeting this receptor.

Comparative Analysis of Transcription Factors TWIST2, GATA3, and HES5 in Glioblastoma Multiforme : Evaluating Biomarker Potential and Therapeutic Targets Using In Silico Methods.

Glioblastoma multiforme (GBM) is characterized by substantial heterogeneity and limited therapeutic options. As molecular approaches to central nervous system (CNS) tumors have gained prominence, this study examined the roles of three genes, TWIST2, GATA3, and HES5, known to be involved in oncogenesis, developmental processes, and maintenance of cancer stem cell properties, which have not yet been extensively studied in GBM. This study is the first to present gene expression data for TWIST2, GATA3, and HES5 specifically within the context of GBM patient survival. Gene expression data for TWIST2, GATA3, and HES5 were collected from GBM and normal brain tissues using datasets from The Cancer Genome Atlas (TCGA) via the Genomic Data Commons (GDC) portal and the Genotype-Tissue Expression (GTEx) database. These data were rigorously analyzed using in silico methods. All three genes were significantly more expressed in GBM tissues than in normal tissues. TWIST2 and GATA3 were linked to lower survival rates in GBM patients. Interestingly, higher HES5 levels were associated with better survival rates, suggesting a complex role that needs more investigation. This study shows that TWIST2, GATA3, and HES5 could help predict outcomes in GBM patients. Our multigene model offers a better understanding of GBM and points to new treatment options, bringing hope for improved therapies and patient outcomes. This research advances our knowledge of GBM and highlights the potential of molecular diagnostics in oncology.

Abstract B005: Denosumab Enhances Cytotoxic T Cell Infiltration by supressing SPP1 Expression in Giant Cell Tumour of Bone

Abstract Denosumab, a RANKL inhibitor, is primarily used to prevent osteoclastogenesis for the treatment of conditions like osteoporosis, bone metastasis, and giant cell tumor of bone (GCTB). RANKL is also key in immunity, activating NFκB and its target genes, including the osteopontin-coding gene SPP1, which is linked with CXCL9:SPP1 macrophage polarization and prognosis. Retrospective studies of bone metastasis have demonstrated improved clinical outcomes when immune checkpoint inhibitors (ICIs) are combined with denosumab. We undertook a study to explore denosumab's direct role in anti-tumor immunity. Single-cell RNA sequencing was performed on nine primary human GCTB samples, including three untreated and six treated solely with denosumab, to exclude confounding treatment factors linked with bone metastasis samples. Furthermore, we utilized immunohistochemistry staining from paired pre- and post-denosumab treated samples from a cohort of nine GCTB patients and conducted pan-cancer analysis on primary tumors, spanning 31 distinct types of cancers, sourced from the Genomic Data Commons Data Portal. Our single-cell analysis of GCTB resulted in a comprehensive cell atlas of this rare disease, revealing denosumab's role in inhibiting SPP1 expression and augmenting cytotoxic T cell infiltration. This was confirmed in nine pairs of pre- and post-denosumab treated GCTB samples using immunohistochemistry staining. The pan-cancer analysis indicated a negative correlation between SPP1 expression and CD8A levels across most tumor types, with the CD8A/SPP1 ratio correlating with clinical outcomes in 14 cancer types. Our research provides robust clinical evidence that denosumab improves anti-tumor immunity by inhibiting SPP1 expression, presenting a significant step towards patient selection for combined ICI and denosumab therapy. Citation Format: Zezhuo Su, Maximus Chun Fai Yeung, Jason Pui Yin CHEUNG, Kelvin Sin Chi Cheung. Denosumab Enhances Cytotoxic T Cell Infiltration by supressing SPP1 Expression in Giant Cell Tumour of Bone [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Tumor Immunology and Immunotherapy; 2024 Oct 18-21; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2024;12(10 Suppl):Abstract nr B005.

Next-generation sequencing (NGS) of anaplastic thyroid carcinoma identifies unreported mutations in 25 recurring genes

Abstract Introduction/Objective Anaplastic thyroid carcinoma (ATC) is a rare, aggressive form of thyroid cancer. Our prior work identified 6 molecular clusters of ATC with a possible de novo group. Secondary analysis of NGS data from 713 ATC cases was undertaken to identify less frequent and unreported gene mutations. Methods/Case Report Deidentified NGS results of 713 ATC cases were obtained from Foundation Medicine (FoundationOne CDx). The results were analyzed for frequency of gene mutations. A search of the literature and genomic databases (TCGA, NCI Genomic Data Commons, and cBioPortal) was performed to determine gene association with thyroid cancers including ATC. Results (if a Case Study enter NA) There are 25 gene mutations present in 5 or more cases that have not previously been reported in ATC and one gene found, MTAP, which has been discussed in a limited number of cases. The gene mutations found are listed from highest to lowest frequency (14.77% to 0.69%) as follows: MTAP, LRP1B, ASXL1, NUP93, MUTYH, NOTCH3, RAD21, CRKL, PIK3R1, FANCA, STAG2, BRCA2, FGF4, FGF3, FGF19, FBXW7, BRCA1, BCORL1, QKI, VHL, PRKN, GATA6, NKX2_1, KDM5C, CCND3, and BAP1. There are also 99 other gene mutations with 4 or fewer cases (0.14% to 0.77%) that have not previously been associated with ATC. MTAP was previously reported to be lost by immunohistochemistry in 6/27 (22.2%) ATC cases; MTAP loss was also reported to indicate a potential CDKN2A abnormality. Our larger data set supports that MTAP is lost in ATC with 98.6% of MTAP- deleted ATC also harboring CDKN2A deletion and 92.3% showing CDKN2B deletion (p-values < 0.0001), supporting MTAP and CDKN2A/B are often deleted together. Other significant genes include the hereditary cancer genes MUTYH, FANCA (autosomal recessive), and BRCA1/2 (autosomal dominant) and genes associated with clonal hematopoiesis. ASXL1 and STAG2 have previously been unreported in ATC while others such as DNMT3A, TET2, and BCOR were also present in our dataset. Conclusion Our findings demonstrate 124 genes mutated in ATC that have not been previously reported. Besides the previously reported MTAP in 6 cases, genes associated with hereditary cancer and clonal hematopoiesis were identified. MTAP is located adjacent to CDKN2A/B and is often codeleted with MTAP at a much higher frequency than previously reported. Identifying associations with other diseases could improve surveillance and options for earlier therapy of ATC. Finally, these findings warrant further research to determine their role in the pathogenesis and treatment targets of ATC.

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.

The downregulation of SASH1 expression promotes breast cancer occurrence and invasion accompanied by the activation of PI3K-Akt-mTOR signaling pathway

SASH1 (SAM and SH3 domain containing 1) has been increasingly reported as a tumor suppressor gene. However, there is limited research on the role of SASH1 in breast cancer. This manuscript aims to investigate the mechanism of SASH1 in the occurrence, development, and prognosis of breast cancer. Firstly, we obtained RNA-sequencing data of the tumors from the Genomic Data Commons data portal website, along with the corresponding clinical information of patients. Pan-cancer analysis was performed to analyze the expression of SASH1 across all tumors. Univariate Cox regression analysis was used to assess the correlation between SASH1 expression and the prognosis of breast cancer patients. Then, immunohistochemistry was utilized to evaluate the expression levels of SASH1, p-Akt, p-PI3K, and p-mTOR in breast cancer tissue. Finally, a cell assay was employed to analyze the impact of SASH1 on the proliferation and invasion of breast cancer cells (MDA-MB-231). The results revealed that SASH1 expression is decreased in BRCA, LUSC, LUAD, CESC, ESCA, and COAD. Meta-analysis also found that SASH1 is downregulated in most tumor tissues, and the expression level of SASH1 in breast cancer was significantly lower than that in the control group (OR = 0.14, 95% CI = 0.08–0.25; P < 0.001). Further experimental validation showed that SASH1 expression is significantly downregulated in breast cancer tissue (38.33%, 23/60), and the overexpression of SASH1 can inhibit the proliferation and invasion of breast cancer cells accompanied by the suppression of PI3K-Akt-mTOR signaling pathway. Additionally, SASH1 overexpression can improve OS and RFS of breast cancer patients.

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.

ExpHRD: an individualized, transcriptome-based prediction model for homologous recombination deficiency assessment in cancer

BackgroundHomologous recombination deficiency (HRD) stands as a clinical indicator for discerning responsive outcomes to platinum-based chemotherapy and poly ADP-ribose polymerase (PARP) inhibitors. One of the conventional approaches to HRD prognostication has generally centered on identifying deleterious mutations within the BRCA1/2 genes, along with quantifying the genomic scars, such as Genomic Instability Score (GIS) estimation with scarHRD. However, the scarHRD method has limitations in scenarios involving tumors bereft of corresponding germline data. Although several RNA-seq-based HRD prediction algorithms have been developed, they mainly support cohort-wise classification, thereby yielding HRD status without furnishing an analogous quantitative metric akin to scarHRD. This study introduces the expHRD method, which operates as a novel transcriptome-based framework tailored to n-of-1-style HRD scoring.ResultsThe prediction model has been established using the elastic net regression method in the Cancer Genome Atlas (TCGA) pan-cancer training set. The bootstrap technique derived the HRD geneset for applying the expHRD calculation. The expHRD demonstrated a notable correlation with scarHRD and superior performance in predicting HRD-high samples. We also performed intra- and extra-cohort evaluations for clinical feasibility in the TCGA-OV and the Genomic Data Commons (GDC) ovarian cancer cohort, respectively. The innovative web service designed for ease of use is poised to extend the realms of HRD prediction across diverse malignancies, with ovarian cancer standing as an emblematic example.ConclusionsOur novel approach leverages the transcriptome data, enabling the prediction of HRD status with remarkable precision. This innovative method addresses the challenges associated with limited available data, opening new avenues for utilizing transcriptomics to inform clinical decisions.

Pan-cancer exploration of oncogenic and clinical impacts revealed that HOXA9 is a diagnostic indicator of tumorigenesis

Homeodomain transcription factor A9 (HOXA9) is a member of the HOX cluster family of transcription factors that are crucially involved in embryo implantation, morphogenesis, body axis development, and endothelial cell differentiation. Despite numerous reports on its aberrant expression in a few malignancies, the molecular and functional complexity of HOXA9 across cancers remains obscure. We aimed to analyze the dynamic role of HOXA9 across cancers by identifying, analyzing, and understanding its multiple modes of regulation and functional implications and identifying possible therapeutic avenues. We conducted a comprehensive analysis to determine the role of HOXA9 across cancers. This approach involved the integration of large-scale datasets from public repositories such as the Genomic Data Commons, specifically the Cancer Genome Atlas (GDC-TCGA), across 33 different cancer types. The multiple modes of HOXA9 regulation by genetic and epigenetic factors were determined using online tools, which comprised experimentally validated observations. Furthermore, downstream pathways were identified by predicting the targets of HOXA9 and by performing functional enrichment analysis. We also assessed the clinical significance of HOXA9 in terms of prognosis and stage stratification. This study evaluated the correlation between HOXA9 and tumor-infiltrating molecules and discussed its association with therapeutically approved antineoplastic drugs. HOXA9 was significantly upregulated in 9 tumors and downregulated in 2 cancers. The deregulation of HOXA9 is primarily attributed to epigenetic factors, including promoter DNA methylation and noncoding RNAs (ncRNAs). The HOXA9 transcription factor interacts with PBX/MEIS cofactors and regulates multiple genes involved in cancer-associated EMT, autophagy, the cell cycle, metabolic pathways, Wnt signaling, TGF-β signaling, the AMPK pathway, PI3K/AKT signaling, and NF-κB signaling, thereby establishing control over downstream mechanisms. Differential expression in various clinical stages across cancers was shown to have prognostic significance and to be correlated with tumor-infiltrating immune molecules. The assessment of the correlation of HOXA9 expression with approved antineoplastic drugs revealed that targeting HOXA9 could be the most reliable strategy for preventing cancer progression. HOXA9 is upregulated in the majority of malignancies and drives cancer progression by regulating multiple signaling mechanisms. Hence, HOXA9 could be a reliable diagnostic indicator and a potential therapeutic candidate for solid cancer types.

PCAS: An Integrated Tool for Multi-Dimensional Cancer Research Utilizing Clinical Proteomic Tumor Analysis Consortium Data.

Proteomics offers a robust method for quantifying proteins and elucidating their roles in cellular functions, surpassing the insights provided by transcriptomics. The Clinical Proteomic Tumor Analysis Consortium database, enriched with comprehensive cancer proteomics data including phosphorylation and ubiquitination profiles, alongside transcriptomics data from the Genomic Data Commons, allow for integrative molecular studies of cancer. The ProteoCancer Analysis Suite (PCAS), our newly developed R package and Shinyapp, leverages these resources to facilitate in-depth analyses of proteomics, phosphoproteomics, and transcriptomics, enhancing our understanding of the tumor microenvironment through features like immune infiltration and drug sensitivity analysis. This tool aids in identifying critical signaling pathways and therapeutic targets, particularly through its detailed phosphoproteomic analysis. To demonstrate the functionality of the PCAS, we conducted an analysis of GAPDH across multiple cancer types, revealing a significant upregulation of protein levels, which is consistent with its important biological and clinical significance in tumors, as indicated in our prior research. Further experiments were used to validate the findings performed using the tool. In conclusion, the PCAS is a powerful and valuable tool for conducting comprehensive proteomic analyses, significantly enhancing our ability to uncover oncogenic mechanisms and identify potential therapeutic targets in cancer research.

Effects of ferroptosis-related gene HSPB1 on acute myeloid leukemia.

The purpose of this study was to investigate the effects and potential mechanisms of ferroptosis-related gene heat shock protein beta-1 (HSPB1) on acute myeloid leukemia (AML). The RNA-seq and clinical data of AML samples were obtained from the Genomic Data Commons database, and the FerrDb database was used to screen the marker, drive and suppressor of ferroptosis. Besides, DESeq2 was applied for differential expression analysis on AML samples and screening for differentially expressed genes (DEGs). The screened DEGs were subjected to the intersection analysis with ferroptosis-related genes to identify the ferroptosis-related DEGs. Next, the functional pathways of ferroptosis-related DEGs were further be discussed by Gene Ontology as well as Kyoto Encyclopedia of Genes and Genomes enrichment analysis of DEGs. Additionally, lasso regression analysis was employed to determine the differential genes related to prognosis in patients with AML and the survival analysis was performed. Subsequently, quantitative real-time polymerase chain reaction and western blot assay were applied to detect the mRNA and protein expression levels of HSPB1 in normal/AML bone marrow tissues and human normal (HS-5)/AML (HL-60) bone marrow cells, respectively. Furthermore, HSPB1 was knocked down to assess the expression changes of glutathione peroxidase 4 and acyl-CoA synthetase long-chain family member 4. Ultimately, the viability and oxidative stress levels of HL-60 were analyzed by Cell Counting Kit-8 and biochemical detection. A total of 4986 DEGs were identified in AML samples, with 3324 up-regulated and 1662 down-regulated. The enrichment analysis illustrated that ferroptosis-related DEGs were significantly enriched in response to metal irons, oxidative stress, and other pathways. After lasso regression analysis, 17 feature genes related to the prognosis of patients with AML were obtained, with HSPB1 exhibiting a significant correlation. The reliability of our models was verified by Cox regression analysis and survival analysis of the hazard model. Furthermore, the outcomes of quantitative real-time polymerase chain reaction and western blot showed that mRNA and protein expression levels of HSPB1 were significantly increased in the AML Group and HL-60 cells. The knockdown of HSPB1 in HL-60 cells reduced the protein level of glutathione peroxidase 4, increased the protein level of acyl-CoA synthetase long-chain family member 4, decreased the cell viability, and aggravated oxidative stress. Ferroptosis-related gene HSPB1 is highly expressed in patients with AML. In addition, HSPB1 may be involved in the occurrence and development of AML by regulating oxidative stress and ferroptosis-related pathways. This study provides new clues for further understanding of AML molecular mechanisms. Also, HSPB1 is expected to be a potential therapeutic target for AML in the future.

Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data.

In recent years, researchers have proven the effectiveness and speediness of machine learning-based cancer diagnosis models. However, it is difficult to explain the results generated by machine learning models, especially ones that utilized complex high-dimensional data like RNA sequencing data. In this study, we propose the binarilization technique as a novel way to treat RNA sequencing data and used it to construct explainable cancer prediction models. We tested our proposed data processing technique on five different models, namely neural network, random forest, xgboost, support vector machine, and decision tree, using four cancer datasets collected from the National Cancer Institute Genomic Data Commons. Since our datasets are imbalanced, we evaluated the performance of all models using metrics designed for imbalance performance like geometric mean, Matthews correlation coefficient, F-Measure, and area under the receiver operating characteristic curve. Our approach showed comparative performance while relying on less features. Additionally, we demonstrated that data binarilization offers higher explainability by revealing how each feature affects the prediction. These results demonstrate the potential of data binarilization technique in improving the performance and explainability of RNA sequencing based cancer prediction models.

Predicting bladder cancer survival with high accuracy: insights from MAPK pathway-related genes

The mitogen-activated protein kinase (MAPK) pathway plays a critical role in tumor development and immunotherapy. Nevertheless, additional research is necessary to comprehend the relationship between the MAPK pathway and the prognosis of bladder cancer (BLCA), as well as its influence on the tumor immune microenvironment. To create prognostic models, we screened ten genes associated with the MAPK pathway using COX and least absolute shrinkage and selection operator (LASSO) regression analysis. These models were validated in the Genomic Data Commons (GEO) cohort and further examined for immune infiltration, somatic mutation, and drug sensitivity characteristics. Finally, the findings were validated using The Human Protein Atlas (HPA) database and through Quantitative Real-time PCR (qRT-PCR). Patients were classified into high-risk and low-risk groups based on the prognosis-related genes of the MAPK pathway. The high-risk group had poorer overall survival than the low-risk group and showed increased immune infiltration compared to the low-risk group. Additionally, the nomograms built using the risk scores and clinical factors exhibited high accuracy in predicting the survival of BLCA patients. The prognostic profiling of MAPK pathway-associated genes represents a potent clinical prediction tool, serving as the foundation for precise clinical treatment of BLCA.

Abstract 6252: Driver indel discovery and allelic imbalance in &amp;gt;9,000 tumor RNA-Seq samples from the Cancer Genome Atlas (TCGA)

Abstract High-throughput DNA sequencing technologies have enabled unbiased screening of genomic alterations such as single nucleotide variants (SNVs) and small insertions/deletions (indels). However, variant analysis using transcriptomic sequencing (RNA-seq) data has not become standard due to challenges in distinguishing true variants, in particular indels, from artifacts that can arise from RNA-seq mapping and library preparation. We have previously developed a tool, RNAIndel, which classifies indels as somatic, germline, or artifacts using a machine learning-based model. Here, we present enhanced somatic indel discovery by incorporating tumor RNA-Seq data with conventional paired tumor-normal DNA-Seq by re-analyzing the Cancer Genome Atlas (TCGA) data using RNAIndel. Our analytic process involves running RNAIndel on 9,101 TCGA tumor RNA-Seq samples across 33 cancer types hosted on Cancer Genomics Cloud by Seven Bridges Genomics (https://www.cancergenomicscloud.org) and comparing the result with the reference somatic indel set generated from paired tumor/normal whole-exome sequencing (WES) data using an ensemble caller developed by NCI Genomic Data Commons (GDC). The comparison validates the RNAIndel variants by matching to the corresponding WES dataset. We also use the GDC indel set to test their expression in RNA-Seq. Indel alignments across sequencing platforms were compared by indelPost, an algorithm that we developed, to perform indel realignment to overcome the differences in mapping algorithms (BWA vs. STAR) and read lengths (100-bp vs. 50-bp) in WES versus RNA-Seq data.The joint DNA/RNA indel analysis provides important insights into the expression profile of indels in known cancer driver genes versus non-drivers. First, indels in driver genes are more likely expressed than those in non-drivers (79% vs. 50%, p &amp;lt; 2.2 × 10-16). Second, mutant allele expression is frequently upregulated for truncating indels in tumor-suppressor genes (TSG) as allelic imbalance was detected in 6% of such variants in TSGs vs. 2% non-drivers. This may indicate a potential second hit leading to loss of the wild-type allele of in the TSGs. Third, allelic imbalance for in-frame indels was also frequent for oncogenes (20%) compared with the non-drivers (7%). The high expression likelihood of driver genes and the enriched indel allele expression for TSGs and oncogenes both support the use of RNA-Seq to enhance driver indel discovery. Indeed, our analysis reveals ~10% additional driver indels by RNA-Seq analysis which are absent from the GDC reference indel set. These indels affect prominent driver genes, most frequently CDKN2A, TP53 and ARID1A. Our experience argues for incorporating de novo indel analysis in RNA-Seq as a standard approach for future cancer genomic analysis. Citation Format: Kohei Hagiwara, Andrew Thrasher, Jinghui Zhang. Driver indel discovery and allelic imbalance in &amp;gt;9,000 tumor RNA-Seq samples from the Cancer Genome Atlas (TCGA) [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 6252.

Abstract 908: SeNMo: A self-normalizing deep learning model for enhanced multi-omics data analysis in oncology

Abstract Multi-omics research has enhanced our understanding of cancer heterogeneity and progression. Investigating molecular data through multi-omics approaches is crucial for unraveling the complex biological mechanisms underlying cancer, thereby enabling more effective diagnosis, treatment, and prevention strategies. However, predicting patient outcomes through integration of all available multi-omics data is still an under-study research direction. Here, we present SeNMo (Self-normalizing Network for Multi-omics), a deep neural network that ensures the zero mean and unit variance of activations across network layers using the self-normalizing technique. Such normalizing techniques are critical in stable and robust learning of deep learning models. SeNMo is particularly efficient in handling multi-omics data characterized by high-width (many features) and low-length (fewer samples) attributes. We trained SeNMo for the task of overall survival of patients using pan-cancer multi-omics data involving 28 cancer sites from the Genomic Data Commons (GDC). The training multi-omics data includes gene expression, DNA methylation, miRNA expression, and protein expression modalities. We tested the model's performance on the Moffitt Cancer Center's internal data involving RNA expression and protein expression data. We evaluated the model’s performance in predicting patient’s overall survival using the concordance index (C-Index), which provides a robust measure of the model's predictive capability. SeNMo performed consistently well in the training regime, reflected by the validation C-Index≥0.6 on GDC's public data. In the testing regime on Moffitt's private data, SeNMo performed with a C-Index of 0.68. The model's performance increased when tested on low-dimensional data or when tested on single omic data such as RNA or protein expression data with a C-Index of 0.7. SeNMo proved to be a mini-foundation model for multi-omics oncology data because it demonstrated robust performance, adaptability across molecular data types, and universal approximator capabilities for the scale of molecular data it was trained on. SeNMo can be further scaled to any cancer site and molecular data type. It can also be fine-tuned for other downstream tasks such as treatment response prediction, risk stratification, patient subgroup identification, and others. Its ability to accurately predict patient outcomes and adapt to various downstream tasks indicates a new era in cancer research and treatment. For future research, SeNMo offers a powerful tool for uncovering deeper insights into the complex nature of cancer and sets a precedent for how artificial intelligence can be leveraged to handle the vast and intricate data in the biomedical field. We believe SeNMo and similar models are poised to transform the oncology landscape, offering hope for more effective, efficient, and patient-centric cancer care. Citation Format: Asim Waqas, Aakash Tripathi, Sabeen Ahmed, Ashwin Mukund, Paul Stewart, Mia Naeini, Hamza Farooq, Ghulam Rasool. SeNMo: A self-normalizing deep learning model for enhanced multi-omics data analysis in oncology [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 908.

Abstract 3480: Alterations in the tumor microenvironment and key signaling pathways in naturally-occurring canine gliomas mirror those seen in human patients

Abstract Background: Malignant gliomas are the most common type of primary brain tumors, with patient outcomes that are generally dismal. Recently, pet dogs have been assessed as a valuable model in which to study gliomas. Gliomas in pet dogs occur at similar rates to those seen in humans, and a recent study comparing canine and human gliomas identified high similarity in terms of mutational rates, aneuploidy, the timing of mutations, and methylation patterns. Here, we further compare naturally-occurring canine and human gliomas, focusing on the tumor microenvironment (TME) and key pathways dysregulated in human gliomas. Methods: Spontaneously occurring gliomas from adult and pediatric human, and canine patients were utilized for this study. RNA, whole genome/whole exome, and bisulfite sequencing data from adult and canine samples were downloaded from the Genomic Data Commons and the National Cancer Institute’s Integrated Canine Data Commons, respectively. Pediatric tumor RNA-seq and genomic data were obtained from the Gabriella Miller Kids First database. Normal adult human and canine brain tissue served as control tissue. Only tumors with a clear diagnosis of astrocytoma, oligodendroglioma, or oligoastrocytoma were included in this study. Results: Overall, we found that key canonical pathways that are altered in human gliomas are also altered in canine gliomas. A comparison of high-grade gliomas in pet dogs to normal canine brain tissues showed significant abnormal gene expression in 3 canonical oncogenic pathways of human GBM: the TP53, RB1, and RTK/RAS/PI3K pathways. Similarities between species included increased expression of PDGF, and receptors PDGFRA and PDGFRB. Frequent copy number alterations and mutations were also observed in PDGFRA and EGFR. Differences between species include the mutation rate of TP53, which we found to be mutated frequently in adults but not in pediatric or canine gliomas, however, expression of TP53 as assessed by RNA-seq was significantly increased in both adult and canine gliomas. IDH1, which drives methylation, was significantly overexpressed in adults but underexpressed in canine. Correspondingly, each of the promotor regions of TERT, PD1 and MGMT was hypermethylated in adults, but hypomethylated in canine. Although an analysis of the TME from RNA-seq data revealed an influx of immune cells in adult, pediatric and canine gliomas, there was a concurrent influx of immunosuppressive cells. Gene expression profiles of astrocytomas and oligodendrogliomas show differences in alterations of immune-related and TME-specific pathways. Conclusion: We found that similar TME and pathway alterations were observed between human and canine gliomas, and that TME alterations were different between canine astrocytomas and oligodendrogliomas. The canine TME, like that in humans, appears to be immunosuppressive. Citation Format: Nadia A. Lanman, Sagar Utturkar, Harish Kothandaraman, Deepika Dhawan, Danni Liu, Min Zhang, Matthew Beyers, Deborah W. Knapp, R. Timothy Bentley. Alterations in the tumor microenvironment and key signaling pathways in naturally-occurring canine gliomas mirror those seen in human patients [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 3480.

Abstract 1852: NCI’s Clinical Proteomic Tumor Analysis Consortium: A proteogenomic cancer analysis program

Abstract The National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (NCI’s CPTAC) is a national effort to accelerate the understanding of cancer biology through the integration of large-scale proteome and genome analysis, or proteogenomics. The program which is composed of a Tumor Characterization Program and a Translational Research Program (proteomics.cancer.gov) aims to leverage the investments made in cancer research by understanding the molecular changes by proteogenomically characterizing cancers and accelerating the basic discovery toward clinical impact. All data and analytical tools are made broadly available to the research community through public databases to maximize utility and public benefit.CPTAC teams have characterized a plethora of treatment-naïve tumor types, including colorectal, ovarian, breast, clear cell renal cell carcinoma, uterine corpus endometrial carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, brain (including adult, pediatric, and adolescent and young adult), head and neck squamous cell carcinoma, and pancreatic ductal adenocarcinoma (proteomics.cancer.gov/resources/milestones-and-publications). Ongoing projects include acute myeloid leukemia (AML), cutaneous melanoma, sarcoma, oligodendroglioma, gastric, prostate, liver, and thyroid cancers. In the PTRC Program, CPTAC is partnering with NCI-sponsored clinical trials to support clinically relevant research projects that would elucidate biological mechanisms of therapeutic response, resistance, and/or toxicity in cancers including AML, multiple myeloma, melanoma, and lung cancers. All mass spectrometry-based proteomic, genomic, and imaging data (histopathology and radiology) are made publicly available at the CPTAC Proteomic Data Commons (pdc.cancer.gov), Genomic Data Commons (gdc.cancer.gov), and the Cancer Imaging Archive (cancerimagingarchive.net)/Imaging Data Commons (imaging.datacommons.cancer.gov) respectively. CPTAC is also supporting development of new proteogenomic data analysis tools (proteomics.cancer.gov/resources/computational-tools). In addition, the CPTAC Assay Portal (assays.cancer.gov) is a public resource populated with mass spectrometry-based targeted proteomic assays developed by the consortium for quantitatively measuring proteins of interest, including those discovered through comprehensive tumor characterization. Lastly, well-characterized monoclonal antibodies targeting cancer-specific proteins and peptides are also made available at CPTAC’s Antibody Portal (antibodies.cancer.gov). Citation Format: Mehdi Mesri, Eunkyung An, Xu Zhang, Jasmin Bavarva, Ana I. Robles, Tara Hiltke, Henry Rodriguez. NCI’s Clinical Proteomic Tumor Analysis Consortium: A proteogenomic cancer analysis program [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 1852.

Abstract 6242: A workflow execution system in a data fabric for integrative cancer analyses

Abstract Cancer researchers are increasingly conducting multi-omic research and performing integrative analyses on combinations of genomic, proteomic, transcriptomic, imaging, single cell and other data modalities. However, it is quite challenging for a researcher to effectively access, aggregate and analyze data and metadata from different data sources in a scalable and reproducible manner, as the individual datasets may be disconnected from each other, and have separate authentication and authorization requirements. We developed a workflow execution system in a data fabric called the Biomedical Research Hub (BRH) to overcome these challenges for academic researchers. The BRH is powered by the Gen3 technology, an open-source Kubernetes based software stack that allows cancer researchers to create their own data fabric and interoperate with data from multiple data sources. The workflow execution system utilizes nextflow and allows researchers to run containerized applications in the cloud in a secure and isolated environment. Data from multiple resources can be combined for analysis using convenient pay models including NIH STRIDES. We plan to demonstrate the application of our system on a scientific use case involving Clonal Hematopoiesis of Indeterminate potential (CHIP), a phenomenon that has been associated with aging, cancer, cardiovascular diseases, infection and all-cause mortality. We run containerized CHIP workflows on the cloud, utilizing two datasets accessible through BRH: i) The Genomic Data Commons, the world’s largest source of harmonized cancer data and ii) BioDataCatalyst, an NHLBI ecosystem that drives discovery and innovation for heart, lung, blood and sleep disorders. Our system is ideally suited for machine learning on large aggregated cancer datasets and federated learning tasks. Citation Format: Aarti Venkat, Pauline Ribeyre, Jawad Qureshi, Sai Shanmukha Narumanchi, J Montgomery Maxwell, Bill Winslow, Sara Volk Garcia, Chris Meyer, Tzintzuni Garcia, Peter Vassilatos, Clinton Malson, Zhenyu Zhang, Robert Grossman. A workflow execution system in a data fabric for integrative cancer analyses [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 6242.

Abstract 3558: An integrated data platform to support the international alliance for cancer early detection

Abstract Cancer early detection is one of the most critical areas of cancer research, as it offers the greatest potential for improving patient outcomes. The International Alliance for Cancer Early Detection (ACED) is a global partnership of world-leading cancer research institutions in the UK and US, established in 2019 to accelerate and revolutionize research in this field. ACED brings together the expertise of the Canary Center at Stanford University, the University of Cambridge, the Knight Cancer Institute at Oregon Health and Sciences University, University College London, and the University of Manchester, together with Cancer Research UK, with the goal of catalyzing new collaborations and research in the field of early detection. One of the major challenges to enabling international collaborations across different institutions is the ability to collect, share and discover datasets between researchers and institutions. To enable collaboration on innovative data science, fundamental functions include, 1) controlled data sharing mechanisms; 2) structured metadata to enable data exploration and data discovery; and 3) enabling easy computational access to that data. To advance researchers' collaboration, ACED began development of an Integrated Data Platform (IDP). The ACED-IDP is based on the Gen3 software platform developed by the University of Chicago's Center for Translational Data Science. Based on software systems originally developed for the NCI's Genomic Data Commons, Gen3 has been used in several other data projects including The Blood Profiling Atlas in Cancer (BloodPAC), Australian BioCommons and numerous other research data platforms. The unique nature of ACED required unique innovations to be made for the development of the IDP. Each of the member institutions within the alliance has different existing computer infrastructures, separate authentication platforms and heterogeneous data types forming the basis of their research. The IDP sought a cloud-ready strategy, while still being cognizant of the extreme costs associated with cloud egress fees that hamper researchers' ability to download data. Additions were made to the Gen3 platform to allow for hybrid cloud support, allowing on-premises as well as cloud object storage systems to be linked to the platform. This innovation permits each institution to share files using the mechanisms that they see fit. To unify the various datasets, the standard Gen3 schema was replaced with one derived from the Fast Healthcare Interoperability Resources (FHIR) standard. To support import from clinical data sets, tooling to enable import and export Observational Medical Outcomes Partnership (OMOP) data has been integrated. With this platform in place, we hope to advance international collaborations and accelerate early cancer detection research. Citation Format: Brian Walsh, Liam Beckman, JD Burchett, Matthew Peterkort, Jordan Lee, Michael Fitzsimons, Peter Vassilatos, Binam Bajracharya, Craig Barnes, Jawad Qureshi, Robert Grossman, Carrie Yakura, Yaozhi Lu, Sarah Burge, Daniel Kelberman, Erin Watson, Kyle Ellrott. An integrated data platform to support the international alliance for cancer early detection [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 3558.

Abstract 7420: Optimizing proteomic data access and analysis in the cloud: Leveraging Terra's integration with the Proteomic Data Commons

Abstract The Proteomic Data Commons (PDC) hosts cancer proteomics data with the goal of making this data available to the public to support development of cancer diagnostics, treatment, and progression tracking. As a part of the Cancer Research Data Commons (CRDC), the Terra platform provides a cloud workbench for the PDC data. FireCloud is a Broad Institute project funded by NCI to empower cancer researchers to access data, run analysis tools and collaborate securely in the cloud. It is powered by Terra, a secure, scalable cloud-native platform developed by the Broad Institute, Microsoft, and Verily, an Alphabet company. It provides batch workflow execution, interactive analysis including data visualization, and ~2,900 publicly available tools with the ability to import more tools from Dockstore. The integration of PDC and Terra enables researchers to leverage the data navigation and file-level search capabilities on the PDC web browser (pdc.cancer.gov) and export selected data manifests to a Terra workspace. This integrated hand-off specifically allows metadata and cloud links to PDC data in Portable Format for Bioinformatics (PFB) to be transferred to Terra and used in analysis with cloud resources. A featured workspace for Terra-PDC integration includes tools for (i) downloading data files and relevant additional metadata; (ii) organizing the data and metadata for running the FragPipe workflow, a comprehensive collection of tools for reading and processing raw Mass Spec (MS) data; and (iii) implementing a pre-configured FragPipe workflow to process isobarically labeled MS data from Tandem Mass Tag (TMT) or Isobaric tags for relative and absolute quantitation (iTRAQ) experiments using information automatically imported from PDC via the PFB import. The pipelines can also be customized as needed to process any type of raw MS data the PDC supports. In addition, Terra connects to the NCI Genomic Data Commons (GDC) and hosts The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET) datasets allowing users to pull in other data types for co-analysis. As a whole, using the CRDC cloud resources, in conjunction with Terra, allows analysis of data on a massive scale, enabling multi-omic integration spanning the genomic and proteomic commons data, with easy sharing of data and tools. Citation Format: Emily LaPlante, Bingxing Huo, DR Mani, Ratna R. Thangudu. Optimizing proteomic data access and analysis in the cloud: Leveraging Terra's integration with the Proteomic Data Commons [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 7420.