The Cancer Genome Atlas Tumor Research Articles

Comparative analysis of the tumor microbiome, molecular profiles, and immune cell abundances by HPV status in mucosal head and neck cancers and their impact on survival

ABSTRACT Head and Neck Squamous Cell Carcinoma (HNSCC) comprises a diverse group of tumors with variable treatment response and prognosis. The tumor microenvironment (TME), which includes microbiome and immune cells, can impact outcomes. Here, we sought to relate the presence of specific microbes, gene expression, and tumor immune infiltration using tumor transcriptomics from The Cancer Genome Atlas (TCGA) and associate these with overall survival (OS). RNA sequencing (RNAseq) from HNSCC tumors in TCGA was processed through the exogenous sequences in tumors and immune cells (exotic) pipeline to identify and quantify low-abundance microbes. The detection of the Papillomaviridae family of viruses assessed HPV status. All statistical analyses were performed using R. A total of 499 RNAseq samples from TCGA were analyzed. HPV was detected in 111 samples (22%), most commonly Alphapapillomavirus 9 (90.1%). The presence of Alphapapillomavirus 9 was associated with improved OS [HR = 0.60 (95%CI: 0.40–0.89, p = .01)]. Among other microbes, Yersinia pseudotuberculosis was associated with the worst survival (HR = 3.88; p = .008), while Pseudomonas viridiflava had the best survival (HR = 0.05; p = .036). Microbial species found more abundant in HPV- tumors included several gram-negative anaerobes. HPV- tumors had a significantly higher abundance of M0 (p < .001) and M2 macrophages (p = .035), while HPV+ tumors had more T regulatory cells (p < .001) and CD8+ T-cells (p < .001). We identified microbes in HNSCC tumor samples significantly associated with survival. A greater abundance of certain anaerobic microbes was seen in HPV tumors and pro-tumorigenic macrophages. These findings suggest that TME can be used to predict patient outcomes and may help identify mechanisms of resistance to systemic therapies.

Abstract PO1-08-12: Genomic Characterization of the Carolina Breast Cancer Study

Abstract Background: Breast cancer is a heterogeneous disease defined by distinct subtypes, mutational profiles, and genomic characteristics. Previous analyses in The Cancer Genome Atlas (TCGA) have been instrumental in understanding the genomic landscape of breast cancer, including identification of key driver mutations, mutational signatures, and distinguishing features of breast cancer subtypes. However, TCGA may not be representative of other population-based breast cancer cohorts, particularly those of earlier stage and including a more diverse set of patients. Our aim was to characterize the genomic landscape of breast cancer in the Carolina Breast Cancer Study (CBCS), a cohort which oversamples Black and young women. Methods: We used targeted sequencing to profile somatic alterations in 1,175 genes from 275 formalin fixed paraffin embedded primary breast tumors in CBCS, 52% of which came from Black women, 41% from women under age 50, and 29% of which were Basal-like based on bulk RNA profiling. We also evaluated TP53 mutation as a source of breast cancer heterogeneity, comparing the type and location of TP53 mutations in each cohort. We assessed chromosome arm-level gains and losses using GISTIC and defined genomic instability as the total number of aneuploid chromosome arms. Intratumoral heterogeneity was assessed with PyClone-VI and defined as the total number and Shannon’s Diversity Index of tumor-specific subclones. For genes mutated in more than 5% of samples and chromosomal aberrations significantly enriched in GISTIC, we estimated the prevalence of alterations and compared to previous distributions in 981 TCGA tumors. Results: Seven genes [TP53 (N=101, 37%), PIK3CA (N=76, 28%), GATA3 (N=32, 12%), CDH1 (N=31, 11%), MAP3K1 (N=22, 8%), KMT2C (N=20, 7%), and CBFB (N =19, 7%)] were mutated in more than 5% of CBCS tumors; all but CBFB which were also highly prevalent in TCGA. While most TP53 hotspot mutations observed in CBCS were previously reported in TCGA, CBCS had twice as many Y220C mutations, one-third fewer R175H mutations, and a non-significantly higher proportion of nonsense mutations than TCGA (20% vs 12%, p = 0.05). Hotspot mutation prevalence in other Luminal-associated driver genes (e.g. PIK3CA, GATA3, CDH1) did not differ by dataset. CBCS tumors showed a higher degree of genomic instability (8 arms vs. 5 arms, p &amp;lt; 0.001) and subclonal diversity than TCGA (48% vs 12% comprised of two or more subclones, p &amp;lt; 0.05). Both datasets showed significant amplifications of 1q and 8q, and deletions of 13q and 17p (where BRCA2 and TP53, respectively, are located), with CBCS having non-significantly higher prevalence of each of these changes. Conclusions: While comparisons should be interpreted in light of technical and population differences between TCGA and CBCS, the overall results show that the suite of commonly altered genes and chromosome arms were highly consistent between TCGA and the diverse CBCS cohort. CBCS tumors tended to display higher genomic instability, intratumoral heterogeneity, and number and diversity of TP53 mutations. This may reflect distinctions in tumor evolutionary state between CBCS and TCGA tumors, and/or could reflect differences in the study populations. Results underscore the importance of considering population characteristics – particularly stage and race – in large-scale genomic contexts, and highlight the importance of diverse cohorts in genomic research. Citation Format: Sarah Van Alsten, Ebonee Butler, Benjamin Calhoun, Michael Love, Charles Perou, Katherine A Hoadley, Melissa Troester. Genomic Characterization of the Carolina Breast Cancer Study [abstract]. In: Proceedings of the 2023 San Antonio Breast Cancer Symposium; 2023 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2024;84(9 Suppl):Abstract nr PO1-08-12.

Abstract NG03: Functional and computational approaches to uncover selection advantages of cancer aneuploidy

Abstract Aneuploidy, including the gain or loss of whole chromosomes or chromosome arms, is a near-universal feature of cancer. We previously applied methods that define chromosome arm aneuploidy to over 10,000 tumors in the Cancer Genome Atlas (TCGA). Cancer subtypes are often characterized by tumor specific patterns of chromosome arm copy number alterations and breakpoints; for example, squamous cell carcinomas (SCCs) from different tissues of origin are characterized by chromosome 3p (chr3p) loss and chromosome 3q (chr3q) gain. From the TCGA aneuploidy data, we developed an algorithm called BISCUT to distinguish peak regions of aneuploidy breakpoints on each chromosome arm. BISCUT identified loci affected by broad copy number alterations that provide fitness advantages or disadvantages both within individual cancer types and across cancers. Our analyses are consistent with selection being the primary driver of aneuploidy events in cancer. We next wanted to validate some BISCUT peaks without a known driver, to identify potential gene deletions that are beneficial in cancer cells. We focused on chromosome 8p (chr8p), as this arm is frequently deleted across cancer types, but no strong tumor suppressors have been identified. Recent advances in genome engineering allow generation of large chromosomal alterations and validation of findings from patient genomic data. For this study, we used our CRISPR-Cas9 arm-deletion system to delete chr8p in human immortalized epithelial cells. Cells with chr8p deletion showed lower amounts of cell death in culture. Knockdown of WRN, one of the two genes in the smallest chr8p BISCUT peak, was sufficient to reproduce this phenotype, suggesting that WRN haploinsufficiency may be beneficial to tumor development. As aneuploidy occurs in tissue-specific patterns, we also wanted to explore the role of chr3 arm aneuploidies in squamous cancers. We again used the CRISPR-Cas9 system to delete one copy of chr3p in a human immortalized lung epithelial cell line similar to the putative cell-of-origin in lung SCC. Consistent with patient data, expression of chr3p genes was decreased upon deletion, as well as increased expression of interferon response genes. Cells with chr3p deletion initially proliferated more slowly than their siblings. Interestingly, after several passages in culture, this proliferation defect was rescued in chr3p deleted cells. Genome sequencing and karyotype analyses suggested that this was partially the result of chr3 duplication, with cells transitioning to a state of chr3q gain. Culturing these cells as organoids also demonstrated that chr3p deletion and chr3q gain can affect basal cell differentiation, consistent with their high frequency in squamous cancers. Our genome engineering approach to model chromosome arm aneuploidies provides a robust model to validate drivers from aneuploidy events, which will be critical to address the gap in our understanding of aneuploidy in cancer. In addition, our methods have identified consequences of individual aneuploidy events, which will lead to new precision oncology targets for patients. Citation Format: Alison M. Taylor. Functional and computational approaches to uncover selection advantages of cancer aneuploidy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr NG03.

Abstract 4921: A user-friendly R Shiny web app for predicting cancer genetic dependencies using deep learning

Abstract Understanding the genetic vulnerabilities (i.e., genetic dependencies) of cancer cells is crucial for developing novel anti-cancer treatments. The Cancer Dependency Map (DepMap) projects, conducted by the Broad Institute and Wellcome Sanger Institute, have performed comprehensive genome-wide CRISPR-Cas9 knockout screens across diverse cancer cell lines. These data resources have enabled computational biologists to glean insights into cancer genetic dependencies using sophisticated computational models. As different genomic mechanisms govern tumorigenesis and genetic vulnerability, the relationship between cancer genomics and genetic dependencies is nonlinear, making the prediction of genetic dependencies challenging. Addressing this challenge, we previously developed a deep learning model, namely DeepDEP, to predict cancer genetic dependencies using multi-omic profiles (Chiu et al. DOI:10.1126/sciadv.abh1275). DeepDEP has a transfer learning scheme that integrates genomic representations captured from unlabeled tumor samples of the Cancer Genome Atlas (TCGA) and genomic features that are predictive of genetic dependencies in cell lines. Such a design enables the application of DeepDEP to both cell lines and tumors. The model demonstrated superior performance compared to various conventional machine learning methods. Prediction results were validated using independent cell-line datasets and patient clinical data. Here, we developed an intuitive web application for DeepDEP which allows users to easily utilize the model on their in-house data. Users can simply indicate whether the query sample is derived from a cancer cell line or a tumor, upload any combination of mutation, gene expression, DNA methylation, and copy number alteration datasets, and view the predicted dependencies of 1,298 cancer-relevant genes. Along with the genetic dependency predictions, users are provided with model performance metrics, detailed gene annotations, and links to useful resources such as Ensembl and COSMIC. Our web application features a variety of interactive visualizations to guide users to interpret the results at the levels of individual genes and pathways. The tool has two supplemental modules to enhance its functionality. One allows users to search for similar cell lines to the query sample within the DepMap project or for similar tumors within the TCGA project. The other supplemental module provides data exploration tools for pre-calculated predictions across all TCGA tumors. In summary, this user-friendly web application provides researchers with access to deep learning predictions of cancer genetic dependencies with various interactive visualization and analysis tools. It is anticipated that this application will facilitate the study of cancer genetic dependencies and enhance the efficiency of anti-cancer drug discovery. Citation Format: Michael J. Kasper, Li-Ju Wang, Michael Ning, Yufei Huang, Yu-Chiao Chiu. A user-friendly R Shiny web app for predicting cancer genetic dependencies using deep learning [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 4921.

Abstract 7035: Characterization of tissue-resident memory T cells in hepatocellular carcinoma through single-cell multi-omics

Abstract As the fifth leading cause of cancer-related mortality, liver cancer imposes a global burden. Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer, with a five-year overall survival rate of less than 20% for advanced-stage cases. Even the most advanced immunotherapy for HCC has disparate response rates in each patient with HCC, which has led to efforts to unravel the underlying factors in the progression of HCC using bioinformatic approaches. In recent decades, numerous studies have explored T-cell subtypes with tissue-specific residency, suggesting their early protective functions and therapeutic potential within tumor microenvironment (TME). This study aimed to understand inter-individual and intercellular TME through single-cell RNA sequencing (scRNA-seq) and single-cell TCR sequencing (scTCR-seq) in five recurrent/non-recurrent HCC patients. We identified tumor-associated tissue-resident memory T (TA-TRM) cells and investigated the heterogeneous characteristics of two TRM clusters (CD69+ and CD103+). Whereas CD69+ TA-TRM showed high expression of genes associated with immunological activation, CD103+ TA-TRM exhibited upregulation of genes related to proliferation capacity. To validate the unique molecular patterns, we analyzed an independent HCC dataset (GSE140228) and observed high concordance of significant core marker genes for TA-TRM clusters between the two datasets. Moreover, we assessed the association of gene expression in TA-TRM cells with overall and recurrence-free survival by conducting survival analysis in two independent large-scale cohorts. The cross-validation approach was applied to estimate risk scores for patients with primary HCC tumors in The Cancer Genome Atlas (TCGA), and the trained model was evaluated using the International Cancer Genome Consortium (ICGC) data. The result of calculating the risk scores clearly indicates that TA-TRM cells are associated with survival in HCC. Therefore, our study characterized two distinct subtypes of TA-TRM cells in HCC, delineated their unique molecular signatures, and established their relevance to survival outcomes. The findings can potentially enhance the understanding of the roles of TA-TRM cells in the context of HCC. Citation Format: Mi-So Park, Hyunbin Cho, Hyeree Kim, Woong-Yang Park, Yong-Han Paik, Yeup Yoon, Wonseok Kang, Hong-Hee Won. Characterization of tissue-resident memory T cells in hepatocellular carcinoma through single-cell multi-omics [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 7035.

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 3634: Copy number alterations in chromosome 19 genes associate with reduced immune infiltrate and adverse outcomes in lung cancer

Abstract Deleterious mutations in STK11/LKB1, KEAP1, and SMARCA4 have been shown to be associated with adverse response to immunotherapy in several lung cancer cohorts. However, the biological and clinical implications of deletions of these genes, which all reside on chromosome 19p (chr19p), has not been extensively described. We sought to first characterize the association between immune infiltration and these copy number changes in chr19p. To do this, we analyzed mutation, copy number, and gene expression data from non-small cell lung cancer cohorts in the Cancer Genome Atlas (TCGA). Chr19p is frequently deleted across cancers in the TCGA dataset, with higher rates in lung adenocarcinoma. In both lung adenocarcinoma and squamous cell carcinoma, nearly half of TCGA tumors have deletion of chr19p including STK11, KEAP1, and SMARCA4, and these tumors are enriched for STK11 mutation. Immune infiltrate (estimated by methylation patterns) was not significantly associated with STK11 mutation but was significantly reduced in tumors with chr19 deletion when controlling for tumor type and overall copy number load. In addition, low-level deletion of any of these genes correlated with decreased expression of immune signaling pathways. Based on these findings, we next wanted to characterize the association between chr19p copy number and immunotherapy response. We previously reported that low-level deletion of STK11 correlated with an immunosuppressive, treatment-refractory phenotype in samples collected from non-small cell lung cancer (NSCLC) patients with resectable disease in a phase II study of neoadjuvant atezolizumab + chemotherapy. To confirm these findings in a larger cohort, we examined the AACR GENIE NSCLC biopharma collective (BPC) dataset. A subset of this cohort included patients treated with immunotherapy (atezolizumab, nivolumab, and/or pembrolizumab) and with copy number data available or GISTIC analysis of individual genes (n = 204). Here, deletion of any of these three genes correlated with worse overall survival, and this was statistically significant for STK11 deletion. Overall, our data suggests that a broader set of patients, defined by copy number changes in these genes, may experience this adverse immunobiology. Further study to elucidate the mechanistic implications of this association is warranted. Citation Format: Brian S. Henick, Yohanna Georgis, Benjamin O. Herzberg, Carla P. Concepcion-Crisol, Alison M. Taylor. Copy number alterations in chromosome 19 genes associate with reduced immune infiltrate and adverse outcomes in lung cancer [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 3634.

Deep learning on tertiary lymphoid structures in hematoxylin-eosin predicts cancer prognosis and immunotherapy response

Tertiary lymphoid structures (TLSs) have been associated with favorable immunotherapy responses and prognosis in various cancers. Despite their significance, their quantification using multiplex immunohistochemistry (mIHC) staining of T and B lymphocytes remains labor-intensive, limiting its clinical utility. To address this challenge, we curated a dataset from matched mIHC and H&E whole-slide images (WSIs) and developed a deep learning model for automated segmentation of TLSs. The model achieved Dice coefficients of 0.91 on the internal test set and 0.866 on the external validation set, along with intersection over union (IoU) scores of 0.819 and 0.787, respectively. The TLS ratio, defined as the segmented TLS area over the total tissue area, correlated with B lymphocyte levels and the expression of CXCL13, a chemokine associated with TLS formation, in 6140 patients spanning 16 tumor types from The Cancer Genome Atlas (TCGA). The prognostic models for overall survival indicated that the inclusion of the TLS ratio with TNM staging significantly enhanced the models’ discriminative ability, outperforming the traditional models that solely incorporated TNM staging, in 10 out of 15 TCGA tumor types. Furthermore, when applied to biopsied treatment-naïve tumor samples, higher TLS ratios predicted a positive immunotherapy response across multiple cohorts, including specific therapies for esophageal squamous cell carcinoma, non-small cell lung cancer, and stomach adenocarcinoma. In conclusion, our deep learning-based approach offers an automated and reproducible method for TLS segmentation and quantification, highlighting its potential in predicting immunotherapy response and informing cancer prognosis.

Abstract 3914: IGSF8 is a novel innate immune checkpoint and cancer immunotherapy target

Abstract Background: Loss of MHC-I antigen presentation is often associated with T-cell excluded tumors, and represents a common mechanism of both primary and acquired resistance to immune checkpoint blockade (ICB) treatment. MHC-I normally acts as a marker of “self” and cells that lack MHC-I are recognized and killed by innate immunity. In this study, we investigated the mechanism of innate immune evasion in tumors with MHC-I antigen presentation defects. Methods: Integrating functional genomics, big data, and artificial intelligence, we discovered that IGSF8 expressed on cancer cells is an evolutionarily conserved innate immune checkpoint. IGSF8 is normally expressed in neuronal tissues and is not essential in vitro or in vivo. However, knockout of IGSF8 in B16-F10 melanoma cell line decreased tumor growth in vivo. For clinical relevance of IGSF8 in tumor immunity, we analyzed genomics, transcriptomics, and clinical data from The Cancer Genome Atlas (TCGA). We developed an antibody (GV20-0251) against IGSF8 and tested its function to induce immune cell killing of cancer cells in vitro and inhibit tumor growth in vivo. Results: IGSF8 has receptors on both natural killer (NK) cells and dendritic cells (DC) to strongly suppress NK cytotoxicity and antigen presentation. In the TCGA tumor profiles, IGSF8 has highest mRNA expression in melanoma and is significantly overexpressed in many cancer types. IGSF8 also shows frequent copy number amplifications. In addition, IGSF8 expression is associated with low antigen presentation, low immune infiltration, and worse overall survival in patients with low MHC class I expression. Immunohistochemistry staining of various cancers showed that IGSF8 expression was primarily observed in malignant cells with low MHC-I. We developed a cross-species reactive antibody against IGSF8 (GV20-0251) which blocks IGSF8 interaction with NK and DC. Flow cytometry of the tumor infiltrating CD45+ cells revealed that IGSF8 inhibition significantly increased NK and dendritic cell infiltration. GV20-0251 enhances NK killing of cancer cells in vitro and increases antigen presentation, NK-mediated cytotoxicity, and T cell signaling in vivo. In multiple syngeneic tumor models (B16-F10, CT26, LLC, 4T1 and EMT6), anti-IGSF8 shows single-agent efficacy and is synergistic with anti-PD1 in controlling tumor growth. Conclusions: IGSF8 is a novel innate immune checkpoint and cancer immunotherapy target. A phase 1 study is ongoing to explore the IGSF8 inhibitor GV20-0251 in patients with advanced or metastatic solid tumors (NCT05669430). Citation Format: X. Shirley Liu, Yulong Li, Xiangyang Wu, Hailing Liu, Huizhu Liu, Caibin Sheng, Qingyang Ding, Yixuan Tang, Chao Liu, Bin Xie, Xi Xiao, Quan Yu, Rongbin Zheng, Xihao Hu, Tengfei Xiao. IGSF8 is a novel innate immune checkpoint and cancer immunotherapy target [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 3914.

Abstract B014: Bioinformatic and in-vitro analyses identify cAMP/PKA/CREB signaling as a major molecular alteration in invasive lobular breast cancer

Abstract Invasive lobular cancer (ILC) is the second most common subtype of breast cancers after invasive carcinoma of no special type (NST) but associated with worse outcomes. Despite the known molecular differences between ILC and NST, ILC treatment is the same as stage matched NST making the development of ILC targeted therapies an urgent need. Therefore, this study was conducted to identify novel molecular alterations in ILC that could be exploited for therapeutic purposes. Differential gene expression and Geneset Enrichment and Variation analyses were performed on RNA-seq data from three large public breast cancer databases – the Sweden Cancerome Analysis Network-Breast (SCAN-B), The Cancer Genome Atlas (TCGA) and Molecular Taxonomy of Breast Cancer International Consortium (METABRIC). Pathways enriched in overlapping differentially expressed genes from these datasets were clustered using Jaccard index to identify novel pathways related to ILC. The cAMP/PKA/CREB signaling was studied in ILC, ILC-like and NST cell lines and patient-derived organoids (POD) using Forskolin, an activator of the pathway. Clinicopathological features of patients with ILC and NST in SCAN-B were similar to prior population-based studies. There was a consistent pattern of up-regulation of cAMP/PKA/CREB related signaling in ILC compared to NST tumors in SCAN-B, TCGA and METABRIC. Treatment with forskolin resulted in a greater increase in phospho-CREB in ILC cell lines and organoids compared to NST. CRISPR deletion of CDH1 in NST cell lines did not alter response of cells to forskolin as measured by phospho-CREB. Co-immunofluorescence supported the enhanced nuclear localization and subsequent phosphorylation of CREB at Serine 133 in ILC cell lines. Forskolin treatment caused growth inhibition in ILC and NST cells, with ILC cell lines being more sensitive to forskolin-mediated growth inhibition. Collectively the data indicate that the cAMP/PKA/CREB signaling is higher in ILC than NST tumors. This was consistent across three separate cancer datasets and was validated in cell lines and organoids. Loss of CDH1 is not sufficient to mediate this phenotype in lobular tumors. Future studies should investigate the mechanisms for differential cAMP/PKA/CREB signaling in ILC and NST and investigate the potential for therapeutic targeting especially in patients with ILC. Citation Format: Abdalla Wedn, Susrutha Puthanmadhom-Narayanan, Adrian Lee, Steffi Oesterreich. Bioinformatic and in-vitro analyses identify cAMP/PKA/CREB signaling as a major molecular alteration in invasive lobular breast cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Advances in Breast Cancer Research; 2023 Oct 19-22; San Diego, California. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_1):Abstract nr B014.

Singleton mutations in large-scale cancer genome studies: uncovering the tail of cancer genome.

Singleton or low-frequency driver mutations are challenging to identify. We present a domain driver mutation estimator (DOME) to identify rare candidate driver mutations. DOME analyzes positions analogous to known statistical hotspots and resistant mutations in combination with their functional and biochemical residue context as determined by protein structures and somatic mutation propensity within conserved PFAM domains, integrating the CADD scoring scheme. Benchmarked against seven other tools, DOME exhibited superior or comparable accuracy compared to all evaluated tools in the prediction of functional cancer drivers, with the exception of one tool. DOME identified a unique set of 32917 high-confidence predicted driver mutations from the analysis of whole proteome missense variants within domain boundaries across 1331 genes, including 1192 noncancer gene census genes, emphasizing its unique place in cancer genome analysis. Additionally, analysis of 8799 TCGA(The Cancer Genome Atlas) and in-house tumor samples revealed 847 potential driver mutations, with mutations in tyrosine kinase members forming the dominant burden, underscoring its higher significance in cancer. Overall, DOME complements current approaches for identifying novel, low-frequency drivers and resistant mutations in personalized therapy.

Abstract A116: TSC1 mutant bladder cancer is characterized by a TSC-associated gene expression signature due to TFE3 transcriptional activity

Abstract Background: Mutations and inactivation of the tumor suppressor gene TSC1 are recurrent (6-10%) events in invasive bladder cancer, but the mechanism by which TSC1 loss promotes bladder cancer development beyond mTORC1 activation is uncertain. The purpose of this study was to assess the functional effects of TSC1 loss in bladder cancer and uncover new therapeutic targets for patients with TSC1 mutations. Materials and methods: We performed differential gene expression and pathway analyses using The Cancer Genome Atlas (TCGA) expression data for TSC1-mutated bladder cancer (TSC1mt-BLCA, n=26) and TSC1 wild-type tumors (TSC1wt-BLCA, n=382) and compared them to an internal cohort of TSC1/TSC2-mutant tumors (n=48). Transcriptomic, epigenetic, and functional analyses, including RNA-seq, H3K27ac ChIP-seq, TFE3 CUT&amp;RUN, ATAC-seq and TFE3 knock-out using CRISPR/Cas9 were conducted on TSC1-mt/wt BLCA cell lines. Results: Comparison of TSC1mt-BLCA and TSC1wt-BLCA TCGA tumors identified 26 genes upregulated in TSC1mt-BLCA out of 60 genes commonly upregulated in Tuberous Sclerosis Complex (TSC) syndromic tumors. Among them, GPNMB showed the greatest increase in expression (median fold change 9.8). GSEA and DESeq2 analyses implicated both mTORC1 hyperactivation and increased lysosomal gene expression in TSC1mt-BLCA. Expression differences in several genes, including GPNMB, and in pS6 were confirmed by immunohistochemistry (IHC) of 5 independent BWH TSC1mt-BLCA and TSC1wt-BLCA specimens each. TFE3, a key regulator of lysosomal gene expression, was elevated in expression and localized to the nucleus in TSC1mt-BLCA by IHC. Molecular studies of TSC1-mt/wt BLCA isogenic cell lines (HCV29, RT4) recapitulated the phenotype found in human tumors. TFE3 was both post-translationally modified (assessed by immunoblot) and predominantly nuclear in TSC1-null cell lines compared to TSC1wt cells, and the localization was partially reversed by rapamycin treatment leading to reduced expression of lysosomal genes. TSC1mt HCV29 cells showed higher expression of multiple genes increased in TSC1mt-BLCA tumors (GPNMB, SQSTM1, GNPDA1). TFE3 knock-out HCV29 clones showed markedly reduced growth, which was not seen with TFE3 KO in TSC1-addback HCV29 cells. GSEA of RNA-seq data indicated that TFE3 KO led to significant reduction in expression of lysosome genes, as well as those involved in ribosome synthesis, DNA replication and DNA excision repair processes. TFE3 CUT&amp;RUN confirmed that TFE3 binds to promoter regions of those genes, thereby directly regulating these pathways. In contrast, TFE3 KO led to an increase in inflammatory and cellular stress genes that could impede TSC1mt cell growth. Conclusions: Our findings show that TSC1mt-BLCA tumors and cell lines have a transcriptional signature similar to that of TSC syndromic tumors and show nuclear localization and transcriptional activation of TFE3. Aberrant TFE3 activation likely contributes to TSC1mt-BLCA development and may therefore be amenable to targeted therapy. Citation Format: Magdalena Losko, Clemens K Probst, Elio Adib, Heng Du, Michelle Hirsch, Krinio Giannikou, David J Kwiatkowski. TSC1 mutant bladder cancer is characterized by a TSC-associated gene expression signature due to TFE3 transcriptional activity [abstract]. In: Proceedings of the AACR-NCI-EORTC Virtual International Conference on Molecular Targets and Cancer Therapeutics; 2023 Oct 11-15; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2023;22(12 Suppl):Abstract nr A116.

Bioinformatic Analysis of The Prognostic Value of A Panel of Six Amino Acid Transporters in Human Cancers.

Solid tumor cells utilize amino acid transporters (AATs) to increase amino acid uptake in response to nutrient-insufficiency. The upregulation of AATs is therefore critical for tumor development and progression. This study identifies the upregulated AATs under amino acid deprived conditions, and further determines the clinicopathological importance of these AATs in evaluating the prognosis of patients with cancers. In this experimental study, the Gene Expression Omnibus (GEO) datasets (GSE62673, GSE26370, GSE125782 and GSE150874) were downloaded from the NCBI website and utilized for integrated differential expression and pathway analysis v0.96, Gene Set Enrichment Analysis (GSEA), and REACTOME analyses to identify the AATs upregulated in response to amino acid deprivation. In addition, The Cancer Genome Atlas (TCGA) datasets with prognostic information were assessed and employed to evaluate the association of identified AATs with patients' prognoses using SurvExpress analysis. Using analysis of NCBI GEO data, this study shows that amino acid deprivation leads to the upregulation of six AAT genes; SLC3A2, SLC7A5, SLC7A1, SLC1A4, SLC7A11 and SLC1A5. GSEA and REACTOME analyses identified altered signaling in cells exposed to amino acid deprivation, such as pathways related to stress responses, the cell cycle and apoptosis. In addition, Principal Component Analysis showed these six AAT genes to be well divided into two distinct clusters in relation to TCGA tumor tissues versus normal counterparts. Finally, Log-Rank analysis confirmed the upregulation of this panel of six AAT genes is correlated with poor prognosis in patients with colorectal, esophageal, kidney and lung cancers. The upregulation of a panel of six AATs is common in several human cancers and may provide a valuable diagnostic tool to evaluate the prognosis of patients with colorectal, esophageal, kidney and lung cancers.

The differential prognostic implications of PD-L1 expression in the outcomes of Filipinos with EGFR-mutant NSCLC treated with tyrosine kinase inhibitors.

The tumor immune microenvironment influences tumor evolution in non-small cell lung cancer (NSCLC). Yet, the prognostic value of programmed death-ligand 1 (PD-L1) in epidermal growth factor receptor (EGFR)-mutant NSCLC remains controversial. Additionally, prognostic studies in Filipinos with EGFR-mutant NSCLC remain unexplored to this day. We prospectively studied the outcomes of EGFR-mutant NSCLC in Filipino cohort, and retrospectively verified the survival trend using The Cancer Genome Atlas (TCGA) cohort. Kaplan-Meier method and generalized linear regression were used to assess survival. Expression and DNA methylation of cluster of differentiation 274 (CD274, gene that codes for PD-L1) were examined from TCGA tumor profiles. Pearson's correlation was used to correlate PD-L1 expression with outcomes associated with occurrence of EGFR mutations, tyrosine kinase inhibitor (TKI) types, and programmed cell death protein 1 (PD-1) expression. Proteome network analysis was used to examine the correlation between drug resistance and PD-L1. PD-L1 positivity was associated with significantly longer progression-free survival (PFS; P=0.0096) but had a significantly contrasting influence in the overall survival (OS; P=0.0011). PD-L1 positivity (in both protein and RNA) was associated with longer median OS (mOS) in exon21 L858R, whereas, negativity was associated with longer mOS in exon19 deletion (exon19del). Stratification (high, low, negative) of PD-L1 expression lacked significant prognostic value (all P>0.05). PD-L1/CD274 expression (P<0.05) and DNA methylation (P<0.001) vary significantly among NSCLC subtypes and in different disease stages. Erlotinib treatment produced the longest median progression-free survival (mPFS; 874 days) relative to other EGFR-TKIs (137-311 days). PD-L1 lacked a significant correlation with EGFR-TKIs. Consistent with the immune-regulation activities of PD-1, higher expression leads to relatively shorter mOS. PD-1 correlated positively with PD-L1 expression and occurrence of exon21 L858R. PD-L1 differentially influenced the outcomes of Filipinos with EGFR-mutant NSCLC. NSCLC subtypes, disease stage, and PD-1 expression may impact the collective outcomes associated with PD-L1 and EGFR-sensitizing mutations.

Deconvolution of cancer cell states by the XDec-SM method.

Proper characterization of cancer cell states within the tumor microenvironment is a key to accurately identifying matching experimental models and the development of precision therapies. To reconstruct this information from bulk RNA-seq profiles, we developed the XDec Simplex Mapping (XDec-SM) reference-optional deconvolution method that maps tumors and the states of constituent cells onto a biologically interpretable low-dimensional space. The method identifies gene sets informative for deconvolution from relevant single-cell profiling data when such profiles are available. When applied to breast tumors in The Cancer Genome Atlas (TCGA), XDec-SM infers the identity of constituent cell types and their proportions. XDec-SM also infers cancer cells states within individual tumors that associate with DNA methylation patterns, driver somatic mutations, pathway activation and metabolic coupling between stromal and breast cancer cells. By projecting tumors, cancer cell lines, and PDX models onto the same map, we identify in vitro and in vivo models with matching cancer cell states. Map position is also predictive of therapy response, thus opening the prospects for precision therapy informed by experiments in model systems matched to tumors in vivo by cancer cell state.

A Genomic Analysis of Esophageal Squamous Cell Carcinoma in Eastern Africa.

Esophageal squamous cell carcinoma (ESCC) comprises 90% of all esophageal cancer cases globally and is the most common histology in low-resource settings. Eastern Africa has a disproportionately high incidence of ESCC. We describe the genomic profiles of 61 ESCC cases from Tanzania and compare them to profiles from an existing cohort of ESCC cases from Malawi. We also provide a comparison to ESCC tumors in The Cancer Genome Atlas. We observed substantial transcriptional overlap with other squamous histologies via comparison with The Cancer Genome Atlas (TCGA) PanCan dataset. DNA analysis revealed known mutational patterns, both genome-wide as well as in genes known to be commonly mutated in ESCC. TP53 mutations were the most common somatic mutation in tumors from both Tanzania and Malawi but were detected at lower frequencies than previously reported in ESCC cases from other settings. In a combined analysis, two unique transcriptional clusters were identified: a proliferative/epithelial cluster and an invasive/migrative/mesenchymal cluster. Mutational signature analysis of the Tanzanian cohort revealed common signatures associated with aging and cytidine deaminase activity (APOBEC) and an absence of signature 29, which was previously reported in the Malawi cohort. This study defines the molecular characteristics of ESCC in Tanzania, and enriches the Eastern African dataset, with findings of overall similarities but also some heterogeneity across two unique sites. Despite a high burden of ESCC in Eastern Africa, genomic investigations in this region are nascent. This represents the largest comprehensive genomic analysis ESCC from sub-Saharan Africa to date.

Tumor collagens predict genetic features and patient outcomes

The extracellular matrix (ECM) is a critical determinant of tumor fate that reflects the output from myriad cell types in the tumor. Collagens constitute the principal components of the tumor ECM. The changing collagen composition in tumors along with their impact on patient outcomes and possible biomarkers remains largely unknown. The RNA expression of the 43 collagen genes from solid tumors in The Cancer Genome Atlas (TCGA) was clustered to classify tumors. PanCancer analysis revealed how collagens by themselves can identify the tissue of origin. Clustering by collagens in each cancer type demonstrated strong associations with survival, specific immunoenvironments, somatic gene mutations, copy number variations, and aneuploidy. We developed a machine learning classifier that predicts aneuploidy, and chromosome arm copy number alteration (CNA) status based on collagen expression alone with high accuracy in many cancer types with somatic mutations, suggesting a strong relationship between the collagen ECM context and specific molecular alterations. These findings have broad implications in defining the relationship between cancer-related genetic defects and the tumor microenvironment to improve prognosis and therapeutic targeting for patient care, opening new avenues of investigation to define tumor ecosystems.

A prognostic and immune infiltration analysis of ZEB2 in pan-cancer

[Objective] ZEB2 is known to be participated in several cancers, yet there is no comprehensive analysis about its function in pan-cancer. This study aimed to investigate the role ZEB2 plays in pan-cancer. [Methods] The data was downloaded from the University of California Santa Cruz (UCSC) Xena and the Cancer Genome Atlas (TCGA). The mRNA expression status of ZEB2 was studied in the TCGA_GTEx samples, TCGA samples and paired samples in TCGA, respectively, and protein expression status was obtained from the University of Alabama at Birmingham Cancer data analysis Portal (UALCAN). Kaplan-Meier analysis was applied to 33 kinds of tumors in TCGA, then among the cancers that ZEB2 can affect prognosis, clinical correlation analysis and univariate and multivariate Cox regression analysis were performed. Furthermore, to confirm the prognostic value of ZEB2 in cancers, nomogram models were constructed on lung adenocarcinoma (LUAD) and stomach adenocarcinoma (STAD). The relationship between ZEB2 mRNA expression and immune cell infiltrates was determined by tumor immune estimation resource (TIMER2.0). Protein-protein interaction (PPI) networks were constructed by the STRING. Functional enrichment analysis was also performed using the “ClusterProfiler” package. [Results] ZEB2 was expressed in most tumors, either upregulated or downregulated, and correlated with clinical characteristics in a part of tumors. The expression level of ZEB2 was also found to impact the prognosis of tumors. Additionally, ZEB2 expression was significantly related to immune cells infiltration level in tumor microenvironment. [Conclusion] we propose that ZEB2 has the potential to be a prognostic biomarker and a promising target for cancer immunotherapy.

Research on the oncogenic role of the house-keeping gene GAPDH in human tumors.

As an internal reference gene, glyceraldehyde-3-phosphate dehydrogenase (GAPDH) plays an important role in glycolysis. While increasing evidence suggests that GAPDH plays a crucial role in tumorigenesis of some cancers, no systematic analysis of GAPDH has been conducted. Here, we sought to analyze the expression of GAPDH and its oncogenic processes in pan-cancer. GAPDH was investigated in The Cancer Genome Atlas (TCGA) tumor types using several bioinformatic tools including Tumor IMmune Estimation Resource (TIMER), Gene Expression Profiling Interactive Analysis (GEPIA), University of ALabama at Birmingham CANcer (UALCAN), cBio Cancer Genomics Portal (cBioPortal), and Search Tool for Recurring Instances of Neighbouring Genes (STRING) for the expression and relationships with prognosis and immune infiltration separately. Through our analysis, we measured the higher expression of GAPDH across the majority of TCGA tumors. GAPDH overexpression predicts poor survival in patients with tumors expressing a high level of GAPDH. Moreover, the genetic changes in GAPDH contributed to an increased mRNA expression. Additionally, GAPDH expression was negatively correlated with immune infiltration involving cancer-associated fibroblasts, neutrophil cell and endothelial. The house-keeping gene GAPDH might be a promising biomarker for pan-cancer prognosis. And GAPDH is not suitable as an internal reference gene for most cancer research, whether RNA or protein analyses.

Regularized survival learning and cross-database analysis enabled identification of colorectal cancer prognosis-related immune genes.

Colon adenocarcinoma is the most common type of colorectal cancer. The prognosis of advanced colorectal cancer patients who received treatment is still very poor. Therefore, identifying new biomarkers for prognosis prediction has important significance for improving treatment strategies. However, the power of biomarker analyses was limited by the used sample size of individual database. In this study, we combined Genotype-Tissue Expression (GTEx) and The Cancer Genome Atlas (TCGA) databases to expand the number of healthy tissue samples. We screened differentially expressed genes between the GTEx healthy samples and TCGA tumor samples. Subsequently, we applied least absolute shrinkage and selection operator (LASSO) regression and multivariate Cox analysis to identify nine prognosis-related immune genes: ANGPTL4, IDO1, NOX1, CXCL3, LTB4R, IL1RL2, CD72, NOS2, and NUDT6. We computed the risk scores of samples based on the expression levels of these genes and divided patients into high- and low-risk groups according to this risk score. Survival analysis results showed a significant difference in survival rate between the two risk groups. The high-risk group had a significantly lower overall survival rate and poorer prognosis. We found the receiver operating characteristic based on the risk score was showed to accurately predict patients' prognosis. These prognosis-related immune genes may be potential biomarkers for colorectal cancer diagnosis and treatment. Our open-source code is freely available from GitHub at https://github.com/gutmicrobes/Prognosis-model.git.