TCGA Cancer Research Articles

Identifying cancer subtypes based on embryonic and hematopoietic stem cell signatures in pan-cancer.

Cancer cells with stem cell-like properties may contribute to cancer development and therapy resistance. The advancement of multi-omics technology has sparked interest in exploring cancer stemness from a multi-omics perspective. However, there is a limited number of studies that have attempted to subtype cancer by combining different types of stem cell signatures. In this study, 10,323 cancer specimens from 33 TCGA cancer types were clustered based on the enrichment scores of six stemness gene sets, representing two types of stem cell backgrounds: embryonic stem cells (ESCs) and hematopoietic stem cells (HSCs). We identified four subtypes of pan-cancer, termed StC1, StC2, StC3 and StC4, which displayed distinct molecular and clinical features, including stemness, genome integrity, intratumor heterogeneity, methylation levels, tumor microenvironment, tumor progression, responses to chemotherapy and immunotherapy, and survival prognosis. Importantly, this subtyping method for pan-cancer is reproducible at the protein level. Our findings indicate that the ESC signature is an adverse prognostic factor in cancer, while the HSC signature and ratio of HSC/ESC signatures are positive prognostic factors. The subtyping of cancer based on ESC and HSC signatures may provide insights into cancer biology and clinical implications of cancer.

THU475 Ovarian Tumor-cell Glucocorticoid Receptor Activity Modulates Cytokine Secretion Promoting Recruitment And Activation of Immunosuppressive Cells Into The Tumor Microenvironment

Abstract Disclosure: M. Taya: None. E. LeBlanc: None. L. Bennett: None. S.D. Conzen: None. Elevated levels of immunosuppressive myeloid-derived suppressor cells (MDSCs) circulate in the peripheral blood or infiltrate both tumors and ascites in ovarian cancer patients, however the cause for this observed phenotype is unknown. MDSCs contribute to tumor immune tolerance primarily via inhibition of cytotoxic T-cells or by activation of immunosuppressive T-regulatory cells. We previously showed that high tumor cell glucocorticoid receptor (GR) expression is associated with a relatively poor prognosis in ovarian cancer patients. Additionally, using primary tumor data retrieved from the ovarian cancer TCGA dataset, we show high tumor GR expression is associated with significantly higher expression of Foxp3, a marker of immunosuppressive T-regulatory cells, as well as CD33, a marker of immunosuppressive myeloid cells, further suggesting an immunosuppressive phenotype in ovarian cancer patients. We thus hypothesize that tumor-intrinsic GR activation in ovarian cancer is associated with increased MDSC infiltration into the tumor microenvironment partly due to the modulation of tumor cell cytokine secretion. To determine whether tumor-cell GR activation modulates the cytokine secretome, we previously showed that GR activation in ovarian cancer cells upregulates secretion of immunomodulatory factors including G-CSF, TGFb and CXCL5. Moreover, co-treatment of cells with a selective GR modulator (SGRM) reverses this effect. Preliminary gene expression data indicate GR mediated cytokine secretion and signaling may involve post-transcriptional or post-translational mechanisms. Considering that elevated levels of these cytokines are necessary for the production and recruitment of MDSCs, these data highlight the relevance of this evident glucocorticoid receptor-cytokine circuit in ovarian cancer cells. Furthermore, we show that this tumor-cell cytokine secretome has the capacity to promote differentiation of MDSCs from precursor myeloid cells in healthy PBMCs. Importantly, measuring expression of activation markers such as Arginase-1 and NOS2 illustrated that these MDSCs are in their functional state. To our knowledge, this is the first evidence that a nuclear receptor in ovarian cancer tumor cells is actively driving MDSC differentiation. Finally, employing ovarian cancer xenograft models as well as a syngeneic ID8 tumor model, treatment with SGRMs demonstrated reduced levels of circulating MDSCs in the peripheral blood as well as tumor infiltrated MDSCs. Using neutralizing antibodies for cytokines, we will now identify key mechanisms, specifically, which targetable immunomodulatory factors are contributing to immune suppression. Based on the insight provided by these data, we propose to determine whether targeting ovarian tumor cell-intrinsic GR activation and resulting MDSC infiltration contributes to improved anti-tumor immune response. Presentation: Thursday, June 15, 2023

Predator: Predicting the impact of cancer somatic mutations on protein-protein interactions.

Since many biological processes are governed by protein-protein interactions, understanding which mutations lead to a disruption in these interactions is profoundly important for cancer research. Most of the existing methods focus on the stability of the protein without considering the specific effects of a mutation on its interactions with other proteins. Here, we focus on somatic mutations that appear on the interface regions of the protein and predict the interactions that would be affected by a mutation of interest. We build an ensemble model, Predator, that classifies the interface mutations as disruptive or nondisruptive based on the predicted effects of mutations on specific protein-protein interactions. We show that Predator outperforms existing approaches in literature in terms of prediction accuracy. We then apply Predator on various TCGA cancer cohorts and perform comprehensive analysis at cohort level, patient level, and gene level in determining the genes whose interface mutations tend to yield a disruption in its interactions. The predictions obtained by Predator shed light on interesting patterns on several genes for each cohort regarding their potential as cancer drivers. Our analyses further reveal that the identified genes and their frequently disrupted partners exhibit patterns of mutually exclusivity across cancer cohorts under study.

Mendelian randomization and transcriptomic analysis reveal an inverse causal relationship between Alzheimer’s disease and cancer

BackgroundAlzheimer’s disease (AD) and cancer are common age-related diseases, and epidemiological evidence suggests an inverse relationship between them. However, investigating the potential mechanism underlying their relationship remains insufficient.MethodsBased on genome-wide association summary statistics for 42,034 AD patients and 609,951 cancer patients from the GWAS Catalog using the two-sample Mendelian randomization (MR) method. Moreover, we utilized two-step MR to identify metabolites mediating between AD and cancer. Furthermore, we employed colocalization analysis to identify genes whose upregulation is a risk factor for AD and demonstrated the genes’ upregulation to be a favorable prognostic factor for cancer by analyzing transcriptomic data for 33 TCGA cancer types.ResultsTwo-sample MR analysis revealed a significant causal influence for increased AD risk on reduced cancer risk. Two-step MR analysis identified very low-density lipoprotein (VLDL) as a key mediator of the negative cause-effect relationship between AD and cancer. Colocalization analysis uncovered PVRIG upregulation to be a risk factor for AD. Transcriptomic analysis showed that PVRIG expression had significant negative correlations with stemness scores, and positive correlations with antitumor immune responses and overall survival in pan-cancer and multiple cancer types.ConclusionAD may result in lower cancer risk. VLDL is a significant intermediate variable linking AD with cancer. PVRIG abundance is a risk factor for AD but a protective factor for cancer. This study demonstrates a causal influence for AD on cancer and provides potential molecular connections between both diseases.

Combining WGCNA and machine learning to construct immune-related EMT patterns to predict HCC prognosis and immune microenvironment.

Hepatocellular carcinoma (HCC) is a malignancy with a very high mortality rate. Because of its high heterogeneity, there is an urgent need to find biomarkers that accurately predict prognosis. Epithelial-mesenchymal transition (EMT) is closely associated with frequent recurrence and high mortality of HCC. Therefore, it is necessary to comprehensively analyze the prognostic value and immunological properties of EMT gene in HCC. In our study, we performed bioinformatics analysis of the TCGA and ICGC liver cancer cohorts and identified the module genes of immune-associated EMTs (iEMT) by Weighted Gene Co-Expression Network Analysis (WGCNA). Further we used machine learning (support vector machines-recursive feature elimination and Lasso) to identify three central iEMT genes (ARMC9, ADAM15 and STC2) and construct iEMT_score. Subsequently, in the training and validation cohorts, it was demonstrated that the overall survival (OS) of patients in the high iEMT_score group was worse than that of patients in the low iEMT_score group. Based on this, we have constructed a nomogram that is easy for clinicians to use. In addition, our study explored differences in pathway enrichment, immunological properties, and sensitivity to common chemotherapy and targeted drugs in different subgroups of iEMT_score. Finally, we showed through in vitro experiments that knockdown of ARMC9 could significantly inhibit the proliferation, migration and invasion of HCC cells BEL7402. Taken together, our findings suggest that iEMT_score is an excellent biomarker for predicting prognosis and provide some new insights for personalized treatment of HCC patients.

Cancer cell employs a microenvironmental neural signal trans-activating nucleus-mitochondria coordination to acquire stemness

Cancer cell receives extracellular signal inputs to obtain a stem-like status, yet how tumor microenvironmental (TME) neural signals steer cancer stemness to establish the hierarchical tumor architectures remains elusive. Here, a pan-cancer transcriptomic screening for 10852 samples of 33 TCGA cancer types reveals that cAMP-responsive element (CRE) transcription factors are convergent activators for cancer stemness. Deconvolution of transcriptomic profiles, specification of neural markers and illustration of norepinephrine dynamics uncover a bond between TME neural signals and cancer-cell CRE activity. Specifically, neural signal norepinephrine potentiates the stemness of proximal cancer cells by activating cAMP-CRE axis, where ATF1 serves as a conserved hub. Upon activation by norepinephrine, ATF1 potentiates cancer stemness by coordinated trans-activation of both nuclear pluripotency factors MYC/NANOG and mitochondrial biogenesis regulators NRF1/TFAM, thereby orchestrating nuclear reprograming and mitochondrial rejuvenating. Accordingly, single-cell transcriptomes confirm the coordinated activation of nuclear pluripotency with mitochondrial biogenesis in cancer stem-like cells. These findings elucidate that cancer cell acquires stemness via a norepinephrine-ATF1 driven nucleus-mitochondria collaborated program, suggesting a spatialized stemness acquisition by hijacking microenvironmental neural signals.

Clinical evidence for a role of E2F1-induced replication stress in modulating tumor mutational burden and immune microenvironment

DNA replication stress (RS) is frequently induced by oncogene activation and is believed to promote tumorigenesis. However, clinical evidence for the role of oncogene-induced RS in tumorigenesis remains scarce, and the mechanisms by which RS promotes cancer development remain incompletely understood. By performing a series of bioinformatic analyses on the oncogene E2F1, other RS-inducing factors, and replication fork processing factors in TCGA cancer database using previously established tools, we show that hyperactivity of E2F1 likely promotes the expression of several of these factors in virtually all types of cancer to induce RS and cytosolic self-DNA production. In addition, the expression of these factors positively correlates with that of ATR and Chk1 that govern the cellular response to RS, the tumor mutational load, and tumor infiltration of immune-suppressive CD4+Th2 cells and myeloid-derived suppressor cells (MDSCs). Consistently, high expression of these factors is associated with poor patient survival. Our study provides new insights into the role of E2F1-induced RS in tumorigenesis and suggests therapeutic approaches for E2F1-overexpressing cancers by targeting genomic instability, cytosolic self-DNA and the tumor immune microenvironment.

Pan-cancer evidence of prognosis, immune infiltration, and immunotherapy efficacy for annexin family using multi-omics data.

The annexin superfamily (ANXA) is made up of 12 calcium (Ca2+) and phospholipid binding protein members that have a high structural homology and play a key function in cancer cells. However, little research has been done on the annexin family's function in pan-cancer. We examined the ANXA family's expression in various tumors through public databases using bioinformatics analysis, assessed the differences in ANXA expression between tumor and normal tissues in pan-cancer, and then investigated the relationship between ANXA expression and patient survival, prognosis, and clinicopathologic traits. Additionally, we investigated the relationships among TCGA cancers' mutations, tumor mutation burden (TMB), microsatellite instability (MSI), immunological subtypes, immune infiltration, tumor microenvironment, immune checkpoint genes, chemotherapeutics sensitivity, and ANXAs expression. cBioPortal was also used to uncover pan-cancer genomic anomalies in the ANXA family, study relationships between pan-cancer ANXA mRNA expression and copy number or somatic mutations, and assess the prognostic values of these variations. Moreover, we investigatedthe relationship between ANXAs expression and effectiveness of immunotherapy in multiple cohorts, including one melanoma (GSE78220), one renal cell carcinoma (GSE67501), and three bladder cancer cohorts (GSE111636, IMvigor210 and our own sequencing dataset (TRUCE-01)), and further analyzed the changes of ANXAs expression before and after treatment (tislelizumab combined with nab-paclitaxel) of bladder cancer. Then, we explored the biological function and potential signaling pathway of ANXAs using gene set enrichment analysis (GSEA), and first conducted immune infiltration analysis with ANXAs family genes expression, copy number, or somatic mutations of bladder cancer by TIMER 2.0. Most cancer types and surrounding normal tissues expressed ANXA differently. ANXA expression was linked to patient survival, prognosis, clinicopathologic features, mutations, TMB, MSI, immunological subtypes, tumor microenvironment, immune cell infiltration, and immune checkpoint gene expression in 33 TCGA cancers, with ANXA family members varied. The anticancer drug sensitivity analysis showed that ANXAs family members were significantly related to a variety of drug sensitivities. In addition, we also discovered that the expression level of ANXA1/2/3/4/5/7/9/10 was positively or negatively correlated with objective responses to anti-PD-1/PD-L1 across multiple immunotherapy cohorts. The immune infiltration analysis of bladder cancer further showed the significant relationships between ANXAs copy number variations or mutation status, and infiltration level of different immune cells. Overall, our analyses confirm the importance of ANXAs expression or genomic alterations in prognosis and immunological features of various cancer and identified ANXA-associated genes that may serve as potential therapeutic targets.

Detecting variation of clonal hematopoiesis using machine-learning in liquid biopsy.

e15027 Background: Tumor‐derived DNA released to the plasma of cancer patients which could be captured and detected by ultra-deep sequencing. NGS provides the technical probability of somatic mutations detection from circulating cell‐free DNA (cfDNA) in plasma samples, while clonal hematopoiesis (CH) mutations affect the accuracy of liquid biopsy for clinical diagnosis or choice of drug and tracing of minimal residual disease (MRD). CH mutation screening of cfDNA samples still an unsolved issue, herein we propose a method that based on multi-variable tree model which screen the CH mutation effectively. Methods: We sequenced 1992 cfDNA (raw depth 35000x) and matched peripheral blood lymphocyte (PBL, raw depth 2000x) samples by 1123 genes target capture sequencing from cohorts which include 962 non-small cell lung cancers (NSCLC), 631 gastric cancers (GC), 379 other non-hematological cancers and 40 healthy normal control. First, we compared the coherence patterns of cfDNA-PBL between CH genes (ASXL1, DNMT3A, TET2) hotspot mutation and tumor associated mutation (EGFR L858R T790M, KRAS G12D G13D, TP53 R43H R141H). Next, we labeled canonical mutations by: point mutation and indels; top frequent tumor genes (from cbioportal collected TCGA cancers); published frequent CH genes; known hotspots and unknown significance genes. Furthermore, hotspot tumor mutation and hotspot CH mutations and other mutations were labeled. kNN classify all mutation to labeled mutations groups, and decided the next steps tree model’s node thresholds build a classify tree model screen the confident CH and somatic mutations. Finally, we validated 6 TP53 CH mutation from healthy normal control by ddPCR. Results: Previous published imbalance test model: 'fisher test p.value > 0.5 & 0.5 < OR < 1.5' only covered 23.2%, 26.9%, 17.5% top 10 hotspot CH mutations (ASXL1, DNMT3A, TET2) correspondingly, while 0.75 quantiles both g_VAF and t_VAF less than 1% and alter reads count less than 20. In terms of tumor associated mutations only 3/155,6/123,7/108,7/71,323/998,16/189 exceed the imbalance test models threshold, especially L858R with median t_VAF 3.4% and t_alter_count 60. Population frequency distribution of CH and tumor genes mutation were different significantly (median 0.776% and 0.569%; p.value < 1.44e-05, F-test). After assign hotspot and CH mutations classify by kNN, The tree model screened 42.9% and 83.6% CH mutations in cancer and healthy normal samples respectively. TP53 were common both in tumor and CH mutation ambiguously, tree model screened 6 TP53 CH mutation (c.12G > A, R43H, G113D, R70C, c.16C > T, A80T) from normal plasma sample all validated by ddPCR. Conclusions: CH mutations were ubiquitous in plasma sample and abundance pattern different from common tumor mutations significantly. CH mutation could be screened by optimal condition assembled decision trees effectively.

RaggedExperiment: the missing link between genomic ranges and matrices in Bioconductor.

The RaggedExperiment R / Bioconductor package provides lossless representation of disparate genomic ranges across multiple specimens or cells, in conjunction with efficient and flexible calculations of rectangular-shaped summaries for downstream analysis. Applications include statistical analysis of somatic mutations, copy number, methylation, and open chromatin data. RaggedExperiment is compatible with multimodal data analysis as a component of MultiAssayExperiment data objects, and simplifies data representation and transformation for software developers and analysts. Measurement of copy number, mutation, single nucleotide polymorphism, and other genomic attributes that may be stored as VCF files produce "ragged" genomic ranges data: i.e. across different genomic coordinates in each sample. Ragged data are not rectangular or matrix-like, presenting informatics challenges for downstream statistical analyses. We present the RaggedExperiment R/Bioconductor data structure for lossless representation of ragged genomic data, with associated reshaping tools for flexible and efficient calculation of tabular representations to support a wide range of downstream statistical analyses. We demonstrate its applicability to copy number and somatic mutation data across 33 TCGA cancer datasets.

Siglec-7/9 are novel immune checkpoints for prostate cancer

Abstract Immune checkpoint-based therapy has led to striking improvements in survival in some types of cancer, but it is ineffective for others, including prostate cancer. Changes in glycosylation, particularly hypersialylation, have been found in diverse malignancies. Glycosylation can result in immune evasion through direct interactions of sialylated glycoproteins on cancer cell surfaces with Siglecs on immune cells including T cells, NK cells, and dendritic cells. Interactions between Siglec-7/9 and sialic acid have been implicated in inhibiting immune response in melanoma, leukemia, and lung cancer. Here, we found that Siglec-7/9 was highly expressed in immune cells including T cells and myeloid cells in the prostate cancer TCGA dataset. Immunohistochemistry demonstrated that Siglec-7/9 ligands were highly expressed in surgically resected prostate cancer tumor tissues but showed no or low expression in adjacent normal tissues. Siglec-7/9 ligands were also found in the prostate cancer cell lines. The disruption of Siglec-7/9 and their ligands by sialidase, which cleaves the sialic acid, or by anti-Siglec-7/9 blocking antibodies, promoted T cell-mediated cytotoxic killing of prostate cancer cells. Furthermore, anti-Siglec-7/9 treatment suppressed prostate cancer tumor growth in a humanized mouse model. Immunohistochemistry demonstrated that prostate cancers in mice treated with anti-Siglec 7/9 antibodies exhibited less Ki67 and CD31, and more cleaved caspase 3 and CD8 compared with the isotype control. This study provides insight into Siglec-7/9-sialic acid targeting strategies for prostate cancer and lays the groundwork for developing a novel class of immunotherapy-based drugs. Supported by grants from DoD (W81XWH2110195) and NIH (#LINK#U01CA226051#ENDLINK#)

Defining multiple layers of intratumor heterogeneity based on variations of perturbations in multi-omics profiling

BackgroundIntratumor heterogeneity (ITH) plays a crucial role in tumor progression, relapse, immune evasion, and drug resistance. Existing ITH quantification methods based on a single molecular level are inadequate to capture ITH evolving from genotype to phenotype. MethodsWe designed a set of information entropy (IE)-based algorithms for quantifying ITH at the genome (somatic copy number alterations and mutations), mRNA, microRNA (miRNA), long non-coding RNA (lncRNA), protein, and epigenome level, respectively. We evaluated the performance of these algorithms by analyzing the correlations between their ITH scores and ITH-associated molecular and clinical features in 33 TCGA cancer types. Moreover, we evaluated the correlations between the ITH measures at different molecular levels by Spearman correlation and clustering analysis. ResultsThe IE-based ITH measures had significant correlations with unfavorable prognosis, tumor progression, genomic instability, antitumor immunosuppression, and drug resistance. The mRNA ITH showed stronger correlations with the miRNA, lncRNA, and epigenome ITH than with the genome ITH, supporting the regulatory relationships of miRNA, lncRNA, and DNA methylation towards mRNA. The protein-level ITH displayed stronger correlations with the transcriptome-level ITH than with the genome-level ITH, supporting the central dogma of molecular biology. Clustering analysis based on the ITH scores identified four subtypes of pan-cancer showing significantly different prognosis. Finally, the ITH integrating the seven ITH measures displayed more prominent properties of ITH than that at a single level. ConclusionsThis analysis provides landscapes of ITH at various molecular levels. Combining the ITH observation from different molecule levels will improve personalized management for cancer patients.

Abstract 1922: PhyloFrame: A machine learning framework for ancestry agnostic disease signatures

Abstract Rapid advancements in genomic sequencing technologies have catalyzed the molecular identification and treatment of previously elusive diseases. Despite these genomic advancements, disparities in precision medicine access and data have resulted in disparate impacts on different socioeconomic groups. Many of the gold standard datasets largely exclude minority populations, compounding their exclusion from medical research. This has resulted in a restricted understanding of cancer and other complex diseases. We demonstrate the effects of ancestral bias in gold standard genomics datasets through ancestral analysis of cancer-related genes in the Cancer Gene Census, spanning 17 ancestral populations. Additionally, we present a machine learning framework, PhyloFrame, that incorporates population genomics data to correct for ancestral bias by creating disease signatures representative of all ancestries. Our ancestral analysis results show that while a majority of the current cancer-related genes in the Cancer Gene Census have below average mutation frequency in non-European populations, there are peaks of ancestrally enriched mutations in ancestry-specific genes related to cancer, which can be targeted using PhyloFrame. PhyloFrame prioritizes gene expression from the cancer genes with high frequency mutations in a given human population in order to capture genes driving disease in each ancestry. It builds on existing disease gene signatures and big-data functional interaction networks to identify ancestry-relevant genes related to a disease, outputing an ancestry-agnostic disease signature. We test PhyloFrame on TCGA cancers with diverse patient populations, such as breast cancer, and compare PhyloFrame's disease signature output to the disease signature output of elastic net runs on cancer samples from a single ancestry. Our results demonstrate that the incorporation of ancestral information allows PhyloFrame to recapitulate disease signatures trained on only one ancestry in a dataset with individuals from many unquantified ancestries. With the incorporation of ancestral information, PhyloFrame is able to create disease signatures with genes pertinent to each ancestral population, even when individuals from those populations are not included in the training data. This work offers a quick and cheap alternative to the mass sequencing that would be required to capture disease-driving genes in minority populations in hopes to contribute to equitable representation in medical research. Citation Format: Leslie A. Smith, James A. Cahill, Kiley Graim. PhyloFrame: A machine learning framework for ancestry agnostic disease signatures [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 1922.

Abstract 654: Ovarian cancer-cell glucocorticoid receptor activity modulates cytokine secretion promoting infiltration of immunosuppressive cells into the tumor microenvironment

Abstract Elevated levels of immunosuppressive myeloid-derived suppressor cells (MDSCs) circulate in the peripheral blood or infiltrate the tumors as well as ascites in ovarian cancer patients, however the cause for this observed phenotype is unknown. MDSCs contribute to tumor immune tolerance primarily via inhibition of cytotoxic T-cells or by activation of immunosuppressive T-regulatory cells. We previously showed that high tumor cell glucocorticoid receptor (GR) expression is associated with a relatively poor prognosis in ovarian cancer patients. Additionally, using primary tumor data retrieved from the ovarian cancer TCGA dataset, we show high tumor GR expression is associated with significantly higher expression of Foxp3, a marker of immunosuppressive T-regulatory cells, as well as CD33, a marker of immunosuppressive myeloid cells, further suggesting an immunosuppressive phenotype in ovarian cancer patients. We thus hypothesize that tumor-intrinsic GR activation in ovarian cancer is associated with increased MDSC infiltration into the tumor microenvironment in part via modulating tumor cell cytokine secretion. To determine whether tumor-cell GR activation modulates the cytokine secretome, we previously showed that GR activation in ovarian cancer cells upregulated secretion of immunomodulatory factors including G-CSF, TGFb and CXCL5. Moreover, co-treatment of cells with a selective GR modulator (SGRM), reverses this secretome effect. Considering that elevated levels of these cytokines are necessary for the production and recruitment of MDSCs, these data highlight the relevance of this evident glucocorticoid receptor-cytokine circuit in ovarian cancer tumor cells. Furthermore, we show that this tumor-cell cytokine secretome has the capacity to promote differentiation of MDSCs from precursor myeloid cells in healthy PBMCs. Importantly, measuring expression of activation markers such as Arginase-1 and NOS2 illustrated that these MDSCs are in their functional state. To our knowledge this is the first evidence that a nuclear receptor in ovarian cancer tumor cells is actively driving MDSC differentiation. Finally, employing ovarian cancer xenograft models as well as a syngeneic ID8 tumor model, treatment with SGRMs demonstrated reduced levels of circulating MDSCs in the peripheral blood as well as tumor infiltrated MDSCs. Using neutralizing antibodies for cytokines, we will now identify key mechanisms, specifically, identify which targetable immunomodulatory factors are contributing to immune suppression. Based on the insight provided by these data, we propose to determine whether targeting ovarian tumor cell-intrinsic GR activation and resulting MDSC infiltration contributes to improved anti-tumor immune response. Citation Format: Manisha Taya, Eleanore LeBlanc, Lynda Bennett, Suzanne D. Conzen. Ovarian cancer-cell glucocorticoid receptor activity modulates cytokine secretion promoting infiltration of immunosuppressive cells into the tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 654.

Abstract 675: Understanding SETDB1-mediated tumor immune evasion mechanism in endometrial cancer

Abstract SETDB1 is characterized as an important epigenetic regulator in many different cancers and involved in transcriptional silencing of many different processes and pathways. It is often overexpressed in many different cancers including colon, melanoma, breast and pancreatic cancers and its high expression is correlated with worse patient outcome. Often associated with repressing complexes such as HUSH and KAP1/KRAB, it has been shown to repress many different genes such as p53 and p21 through histone H3K9 methylation. In addition, it has been implicated in regulating transposable elements (TEs), primarily of the LTR types as well as SINEs and LINEs. Activation of TEs is key to induction of anti-viral response genes such as the type I interferon pathway and immune-stimulated genes (ISGs) which in turn promote innate immune response against the tumor. Here, we show that SETDB1 promotes tumor growth in endometrial cancer possibly through establishing an immune evasion from the innate immune system. Mice subcutaneously injected with SETDB1 depleted endometrial cancer cells show prolonged survival time and reduced tumorigenesis. In NSG mice, we demonstrate that subcutaneously injected endometrial cancer cell, ISHIKAWA, shows both increased penetration and recruitment of macrophages in the tumor when SETDB1 is knocked out. Our macrophage-cancer cells co-culture in-vitro assay suggests that macrophages induce greater killing in SETDB1-/- cells when stimulated into M1 phenotype more so than in WT cancer cells. Further, tumor IHC analysis shows greater proliferation capacity for control tumor relative to SETDB1-/- as evident by greater mitotic figures per unit area and Ki67 staining score. We further show that cytokines such as CCL5 and CXCL9 (previously reported for their positive correlation with T-cell infiltration in tumor) are significantly upregulated in SETDB1 knockout tumors relative to non-targeting control tumors (NT) as well as in cell lines. Endometrial cancer TCGA data show significant negative correlation between SETDB1 and markers of T-cell infiltration such as CD8A and IFNG as well as cytokines CCL5 and CXCL9. Overall, these findings show important implication of SETDB1 in macrophage recruitment and macrophage-mediated killing of cancer cells in addition to its role in promoting tumor cells proliferation. Citation Format: Kiarash Salari, Matthew Wells, Eleanor R. Johnson, Tianyue Li, Jiaqing Hao, Bing Li, Shujie Yang. Understanding SETDB1-mediated tumor immune evasion mechanism in endometrial cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 675.

Abstract 527: Targeting citric acid cycle anaplerosis reverses the immunosuppressive phenotype of CD11b+Gr1+ myeloid cells in ovarian cancer

Abstract Immunosuppressive myeloid cells (CD11b+Gr1+) are a major constituent of the epithelial ovarian cancer (EOC) microenvironment that confer immunosuppression and promote tumor growth. We recently showed that the EOC microenvironment can induce metabolic reprogramming inCD11b+Gr1+ cells by increasing oxidative phosphorylation via TCA cycle, resulting in increased immunosuppressive ability. Increased TCA cycle activity was due to glutamine anaplerosis, which was facilitated by the upregulation of DLST (dihydrolipoamide succinyl transferase), the E2transferase subunit of α-KGDC (alpha-ketoglutarate dehydrogenase complex). We selected CPI-613 (Devimistat), an orphan metabolic drug, being actively tested in clinical trials to target the a-KGDC complex. We utilized the syngeneic ID8p53-/− model, a high grade serous ovarian cancer, to determine if CPI-613 may regulate CD11b+Gr1+ cell metabolism and immunosuppressive function and restore an antitumor immune response that inhibits EOC. One week after inducing tumors, mice were treated with CPI-613 (2.5mg/kg body weight) twice a week by IP injections or with vehicle (corn oil, control). CPI-613 treated mice showed significantly improved overall survival (median survival 75 days versus 48 days in control), decreased tumor progression and burden.CPI-613 decreased the number of CD11b+Gr1+ myeloid cells in ascites, and more importantly, reduced the intracellular immunosuppressive markers arginase 1 and IL-1β, indicating that the immunosuppressive potential was diminished, which was also validated by reversal of T cell suppression by CPI-613. The alleviation of immunosuppression was complemented by remarkable increase in CD8+ T effector cells. Further, combination of CPI-613 with anti-PD1(100ug/mice, every 5th day for 4 weeks) significantly improved survival and potentiated the CD8+mediated anti-tumor response. CPI-613 inhibited expression of DLST and immunosuppressive markers in the CD11b+Gr1+ myeloid cells, accompanied by decreased oxidative phosphorylation and decreased glutamine anaplerosis. Interestingly CPI-613 treatment also decreased the tumor associated macrophages, increased the M1 marker CD38 and decreased M2 marker EGR2, which reflected in increased M1/M2 macrophage ratio. Increased glutamine demand and addiction have been shown in variety of cancers, including EOC. We found that DLST expression corelates with worse progression free survival in the ovarian cancer TCGA dataset, implying that DLST is important in EOC cells. As a result, DLST targeting of aberrant glutamine metabolism will have the dual benefit of reducing CD11b+Gr1+ cell-mediated immunosuppression while also directly inhibiting tumor growth. Thus CPI-613 represents an attractive drug to target glutamine addiction and reduce immunosuppressive environment plaguing ovarian cancer. Citation Format: Mary Priyanka Udumula, Harshit Singh, Faraz Rashid, Miriana Hijaz, Radhika Gogoi, Philip Philip, Adnan Munkarah, Shailendra Giri, Ramandeep Rattan. Targeting citric acid cycle anaplerosis reverses the immunosuppressive phenotype of CD11b+Gr1+ myeloid cells in ovarian cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 527.

The prognostic value of RHBDF2 in Pan-Cancer, and its correlation with cell Adhesion of Hepatocellular Carcinoma

ABSTRACT The impact of RHBDF2 on the expression and potential function in many cancers is still unknown. Therefore, the expression and methylation modification of RHBDF2 were evaluated across TCGA cancers in this study. Moreover, two methods, COX regression and Kaplan-Meier, were utilized for analyses of the prognoses of RHBDF2 in patients. Besides, the association between RHBDF2 and immune microenvironment, mutation, tumor mutation burden and microsatellite instability was analyzed with Pearson correlation. We verified RHBDF2 expression in hepatocellular carcinoma (HCC) compared with normal cell and tissue samples, detected the effects of RHBDF2 knockdown on biological functions in HCC cells, and detected CD4, CD8 and CD68 expression in hepatocellular carcinoma tissues and paired normal tissues. Given these results, the significant mRNA overexpression and promoter hypomethylation of RHBDF2 in various tumor types was showed, and a clear relationship between RHBDF2 overexpression and unfavourable overall survival and progression-free survival was observed, including liver hepatocellular carcinoma (LIHC), glioma (GBMLGG) and pancreatic adenocarcinoma (PAAD). Additionally, hypomethylation of RHBDF2 can affect the overall survival in some tumors. Furthermore, a clear correlation between RHBDF2 and infiltration of immune cells, immune-related molecules, TMB and MSI was observed. Besides, RHBDF2 expression is upregulated in HCC cells and tissues, and RHBDF2 knockdown could decrease the cell adhesion ability of HCC cells. More importantly, the expression of CD4, CD8 and CD68 was higher in HCC tissues. Altogether, the research denoted that RHBDF2 can be a prognostic biomarker for cancers according to these results and participate in cell adhesion of HCC cells.

MUC16 promotes triple-negative breast cancer lung metastasis by modulating RNA-binding protein ELAVL1/HUR

BackgroundTriple-negative breast cancer (TNBC) is highly aggressive with an increased metastatic incidence compared to other breast cancer subtypes. However, due to the absence of clinically reliable biomarkers and targeted therapy in TNBC, outcomes are suboptimal. Hence, there is an urgent need to understand biological mechanisms that lead to identifying novel therapeutic targets for managing metastatic TNBC.MethodsThe clinical significance of MUC16 and ELAVL1 or Hu antigen R (HuR) was examined using breast cancer TCGA data. Microarray was performed on MUC16 knockdown and scramble TNBC cells and MUC16-associated genes were identified using RNA immunoprecipitation and metastatic cDNA array. Metastatic properties of MUC16 were evaluated using tail vein experiment. MUC16 and HuR downstream pathways were confirmed by ectopic overexpression of MUC16-carboxyl-terminal (MUC16-Cter), HuR and cMyc as well as HuR inhibitors (MS-444 and CMLD-2) in TNBC cells.ResultsMUC16 was highly expressed in TNBC and correlated with its target HuR. Depletion of MUC16 showed decreased invasion, migration, and colony formation abilities of human and mouse TNBC cells. Mice injected with MUC16 depleted cells were less likely to develop lung metastasis (P = 0.001). Notably, MUC16 and HuR were highly expressed in the lung tropic TNBC cells and lung metastases. Mechanistically, we identified cMyc as a HuR target in TNBC using RNA immunoprecipitation and metastatic cDNA array. Furthermore, MUC16 knockdown and pharmacological inhibition of HuR (MS-444 and CMLD-2) in TNBC cells showed a reduction in cMyc expression. MUC16-Cter or HuR overexpression models indicated MUC16/HuR/cMyc axis in TNBC cell migration.ConclusionsOur study identified MUC16 as a TNBC lung metastasis promoter that acts through HuR/cMyc axis. This study will form the basis of future studies to evaluate the targeting of both MUC16 and HuR in TNBC patients.

Abstract P6-14-09: Non-canonical Wnt/Ror2 signaling regulates tumor cell invasion and dissemination in breast cancer through cell-matrix crosstalk

Abstract BACKGROUND: Metastasis is the main cause of mortality for breast cancer patients. The early steps of cancer metastasis require that tumor cells actively invade and disseminate from the primary tumor to distant organs. Cell-extracellular matrix (ECM) interactions represent fundamental interactions during tumor invasion and metastasis, yet how particular signal-transduction factors prompt the conversion of tumor cells into migratory populations capable of systemic dissemination remains elusive. OBJECTIVES: Wnt signaling is a known regulator of cell fate, migration, and polarity during various key biological processes. We previously discovered an inverse correlation between the canonical Wnt signature and a non-canonical Wnt receptor, Ror2, across breast cancers in TCGA. The objective of this study is to investigate how canonical and non-canonical Wnt signaling orchestrate tumor cell behavior during cancer invasion and metastasis. METHODS: We used clinically relevant syngeneic TP53-null Genetically Engineered Mouse Model (GEMM) tumors that molecularly reflect the different breast cancer intrinsic subtypes. In vivo transplantation of GEMM models and in vitro 3D tumor organoids were employed to elucidate the molecular mechanism underlying the migration, invasion, and metastasis of tumor cells upon genetic perturbation of Wnt/Ror2 signaling. RESULTS/DISCUSSION: From 3-dimensional (3D) tumor organoid models, we identified novel transcriptional alterations encompassing cell-cell adhesion, cytoskeletal remodeling, and ECM organization upon Ror2 loss. At protein levels, we discovered a significant increase in collagen fibril organization and integrin-α5 and integrin-β3 expression following Ror2 depletion. In addition, the matrisomal protein fibronectin (FN) was concomitantly upregulated and assembled in Ror2-deficient tumor cells at sites of invasion. Consequently, we observed FAK activation and actin cytoskeleton alterations in Ror2-deficient tumor cells, leading to a promigratory tumor cell behavior. The altered cytoskeleton and cell-ECM interaction enhanced the rigidity of the Ror2-depleted cells. Furthermore, Inhibition of either integrin or FAK activation abrogated the increased invasion driven by Ror2-loss. Unexpectedly, these changes were distinct from processes regulated by Wnt/ß-catenin activation. From both spontaneous metastasis and experimental metastasis models, we discovered a shift from canonical to non-canonical Wnt signaling throughout the progression of lung metastasis, indicating that the balance of Wnt signaling may serve as a switch to dictate cell functions at different stages of metastatic development. Consistently, we found enhanced initial colonization in the lungs when Ror2 is depleted in these breast cancer cells. Together, these studies provide new insight into how canonical and alternative Wnt pathways coordinate cell-cell and cell-ECM exchanges during breast cancer progression and metastasis. Supported by grant NIH-CA016303 Citation Format: Hongjiang Si, Na Zhao, Andrea Pedroza, Chad Creighton, Jeffrey Rosen, Kevin Roarty. Non-canonical Wnt/Ror2 signaling regulates tumor cell invasion and dissemination in breast cancer through cell-matrix crosstalk [abstract]. In: Proceedings of the 2022 San Antonio Breast Cancer Symposium; 2022 Dec 6-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2023;83(5 Suppl):Abstract nr P6-14-09.

Characterization of the immune cell landscape in CRC: Clinical implications of tumour-infiltrating leukocytes in early- and late-stage CRC.

Colorectal cancer (CRC) is the third leading cause of cancer-related deaths globally. Tumour-infiltrating leukocytes play an important role in cancers, including CRC. We therefore sought to characterize the impact of tumour-infiltrating leukocytes on CRC prognosis. To determine whether the immune cell profile within CRC tissue could influence prognosis, we employed three computational methodologies (CIBERSORT, xCell and MCPcounter) to predict abundance of immune cell types, based on gene expression. This was done using two patient cohorts, TCGA and BC Cancer Personalized OncoGenomics (POG). We observed significant differences in immune cell composition between CRC and normal adjacent colon tissue, as well as differences in based on method of analysis. Evaluation of survival based on immune cell types revealed dendritic cells as a positive prognostic marker, consistently across methodologies. Mast cells were also found to be a positive prognostic marker, but in a stage-dependent manner. Unsupervised cluster analysis demonstrated that significant differences in immune cell composition has a more pronounced effect on prognosis in early-stage CRC, compared to late-stage CRC. This analysis revealed a distinct group of individuals with early-stage CRC which have an immune infiltration signature that indicates better survival probability. Taken together, characterization of the immune landscape in CRC has provided a powerful tool to assess prognosis. We anticipate that further characterization of the immune landscape will facilitate use of immunotherapies in CRC.