Cancer Genome Research Articles

In Memoriam: Charis Eng MD PhD (1962–2024)

Matthew Ringel On behalf of the entire editorial team of Endocrine-Related Cancer, and personally as trainees and collaborators who at present serve as associate or senior editors of the Journal, it is with profound sadness that we write this memorial to Prof. Charis Eng, M.D., Ph.D who passed away on August 13, 2024. Prof. Eng served as Editor-in-Chief of Endocrine-Related Cancer from 2011-2021 and was dedicated to the Journal before, during, and after she served in that role. She had remarkable impact on Endocrine-Related Cancer moving the journal forward with great vision and energy while maintaining the strongest commitments to publishing the highest quality original research and reviews applying fair, ethical, and rigorous peer-review processes. Charis was a world-recognized leader in cancer genomics and clinical genetics medicine who enabled growth in Endocrine-Related Cancer in these and other areas critical for advancement of research and clinical care of endocrine cancers. She will be remembered by all of us as a superb and consequential researcher, editor, physician, leader, teacher, mentor, colleague, and friend.

Abstract A005: APC mutations dysregulate alternative polyadenylation in cancer

Abstract Alternative polyadenylation (APA) affects most human genes and is recurrently dysregulated in cancers. However, the mechanistic origins of this dysregulation are incompletely understood. We completed an unbiased computational analysis of molecular regulators of poly(A) site selection across The Cancer Genome Atlas and identified that colorectal adenocarcinoma is distinct from all other cancer subtypes. We linked this distinction to the frequent presence of loss-of-function APC mutations in colorectal adenocarcinoma, which were strongly associated with expression of long 3′ UTRs relative to tumors lacking APC mutations. APC knockout similarly dysregulated APA in human colon organoids, and reduced APC expression was associated with APA dysregulation in tumor types lacking APC mutations. Building on previous work that identified APC as an RNA-binding protein that preferentially binds 3′ UTRs during mouse neurogenesis, we found that APC binding is most significantly enriched just upstream of proximal poly(A) sites within 3′ UTRs. Our results suggest that APC promotes proximal poly(A) site use and that APC loss contributes to pervasive APA dysregulation in human cancers. Citation Format: Austin M Gabel, Andrea E Belliville, James D Thomas, Jose Mario B Pineda, Robert K Bradley. APC mutations dysregulate alternative polyadenylation in cancer [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr A005.

Abstract A020: LncTransformer: Identifying novel prognostic long-noncoding RNA biomarkers for pancreatic cancer using deep learning

Abstract Pancreatic cancer remains a formidable challenge due to high mortality rates and limited therapeutic options due to late detection of the cancer. Recent literature has focused on investigating the potential of long noncoding RNAs (lncRNAs) as biomarkers in several cancers. To identify significantly altered expression of lncRNA in pancreatic cancer, I collected information from the Cancer Genome Atlas (TCGA) and extracted RNA- sequencing (RNA-seq) transcriptomic profiles of pancreatic carcinomas. Out of 60,660 gene transcripts shared between 151 pancreatic cancer patients, 38 lncRNAs were identified to be significantly differentially expressed. To find the lncRNAs related to metastatic progression of pancreatic cancer, different machine learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest classifier (RFC), and a custom autoencoder model were trained to identify potential prognostic biomarkers. The custom deep learning model predicted metastatic prognostic biomarkers with a 92% accuracy, with the second-best accuracies coming from the SVM and RFC models, having an accuracy of 76%. Using the deep learning model, the following novel lncRNA and gene biomarkers were identified: LINC01300, SERPINB13, AC010789.1, TMPRSS15, DUSP5- DT, AL513128.3, MIR205HG, LINC00486, RF00019, LINC01115, and AC133530.1. Further investigating these results, specifically the identified lncRNA, the LINC01300 was identified to be potentially circulating in the blood. If further analysis proves this hypothesis correct then LINC01300 along with other identified genes could serve as biomarkers in a liquid biopsy. The identified biomarkers would be especially helpful in rural areas with lower accessibility to healthcare as it would allow for an easier, more efficient, and cost-effective method of pancreatic cancer detection. Based on these findings, further investigations of this gene panel in vitro and in vivo will be conducted, as they could be targeted for improved outcomes in pancreatic cancer patients and assist in the diagnosis of metastatic progression based on RNA-seq data of primary pancreatic tumors or the development of a liquid biopsy. Citation Format: Shivali Singh. LncTransformer: Identifying novel prognostic long-noncoding RNA biomarkers for pancreatic cancer using deep learning [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr A020.

Abstract B017: Discovery of cancer-specific small RNAs using a new unbiased analysis of cohort sequence data

Abstract It is well-established that small RNAs such as microRNAs can have important roles as oncogenes or tumor suppressors. The majority of studies of small RNAs in cancer have focused on examining the often subtle deregulation of specific, well-characterized molecules, aiming to understand their role in tumorigenesis. However, these efforts have predominantly concentrated on known, well-annotated RNA sequences, potentially overlooking novel, cancer-specific molecules. In this study, we sought to apply a new computational approach to uncover small RNA molecules that are uniquely associated with cancer and are completely absent from all non- cancerous tissues. To achieve this, we developed an unbiased approach that can detect cancer- specific sequences even if they are lowly abundant, do not overlap with existing genome annotations and even if they do not map to the human reference genome. We applied our framework to hundreds of small RNA-seq samples across a diverse range of cancer types from two large-scale public resources: The Cancer Genome Atlas (TCGA) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). Through this comprehensive analysis, we identified a series of small RNA molecules that are exclusively present in tumor samples and completely absent in adjacent normal tissues across multiple cancer types. Furthermore, these molecules were not detected in a large cohort comprising over 2,500 non-cancerous public samples, underscoring their specificity to cancerous conditions. Some of these molecules are also associated with different patient metadata. For example, one of these candidates exhibited significant correlation with poor patient survival in both Lung Adenocarcinoma and Endometrial Carcinoma within both the TCGA and CPTAC datasets. The discovery of such cancer-specific small RNAs holds immense potential for clinical applications. These molecules could serve as highly specific diagnostic and prognostic biomarkers, aiding in the early detection and stratification of cancers. Moreover, their unique presence in cancer cells, positions them as promising targets for the development of novel therapeutic interventions. In summary, our findings highlight the importance of exploring the full spectrum of small RNAs in cancer research. By moving beyond the constraints of known annotations, we have uncovered previously unknown molecules that could improve cancer diagnostics, prognostics, and treatment. Citation Format: Panagiotis Kalogeropoulos, Maria Alejandra Ulloa, Marc Friedländer. Discovery of cancer-specific small RNAs using a new unbiased analysis of cohort sequence data [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: RNAs as Drivers, Targets, and Therapeutics in Cancer; 2024 Nov 14-17; Bellevue, Washington. Philadelphia (PA): AACR; Mol Cancer Ther 2024;23(11_Suppl):Abstract nr B017.

Expression levels of some genes in the MAPK pathway (DUSP1, DUSP2, DUSP4, DUSP6 and DUSP10) in eyelid tumor tissue

Abstract Objectives To control mitogen-activated protein kinases (MAPK) signaling pathways involved in the onset and progression of cancer, dual specificity phosphatases (DUSPs/MKP) are essential. This study seeks to detect the correlation between eyelid tumors and the genes DUSPs, known for their influence on MAPK signaling pathways. Additionally, we aim to juxtapose our findings with analyses from various bioinformatics databases. Methods Expression levels of relevant genes in cDNA samples were determined by quantitative PCR method. Open-access databases were used for mutation analysis of relevant genes, mRNA expression changes, and survival analyses, and the STRING database was used for protein-protein interactions. Results It was found that the expression of DUSP1 and DUSP2 showed a significant decrease in the tumor tissue, while a significant increase was detected in the DUSP4 and DUSP6 genes. Additionally, when we compared the study genes with the Cancer Genome Atlas program cancer cohorts, it was found that the DUSP1 and DUSP10 gene expression profiles were downregulated in uveal melanoma compared to other cancer cohorts. Conclusions Significant and obvious changes were observed in the DUSP genes we studied in eyelid tumors. However, the relationship between these genes and cancer must be studied more. Considering that these enzymes are effective in cell proliferation, differentiation, and apoptosis, it would be appropriate to plan comprehensive studies on their interactions with other proteins they interact with in the MAPK pathway.

A novel platelets-related gene signature for predicting prognosis, immune features and drug sensitivity in gastric cancer

BackgroundPlatelets can dynamically regulate tumor development and progression. Nevertheless, research on the predictive value and specific roles of platelets in gastric cancer (GC) is limited. This research aims to establish a predictive platelets-related gene signature in GC with prognostic and therapeutic implications.MethodsWe downloaded the transcriptome data and clinical materials of GC patients (n=378) from The Cancer Genome Atlas (TCGA) database. Prognostic platelets-related genes screened by univariate Cox regression were included in Least Absolute Shrinkage and Selection Operator (LASSO) analysis to construct a risk model. Kaplan-Meier curves and receiver operating characteristic curves (ROCs) were performed in the TCGA cohort and three independent validation cohorts. A nomogram integrating the risk score and clinicopathological features was constructed. Functional enrichment and tumor microenvironment (TME) analyses were performed. Drug sensitivity prediction was conducted through The Cancer Therapeutics Response Portal (CTRP) database. Finally, the expression of ten signature genes was validated by quantitative real-time PCR (qRT-PCR).ResultsA ten-gene (SERPINE1, ANXA5, DGKQ, PTPN6, F5, DGKB, PCDH7, GNG11, APOA1, and TF) predictive risk model was finally constructed. Patients were categorized as high- or low-risk using median risk score as the threshold. The area under the ROC curve (AUC) values for the 1-, 2-, and 3-year overall survival (OS) in the training cohort were 0.670, 0.695, and 0.707, respectively. Survival analysis showed a better OS in low-risk patients in the training and validation cohorts. The AUCs of the nomogram for predicting 1-, 2-, and 3-year OS were 0.708, 0.763, and 0.742, respectively. TME analyses revealed a higher M2 macrophage infiltration and an immunosuppressive TME in the high-risk group. Furthermore, High-risk patients tended to be more sensitive to thalidomide, MK-0752, and BRD-K17060750.ConclusionThe novel platelets-related genes signature we identified could be used for prognosis and treatment prediction in GC.

Deciphering the Role of ASPM in Breast Cancer: A Comprehensive Multicohort Study

Background: Assembly factor for spindle microtubules (ASPM) has gained significant attention in cancer research due to its association with tumor growth and progression. Through the analysis of large-scale genomic datasets, ASPM has been identified as the top upregulated gene in breast cancer (BC), characterized by high proliferation. This multicohort study aimed to investigate the clinicopathological and prognostic significance of ASPM mRNA and protein expression in BC. Methods: ASPM mRNA expression was assessed using the Cancer Genome Atlas (TCGA) BC cohort and has been further validated in the Molecular Taxonomy of BC International Consortium (METABRIC) (n = 1980), The Uppsala cohort (n = 249), in addition to the combined multicentric cohort (n = 7252). ASPM protein expression was evaluated in a large BC cohort (n = 1300) using immunohistochemistry. The correlations between ASPM expression, clinicopathological parameters, molecular subtypes and outcome were assessed. The response to taxane treatment was compared to the clinical prognosis of ASPM using the ROC plotter. Results: High ASPM mRNA and protein expression were significantly associated with aggressive BC features and poor survival across all cohorts. The association with poor outcomes was maintained in the adjuvant chemotherapy and radio-therapy-treated patients. Responders to taxane treatment showed significantly elevated ASPM levels compared to non-responders. Conclusions: High ASPM expression predicts poor prognosis in BC. It may play a role in treatment resistance within a specific subgroup of patients. Further clinical trials are warranted to explore the potential of ASPM as a target for therapeutic interventions in cancer.

A Systematic Identification of RNA-Binding Proteins (RBPs) Driving Aberrant Splicing in Cancer

Background: Alternative Splicing (AS) is a post-transcriptional process that allows a single RNA to produce different mRNA variants and, in some cases, multiple proteins. Various processes, many yet to be discovered, regulate AS. This study focuses on regulation by RNA-binding proteins (RBPs), which are not only crucial for splicing regulation but also linked to cancer prognosis and are emerging as therapeutic targets for cancer treatment. CLIP-seq experiments help identify where RBPs bind on nascent transcripts, potentially revealing changes in splicing status that suggest causal relationships. Selecting specific RBPs for CLIP-seq experiments is often driven by a priori hypotheses. Results: We developed an algorithm to detect RBPs likely related to splicing changes between conditions by integrating several CLIP-seq databases and a differential splicing detection algorithm. This work refines a previous study by improving splicing event prediction, testing different enrichment statistics, and performing additional validation experiments. The new method provides more accurate predictions and is included in the Bioconductor package EventPointer 3.14. We tested the algorithm in four experiments involving knockdowns of seven different RBPs. The algorithm accurately assessed the statistical significance of these RBPs using only splicing alterations. Additionally, we applied the algorithm to study sixteen cancer types from The Cancer Genome Atlas (TCGA) and three from TARGET. We identified relationships between RBPs and various cancer types, including alterations in CREBBP and MBNL2 in adenocarcinomas of the lung, liver, prostate, rectum, stomach, and colon. Some of these findings are validated in the literature, while others are novel. Conclusions: The developed algorithm enhances the ability to predict and understand RBP-related splicing changes, offering more accurate predictions and novel insights into cancer-related splicing alterations. This work highlights the potential of RBPs as therapeutic targets and contributes to the broader understanding of their roles in cancer biology.

A05 - HPB Interrogating the spatial biology of pancreatic cancer-associated fibroblasts

Abstract Background Cancer-associated fibroblasts (CAFs) are key players in the fibrotic tumour microenvironment (TME) of pancreatic cancer. Recent evidence has demonstrated the existence of multiple CAF subtypes, however there is relatively little evidence validating and corroborating these CAF subtypes in resected human pancreatic cancer. Furthermore, the spatial interactions between CAF subtypes and the epithelial and immune compartments of the pancreatic cancer TME is not well understood. Method We designed and optimised a bespoke 8-plex immunofluorescence panel for the purposes of CAF subtype profiling and elucidation of CAF spatial biology: panCK, FAP, SMA, CTGF, S100A4, IL6, LIF, PDGFR. This was applied to a cohort of n=215 resected pancreatic cancer, well characterised with highly granular clinico-pathological and transcriptomic data through the Australian Pancreatic Cancer Genome Initiative. Spatial analysis was performed with novel computational biology techniques. Results We demonstrate seven CAF subtypes which cluster to form distinct, highly prognostic “enrichment profiles” (EPs). The cold EP represents quiescent CAFs in close proximity to immune cells (multivariate HR 0.45, 95% CI 0.30-0.68, p<0.001), and the fibrotic EP represents activated CAFs in close proximity to epithelial cells (multivariate HR 2.22, 95% CI 1.48-3.35, p<0.001). We also demonstrate the prognostic power of high spatial entropy (loss of tissue architecture, correlating with the fibrotic EP) vs low spatial entropy (preservation of tissue architecture, correlating with the cold EP) (HR 2.22, 95% CI 1.47-3.35, p<0.001). Conclusion We begin to unravel the elusive spatial biology of pancreatic CAFs in a large resected human cohort. We see changes in immune cell infiltrate, modulation of cellular relationships, and greater tissue disorganisation as a result of CAF activation. These features are highly prognostic and we hypothesise they are driven by changes in crosstalk between TME compartments. Current work focuses on application of these techniques to a large resected neoadjuvantly-treated cohort, allowing further interrogation of the notorious fibrotic post-neoadjuvant pancreatic cancer TME. If accepted for presentation we look forward to presenting both cohorts simultaneously.

Abstract A067: lncTransformer: Identifying novel prognostic biomarkers for pancreatic cancer using deep learning models

Abstract Pancreatic cancer remains a formidable challenge due to high mortality rates and limited therapeutic options due to late detection of the cancer. Recent literature has focused on investigating the potential of long noncoding RNAs (lncRNAs) as biomarkers in several cancers. To identify significantly altered expression of lncRNA in pancreatic cancer, I collected information from the Cancer Genome Atlas (TCGA) and extracted RNA-sequencing (RNA-seq) transcriptomic profiles of pancreatic carcinomas. Out of 60,660 gene transcripts shared between 151 pancreatic cancer patients, 38 lncRNAs were identified to be significantly differentially expressed. To find the lncRNAs related to metastatic progression of pancreatic cancer, different machine learning algorithms, including logistic regression (LR), support vector machine (SVM), random forest classifier (RFC), and a custom autoencoder model were trained to identify potential prognostic biomarkers. The custom deep learning model predicted metastatic prognostic biomarkers with a 92% accuracy, with the second-best accuracies coming from the SVM and RFC models, having an accuracy of 76%. Using the deep learning model, the following novel lncRNA and gene biomarkers were identified: LINC01300, SERPINB13, AC010789.1, TMPRSS15, DUSP5-DT, AL513128.3, MIR205HG, LINC00486, RF00019, LINC01115, and AC133530.1. Further investigating these results, specifically the identified lncRNA, the LINC01300 was identified to be potentially circulating in the blood. If further analysis proves this hypothesis correct then LINC01300 along with other identified genes could serve as a biomarker panel for a liquid biopsy. The identified biomarkers would be especially helpful in rural areas with lower accessibility to healthcare as it would allow for an easier, more efficient, and cost-effective method of pancreatic cancer detection. Based on these findings, further investigations of this gene panel are being conducted, in vitro and in vivo, to create a novel liquid biopsy to assist in the prognostic diagnosis of metastatic progression of pancreatic cancer. Citation Format: Shivali Singh. lncTransformer: Identifying novel prognostic biomarkers for pancreatic cancer using deep learning models [abstract]. In: Proceedings of the AACR Special Conference: Liquid Biopsy: From Discovery to Clinical Implementation; 2024 Nov 13-16; San Diego, CA. Philadelphia (PA): AACR; Clin Cancer Res 2024;30(21_Suppl):Abstract nr A067.

Leveraging homologous recombination deficiency for sarcoma

Abstract Background Homologous recombination deficiency (HRD) in tumors correlates with poor prognosis and metastases development. Determining HRD is of major clinical relevance as it can be treated with PARP inhibitors (PARPi). HRD remains poorly investigated in sarcoma, a rare and heterogeneous cancer of mesenchymal origin. Objective We aimed (i) to investigate predictive biomarkers of HRD in several independent sarcoma cohorts using a cross-functional strategy by combining genomic, transcriptomic and phenotypic approaches and (ii) to evaluate the therapeutic potential of PARPi and DNA damage response (DDR)-based therapies ex vivo. Materials and methods We performed a comprehensive genomic and transcriptomic characterization of sarcoma using datasets from The Cancer Genome Atlas (TCGA) and Therapeutically Applicable Research to Generate Effective Treatments (TARGET), and our own bone and soft tissue sarcoma cohorts. We evaluated PARP1/2 and WEE1 inhibition ex vivo in patient-derived sarcoma cell models as monotherapy and in combination with chemotherapeutic agents to identify synergistic effects. Results Firstly, we identified genomic traits of HRD in a subset of sarcomas associated with molecular alterations in homologous recombination repair (HRR) pathway genes and high chromosomal instability. Secondly, we identified and validated distinct SARC-HRD transcriptional signatures that predicted sensitivity to PARPi. Finally, we showed functional defects in HRR in sarcoma cells that were associated with functional dependency towards PARPi and WEE1i and support the clinical use of RAD51 as a predictive biomarker for PARPi sensitivity. Conclusion We provide a personalized oncological approach to potentially improve the treatment of sarcoma patients. We encourage the evaluation of gene expression signatures to enhance the identification of patients who might benefit from DDR-based therapies.

Meeting abstracts from the Annual Conference “Clinical Genetics of Cancer 2023”

Meeting abstracts from the Annual Conference “Clinical Genetics of Cancer 2023”

A pyroptosis-related lncRNA risk model for the prediction of prognosis and immunotherapy response in head and neck squamous cell carcinoma

BackgroundRecent research has highlighted pyroptosis as a key factor in cancer progression. This study aims to explore the association between pyroptosis-related signatures and overall survival (OS) in head and neck squamous cell carcinoma (HNSC) and develop a pyroptosis-related long non-coding RNA (lncRNA) risk model to predict prognosis and response to immunotherapy in HNSC.MethodsWe extracted expression data for 18 pyroptosis-related genes and identified lncRNA probes specific to HNSC by using datasets from the Gene Expression Omnibus (GEO) and The Cancer Genome Atlas (TCGA). Consensus clustering was performed to categorize HNSC patients into distinct subtypes. A six-lncRNA risk score model was constructed through univariate and least absolute shrinkage and selection operator (LASSO) Cox regression analyses. We evaluated the predictive ability of the lncRNA model for patients’ survival and immunotherapy response. Gene expression was evaluated using immunohistochemistry (IHC) and Reverse Transcription Quantitative Polymerase Chain Reaction (RT-qPCR).ResultsOur analysis revealed two distinct pyroptosis-related subtypes in HNSC patients, Cluster A and Cluster B. Notably, patients in Cluster B exhibited significantly poorer overall survival compared to those in Cluster A. Through differential expression analysis, we identified six lncRNAs (AC002331.1, CTA-384D8.35, RP11-291B21.2, AC006262.5, RP1-27K12.2, and RP11-54H7.4) that were differentially expressed between these clusters. A 6-lncRNA risk score model was developed, which successfully stratified patients into high- and low-risk groups with distinct overall survival outcomes. Validation using RT-qPCR confirmed the differential expression of these six lncRNAs in HNSC tumor tissues compared to adjacent normal tissues, we found that the expression of CTA-384D8.35 was significantly increased in the tumor group (t=-6.203, P<0.001). Furthermore, the 6-lncRNA risk score model demonstrated a significant association with patient response to immunotherapy, with the low-risk group exhibiting a higher objective response rate to immune checkpoint blockade (ICB) therapy and longer survival compared to the high-risk group.ConclusionOur study underscores the role of pyroptosis signatures in HNSC prognosis and identifies two distinct pyroptosis subtypes with differing survival outcomes. The six-lncRNA risk score model offers a valuable tool for predicting patient prognosis and potential benefits from ICB therapy. These findings highlight the importance of pyroptosis and associated lncRNAs in the tumor microenvironment, paving the way for novel targeted therapies in HNSC.

CNSC-08. PROGNOSTIC BIOMARKER DISCOVERY THROUGH PROTEOMIC ANALYSIS OF GLIOBLASTOMA MULTIFORME

Abstract BACKGROUND Glioblastoma multiforme (GBM) is an aggressively malignant brain tumor known for its high recurrence rates and dismal survival outcomes. Despite advancements in surgical and therapeutic strategies, the prognosis for GBM patients remains poor. This study applies proteomic analysis to postoperative GBM tumor specimens to identify molecular markers that could enhance prognosis prediction. Through the analysis of protein expression profiles before and after tumor recurrence, we identified significant alterations in protein pathways via Gene Set Enrichment Analysis (GSEA), highlighting potential biomarkers for disease outcome prediction. METHODS We employed Sequential Window Acquisition of All Theoretical Fragment Ion Spectra (SWATH-MS) proteomics to analyze protein samples from both primary and recurrent GBM patients. Gene Set Enrichment Analysis (GSEA) was utilized to compare protein expression profiles and discern significantly altered pathways. RESULTS Analysis revealed 778 differentially expressed proteins (DEPs), with notable alterations in pathways such as glycolysis, interleukin signaling, and MAPK signaling. Proteins such as TOLLIP, ENO1, and ITGA2B emerged as potential prognostic markers. Validation through an online tumor prognostic analysis platform (ToPP), integrated with The Cancer Genome Atlas (TCGA) data, facilitated the development of a prognostic risk score model. This model effectively categorized patients into high and low-risk groups based on their protein expression profiles, enhancing the prediction of patient outcomes. The proteins and pathways identified offer new insights into the molecular dynamics of GBM postoperatively. The association between protein expression profiles and patient prognosis underscores the potential of these markers in developing customized therapeutic strategies. Additionally, the prognostic risk score model serves as a valuable tool for improving prognostic accuracy and could guide personalized treatment plans. CONCLUSION Proteomic profiling of GBM tumors significantly contributes to uncovering the molecular mechanisms driving this aggressive cancer and facilitates the development of dependable prognostic tools. These findings not only affirm the critical role of proteomics in cancer research but also advance personalized medicine strategies in GBM management.

CNSC-48. DIFFERENTIAL GENE EXPRESSION UNDERLYING EPILEPTOGENICITY IN PATIENTS WITH GLIOMAS

Abstract BACKGROUND Seizures are a common sequela for patients suffering from gliomas. Molecular properties are known to influence the initiation of seizures that may influence tumor growth. Different levels of gene expression associated with seizures related to gliomas remain unclear. We analyzed RNA sequencing of gliomas to further probe these differences. METHODS Total RNA sequencing was obtained from The Cancer Genome Atlas - Lower Grade Glioma project, comprised of 2021 WHO classification low grade gliomas, including IDH-mutant and IDH-wild type, to distinguish differential expression in patients who did and did not experience seizures. Utilizing QIAGEN Ingenuity Pathways Analysis, we identified canonical and functional pathways to further characterize differential expression. RESULTS Of 289 patients with gliomas, 83 (28.7%) had available information regarding seizure occurrence prior to intervention and other pertinent variables of interest. Of these, 50 (60.2%) were allocated to the seizure group. When comparing the level of RNA expression from these tumors between the seizure and non-seizure groups, 52 genes that were significantly differentially regulated were identified. We found canonical pathways that were altered, most significantly RhoGDI and Semaphorin Neuronal Repulsive signaling. Functional gene analysis revealed tumors that promoted seizures had significantly increased functional gene sets involving neuronal differentiation and synaptogenesis. CONCLUSIONS In the setting of gliomas, differences in tumor gene expression exist between individuals with and without seizures, despite similarities in patient demographics and other tumor characteristics. There are significant differences in gene expression associated with neuron development and synaptogenesis, ultimately suggesting a mechanistic role of a tumor-neuron synapse in seizure initiation.

EPCO-46. SPATIAL TRANSCRIPTOMICS ANALYSIS OF GLIOBLASTOMA REVEALS THREE DISTINCT REGIONAL PROGRAMS OF T-CELL INFILTRATION

Abstract Glioblastoma (GBM) resists T-cell killing by developing an immunosuppressive tumor microenvironment. Transcriptomic technologies, such as single-cell RNA sequencing (scRNA-seq), have characterized immune cell phenotypes and putative cell-cell interactions in GBM. However, the tumor microenvironment surrounding T-cells is still not well characterized, motivating the need for spatial analyses. Two publicly-available GBM spatial transcriptomic datasets were analyzed, with one discovery cohort (N=18) and one validation cohort (N=12). Using cell deconvolution with a scRNA-seq reference dataset, areas of high T-cell infiltration were identified and clustered into regional programs based on transcriptomic signatures. To highlight which ligand-receptor interactions are most prominent in these regional programs, a novel spatial enrichment algorithm was applied. The impact of spatially enriched ligand-receptor interactions on overall survival was evaluated using bulk RNA-sequencing data from The Cancer Genome Atlas (TCGA). Three distinct T-cell regional programs (transitional, immunosuppressive, and infiltrative) were found across both the discovery and validation cohort. The infiltrative T-cell regional program resided on the periphery of the tumor with a neural progenitor phenotype (NRGN, VSNL1, SLC17A7). In contrast, the immunosuppressive T-cell region had a higher proportion of malignant cells and a greater mesenchymal tumor signature (ANXA1, CD44, complement pathway). Furthermore, ligand-receptor enrichment analysis showed that the immunosuppressive region had higher tumor and tumor-associated macrophage (TAM) signaling towards CD4 T-cells along the collagen, midkine, and osteopontin pathways. TCGA survival analysis showed that higher collagen and midkine signals were associated with worse overall and disease free survival. Our results identified three distinct regional programs in GBM and highlight a set of signaling pathways associated with T-cell suppression. Our findings suggest that the disruption of these pathways could reduce immunosuppressive signals from tumor and TAMs to T-cells. Subsequent validation with spatial proteomics data in glioblastoma is ongoing.

CSIG-28. DISSECTING THE FUNCTIONAL IMPAIRMENT OF WILD-TYPE P53 IN HUMAN GLIOBLASTOMA

Abstract Glioblastoma (GBM) is the most common malignant brain tumor in adults and is characterized by heterogeneous nature, invasive potential, and poor treatment response. Work from The Cancer Genome Atlas (TCGA) identified the tumor suppressor gene TP53 as a GBM driver. In contrast to several cancers, only ~35% of GBM cases carry mutations in TP53. Work from our lab showed that TP53 mutations emerge as ‘late’ events during GBM growth. Therefore, we hypothesized that wild-type (wt) p53 function is impaired in GBM. We used the TCGA data of 591 GBM patients to compare the clinical and molecular characteristics of wt and mutant TP53 patients. Gene expression data were analyzed to compare the correlation of TP53 with the expression of key p53 regulators and identify differentially expressed regulators between our groups. Furthermore, we evaluated the expression of p53 and selected regulators in patient-derived cells (PDCs) and control lines with known TP53 status. Our results show that wt TP53 patients live less than mutant patients and both groups respond similarly to treatment. No differences in age, sex, race, TP53 levels, and MGMT methylation status were observed between the groups. Gene expression analyses revealed that MAPK8, a p53 activator, negatively correlates with TP53 in the wt group. Moreover, ~21% of p53 regulators are differentially expressed between wt and mutant patients. Among these, we selected SIRT3, a p53 repressor, for experimental validation. We verified the p53 genetic status of PDCs through western blot and confirmed Sirt3 expression in wt and mutant TP53 PDCs. By CRISPR-Cas9-based genomic editing, we are currently evaluating the impact of SIRT3 knockout in GBM-PDC and patient-derived xenografts. We expect that SIRT3 knockout will restore p53 function in the wt group. In conclusion, our results suggest that p53 function is impaired in wt patients and provide insights into mechanisms affecting its activity.

Integrating transcriptomic data and digital pathology for NRG-based prediction of prognosis and therapy response in gastric cancer

Background Cancer is characterized by its ability to resist cell death, and emerging evidence suggests a potential correlation between non-apoptotic regulated cell death (RCD), tumor progression, and therapy response. However, the prognostic significance of non-apoptotic RCD-related genes (NRGs) and their relationships with immune response in gastric cancer (GC) remain unclear. Methods In this study, RNA-seq gene expression and clinical information of GC patients were acquired from The Cancer Genome Atlas and the Gene Expression Omnibus databases. Cox and LASSO regression analyses were used to construct the NRG signature. Moreover, we developed a deep learning model based on ResNet50 to predict the NRG signature from digital pathology slides. The expression of signature hub genes was validated using real-time quantitative PCR and single-cell RNA sequencing data. Results We identified 13 NRGs as signature genes for predicting the prognosis of patients with GC. The high-risk group, characterized by higher NRG scores, demonstrated a shorter overall survival rate, increased immunosuppressive cell infiltration, and immune dysfunction. Moreover, associations were observed between the NRG signature and chemotherapeutic drug responsiveness, as well as immunotherapy effectiveness in GC patients. Furthermore, the deep learning model effectively stratified GC patients based on the NRG signature by leveraging morphological variances, showing promising results for the classification of GC patients. Validation experiments demonstrated that the expression level of SERPINE1 was significantly upregulated in GC, while the expression levels of GPX3 and APOD were significantly downregulated. Conclusion The NRG signature and its deep learning model have significant clinical implications, highlighting the importance of individualized treatment strategies based on GC subtyping. These findings provide valuable insights for guiding clinical decision-making and treatment approaches for GC.

PRMT5/WDR77 Enhances the Proliferation of Squamous Cell Carcinoma via the ΔNp63α-p21 Axis

Protein arginine methyltransferase 5 (PRMT5) is a critical oncogenic factor in various cancers, and its inhibition has shown promise in suppressing tumor growth. However, the role of PRMT5 in squamous cell carcinoma (SCC) remains largely unexplored. In this study, we analyzed SCC patient data from The Cancer Genome Atlas (TCGA) and the Cancer Dependency Map (DepMap) to investigate the relationship between PRMT5 and SCC proliferation. We employed competition-based cell proliferation assays, 3-(4,5-dimethylthiazol-2-yl)-2,5-diphenyl-2H-tetrazolium bromide (MTT) assays, flow cytometry, and in vivo mouse modeling to examine the regulatory roles of PRMT5 and its binding partner WDR77 (WD repeat domain 77). We identified downstream targets, including the p63 isoform ΔNp63α and the cyclin-dependent kinase inhibitor p21, through single-cell RNA-seq, RT-qPCR, and Western blot analyses. Our findings demonstrate that upregulation of PRMT5 and WDR77 correlates with the poor survival of head and neck squamous cell carcinoma (HNSCC) patients. PRMT5/WDR77 regulates the HNSCC-specific transcriptome and facilitates SCC proliferation by promoting cell cycle progression. The PRMT5 and WDR77 stabilize the ΔNp63α Protein, which in turn, inhibits p21. Moreover, depletion of PRMT5 and WDR77 repress SCC in vivo. This study reveals for the first time that PRMT5 and WDR77 synergize to promote SCC proliferation via the ΔNp63α-p21 axis, highlighting a novel therapeutic target for SCC.

Post-transcriptional control drives Aurora kinase A expression in human cancers

Aurora kinase A (AURKA) is a major regulator of the cell cycle. A prominent association exists between high expression of AURKA and cancer, and impairment of AURKA levels can trigger its oncogenic activity. In order to explore the contribution of post-transcriptional regulation to AURKA expression in different cancers, we carried out a meta-analysis of -omics data of 18 cancer types from The Cancer Genome Atlas (TCGA). Our study confirmed a general trend for increased AURKA mRNA in cancer compared to normal tissues and revealed that AURKA expression is highly dependent on post-transcriptional control in several cancers. Correlation and clustering analyses of AURKA mRNA and protein expression, and expression of AURKA-targeting hsa-let-7a miRNA, unveiled that hsa-let-7a is likely involved to varying extents in controlling AURKA expression in cancers. We then measured differences in the short/long ratio (SLR) of the two alternative cleavage and polyadenylation (APA) isoforms of AURKA mRNA across cancers compared to the respective healthy counterparts. We suggest that the interplay between APA and hsa-let-7a targeting of AURKA mRNA may influence AURKA expression in some cancers. hsa-let-7a and APA may also independently contribute to altered AURKA levels. Therefore, we argue that AURKA mRNA and protein expression are often discordant in cancer as a result of dynamic post-transcriptional regulation.