Pan-cancer Dataset Research Articles

EPCO-03. A COMPREHENSIVE SINGLE-CELL ATLAS OF SCHWANNOMA REVEALS CONSERVED CELLULAR STATES ACROSS DIFFERENT GENETIC BACKGROUNDS AND ANATOMIC LOCATIONS

Abstract BACKGROUND Despite being associated with molecular alterations in the NF2 gene, there is limited understanding of the gene expression programs that govern the behavior of schwannomas both in sporadic cases as well as in patients with schwannoma predisposition syndromes, and also with respect to their anatomic location. METHODS We characterized the transcriptional heterogeneity of schwannomas across different genetic backgrounds and anatomic locations at the single-cell level. Tumor samples from patients undergoing resection for symptomatic schwannoma were dissociated and single cells were processed for full-transcript whole transcriptome profiling via Smart-seq2. Our cohort of 22 schwannomas, includes 5 tumors from schwannomatosis patients, 11 tumors from neurofibromatosis type 2 patients, and 6 sporadic tumors – 3 of which had grown after radiation treatment. Anatomically, 12 tumors were excised from cranial nerves (11 vestibular, 1 accessory) and 10 from spinal and other peripheral nerves. RESULTS We profiled 11,373 cells, including 4725 neoplastic cells, 2425 fibroblasts, 1314 T cells, 1258 endothelial cells, 969 myeloid cells, 503 pericytes and 240 Schwann cells. Copy number alteration analyses reveal evidence of intratumoral polyclonality (supporting the hypothesis of multiple sites of origin for a tumor). Using non-negative matrix factorization, we identify 6 distinct transcriptional metaprograms, with gene signatures related to stress, myelin production, antigen presentation, interferon signaling, glycolysis and extracellular matrix. After comparison with a pan-cancer gene expression dataset, we note unique interferon and antigen presentation metaprograms in schwannoma, with the latter being mainly characterized by expression of MHC Class II genes. Overall, we observe conserved expression of these metaprograms across anatomic locations and genetic backgrounds, with the exception of decreased interferon signaling for vestibular tumors. CONCLUSION We provide a comprehensive single-cell characterization of schwannoma spanning diverse genetic and anatomic backgrounds. We describe unique transcriptional metaprograms for antigen presentation and interferon signaling, which might underlie mechanisms for immune evasion.

DCLRE1B promotes tumor progression and predicts immunotherapy response through METTL3-mediated m6A modification in pancreatic cancer

BackgroundDCLRE1B is a 5’-to-3’ exonuclease, which is involved in repairing ICL-related DNA damage. DCLRE1B has been reported to cause poor prognosis in a variety of cancers. Nonetheless, there is no research on DCLRE1B’s biological role in pan-cancer datasets. Thus, ascertaining the processes via which DCLRE1B modulates tumorigenesis was the goal of the extensive bioinformatics investigation of pan-cancer datasets in the present research.MethodsIn our research, employing internet websites and databases including TIMER, GEPIA, TISIDB, Kaplan–Meier Plotter, SangerBox, cBioPortal, and LinkedOmics, DCLRE1B-related data in numerous tumors were extracted. To ascertain the association among DCLRE1B expression, prognosis, genetic changes, and tumor immunity, the pan-cancer datasets were examined. The DCLRE1B’s biological roles in pancreatic cancer cells were ascertained by employing wound healing, in vitro CCK-8, and MeRIP-qPCR assays.ResultAccording to the pan-cancer analysis, in numerous solid tumors, DCLRE1B upregulation was observed. Expression of DCLRE1B was found to be substantially related to the cancer patients’ prognoses. Similarly, expression of DCLRE1B exhibited substantial association with immune cells in several cancer types. DCLRE1B expression correlated with immune checkpoint (ICP) gene expression and impacted immunotherapy sensitivity. According to in vitro trials, DCLRE1B promoted PC cells’ proliferation and migration capacities. Also, according to GSEA enrichment analysis, DCLRE1B might participate in the JAK-STAT signaling pathway, which was confirmed by western blotting. In addition, we also found that the downregulation of DCLRE1B may be regulated by METTL3-mediated m6A modification.ConclusionsIn human cancer, the overexpression of DCLRE1B was generally observed, which aided cancer onset and advancement via a variety of processes comprising control of the immune cells’ tumor infiltration. According to this study’s findings, in a few malignant tumors, DCLRE1B is a candidate immunotherapeutic and prognostic biomarker.

Multiomics dynamic learning enables personalized diagnosis and prognosis for pancancer and cancer subtypes.

Artificial intelligence (AI) approaches in cancer analysis typically utilize a 'one-size-fits-all' methodology characterizing average patient responses. This manner neglects the diverse conditions in the pancancer and cancer subtypes of individual patients, resulting in suboptimal outcomes in diagnosis and treatment. To overcome this limitation, we shift from a blanket application of statistics to a focus on the explicit recognition of patient-specific abnormalities. Our objective is to use multiomics data to empower clinicians with personalized molecular descriptions that allow for customized diagnosis and interventions. Here, we propose a highly trustworthy multiomics learning (HTML) framework that employs multiomics self-adaptive dynamic learning to process each sample with data-dependent architectures and computational flows, ensuring personalized and trustworthy patient-centering of cancer diagnosis and prognosis. Extensive testing on a 33-type pancancer dataset and 12 cancer subtype datasets underscored the superior performance of HTML compared with static-architecture-based methods. Our findings also highlighting the potential of HTML in elucidating complex biological pathogenesis and paving the way for improved patient-specific care in cancer treatment.

Computational drug prediction in hepatoblastoma by integrating pan-cancer transcriptomics with pharmacological response.

Hepatoblastoma (HB) is the predominant form of pediatric liver cancer, though it remains exceptionally rare. While treatment outcomes for children with HB have improved, patients with advanced tumors face limited therapeutic choices. Additionally, survivors often suffer from long-term adverse effects due to treatment, including ototoxicity, cardiotoxicity, delayed growth, and secondary tumors. Consequently, there is a pressing need to identify new and effective therapeutic strategies for patients with HB. Computational methods to predict drug sensitivity from a tumor's transcriptome have been successfully applied for some common adult malignancies, but specific efforts in pediatric cancers are lacking because of the paucity of data. In this study, we used DrugSense to assess drug efficacy in patients with HB, particularly those with the aggressive C2 subtype associated with poor clinical outcomes. Our method relied on publicly available collections of pan-cancer transcriptional profiles and drug responses across 36 tumor types and 495 compounds. The drugs predicted to be most effective were experimentally validated using patient-derived xenograft models of HB grown in vitro and in vivo. We thus identified 2 cyclin-dependent kinase 9 inhibitors, alvocidib and dinaciclib as potent HB growth inhibitors for the high-risk C2 molecular subtype. We also found that in a cohort of 46 patients with HB, high cyclin-dependent kinase 9 tumor expression was significantly associated with poor prognosis. Our work proves the usefulness of computational methods trained on pan-cancer data sets to reposition drugs in rare pediatric cancers such as HB, and to help clinicians in choosing the best treatment options for their patients.

Pan-Cancer Analysis of Copy-Number Features Identifies Recurrent Signatures and a Homologous Recombination Deficiency Biomarker to Predict Poly (ADP-Ribose) Polymerase Inhibitor Response.

Copy-number (CN) features reveal the molecular state of cancers and may have predictive and prognostic value in the treatment of cancer. We sought to apply published CN analysis methods to a large pan-cancer data set and characterize ubiquitous CN signatures across tumor types, including potential utility for treatment selection. We analyzed the landscape of CN features in 260,333 pan-cancer samples. We examined the association of 10 signatures with genomic alterations and clinical characteristics and trained a machine learning classifier using CN and insertion and deletion features to detect homologous recombination deficiency signature (HRDsig) positivity. Clinical outcomes were assessed using a real-world clinicogenomic database (CGDB) of comprehensive genomic profiling linked to deidentified, electronic health record-derived clinical data. CN signatures were prevalent across cancer types and associated with diverse processes including focal tandem duplications, seismic amplifications, genome-wide loss of heterozygosity (gLOH), and HRD. Our novel HRDsig outperformed gLOH in predicting BRCAness and effectively distinguished biallelic BRCA and homologous recombination-repair wild-type (HRRwt) samples pan-tumor, demonstrating high sensitivity to detect biallelic BRCA in ovarian (93%) and other HRD-associated cancers (80%-87%). Pan-tumor prevalence of HRDsig was 6.4%. HRRwt cases represented a significant fraction of the HRDsig-positive cohort, likely reflecting a population with nongenomic mechanisms of HRD. In ovarian and prostate CGDBs, HRDsig identified more patients than gLOH and had predictive value for poly (ADP-ribose) polymerase inhibitor (PARPi) benefit. Tumor CN profiles are informative, revealing diverse processes active in cancer. We describe the landscape of 10 CN signatures in a large pan-cancer cohort, including two associated with HRD. We trained a machine learning-based HRDsig that robustly identified BRCAness and associated with biallelic BRCA pan-tumor, and was predictive of PARPi benefit in real-world ovarian and prostate data sets.

Selenium metabolism heterogeneity in pan-cancer: insights from bulk and single-cell RNA sequencing.

Selenium, a natural microelement with both nutritional and toxicological properties, is intertwined with tumorigenesis and progression. However, it is not fully understood how selenium metabolism affects immune response and cancer biology. We estimated selenium metabolism by Gene Set Enrichment Analysis (GSEA) to delineate the selenium metabolism landscape using The Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), Cancer Cell Line Encyclopedia (CCLE) and a integrated pan-cancer single-cell dataset. We systematically explored the prognostic implications of selenium metabolism and selenium-related regulatory patterns. The therapeutic value of selenium metabolism was explored through machine learning and examined in several immunotherapy cohorts. The heterogeneity and underlying mechanism of selenium metabolism were investigated by cell‒cell communication analysis at the single-cell level. A GSEA analysis based on 86 genes was used to evaluate the selenium metabolism landscape. The selenium metabolism score exhibited prognostic value in predicting the lower risk of mortality, possibly due to its correlation with multiple cancer hallmarks, including a positive correlation with complement (R = 0.761, P < 0.001), inflammatory response (R = 0.663, P < 0.001), apoptosis (R = 0.626, P < 0.001), hypoxia (R = 0.587, P < 0.001), reactive oxygen species (ROS) (R = 0.558, P < 0.001), and interferon gamma response (R = 0.539, P < 0.001). We also observed heterogeneity in the relationship between selenium metabolism and immunity across different cancers. Based on selenium-related genes, we constructed a machine learning model with area under the ROC curve (AUC) of 0.82 in predicting immune checkpoint inhibitor (ICI)-based immunotherapy response. Single-cell selenium metabolism quantification revealed that adjacent and tumor tissues had higher selenium metabolism compared with normal tissues, especially in epithelial cells, fibroblasts and macrophages. The communication between high-selenium epithelium and high-selenium fibroblast was significantly higher than other cells, especially in cytokines, chemokines, collagen, Wnt, VEGF, IGF and FGF pathways. Our study provides a comprehensive landscape of selenium metabolism levels and diverse regulatory patterns in different cancers, deepening the understanding of selenium's roles in tumorigenesis and immunity.

Increased KRAS G12C Prevalence, High Tumor Mutational Burden, and Specific Mutational Signatures Are Associated With MUTYH Mutations: A Pan-Cancer Analysis.

The aim of this study was to determine the pan-cancer landscape of MUTYH alterations and the relationship between MUTYH mutations and potentially actionable biomarkers such as specific genomic alterations, tumor mutational burden, and mutational signatures. We used a large pan-cancer comprehensive genomic dataset from patients profiled (tissue next generation sequencing) during routine clinical care. Overall, 2.8% of 229 120 solid tumors had MUTYH alterations, of which 55% were predicted germline. Thirty tumor types had a 2% or greater MUTYH mutation rate. MUTYH-altered versus -WT cancers had significantly higher tumor mutational burden and more frequent alterations in KRAS G12C, but not in KRAS in general; these observations were statistically significant, especially in colorectal cancers. Across cancers, PD-L1 expression levels (immunohistochemistry) were not associated with MUTYH alteration status. In silico computation demonstrated that MUTYH mutational signatures are associated with higher levels of hydrophobicity (which may reflect higher immunogenicity of neoantigens) relative to several other signature types such as microsatellite instability. Survival of patients with MUTYH-altered versus -WT tumors was similar. In conclusion, comprehensive genomic profiling suggests that several features of MUTYH-altered cancers may be pharmacologically targetable. Drugs such as sotorasib (targeting KRAS G12C) and immune checkpoint inhibitors, targeting the increased mutational load and higher neo-antigen hydrophobicity/immunogenicity merit investigation in MUTYH-mutated malignancies.

Eph receptor B6 shapes a cold immune microenvironment, inhibiting anti-cancer immunity and immunotherapy response in bladder cancer.

The role of Eph receptors and related ephrin (EFN) ligands (as the largest family of transmembrane-bound RTKs) in immunomodulation in many types of cancer, especially bladder cancer (BLCA), is scarcely known. A pan-cancer dataset was retrieved from The Cancer Genome Atlas (TCGA) to explore the relation between Eph receptor/EFN ligand family genes and immunomodulators and tumor-infiltrated immune cells (TIICs). Local BLCA, GSE32894, and GSE31684 cohorts were applied to validate. The IMvigor210 cohort was employed to explore the relationship between EPHB6 and immunotherapy response. Moreover, association between EPHB6 and molecular subtype was investigated to explore potential therapeutic strategies. Immunohistochemical staining of CD8 and CD68 was performed to validate the correlation between EPHB6 and TIICs. The pan-cancer analysis revealed variations in the immunological effects of Eph receptor/EFN ligand family genes across different types of cancer. EPHB6 expression negatively correlated with the expression of the majority of immunomodulators (including HLA and immune checkpoints), and CD8 T cells and macrophages in both the TCGA-BLCA and validation BLCA cohorts, shaping a cold immune microenvironment with inhibited immunity. In the IMvigor210 cohort, patients with high-EPHB6 highly correlated with a non-inflamed, low PD-L1 expression immune phenotype, and correspondingly, with less responders to immunotherapy. The high-EPHB6 group, enriched with the basal subtype, presented significantly fewer TP53 and more FGFR3 genomic alterations. Finally, a novel EPHB6-related Genes signature, with reliable and robust ability in prognosis prediction, was constructed. This study comprehensively investigated the immunological effects of Eph receptor/EFN ligand family genes pan-cancer, and specially identified the immunosuppressive role of EPHB6 in BLCA. Furthermore, EPHB6 may predict the molecular subtype and prognosis of BLCA, and serve as a novel therapeutic target to improve the sensitivity of immunotherapy.

Impact of apolipoprotein A1 on tumor immune microenvironment, clinical prognosis and genomic landscape in hepatocellular carcinoma.

Current knowledge on apolipoprotein A1 (APOA1) in hepatocellular carcinoma (HCC) is fragmented and even contradictory. Multi-dimensional analyses are required to comprehensively elucidate its value and underlying mechanism. We collected 49 RNA-seq datasets, 40 cell line types data and 70 scRNA pan-cancer datasets public available, including 17 HCC datasets (1754 tumor samples), and enrolled 73 pairs of HCC tissue and 516 blood samples independently from our clinics. APOA1 impacting on the HCC tumor microenvironment (TME) was analyzed using intensive data mining. Methylation sequencing, flow cytometry, quantitative PCR, western blot, immunohistochemistry and clinical chemistry assays were conducted for wet experimental investigation. The APOA1 ontology fingerprint indicated that it played various crucial biological roles in HCC, primarily involved in cholesterol efflux. Consistent findings at histology, serology, and clinical follow-up revealed that high APOA1 was a good prognosis indicator of HCC. Hypermethylation in the APOA1 promoter region was found in clinical samples which is in accordance with the reduction of APOA1 in HCC. The cell cycle, DNA replication, mismatch repair pathways, and tumor cell proliferation were less observed in the HCC APOA1high subgroup. The favorable immunoregulatory abilities of APOA1 showed interesting findings: a positive correlation between APOA1 and anti-tumor immune cells (NK, CD8+T cells) and a negative association with immune cells exerting immunosuppressive effects, including M2 macrophages. This is an integrative multidimensional exploration of APOA1 using bioinformatics and experiments. Both the prognostic value and anti-tumor effects based on APOA1 panoramic exploration in the HCC TME demonstrate a new potential clinical target for HCC assessment and intervention in the future.

A pan-cancer analysis of the oncogenic and immunological roles of apolipoprotein F (APOF) in human cancer

BackgroundApolipoprotein F (APOF) has been less studied in cancers. Thus, we aimed to perform a pan-cancer analysis of the oncogenic and immunological effects of APOF on human cancer.MethodsA standardized TCGA pan-cancer dataset was downloaded. Differential expression, clinical prognosis, genetic mutations, immune infiltration, epigenetic modifications, tumor stemness and heterogeneity were analyzed. We conducted all analyses through software R (version 3.6.3) and its suitable packages.ResultsOverall, we found that the common cancers differentially expressed between tumor and normal samples and prognostic-associated were BRCA, PRAD, KIRP, and LIHC in terms of overall survival (OS), disease-free survival (DFS) and progression-free survival (PFS). The pan-cancer Spearman analysis showed that the mRNA expression of APOF was negatively correlated with four tumor stemness indexes (DMPss, DNAss, ENHss, and EREG-METHss) with statistical significance for PRAD and was positively correlated for LIHC. In terms of BRCA and PRAD patients, we found negative correlation of APOF with TMB, MSI, neo, HRD and LOH. The mutation frequencies of BRCA and LIHC were 0.3%. APOF expression was negatively correlated with immune infiltration and positively correlated with tumor purity for PRAD patients. The mRNA expression of APOF was negatively associated with most TILs for LIHC, B cells, CD4+ T cells, neutrophils, macrophages and dendritic cells, but was positively associated with CD8+ T cells.ConclusionsOur pan-cancer study offered a relatively comprehensive understanding of the roles of APOF on BRCA, PRAD, KIRP, and LIHC.

Artificial intelligence (AI) –powered spatial analysis of macrophages in tumor microenvironment and its association with interferon-gamma (IFNG) signature and immune phenotype (IP) in pan-cancer dataset.

2621 Background: Macrophages are known to play a crucial role in cancer progression, but few studies have reported macrophage spatial distribution and its association with genomic signatures and tumor-infiltrating lymphocytes (TIL). Here, we applied an AI-powered Whole Slide Image (WSI) analyzer in The Cancer Genome Atlas (TCGA) pan-cancer dataset to show correlation between specific localization of macrophage and anti-tumor activity, which is reflected by IFNG signature and IP based on spatial TIL density. Methods: For spatial macrophage analysis in TCGA pan-cancer dataset, we used Lunit SCOPE IO, an AI-powered H&amp;E WSI analyzer, which identifies and quantifies various classes of cells such as tumor cell, macrophage, and TIL within cancer or stroma area. IPs were defined as follows: inflamed IP (IIP) as high intratumoral TIL (iTIL) and stromal TIL (sTIL); immune-excluded IP (IEP) as low iTIL and high sTIL; immune-desert IP (IDP) as low TIL overall. The immune cell profiling and quantification of the IFNG signature score were analyzed by using CIBERSORT and RSEM. Results: In TCGA dataset (N = 7,402), median macrophage density in total TME area (tMP) was 19.9 /mm2 (interquartile range [IQR] 9.0-44.9), and it was highly enriched in lung adenocarcinoma (median 65.1, IQR 34.3-110.0), lung squamous cell carcinoma (61, 32.4-101.3), and head and neck squamous cell carcinoma (39.4, 19.6-77.3). Based on IP classification, tMP was highly enriched in IIP (37.8, 18.7-73.8) compared to IEP (25.5, 13.8-48.6) and IDP (8.9, 4.4-17.1). Interestingly, spatial analysis showed that intratumoral macrophage density (iMP) in IIP was highly enriched compared to the others (mean fold change 2.57) and the difference of stromal macrophage density (sMP) among IPs was relatively smaller (mean fold change x1.85). Classified into four groups based on median values of iMP and sMP, high-iMP/high-sMP group had the highest proportion of IIP (47.3% vs 18.7%, p &lt; 0.001), whereas low-iMP/high-sMP group had the highest one of IEP (54.9% vs 32.3%, p &lt; 0.001) compared to the others, respectively. Moreover, the high-iMP/high-sMP group had the highest level of IFNG signature score among 4 groups (x2.13 times higher, p &lt; 0.001). Notably, both iMP and sMP were significantly correlated with CD68 expression (rho 0.292 and 0.287) but CSF1R expression was more strongly correlated with iMP (rho 0.259) compared to sMP (rho 0.078). Conclusions: IIP, IFNG signature, and CSF1R expression are more strongly correlated with intratumoral macrophage density versus stromal macrophage density, suggesting a relationship between macrophage location and function in the TME warranting further investigation.

Exploring expression levels of HER2, HER3, MET, Claudin18.2, and MUC16 across 16 cancer types using an artificial intelligence-powered immunohistochemistry analyzer.

3135 Background: Quantifying tumor-associated antigen (TAA) expression levels on tumor cells (TC) from immunohistochemistry (IHC) stained slides has been widely used to predict response to novel anti-tumor agents. However, obtaining the precise quantification of TAA expression levels from various cancer types is still challenging. To quantify five targets of interest, we applied an artificial intelligence (AI)-powered analyzer to 16 cancer types. Methods: AI-powered whole slide image (WSI) analyzers were developed using IHC positive or negative tumor cells (TC) from PD-L1 22C3 pharmDx and HER2 4B5 datasets, annotated by pathologists. The universal model was trained by IHC-positive or negative TCs from all datasets, while the second model was trained by the intensity of positive (from 1+ to 3+) or negative TCs from the HER2 dataset. Both AI models were applied to the pan-cancer tissue microarray dataset (n = 1,370) with MUC16, CLDN18.2, MET, HER3, and HER2 IHC staining, respectively. The expression levels of those TAAs were calculated with tumor proportion score (TPS) or ASCO/CAP scoring criteria for HER2 in 16 cancer types including biliary tract, breast, colorectum, esophagus, head and neck, kidney, liver, lung, ovary, bladder, pancreas, prostate, stomach, thyroid, cervical, and endometrial cancer. Results: Based on the TPS of 1% or more, the positivity of MUC16 and CLDN18.2 were most frequently observed in ovary (51.6%) and kidney (24.2%), respectively, and MET and HER3 in colorectum (63.8%; 40.5%). The five most highly expressed tumors and their frequency of TPS≥1% are shown. HER2 3+ tumor cells were most highly expressed in the breast, followed by the bladder. Conclusions: This study describes the TAA expression levels in various tumors using an AI-powered IHC analyzer. This method could be useful for exploring target cancer types and predicting response to novel TAA targeted agents. [Table: see text]

Amplification of wild-type RET and clinical response to selpercatinib for non–small-cell lung cancer (NSCLC).

9123 Background: RET rearrangements and RET kinase domain mutations represent targetable genomic alterations in various cancer types. However, the frequency and characteristics of RET amplification in cancer and its potential role as a targetable oncogenic driver are not well-characterized. Methods: In a pan-cancer cohort of patients at the Dana-Farber Cancer Institute (DFCI) whose tumors underwent next-generation sequencing (NGS), we evaluated the frequency of wild-type (wt) RET amplification (defined as ≥6 copies) in the absence of RET rearrangements or activating mutations. We validated our findings using merged data from the TCGA, GENIE, and China Pan-Cancer datasets. Results: The frequency of wt RET amplification across all cancers was 0.08% (28/34,463) in the DFCI cohort and 0.16% (145/91,466) in the validation cohort. In NSCLC, the frequency of RET amplification was 0.10% (5/4773) in the DFCI cohort and 0.13% (15/11,622) in the validation cohort. Other cancer types with RET amplification included hepatobiliary cancer, prostate cancer, and breast cancer, among others. For 22 RET-amplified cases with available copy number data , the median RET gene copy number was 7 (range 6-36). Among 20 RET-amplified NSCLCs, 10 had no other known driver mutations, and 10 had concurrent driver mutations (including MET exon 14 skipping, MET amplification, KRAS G12C/S/D, EGFR L858R, EGFR exon 20 insertion, and EGFR exon 19 deletion). In cases with available smoking status, 5 out of 6 patients with RET-amplified NSCLC had a history of tobacco use. A 69-year-old man with unresectable stage III NSCLC had disease recurrence after chemoradiation and durvalumab. Both the initial and recurrent biopsy samples showed focal high-level RET amplification (22 and 28 copies, respectively); genomic and RNA sequencing showed no other driver mutations or RET rearrangements. He experienced partial response to the selective RET inhibitor selpercatinib with decrease of both intracranial and systemic tumor burden. Conclusions: Amplification of wt RET represents a novel, targetable, rare molecular subset of NSCLC and other cancers.

Hallmarks of transcriptional intratumour heterogeneity across a thousand tumours.

Each tumour contains diverse cellular states that underlie intratumour heterogeneity (ITH), a central challenge of cancer therapeutics1. Dozens of recent studies have begun to describe ITH by single-cell RNA sequencing, but each study typically profiled only a small number of tumours and provided a narrow view of transcriptional ITH2. Here we curate, annotate and integrate the data from 77 different studies to reveal the patterns of transcriptional ITH across 1,163 tumour samples covering 24 tumour types. Among the malignant cells, we identify 41 consensus meta-programs, each consisting of dozens of genes that are coordinately upregulated in subpopulations of cells within many tumours. The meta-programs cover diverse cellular processes including both generic (for example, cell cycle and stress) and lineage-specific patterns that we map into 11 hallmarks of transcriptional ITH. Most meta-programs of carcinoma cells are similar to those identified in non-malignant epithelial cells, suggesting that a large fraction of malignant ITH programs are variable even before oncogenesis, reflecting the biology of their cell of origin. We further extended the meta-program analysis to six common non-malignant cell types and utilize these to map cell-cell interactions within the tumour microenvironment. In summary, we have assembled a comprehensive pan-cancer single-cell RNA-sequencing dataset, which is available through the Curated Cancer Cell Atlas website, and leveraged this dataset to carry out a systematic characterization of transcriptional ITH.

The prognostic value and immunological role of CD44 in pan-cancer study

To investigate the correlation between cluster of differentiation-44 (CD44) expression and immunotherapy response and identify its possible predictive value in pan-cancer. Datasets of 33 cancer types from The Cancer Genome Atlas (TCGA) database were applied to investigate the relationship of CD44 expression with prognosis, tumor mutational burden (TMB), and microsatellite instability (MSI), and determine its potential prognostic value in pan-cancer. Patients were split into high-risk and low-risk cancer groups based on the survival outcomes of various cancer types. Additionally, the underlying mechanisms of CD44 in the tumor microenvironment (TME) were analyzed using ESTIMATE and CIBERSORT algorithms and Gene Set Enrichment Analysis (GSEA). Subsequently, the biological role of CD44 at single-cell level was investigated using CancerSEA database. Variable expression levels of CD44 between tumor and adjacent normal tissues were identified in pan-cancer datasets, further survival analysis revealed that CD44 expression was associated with multiple clinical annotations and survival indicators. Besides, the expression of CD44 was significantly associated with TMB and MSI in 10 types and 6 types of cancer, respectively, indicating it could be exploited as a potential biomarker predicting immunotherapy outcomes. Meanwhile, CD44 could influence several crucial immune cell-related pathways. and the results revealed by CancerSEA database denoted the correlation of CD44 with malignant phenotype and functional states, further indicating it can serve as a potential therapeutic target in cancer management. Our study demonstrated that CD44 shows great promise as a prognostic biomarker in numerous cancers, which will assist in developing new strategies in cancer management.

Abstract 3132: CLUMPS-PTM: Spatial clustering of post-translational modifications across cancer types

Abstract Post-translational modifications (PTMs) play key roles in regulating cell signaling in health and disease processes. Recent advances in mass spectrometry enable comprehensive investigation of PTMs. However, identifying functionally relevant PTMs and protein domains implicated in disease remains a key challenge. We present CLUMPS-PTM, an algorithm for spatial clustering of PTMs to propose functional domains. We apply CLUMPS-PTM to the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset (one of the largest proteogenomic pan-cancer PTM datasets) comprising 1110 samples spanning 11 tumor types. First, we map over 14K phospho-sites and 13K acetyl-sites detected in the CPTAC dataset to protein data bank (PDB) files. We expand this to the predicted AlphaFold proteome and recover additional 12K phospho-sites and 10K acetyl-sites exceeding model confidence (pLDDT) of 70%. Consistent with previous research, phospho-sites are found on residues with lower AlphaFold prediction confidence than acetyl-sites due to their abundance in unstructured domains (p-value &amp;lt; 1e-4). To identify functionally relevant protein domains in the pan-cancer context, we characterize the 1110 samples by applying SignatureAnalyzer, a Bayesian non-negative matrix factorization method, to discover multi-omic patterns. This analysis identifies strong signatures associated with DNA damage response (DDR) and stratifies the CPTAC cohort into DDR-high (n=442) and DDR-low (n=668) groups. Next, we applied CLUMPS-PTM to differentially modified phospho/acetyl-sites between these groups. We identified 22 proteins (including GMPS, NCBP1, and ANPEP (CD13)) with significant clusters of differentially phosphorylated sites (q-value &amp;lt; 0.1) in DDR-high samples. ANPEP is a zinc-dependent aminopeptidase with a significant cluster (q-value = 0.0129) of differentially phosphorylated sites in the ERAP1-C domain that faces peptidase active sites known to have proliferative activity via the MAPK pathway. CLUMPS-PTM also identified a significant cluster of acetyl-sites on ARID1A (a SWI/SNF chromatin remodeler known to be mutated in cancer) in DDR-high samples (q-value = 0.085). The ARID1A cluster is on the C-terminal tail of the protein within the glucocorticoid receptor (GR) domain, an upstream regulator of proteins that increase catabolism, reduce inflammation, and increase cell survival. Increased acetylation may block a ubiquitination site at the C-terminus of the protein, resulting in increased protein levels, likely due to reduced ARID1A degradation, and increased GR signaling. Overall, CLUMPS-PTM is an open-source tool that allows near proteome-wide spatial analysis with the growing availability of PTM data. We anticipate that it will be useful to the broader proteomic community for the discovery of novel targets and generation of insights into functional mechanisms. Citation Format: Shankara K. Anand, Yifat Geffen, Yo Akiyama, Francois Aguet, Gad Getz. CLUMPS-PTM: Spatial clustering of post-translational modifications across cancer types [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 3132.

Abstract 4877: Mutant or overexpressed SF3B1 facilitates endometrial cancer tumorigenesis

Abstract Aberrant expression or mutations in core splicing factors including U2AF1, SRSF2, ZRSR2, and SF3B1 found to drive tumorigenesis of various malignant tumors e.g., myelodysplastic syndromes, chronic lymphocytic leukemia, uveal melanoma, and breast cancer. Importantly, the amplifications and mutations in SF3B1, the pioneer splicing factor, are reportedly far more common than those in other splicing factors. However, the functional relevance and underlying mechanisms of dysregulated SF3B1 in endometrial cancer are not investigated. Endometrial cancer is the fourth most common cancer in women in the United States and there will be about 65,950 new endometrial cancer cases in the current year alone with 12,550 estimated cancer deaths in the United States. In this study, we thus investigated the role of SF3B1 in endometrial adenocarcinoma. We found SF3B1 is one of the most frequently amplified or mutated splicing factors in endometrial cancer. Specifically, analysis of The Cancer Genome Atlas (TCGA) uterine pan-cancer dataset revealed that up to 9% of endometrial cancers have SF3B1 mutations or amplification. Additionally, we showed that the SF3B1 protein is overexpressed in human as well as mouse endometrial tumor samples. Further analysis found elevated SF3B1 protein in multiple endometrial cancer cell lines even in the absence of SF3B1 mutations. However, we did not observe any altered expression at the transcript level. Further, we identified a correlation between the over-expressed SF3B1 levels and the tumorigenicity/invasive potential of cancer cell lines. For instance, endometrial cancer cell lines (Ishikawa, AN3CA, KLE) with over-expressed SF3B1 levels were much more tumorigenic and invasive than those with low endogenous SF3B1 levels (normal human endometrial epithelial cells, RL-95-2 cells). Moreover, cell viability assays revealed that Ishikawa, AN3CA, and KLE cells were more susceptible to the loss of SF3B1 or treatment with the SF3B1 inhibitor Pladienolide-B (PLAD-B) than RL-95-2 cells. To further ensure that overexpressed or mutated SF3B1 drives endometrial tumorigenesis, we transiently transfected an empty vector or a vector expressing FLAG-tagged wild-type SF3B1 or vector with mutated SF3B1 construct R957Q into RL-95-2 cells which have minimal endogenous SF3B1. Analysis of proliferation assays revealed that cells overexpressing SF3B1 proliferated significantly more than cells expressing empty vector. Intriguingly, we observed that RL-95-2 cells over-expressing R957Q mutants were even more proliferative than the cells expressing wild-type SF3B1. Together, this data suggest that SF3B1 over-expression and mutation play a crucial oncogenic role in endometrial cancer, and these findings may support the development of SF3B1 inhibitors to treat this disease. Citation Format: Pooja Popli, Xinchao Deng, Premal Thaker, Ramakrishna Kommagani. Mutant or overexpressed SF3B1 facilitates endometrial cancer tumorigenesis. [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 4877.

Systematic pan-cancer analysis identifies SLC31A1 as a biomarker in multiple tumor types

BackgroundSolute Carrier Family 31 Member 1 (SLC31A1) has recently been identified as a cuproptosis-regulatory gene. Recent studies have indicated that SLC31A1 may play a role in colorectal and lung cancer tumorigenesis. However, the role of SLC31A1 and its cuproptosis-regulatory functions in multiple tumor types remains to be further elucidated.MethodsOnline websites and datasets such as HPA, TIMER2, GEPIA, OncoVar, and cProSite were used to extract data on SLC31A1 in multiple cancers. DAVID and BioGRID were used to conduct functional analysis and construct the protein–protein interaction (PPI) network, respectively. The protein expression data of SLC31A1 was obtained from the cProSite database.ResultsThe Cancer Genome Atlas (TCGA) datasets showed increased SLC31A1 expression in tumor tissues compared with non-tumor tissues in most tumor types. In patients with tumor types including adrenocortical carcinoma, low-grade glioma, or mesothelioma, higher SLC31A1 expression was associated with shorter overall survival and disease-free survival. S105Y was the most prevalent point mutation in SLC31A1 in TCGA pan-cancer datasets. Moreover, SLC31A1 expression was positively correlated with the infiltration of immune cells such as macrophages and neutrophils in tumor tissues in several tumor types. Functional enrichment analysis showed that SLC31A1 co-expressed genes were involved in protein binding, integral components of the membrane, metabolic pathways, protein processing, and endoplasmic reticulum. Copper Chaperone For Superoxide Dismutase, Phosphatidylinositol-4,5-Bisphosphate 3-Kinase Catalytic Subunit Alpha and Solute Carrier Family 31 Member 2 were copper homeostasis-regulated genes shown in the PPI network, and their expression was positively correlated with SLC31A1. Analysis showed there was a correlation between SLC31A1 protein and mRNA in various tumors.ConclusionsThese findings demonstrated that SLC31A1 is associated with multiple tumor types and disease prognosis. SLC31A1 may be a potential key biomarker and therapeutic target in cancers.

CCNE1 is a predictive and immunotherapeutic indicator in various cancers including UCEC: a pan-cancer analysis

BackgroundCCNE1 plays an important oncogenic role in several tumors, especially high-stage serous ovarian cancer and endometrial cancer. Nevertheless, the fundamental function of CCNE1 has not been explored in multiple cancers. Therefore, bioinformatics analyses of pan-cancer datasets were carried out to explore how CCNE1 regulates tumorigenesis.MethodsA variety of online tools and cancer databases, including GEPIA2, SangerBox, LinkedOmics and cBioPortal, were applied to investigate the expression of CCNE1 across cancers. The pan-cancer datasets were used to search for links between CCNE1 expression and prognosis, DNA methylation, m6A level, genetic alterations, CCNE1-related genes, and tumor immunity. We verified that CCNE1 has biological functions in UCEC cell lines using CCK-8, EdU, and Transwell assays.ResultsIn patients with different tumor types, a high mRNA expression level of CCNE1 was related to a poor prognosis. Genes related to CCNE1 were connected to the cell cycle, metabolism, and DNA damage repair, according to GO and KEGG enrichment analyses. Genetic alterations of CCNE1, including duplications and deep mutations, have been observed in various cancers. Immune analysis revealed that CCNE1 had a strong correlation with TMB, MSI, neoantigen, and ICP in a variety of tumor types, and this correlation may have an impact on the sensitivity of various cancers to immunotherapy. CCK-8, EdU and Transwell assays suggested that CCNE1 knockdown can suppress UCEC cell proliferation, migration and invasion.ConclusionOur study demonstrated that CCNE1 is upregulated in multiple cancers in the TCGA database and may be a promising predictive biomarker for the immunotherapy response in some types of cancers. Moreover, CCNE1 knockdown can suppress the proliferation, migration and invasion of UCEC cells.

TLimmuno2: predicting MHC class II antigen immunogenicity through transfer learning.

Major histocompatibility complex (MHC) class II molecules play a pivotal role in antigen presentation and CD4+ T cell response. Accurate prediction of the immunogenicity of MHC class II-associated antigens is critical for vaccine design and cancer immunotherapies. However, current computational methods are limited by insufficient training data and algorithmic constraints, and the rules that govern which peptides are truly recognized by existing T cell receptors remain poorly understood. Here, we build a transfer learning-based, long short-term memory model named 'TLimmuno2' to predict whether epitope-MHC class II complex can elicit T cell response. Through leveraging binding affinity data, TLimmuno2 shows superior performance compared with existing models on independent validation datasets. TLimmuno2 can find real immunogenic neoantigen in real-world cancer immunotherapy data. The identification of significant MHC class II neoantigen-mediated immunoediting signal in the cancer genome atlas pan-cancer dataset further suggests the robustness of TLimmuno2 in identifying really immunogenic neoantigens that are undergoing negative selection during cancer evolution. Overall, TLimmuno2 is a powerful tool for the immunogenicity prediction of MHC class II presented epitopes and could promote the development of personalized immunotherapies.