Clinical Proteomic Tumor Analysis Consortium Research Articles

The cuproptosis-related gene UBE2D2 functions as an immunotherapeutic and prognostic biomarker in pan-cancer.

Cuproptosis, as a unique modality of regulated cell death, requires the involvement of ubiquitin-binding enzyme UBE2D2. However, the prognostic and immunotherapeutic values of UBE2D2 in pan-cancer remain largely unknown. Using UCSC Xena, TIMER, Clinical Proteomic Tumor Analysis Consortium (CPTAC), and Human Protein Atlas (HPA) databases, we aimed to explore the differential expression pattern of UBE2D2 across multiple cancer types and to evaluate its association with patient prognosis, clinical features, and genetic variations. The association between UBE2D2 and immunotherapy response was assessed by gene set enrichment analysis, tumor microenvironment, immune gene co-expression and drug half maximal inhibitory concentration (IC50) analysis. The mRNA and protein levels of UBE2D2 were markedly elevated in most cancer types, and UBE2D2 exhibited prognostic significance in liver hepatocellular carcinoma (LIHC), kidney chromophobe (KICH), uveal melanomas (UVM), cervical squamous cell carcinoma and endocervical adenocarcinoma (CESC), and kidney renal papillary cell carcinoma (KIRP). UBE2D2 expression was correlated with clinical features, tumor mutation burden, microsatellite instability, and anti-tumor drug resistance in several tumor types. Gene enrichment analysis showed that UBE2D2 was significantly associated with immune-related pathways. The expression level of UBE2D2 was correlated with immune cell infiltration, including CD4 + T cells、Macrophages M2、CD8 + T cells in pan-cancer. PDCD1, CD274 and CTLA4 expression levels were positively correlated with UBE2D2 level in multiple cancers. We comprehensively investigated the potential value of UBE2D2 as a prognostic and immunotherapeutic predictor for pan-cancer, providing a novel insight for cancer immunotherapy.

Pan-cancer analysis of prognostic and immunological role of IL4I1 in human tumors: a bulk omics research and single cell sequencing validation

BackgroundInterleukin-4 inducible gene 1 (IL4I1) regulates tumor progression in numerous tumor types. However, its correlation with immune infiltration and prognosis of patients in a pan-cancer setting remains unclear.MethodsData from the Cancer Genome Atlas (TCGA), Genotype-Tissue Expression (GTEx), UALCAN, Clinical Proteomic Tumor Analysis Consortium (CPTAC), Gene Expression Omnibus (GEO), cBioPortal, Cancer Single-cell State Atlas (CancerSEA), and Tumor IMmune Estimation Resource(TIMER) databases were used to evaluate IL4I1 expression, clinical features and prognostic effects, gene set enrichment, and correlation with immune cell infiltration, as well as the relationship between IL4I1 methylation and expression and survival prognosis. Correlations with 192 anticancer drugs were also analyzed.ResultsIL4I1 was significantly overexpressed in the majority of tumors, and the imbalance of IL4I1 was significantly correlated with overall survival and pathological stage. Moreover, total IL4I1 protein was increased in cancer. Therefore, IL4I1 may be used as a prognostic biomarker or protective factor in numerous types of cancer. The methylation level of IL4I1 may also be used as a prognostic marker. The functional enrichment of IL4I1 was closely related to the immunomodulatory pathway. In addition, the level of tumor-associated macrophage infiltration was positively correlated with the expression of IL4I1 in pan-cancerous tissues. scRNA-seq analysis suggested that IL4I1 differ significantly among different cells in the tumor microenvironment and was most enriched in macrophages. Various immune checkpoint genes were positively correlated with IL4I1 expression in most tumors. In addition, patients with high IL4I1 expression may be resistant to BMS-754807 and docetaxel, but sensitive to temozolomide.ConclusionIL4I1 may play a role as promoter of cancer and prognostic indicator in patients. High expression of IL4I1 is associated with the state of tumor immunosuppression and may contribute to tumor-associated macrophage invasion. Therefore, IL4I1 may be a new therapeutic target for the treatment and prognosis of patients with cancer.

Prediction of Responsiveness to PD-L1/PD-1 Inhibitors Using miRNA Profiles Associated With PD-L1 Expression in Lung Adenocarcinoma and Squamous Cell Carcinoma.

MicroRNAs (miRNAs) regulate programmed cell death ligand 1 (PD-L1) and play a crucial role in tumor immune response. However, the relationship between miRNA expression patterns and PD-L1 remains unclear in both lung adenocarcinoma (LUAD) and lung squamous cell carcinoma (LUSC). We investigated PD-L1-related miRNAs that can predict treatment response in patients treated with PD-L1/PD-1 inhibitors. We selected miRNAs that were correlated with PD-L1 expression within the LUAD and LUSC datasets obtained from The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC). We validated whether the miRNA profile could be used to predict the prognosis of patients treated with PD-L1/PD-1 inhibitors. Based on four public datasets, we selected 66 and 23 miRNAs associated with PD-L1 expression in LUAD and LUSC, respectively. From the above miRNAs, we identified 5 miRNAs in LUSC and 1 miRNA in LUAD that could predict the response to PD-L1/PD-1 inhibitors in a validation set of patients treated with PD-L1/PD-1 inhibitors. In LUSC, the miRNA profile exhibited a high predictive capability for the response to PD-L1/PD-1 treatment [area under the curve (AUC)=0.963] and accurately predicted prognosis (p=0.031). In LUAD, the miRNA profile was relatively less predictive than in LUSC (AUC=0.691 and p=0.213). Additionally, we observed variations in the PD-L1-associated miRNA profiles, as well as in the associated pathways, between LUAD and LUSC. The PD-L1-associated miRNA profile may predict treatment response in LUSC patients treated with PD-L1/PD-1 inhibitors and help select the PD-L1/PD-1 inhibitor treatment group.

TIMM17A overexpression in lung adenocarcinoma and its association with prognosis

Lung adenocarcinoma (LUAD), a leading cause of cancer-related mortality worldwide, demands a deeper understanding of its molecular mechanisms and the identification of reliable biomarkers for better diagnosis and targeted therapy. Leveraging data from the Cancer Genome Atlas (TCGA), the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and the Human Protein Atlas (HPA), we investigated the mRNA and protein expression profiles of TIMM17A and assessed its prognostic significance through Kaplan–Meier survival curves and Cox regression analysis. Through Gene Set Enrichment Analysis, we explored the regulatory mechanisms of TIMM17A in LUAD progression and demonstrated its role in modulating the proliferative capacity of A549 cells, a type of LUAD cell, via in vitro experiments. Our results indicate that TIMM17A is significantly upregulated in LUAD tissues, correlating with clinical staging, lymph node metastasis, overall survival, and progression-free survival, thereby establishing it as a critical independent prognostic factor. The construction of a nomogram model further enhances our ability to predict patient outcomes. Knockdown of TIMM17A inhibited the growth of LUAD cells. The potential of TIMM17A as a biomarker and therapeutic target for LUAD presents a promising pathway for improving patient diagnosis and treatment strategies.

Comprehensive Assessment of the Intrinsic Pancreatic Microbiome.

We sought to comprehensively profile tissue and cyst fluid in patients with benign, precancerous, and cancerous conditions of the pancreas to characterize the intrinsic pancreatic microbiome. Small studies in pancreatic ductal adenocarcinoma (PDAC) and intraductal papillary mucinous neoplasm (IPMN) have suggested that intra-pancreatic microbial dysbiosis may drive malignant transformation. Pancreatic samples were collected at the time of resection from 109 patients. Samples included tumor tissue (control, n=20; IPMN, n=20; PDAC, n=19) and pancreatic cyst fluid (IPMN, n=30; SCA, n=10; MCN, n=10). Assessment of bacterial DNA by quantitative PCR and 16S ribosomal RNA gene sequencing was performed. Downstream analyses determined the relative abundances of individual taxa between groups and compared intergroup diversity. Whole-genome sequencing data from 140 patients with PDAC in the National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium (CPTAC) were analyzed to validate findings. Sequencing of pancreatic tissue yielded few microbial reads regardless of diagnosis, and analysis of pancreatic tissue showed no difference in the abundance and composition of bacterial taxa between normal pancreas, IPMN, or PDAC groups. Low-grade dysplasia (LGD) and high-grade dysplasia (HGD) IPMN were characterized by low bacterial abundances with no difference in tissue composition and a slight increase in Pseudomonas and Sediminibacterium in HGD cyst fluid. Decontamination analysis using the CPTAC database confirmed a low-biomass, low-diversity intrinsic pancreatic microbiome that did not differ by pathology. Our analysis of the pancreatic microbiome demonstrated very low intrinsic biomass that is relatively conserved across diverse neoplastic conditions and thus unlikely to drive malignant transformation.

Zinc finger Protein207 orchestrates glioma migration through regulation of epithelial-mesenchymal transition.

Glioma represents the predominant primary malignant brain tumor. For several years, molecular profiling has been instrumental in the management and therapeutic stratification of glioma, providing a deeper understanding of its biological complexity. Accumulating evidence unveils the putative involvement of zinc finger proteins (ZNFs) in cancer. This study aimed to elucidate the role and significance of ZNF207 in glioma. Utilizing online data such as The Cancer Genome Atlas (TCGA), the Chinese Glioma Genome Atlas (CGGA), the Genotype-Tissue Expression (GTEx) project, the Clinical Proteomic Tumor Analysis Consortium (CPTAC), and the Human Protein Atlas (HPA) databases, in conjunction with bioinformatics methodologies including GO, KEGG, GSEA, CIBERSORT immune cell infiltration estimation, and protein-protein interaction (PPI) analysis, enabled a comprehensive exploration of ZNF207's involvement in gliomagenesis. Immunohistochemistry and RT-PCR techniques were employed to validate the expression level of ZNF207 in glioma samples. Subsequently, the biological effects of ZNF207 on glioma cells were explored through in vitro assays. Our results demonstrate elevated expression of ZNF207 in gliomas, correlating with unfavorable patient outcomes. Stratification analyses were used to delineate the prognostic efficacy of ZNF207 in glioma with different clinicopathological characteristics. Immunocorrelation analysis revealed a significant association between ZNF207 expression and the infiltration levels of T helper cells, macrophages, and natural killer (NK) cells. Utilizing ZNF207 expression and clinical features, we constructed an OS prediction model and displayed well discrimination with a C-index of 0.861. Moreover, the strategic silencing of ZNF207 attenuated glioma cell advancement, evidenced by diminished cellular proliferation, weakened cell tumorigenesis, augmented apoptotic activity, and curtailed migratory capacity alongside the inhibition of the epithelial-mesenchymal transition (EMT) pathway. ZNF207 may identify as a prospective biomarker and therapeutic candidate for glioma prevention, providing valuable insights into understanding glioma pathogenesis and treatment strategies.

Abstract LB012: Evaluating computational approaches for CPTAC pan-cancer cross-cohort protein expression comparison

Abstract Recently, the National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (CPTAC) has generated harmonized genomic, transcriptomic, proteomic, and clinical data for >1000 tumors in 10 cohorts to facilitate pan-cancer discovery research. However, protein expression comparison across CPTAC cohorts remains challenging due to non-uniform missing data and protein expression distribution patterns across tumor types. Here we present our efforts to evaluate various missing data handling and normalization strategies to generate a normalized pan-cancer protein expression dataset. First, we developed a novel algorithm to select robustly expressed proteins in tumors in any of the CPTAC cohorts, Second, we applied a cohort hybrid imputation approach to protein abundance values from FragPipe within each cohort based on protein expression distribution patterns. Third, we calculated iBAQ using protein abundance value and applied global quantile normalization or smooth quantile normalization methods. To assess if our missing data imputation and normalization strategy affected downstream analyses, we compared the fold change in differential protein expression between tumor and matched normal for each cohort using non-normalized, global quantile normalized and smooth quantile normalized protein iBAQ values. Our results demonstrate a strong correlation in fold change between global quantile normalized data and non-normalized data (Pearson r = 0.97 (ccRCC), r = 0.96 (COAD), r = 0.99 (LUAD) and r = 0.99 (LSCC)). Similar results were observed comparing smooth quantile normalized data to non-normalized data (Pearson r = 1.00 for ccRCC, COAD, LUAD, and LSCC), indicating both normalization methods retained biological differences between tumor and matched normal tissues within cohorts. Lastly, we identified several proteins (ERAP2, CA9, GSTM3, MX1, STAT1) whose protein and RNA expression were highly correlated across eight CPTAC cohorts (r > 0.7 for COAD, BRCA, LUAD, ccRCC, PDAC, UCEC, HNSCC, and LSCC). We then compared their protein expression rank across CPTAC cohorts with their RNA expression rank across corresponding TCGA cohorts. Specifically, median log2(iBAQ) of CPTAC and median log2(TPM) of TCGA are calculated for those proteins within indications, then indications are ranked by median log2(iBAQ) and median log2(TPM) in CPTAC and TCGA, respectively. Weighted rank correlation was used to measure rank agreement. Global quantile normalization has the highest rank correlation (weighted rank correlation between 0.597 to 0.931) compared to smooth quantile normalization or without normalization. These results suggest that combination of cohort hybrid imputation and global quantile normalization is a reasonable approach to generate a normalized CPTAC pan-cancer protein dataset that could be leveraged to interrogate protein expression across different cancer types. Citation Format: Jixin Wang, Xiaowen Tian, Wen Yu, John Bullen, Elaine Hurt, Wenyan Zhong. Evaluating computational approaches for CPTAC pan-cancer cross-cohort protein expression comparison [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB012.

Abstract LB004: Deciphering oncogenic dedifferentiation: A proteomic approach to quantifying stemness scores and unveiling druggable targets

Abstract Cancer progression and therapeutic resistance are closely linked to the acquisition of a stemness phenotype. In this context, we introduce a novel protein expression-based stemness index (PROTsi) designed to evaluate oncogenic dedifferentiation in relation to histopathology, molecular features, and clinical outcomes in tumor samples. The methodology involved the application of a machine learning model to predict the stemness molecular phenotype, leveraging proteomic data. The prediction model was built from human pluripotent stem cells from the Human Induced Pluripotent Stem Cells Consortium (HipSci). Using datasets sourced from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) across eleven distinct tumor types, we validate PROTsi's effectiveness in accurately quantifying stem-like features. Integrating the PROTsi with gene expression, DNA methylation, microRNA expression, copy number alteration, and protein post-translational modification data such as protein acetylation, glycosylation, and phosphorylation, we unveiled proteogenomic associations related to stemness. This comprehensive analysis enabled the identification of proteins and modified proteins functioning as active nodes within transcriptional networks, thereby steering the cancer aggressiveness. Proteins highly correlated with stemness were identified as potential drug targets both tumor-specific and shared among tumor types. The stemness-associated proteins we identified demonstrate prognostic value for clinical outcomes, as confirmed through immunohistochemistry in independent samples. This underscores PROTsi's efficacy as a valuable tool for selecting both prognostic protein and drug targets. This dual functionality enhances its utility in customizing anti-cancer therapy and propels the clinical development of effective cures for diverse cancer patient populations. In conclusion, our study not only introduces PROTsi as a robust method for evaluating stemness in cancer but also sheds light on potential opportunities for targeted therapies, emphasizing the significance of personalized treatment strategies in the pursuit of more effective cancer interventions. Citation Format: Iga Kołodziejczak-Guglas, Renan Simões, Elizabeth G. Demicco, Rossana L. Segura, Alexander J. Lazar, Weiping Ma, Erik Storrs, Francesca Petralia, Antonio Colaprico, Felipe da Leprevost, Pietro Pugliese, Michele Ceccarelli, Alexey I. Nesvizhski, Bożena Kamińska, Bing Zhang, Henry Rodriguez, Mehdi Mesri, Ana I. Robles, Clinical Proteomic Tumor Analysis Consortium, Li Ding, Tathiane M. Malta, Maciej Wiznerowicz. Deciphering oncogenic dedifferentiation: A proteomic approach to quantifying stemness scores and unveiling druggable targets [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB004.

Abstract LB146: Multi Omics analyses identified novel loci and genes for lung cancer risk among European Descendants

Abstract Introduction Genome-wide association studies (GWAS) have identified >60 genomic loci for lung cancer risk. However, causal genes and the underlying biological mechanisms for most of these loci remain unknown. Therefore, we conducted a multi-omics study to identify lung cancer susceptibility genes. Method We first conducted a transcriptome-wide association study (TWAS) using S-PrediXcan framework. Whole transcriptome data from adjacent-normal lung tissue samples and genomic data from 304 European-ancestry lung cancer patients were used to build genetic prediction models for expression levels of protein-coding genes and lincRNAs. The prediction models were then applied to GWAS meta-analysis results for lung cancer with 39,363 cases and 621,480 controls of European descents from TRICL-ILCCO, the UK Biobank, and FinnGen. COJO was used to assess whether those significant genes were independent of proximal (±1.5 Mb) GWAS-identified risk variants. To replicate our TWAS-identified genes, we used data from GTEx version 8 normal lung tissue (N=444) to build imputation models and then run association analyses. We acquired gene (N=107) and protein expression data (N=111) from tumor and matched adjacent-normal lung tissues from European-ancestry participants in the Clinical Proteomic Tumor Analysis Consortium. Paired Wilcoxon rank sum tests were then conducted to assess whether identified genes and their corresponding proteins were expressed differentially. Results Out of 18,850 genes (read count >6 and TPM >0.1 in at least 20% samples), 6,754 protein-coding genes and 802 lincRNAs were predicted reliably using genetic variants (R>0.1 and P<0.05). Among them, genetically proxied expression levels of 24 genes located at 15 loci were significantly associated with lung cancer risk at a Bonferroni adjusted P <0.05. Four genes were located at four novel loci, including SERF1A at 5q13.2, SMIM33 at 5q31.2, MUC5B at 11p15.5, and RASSF10 at 11p15.2. Six putative candidate genes, namely H4C13, HCP5B, STN1, COPS2, SKIV2L, and LINC00243 resided in previously GWAS-identified lung cancer loci but have not been reported. Of these six genes, SKIV2L (Padjust=1.49e-5) and LINC00243 (Padjust=3.47e-5) were independently associated with lung cancer risk even after adjusting for GWAS-identified variants. Among 24 identified genes, 16 showed a consistent significant association with lung cancer risk (p<0.05) based on the prediction models built using GTEx data. Of ten unreported or novel genes, MUC5B, SMIM33, SERF1A, STN1, COPS2, and LINC00243 showed differential gene expression at P <0.05 and consistent association direction with TWAS results. Three proteins, STN1, COPS2, and SKIV2L, showed differential expression in tumor and adjacent-normal tissue at P<0.05, which is consistent with our TWAS direction. Conclusion Our findings offer novel insights into lung cancer carcinogenesis by uncovering new genes and loci. Citation Format: Shuai Xu, Yaohua Yang, Tianying Zhao, Jiajun Shi, Jie Ping, Wanqing Wen, Hui Cai, Xingyi Guo, Ran Tao, Xiao-Ou Shu, Wei Zheng, Jirong Long, Qiuyin Cai. Multi Omics analyses identified novel loci and genes for lung cancer risk among European Descendants [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB146.

ERH Impacts Patient Prognosis and Tumor Immune Microenvironment: A Pan-Cancer Analysis.

The enhancer of rudimentary homolog (ERH) has been shown to play significant roles in tumorigenesis and progression. However, few systematic pan-cancer analyses about ERH have been described. From the tumor immune estimation resource web server2.0 (TIMER2.0), the Genotype-Tissue Expression database (GTEx) and the Gene Expression Profile Interactive Analysis version 2 (GEPIA2) databases, we explored the expression profiles and prognostic significance of ERH in 33 cancers. The Clinical Proteomic Tumor Analysis Consortium (CPTAC) and the Human Protein Atlas (HPA) databases were further used to examine the differential expression of ERH at the protein level. The genetic alteration profile was obtained from the cBioPortal. The correlation between ERH expression and the quantities of immune infiltrating cells was examined by the TIMER tool. Spearman's correlation test was conducted to analyze the association between ERH expression status and a number of prognostic indicators, including immune checkpoints, TMB, MSI, immune neoantigen, MMR genes, and DNA methyltransferases. ProteinProtein Interaction analyses were performed in the String and GeneMANIA databases, and enrichment analysis and predicted signaling pathways were identified through GO and KEGG. To make our results convincing, we validated them in six datasets in the Gene Expression Omnibus (GEO) database. In addition, we verified the expression of ERH between gastric cancer tissues and adjacent normal tissues by RT-qPCR. ERH expression was elevated in numerous tumors, and it was not associated with the patient's prognosis. Furthermore, the quantities of immune infiltrating cells and immune checkpoint genes were remarkably associated with ERH. TMB and MSI were related to ERH expression in 14 and 15 cancer types, respectively. Moreover, the expression of ERH was strongly associated with MMR defects in multiple cancer types, and almost all tumors showed coexpression of ERH and four DNA methyltransferases. The results of GO and KEGG analysis confirmed that ERH potentially impacts several important signaling pathways. Both the GEO datasets and the RT-qPCR experiment validated our previous analysis. Our pan-cancer analysis demonstrated the characterization of ERH in multiple tumors. ERH may be a valuable novel biological indicator for assessing immunotherapy efficacy and prognosis in various malignancies.

ITGAL expression in non-small-cell lung cancer tissue and its association with immune infiltrates.

Integrin subunit alpha L (ITGAL) encodes an integrin component of LFA-1 and is a membrane receptor molecule widely expressed on leukocytes. It plays a key role in the interaction between white blood cells and other cells. There was a significant correlation between the expression of ITGAL and the tumor microenvironment in a number of cancers. However, experimental studies targeting ITGAL and immune cell infiltration in non-small-cell lung cancer (NSCLC) and the response to immune checkpoint inhibitor therapy are lacking. Data were obtained from The Cancer Genome Atlas (TCGA), Gene Expression Omnibus (GEO), and Clinical Proteomic Tumor Analysis Consortium (CPTAC) databases to explore the relationship between ITGAL expression and prognosis, as well as the immune cell infiltration in patients with NSCLC. In addition, immunohistochemical staining for ITGAL and multiplex immunofluorescence (mIF) staining for ITGAL, CD20, CD68, CD4, and CD8 from tissue microarrays containing 118 tumor tissues and paired paracancerous tissues from patients with NSCLC were performed. The correlation between ITGAL expression and clinical factors, as well as the immunophenotypes of tumor-infiltrating immune cells, were also analyzed. In NSCLC tumor tissues, ITGAL was downregulated compared with matched paracancerous tissues, and low ITGAL expression was associated with a poor prognosis of NSCLC patients. Subsequently, immunohistochemistry results for tissue microarray showed that ITGAL expression was mainly elevated in tumor stroma and areas with highly infiltrated immune cells. ITGAL expression was higher in paracancerous tissues than tumor tissues. Furthermore, mIF results indicated that the patients with ITGAL-high expression tend had significantly higher CD8+ T cells, CD68+ macrophages, CD4+ T cells, and CD20+ B cells infiltration in their tumor tissues. Immunophenotypes were classified into three categories, that is deserted, excluded, and inflamed types, according to each kind of immune cell distribution in or around the cancer cell nest. MIF results showed that ITGAL expression level was correlated with the immunophenotypes. Furthermore, ITGAL expression was associated with the prognosis of NSCLC in patients with immune checkpoint inhibitor therapy and the patients with high ITGAL expression tends have better outcomes. ITGAL may be used as a biomarker for assessing the immune microenvironment in patients with NSCLC.

Prognostic Cell Death Index for Lung Adenocarcinoma: A Comprehensive Transcriptome-Based Analysis of Twelve Programmed Cell Death Pattern Genes.

Lung adenocarcinoma (LUAD) is a prominent contributor to global cancer mortality, characterized by constrained prognosis. This study aimed to develop a novel prognostic indicator, the Cell Death Index (CDI), utilizing twelve programmed cell death (PCD) pattern genes, to predict the immune infiltration and prognosis in LUAD patients. We collected PCD-related genes and identified prognostic PCD genes in the Cancer Genome Atlas (TCGA)-LUAD dataset, and made rigorous validation in the Clinical Proteomic Tumor Analysis Consortium (CPTAC)-LUAD cohorts. CDI was calculated using a multivariable Cox regression model. Functional enrichment and tumor microenvironment were evaluated. Drug sensitivity prediction and nomogram development were performed to assess CDI's potential value. The results revealed 10 PCD genes (ERO1A, CDK5R1, TRIM6, DNASE2B, ITPRIP, MRGPRX2, FGA, NDUFA13, NLRP2, and CD68) significantly associated with LUAD prognosis. The CDI was constructed and showed high accuracy in predicting patient survival with C-index values of 0.801 and 0.794 in the prognosis cohort and validation cohort, respectively. CDI is also indicative of variations in biological functions, tumor microenvironment, and immune cell infiltration including neutrophils, activated mast cells, activated dendritic cells, M0 macrophages, resting natural killer cells, γδT cells, and activated memory CD4+T cells. Furthermore, drug sensitivity analysis indicated potential targeted strategies. The CDI, based on PCD genes, serves as a robust prognostic tool for LUAD, offering profound insights into tumor biology, immune response, and personalized treatment strategies. This study underscores the pivotal role of PCD mechanisms in LUAD pathogenesis and identifies potential therapeutic targets.

Graph Perceiver Network for Lung Tumor and Bronchial Premalignant Lesion Stratification from Histopathology

Bronchial premalignant lesions (PMLs) precede the development of invasive lung squamous carcinoma (LUSC), posing a significant challenge in distinguishing those likely to advance to LUSC from those that might regress without intervention. In this context, we present a novel computational approach, the Graph Perceiver Network (GRAPE-Net), leveraging hematoxylin and eosin (H&E) stained whole slide images (WSIs) to stratify endobronchial biopsies of PMLs across a spectrum from normal to tumor lung tissues. GRAPE-Net outperforms existing frameworks in classification accuracy predicting LUSC, lung adenocarcinoma (LUAD), and non-tumor (normal) lung tissue on The Cancer Genome Atlas (TCGA) and Clinical Proteomic Tumor Analysis Consortium (CPTAC) datasets containing lung resection tissues while efficiently generating pathologist-aligned, class-specific heatmaps. The network was further tested using endobronchial biopsies from two data cohorts, containing normal to carcinoma in situ histology, and it demonstrated a unique capability to differentiate carcinoma in situ lung squamous PMLs based on their progression status to invasive carcinoma. The network may have utility in stratifying PMLs for chemoprevention trials or more aggressive follow-up.

Abstract 1851: Comprehensive multi-level proteomics characterization of gastric cancer

Abstract Gastric cancer is the leading cause of cancer related deaths worldwide, accounts for over 700,000 deaths each year. There is an urgent need for advancing therapeutic strategies for patients with gastric cancer. Addressing this need, the Clinical Proteomics Tumor Analysis Consortium (CPTAC) has conducted a comprehensive characterization of gastric cancer, aiming to elucidate the complex molecular mechanisms driving this cancer type, and to discover underlying cancer biology that will form the basis for developing new approaches for precision medicine. This comprehensive study includes an integrated multi-level proteomics analysis of 165 surgically resected gastric cancer and 41 normal adjacent tissues. We employed a multi-faceted approach, analyzing the proteome, phosphoproteome and glycoproteome, along with other protein modifications including ubiquitination, acetylation, and tyrosine phosphorylation. Furthermore, we extended our research to include 74 pairs of early-onset gastric cancer and normal adjacent tissues, analyzed using a proteomic approach. We can totally identify more than 12,000 protein groups with median identification of 9750 protein groups per sample. This expansive dataset helps constructing a comprehensive multi-omics atlas of gastric cancer, offering a deep understanding into the disease's molecular landscape. The depth of this study was enhanced by incorporating multi-level proteomics data across 6 tiers, facilitating an unprecedented multi-level proteomics analysis. Our integrated approach allows us to illustrate the changes in protein expression, protein modifications, signaling pathways, and protein networks during tumorigeneis. This analysis uncovers potential therapeutic targets for personalized treatments. Overall, our study provides a multi-omics atlas for gastric cancer, a profound insight into gastric cancer biology, contributing significantly to the understanding of its pathogenesis. It could pave the way for precise treatments for gastric cancer, underscoring the potential of multi-level proteomics analyses in advancing personalized medicine. Citation Format: Yuefan Wang, Clinical Proteomic Tumor Analysis Consortium (CPTAC). Comprehensive multi-level proteomics characterization of gastric cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1851.

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

Abstract The National Cancer Institute’s Clinical Proteomic Tumor Analysis Consortium (NCI’s CPTAC) is a national effort to accelerate the understanding of cancer biology through the integration of large-scale proteome and genome analysis, or proteogenomics. The program which is composed of a Tumor Characterization Program and a Translational Research Program (proteomics.cancer.gov) aims to leverage the investments made in cancer research by understanding the molecular changes by proteogenomically characterizing cancers and accelerating the basic discovery toward clinical impact. All data and analytical tools are made broadly available to the research community through public databases to maximize utility and public benefit.CPTAC teams have characterized a plethora of treatment-naïve tumor types, including colorectal, ovarian, breast, clear cell renal cell carcinoma, uterine corpus endometrial carcinoma, lung adenocarcinoma, lung squamous cell carcinoma, brain (including adult, pediatric, and adolescent and young adult), head and neck squamous cell carcinoma, and pancreatic ductal adenocarcinoma (proteomics.cancer.gov/resources/milestones-and-publications). Ongoing projects include acute myeloid leukemia (AML), cutaneous melanoma, sarcoma, oligodendroglioma, gastric, prostate, liver, and thyroid cancers. In the PTRC Program, CPTAC is partnering with NCI-sponsored clinical trials to support clinically relevant research projects that would elucidate biological mechanisms of therapeutic response, resistance, and/or toxicity in cancers including AML, multiple myeloma, melanoma, and lung cancers. All mass spectrometry-based proteomic, genomic, and imaging data (histopathology and radiology) are made publicly available at the CPTAC Proteomic Data Commons (pdc.cancer.gov), Genomic Data Commons (gdc.cancer.gov), and the Cancer Imaging Archive (cancerimagingarchive.net)/Imaging Data Commons (imaging.datacommons.cancer.gov) respectively. CPTAC is also supporting development of new proteogenomic data analysis tools (proteomics.cancer.gov/resources/computational-tools). In addition, the CPTAC Assay Portal (assays.cancer.gov) is a public resource populated with mass spectrometry-based targeted proteomic assays developed by the consortium for quantitatively measuring proteins of interest, including those discovered through comprehensive tumor characterization. Lastly, well-characterized monoclonal antibodies targeting cancer-specific proteins and peptides are also made available at CPTAC’s Antibody Portal (antibodies.cancer.gov). Citation Format: Mehdi Mesri, Eunkyung An, Xu Zhang, Jasmin Bavarva, Ana I. Robles, Tara Hiltke, Henry Rodriguez. NCI’s Clinical Proteomic Tumor Analysis Consortium: A proteogenomic cancer analysis program [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1852.

Abstract 3955: A proteogenomic study of high-grade glioma among adolescents and young adults

Abstract To unravel the molecular mechanisms underlying high-grade glioma (HGG) in adolescent and young adult (AYA) patients, we conducted a comprehensive proteogenomic analysis for 34 AYA (age 15-40) and 59 pediatric (age 0-15) HGG cases. Our approach involved whole genome sequencing, methylation profiling, RNA sequencing, and a suite of mass spectrometry-based proteomic experiments, including global proteomic, phosphoproteomic, and glycoproteomic profiling. The proteomics study successfully identified and quantified approximately 11,000 proteins, 33,000 phosphosites, and 3000 glycopeptides with a 50% missing filtering threshold. To identify the unique characteristics of AYA HGG in contrast to both pediatric and adult HGG, we further integrated a proteogenomic dataset of 99 adult GBM tumors previously published by CPTAC and collaborators (PMID: 33577785). Our study unveiled a collection of mutations, copy number variations, epigenetic modifications and gene fusions that exhibited different frequencies across tumors from pediatric, AYA, and adult patients. Moreover, the influence of these genetic variations on RNA/protein activities also varied across different age groups. Clustering analysis using age-dependent molecular profiles revealed striking differences in RNA/protein/phosphosite activities between patients aged 15-26 and 26-40. Additionally, significantly better overall survival (OS) was observed in the 26-40 age group compared to all other age groups, highlighting distinct biological characteristics within the AYA category that differentiate adolescents from young adults.By leveraging proteogenomic datasets from normal brain tissues (PMID: 30518843), we identified genes, proteins, and pathways with differential age-dependent molecular profiles between tumor and normal brain tissues. Notably, this analysis highlighted significant alterations in proteins from the oxidative phosphorylation, tricarboxylic acid (TCA) cycle, and myelin sheath pathways in tumors compared to normal tissues, some of which were found to be sex-specific.To search for prognostic markers, we introduced a novel approach called Trans-Population Survival Analysis through Interpolation of Age-Dependent Tumor Molecular Profiles. Applying this method on the kinase activity scores derived from the phosphoproteomic data, we identified 150 kinases whose activities were significantly associated with OS among AYA patients. Additionally, employing a causal network analysis tool, we pinpointed six kinases causally linked to OS, suggesting their potential as treatment targets. This study not only characterizes the molecular landscape of AYA HGG but also explores the disease trajectory across the lifespan. Our findings shed light on the intricate interplay of various factors influencing glioma etiology and patient outcomes, paving the way for a deeper understanding of HGG in the AYA population. Citation Format: Nicole Tignor, Mateusz P. Koptyra, Shrabanti Chowdhury, Weiping Ma, Jo Lynne Rokita, Marina Gritsenko, Xiaoyu Song, Giacomo B. Marino, Eden Z. Deng, Francesca Petralia, Azra Krek, Dmitry Rykunov, Felipe da Veiga Leprevost, Noshad Hosseini, Komal S. Rathi, Yingwei Hu, Simona Migliozzi, Tomer Yaron, Weijia Fu, Bo Zhang, Yuankun Zhu, Miguel A. Brown, Jeffrey R. Whiteaker, Clinical Proteomics Tumor Analysis Consortium (CPTAC), Children’s Brain Tumor Network (CBTN), Mehdi Mesri, Ana I. Robles, Karin Rodland, Lewis C. Cantley, Antonio Iavarone, Kenneth Aldape, Marcin Cieślik, Alexey I. Nesvizhskii, Joseph E. Ippolito, Joshua B. Rubin, Amanda G. Paulovich, Hui Zhang, Avi Ma'ayan, Tao Liu, Phillip B. Storm, Adam C. Resnick, Brian R. Rood, Pei Wang. A proteogenomic study of high-grade glioma among adolescents and young adults [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 3955.

Abstract 6210: The cancer omics and drug experimental response dataset (CODERData): A harmonized benchmark dataset for machine learning models of drug response prediction

Abstract Background: Determining a patient’s response to a specific therapy is a vital step in developing personalized cancer treatment. Personalized treatment relies on two key technological advancements: algorithms tailored to each patient’s molecular profiles, and comprehensive datasets detailing the effects of patient diversity on drug response. Despite recent advancements in machine learning-based models, each new algorithm requires curation of numerous datasets, which poses a significant burden to researchers who need to evaluate and compare various algorithms. Currently, datasets include extensive genomics, transcriptomics, proteomics and metabolomics measurements along with drug response data (i.e., cell viability) for each cell line, collected through the Cancer Cell Line Encyclopedia (CCLE) and other initiatives. Additional omics data is available for tumor organoids and patient samples generated by consortia such as Human Cancer Model Initiative (HCMI) and the Clinical Proteomic Tumor Analysis Consortium (CPTAC). However, to date there has been little public effort to annotate and harmonize these data across repositories. Methods: To address this need, we developed the Python package coderdata that collates the most up-to-date cancer omics and drug response data. We curated four public cancer datasets, establishing reproducible pipelines that standardize data retrieval, formatting, and integration so the datasets can be built on a cluster if needed. The datasets undergo bi-monthly synchronization with FigShare, ensuring the data is up to date and available. Scientists can use coderdata to retrieve and format data on the command line interface as well as within Python scripts. Results: Coderdata is a straightforward Python package that will enable the development and benchmarking of diverse machine learning applications in cancer research. At its core, it represents reproducible data harmonization that enables heterogeneous datasets to be analyzed in bulk. This Python package downloads and standardizes data from multiple consortia-related resources including HCMI, CPTAC, CCLE, and BeatAML, together representing 4931 multi-omics samples from across 250+ cancer types. These datasets include cancer data metrics such as copy number, mutation, transcriptomics, proteomics, miRNA, methylation, and drug response data. Conclusions: The applications of machine learning in cancer biology are hampered by the distributed nature of existing datasets. As such, the collection and standardization of data by coderdata substantially reduces the time investment required for data curation in cancer research. Direct access to benchmark multi-omics and drug response datasets enables scientists to focus on algorithm development for tumor drug response prediction, potentially accelerating the discovery of therapeutic strategies. Citation Format: Jeremy Jacobson, Sydney Schwartz, M. Ryan Weil, Neeraj Kumar, Sara Gosline. The cancer omics and drug experimental response dataset (CODERData): A harmonized benchmark dataset for machine learning models of drug response prediction [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 6210.

Abstract 2339: GPNotebook: A toolkit for identifying cancer-related glycosylation alterations in glycoproteomic analysis

Abstract This study is dedicated to the development and application of 'GPNotebook', an innovative Python-based data analysis tool, specifically designed to enhance glycopeptide research in cancer studies by enabling the detailed investigation of glycosylation patterns and their correlation with various clinical cancer phenotypes. In our study, we recognize the critical role of protein glycosylation in biological processes and its relevance in cancer. To address the lack of specialized tools for glycoproteomic analysis, we developed GPNotebook, a comprehensive Python package designed for detailed investigation of intact glycopeptides (IGPs). This tool integrates multiple functionalities, including statistical profiling, differential expression analysis, categorization of glycosylation subtypes, and exploration of the interactions between glycosylation and phosphorylation. It also encompasses survival analysis and assessments of glycosylation enzymes. We applied GPNotebook in a research project focused on 10 cancer types collected from Clinical Proteomic Tumor Analysis Consortium (CPTAC) projects to validate its efficacy and demonstrate its wide-ranging capabilities. Through this application, we were able to analyze and interpret complex glycoproteomic data, revealing potential IGPs as biomarkers for cancer detection and differentiation of cancer subtypes. We identified hundreds of cancer-specific IGPs, with several showing promise as early-stage cancer biomarkers. In addition, our analysis revealed unique glycosylation patterns linked to specific molecular subtypes and immune cell distributions in tumor microenvironments. We also clarified the roles of certain glycosylation enzymes across different cancer types, highlighting their potential significance in cancer progression. GPNotebook has proven to be a critical tool in glycopeptide research. Our findings not only enhance the understanding of glycosylation in cancer but also demonstrate GPNotebook's potential in identifying novel biomarkers and providing insights into cancer subtype differentiation. This study positions GPNotebook as a valuable resource for exploring the complex relationships between protein glycosylation and cancer phenotypes. Citation Format: Hui Zhang, Trung Hoang, Yingwei Hu. GPNotebook: A toolkit for identifying cancer-related glycosylation alterations in glycoproteomic analysis [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 2339.

Abstract 4909: An RNA-based model for tertiary lymphoid structure (TLS) prediction and classification in pancreatic adenocarcinoma (PDAC)

Abstract Although TLS status possesses prognostic significance in PDAC and can potentially affect chemotherapy outcomes, there is currently a notable lack of RNA sequencing (RNA-seq) models that specialize in TLS identification and classification in PDAC. Here, we developed a model for predicting TLS status (high or low) based on RNA-seq data. Design: Hematoxylin and eosin (H&E) whole slide images of PDAC samples from The Cancer Genome Atlas (TCGA, n = 118) and Clinical Proteomic Tumor Analysis Consortium (CPTAC, n = 129) were used to detect intratumoral and borderline TLSs followed by TLS density measurements (units/mm2) by an experienced pathologist. The samples were then stratified into TLS-high and TLS-low groups based on median density values. Next, we used deconvolution by Kassandra algorithm to identify cell subtypes enriched in each TLS group based on gene expression (RNA-seq) data. Calculation of ssGSEA scores for gene signatures corresponding to cell subtypes and TLS structures was performed, along with survival analysis. Differential expression analysis between TLS-high and TLS-low samples, followed by functional enrichment (|logFC| > 2; padj < 0.01), was conducted. The LightGBM gradient boosting classifier was then trained on ranked expression data with sequential feature selection to predict TLS-high and TLS-low groups. We trained the model with H&E staining annotations and ranked RNA expression data from TCGA or CPTAC samples (total n = 167). The remaining 80 were designated as hold-out samples. The weighted F1 score was computed as a performance metric. Findings: Median density of detected TLSs in the TCGA and CPTAC samples was 0.012 units/mm2. Kassandra deconvolution revealed B-cell enrichment, but fibroblast and M2 macrophage depletion in the TLS-high group. Our calculated ssGSEA scores of previously described TLS gene signatures, along with those of different B-cell subtypes and follicular dendritic cells, showed significant association with the TLS-high group. Genes associated with B-cell proliferation, differentiation, and signaling (CD19, CD22, CD79A, CD79B, and CR2) were also upregulated in this group. Comparing the performance of our RNA-based model on the validation dataset with manual TLS classification by a pathologist, we obtained an F1 weighted score of 0.72 and ROC-AUC score of 0.77. Thus, the TLS predictions by our model concurred with the TLS classification based on H&E annotations and pathological evaluation. Moreover, patients in the predicted TLS-low group had worse overall survival (OS) compared to the TLS-high group (Log(HR) = 0.76; 95% CI [0.04; 1.48]; p<0.05). We present an RNA-based model that stratifies PDAC samples as TLS-high or TLS-low, with predictions that conform to pathological findings. We also found TLS-low samples to associate with worse OS, thus offering an objective means to predict prognoses of PDAC patients based on TLS status. Citation Format: Alexandra Livanova, Andrey Tyshevich, Andrey Kravets, Stanislav Kurpe, Nadezhda Lukashevich, Dmitry Ivchenkov, Daniil Dymov, Anna Belozerova, Kirill Kryukov, Aleksandr Sarachakov, Viktor Svekolkin, Vladimir Kushnarev. An RNA-based model for tertiary lymphoid structure (TLS) prediction and classification in pancreatic adenocarcinoma (PDAC) [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 4909.

Abstract 1694: Using high-throughput screening to identify DNA response elements that sense cancer dysregulated pathways

Abstract Earli is developing a highly sensitive, orthogonal approach that is designed to be delivered via IV injection and uses a genetic construct to usurp dysregulated pathways, and actively forces cancer cells to drive the expression of a detectable ‘synthetic’ biomarker. The key element of our cancer-activated construct that drives specificity and sensitivity of expression is a promoter sequence, synthetically engineered to leverage transcription factors (TFs) activated in cancer pathways. Acting as a molecular sensor for dysregulated TF levels, the DNA construct expresses a measurable biomarker in cancer cells, while remaining transcriptionally silent in normal adjacent tissues and benign lesions. Previously, we described a multiomics factor analysis (MOFA) to interrogate 200 non-small cell lung cancer (NSCLC) and matched normal adjacent tissue samples comprised of RNA-seq based transcriptional profiling, and MS-based proteomics and phospho-proteomics from the Clinical Proteomic Tumor Analysis Consortium (CPTAC) dataset, and generated a list of top TFs dysregulated in NSCLC. In this study, we show the capability to design, engineer, and test cancer specific activity of combinations of response elements using a Massively Parallel Reporter Assay (MPRA) high-throughput pooled screening (HTS) method. More than 2,000 unique sequences of homotypic or heterotypic TF binding sites corresponding to the top 100 TFs were arrayed with diverse spacing and individually barcoded for the high-throughput screens in NSCLC cell lines. To validate our top hits, we individually tested 10 sequences from the top 25% and 8 sequences from the bottom 25%. Nine of the top sequences produced signals 5-10X higher than the background, whereas only one of the eight bottom sequences produced signal higher than the background. We have individually validated 30 different response elements and found that 28 of 30 drove the expected expression, with the majority of response elements (n=20) showing expression 5-10X higher than background. Remarkably, two of the engineered response elements were able to drive expression 20-30X higher in a specific cell line activated by WNT-signaling, suggesting that these response elements can sense specific cancer dysregulated pathways. Our lead response elements are being tested across a broad range of in vitro primary tumor lines with diverse genetic backgrounds and transcriptional profiles prior to in vivo testing using our imaging platform to distinguish malignant lung nodules. In summary, we have established a robust screening platform to identify response elements that can be activated by cancer dysregulated pathways. We are currently expanding this platform to other cancer models and indications, and leveraging this to explore more complex response elements that use combinations of TFs and natural spacing. Citation Format: Elizabeth Stroebele, Yue (Wendy) Zhang, Ishan Podar, Chloe Xia, Ajda Rojc, Dariusz Wodziak, Maggie Louie, David Suhy. Using high-throughput screening to identify DNA response elements that sense cancer dysregulated pathways [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 1 (Regular Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(6_Suppl):Abstract nr 1694.