Abstract
Pan-cancer multi-omics data produced from integrated genomic, epigenomic, transcriptomic, proteomic, and post-translational modification (PTM) profiling of a wide variety of cancer types ...holds great potential for understanding cancer biology and generating therapeutic hypotheses. To realize this potential and make analysis and visualization of these complex and interconnected data easily accessible to cancer biologists and clinicians, we have developed a web portal, LinkedOmicsKB. The web portal provides access to a harmonized proteogenomic dataset of over 1000 patient samples covering 10 cancer cohorts from the Clinical Proteomic Tumor Analysis Consortium (CPTAC). We calculated associations between methylation, copy number variation (CNV), RNA, protein, and phosphosite data for each gene and further correlated the proteogenomics data with clinical and computed molecular phenotypes. All results are stored in a MongoDB database and visualizations are provided for exploring pan-cancer multi-omics relationships as well as individual statistics. We demonstrate the utility of LinkedOmicsKB to provide insights into clinical phenotypes, somatic mutations, and understudied genes. In the pan-cancer CPTAC data, overall survival was correlated with proteins involved in protein hydroxylation, including PLOD1 and PLOD2. Additionally, two phosphorylation sites on the tumor suppressor MIG-6 were associated with worse survival. These sites were also associated with hypoxia, MAPK, and EGFR pathway activity scores, suggesting a relationship between the signaling in these pathways and cancer prognosis. STK17B is an understudied kinase that regulates apoptosis. We found STK17B was upregulated in 6 cancer types at the protein level but only two at the RNA level. The protein abundance of STK17B was highly associated with immune scores and JAK/STAT signaling, supporting a role for STK17B in the immune response. We identified 3 phosphorylation sites on STK17B, which were associated with EGF pathway activity scores and immune-related scores. LinkedOmicsKB is a valuable tool that can be used to generate biological and clinical insights into any gene, phosphosite, mutation, or phenotype.
Citation Format: Sara R. Savage, Yuxing Liao, Yongchao Dou, Zhiao Shi, Xinpei Yi, Wen Jiang, Jonathan T. Lei, Bing Zhang. LinkedOmicsKB: A web portal to explore pan-cancer molecular and phenotype associations 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 6575.
HST Goddard High-Resolution Spectrograph observations of the 1216, 2600, and 2800 A spectral regions are analyzed for the spectroscopic binary system Capella, obtained at orbital phase 0.26 with ...3.27-3.57 km/s resolution and high SNR. The column densities of H I, D I, Mg II, and Fe II for the local interstellar medium along this 12.5 pc line of sight, together with estimates of the temperature and turbulent velocity are inferred. It is inferred that the atomic deuterium/hydrogen ratio by number is 1.65(+0.07, -0.18) x 10 exp -5 for this line of sight. Galactic evolution calculations indicate that the primordial D/H ratio probably lies in the range of (1.5-3) x (D/H)LISM. If H0 = 80 km/s Mpc, as recent evidence suggests, then the baryonic density in units of the Einstein-de Sitter closure density is 0.023-0.031. Thus the universe is argued to expand forever, unless nonbaryonic matter greatly exceeds the amount of baryonic matter.
Abstract
Although high-grade serous ovarian cancers (HGSOC) are highly chemosensitive with an 85% initial response rate to platinum-based chemotherapy, 15% of patients are “exceptional ...nonresponders,” with platinum-refractory tumors that remain stable or progress during treatment. Unfortunately, we have no predictive biomarkers to identify refractory patients up front, and they receive futile chemotherapy through which most patients become too ill to be eligible for clinical trials. Hence, no progress has been made in treating these deadly tumors. The goal of this study is to identify mechanisms of platinum refractoriness to: i) predict refractory HGSOCs up front and ii) identify potential new drug targets in refractory disease to point to desperately needed new therapeutic approaches. Of note, 80-90% of patients who are initially platinum responsive will relapse and develop platinum-resistant disease, and it is possible that findings in platinum-refractory tumors might also provide insights into platinum-resistant tumors. Our NCI Clinical Proteomic Tumor Analysis Consortium (CPTAC)-funded approach combines genomic and proteomic (“proteogenomic”) analyses of both preclinical models (0, 8, and 24 hours post-platinum exposure) and treatment-naïve human tumors. For preclinical models, we studied a well-characterized collection of patient-derived xenograft (PDX) models (10 sensitive, 10 refractory), as well as intrapatient HGSOC cell line pairs derived from patients before and after the development of platinum resistance. For the PDX models, proteogenomic profiling included RNASeq, WES, global proteomics, and phosphoproteomics at all 3 time points (0, 8, 24 hours). For cell line models (3 sensitive, 3 resistant), proteogenomic profiling was performed at all 3 timepoints (0, 8, 24 hours), and experiments were performed in complete biologic triplicate. Analyses included RNASeq, WES, global proteomics, phosphoproteomics, ubiquitin proteome, acetylated proteome, and pTyr. A large collection of 275 human HGSOCs (an approximate equal balance of platinum sensitive and refractory tumors) is currently undergoing proteomic profiling, and genomic profiles (WGS, RNASeq) will be performed on a subset. In parallel, we have performed a comprehensive review of 31 years of published work on platinum responses of human cancers, identifying ~700 genes implicated in the response and scoring each gene with respect to strength of the published evidence. Using a Bayesian approach, we are integrating the curated candidates from the literature with our empirical proteogenomic datasets to identify a candidate signature for detecting platinum-refractory disease prior to chemotherapy. We are also performing gene-regulatory network analysis to identify potential drivers of chemo response. NextGen, targeted, multiplex, multiple reaction monitoring mass spectrometry-based assays are being developed to quantify proteins in the signature for validation studies, using independent patient cohorts.
This abstract is also being presented as Poster A62.
Citation Format: Jacob J. Kennedy, Shrabanti Chowdhury, Sara R. Savage, Xiaonan Hou, Catherine J. Huntoon, Richard G. Ivey, Qing Yu, Chenwei Lin, Dongqing Huang, Lei Zhao, Uliana J. Voytovich, Regine M. Schoenherr, Zahra Shire, Steven J. Skates, Jeffrey R. Whiteaker, Andrew N. Hoofnagle, Samuel C. Mok, Bing Zhang, Larry M. Karnitz, S. John Weroha, Steven P. Gygi, Scott H. Kaufmann, Pei Wang, Michael J. Birrer, Amanda G. Paulovich. Proteogenomic approach to identify mechanisms of platinum refractoriness in high-grade serous ovarian cancers abstract. In: Proceedings of the AACR Special Conference on Advances in Ovarian Cancer Research; 2019 Sep 13-16, 2019; Atlanta, GA. Philadelphia (PA): AACR; Clin Cancer Res 2020;26(13_Suppl):Abstract nr PR02.
Gene set analysis plays a critical role in the functional interpretation of omics data. Although this is typically done for one omics experiment at a time, there is an increasing need to combine gene ...set analysis results from multiple experiments performed on the same or different omics platforms, such as in multi-omics studies. Integrating results from multiple experiments is challenging, and annotation redundancy between gene sets further obscures clear conclusions. We propose to use a weighted set cover algorithm to reduce redundancy of gene sets identified in a single experiment. Next, we use affinity propagation to consolidate similar gene sets identified from multiple experiments into clusters and to automatically determine the most representative gene set for each cluster. Using three examples from over representation analysis and gene set enrichment analysis, we showed that weighted set cover outperformed a previously published set cover method and reduced the number of gene sets by 52-77%. Focusing on overlapping genes between the list of input genes and the enriched gene sets in over-representation analysis and leading-edge genes in gene set enrichment analysis further reduced the number of gene sets. A use case combining enrichment analysis results from RNA-Seq and proteomics data comparing basal and luminal A breast cancer samples highlighted the known difference in proliferation and DNA damage response. Finally, we used these algorithms for a pan-cancer survival analysis. Our analysis clearly revealed prognosis-related pathways common to multiple cancer types or specific to individual cancer types, as well as pathways associated with prognosis in different directions in different cancer types. We implemented these two algorithms in an R package, Sumer, which generates tables and static and interactive plots for exploration and publication. Sumer is publicly available at https://github.com/bzhanglab/sumer.
Abstract
Background: Molecularly targeted therapies are critical for improving cancer treatment. Since proteins are the targets of these therapies and functional effectors of genomic aberrations, ...proteogenomics data from the Clinical Proteomics Tumor Analysis Consortium (CPTAC) provides an unprecedented opportunity to characterize existing and future therapeutic targets for cancer treatment.
Approach: CPTAC proteogenomics data from >1000 cancer patients spanning 10 cancer types was used to evaluate current and potential therapeutic targets curated from four databases. Cell line data from DepMap was further integrated to distinguish causations from associations. Computational pipelines were deployed to identify synthetic lethality for targeting tumor suppressor loss and to prioritize tumor associated antigens as immunotherapy targets.
Results: We systematically collected 3050 druggable proteins and classified them into 5 tiers to facilitate different applications such as companion diagnostics, drug repurposing, and new therapy development. Many druggable proteins showed poor mRNA-protein correlation, including secreted proteins and proteins whose abundance was correlated with their interaction partners instead of cognate mRNA, highlighting the necessity of direct proteomic quantification of drug targets. 618 druggable proteins showed both overexpression in tumors compared to normal and significant dependency in CRISPR-Cas9 screens of cell lines of the same lineage. Notably, PAK1, a kinase targeted by investigational drugs, demonstrated both overexpression and dependency in all cancer types. A similar analysis of phosphoproteomics data focusing on known activating sites of druggable proteins further revealed targetable dependencies driven by protein hyperactivation. The phosphosite pS50 on PTPN1, a phosphatase targeted by experimental drugs, was increased in 7 cancer types and PTPN1 demonstrated dependency in related cancer cell lines. Based on tumor proteogenomic data and cell line CRISPR-Cas9 screen data, we identified synthetic lethality for difficult to target tumor suppressor losses, revealing TP53 mutations as a candidate biomarker to select breast cancer patients for CHEK1 inhibition, and endometrial cancer patients for treatment with doxorubicin. We identified 140 proteins whose expression was restricted in normal tissues but abnormal in tumors. Experimental analysis of peptides predicted to have high binding affinity to the most common allotype HLA-A02 for 7 prioritized proteins identified 21 peptides from 5 proteins with both strong binding affinity and immunogenicity which could be further investigated as immunotherapy targets.
Conclusion: We generate a comprehensive resource of protein and peptide targets that covers multiple therapeutic modalities. This unique resource will pave the way for repurposing of currently available drugs and developing new drugs for cancer treatment.
Citation Format: Jonathan T. Lei, Sara R. Savage, Xinpei Yi, Bo Wen, Hongwei Zhao, Lauren K. Somes, Paul W. Shafer, Yongchao Dou, Qiang Gao, Valentina Hoyos, Bing Zhang. Pan-cancer proteogenomics expands the landscape of therapeutic targets. 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 5726.
Abstract
We prospectively collected matched tumor specimens, adjacent non-tumor tissues, and blood samples from 110 colon cancer patients and analyzed the samples using seven omics platforms, ...including whole-exome sequencing, copy number arrays, RNA-Seq, miRNA-Seq, label-free global proteomics, isobaric tandem mass tag (TMT) labeling-based global proteomics, and TMT-based phosphoproteomics. Comparative proteomic and phosphoproteomic analysis of paired tumor and adjacent normal samples produced the first comprehensive catalogue of colon cancer-associated proteins and phosphosites, including known and putative new biomarkers and drug targets. These cancer-associated proteins and phosphosites had very little overlap with known cancer genes in the Cancer Gene Census, providing a novel information layer to our knowledge about colon cancer. One notable finding in differential proteome analysis is the identification of several cancer/testis antigens that were recurrently over-expressed in tumors compared to adjacent normal tissue, including IGF2BP3 (51%), SPAG1 (14%), and ATAD2 (8%). Through integrative analysis of the whole-exome sequencing, RNA-Seq, and proteomics data, we further predicted personalized neoantigens for 38% of the patients. In total, we found proteomics-supported neoantigens or cancer/testis antigens for 78% of the tumors in this cohort, demonstrating the potential of proteogenomics in identifying tumor antigens for cancer vaccine development. Proteomics data complemented somatic copy number analysis results and showed that multiple somatic copy number deletion events converge to repress the endocytosis pathway, suggesting its tumor suppressor role in colon cancer. In addition to reinforcing or complementing genomic findings, proteogenomic integration may also contradict genomics data-based inferences and lead to unexpected discoveries and therapeutic opportunities. Proteomics data identified SOX9 as an oncogene in colon cancer, whereas it was predicted to be a tumor suppressor based on somatic mutation data in the TCGA study. Phosphoproteomics data revealed a dual role of Rb phosphorylation in promoting proliferation and repressing apoptosis in colon cancer, clarifying the long-standing puzzle of colon cancer-specific amplification of this tumor suppressor and highlighting a unique opportunity for targeting Rb phosphorylation in colon cancer. Microsatellite instability status has been approved by the FDA as a biomarker for selecting patients for checkpoint inhibitor therapy in colorectal and other solid tumors. However, many MSI-high tumors fail to respond to checkpoint inhibition. Our proteogenomic analysis identified a subtype-specific association between increased glycolysis and decreased CD8 T cell infiltration in MSI-high colon tumors, suggesting glycolysis as a target for overcoming immune evasion in this MSI-H tumors. We make the primary and processed datasets available in publicly accessible data repositories and portals to allow broad use of these datasets for new biological discoveries and therapeutic hypothesis generation.
Citation Format: Bing Zhang, Suhas Vasaikar, Chen Huang, Xiaojing Wang, Vladislav A. Petyuk, Sara R. Savage, Bo Wen, Yongchao Dou, Yun Zhang, Zhiao Shi, Osama A. Arshad, Marina A. Gritsenko, Lisa J. Zimmerman, Jason E. McDermott, Therese R. Clauss, Ronald J. Moore, Rui Zhao, Matthew E. Monroe, Yi-Ting Wang, Matthew C. Chambers, Robbert J. Slebos, Ken S. Lau, Qianxing Mo, Li Ding, Matthew Ellis, Mathangi Thiagarajan, Christopher R. Kinsinger, Henry Rodriguez, Richard D. Smith, Karin D. Rodland, Daniel C. Liebler, Tao Liu, CPTAC Investigators. Proteogenomic characterization of human colon cancer reveals new therapeutic opportunities abstract. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr LB-006.
The objective of this study was to determine the role of individual NFAT isoforms in TNFα-induced retinal leukostasis. To this end, human retinal microvascular endothelial cells (HRMEC) transfected ...with siRNA targeting individual NFAT isoforms were treated with TNFα, and qRT-PCR was used to examine the contribution of each isoform to the TNFα-induced upregulation of leukocyte adhesion proteins. This showed that NFATc1 siRNA increased ICAM1 expression, NFATc2 siRNA reduced CX3CL1, VCAM1, SELE, and ICAM1 expression, NFATc3 siRNA increased CX3CL1 and SELE expression, and NFATc4 siRNA reduced SELE expression. Transfected HRMEC monolayers were also treated with TNFα and assayed using a parallel plate flow chamber, and both NFATc2 and NFATc4 knockdown reduced TNFα-induced cell adhesion. The effect of isoform-specific knockdown on TNFα-induced cytokine production was also measured using protein ELISAs and conditioned cell culture medium, and showed that NFATc4 siRNA reduced CXCL10, CXCL11, and MCP-1 protein levels. Lastly, the CN/NFAT-signaling inhibitor INCA-6 was shown to reduce TNFα-induced retinal leukostasis in vivo. Together, these studies show a clear role for NFAT-signaling in TNFα-induced retinal leukostasis, and identify NFATc2 and NFATc4 as potentially valuable therapeutic targets for treating retinopathies in which TNFα plays a pathogenic role.
Alzheimer's disease (AD) is highly heritable and recent studies have identified over 20 disease-associated genomic loci. Yet these only explain a small proportion of the genetic variance, indicating ...that undiscovered loci remain. Here, we performed a large genome-wide association study of clinically diagnosed AD and AD-by-proxy (71,880 cases, 383,378 controls). AD-by-proxy, based on parental diagnoses, showed strong genetic correlation with AD (r
= 0.81). Meta-analysis identified 29 risk loci, implicating 215 potential causative genes. Associated genes are strongly expressed in immune-related tissues and cell types (spleen, liver, and microglia). Gene-set analyses indicate biological mechanisms involved in lipid-related processes and degradation of amyloid precursor proteins. We show strong genetic correlations with multiple health-related outcomes, and Mendelian randomization results suggest a protective effect of cognitive ability on AD risk. These results are a step forward in identifying the genetic factors that contribute to AD risk and add novel insights into the neurobiology of AD.
Lipoedema is a chronic adipose tissue disorder mainly affecting women, causing excess subcutaneous fat deposition on the lower limbs with pain and tenderness. There is often a family history of ...lipoedema, suggesting a genetic origin, but the contribution of genetics is currently unclear. A tightly phenotyped cohort of 200 lipoedema patients was recruited from two UK specialist clinics. Objective clinical characteristics and measures of quality of life data were obtained. In an attempt to understand the genetic architecture of the disease better, genome-wide single nucleotide polymorphism (SNP) genotype data were obtained, and a genome wide association study (GWAS) was performed on 130 of the recruits. The analysis revealed genetic loci suggestively associated with the lipoedema phenotype, with further support provided by an independent cohort taken from the 100,000 Genomes Project. The top SNP rs1409440 (ORmeta ≈ 2.01, Pmeta ≈ 4 x 10-6) is located upstream of LHFPL6, which is thought to be involved with lipoma formation. Exactly how this relates to lipoedema is not yet understood. This first GWAS of a UK lipoedema cohort has identified genetic regions of suggestive association with the disease. Further replication of these findings in different populations is warranted.
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DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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