Bimodality in pan-cancer proteomics reveals new opportunities for biomarker discovery DOI Open Access
Wen Jiang,

Damián E. Bikiel,

Jan Zaucha

и другие.

bioRxiv (Cold Spring Harbor Laboratory), Год журнала: 2025, Номер unknown

Опубликована: Март 12, 2025

Summary Bimodal protein expression, characterized by the distribution of expression with two modes, is linked to phenotypic variation across various biological systems. Whereas previous studies focused on RNA data, we developed a bimodality model tailored for proteomics enhance identification cancer-associated biomarkers and targets, facilitating precision oncology. We analyzed data from cancer types identified 2401 tumor-associated bimodal proteins. These proteins were evaluated pathway enrichment, revealing significant associations critical pathways, such as metabolism non-essential amino acids, interaction between extracellular matrix its receptors cell surface, central carbon in cancer. Utilizing an AI-enhanced knowledge graph, further delineated common patterns among pan-cancer A case study TROP2 colon adenocarcinoma highlighted upregulation MYC WNT/β-catenin signaling pathways down-regulation inflammatory interferon-related TROP2-high group. The difference TROP2-low groups underscored significance determining heterogeneity differences vulnerability, which can inform treatment decisions. Our findings show value uncovering novel advancing medicine, setting precedent multi-omics integration clinical validation.

Язык: Английский

Pan-cancer analysis of post-translational modifications reveals shared patterns of protein regulation DOI Creative Commons
Yifat Geffen, Shankara Anand, Yo Akiyama

и другие.

Cell, Год журнала: 2023, Номер 186(18), С. 3945 - 3967.e26

Опубликована: Авг. 1, 2023

Post-translational modifications (PTMs) play key roles in regulating cell signaling and physiology both normal cancer cells. Advances mass spectrometry enable high-throughput, accurate, sensitive measurement of PTM levels to better understand their role, prevalence, crosstalk. Here, we analyze the largest collection proteogenomics data from 1,110 patients with profiles across 11 types (10 National Cancer Institute's Clinical Proteomic Tumor Analysis Consortium [CPTAC]). Our study reveals pan-cancer patterns changes protein acetylation phosphorylation involved hallmark processes. These revealed subsets tumors, different types, including those dysregulated DNA repair driven by phosphorylation, altered metabolic regulation associated immune response acetylation, affected kinase specificity crosstalk between modified histone regulation. Overall, this resource highlights rich biology governed PTMs exposes potential new therapeutic avenues.

Язык: Английский

Процитировано

70
Pan-cancer proteogenomics connects oncogenic drivers to functional states DOI Creative Commons
Yize Li, Eduard Porta‐Pardo, Collin Tokheim

и другие.

Cell, Год журнала: 2023, Номер 186(18), С. 3921 - 3944.e25

Опубликована: Авг. 1, 2023

Cancer driver events refer to key genetic aberrations that drive oncogenesis; however, their exact molecular mechanisms remain insufficiently understood. Here, our multi-omics pan-cancer analysis uncovers insights into the impacts of cancer drivers by identifying significant cis-effects and distal trans-effects quantified at RNA, protein, phosphoprotein levels. Salient observations include association point mutations copy-number alterations with rewiring protein interaction networks, notably, most genes converge toward similar states denoted sequence-based kinase activity profiles. A correlation between predicted neoantigen burden measured T cell infiltration suggests potential vulnerabilities for immunotherapies. Patterns hallmarks vary polygenic abundance ranging from uniform heterogeneous. Overall, work demonstrates value comprehensive proteogenomics in understanding functional oncogenic links development, surpassing limitations studying individual types.

Язык: Английский

Процитировано

54
Pan-cancer proteogenomics characterization of tumor immunity DOI Creative Commons
Francesca Petralia, Weiping Ma, Tomer M. Yaron

и другие.

Cell, Год журнала: 2024, Номер 187(5), С. 1255 - 1277.e27

Опубликована: Фев. 1, 2024

Despite the successes of immunotherapy in cancer treatment over recent decades, less than <10%–20% cases have demonstrated durable responses from immune checkpoint blockade. To enhance efficacy immunotherapies, combination therapies suppressing multiple evasion mechanisms are increasingly contemplated. better understand cell surveillance and diverse tumor tissues, we comprehensively characterized landscape more 1,000 tumors across ten different cancers using CPTAC pan-cancer proteogenomic data. We identified seven distinct subtypes based on integrative learning type compositions pathway activities. then thoroughly categorized unique genomic, epigenetic, transcriptomic, proteomic changes associated with each subtype. Further leveraging deep phosphoproteomic data, studied kinase activities subtypes, which revealed potential subtype-specific therapeutic targets. Insights this work will facilitate development future strategies precision targeting existing agents.

Язык: Английский

Процитировано

32
Pan-cancer proteogenomics expands the landscape of therapeutic targets DOI Creative Commons
Sara R. Savage, Xinpei Yi, Jonathan T. Lei

и другие.

Cell, Год журнала: 2024, Номер 187(16), С. 4389 - 4407.e15

Опубликована: Июнь 24, 2024

Fewer than 200 proteins are targeted by cancer drugs approved the Food and Drug Administration (FDA). We integrate Clinical Proteomic Tumor Analysis Consortium (CPTAC) proteogenomics data from 1,043 patients across 10 types with additional public datasets to identify potential therapeutic targets. Pan-cancer analysis of 2,863 druggable reveals a wide abundance range identifies biological factors that affect mRNA-protein correlation. Integration proteomic tumors genetic screen cell lines protein overexpression- or hyperactivation-driven dependencies, enabling accurate predictions effective drug Proteogenomic identification synthetic lethality provides strategy target tumor suppressor gene loss. Combining proteogenomic MHC binding prediction prioritizes mutant KRAS peptides as promising neoantigens. Computational shared tumor-associated antigens followed experimental confirmation nominates immunotherapy These analyses, summarized at https://targets.linkedomics.org, form comprehensive landscape peptide targets for companion diagnostics, repurposing, therapy development.

Язык: Английский

Процитировано

25
DrugMap: A quantitative pan-cancer analysis of cysteine ligandability DOI
Mariko Takahashi, Harrison B. Chong, Siwen Zhang

и другие.

Cell, Год журнала: 2024, Номер 187(10), С. 2536 - 2556.e30

Опубликована: Апрель 22, 2024

Язык: Английский

Процитировано

21
Mass-spectrometry-based proteomics: from single cells to clinical applications DOI
Tiannan Guo, Judith A. Steen, Matthias Mann

и другие.

Nature, Год журнала: 2025, Номер 638(8052), С. 901 - 911

Опубликована: Фев. 26, 2025

Язык: Английский

Процитировано

2
Integrative multi-omic cancer profiling reveals DNA methylation patterns associated with therapeutic vulnerability and cell-of-origin DOI Creative Commons
Wen-Wei Liang, Rita Jui-Hsien Lu, Reyka G. Jayasinghe

и другие.

Cancer Cell, Год журнала: 2023, Номер 41(9), С. 1567 - 1585.e7

Опубликована: Авг. 14, 2023

DNA methylation plays a critical role in establishing and maintaining cellular identity. However, it is frequently dysregulated during tumor development closely intertwined with other genetic alterations. Here, we leveraged multi-omic profiling of 687 tumors matched non-involved adjacent tissues from the kidney, brain, pancreas, lung, head neck, endometrium to identify aberrant associated RNA protein abundance changes build Pan-Cancer catalog. We uncovered lineage-specific epigenetic drivers including hypomethylated FGFR2 endometrial cancer. showed that hypermethylated STAT5A pervasive regulon downregulation immune cell depletion, suggesting regulation expression constitutes molecular switch for immunosuppression squamous tumors. further demonstrated subtype-enrichment information can explain cell-of-origin, intra-tumor heterogeneity, phenotypes. Overall, identified cis-acting events drive transcriptional translational changes, shedding light on tumor's landscape its cell-of-origin.

Язык: Английский

Процитировано

34
A proteogenomics data-driven knowledge base of human cancer DOI Creative Commons
Yuxing Liao, Sara R. Savage, Yongchao Dou

и другие.

Cell Systems, Год журнала: 2023, Номер 14(9), С. 777 - 787.e5

Опубликована: Авг. 23, 2023

Язык: Английский

Процитировано

26
Integration of pan-omics technologies and three-dimensional in vitro tumor models: an approach toward drug discovery and precision medicine DOI Creative Commons

Anmi Jose,

Pallavi Kulkarni,

Jaya Thilakan

и другие.

Molecular Cancer, Год журнала: 2024, Номер 23(1)

Опубликована: Март 9, 2024

Abstract Despite advancements in treatment protocols, cancer is one of the leading cause deaths worldwide. Therefore, there a need to identify newer and personalized therapeutic targets along with screening technologies combat cancer. With advent pan-omics technologies, such as genomics, transcriptomics, proteomics, metabolomics, lipidomics, scientific community has witnessed an improved molecular metabolomic understanding various diseases, including In addition, three-dimensional (3-D) disease models have been efficiently utilized for pathophysiology tools drug discovery. An integrated approach utilizing 3-D vitro tumor led intricate network encompassing signalling pathways cross-talk solid tumors. present review, we underscore current trends omics highlight their role genotypic-phenotypic co-relation respect models. We further discuss challenges associated provide our outlook on future applications these discovery precision medicine management Graphical

Язык: Английский

Процитировано

15
TMO-Net: an explainable pretrained multi-omics model for multi-task learning in oncology DOI Creative Commons

Feng-ao Wang,

Zhenfeng Zhuang,

Feng Gao

и другие.

Genome biology, Год журнала: 2024, Номер 25(1)

Опубликована: Июнь 6, 2024

Abstract Cancer is a complex disease composing systemic alterations in multiple scales. In this study, we develop the Tumor Multi-Omics pre-trained Network (TMO-Net) that integrates multi-omics pan-cancer datasets for model pre-training, facilitating cross-omics interactions and enabling joint representation learning incomplete omics inference. This enhances sample empowers various downstream oncology tasks with datasets. By employing interpretable learning, characterize contributions of distinct features to clinical outcomes. The TMO-Net serves as versatile framework cross-modal oncology, paving way tumor omics-specific foundation models.

Язык: Английский

Процитировано

9