Refining Classification of Cholangiocarcinoma Subtypes via Proteogenomic Integration Reveals New Therapeutic Prospects

Cho, Soo Young; Hwang, Heeyoun; Kim, Yun-Hee; Yoo, Byong Chul; Han, Nayoung; Kong, Sun‐Young; Baek, Min-Jeong; Kim, Kyung‐Hee; Lee, Mi Rim; Park, Jae Gwang; Han, Sung‐Sik; Lee, Woojin; Park, Charny; Park, Jong Bae; Kim, Jin Young; Park, Sang-Jae; Woo, Sang Myung

doi:10.1053/j.gastro.2023.02.045

Cited by 14 publications

(1 citation statement)

References 46 publications

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“…Lin et al [9] described the immunogenomic traits of iCCA, and Song et al [10] depicted the molecularly distinct features of two histological subtypes of iCCA. In addition, Cho et al [11] identified three clinically supported iCCA subtypes (stem-like, poorly immunogenic, and metabolism) and found that NCT-501 (an IDH1 family member inhibitor) exhibited synergism with nanoparticle albumin-bound paclitaxel for the stem-like subtype in an iCCA organoid model. These classifications provide us with molecular phenotypes to understand prognostic differences among iCCA patients.…”

Section: Introductionmentioning

confidence: 99%

Multiomics analysis reveals metabolic subtypes and identifies diacylglycerol kinase α (DGKA) as a potential therapeutic target for intrahepatic cholangiocarcinoma

Liu,

Wang,

Zhao

et al. 2023

Cancer Communications

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BackgroundIntrahepatic cholangiocarcinoma (iCCA) is a highly heterogeneous and lethal hepatobiliary tumor with few therapeutic strategies. The metabolic reprogramming of tumor cells plays an essential role in the development of tumors, while the metabolic molecular classification of iCCA is largely unknown. Here, we performed an integrated multiomics analysis and metabolic classification to depict differences in metabolic characteristics of iCCA patients, hoping to provide a novel perspective to understand and treat iCCA.MethodsWe performed integrated multiomics analysis in 116 iCCA samples, including whole‐exome sequencing, bulk RNA‐sequencing and proteome analysis. Based on the non‐negative matrix factorization method and the protein abundance of metabolic genes in human genome‐scale metabolic models, the metabolic subtype of iCCA was determined. Survival and prognostic gene analyses were used to compare overall survival (OS) differences between metabolic subtypes. Cell proliferation analysis, 5‐ethynyl‐2'‐deoxyuridine (EdU) assay, colony formation assay, RNA‐sequencing and Western blotting were performed to investigate the molecular mechanisms of diacylglycerol kinase α (DGKA) in iCCA cells.ResultsThree metabolic subtypes (S1‐S3) with subtype‐specific biomarkers of iCCA were identified. These metabolic subtypes presented with distinct prognoses, metabolic features, immune microenvironments, and genetic alterations. The S2 subtype with the worst survival showed the activation of some special metabolic processes, immune‐suppressed microenvironment and Kirsten rat sarcoma viral oncogene homolog (KRAS)/AT‐rich interactive domain 1A (ARID1A) mutations. Among the S2 subtype‐specific upregulated proteins, DGKA was further identified as a potential drug target for iCCA, which promoted cell proliferation by enhancing phosphatidic acid (PA) metabolism and activating mitogen‐activated protein kinase (MAPK) signaling.ConclusionVia multiomics analyses, we identified three metabolic subtypes of iCCA, revealing that the S2 subtype exhibited the poorest survival outcomes. We further identified DGKA as a potential target for the S2 subtype.

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Section: Introductionmentioning

confidence: 99%

Multiomics analysis reveals metabolic subtypes and identifies diacylglycerol kinase α (DGKA) as a potential therapeutic target for intrahepatic cholangiocarcinoma

Liu,

Wang,

Zhao

et al. 2023

Cancer Communications

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Omics‐Enhanced Nanomedicine for Cancer Therapy

Qi,

Li,

Liu

et al. 2024

Advanced Materials

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Cancer nanomedicine has emerged as a promising approach to overcome the limitations of conventional cancer therapies, offering enhanced efficacy and safety in cancer management. However, the inherent heterogeneity of tumors presents increasing challenges for the application of cancer nanomedicine in both diagnosis and treatment. This heterogeneity necessitates the integration of advanced and high‐throughput analytical techniques to tailor nanomedicine strategies to individual tumor profiles. Omics technologies, encompassing genomics, epigenomics, transcriptomics, proteomics, metabolomics, and more, provide unparalleled insights into the molecular and cellular mechanisms underlying cancer. By dissecting tumor heterogeneity across multiple levels, these technologies offer robust support for the development of personalized and precise cancer nanomedicine strategies. In this review, the principles, techniques, and applications of key omics technologies are summarized. Especially, the synergistic integration of omics and nanomedicine in cancer therapy is explored, focusing on enhanced diagnostic accuracy, optimized therapeutic strategies and the assessment of nanomedicine‐mediated biological responses. Moreover, this review addresses current challenges and outlines future directions in the field of omics‐enhanced nanomedicine. By offering valuable insights and guidance, this review aims to advance the integration of omics with nanomedicine, ultimately driving improved diagnostic and therapeutic strategies for cancer.

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