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Plasma proteome atlas for differentiating tumor stage and post-surgical prognosis of hepatocellular carcinoma and cholangiocarcinoma.

ABSTRACT: Although mass spectrometry-based plasma proteomics enables sensitive and large-scale discovery and validation of biomarkers for various diseases, its integrative application to hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) is not well investigated. Therefore, we analyzed albumin- and immunoglobulin G-depleted plasma samples from 148 and 60 patients with HCC and CCA, respectively, using liquid chromatography-tandem mass spectrometry. The algorithm used to measure the content of each protein was the percentage of exponentially modified protein abundance index. From 5320 proteins assayed in plasma, 53 and 25 biomarker candidates were identified for HCC and CCA, respectively. The abundance of six and two HCC markers particularly protruded in stage II and III, respectively, whereas plasma serine protease inhibitor was the sole marker the level of which steadily decreased with CCA progression. From a prognostic facet, we showed candidate markers and their cutoff levels for evaluating probability of tumor recurrence and patient survival period. Combination Kaplan-Meier models showed that HCC stage III or IV and both the content of alpha-2-HS-glycoprotein and apolipoprotein CIII <0.2% exhibited the poorest post-surgical recurrence-free and overall survivals. Furthermore, the content of afamin ?0.2% played a significant role on the poor prognosis in patients with CCA. Our findings, taken together, characterized novel plasma biomarker signatures in dissecting tumor stages and post-surgical outcomes of HCC and CCA.

SUBMITTER: Chang TT

PROVIDER: S-EPMC7449477 | biostudies-literature | 2020

REPOSITORIES: biostudies-literature

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Plasma proteome atlas for differentiating tumor stage and post-surgical prognosis of hepatocellular carcinoma and cholangiocarcinoma.

Chang Ting-Tsung TT Ho Cheng-Hsun CH

PloS one 20200826 8

Although mass spectrometry-based plasma proteomics enables sensitive and large-scale discovery and validation of biomarkers for various diseases, its integrative application to hepatocellular carcinoma (HCC) and cholangiocarcinoma (CCA) is not well investigated. Therefore, we analyzed albumin- and immunoglobulin G-depleted plasma samples from 148 and 60 patients with HCC and CCA, respectively, using liquid chromatography-tandem mass spectrometry. The algorithm used to measure the content of each ...[more]

PMID: 32845921

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Project description:ObjectivesTo establish a nomogram based on preoperative laboratory study variables using least absolute shrinkage and selection operator (LASSO) regression for differentiating combined hepatocellular cholangiocarcinoma (cHCC) from intrahepatic cholangiocarcinoma (iCCA).MethodsWe performed a retrospective analysis of iCCA and cHCC patients who underwent liver resection. Blood signatures were established using LASSO regression, and then, the clinical risk factors based on the multivariate logistic regression and blood signatures were combined to establish a nomogram for a differential preoperative diagnosis between iCCA and cHCC. The differential accuracy ability of the nomogram was determined by Harrell's index (C-index) and decision curve analysis, and the results were validated using a validation set. Furthermore, patients were categorized into two groups according to the optimal cut-off values of the nomogram-based scores, and their survival differences were assessed using Kaplan-Meier curves.ResultsA total of 587 patients who underwent curative liver resection for iCCA or cHCC between January 2008 and December 2017 at West China Hospital were enrolled in this study. The cHCC score was based on the personalized levels of the seven laboratory study variables. On multivariate logistic analysis, the independent factors for distinguishing cHCC were age, sex, biliary duct stones, and portal hypertension, all of which were incorporated into the nomogram combined with the cHCC-score. The nomogram had a good discriminating capability, with a C-index of 0.796 (95% CI, 0.752-0.840). The calibration plot for distinguishing cHCC from iCCA showed optimal agreement between the nomogram prediction and actual observation in the training and validation sets. The decision curves indicated significant clinical usefulness.ConclusionThe nomogram showed good accuracy for the differential diagnosis between iCCA and cHCC preoperatively, and therapeutic decisions would improve if it was applied in clinical practice.

Project description:Background & aimsIntratumor molecular heterogeneity is a key feature of tumorigenesis and is linked to treatment failure and patient prognosis. Herein, we aimed to determine what drives tumor cell evolution by performing single-cell transcriptomic analysis.MethodsWe analyzed 46 hepatocellular carcinoma (HCC) and intrahepatic cholangiocarcinoma (iCCA) biopsies from 37 patients enrolled in interventional studies at the NIH Clinical Center, with 16 biopsies collected before and after treatment from 7 patients. We developed a novel machine learning-based consensus clustering approach to track cellular states of 57,000 malignant and non-malignant cells including tumor cell transcriptome-based functional clonality analysis. We determined tumor cell relationships using RNA velocity and reverse graph embedding. We also studied longitudinal samples from 4 patients to determine tumor cellular state and its evolution. We validated our findings in bulk transcriptomic data from 488 patients with HCC and 277 patients with iCCA.ResultsUsing transcriptomic clusters as a surrogate for functional clonality, we observed an increase in tumor cell state heterogeneity which was tightly linked to patient prognosis. Furthermore, increased functional clonality was accompanied by a polarized immune cell landscape which included an increase in pre-exhausted T cells. We found that SPP1 expression was tightly associated with tumor cell evolution and microenvironmental reprogramming. Finally, we developed a user-friendly online interface as a knowledge base for a single-cell atlas of liver cancer.ConclusionsOur study offers insight into the collective behavior of tumor cell communities in liver cancer as well as potential drivers of tumor evolution in response to therapy.Lay summaryIntratumor molecular heterogeneity is a key feature of tumorigenesis that is linked to treatment failure and patient prognosis. In this study, we present a single-cell atlas of liver tumors from patients treated with immunotherapy and describe intratumoral cell states and their hierarchical relationship. We suggest osteopontin, encoded by the gene SPP1, as a candidate regulator of tumor evolution in response to treatment.

Dataset Information

Plasma proteome atlas for differentiating tumor stage and post-surgical prognosis of hepatocellular carcinoma and cholangiocarcinoma.

Publications

Plasma proteome atlas for differentiating tumor stage and post-surgical prognosis of hepatocellular carcinoma and cholangiocarcinoma.

Similar Datasets

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