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Cell of Origin in Human Samples: Lessons from Genomic Studies

This article explores the different cells of origin in liver cancer, the liver cancer microenvironment, and the global data ecosystem of integrated systems biology for classifying and guiding interventions for liver cancer.

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Cell of Origin in Human Samples: Lessons from Genomic Studies

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  1. Cell of Origin in Human Samples: Lessons from Genomic Studies Anuradha Budhu, Ph.D. Laboratory of Human Carcinogenesis, NCI NCI CCR Liver Cancer Program *no disclosures

  2. Liver Cancer Background • HCC • 90% Primary Liver Cancer (PLC) • Highest incidence in Asia & Sub-Saharan Africa • Main risk factors: viral hepatitis (B and/or C), OH abuse, fatty liver disease/diabetes (MetS) • Cofactors: aflatoxin B1, tobacco • CCA • 2nd most common PLC • Highest incidence in Southeast Asia • Risk factors: primary sclerosing cholangitis, biliary duct cysts, hepatolithiasis, parasitic biliary fluke infestation • Shared risk factors with HCC: viral hepatitis (CCA developing in the cirrhotic liver)

  3. Liver Cancer Cells of Origin • Different types of PLC can originate in the liver dependent on the transformation event and the cell type undergoing the transformation • Mature hepatocytes or cholangiocytes might give rise to distinct tumors (HCC, CCA) Self-renewing • Hepatic stem/progenitor cells;oval cells residing in the terminal branches of the biliary tree (canals of Hering); transform to HCC or transdifferentiate to biliary-like cells that transform to CCA • Reversible ductal metaplasia and dedifferentiation of hepatocytes upon injury to hepatocyte-derived progenitor-like cells De-differentiation/transdifferentiation Adapted from bioninja.com.au

  4. Liver Cancer Microenvironment • The developing tumor requires a specific microenvironment • 90% of liver tumors develop under conditions of chronic inflammation • Changes in the extracellular matrix, signaling between parenchymal and nonparenchymal cells, immune dysfunction Adapted from bioninja.com.au

  5. Global Data Ecosystem of Integrated Systems Biology to Classify Liver Cancer and Guide Interventions Biobank & Information Commons Biomarker-guided Interventions Omics-based classification Target Identification Tumor Diagnosis Liver • Impact: • Quality of life • Overall survival Blood Prognosis Integrated classifications Urine Treatment response Tumor cells Clinical Clinical & histopathology classifications Drug toxicity Mouse models Modified from Wang XW & Thorgeirsson SS. HepaticOncology 2014

  6. Hepatic Nontumor Tissue NM M A Microenvironment Gene Expression Signature of HCC Metastasis Gene expression array M NM Th1 Th2 CSF1 Budhu et al, Cancer Cell, 2006

  7. Microenvironment Clusters in HCC Related to Prognosis Gene expression array Hepatic Nontumor Tissue (FFPE) Hoshida, NEJM, 2008

  8. Immune Phenotyping of HCC in TCGA-RNASeq Indicates Alteration of the Tumor Microenvironment TCGA Cell, 2017

  9. Global Transcriptomics Identifies Tumor Subtypes of Thai HCC related to Patient Outcome HCC (n=62) Chaisaingmongkol & Budhu et al, Cancer Cell, 2017

  10. Integrative Genomic Characterization of HCC TCGA, Cell, 2017

  11. Molecular Classification of HCC Ethnicity Research Group Hoshida/Golub Caucasian Asian/Caucasian Thorgeirsson Wang Asian Caucasian Zucman-Rossi Llovet Caucasian Asian Kim/Monden/Hui Caucasian Buendia iClust 3 iClust 1 iClust 2 TCGA Caucasian/Asian TIGER-LC Common C2 Common C1 Asian Modified from Hoshida Y et al Semin Liver Dis 39: 35–51, 2010

  12. Integrated Characterization of Reveals Molecular Distinctions Among CCA Types and an IDH-mut Subgroup TCGA, 2017

  13. Genomic and Genetic Integrative Clustering of iCCA Reveals Four Prognosis Subtypes Goeppert et al, Hepatology, 2019

  14. Global Transcriptomics Identifies Common Tumor Subtypes of Thai HCC and CCA HCC (n=62) CCA (n=91) Hoshida Y, et al., PLoS ONE, 2(11): e1195, 2007 Chaisaingmongkol & Budhu et al, Cancer Cell, 2017

  15. Global Transcriptomics Identifies Tumor Subtypes of Thai ICC Fluke + Fluke - Jusakul et al, Cancer Dis, 2017

  16. Genomic Characterization of CCA • Chromosome Instability • Cell cycle events • Oncogene mutations • More aggressive • Activation of immune response • Less aggressive Modified from Sia et al, Gastro, 2017

  17. The Liver Cancer Puzzle: Levels of Diversity Level 1 Level 2 EJ Fox & LA Loeb. Nature 512: 143-4, 2014

  18. Understanding Tumor Cell Communities by Single Cell Analysis Possible Lessons Single Cell Isolation/Capture DEPArray FACS Chromium C1 • Tumor cell community and its cellular hierarchy • Tumor microenvironment • Collective behavior/regulation • Common driver signaling • Mechanisms of tumor resistance to therapy Qb3.berkeley.edu abcam.com siliconbiosystems.com DNA/RNA Amplification • DNA seq • RNA seq • Other Omics: Proteomics; Metabolomics; Cellular imaging

  19. Heterogeneity of Stem/Progenitor-like HCC Cells HCC Organoids scRNA sequencing EpCAM CD133 CD24 Zheng et al, Hepatology, 2018

  20. Single Cell Analysis of HCC defines EpCAM clusters Ho et al, Cancer Let, 2019

  21. Expression Heterogeneity of Cells in the HCC and CCA Ecosystem Ma et al, In Press, Cancer Cell 2019

  22. Cell Lineage Varies Among Malignant Cells with Poor Prognosis of Higher Diversity Tumors Ma et al, In Press, Cancer Cell 2019

  23. Single Cell RNASeq Identifies Distinct Hepatic Populations Discrete Cell Populations Characterization of the Resident Liver Cells MacParlard et al, Nat Comm, 2018

  24. Summary • Molecular and integrated characterization of primary liver cancers has shed light on the dysregulated molecules and pathways which may be interrogated for clinical benefit. • HCC and CCA are comprised of molecular subtypes and driver genes related to cell proliferation and immune alterations and patient outcome. • The liver microenvironment plays a significant role in liver cancer with molecular clusters related to immune/inflammatory processes. • While unique subtypes are found in HCC and CCA, certain molecular subtypes are shared. • Single-cell analysis rather than bulk tumor is providing a more comprehensive assessment of PLC heterogeneity and distinct cell populations related to patient outcome.

  25. Limitations and Future Directions • Sample size effects, particularly for rare PLC types • Limitations and cross-comparisons among platforms used for analyses • Role of race/ethnicity/health disparity on subtypes • Role of etiology on subtypes • Additional studies of early stage PLC, chronic liver disease • Exploration and integration of ‘omics’ and pathways: proteomics, metabolomics, microbiome • Characterization of the liver and tumors by single cell studies

  26. Acknowledgements • Laboratory of Human Carcinogenesis/National Cancer Institute • NCI CCR Liver Cancer Program • TIGER-LC Consortium • TCGA • Clinical and Research Teams • Patients

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