Subtopic Deep Dive
Intrahepatic Cholangiocarcinoma Molecular Pathology
Research Guide
What is Intrahepatic Cholangiocarcinoma Molecular Pathology?
Intrahepatic cholangiocarcinoma molecular pathology studies genomic mutations, fusions like FGFR2 and IDH1/2, and epigenetic alterations in tumors originating from intrahepatic bile ducts.
Researchers use next-generation sequencing to profile somatic mutations and gene fusions in intrahepatic cholangiocarcinoma (ICC). Key findings include two molecular classes with distinct outcomes (Sia et al., 2013, 578 citations) and FGFR2 fusions defining a unique subtype (Arai et al., 2013, 535 citations). Over 10 papers from the list detail these profiles, with Nakamura et al. (2015) mapping genomic spectra across biliary tract cancers (1227 citations).
Why It Matters
Molecular pathology identifies actionable mutations like IDH1/2 and FGFR fusions, enabling targeted therapies that improve survival in ICC, an aggressive cancer with poor prognosis. Nakamura et al. (2015) revealed genomic spectra supporting precision oncology, while Arai et al. (2013) defined FGFR2 fusion-positive subtypes responsive to tyrosine kinase inhibitors. Sia et al. (2013) classified ICC into proliferation and inflammation classes, predicting outcomes and guiding treatment. Bañales et al. (2020) highlight these insights for mechanism-based management (2290 citations).
Key Research Challenges
Heterogeneous Mutation Profiles
ICC tumors show variable mutations like TP53, KRAS, and IDH1/2 across patients, complicating universal therapies. Nakamura et al. (2015) identified diverse genomic spectra in biliary tract cancers. Sia et al. (2013) noted two classes with differing outcomes, requiring subtype-specific approaches.
Rare FGFR Fusion Detection
FGFR2 fusions occur in 10-15% of ICC but are hard to detect without targeted NGS panels. Arai et al. (2013) defined this subtype using fusion gene analysis. Ross et al. (2014) used NGS to uncover fusions for targeted therapy routes.
Epigenetic Mechanism Gaps
MicroRNA dysregulation affects growth and chemotherapy response, but causal roles remain unclear. Meng et al. (2006) linked miRNAs to cholangiocarcinoma cell behavior (980 citations). Integrating epigenetics with genomics challenges classification per WHO 2019 standards (Nagtegaal et al., 2019).
Essential Papers
The 2019 WHO classification of tumours of the digestive system
Irıs D. Nagtegaal, Robert D. Odze, David S. Klimstra et al. · 2019 · Histopathology · 3.8K citations
Contains fulltext : 229796.pdf (Publisher’s version ) (Open Access)
Cholangiocarcinoma 2020: the next horizon in mechanisms and management
Jesús M. Bañales, José J.G. Marı́n, Ángela Lamarca et al. · 2020 · Nature Reviews Gastroenterology & Hepatology · 2.3K citations
Cholangiocarcinoma: current knowledge and future perspectives consensus statement from the European Network for the Study of Cholangiocarcinoma (ENS-CCA)
Jesús M. Bañales, Vincenzo Cardinale, Guido Carpino et al. · 2016 · Nature Reviews Gastroenterology & Hepatology · 1.3K citations
Genomic spectra of biliary tract cancer
Hiromi Nakamura, Yasuhito Arai, Yasushi Totoki et al. · 2015 · Nature Genetics · 1.2K citations
Involvement of Human Micro-RNA in Growth and Response to Chemotherapy in Human Cholangiocarcinoma Cell Lines
Fanyin Meng, Roger Henson, Molly Lang et al. · 2006 · Gastroenterology · 980 citations
Forty-Year Trends in Cholangiocarcinoma Incidence in the U.S.: Intrahepatic Disease on the Rise
Supriya K. Saha, Andrew X. Zhu, Charles S. Fuchs et al. · 2016 · The Oncologist · 774 citations
Abstract Background. Challenges in the diagnosis and classification of cholangiocarcinoma have made it difficult to quantify the true incidence of this highly aggressive malignancy. Methods. We ana...
Cholangiocarcinoma
Paul J. Brindley, Melinda Bachini, Sumera I. Ilyas et al. · 2021 · Nature Reviews Disease Primers · 768 citations
Cholangiocarcinoma (CCA) is a highly lethal adenocarcinoma of the hepatobiliary system, which can be classified as intrahepatic, perihilar and distal. Each anatomic subtype has distinct genetic abe...
Reading Guide
Foundational Papers
Start with Sia et al. (2013, 578 citations) for ICC molecular classes with outcome differences, Arai et al. (2013, 535 citations) for FGFR2 fusions, and Meng et al. (2006, 980 citations) for miRNA roles in growth and chemotherapy.
Recent Advances
Study Bañales et al. (2020, 2290 citations) for management horizons, Nagtegaal et al. (2019, 3821 citations) for WHO classification, and Brindley et al. (2021, 768 citations) for subtype genetics.
Core Methods
Core techniques are NGS for genomic profiling (Nakamura et al., 2015; Ross et al., 2014), integrative clustering for classes (Sia et al., 2013), and fusion PCR/sequencing for drivers (Arai et al., 2013).
How PapersFlow Helps You Research Intrahepatic Cholangiocarcinoma Molecular Pathology
Discover & Search
PapersFlow's Research Agent uses searchPapers and citationGraph to map ICC molecular pathology from 250M+ OpenAlex papers, starting with Nakamura et al. (2015) on genomic spectra (1227 citations), then findSimilarPapers for FGFR fusions like Arai et al. (2013), and exaSearch for IDH1/2 mutation cohorts.
Analyze & Verify
Analysis Agent applies readPaperContent to extract mutation frequencies from Sia et al. (2013), verifies claims with CoVe against WHO classification (Nagtegaal et al., 2019), and runs PythonAnalysis for survival statistics from class divisions using pandas on supplementary data, with GRADE grading for evidence strength in targeted therapy claims.
Synthesize & Write
Synthesis Agent detects gaps in FGFR2 therapy resistance post-Arai et al. (2013), flags contradictions between miRNA studies (Meng et al., 2006) and genomic profiles, while Writing Agent uses latexEditText, latexSyncCitations for ICC review papers, and latexCompile for biomarker tables with exportMermaid for mutation pathway diagrams.
Use Cases
"Extract mutation frequencies from ICC NGS datasets and plot survival curves by class."
Research Agent → searchPapers('intrahepatic cholangiocarcinoma NGS') → Analysis Agent → readPaperContent(Sia 2013) → runPythonAnalysis(pandas/matplotlib on supp data) → Kaplan-Meier plot output with GRADE-verified p-values.
"Draft LaTeX review on FGFR2 fusions in ICC with cited figures."
Synthesis Agent → gap detection(Arai 2013 + Ross 2014) → Writing Agent → latexGenerateFigure(FGFR pathway) → latexSyncCitations(10 papers) → latexCompile → PDF with synced bibtex and mermaid diagrams.
"Find code for analyzing ICC genomic variants from papers."
Research Agent → searchPapers('ICC NGS analysis code') → Code Discovery → paperExtractUrls(Nakamura 2015 supp) → paperFindGithubRepo → githubRepoInspect → Verified R/Python scripts for variant calling.
Automated Workflows
Deep Research workflow conducts systematic review of 50+ ICC papers via searchPapers → citationGraph(Nakamura 2015 hub) → structured report with molecular classes from Sia et al. (2013). DeepScan applies 7-step CoVe to verify FGFR fusion prevalence across Arai et al. (2013) and Ross et al. (2014), with Python checkpoints. Theorizer generates hypotheses on miRNA-genomic interactions from Meng et al. (2006) + Bañales et al. (2020).
Frequently Asked Questions
What defines intrahepatic cholangiocarcinoma molecular pathology?
It examines genomic mutations (IDH1/2, TP53), FGFR fusions, and epigenetic changes like miRNAs in ICC tumors using NGS, per Nakamura et al. (2015) and Sia et al. (2013).
What are key methods in this subtopic?
Next-generation sequencing for mutation profiling (Nakamura et al., 2015; Ross et al., 2014), integrative analysis for molecular classes (Sia et al., 2013), and fusion detection for FGFR2 subtypes (Arai et al., 2013).
What are the most cited papers?
Top papers include WHO classification (Nagtegaal et al., 2019, 3821 citations), Bañales et al. (2020, 2290 citations), and Nakamura et al. (2015, 1227 citations) on genomic spectra.
What open problems exist?
Challenges include integrating epigenetics (Meng et al., 2006) with genomics for better classification, overcoming intratumor heterogeneity, and expanding targeted therapies beyond FGFR/IDH per Bañales et al. (2020).
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