Multiomic Analysis Reveals Comprehensive Tumor Heterogeneity and Distinct Immune Subtypes in Multifocal Intrahepatic Cholangiocarcinoma
Bibliographic record
Abstract
PURPOSE: Targeted therapy and immunotherapy are transforming the treatment approach for intrahepatic cholangiocarcinoma (ICC). However, little is known about the intertumor heterogeneity (ITH) of multifocal ICC and its impacts on patient response to these treatments. We aimed to characterize the immunogenomic and epigenomic heterogeneity of multifocal ICC to guide treatment decision making. EXPERIMENTAL DESIGN: We obtained 66 tumor samples from 16 patients with multifocal ICC and characterized the tumor and immune heterogeneity using whole-exome sequencing, bulk and single-cell RNA sequencing, methylation microarray, and multiplex immunostaining. Patients were divided into high- or low-ITH groups according to the median ITH index. Two independent cohorts were used to validate findings. Responses to anti-PD-1 therapy were assessed. RESULTS: Multifocal ICC presented considerable intertumor genomic, transcriptional, and epigenomic heterogeneity within a patient in high ITH group. The immune profile among multiple tumors within a patient was relatively less heterogeneous in high- or low-ITH group, and consistent responses of multiple tumors to anti-PD-1 immunotherapy were observed. Unsupervised clustering of immune markers identified one low and one high immune subtype, with higher immune cell infiltration, closer tumor-immune cell interactions, and upregulated IFN-signature expression in high-immune subtype. Determining expression levels of CD8B and ICOS facilitated this immune classification and prediction of patient prognosis. Finally, promoter DNA methylation contributed to different immune profiles of two subtypes by regulating immune-gene expression. CONCLUSIONS: There is comprehensive heterogeneity in the genome, transcriptome, and epigenome of multifocal ICC. On the basis of the less heterogeneous immune profile of ICC, we suggest an immune classification that stratifies patients' prognosis and may support personalized immunotherapy.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.002 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".