A multimodal atlas of hepatocellular carcinoma reveals convergent evolutionary paths and 'bad apple' effect on clinical trajectory.

BACKGROUND & AIMS: Hepatocellular Carcinoma (HCC) is a highly fatal cancer characterized by high intra-tumor heterogeneity (ITH). A panoramic understanding of its tumor evolution, in relation to its clinical trajectory, may provide novel prognostic and treatment strategies. METHODS: Through the Asia-Pacific Hepatocellular Carcinoma (AHCC) trials group (NCT03267641), we recruited one of the largest prospective cohorts of HCC with over 600 whole genome and transcriptome samples from 123 treatment-naïve patients. RESULTS: Using a multi-region sampling approach, we revealed seven convergent genetic evolutionary paths governed by the early driver mutations, late copy number variations and viral integrations, which stratify patient clinical trajectories after surgical resection. Furthermore, such evolutionary paths shaped the molecular profiles, leading to distinct transcriptomic subtypes. Most significantly, although we found the coexistence of multiple transcriptomic subtypes within certain tumors, patient prognosis was best predicted by the most aggressive cell fraction of the tumor, rather than by overall degree of transcriptomic ITH level - a phenomenon we termed the 'bad apple' effect. Finally, we found that characteristics throughout early and late tumor evolution provide significant and complementary prognostic power in predicting patient survival. CONCLUSIONS: Taken together, our study generated a comprehensive landscape of evolutionary history for HCC and provided a rich multi-omics resource for understanding tumor heterogeneity and clinical trajectories. CLINICAL TRIAL NUMBER: NCT03267641 (Observational cohort) IMPACT AND IMPLICATIONS: This prospective study, utilizing comprehensive multi-sector, multi-omics sequencing and clinical data from surgically resected HCC, reveals critical insights into the role of tumor evolution and intra-tumor heterogeneity (ITH) in determining the prognosis of Hepatocellular Carcinoma (HCC). These findings are invaluable for oncology researchers and clinicians, as they underscore the influence of distinct evolutionary paths and the 'bad apple' effect, where the most aggressive tumor fraction dictates disease progression. These insights not only enhance prognostic accuracy post-surgical resection but also pave the way for developing personalized therapies tailored to specific tumor evolutionary and transcriptomic profiles. The co-existence of multiple sub-types within the same tumor prompts a re-appraisal of the utilities of depending on single samples to represent the entire tumor and suggests the need for clinical molecular imaging. This research thus marks a significant step forward in the clinical understanding and management of HCC, underscoring the importance of integrating tumor evolutionary dynamics and multi-omics biomarkers into therapeutic decision-making.

Dynamic phenotypic heterogeneity and the evolution of multiple RNA subtypes in hepatocellular carcinoma: the PLANET study.

Intra-tumor heterogeneity (ITH) is a key challenge in cancer treatment, but previous studies have focused mainly on the genomic alterations without exploring phenotypic (transcriptomic and immune) heterogeneity. Using one of the largest prospective surgical cohorts for hepatocellular carcinoma (HCC) with multi-region sampling, we sequenced whole genomes and paired transcriptomes from 67 HCC patients (331 samples). We found that while genomic ITH was rather constant across stages, phenotypic ITH had a very different trajectory and quickly diversified in stage II patients. Most strikingly, 30% of patients were found to contain more than one transcriptomic subtype within a single tumor. Such phenotypic ITH was found to be much more informative in predicting patient survival than genomic ITH and explains the poor efficacy of single-target systemic therapies in HCC. Taken together, we not only revealed an unprecedentedly dynamic landscape of phenotypic heterogeneity in HCC, but also highlighted the importance of studying phenotypic evolution across cancer types.

Obtaining spatially resolved tumor purity maps using deep multiple instance learning in a pan-cancer study.

Tumor purity is the percentage of cancer cells within a tissue section. Pathologists estimate tumor purity to select samples for genomic analysis by manually reading hematoxylin-eosin (H&E)-stained slides, which is tedious, time consuming, and prone to inter-observer variability. Besides, pathologists' estimates do not correlate well with genomic tumor purity values, which are inferred from genomic data and accepted as accurate for downstream analysis. We developed a deep multiple instance learning model predicting tumor purity from H&E-stained digital histopathology slides. Our model successfully predicted tumor purity in eight The Cancer Genome Atlas (TCGA) cohorts and a local Singapore cohort. The predictions were highly consistent with genomic tumor purity values. Thus, our model can be utilized to select samples for genomic analysis, which will help reduce pathologists' workload and decrease inter-observer variability. Furthermore, our model provided tumor purity maps showing the spatial variation within sections. They can help better understand the tumor microenvironment.

Genomic landscape of lung adenocarcinoma in East Asians.

Lung cancer is the world's leading cause of cancer death and shows strong ancestry disparities. By sequencing and assembling a large genomic and transcriptomic dataset of lung adenocarcinoma (LUAD) in individuals of East Asian ancestry (EAS; n = 305), we found that East Asian LUADs had more stable genomes characterized by fewer mutations and fewer copy number alterations than LUADs from individuals of European ancestry. This difference is much stronger in smokers as compared to nonsmokers. Transcriptomic clustering identified a new EAS-specific LUAD subgroup with a less complex genomic profile and upregulated immune-related genes, allowing the possibility of immunotherapy-based approaches. Integrative analysis across clinical and molecular features showed the importance of molecular phenotypes in patient prognostic stratification. EAS LUADs had better prediction accuracy than those of European ancestry, potentially due to their less complex genomic architecture. This study elucidated a comprehensive genomic landscape of EAS LUADs and highlighted important ancestry differences between the two cohorts.

PSiTE: a Phylogeny guided Simulator for Tumor Evolution.

SUMMARY: Simulating realistic clonal dynamics of tumors is an important topic in cancer genomics. Here, we present Phylogeny guided Simulator for Tumor Evolution, a tool that can simulate different types of tumor samples including single sector, multi-sector bulk tumor as well as single-cell tumor data under a wide range of evolutionary trajectories. Phylogeny guided Simulator for Tumor Evolution provides an efficient tool for understanding clonal evolution of cancer. AVAILABILITY AND IMPLEMENTATION: PSiTE is implemented in Python and is available at https://github.com/hchyang/PSiTE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.