Biology subjects

Heim, M. H.

Publications and source records attributed to Heim, M. H..

5 recordsLinked to original sources

bioRxiv2026

High regeneration-associated stress defines a distinct HCC subgroup with therapeutically exploitable vulnerabilities

Background: To date, no precision oncology approach has been established for HCC. Despite the diverse underlying causes, HCC development exhibits a uniform pathophysiology characterised by chronic hyper-proliferation, resulting from hepatocyte apoptosis and compensatory liver regeneration. This chronic hyper-proliferative pressure, termed regeneration stress, drives genomic instability during HCC onset, yet its therapeutic potential remains poorly explored. This study aimed to identify targetable vulnerabilities tied to regeneration stress and establish clinically applicable markers for treatment stratification. Methods: Weighted gene co-expression network analysis (WGCNA) was applied on external bulk RNA-seq datasets to define a LIVer REgeneration Stress Signature (LIVRESS). The signature was functionally validated using HCC patient-derived organoids (HCC-Org), and vulnerabilities were mapped using mid-throughput drug screening, single-molecule and single-cell assays, and multi-omic integration. Results: High LIVRESS scores, characterised by enrichment in replication, mitotic and DNA damage repair pathways, identified a subset of HCC patients with aggressive disease and poorer survival across aetiologies. HCC-Org with high LIVRESS scores displayed exquisite sensitivity to multiple inhibitors of the checkpoint kinase ATR. Although HCC-Org models exhibited a baseline reduction in replication fork speed, sensitivity to ATR inhibitor (ATRi) was decoupled from replication fork dynamics and rather linked to intrinsic mitotic instability. ATR inhibition triggers mitotic failure and apoptosis in LIVRESSHigh HCC-Org. This killing effect was significantly potentiated by combining ATRi with PARPi or WEE1i. Multi-omic integration identified KPNA2 as a surrogate biomarker of ATRi sensitivity. Conclusion: Our findings demonstrate that a subset of HCC-Org, characterised by high liver regeneration-associated stress, is vulnerable to ATRi-based therapies. By focusing on a comprehensive regenerative stress model, we establish a framework to stratify HCC patients and implement biomarker-driven, ATR-based therapies for HCC patients with advanced disease. Impact and implications: Regeneration stress is a key factor that drives genomic instability in HCC, providing a basis for the LIVRESS to identify patients dependent on ATR-mediated checkpoints. These findings reveal a conceptual shift for researchers and trialists: ATRi efficacy is decoupled from replication fork dynamics and instead leverages mitotic fragility. Practically, the LIVRESS and its IHC surrogate marker (KPNA2) offer a scalable roadmap for physicians to improve patient stratification in ATRi-based precision oncology trials. While requiring prospective validation, these results pave the way toward biomarker-driven therapies for advanced HCC.

cancer biology↗

bioRxiv2024

Meta-analysis reveals limited reproducibility of lncRNA-related findings in hepatocellular carcinoma research

Background & AimsThe human genome comprises tens of thousands of long non-coding RNAs (lncRNAs), whose functionality is highly debated. In the field of hepatocellular carcinoma (HCC) research, as for other cancer types, lncRNAs are increasingly reported to act as oncogenes or tumor suppressors and are put forward as useful diagnostic or prognostic biomarkers. Here, we investigate the reliability of these claims by performing a meta-analysis of the associations between HCC and lncRNAs reported in the scientific literature, and by assessing lncRNA expression patterns in two HCC patient cohorts. Approach & ResultsWhile nowadays up to 6% of all HCC-related publications cite lncRNAs, we show that most reported associations between HCC and lncRNAs have not been reproduced. In general, lncRNAs are less often differentially expressed between HCC tissues and controls compared to protein-coding genes. However, HCC-associated lncRNAs are frequently up-regulated in tumor samples, consistent with the fact that they are often selected based on transcriptome-wide comparative analyses. We perform a detailed examination of the 25 lncRNAs that are most frequently cited in association with HCC. For 10 out of these 25 lncRNAs, including well known lncRNAs such as MALAT1, NEAT1, H19 and XIST, we identify important conflicts between the biological roles and expression patterns previously reported for them in HCC and the expression patterns that we observe here. Finally, we observe that HCC-associated publications that cite lncRNAs are retracted three times more often than publications that cite protein-coding genes. ConclusionsOur results thus highlight the poor reproducibility of lncRNA-related claims in association with HCC, which is problematic in a context where new biomarkers and molecular targets for therapy are greatly needed.

cancer biology↗

bioRxiv2021

Multi-omics subtyping of hepatocellular carcinoma patients using a Bayesian network mixture model

Comprehensive molecular characterization of cancer subtypes is essential for predicting clinical outcomes and searching for personalized treatments. We present bnClustOmics, a statistical model and computational tool for multi-omics unsupervised clustering, which serves a dual purpose: Clustering patient samples based on a Bayesian network mixture model and learning the networks of omics variables representing these clusters. The discovered networks encode interactions among all omics variables and provide a molecular characterization of each patient subgroup. We conducted simulation studies that demonstrated the advantages of our approach compared to other clustering methods in the case where the generative model is a mixture of Bayesian networks. We applied bnClustOmics to a hepatocellular carcinoma (HCC) dataset comprising genome (mutation and copy number), transcriptome, proteome, and phosphoproteome data. We identified three main HCC subtypes together with molecular characteristics, some of which are associated with survival even when adjusting for the clinical stage. Cluster-specific networks shed light on the links between genotypes and molecular phenotypes of samples within their respective clusters and suggest targets for personalized treatments. Author summaryMulti-omics approaches to cancer subtyping can provide more insights into molecular changes in tumors compared to single-omics approaches. However, most multi-omics clustering methods do not take into account that gene products interact, for example, as parts of protein complexes or signaling networks. Here we present bnClustOmics, a Bayesian network mixture model for unsupervised clustering of multi-omics data, which can represent dependencies among molecular changes of various omics types explicitly. Unlike other approaches that use data from public interaction databases as ground truth, bnClustOmics learns the dependencies between genes from the analyzed multi-omics dataset. At the same time, our approach can also account for prior knowledge from public interaction databases and use it to guide network learning without losing the ability to learn new dependencies. We applied bnClustOmics to a multi-omics HCC dataset and identified three subtypes similar to those identified in other HCC studies. The cluster-specific networks learned by bnClustOmics revealed additional insights into the molecular characterization of the discovered subgroups and highlighted the changes in signaling networks leading to distinct HCC phenotypes.

systems biology↗

bioRxiv2021

Proteogenomic characterization of hepatocellular carcinoma

We performed a proteogenomic analysis of hepatocellular carcinomas (HCCs) across clinical stages and etiologies. We identified pathways differentially regulated on the genomic, transcriptomic, proteomic and phosphoproteomic levels. These pathways are involved in the organization of cellular components, cell cycle control, signaling pathways, transcriptional and translational control and metabolism. Analyses of CNA-mRNA and mRNA-protein correlations identified candidate driver genes involved in epithelial-to-mesenchymal transition, the Wnt-{beta}-catenin pathway, transcriptional control, cholesterol biosynthesis and sphingolipid metabolism. The activity of targetable kinases aurora kinase A and CDKs was upregulated. We found that CTNNB1 mutations are associated with altered phosphorylation of proteins involved in actin filament organization, whereas TP53 mutations are associated with elevated CDK1/2/5 activity and altered phosphorylation of proteins involved in lipid and mRNA metabolism. Integrative clustering identified HCC subgroups with distinct regulation of biological processes, metabolic reprogramming and kinase activation. Our analysis provides insights into the molecular processes underlying HCCs.

genomics↗

bioRxiv2021

LncRNA analyses reveal increased levels of non-coding centromeric transcripts in hepatocellular carcinoma

The search for new biomarkers and drug targets for hepatocellular carcinoma (HCC) has spurred an interest in long non-coding RNAs (lncRNAs), often proposed as oncogenes or tumor suppressors. Furthermore, lncRNA expression patterns can bring insights into the global de-regulation of cellular machineries in tumors. Here, we examine lncRNAs in a large HCC cohort, comprising RNA-seq data from paired tumor and adjacent tissue biopsies from 114 patients. We find that numerous lncRNAs are differentially expressed between tumors and adjacent tissues and between tumor progression stages. Although we find strong differential expression for most lncRNAs previously associated with HCC, the expression patterns of several prominent HCC-associated lncRNAs disagree with their previously proposed roles. We examine the genomic characteristics of HCC-expressed lncRNAs and reveal an enrichment for repetitive elements among the lncRNAs with the strongest expression increases in advanced-stage tumors. This enrichment is particularly striking for lncRNAs that overlap with satellite repeats, a major component of centromeres. Consistently, we find increased non-coding RNA transcription from centromeres in tumors, in the majority of patients, suggesting that aberrant centromere activation takes place in HCC.

cancer biology↗