Bi-allelic hydroxymethylbilane synthase inactivation defines a homogenous clinico-molecular subtype of hepatocellular carcinoma

Molina, Laura; Zhu, Junjie; Trépo, Eric; Bayard, Quentin; Amaddeo, Giuliana; Blanc, Jean‐Frédéric; Caldéraro, Julien; Ma, Xiaochao; Zucman‐Rossi, Jessica; Letouzé, Éric; Bail, Brigitte Le; Chiche, Laurence; Bioulac‐Sage, Paulette; Balabaud, Charles; Possenti, Laurent; Decraecker, Marie; Paradis, Valérie; Laurent, Alexis

doi:10.1016/j.jhep.2022.05.018

Cited by 20 publications

(17 citation statements)

References 57 publications

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“…Tumor heterogeneity is an important factor affecting the survival and prognosis of patients with cancer. Molecular stratification has been applied to multiple malignancies in order to help inform appropriate clinical decisions, including prostate cancer (Tang et al, 2022), breast cancer (Wolf et al, 2022), hepatocellular carcinoma (Molina et al, 2022), intrahepatic cholangiocarcinoma (Martin-Serrano et al, 2022) and small cell lung cancer (Rudin et al, 2019). It was demonstrated that multiple molecular subtypes of cervical cancer may be uncovered from different aspects (Meijer and Steenbergen, 2017).…”

Section: Discussionmentioning

confidence: 99%

Characterization of the microenvironment in different immune-metabolism subtypes of cervical cancer with prognostic significance

Lai

Liu²,

Li³

et al. 2023

Front. Genet.

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Introduction: Immune cell infiltration and metabolic reprogramming may have great impact on the tumorigenesis and progression of malignancies. The interaction between these two factors in cervical cancer remains to be clarified. Here we constructed a gene set containing immune and metabolism related genes and we applied this gene set to molecular subtyping of cervical cancer.Methods: Bulk sequencing and single-cell sequencing data were downloaded from the Cancer Genome Atlas (TCGA) database and Gene Expression Omnibus (GEO) database respectively. Immune and metabolism related genes were collected from Immport and Kyoto encyclopedia of genes and genomes (KEGG) database respectively. Unsupervised consensus clustering was performed to identify the molecular subtypes. Cibersort was applied to evaluate the immune cells infiltration status. Differential expression analysis and Gene set enrichment analysis (GSEA) were performed to characterize the molecular pattern of different subtypes. Multivariate Cox regression analysis was used for prognosis prediction model construction and receiver operating characteristic (ROC) curve was used for performance evaluation. The hub genes in the model were verified in single-cell sequencing dataset and clinical specimens. In vitro experiments were performed to validate the findings in our research.Results: Three subtypes were identified with prognostic implications. C1 subgroup was in an immunosuppressive state with activation of mitochondrial cytochrome P450 metabolism, C2 had poor immune cells infiltration and was characterized by tRNA anabolism, and the C3 subgroup was in an inflammatory state with activation of aromatic amino acid synthesis. The area under the ROC curve of the constructed model was 0.8, which showed better performance than clinical features. IMPDH1 was found to be significantly upregulated in tumor tissue and it was demonstrated that IMPDH1 could be a novel therapeutic target in vitro.Discussion: In summary, our findings suggested novel molecular subtypes of cervical cancer with distinct immunometabolic profiles and uncovered a novel therapeutic target.

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Section: Discussionmentioning

confidence: 99%

Characterization of the microenvironment in different immune-metabolism subtypes of cervical cancer with prognostic significance

Lai

Liu²,

Li³

et al. 2023

Front. Genet.

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“…Heme biosynthesis is closely linked to the TCA cycle, from which it derives succinyl-CoA [17]. This cataplerotic reaction is likely taking place in HMBS-deficient HCCs, as suggested by the compensatory upregulation of citrate synthase observed in these tumors [13]. Among the consequences of exacerbated succinyl-CoA consumption can be a depletion in the levels of its precursor -ketoglutarate (-KG).…”

Section: J O U R N a L P R E -P R O O Fmentioning

confidence: 99%

“…The notion that metabolic genes could act as tumor suppressors was already put forward about twenty years ago, when, for instance, mice deficient in methionine-adenosyltransferase-1 were found to spontaneously develop HCC [11], or mutations in tricarboxylic acid cycle (TCA) genes were identified as cancer drivers [12]. Now, Molina and collaborators delved into this issue by analyzing HMBS mutations in four HCC tissues from three diagnosed AIP patients, and screening two large cohorts of sporadic HCC cases (754 total) [13].…”

mentioning

confidence: 99%

Hydroxymethylbilane synthase (aka porphobilinogen deaminase): A novel metabolic tumor suppressor gene in hepatocellular carcinoma

Fontanellas¹,

Avila²

2022

Journal of Hepatology

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“…We used an in-house series of 401 liver cancers with matched whole exome (WES, n=275) / genome (WGS, n=126) sequencing and RNA-seq data to benchmark the performance of SpliceAI. Initial bioinformatic analyses (alignment and variant calling) were already performed in previous studies (19)(20)(21)(22)(23)(24)(25) and we directly used processed data in this study. We then analyzed a large pan-cancer series comprising 17,714 tumor samples across 25 cancer types from the ICGC-PCAWG (n=2,699 WGS) (26), Hartwig Medical Foundation (n=4,818 WGS) (27) and TCGA (n=10,197 WES) (28) projects.…”

Section: Datasetsmentioning

confidence: 99%

“…We used an in-house series of 401 liver cancers (19)(20)(21)(22)(23)(24)(25) with whole exome (n=275) or whole genome sequencing (n=126) and matched RNA-seq data to predict and validate cryptic splice mutations using SpliceAI, MaxEntScan and GeneSplicer (Figure 1A). A total of 1,704,461 somatic mutations were analyzed with the three tools.…”

Section: Spliceai Outperforms Traditional Tools For Predicting Crypti...mentioning

confidence: 99%

Discovering cryptic splice mutations in cancers via a deep neural network framework

Teboul

Grabias

Zucman‐Rossi

et al. 2022

Preprint

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Somatic mutations can disrupt splicing regulatory elements and have dramatic effects on cancer genes, yet the functional consequences of mutations located in extended splice regions is difficult to predict. Here, we use a deep neural network (SpliceAI) to characterize the landscape of splice-altering mutations in cancer. In our in-house liver cancer series, SpliceAI uncovers many cryptic splice mutations, located outside essential splice sites, that validate at a high rate in matched RNA-seq data. We then extend the analysis to a large pan-cancer cohort of 18,115 tumors, revealing >100,000 cryptic splice mutations. Taking into account these mutations increases the power of driver gene discovery, revealing >100 new candidate driver genes. It also reveals new driver mutations in known cancer genes, doubling the frequency of splice alterations in tumor suppressor genes. Mutational signature analysis reveals the mutational processes that give rise to splice mutations in each cancer type, with an enrichment of signatures related to clock-like processes and DNA repair deficiency. Altogether, this work sheds light on the causes and impact of cryptic splice mutations in cancer, and highlights the power of deep learning approaches to better annotate the functional consequences of mutations in oncology.

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Bi-allelic hydroxymethylbilane synthase inactivation defines a homogenous clinico-molecular subtype of hepatocellular carcinoma

Cited by 20 publications

References 57 publications

Characterization of the microenvironment in different immune-metabolism subtypes of cervical cancer with prognostic significance

Characterization of the microenvironment in different immune-metabolism subtypes of cervical cancer with prognostic significance

Hydroxymethylbilane synthase (aka porphobilinogen deaminase): A novel metabolic tumor suppressor gene in hepatocellular carcinoma

Discovering cryptic splice mutations in cancers via a deep neural network framework

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