Integrated Genomic Analysis Identifies Driver Genes and Cisplatin-Resistant Progenitor Phenotype in Pediatric Liver Cancer

Abstract

Pediatric liver cancers (PLC) comprise diverse diseases affecting infants, children, and adolescents. Despite overall good prognosis, PLCs display heterogeneous response to chemotherapy. Integrated genomic analysis of 126 pediatric liver tumors showed a continuum of driver mechanisms associated with patient age, including new targetable oncogenes. In 10% of patients with hepatoblastoma, all before three years old, we identified a mosaic premalignant clonal expansion of cells altered at the 11p15.5 locus. Analysis of spatial and longitudinal heterogeneity revealed an important plasticity between "hepatocytic," "liver progenitor," and "mesenchymal" molecular subgroups of hepatoblastoma. We showed that during chemotherapy, "liver progenitor" cells accumulated massive loads of cisplatin-induced mutations with a specific mutational signature, leading to the development of heavily mutated relapses and metastases. Drug screening in PLC cell lines identified promising targets for cisplatin-resistant progenitor cells, validated in mouse xenograft experiments. These data provide new insights into cisplatin resistance mechanisms in PLC and suggest alternative therapies. SIGNIFICANCE: PLCs are deadly when they resist chemotherapy, with limited alternative treatment options. Using a multiomics approach, we identified PLC driver genes and the cellular phenotype at the origin of cisplatin resistance. We validated new treatments targeting these molecular features in cell lines and xenografts.This article is highlighted in the In This Issue feature, p. 2355.

Figure 1.. Genomic landscape of pediatric liver cancers.

a Number of somatic mutations and structural variants identified in 65 pediatric liver cancers (PLC) by whole genome sequencing (WGS). Samples are ordered by diagnosis and mutation burden. Alteration types are indicated with a color code, and specific structural variant phenotypes are highlighted. b Heatmap representation of driver alterations across 122 PLC analyzed by WGS or whole exome sequencing (WES). Samples are ordered by diagnosis and patient age at sampling. c Frequency of copy-number alterations along the genome for 122 PLC analyzed by WGS or WES. The top axis indicates the frequency of low-amplitude changes (gains, losses and losses of heterozygosity (LOH)); the bottom axis indicates the frequency of high-amplitude changes (focal amplifications and homozygous deletions). Target genes of amplifications and homozygous deletions are indicated. Correlation between gene expression (variance-stabilized) and copy-number is displayed for 3 selected genes.

Figure 2.. Pre-malignant clonal expansions with 11p15 alteration in hepatoblastoma patients.

a Identification of a copy-neutral LOH (cn-LOH) at 11p15 locus in the non-tumor liver of patient #3559. B Allele frequencies (BAF) of heterozygous single-nucleotide polymorphisms (SNPs) are represented along chromosome 11. SNPs with a BAF greater (resp. lower) than 0.5 in the tumor are colored in red (resp. blue). In the cn-LOH region, red (resp. blue) SNPs correspond to those for which the B allele was retained (resp. lost). The same BAF imbalance is identified in the non-tumor sample, with the same boundaries, demonstrating the presence of the cn-LOH. The amplitude of BAF changes indicate that the cn-LOH is present in 30% of cells in the non-tumor sample. b Pre-malignant expansions with cn-LOH at 11p15 locus were identified in 6 hepatoblastoma patients. The proportion of non-tumor cells harboring the alteration is indicated below. Copy-neutral LOH became clonal in matched hepatoblastoma that had acquired in addition activating CTNNB1 mutations. c Expression levels of IGF2 and the β-catenin targets GLUL, LGR5, AXIN2 (2^-ΔΔCt ) in tumor and non-tumor samples from patients with and without 11p15.5 cn-LOH premalignant expansions. d Representative areas of FFPE slides from 2 HB patients: patient #3559 with mosaic 11p15.5 cn-LOH and control patient #4217. Four types of staining were performed: hematoxylin and eosin staining (H&E), IGF2 RNAscope in situ hybridization, and β-catenin and Glutamine synthetase immunostainings.

Figure 3.. Molecular plasticity of hepatoblastoma across three differentiation states.

a Gene expression-based classification of hepatoblastoma. Unsupervised hierarchical clustering of 100 HB samples from 64 patients and 4 non-tumor liver samples revealed 4 molecular groups. Clinical and molecular annotations are depicted below the dendrogram, with p-values indicating their association with molecular groups. A heatmap shows the expression of key transcription factors (TF) and marker genes representative of each group, as well as molecular scores of hepatic differentiation, cell proliferation and immune infiltration. b Top: Proportion of transcriptomic groups and a selection of driver alteration frequencies at different steps of hepatoblastoma progression. Middle: Transcriptomic group switches identified in 24 patients with multiple sampling, including patients with pre/post-chemotherapy samples, synchronous samples at distinct locations in the primary tumor, and/or paired primary and relapse/metastasis. The number of patients with/without molecular switch is indicated for each type of multiple sampling, and the transcriptomic switch is represented by a color code on the arrows. Bottom: Examples of histological heterogeneity matching transcriptomic group switches. c Projection of hepatoblastomas and non-tumor liver samples over two independent methylation components. Hepatoblastomas are colored by their transcriptomic group, and samples from a same patient are linked with black lines. d Association of intra-sample histological heterogeneity with transcriptomic groups. e Association of intra-tumor histological heterogeneity with 11p15.5 locus alteration.

Figure 4.. Single-nucleus RNA-seq reveals molecular plasticity along tumor progression in one patient.

a Virtual copy-number profiles discriminate tumor and normal cells. In agreement with WES data, all tumor cells display gains at 1_q, 2_q, 5_q and 15_q whereas the X_q gain is specific to metastatic cells. b Uniform manifold approximation and projection (UMAP) of tumor nuclei in the primary tumor (top) and metastasis (bottom). Mean expression of marker genes for each differentiation state are displayed with a color code. The last UMAP on the right shows the assignment of each cell cluster to ‘Mesenchymal’, ‘Liver Progenitor’ or ‘Hepatocytic’ cell type based on the expression of marker genes. c Schematic representation of genetic and non-genetic evolution in patient #3981. LCA: Last Common Ancestor; M: Mesenchymal; LP: Liver Progenitor; H: Hepatocytic.

Figure 5.. Massive load of cisplatin-induced mutations in chemoresistant hepatoblastomas.

a Single base substitution (SBS) signatures identified in pediatric liver cancers (PLC). Each signature is displayed according to the 96-substitution classification defined by substitution type and sequence context immediately 5′ and 3′ to the mutated base. b Unsupervised classification of 65 PLC genomes based on their mutational signature exposures. Clinical and molecular annotations are depicted below the dendrogram. Bar graphs indicate the proportion of the 4 single base substitution (SBS), 2 doublet base substitution (DBS) and 5 indel (ID) signatures in each sample. c Number of subclonal (left) and clonal (right) mutations attributed to signature SBS35 (cisplatin) in hepatoblastoma samples according to sample type (pre-chemotherapy biopsy, primary tumor or relapse / metastasis) and molecular group (Hepatocytic or Mesenchymal vs. Liver Progenitor). d Phylogenetic trees reconstructed for 8 HB patients with primary tumors and relapses / metastases analyzed by WGS or WES. The time and molecular group of each sample is shown above the trees, together with chemotherapy treatment. Driver alterations are indicated. Branch lengths are proportional to the number of mutations acquired, with a color code indicating the contribution of each mutational signature.

Figure 6.. New therapeutic strategies targeting hepatoblastoma based on molecular features.

a Heatmap representation of somatic alterations in 9 pediatric liver cancer cell lines (PL-CCL) and corresponding patient age at diagnosis (Top). b Correlation between sensitivity to cisplatin assessed with area under the curve (AUC) and ‘liver progenitor’ 7-genes expression signature. Color gradient intensity reflects levels of ‘Liver Progenitor’ signature. Pearson correlation test was performed. c Accumulation of SBS35 signature in 4/5 PL-CCL. Trapezium indicates cell passaging and triangle symbolized cell line limit dilution and clonal expansion. d Correlation between sensitivity to neutralizing antibody anti-IGF2 (AUC) and IGF2 expression in 5 PL-CCL (Pearson test). e Trametinib sensitivity (AUC) in 9 PL-CCL (top) and corresponding genetic alterations in Ras MAPK pathway (bottom). f Volcano plot representing differentially expressed genes in HB belonging to Hepatocytic (H-hot and H-cold) and ‘Liver Progenitor’ transcriptomic subgroups. g PLK1, BIRC5 and CHEK1 interactions and roles in cell cycle, DNA damage response and apoptosis. h Drug response assessed with AUC in 9 PL-CCL. Color gradient intensity reflects levels of ‘Liver Progenitor’ signature. Pearson correlations are performed between AUC and levels of ‘Liver progenitor’ signature. i Sensitivity to Cisplatin, Doxorubicin and BI-2536 in vivo in HepG2 xenografts from 24 nude mice and j mice weight follow-up. Mann-Whitney-Wilcoxon tests were performed at day 37.