The genetic etiology of childhood cancers still remains largely unknown. It is therefore essential to develop novel strategies to unravel the spectrum of pediatric cancer genes. Statistical network modeling techniques have emerged as powerful methodologies for enabling the inference of gene-disease relationship and have been performed on adult but not pediatric cancers. We performed a deep multi-layer understanding of pan-cancer transcriptome data selected from the Treehouse Childhood Cancer Initiative through a co-expression network analysis. We identified six modules strongly associated with pediatric tumor histotypes that were functionally linked to developmental processes. Topological analyses highlighted that pediatric cancer predisposition genes and potential therapeutic targets were central regulators of cancer-histotype specific modules. A module was related to multiple pediatric malignancies with functions involved in DNA repair and cell cycle regulation. This canonical oncogenic module gathered most of the childhood cancer predisposition genes and clinically actionable genes. In pediatric acute leukemias, the driver genes were co-expressed in a module related to epigenetic and post-transcriptional processes, suggesting a critical role of these pathways in the progression of hematologic malignancies. This integrative pan-cancer study provides a thorough characterization of pediatric tumor-associated modules and paves the way for investigating novel candidate genes involved in childhood tumorigenesis.

Trastuzumab has significantly improved the overall survival of patients with HER2+ metastatic breast cancer (MBC). However, outcomes can vary, with patients progressing within 1 year of treatment or exceptional cases of complete response to trastuzumab for ≥10 years. Identification of the underlying genomic aberrations of “exceptional responders (ExRs)” compared to “rapid non-responders (NRs)” increases our understanding of the mechanisms involved in MBC progression and identification of biomarkers of trastuzumab response and resistance. Whole-exome sequencing was performed on six ExRs compared to five NR. The overall fraction of genome copy number alteration (CNA) burden was higher in NR patients (P = 0.07), while more significantly pronounced in copy number gains (P = 0.03) in NR compared to ExRs. Delineation of the distribution of CNA burden across the genome identified a greater degree of CNA burden in NR within Chr8 (P = 0.02) and in Chr17 (P = 0.06) and conferred a statistically significant benefit in overall survival. Clinical trial number: NCT01722890 [ICORG 12/09].

Molecular and histopathological analysis of melanoma subtypes has revealed distinct epidemiological, genetic, and clinical features. However, immunotherapy for advanced metastatic melanoma patients does not differ based on subtype. Response to immune checkpoint inhibitors (ICI) has been shown to vary, therefore, predictive biomarkers are needed in the design of precision treatments. Targeted sequencing and histopathological analysis (CD8 and CD20 immunohistochemistry) were performed on subtypes of metastatic melanoma (cutaneous melanoma (CM, n = 10); head and neck melanoma (HNM, n = 7); uveal melanoma (UM, n = 4); acral lentiginous melanoma (AM, n = 1) and mucosal melanoma (MM, n = 1) treated with ICI). Progression-free survival (PFS) was significantly associated with high CD8 expression (p = 0.025) and mutations in DNA damage repair (DDR) pathway genes (p = 0.012) in all subtypes but not with CD20 expression. Our study identified that immune cell infiltration and DDR gene mutations may have an impact in response to ICI treatment in metastatic melanoma but differs among subtypes. Therefore, a comprehensive understanding of the immune infiltration cells’ role and DDR gene mutations in metastatic melanoma may identify prognostic biomarkers.

Pancreatic ductal adenocarcinoma (PDAC) is a highly fatal disease with limited treatment options. PARP inhibitors (PARPi) have shown promise in treating PDAC with homologous recombination deficiency (HRD), but rapid acquisition of resistance limits their efficacy. Our objective is to investigate mechanisms of resistance to PARPi in BRCA2‐mutant PDAC cells and identify potential therapeutic targets to modulate this resistance. We developed olaparib‐ and talazoparib‐resistant Capan‐1 cell lines and characterised their resistance profiles using viability assays, RNA sequencing and metabolomic profiling. We also developed a cisplatin‐resistant Capan‐1 cell line to compare resistance mechanisms between PARPi and platinum agents. Both olaparib‐ and talazoparib‐resistant cells showed cross‐resistance to other PARPi and oxaliplatin, but not to gemcitabine or 5‐FU. Talazoparib‐resistant cells exhibited a similar resistance profile to cisplatin‐resistant cells, including decreased PARP1 expression and altered metabolomic profiles. RNA sequencing and metabolomic profiling revealed significant enrichment of metabolic pathways, including oxidative phosphorylation and glycolysis, in resistant cells. Our study highlights the complexity of resistance mechanisms to PARPi in PDAC and identifies potential therapeutic targets in metabolism. The differences in the resistance profiles between olaparib and talazoparib suggest that PARP‐trapping potency may play a role in resistance development. Further research is needed to validate these findings and explore novel therapeutic strategies to overcome resistance to PARPi in PDAC.

E13019 Background: The introduction of anti-HER2 therapies such as trastuzumab for HER2+ metastatic breast cancer (MBC) has led to significant improvements to disease progression. We and others have reported long-term durable complete response to trastuzumab in patients with HER2+ MBC. We hypothesise that genomic copy number alteration (CNA) and tumour mutational burden (TMB) may act as a prognostic measure of predicting response to trastuzumab in long-term survivors (LTS) compared to short-term survivors (STS). Using whole-genome sequencing (WGS) to detect these alterations, we aim to identify the molecular characteristics of primary and metastatic lesions in patients with metastatic HER2+ breast cancer. Methods: WGS was performed on samples from two patients from a HER2+ MBC cohort; a of long-term survivor (PFS 27 months; OS > 60 months) and a short-term survivor (PFS 4 months; OS < 14 months). Primary tumours, adjacent normal tissue and metastases were sequenced at a mean depth of 60X. Reads were aligned on the hg38 reference genome using Burrows-Wheeler Aligner (bwa), CNA were detected using Control-FREEC to evaluate total CNA burden. Single nucleotide variants were detected using GATK Mutect2 to calculate TMB. Results: Analysis of DNA chromosome disruption (fraction of the genome amplified/deleted) revealed a higher overall fraction of disrupted genome in the LTS primary tumour and metastasis compared with the STS (0.061 and 0.064 vs 0.031 and 0.047, respectively). Further delineation of the distribution of CNA burden in all genomes identified chr1 as the most altered in the STS samples whereas chr17 was the most disrupted in the LTS samples. HER2 and CDK12 were amplified in both LTS and STS samples (4 copies in LTS primary tumour and metastasis, 18 and 14 copies in STS primary tumour and metastasis respectively). A high TMB was estimated in both samples but was higher in the STS primary tumour than in the LTS one (161 vs 142, respectively). TMB was increased in LTS metastatic lesions. NRAS mutation was found in LTS primary tumour but was lost in the metastasis. Conclusions: This pilot study highlights the potential for whole-genome sequencing to identify CNA and TMB in HER2+ MBC in patients who display long-term and short-term survival. Further samples from this cohort need to be studied to investigate the relationship between CNA burden and HER2+ MBC patient survival.

L’hétérogénéité tumorale est une conséquence directe de la dynamique évolutive non observable de la croissance des tumeurs. Elle fait partie des challenges actuels en oncologie car elle est à l’origine de la résistance aux thérapies anti-cancer actuelles, soit par l’expansion d’une population clonale présente résistante, soit par une résistance acquise à la suite de l’exposition aux traitements. Pouvoir caractériser ces clones permettrait de mieux comprendre l’évolution des cancers, et ainsi identifier de nouveaux marqueurs pour les futures thérapies ciblées. C’est dans cet esprit que SomaSnake a été développé : permettre des analyses reproductibles et capables d’apporter une aide pour la compréhension des réponses des cancers aux traitements actuels. SomaSnake est un outil bioinformatique qui a pour but d’harmoniser et de faciliter les analyses de données de caractérisation des génomes tumoraux, particulièrement dans le cas où ces analyses sont étagées dans le temps. En effet, cette tâche peut s’avérer complexe lorsqu’il s’agit d’être en mesure de retrouver avec exactitude les mêmes résultats à partir d’une réanalyse à distance des mêmes données brutes, mais également lorsqu’il s’agit d’en intégrer de nouvelles. SomaSnake est un package conda facile d’installation et d’utilisation permettant de réaliser des analyses classiques telles que le traitement des reads, l’appel des variants somatiques et leur annotation, mais aussi des analyses plus complexes visant à reconstruire les populations clonales et sous clonales des tumeurs.