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van Leenders, G. J. L. H.

Publications and source records attributed to van Leenders, G. J. L. H..

3 recordsLinked to original sources

Integrative analysis of patient-derived tumoroids and ex vivo organoid modeling of ARID1A loss in bladder cancer reveals therapeutic molecular targets

Somatic mutations in ARID1A (AT-rich interactive domain-containing protein 1A) are present in approximately 25% of bladder cancers (BC) and are associated with poor prognosis. With a view to discover effective treatment options for ARID1A-deficient BC patients, we set out to identify targetable effectors dysregulated consequent to ARID1A deficiency. Integrative analyses of ARID1A depletion in normal organoids and data mining in publicly available datasets revealed upregulation of DNA repair and cell cycle-associated genes consequent to loss of ARID1A and identified CHEK1 (Checkpoint kinase 1) and chromosomal passenger complex member BIRC5 (Baculoviral IAP Repeat Containing 5) as therapeutically drug-able candidate molecular effectors. Ex vivo treatment of patient-derived BC tumoroids with clinically advanced small molecule inhibitors targeting CHEK1 or BIRC5 was associated with increased DNA damage signalling and apoptosis, and selectively induced cell death in tumoroids lacking ARID1A protein expression. Thus, integrating public datasets with patient-derived organoid modelling and ex-vivo drug testing can uncover key molecular effectors and mechanisms of oncogenic transformation, potentially leading to novel therapeutic strategies. Our data point to ARID1A protein expression as a suitable candidate biomarker for the selection of BC patients responsive to therapies targeting BIRC5 and CHEK1.

cancer biology↗

T Cell-to-Stroma Enrichment (TSE) score: a gene expression metric that predicts response to immune checkpoint inhibitors in patients with urothelial cancer

Immune checkpoint inhibitors (ICIs) improve overall survival in patients with metastatic urothelial cancer (mUC). To identify predictive markers of response, whole-genome DNA (n=70) and RNA-sequencing (n=41) were performed using fresh metastatic biopsies prior to treatment with pembrolizumab. PD-L1 combined positivity score did not, whereas tumor mutational burden and APOBEC mutagenesis modestly predicted response. Using gene expression analysis, we defined the T cell-to-stroma enrichment (TSE) score, a signature-based metric that captures the relative abundance of T cells and stromal cells. Patients with a positive and negative TSE score show progression-free survival rates at 6 months of 67 and 0%, respectively. The TSE score was captured by immunofluorescence in tumor tissue, and validated in two independent ICI-treated cohorts of patients with mUC (IMvigor210) and muscle-invasive UC (ABACUS). In conclusion, the TSE score represents a clinically applicable marker that potentially aids in prospectively selecting patients with mUC for ICI treatment.

cancer biology↗

Genomic and transcriptomic landscape of advanced renal cell cancer to individualize treatment strategy

BackgroundDifferences in the clinical course and treatment responses in individual patients with advanced renal cell carcinoma (RCC) can largely be explained by the different genomics of this disease. To improve the personalized treatment strategy and survival outcomes for patients with advanced RCC, the genomic make-up in patients with advanced RCC was investigated to identify putative actionable mutations and signatures. MethodsIn this prospective multicenter study (NCT01855477), whole-genome sequencing (WGS) data of locally advanced and metastatic tissue biopsies and matched whole-blood samples were collected from 91 patients with histopathologically confirmed RCC. WGS data were analyzed for small somatic variants, copy-number alterations and structural variants. For a subgroup of patients, RNA sequencing (RNA-Seq) data could be analyzed. RNA-Seq data were clustered on immunogenic and angiogenic gene expression patterns according to a previously developed angio-immunogenic gene signature. ResultsFor papillary and clear cell RCC, putative actionable drug targets were detected by WGS in 100% of the patients. RNA-Seq data of clear cell and papillary RCC were clustered using a previously developed angio-immunogenic gene signature. Analyses of driver mutations and RNA-Seq data revealed clear differences among different RCC subtypes, showing the added value of WGS and RNA-Seq over clinicopathological data. ConclusionsBy improving both histological subtyping and the selection of treatment according to actionable targets and immune signatures, WGS and RNA-Seq may improve therapeutic decision making for most patients with advanced RCC, including patients with non-clear cell RCC for whom no standard treatment is available to data. Prospective clinical trials are needed to evaluate the impact of genomic and transcriptomic diagnostics on survival outcome for advanced RCC patients.

cancer biology↗