Spatial heterogeneity in medulloblastoma

Spatial heterogeneity of transcriptional and genetic markers between physically isolated biopsies of a single tumor poses major barriers to the identification of biomarkers and the development of targeted therapies that will be effective against the entire tumor. We analyzed the spatial heterogeneity of multiregional biopsies from 35 patients, using a combination of transcriptomic and genomic profiles. Medulloblastomas (MBs), but not high-grade gliomas (HGGs), demonstrated spatially homogeneous transcriptomes, which allowed for accurate subgrouping of tumors from a single biopsy. Conversely, somatic mutations that affect genes suitable for targeted therapeutics demonstrated high levels of spatial heterogeneity in MB, malignant glioma, and renal cell carcinoma (RCC). Actionable targets found in a single MB biopsy were seldom clonal across the entire tumor, which brings the efficacy of monotherapies against a single target into question. Clinical trials of targeted therapies for MB should first ensure the spatially ubiquitous nature of the target mutation

Inconsistency in large pharmacogenomic studies

Cancer cell line studies have long been used to test efficacy of therapeutic agents and to explore genomic factors predictive of response(1,2). Two large-scale pharmacogenomic studies were published recently(3,4); each assayed a panel of several hundred cancer cell lines for gene expression, copy number, genome sequence, and pharmacological response to multiple anti-cancer drugs. The resulting datasets present a unique opportunity to characterize mechanisms associated with drug response, with 471 cell lines and 15 drugs assayed in both. However, while gene expression is well correlated between studies, the measured pharmacologic drugs response is highly discordant. This poor correspondence is surprising as both studies assessed drug response using common estimators: the IC(50) (concentration at which the drug inhibited 50% of the maximal cellular growth), and the AUC (area under the activity curve measuring dose response)(5). For drugs screened in both studies, only one had a Spearman correlation coefficient in measured response greater than 0.6. Importantly these results are also reflected in inconsistent associations between genomic features and drug response. Although the source of inconsistencies in drug response measures between these two well-controlled studies remains uncertain, it makes drawing firm conclusions about response very difficult and has potential implications for using these outcome measures to assess gene-drug relationships or select potential anti-cancer drugs based on their reported results. Our findings suggest standardization of response measurement protocols in pharmacogenomic studies is essential before such studies can live up to their promise

Biomarker analysis from CheckMate 214: nivolumab plus ipilimumab versus sunitinib in renal cell carcinoma

Background The phase 3 CheckMate 214 trial demonstrated higher response rates and improved overall survival with nivolumab plus ipilimumab versus sunitinib in first-line therapy for advanced clear-cell renal cell carcinoma (RCC). An unmet need exists to identify patients with RCC who are most likely to benefit from treatment with nivolumab plus ipilimumab.Methods In exploratory analyses, pretreatment levels of programmed death ligand 1 were assessed by immunohistochemistry. Genomic and transcriptomic biomarkers (including tumor mutational burden and gene expression signatures) were also investigated.Results Biomarkers previously associated with benefit from immune checkpoint inhibitor-containing regimens in RCC were not predictive for survival in patients with RCC treated with nivolumab plus ipilimumab. Analysis of gene expression identified an association between an inflammatory response and progression-free survival with nivolumab plus ipilimumab.Conclusions The exploratory analyses reveal relationships between molecular biomarkers and provide supportive data on how the inflammation status of the tumor microenvironment may be important for identifying predictive biomarkers of response and survival with combination immunotherapy in patients with RCC. Further validation may help to provide biomarker-driven precision treatment for patients with RCC