Barriers to accrual and enrollment in brain tumor trials.

Many factors contribute to the poor survival of malignant brain tumor patients, some of which are not easily remedied. However, one contributor to the lack of progress that may be modifiable is poor clinical trial accrual. Surveys of brain tumor patients and neuro-oncology providers suggest that clinicians do a poor job of discussing clinical trials with patients and referring patients for clinical trials. Yet, data from the Cancer Action Network of the American Cancer Society suggest that most eligible oncology patients asked to enroll on a clinical trial will agree to do so. To this end, the Society for Neuro-Oncology (SNO) in collaboration with the Response Assessment in Neuro-Oncology (RANO) Working Group, patient advocacy groups, clinical trial cooperative groups, including the Adult Brain Tumor Consortium (ABTC), and other partners are working together with the intent to double clinical trial accrual over the next 5 years. Here we describe the factors contributing to poor clinical trial accrual in neuro-oncology and offer possible solutions.

Renal medullary carcinomas depend upon SMARCB1 loss and are sensitive to proteasome inhibition.

Renal medullary carcinoma (RMC) is a rare and deadly kidney cancer in patients of African descent with sickle cell trait. We have developed faithful patient-derived RMC models and using whole-genome sequencing, we identified loss-of-function intronic fusion events in one SMARCB1 allele with concurrent loss of the other allele. Biochemical and functional characterization of these models revealed that RMC requires the loss of SMARCB1 for survival. Through integration of RNAi and CRISPR-Cas9 loss-of-function genetic screens and a small-molecule screen, we found that the ubiquitin-proteasome system (UPS) was essential in RMC. Inhibition of the UPS caused a G2/M arrest due to constitutive accumulation of cyclin B1. These observations extend across cancers that harbor SMARCB1 loss, which also require expression of the E2 ubiquitin-conjugating enzyme, UBE2C. Our studies identify a synthetic lethal relationship between SMARCB1-deficient cancers and reliance on the UPS which provides the foundation for a mechanism-informed clinical trial with proteasome inhibitors.

Leveraging molecular datasets for biomarker-based clinical trial design in glioblastoma.

BACKGROUND: Biomarkers can improve clinical trial efficiency, but designing and interpreting biomarker-driven trials require knowledge of relationships among biomarkers, clinical covariates, and endpoints. We investigated these relationships across genomic subgroups of glioblastoma (GBM) within our institution (DF/BWCC), validated results in The Cancer Genome Atlas (TCGA), and demonstrated potential impacts on clinical trial design and interpretation. METHODS: We identified genotyped patients at DF/BWCC, and clinical associations across 4 common GBM genomic biomarker groups were compared along with overall survival (OS), progression-free survival (PFS), and survival post-progression (SPP). Significant associations were validated in TCGA. Biomarker-based clinical trials were simulated using various assumptions. RESULTS: Epidermal growth factor receptor (EGFR)(+) and p53(-) subgroups were more likely isocitrate dehydrogenase (IDH) wild-type. Phosphatidylinositol-3 kinase (PI3K)(+) patients were older, and patients with O6-DNA methylguanine-methyltransferase (MGMT)-promoter methylation were more often female. OS, PFS, and SPP were all longer for IDH mutant and MGMT methylated patients, but there was no independent prognostic value for other genomic subgroups. PI3K(+) patients had shorter PFS among IDH wild-type tumors, however, and no DF/BWCC long-term survivors were either EGFR(+) (0% vs 7%, P = .014) or p53(-) (0% vs 10%, P = .005). The degree of biomarker overlap impacted the efficiency of Bayesian-adaptive clinical trials, while PFS and OS distribution variation had less impact. Biomarker frequency was proportionally associated with sample size in all designs. CONCLUSIONS: We identified several associations between GBM genomic subgroups and clinical or molecular prognostic covariates and validated known prognostic factors in all survival periods. These results are important for biomarker-based trial design and interpretation of biomarker-only and nonrandomized trials.