The Neurologic Assessment in Neuro-Oncology (NANO) scale: A tool to assess neurologic function for integration into the Response Assessment in Neuro-Oncology (RANO) criteria

__Background__ The Macdonald criteria and the Response Assessment in Neuro-Oncology (RANO) criteria defne radiologic parameters to classify therapeutic outcome among patients with malignant glioma and specify that clinical status must be incorporated and prioritized for overall assessment. But neither provides specifc parameters to do so. We hypothesized that a standardized metric to measure neurologic function will permit more effective overall response assessment in neuro-oncology. __Methods__ An international group of physicians including neurologists, medical oncologists, radiation oncologists, and neurosurgeons with expertise in neuro-oncology drafted the Neurologic Assessment in Neuro-Oncology (NANO) scale as an objective and quantifable metric of neurologic function evaluable during a routine offce examination. The scale was subsequently tested in a multicenter study to determine its overall reliability, interobserver variability, and feasibility. __Results__ The NANO scale is a quantifable evaluation of 9 relevant neurologic domains based on direct observation and testing conducted during routine offce visits. The score defnes overall response criteria. A prospective, multinational study noted a >90% inter-observer agreement rate with kappa statistic ranging from 0.35 to 0.83 (fair to almost perfect agreement), and a median assessment time of 4 minutes (interquartile range, 3-5). __Conclusion__ The NANO scale provides an objective clinician-reported outcome of neurologic function with high inter-observer agreement. It is designed to combine with radiographic assessment to provide an overall assessment of outcome for neuro-oncology patients in clinical trials and in daily practice. Furthermore, it complements existing patient-reported outcomes and cognition testing to combine for a global clinical outcome assessment of well-being among brain tumor patients

R-MPV followed by high-dose chemotherapy with TBC and autologous stem-cell transplant for newly diagnosed primary CNS lymphoma

High-dose methotrexate-based chemotherapy is the mainstay of treatment of primary central nervous system lymphoma (PCNSL), but relapses remain frequent. High-dose chemotherapy (HDC) with autologous stem-cell transplant (ASCT) may provide an alternative to address chemoresistance and overcome the blood-brain barrier. In this single-center phase-2 study, newly diagnosed PCNSL patients received 5 to 7 cycles of chemotherapy with rituximab, methotrexate (3.5 g/m(2)), procarbazine, and vincristine (R-MPV). Those with a complete or partial response proceeded with consolidation HDC with thiotepa, cyclophosphamide, and busulfan, followed by ASCT and no radiotherapy. Primary end point was 1-year progression-free survival (PFS), N = 32. Median age was 57, and median Karnofsky performance status 80. Following R-MPV, objective response rate was 97%, and 26 (81%) patients proceeded with HDC-ASCT. Among all patients, median PFS and overall survival (OS) were not reached (median follow-up: 45 months). Two-year PFS was 79% (95% confidence interval [CI], 58-90), with no events observed beyond 2 years. Two-year OS was 81% (95% CI, 63-91). In transplanted patients, 2-year PFS and OS were 81%. There were 3 treatment-related deaths. Prospective neuropsychological evaluations suggested relatively stable cognitive functions posttransplant. In conclusion, this treatment was associated with excellent disease control and survival, an acceptable toxicity profile, and no evidence of neurotoxicity thus far. This trial was registered at www.clinicaltrials.gov as NCT00596154

Sequencing and curation strategies for identifying candidate glioblastoma treatments

Abstract Background Prompted by the revolution in high-throughput sequencing and its potential impact for treating cancer patients, we initiated a clinical research study to compare the ability of different sequencing assays and analysis methods to analyze glioblastoma tumors and generate real-time potential treatment options for physicians. Methods A consortium of seven institutions in New York City enrolled 30 patients with glioblastoma and performed tumor whole genome sequencing (WGS) and RNA sequencing (RNA-seq; collectively WGS/RNA-seq); 20 of these patients were also analyzed with independent targeted panel sequencing. We also compared results of expert manual annotations with those from an automated annotation system, Watson Genomic Analysis (WGA), to assess the reliability and time required to identify potentially relevant pharmacologic interventions. Results WGS/RNAseq identified more potentially actionable clinical results than targeted panels in 90% of cases, with an average of 16-fold more unique potentially actionable variants identified per individual; 84 clinically actionable calls were made using WGS/RNA-seq that were not identified by panels. Expert annotation and WGA had good agreement on identifying variants [mean sensitivity = 0.71, SD = 0.18 and positive predictive value (PPV) = 0.80, SD = 0.20] and drug targets when the same variants were called (mean sensitivity = 0.74, SD = 0.34 and PPV = 0.79, SD = 0.23) across patients. Clinicians used the information to modify their treatment plan 10% of the time. Conclusion These results present the first comprehensive comparison of technical and machine augmented analysis of targeted panel and WGS/RNA-seq to identify potential cancer treatments