Sex differences in the incidence and outcome of human disease are broadly recognized, but in most cases, not sufficiently understood to enable sex-specific approaches to treatment. Glioblastoma (GBM), the most common malignant brain tumor, provides a case in point. Despite well-established differences in incidence and emerging indications of differences in outcome, there are few insights that distinguish male and female GBM at the molecular level or allow specific targeting of these biological differences. Here, using a quantitative imaging-based measure of response, we found that standard therapy is more effective in female compared to male GBM patients. We then applied a computational algorithm to linked GBM transcriptome and outcome data and identified sex-specific molecular subtypes of GBM in which cell cycle and integrin signaling are the critical determinants of survival for male and female patients, respectively. The clinical utility of cell cycle and integrin signaling pathway signatures was further established through correlations between gene expression and in vitro chemotherapy sensitivity in a panel of male and female patient-derived GBM cell lines. Together these results suggest that greater precision in GBM molecular subtyping can be achieved through sex-specific analyses and that improved outcomes for all patients might be accomplished by tailoring treatment to sex differences in molecular mechanisms.

Aims To assess the effect of a multi-component primary care (PC)-delivered BI for reducing risky drug use (RDU) among patients identified by screening. Design Multicenter single-blind two-arm randomized controlled trial of patients enrolled from February 2011 to November 2012 with 3-month follow-up. Randomization and allocation to trial group were computer-generated. Setting Primary care waiting rooms of 5 federally qualified health centers (FQHCs) in Los Angeles County (LAC), USA. Participants 334 adult primary care patients (171 intervention; 163 control) with RDU scores (4–26) on the WHO Alcohol, Smoking and Substance Involvement Screening Test (ASSIST) self-administered on tablet PCs; 261 (78%) completed follow-up. Mean age was 41.7 years; 63% were male; 38% were Caucasian. Intervention(s) and Measurement Intervention patients received brief (typically 3–4 minutes) clinician advice to quit/reduce their drug use reinforced by a video doctor message, health education booklet, and up to two 20–30 minute follow-up telephone drug use coaching sessions. Controls received usual care and cancer screening information. Primary outcome was patient self-reported use of highest scoring drug (HSD) at follow-up. Findings Intervention and control patients reported equivalent baseline HSD use; at follow-up, after adjustment for covariates in a linear regression model, intervention patients reported using their HSD an average of 2.21 fewer days in the previous month than controls (p<0.005). No compensatory increases in use of other measured substances were found (p>0.10). Conclusions A clinician-delivered brief intervention with follow-up counseling calls may decrease drug use among risky users compared with usual care in low-income community health centers of Los Angeles County, USA.

Glioblastoma (GBM) is a heterogeneous and lethal brain cancer. These tumors are followed using magnetic resonance imaging (MRI), which is unable to precisely identify tumor cell invasion, impairing effective surgery and radiation planning. We present a novel hybrid model, based on multiparametric intensities, which combines machine learning (ML) with a mechanistic model of tumor growth to provide spatially resolved tumor cell density predictions. The ML component is an imaging data-driven graph-based semi-supervised learning model and we use the Proliferation-Invasion (PI) mechanistic tumor growth model. We thus refer to the hybrid model as the ML-PI model. The hybrid model was trained using 82 image-localized biopsies from 18 primary GBM patients with pre-operative MRI using a leave-one-patient-out cross validation framework. A Relief algorithm was developed to quantify relative contributions from the data sources. The ML-PI model statistically significantly outperformed (p < 0.001) both individual models, ML and PI, achieving a mean absolute predicted error (MAPE) of 0.106 ± 0.125 versus 0.199 ± 0.186 (ML) and 0.227 ± 0.215 (PI), respectively. Associated Pearson correlation coefficients for ML-PI, ML, and PI were 0.838, 0.518, and 0.437, respectively. The Relief algorithm showed the PI model had the greatest contribution to the result, emphasizing the importance of the hybrid model in achieving the high accuracy.

BACKGROUND: MRI-based modeling of tumor cell density (TCD) can significantly improve targeted treatment of Glioblastoma (GBM). Unfortunately, interpatient variability limits the predictive ability of many modeling approaches. We present a Transfer Learning (TL) method that generates individualized patient models, grounded in the wealth of population data, while also detecting and adjusting for interpatient variabilities based on each patient’s own histologic data. METHODS: We recruited primary GBM patients undergoing image-guided biopsies and preoperative imaging including contrast-enhanced MRI (CE-MRI), Dynamic-Susceptibility-Contrast (DSC)-MRI, and Diffusion Tensor Imaging (DTI). We calculated relative cerebral blood volume (rCBV) from DSC-MRI and mean diffusivity (MD) and fractional anisotropy (FA) from DTI. Following image coregistration, we assessed TCD for each biopsy and identified corresponding localized MRI measurements. We then explored a range of univariate and multivariate predictive models of TCD based on MRI measurements in a generalized one-model-fits-all (OMFA) approach. We then implemented both univariate and multivariate individualized TL predictive models, which harness the available population level data but allow for individual variability in their predictions. Finally, we compared Pearson correlation coefficients and mean absolute error between the individualized TL versus generalized OMFA models. RESULTS: TCD significantly correlated with rCBV (r=0.33,p<0.0001) and T1+C (r=0.36,p<0.0001) on univariate analysis after correcting for multiple comparisons. With single variable modeling (using rCBV), TL increased predictive performance (r=0.53, MAE=15.19%) compared to OMFA (r=0.27, MAE=17.79%). With multivariate modeling, TL further improved performance (r=0.88, MAE 5.66%) compared to OMFA (r=0.39, MAE=16.55%). CONCLUSION: TL significantly improves predictive modeling performance for quantifying tumor cell density in GBM.