Robust Cluster Prediction Across Data Types Validates Association of Sex and Therapy Response in GBM.

Background: Previous studies have described sex-specific patient subtyping in glioblastoma. The cluster labels associated with these "legacy data" were used to train a predictive model capable of recapitulating this clustering in contemporary contexts.

Methods: We used robust ensemble machine learning to train a model using gene microarray data to perform multi-platform predictions including RNA-seq and potentially scRNA-seq.

A Pan-Cancer Analysis of Age and Sex Differences in Cancer Incidence and Survival in the United States, 2001-2020.

Background: In cancer, age and sex are often studied individually, but the impact of the intersection of these factors on cancer incidence and survival remains unclear. Using population-level data, we provide an up-to-date analysis of the impact of sex and age on cancer incidence and survival.

Methods: Using data from the United States Cancer Statistics public use research database and the Centers for Disease Control and Prevention's National Program of Cancer Registries Survival database, we assessed sex and age differences in the incidence and survival of malignant cancers diagnosed from 2001 to 2020.

Interactions Between Ploidy and Resource Availability Shape Clonal Evolution in Glioblastoma.

Glioblastoma (GBM) is the most aggressive form of primary brain tumor. The infiltrative nature of GBM makes complete surgical resection impossible. The selective forces that govern gliomagenesis are strong, shaping the composition of tumor cells during the initial progression to malignancy with late consequences for invasiveness and therapy response.

Development and validation of a molecular classifier of meningiomas.

Background: Meningiomas exhibit considerable clinical and biological heterogeneity. We previously identified four distinct molecular groups (immunogenic, NF2-wildtype, hypermetabolic, proliferative) that address much of this heterogeneity. Despite the utility of these groups, the stochasticity of clustering methods and the use of multi-omics data for discovery limits the potential for classifying prospective cases.

Machine Learning-based Prognostic Subgrouping of Glioblastoma: A Multi-center Study.

Background: Glioblastoma is the most aggressive adult primary brain cancer, characterized by significant heterogeneity, posing challenges for patient management, treatment planning, and clinical trial stratification.

Methods: We developed a highly reproducible, personalized prognostication and clinical subgrouping system using machine learning (ML) on routine clinical data, MRI, and molecular measures from 2,838 demographically diverse patients across 22 institutions and 3 continents. Patients were stratified into favorable, intermediate, and poor prognostic subgroups (I, II, III) using Kaplan-Meier analysis (Cox proportional model and hazard ratios [HR]).