Predicting outcomes in female germ cell tumors using a modified International Germ Cell Cancer Collaborative Group classification system to guide management.

OBJECTIVE: We previously developed preoperative and pre-chemotherapy modified versions of the male International Germ Cell Cancer Collaborative Group (IGCCCG) prognostic model and assessed it in female patients with germ cell tumors (GCTs). We sought to validate these modified IGCCCG (mIGCCCG) models in a new cohort. METHODS: We queried institutional databases for female patients with GCTs treated at Memorial Sloan Kettering Cancer Center from 1/1/1990-6/1/2020. The mIGCCCG model classifies patients with non-dysgerminomas as good, intermediate, or poor risk based on tumor markers using male IGCCCG cutoffs and absence/presence of non-pulmonary/peritoneal visceral metastasis. In dysgerminomas, good- and intermediate-risk groups are defined by absence/presence of non-pulmonary/peritoneal visceral metastasis. Progression-free survival (PFS) and overall survival (OS) were estimated for each group in the validation and combined original and validation cohorts. Associations between individual clinical factors and outcomes were evaluated. RESULTS: Among 183 female patients with GCTs, clinical characteristics and outcomes were similar between the original (n = 93) and validation (n = 90) cohorts. In multivariable models, higher stage, older age, and non-dysgerminoma histology predicted worse PFS and OS (p < 0.05). Among 162 patients who received chemotherapy, preoperative and pre-chemotherapy mIGCCCG models were significantly associated with PFS and OS (p < 0.001 for all groups). With the preoperative model, 3-year PFS rates were 94%, 76%, and 50% in the good-, intermediate-, and poor-risk patients, respectively; OS rates were 96%, 86%, and 52%, respectively. Even within stage groups, mIGCCCG risk classifications were associated with clinical outcomes. CONCLUSIONS: A female-specific mIGCCCG risk model effectively stratifies patients and should be incorporated into clinical trials.

MDSINE: Microbial Dynamical Systems INference Engine for microbiome time-series analyses.

Predicting dynamics of host-microbial ecosystems is crucial for the rational design of bacteriotherapies. We present MDSINE, a suite of algorithms for inferring dynamical systems models from microbiome time-series data and predicting temporal behaviors. Using simulated data, we demonstrate that MDSINE significantly outperforms the existing inference method. We then show MDSINE's utility on two new gnotobiotic mice datasets, investigating infection with Clostridium difficile and an immune-modulatory probiotic. Using these datasets, we demonstrate new capabilities, including accurate forecasting of microbial dynamics, prediction of stable sub-communities that inhibit pathogen growth, and identification of bacteria most crucial to community integrity in response to perturbations.

Interactions between Gut Microbiota, Host Genetics and Diet Modulate the Predisposition to Obesity and Metabolic Syndrome.

Obesity, diabetes, and metabolic syndrome result from complex interactions between genetic and environmental factors, including the gut microbiota. To dissect these interactions, we utilized three commonly used inbred strains of mice-obesity/diabetes-prone C57Bl/6J mice, obesity/diabetes-resistant 129S1/SvImJ from Jackson Laboratory, and obesity-prone but diabetes-resistant 129S6/SvEvTac from Taconic-plus three derivative lines generated by breeding these strains in a new, common environment. Analysis of metabolic parameters and gut microbiota in all strains and their environmentally normalized derivatives revealed strong interactions between microbiota, diet, breeding site, and metabolic phenotype. Strain-dependent and strain-independent correlations were found between specific microbiota and phenotypes, some of which could be transferred to germ-free recipient animals by fecal transplantation. Environmental reprogramming of microbiota resulted in 129S6/SvEvTac becoming obesity resistant. Thus, development of obesity/metabolic syndrome is the result of interactions between gut microbiota, host genetics, and diet. In permissive genetic backgrounds, environmental reprograming of microbiota can ameliorate development of metabolic syndrome.

Improving microbial fitness in the mammalian gut by in vivo temporal functional metagenomics.

Elucidating functions of commensal microbial genes in the mammalian gut is challenging because many commensals are recalcitrant to laboratory cultivation and genetic manipulation. We present Temporal FUnctional Metagenomics sequencing (TFUMseq), a platform to functionally mine bacterial genomes for genes that contribute to fitness of commensal bacteria in vivo. Our approach uses metagenomic DNA to construct large-scale heterologous expression libraries that are tracked over time in vivo by deep sequencing and computational methods. To demonstrate our approach, we built a TFUMseq plasmid library using the gut commensal Bacteroides thetaiotaomicron (Bt) and introduced Escherichia coli carrying this library into germfree mice. Population dynamics of library clones revealed Bt genes conferring significant fitness advantages in E. coli over time, including carbohydrate utilization genes, with a Bt galactokinase central to early colonization, and subsequent dominance by a Bt glycoside hydrolase enabling sucrose metabolism coupled with co-evolution of the plasmid library and E. coli genome driving increased galactose utilization. Our findings highlight the utility of functional metagenomics for engineering commensal bacteria with improved properties, including expanded colonization capabilities in vivo.