High-throughput sequencing technologies allow easy characterization of the human microbiome, but the statistical methods to analyze microbiome data are still in their infancy. Differential abundance methods aim at detecting associations between the abundances of bacterial species and subject grouping factors. The results of such methods are important to identify the microbiome as a prognostic or diagnostic biomarker or to demonstrate efficacy of prodrug or antibiotic drugs. Because of a lack of benchmarking studies in the microbiome field, no consensus exists on the performance of the statistical methods. We have compared a large number of popular methods through extensive parametric and nonparametric simulation as well as real data shuffling algorithms. The results are consistent over the different approaches and all point to an alarming excess of false discoveries. This raises great doubts about the reliability of discoveries in past studies and imperils reproducibility of microbiome experiments. To further improve method benchmarking, we introduce a new simulation tool that allows to generate correlated count data following any univariate count distribution; the correlation structure may be inferred from real data. Most simulation studies discard the correlation between species, but our results indicate that this correlation can negatively affect the performance of statistical methods.

Central nervous system (CNS) relapse carries a poor prognosis in diffuse large B-cell lymphoma (DLBCL). Integrating biomarkers into the CNS–International Prognostic Index (CNS-IPI) risk model may improve identification of patients at high risk for developing secondary CNS disease. CNS relapse was analyzed in 1418 DLBCL patients treated with obinutuzumab or rituximab plus cyclophosphamide, doxorubicin, vincristine, prednisone chemotherapy in the phase 3 GOYA study. Cell of origin (COO) was assessed using gene-expression profiling. BCL2 and MYC protein expression was analyzed by immunohistochemistry. The impact of CNS-IPI, COO, and BCL2/MYC dual-expression status on CNS relapse was assessed using a multivariate Cox regression model (data available in n = 1418, n = 933, and n = 688, respectively). High CNS-IPI score (hazard ratio [HR], 4.0; 95% confidence interval [CI], 1.3-12.3; P = .02) and activated B-cell‒like (ABC) (HR, 5.2; 95% CI, 2.1-12.9; P = .0004) or unclassified COO subtypes (HR, 4.2; 95% CI, 1.5-11.7; P = .006) were independently associated with CNS relapse. BCL2/MYC dual-expression status did not impact CNS relapse risk. Three risk subgroups were identified based on the presence of high CNS-IPI score and/or ABC/unclassified COO (CNS-IPI-C model): low risk (no risk factors, n = 450 [48.2%]), intermediate risk (1 factor, n = 408 [43.7%]), and high risk (both factors, n = 75 [8.0%]). Two-year CNS relapse rates were 0.5%, 4.4%, and 15.2% in the respective risk subgroups. Combining high CNS-IPI and ABC/unclassified COO improved CNS relapse prediction and identified a patient subgroup at high risk for developing CNS relapse. The study was registered at www.clinicaltrials.gov as #NCT01287741.

Purpose To perform an updated analysis of the randomized phase III GADOLIN trial in patients with rituximab-refractory indolent non-Hodgkin lymphoma treated with obinutuzumab (GA101; G) and bendamustine (B). Patients and Methods Patients with histologically documented, rituximab-refractory CD20 indolent non-Hodgkin lymphoma received G 1,000 mg (days 1, 8, and 15, cycle 1; day 1, cycles 2 to 6) plus B 90 mg/m/d (days 1 and 2, all cycles) or B 120 mg/m/d monotherapy. Patients who did not experience disease progression with G-B received G maintenance (1,000 mg every 2 months) for up to 2 years. The primary end point was progression-free survival (PFS). Results Of 413 randomly assigned patients (intention-to-treat [ITT]: G-B, n = 204; B monotherapy, n = 209), 335 had follicular lymphoma (FL; G-B, n = 164; B monotherapy, n = 171). After a median follow-up of 31.8 months, median PFS in ITT patients was 25.8 months (G-B) and 14.1 months (B monotherapy; hazard ratio [HR], 0.57; 95% CI, 0.44 to 0.73; P < .001). Overall survival (OS) also was prolonged (HR, 0.67; 95% CI, 0.47 to 0.96; P = .027). PFS and OS benefits were similar in patients with FL. Grade 3 to 5 adverse events (AEs) were reported by 148 (72.5%) and 133 (65.5%) patients in the G-B and B monotherapy arms, respectively, most commonly neutropenia (G-B, 34.8%; B monotherapy, 27.1%), thrombocytopenia (10.8% and 15.8%), anemia (7.4% and 10.8%), and infusion-related reactions (9.3% and 3.4%). Serious AEs occurred in 89 G-B patients (43.6%) and 75 B monotherapy patients (36.9%); fatal AEs occurred in 16 (7.8%) and 13 (6.4%), respectively. Conclusion This updated analysis confirms the PFS benefit for G-B shown in the primary analysis. A substantial OS benefit also was demonstrated in the ITT population and in patients with FL. Toxicity was similar for both treatments.