A Revised Markov Model Evaluating Oophorectomy at the Time of Hysterectomy for Benign Indication: Age 65 Years Revisited.

OBJECTIVE: To perform an updated Markov modeling to assess the optimal age for bilateral salpingo-oophorectomy (BSO) at the time of hysterectomy for benign indication. METHODS: We performed a literature review that assessed hazard ratios (HRs) for mortality by disease, age, hysterectomy with or without BSO, and estrogen therapy use. Base mortality rates were derived from national vital statistics data. A Markov model from reported HRs predicted the proportion of the population staying alive to age 80 years by 1-year and 5-year age groups at time of surgery, from age 45 to 55 years. Those younger than age 50 years were modeled as either taking postoperative estrogen or not; those 50 and older were modeled as not receiving estrogen. Computations were performed with R 3.5.1, using Bayesian integration for HR uncertainty. RESULTS: Performing salpingo-oophorectomy before age 50 years for those not taking estrogen yields a lower survival proportion to age 80 years than hysterectomy alone before age 50 years (52.8% [Bayesian CI 40.7-59.7] vs 63.5% [Bayesian CI 62.2-64.9]). At or after age 50 years, there were similar proportions of those living to age 80 years with hysterectomy alone (66.4%, Bayesian CI 65.0-67.6) compared with concurrent salpingo-oophorectomy (66.9%, Bayesian CI 64.4-69.0). Importantly, those taking estrogen when salpingo-oophorectomy was performed before age 50 years had similar proportions of cardiovascular disease, stroke, and people living to age 80 years as those undergoing hysterectomy alone or those undergoing hysterectomy and salpingo-oophorectomy at age 50 years and older. CONCLUSION: This updated Markov model argues for the consideration of concurrent salpingo-oophorectomy for patients who are undergoing hysterectomy at age 50 and older and suggests that initiating estrogen in those who need salpingo-oophorectomy before age 50 years mitigates increased mortality risk.

A COMPOSITIONAL MODEL TO ASSESS EXPRESSION CHANGES FROM SINGLE-CELL RNA-SEQ DATA.

On the problem of scoring genes for evidence of changes in the distribution of single-cell expression, we introduce an empirical Bayesian mixture approach and evaluate its operating characteristics in a range of numerical experiments. The proposed approach leverages cell-subtype structure revealed in cluster analysis in order to boost gene-level information on expression changes. Cell clustering informs gene-level analysis through a specially-constructed prior distribution over pairs of multinomial probability vectors; this prior meshes with available model-based tools that score patterns of differential expression over multiple subtypes. We derive an explicit formula for the posterior probability that a gene has the same distribution in two cellular conditions, allowing for a gene-specific mixture over subtypes in each condition. Advantage is gained by the compositional structure of the model not only in which a host of gene-specific mixture components are allowed but also in which the mixing proportions are constrained at the whole cell level. This structure leads to a novel form of information sharing through which the cell-clustering results support gene-level scoring of differential distribution. The result, according to our numerical experiments, is improved sensitivity compared to several standard approaches for detecting distributional expression changes.

Enrichment of melanoma-associated T cells in 6-thioguanine-resistant T cells from metastatic melanoma patients.

This study examines whether 6-thioguanine resistant T cells (mutant) from metastatic melanoma patients are enriched for melanoma-associated T cells compared to T cells obtained analogously without thioguanine selection (wild-type). Melanoma-associated antigen pentamer staining was performed on 5 tumour and 9 peripheral blood samples from metastatic melanoma patients. T cell receptor beta chain repertoire was examined via Sanger sequencing of mutant and wild-type in blood and tumour from metastatic melanoma patients at times of tumour progression (n = 8) and via Illumina sequencing in tumour derived T cells and in uncultured T cells (uncultured), wild-type and mutant from blood before and after immune checkpoint blockade (n = 1). Mutant from tumour (3 of 5; P < 0.001), but not blood (0 of 9), were enriched compared to wild-type for binding melanoma-associated antigen pentamers. T cell receptor beta analysis in patients with tumour progression (n = 8) detected increased melanoma associated T cells in mutant compared to wild-type from blood (Monte Carlo P = 10). Comparison of blood samples before and after immune checkpoint blockade with prior tumor from one metastatic melanoma patient detected increased T cell receptor beta sharing between tumour and mutant compared to tumour and wild-type or tumour and uncultured: 11.0% (72/656), 1.5% (206/13 639) and 1.3% (381/29 807), respectively (Monte Carlo P = 10 for mutant versus wild-type and mutant versus uncultured). These data demonstrate that mutant in metastatic melanoma patients are enriched for melanoma-associated T cells and are candidate probes to study in vivo melanoma-reactive T cells.