Cytomegalovirus seropositivity is associated with improved responses to CD38 monoclonal antibody therapies in multiple myeloma: A single-centre retrospective study.

The CD38-targeting monoclonal antibodies (CD38 mAbs) are well-established therapies in multiple myeloma (MM), but responses to treatment are not always deep or durable. Natural killer (NK) cells deficient in Fc epsilon receptor gamma subunits, known as g-NK cells, are found in higher numbers among individuals exposed to cytomegalovirus (CMV) and are able to potentiate the efficacy of daratumumab in vivo. Here, we present a single-centre, retrospective analysis of 136 patients with MM with known CMV serostatus who received a regimen containing a CD38 mAb (93.4% daratumumab and 6.6% isatuximab). CMV seropositivity was associated with an increased overall response rate to treatment regimens containing a CD38 mAb (odds ratio 2.65, 95% confidence interval [CI] 1.17-6.02). However, CMV serostatus was associated with shorter time to treatment failure in a multivariate Cox model (7.8 vs. 8.8 months in the CMV-seropositive vs. CMV-seronegative groups respectively, log-rank p = 0.18, hazard ratio 1.98, 95% CI 1.25-3.12). Our data suggest that CMV seropositivity may predict better response to CD38 mAbs, although this did not correspond to longer time to treatment failure. Larger studies directly quantitating g-NK cells are required to fully understand their effect on CD38 mAb efficacy in MM.

Financial Conflicts of Interest in Public Comments on Medicare National Coverage Determinations of Medical Devices.

This study reviewed public comments for all Medicare National Coverage Determinations between June 2019 and 2022 on select pulmonary and cardiac devices to determine whether financial conflicts of interest were disclosed.

Estimation of effects of contact tracing and mask adoption on COVID-19 transmission in San Francisco: a modeling study.

The current COVID-19 pandemic has spurred concern about what interventions may be effective at reducing transmission. The city and county of San Francisco imposed a shelter-in-place order in March 2020, followed by use of a contact tracing program and a policy requiring use of cloth face masks. We used statistical estimation and simulation to estimate the effectiveness of these interventions in San Francisco. We estimated that self-isolation and other practices beginning at the time of San Francisco's shelter-in-place order reduced the effective reproduction number of COVID-19 by 35.4% (95% CI, -20.1%-81.4%). We estimated the effect of contact tracing on the effective reproduction number to be a reduction of approximately 44% times the fraction of cases that are detected, which may be modest if the detection rate is low. We estimated the impact of cloth mask adoption on reproduction number to be approximately 8.6%, and note that the benefit of mask adoption may be substantially greater for essential workers and other vulnerable populations, residents return to circulating outside the home more often. We estimated the effect of those interventions on incidence by simulating counterfactual scenarios in which contact tracing was not adopted, cloth masks were not adopted, and neither contact tracing nor cloth masks was adopted, and found increases in case counts that were modest, but relatively larger than the effects on reproduction numbers. These estimates and model results suggest that testing coverage and timing of testing and contact tracing may be important, and that modest effects on reproduction numbers can nonetheless cause substantial effects on case counts over time.

Exploring genetic interaction manifolds constructed from rich single-cell phenotypes.

How cellular and organismal complexity emerges from combinatorial expression of genes is a central question in biology. High-content phenotyping approaches such as Perturb-seq (single-cell RNA-sequencing pooled CRISPR screens) present an opportunity for exploring such genetic interactions (GIs) at scale. Here, we present an analytical framework for interpreting high-dimensional landscapes of cell states (manifolds) constructed from transcriptional phenotypes. We applied this approach to Perturb-seq profiling of strong GIs mined from a growth-based, gain-of-function GI map. Exploration of this manifold enabled ordering of regulatory pathways, principled classification of GIs (e.g., identifying suppressors), and mechanistic elucidation of synergistic interactions, including an unexpected synergy between CBL and CNN1 driving erythroid differentiation. Finally, we applied recommender system machine learning to predict interactions, facilitating exploration of vastly larger GI manifolds.

KRASG12C inhibition produces a driver-limited state revealing collateral dependencies.

Inhibitors targeting KRASG12C, a mutant form of the guanosine triphosphatase (GTPase) KRAS, are a promising new class of oncogene-specific therapeutics for the treatment of tumors driven by the mutant protein. These inhibitors react with the mutant cysteine residue by binding covalently to the switch-II pocket (S-IIP) that is present only in the inactive guanosine diphosphate (GDP)-bound form of KRASG12C, sparing the wild-type protein. We used a genome-scale CRISPR interference (CRISPRi) functional genomics platform to systematically identify genetic interactions with a KRASG12C inhibitor in cellular models of KRASG12C mutant lung and pancreatic cancer. Our data revealed genes that were selectively essential in this oncogenic driver-limited cell state, meaning that their loss enhanced cellular susceptibility to direct KRASG12C inhibition. We termed such genes "collateral dependencies" (CDs) and identified two classes of combination therapies targeting these CDs that increased KRASG12C target engagement or blocked residual survival pathways in cells and in vivo. From our findings, we propose a framework for assessing genetic dependencies induced by oncogene inhibition.