Safety and efficacy of letetresgene autoleucel alone or with pembrolizumab for relapsed/refractory multiple myeloma.

This pilot study assessed the safety and efficacy of letetresgene autoleucel (lete-cel; GSK3377794), a genetically modified autologous T-cell therapy targeting New York esophageal squamous cell carcinoma-1 (NY-ESO-1)/L antigen family member 1 isoform A (LAGE-1a)-positive myeloma cells, alone or in combination with pembrolizumab in patients with relapsed/refractory multiple myeloma. Eligible patients expressed NY-ESO-1 and/or LAGE-1a and either HLA-A∗02:01, ∗02:05, or ∗02:06. Patients received lete-cel single infusion alone (arm 1) or with pembrolizumab (arm 2). 127 patients were screened, and 6 patients (3 per arm) were enrolled; patients in arm 1 and 2 received lete-cel alone, or with pembrolizumab, respectively. All patients exhibited grade 3/4 cytopenias, which resolved or improved to grade 1. One patient (arm 1) had grade 3/4 lete-cel-related adverse events (AEs); 2 patients (arm 2) had grade 3/4 AEs related to lete-cel and lymphodepletion. Three patients with grade 1/2 cytokine release syndrome (CRS) exhibited elevated post-lete-cel interleukin-6 levels versus those without CRS. Pooled overall response rate was 50% including 1 patient each with confirmed clinical response, very good clinical response, and partial response, and progression-free survival ranged from 1.3 to 5.2 months. Responders (arm 1: n = 1; arm 2: n = 2) had a time-to-response of 3 weeks, duration of response of 2.1 months. Two responders, but no nonresponders, exhibited elevated cytokine levels after lete-cel infusion. Lete-cel had a manageable safety profile and demonstrated clear but transient antitumor activity in patients with relapsed/refractory multiple myeloma. This trial was registered at www.clinicaltrials.gov as #NCT03168438.

Identification of Clinical Phenotypes in Septic Patients Presenting With Hypotension or Elevated Lactate.

Introduction: Targeted therapies for sepsis have failed to show benefit due to high variability among subjects. We sought to demonstrate different phenotypes of septic shock based solely on clinical features and show that these relate to outcome. Methods: A retrospective analysis was performed of a 1,023-subject cohort with early septic shock from the ProCESS trial. Twenty-three clinical variables at baseline were analyzed using hierarchical clustering, with consensus clustering used to identify and validate the ideal number of clusters in a derivation cohort of 642 subjects from 20 hospitals. Clusters were visualized using heatmaps over 0, 6, 24, and 72 h. Clinical outcomes were 14-day all-cause mortality and organ failure pattern. Cluster robustness was confirmed in a validation cohort of 381 subjects from 11 hospitals. Results: Five phenotypes were identified, each with unique organ failure patterns that persisted in time. By enrollment criteria, all patients had shock. The two high-risk phenotypes were characterized by distinct multi-organ failure patterns and cytokine signatures, with the highest mortality group characterized most notably by liver dysfunction and coagulopathy while the other group exhibited primarily respiratory failure, neurologic dysfunction, and renal dysfunction. The moderate risk phenotype was that of respiratory failure, while low-risk phenotypes did not have a high degree of additional organ failure. Conclusions: Sepsis phenotypes with distinct biochemical abnormalities may be identified by clinical characteristics alone and likely provide an opportunity for early clinical actionability and prognosis.

APT-MCMC, a C++/Python implementation of Markov Chain Monte Carlo for parameter identification.

The inverse problem associated with fitting parameters of an ordinary differential equation (ODE) system to data is nonlinear and multimodal, which is of great challenge to gradient-based optimizers. Markov Chain Monte Carlo (MCMC) techniques provide an alternative approach to solving these problems and can escape local minima by design. APT-MCMC was created to allow users to setup ODE simulations in Python and run as compiled C++ code. It combines affine-invariant ensemble of samplers and parallel tempering MCMC techniques to improve the simulation efficiency. Simulations use Bayesian inference to provide probability distributions of parameters, which enable analysis of multiple minima and parameter correlation. Benchmark tests result in a 20×-60× speedup but 14% increase in memory usage against emcee, a similar MCMC package in Python. Several MCMC hyperparameters were analyzed: number of temperatures, ensemble size, step size, and swap attempt frequency. Heuristic tuning guidelines are provided for setting these hyperparameters.

Discrete Dynamical Modeling of Influenza Virus Infection Suggests Age-Dependent Differences in Immunity.

Immunosenescence, an age-related decline in immune function, is a major contributor to morbidity and mortality in the elderly. Older hosts exhibit a delayed onset of immunity and prolonged inflammation after an infection, leading to excess damage and a greater likelihood of death. Our study applies a rule-based model to infer which components of the immune response are most changed in an aged host. Two groups of BALB/c mice (aged 12 to 16 weeks and 72 to 76 weeks) were infected with 2 inocula: a survivable dose of 50 PFU and a lethal dose of 500 PFU. Data were measured at 10 points over 19 days in the sublethal case and at 6 points over 7 days in the lethal case, after which all mice had died. Data varied primarily in the onset of immunity, particularly the inflammatory response, which led to a 2-day delay in the clearance of the virus from older hosts in the sublethal cohort. We developed a Boolean model to describe the interactions between the virus and 21 immune components, including cells, chemokines, and cytokines, of innate and adaptive immunity. The model identifies distinct sets of rules for each age group by using Boolean operators to describe the complex series of interactions that activate and deactivate immune components. Our model accurately simulates the immune responses of mice of both ages and with both inocula included in the data (95% accurate for younger mice and 94% accurate for older mice) and shows distinct rule choices for the innate immunity arm of the model between younger and aging mice in response to influenza A virus infection.IMPORTANCE Influenza virus infection causes high morbidity and mortality rates every year, especially in the elderly. The elderly tend to have a delayed onset of many immune responses as well as prolonged inflammatory responses, leading to an overall weakened response to infection. Many of the details of immune mechanisms that change with age are currently not well understood. We present a rule-based model of the intrahost immune response to influenza virus infection. The model is fit to experimental data for young and old mice infected with influenza virus. We generated distinct sets of rules for each age group to capture the temporal differences seen in the immune responses of these mice. These rules describe a network of interactions leading to either clearance of the virus or death of the host, depending on the initial dosage of the virus. Our models clearly demonstrate differences in these two age groups, particularly in the innate immune responses.