Themis suppresses the effector function of CD8+ T cells in acute viral infection

Long-Term Follow-Up Defines the Population That Benefits from Early Interception in a High-Risk Smoldering Multiple Myeloma Clinical Trial Using the Combination of Ixazomib, Lenalidomide, and Dexamethasone

Background

Early therapeutic intervention in high-risk SMM (HR-SMM) has demonstrated benefit in previous studies of lenalidomide with or without dexamethasone. Triplets and quadruplet studies have been examined in this same population. However, to date, none of these studies examined the impact of depth of response on long-term outcomes of participants treated with lenalidomide-based therapy, and whether the use of the 20/2/20 model or the addition of genomic alterations can further define the population that would benefit the most from early therapeutic intervention. Here, we present the results of the phase II study of the combination of ixazomib, lenalidomide, and dexamethasone in patients with HR-SMM with long-term follow-up and baseline single-cell tumor and immune sequencing that help refine the population to be treated for early intervention studies.

Methods

This is a phase II trial of ixazomib, lenalidomide, and dexamethasone (IRD) in HR-SMM. Patients received 9 cycles of induction therapy with ixazomib 4mg on days 1, 8, and 15; lenalidomide 25mg on days 1-21; and dexamethasone 40mg on days 1, 8, 15, and 22. The induction phase was followed by maintenance with ixazomib 4mg on days 1, 8, and 15; and lenalidomide 15mg d1-21 for 15 cycles for 24 months of treatment. The primary endpoint was progression-free survival after 2 years of therapy. Secondary endpoints included depth of response, biochemical progression, and correlative studies included single-cell RNA sequencing and/or whole-genome sequencing of the tumor and single-cell sequencing of immune cells at baseline.

Results

Fifty-five patients, with a median age of 64, were enrolled in the study. The overall response rate was 93%, with 31% of patients achieving a complete response and 45% achieving a very good partial response or better. The most common grade 3 or greater treatment-related hematologic toxicities were neutropenia (16 patients; 29%), leukopenia (10 patients; 18%), lymphocytopenia (8 patients; 15%), and thrombocytopenia (4 patients; 7%). Non-hematologic grade 3 or greater toxicities included hypophosphatemia (7 patients; 13%), rash (5 patients; 9%), and hypokalemia (4 patients; 7%). After a median follow-up of 50 months, the median progression-free survival (PFS) was 48.6 months (95% CI: 39.9 - not reached; NR) and median overall survival has not been reached. Patients achieving VGPR or better had a significantly better progression-free survival (p<0.001) compared to those who did not achieve VGPR (median PFS 58.2 months vs. 31.3 months). Biochemical progression preceded or was concurrent with the development of SLiM-CRAB criteria in eight patients during follow-up, indicating that biochemical progression is a meaningful endpoint that correlates with the development of end-organ damage. High-risk 20/2/20 participants had the worst PFS compared to low- and intermediate-risk participants. The use of whole genome or single-cell sequencing of tumor cells identified high-risk aberrations that were not identified by FISH alone and aided in the identification of participants at risk of progression. scRNA-seq analysis revealed a positive correlation between MHC class I expression and response to proteasome inhibition and at the same time a decreased proportion of GZMB+ T cells within the clonally expanded CD8+ T cell population correlated with suboptimal response.

Conclusions

Ixazomib, lenalidomide and dexamethasone in HR-SMM demonstrates significant clinical activity with an overall favorable safety profile. Achievement of VGPR or greater led to significant improvement in time to progression, suggesting that achieving deep response is beneficial in HR-SMM. Biochemical progression correlates with end-organ damage. Patients with high-risk FISH and lack of deep response had poor outcomes. ClinicalTrials.gov identifier: ( NCT02916771 ).

SHP-1 Regulates CD8+ T Cell Effector Function but Plays a Subtle Role with SHP-2 in T Cell Exhaustion Due to a Stage-Specific Nonredundant Functional Relay

SHP-1 (Src homology region 2 domain-containing phosphatase 1) is a well-known negative regulator of T cells, whereas its close homolog SHP-2 is the long-recognized main signaling mediator of the PD-1 inhibitory pathway. However, recent studies have challenged the requirement of SHP-2 in PD-1 signaling, and follow-up studies further questioned the alternative idea that SHP-1 may replace SHP-2 in its absence. In this study, we systematically investigate the role of SHP-1 alone or jointly with SHP-2 in CD8+ T cells in a series of gene knockout mice. We show that although SHP-1 negatively regulates CD8+ T cell effector function during acute lymphocytic choriomeningitis virus (LCMV) infection, it is dispensable for CD8+ T cell exhaustion during chronic LCMV infection. Moreover, in contrast to the mortality of PD-1 knockout mice upon chronic LCMV infection, mice double deficient for SHP-1 and SHP-2 in CD8+ T cells survived without immunopathology. Importantly, CD8+ T cells lacking both phosphatases still differentiate into exhausted cells and respond to PD-1 blockade. Finally, we found that SHP-1 and SHP-2 suppressed effector CD8+ T cell expansion at the early and late stages, respectively, during chronic LCMV infection.

Machine Learning Made Easy (MLme): a comprehensive toolkit for machine learning-driven data analysis

BACKGROUND

Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance.

RESULTS

To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating 4 essential functionalities-namely, Data Exploration, AutoML, CustomML, and Visualization-MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on 6 distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations.

CONCLUSION

MLme serves as a valuable resource for leveraging ML to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.

Themis controls T cell activation, effector functions, and metabolism of peripheral CD8+ T cells

Themis is important in regulating positive selection of thymocytes during T cell development, but its role in peripheral T cells is less understood. Here, we investigated T cell activation and its sequelae using a tamoxifen-mediated, acute Themis deletion mouse model. We find that proliferation, effector functions including anti-tumor killing, and up-regulation of energy metabolism are severely compromised. This study reveals the phenomenon of peripheral adaptation to loss of Themis, by demonstrating direct TCR-induced defects after acute deletion of Themis that were not evident in peripheral T cells chronically deprived of Themis in dLck-Cre deletion model. Peripheral adaptation to long-term loss was compared using chronic versus acute tamoxifen-mediated deletion and with the (chronic) dLck-Cre deletion model. We found that upon chronic tamoxifen-mediated Themis deletion, there was modulation in the gene expression profile for both TCR and cytokine signaling pathways. This profile overlapped with (chronic) dLck-Cre deletion model. Hence, we found that peripheral adaptation induced changes to both TCR and cytokine signaling modules. Our data highlight the importance of Themis in the activation of CD8+ T cells.

Machine Learning Made Easy (MLme): A Comprehensive Toolkit for Machine Learning-Driven Data Analysis

Background

Machine learning (ML) has emerged as a vital asset for researchers to analyze and extract valuable information from complex datasets. However, developing an effective and robust ML pipeline can present a real challenge, demanding considerable time and effort, thereby impeding research progress. Existing tools in this landscape require a profound understanding of ML principles and programming skills. Furthermore, users are required to engage in the comprehensive configuration of their ML pipeline to obtain optimal performance.

Results

To address these challenges, we have developed a novel tool called Machine Learning Made Easy (MLme) that streamlines the use of ML in research, specifically focusing on classification problems at present. By integrating four essential functionalities, namely Data Exploration, AutoML, CustomML, and Visualization, MLme fulfills the diverse requirements of researchers while eliminating the need for extensive coding efforts. To demonstrate the applicability of MLme, we conducted rigorous testing on six distinct datasets, each presenting unique characteristics and challenges. Our results consistently showed promising performance across different datasets, reaffirming the versatility and effectiveness of the tool. Additionally, by utilizing MLme's feature selection functionality, we successfully identified significant markers for CD8+ naive (BACH2), CD16+ (CD16), and CD14+ (VCAN) cell populations.

Conclusion

MLme serves as a valuable resource for leveraging machine learning (ML) to facilitate insightful data analysis and enhance research outcomes, while alleviating concerns related to complex coding scripts. The source code and a detailed tutorial for MLme are available at https://github.com/FunctionalUrology/MLme.