Biology subjects

McKinney, B.

Publications and source records attributed to McKinney, B..

3 recordsLinked to original sources

bioRxiv2026

CD19 CAR Engineered T Regulatory Cells Promote Durable Immune Tolerance in Experimental Systemic Lupus Erythematosus

While the immune reset induced by CD19 CAR T therapies has shown striking promise in systemic lupus erythematosus (SLE), the nature and duration of benefit mediated by B cell depletion remains unclear. Loss of tolerance in SLE is driven by extrafollicular and germinal center interactions between dendritic cells and self-reactive T and B cells. We hypothesized that CD19 CAR-engineered regulatory T cells (CAR19 EngTreg), capable of suppressing multiple autoimmune effector cell types, might promote long-term tolerance without producing high-levels of inflammatory mediators. To generate CAR19 EngTreg, we combined CD19 CAR delivery with homology directed repair at the FOXP3 locus to drive constitutive, stable FOXP3 expression in CD4 T cells. CAR19 EngTreg expressed canonical Treg markers; exhibited minimal inflammatory cytokine production; and blocked human B cell differentiation into antibody secreting plasma cells. In humanized mouse models, transfer of CAR19 EngTreg led to a reduction in B cell numbers with low inflammatory cytokine production relative to CD19 CAR Teff, and reduced tissue inflammation. In a short-term, in vivo imiquimod-induced inflammatory model, murine CAR19 EngTreg reduced skin pathology, spleen size, and inflammatory cytokines. Finally, in two distinct mouse models of lupus, CAR19 EngTreg persisted for >4 months leading to reduced autoimmune T and B effector populations, sustained reduction in multiple autoantibodies and improved renal function and histopathology. Thus, CAR19 EngTreg may offer a promising and durable therapeutic avenue for B cell-mediated autoimmune disorders capable of long-term regulation of both autoreactive B and T cells.

immunology↗

bioRxiv2021

APOBEC mutagenesis inhibits breast cancer growth through induction of a T cell-mediated antitumor immune response

The APOBEC family of cytidine deaminases is one of the most common endogenous sources of mutations in human cancer. Genomic studies of tumors have found that APOBEC mutational signatures are particularly enriched in the HER2 subtype of breast cancer and have been associated with immunotherapy response in diverse cancer types. However, the direct consequences of APOBEC mutagenesis on the tumor immune microenvironment have not been thoroughly investigated. To address this, we developed syngeneic murine mammary tumor models with inducible expression of APOBEC3B. We found that APOBEC activity induces an antitumor adaptive immune response and CD4+ T cell-mediated tumor growth inhibition. While polyclonal APOBEC tumors had a moderate growth defect, clonal APOBEC tumors were almost completely rejected by the immune system, suggesting that APOBEC-mediated genetic heterogeneity limits the antitumor adaptive immune response. Consistent with the observed immune infiltration in APOBEC tumors, APOBEC activity sensitized HER2-driven breast tumors to checkpoint inhibition. In human breast cancers, the relationship between APOBEC mutagenesis and immunogenicity varied by breast cancer subtype and the frequency of subclonal mutations. This work provides a mechanistic basis for the sensitivity of APOBEC tumors to checkpoint inhibitors and suggests a rationale for using APOBEC mutational signatures as a biomarker predicting immunotherapy response in HER2-positive breast cancers. SIGNIFICANCEAPOBEC mutational signatures are observed in many cancers, yet the consequences of these mutations on the tumor immune microenvironment are not well understood. Using a novel mouse model, we show that APOBEC activity sensitizes HER2-driven mammary tumors to checkpoint inhibition and could inform immunotherapy treatment strategies for HER2-positive breast cancer patients.

cancer biology↗

bioRxiv2020

Consensus Features Nested Cross-Validation

MotivationFeature selection can improve the accuracy of machine learning models, but appropriate steps must be taken to avoid overfitting. Nested cross-validation (nCV) is a common approach that chooses the classification model and features to represent a given outer fold based on features that give the maximum inner-fold accuracy. Differential privacy is a related technique to avoid overfitting that uses a privacy preserving noise mechanism to identify features that are stable between training and holdout sets. MethodsWe develop consensus nested CV (cnCV) that combines the idea of feature stability from differential privacy with nested CV. Feature selection is applied in each inner fold and the consensus of top features across folds is a used as a measure of feature stability or reliability instead of classification accuracy, which is used in standard nCV. We use simulated data with main effects, correlation, and interactions to compare the classification accuracy and feature selection performance of the new cnCV with standard nCV, Elastic Net optimized by CV, differential privacy, and private Evaporative Cooling (pEC). We also compare these methods using real RNA-Seq data from a study of major depressive disorder. ResultsThe cnCV method has similar training and validation accuracy to nCV, but cnCV has much shorter run times because it does not construct classifiers in the inner folds. The cnCV method chooses a more parsimonious set of features with fewer false positives than nCV. The cnCV method has similar accuracy to pEC and cnCV selects stable features between folds without the need to specify a privacy threshold. We show that cnCV is an effective and efficient approach for combining feature selection with classification. AvailabilityCode available at https://github.com/insilico/cncv. Contactbrett.mckinney@utulsa.edu Supplementary information:

bioinformatics↗