Post-infusion PD-1+CD8+CAR-T cells identify patients responsive to CD19-CAR-T therapy in non-Hodgkin's lymphoma.

Chimeric antigen receptor T cell therapy (CAR-T) has revolutionized treatment for relapsed/refractory (r/r) B-cell non-Hodgkin's lymphoma (NHL). Robust biomarkers and a complete understanding of CAR-T cell function in the post-infusion phase remain limited. Here we used a 37-color spectral flow cytometry panel to perform high dimensional single cell analysis of post-infusion samples in 26 patients treated with CD28 co-stimulatory domain containing commercial CAR-T (CD28-CAR-T) for NHL and focused on computationally gated CD8+ CAR-T cells. We found that the presence of post-infusion PD-1+ CD8+ CAR-T cells at the Day 14 timepoint highly correlated with the ability to achieve complete response (CR) by 6 months. Further analysis identified multiple subtypes of CD8+ PD-1+ CAR-T cells including PD-1+ TCF1+ stem-like CAR-T cells and PD-1+ TIM3+ effector-like CAR-T cells that correlated with improved clinical outcomes such as response and progression free survival. Additionally, we identified a subset of PD-1+ CD8+ CAR+ T cells with effector-like function that was increased in patients who achieved a CR and was associated with Grade 3 or higher immune effector cell-associated neurotoxicity syndrome. Here we identified robust biomarkers of response to CD28-CAR-T and highlight the importance of PD-1 positivity in CD8+ CAR-T cells post-infusion in achieving CR.

mRNA vaccines against SARS-CoV-2 induce divergent antigen-specific T-cell responses in patients with lung cancer.

BACKGROUND: The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) Omicron variant is highly transmissible and evades pre-established immunity. Messenger RNA (mRNA) vaccination against ancestral strain spike protein can induce intact T-cell immunity against the Omicron variant, but efficacy of booster vaccination in patients with late-stage lung cancer on immune-modulating agents including anti-programmed cell death protein 1(PD-1)/programmed death-ligand 1 (PD-L1) has not yet been elucidated. METHODS: We assessed T-cell responses using a modified activation-induced marker assay, coupled with high-dimension flow cytometry analyses. Peripheral blood mononuclear cells (PBMCs) were stimulated with various viral peptides and antigen-specific T-cell responses were evaluated using flow cytometry. RESULTS: Booster vaccines induced CD8+ T-cell response against the ancestral SARS-CoV-2 strain and Omicron variant in both non-cancer subjects and patients with lung cancer, but only a marginal induction was detected for CD4+ T cells. Importantly, antigen-specific T cells from patients with lung cancer showed distinct subpopulation dynamics with varying degrees of differentiation compared with non-cancer subjects, with evidence of dysfunction. Notably, female-biased T-cell responses were observed. CONCLUSION: We conclude that patients with lung cancer on immunotherapy show a substantial qualitative deviation from non-cancer subjects in their T-cell response to mRNA vaccines, highlighting the need for heightened protective measures for patients with cancer to minimize the risk of breakthrough infection with the Omicron and other future variants.

Single-Cell RNA-Seq Analysis of Patient Myeloid-Derived Suppressor Cells and the Response to Inhibition of Bruton's Tyrosine Kinase.

Myeloid-derived suppressor cell (MDSC) levels are elevated in patients with cancer and contribute to reduced efficacy of immune checkpoint therapy. MDSC express Bruton's tyrosine kinase (BTK) and BTK inhibition with ibrutinib, an FDA-approved irreversible inhibitor of BTK, leads to reduced MDSC expansion/function in mice and significantly improves the antitumor activity of anti-PD-1 antibody treatments. Single-cell RNA sequencing (scRNA-seq) was used to characterize the effect of ibrutinib on gene expression of fluorescence-activated cell sorting-enriched MDSC from patients with different cancer types [breast, melanoma, head and neck squamous cell cancer (HNSCC)]. Melanoma patient MDSC were treated in vitro for 4 hours with 5 µmol/L ibrutinib or DMSO, processed for scRNA-seq using the Chromium 10× Genomics platform, and analyzed via the Seurat v4 standard integrative workflow. Baseline gene expression of MDSC from patients with breast, melanoma, and HNSCC cancer revealed similarities among the top expressed genes. In vitro ibrutinib treatment of MDSC from patients with melanoma resulted in significant changes in gene expression. GBP1, IL-1ß, and CXCL8 were among the top downregulated genes whereas RGS2 and ABHD5 were among the top upregulated genes (P < 0.001). Double positive CD14+CD15+ MDSC and PMN-MDSC responded similarly to BTK inhibition and exhibited more pronounced gene changes compared with early MDSC and M-MDSC. Pathway analysis revealed significantly downregulated pathways including TREM1, nitric oxide signaling, and IL-6 signaling (P < 0.004). IMPLICATIONS: scRNA-seq revealed characteristic gene expression patterns for MDSC from different patients with cancer and BTK inhibition led to the downregulation of multiple genes and pathways important to MDSC function and migration.

Multiplexed assay of variant effect reveals residues of functional importance in the BRCA1 coiled-coil and serine cluster domains.

Delineating functionally normal variants from functionally abnormal variants in tumor suppressor proteins is critical for cancer surveillance, prognosis, and treatment options. BRCA1 is a protein that has many variants of uncertain significance which are not yet classified as functionally normal or abnormal. In vitro functional assays can be used to identify the functional impact of a variant when the variant has not yet been categorized through clinical observation. Here we employ a homology-directed repair (HDR) reporter assay to evaluate over 300 missense and nonsense BRCA1 variants between amino acid residues 1280 and 1576, which encompasses the coiled-coil and serine cluster domains. Functionally abnormal variants tended to cluster in residues known to interact with PALB2, which is critical for homology-directed repair. Multiplexed results were confirmed by singleton assay and by ClinVar database variant interpretations. Comparison of multiplexed results to designated benign or likely benign or pathogenic or likely pathogenic variants in the ClinVar database yielded 100% specificity and 100% sensitivity of the multiplexed assay. Clinicians can reference the results of this functional assay for help in guiding cancer treatment and surveillance options. These results are the first to evaluate this domain of BRCA1 using a multiplexed approach and indicate the importance of this domain in the DNA repair process.

Adjusting for gene-specific covariates to improve RNA-seq analysis.

SUMMARY: This article suggests a novel positive false discovery rate (pFDR) controlling method for testing gene-specific hypotheses using a gene-specific covariate variable, such as gene length. We suppose the null probability depends on the covariate variable. In this context, we propose a rejection rule that accounts for heterogeneity among tests by using two distinct types of null probabilities. We establish a pFDR estimator for a given rejection rule by following Storey's q-value framework. A condition on a type 1 error posterior probability is provided that equivalently characterizes our rejection rule. We also present a suitable procedure for selecting a tuning parameter through cross-validation that maximizes the expected number of hypotheses declared significant. A simulation study demonstrates that our method is comparable to or better than existing methods across realistic scenarios. In data analysis, we find support for our method's premise that the null probability varies with a gene-specific covariate variable. AVAILABILITY AND IMPLEMENTATION: The source code repository is publicly available at https://github.com/hsjeon1217/conditional_method.

graph-GPA 2.0: improving multi-disease genetic analysis with integration of functional annotation data.

Genome-wide association studies (GWAS) have successfully identified a large number of genetic variants associated with traits and diseases. However, it still remains challenging to fully understand the functional mechanisms underlying many associated variants. This is especially the case when we are interested in variants shared across multiple phenotypes. To address this challenge, we propose graph-GPA 2.0 (GGPA 2.0), a statistical framework to integrate GWAS datasets for multiple phenotypes and incorporate functional annotations within a unified framework. Our simulation studies showed that incorporating functional annotation data using GGPA 2.0 not only improves the detection of disease-associated variants, but also provides a more accurate estimation of relationships among diseases. Next, we analyzed five autoimmune diseases and five psychiatric disorders with the functional annotations derived from GenoSkyline and GenoSkyline-Plus, along with the prior disease graph generated by biomedical literature mining. For autoimmune diseases, GGPA 2.0 identified enrichment for blood-related epigenetic marks, especially B cells and regulatory T cells, across multiple diseases. Psychiatric disorders were enriched for brain-related epigenetic marks, especially the prefrontal cortex and the inferior temporal lobe for bipolar disorder and schizophrenia, respectively. In addition, the pleiotropy between bipolar disorder and schizophrenia was also detected. Finally, we found that GGPA 2.0 is robust to the use of irrelevant and/or incorrect functional annotations. These results demonstrate that GGPA 2.0 can be a powerful tool to identify genetic variants associated with each phenotype or those shared across multiple phenotypes, while also promoting an understanding of functional mechanisms underlying the associated variants.

LRT: Integrative analysis of scRNA-seq and scTCR-seq data to investigate clonal differentiation heterogeneity.

Single-cell RNA sequencing (scRNA-seq) data has been widely used for cell trajectory inference, with the assumption that cells with similar expression profiles share the same differentiation state. However, the inferred trajectory may not reveal clonal differentiation heterogeneity among T cell clones. Single-cell T cell receptor sequencing (scTCR-seq) data provides invaluable insights into the clonal relationship among cells, yet it lacks functional characteristics. Therefore, scRNA-seq and scTCR-seq data complement each other in improving trajectory inference, where a reliable computational tool is still missing. We developed LRT, a computational framework for the integrative analysis of scTCR-seq and scRNA-seq data to explore clonal differentiation trajectory heterogeneity. Specifically, LRT uses the transcriptomics information from scRNA-seq data to construct overall cell trajectories and then utilizes both the TCR sequence information and phenotype information to identify clonotype clusters with distinct differentiation biasedness. LRT provides a comprehensive analysis workflow, including preprocessing, cell trajectory inference, clonotype clustering, trajectory biasedness evaluation, and clonotype cluster characterization. We illustrated its utility using scRNA-seq and scTCR-seq data of CD8+ T cells and CD4+ T cells with acute lymphocytic choriomeningitis virus infection. These analyses identified several clonotype clusters with distinct skewed distribution along the differentiation path, which cannot be revealed solely based on scRNA-seq data. Clones from different clonotype clusters exhibited diverse expansion capability, V-J gene usage pattern and CDR3 motifs. The LRT framework was implemented as an R package 'LRT', and it is now publicly accessible at https://github.com/JuanXie19/LRT. In addition, it provides two Shiny apps 'shinyClone' and 'shinyClust' that allow users to interactively explore distributions of clonotypes, conduct repertoire analysis, implement clustering of clonotypes, trajectory biasedness evaluation, and clonotype cluster characterization.

Long-Term Impact of Daily E-cigarette Exposure on the Lungs of Asthmatic Mice.

INTRODUCTION: Although the greater popularity of electronic cigarettes (EC) among asthmatics is alarming, there is limited knowledge of the long-term consequences of EC exposure in asthmatics. AIMS AND METHODS: Mild asthmatic C57/BL6J adult male and female mice were established by intranasal insufflation with three combined allergens. The asthmatic and age and sex-matched' naïve mice were exposed to air, nicotine-free (propylene glycol [PG]/vegetable glycerin [VG]-only), or PG/VG+Nicotine, 4 hours daily for 3 months. The effects of EC exposure were accessed by measuring cytokines in bronchoalveolar lavage, periodic acid-schiff (PAS) staining, mitochondrial DNA copy numbers (mtCN), and the transcriptome in the lung. Significance was false discovery rate <0.2 for transcriptome and 0.05 for the others. RESULTS: In asthmatic mice, PG/VG+Nicotine increased PAS-positive cells and IL-13 compared to mice exposed to air and PG/VG-only. In naïve mice exposed to PG/VG+Nicotine and PG/VG-only, higher INF-γ was observed compared to mice exposed only to air. PG/VG-only and PG/VG+Nicotine had significantly higher mtCN compared to air exposure in asthmatic mice, while the opposite pattern was observed in non-asthmatic naïve mice. Different gene expression patterns were profoundly found for asthmatic mice exposed to PG/VG+Nicotine compared to PG/VG-only, including genes involved in mitochondrial dysfunction, oxidative phosphorylation, and p21-activated kinase (PAK) signaling. CONCLUSIONS: This study provides experimental evidence of the potential impact of nicotine enhancement on the long-term effects of EC in asthmatics compared to non-asthmatics. IMPLICATIONS: The findings from this study indicate the potential impact of EC in asthmatics by addressing multiple biological markers. The long-term health outcomes of EC in the susceptible group can be instrumental in supporting policymaking and educational campaigns and informing the public, healthcare providers, and EC users about the underlying risks of EC use.

Statistical Power Analysis for Designing Bulk, Single-Cell, and Spatial Transcriptomics Experiments: Review, Tutorial, and Perspectives.

Gene expression profiling technologies have been used in various applications such as cancer biology. The development of gene expression profiling has expanded the scope of target discovery in transcriptomic studies, and each technology produces data with distinct characteristics. In order to guarantee biologically meaningful findings using transcriptomic experiments, it is important to consider various experimental factors in a systematic way through statistical power analysis. In this paper, we review and discuss the power analysis for three types of gene expression profiling technologies from a practical standpoint, including bulk RNA-seq, single-cell RNA-seq, and high-throughput spatial transcriptomics. Specifically, we describe the existing power analysis tools for each research objective for each of the bulk RNA-seq and scRNA-seq experiments, along with recommendations. On the other hand, since there are no power analysis tools for high-throughput spatial transcriptomics at this point, we instead investigate the factors that can influence power analysis.

MicroRNA profiles of dry secretions through the first three weeks of the dry period from Holstein cows.

In dairy cows, the period from the end of lactation through the dry period and into the transition period, requires vast physiological and immunological changes critical to mammary health. The dry period is important to the success of the next lactation and intramammary infections during the dry period will adversely alter mammary function, health and milk production for the subsequent lactation. MicroRNAs (miRNAs) are small non-coding RNAs that can post transcriptionally regulate gene expression. We sought to characterize the miRNA profile in dry secretions from the last day of lactation to 3, 10, and 21 days post dry-off. We identified 816 known and 80 novel miRNAs. We found 46 miRNAs whose expression significantly changed (q-value < 0.05) over the first three weeks of dry-off. Additionally, we examined the slopes of random regression models of log transformed normalized counts and cross analyzed the 46 significantly upregulated and downregulated miRNAs. These miRNAs were found to be associated with important components of pregnancy, lactation, as well as inflammation and disease. Detailing the miRNA profile of dry secretions through the dry-off period provides insight into the biology at work, possible means of regulation, components of resistance and/or susceptibility, and outlets for targeted therapy development.