Tumor Regression Following Engineered Polyomavirus-Specific T Cell Therapy in Immune Checkpoint Inhibitor-Refractory Merkel Cell Carcinoma.

Although immune check-point inhibitors (CPIs) revolutionized treatment of Merkel cell carcinoma (MCC), patients with CPI-refractory MCC lack effective therapy. More than 80% of MCC express T-antigens encoded by Merkel cell polyomavirus, which is an ideal target for T-cell receptor (TCR)-based immunotherapy. However, MCC often repress HLA expression, requiring additional strategies to reverse the downregulation for allowing T cells to recognize their targets. We identified TCRMCC1 that recognizes a T-antigen epitope restricted to human leukocyte antigen (HLA)-A*02:01. Seven CPI-refractory metastatic MCC patients received CD4 and CD8 T cells transduced with TCRMCC1 (TTCR-MCC1) preceded either by lymphodepleting chemotherapy or an HLA-upregulating regimen (single-fraction radiation therapy (SFRT) or systemic interferon gamma (IFNγ)) with concurrent avelumab. Two patients who received preceding SFRT and IFNγ respectively experienced tumor regression. One experienced regression of 13/14 subcutaneous lesions with 1 'escape' lesion and the other had delayed tumor regression in all lesions after initial progression. Although TTCR-MCC1 cells with an activated phenotype infiltrated tumors including the 'escape' lesion, all progressing lesions transcriptionally lacked HLA expression. While SFRT/IFNγ did not immediately upregulate tumor HLA expression, a secondary endogenous antigen-specific T cell infiltrate was detected in one of the regressing tumors and associated with HLA upregulation, indicating in situ immune responses have the potential to reverse HLA downregulation. Indeed, supplying a strong co-stimulatory signal via a CD200R-CD28 switch receptor allows TTCR-MCC1 cells to control HLA-downregulated MCC cells in a xenograft mouse model, upregulating HLA expression. Our results demonstrate the potential of TCR gene therapy for metastatic MCC and propose a next strategy for overcoming epigenetic downregulation of HLA in MCC.

ADTnorm: Robust Integration of Single-cell Protein Measurement across CITE-seq Datasets.

CITE-seq enables paired measurement of surface protein and mRNA expression in single cells using antibodies conjugated to oligonucleotide tags. Due to the high copy number of surface protein molecules, sequencing antibody-derived tags (ADTs) allows for robust protein detection, improving cell-type identification. However, variability in antibody staining leads to batch effects in the ADT expression, obscuring biological variation, reducing interpretability, and obstructing cross-study analyses. Here, we present ADTnorm (https://github.com/yezhengSTAT/ADTnorm), a normalization and integration method designed explicitly for ADT abundance. Benchmarking against 14 existing scaling and normalization methods, we show that ADTnorm accurately aligns populations with negative- and positive-expression of surface protein markers across 13 public datasets, effectively removing technical variation across batches and improving cell-type separation. ADTnorm enables efficient integration of public CITE-seq datasets, each with unique experimental designs, paving the way for atlas-level analyses. Beyond normalization, ADTnorm includes built-in utilities to aid in automated threshold-gating as well as assessment of antibody staining quality for titration optimization and antibody panel selection. Applying ADTnorm to a published COVID-19 CITE-seq dataset allowed for identifying previously undetected disease-associated markers, illustrating a broad utility in biological applications.

The tidyomics ecosystem: enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis and integration. Addressing these challenges, Bioconductor provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming offers a revolutionary data organization and manipulation standard. Here we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analyzing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas, spanning six data frameworks and ten analysis tools.

The tidyomics ecosystem: Enhancing omic data analyses.

The growth of omic data presents evolving challenges in data manipulation, analysis, and integration. Addressing these challenges, Bioconductor1 provides an extensive community-driven biological data analysis platform. Meanwhile, tidy R programming2 offers a revolutionary standard for data organisation and manipulation. Here, we present the tidyomics software ecosystem, bridging Bioconductor to the tidy R paradigm. This ecosystem aims to streamline omic analysis, ease learning, and encourage cross-disciplinary collaborations. We demonstrate the effectiveness of tidyomics by analysing 7.5 million peripheral blood mononuclear cells from the Human Cell Atlas3, spanning six data frameworks and ten analysis tools.

RAIN: machine learning-based identification for HIV-1 bNAbs.

Broadly neutralizing antibodies (bNAbs) are promising candidates for the treatment and prevention of HIV-1 infections. Despite their critical importance, automatic detection of HIV-1 bNAbs from immune repertoires is still lacking. Here, we develop a straightforward computational method for the Rapid Automatic Identification of bNAbs (RAIN) based on machine learning methods. In contrast to other approaches, which use one-hot encoding amino acid sequences or structural alignment for prediction, RAIN uses a combination of selected sequence-based features for the accurate prediction of HIV-1 bNAbs. We demonstrate the performance of our approach on non-biased, experimentally obtained and sequenced BCR repertoires from HIV-1 immune donors. RAIN processing leads to the successful identification of distinct HIV-1 bNAbs targeting the CD4-binding site of the envelope glycoprotein. In addition, we validate the identified bNAbs using an in vitro neutralization assay and we solve the structure of one of them in complex with the soluble native-like heterotrimeric envelope glycoprotein by single-particle cryo-electron microscopy (cryo-EM). Overall, we propose a method to facilitate and accelerate HIV-1 bNAbs discovery from non-selected immune repertoires.

Long-term Remissions Following CD20-Directed Chimeric Antigen Receptor-Adoptive T-cell Therapy.

Chimeric antigen receptor (CAR) T-cell therapy produces high response rates in refractory B-cell non-Hodgkin lymphoma, but long-term data are minimal to date. In this study, we present long-term follow-up of a pilot trial testing a CD20-targeting third-generation CAR in patients with relapsed B-cell lymphomas following cyclophosphamide-only lymphodepletion. Two of the three patients in the trial, with mantle cell lymphoma and follicular lymphoma, had remissions lasting more than 7 years, though they ultimately relapsed. The absence of B-cell aplasia in both patients suggested a lack of functional CAR T-cell persistence, leading to the hypothesis that endogenous immune responses were responsible for these long-term remissions. Correlative immunologic analyses supported this hypothesis, with evidence of new humoral and cellular antitumor immune responses proximal to clinical response time points. Collectively, our results suggest that CAR T-cell therapy may facilitate epitope spreading and endogenous immune response formation in lymphomas. Significance: Two of three patients treated with CD20-targeted CAR T-cell therapy had long-term remissions, with evidence of endogenous antitumor immune response formation. Further investigation is warranted to develop conditions that promote epitope spreading in lymphomas.

An approach for integrating multimodal omics data into sparse and interpretable models.

Using omics data, a common goal is to identify a concise set of variables that predict a clinical endpoint from an extensive pool. In a recent paper published in Nature Biotechnology, Hédou et al.1 introduced Stabl, a computational method crafted to identify sparse yet robust signatures linked to endpoints.

Response to tumor-infiltrating lymphocyte adoptive therapy is associated with preexisting CD8+ T-myeloid cell networks in melanoma.

Adoptive cell therapy (ACT) using ex vivo-expanded tumor-infiltrating lymphocytes (TILs) can eliminate or shrink metastatic melanoma, but its long-term efficacy remains limited to a fraction of patients. Using longitudinal samples from 13 patients with metastatic melanoma treated with TIL-ACT in a phase 1 clinical study, we interrogated cellular states within the tumor microenvironment (TME) and their interactions. We performed bulk and single-cell RNA sequencing, whole-exome sequencing, and spatial proteomic analyses in pre- and post-ACT tumor tissues, finding that ACT responders exhibited higher basal tumor cell-intrinsic immunogenicity and mutational burden. Compared with nonresponders, CD8+ TILs exhibited increased cytotoxicity, exhaustion, and costimulation, whereas myeloid cells had increased type I interferon signaling in responders. Cell-cell interaction prediction analyses corroborated by spatial neighborhood analyses revealed that responders had rich baseline intratumoral and stromal tumor-reactive T cell networks with activated myeloid populations. Successful TIL-ACT therapy further reprogrammed the myeloid compartment and increased TIL-myeloid networks. Our systematic target discovery study identifies potential T-myeloid cell network-based biomarkers that could improve patient selection and guide the design of ACT clinical trials.

Signaling via a CD27-TRAF2-SHP-1 axis during naive T cell activation promotes memory-associated gene regulatory networks.

The interaction of the tumor necrosis factor receptor (TNFR) family member CD27 on naive CD8+ T (Tn) cells with homotrimeric CD70 on antigen-presenting cells (APCs) is necessary for T cell memory fate determination. Here, we examined CD27 signaling during Tn cell activation and differentiation. In conjunction with T cell receptor (TCR) stimulation, ligation of CD27 by a synthetic trimeric CD70 ligand triggered CD27 internalization and degradation, suggesting active regulation of this signaling axis. Internalized CD27 recruited the signaling adaptor TRAF2 and the phosphatase SHP-1, thereby modulating TCR and CD28 signals. CD27-mediated modulation of TCR signals promoted transcription factor circuits that induced memory rather than effector associated gene programs, which are induced by CD28 costimulation. CD27-costimulated chimeric antigen receptor (CAR)-engineered T cells exhibited improved tumor control compared with CD28-costimulated CAR-T cells. Thus, CD27 signaling during Tn cell activation promotes memory properties with relevance to T cell immunotherapy.

Enhancing immunogenic responses through CDK4/6 and HIF2α inhibition in Merkel cell carcinoma.

Approximately 50% of Merkel cell carcinoma (MCC) patients facing this highly aggressive skin cancer initially respond positively to PD-1-based immunotherapy. Nevertheless, the recurrence of MCC post-immunotherapy emphasizes the pressing need for more effective treatments. Recent research has highlighted Cyclin-dependent kinases 4 and 6 (CDK4/6) as pivotal cell cycle regulators gaining prominence in cancer studies. This study reveals that the CDK4/6 inhibitor, palbociclib can enhance PD-L1 gene transcription and surface expression in MCC cells by activating HIF2α. Inhibiting HIF2α with TC-S7009 effectively counteracts palbociclib-induced PD-L1 transcription and significantly intensifies cell death in MCC. Simultaneously, co-targeting CDK4/6 and HIF2α boosts ROS levels while suppressing SLC7A11, a key regulator of cellular redox balance, promoting ferroptosis- a form of immunogenic cell death linked to iron. Considering the rising importance of immunogenic cell death in immunotherapy, this strategy holds promise for improving future MCC treatments, markedly increasing immunogenic cell death various across various MCC cell lines, thus advancing cancer immunotherapy.

Single-cell Profiling Uncovers a Muc4-Expressing Metaplastic Gastric Cell Type Sustained by Helicobacter pylori-driven Inflammation.

Mechanisms for Helicobacter pylori (Hp)-driven stomach cancer are not fully understood. In a transgenic mouse model of gastric preneoplasia, concomitant Hp infection and induction of constitutively active KRAS (Hp+KRAS+) alters metaplasia phenotypes and elicits greater inflammation than either perturbation alone. Gastric single-cell RNA sequencing showed that Hp+KRAS+ mice had a large population of metaplastic pit cells that expressed the intestinal mucin Muc4 and the growth factor amphiregulin. Flow cytometry and IHC-based immune profiling revealed that metaplastic pit cells were associated with macrophage and T-cell inflammation. Accordingly, expansion of metaplastic pit cells was prevented by gastric immunosuppression and reversed by antibiotic eradication of Hp. Finally, MUC4 expression was significantly associated with proliferation in human gastric cancer samples. These studies identify an Hp-associated metaplastic pit cell lineage, also found in human gastric cancer tissues, whose expansion is driven by Hp-dependent inflammation. Significance: Using a mouse model, we have delineated metaplastic pit cells as a precancerous cell type whose expansion requires Hp-driven inflammation. In humans, metaplastic pit cells show enhanced proliferation as well as enrichment in precancer and early cancer tissues, highlighting an early step in the gastric metaplasia to cancer cascade.

Single-cell protein expression profiling resolves circulating and resident memory T cell diversity across tissues and infection contexts.

Memory CD8+ T cells can be broadly divided into circulating (TCIRCM) and tissue-resident memory T (TRM) populations. Despite well-defined migratory and transcriptional differences, the phenotypic and functional delineation of TCIRCM and TRM cells, particularly across tissues, remains elusive. Here, we utilized an antibody screening platform and machine learning prediction pipeline (InfinityFlow) to profile >200 proteins in TCIRCM and TRM cells in solid organs and barrier locations. High-dimensional analyses revealed unappreciated heterogeneity within TCIRCM and TRM cell lineages across nine different organs after either local or systemic murine infection models. Additionally, we demonstrated the relative effectiveness of strategies allowing for the selective ablation of TCIRCM or TRM populations across organs and identified CD55, KLRG1, CXCR6, and CD38 as stable markers for characterizing memory T cell function during inflammation. Together, these data and analytical framework provide an in-depth resource for memory T cell classification in both steady-state and inflammatory conditions.

An information theoretic approach to detecting spatially varying genes.

A key step in spatial transcriptomics is identifying genes with spatially varying expression patterns. We adopt an information theoretic perspective to this problem by equating the degree of spatial coherence with the Jensen-Shannon divergence between pairs of nearby cells and pairs of distant cells. To avoid the notoriously difficult problem of estimating information theoretic divergences, we use modern approximation techniques to implement a computationally efficient algorithm designed to scale with in situ spatial transcriptomics technologies. In addition to being highly scalable, we show that our method, which we call maximization of spatial information (Maxspin), improves accuracy across several spatial transcriptomics platforms and a variety of simulations when compared with a variety of state-of-the-art methods. To further demonstrate the method, we generated in situ spatial transcriptomics data in a renal cell carcinoma sample using the CosMx Spatial Molecular Imager and used Maxspin to reveal novel spatial patterns of tumor cell gene expression.

Intradermal skin test with mRNA vaccines as a surrogate marker of T cell immunity in immunocompromised patients.

OBJECTIVES: Intradermal skin test (IDT) with mRNA vaccines may represent a simple, reliable, and affordable tool to measure T cell response in immunocompromised patients who failed to mount serological responses following vaccination with mRNA covid-19 vaccines. METHODS: We compared anti-SARS-CoV-2 antibodies and cellular responses in vaccinated immunocompromised patients (n = 58), healthy seronegative naive controls (NC, n = 8), and healthy seropositive vaccinated controls (VC, n = 32) by Luminex, spike-induced IFN-γ Elispot and an IDT. A skin biopsy 24 h after IDT and single-cell RNAseq was performed in three vaccinated volunteers. RESULTS: Twenty-five percent of seronegative NC had a positive Elispot (2/8) and IDT (1/4), compared to 95% (20/21) and 93% (28/30) in seropositive VC, respectively. Single-cell RNAseq data in the skin of VC showed a predominant mixed population of effector helper and cytotoxic T cells. The TCR repertoire revealed 18/1064 clonotypes with known specificities against SARS-CoV-2, among which six were spike-specific. Seronegative immunocompromised patients with positive Elispot and IDT were in 83% (5/6) treated with B cell-depleting reagents, while those with negative IDT were all transplant recipients. CONCLUSIONS: Our results indicate that delayed local reaction to IDT reflects vaccine-induced T-cell immunity opening new perspectives to monitor seronegative patients and elderly populations with waning immunity.

Differentiation of IL-26+ TH17 intermediates into IL-17A producers via epithelial crosstalk in psoriasis.

Interleukin (IL)-26 is a TH17 cytokine with known antimicrobial and pro-inflammatory functions. However, the precise role of IL-26 in the context of pathogenic TH17 responses is unknown. Here we identify a population of blood TH17 intermediates that produce high levels of IL-26 and differentiate into IL-17A-producing TH17 cells upon TGF-ß1 exposure. By combining single cell RNA sequencing, TCR sequencing and spatial transcriptomics we show that this process occurs in psoriatic skin. In fact, IL-26+ TH17 intermediates infiltrating psoriatic skin induce TGF-ß1 expression in basal keratinocytes and thereby promote their own differentiation into IL-17A-producing cells. Thus, our study identifies IL-26-producing cells as an early differentiation stage of TH17 cells that infiltrates psoriatic skin and controls its own maturation into IL17A-producing TH17 cells, via epithelial crosstalk involving paracrine production of TGF-ß1.

Antibody Correlates of Protection From Severe Respiratory Syncytial Virus Disease in a Vaccine Efficacy Trial.

Background: Respiratory syncytial virus (RSV) can cause serious lung infections in young children and there is currently no available vaccine. Methods: We used complementary statistical frameworks to analyze 4 RSV serology measurements in mothers and their infants in South Africa who participated in a phase 3 maternal immunization trial of an RSV F protein nanoparticle vaccine as correlates of risk and of protection against different RSV disease endpoints. Results: We found evidence to support each antibody measurement-encompassing RSV-neutralizing antibodies and F surface glycoprotein-binding antibodies-as an inverse correlate of risk of RSV-associated acute lower respiratory tract infection with severe hypoxia in at least 1 framework, with vaccine-induced fold-rise from the maternal enrollment to day 14 samples of anti-F immunoglobulin G (IgG) binding antibodies having the most consistent evidence. This evidence includes a significant association of fold-rise anti-F IgG with vaccine efficacy (VE); achieving a baseline covariate-adjusted VE of 75% requires a vaccine-induced maternal anti-F IgG fold-rise of around 16. Neither multivariable logistic regression nor superlearning analyses showed benefit to including multiple time points or assays in the same model, suggesting a parsimonious correlate. Post hoc exploratory analyses supported adherence of vaccine-induced maternal anti-F IgG fold-rise to the Prentice criteria for a valid surrogate endpoint. Conclusions: Our results suggest that the vaccine induced protective anti-F antibody responses. If this finding is confirmed, VE could potentially be augmented by increasing these responses.

Defining cellular population dynamics at single-cell resolution during prostate cancer progression.

Advanced prostate malignancies are a leading cause of cancer-related deaths in men, in large part due to our incomplete understanding of cellular drivers of disease progression. We investigate prostate cancer cell dynamics at single-cell resolution from disease onset to the development of androgen independence in an in vivo murine model. We observe an expansion of a castration-resistant intermediate luminal cell type that correlates with treatment resistance and poor prognosis in human patients. Moreover, transformed epithelial cells and associated fibroblasts create a microenvironment conducive to pro-tumorigenic immune infiltration, which is partially androgen responsive. Androgen-independent prostate cancer leads to significant diversification of intermediate luminal cell populations characterized by a range of androgen signaling activity, which is inversely correlated with proliferation and mRNA translation. Accordingly, distinct epithelial populations are exquisitely sensitive to translation inhibition, which leads to epithelial cell death, loss of pro-tumorigenic signaling, and decreased tumor heterogeneity. Our findings reveal a complex tumor environment largely dominated by castration-resistant luminal cells and immunosuppressive infiltrates.

Transcriptional atlas of the human immune response to 13 vaccines reveals a common predictor of vaccine-induced antibody responses.

Systems vaccinology has defined molecular signatures and mechanisms of immunity to vaccination. However, comparative analysis of immunity to different vaccines is lacking. We integrated transcriptional data of over 3,000 samples, from 820 adults across 28 studies of 13 vaccines and analyzed vaccination-induced signatures of antibody responses. Most vaccines induced signatures of innate immunity and plasmablasts at days 1 and 7, respectively, after vaccination. However, the yellow fever vaccine induced an early transient signature of T and B cell activation at day 1, followed by delayed antiviral/interferon and plasmablast signatures that peaked at days 7 and 14-21, respectively. Thus, there was no evidence for a 'universal signature' that predicted antibody response to all vaccines. However, accounting for the asynchronous nature of responses, we defined a time-adjusted signature that predicted antibody responses across vaccines. These results provide a transcriptional atlas of immunity to vaccination and define a common, time-adjusted signature of antibody responses.

Pan-vaccine analysis reveals innate immune endotypes predictive of antibody responses to vaccination.

Several studies have shown that the pre-vaccination immune state is associated with the antibody response to vaccination. However, the generalizability and mechanisms that underlie this association remain poorly defined. Here, we sought to identify a common pre-vaccination signature and mechanisms that could predict the immune response across 13 different vaccines. Analysis of blood transcriptional profiles across studies revealed three distinct pre-vaccination endotypes, characterized by the differential expression of genes associated with a pro-inflammatory response, cell proliferation, and metabolism alterations. Importantly, individuals whose pre-vaccination endotype was enriched in pro-inflammatory response genes known to be downstream of nuclear factor-kappa B showed significantly higher serum antibody responses 1 month after vaccination. This pro-inflammatory pre-vaccination endotype showed gene expression characteristic of the innate activation state triggered by Toll-like receptor ligands or adjuvants. These results demonstrate that wide variations in the transcriptional state of the immune system in humans can be a key determinant of responsiveness to vaccination.

The Immune Signatures data resource, a compendium of systems vaccinology datasets.

Vaccines are among the most cost-effective public health interventions for preventing infection-induced morbidity and mortality, yet much remains to be learned regarding the mechanisms by which vaccines protect. Systems immunology combines traditional immunology with modern 'omic profiling techniques and computational modeling to promote rapid and transformative advances in vaccinology and vaccine discovery. The NIH/NIAID Human Immunology Project Consortium (HIPC) has leveraged systems immunology approaches to identify molecular signatures associated with the immunogenicity of many vaccines. However, comparative analyses have been limited by the distributed nature of some data, potential batch effects across studies, and the absence of multiple relevant studies from non-HIPC groups in ImmPort. To support comparative analyses across different vaccines, we have created the Immune Signatures Data Resource, a compendium of standardized systems vaccinology datasets. This data resource is available through ImmuneSpace, along with code to reproduce the processing and batch normalization starting from the underlying study data in ImmPort and the Gene Expression Omnibus (GEO). The current release comprises 1405 participants from 53 cohorts profiling the response to 24 different vaccines. This novel systems vaccinology data release represents a valuable resource for comparative and meta-analyses that will accelerate our understanding of mechanisms underlying vaccine responses.