Cytotoxic T Cells Targeting Spike Glycoprotein Are Associated with Hybrid Immunity to SARS-CoV-2

mRNA vaccination of individuals with prior SARS-CoV-2 infection provides superior protection against breakthrough infections with variants of concern compared with vaccination in the absence of prior infection. However, the immune mechanisms by which this hybrid immunity is generated and maintained are unknown. Whereas genetic variation in spike glycoprotein effectively subverts neutralizing Abs, spike-specific T cells are generally maintained against SARS-CoV-2 variants. Thus, we comprehensively profiled human T cell responses against the S1 and S2 domains of spike glycoprotein in a cohort of SARS-CoV-2-naive (n = 13) or -convalescent (n = 17) individuals who received two-dose mRNA vaccine series and were matched by age, sex, and vaccine type. Using flow cytometry, we observed that the overall functional breadth of CD4 T cells and polyfunctional Th1 responses was similar between the two groups. However, polyfunctional cytotoxic CD4 T cell responses against both S1 and S2 domains trended higher among convalescent subjects. Multimodal single-cell RNA sequencing revealed diverse functional programs in spike-specific CD4 and CD8 T cells in both groups. However, convalescent individuals displayed enhanced cytotoxic and antiviral CD8 T cell responses to both S1 and S2 in the absence of cytokine production. Taken together, our data suggest that cytotoxic CD4 and CD8 T cells targeting spike glycoprotein may partially account for hybrid immunity and protection against breakthrough infections with SARS-CoV-2.

Duration of viral infectiousness and correlation with symptoms and diagnostic testing in non-hospitalized adults during acute SARS-CoV-2 infection: A longitudinal cohort study

BACKGROUND

Guidelines for SARS-CoV-2 have relied on limited data on duration of viral infectiousness and correlation with COVID-19 symptoms and diagnostic testing.

METHODS

We enrolled ambulatory adults with acute SARS-CoV-2 infection and performed serial measurements of COVID-19 symptoms, nasal swab viral RNA, nucleocapsid (N) and spike (S) antigens, and replication-competent SARS-CoV-2 by viral growth in culture. We determined average time from symptom onset to a first negative test result and estimated risk of infectiousness, as defined by positive viral growth in culture.

RESULTS

Among 95 adults, median [interquartile range] time from symptom onset to first negative test result was 9 [5] days, 13 [6] days, 11 [4] days, and >19 days for S antigen, N antigen, culture growth, and viral RNA by RT-PCR, respectively. Beyond two weeks, virus growth and N antigen titers were rarely positive, while viral RNA remained detectable among half (26/51) of participants tested 21-30 days after symptom onset. Between 6-10 days from symptom onset, N antigen was strongly associated with culture positivity (relative risk=7.61, 95% CI: 3.01-19.22), whereas neither viral RNA nor symptoms were associated with culture positivity. During the 14 days following symptom onset, the presence of N antigen remained strongly associated (adjusted relative risk=7.66, 95% CI: 3.96-14.82) with culture positivity, regardless of COVID-19 symptoms.

CONCLUSIONS

Most adults have replication-competent SARS-CoV-2 for 10-14 after symptom onset. N antigen testing is a strong predictor of viral infectiousness and may be a more suitable biomarker, rather than absence of symptoms or viral RNA, to discontinue isolation within two weeks from symptom onset.

Cytomegalovirus-vaccine-induced unconventional T cell priming and control of SIV replication is conserved between primate species

Strain 68-1 rhesus cytomegalovirus expressing simian immunodeficiency virus (SIV) antigens (RhCMV/SIV) primes MHC-E-restricted CD8+ T cells that control SIV replication in 50%-60% of the vaccinated rhesus macaques. Whether this unconventional SIV-specific immunity and protection is unique to rhesus macaques or RhCMV or is intrinsic to CMV remains unknown. Here, using cynomolgus CMV vectors expressing SIV antigens (CyCMV/SIV) and Mauritian cynomolgus macaques, we demonstrate that the induction of MHC-E-restricted CD8+ T cells requires matching CMV to its host species. RhCMV does not elicit MHC-E-restricted CD8+ T cells in cynomolgus macaques. However, cynomolgus macaques vaccinated with species-matched 68-1-like CyCMV/SIV mounted MHC-E-restricted CD8+ T cells, and half of the vaccinees stringently controlled SIV post-challenge. Protected animals manifested a vaccine-induced IL-15 transcriptomic signature that is associated with efficacy in rhesus macaques. These findings demonstrate that the ability of species-matched CMV vectors to elicit MHC-E-restricted CD8+ T cells that are required for anti-SIV efficacy is conserved in nonhuman primates, and these data support the development of HCMV/HIV for a prophylactic HIV vaccine.

Imprinted SARS-CoV-2-specific memory lymphocytes define hybrid immunity

Immune memory is tailored by cues that lymphocytes perceive during priming. The severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) pandemic created a situation in which nascent memory could be tracked through additional antigen exposures. Both SARS-CoV-2 infection and vaccination induce multifaceted, functional immune memory, but together, they engender improved protection from disease, termed hybrid immunity. We therefore investigated how vaccine-induced memory is shaped by previous infection. We found that following vaccination, previously infected individuals generated more SARS-CoV-2 RBD-specific memory B cells and variant-neutralizing antibodies and a distinct population of IFN-γ and IL-10-expressing memory SARS-CoV-2 spike-specific CD4+ T cells than previously naive individuals. Although additional vaccination could increase humoral memory in previously naive individuals, it did not recapitulate the distinct CD4+ T cell cytokine profile observed in previously infected subjects. Thus, imprinted features of SARS-CoV-2-specific memory lymphocytes define hybrid immunity.

Interleukin-15 response signature predicts RhCMV/SIV vaccine efficacy

Simian immunodeficiency virus (SIV) challenge of rhesus macaques (RMs) vaccinated with strain 68–1 Rhesus Cytomegalovirus (RhCMV) vectors expressing SIV proteins (RhCMV/SIV) results in a binary outcome: stringent control and subsequent clearance of highly pathogenic SIV in ~55% of vaccinated RMs with no protection in the remaining 45%. Although previous work indicates that unconventionally restricted, SIV-specific, effector-memory (EM)-biased CD8⁺ T cell responses are necessary for efficacy, the magnitude of these responses does not predict efficacy, and the basis of protection vs. non-protection in 68–1 RhCMV/SIV vector-vaccinated RMs has not been elucidated. Here, we report that 68–1 RhCMV/SIV vector administration strikingly alters the whole blood transcriptome of vaccinated RMs, with the sustained induction of specific immune-related pathways, including immune cell, toll-like receptor (TLR), inflammasome/cell death, and interleukin-15 (IL-15) signaling, significantly correlating with subsequent vaccine efficacy. Treatment of a separate RM cohort with IL-15 confirmed the central involvement of this cytokine in the protection signature, linking the major innate and adaptive immune gene expression networks that correlate with RhCMV/SIV vaccine efficacy. This change-from-baseline IL-15 response signature was also demonstrated to significantly correlate with vaccine efficacy in an independent validation cohort of vaccinated and challenged RMs. The differential IL-15 gene set response to vaccination strongly correlated with the pre-vaccination activity of this pathway, with reduced baseline expression of IL-15 response genes significantly correlating with higher vaccine-induced induction of IL-15 signaling and subsequent vaccine protection, suggesting that a robust de novo vaccine-induced IL-15 signaling response is needed to program vaccine efficacy. Thus, the RhCMV/SIV vaccine imparts a coordinated and persistent induction of innate and adaptive immune pathways featuring IL-15, a known regulator of CD8⁺ T cell function, that support the ability of vaccine-elicited unconventionally restricted CD8⁺ T cells to mediate protection against SIV challenge.

SIV insert-expressing vaccine vectors based on strain 68–1 RhCMV elicit robust, highly effector-memory-biased, unconventionally restricted T cell responses that are associated with an unprecedented level of SIV control after challenge (replication arrest leading to clearance) in slightly over half of vaccinated monkeys. Since efficacy among monkeys vaccinated with the effective 68–1 vaccine is not predicted by standard measures of immunogenicity, we used functional genomics analysis of RhCMV/SIV vaccinated monkeys with known challenge outcomes to identify immune correlates of protection. We found that vaccine efficacy significantly correlates with a vaccine-induced response to IL-15 that includes modulation of immune cell, inflammation, TLR signaling, and cell death programming response pathways. These data suggest that RhCMV/SIV efficacy results from a coordinated and sustained vaccine-mediated induction of innate and adaptive immune pathways featuring IL-15, a known regulator of CD8⁺ effector-memory T cell function, that cooperates with vaccine-elicited CD8⁺ T cells to mediate efficacy.

Abstract 1679: Neoepitope screening in colorectal cancer patients by custom exome panel profiling somatic mutations

Colorectal cancer (CRC) is one of the major causes of global mortality. Recent clinical advances in T cell therapy using antibodies that block inhibitory T cell signals have been transforming cancer therapy. Studies show that somatic mutations in protein coding regions produce tumor-specific antigens, or neoantigens. CRC warrants deeper investigation into the non-synonymous exonic mutations that seem to be drivers in tumor development. Tumor mutation burden (TMB) and the abundance of neoantigens are highly correlated and it has been shown that TMB can be estimated using targeted sequencing panels. The aim of this study is to investigate 10 matched tumor/normal CRC samples using a panel targeting 422 genes covering about 1.7 Mb to screen for potential neoepitope candidates. Although sequencing and bioinformatics pipelines are developed to effectively filter neoantigens, it is still unclear which antigens elicit a T cell response. A review of the tools used in neoepitope prediction shows that it is not only a very complex task, but demands high sequencing depth. Comparing currently available tools will enable us to understand the strengths and shortcomings of each algorithm, and develop a custom pipeline framework.

Note: This abstract was not presented at the meeting.

Citation Format: Raghavee Venkatramanan, Kerry Deutsch, Inah Golez, Pallavi Shroff, Spencer Seale, Brendan Ralph, Sally. Dow, Evan Anderson, Saman Tahir, Anjali Malge, Corey Braastad, Steven Anderson. Neoepitope screening in colorectal cancer patients by custom exome panel profiling somatic mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1679.

Abstract 3418: Cross-comparison of targeted gene expression technologies for patient stratification

Colorectal cancer (CRC) is one of the major causes of global cancer mortality. Until recently, KRAS has been the only predictive biomarker for anti-EGFR therapy for metastatic CRC, and yet predicting prognosis in clinical practice is still poor. Therefore, a more accurate method for prognosis of CRC patients is needed. Gene expression profiling has shown great promise in predicting prognosis of individual patients in diverse cancers. The development of RNA-sequencing has greatly facilitated identification of biomarkers that can be used to stratify patients for targeted therapies. Despite the decrease in cost of sequencing in last few years, the time and the resources needed for analysis limit its use in clinical trials for patient selection. Targeted gene expression technologies like qPCR and NanoString enable highly customizable assays that can be conveniently performed for patient recruitment. The aim of this study was to investigate potential alternatives for gene profiling using a novel NanoString Plex Set technology. The Plex Set system comes with prepackaged and custom code sets in identifying genetic markers. Up to 8 samples can be pooled to each nCounter cartridge lane, enabling a total of 96 samples per run, thus making the total cost relatively affordable. For this study, gene expression signature was developed using RNA-Seq data where we have profiled 74 CRC samples, 20 of which have matching normal samples. A RAS signature score based on expression profile was calculated for each sample. In order to look for potential gene signatures, differential gene expression analysis was performed between the following groups: (a) samples with high versus those with low RAS signature scores in the 54 CRC, (b) KRAS mutant versus wild-type samples, and (c) tumor versus normal samples in the clinical study. We hypothesized that our genes of interest are most likely significantly differentially expressed in one of these groups. The counts of significantly expressed gene for the groups (a-c) are 1560 and 34, respectively, and we are working on the third case. Therefore, significantly deferentially expressed genes between groups were selected and ranked based on frequency of occurrence. These genes of interest are being analyzed using NanoString Plex Set and qPCR to evaluate the potential of using NanoString Plex Set system for targeted gene expression profiling. Results of these analyses will be presented.

Citation Format: Raghavee Venkatramanan, Tuuli Saloranta, Inah Golez, Elliot Swanson, Kimberly Kruse, Vickie Satele, Saman Tahir, Sally Dow, Evan Anderson, Briana Hudson, Spencer Chee, Kerry Deutsch, Steve Anderson, Fang Yin Lo, Anup Madan. Cross-comparison of targeted gene expression technologies for patient stratification [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3418.

Abstract 5379: Stratification of colorectal cancer patients based on various sequencing platforms and tumor mutational burden

Colorectal cancer (CRC) is the third most common type of cancer in the United States. Although chemotherapy, radiation and targeted therapies can improve survival rates, recent studies have shown the potential benefit of immunotherapies to improve outcomes for patients with advanced CRC. Targeted therapies that use monoclonal antibodies (mAbs) to EGFR have been shown to benefit some CRC patients. Until recently, KRAS has been the only predictive biomarker for anti-EGFR therapy for metastatic CRC. However, 40% to 60% of patients with wild-type KRAS do not respond to anti-EGFR therapy. Therefore, to accurately predict patients' response to treatments and improve clinical outcomes, additional prediction and treatment methods are imperative. One of the many efforts to improve prediction for CRC patient's response to the anti-EGFR therapy is the development of gene expression based RAS signature scores for identification of RAS activated tumors independent of mutations in the KRAS gene. There is also considerable effort being placed on combinations of targeted therapy and immunotherapies to improve responses for these cancers. Previously, we have stratified 55 CRC samples by applying a RAS gene signature score which measures MEK pathway functional output independent of tumor genotype. We showed that samples that have RAS activating mutations such as KRAS and BRAF have significant higher RAS scores (p<0.001). Here we investigate 120 colorectal cancer patients using exome sequencing, targeted panel sequencing, and RNA sequencing. Specifically, we acquired 40 normal-tumor pair samples and performed GATK tumor-normal pair mutation calling. Among these 120 samples, 65 of them are from patients treated with either cetuximab or panitumumab. Continued from our previous study, we stratified these samples based on calculated RAS scores, and showed how the results correlate with clinical outcome. Tumor mutational burden (TMB) can help predict the likelihood that a patient will benefit from certain immunotherapies; however, clinical applicability is limited due to cost and computing requirements of whole-exome sequencing to detect variants with high degree of accuracy. Therefore it would be helpful if TMB can be identified based on targeted sequencing. Here we investigate how TMB calculated based on different sequencing platform correlates with each other and whether targeted sequencing panel can be used instead of exome sequencing for stratifying patients. In addition, we investigated if Illumina's global screening array can be used for TMB analysis. Furthermore, to investigate the potential immune reactivity in these CRC samples, and thereby the potential benefit of immunotherapy, we performed in-silico prediction of neo-antigens and peptide binding affinity between tumor antigens derived from mutations and human HLA alleles.

Citation Format: Fang Yin Lo, Claire Olson, Kerry Deutsch, Tuuli Saloranta, Inah Golez, Timothy Yeatman, Steven Anderson, Anup Madan. Stratification of colorectal cancer patients based on various sequencing platforms and tumor mutational burden [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5379.

Abstract 3578: Unmasking T-cell heterogeneity via single cell transcriptomic profiling

Single-cell gene expression analysis has been shown to delineate cellular heterogeneity. Such analyses result in the discovery of genes that identify subtypes of cells, or that mark intermediate states during a biological process. Cellular heterogeneity, plasticity, and diversity of T cells facilitate a wide range of functional flexibility that give rise to a remarkable breadth of potential responses against many different pathogens and in various disease settings. Understanding how T cells leverage these cellular characteristics particularly during dynamic processes such as development, differentiation, and antigenic response is important and well suited for single-cell analysis. Unbiased approaches to evaluate cellular states and subpopulations of single T cells can identify important aspects that may be concealed by targeted approaches such as FACS sorting on cell-surface antigens, or bulk expression analysis. Resolving these complexities in T cells is of particular interest as classical phenotypic criteria has been deemed insufficient for distinguishing different T cell subtypes and transitional states, particularly in the context of dysfunctional T cell states in autoimmunity and tumor-related exhaustion. We activated purified 2.5 million CD4+ T cells from Cynomolgus monkey (M. fascicularis) using equal amounts of biotinylated antibodies against CD2 (human), CD3 (primate) and CD283 (human). To determine the single cell transcriptomic profile of these activated T cells, we performed drop seq sequencing at 0 hours and 24 hours post activation. Cells at both time points were split into four technical replicates to determine variability in the sequencing process. In addition, RNA was also isolated from these cells and whole transcriptome analysis was performed using Illumina's Next Generation Sequencing technology. Our analysis show how cellular subpopulations can be identified from transcriptional data, and derive characteristic gene expression signatures that distinguish these states. We ordered single cells in pseudotime, placing them along a trajectory corresponding to individual cell's asynchronous progression during activation. Monocle was used for this purpose as it orders cells by learning an explicit principal graph from the single cell genomics data with Reversed Graph Embedding, an advanced machine learning technique, which robustly and accurately resolves complicated biological processes. This information could not have been obtained from bulk RNA-Seq analysis. Our results show that single-cell RNA-Seq is a powerful technique to study the cellular heterogeneity in T cells, a paradigm that will be of great value in the development of immunotherapeutic strategies.

Citation Format: Mukta Dutta, Tuuli Saloranta, Inah Golez, Kerry Deutsch, Cara Lord, Vickie Satele, Steve Anderson, Anup Madan. Unmasking T-cell heterogeneity via single cell transcriptomic profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 3578.