Cmai: Predicting Antigen-Antibody Interactions from Massive Sequencing Data.

The interaction between antigens and antibodies (B cell receptors, BCRs) is the key step underlying the function of the humoral immune system in various biological contexts. The capability to profile the landscape of antigen-binding affinity of a vast number of BCRs will provide a powerful tool to reveal novel insights at unprecedented levels and will yield powerful tools for translational development. However, current experimental approaches for profiling antibody-antigen interactions are costly and time-consuming, and can only achieve low-to-mid throughput. On the other hand, bioinformatics tools in the field of antibody informatics mostly focus on optimization of antibodies given known binding antigens, which is a very different research question and of limited scope. In this work, we developed an innovative Artificial Intelligence tool, Cmai, to address the prediction of the binding between antibodies and antigens that can be scaled to high-throughput sequencing data. Cmai achieved an AUROC of 0.91 in our validation cohort. We devised a biomarker metric based on the output from Cmai applied to high-throughput BCR sequencing data. We found that, during immune-related adverse events (irAEs) caused by immune-checkpoint inhibitor (ICI) treatment, the humoral immunity is preferentially responsive to intracellular antigens from the organs affected by the irAEs. In contrast, extracellular antigens on malignant tumor cells are inducing B cell infiltrations, and the infiltrating B cells have a greater tendency to co-localize with tumor cells expressing these antigens. We further found that the abundance of tumor antigen-targeting antibodies is predictive of ICI treatment response. Overall, Cmai and our biomarker approach filled in a gap that is not addressed by current antibody optimization works nor works such as AlphaFold3 that predict the structures of complexes of proteins that are known to bind.

Differences in systemic immune parameters in individuals with lung cancer according to race.

Introduction: Racial and ethnic disparities in the presentation and outcomes of lung cancer are widely known. To evaluate potential factors contributing to these observations, we measured systemic immune parameters in Black and White patients with lung cancer. Methods: Patients scheduled to receive cancer immunotherapy were enrolled in a multi-institutional prospective biospecimen collection registry. Clinical and demographic information were obtained from electronic medical records. Pre-treatment peripheral blood samples were collected and analyzed for cytokines using a multiplex panel and for immune cell populations using mass cytometry. Differences between Black and White patients were determined and corrected for multiple comparisons. Results: A total of 187 patients with non-small cell lung cancer (Black, 19; White, 168) were included in the analysis. There were no significant differences in baseline characteristics between Black and White patients. Compared to White patients, Black patients had significantly lower levels of CCL23 and CCL27, and significantly higher levels of CCL8, CXCL1, CCL26, CCL25, CCL1, IL-1 b, CXCL16, and IFN-γ (all P <0.05, FDR<0.1). Black patients also exhibited greater populations of non-classical CD16+ monocytes, NKT-like cells, CD4+ cells, CD38+ monocytes, and CD57+ gamma delta T cells (all P <0.05). Conclusions: Black and White patients with lung cancer exhibit several differences in immune parameters, with Black patients exhibiting greater levels of numerous pro-inflammatory cytokines and cell populations. The etiology and clinical significance of these differences warrant further evaluation.

Highly variable timing renders immunotherapy efficacy and toxicity impractical biomarkers of one another in clinical practice.

Background: A useful clinical biomarker requires not only association but also a consistent temporal relationship. For instance, chemotherapy-induced neutropenia and epidermal growth-factor inhibitor-related acneiform rash both occur within weeks of treatment initiation, thereby providing information prior to efficacy assessment. Although immune checkpoint inhibitor (ICI)-associated immune-related adverse events (irAE) have been associated with therapeutic benefit, irAE may have delayed and highly variable onset. To determine whether ICI efficacy and irAE could serve as clinically useful biomarkers for predicting each other, we determined the temporal relationship between initial efficacy assessment and irAE onset in a diverse population treated with ICI. Methods: Using two-sided Fisher exact and Cochran-Armitage tests, we determined the relative timing of initial efficacy assessment and irAE occurrence in a cohort of 155 ICI-treated patients (median age 68 years, 40% women). Results: Initial efficacy assessment was performed a median of 50 days [interquartile range (IQR) 39-59 days] after ICI initiation; median time to any irAE was 77 days (IQR 28-145 days) after ICI initiation. Median time to first irAE was 42 days (IQR 20-88 days). Overall, 58% of any irAE and 47% of first irAE occurred after initial efficacy assessment. For clinically significant (grade ≥2) irAE, 60% of any and 53% of first occurred after initial efficacy assessment. The likelihood of any future irAE did not differ according to response (45% for complete or partial response vs. 47% for other cases; P=1). In landmark analyses controlling for clinical and toxicity follow-up, patients demonstrating greater tumor shrinkage at initial efficacy assessment were more likely to develop future grade ≥2 (P=0.05) and multi-organ (P=0.02) irAE. Conclusions: In contrast to that seen with chemotherapy and molecularly targeted therapies, the temporal relationship between ICI efficacy and toxicity is complex and bidirectional. In practice, neither parameter can be routinely relied on as a clinical biomarker to predict the other.

Distinct autoantibody profiles across checkpoint inhibitor types and toxicities.

Immune checkpoint inhibitors (ICI) are increasingly used in combination. To understand the effects of different ICI categories, we characterized changes in circulating autoantibodies in patients enrolled in the E4412 trial (NCT01896999) of brentuximab vedotin (BV) plus ipilimumab, BV plus nivolumab, or BV plus ipilimumab-nivolumab for Hodgkin Lymphoma. Cycle 2 Day 1 (C2D1) autoantibody levels were compared to pre-treatment baseline. Across 112 autoantibodies tested, we generally observed increases in ipilimumab-containing regimens, with decreases noted in the nivolumab arm. Among 15 autoantibodies with significant changes at C2D1, all nivolumab cases exhibited decreases, with more than 90% of ipilimumab-exposed cases showing increases. Autoantibody profiles also showed differences according to immune-related adverse event (irAE) type, with rash generally featuring increases and liver toxicity demonstrating decreases. We conclude that dynamic autoantibody profiles may differ according to ICI category and irAE type. These findings may have relevance to clinical monitoring and irAE treatment.

The evolution of lung adenocarcinoma precursors is associated with chromosomal instability and transition from innate to adaptive immune response/evasion.

Studying lung adenocarcinoma (LUAD) early carcinogenesis is challenging, primarily due to the lack of LUAD precursors specimens. We amassed multi-omics data from 213 LUAD and LUAD precursors to identify molecular features underlying LUAD precancer evolution. We observed progressively increasing mutations, chromosomal aberrations, whole genome doubling and genomic instability from precancer to invasive LUAD, indicating aggravating chromosomal instability (CIN). Telomere shortening, a crucial genomic alteration linked to CIN, emerged at precancer stage. Moreover, later-stage lesions demonstrated increasing cancer stemness and decreasing alveolar identity, suggesting epithelial de-differentiation during early LUAD carcinogenesis. The innate immune cells progressively diminished from precancer to invasive LUAD, concomitant with a gradual recruitment of adaptive immune cells (except CD8+ and gamma-delta T cells that decreased in later stages) and upregulation of numerous immune checkpoints, suggesting LUAD precancer evolution is associated with a shift from innate to adaptive immune response and immune evasion mediated by various mechanisms.

Use of artificial intelligence chatbots in clinical management of immune-related adverse events.

BACKGROUND: Artificial intelligence (AI) chatbots have become a major source of general and medical information, though their accuracy and completeness are still being assessed. Their utility to answer questions surrounding immune-related adverse events (irAEs), common and potentially dangerous toxicities from cancer immunotherapy, are not well defined. METHODS: We developed 50 distinct questions with answers in available guidelines surrounding 10 irAE categories and queried two AI chatbots (ChatGPT and Bard), along with an additional 20 patient-specific scenarios. Experts in irAE management scored answers for accuracy and completion using a Likert scale ranging from 1 (least accurate/complete) to 4 (most accurate/complete). Answers across categories and across engines were compared. RESULTS: Overall, both engines scored highly for accuracy (mean scores for ChatGPT and Bard were 3.87 vs 3.5, p<0.01) and completeness (3.83 vs 3.46, p<0.01). Scores of 1-2 (completely or mostly inaccurate or incomplete) were particularly rare for ChatGPT (6/800 answer-ratings, 0.75%). Of the 50 questions, all eight physician raters gave ChatGPT a rating of 4 (fully accurate or complete) for 22 questions (for accuracy) and 16 questions (for completeness). In the 20 patient scenarios, the average accuracy score was 3.725 (median 4) and the average completeness was 3.61 (median 4). CONCLUSIONS: AI chatbots provided largely accurate and complete information regarding irAEs, and wildly inaccurate information ("hallucinations") was uncommon. However, until accuracy and completeness increases further, appropriate guidelines remain the gold standard to follow.

Geographic Accessibility and Completion of Initial Low-Dose CT-Based Lung Cancer Screening in an Urban Safety-Net Population.

BACKGROUND: Recent modifications to low-dose CT (LDCT)-based lung cancer screening guidelines increase the number of eligible individuals, particularly among racial and ethnic minorities. Because these populations disproportionately live in metropolitan areas, we analyzed the association between travel time and initial LDCT completion within an integrated, urban safety-net health care system. METHODS: Using Esri's StreetMap Premium, OpenStreetMap, and the r5r package in R, we determined projected private vehicle and public transportation travel times between patient residence and the screening facility for LDCT ordered in March 2017 through December 2022 at Parkland Memorial Hospital in Dallas, Texas. We characterized associations between travel time and LDCT completion in univariable and multivariable analyses. We tested these associations in a simulation of 10,000 permutations of private vehicle and public transportation distribution. RESULTS: A total of 2,287 patients were included in the analysis, of whom 1,553 (68%) completed the initial ordered LDCT. Mean age was 63 years, and 73% were underrepresented minorities. Median travel time from patient residence to the LDCT screening facility was 17 minutes by private vehicle and 67 minutes by public transportation. There was a small difference in travel time to the LDCT screening facility by public transportation for patients who completed LDCT versus those who did not (67 vs 66 min, respectively; P=.04) but no difference in travel time by private vehicle for these patients (17 min for both; P=.67). In multivariable analysis, LDCT completion was not associated with projected travel time to the LDCT facility by private vehicle (odds ratio, 1.01; 95% CI, 0.82-1.25) or public transportation (odds ratio, 1.14; 95% CI, 0.89-1.44). Similar results were noted across travel-type permutations. Black individuals were 29% less likely to complete LDCT screening compared with White individuals. CONCLUSIONS: In an urban population comprising predominantly underrepresented minorities, projected travel time is not associated with initial LDCT completion in an integrated health care system. Other reasons for differences in LDCT completion warrant investigation.

Transferring care to enhance access to early-phase cancer clinical trials: Protocol to evaluate a novel program.

Involving diverse populations in early-phase (phase I and II) cancer clinical trials is critical to informed therapeutic development. However, given the growing costs and complexities of early-phase trials, trial activation and enrollment barriers may be greatest for these studies at healthcare facilities that provide care to the most diverse patient groups, including those in historically underserved communities (e.g., safety-net healthcare systems). To promote diverse and equitable access to early-phase cancer clinical trials, we are implementing a novel program for the transfer of care to enhance access to early-phase cancer clinical trials. We will then perform a mixed-methods study to determine perceptions and impact of the program. Specifically, we will screen, recruit, and enroll diverse patients from an urban, integrated safety-net healthcare system to open and active early-phase clinical trials being conducted in a university-based cancer center. To evaluate this novel program, we will: (1) determine program impact and efficiency; and (2) determine stakeholder experience with and perceptions of the program. To achieve these goals, we will conduct preliminary cost analyses of the program. We will also conduct surveys and interviews with patients and caregivers to elucidate program impact, challenges, and areas for improvement. We hypothesize that broadening access to early-phase cancer trials conducted at experienced centers may improve equity and diversity. In turn, such efforts may enhance the efficiency and generalizability of cancer clinical research.

Effect of Patient Navigation on Completion of Lung Cancer Screening in Vulnerable Populations.

BACKGROUND: Although low-dose, CT-based lung cancer screening (LCS) can decrease lung cancer mortality in high-risk individuals, the process may be complex and pose challenges to patients, particularly those from minority underinsured and uninsured populations. We conducted a randomized controlled trial of telephone-based navigation for LCS within an integrated, urban, safety-net health care system. PATIENTS AND METHODS: Patients eligible for LCS were randomized (1:1) to usual care with or without navigation at Parkland Health in Dallas, Texas. The primary endpoint was completion of the first 3 consecutive steps in a patient's LCS process. We explored differences in completion of LCS steps between navigation and usual care groups, controlling for patient characteristics using the chi-square test. RESULTS: Patients (N=447) were randomized to either navigation (n=225) or usual care (n=222). Mean patient age was 62 years, 46% were female, and 69% were racial/ethnic minorities. There was no difference in completion of the first 3 steps of the LCS algorithm between arms (12% vs 9%, respectively; P=.30). For ordered LCS steps, completion rates were higher among patients who received navigation (86% vs 79%; P=.03). The primary reason for step noncompletion was lack of order placement. CONCLUSIONS: In this study, lack of order placement was a key reason for incomplete LCS steps. When orders were placed, patients who received navigation had higher rates of completion. Clinical team education and enhanced electronic health record processes to simplify order placement, coupled with patient navigation, may improve LCS in safety-net health care systems.

Incidence, Correlates, and Prognostic Significance of Mixed Response in Advanced Non-small Cell Lung Cancer.

BACKGROUND: Mixed response (MR), a scenario featuring discordant tumor changes, has been reported primarily with targeted therapies or immunotherapy. We determined the incidence and prognostic significance of MR in advanced non-small cell lung cancer (NSCLC) treated with cytotoxic chemotherapy. PATIENTS AND METHODS: We analyzed patient-level data from ECOG-ACRIN E5508 (carboplatin-paclitaxel + bevacizumab induction followed by randomization to maintenance therapy regimens). For patients with at least 2 target lesions and available measurements after cycle 2, we characterized response as homogeneous response (HR, similar behavior of all lesions), MR (similar behavior but >30% difference in magnitude of best and least responding lesions), or true mixed response (TMR, best and least responding lesions showing different behavior: ≥10% growth versus ≥10% shrinkage). We compared category characteristics using Mann-Whitney U and Chi-square tests, and overall survival (OS) using log-rank test and Cox models. RESULTS: Among 965 evaluable patients, HR occurred in 609 patients (63%), MR in 208 (22%), and TMR in 148 (15%). Median OS was 13.6 months for HR, 12.0 months for MR, and 7.6 months for TMR (P < .001). Compared to HR, TMR had inferior OS among stable disease cases (HR 1.62; 95% CI, 1.23-2.12; P < .001) and a trend toward inferior OS among progressive disease cases (HR 1.39; 95% CI, 0.83-2.33; P = .2). In multivariate analysis, TMR was associated with worse OS (HR 1.48; 95% CI, 1.22-1.79; P < .001). CONCLUSION: True mixed response occurs in a substantial minority of lung cancer cases treated with chemotherapy and independently confers poor prognosis.

Emerging Strategies in Lung Cancer Screening: Blood and Beyond.

BACKGROUND: Although low dose computed tomography (LDCT)-based lung cancer screening (LCS) can decrease lung cancer-related mortality among high-risk individuals, it remains an imperfect and substantially underutilized process. LDCT-based LCS may result in false-positive findings, which can lead to invasive procedures and potential morbidity. Conversely, current guidelines may fail to capture at-risk individuals, particularly those from under-represented minority populations. To address these limitations, numerous biomarkers have emerged to complement LDCT and improve early lung cancer detection. CONTENT: This review focuses primarily on blood-based biomarkers, including protein, microRNAs, circulating DNA, and methylated DNA panels, in current clinical development for LCS. We also examine other emerging biomarkers-utilizing airway epithelia, exhaled breath, sputum, and urine-under investigation. We highlight challenges and limitations of biomarker testing, as well as recent strategies to integrate molecular strategies with imaging technologies. SUMMARY: Multiple biomarkers are under active investigation for LCS, either to improve risk-stratification after nodule detection or to optimize risk-based patient selection for LDCT-based screening. Results from ongoing and future clinical trials will elucidate the clinical utility of biomarkers in the LCS paradigm.

Telemedicine and Cancer Clinical Research: Opportunities for Transformation.

ABSTRACT: Telemedicine represents an established mode of patient care delivery that has and will continue to transform cancer clinical research. Through telemedicine, opportunities exist to improve patient care, enhance access to novel therapies, streamline data collection and monitoring, support communication, and increase trial efficiency. Potential challenges include disparities in technology access and literacy, physical examination performance, biospecimen collection, privacy and security concerns, coverage of services by insurance, and regulatory considerations. Coupled with artificial intelligence, telemedicine may offer ways to reach geographically dispersed candidates for narrowly focused cancer clinical trials, such as those targeting rare genomic subsets. Collaboration among clinical trial staff, clinicians, regulators, professional societies, patients, and their advocates is critical to optimize the benefits of telemedicine for clinical cancer research.

Contemporary Trends in Reviewing Test Results Through the Electronic Patient Portal Among Patients With Cancer.

This cohort study among patients with cancer examines changes in the time from posting of test results in the electronic health record to patient viewing in the patient portal before and after implementation of the 21st Century Cures Act.

pan-MHC and cross-Species Prediction of T Cell Receptor-Antigen Binding.

Profiling the binding of T cell receptors (TCRs) of T cells to antigenic peptides presented by MHC proteins is one of the most important unsolved problems in modern immunology. Experimental methods to probe TCR-antigen interactions are slow, labor-intensive, costly, and yield moderate throughput. To address this problem, we developed pMTnet-omni, an Artificial Intelligence (AI) system based on hybrid protein sequence and structure information, to predict the pairing of TCRs of αß T cells with peptide-MHC complexes (pMHCs). pMTnet-omni is capable of handling peptides presented by both class I and II pMHCs, and capable of handling both human and mouse TCR-pMHC pairs, through information sharing enabled this hybrid design. pMTnet-omni achieves a high overall Area Under the Curve of Receiver Operator Characteristics (AUROC) of 0.888, which surpasses competing tools by a large margin. We showed that pMTnet-omni can distinguish binding affinity of TCRs with similar sequences. Across a range of datasets from various biological contexts, pMTnet-omni characterized the longitudinal evolution and spatial heterogeneity of TCR-pMHC interactions and their functional impact. We successfully developed a biomarker based on pMTnet-omni for predicting immune-related adverse events of immune checkpoint inhibitor (ICI) treatment in a cohort of 57 ICI-treated patients. pMTnet-omni represents a major advance towards developing a clinically usable AI system for TCR-pMHC pairing prediction that can aid the design and implementation of TCR-based immunotherapeutics.

Longitudinal nucleocapsid antibody testing reveals undocumented SARS-CoV-2 infections in patients with lung cancer.

Patients diagnosed with lung cancer (LC) exhibit increased susceptibility to SARS-CoV-2 infection. Rodilla et al. monitor the levels of plasma anti-nucleocapsid antibodies within a cohort of fully vaccinated LC patients and reveal that the actual infection rate is nearly twice the documented rate, indicating a significant prevalence of unreported cases.

Initial Steps in Creating a Patient-Centric Addendum to Clinical Trial Informed Consent Forms.

Introduction: The purpose of the informed consent form (ICF) is to outline the risks and benefits of an interventional clinical trial to potential participants. The aim of this study was to explore the feasibility of a short addendum to the ICF, summarizing key points most relevant to potential participants. Methods: A sample of 20 ICFs was reviewed against the requirements of the U.S. federal regulation documents and assessed for readability. Alongside the ICF review, we conducted focus groups and one-on-one interviews with people with lung cancer (n = 9) to learn what information was most important when considering participation in a clinical trial using a hypothetical phase 3 ICF. Results: The 20 ICFs reviewed were from phases 1 to 3, expanded-access, and single-patient trials covering predominantly NSCLC; 60% were global. The mean length of the ICFs was 21 (range: 15-34) pages. The average reading level was tenth grade whereas the average U.S. reading level was eighth grade. Readability varied by section, the "purpose of the study" section had the highest reading level. In the qualitative research component, participants were "overwhelmed" by the hypothetical ICF. Participants were also asked to list information for the addendum; their suggestions broadly map to federal regulations. An addendum with reference to sections in the ICF for additional details was well received. Conclusions: The variations in ICF architecture and readability make it difficult for patients to make an informed decision to participate in a clinical trial. Implications extend beyond lung cancer, highlighting key areas for ICF improvements and providing a roadmap for developing a patient-centric addendum.

Tumor Treating Fields therapy with standard systemic therapy versus standard systemic therapy alone in metastatic non-small-cell lung cancer following progression on or after platinum-based therapy (LUNAR): a randomised, open-label, pivotal phase 3 study.

BACKGROUND: Tumor Treating Fields (TTFields) are electric fields that disrupt processes critical for cancer cell survival, leading to immunogenic cell death and enhanced antitumour immune response. In preclinical models of non-small-cell lung cancer, TTFields amplified the effects of chemotherapy and immune checkpoint inhibitors. We report primary results from a pivotal study of TTFields therapy in metastatic non-small-cell lung cancer. METHODS: This randomised, open-label, pivotal phase 3 study recruited patients at 130 sites in 19 countries. Participants were aged 22 years or older with metastatic non-small-cell lung cancer progressing on or after platinum-based therapy, with squamous or non-squamous histology and ECOG performance status of 2 or less. Previous platinum-based therapy was required, but no restriction was placed on the number or type of previous lines of systemic therapy. Participants were randomly assigned (1:1) to TTFields therapy and standard systemic therapy (investigator's choice of immune checkpoint inhibitor [nivolumab, pembrolizumab, or atezolizumab] or docetaxel) or standard therapy alone. Randomisation was performed centrally using variable blocked randomisation and an interactive voice-web response system, and was stratified by tumour histology, treatment, and region. Systemic therapies were dosed according to local practice guidelines. TTFields therapy (150 kHz) was delivered continuously to the thoracic region with the recommendation to achieve an average of at least 18 h/day device usage. The primary endpoint was overall survival in the intention-to-treat population. The safety population included all patients who received any study therapy and were analysed according to the actual treatment received. The study is registered with ClinicalTrials.gov, NCT02973789. FINDINGS: Between Feb 13, 2017, and Nov 19, 2021, 276 patients were enrolled and randomly assigned to receive TTFields therapy with standard therapy (n=137) or standard therapy alone (n=139). The median age was 64 years (IQR 59-70), 178 (64%) were male and 98 (36%) were female, 156 (57%) had non-squamous non-small-cell lung cancer, and 87 (32%) had received a previous immune checkpoint inhibitor. Median follow-up was 10·6 months (IQR 6·1-33·7) for patients receiving TTFields therapy with standard therapy, and 9·5 months (0·1-32·1) for patients receiving standard therapy. Overall survival was significantly longer with TTFields therapy and standard therapy than with standard therapy alone (median 13·2 months [95% CI 10·3-15·5] vs 9·9 months [8·1-11·5]; hazard ratio [HR] 0·74 [95% CI 0·56-0·98]; p=0·035). In the safety population (n=267), serious adverse events of any cause were reported in 70 (53%) of 133 patients receiving TTFields therapy plus standard therapy and 51 (38%) of 134 patients receiving standard therapy alone. The most frequent grade 3-4 adverse events were leukopenia (37 [14%] of 267), pneumonia (28 [10%]), and anaemia (21 [8%]). TTFields therapy-related adverse events were reported in 95 (71%) of 133 patients; these were mostly (81 [85%]) grade 1-2 skin and subcutaneous tissue disorders. There were three deaths related to standard therapy (two due to infections and one due to pulmonary haemorrhage) and no deaths related to TTFields therapy. INTERPRETATION: TTFields therapy added to standard therapy significantly improved overall survival compared with standard therapy alone in metastatic non-small-cell lung cancer after progression on platinum-based therapy without exacerbating systemic toxicities. These data suggest that TTFields therapy is efficacious in metastatic non-small-cell lung cancer and should be considered as a treatment option to manage the disease in this setting. FUNDING: Novocure.

T-cell tolerant fraction as a predictor of immune-related adverse events.

BACKGROUND: Immune checkpoint inhibitor (ICI) therapies may cause unpredictable and potentially severe autoimmune toxicities termed immune-related adverse events (irAEs). Because T cells mediate ICI effects, T cell profiling may provide insight into the risk of irAEs. Here we evaluate a novel metric-the T-cell tolerant fraction-as a predictor of future irAEs. METHODS: We examined T-cell receptor beta (TRB) locus sequencing from baseline pretreatment samples from an institutional registry and previously published studies. For each patient, we used TRB sequences to calculate the T-cell tolerant fraction, which was then assessed as a predictor of future irAEs (classified as Common Terminology Criteria for Adverse Event grade 0-1 vs grade ≥2). We then compared the tolerant fraction to TRB clonality and diversity. Finally, the tolerant fraction was assessed on (1) T cells enriched against napsin A, a potential autoantigen of irAEs; (2) thymic versus peripheral blood T cells; and (3) TRBs specific for various infections and autoimmune diseases. RESULTS: A total of 77 patients with cancer (22 from an institutional registry and 55 from published studies) receiving ICI therapy (43 CTLA4, 19 PD1/PDL1, 15 combination CTLA4+PD1/PDL1) were included in the study. The tolerant fraction was significantly lower in cases with clinically significant irAEs (p<0.001) and had an area under the receiver operating curve (AUC) of 0.79. The tolerant fraction was lower for each ICI treatment category, reaching statistical significance for CTLA4 (p<0.001) and demonstrating non-significant trends for PD1/PDL1 (p=0.21) and combination ICI (p=0.18). The tolerant fraction for T cells enriched against napsin A was lower than other samples. The tolerant fraction was also lower in thymic versus peripheral blood samples, and lower in some (multiple sclerosis) but not other (type 1 diabetes) autoimmune diseases. In our study cohort, TRB clonality had an AUC of 0.62, and TRB diversity had an AUC of 0.60 for predicting irAEs. CONCLUSIONS: Among patients receiving ICI, the baseline T-cell tolerant fraction may serve as a predictor of clinically significant irAEs.