Quantitation of cardiac troponin I in cancer patients treated with immune checkpoint inhibitors: a case-control study.

OBJECTIVES: Immune checkpoint inhibitors (ICIs) cause a variety of toxicities, including immune-related adverse events (irAEs), but there are no biomarkers to predict their development. Guidelines recommend measuring circulating cardiac troponin I (cTnI) during ICI therapy to detect related cardiotoxicities. Moreover, elevated cTnI has also been associated with worse outcomes in non-cardiac patients, including cancer. Thus here, we investigated whether cTnI levels were higher in patients with irAEs. METHODS: The study consisted of three groups; 21 cancer patients undergoing ICI immunotherapies who presented with irAEs, four patients without irAEs, and 20 healthy controls. Patient samples were assessed at baseline (n=25), during ICI treatment (n=25, median=6 weeks of treatment) and at toxicity (n=6, median=13 weeks of treatment). In addition to blood high sensitivity cardiac troponin I (hs-cTnI), anti-thyroglobulin (TG) and anti-thyroid peroxidase (TPO) antibodies were also quantitated to detect thyroid dysfunction, constituting the second leading toxicity (23.8%) after pneumonitis (28.6%). RESULTS: Four patients with irAEs (n=4/21; 19%) and one without irAEs (n=1/4; 25%) showed higher hs-cTnI levels at any time-point; the remaining had physiological levels. None of these patients developed cardiotoxicity. Concurrent elevated levels of anti-thyroid antibodies and hs-cTnI were detected in one patient with thyroid dysfunction (n=1/5, 20%). However, these antibodies were also elevated in three patients (n=3/16, 19%) with non-thyroid irAEs and in up to 40% of healthy controls. CONCLUSIONS: hs-cTnI was not elevated in patients with irAEs, but larger studies are needed to confirm these observations.

Divergent prognostic effects of pre-existing and treatment-emergent thyroid dysfunction in patients treated with immune checkpoint inhibitors.

BACKGROUND: Thyroid dysfunction is among the most common autoimmune diseases and immune checkpoint inhibitor (ICI)-induced immune-related adverse events (irAE). We determined the association between longitudinal thyroid function and clinical outcomes in patients treated with ICI. METHODS: We identified all patients treated with ICI at UT Southwestern Medical Center from January 1, 2011, through December 31, 2020. We defined normal thyroid stimulating hormone (TSH) and free thyroxine (FT4) levels according to institutional reference range. We defined clinical thyroid dysfunction using established criteria incorporating labs and treatment. We determined the association between thyroid function and overall survival (OS) using Kaplan-Meier curves, log-rank tests, and multivariate Cox proportional hazards model. RESULTS: A total of 1781 patients were included in analyses, of whom 381 (21%) had abnormal baseline TSH. Patients with abnormal baseline TSH were more likely to be female, have kidney cancer, and initiate levothyroxine after ICI initiation (all P < 0.001). Patients with abnormal baseline TSH had inferior OS (median 16 vs 27 months; P < 0.001). Among patients with normal baseline TSH, those who had abnormal TSH after ICI initiation had improved OS (median 41 vs 22 months; P < 0.001). In a multivariate Cox model, abnormal baseline TSH was associated with worse OS (HR 1.62; 95% CI, 1.30-2.02; P < 0.001), while initiation of levothyroxine after ICI initiation was associated with improved OS (HR 0.62; 95% CI, 0.44-0.88; P = 0.008). CONCLUSIONS: ICI-induced thyroid dysfunction is associated with improved survival, although abnormal TSH prior to ICI initiation is associated with inferior survival. PRECIS: Thyroid abnormalities occur commonly in the general population and as immunotherapy toxicities. We found that immunotherapy-induced thyroid dysfunction is associated with better survival, but pre-existing thyroid abnormalities convey worse outcomes.

Patient familiarity with, understanding of, and preferences for clinical trial endpoints and terminology.

BACKGROUND: Although there is increased attention to designing and explaining clinical trials in ways that are clinically meaningful for patients, there is limited information on patient preferences, understanding, and perceptions of this content. METHODS: Maximum difference scaling (MaxDiff) methodology was used to develop a survey for assessing patients' understanding of 19 clinical terms and perceived importance of 9 endpoint surrogate phrases used in clinical trials and consent forms. The survey was administered electronically to individuals with metastatic breast cancer affiliated with the Metastatic Breast Cancer Alliance. Analyses were performed using Bayesian P values with statistical software. RESULTS: Among 503 respondents, 77% had a college degree, 70% were diagnosed with metastatic disease ≥2 years before survey completion, and 77% had received ≥2 lines of systemic therapy. Less than 35% of respondents reported understanding "fairly well" the terms symptomatic progression, duration of disease control, time to treatment cessation, and endpoints. Income level and time since onset of metastatic disease correlated with comprehension. Patients who had received ≥6 lines of therapy perceived that time until serious side effects (P < .001) and time on therapy (P < .001) were more important compared with those who had received only 1 line of therapy. Positively phrased parameters were associated with increased perceived importance. CONCLUSIONS: Even among educated, heavily pretreated patients, many commonly used clinical research terms are poorly understood. Comprehension and the perceived importance of trial endpoints vary over the course of disease. These observations may inform the design, discussion, and reporting of clinical trials.

Statin Intolerance, Anti-HMGCR Antibodies, and Immune Checkpoint Inhibitor-Associated Myositis: A "Two-Hit" Autoimmune Toxicity or Clinical Predisposition?

Immune-related adverse events induced by immune checkpoint inhibitor (ICI) therapy may affect diverse organ systems, including skeletal and cardiac muscle. ICI-associated myositis may result in substantial morbidity and occasional mortality. We present a case of a patient with advanced non-small cell lung cancer who developed grade 4 myositis with concurrent myocarditis early after initiation of anti-programmed death ligand 1 therapy (durvalumab). Autoantibody analysis revealed marked increases in anti-3-hydroxy-3-methylglutaryl-coenzyme A reductase antibody levels that preceded clinical toxicity, and further increased during toxicity. Notably, the patient had a history of intolerable statin myopathy, which had resolved clinically after statin discontinuation and prior to ICI initiation. This case demonstrates a potential association between statin exposure, autoantibodies, and ICI-associated myositis.

Late-Onset Immunotherapy Toxicity and Delayed Autoantibody Changes: Checkpoint Inhibitor-Induced Raynaud's-Like Phenomenon.

Immune checkpoint inhibitor (ICI)-induced immune-related adverse events (irAEs) may affect almost any organ system and occur at any point during therapy. Autoantibody analysis may provide insight into the mechanism, nature, and timing of these events. We report a case of ICI-induced late-onset Raynaud's-like phenomenon in a patient receiving combination immunotherapy. A 53-year-old woman with advanced non-small lung cancer received combination anti-cytotoxic T-lymphocyte antigen 4 and anti-programmed death 1 ICI therapy. She developed early (hypophysitis at 4 months) and late (Raynaud's at >20 months) irAEs. Longitudinal assessment of 124 autoantibodies was correlated with toxicity. Although autoantibody levels were generally stable for the first 18 months of therapy, shortly before the development of Raynaud's, a marked increase in multiple autoantibodies was observed. This case highlights the potential for delayed autoimmune toxicities and provides potential biologic insights into the dynamic nature of these events. KEY POINTS: A patient treated with dual anti-PD1 and anti-CTLA4 therapy developed Raynaud's-like signs and symptoms more than 18 months after starting therapy. In this case, autoantibody changes became apparent shortly before onset of clinical toxicity. This case highlights the potential for late-onset immune-related adverse events checkpoint inhibitors, requiring continuous clinical vigilance. The optimal duration of checkpoint inhibitor therapy in patients with profound and prolonged responses remains unclear.

Investigational Biomarkers for Checkpoint Inhibitor Immune-Related Adverse Event Prediction and Diagnosis.

BACKGROUND: Immune checkpoint inhibitors (ICIs) have revolutionized the treatment of multiple cancers. However, these promising therapies may also cause immune-related adverse events (irAEs) in a substantial proportion of patients. These autoimmune phenomena may affect almost any organ system and may occur at almost any point in therapy. In some instances, these toxicities are life-threatening and potentially permanent. Diverse clinical presentation and unpredictable timing further complicate their anticipation and diagnosis. CONTENT: To improve patient safety and selection for ICI use, biomarkers for irAE diagnosis and prediction are under development. Clinicians may use traditional laboratory markers such as routine chemistries, creatinine clearance, thyroid function tests, and serum cortisol/adrenocorticotrophic hormone to monitor for specific irAEs, but noted aberrations may not necessarily represent an immune-mediated etiology. Novel biomarkers have the potential to be more specific to assist in the diagnosis of irAEs. The prediction of irAEs is more challenging. Apart from a history of autoimmune disease, no other clinical parameters are routinely used to project risk. Biomarker candidates under investigation for irAE diagnosis and prediction include blood cell analysis, chemokines/cytokines, autoantibodies, and genetic predisposition, such as human leukocyte antigen haplotype. Among other emerging candidates are immune-cell subsets, T-cell repertoire, fecal microbiome, tumor genomics, and radiomic characterization. SUMMARY: Several conventional laboratory indexes of end-organ dysfunction are currently in routine clinical use for irAE monitoring and diagnosis. Novel biomarkers for the prediction and diagnosis of these irAEs, which primarily characterize patient immune function, represent an area of active investigation.

Type and case volume of health care facility influences survival and surgery selection in cases with early-stage non-small cell lung cancer.

BACKGROUND: With the expansion of non-small cell lung cancer (NSCLC) screening methods, the percentage of cases with early-stage NSCLC is anticipated to increase. Yet it remains unclear how the type and case volume of the health care facility at which treatment occurs may affect surgery selection and overall survival for cases with early-stage NSCLC. METHODS: A total of 332,175 cases with the American Joint Committee on Cancer (AJCC) TNM stage I and stage II NSCLC who were reported to the National Cancer Data Base (NCDB) by 1302 facilities were studied. Facility type was characterized in the NCDB as community cancer program (CCP), comprehensive community cancer program (CCCP), academic/research program (ARP), or integrated network cancer program (INCP). Each facility type was dichotomized further into high-volume or low-volume groups based on the case volume. Multivariate Cox proportional hazard models, the logistic regression model, and propensity score matching were used to evaluate differences in survival and surgery selection among facilities according to type and volume. RESULTS: Cases from ARPs were found to have the longest survival (median, 16.4 months) and highest surgery rate (74.8%), whereas those from CCPs had the shortest survival (median, 9.7 months) and the lowest surgery rate (60.8%). The difference persisted when adjusted by potential confounders. For cases treated at CCPs, CCCPs, and ARPs, high-volume facilities had better survival outcomes than low-volume facilities. In facilities with better survival outcomes, surgery was performed for a greater percentage of cases compared with facilities with worse outcomes. CONCLUSIONS: For cases with early-stage NSCLC, both facility type and case volume influence surgery selection and clinical outcome. Higher surgery rates are observed in facilities with better survival outcomes.

Increasing Numbers and Reported Adverse Events in Patients with Lung Cancer Undergoing Inpatient Lung Biopsies: A Population-Based Analysis.

INTRODUCTION: The use of molecular biomarkers to guide lung cancer management has led to increasing frequency and amounts of tissue required for repeat lung biopsies. While patient safety and reporting of adverse events has been increasingly emphasized in recent decades, the safety of lung biopsies in patients with lung cancer has only been studied in small cohorts. We therefore analyzed adverse events in patients with lung cancer undergoing lung biopsies in the National Hospital Discharge Survey (NHDS) database. METHODS: Data were abstracted using ICD-9 lung cancer diagnosis (162.X) and lung biopsy procedure codes (33.20, 33.24, 33.25, 33.26, 33.27, 33.28) from 2001 to 2010. Agency for Healthcare Research and Quality (AHRQ) Patient-Safety Indicators (PSI) were used to identify hospital-acquired adverse events. Weighted analyses were performed using SAS version 9.4. RESULTS: A total of 540,747 patients were included for analysis. The number of biopsies increased over time, from 51,221 in 2001, to 63,239 in 2010 (P < 0.001). Overall, 159,683 (30%) patients suffered ≥ 1-PSI event during their hospitalization. Incidence of PSI varied by biopsy type: bronchoscopic (26%), percutaneous (34%), surgical (39%). The proportion of patients with ≥ 1 PSI event increased from 24% in 2001 to 38% in 2010 (P < 0.001). Patients with ≥ 1 PSI had longer length of stay (mean, 11.6 vs 8.1 days; P < 0.001) and higher in-hospital mortality (adjusted odds ratio, 5.9, 95% CI 3.9-8.9; P < 0.001). CONCLUSIONS: The frequency of lung biopsies performed and rate of documented adverse events in hospitalized lung cancer patients have increased. These findings have policy, funding, research, and practice implications.

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.

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.

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.

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.

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.

Antibiotic Prescriptions in Lung Cancer and Melanoma Populations: Differences With Potential Clinical Implications in the Immunotherapy Era.

INTRODUCTION: Antibiotic exposure is associated with worse clinical outcomes in patients receiving immune checkpoint inhibitors (ICI). We analyzed antibiotic prescription patterns in lung cancer and melanoma, two malignancies in which ICI are used broadly across stages. METHODS: We performed a retrospective cohort study of adults in the U.S. Veterans Affairs (VA) medical system diagnosed with lung cancer or melanoma from 2003 to 2016. We defined antibiotic exposure as receipt of a prescription for a systemic antibacterial agent between 6 months before and 6 months after cancer diagnosis. Demographics, clinical variables, prescriptions, and diagnostic codes were abstracted from the VA Corporate Data Warehouse. Antibiotic exposure was compared using t tests, Chi-square, and multivariate analyses. RESULTS: A total of 310,321 patients (280,068 lung cancer, 30,253 melanoma) were included in the analysis. Antibiotic exposure was more common among patients with lung cancer (42% vs. 24% for melanoma; P < .001). Among antibiotic-exposed patients, those with lung cancer were more likely to receive prescriptions for multiple antibiotics (47% vs. 30% for melanoma; P < .001). In multivariate analyses, antibiotic exposure was associated with lung cancer diagnosis (HR 1.50; 95% CI, 1.46-1.55), comorbidity score (HR 1.08; 95% CI, 1.08-1.09), non-white race (HR 1.11; 95% CI, 1.06-1.17), and female gender (HR 1.31; 95% CI, 1.24-1.37). CONCLUSION: Among cancer patients, antibiotics are prescribed frequently. Antibiotic exposure is more common in certain cancer types and patient populations. Given the negative effect antibiotic exposure has on immunotherapy outcomes, these observations may have clinical and healthy policy implications.

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.

Systemic immune parameters after prior radiation therapy in patients receiving immune checkpoint inhibitors.

INTRODUCTION: Preclinical studies have demonstrated the ability of radiation therapy (RT) to augment immune response and tumor control by immune checkpoint inhibitors (ICI). However, numerous clinical trials combining RT and ICI have yielded relatively disappointing results. To improve understanding of optimal use of these therapies, we assessed systemic immune effects of prior RT in patients receiving ICI. METHODS AND MATERIALS: Pre- and post-ICI blood samples were collected from patients enrolled in a prospective immunotherapy biospecimen protocol. Mutiplex panels of 40 cytokines and 120 autoantibodies (Ab) were analyzed. We identified differences in these parameters according to receipt, timing, and type of prior RT. We calculated P values using the Pearson product-moment correlation coefficient and false discovery rate (FDR) using the Benjamini-Hochberg Procedure. RESULTS: Among 277 total patients, 69 (25%) received RT in the 6 months prior to ICI initiation. Among RT-treated patients, 23 (33%) received stereotactic RT, and 33 (48%) received curative intent RT. There was no significant difference in demographics or type of immunotherapy between patients according to prior RT exposure. Baseline complement C8 Ab and MIP-1d/CCL15 were significantly higher among patients with prior RT. For MIP-1d/CCL15, only prior stereotactic RT was associated with significant differences. CONCLUSIONS: Prior RT is associated with few changes in systemic immune parameters in patients receiving ICI. The underlying mechanisms and optimal approach to harnessing the potential synergy of RT and ICI require further prospective clinical investigation.

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.