Elizabeth Phillips

Importance; Stevens-Johnson Syndrome and Toxic Epidermal Necrolysis (SJS/TEN) are rare, potentially fatal adverse drug reactions. As the use of immune checkpoint inhibitors (ICIs) expands, their role as direct inducers or synergistic contributors to SJS/TEN remains incompletely characterized.

Objective: To determine whether ICIs are independent risk factors for SJS/TEN, evaluate their interactions with known culprit drugs, and assess their impact on latency and mortality.

Design: Cross-sectional analysis of adverse event reports submitted to the U.S. Food and Drug Administration Adverse Event Reporting System (FDA FAERS) between January 2013 and December 2023, sanitized and de-duplicated. Logistic regression and Cox models were used to assess predictors of SJS/TEN development, mortality, and latency.

Setting: Global pharmacovigilance reports submitted to FAERS.

Participants: A total of 17,495 unique and de-identified patients reported SJS/TEN, of 13,986,839 total reports.

Exposures: Suspected causative drugs, including ICIs.

Main Outcomes and Measures: Primary outcomes were the adjusted odds of developing SJS/TEN, time-to-event (TTE) of reaction/drug latency, and all-cause mortality. Depending on the analysis, covariates included age, sex, number of concomitant drugs, cancer diagnosis, and specific drug exposures.

Results: Of 17,495 SJS/TEN cases (median age 53 years, 37.6% male), 970 (5.5%) had ICI exposure and 653 (3.7%) listed an ICI as the primary suspect. ICI exposure was associated with developing SJS/TEN (adjusted OR, 6.69; 95% CI, 6.19-7.23) while controlling for age, exposure to strong and weak culprits, number of concomitant drugs, and cancer diagnosis. ICI increases SJS-TEN risk among patients exposed to allopurinol (OR, 4.35; 95% CI, 3.12-6.06) and TMP-SMX (OR, 5.68; 95% CI, 4.05-7.95) with the same covariates. Among patients with small-molecule-induced SJS/TEN, mortality was strongly associated with ICI exposure (particularly exposure to multiple ICI, OR, 7.31; 95% CI, 3.09-17.27). Among all SJS/TEN cases, ICI exposure was associated with delayed onset, compared to cancer patients not exposed to ICI and non-cancer patients (median 20 vs 14 vs 13 days; P < .0001).

Conclusions and Relevance: ICIs are associated with increased SJS/TEN risk, both independently and in combination with known culprit drugs, and may delay disease onset. These findings support increased vigilance in prescribing known culprits alongside ICI.

Co-trimoxazole is a leading global cause of severe cutaneous adverse drug reactions (SCAR) including Stevens-Johnson syndrome/toxic epidermal necrolysis (SJS/TEN) and drug reaction with eosinophilia and systemic symptoms (DRESS). Co-trimoxazole-induced SCAR are associated with HLA class I alleles including HLA-B*13:01 and HLA-B*38:02 in Southeast Asian (SEA) populations. However, the global generalizability of these associations is unknown but critical for population-appropriate risk stratification and diagnosis.
To determine HLA risk factors associated with co-trimoxazole-induced SJS/TEN and DRESS in populations from the United States (US) and South Africa (SA).
We performed high-resolution HLA typing on dermatologist-adjudicated co-trimoxazole-induced SCAR patients in the US (n=63) and SA (n=26) compared to population controls. Peptide binding and docking analyses were performed using MHCcluster2.0 and CB-Dock2.
In a multiple logistic regression model, HLA-B*44:03 (Pc<0.001, OR: 4.08), HLA-B*38:01 (Pc<0.001, OR: 5.66), and HLA-C*04:01 (Pc=0.003, OR: 2.50) were independently associated with co-trimoxazole-induced SJS/TEN in the US. HLA-B*44:03 was also associated with co-trimoxazole-induced DRESS in SA (Pc=0.019, OR: 10.69). Distinct HLA-B variants with shared peptide binding specificities (SPBS) and HLA-C*04:01 identified 94% and 78% of co-trimoxazole-induced SJS/TEN and DRESS in the US, respectively. The SEA risk allele HLA-B*13:01, with SPBS to HLA-B*44:03, was identified in just 1/63 US SCAR patients.
HLA alleles with SPBS to SEA-related risk alleles including HLA-B*44:03 (SPBS with HLA-B*13:01) and HLA-B*38:01 (SPBS with HLA-B*38:02) but also HLA-C*04:01 predisposed to co-trimoxazole-induced SCAR in the US and SA. These findings provide biological plausibility and strategies for global risk prediction and diagnosis of co-trimoxazole-induced SCAR.
HLA alleles including HLA-B*13:01 and HLA-B*38:02 are risk factors for co-trimoxazole-induced SCAR in Asian populations. However, the generalizability of these associations to other global populations is unknown but critical for population-appropriate risk stratification and diagnosis.
HLA alleles with shared peptide binding specificities (SPBS) to Asian-related risk alleles including HLA-B*44:03 (SPBS with HLA-B*13:01) and HLA-B*38:01 (SPBS with HLA-B*38:02) but also HLA-C*04:01 predisposed to co-trimoxazole-induced SCAR in the US and South Africa.
HLA alleles previously associated with co-trimoxazole-induced SCAR do not identify risk across populations. However, HLA alleles with SPBS provide biological plausibility and strategies for global and population-appropriate clinical risk stratification and diagnosis of cotrimoxazole-induced SCAR.

Stevens-Johnson syndrome and toxic epidermal necrolysis (SJS/TEN) is a rare but life-threatening cutaneous drug reaction mediated by human leukocyte antigen (HLA) class I-restricted CD8+ T-cells. To obtain an unbiased assessment of SJS/TEN cellular immunopathogenesis, we performed single-cell (sc) transcriptome, surface proteome, and TCR sequencing on unaffected skin, affected skin, and blister fluid from 17 SJS/TEN patients. From 119,784 total cells, we identified 16 scRNA-defined subsets, confirmed by subset-defining surface protein expression. Keratinocytes upregulated HLA and IFN-response genes in the affected skin. Cytotoxic CD8+ T-cell subpopulations of expanded and unexpanded TCRαβ clonotypes were shared in affected skin and blister fluid but absent or unexpanded in SJS/TEN unaffected skin. SJS/TEN blister fluid is a rich reservoir of oligoclonal CD8+ T-cells with an effector phenotype driving SJS/TEN pathogenesis. This multiomic database will act as the basis to define antigen-reactivity, HLA restriction, and signatures of drug-antigen-reactive T-cell clonotypes at a tissue level.

Background: Electronic health records (EHR) are increasingly used for studying multimorbidities. However, concerns about accuracy, completeness, and EHRs being primarily designed for billing and administration raise questions about the consistency and reproducibility of EHR-based multimorbidity research.

Methods: Utilizing phecodes to represent the disease phenome, we analyzed pairwise comorbidity strengths using a dual logistic regression approach and constructed multimorbidity as an undirected weighted graph. We assessed the consistency of the multimorbidity networks within and between two major EHR systems at local (nodes and edges), meso (neighboring patterns), and global (network statistics) scales. We present case studies to identify disease clusters and uncover clinically interpretable disease relationships. We provide an interactive web tool and a knowledge base combing data from multiple sources for online multimorbidity analysis.

Findings: Analyzing data from 500,000 patients across Vanderbilt University Medical Center and Mass General Brigham health systems, we observed a strong correlation in disease frequencies ( Kendall’s ι− = 0.643) and comorbidity strengths (Pearson π = 0.79). Consistent network statistics across EHRs suggest a similar structure of multimorbidity networks at various scales. Comorbidity strengths and similarities of multimorbidity connection patterns align with the disease genetic correlations. Graph-theoretic analyses revealed a consistent core-periphery structure, implying efficient network clustering through threshold graph construction. Using hydronephrosis as a case study, we demonstrated the network’s ability to uncover clinically relevant disease relationships and provide novel insights.

Interpretation: Our findings demonstrate the robustness of large-scale EHR data for studying complex disease interactions. The alignment of multimorbidity patterns with genetic data suggests the potential utility for uncovering shared etiology of diseases. The consistent core-periphery network structure offers a strategic approach to analyze disease clusters. This work also sets the stage for advanced disease modeling, with implications for precision medicine.

Funding: VUMC Biostatistics Development Award, UL1 TR002243, R21DK127075, R01HL140074, P50GM115305, R01CA227481