Incidence and Risk Factors for Pneumonitis Associated With Checkpoint Inhibitors in Advanced Non-Small Cell Lung Cancer: A Single Center Experience

INTRODUCTION: Immune checkpoint inhibitor (ICI) pneumonitis causes substantial morbidity and mortality. Estimates of real-world incidence and reported risk factors vary substantially. METHODS: We conducted a retrospective review of 419 patients with advanced non-small cell lung cancer (NSCLC) who were treated with anti-PD-(L)1 with or without anti-CTLA-4 therapy. Clinical, imaging, and microbiological data were evaluated by multidisciplinary adjudication teams. The primary outcome of interest was grade ≥2 (CTCAEv5) pneumonitis. Clinicopathologic variables, tobacco use, cancer therapies, and preexisting lung disease were assessed for univariate effects using Cox proportional hazards models. We created multivariate Cox proportional hazards models to assess risk factors for pneumonitis and mortality. Pneumonitis, pneumonia, and progression were modeled as time-dependent variables in mortality models. RESULTS: We evaluated 419 patients between 2013 and 2021. The cumulative incidence of pneumonitis was 9.5% (40/419). In a multivariate model, pneumonitis increased the risk for mortality (HR 1.6, 95% CI, 1.0-2.5), after adjustment for disease progression (HR 1.6, 95% CI, 1.4-1.8) and baseline shortness of breath (HR 1.5, 95% CI, 1.2-2.0). Incomplete resolution was more common with more severe pneumonitis. Interstitial lung disease was associated with higher risk for pneumonitis (HR 5.4, 95% CI, 1.1-26.6), particularly in never smokers (HR 26.9, 95% CI, 2.8-259.0). CONCLUSION: Pneumonitis occurred at a high rate and significantly increased mortality. Interstitial lung disease, particularly in never smokers, increased the risk for pneumonitis

Human herpesvirus 6 reactivation and disease are infrequent in chimeric antigen receptor T-cell therapy recipients

•HHV-6 reactivation in plasma occurred in 6% and possible HHV-6 encephalitis in 0.2% of patients within 12 weeks after CARTx.•HHV-6 reactivation and disease are infrequent after CARTx, and routine HHV-6 monitoring is not warranted.[Display omitted]Human herpesvirus 6B (HHV-6B) reactivation and disease are increasingly reported after chimeric antigen receptor (CAR) T-cell therapy (CARTx). HHV-6 reactivation in the CAR T-cell product was recently reported, raising questions about product and patient management. Because of overlapping manifestations with immune effector cell–associated neurotoxicity syndrome, diagnosing HHV-6B encephalitis is challenging. We provide 2 lines of evidence assessing the incidence and outcomes of HHV-6B after CARTx. First, in a prospective study with weekly HHV-6B testing for up to 12 weeks after infusion, HHV-6B reactivation occurred in 8 of 89 participants; 3 had chromosomally integrated HHV-6 and were excluded, resulting in a cumulative incidence of HHV-6B reactivation of 6% (95% confidence interval [CI], 2.2-12.5). HHV-6B detection was low level (median peak, 435 copies per mL; interquartile range, 164-979) and did not require therapy. Second, we retrospectively analyzed HHV-6B detection in the blood and/or cerebrospinal fluid (CSF) within 12 weeks after infusion in CARTx recipients. Of 626 patients, 24 had symptom-driven plasma testing, with detection in 1. Among 34 patients with CSF HHV-6 testing, 1 patient had possible HHV-6 encephalitis for a cumulative incidence of 0.17% (95% CI, 0.02-0.94), although symptoms improved without treatment. Our data demonstrate that HHV-6B reactivation and disease are infrequent after CARTx. Routine HHV-6 monitoring is not warranted.Kampouri and colleagues report on human herpesvirus 6B (HHV-6B) reactivation and disease following chimeric antigen receptor (CAR) T-cell therapy. A prospective study of 89 patients tested weekly revealed reactivation in 6%, with low-level detection requiring no therapy. In a retrospective study of 626 patients, 24 had symptom-driven plasma testing, with 1 patient testing positive; in 34 patients receiving symptom-driven cerebrospinal fluid testing, 1 patient had HHV-6 that resolved without therapy, with a cumulative incidence of 0.17%. HHV-6B reactivation is infrequent following CAR T-cell therapy, suggesting routine testing is not indicated

Machine Learning-Based Time-Series Clustering Identifies Archetypal Trajectories of Hematotoxicity after CAR T-Cell Therapy

BACKGROUND Severe hematotoxicity has been reported in a subset of patients (pts) after chimeric antigen receptor (CAR) T-cell therapy, leading to severe infections, transfusion dependency, and worse outcomes. Early identification of pts at risk for severe hematotoxicity could inform the consideration of an autologous stem cell boost, allogeneic HCT, and/or additional infectious prophylaxis. While distinct archetypal trajectories of blood count recovery have been proposed (e.g., quick vs. intermittent vs. aplastic), manual trajectory classification is subjective and labor-intensive. To address these limitations, we automated the identification of distinct trajectories of hematotoxicity by applying time-series clustering to longitudinal absolute neutrophil count (ANC) data from >400 pts treated with CAR T-cell therapy. Next, we sought to identify factors associated with the identified trajectories and to assess the predictive ability of the CAR-HEMATOTOX score (Rejeski, Blood 2021). STUDY DESIGN AND METHODS Adults ≥18 years of age who received their first infusion of CAR T cells for hematologic malignancies with commercial or investigational products at the Fred Hutchinson Cancer Center (2013-2023) were included. ANC trajectories were clustered using non-supervised longitudinal k-means based on Euclidean distances using the latrend and kml packages in R 4.1.3. We applied logistic regression to pre-lymphodepletion (pre-LD) variables to predict poor/very poor ANC recovery. Sensitivity and specificity were computed using a probability threshold based on the Youden criteria. RESULTS A total of 509 pts were identified; 106 were excluded due to insufficient data, with 403 pts included for the analysis. The most common disease types were aggressive NHL (n = 161; 40%), indolent NHL (n = 82; 20%), and ALL (n = 74; 18%). CAR T-cell products were axi-cel, n= 101 (25%); brexu-cel, n = 24 (6%); cilta-cel, n = 21 (5%); liso-cel, n = 46 (11%); ide-cel, n = 25 (6%); tisa-cel, 12 (3%); and investigational CD19 or CD20 CAR T-cell products, n = 174 (43%). As shown in Figure 1A, the ANC longitudinal data clustered into four distinct trajectories as follows: 1) high nadir followed by rapid recovery (“very good recovery”), n = 294 (73%); 2) low nadir followed by rapid recovery (“good recovery”), n = 87 (22%); 3) low nadir followed by intermittent recovery (“poor recovery”), n = 13 (3%); 4) aplastic phenotype (“very poor recovery”), n = 9 (2%). In univariate logistic regression, ALL (reference: aggressive NHL; OR = 5.43, 95% CI, 1.71-20.6, p = 0.006), pre-LD lower ANC (OR = 3.33 per log 10 , 95% CI, 1.82-6.25, p 90%) but low specificity (high/low: 31%, continuous: 58%). To improve risk prediction of poor/very poor ANC recovery, we built a logistic regression model using restricted cubic splines - allowing for non-linear effects - including pre-LD ANC, platelet, Hb, LDH, CRP, and ferritin, which showed higher discrimination (C-index: 0.91), high sensitivity (88%), and higher specificity (79%) ( Figure 1B). CONCLUSION We introduce an automated, scalable, and disease-agnostic framework to analyze hematologic toxicity after CAR T-cell therapy. K-means clustering of longitudinal ANC data categorized pts into archetypal trajectories of hematologic recovery. A logistic regression model using pre-LD ANC, platelet, Hb, LDH, CRP, and ferritin showed improved discrimination and sensitivity compared to CAR-HEMATOTOX in our training set, but both models had low specificity (poor ability to “rule in” pts at risk of severe hematotoxicity) and thus low clinical utility at this time. We plan to further improve our predictive models by incorporating post-infusion biomarkers of systemic inflammation that we have previously shown to be associated with delayed count recovery after CAR T-cell therapy (Juluri, Blood Advances 2022)

Identification and Prediction of Severe Hematologic Toxicity after CAR T-Cell Therapy Using Machine Learning-Based Time-Series Clustering

Severe hematologic toxicity is a significant complication associated with CAR T-cell therapy, leading to infections, transfusion dependency, and mortality. Using data from >400 CAR T-cell patients (pts), we hypothesized a time-series clustering-based approach could: i) automate the identification of pts with impaired absolute neutrophil count (ANC) recovery, ii) enable the identification of factors associated with ANC recovery, and iii) assess predictive models of hematologic toxicity after CAR T-cell therapy. Adults who received their first infusion of CAR T cells for hematologic malignancies with commercial or investigational products at our center (2013-2023) were included. ANC trajectories were clustered using non-supervised longitudinal k-means based on Euclidean distances. Sensitivity and specificity were computed based on the Youden criteria. 403 pts were included. The most common disease types were aggressive NHL (n = 161; 40%), indolent NHL (n = 82; 20%), ALL (n = 74; 18%), and MM/PCL (n = 44; 11%). The most common CAR T-cell products were investigational CD19 or CD20 CAR T-cell products (n = 174; 43%), axi-cel (n = 101; 25%), and liso-cel (n = 46; 11%). The longitudinal ANC data clustered into 4 distinct trajectories: 1) very good recovery (high nadir followed by rapid recovery), n = 294 (73%); 2) good recovery (low nadir followed by rapid recovery), n = 87 (22%); 3) poor recovery (low nadir followed by intermittent recovery), n = 13 (3%); 4) very poor recovery (aplastic phenotype), n = 9 (2%) (Figure 1). Pts with poor/very poor ANC recovery had significantly shorter overall survival than those with good/very good recovery (median 3 vs. 19 months, p < 0.001; Figure 2). 100-day mortality in pts with in very good/good vs. poor/very poor recovery was 62% vs. 11%, respectively (p < 0.001). In univariate logistic regression, predictors of poor/very poor recovery included disease type and inflammatory biomarkers (Table 1). Next, we assessed the ability of the CAR-HEMATOTOX score (high vs. low risk; Rejeski, Blood 2021) to predict poor/very poor ANC recovery. Due to missing data, day +0 rather than pre-lymphodepletion (LD) values were used for CRP and ferritin. The specificity and sensitivity of the CAR-HEMATOTOX were 31% and 100%, respectively. A logistic regression model using restricted cubic splines and including pre-LD ANC, peak CRP, and peak ferritin showed high discrimination (C-index: 0.91) with 74% specificity and 95% sensitivity. We introduce an automated and scalable framework that successfully identifies pts with the most severe hematologic toxicity after CAR T-cell therapy, and specifically those displaying an “aplastic” trajectory. We identified key factors associated with poor ANC recovery. A new model including peak inflammatory biomarkers showed improved performance compared to the CAR-HEMATOTOX score