Regulatory approval of immune checkpoint inhibitors (ICIs) was based on results of large, randomized clinical trials, resulting in limited outcomes data in patient cohorts typically underrepresented in such trials. The objective of this study was to evaluate the efficacy and safety of ICIs in these unique patient cohorts. This is a multicenter, retrospective analysis of real-world data at six academic and community clinics in the United States from 1 January 2011 to 1 April 2018. Patients were included if they had received at least one cycle of ICI treatment. Unique patient cohorts included age > 75 years, non-White race, positive smoking history, ECOG performance status (PS) ≥ 2, BMI ≥ 30 kg/m², autoimmune diseases (AIDs), chronic viral infections (CVI), extensive prior lines of therapy (LOTs), or >three metastatic sites. Immune-related adverse events (irAEs), overall survival (OS), and time to treatment failure were evaluated in the entire cohort and in NSCLC patients treated with PD-(L)1 monotherapy. Outcomes and their association with unique patient cohorts were compared on univariate analysis and multivariate analysis to those without a particular characteristic in the entire NSCLC PD-(L)1 monotherapy cohorts. In total, 1453 patients were included: 56.5%—smokers, 30.4%—non-White, 22.8%—elderly, 20.8%—ECOG PS ≥ 2, 15.7%—history of AIDs, and 4.7%—history of CVI. The common ICIs were nivolumab (37.1%) and pembrolizumab (22.2%). Black patients, compared to White patients, experienced fewer irAEs (OR 0.54, p < 0.001). An ECOG PS of ≥2 (HR = 2.01, p < 0.001) and an increased number of previous LOTs were associated with poor OS (the median OS of 26.2 vs. 16.2 vs. 9.6 months for one vs. two vs. three prior LOTs, p < 0.001). The above results were confirmed in anti-PD-(L)1 monotherapy non-small cell lung cancer patients (n = 384). Overall, ICIs were safe and efficacious in these typically underrepresented patient cohorts. We noted ECOG PS ≥ 2 and an increased prior LOTs were associated with poor ICI efficacy, and Black patients, compared to White patients, experienced fewer irAEs.

Acute graft-versus-host disease (aGvHD) remains a major cause of morbidity and mortality after allogeneic hematopoietic stem cell transplantation (HSCT). We performed RNA analysis of 1408 candidate genes in bone marrow samples obtained from 167 patients undergoing HSCT. RNA expression data were used in a machine learning algorithm to predict the presence or absence of aGvHD using either random forest or extreme gradient boosting algorithms. Patients were randomly divided into training (2/3 of patients) and validation (1/3 of patients) sets. Using post-HSCT RNA data, the machine learning algorithm selected 92 genes for predicting aGvHD that appear to play a role in PI3/AKT, MAPK, and FOXO signaling, as well as microRNA. The algorithm selected 20 genes for predicting survival included genes involved in MAPK and chemokine signaling. Using pre-HSCT RNA data, the machine learning algorithm selected 400 genes and 700 genes predicting aGvHD and overall survival, but candidate signaling pathways could not be specified in this analysis. These data show that NGS analyses of RNA expression using machine learning algorithms may be useful biomarkers of aGvHD and overall survival for patients undergoing HSCT, allowing for the identification of major signaling pathways associated with HSCT outcomes and helping to dissect the complex steps involved in the development of aGvHD. The analysis of pre-HSCT bone marrow samples may lead to pre-HSCT interventions including choice of remission induction regimens and modifications in patient health before HSCT.

Lymphoma patients are at greater risk of severe consequences from COVID-19 infection, yet most reports of COVID-19-associated outcomes were published before the advent of COVID-19 vaccinations and monoclonal antibodies (mAbs). In this retrospective study, we report the real-world outcomes of 68 lymphoma or CLL patients who developed COVID-19 infection during the omicron surge in the US. We found that 34% of patients were hospitalized as a result of COVID-19 infection. The death rate due to COVID-19 was 9% (6/68) in the overall population and 26% (6/23) in hospitalized patients. During the preintervention COVID-19 era, the mortality rate reported in cancer patients was 34%, which increased to 60.2% in hospitalized patients. Thus, the death rates in our study were much lower when compared to those in cancer patients earlier in the pandemic, and may be attributed to modern interventions. In our study, 60% (18/30) of patients with serology data available did not develop anti-COVID-19 spike protein antibodies following vaccination. Most patients (74%, 17/23) who were hospitalized due to COVID-19 infection did not receive COVID-19 mAb treatment. Our results pointed to the importance of humoral immunity and the protective effect of COVID-19 mAbs in improving outcomes in lymphoma patients.

Introduction: Anxiety and depression can affect immune function, yet little is known about their impact on immune checkpoint inhibitor (ICI) therapy outcomes. We investigated associations between an existing anxiety or depression diagnosis and ICI outcomes. Methods: In this secondary analysis, multicenter retrospective real‐world data were abstracted from medical charts. Patients included received ≥ 1 dose of anti‐PD‐1 or anti‐PD‐L1 monotherapy. Key variables abstracted were anxiety/depression diagnosis at treatment initiation, ICI therapy outcomes (immune‐related adverse events, irAEs; overall survival, OS; time to treatment failure, TTF), and other sociodemographic and clinical factors. Results: Of the 913 patients, 11% and 12% had an existing anxiety or depression diagnosis, respectively. Rates of any grade irAEs were 32% overall, and 44% and 37% among those with anxiety or depression history, respectively. In the multivariable analysis, patients with an anxiety diagnosis had a greater likelihood of experiencing irAEs (OR = 1.80; 95% CI = 1.16–2.79, p = 0.009) and better OS (HR = 0.74; 95% CI = 0.54–1.00, p = 0.048) compared to those without an anxiety diagnosis. Depression diagnosis was not significantly associated with irAEs, OS, or TTF. In multivariable sensitivity analyses restricted to patients with non‐small cell lung cancer (NSCLC, n = 417), those with an anxiety diagnosis had a trend toward better OS (HR = 0.66; 95% CI = 0.43–1.01; p = 0.056) and longer TTF (HR = 0.71; 95% CI = 0.50–1.02; p = 0.063) than those without an anxiety diagnosis, while irAEs did not vary significantly by anxiety. Conclusion: Pre‐existing anxiety diagnosis may impact clinical outcomes for patients receiving anti‐PD‐1 or anti‐PD‐L1 treatments. Links between psychosocial factors and ICI outcomes should be further examined in translational and prospective studies.

Background CD19-directed chimeric antigen receptor (CAR) T-cell therapy has revolutionized cancer care for patients with relapsed or refractory (R/R) diffuse large B-cell lymphoma (DLBCL). Despite impressive responses seen with CAR T-cell treatment, nearly 30–50% of patients will relapse with very poor outcomes and the mechanism of relapse if oftentimes unknown. Next-generation sequencing (NGS) is a novel technology that can detect genomic biomarkers and provide insight into treatment resistance and cancer prognosis. This report highlights a case where NGS was able to detect the presence of the CAR T-cell construct in a patient after CAR T-cell therapy relapse. Case presentation We present a 20-year-old, White, male patient with R/R DLBCL who was treated with CD19 CAR T-cell therapy and relapsed approximately six months after infusion. Biopsy showed CD19-negative disease. Interestingly, NGS detected the presence of the CAR T-cell construct even after the patient progressed following CAR T-cell therapy, suggesting continued persistence of the CAR T-cells. Conclusions Prognosis of patients who relapse after CAR T-cell therapy for R/R DLBCL remains poor as mechanisms and predictors of relapse are not well understood. It is necessary to consider how novel technologies can be incorporated into the prognostication and monitoring of response to CAR T-cell therapy. In our case, the ability of NGS to distinguish the CAR T-cell product suggests that NGS may have the potential to ascertain CAR T-cell response and provide insight into the purported mechanism of relapse after CAR T-cell therapy. This report highlights a potentially new approach to monitoring patients with R/R DLBCL following CAR T-cell therapy using NGS technology.