Toxicity of immune checkpoint inhibitors such as ipilimumab and nivolumab is likely associated with clinical efficacy. In this study, we aim to evaluate this association for pembrolizumab. To this... Show moreToxicity of immune checkpoint inhibitors such as ipilimumab and nivolumab is likely associated with clinical efficacy. In this study, we aim to evaluate this association for pembrolizumab. To this end, data of 147 patients included in the Dutch cohort of the Pembrolizumab Expanded Access Program were collected. All data were collected prospectively. Patients with adverse events (AEs) at any time during therapy showed a higher chance of achieving disease control compared with patients without AEs (low-grade AEs vs. no AEs: odds ratio=12.8, P=0.0002, high-grade AEs vs. no AEs: odds ratio=38.5, P=0.0001) according to a multivariate logistic regression analysis. In addition, Cox regression analysis showed a lower risk of death (hazard ratio: 0.51, 95% confidence interval: 0.28-0.97) and disease progression (hazard ratio: 0.54, 95% confidence interval: 0.30-0.98) over time for patients with high-grade AEs at any time during therapy compared with patients without AEs during therapy. To correct for time dependency of occurrence of AEs, a pseudolandmark analysis at 6 months of therapy was performed. Although significance was lost (Wald test P>0.05), prolonged survival in 3 patients who stopped therapy within 6 months due to the occurrence of AEs was observed, suggesting the potential treatment benefit despite the premature ending of therapy. The occurrence of high-grade toxicity at any time during treatment was associated with higher objective response rates, progression-free survival, and overall survival. There remains a need to assess the predictive value of early occurring AEs on patient survival. Show less
Unprecedented successes regarding cancer immunotherapy have been achieved, in which therapeutic agents are used to target immune cells rather than cancer cells. The most effective immunotherapy to... Show moreUnprecedented successes regarding cancer immunotherapy have been achieved, in which therapeutic agents are used to target immune cells rather than cancer cells. The most effective immunotherapy to date is the group of immune checkpoint inhibitors (CPI), targeting, for example, cytotoxic T-lymphocyte-associated antigen 4 (CTLA-4) or programmed cell death protein (PD-1). TThe combination of these therapies (anti-PD-1 with anti-CTLA-4) induces high response rates, and seem to be increased further when applied in early-stage disease. However, combined CTLA-4 plus PD-1 blockade causes frequent high-grade immune-related adverse events (irAE). To date, research on biological mechanism of irAEs is scarce and no widely accepted biomarkers predicting onset of severe irAEs have been identified. The similarity of irAEs to autoimmune disorders fuels the hypothesis that irAEs may be linked to susceptible genetic loci related to various autoimmune diseases. In this review, we extensively searched for susceptible loci associated with various autoimmune diseases, and pooled them in groups most likely to be associated with CPI-induced irAEs. These sets could be used in future research on predicting irAEs and guide physicians in a more refined and personal manner. Show less
Introduction: Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds-urging the quest for predictive biomarkers.... Show moreIntroduction: Immunotherapy is regarded as one of the major breakthroughs in cancer treatment. Despite its success, only a subset of patients responds-urging the quest for predictive biomarkers. We hypothesize that artificial intelligence (AI) algorithms can automatically quantify radiographic characteristics that are related to and may therefore act as noninvasive radiomic biomarkers for immunotherapy response.Patients and methods: In this study, we analyzed 1055 primary and metastatic lesions from 203 patients with advanced melanoma and non-small-cell lung cancer (NSCLC) undergoing anti-PD1 therapy. We carried out an AI-based characterization of each lesion on the pretreatment contrast-enhanced CT imaging data to develop and validate a noninvasive machine learning biomarker capable of distinguishing between immunotherapy responding and nonresponding. To define the biological basis of the radiographic biomarker, we carried out gene set enrichment analysis in an independent dataset of 262 NSCLC patients.Results: The biomarker reached significant performance on NSCLC lesions (up to 0.83 AUC, P < 0.001) and borderline significant for melanoma lymph nodes (0.64 AUC, P = 0.05). Combining these lesion-wide predictions on a patient level, immunotherapy response could be predicted with an AUC of up to 0.76 for both cancer types (P < 0.001), resulting in a 1-year survival difference of 24% (P = 0.02). We found highly significant associations with pathways involved in mitosis, indicating a relationship between increased proliferative potential and preferential response to immunotherapy.Conclusions: These results indicate that radiographic characteristics of lesions on standard-of-care imaging may function as noninvasive biomarkers for response to immunotherapy, and may show utility for improved patient stratification in both neoadjuvant and palliative settings. Show less
