scholarly journals Comprehensive Statistical Exploration of Prognostic (Bio-)Markers for Responses to Immune Checkpoint Inhibitor in Patients with Non-Small Cell Lung Cancer

Cancers ◽  
2021 ◽  
Vol 14 (1) ◽  
pp. 75
Author(s):  
Stefanie Hiltbrunner ◽  
Meta-Lina Spohn ◽  
Ramona Wechsler ◽  
Dilara Akhoundova ◽  
Lorenz Bankel ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Patient Group ◽  
Cell Lung Cancer ◽  
Immune Checkpoint ◽  
Tumor Size ◽  
Small Cell ◽  
Size Reduction ◽  
Small Cell Lung ◽  
Nsclc Patients

Metastatic non-small cell lung cancer (NSCLC) patients treated with immune checkpoint inhibitors (ICIs) may suffer from heavy side effects and not all patients benefit from the treatment. We conducted a comprehensive statistical analysis to identify promising (bio-)markers for treatment response. We analyzed retrospective data from NSCLC patients treated with ICIs in first- or further-line therapy settings at the University Hospital Zurich. We investigated 16 possible prognostic markers with respect to overall survival, tumor size reduction, and the development of an immune-related adverse event (irAE) and assessed the robustness of our results. For the further-line patient group, the most significant result was that increased basophil counts were associated with increased odds of tumor size reduction within three months and with the development of an irAE. For the first-line patient group, the most significant results were that increased lymphocyte counts, the histology of adenocarcinoma, and the intake of non-steroidal anti-rheumatic drugs (NSAR) were associated with decreased hazards of dying. Our study yielded new hypotheses for predictive (bio-)markers for response to ICIs in NSCLC patients. The possibly beneficial role of high basophil counts is a particularly interesting finding. Our results should be tested on independent data in a prospective fashion.

scholarly journals Subunits of ARID1 serve as novel biomarkers for the sensitivity to immune checkpoint inhibitors and prognosis of advanced non-small cell lung cancer.

2020 ◽  
Author(s):  
Dantong Sun ◽  
Lu Tian ◽  
Yan Zhu ◽  
Yang Wo ◽  
Qiaoling Liu ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Small Cell ◽  
Small Cell Lung ◽  
Novel Biomarkers ◽  
Nsclc Patients

Abstract Introduction Patients with advanced non-small cell lung cancer (NSCLC) benefit from treatment with immune checkpoint inhibitors (ICIs). Biomarkers such as programmed death-ligand 1 (PD-L1), the tumor mutational burden (TMB) and the mismatch repair (MMR) status are used to predict the prognosis of ICIs therapy. Nevertheless, novel biomarkers need to be further investigated, and a systematic prognostic model is needed for the evaluation of the survival risks of ICIs treatment.Methods A cohort of 240 patients who received ICIs from the cBioPortal for Cancer Genomics was evaluated in this research. Clinical information and targeted sequencing data were acquired for analyses. The Kaplan-Meier plot method was used to perform survival analyses, and selected variables were then confirmed by a novel nomogram constructed by the “rms” package of R software.Results Seven percent of the NSCLC patients harbored ARID1A mutations, while 4% of the NSCLC patients harbored ARID1B mutations. Mutations in ARID1A and ARID1B were confirmed to be associated with sensitivity to ICIs. Patients harboring these mutations were found to have a better response to treatment (ARID1A: P=0.045; ARID1B: P=0.034) and prolonged progression-free survival (ARID1B: P=0.032). Here, a novel nomogram was constructed to predict the prognosis of ICIs treatment. Elevation of the TMB, enhanced expression of PD-L1 and activation of the antigen presentation process and cellular immunity were found to be correlated with ARID1A and ARID1B mutations.Conclusion ARID1A and ARID1B could serve as novel biomarkers for the prognosis and sensitivity to ICIs of advanced NSCLC.

Plasma-derived cfDNA to reveal potential biomarkers of response prediction and monitoring in non-small cell lung cancer (NSCLC) patients on immunotherapy.

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 9588-9588
Author(s):  
Francesco Vallania ◽  
Karen Assayag ◽  
Peter Ulz ◽  
Adam Drake ◽  
Hayley Warsinske ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Transcription Factor ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Immune Checkpoint ◽  
Response Prediction ◽  
Small Cell ◽  
Cellular Composition ◽  
Small Cell Lung ◽  
Nsclc Patients

9588 Background: Immune checkpoint inhibitors have shown promising results in many advanced cancers, but the response rate remains low. Various molecular and cellular biomarkers, such as elevated tumor-infiltrating cytotoxic T cells and Natural Killer (NK) cells at baseline, are associated with response. Blood-based biomarkers to predict or monitor response remain challenging to develop. Here we investigate the potential of cell-free DNA (cfDNA) biomarkers to predict response to the PD-1 immune checkpoint inhibitor nivolumab in patients with refractory metastatic non-small cell lung cancer (NSCLC). Methods: Plasma from stage IV NSCLC patients enrolled in ALCINA (NCT02866149) was collected before (baseline, BL, n = 30) and at week 8 (W8, n = 17) of nivolumab therapy. Response was determined using RECIST 1.1 (responders n = 5; non-responders n = 25). Whole-genome sequencing was performed to characterize cfDNA fragments. Tumor fraction (TF) was assessed using ichorCNA. Cellular composition was estimated by deconvolution of cfDNA co-fragmentation patterns, and transcription factor activity was estimated by measuring binding site accessibility across the genome. Results: Although estimated TF at baseline did not predict response to nivolumab, NK cell levels estimated by cell-mixture deconvolution were significantly higher in responders at BL (p < 0.05). Furthermore, estimated monocyte levels at W8 strongly correlated with overall survival (r = 0.75, p < 0.0005, HR = 15.02) and were significantly higher in responders (p < 0.05). By evaluating changes in transcription factor binding activity, we identified factors with greater accessibility in non-responders at baseline (DEAF1, THAP11) and W8 (DUX4, PDX-1). Conclusions: Plasma cfDNA signatures may be useful for response prediction and monitoring in NSCLC patients on immunotherapy. Our results suggest that changes in the immune system, as reflected by cellular composition and transcriptional activity inferred from cfDNA, may provide biological insights beyond TF alone that may benefit biomarker discovery and drug target identification.

Impact of SWI/SNF complex mutations in patients with non-small cell lung cancer (NSCLC) treated with immune checkpoint inhibitors: Immuno-oncology biomarker study in LC-SCRUM-Japan (LC-SCRUM-IBIS).

2020 ◽  
Vol 38 (15_suppl) ◽  
pp. 9530-9530
Author(s):  
Kiyotaka Yoh ◽  
Shingo Matsumoto ◽  
Naoki Furuya ◽  
Kazumi Nishino ◽  
Shingo Miyamoto ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Clinical Outcomes ◽  
Cell Lung Cancer ◽  
Immune Checkpoint ◽  
Checkpoint Inhibitors ◽  
Progression Free Survival ◽  
Small Cell ◽  
Small Cell Lung ◽  
Nsclc Patients

9530 Background: The SWI/SNF chromatin remodeling complex is reported to be involved in sensitivity and resistance to immune checkpoint inhibitor (ICI). However, their role in non-small cell lung cancer (NSCLC) remains unclear. We examined the relationship between SWI/SNF complex mutations and clinical outcomes of ICI in patients with NSCLC. Methods: Of 1017 lung cancer patients enrolled in LC-SCRUM-IBIS, 350 patients were analyzable for whole-exome sequencing (WES). WES data were used to analyze the presence of mutations in 29 major subunits of the SWI/SNF complexes. ARID1A and SMARCA4 mutations were also evaluated in a targeted NGS panel (Oncomine comprehensive assay, OCA). PD-L1 expression by 22C3, tissue tumor mutational burden (tTMB) by WES, STK11 and KEAP1 mutations by WES or OCA were also assessed. Durable clinical benefit (DCB) including CR, PR and SD > 6 mos to ICI, progression-free survival (PFS) and overall survival (OS) were compared in status of each of SWI/SNF complex mutations and other factors. Results: At least one mutation in any subunits of the SWI/SNF complex was present in 28% of NSCLC patients. The most common mutated subcomplexes were SMARCA4 (12%), BAF (7%: ARID1A, 4%), non-canonical BAF (3%), PBAF (3%), and SMARCA2 (2%). Of 101 NSCLC patients treated with PD-1/PD-L1 inhibitors, SMARCA4 mutations tended to be associated with lower DCB (16 vs 31%) and shorter median PFS (1.9 vs 3.6 m) and OS (7.4 vs 18.1m). Patients with ARID1A mutations tended to have better clinical outcomes (DCB, 40 vs 28%) compared to those without mutations. No significant associations were found between PD-L1 expression and SMARCA4 or ARID1A mutations. Patients with STK11/KEAP1 mutations had lower rate of PD-L1 expression (TPS > 50%) (18% vs 48%, P = 0.03) and worse clinical outcomes (DCB, 6 vs 33%) compared to those without mutations. There was no significant association between a tTMB status and clinical outcome. Conclusions: SMARCA4 and ARID1A mutations appear to affect clinical outcomes of ICI in NSCLC patients. These findings indicate that SWI/SNF complex mutations may serve as a predictive biomarker for ICI in NSCLC patients.

scholarly journals P3.02c-066 HLA-A2 Status and Immune Checkpoint Inhibitors in Advanced Non-Small Cell Lung Cancer (NSCLC) Patients

2017 ◽  
Vol 12 (1) ◽  
pp. S1316
Author(s):  
Laura Mezquita ◽  
Melinda Charrier ◽  
Edouard Auclin ◽  
Louise Dupraz ◽  
Jordi Remon ◽  
...  
Keyword(s):  
Lung Cancer ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Immune Checkpoint ◽  
Immune Checkpoint Inhibitors ◽  
Checkpoint Inhibitors ◽  
Small Cell ◽  
Small Cell Lung ◽  
Nsclc Patients

Longitudinal tumor size and NLR as predictive factors of individual survival compared to their baseline values in patients with non-small cell lung cancer treated with durvalumab.

2019 ◽  
Vol 37 (15_suppl) ◽  
pp. e20047-e20047
Author(s):  
Kirill Zhudenkov ◽  
Sergey Gavrilov ◽  
Kirill Peskov ◽  
Gabriel Helmlinger ◽  
Sergey Aksenov
Keyword(s):  
Lung Cancer ◽  
Longitudinal Data ◽  
Small Cell Lung Cancer ◽  
Cell Lung Cancer ◽  
Tumor Size ◽  
Predictive Performance ◽  
Small Cell ◽  
Small Cell Lung ◽  
Prognostic Accuracy ◽  
Nsclc Patients

e20047 Background: Our ability to accurately predict survival of patients with non-small cell lung cancer (NSCLC) while on treatment is limited. Prognostic markers such as stage and tumor size are well established, while neutrophil-to-lymphocytes ratio (NLR) and other hemogram measurements have recently been studied. Gain in prognostic accuracy of these markers when measured longitudinally has not been established. Methods: There were 679 NSCLC patients (Stage 3 or 4, ECOG PS 0 or 1) from clinical studies of durvalumab 10 mg/kg every two weeks (NCT02087423 and NCT01693562). We developed three models of overall survival (OS) all with ECOG as covariate: a Cox proportional hazards model with baseline tumor sum-of-longest-diameters (SLD) and NLR as covariates (COX); a joint model of OS and longitudinal SLD and baseline NLR (JM SLD); and a joint model of OS and longitudinal SLD and NLR (JM SLD&NLR). We compared prognostic accuracy of these markers measured longitudinally vs. at baseline, using predicted probability of OS at 12 months after start of durvalumab as a prognostic score. We evaluated predictive performance of the models using area under the receiver-operating characteristic curve (ROC AUC) describing trade-off between true and false positives (i.e., survival past 12 months). The AUCs were calculated for patients in the dataset using longitudinal data up to different cut-offs. Results: The AUC for all patients starting durvalumab using baseline ECOG, SLD and NLR was 0.73, while it decreased to 0.64 for patients surviving to 6 months, compared to 0.50 for noninformative models. The AUC using longitudinal information for SLD and NLR was larger the more longitudinal data was used for prediction and was 0.81 using 6 months’ worth of data. Conclusions: Using longitudinal information for SLD and NLR increased individual predictive performance of these markers compared to only baseline information in NSCLC patients. [Table: see text]

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