Blood platelet RNA profiles do not enable for nivolumab response prediction at baseline in patients with non-small cell lung cancer.

BACKGROUND: Anti-PD-(L)1 immunotherapy has emerged as a promising treatment approach for non-small cell lung cancer (NSCLC), though the response rates remain low. Pre-treatment response prediction may improve patient allocation for immunotherapy. Blood platelets act as active immune-like cells, thereby constraining T-cell activity, propagating cancer metastasis, and adjusting their spliced mRNA content. OBJECTIVE: We investigated whether platelet RNA profiles before start of nivolumab anti-PD1 immunotherapy may predict treatment responses. METHODS: We performed RNA-sequencing of platelet RNA samples isolated from stage III-IV NSCLC patients before treatment with nivolumab. Treatment response was scored by the RECIST-criteria. Data were analyzed using a predefined thromboSeq analysis including a particle-swarm-enhanced support vector machine (PSO/SVM) classification algorithm. RESULTS: We collected and processed a 286-samples cohort, separated into a training/evaluation and validation series and subjected those to training of the PSO/SVM-classification algorithm. We observed only low classification accuracy in the 107-samples validation series (area under the curve (AUC) training series: 0.73 (95% -CI: 0.63-0.84, n = 88 samples), AUC evaluation series: 0.64 (95% -CI: 0.51-0.76, n = 91 samples), AUC validation series: 0.58 (95% -CI: 0.45-0.70, n = 107 samples)), employing a five-RNAs biomarker panel. CONCLUSIONS: We concluded that platelet RNA may have minimally discriminative capacity for anti-PD1 nivolumab response prediction, with which the current methodology is insufficient for diagnostic application.

Prognostic Value of Nivolumab Clearance in Non-Small Cell Lung Cancer Patients for Survival Early in Treatment.

INTRODUCTION: Immune checkpoint inhibitors improved survival of advanced stage non-small cell lung cancer patients, but the overall response rate remains low. A biomarker that identifies non-responders would be helpful to allow treatment decisions. Clearance of immune checkpoint inhibitors is related to treatment response, but its prognostic potential early in treatment remains unknown. Our primary aim was to investigate the prognostic potential of nivolumab clearance for overall survival early in treatment. Our secondary aim was to evaluate the performance of nivolumab clearance as prognostic biomarker. PATIENTS AND METHODS: Individual estimates of nivolumab clearances at first dose, 6 and 12 weeks after treatment initiation were obtained via nonlinear mixed-effects modelling. Prognostic value of nivolumab clearance was estimated using univariate Cox regression at first dose and for the ratios between 6 and 12 weeks to first dose. The performance of nivolumab clearance as biomarker was assessed by calculating sensitivity and specificity. RESULTS: During follow-up of 75 months, 69 patients were included and 865 died. Patients with a nivolumab clearance ≥ 7.3 mL/h at first dose were more likely to die compared to patients with a nivolumab clearance < 7.3 mL/h at first dose (hazard ratio [HR] = 3.55, 955 CI 1.75-7.20). The HRs of dose nivolumab clearance ratios showed similar results with a HR of 3.93 (955 CI 1.66-9.32) for 6 weeks to first-dose clearance ratio at a 0.953 cut-point and a HR of 2.96 (955 CI 1.32-6.64) for 12 weeks to first-dose clearance ratio at a cut-point of 0.814. For nivolumab clearance at all early time points, sensitivity was high (≥ 0.95) but specificity was low (0.11-0.29). CONCLUSION: Nivolumab clearance is indicative of survival early in treatment. Our results encourage to further assess the prognostic potential of immunotherapy clearance.

PD-1T TILs as a Predictive Biomarker for Clinical Benefit to PD-1 Blockade in Patients with Advanced NSCLC.

PURPOSE: Durable clinical benefit to PD-1 blockade in non-small cell lung cancer (NSCLC) is currently limited to a small fraction of patients, underlining the need for predictive biomarkers. We recently identified a tumor-reactive tumor-infiltrating T lymphocyte (TIL) pool, termed PD-1T TILs, with predictive potential in NSCLC. Here, we examined PD-1T TILs as biomarker in NSCLC. EXPERIMENTAL DESIGN: PD-1T TILs were digitally quantified in 120 baseline samples from advanced NSCLC patients treated with PD-1 blockade. Primary outcome was disease control (DC) at 6 months. Secondary outcomes were DC at 12 months and survival. Exploratory analyses addressed the impact of lesion-specific responses, tissue sample properties, and combination with other biomarkers on the predictive value of PD-1T TILs. RESULTS: PD-1T TILs as a biomarker reached 77% sensitivity and 67% specificity at 6 months, and 93% and 65% at 12 months, respectively. Particularly, a patient group without clinical benefit was reliably identified, indicated by a high negative predictive value (NPV) (88% at 6 months, 98% at 12 months). High PD-1T TILs related to significantly longer progression-free (HR 0.39, 95% CI, 0.24-0.63, P < 0.0001) and overall survival (HR 0.46, 95% CI, 0.28-0.76, P < 0.01). Predictive performance was increased when lesion-specific responses and samples obtained immediately before treatment were assessed. Notably, the predictive performance of PD-1T TILs was superior to PD-L1 and tertiary lymphoid structures in the same cohort. CONCLUSIONS: This study established PD-1T TILs as predictive biomarker for clinical benefit to PD-1 blockade in patients with advanced NSCLC. Most importantly, the high NPV demonstrates an accurate identification of a patient group without benefit. See related commentary by Anagnostou and Luke, p. 4835.

Cell-Free DNA at Diagnosis for Stage IV Non-Small Cell Lung Cancer: Costs, Time to Diagnosis and Clinical Relevance.

Tissue biopsies can be burdensome and are only effective in 10-30% of patients with metastasized non-small-cell lung cancer (mNSCLC). Next-generation sequencing (NGS) on cell-free DNA (cfDNA) might be an attractive alternative. We evaluated the costs, throughput time, and diagnostic yield of two diagnostic scenarios with tissue and cfDNA for mNSCLC patients, compared to diagnostics based on tissue biopsy alone. Data were retrieved from 209 stage IV NSCLC patients included in 10 hospitals in the Netherlands in the observational Lung cancer Early Molecular Assessment (LEMA) trial. Discrete event simulation was developed to compare three scenarios, using LEMA data as input where possible: (1) diagnostics with "tissue only"; (2) diagnostics with "cfDNA first", and subsequent tissue biopsy if required (negative for EGFR, BRAF ALK, ROS1); (3) cfDNA if tissue biopsy failed ("tissue first"). Scenario- and probabilistic analyses were performed to quantify uncertainty. In scenario 1, 84% (Credibility Interval [CrI] 70-94%) of the cases had a clinically relevant test result, compared to 93% (CrI 86-98%) in scenario 2, and 93% (CrI 86-99%) in scenario 3. The mean throughput time was 20 days (CrI 17-23) pp in scenario 1, 9 days (CrI 7-11) in scenario 2, and 19 days (CrI 16-22) in scenario 3. Mean costs were €2304 pp (CrI €2067-2507) in scenario 1, compared to €3218 (CrI €3071-3396) for scenario 2, and €2448 (CrI €2382-2506) for scenario 3. Scenarios 2 and 3 led to a reduction in tissue biopsies of 16% and 9%, respectively. In this process-based simulation analysis, the implementation of cfDNA for patients with mNSCLC resulted in faster completion of molecular profiling with more identified targets, with marginal extra costs in scenario 3.

Clinical Utility of Plasma-Based Comprehensive Molecular Profiling in Advanced Non-Small-Cell Lung Cancer.

Comprehensive molecular profiling (CMP) plays an essential role in clinical decision making in metastatic non-small-cell lung cancer (mNSCLC). Circulating tumor DNA (ctDNA) analysis provides possibilities for molecular tumor profiling. In this study, we aim to explore the additional value of centralized ctDNA profiling next to current standard-of-care protocolled tissue-based molecular profiling (SoC-TMP) in the primary diagnostic setting of mNSCLC in the Netherlands. METHODS: Pretreatment plasma samples from 209 patients with confirmed mNSCLC were analyzed retrospectively using the NGS AVENIO ctDNA Targeted Kit (Roche Diagnostics, Basel, Switzerland) and compared with paired prospective pretreatment tissue-based molecular profiling from patient records. The AVENIO panel is designed to detect single-nucleotide variants, copy-number variations, insertions or deletions, and tyrosine kinase fusion in 17 genes. RESULTS: Potentially targetable drivers were detected with SoC-TMP alone in 34.4% of patients. Addition of clonal hematopoiesis of indeterminate potential-corrected, plasma-based CMP increased this to 39.7% (P < .001). Concordance between SoC-TMP and plasma-CMP was 86.6% for potentially targetable drivers. Clinical sensitivity of plasma-CMP was 75.2% for any oncogenic driver. Specificity and positive predictive value were more than 90% for all oncogenic drivers. CONCLUSION: Plasma-CMP is a reliable tool in the primary diagnostic setting, although it cannot fully replace SoC-TMP. Complementary profiling by combined SoC-TMP and plasma-CMP increased the proportion of patients who are eligible for targeted treatment.

An Automated Correction Algorithm (ALPACA) for ddPCR Data Using Adaptive Limit of Blank and Correction of False Positive Events Improves Specificity of Mutation Detection.

BACKGROUND: Bio-Rad droplet-digital PCR is a highly sensitive method that can be used to detect tumor mutations in circulating cell-free DNA (cfDNA) of patients with cancer. Correct interpretation of ddPCR results is important for optimal sensitivity and specificity. Despite its widespread use, no standardized method to interpret ddPCR data is available, nor have technical artifacts affecting ddPCR results been widely studied. METHODS: False positive rates were determined for 6 ddPCR assays at variable amounts of input DNA, revealing polymerase induced false positive events (PIFs) and other false positives. An in silico correction algorithm, known as the adaptive LoB and PIFs: an automated correction algorithm (ALPACA), was developed to remove PIFs and apply an adaptive limit of blank (LoB) to individual samples. Performance of ALPACA was compared to a standard strategy (no PIF correction and static LoB = 3) using data from commercial reference DNA, healthy volunteer cfDNA, and cfDNA from a real-life cohort of 209 patients with stage IV nonsmall cell lung cancer (NSCLC) whose tumor and cfDNA had been molecularly profiled. RESULTS: Applying ALPACA reduced false positive results in healthy cfDNA compared to the standard strategy (specificity 98 vs 88%, P = 10-5) and stage IV NSCLC patient cfDNA (99 vs 93%, P = 10-11), while not affecting sensitivity in commercial reference DNA (70 vs 68% P = 0.77) or patient cfDNA (82 vs 88%, P = 0.13). Overall accuracy in patient samples was improved (98 vs 92%, P = 10-7). CONCLUSIONS: Correction of PIFs and application of an adaptive LoB increases specificity without a loss of sensitivity in ddPCR, leading to a higher accuracy in a real-life cohort of patients with stage IV NSCLC.

Nivolumab in pre-treated advanced non-small cell lung cancer: long term follow up data from the Dutch expanded access program and routine clinical care.

BACKGROUND: For advanced non-small cell lung cancer anti-PD-1 treatment has become standard care in first and second line treatment in recent years. Because many of the clinical trials with anti-PD-1 drugs have only recently been completed, long term follow up data of patients treated with these agents is scarce, even more so of patients treated in real life clinical care. We present long term follow up of patients treated with nivolumab. METHODS: Two hundred forty-eight patients with pre-treated, advanced NSCLC who received nivolumab between August 2015 and December 2018 were included in this retrospective cohort study. Overall survival and progression free survival rates were calculated for the total cohort and for subgroups defined by clinical characteristics, responses to treatment, and other parameters. Data on further lines of treatment and characteristics of long term survivors were also collected. RESULTS: Median overall survival in the total cohort was 8.1 months, median progression free survival was 2.8 months. Overall survival after two and three years was 23.8% and 17.1%, respectively. Good ECOG performance scores, absence of liver metastases, experiencing treatment-related toxicity, and response to nivolumab were significantly associated with longer overall survival and progression free survival. Three-year survival rate among patients with an objective response was 55.3%. Survival for more than two years without subsequent therapy after nivolumab was observed in 13.3% of patients. CONCLUSIONS: The results from our study confirm that long term survival rates of patients treated with nivolumab for advanced NSCLC in a real world clinical setting are comparable to survival rates shown in clinical trials.

Pembrolizumab for the treatment of non-small cell lung cancer.

INTRODUCTION: In the last years, a spectacular development of immunotherapeutic agents aimed at the PD-1/PD-L1 axis has taken place. This development of these checkpoint inhibitors has greatly influenced our approach to the treatment of lung cancer in first and second line. The limited toxicity profile and the ability to treat for prolonged periods, even in smokers, is a welcome expansion of the therapeutic arsenal of the oncologist. Areas covered: This review highlights the results of recent clinical trials on pembrolizumab for the treatment of non-small cell lung cancer. The authors discuss both first and second line treatment with pembrolizumab as monotherapy and in combination therapies. Additionally, implications of the PD-L1 immunohistochemistry assay with the 22C3 antibody and its use in clinical practice and trials is discussed. Expert commentary: A higher overall response, overall survival and a moderate toxicity profile is observed with the use of pembrolizumab, compared to chemotherapy, in both first and second line. These promising results have already translated into the registration of pembrolizumab in first and second line in patients with a high expression of PD-L1. However, as PD-L1 staining does not sufficiently discriminate responders from non-responders for all checkpoint inhibitors, there still is a need for a better predictive biomarker.