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.

Comparing modeling strategies combining changes in multiple serum tumor biomarkers for early prediction of immunotherapy non-response in non-small cell lung cancer.

BACKGROUND: Patients treated with immune checkpoint inhibitors (ICI) are at risk of adverse events (AEs) even though not all patients will benefit. Serum tumor markers (STMs) are known to reflect tumor activity and might therefore be useful to predict response, guide treatment decisions and thereby prevent AEs. OBJECTIVE: This study aims to compare a range of prediction methods to predict non-response using multiple sequentially measured STMs. METHODS: Nine prediction models were compared to predict treatment non-response at 6-months (n = 412) using bi-weekly CYFRA, CEA, CA-125, NSE, and SCC measurements determined in the first 6-weeks of therapy. All methods were applied to six different biomarker combinations including two to five STMs. Model performance was assessed based on sensitivity, while model training aimed at 95% specificity to ensure a low false-positive rate. RESULTS: In the validation cohort, boosting provided the highest sensitivity at a fixed specificity across most STM combinations (12.9% -59.4%). Boosting applied to CYFRA and CEA achieved the highest sensitivity on the validation data while maintaining a specificity >95%. CONCLUSIONS: Non-response in NSCLC patients treated with ICIs can be predicted with a specificity >95% by combining multiple sequentially measured STMs in a prediction model. Clinical use is subject to further external validation.

Prospective Detection of Early Lung Cancer in Patients With COPD in Regular Care by Electronic Nose Analysis of Exhaled Breath.

BACKGROUND: Patients with COPD are at high risk of lung cancer developing, but no validated predictive biomarkers have been reported to identify these patients. Molecular profiling of exhaled breath by electronic nose (eNose) technology may qualify for early detection of lung cancer in patients with COPD. RESEARCH QUESTION: Can eNose technology be used for prospective detection of early lung cancer in patients with COPD? STUDY DESIGN AND METHODS: BreathCloud is a real-world multicenter prospective follow-up study using diagnostic and monitoring visits in day-to-day clinical care of patients with a standardized diagnosis of asthma, COPD, or lung cancer. Breath profiles were collected at inclusion in duplicate by a metal-oxide semiconductor eNose positioned at the rear end of a pneumotachograph (SpiroNose; Breathomix). All patients with COPD were managed according to standard clinical care, and the incidence of clinically diagnosed lung cancer was prospectively monitored for 2 years. Data analysis involved advanced signal processing, ambient air correction, and statistics based on principal component (PC) analysis, linear discriminant analysis, and receiver operating characteristic analysis. RESULTS: Exhaled breath data from 682 patients with COPD and 211 patients with lung cancer were available. Thirty-seven patients with COPD (5.4%) demonstrated clinically manifest lung cancer within 2 years after inclusion. Principal components 1, 2, and 3 were significantly different between patients with COPD and those with lung cancer in both training and validation sets with areas under the receiver operating characteristic curve of 0.89 (95% CI, 0.83-0.95) and 0.86 (95% CI, 0.81-0.89). The same three PCs showed significant differences (P < .01) at baseline between patients with COPD who did and did not subsequently demonstrate lung cancer within 2 years, with a cross-validation value of 87% and an area under the receiver operating characteristic curve of 0.90 (95% CI, 0.84-0.95). INTERPRETATION: Exhaled breath analysis by eNose identified patients with COPD in whom lung cancer became clinically manifest within 2 years after inclusion. These results show that eNose assessment may detect early stages of lung cancer in patients with COPD.

Modeling strategies to analyse longitudinal biomarker data: An illustration on predicting immunotherapy non-response in non-small cell lung cancer.

Serum tumor markers acquired through a blood draw are known to reflect tumor activity. Their non-invasive nature allows for more frequent testing compared to traditional imaging methods used for response evaluations. Our study aims to compare nine prediction methods to accurately, and with a low false positive rate, predict progressive disease despite treatment (i.e. non-response) using longitudinal tumor biomarker data. Bi-weekly measurements of CYFRA, CA-125, CEA, NSE, and SCC were available from a cohort of 412 advanced stage non-small cell lung cancer (NSCLC) patients treated up to two years with immune checkpoint inhibitors. Serum tumor marker measurements from the first six weeks after treatment initiation were used to predict treatment response at 6 months. Nine models with varying complexity were evaluated in this study, showing how longitudinal biomarker data can be used to predict non-response to immunotherapy in NSCLC patients.

Detection and localization of early- and late-stage cancers using platelet RNA.

Cancer patients benefit from early tumor detection since treatment outcomes are more favorable for less advanced cancers. Platelets are involved in cancer progression and are considered a promising biosource for cancer detection, as they alter their RNA content upon local and systemic cues. We show that tumor-educated platelet (TEP) RNA-based blood tests enable the detection of 18 cancer types. With 99% specificity in asymptomatic controls, thromboSeq correctly detected the presence of cancer in two-thirds of 1,096 blood samples from stage I-IV cancer patients and in half of 352 stage I-III tumors. Symptomatic controls, including inflammatory and cardiovascular diseases, and benign tumors had increased false-positive test results with an average specificity of 78%. Moreover, thromboSeq determined the tumor site of origin in five different tumor types correctly in over 80% of the cancer patients. These results highlight the potential properties of TEP-derived RNA panels to supplement current approaches for blood-based cancer screening.

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.

eNose analysis for early immunotherapy response monitoring in non-small cell lung cancer.

OBJECTIVES: Exhaled breath analysis by electronic nose (eNose) has shown to be a potential predictive biomarker before start of anti-PD-1 therapy in patients with non-small cell lung carcinoma (NSCLC). We hypothesized that the eNose could also be used as an early monitoring tool to identify responders more accurately at early stage of treatment when compared to baseline. In this proof-of-concept study we aimed to definitely discriminate responders from non-responders after six weeks of treatment. MATERIALS AND METHODS: This was a prospective observational study in patients with advanced NSCLC eligible for anti-PD-1 treatment. The efficacy of treatment was assessed by the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1 at 3-month follow-up. We analyzed SpiroNose exhaled breath data of 94 patients (training cohort n = 62, validation cohort n = 32). Data analysis involved signal processing and statistics based on Independent Samples T-tests and Linear Discriminant Analysis (LDA) followed by Receiver Operating Characteristic (ROC) analysis. RESULTS: In the training cohort, a specificity of 73% was obtained at a 100% sensitivity level to identify objective responders. The Area Under the Curve (AUC) was 0.95 (CI: 0.89-1.00). In the validation cohort, these results were confirmed with an AUC of 0.97 (CI: 0.91-1.00). CONCLUSION: Exhaled breath analysis by eNose early during treatment allows for a highly accurate, non-invasive and low-cost identification of advanced NSCLC patients who benefit from anti-PD-1 therapy.

Validation of a clinical blood-based decision aid to guide immunotherapy treatment in patients with non-small cell lung cancer.

BACKGROUND: The widespread introduction of immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) has led to durable responses but still many patients fail and are treated beyond progression. OBJECTIVE: This study investigated whether readily available blood-based tumor biomarkers allow accurate detection of early non-responsiveness, allowing a timely switch of therapy and cost reduction. METHODS: In a prospective, observational study in patients with NSCLC treated with nivolumab or pembrolizumab, five serum tumor markers were measured at baseline and every other week. Six months disease control as determined by RECIST was used as a measure of clinical response. Patients with a disease control <  6 months were deemed non-responsive. For every separate tumor marker a criterion for predicting of non-response was developed. Each marker test was defined as positive (predictive of non-response) if the value of that tumor marker increased at least 50% from the value at baseline and above a marker dependent minimum value to be determined. Also, tests based on combination of multiple markers were designed. Specificity and sensitivity for predicting non-response was calculated and results were validated in an independent cohort. The target specificity of the test for detecting non-response was set at >  95%, in order to allow its safe use for treatment decisions. RESULTS: A total of 376 patients (training cohort: 180, validation cohort: 196) were included in our analysis. Results for the specificity of the single marker tests in the validation set were CEA: 98·3% (95% CI: 90·9-100%), NSE: 96·5% (95% CI: 87·9-99·6%), SCC: 96·5% (95% CI: 88·1-99·6%), Cyfra21·1 : 91.8% (95% CI: 81·9-97·3%), and CA125 : 86·0% (95% CI: 74·2-93·7%). A test based on the combination of Cyfra21.1, CEA and NSE accurately predicted non-response in 32.3% (95% CI 22.6-43.1%) of patients 6 weeks after start of immunotherapy. Survival analysis showed a significant difference between predicted responders (Median PFS: 237 days (95% CI 184-289 days)) and non-responders (Median PFS: 58 days (95% CI 46-70 days)) (p <  0.001). CONCLUSIONS: Serum tumor marker based tests can be used for accurate detection of non-response in NSCLC, thereby allowing early and safe discontinuation of immunotherapy in a significant subset of patients.

Imatinib in patients with severe COVID-19: a randomised, double-blind, placebo-controlled, clinical trial.

BACKGROUND: The major complication of COVID-19 is hypoxaemic respiratory failure from capillary leak and alveolar oedema. Experimental and early clinical data suggest that the tyrosine-kinase inhibitor imatinib reverses pulmonary capillary leak. METHODS: This randomised, double-blind, placebo-controlled, clinical trial was done at 13 academic and non-academic teaching hospitals in the Netherlands. Hospitalised patients (aged ≥18 years) with COVID-19, as confirmed by an RT-PCR test for SARS-CoV-2, requiring supplemental oxygen to maintain a peripheral oxygen saturation of greater than 94% were eligible. Patients were excluded if they had severe pre-existing pulmonary disease, had pre-existing heart failure, had undergone active treatment of a haematological or non-haematological malignancy in the previous 12 months, had cytopenia, or were receiving concomitant treatment with medication known to strongly interact with imatinib. Patients were randomly assigned (1:1) to receive either oral imatinib, given as a loading dose of 800 mg on day 0 followed by 400 mg daily on days 1-9, or placebo. Randomisation was done with a computer-based clinical data management platform with variable block sizes (containing two, four, or six patients), stratified by study site. The primary outcome was time to discontinuation of mechanical ventilation and supplemental oxygen for more than 48 consecutive hours, while being alive during a 28-day period. Secondary outcomes included safety, mortality at 28 days, and the need for invasive mechanical ventilation. All efficacy and safety analyses were done in all randomised patients who had received at least one dose of study medication (modified intention-to-treat population). This study is registered with the EU Clinical Trials Register (EudraCT 2020-001236-10). FINDINGS: Between March 31, 2020, and Jan 4, 2021, 805 patients were screened, of whom 400 were eligible and randomly assigned to the imatinib group (n=204) or the placebo group (n=196). A total of 385 (96%) patients (median age 64 years [IQR 56-73]) received at least one dose of study medication and were included in the modified intention-to-treat population. Time to discontinuation of ventilation and supplemental oxygen for more than 48 h was not significantly different between the two groups (unadjusted hazard ratio [HR] 0·95 [95% CI 0·76-1·20]). At day 28, 15 (8%) of 197 patients had died in the imatinib group compared with 27 (14%) of 188 patients in the placebo group (unadjusted HR 0·51 [0·27-0·95]). After adjusting for baseline imbalances between the two groups (sex, obesity, diabetes, and cardiovascular disease) the HR for mortality was 0·52 (95% CI 0·26-1·05). The HR for mechanical ventilation in the imatinib group compared with the placebo group was 1·07 (0·63-1·80; p=0·81). The median duration of invasive mechanical ventilation was 7 days (IQR 3-13) in the imatinib group compared with 12 days (6-20) in the placebo group (p=0·0080). 91 (46%) of 197 patients in the imatinib group and 82 (44%) of 188 patients in the placebo group had at least one grade 3 or higher adverse event. The safety evaluation revealed no imatinib-associated adverse events. INTERPRETATION: The study failed to meet its primary outcome, as imatinib did not reduce the time to discontinuation of ventilation and supplemental oxygen for more than 48 consecutive hours in patients with COVID-19 requiring supplemental oxygen. The observed effects on survival (although attenuated after adjustment for baseline imbalances) and duration of mechanical ventilation suggest that imatinib might confer clinical benefit in hospitalised patients with COVID-19, but further studies are required to validate these findings. FUNDING: Amsterdam Medical Center Foundation, Nederlandse Organisatie voor Wetenschappelijk Onderzoek/ZonMW, and the European Union Innovative Medicines Initiative 2.

Modeling Diagnostic Strategies to Manage Toxic Adverse Events following Cancer Immunotherapy.

BACKGROUND: Although immunotherapy (IMT) provides significant survival benefits in selected patients, approximately 10% of patients experience (serious) immune-related adverse events (irAEs). The early detection of adverse events will prevent irAEs from progressing to severe stages, and routine testing for irAEs has become common practice. Because a positive test outcome might indicate a clinically manifesting irAE that requires treatment to (temporarily) discontinue, the occurrence of false-positive test outcomes is expected to negatively affect treatment outcomes. This study explores how the UPPAAL modeling environment can be used to assess the impact of test accuracy (i.e., test sensitivity and specificity), on the probability of patients entering palliative care within 11 IMT cycles. METHODS: A timed automata-based model was constructed using real-world data and expert consultation. Model calibration was performed using data from 248 non-small-cell lung cancer patients treated with nivolumab. A scenario analysis was performed to evaluate the effect of changes in test accuracy on the probability of patients transitioning to palliative care. RESULTS: The constructed model was used to estimate the cumulative probabilities for the patients' transition to palliative care, which were found to match real-world clinical observations after model calibration. The scenario analysis showed that the specificity of laboratory tests for routine monitoring has a strong effect on the probability of patients transitioning to palliative care, whereas the effect of test sensitivity was limited. CONCLUSION: We have obtained interesting insights by simulating a care pathway and disease progression using UPPAAL. The scenario analysis indicates that an increase in test specificity results in decreased discontinuation of treatment due to suspicion of irAEs, through a reduction of false-positive test outcomes.

Prognostic Value of Deep Learning-Mediated Treatment Monitoring in Lung Cancer Patients Receiving Immunotherapy.

BACKGROUND: Checkpoint inhibitors provided sustained clinical benefit to metastatic lung cancer patients. Nonetheless, prognostic markers in metastatic settings are still under research. Imaging offers distinctive advantages, providing whole-body information non-invasively, while routinely available in most clinics. We hypothesized that more prognostic information can be extracted by employing artificial intelligence (AI) for treatment monitoring, superior to 2D tumor growth criteria. METHODS: A cohort of 152 stage-IV non-small-cell lung cancer patients (NSCLC) (73 discovery, 79 test, 903CTs), who received nivolumab were retrospectively collected. We trained a neural network to identify morphological changes on chest CT acquired during patients' follow-ups. A classifier was employed to link imaging features learned by the network with overall survival. RESULTS: Our results showed significant performance in the independent test set to predict 1-year overall survival from the date of image acquisition, with an average area under the curve (AUC) of 0.69 (p < 0.01), up to AUC 0.75 (p < 0.01) in the first 3 to 5 months of treatment, and 0.67 AUC (p = 0.01) for durable clinical benefit (6 months progression-free survival). We found the AI-derived survival score to be independent of clinical, radiological, PDL1, and histopathological factors. Visual analysis of AI-generated prognostic heatmaps revealed relative prognostic importance of morphological nodal changes in the mediastinum, supraclavicular, and hilar regions, lung and bone metastases, as well as pleural effusions, atelectasis, and consolidations. CONCLUSIONS: Our results demonstrate that deep learning can quantify tumor- and non-tumor-related morphological changes important for prognostication on serial imaging. Further investigation should focus on the implementation of this technique beyond thoracic imaging.

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.

A Serum Protein Classifier Identifying Patients with Advanced Non-Small Cell Lung Cancer Who Derive Clinical Benefit from Treatment with Immune Checkpoint Inhibitors.

PURPOSE: Pretreatment selection of patients with non-small cell lung cancer (NSCLC) who would derive clinical benefit from treatment with immune checkpoint inhibitors (CPIs) would fulfill an unmet clinical need by reducing unnecessary toxicities from treatment and result in substantial health care savings. EXPERIMENTAL DESIGN: In a retrospective study, mass spectrometry (MS)-based proteomic analysis was performed on pretreatment sera derived from patients with advanced NSCLC treated with nivolumab as part of routine clinical care (n = 289). Machine learning combined spectral and clinical data to stratify patients into three groups with good ("sensitive"), intermediate, and poor ("resistant") outcomes following treatment in the second-line setting. The test was applied to three independent patient cohorts and its biology was investigated using protein set enrichment analyses (PSEA). RESULTS: A signature consisting of 274 MS features derived from a development set of 116 patients was associated with progression-free survival (PFS) and overall survival (OS) across two validation cohorts (N = 98 and N = 75). In pooled analysis, significantly better OS was demonstrated for "sensitive" relative to "not sensitive" patients treated with nivolumab; HR, 0.58 (95% confidence interval, 0.38-0-87; P = 0.009). There was no significant association with clinical factors including PD-L1 expression, available from 133 of 289 patients. The test demonstrated no significant association with PFS or OS in a historical cohort (n = 68) of second-line NSCLC patients treated with docetaxel. PSEA revealed proteomic classification to be significantly associated with complement and wound-healing cascades. CONCLUSIONS: This serum-derived protein signature successfully stratified outcomes in cohorts of patients with advanced NSCLC treated with second-line PD-1 CPIs and deserves further prospective study.

Cell-free DNA in the supernatant of pleural effusion can be used to detect driver and resistance mutations, and can guide tyrosine kinase inhibitor treatment decisions.

OBJECTIVES: Molecular profiling of tumours has become the mainstay of diagnostics for metastasised solid malignancies and guides personalised treatment, especially in nonsmall cell lung cancer (NSCLC). In current practice, it is often challenging to obtain sufficient tumour material for reliable molecular analysis. Cell-free DNA (cfDNA) in blood or other bio-sources could present an alternative approach to obtain genetic information from the tumour. In a retrospective cohort we analysed the added value of cfDNA analysis in pleural effusions for molecular profiling. METHODS: We retrospectively analysed both the supernatant and the cell pellet of 44 pleural effusions sampled from 39 stage IV patients with KRAS (n=23) or EGFR (n=16) mutated tumours to detect the original driver mutation as well as for EGFR T790M resistance mutations. Patients were diagnosed with either NSCLC (n=32), colon carcinoma (n=4), appendiceal carcinoma (n=2) or adenocarcinoma of unknown primary (n=1). Samples collected in the context of routine clinical care were stored at the Netherlands Cancer Institute biobank. We used droplet digital PCR for analysis. RESULTS: The driver mutation could be detected in 36 of the 44 pleural effusions by analysis of both the supernatant (35 out of 44 positive) and the cell pellet (31 out of 44 positive). In seven out of 20 pleural effusions from patients with EGFR mutation-positive tumours, a T790M mutation was detected. All seven supernatants and cell pellets were positive. CONCLUSIONS: cfDNA in pleural effusion can be used to detect driver mutations as well as resistance mechanisms like EGFR T790M in pleural effusion with high accuracy and is therefore a valuable bio-source.

Influenza vaccination in patients with lung cancer receiving anti-programmed death receptor 1 immunotherapy does not induce immune-related adverse events.

BACKGROUND: Influenza vaccination is recommended in patients with cancer to reduce influenza-related complications. Recently, more immune-related adverse events (irAEs) were demonstrated in patients with lung cancer who were vaccinated with the trivalent seasonal influenza vaccine during anti-programmed death receptor 1 (PD-1) immunotherapy. Confirmation of these findings is essential before recommendations on influenza vaccination may be revoked. METHODS: In this cohort study in patients with lung cancer receiving nivolumab 3 mg/kg every 2 weeks during two influenza seasons (2015/16-2016/17), irAEs have been monitored. Incidence, timing and severity of irAEs were compared between vaccinated patients and non-vaccinated patients. FINDINGS: In a compassionate use programme, 127 patients with lung cancer had been treated with at least one dose of nivolumab during two national influenza vaccination campaigns from September until December of 2015 and 2016. Forty-two patients had received the influenza vaccine, and 85 patients were not vaccinated. Median follow-up period was 118 days (interquartile range 106-119). Mean age was 64 years (range 46-83). In vaccinated and non-vaccinated patients, the incidence of irAEs was 26% and 22%, respectively, rate ratio 1.20 (95% confidence interval [CI] 0.51-2.65). The incidence of serious irAEs was 7% and 4%, respectively, rate ratio 2.07 (95% CI 0.28-15.43). Influenza vaccination while receiving nivolumab did not result in significant differences in the rates of discontinuation, death, clinical deterioration or tumour response between the groups. INTERPRETATION: Influenza vaccination in patients with lung cancer receiving anti-PD-1 immunotherapy does not induce irAEs in our cohort. With this result, influenza vaccination should not be deterred from this group of patients.

Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets.

Blood-based liquid biopsies, including tumor-educated blood platelets (TEPs), have emerged as promising biomarker sources for non-invasive detection of cancer. Here we demonstrate that particle-swarm optimization (PSO)-enhanced algorithms enable efficient selection of RNA biomarker panels from platelet RNA-sequencing libraries (n = 779). This resulted in accurate TEP-based detection of early- and late-stage non-small-cell lung cancer (n = 518 late-stage validation cohort, accuracy, 88%; AUC, 0.94; 95% CI, 0.92-0.96; p < 0.001; n = 106 early-stage validation cohort, accuracy, 81%; AUC, 0.89; 95% CI, 0.83-0.95; p < 0.001), independent of age of the individuals, smoking habits, whole-blood storage time, and various inflammatory conditions. PSO enabled selection of gene panels to diagnose cancer from TEPs, suggesting that swarm intelligence may also benefit the optimization of diagnostics readout of other liquid biopsy biosources.

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.