Detection of dendritic cell subsets in the tumor microenvironment by multiplex immunohistochemistry.

Dendritic cells (DCs) are essential in antitumor immunity. In humans, three main DC subsets are defined: two types of conventional DCs (cDC1s and cDC2s) and plasmacytoid DCs (pDCs). To study DC subsets in the tumor microenvironment (TME), it is important to correctly identify them in tumor tissues. Tumor-derived DCs are often analyzed in cell suspensions in which spatial information about DCs which can be important to determine their function within the TME is lost. Therefore, we developed the first standardized and optimized multiplex immunohistochemistry panel, simultaneously detecting cDC1s, cDC2s, and pDCs within their tissue context. We report on this panel's development, validation, and quantitative analysis. A multiplex immunohistochemistry panel consisting of CD1c, CD303, X-C motif chemokine receptor 1, CD14, CD19, a tumor marker, and DAPI was established. The ImmuNet machine learning pipeline was trained for the detection of DC subsets. The performance of ImmuNet was compared with conventional cell phenotyping software. Ultimately, frequencies of DC subsets within several tumors were defined. In conclusion, this panel provides a method to study cDC1s, cDC2s, and pDCs in the spatial context of the TME, which supports unraveling their specific roles in antitumor immunity.

Proactive monitoring of drug-drug interactions between direct oral anticoagulants and small-molecule inhibitors in patients with non-small cell lung cancer.

BACKGROUND: Small-molecule inhibitors (SMIs) have revolutionised the treatment of non-small cell lung cancer (NSCLC). However, SMI-induced drug-drug interactions (DDIs) with frequently co-administered direct oral anticoagulants (DOACs), increase thromboembolic and bleeding risks. This study investigated and proactively managed the consequences of DOAC-SMI DDIs. METHODS: This prospective, observational study enrolled patients with NSCLC concomitantly using a DOAC and SMI. The primary outcome was the proportion of patients with DOAC plasma trough (Ctrough) and peak (Cpeak) concentrations outside expected ranges. Secondary outcomes included DOAC treatment modifications, incidence of bleeding and thromboembolic events and feasibility evaluation of pharmacokinetically guided DOAC dosing. RESULTS: Thirty-three patients were analysed. Thirty-nine percent (13/33) had DOAC Ctrough and/or Cpeak were outside the expected ranges in 39% (13/33). In 71% (5/7) of patients with DOAC concentrations quantified before and during concurrent SMI use, DOAC Ctrough and/or Cpeak increased or decreased >50% upon SMI initiation. In all patients in whom treatment modifications were deemed necessary, DOAC concentrations were adjusted to within the expected ranges. CONCLUSION: Proactive monitoring showed that a substantial proportion of patients had DOAC concentrations outside the expected ranges. DOAC concentrations were successfully normalised after treatment modifications. These results highlight the importance of proactive monitoring of DOAC-SMI DDIs to improve treatment in patients with NSCLC.

Clinical perspectives on serum tumor marker use in predicting prognosis and treatment response in advanced non-small cell lung cancer.

The optimal positioning and usage of serum tumor markers (STMs) in advanced non-small cell lung cancer (NSCLC) care is still unclear. This review aimed to provide an overview of the potential use and value of STMs in routine advanced NSCLC care for the prediction of prognosis and treatment response. Radiological imaging and clinical symptoms have shown not to capture a patient's entire disease status in daily clinical practice. Since STM measurements allow for a rapid, minimally invasive, and safe evaluation of the patient's tumor status in real time, STMs can be used as companion decision-making support tools before start and during treatment. To overcome the limited sensitivity and specificity associated with the use of STMs, tests should only be applied in specific subgroups of patients and different test characteristics should be defined per clinical context in order to answer different clinical questions. The same approach can similarly be relevant when developing clinical applications for other (circulating) biomarkers. Future research should focus on the approaches described in this review to achieve STM test implementation in advanced NSCLC care.

Pre-analytical stability of the CEA, CYFRA 21.1, NSE, CA125 and HE4 tumor markers.

BACKGROUND: For lung cancer, circulating tumor markers (TM) are available to guide clinical treatment decisions. To ensure adequate accuracy, pre-analytical instabilities need to be known and addressed in the pre-analytical laboratory protocols. OBJECTIVE: This study investigates the pre-analytical stability of CA125, CEA, CYFRA 21.1, HE4 and NSE for the following pre-analytical variables and procedures; i) whole blood stability, ii) serum freeze-thaw cycles, iii) electric vibration mixing and iv) serum storage at different temperatures. METHODS: Left-over patient samples were used and for every investigated variable six patient samples were used and analysed in duplicate. Acceptance criteria were based on analytical performance specifications based on biological variation and significant differences with baseline. RESULTS: Whole blood was stable for at least 6 hours for all TM except for NSE. Two freeze-thaw cycles were acceptable for all TM except CYFRA 21.1. Electric vibration mixing was allowed for all TM except for CYFRA 21.1. Serum stability at 4°C was 7 days for CEA, CA125, CYFRA 21.1 and HE4 and 4 hours for NSE. CONCLUSIONS: Critical pre-analytical processing step conditions were identified that, if not taken into account, will result in reporting of erroneous TM results.

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.

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.

Impact of time-to-treatment on survival for advanced non-small cell lung cancer patients in the Netherlands: a nationwide observational cohort study.

BACKGROUND: The assumption that more rapid treatment improves survival of advanced non-small cell lung cancer (NSCLC) has not yet been proven. We studied the relation between time-to-treatment and survival in advanced stage NSCLC patients in a large multicentric nationwide retrospective cohort. Additionally, we identified factors associated with delay. METHOD: We selected 10 306 patients, diagnosed and treated between 2014 and 2019 for clinical stage III and IV NSCLC, from the Netherlands Cancer Registry that includes nationwide data from 109 Dutch hospitals. Associations between survival and time-to-treatment were tested with Cox proportional hazard regression analyses. Time-to-treatment was adjusted for multiple covariates including diagnostic procedures and type of therapy. Factors associated with delay were identified by multilevel logistic regression. RESULTS: Risk of death significantly decreased with longer time-to-treatment for stage III patients receiving only radiotherapy (adjusted HR, aHR >21 days: 0.59 (95% CI 0.48 to 0.73)) or any type of systemic therapy (aHR >49 days: 0.72 (95% CI 0.56 to 0.91)) and stage IV patients receiving chemotherapy and/or immunotherapy (aHR >21 days: 0.81 (95% CI 0.73 to 0.88)). No significant association was found for stage III patients treated with chemoradiotherapy and stage IV patients treated with targeted therapy. More complex diagnostic procedures often delay treatment. CONCLUSION: Although in general it is important to start treatment as early as possible, our study finds no evidence that a more rapid start of treatment improves outcomes in advanced stage NSCLC patients. The benefit of urgent treatment is probably confounded by unmeasured patient and tumour characteristics and, clinical urgency dictating timelines of treatment. Time-to-treatment and its impact should be continuously evaluated as therapeutic strategies continue to evolve and improve.

Scan-based competing death risk model for re-evaluating lung cancer computed tomography screening eligibility.

BACKGROUND: A baseline computed tomography (CT) scan for lung cancer (LC) screening may reveal information indicating that certain LC screening participants can be screened less, and instead require dedicated early cardiac and respiratory clinical input. We aimed to develop and validate competing death (CD) risk models using CT information to identify participants with a low LC risk and a high CD risk. METHODS: Participant demographics and quantitative CT measures of LC, cardiovascular disease and chronic obstructive pulmonary disease were considered for deriving a logistic regression model for predicting 5-year CD risk using a sample from the National Lung Screening Trial (n=15 000). Multicentric Italian Lung Detection data were used to perform external validation (n=2287). RESULTS: Our final CD model outperformed an external pre-scan model (CD Risk Assessment Tool) in both the derivation (area under the curve (AUC) 0.744 (95% CI 0.727-0.761) and 0.677 (95% CI 0.658-0.695), respectively) and validation cohorts (AUC 0.744 (95% CI 0.652-0.835) and 0.725 (95% CI 0.633-0.816), respectively). By also taking LC incidence risk into consideration, we suggested a risk threshold where a subgroup (6258/23 096 (27%)) was identified with a number needed to screen to detect one LC of 216 (versus 23 in the remainder of the cohort) and ratio of 5.41 CDs per LC case (versus 0.88). The respective values in the validation cohort subgroup (774/2287 (34%)) were 129 (versus 29) and 1.67 (versus 0.43). CONCLUSIONS: Evaluating both LC and CD risks post-scan may improve the efficiency of LC screening and facilitate the initiation of multidisciplinary trajectories among certain participants.

Pharmacokinetically-guided dosing to improve the efficacy of brigatinib in non-small cell lung cancer patients.

Brigatinib was recently approved for the treatment of anaplastic lymphoma kinase-positive non-small cell lung cancer and is dosed according to a one-dose-fits-all paradigm. We aimed to identify a pharmacokinetically-guided precision dosing strategy to improve treatment response with brigatinib through simulations using a previously published pharmacokinetic-pharmacodynamic model. Dosing strategies explored were the approved 180 mg QD; the highest tolerable dose tested in clinical trials: 240 mg QD; and two precision dosing strategies targeting the median trough concentrations following 180 mg QD, and 240 mg QD. We investigated the impact of alternative dosing regimens on progression-free survival (PFS), overall survival (OS) and the probability of developing a grade ≥2 rash or grade ≥2 amylase increase. Median PFS and OS increased by 1.6 and 7.8 months, respectively between the currently approved dosing strategy and precision dosing to the median trough concentration of the 240 mg dosing strategy, with only a minor increase in the probability of developing toxicity.

Hyperhydration with cisplatin does not influence pemetrexed exposure.

Pemetrexed is a cytotoxic drug for first-line treatment of lung cancer. It is often combined with other anticancer drugs such as cisplatin or carboplatin. In clinical practice, hyperhydration regimens are applied to overcome cisplatin-related nephrotoxicity. As pemetrexed is almost completely eliminated from the body by the kidneys, hyperhydration can result in augmented clearance. Furthermore, administration of large quantities of fluid may increase the volume of distribution of pemetrexed. Pharmacokinetics and, thus, efficacy and toxicity may be influenced by hyperhydration. This has not yet been properly studied. We performed a population pharmacokinetic analysis to assess hyperhydration as a covariate for pemetrexed clearance and for volume of distribution A relevant change was defined as >25% increase in clearance or volume of distribution. In our extensive dataset of 133 individuals, we found that hyperhydration did not significantly or relevantly explain variability in pemetrexed clearance (unchanged, P = .196) or volume of distribution (+7% change, P = .002), despite a power of >99% to detect a relevant change. Therefore, dose adjustments of pemetrexed are not required during hyperhydration with cisplatin.

Dysregulated Innate and Adaptive Immune Responses Discriminate Disease Severity in COVID-19.

The clinical spectrum of COVID-19 varies and the differences in host response characterizing this variation have not been fully elucidated. COVID-19 disease severity correlates with an excessive proinflammatory immune response and profound lymphopenia. Inflammatory responses according to disease severity were explored by plasma cytokine measurements and proteomics analysis in 147 COVID-19 patients. Furthermore, peripheral blood mononuclear cell cytokine production assays and whole blood flow cytometry were performed. Results confirm a hyperinflammatory innate immune state, while highlighting hepatocyte growth factor and stem cell factor as potential biomarkers for disease severity. Clustering analysis revealed no specific inflammatory endotypes in COVID-19 patients. Functional assays revealed abrogated adaptive cytokine production (interferon-γ, interleukin-17, and interleukin-22) and prominent T-cell exhaustion in critically ill patients, whereas innate immune responses were intact or hyperresponsive. Collectively, this extensive analysis provides a comprehensive insight into the pathobiology of severe to critical COVID-19 and highlights potential biomarkers of disease severity.

Toxicity of pemetrexed during renal impairment explained-Implications for safe treatment.

Pemetrexed is an important component of first line treatment in patients with non-squamous non-small cell lung cancer. However, a limitation is the contraindication in patients with renal impairment due to hematological toxicity. Currently, it is unknown how to safely dose pemetrexed in these patients. The aim of our study was to elucidate the relationship between pemetrexed exposure and toxicity to support the development of a safe dosing regimen in patients with renal impairment. A population pharmacokinetic/pharmacodynamic analysis was performed based on phase II study results in three patients with renal dysfunction, supplemented with data from 106 patients in early clinical studies. Findings were externally validated with data of different pemetrexed dosing regimens. Alternative dosing regimens were evaluated using the developed model. We found that pemetrexed toxicity was driven by the time above a toxicity threshold concentration. The threshold for vitamin-supplemented patients was 0.110 mg/mL (95% CI: 0.092-0.146 mg/mL). It was observed that in patients with renal impairment (estimated glomerular filtration rate [eGFR]: <45 mL/min) the approved dose of 500 mg/m2 would yield a high probability of severe neutropenia in the range of 51.0% to 92.6%. A pemetrexed dose of 20 mg for patients (eGFR: 20 mL/min) is shown to be neutropenic-equivalent to the approved dose in patients with adequate renal function (eGFR: 90 mL/min), but would result in an approximately 13-fold lower area under the concentration-time curve. The pemetrexed exposure-toxicity relationship is explained by a toxicity threshold and substantially different from previously thought. Without prophylaxis for toxicity, it is unlikely that a therapeutic dose can be safely administered to patients with renal impairment.

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.

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.

Mutational signature analysis in non-small cell lung cancer patients with a high tumor mutational burden.

BACKGROUND: Lung cancer is the leading cause of cancer death worldwide. With the growing number of targeted therapies and the introduction of immuno-oncology (IO), personalized medicine has become standard of care in patients with metastatic disease. The development of predictive and prognostic biomarkers is of great importance. Mutational signatures harbor potential clinical value as predictors of therapy response in cancer. Here we set out to investigate particular mutational processes by assessing mutational signatures and associations with clinical features, tumor mutational burden (TMB) and targetable mutations. METHODS: In this retrospective study, we studied tumor DNA from patients with non-small cell lung cancer (NSCLC) irrespective of stage. The samples were sequenced using a 2 megabase (Mb) gene panel. On each sample TMB was determined and defined as the total number of single nucleotide mutations per Mb (mut/Mb) including non-synonymous mutations. Mutational signature profiling was performed on tumor samples in which at least 30 somatic single base substitutions (SBS) were detected. RESULTS: In total 195 samples were sequenced. Median total TMB was 10.3 mut/Mb (range 0-109.3). Mutational signatures were evaluated in 76 tumor samples (39%; median TMB 15.2 mut/Mb). SBS signature 4 (SBS4), associated with tobacco smoking, was prominently present in 25 of 76 samples (33%). SBS2 and/or SBS13, both associated with activity of the AID/APOBEC family of cytidine deaminases, were observed in 11 of 76 samples (14%). SBS4 was significantly more present in early stages (I and II) versus advanced stages (III and IV; P = .005). CONCLUSION: In a large proportion of NSCLC patients tissue panel sequencing with a 2 Mb panel can be used to determine the mutational signatures. In general, mutational signature SBS4 was more often found in early versus advanced stages of NSCLC. Further studies are needed to determine the clinical utility of mutational signature analyses.

First-Line Nivolumab in Stage IV or Recurrent Non-Small-Cell Lung Cancer.

BACKGROUND: Nivolumab has been associated with longer overall survival than docetaxel among patients with previously treated non-small-cell lung cancer (NSCLC). In an open-label phase 3 trial, we compared first-line nivolumab with chemotherapy in patients with programmed death ligand 1 (PD-L1)-positive NSCLC. METHODS: We randomly assigned, in a 1:1 ratio, patients with untreated stage IV or recurrent NSCLC and a PD-L1 tumor-expression level of 1% or more to receive nivolumab (administered intravenously at a dose of 3 mg per kilogram of body weight once every 2 weeks) or platinum-based chemotherapy (administered once every 3 weeks for up to six cycles). Patients receiving chemotherapy could cross over to receive nivolumab at the time of disease progression. The primary end point was progression-free survival, as assessed by means of blinded independent central review, among patients with a PD-L1 expression level of 5% or more. RESULTS: Among the 423 patients with a PD-L1 expression level of 5% or more, the median progression-free survival was 4.2 months with nivolumab versus 5.9 months with chemotherapy (hazard ratio for disease progression or death, 1.15; 95% confidence interval [CI], 0.91 to 1.45; P=0.25), and the median overall survival was 14.4 months versus 13.2 months (hazard ratio for death, 1.02; 95% CI, 0.80 to 1.30). A total of 128 of 212 patients (60%) in the chemotherapy group received nivolumab as subsequent therapy. Treatment-related adverse events of any grade occurred in 71% of the patients who received nivolumab and in 92% of those who received chemotherapy. Treatment-related adverse events of grade 3 or 4 occurred in 18% of the patients who received nivolumab and in 51% of those who received chemotherapy. CONCLUSIONS: Nivolumab was not associated with significantly longer progression-free survival than chemotherapy among patients with previously untreated stage IV or recurrent NSCLC with a PD-L1 expression level of 5% or more. Overall survival was similar between groups. Nivolumab had a favorable safety profile, as compared with chemotherapy, with no new or unexpected safety signals. (Funded by Bristol-Myers Squibb and others; CheckMate 026 ClinicalTrials.gov number, NCT02041533 .).

Rationale and design of the PRAETORIAN-COVID trial: A double-blind, placebo-controlled randomized clinical trial with valsartan for PRevention of Acute rEspiraTORy dIstress syndrome in hospitAlized patieNts with SARS-COV-2 Infection Disease.

There is much debate on the use of angiotensin receptor blockers (ARBs) in severe acute respiratory syndrome-coronavirus-2 (SARS-CoV-2)-infected patients. Although it has been suggested that ARBs might lead to a higher susceptibility and severity of SARS-CoV-2 infection, experimental data suggest that ARBs may reduce acute lung injury via blocking angiotensin-II-mediated pulmonary permeability, inflammation, and fibrosis. However, despite these hypotheses, specific studies on ARBs in SARS-CoV-2 patients are lacking. METHODS: The PRAETORIAN-COVID trial is a multicenter, double-blind, placebo-controlled 1:1 randomized clinical trial in adult hospitalized SARS-CoV-2-infected patients (n = 651). The primary aim is to investigate the effect of the ARB valsartan compared to placebo on the composite end point of admission to an intensive care unit, mechanical ventilation, or death within 14 days of randomization. The active-treatment arm will receive valsartan in a dosage titrated to blood pressure up to a maximum of 160 mg bid, and the placebo arm will receive matching placebo. Treatment duration will be 14 days, or until the occurrence of the primary end point or until hospital discharge, if either of these occurs within 14 days. The trial is registered at clinicaltrials.gov (NCT04335786, 2020). SUMMARY: The PRAETORIAN-COVID trial is a double-blind, placebo-controlled 1:1 randomized trial to assess the effect of valsartan compared to placebo on the occurrence of ICU admission, mechanical ventilation, and death in hospitalized SARS-CoV-2-infected patients. The results of this study might impact the treatment of SARS-CoV-2 patients globally.

Comparability of PD-L1 immunohistochemistry assays for non-small-cell lung cancer: a systematic review.

Programmed cell death ligand 1 (PD-L1) immunohistochemistry is used to determine which patients with advanced non-small-cell lung cancer (NSCLC) respond best to treatment with PD-L1 inhibitors. For each inhibitor, a unique immunohistochemical assay was developed. This systematic review gives an up-to-date insight into the comparability of standardised immunohistochemical assays and laboratory-developed tests (LDTs), focusing specifically on tumour cell (TC) staining and scoring. A systematic search was performed identifying publications that assessed interassay, interobserver and/or interlaboratory concordance of PD-L1 assays and LDTs in tissue of NSCLC patients. Of 4294 publications identified through the systematic search, 27 fulfilled the inclusion criteria and were of sufficient methodological quality. Studies assessing interassay concordance found high agreement between assays 22C3, 28-8 and SP263 and properly validated LDTs, and lower concordance for comparisons involving SP142. A decrease in concordance, however, is seen with use of cut-offs, which hampers interchangeability of PD-L1 immunohistochemistry assays and LDTs. Studies assessing interobserver concordance found high agreement for all assays and LDTs, but lower agreement with use of a 1% cut-off. This may be problematic in clinical practice, as discordance between pathologists at this cut-off may result in some patients being denied valuable treatment options. Finally, five studies assessed interlaboratory concordance and found moderate to high agreement levels for various assays and LDTs. However, to assess the actual existence of interlaboratory variation in PD-L1 testing and PD-L1 positivity in clinical practice, studies using real-world clinical pathology data are needed.

A continuous responder algorithm to optimize clinical management of small-cell lung cancer with progastrin-releasing peptide as a simple blood test.

This study aimed to investigate whether changes in progastrin-releasing peptide (ProGRP) levels correlate with treatment response and can be used to optimize clinical management of patients with small-cell lung cancer. Patients with small-cell lung cancer (any stage) receiving chemotherapy were eligible. ProGRP was measured in serum/plasma at baseline and after each chemotherapy cycle using the Elecsys® ProGRP assay (Roche Diagnostics). Treatment response was assessed by computed tomography scan. The primary objective was to examine whether changes in ProGRP levels correlated with computed tomography scan results after two cycles of chemotherapy. The prognostic value of ProGRP among patients receiving first-line chemotherapy was also assessed. Overall, 261 patients from six centers were eligible. Among patients with elevated baseline ProGRP (>100 pg/mL), a ProGRP decline after Cycle 2 was associated with nonprogression (area under the curve: 84%; 95% confidence interval: 72.8-95.1; n = 141). ProGRP changes from baseline to end of Cycle 1 were predictive of response, as determined by computed tomography scan 3 weeks later (area under the curve: 87%; 95% confidence interval: 74.1-99.2; n = 137). This was enhanced by repeat measurements, with a 92% area under the curve (95% confidence interval: 85.3-97.8) among patients with ProGRP data after both Cycles 1 and 2 (n = 123); if a patient experienced a ≥25% decline in ProGRP after Cycle 1, and ProGRP remained stable or decreased after Cycle 2, the probability of finding progression on the interim computed tomography scan at the end of Cycle 2 was almost zero (sensitivity: 100%, specificity: 71%). Both ProGRP levels at baseline and at the end of first-line chemotherapy were prognostic; the latter provided a moderately improved hazard ratio of 2.43 (95% confidence interval: 1.33-4.46; n = 110) versus 1.87 (95% confidence interval: 1.04-3.37; n = 216). In summary, for patients with small-cell lung cancer and elevated baseline ProGRP levels, ProGRP may be a simple, reliable, and repeatable tool for monitoring response to chemotherapy and provide valuable prognostic information.

Implementation of a Multicenter Biobanking Collaboration for Next-Generation Sequencing-Based Biomarker Discovery Based on Fresh Frozen Pretreatment Tumor Tissue Biopsies.

BACKGROUND: The discovery of novel biomarkers that predict treatment response in advanced cancer patients requires acquisition of high-quality tumor samples. As cancer evolves over time, tissue is ideally obtained before the start of each treatment. Preferably, samples are freshly frozen to allow analysis by next-generation DNA/RNA sequencing (NGS) but also for making other emerging systematic techniques such as proteomics and metabolomics possible. Here, we describe the first 469 image-guided biopsies collected in a large collaboration in The Netherlands (Center for Personalized Cancer Treatment) and show the utility of these specimens for NGS analysis. PATIENTS AND METHODS: Image-guided tumor biopsies were performed in advanced cancer patients. Samples were fresh frozen, vital tumor cellularity was estimated, and DNA was isolated after macrodissection of tumor-rich areas. Safety of the image-guided biopsy procedures was assessed by reporting of serious adverse events within 14 days after the biopsy procedure. RESULTS: Biopsy procedures were generally well tolerated. Major complications occurred in 2.1%, most frequently consisting of pain. In 7.3% of the percutaneous lung biopsies, pneumothorax requiring drainage occurred. The majority of samples (81%) contained a vital tumor percentage of at least 30%, from which at least 500 ng DNA could be isolated in 91%. Given our preset criteria, 74% of samples were of sufficient quality for biomarker discovery. The NGS results in this cohort were in line with those in other groups. CONCLUSION: Image-guided biopsy procedures for biomarker discovery to enable personalized cancer treatment are safe and feasible and yield a highly valuable biobank. The Oncologist 2017;22:33-40Implications for Practice: This study shows that it is safe to perform image-guided biopsy procedures to obtain fresh frozen tumor samples and that it is feasible to use these biopsies for biomarker discovery purposes in a Dutch multicenter collaboration. From the majority of the samples, sufficient DNA could be yielded to perform next-generation sequencing. These results indicate that the way is paved for consortia to prospectively collect fresh frozen tumor tissue.