Comparing deep learning and pathologist quantification of cell-level PD-L1 expression in non-small cell lung cancer whole-slide images.

Programmed death-ligand 1 (PD-L1) expression is currently used in the clinic to assess eligibility for immune-checkpoint inhibitors via the tumor proportion score (TPS), but its efficacy is limited by high interobserver variability. Multiple papers have presented systems for the automatic quantification of TPS, but none report on the task of determining cell-level PD-L1 expression and often reserve their evaluation to a single PD-L1 monoclonal antibody or clinical center. In this paper, we report on a deep learning algorithm for detecting PD-L1 negative and positive tumor cells at a cellular level and evaluate it on a cell-level reference standard established by six readers on a multi-centric, multi PD-L1 assay dataset. This reference standard also provides for the first time a benchmark for computer vision algorithms. In addition, in line with other papers, we also evaluate our algorithm at slide-level by measuring the agreement between the algorithm and six pathologists on TPS quantification. We find a moderately low interobserver agreement at cell-level level (mean reader-reader F1 score = 0.68) which our algorithm sits slightly under (mean reader-AI F1 score = 0.55), especially for cases from the clinical center not included in the training set. Despite this, we find good AI-pathologist agreement on quantifying TPS compared to the interobserver agreement (mean reader-reader Cohen's kappa = 0.54, 95% CI 0.26-0.81, mean reader-AI kappa = 0.49, 95% CI 0.27-0.72). In conclusion, our deep learning algorithm demonstrates promise in detecting PD-L1 expression at a cellular level and exhibits favorable agreement with pathologists in quantifying the tumor proportion score (TPS). We publicly release our models for use via the Grand-Challenge platform.

Lung cancer biomarkers: Raising the clinical value of the classical and the new ones.

Blood-based diagnostics for lung cancer support the diagnosis, estimation of prognosis, prediction, and monitoring of therapy response in lung cancer patients. The clinical utility of serum tumor markers has considerably increased due to developments in serum protein tumor markers analytics and clinical biomarker studies, the exploration of preanalytical and influencing conditions, the interpretation of biomarker combinations and individual biomarker kinetics, as well as the implementation of biostatistical models. In addition, circulating tumor DNA (ctDNA) and other liquid biopsy markers are playing an increasingly prominent role in the molecular tumor characterization and the monitoring of tumor evolution over time. Thus, modern lung cancer biomarkers may considerably contribute to an individualized companion diagnostics and provide a sensitive guidance for patients throughout the course of their disease. In this special edition on Tumor Markers in Lung Cancer, experts summarize recent developments in clinical laboratory diagnostics of lung cancer and give an outlook on future challenges and opportunities.

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.

A Dysfunctional T-cell Gene Signature for Predicting Nonresponse to PD-1 Blockade in Non-small Cell Lung Cancer That Is Suitable for Routine Clinical Diagnostics.

PURPOSE: Because PD-1 blockade is only effective in a minority of patients with advanced-stage non-small cell lung cancer (NSCLC), biomarkers are needed to guide treatment decisions. Tumor infiltration by PD-1T tumor-infiltrating lymphocytes (TIL), a dysfunctional TIL pool with tumor-reactive capacity, can be detected by digital quantitative IHC and has been established as a novel predictive biomarker in NSCLC. To facilitate translation of this biomarker to the clinic, we aimed to develop a robust RNA signature reflecting a tumor's PD-1T TIL status. EXPERIMENTAL DESIGN: mRNA expression analysis using the NanoString nCounter platform was performed in baseline tumor samples from 41 patients with advanced-stage NSCLC treated with nivolumab that were selected on the basis of PD-1T TIL infiltration by IHC. Samples were included as a training cohort (n = 41) to develop a predictive gene signature. This signature was independently validated in a second cohort (n = 42). Primary outcome was disease control at 12 months (DC 12 m), and secondary outcome was progression-free and overall survival. RESULTS: Regularized regression analysis yielded a signature using 12 out of 56 differentially expressed genes between PD-1T IHC-high tumors from patients with DC 12 m and PD-1T IHC-low tumors from patients with progressive disease (PD). In the validation cohort, 6/6 (100%) patients with DC 12 m and 23/36 (64%) with PD were correctly classified with a negative predictive value (NPV) of 100% and a positive predictive value of 32%. CONCLUSIONS: The PD-1T mRNA signature showed a similar high sensitivity and high NPV as the digital IHC quantification of PD-1T TIL. This finding provides a straightforward approach allowing for easy implementation in a routine diagnostic clinical setting.

Integrating treatment cost reduction strategies and biomarker research to reduce costs and personalize expensive treatments: an example of a self-funding trial in non-small cell lung cancer.

Personalization of treatment offers the opportunity to treat patients more effectively based on their dominant disease-specific features. The increasing number and types of treatment, and the high costs associated with these treatments, however, demand new approaches that improve patient selection while reducing treatment-associated costs to ensure sustainable healthcare. The DEDICATION-1 trial has been designed to investigate the non-inferiority of lower dosing regimens when compared to standard of care dosing regimens as a potential effective treatment cost reduction strategy to reduce costs of treatment with expensive immune checkpoint inhibitors in non-small cell lung cancer. If non-inferiority is confirmed, lower dosing regimens could be implemented for all therapeutic indications of pembrolizumab. The cost savings obtained within the trial are partly reinvested in biomarker research to improve the personalization of pembrolizumab treatment. The implementation of these biomarkers will potentially lead to additional cost savings by preventing ineffective pembrolizumab exposure, thereby further reducing the financial pressure on healthcare systems. The concepts discussed within this perspective can be applied both to other anticancer agents, as well as to treatments prescribed outside the oncology field.

Cerebrospinal Fluid Concentration of the RET Inhibitor Pralsetinib: A Case Report.

Introduction: Pralsetinib is used to treat metastatic RET fusion-positive non-small cell lung cancer. Preclinical studies of pralsetinib have shown blood-brain barrier (BBB) penetration and intracranial activity. The intracranial efficacy of pralsetinib in patients with brain metastasis is considered to be greater compared to older multikinase tyrosine kinase inhibitors. However, CSF concentrations of pralsetinib in patients are not well described in the literature. Case Presentation: We report a case of a patient with RET fusion-positive NSCLC treated with pralsetinib. Despite extracranial clinical and radiological remission, the patient developed progressive brain metastasis during treatment with pralsetinib. We measured the pralsetinib concentration in plasma and in CSF to determine the CSF-to-unbound plasma ratio. The measured pralsetinib concentrations in plasma and CSF were 1,951 ng/mL (∼57 unbound) and 14 ng/mL, respectively, reflecting a CSF-to-unbound plasma concentration ratio of 0.25. Our findings were compared with data from the literature. Conclusion: We showed that pralsetinib penetrates the CSF well and is expected to be an effective treatment for brain metastasis of RET fusion-positive NSCLC. Lack of intracranial efficacy is more likely to be caused by intrinsic or acquired tumor resistance instead of suboptimal exposure of pralsetinib in the brain.

Impact of time-to-treatment on survival for early-stage non-small cell lung cancer in The Netherlands-a nationwide observational cohort study.

Background: Varied outcomes on the relation between time-to-treatment and survival in early-stage non-small cell lung cancer (NSCLC) patients are reported. We examined this relation in a large multicentric retrospective cohort study and identified factors associated with extended time-to-treatment. Methods: We included 9,536 patients with clinical stage I-II NSCLC, diagnosed and treated in 2014-2019, from the Netherlands Cancer Registry that includes nation-wide data. Time-to-treatment was defined as the number of days between first outpatient visit for suspected lung cancer and start of treatment. The effect of extended time-to-treatment beyond the first quartile and survival was studied with Cox proportional hazard regression. Analyses were stratified for stage and type of therapy. Time-to-treatment was adjusted for multiple covariates including performance status and socioeconomic status. Factors associated with treatment delay were identified by multilevel logistic regression. Results: Median time-to-treatment was 47 days [interquartile range (IQR): 34-65] for stage I and 46 days (IQR: 34-62) for stage II. The first quartile extended to 33 days for both stages. Risk of death increased significantly with extended time-to-treatment for surgical treatment of clinical stage II patients [adjusted hazard ratio (aHR) >33 days: 1.36, 95% confidence intervals (CI): 1.09-1.70], but not in stage II patients treated with radiotherapy or in stage I patients. Causes of prolonged time-to-treatment were multifactorial including diagnostic tests, such as endoscopic ultrasound (EUS) or endobronchial ultrasound (EBUS). Conclusions: Clinical stage II patients benefit from fast initiation of surgical treatment. Surprisingly this appears to be accounted for by patients who are clinically stage II but pathologically stage I. Further study is needed on characterizing these patients and the significance of lymph node- or distant micrometastasis in guiding time-to-treatment and treatment strategy.

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.

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.

Sleep Hypoventilation is Common in Diurnal Normocapnic COPD Patients with Severe or Very Severe Obstruction and is Not Associated with Body Mass Index.

Sleep hypoventilation (SH) is common in COPD patients with diurnal hypercapnia, however there are little data on the presence of SH in COPD patients with diurnal normocapnia. In this study the prevalence of SH in stable normocapnic COPD patients with severe or very severe obstruction (GOLD stages III and IV) was evaluated across body mass index (BMI) classes and associations between SH and body composition measures were explored. A total of 56 diurnal normocapnic COPD patients, of whom 17 normal-weight (COPDNW), 18 overweight (COPDOW) and 21 obese (COPDOB), underwent polysomnography to objectify SH and bioelectrical impedance analysis to assess body composition. The overall prevalence of SH was 66.1% and was not different across BMI classes. Logistic regression models indicated that SH was not associated with waist-to-hip ratio, body fat percentage and fat-free mass index. Our data indicate that SH is common in diurnal normocapnic COPD patients with severe or very severe obstruction and is not associated with BMI or body composition.

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.

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.

Serum tumor markers for response prediction and monitoring of advanced lung cancer: A review focusing on immunotherapy and targeted therapies.

BACKGROUND: The value of serum tumor markers (STMs) in the current therapeutic landscape of lung cancer is unclear. OBJECTIVE: This scoping review gathered evidence of the predictive, prognostic, and monitoring value of STMs for patients with advanced lung cancer receiving immunotherapy (IT) or targeted therapy (TT). METHODS: Literature searches were conducted (cut-off: May 2022) using PubMed and Cochrane CENTRAL databases. Medical professionals advised on the search strategies. RESULTS: Study heterogeneity limited the evidence and inferences from the 36 publications reviewed. While increased baseline levels of serum cytokeratin 19 fragment antigen (CYFRA21-1) and carcinoembryonic antigen (CEA) may predict IT response, results for TT were less clear. For monitoring IT-treated patients, STM panels (including CYFRA21-1, CEA, and neuron-specific enolase) may surpass the power of single analyses to predict non-response. CYFRA21-1 measurement could aid in monitoring TT-treated patients, but the value of CEA in this context requires further investigation. Overall, baseline and dynamic changes in individual or combined STM levels have potential utility to predict treatment outcome and for monitoring of patients with advanced lung cancer. CONCLUSIONS: In advanced lung cancer, STMs provide additional relevant clinical information by predicting treatment outcome, but further standardization and validation is warranted.

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.

A Systematic Evaluation of Cost-Saving Dosing Regimens for Therapeutic Antibodies and Antibody-Drug Conjugates for the Treatment of Lung Cancer.

BACKGROUND: Expensive novel anticancer drugs put a serious strain on healthcare budgets, and the associated drug expenses limit access to life-saving treatments worldwide. OBJECTIVE: We aimed to develop alternative dosing regimens to reduce drug expenses. METHODS: We developed alternative dosing regimens for the following monoclonal antibodies used for the treatment of lung cancer: amivantamab, atezolizumab, bevacizumab, durvalumab, ipilimumab, nivolumab, pembrolizumab, and ramucirumab; and for the antibody-drug conjugate trastuzumab deruxtecan. The alternative dosing regimens were developed by means of modeling and simulation based on the population pharmacokinetic models developed by the license holders. They were based on weight bands and the administration of complete vials to limit drug wastage. The resulting dosing regimens were developed to comply with criteria used by regulatory authorities for in silico dose development. RESULTS: We found that alternative dosing regimens could result in cost savings that range from 11 to 28%, and lead to equivalent pharmacokinetic exposure with no relevant increases in variability in exposure. CONCLUSIONS: Dosing regimens based on weight bands and the use of complete vials to reduce drug wastage result in less expenses while maintaining equivalent exposure. The level of evidence of our proposal is the same as accepted by regulatory authorities for the approval of alternative dosing regimens of other monoclonal antibodies in oncology. The proposed alternative dosing regimens can, therefore, be directly implemented in clinical practice.

Therapeutic drug monitoring guided dosing versus standard dosing of alectinib in advanced ALK positive non-small cell lung cancer patients: Study protocol for an international, multicenter phase IV randomized controlled trial (ADAPT ALEC).

Background: Alectinib is first-line therapy in patients with stage IV non-small cell lung carcinoma (NSCLC) and an anaplastic lymphoma kinase (ALK) fusion. A shorter median progression-free survival (mPFS) was observed when alectinib minimum plasma concentrations during steady state (Cmin,SS) were below 435 ng/mL. This may suggest that patients should have an alectinib Cmin,SS ≥ 435 ng/mL for a more favorable outcome. This potential target could be attained by using therapeutic drug monitoring (TDM), i.e. adjusting the dose based on measured plasma trough concentrations. Hypothetically, this will increase mPFS, but this has not yet been evaluated in a randomized controlled trial (RCT). Therefore, the ADAPT ALEC trial is designed, with the primary objective to prolong mPFS in NSCLC patients treated with alectinib by using TDM. Methods: ADAPT ALEC is a multicenter, phase IV RCT, in which patients aged ≥ 18 years with advanced ALK positive (+) NSCLC eligible for alectinib in daily care are enrolled. Participants will be randomized (1:1 ratio) into intervention arm A (TDM) or B (control), stratified by brain metastases and prior ALK treatments. Starting dose in both arms is the approved flat fixed dose of alectinib 600 mg taken twice daily with food. In case of alectinib Cmin,SS < 435 ng/mL, arm A will receive increased doses of alectinib till Cmin,SS ≥ 435 ng/mL when considered tolerable. The primary outcome is mPFS, where progressive disease is defined according to RECIST v1.1 or all-cause death and assessed by CT-scans and MRI brain. Secondary endpoints are feasibility and tolerability of TDM, patient and physician adherence, overall response rate, median overall survival, intracranial PFS, quality of life, toxicity, alectinib-M4 concentrations and cost-effectiveness of TDM. Exploratory endpoints are circulating tumor DNA and body composition. Discussion: The ADAPT ALEC will show whether treatment outcomes of patients with advanced ALK+ NSCLC improve when using TDM-guided dosing of alectinib instead of fixed dosing. The results will provide high quality evidence for deciding whether TDM should be implemented as standard of care and this will have important consequences for the prescribing of alectinib. Clinical trial registration: ClinicalTrials.gov, identifier NCT05525338.

Feasibility of an Individualized Dispensing Program for Patients Prescribed Oral Anticancer Drugs to Prevent Waste.

PURPOSE: Waste of oral anticancer drugs (OACDs) causes financial and environmental burdens. This study evaluates the feasibility of an individualized dispensing program to prevent waste of OACDs. METHODS: Adult patients were dispensed individualized quantities of niraparib, abiraterone, enzalutamide, ruxolitinib, osimertinib, or imatinib as standard care, during the first 6 months of treatment. The first 50 patients participated in an feasibility evaluation conform five domains of Bowen's Framework. (1) implementation: reach (eligible patients included) and protocol fidelity (executions following protocol) assessed from pharmacy data, (2) acceptability: rated from 1 to 10 and agreement with theoretical framework acceptability domains via a survey among patients and pharmacy technicians, (3) practicality: program's costs, (4) effect: compared with previous practice (full package supply per month), defined as difference in unused OACD unit doses and net cost-savings, and (5) demand: potential scale-up of the program by including more OACDs. RESULTS: Participants' median age was 67 (interquartile range [IQR], 58-71) years, and 76% was male. (1) Implementation: reach and protocol fidelity were 89% and 90%, respectively. (2) Acceptability was high among patients (median, 9; IQR, 8-9) and pharmacy technicians (median, 7; IQR, 6-8). All acceptability domains were agreed on. (3) Practicality: program costs were €4,289. (4) Effect: unused OACD unit doses were reduced by 34%, causing net cost-savings of €693 per discontinued patient. (5) Demand: the program could be scaled up to seven times by including all OACDs. CONCLUSION: Individualized dispensing for patients prescribed OACDs is feasible for preventing waste in terms of implementation, acceptability, practicality, effect, and demand.

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.

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.

Renal function-based versus standard dosing of pemetrexed: a randomized controlled trial.

PURPOSE: Pemetrexed is a chemotherapeutic drug in the treatment of non-small cell lung cancer and mesothelioma. Optimized dosing of pemetrexed based on renal function instead of body surface area (BSA) is hypothesized to reduce pharmacokinetic variability in systemic exposure and could therefore improve treatment outcomes. The aim of this study is to compare optimized dosing to standard BSA-based dosing. METHODS: A multicenter randomized (1:1) controlled trial was performed to assess superiority of optimized dosing versus BSA-based dosing in patients who were eligible for pemetrexed-based chemotherapy. The individual exposure to pemetrexed in terms of area under the concentration-time curve (AUC) was determined. The fraction of patients attaining to a predefined typical target AUC (164 mg × h/L ± 25%) was calculated. RESULTS: A total of 81 patients were included. Target attainment was not statistically significant different between both arms (89% vs. 84% (p = 0.505)). The AUC of pemetrexed was similar between the optimized dosing arm (n = 37) and the standard of care arm (n = 44) (155 mg × h/L vs 160 mg × h/L (p = 0.436). CONCLUSION: We could not show superiority of optimized dosing of pemetrexed in patients with an adequate renal function does not show added value on the attainment of a pharmacokinetic endpoint, safety, nor QoL compared to standard of care dosing. CLINICAL TRIAL NUMBER: Clinicaltrials.gov identifier: NCT03655821.