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Objectives: Timely diagnosis of lung cancer (LC) is crucial to achieve optimal patient care and outcome. Moreover, the number of procedures required to obtain a definitive diagnosis can have a large influence on the life expectancy of a patient. Here, adherence with existing Dutch guidelines for timeliness and type and number of invasive and imaging procedures was assessed., Materials and Methods: 1096 patients with suspected LC were enrolled in this multicenter prospective study (NL9146). The overall survival, time from referral to the first appointment with the pulmonologist, time to diagnosis and treatment, and the number of imaging and invasive procedures were evaluated. Patients were divided into different diagnostic groupsearly- and advanced stage non-small-cell lung cancer (NSCLC), small-cell lung cancer (SCLC), large cell neuroendocrine carcinoma of the lung (LCNEC), patients without LC and patients without a definitive diagnosis., Results: The majority of patients (66 %) received a definitive diagnosis within 5 weeks, although the time to diagnosis of early-stage LC patients and patients without LC was significantly longer comparted to advanced stage LC. An increase in invasive procedures was seen for early-stage LC compared to advanced stage LC and for 13 % of the advanced stage non-squamous NSCLC patients up to three additional invasive procedures were performed solely to obtain sufficient material for NGS. For patients without a definitive diagnosis, 50 % did undergo at least one invasive procedure, while 11 % did not wish to undergo any invasive procedures., Conclusion: These insights could aid in improved LC diagnostics and efficient implementation of new techniques like liquid biopsy and artificial intelligence. This may lead to more timely LC care, a decreased number of invasive procedures, less variability between the diagnostic trajectory of different patients and aid in obtaining a definitive diagnosis for all patients., Competing Interests: Declaration of competing interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2024 The Author(s). Published by Elsevier B.V. All rights reserved.)

Background: Predictive biomarkers of immune checkpoint inhibitor (ICI) efficacy are currently lacking for non-small cell lung cancer (NSCLC). Here, we describe the results from the Anti-PD-1 Response Prediction DREAM Challenge, a crowdsourced initiative that enabled the assessment of predictive models by using data from two randomized controlled clinical trials (RCTs) of ICIs in first-line metastatic NSCLC., Methods: Participants developed and trained models using public resources. These were evaluated with data from the CheckMate 026 trial (NCT02041533), according to the model-to-data paradigm to maintain patient confidentiality. The generalizability of the models with the best predictive performance was assessed using data from the CheckMate 227 trial (NCT02477826). Both trials were phase III RCTs with a chemotherapy control arm, which supported the differentiation between predictive and prognostic models. Isolated model containers were evaluated using a bespoke strategy that considered the challenges of handling transcriptome data from clinical trials., Results: A total of 59 teams participated, with 417 models submitted. Multiple predictive models, as opposed to a prognostic model, were generated for predicting overall survival, progression-free survival, and progressive disease status with ICIs. Variables within the models submitted by participants included tumor mutational burden (TMB), programmed death ligand 1 (PD-L1) expression, and gene-expression-based signatures. The best-performing models showed improved predictive power over reference variables, including TMB or PD-L1., Conclusions: This DREAM Challenge is the first successful attempt to use protected phase III clinical data for a crowdsourced effort towards generating predictive models for ICI clinical outcomes and could serve as a blueprint for similar efforts in other tumor types and disease states, setting a benchmark for future studies aiming to identify biomarkers predictive of ICI efficacy., Trial Registration: CheckMate 026; NCT02041533, registered January 22, 2014. CheckMate 227; NCT02477826, registered June 23, 2015., (© 2024. The Author(s).)

Objectives: Pathologic subtyping of tissue biopsies is the gold standard for the diagnosis of lung cancer (LC), which could be complicated in cases of e.g. inconclusive tissue biopsies or unreachable tumors. The diagnosis of LC could be supported in a minimally invasive manner using protein tumor markers (TMs) and circulating tumor DNA (ctDNA) measured in liquid biopsies (LBx). This study evaluates the performance of LBx-based decision-support algorithms for the diagnosis of LC and subtyping into small- and non-small-cell lung cancer (SCLC and NSCLC) aiming to directly impact clinical practice., Materials and Methods: In this multicenter prospective study (NL9146), eight protein TMs (CA125, CA15.3, CEA, CYFRA 21-1, HE4, NSE, proGRP and SCCA) and ctDNA mutations in EGFR, KRAS and BRAF were analyzed in blood of 1096 patients suspected of LC. The performance of individual and combined TMs to identify LC, NSCLC or SCLC was established by evaluating logistic regression models at pre-specified positive predictive values (PPV) of ≥95% or ≥98%. The most informative protein TMs included in the multi-parametric models were selected by recursive feature elimination., Results: Single TMs could identify LC, NSCLC and SCLC patients with 46%, 25% and 40% sensitivity, respectively, at pre-specified PPVs. Multi-parametric models combining TMs and ctDNA significantly improved sensitivities to 65%, 67% and 50%, respectively., Conclusion: In patients suspected of LC, the LBx-based decision-support algorithms allowed identification of about two-thirds of all LC and NSCLC patients and half of SCLC patients. These models therefore show clinical value and may support LC diagnostics, especially in patients for whom pathologic subtyping is impossible or incomplete., Competing Interests: Declaration of Competing Interest The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper., (Copyright © 2023 The Authors. Published by Elsevier B.V. All rights reserved.)