ObjectivesThe landmark ADAURA study recently demonstrated a significant disease-free survival benefit of adjuvant osimertinibin patients with resected EGFR-mutated lung adenocarcinoma. However,... Show moreObjectivesThe landmark ADAURA study recently demonstrated a significant disease-free survival benefit of adjuvant osimertinibin patients with resected EGFR-mutated lung adenocarcinoma. However, data on prevalence rates and stage distribution of EGFR mutations in non-small cell lung cancer in Western populations are limited since upfront EGFR testing in early stage lung adenocarcinoma is not common practice. Here, we present a unique, real-world, unselected cohort of lung adenocarcinoma to aid in providing a rationale for routine testing of early stage lung cancers for EGFR mutations in the West-European population.Material and methodsWe performed routine unbiased testing of all cases, regardless of TNM stage, with targeted next-generation sequencing on 486 lung adenocarcinoma cases between 01- January 2014 and 01 February 2020. Clinical and pathological data, including co-mutations and morphology, were collected. EGFR-mutated cases were compared to KRAS-mutated cases to investigate EGFR-specific characteristics.ResultsIn total, 53 of 486 lung adenocarcinomas (11%) harboured an EGFR mutation. In early stages (stage 0-IIIA), the prevalence was 13%, versus 9% in stage IIIB-IV. Nine out of 130 (7%) stage IB-IIIA patients fit the ADAURA criteria. Early stage cases harboured more L858R mutations (p = 0.02), fewer exon 20 insertions (p = 0.048), fewer TP53 co-mutations (p = 0.007), and were more frequently never smokers (p = 0.04) compared to late stage cases with EGFR mutations. The KRAS-mutated cases were distributed more evenly across TNM stages compared to the EGFR-mutated cases.ConclusionAs (neo-)adjuvant targeted therapy regimes enter the field of lung cancer treatment, molecular analysis of early stage non-small cell lung cancer becomes relevant. Testing for EGFR mutations in early stage lung adenocarcinoma holds a substantial yield in our population, as our number needed to test ratio for adjuvant osimertinib was 14.4. The observed differences between early and late stage disease warrant further analysis to work towards better prognostic stratification and more personalised treatment. Show less
Hondelink, L.M.; Schrader, A.M.R.; Aghmuni, G.A.; Solleveld-Westerink, N.; Cleton-Jansen, A.M.; Egmond, D. van; ... ; Cohen, D. 2022
Introduction: Since the approval of neurotrophic tropomyosin receptor kinase (NTRK) tyrosine kinase inhibitors for fist-line advanced stage pan-cancer therapy, pathologists and molecular biologists... Show moreIntroduction: Since the approval of neurotrophic tropomyosin receptor kinase (NTRK) tyrosine kinase inhibitors for fist-line advanced stage pan-cancer therapy, pathologists and molecular biologists have been facing a complex question: how should the large volume of specimens be screened for NTRK fusions? Immunohistochemistry is fast and cheap, but the sensitivity compared to RNA NGS is unclear.Methods: We performed RNA-based next-generation sequencing on 1,329 cases and stained 24 NTRK-rearranged cases immunohistochemically with pan-TRK (ERP17341). Additionally, we performed a meta-analysis of the literature. After screening 580 studies, 200 additional NTRK-rearranged cases from 13 studies, analysed with sensitive molecular diagnostics as well as pan-TRK IHC, were included.Results: In the included 224 NTRK-rearranged solid tumours, the sensitivity for pan-TRK IHC was 82% and the false-negative rate was 18%. NTRK3 fusions had more false negatives (27%) compared to NTRK1 (6%) and NTRK2 (14%) (p = 0.0006). Membranous, nuclear and peri-nuclear staining patterns strongly correlated with different fusion products, with membranous staining being more prevalent in NTRK1 and NTRK2, nuclear in NTRK3, and perinuclear in NTRK1.Conclusion: Despite a reduction in the number of molecular analysis, using pan-TRK immunohistochemistry as a prescreening method to detect NTRK fusions in solid tumours will miss 18% of all NTRK-fused cases (especially involving NTRK3). Therefore, the most comprehensive and optimal option to detect NTRK fusions is to perform molecular testing on all eligible cases. However, in case of financial or logistical limitations, an immunohistochemistry-first approach is defensible in tumours with a low prevalence of NTRK fusions. (C) 2022 The Author(s). Published by Elsevier Ltd. This is an open access article under the CC BY license (http://creativecommons.org/licenses/by/4.0/). Show less
Hondelink, L.M.; Schrader, A.M.R.; Aghmuni, G.A.; Solleveld-Westerink, N.; Cleton-Jansen, A.M.; Egmond, D. van; ... ; Cohen, D. 2022
Objectives: Programmed death-ligand 1 (PD-L1) is the only approved predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). However, predictive PD-L1 immunohistochemistry is... Show moreObjectives: Programmed death-ligand 1 (PD-L1) is the only approved predictive biomarker for immunotherapy in non-small cell lung cancer (NSCLC). However, predictive PD-L1 immunohistochemistry is subject to interobserver variability. We hypothesized that a pathologist's personality influences the interobserver variability and diagnostic accuracy of PD-L1 immunoscoring.& nbsp;Materials and Methods: Seventeen pathologists performed PD-L1 immunoscoring on 50 resected NSCLC tumors in three categories (< 1%;1-49%;>= 50%). Also, the pathologists completed a certified personality test (NEO-PI-r), assessing five personality traits: neuroticism, extraversion, openness, altruism and conscientiousness.& nbsp;Results: The overall agreement among pathologists for a series of 47 tumors was substantial (kappa = 0.63). Of these, 23/47 (49%) tumors were entirely negative or largely positive, resulting in a kappa value of 0.93. The remaining 24/47 (51%) tumors had a PD-L1 score around the cutoff value, generating a kappa value of 0.32. Pathologists with high scores for conscientiousness (careful, diligent) had the least interobserver variability (r = 0.6, p = 0.009). Also, they showed a trend towards higher sensitivity (74% vs. 68%, p = 0.4), specificity (86% vs. 82%, p = 0.3) and percent agreement (83% vs. 79%, p = 0.3), although not significant. In contrast, pathologists with high scores for neuroticism (sensitive, anxious) had significantly lower specificity (80% vs. 87%, p = 0.03) and percent agreement (78% vs. 85%, p = 0.03). Also, a trend towards high interobserver variability (r =-0.3, p = 0.2) and lower sensitivity (68% vs. 74%, p = 0.3) was observed, although not significant. Pathologists with relatively high scores for conscientiousness scored fewer tumors PD-L1 positive at the >= 1% cut-off (r =-0.5, p = 0.03). In contrast, pathologists with relatively high scores for neuroticism score more tumors PD-L1 positive at > 1% (r = 0.6, p = 0.017) and >= 50% cut-offs (r = 0.6, p = 0.009).& nbsp;Conclusions: This study is the first to demonstrate the impact of a pathologist's personality on the interobserver variability and diagnostic accuracy of immunostaining, in the context of PD-L1 in NSCLC. Larger studies are needed for validation of these findings. Show less
Hondelink, L.M.; Huyuk, M.; Postmus, P.E.; Smit, V.T.H.B.M.; Blom, S.; Thusen, J.H. von der; Cohen, D. 2021
Aims Immunohistochemical programmed death-ligand 1 (PD-L1) staining to predict responsiveness to immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) has several drawbacks: a... Show moreAims Immunohistochemical programmed death-ligand 1 (PD-L1) staining to predict responsiveness to immunotherapy in patients with advanced non-small cell lung cancer (NSCLC) has several drawbacks: a robust gold standard is lacking, and there is substantial interobserver and intraobserver variance, with up to 20% discordance around cutoff points. The aim of this study was to develop a new deep learning-based PD-L1 tumour proportion score (TPS) algorithm, trained and validated on a routine diagnostic dataset of digitised PD-L1 (22C3, laboratory-developed test)-stained samples. Methods and results We designed a fully supervised deep learning algorithm for whole-slide PD-L1 assessment, consisting of four sequential convolutional neural networks (CNNs), using aiforia create software. We included 199 whole slide images (WSIs) of 'routine diagnostic' histology samples from stage IV NSCLC patients, and trained the algorithm by using a training set of 60 representative cases. We validated the algorithm by comparing the algorithm TPS with the reference score in a held-out validation set. The algorithm had similar concordance with the reference score (79%) as the pathologists had with one another (75%). The intraclass coefficient was 0.96 and Cohen's kappa coefficient was 0.69 for the algorithm. Around the 1% and 50% cutoff points, concordance was also similar between pathologists and the algorithm. Conclusions We designed a new, deep learning-based PD-L1 TPS algorithm that is similarly able to assess PD-L1 expression in daily routine diagnostic cases as pathologists. Successful validation on routine diagnostic WSIs and detailed visual feedback show that this algorithm meets the requirements for functioning as a 'scoring assistant'. Show less
Cohen, D.; Hondelink, L.M.; Solleveld-Westerink, N.; Uljee, S.M.; Ruano, D.; Cleton-Jansen, A.M.; ... ; Wezel, T. van 2020
Introduction: Frequently, patients with locally advanced or metastatic NSCLC are screened for mutations and fusions. In most laboratories, molecular workup includes a multitude of tests:... Show moreIntroduction: Frequently, patients with locally advanced or metastatic NSCLC are screened for mutations and fusions. In most laboratories, molecular workup includes a multitude of tests: immunohistochemistry (ALK, ROS1, and programmed death-ligand 1 testing), DNA sequencing, in situ hybridization for fusion, and amplification detection. With the fast-emerging new drugs targeting specific fusions and exon-skipping events, this procedure harbors a growing risk of tissue exhaustion.Methods: In this study, we evaluated the benefit of anchored, multiplexed, polymerase chain reaction-based targeted RNA sequencing (RNA next-generation sequencing [NGS]) in the identification of gene fusions and exon-skipping events in patients, in which no pathogenic driver mutation was found by DNA-based targeted cancer hotspot NGS (DNA NGS). We analyzed a cohort of stage IV NSCLC cases from both in-house and referral hospitals, consisting 38.5% cytology samples and 61.5% microdissected histology samples, mostly core needle biopsies. We compared molecular findings in a parallel workup (DNA NGS and RNA NGS, cohort 1, n = 198) with a sequential workup (DNA NGS followed by RNA NGS in selected cases, cohort 2, n = 192). We hypothesized the sequential workup to be the more efficient procedure.Results: In both cohorts, a maximum of one oncogenic driver mutation was found per case. This is in concordance with large, whole-genome databases and suggests that it is safe to omit RNA NGS when a clear oncogenic driver is identified in DNA NGS. In addition, this reduced the number of necessary RNA NGS to only 53% of all cases. The tumors of never smokers, however, were enriched for fusions and exon-skipping events (32% versus 4% in former and current smokers, p = 0.00), and therefore benefited more often from the shorter median turnaround time of the parallel approach (15 d versus only 9 d in the parallel workup).Conclusions: We conclude that sequentially combining DNA NGS and RNA NGS is the most efficient strategy for mutation and fusion detection in smoking-associated NSCLC, whereas for never smokers we recommend a parallel approach. This approach was shown to be feasible on small tissue samples including for cytology tests, can drastically reduce the complexity and cost of molecular workup, and also provides flexibility in the constantly evolving landscape of actionable targets in NSCLC. (C) 2020 International Association for the Study of Lung Cancer. Published by Elsevier Inc. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/). Show less
