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Background: Atypical EGFR mutations occur in 10%-30% of non-small-cell lung cancer (NSCLC) patients with EGFR mutations and their sensitivity to classical epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKI) is highly heterogeneous. Patients harboring one group of uncommon, recurrent EGFR mutations (G719X, S768I, L861Q) respond to EGFR-TKI. Exon 20 insertions are mostly insensitive to EGFR-TKI but display sensitivity to exon 20 inhibitors. Clinical outcome data of patients with very rare point and compound mutations upon systemic treatments are still sparse to date., Patients and Methods: In this retrospective, multicenter study of the national Network Genomic Medicine (nNGM) in Germany, 856 NSCLC cases with atypical EGFR mutations including co-occurring mutations were reported from 12 centers. Clinical follow-up data after treatment with different EGFR-TKIs, chemotherapy and immune checkpoint inhibitors were available from 260 patients. Response to treatment was analyzed in three major groups: (i) uncommon mutations (G719X, S7681, L861Q and combinations), (ii) exon 20 insertions and (iii) very rare EGFR mutations (very rare single point mutations, compound mutations, exon 18 deletions, exon 19 insertions)., Results: Our study comprises the largest thus far reported real-world cohort of very rare EGFR single point and compound mutations treated with different systemic treatments. We validated higher efficacy of EGFR-TKI in comparison to chemotherapy in group 1 (uncommon), while most exon 20 insertions (group 2) were not EGFR-TKI responsive. In addition, we found TKI sensitivity of very rare point mutations (group 3) and of complex EGFR mutations containing exon 19 deletions or L858R mutations independent of the combination partner. Notably, treatment responses in group 3 (very rare) were highly heterogeneous. Co-occurring TP53 mutations exerted a non-significant trend for a detrimental effect on outcome in EGFR-TKI-treated patients in groups 2 and 3 but not in group 1., Conclusions: Based on our findings, we propose a novel nNGM classification of atypical EGFR mutations., Competing Interests: Disclosure MJ: speaker’s honoraria and/or advisory board from Roche, Boehringer, Amgen, AstraZeneca, Novartis. AT: honoraria, consulting and/or travel support from Lilly, Pfizer, Boehringer, Celgene, MSD, BMS, Amgen, Roche, Takeda, GSK. JLVR: consulting and/or travel support from Roche, Lilly. MR: research funding from BMS. AJ: speaker honoraria, advisory board and/or travel support from Amgen, Archer, Astra Zeneca, Bayer, Biocartis, Boerhinger Ingelheim, BMS, Merck, MSD, Novartis, Pfizer, QuiP GmbH, Roche, Thermo Fisher. IB: honoraria, consulting and/or travel support from AstraZeneca, Novartis, BMS, Bayer. HDH: travel, accommodations or other expenses from Johnson & Johnson, Boehringer Ingelheim, Bristol-Myers Squibb and Amgen. JAS: honoraria, consulting and/or travel support from AstraZeneca, Novartis, Amgen, Roche; research funding from AstraZeneca. JA: consulting and/or travel support from AstraZeneca, Boehringer Ingelheim, BMS, Roche. JB: honoraria from Novartis. FCS: honoraria from Pfizer, Takeda; research funding from Roche; travel support from Lilly. MW: research funding from Roche; honoraria and/or advisory board fees from Roche, AstraZeneca, Boehringer, Kite, Novartis, Merk, BMS, Heidelberg Pharma; non-financial support from AstraZeneca, BMS, Glenmark. SMB: honoraria and/or advisory board from Amgen, AstraZeneca, Roche, Novartis, GSK, MSD, Targos Molecular Health, Merck, Onkowisse, QuIP GmbH and advisory board honoraria, speaker fees and non-financial support from Janssen and BMS. CB: honoraria and/or advisory board from AstraZeneca, Bayer, Berlin Chemie, BMS, Merck, Roche, Novartis, Sanofi, Lilly/ImClone and travel expenses from BMS, Merck, Pfizer and Sanofi. RB: employment with Targos Molecular Path Inc, Kassel, Germany, leadership role as Chief Scientific Officer at Targos Molecular Path Inc, stock from Targos Molecular Path Inc. JW: consulting and/or travel support from Amgen, AstraZeneca, Bayer, Blueprint, BMS, Boehringer-Ingelheim, Chugai, Daiichi Sankyo, Ignyta, Janssen, Lilly, Loxo, MSD, Novartis, Pfizer, Roche, Seattle Genetics, Takeda; research funding from BMS, Janssen Pharmaceutica, Novartis, Pfizer. SL: advisory board, speaker honoraria and travel support from Lilly, Sanofi, BerGenBio, Novartis, Boehringer Ingelheim, BMS, Roche, Astra Zeneca, MSD, Sanofi-Aventis, Janssen, Takeda, Daiichi-Sankyo and research funding from Roche, BMS and BerGenBio. All other authors have declared no conflicts of interest., (Copyright © 2022 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Introduction: Tumor mutational burden (TMB) is a quantitative assessment of the number of somatic mutations within a tumor genome. Immunotherapy benefit has been associated with TMB assessed by whole-exome sequencing (wesTMB) and gene panel sequencing (psTMB). The initiatives of Quality in Pathology (QuIP) and Friends of Cancer Research have jointly addressed the need for harmonization among TMB testing options in tissues. This QuIP study identifies critical sources of variation in psTMB assessment., Methods: A total of 20 samples from three tumor types (lung adenocarcinoma, head and neck squamous cell carcinoma, and colon adenocarcinoma) with available WES data were analyzed for psTMB using six panels across 15 testing centers. Interlaboratory and interplatform variation, including agreement on variant calling and TMB classification, were investigated. Bridging factors to transform psTMB to wesTMB values were empirically derived. The impact of germline filtering was evaluated., Results: Sixteen samples had low interlaboratory and interpanel psTMB variation, with 87.7% of pairwise comparisons revealing a Spearman's ρ greater than 0.6. A wesTMB cut point of 199 missense mutations projected to psTMB cut points between 7.8 and 12.6 mutations per megabase pair; the corresponding psTMB and wesTMB classifications agreed in 74.9% of cases. For three-tier classification with cut points of 100 and 300 mutations, agreement was observed in 76.7%, weak misclassification in 21.8%, and strong misclassification in 1.5% of cases. Confounders of psTMB estimation included fixation artifacts, DNA input, sequencing depth, genome coverage, and variant allele frequency cut points., Conclusions: This study provides real-world evidence that all evaluated panels can be used to estimate TMB in a routine diagnostic setting and identifies important parameters for reliable tissue TMB assessment that require careful control. As complex or composite biomarkers beyond TMB are likely playing an increasing role in therapy prediction, the efforts by QuIP and Friends of Cancer Research also delineate a general framework and blueprint for the evaluation of such assays., (Copyright © 2020 International Association for the Study of Lung Cancer. Published by Elsevier Inc. All rights reserved.)