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Background: The efficacy of checkpoint inhibitors for non-small cell lung cancer (NSCLC) with MET exon 14 skipping (METΔ14ex) remains controversial., Materials and Methods: 110 consecutive METΔ14ex NSCLC patients receiving first-line chemotherapy (CHT) and/or immunotherapy (IO) in 10 German centers between 2016-2022 were analyzed., Results: Combined CHT-IO was given to 35/110 (32%) patients, IO alone to 43/110 (39%), and CHT to 32/110 (29%) upfront. Compared to CHT, CHT-IO showed longer progression-free survival (median PFS 6 vs. 2.5 months, p = 0.004), more objective responses (ORR 49% vs. 28%, p = 0.086) and numerically longer overall survival (OS 16 vs. 10 months, p = 0.240). For IO monotherapy, OS (14 vs. 16 months) and duration of response (26 vs. 22 months) were comparable to those of CHT-IO. Primary progressive disease (PD) was more frequent with IO compared to CHT-IO (13/43 vs. 3/35, p = 0.018), particularly for never-smokers (p = 0.041). Higher PD-L1 TPS were not associated with better IO outcomes, but TP53 mutated tumors showed numerically improved ORR (56% vs. 32%, p = 0.088) and PFS (6 vs. 3 months, p = 0.160), as well as longer OS in multivariable analysis (HR=0.54, p = 0.034) compared to their wild-type counterparts. Any second-line treatment was administered to 35/75 (47%) patients, with longer survival for capmatinib or tepotinib compared to crizotinib (PFS 10 vs. 3 months, p = 0.013; OS 16 vs. 13 months, p = 0.270)., Conclusion: CHT-IO is superior to CHT, and IO alone also effective for METΔ14ex NSCLC, especially in the presence of TP53 mutations and independent of PD-L1 expression, but never-smokers are at higher risk of primary PD., Competing Interests: Declaration of Competing Interest The authors declare the following financial interests/personal relationships which may be considered as potential competing interests: JK: speaker’s honoraria from BMS, AstraZeneca and Pfizer, travel grants from Takeda, advisory board honoraria from Takeda, Roche and AstraZeneca. DM: advisory board/lecture fees from AstraZeneca, BMS, Boehringer Ingelheim, Lilly, MSD, Novartis, Roche, Sanofi and Takeda. DiKa: advisory boards/speaker´s honoraria from BMS, Boehringer-Ingelheim, MSD, Roche, Janssen, Pfizer, AstraZeneca; support for attending meetings from Novartis, Boehringer-Ingelheim. MH: advisory board, speaker’s honoraria and travel grants from Boehringer-Ingelheim. BH: advisory board/lecture fees from AstraZeneca, Boehringer Ingelheim, BMS, MSD, Roche, Pfizer. MF: grants from AstraZeneca, BMS, MSD, and Roche; consulting fees from AstraZeneca, MSD, Roche, BMS; speaker´s honoraria from AstraZeneca, MSD, Roche, BMS. MK: speaker’s honoraria and travel grants from Veracyte Inc. AV: speaker’s honoraria from AstraZeneca. MA: speaker’s honoraria from Boehringer Ingelheim. CG: advisory board/lecture fees from Amgen, AstraZeneca, BMS, Boehringer Ingelheim, Eli Lilly, Takeda, MSD, Novartis, Pfizer, Roche, AbbVie, Tesaro/GSK and Blueprints Medicines. AT: consulting fees from Boehringer Ingelheim, Daiichi, Astra Zeneca, Roche, Pfizer, BMS, MSD, Sanofi, Lilly, Novartis; speaker’s honoraria from Boehringer Ingelheim, Daiichi, Astra Zeneca, Roche, Pfizer, BMS, MSD, Sanofi, Lilly, Novartis; travel grants from Sanofi, Janssen, Daiichi; BMS, MSD, AstraZeneca. MR: advisory board/lecture fees from Amgen, AstraZeneca, BMS, Boehringer, Lilly, Merck, MSD, Novartis, Pfizer, Roche, Samsung. NF: grants from Roche, consulting fees from AbbVie, Amgen, AstraZeneca, BeiGene, Berlinchemie, Boehringer Ingelheim, Bristol Myers&Squibb, Lilly, Merck Sharp&Dohme, Merck, Novartis, Pfizer, Roche, Sanofi, Takeda; support for attending meetings from Amgen, AstraZeneca, BMS, Janssen, Lilly, Takeda. AS: advisory board honoraria from BMS, AstraZeneca, ThermoFisher, Novartis, speaker’s honoraria from BMS, Illumina, AstraZeneca, Novartis, ThermoFisher, MSD, Roche, and research funding from Chugai and BMS. MT: research funding from AstraZeneca, BMS, Merck, Roche, Takeda; speaker’s honoraria from AstraZeneca, Beigene, Novartis, Eli Lilly, BMS, MSD, Roche, Celgene, Takeda, AbbVie, Boehringer Ingelheim, Pfizer, Eli Lilly, MSD, Takeda, Pfizer, Chugai, Daiichi Sankyo, GlaxoSmithKline, Janssen Oncology, Merck, Sanofi and travel grants from AstraZeneca, BMS, MSD, Novartis, Daiichi Sankyo, Janssen Oncology, Lilly, Merck, Pfizer, Roche, Sanofi, Takeda, Boehringer Ingelheim. PC: research funding from AstraZeneca, Amgen, Boehringer Ingelheim, Novartis, Roche, and Takeda, speaker’s honoraria from AstraZeneca, Janssen, Novartis, Roche, Pfizer, Thermo Fisher, Takeda, support for attending meetings from AstraZeneca, Eli Lilly, Daiichi Sankyo, Gilead, Novartis, Pfizer, Takeda, and personal fees for participating to advisory boards from AstraZeneca, Boehringer Ingelheim, Chugai, Pfizer, Novartis, MSD, Takeda and Roche, all outside the submitted work. All other authors have no conflicts of interest to declare., (Copyright © 2024 The Authors. Published by Elsevier Ltd.. All rights reserved.)

Mudstones and shales serve as natural barrier rocks in various geoenergy applications. Although many studies have investigated their mechanical properties, characterizing these parameters at the microscale remains challenging due to their fine-grained nature and susceptibility to microstructural damage introduced during sample preparation. This study aims to investigate the micromechanical properties of clay matrix composite in mudstones by combining high-speed nanoindentation mapping and machine learning data analysis. The nanoindentation approach effectively captured the heterogeneity in high-resolution mechanical property maps. Utilizing machine learning-based k -means clustering, the mechanical characteristics of matrix clay, brittle minerals, as well as measurements on grain boundaries and structural discontinuities (e.g., cracks) were successfully distinguished. The classification results were validated through correlation with broad ion beam-scanning electron microscopy images. The resulting average reduced elastic modulus ( E r ) and hardness ( H ) values for the clay matrix were determined to be 16.2 ± 6.2 and 0.5 ± 0.5 GPa, respectively, showing consistency across different test settings and indenter tips. Furthermore, the sensitivity of indentation measurements to various factors was investigated, revealing limited sensitivity to indentation depth and tip geometry (when comparing Cube corner and Berkovich tip in a small range of indentation depth variations), but decreased stability at lower loading rates. Box counting and bootstrapping methods were applied to assess the representativeness of parameters determined for the clay matrix. A relatively small dataset (indentation number = 60) is needed to achieve representativeness, while the main challenges is to cover a representative mapping area for clay matrix characterization. Overall, this study demonstrates the feasibility of high-speed nanoindentation mapping combined with data analysis for micromechanical characterization of the clay matrix in mudstones, paving the way for efficient analysis of similar fine-grained sedimentary rocks., Supplementary Information: The online version contains supplementary material available at 10.1007/s40948-024-00864-9., Competing Interests: Competing interestsThe authors declare no competing interests., (© The Author(s) 2024.)