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Cetuximab induces responses in about 13% of head and neck squamous cell carcinomas (HNSCC). We describe the molecular mechanism of acquired resistance to cetuximab, which could be overcome by switching to a different anti-EGFR antibody. Biopsies were collected at three different time points: before the start of cetuximab (PRE-cetux), at acquired resistance to cetuximab (AR-cetux), and at acquired resistance to duligotuzumab (AR-duligo). Biopsies were analyzed using tumor and normal whole-exome sequencing, RNASeq, and targeted panel sequencing with ultra-deep coverage to generate differential mutation and expression profiles. WES and targeted sequencing analysis identified an EGFR p.G465R extracellular domain mutation in AR-cetux biopsy. Furthermore, RNASeq confirmed the expression of this mutation in the tumor tissue. This mutation prevented the binding of cetuximab to EGFR and was not present in PRE-cetux and AR-duligo biopsies, suggesting a potential mechanism of acquired resistance to cetuximab. Molecular dynamic simulations confirmed that duligotuzumab effectively binds EGFR with a p.G465R mutation. Interestingly, the p.G465R mutation improved the stability of the duligotuzumab-EGFR complex as compared to the wild-type EGFR. This is the first report of an EGFR ECD mutation associated with acquired resistance to cetuximab, posing a need for further validation. We suggest appropriate serial mutational profiling to identify ECD mutations should be considered for select patients with initial cetuximab benefit., (© 2024. The Author(s), under exclusive licence to Springer Nature America, Inc.)

Molecular alterations in cancer can cause phenotypic changes in tumor cells and their micro-environment. Routine histopathology tissue slides - which are ubiquitously available - can reflect such morphological changes. Here, we show that deep learning can consistently infer a wide range of genetic mutations, molecular tumor subtypes, gene expression signatures and standard pathology biomarkers directly from routine histology. We developed, optimized, validated and publicly released a one-stop-shop workflow and applied it to tissue slides of more than 5000 patients across multiple solid tumors. Our findings show that a single deep learning algorithm can be trained to predict a wide range of molecular alterations from routine, paraffin-embedded histology slides stained with hematoxylin and eosin. These predictions generalize to other populations and are spatially resolved. Our method can be implemented on mobile hardware, potentially enabling point-of-care diagnostics for personalized cancer treatment. More generally, this approach could elucidate and quantify genotype-phenotype links in cancer., Competing Interests: Competing interests JNK has an informal, unpaid advisory role at Pathomix (Heidelberg, Germany) which does not relate to this research. JNK declares no other relationships or competing interests. All other authors declare no competing interests.