This is a repository copy of Pan-cancer image-based detection of clinically actionable genetic alterations.

and tluedde@ukaachen.de 34 35 Precision treatment of cancer relies on genetic alterations which are diagnosed by molecular 36 biology assays. 1 These tests can be a bottleneck in oncology workflows because of high turna-37 round time, tissue usage and costs. 2 Here, we show that deep learning can predict point muta-38 tions, molecular tumor subtypes and immune-related gene expression signatures 3,4 directly 39 from routine histological images of tumor tissue. We developed and systematically optimized 40 a one-stop-shop workflow and applied it to more than 4000 patients with breast 5 , colon and 41 rectal 6 , head and neck 7 , lung 8,9 , pancreatic 10 , prostate 11 cancer, melanoma 12 and gastric 13 can-42 cer. Together, our findings show that a single deep learning algorithm can predict clinically ac-43 tionable alterations from routine histology data. Our method can be implemented on mobile 44 hardware 14 , potentially enabling point-of-care diagnostics for personalized cancer treatment 45 in individual patients. 46 Clinical guidelines recommend molecular testing of tumor tissue for most patients with advanced 47 209 The results are in part based upon data generated by the TCGA Research Network: http://can-210 cergenome.nih.gov/. Our funding sources are as follows. J.N.K.: RWTH University Aachen (START 211

Immunohistochemistry plays an indispensable role in accurate diagnosis of malignant mesothelioma, particularly in morphologically challenging cases and in biopsy and cytology specimens, where tumor architecture is difficult or impossible to evaluate. Application of a targeted panel of mesothelialand epithelial-specific markers permits correct identification of tumor lineage in the vast majority of cases.An immunopanel including two mesothelial markers (calretinin, CK5/6, WT-1, or D2-40) and two epithelial markers (MOC-31 and claudin-4) offers good sensitivity and specificity, with adjustments as appropriate for the differential diagnosis. Once mesothelial lineage is established, malignancy-specific studies can help verify a diagnosis of malignant mesothelioma. BAP1 loss, CDKN2A homozygous deletion, and MTAP loss are highly specific markers of malignancy in a mesothelial lesion, and they attain acceptable diagnostic sensitivity when applied as a diagnostic panel. Novel markers of malignancy, such as 5-hmC loss and increased EZH2 expression, are promising, but have not yet achieved widespread clinical adoption. Some diagnostic markers also have prognostic significance, and PD-L1 immunohistochemistry may predict tumor response to immunotherapy. Application and interpretation of these immnuomarkers should always be guided by clinical history, radiographic findings, and above all histomorphology.

The Cancer Genome Atlas (TCGA) is one of the largest biorepositories of digital histology. Deep learning (DL) models have been trained on TCGA to predict numerous features directly from histology, including survival, gene expression patterns, and driver mutations. However, we demonstrate that these features vary substantially across tissue submitting sites in TCGA for over 3,000 patients with six cancer subtypes. Additionally, we show that histologic image differences between submitting sites can easily be identified with DL. Site detection remains possible despite commonly used color normalization and augmentation methods, and we quantify the image characteristics constituting this site-specific digital histology signature. We demonstrate that these site-specific signatures lead to biased accuracy for prediction of features including survival, genomic mutations, and tumor stage. Furthermore, ethnicity can also be inferred from site-specific signatures, which must be accounted for to ensure equitable application of DL. These site-specific signatures can lead to overoptimistic estimates of model performance, and we propose a quadratic programming method that abrogates this bias by ensuring models are not trained and validated on samples from the same site.

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