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

The results of this study demonstrate that muscle function progressively declines in the Cy/+ rat model of CKD. Because whole muscle mass and architecture do not vary between CKD and NL, but CKD muscles show reduction in individual fiber CSA, our data suggest that the functional decline is related to increased muscle fiber atrophy.

A model’s ability to express its own predictive uncertainty is an essential attribute for maintaining clinical user confidence as computational biomarkers are deployed into real-world medical settings. In the domain of cancer digital histopathology, we describe a clinically-oriented approach to uncertainty quantification for whole-slide images, estimating uncertainty using dropout and calculating thresholds on training data to establish cutoffs for low- and high-confidence predictions. We train models to identify lung adenocarcinoma vs. squamous cell carcinoma and show that high-confidence predictions outperform predictions without uncertainty, in both cross-validation and testing on two large external datasets spanning multiple institutions. Our testing strategy closely approximates real-world application, with predictions generated on unsupervised, unannotated slides using predetermined thresholds. Furthermore, we show that uncertainty thresholding remains reliable in the setting of domain shift, with accurate high-confidence predictions of adenocarcinoma vs. squamous cell carcinoma for out-of-distribution, non-lung cancer cohorts.

Sickle cell disease (SCD) patients are at a four- to 100-fold increased risk for thrombosis compared with the general population, although the mechanisms and risk factors are not clear. We investigated the incidence and predictors for thrombosis in a retrospective, longitudinal cohort of 1193 pediatric and adult SCD patients treated at our institution between January 2008 and December 2017. SCD diagnosis and thrombotic complications were identified using International Classification of Diseases coding and verified through medical chart review. Clinical and laboratory data were extracted from the medical records. With a median follow-up of 6.4 years, 208 (17.4%) SCD patients experienced 352 thrombotic events (64 strokes, 288 venous thromboembolisms [VTE]). Risk factors for stroke included older age and HbSS/Sβ0-genotype and a lower hemoglobin (Hb) F% in the subset of HbSS/Sβ0-genotype patients (P < .05). VTE risk was independently associated with lower estimated glomerular filtration rate, hydroxyurea (HU) use, HbSS/Sβ0 genotype, and higher white blood cell (WBC) counts and Hb (P ≤ .03). Two thrombomodulin gene variants previously associated with thrombosis in the general African American population, THBD rs2567617 (minor allele frequency [MAF] 0.25; odds ratio [OR], 1.5; P = .049) and THBD rs1998081 (MAF, 0.24; OR, 1.5; P = .059), were associated with thrombosis in this cohort. In summary, thrombotic complications are common, and several traditional and SCD-specific risk factors are associated with thrombotic risk. Future studies integrating clinical, laboratory, and genetic risk factors may improve our understanding of thrombosis and guide intervention practices in SCD.