A randomized phase 2 study of temsirolimus and cetuximab versus temsirolimus alone in recurrent/metastatic, cetuximab‐resistant head and neck cancer: The MAESTRO study

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/155947/1/cncr32929_am.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/155947/2/cncr32929.pd

A randomized phase 2 network trial of tivantinib plus cetuximab versus cetuximab in patients with recurrent/metastatic head and neck squamous cell carcinoma

Peer Reviewedhttps://deepblue.lib.umich.edu/bitstream/2027.42/154935/1/cncr32762.pdfhttps://deepblue.lib.umich.edu/bitstream/2027.42/154935/2/cncr32762_am.pd

Uncertainty-Informed Deep Learning Models Enable High-Confidence Predictions for Digital Histopathology

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 novel, clinically-oriented approach to uncertainty quantification (UQ) 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 UQ, 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 UQ 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

Predictions of CCR1 Chemokine Receptor Structure and BX 471 Antagonist Binding Followed by Experimental Validation

A major challenge in the application of structure-based drug design methods to proteins belonging to the superfamily of G protein-coupled receptors (GPCRs) is the paucity of structural information (1). The 19 chemokine receptors, belonging to the Class A family of GPCRs, are important drug targets not only for autoimmune diseases like multiple sclerosis but also for the blockade of human immunodeficiency virus type 1 entry (2). Using the MembStruk computational method (3), we predicted the three-dimensional structure of the human CCR1 receptor. In addition, we predicted the binding site of the small molecule CCR1 antagonist BX 471, which is currently in Phase II clinical trials (4). Based on the predicted antagonist binding site we designed 17 point mutants of CCR1 to validate the predictions. Subsequent competitive ligand binding and chemotaxis experiments with these mutants gave an excellent correlation to these predictions. In particular, we find that Tyr-113 and Tyr-114 on transmembrane domain 3 and Ile-259 on transmembrane 6 contribute significantly to the binding of BX 471. Finally, we used the predicted and validated structure of CCR1 in a virtual screening validation of the Maybridge data base, seeded with selective CCR1 antagonists. The screen identified 63% of CCR1 antagonists in the top 5% of the hits. Our results indicate that rational drug design for GPCR targets is a feasible approach

Pan-cancer image-based detection of clinically actionable genetic alterations

Molecular alterations in cancer can cause phenotypic changes in tumor cells and their microenvironment. 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 5,000 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

Synthesis of glutamic acid analogs as potent inhibitors of leukotriene A(4) hydrolase

Leukotriene B-4 (LTB4) is a potent pro-inflammatory mediator that has been implicated in the pathogenesis of multiple diseases, including psoriasis, inflammatory bowel disease, multiple sclerosis and asthma. As a method to decrease the level of LTB4 and possibly identify novel treatments, inhibitors of the LTB4 biosynthetic enzyme, leukotriene A(4) hydrolase (LTA(4)-h), have been explored. Here we describe the discovery of a potent inhibitor of LTA(4)-h, arylamide of glutamic acid 4f, starting from the corresponding glycinamide 2. Analogs of 4f are then described, focusing on compounds that are both active and stable in whole blood. This effort culminated in the identification of amino alcohol 12a and amino ester 6b which meet these criteria

Deep learning generates synthetic cancer histology for explainability and education

Abstract Artificial intelligence methods including deep neural networks (DNN) can provide rapid molecular classification of tumors from routine histology with accuracy that matches or exceeds human pathologists. Discerning how neural networks make their predictions remains a significant challenge, but explainability tools help provide insights into what models have learned when corresponding histologic features are poorly defined. Here, we present a method for improving explainability of DNN models using synthetic histology generated by a conditional generative adversarial network (cGAN). We show that cGANs generate high-quality synthetic histology images that can be leveraged for explaining DNN models trained to classify molecularly-subtyped tumors, exposing histologic features associated with molecular state. Fine-tuning synthetic histology through class and layer blending illustrates nuanced morphologic differences between tumor subtypes. Finally, we demonstrate the use of synthetic histology for augmenting pathologist-in-training education, showing that these intuitive visualizations can reinforce and improve understanding of histologic manifestations of tumor biology