SUMMARY Many cancer cells consume large quantities of glutamine to maintain TCA cycle anaplerosis and support cell survival. It was therefore surprising when RNAi screening revealed that suppression of citrate synthase (CS), the first TCA cycle enzyme, prevented glutamine-withdrawal-induced apoptosis. CS suppression reduced TCA cycle activity and diverted oxaloacetate, the substrate of CS, into production of the nonessential amino acids aspartate and asparagine. We found that asparagine was necessary and sufficient to suppress glutamine-withdrawal-induced apoptosis without restoring the levels of other nonessential amino acids or TCA cycle intermediates. In complete medium, tumor cells exhibiting high rates of glutamine consumption underwent rapid apoptosis when glutamine-dependent asparagine synthesis was suppressed and expression of asparagine synthetase was statistically correlated with poor prognosis in human tumors. Coupled with the success of L-asparaginase as a therapy for childhood leukemia, the data suggest that intracellular asparagine is a critical suppressor of apoptosis in many human tumors.

SUMMARY CD95 (Fas/APO-1), when bound by its cognate ligand CD95L, induces cells to die by apoptosis. We now show that elimination of CD95 or CD95L results in a form of cell death that is independent of caspase-8, RIPK1/MLKL, and p53, is not inhibited by Bcl-xL expression, and preferentially affects cancer cells. All tumors that formed in mouse models of low-grade serous ovarian cancer or chemically induced liver cancer with tissue specific deletion of CD95 still expressed CD95, suggesting that cancer cannot form in the absence of CD95. Death induced by CD95R/L elimination (DICE) is characterized by an increase in cell size and production of mitochondrial ROS, and DNA damage. It resembles a necrotic form of mitotic catastrophe. No single drug was found to completely block this form of cell death, and it could also not be blocked by the knockdown of a single gene, making it a promising new way to kill cancer cells.

Patient-specific induced pluripotent stem cells (iPSCs) represent a novel system for modeling human genetic disease and could develop into a key drug discovery platform. We recently reported disease-specific phenotypes in iPSCs from familial dysautonomia (FD) patients. FD is a rare but fatal genetic disorder affecting neural crest lineages. Here we demonstrate the feasibility of performing a primary screen in FD-iPSC derived neural crest precursors. Out of 6,912 compounds tested we characterized 8 hits that rescue expression of IKBKAP, the gene responsible for FD. One of those hits, SKF-86466, is shown to induce IKBKAP transcription via modulation of intracellular cAMP levels and PKA dependent CREB phosphorylation. SKF-86466 also rescues IKAP protein expression and the disease-specific loss of autonomic neuron marker expression. Our data implicate alpha-2 adrenergic receptor activity in regulating IKBKAP expression and demonstrate that small molecule discovery in an iPSC-based disease model can identify candidate drugs for potential therapeutic intervention.

Upper tract urothelial carcinoma (UTUC) is characterized by a distinctly aggressive clinical phenotype. To define the biological features driving this phenotype, we performed an integrated analysis of whole-exome and RNA sequencing of UTUC. Here we report several key insights from our molecular dissection of this disease: 1) Most UTUCs are luminal-papillary; 2) UTUC has a T-cell depleted immune contexture; 3) High FGFR3 expression is enriched in UTUC and correlates with its T-cell depleted immune microenvironment; 4) Sporadic UTUC is characterized by a lower total mutational burden than urothelial carcinoma of the bladder. Our findings lay the foundation for a deeper understanding of UTUC biology and provide a rationale for the development of UTUC-specific treatment strategies.

Convolutional neural networks (CNN) have been the most popular deep learning architectures used for image classification in cancer ( Fig. 1 ). CNNs apply a series of nonlinear transformations to structured data (such as raw pixels of an image) to learn relevant features automatically, unlike conventional machine learning models that frequently require manual feature curation. On the fl ip side, it is diffi cult to tell what features are learned by the CNNs, making them what many have referred to as a "black box." One consequence is that images used for CNNs should be carefully prepro cessed to reduce the risk that the model learns from image artifacts. There are two major approaches for CNN models; one is transfer learning that uses images from a large collection of natural objects (such as in ImageNet) to train the initial layers of a model (where the model learns to identify general features such as shapes and edges) and then uses the diseasespecifi c data to fi ne-tune the training parameters in the last layers. The second variation of CNN is based on an autoencoder where the model learns background features from a subset of representative images and encodes a compressed representation of the basic features later used to initialize the CNN. In the CAMELYON16 Challenge-a crowdsourced competition to identify and classify lymph node metastasis in patients with breast cancer from whole slide images (WSI) of hematoxylin and eosin (H&E)-stained tumors-25 of the 32 submitted algorithms were CNNs, and the top 5 classifi cation models were exclusively based on transfer learning, which were GoogLeNet, ResNet, VGG-16 ( 2 ). Khosravi and colleagues trained and tested several state-of-the-art deep learning models to classify WSI from H&E-stained tumor tissues of The Cancer Genome Atlas (TCGA) cohort and reported on the

We describe Exome Cancer Test v1.0 (EXaCT-1), the first New York State-Department of Health-approved whole-exome sequencing (WES)-based test for precision cancer care. EXaCT-1 uses HaloPlex (Agilent) target enrichment followed by next-generation sequencing (Illumina) of tumour and matched constitutional control DNA. We present a detailed clinical development and validation pipeline suitable for simultaneous detection of somatic point/indel mutations and copy-number alterations (CNAs). A computational framework for data analysis, reporting and sign-out is also presented. For the validation, we tested EXaCT-1 on 57 tumours covering five distinct clinically relevant mutations. Results demonstrated elevated and uniform coverage compatible with clinical testing as well as complete concordance in variant quality metrics between formalin-fixed paraffin embedded and fresh-frozen tumours. Extensive sensitivity studies identified limits of detection threshold for point/indel mutations and CNAs. Prospective analysis of 337 cancer cases revealed mutations in clinically relevant genes in 82% of tumours, demonstrating that EXaCT-1 is an accurate and sensitive method for identifying actionable mutations, with reasonable costs and time, greatly expanding its utility for advanced cancer care.

Dengue virus (DENV) infections are vectored by mosquitoes and constitute one of the most prevalent infectious diseases in many parts of the world, affecting millions of people annually. Current treatments for DENV infections are nonspecific and largely ineffective. In this study, we describe the adaptation of a high-content cell-based assay for screening against DENV-infected cells to identify inhibitors and modulators of DENV infection. Using this high-content approach, we monitored the inhibition of test compounds on DENV protein production by means of immunofluorescence staining of DENV glycoprotein envelope, simultaneously evaluating cytotoxicity in HEK293 cells. The adapted 384-well microtiter-based assay was validated using a small panel of compounds previously reported as having inhibitory activity against DENV infections of cell cultures, including compounds with antiviral activity (ribavirin), inhibitors of cellular signaling pathways (U0126), and polysaccharides that are presumed to interfere with virus attachment (carrageenan). A screen was performed against a collection of 5,632 well-characterized bioactives, including U.S. Food and Drug Administration-approved drugs. Assay control statistics show an average Z' of 0.63, indicative of a robust assay in this cell-based format. Using a threshold of >80% DENV inhibition with <20% cellular cytotoxicity, 79 compounds were initially scored as positive hits. A follow-up screen confirmed 73 compounds with IC 50 potencies ranging from 60 nM to 9 mM and yielding a hit rate of 1.3%. Over half of the confirmed hits are known to target transporters, receptors, and protein kinases, providing potential opportunity for drug repurposing to treat DENV infections. In summary, this assay offers the opportunity to screen libraries of chemical compounds, in an effort to identify and develop novel drug candidates against DENV infections.

Due to the numerous challenges in hit identification from random RNAi screening, we have examined current practices with a discovery of a variety of methodologies employed and published in many reports; majority of them, unfortunately, do not address the minimum associated criteria for hit nomination, as this could potentially have been the cause or may well be the explanation as to the lack of confirmation and follow up studies, currently facing the RNAi field. Overall, we find that these criteria or parameters are not well defined, in most cases arbitrary in nature, and hence rendering it extremely difficult to judge the quality of and confidence in nominated hits across published studies. For this purpose, we have developed a simple method to score actives independent of assay readout; and provide, for the first time, a homogenous platform enabling cross-comparison of active gene lists resulting from different RNAi screening technologies. Here, we report on our recently developed method dedicated to RNAi data output analysis referred to as the BDA method applicable to both arrayed and pooled RNAi technologies; wherein the concerns pertaining to inconsistent hit nomination and off-target silencing in conjugation with minimal activity criteria to identify a high value target are addressed. In this report, a combined hit rate per gene, called “H score”, is introduced and defined. The H score provides a very useful tool for stringent active gene nomination, gene list comparison across multiple studies, prioritization of hits, and evaluation of the quality of the nominated gene hits.