Homothorax plays autonomous and nonautonomous roles in proximodistal axis formation and migration of the Drosophila renal tubules.

The Drosophila Malpighian tubules (MpTs) serve as a functional equivalent of the mammalian renal tubules. The MpTs are composed of two pairs of epithelial tubes that bud from the midgut-hindgut boundary during embryogenesis. The MpT primordia grow, elongate and migrate through the body cavity to assume their final position and shape. The stereotypic pattern of MpT migration is regulated by multiple intrinsic and extrinsic signals, many of which are still obscure. In this work, we implicate the TALE-class homeoprotein Homothorax (Hth) in MpT patterning. We show that in the absence of Hth the tubules fail to rearrange and migrate. Hth plays both autonomous and nonautonomous roles in this developmental process. Within the tubules Hth is required for convergent extension and for defining distal versus proximal cell identities. The difference between distal and proximal cell identities seems to be required for proper formation of the leading loop. Outside the tubules, wide-range mesodermal expression of Hth is required for directing anterior migration. The nonautonomous effects of Hth on MpT migration can be partially attributed to its effects on homeotic determination along the anterior posterior axis of the embryo and to its effects on stellate cell (SC) incorporation into the MpT.

A deep-learning framework to predict cancer treatment response from histopathology images through imputed transcriptomics.

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an indirect two-step approach consisting of (1) DeepPT, a deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response to targeted and immune therapies from the inferred expression values. We show that DeepPT successfully predicts transcriptomics in all 16 The Cancer Genome Atlas cohorts tested and generalizes well to two independent datasets. ENLIGHT-DeepPT successfully predicts true responders in five independent patient cohorts involving four different treatments spanning six cancer types, with an overall odds ratio of 2.28 and a 39.5% increased response rate among predicted responders versus the baseline rate. Notably, its prediction accuracy, obtained without any training on the treatment data, is comparable to that achieved by directly predicting the response from the images, which requires specific training on the treatment evaluation cohorts.

Prediction of cancer treatment response from histopathology images through imputed transcriptomics.

Advances in artificial intelligence have paved the way for leveraging hematoxylin and eosin (H&E)-stained tumor slides for precision oncology. We present ENLIGHT-DeepPT, an approach for predicting response to multiple targeted and immunotherapies from H&E-slides. In difference from existing approaches that aim to predict treatment response directly from the slides, ENLIGHT-DeepPT is an indirect two-step approach consisting of (1) DeepPT, a new deep-learning framework that predicts genome-wide tumor mRNA expression from slides, and (2) ENLIGHT, which predicts response based on the DeepPT inferred expression values. DeepPT successfully predicts transcriptomics in all 16 TCGA cohorts tested and generalizes well to two independent datasets. Our key contribution is showing that ENLIGHT-DeepPT successfully predicts true responders in five independent patients' cohorts involving four different treatments spanning six cancer types with an overall odds ratio of 2.44, increasing the baseline response rate by 43.47% among predicted responders, without the need for any treatment data for training. Furthermore, its prediction accuracy on these datasets is comparable to a supervised approach predicting the response directly from the images, which needs to be trained and tested on the same cohort. ENLIGHT-DeepPT future application could provide clinicians with rapid treatment recommendations to an array of different therapies and importantly, may contribute to advancing precision oncology in developing countries.

Clinically oriented prediction of patient response to targeted and immunotherapies from the tumor transcriptome.

BACKGROUND: Precision oncology is gradually advancing into mainstream clinical practice, demonstrating significant survival benefits. However, eligibility and response rates remain limited in many cases, calling for better predictive biomarkers. METHODS: We present ENLIGHT, a transcriptomics-based computational approach that identifies clinically relevant genetic interactions and uses them to predict a patient's response to a variety of therapies in multiple cancer types without training on previous treatment response data. We study ENLIGHT in two translationally oriented scenarios: personalized oncology (PO), aimed at prioritizing treatments for a single patient, and clinical trial design (CTD), selecting the most likely responders in a patient cohort. FINDINGS: Evaluating ENLIGHT's performance on 21 blinded clinical trial datasets in the PO setting, we show that it can effectively predict a patient's treatment response across multiple therapies and cancer types. Its prediction accuracy is better than previously published transcriptomics-based signatures and is comparable with that of supervised predictors developed for specific indications and drugs. In combination with the interferon-γ signature, ENLIGHT achieves an odds ratio larger than 4 in predicting response to immune checkpoint therapy. In the CTD scenario, ENLIGHT can potentially enhance clinical trial success for immunotherapies and other monoclonal antibodies by excluding non-responders while overall achieving more than 90% of the response rate attainable under an optimal exclusion strategy. CONCLUSIONS: ENLIGHT demonstrably enhances the ability to predict therapeutic response across multiple cancer types from the bulk tumor transcriptome. FUNDING: This research was supported in part by the Intramural Research Program, NIH and by the Israeli Innovation Authority.

Escort cell encapsulation of Drosophila germline cells is maintained by irre cell recognition module proteins.

Differentiation of germline stem cells (GSCs) in the Drosophila ovary is induced by somatic escort cells (ECs), which extend membrane protrusions encapsulating the germline cells (GCs). Germline encapsulation requires activated epidermal growth factor receptor (Egfr) signaling within the ECs, following secretion of its ligands from the GCs. We show that the conserved family of irre cell recognition module (IRM) proteins is essential for GC encapsulation by ECs, with a requirement for roughest (rst) and kin of irre (kirre) in the germline and for sticks and stones (sns) and hibris (hbs) in ECs. In the absence of IRM components in their respective cell types, EC extensions are reduced concomitantly with a decrease in Egfr signaling in these cells. Reintroducing either activated Egfr in the ECs, or overexpressing its ligand Spitz (Spi) from the germline, rescued the requirement for IRM proteins in both cell types. These experiments introduce novel essential components, the IRM proteins, into the process of inductive interactions between GCs and ECs, and imply that IRM-mediated activity is required upstream of the Egfr signaling.