An image-based screen for secreted proteins involved in breast cancer G0 cell cycle arrest.

Secreted proteins regulate the balance between cellular proliferation and G0 arrest and therefore play important roles in tumour dormancy. Tumour dormancy presents a significant clinical challenge for breast cancer patients, where non-proliferating, G0-arrested cancer cells remain at metastatic sites, below the level of clinical detection, some of which can re-enter proliferation and drive tumour relapse. Knowing which secreted proteins can regulate entry into and exit from G0 allows us to manipulate their signalling to prevent tumour relapse. To identify novel secreted proteins that can promote breast cancer G0 arrest, we performed a secretome-wide, image-based screen for proteins that increase the fraction of cells in G0 arrest. From a secretome library of 1282 purified proteins, we identified 29 candidates that promote G0 arrest in non-transformed and transformed breast epithelial cells. The assay we have developed can be adapted for use in other perturbation screens in other cell types. All datasets have been made available for re-analysis and our candidate proteins are presented for alternative bioinformatic refinement or further experimental follow up.

A data science roadmap for open science organizations engaged in early-stage drug discovery.

The Structural Genomics Consortium is an international open science research organization with a focus on accelerating early-stage drug discovery, namely hit discovery and optimization. We, as many others, believe that artificial intelligence (AI) is poised to be a main accelerator in the field. The question is then how to best benefit from recent advances in AI and how to generate, format and disseminate data to enable future breakthroughs in AI-guided drug discovery. We present here the recommendations of a working group composed of experts from both the public and private sectors. Robust data management requires precise ontologies and standardized vocabulary while a centralized database architecture across laboratories facilitates data integration into high-value datasets. Lab automation and opening electronic lab notebooks to data mining push the boundaries of data sharing and data modeling. Important considerations for building robust machine-learning models include transparent and reproducible data processing, choosing the most relevant data representation, defining the right training and test sets, and estimating prediction uncertainty. Beyond data-sharing, cloud-based computing can be harnessed to build and disseminate machine-learning models. Important vectors of acceleration for hit and chemical probe discovery will be (1) the real-time integration of experimental data generation and modeling workflows within design-make-test-analyze (DMTA) cycles openly, and at scale and (2) the adoption of a mindset where data scientists and experimentalists work as a unified team, and where data science is incorporated into the experimental design.