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Reproducibility is essential to open science, as there is limited relevance for findings that can not be reproduced by independent research groups, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, scrutinized, challenged, and built upon. However, the intrinsic complexity and continuous growth of biomedical data makes it increasingly difficult to process, analyze, and share with the community in a FAIR (findable, accessible, interoperable, and reusable) manner. To overcome these issues, we created a cloud-based platform called ORCESTRA (orcestra.ca), which provides a flexible framework for the reproducible processing of multimodal biomedical data. It enables processing of clinical, genomic and perturbation profiles of cancer samples through automated processing pipelines that are user-customizable. ORCESTRA creates integrated and fully documented data objects with persistent identifiers (DOI) and manages multiple dataset versions, which can be shared for future studies., It is no secret that a significant part of scientific research is difficult to reproduce. Here, the authors present a cloud-computing platform called ORCESTRA that facilitates reproducible processing of multimodal biomedical data using customizable pipelines and well-documented data objects.

In the past few decades, major initiatives have been launched around the world to address chemical safety testing. These efforts aim to innovate and improve the efficacy of existing methods with the long-term goal of developing new risk assessment paradigms. The transcriptomic and toxicological profiling of mammalian cells has resulted in the creation of multiple toxicogenomic datasets and corresponding tools for analysis. To enable easy access and analysis of these valuable toxicogenomic data, we have developed ToxicoDB (toxicodb.ca), a free and open cloud-based platform integrating data from large in vitro toxicogenomic studies, including gene expression profiles of primary human and rat hepatocytes treated with 231 potential toxicants. To efficiently mine these complex toxicogenomic data, ToxicoDB provides users with harmonized chemical annotations, time- and dose-dependent plots of compounds across datasets, as well as the toxicity-related pathway analysis. The data in ToxicoDB have been generated using our open-source R package, ToxicoGx (github.com/bhklab/ToxicoGx). Altogether, ToxicoDB provides a streamlined process for mining highly organized, curated, and accessible toxicogenomic data that can be ultimately applied to preclinical toxicity studies and further our understanding of adverse outcomes.

Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose–response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0, https://pharmacodb.ca/), we present (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug–response analysis such as tissue distribution of dose–response metrics and biomarker analysis; and (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug–response phenotypes of cancer models., Graphical Abstract Graphical abstractPharmacoDB 2.0 : Improving scalability and transparency of in vitro pharmacogenomics analysis.

Cancer pharmacogenomics studies provide valuable insights into disease progression and associations between genomic features and drug response. PharmacoDB integrates multiple cancer pharmacogenomics datasets profiling approved and investigational drugs across cell lines from diverse tissue types. The web-application enables users to efficiently navigate across datasets, view and compare drug dose-response data for a specific drug-cell line pair. In the new version of PharmacoDB (version 2.0,https://pharmacodb.ca/), we present: (i) new datasets such as NCI-60, the Profiling Relative Inhibition Simultaneously in Mixtures (PRISM) dataset, as well as updated data from the Genomics of Drug Sensitivity in Cancer (GDSC) and the Genentech Cell Line Screening Initiative (gCSI); (ii) implementation of FAIR data pipelines using ORCESTRA and PharmacoDI; (iii) enhancements to drug response analysis such as tissue distribution of dose-response metrics and biomarker analysis; (iv) improved connectivity to drug and cell line databases in the community. The web interface has been rewritten using a modern technology stack to ensure scalability and standardization to accommodate growing pharmacogenomics datasets. PharmacoDB 2.0 is a valuable tool for mining pharmacogenomics datasets, comparing and assessing drug response phenotypes of cancer models.HIGHLIGHTSPharmacoDB 2.0 includes new and updated large pharmacogenomic datasets. The data processing for PharmacoDB is made fully reproducible through the use of the ORCESTRA platform and automated data ingestion pipelinesThe new release contains enriched annotations for drugs and cell lines via connectivity to external databases, as well as new analytical methods for tissue-specific and pan-cancer biomarker discoveryThe new version of PharmacoDB incorporates a scalable and reproducible framework that can accelerate the implementation of analytical pipelines including machine learning/AI for biomarker discovery in the future

Quantifying response to drug treatment in mouse models of human cancer is important for treatment development and assignment, and yet remains a challenging task. A preferred measure to quantify this response should take into account as much of the experimental data as possible, i.e. both tumor size over time and the variation among replicates. We propose a theoretically grounded measure, KuLGaP, to compute the difference between the treatment and control arms. KuLGaP is more selective than currently existing measures, reduces the risk of false positive calls and improves translation of the lab results to clinical practice.

Reproducibility is essential to Open Science. If a finding cannot be reproduced by independent research groups its relevance is extremely limited, regardless of its validity. It is therefore crucial for scientists to describe their experiments in sufficient detail so they can be reproduced, challenged, and built upon. However, due to recent technological advances in the biological and computational sciences, experimental protocols, data analysis and interpretation have become increasingly complex. This has made reproducing research findings more challenging, with some researchers going as far as suggesting that the biomedical sciences are experiencing a "reproducibility crisis". In order to overcome these issues we developed ORCESTRA, a cloud-based platform that provides a transparent, reproducible and flexible computational framework for processing and sharing high-throughput multimodal biomedical data. The platform enables processing of genomic and pharmacological profiles of cancer samples through the use of automated processing pipelines executed by Pachyderm, a data versioning and orchestration tool. ORCESTRA creates an integrated and fully documented data object known as a PharmacoSet (PSet) for future analyses using the Bioconductor PharmacoGx package. A PSet includes cell line and drug annotations, along with molecular and pharmacological data from the largest studies and consortia. Our platform is currently being expanded to additional data types, which includes toxicogenomics, xenographic pharmacogenomic data, radiomics, and clinical genomic data. The automated pipelines can be accessed via a web interface (www.orcestra.ca). Users can view and download existing dataset or request a new one by selecting pipeline parameters. The web application provides features to improve user experience, and to accommodate different scenarios for ORCESTRA deployment. They include a personal account to save PSets, a dashboard to check the status of a requested pipeline, email notification upon the pipeline completion, handling pipeline requests while the Pachyderm cluster is offline, and “manual push” of the pipeline requests once the cluster becomes online. Funding: This project is supported by CIHR, under the frame of ERA PerMed. Citation Format: Anthony Mammoliti, Petr Smirnov, Minoru Nakano, Zhaleh Safikhani, Sisira Nair, Arvind Singh Mer, Chantal Ho, Gangesh Beri, Benjamin Haibe-Kains. ORCESTRA: A platform for orchestrating and sharing high-throughput multimodal data analyses [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PR-07.