Case Study |

Biomarker informatics


Our client has an ex vivo tumor evaluation platform that maintains the tumors cellular structure. This platform collected data on the efficacy of various drug treatment candidates with the intention of biomarker identification moving towards personalized medicine. We defined strategies and technologies and provided bioinformatics and statistical analysis on data from patient samples provided by the client. We performed an assay statistical analysis resulting in data interpretations and visualizations in order to guide their bioinformatics strategy since they did not have in house bioinformatics capabilities. One of our informatics Subject Matter Experts (SME) took lead for both the strategy and implementation aspect of this project while working alongside our client’s team. This work has contributed to a publication (Link to Publication).

The Project

Strategy and Requirement Gathering

Talked to stakeholders and evaluated appropriate technologies (Python, AWS) for bioinformatics support and analysis

Selected appropriate visualization tools required to produce publication quality visualizations

Defined and road mapped future technology platform to streamline and automate the way in which project data analysis is performed

Work with the R&D and immuno-oncology scientific teams to bridge bioinformatic or data science expertise to a growing number of projects.

Analysis and Informatics Execution

DNA SNPs mutation identified by Dicer1 analysis across multiple cell lines

Analyzed RNA transcriptomics by Nanostring and RNA Seq, interrogate high-dimensional flow and mass cytometry, integrate quantitative pathology and other next generation approaches.

Derived signatures, patterns, mechanisms and other biological phenomena that associate to therapy response across dozens of solid tumor types and in response to dozens of drug regimens and combinations.

Implemented Python data visualization scripts

Statistical analysis across multiple studies used for publication

Established initial AWS infrastructure and compute resources

Used AWS Elastic Cloud Computing in order to ramp up for future analysis


  • Arrayo was able to propose a strategy based on the specific needs of our client taking account for their in-house capabilities. The road map was utilized to show future value of implementing a data science and analytics platform to the C suite. All informatics pipeline and NGS analysis and visualization tasks were prioritized and completed. The results of these analysis and visualizations was crucial to the publication (Link to Publication), as well as supporting overall direction and strategy of the company.