A Cambridge-based company has dedicated its mission to pushing the borders of gene & cell therapy. It engineered a novel class of injectable therapies to selectively activate and regulate T cells within the patient’s body to improve the treatment of cancer, infectious disease, and autoimmune disease. With the encouraging results, the company made the strategic decision to scale and grow into a clinical-stage company by expanding its development work in-house. However, the process of scaling posted its own set of obstacles, as the success of the individual projects relied heavily on seamless coordination across teams and the exchange of data-driven insights. And the newly experienced scarcity of computational resources and data analytics capacity has made this coordination and exchange challenging. To establish the data analysis infrastructure required for such work, the company has utilized Omics Playground as its unified cloud solution.
Interdisciplinary collaboration faced difficulties when trying to bridge the gap between biology and computational science, resulting in time-consuming analytical reruns.
The increased need to process, analyze and interpret large data sets created an analytical bottleneck that significantly slowed the pipeline’s progress.
The complexity and variability of biological data, as well as the changing scientific objective, resulted in significant management efforts of different computational tools and algorithms.
decrease in time spent on interdisciplinary data handover and analytical reruns.
were required on average to analyze and interpret an omics data set, compared to the initial 4 weeks.
increased throughput across their screening funnel, due to a unified and flexible analytics platform.
Read about the perspective of an Executive:
“Accelerate Biotech Innovation: Impacts of Modern Transcriptomics Data Analytics Platforms”
# of employees in 2023: 50-100
Field: Gene & Cell Therapies
Headquarters: Cambridge, UK
The company has been designing novel therapeutics to modify the immune system in a targeted and specific manner to fight diseases – from cancer to autoimmune disease. To bring these promising new therapeutics to market, they chose to evolve the company from a research-focused company into a clinical-stage organization.
Part of their strategy was to internalize early development activities, such as cell line development, and ensure the quality of their work. While the research team required flexibility in their bioinformatics solutions, the early development teams needed more structure and control for their data analysis. Both teams required seamless knowledge and data exchange.
However, the sudden surge in R&D activities led to a significant increase in data processing demands, which in turn caused computational bottlenecks.
Their bioinformatics team was overwhelmed by data analytics requests, and as the teams relied heavily on personal interactions to make analytical requests and share data insights, the analytics process was constantly interrupted.
It became increasingly difficult for the leaders to key pieces of information across the company, which challenges the reproducibility of the results.
Just by chance, Chris M, Head of Bioinformatics and Data Science, found Omicy Playground and saw that it provided out-of-the-box omics analytics functionalities that could levitate the pressure from the computational team and empower their scientists to interpret the data independently while offering both structure and the flexibility needed to support the discovery and process development.
“Omics Playground made data analysis an intuitive user experience for everyone – even our biologists – while still centralizing and standardizing data analysis across teams”, he commented, “…and by having all omics data analyzed in one place, BigOmics can collate related datasets as collections of scientific questions and answers, providing deeper context and improving R&D productivity. For example, our researchers can see why a certain peptide is more effective at activating T-cells to fight a type of cancer since all the experiments associated with answering this question are packed together […]. Now our teams can work efficiently and stay in sync. This more streamlined approach to coordinating data transfer not only reduces time spent asking and waiting for status updates but allows us to find the most promising therapeutic candidates.”
BigOmics can streamline processes and eliminate time-consuming manual and repetitive interrogations while adding structure to data analytics processes and bringing standardization to their entire workflow.
With the focus on getting treatments to patients faster, Chris’ team has introduced the data analytics infrastructure that will allow them to move efficiently and effectively as an organization from early ideas to products that will improve lives.
Since RNA-Seq data is analyzed through state-of-the-art algorithms, data can be directly handed over for interpretation to biologists.
Newly acquired data sets can be compared to past results and more than 50,000 pathways and gene sets, providing the necessary context for scientific breakthroughs.
Being freed from repetitive analysis, bioinformaticians can focus their efforts on bespoke analysis.
BigOmics accelerates time-to-discovery by eliminating time-consuming manual and repetitive interrogations, allowing one to achieve milestones faster.
Across the numerous projects, and many decisions have to be made rapidly yet confidently. Having access to the records of past experiments empowers executives to make fast, data-driven decisions.
Since BigOmics has improved data reproducibility, it’s much easier for leaders to find the most promising target candidate for therapeutic approaches.
Repetitive and time-consuming data analysis iterations can be avoided since biologists are enabled to gain insights from their data directly from the platform.
Everyone can easily access the same version of data and results shared in team meetings since scientists present directly from Omics Playground.
Bioinformatics resources are conserved for more bespoke analysis, as the data and knowledge transfer is streamlined successfully.
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