U Penn: Streamlining Cancer Research through No-Code Workflows

The Partner

Led by Dr. Avery Posey, the Posey Laboratory at Penn Medicine focuses on the development of novel cancer therapies for humans and dogs that genetically alter cancer patients’ own T cells to improve the ability of the immune system to fight cancer. This research involves antigen discovery to identify tumor-specific targets, engineering strategies to surmount the tumor microenvironment, and altering the signaling influences of T cells to develop robust anti-tumor efficacy.

The Project

We partnered with John Keane, a member of Dr. Avery Posey's Lab and utilized g.nome^® to analyze RNA-seq data. John aimed to create custom visualizations for studying upregulated and downregulated genes, perform gene set enrichment analysis (GSEA), and conduct pathway analysis. The insights gained from this project will contribute significantly to future projects in the field of immunology.

The Problem

Prior to adopting g.nome, John and his team were encountering several challenges in completing their project:

Software Limitations

• Constraints with Other Platforms: The current software being used by Penn Medicine presented limitations that hindered John's team. They were unable to make necessary adjustments to workflow parameters and fine-tune their analyses effectively without support from the core facility.
• Lack of Workflow Flexibility: The research being done required a high degree of iteration and workflow flexibility. The current software prevented research scientists from optimizing their pipeline to address specific research questions adequately. 

Coding Barrier

• Knowledge Deficit: To obtain usable outputs, John would have had to invest a substantial amount of time in learning coding languages for manual analysis, a process that would delay research progress.
• Resource Dependence: Collaborating with bioinformatics resources was therefore necessary, introducing a dependency on others and creating project delays.

Extended Timelines

• Analysis Delays: Utilizing external resources in the iteration process could take up to two months to complete. This extended timeline had a significant impact on research progress, causing delays in obtaining insights and making informed decisions.
• Timeliness of Insights: In the rapidly evolving field of cancer research, timely access to insights was critical to stay ahead and effectively contribute to advancements in immunology.

The Solution

The g.nome platform effectively addressed these challenges by providing:

Flexible Workflow Parameters

• Customization: g.nome addressed the need for workflow flexibility by allowing John to make adjustments to workflow parameters as needed. This enabled the team to tailor their analysis workflows to specific research questions, eliminating constraints.

• Adaptability: g.nome's flexibility allowed for real-time adjustments in response to emerging research needs, ensuring that the team could optimize their analyses effectively.

No-Coding Approach

• Coding Independence: g.nome empowered John to work independently, without the need for extensive coding knowledge or dependence on other coding resources. This eliminated the coding barrier and allowed him to focus on the research itself rather than mastering programming languages or managing the work of others.

• User-Friendly Interface: g.nome's user-friendly interface and tools simplified the data analysis process, ensuring all researchers could harness the full power of the platform without extensive technical training.

Increased Efficiency

• Streamlined Workflow: g.nome streamlined the entire analysis process, from data input to interpretation of results. This efficiency significantly reduced the timeline for data processing, enabling John and his team to obtain insights in a fraction of the time previously required.

• Rapid Access to Insights: g.nome's capabilities facilitated quicker access to meaningful insights, allowing for timely decision-making and project advancement. Integration with Jupyter notebooks allowed John to create custom visualizations within the platform and modify them as necessary, accelerating research progress.

The Results

The g.nome platform effectively addressed the initial challenges faced by John and his team. It empowered John to independently process and analyze the data without the need for extensive coding knowledge or assistance from a bioinformatics core. John was able to manipulate the data to focus on genes of interest, facilitating the identification of overexpressed or underexpressed genes critical to their research.

The insights gained from g.nome will significantly inform John's future work in immunology. Pathway analysis allowed him to concentrate on genes associated with T cell proliferation and activation, leading to a better understanding of how specific cytokines enhance the antitumor function of CAR-T cells. The time required for analysis was also significantly reduced compared to manual methods, resulting in more efficient data processing.

Conclusion

Collaborating with Almaden Genomics and utilizing the g.nome platform helped accelerate the Posey Laboratory's cancer immunology research. By effectively addressing critical challenges, g.nome streamlined workflows, eliminated coding obstacles, and significantly expedited the analysis process. This not only enhanced research efficiency but also enabled deeper insights through custom visualizations in Jupyter Notebooks. This partnership underscores the transformative potential of the g.nome platform in scientific inquiry.