Introduction
PERsonalized Single-Cell Expression-Based Planning for Treatments In ONcology, or PERCEPTION, is an artificial intelligence (AI)–based tool developed by researchers at the National Institute of Health (NIH) that employs single-cell RNA sequencing technology (scRNA-seq) to tackle the challenge of predicting patient responses to cancer treatment. This innovative AI approach has demonstrated potential to offer a more precise and personalized method for forecasting patient outcomes and identifying drug resistance.
How the PERCEPTION AI model works
PERCEPTION utilizes single-cell transcriptomics to identify the diverse genetic profiles within a tumor, offering insights that traditional bulk RNA sequencing cannot, such as response to monotherapy and combination treatments.
The PERCEPTION pipeline has three main steps:
- Building Drug-Response Models: Initial models are created using bulk expression data from large-scale drug screens.
- Tuning with Single-Cell Expression Data: This step utilizes Elastic net linear models, which are refined with single-cell expression data to enhance their predictive accuracy. The “elastic net” component refers to a specific method used to train the models, which helps to control complexity and potentially improve accuracy. .
- Predicting Clinical Response: The refined models are then used to predict treatment responses based on single-cell transcriptomics from patient tumors.
PERCEPTION’s approach identifies the most treatment resistant transcriptional clones within a tumor sample and uses these to predict overall clinical response.
Development & Clinical Validation
PERCEPTION researchers built predictive AI models using 44 FDA approved cancer therapeutics. The researchers were able to predict cellular response to individuals and combinations of drugs. PERCEPTION has been tested and validated using data from clinical trials for multiple myeloma and breast cancer, where it accurately predicted patient responses to targeted therapies. Additionally, it has been able to capture the development of resistance in non-small cell lung cancer (NSLSC) patients treated with tyrosine kinase inhibitors.
Compared to existing bulk response models, PERCEPTION consistently outperforms in predicting treatment outcomes. Its ability to incorporate tumor heterogeneity through single-cell data makes it a powerful tool for precision oncology and medicine. By identifying the most resistant clones, PERCEPTION provides oncologists with a clearer understanding of how a tumor is likely to respond to treatment, thereby enabling more precise and effective therapy choices.
The Future of Oncology Powered with AI and Single-Cell Analysis
The integration of PERCEPTION into clinical practice has the potential to transform cancer treatment. This tool provides a more detailed and accurate assessment of tumor behavior, significantly enhancing the personalization of treatment strategies. As a result, patient outcomes could improve dramatically, with therapies tailored to the specific genetic makeup of each tumor.
One main challenge is that single-cell data has not yet been widely adopted in clinical settings, limiting the full potential of such advanced tools. However, as the availability of single-cell data increases, the accuracy of PERCEPTION will continue to improve. This tool offers a data-driven and highly accurate method for predicting treatment responses, propelling the advancement of personalized medicine. By moving away from reliance on bulk tumor data, which often fails to capture the full complexity of cancer, PERCEPTION lays a more robust foundation for developing individualized treatment plans.
Conclusion
The PERCEPTION study demonstrates proof-of-concept for use of AI models with single-cell transcriptomics to predict patient responses and resistance to cancer treatments with high accuracy. This study shows the potential for personalized treatment plans where patients are matched with drugs proven to be effective against their cancer. The team behind PERCEPTION hope that this study encourages the adoption of single-cell profiling in clinical settings.
For more details, the full study on PERCEPTION can be accessed here.
Outsourcing Bioinformatics Analysis: How Bridge Informatics (BI) Can Help
We are passionate about empowering life science companies with cutting-edge technologies. BI’s data scientists prioritize studying, understanding, and reporting on the latest developments so we can advise our clients confidently. Our bioinformaticians are trained bench biologists, so they understand the biological questions driving your computational analysis.
From pipeline development and software engineering to deploying your existing bioinformatic tools, BI can help you on every step of your research journey. As experts across data types from leading sequencing platforms, we can help you tackle the challenging computational tasks of storing, analyzing and interpreting genomic and transcriptomic data. Click here to schedule a free introductory call with a member of our team.
Tyler Kolisnik, PhD, Data Scientist, Bridge Informatics
In his role as Data Scientist, Tyler helps clients transform complex data into actionable insights. A specialist in bioinformatics, his expertise includes high-throughput sequencing, data analytics, pipeline development, SQL databasing, and R and Python programming.
Tyler previously worked as a Bioinformatician at Imagia-Canexia Health, Rancho Biosciences, and GenomeDx Biosciences. He completed his PhD at Massey University in Auckland, New Zealand in collaboration with the Genome Sciences Centre in Vancouver. His research focused on the development of machine learning models and tools for improving cancer prognosis and treatment. If you’re interested in reaching out, please email [email protected] or [email protected]
