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Pan-Cancer Drug Response Prediction Using Integrative Principal Component Regression
Qingzhi Liu1, Gen Li1, Veerabhadran Baladandayuthapani1
1Department of Biostatistics, University of Michigan, Ann Arbor, MI, 48109, USA.
Abstract:
The pursuit of precision oncology heavily relies on large-scale genomic and pharmacological data garnered from preclinical cancer model systems such as cell lines. While cell lines are instrumental in understanding the interplay between genomic programs and drug response, it well-established that they are not fully representative of patient tumors. Development of integrative methods that can systematically assess the commonalities between patient tumors and cell-lines can help bridge this gap. To this end, we introduce the Integrative Principal Component Regression (iPCR) model which uncovers both joint and model-specific structured variations in the genomic data of cell lines and patient tumors through matrix decompositions. The extracted joint variation is then used to predict patient drug responses based on the pharmacological data from preclinical models. Moreover, the interpretability of our model allows for the identification of key driver genes and pathways associated with the treatment-specific response in patients across multiple cancers. We demonstrate that the outputs of the iPCR model can assist in inferring both model-specific and shared co-expression networks between cell lines and patients. We show that iPCR performs favorably compared to competing approaches in predicting patient drug responses, in both simulation studies and real-world applications, in addition to identifying key genomic drivers of cancer drug responses.
Insights
Precision oncology uses cell lines to study cancer, but they don't fully represent patient tumors. Our new Integrative Principal Component Regression (iPCR) model predicts patient drug responses using genomic data from cell lines and tumors.
Area of Science:
- Genomics
- Computational Biology
- Precision Oncology
Background:
- Cell lines are crucial for cancer research but have limitations in representing patient tumors.
- Bridging the gap between cell line and patient tumor data is essential for precision oncology.
- Existing methods lack systematic ways to assess commonalities between cell line and patient tumor genomic data.
Purpose of the Study:
- To develop an integrative method for assessing shared variations between cell line and patient tumor genomic data.
- To predict patient drug responses using preclinical pharmacological data and integrated genomic variations.
- To identify key genes and pathways driving cancer drug responses in patients.
Main Methods:
- Introduced the Integrative Principal Component Regression (iPCR) model.
- Utilized matrix decompositions to uncover joint and model-specific variations in genomic data.
- Applied the model to predict patient drug responses based on cell line pharmacological data.
Main Results:
- iPCR effectively uncovers shared and model-specific genomic variations.
- The model accurately predicts patient drug responses, outperforming competing methods.
- Identified key driver genes and pathways associated with treatment-specific responses across multiple cancers.
- Facilitated inference of co-expression networks between cell lines and patients.
Conclusions:
- The iPCR model provides a powerful tool for integrating cell line and patient tumor genomic data.
- This approach enhances the prediction of patient drug responses in precision oncology.
- iPCR aids in identifying novel therapeutic targets and understanding cancer biology.
