Predicting Tumor Microenvironment and Immunotherapy Response in Lung Cancer
Author Information
Author(s): Sushant Patkar, Alex Chen, Alina Basnet, Amber Bixby, Rahul Rajendran, Rachel Chernet, Susan Faso, Prashanth Ashok Kumar, Devashish Desai, Ola El-Zammar, Christopher Curtiss, Saverio J. Carello, Michel R. Nasr, Peter Choyke, Stephanie Harmon, Baris Turkbey, Tamara Jamaspishvili
Primary Institution: National Cancer Institute, National Institutes of Health, Bethesda, MD, USA
Hypothesis
Can a deep learning approach accurately predict tumor microenvironment composition and immunotherapy response from histopathology images in non-small cell lung cancer?
Conclusion
The HistoTME model effectively predicts tumor microenvironment signatures and improves prognostication for lung cancer patients receiving immunotherapy.
Supporting Evidence
- HistoTME achieved an average Pearson correlation of 0.5 with ground truth on independent tumor cohorts.
- HistoTME-predicted microenvironment signatures improved prognostication of lung cancer patients receiving immunotherapy.
- The model was validated on cohorts of 865 and 333 NSCLC patients.
- HistoTME identified more patients responding to first-line immune checkpoint inhibitor therapy.
- Statistical analysis showed significant differences in overall survival based on TME status.
- Interactions among TME signatures were found to improve prediction of immunotherapy response.
- The model's predictions were correlated with actual cellular abundances from immunohistochemistry data.
- HistoTME provides a cost-effective method for assessing tumor microenvironment from routine pathology slides.
Takeaway
Researchers created a computer program that looks at images of lung cancer to help doctors understand how the tumor environment affects treatment success.
Methodology
The study used a weakly supervised deep learning model trained on histopathology images and transcriptomics data to predict tumor microenvironment signatures.
Potential Biases
Potential biases may arise from the retrospective nature of the study and lack of external validation datasets.
Limitations
The model's predictions are based on bulk transcriptomics data, which may not capture spatial distribution of cell types.
Participant Demographics
The study involved 652 patients with non-small cell lung cancer, including those treated with immunotherapy.
Statistical Information
P-Value
0.0012
Confidence Interval
[95% CI: 0.61-0.88]
Statistical Significance
p<0.05
Digital Object Identifier (DOI)
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