Deep learning-based cancer patient stratification
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presenting methods for stratify cancer patients using deep learning and multi-omics (transcriptome, genome, epigenome, etc.)
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Deep learning-based cancer patient stratification
- 1. Deep learning-based cancer patient stratification is here Altuna Akalin @AltunaAkalin http://bioinformatics.mdc-berlin.de
- 2. Cancer-related burden 9.5 Million cancer-related deaths per year $80 Billion direct medical costs per year just in the US https://www.cancer.gov/about-cancer/understanding/statistics https://www.cancer.org/cancer/cancer-basics/economic-impact-of-cancer.html $2.7 Billion Cost of developing a single cancer drug
- 3. Why is this so? Pre-clinical Research Clinical Trials Approval Prescription Pathology/ Medical History Imaging Tumor Location Ethnicity Age Gender ? Missing info on Omics from biopsies Tumor
- 4. Why is this so? Pre-clinical Research Clinical Trials Approval Prescription Genome Transcriptome Complex patterns control clinical variables: i.e subtype, drug response Genome Transcriptome Narrow focus
- 5. Failing Trials: undiscovered mechanisms of action and targets Sorafenib BRAF Melanoma VEGFR2
- 6. https://github.com/BIMSBbioinfo/maui Ronen et al., 2019, Life Science Alliance multi-omics patters for clinical variable modelling (latent factors)
- 7. Latent factors: intuitive explanation Hair color pose smile Black Brown Blonde noHair Front side Turn 100 200 300 400 Head tilt Eye color Latent factors -> pattern of pixels -> facial features
- 8. Consensus Molecular Subtypes (CMS) for colorectal cancers Guinney et. al., Nat Med. 2015. • CMS are mostly driven by gene-expression • 19 % can not be assigned to a subtype
- 9. Can we predict CMS status from latent factors ?
- 10. We can refine subtypes based on latent factors 2 1 0 1 2 3 4 5 6 reduced dimension 1 (UMAP) 0 1 2 3 4 5 6 reduced dimension 2 (UMAP) maui maui 0 1 2 3 4 5 2 1 0 1 2 3 4 5 6 reduced dimension 1 (UMAP) 0 1 2 3 4 5 6 reduced dimension 2 (UMAP) CMS label CMS CMS1 CMS2 CMS3 CMS4 0 20 40 60 timeline (months) 0.0 0.2 0.5 0.8 1.0 overall survival log-rank p=0.000
- 11. And it is fast: runtime
- 12. Does it work on other cancers? Cell lines PDX/organoids Human biopsies Transfer patterns between tumor models + biopsies
- 13. Does it work on other cancers? Acceptable Acceptable Acceptable Acceptable Acceptable Acceptable Acceptable Acceptable Acceptable Acceptable Strong Strong Strong Strong Strong Strong Strong Strong Strong Strong Pheochromocytoma (TCGA−PCPG) Breast (TCGA−BRCA) Brain (TCGA−LGG) Testicular (TCGA−TGCT) Thyroid (TCGA−THCA) Colon (TCGA−COAD) Cervical (TCGA−CESC) Esophageal (TCGA−ESCA) Colorectal (TCGA−READ&COAD) Uterine (TCGA−UCEC) 0.4 0.5 0.6 0.7 Survival Prediction Accuracy standard of care arcas Latent factors Clinical variables
- 14. More coherent subtypes on other cancers as well
- 15. Beyond subtypes: Benefit of multi-omics for modeling drug response 0.0 0.1 0.2 0.3 0.4 BRAF mutation Latent factors Prediction performance Trametinib Response Prediction BRAF_V600E DNMT3A EPHA2 EZH2 MFNG SUZ12 HCFC1 VIM TNPO1 KRAS_G12V 0.00 0.25 0.50 0.75 1.00 Variable Importance Variable Name TRAMETINIB
- 16. Outlook for ML/AI applications Sequencing prices Drug dev. & Treatment cost Data availability Machine learning methods $2.7 Billion Cost of developing a single cancer drug
- 17. We are looking for collaborations and partnerships • Platform for omics data with our DL/ML methods • Looking for: • Clinical variables: response, survival, resistance, etc. • Tumor models or primary tumors • Small samples (n=15-20) can work, we do data augmentation Reach out via: altuna.akalin@mdc-berlin.de
- 18. Acknowledgements Dr. Bora Uyar - Clinical variable modelling - Other cancers Dr. Vedran Franke - Drug-response modelling - Disease model integration Dr. Jonathan Ronen - Deep learning development Slides at: https://www.slideshare.net/altunaakalin