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Gene Expression Profiling Predicts Clinical Outcome of Breast Cancer Motivation Observation : breast-cancer patients at same stage have different outcomes Problem : existing outcome predictors are poor - lymph nodes - histological grade

  1. Gene Expression Profiling Predicts Clinical Outcome of Breast Cancer

  2. Motivation Observation : breast-cancer patients at same stage have different outcomes Problem : existing outcome predictors are poor 
 - lymph nodes 
 - histological grade Goals : 
 - identify categories of breast cancer 
 - predict outcome based on gene expression 
 - decide therapy accordingly

  3. Treatment “Chemotherapy or hormonal therapy reduces risk of distant metastases by approximately one-third; however 70-80% of patients receiving this treatment would have survived without it.” A main contribution of this method is lower false positives.

  4. Result Overview

  5. Result Overview gets therapy needs therapy

  6. Result Overview needs therapy does not need therapy 100 100 Recommend Therapy % Recommend Therapy % 75 75 50 50 25 25 0 0 St Gallen NIH Prognosis Profile St Gallen NIH Prognosis Profile

  7. Study 1. unsupervised clustering, look for tumor categories 2. supervised learning, find prognosis reporter genes

  8. Study 1. unsupervised clustering, look for tumor categories 2. supervised learning, find prognosis reporter genes

  9. Unsupervised Hierarchical Clustering: Dendogram http://youtu.be/XJ3194AmH40?t=5m

  11. 2 categories

  12. 4 categories

  13. page 531

  14. page 531 co-regulates with ER- α

  15. page 531

  16. page 531 co-regulates with lymphocytic infiltrate

  17. page 531

  18. Study 1. unsupervised clustering, look for tumor categories 2. supervised learning, find prognosis reporter genes

  19. Study 1. unsupervised clustering, look for tumor categories 2. supervised learning, find prognosis reporter genes

  20. Method Start with 25K genes (some double counting) From these, identify 5K that are significantly regulated From these, identify 231 significantly associated with disease outcome From these, identify 70 as classification features

  21. Supervised Learning on Prognosis Signatures

  22. page 532 Training Data (78 tumors)

  23. page 532 Test Data (19 tumors)

  24. Supervised Learning on ER and BRCA1 Signatures

  25. page 533

  26. page 533

  27. Summary New ideas (for 826): 
 - new label: clinical outcome 
 - use of unsupervised learning 
 - accuracy vs. sensitivity tradeoffs

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