Classifiers currently being developed in
multiple myeloma, including classifiers
predicting response
Martin Bøgsted
Depart...
Outline
• Vocabulary and purpose of the presentation
• Genomics based chemosensitivity – research strategy
• Selection of ...
Vocabulary
Oldenhuis et al., European Journal of Cancer, 2008
• Prognostic biomarker: ”… provides information about the
pa...
Genomics based chemosensitivity
reseach strategy
Establish relevant
cell lines
Drug
Retrospective
patient data
Measure tox...
Selection of predictive genes
Possible approaches
Toxicity:
T = (T1,…,T60)
Expressions:
GE1 = (GE1,1,…,GE1,60)
.
.
.
GE100...
Prediction of patient chemosensitivity
Possible approaches
Retrospective
patient data
Select predictive genes
(by biostati...
Example 1: Principal component analysis and logistic regression
Potti et al. , Nature Medicine, 2006
• Model based on NCI6...
Example 2: Pearson correlation, statistical learning and
majority vote, (Knudsen, MPI*)
• Model based on
NCI60 cell lines....
Preliminary results – Multiple Myeloma patients
Knudsen, MPI and Data from Sonneveld and van Duin
Discussion
1. How do we initiate a pilot study study based on existing litterature,
data from our labs, our bioinformatica...
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Dias nummer 1

  1. 1. Classifiers currently being developed in multiple myeloma, including classifiers predicting response Martin Bøgsted Department of Haematology
  2. 2. Outline • Vocabulary and purpose of the presentation • Genomics based chemosensitivity – research strategy • Selection of predictive genes • Prediction of chemosensitivity • Example 1 • Example 2 • Preliminary results – Multiple Myeloma patients • Discussion
  3. 3. Vocabulary Oldenhuis et al., European Journal of Cancer, 2008 • Prognostic biomarker: ”… provides information about the patients overall cancer outcome, regardless of therapy …” • Predictive biomarker: ”…gives information on the effect of a therapeutic intervention in a patient …” Purpose of the presentation • The purpose of the talk is to show examples of predictive genes expressed in cancer/haematological malignancies and to facilitate a discussion on how these ideas can be pursued in Multiple Myeloma.
  4. 4. Genomics based chemosensitivity reseach strategy Establish relevant cell lines Drug Retrospective patient data Measure toxicity (e.g. GI50, TGI, LC50) Gene Expression profile (DNA microarray or Q-RT-PCR) Select predictive genes (by biostatistics algorithms) Predict patients chemosensitivity (by biostatistics algorithms) Gene Expression profile (DNA microarray or Q-RT-PCR) In vitro system In vivo system Possible training set
  5. 5. Selection of predictive genes Possible approaches Toxicity: T = (T1,…,T60) Expressions: GE1 = (GE1,1,…,GE1,60) . . . GE100 = (GE100,1,…,GE100,60) B. Correlation analysis 1. If we for gene j has a significant Pearson correlation coefficient between the toxicity vector T and gene vector j choose gene j A. Sensitivity analysis 1. Choose highly resistent cell lines 2. Choose highly sensitive cell lines 3. If gene j is differentially expressed between highly resistant cell lines and highly sensitive cell lines choose gene j Establish relevant cell lines Drug Measure toxicity (e.g. GI50, TGI, LC50) Gene Expression Profile (cDNA microarray or Q-RT-PCR) Select predictive genes (by biostatistics algorithms) In vitro system
  6. 6. Prediction of patient chemosensitivity Possible approaches Retrospective patient data Select predictive genes (by biostatistics algorithms) Predict patients chemosensitivity (by biostatistics algorithms) Gene Expression profile (cDNA microarray or Q-RT-PCR) In vivo system Possible training set A. Logistic regression 1. log odds ratio for remission is linear dependent on the significant PCs of GE. 2. PCA on a patient GEP is performed and used in the predictive model B. Absolute quantification 1. Expression of the chosen genes are summed for each patient. 2. The quantile of the is found relative to a reference population C. Classification algorithms For a training set, the n-dim. space of GEPs is divided into a response vs. non-response region by classfication algorithms. Decision is based on majority vote. Validated by cross validation. Expression of chosen genes from cell lines: GE=(G1,…,Gn)
  7. 7. Example 1: Principal component analysis and logistic regression Potti et al. , Nature Medicine, 2006 • Model based on NCI60 cell lines. • Differentially expressed genes between highly sensitive and resistant cell lines • A predictive model based on cell line response described by logistic regression on the principal components of selected genes. Breast cancer example See also comments by Coombes et al., Nature Medicine, 2007
  8. 8. Example 2: Pearson correlation, statistical learning and majority vote, (Knudsen, MPI*) • Model based on NCI60 cell lines. • Genes selected by correlation analysis • Patients response predicted by • Classification algorithms *Medical Prognosis Institute, DK
  9. 9. Preliminary results – Multiple Myeloma patients Knudsen, MPI and Data from Sonneveld and van Duin
  10. 10. Discussion 1. How do we initiate a pilot study study based on existing litterature, data from our labs, our bioinformatical and –statistical expertise? 2. What data do we have, are they comparable, are they in a suitable quality (i.e. Affymetrix 133a) and format, do we have sufficient medical records for the patients, what else do we need? 3. Store data in a format, so that various approaches for predicting responses can be performed e.g. a common research data bases, based on Good Bioinformatical Practice (GoBib). 4. If the pilot study is positive, how do we organize a prospective trial and later a random intervention trial? 5. Organisation? Do we want to pursue similar ideas for Multiple Myeloma?
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