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ANTICANCER THIAZOLIDINONES DESIGN: Mining of 60-Cell Lines Experimental Data

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The Developmental Therapeutics Program (DTP) of National Cancer Institute (NCI; USA) provides 60-cell line anticancer screen of supplied compounds with the goal of identifying chemical leads and biological mechanisms. The results of utilizing this screen with novel 4-thiazolidinones formed in-house database. Then, in order to discovery some encapsulated knowledge about anticancer activity mechanism and to create a rational background for further QSAR modelling, data mining was performed. Since DTP 60-cell line screening is a two-stage process, with the first evaluation of all compounds against the 60 cell lines at a single dose of 10 µM and the second evaluation of only active compounds at five doses (including 10 µM), the comparative analysis of both stages results was performed. The aim was to answer: “Has same dose results of this two stages enough statistical similarity to be treated together in future QSAR modelling?” Using Student’s t-test of residuals it was found, that null-hypothesis about normal distribution of residuals with zero mean is rejected for 41 from 60 cell lines with 5% level of significance. Thus the homogeneity between this two data samples was declined, and further only first stage results were used.
COMPARE-analysis, based on pattern recognition algorithm, showed that studied 4-thiazolidinones activity does not belong to any of well-known anticancer mechanisms. Therefore, Principal Components Analysis and neural network approaches were applied to discover and recognize possible mechanisms of biological action. Using relational sensitivity data, 66 Cohonen’s Self-Organizing Map was created and trained. The distribution of activities in the neural network gives a possibility to distinguish three classes: two different mechanisms (A and C) and mixed one (B). The similarity and difference in cell lines sensitivities for described mechanisms are pointed.
Since there are three classes of mechanisms, it is necessary to construct three data samples for three QSAR models. Each sample contains active compounds of respective class and all non-active structures. In the other hand, activities of non-active compounds have to be normally distributed with mean growth percent of cancer cells = 100% and unknown variance. Multiple evaluating of t-test with slow change of cut-off resulted in the first failure to reject null-hypothesis with minimum growth percent = 86%. Simply saying, all compounds with mean growth percent above 86% have to be treated as non-active. Such introduction of the border between active and non-active compounds let us to form rational data arrays for further QSAR investigations.

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ANTICANCER THIAZOLIDINONES DESIGN: Mining of 60-Cell Lines Experimental Data

  1. 1. ANTICANCER THIAZOLIDINONES DESIGN: Mining of 60-Cell Lines Experimental Data Oleg Devinyak, Roman Lesyk Uzhgorod National University Danylo Halytsky Lviv National Medical Univeristy
  2. 2. 2 BACKGROUND NCI-60 DTP Human Tumor Cell Line Screen non-active Conc: 10-5 M active First Stage Second Stage Conc: 10-5 M
  3. 3. 3 PROBLEMS 1. Have the same dose results of this two stages enough statistical similarity to be treated together in future QSAR modelling? 2. Where is a rational border between active and inactive compounds? 3. Is there different mechanisms of antitumor action associated with investigated compounds?
  4. 4. 4 SAME DOSE RESULTS ANALYSIS HYPOTHESIS: Same dose results are homogenous Conclusion 1. This results can be combined together to increase overall data amount. Conclusion 2. Deviation in the results for same compounds is an error of the experiment Conclusion 3. This experimental error is a minimal error for any QSAR model based on this data
  5. 5. 5 Null hypothesis: deviations in the results for same compounds is a normally distributed random sample with zero mean and unknown variance. SAME DOSE RESULTS ANALYSIS 60 cell lines results for 73 pairs of compounds Student’s t-test 41 cell lines – null hypothesis is rejected 19 cell lines – fail to reject null hypothesis
  6. 6. 6 SAME DOSE RESULTS ANALYSIS
  7. 7. 7 SAME DOSE RESULTS ANALYSIS
  8. 8. 8 RATIONAL BORDER BETWEEN ACTIVITY AND INACTIVITY
  9. 9. 9 A SEARCH FOR DIFFERENT ANTITUMOR MECHANISMS Principal component analysis Principal Components PC1 PC2 PC3 PC4 PC5 PC6 PC7 PC8 PC9 PC10 Explained variance, % 67,48 3,40 2,28 2,08 1,92 1,86 1,72 1,64 1,42 1,24 Decrease in explained variance, % 64.08 1.19 0.20 0.17 0.05 0.14 0.08 0.22 0.19 0.07
  10. 10. 10 A SEARCH FOR DIFFERENT ANTITUMOR MECHANISMS Self-organizing maps
  11. 11. 11 A SEARCH FOR DIFFERENT ANTITUMOR MECHANISMS Self-organizing maps ∑ −−= )100( ii GIIA ,
  12. 12. 12 SENSITIVITY PATTERNS Most sensitive lines NCI-H460 MDA-MB-231/ATCC М14 SK-MEL-5
  13. 13. 13 SENSITIVITY PATTERNS Most insensitive lines HOP-62 OVCAR-5 SK-OV-3 SNB-19
  14. 14. 14 SUMMARY • The homogeneity of NCI DTP results obtained from different testing stages is rejected • About 4% of testing results are extreme errors • Rational border between active and non- active compunds is introduced • Two independent and one mixed mechanisms of 4-thiazolidinones antitumor activity are identified • Some selectivity linked with different modes of action for separate cell lines is highlighted
  15. 15. 15 THANK YOU FOR ATTENTION!

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