CHAPTER 8 ONCOGENIC MARKER DETECTION FROM P53 MUTANT AMINO-ACID SUBSTITUTIONS

Transcription

1 134 CHAPTER 8 ONCOGENIC MARKER DETECTION FROM P53 MUTANT AMINO-ACID SUBSTITUTIONS The recent past has witnessed a rapid rise in the utilization of computational techniques to aid and accelerate biological experiments by providing improved focus for wet-lab investigations that would otherwise be a heavy labour and resource-intensive task (Onisko et al, 2011;Huang et al, 2011; Familil et al, 2012). This chapter details the Genetic Mutant Marker Extraction (GMME) methodology based on Naïve Bayes learning to detect the co-occurring mutations that have the capacity to reinstate P53 to normalcy. The proposed GMME methodology aimed at predicting the possible hot spot cancer, strong rescue and weak rescue mutants that have the capacity to reinstate normal P53 transcriptional activity as detailed in this chapter. 8.1 P53 AMINO-ACID SUSBTITUTIONS The training P53 mutation (amino-acid substitutions) data adopted for this research was obtained by yeast P53 functional assays (Brachmann, 2005; Danziger et al, 2006). The mutation data comprised of amino-acid substitutions from 1-site until 6-site. The data required pre-processing before execution of the proposed GMME methodology. The proposed framework for P53 amino-acid substitution detection is detailed in the next section.

2 8.2 ONCOGENIC MARKER EXTRACTION FRAMEWORK FOR CANCER PREDICTION 135 The methodology adopted in this research work to predict the P53 amino-acid substitutions is diagrammatically presented in Figure 8.1. Figure 8.1 Oncomutation marker extraction framework for cancer prediction

3 136 In order to formulate the GMME technique, three major requirements were to be met. Primarily, the raw P53 mutation data had to be pre-processed to retrieve the mutations on a site-wise basis. The data was pre-processed to manipulate the large dataset comprising of P53 cancer mutants and their cooccurring (2-site, 3-site, 4-site) mutations spanning nearly 16,700 records. Secondly, a suitable prediction technique had to be chosen to classify the P53 amino-acid substitutions as active/inactive. For this reason, a comparison on the predictive performance was drawn among the well-known and outstanding classifiers to detect the status of site-wise P53 amino-acid substitutions. Finally a Naïve Bayes-based learning approach was devised to identify and extract the hot spot cancer/strong rescue/weak rescue mutations Data Pre-Processing of P53 Amino-Acid Substitutions Initially the P53 amino-acid substitution data contained innumerable missing values. Hence, all the records that had well-defined values for each of the sites were filtered separately as 1-site, 2-site, 3-site, 4-site, 5-site and 6-site mutation data. This pre-processed data held precise information on the cancer mutants and their co-occurring mutation sites (1-site to 6-site mutations). The data comprised of 16,699 P53 mutant records each depicting amino acid substitutions at the different codon positions as depicted in Table 8.1. Table 8.1 Site-wise P53 amino-acid substitution records Site Active Records Inactive Records Total No.of Records ,496 16, Training records 16,699 The mutations sites varied from one to six. The 1-site data represented the independent P53 mutations while the 2-site to 6-site data represented the co-occurring mutations that have the capacity to turn the

4 137 previous site mutations cancerous (inactive) or non-cancerous (active). However, rescue markers could be detected only from the second, third and fourth site recombination records since 1-site data were independent mutations while the 5-site and 6-site site data contained very few records that led to reasonably low cross-validation accuracy and hence believed to be insufficient to train the classifier. The 1-site data constituted fifty-four hot spot cancer mutants of which fourteen were rescueable as revealed by the proposed GMME technique. The second, third and fourth site mutation records constituted the training dataset Learning Methodology for Cancer Prediction from P53 Amino- Acid Substitutions A comparison of five prediction techniques viz, Naïve Bayes, Nearest Neighbor, Decision Tree, Random Forest and Ababoost with Decision Stump learning was carried out on the P53 training dataset. The Naïve Bayes classifier generated optimal accuracy, MCC, AUC on the 2-site, 3-site and 4-site recombinations in comparatively less time. This led to the formulation of a general data mining framework to categorize the mutant types as hot spot cancer, strong rescue and weak rescue mutants. The Naïve Bayes Learning algorithm presented a supervised learning methodology that depicted reasonably good performance in prediction of P53 activity from amino-acid substitutions. This guided the research focus to utilize the Naïve Bayesian probabilities to formulate the GMME methodology Genetic Mutant Marker Extraction Methodology The P53 mutant marker prediction hierarchy is depicted in Figure 8.2. After executing the Naïve Bayes algorithm on the site-wise P53 amino-acid substitutions, the GMME methodology was applied as described below.

5 138 Training Data P53 Mutants (Amino-acid Substitutions) Naïve Bayes Learning Inactive Figure Active GMME Technique Hot Spot Cancer Mutants Strong Rescue Mutants Weak Rescue Mutants Figure 8.2 P53 mutant marker prediction hierarchy The GMME methodology to relate and extract the cancer/rescue mutations at each recombination site is as follows. The Conditional Probability Values (CPV) and Classification Function Values (CFV) for each amino acid substitution in the 2-site, 3-site and 4-site training data were calculated according to Eq. (8.1) and Eq. (8.2) respectively (Han and Kamber, 2006). CPV=P (X CL) = (8.1) CFV= P (CL X) =P (CL) * (8.2) P53 mutation (amino-acid substitution) and CL denotes the target class (Active/Inactive). The number of recombinant mutation 8.1) and (8.2) correspond to take up both positive

6 139 and negative values while CPV can span only positive values ranging from zero to one. The algorithmic presentation of the GMME methodology is depicted below. Input: Pre-processed training mutation data (1-site, 2-site, 3-site, 4-site) Output: Cancer/Strong Rescue/Weak Rescue mutations Algorithm: under consideration (4 in this research).. Step 3: Let i=1; // To detect cancer mutations Step 4: X=i+1; Step 5: For each data record in the subset Step 5.2: Move to next record. Step 5.3 Else store as NON-CANCER mutant and repeat steps and Step 6: i=i+1; Step 7: While (i>1 && i, j=i+1<n) //Co-occurring mutations (N=4) Step 7.1: for (k=1, m=1; k<=i; k++, m++) xm = k; //x1-site 1, x2-site 2 Step 7.2: Obtain the conditional probability values (c.p.v) value for each record in each xm in this training subset as follows: P (X CL) = Step 7.3: Obtain the classification function distribution (c.f.v) for each record in each xm. P (CL X) =P (CL) *

7 140 Step 7.4: If at site i for xm) then Step 7.4.1: Store mutation as STRONG RESCUE mutations at site i. Step 7.4.2: Verify with co-occurring mutations in the higher sites (up to site 4). Step 7.4.3: If ((c.f.v) && (c.p.v) values are high for class ( in higher sites, then Step : Store corresponding mutation sites as WEAK RESCUE. Step : Remove such mutations from STRONG RESCUE list. Step 7.5: Else If c.p.v value for mutation in site-i is low then ignore // It has no rescue mutant in this subset. Step7.6: Else If c.p.v value for site i is low For every combination in this subset Step 7.6.1: Detect mutations that have high CPV Step 7.6.2: Store as Strong Rescue mutant for mutation in site-i. Step 7.6.3: Repeat steps and Step7.7: Else If c.p.v value is HIGH for both classes then Step 7.7.1: Detect mutations that have high CPV Step 7.7.2: Store as Strong Rescue mutant for mutation in site-i. Step 7.7.3: Repeat steps and End While Step 8: End The first step in GMME was to detect the Naïve Bayes probabilities defined by the Conditional Probability Value (CPV) and Classification Function

8 141 Value (CFV) of each amino-acid substitution with respect to the target (Active/Inactive) class. The probability values indicated clearly the contribution P53 mutation. The CPV and CFV took up one of the two values: Probability status High (PSH) for CFV is depicted by bability status Low (PSL). A low value indicated least contribution to the target class and a high value indicated the most contribution to the target class. The amino acid substitutions in each site were individually investigated in the GMME technique. A co-occurring amino-acid substitution is i CFV were high (PSH) with class Active at that particular site. A co-occurring rescue mutation for a hot spot cancer mutant was added to the Strong Rescue all higher order recombinant sites (i+1<=4). A co-occurring rescue mutation for a cancer mutant was added to the Weak Rescue mutant list iff the CFV or CPV recombination site (i+1<=4). The amino-acid substitutions that tend to be tes were termed to be hot spot cancer mutants. The CPV The obtained values were compared with respect to the target class x i -acid substitution at mutants, [C] denote the set of hot spot Cancer mutants and [WR], [SR] denote the set of Weak Rescue mutants and Strong Rescue mutants respectively. Let s/class of aminothe Inactive status/class. The principle adopted in the GMME methodology to categorize the amino acid substitutions under the different mutant types can be concisely denoted by the following equations:

9 x i 142 [ R] X : ( xicpv && xicfv == PSH): Active Class (A) (8.3) [ SR] x j [ R] : 4 j i 1x j ( x && jcpv x == PSH): Active Class (A) (8.4) jcfv x j [ WR] [ R] : 4 j i 1x j ( x jcpv x == PSH) : Inactive Class (IA) (8.5) jcfv x i [ C] X : 4 i 2 x i ( x && icpv xicfv == PSH): Inactive Class (IA) (8.6) x icpv, x icfv, denote the CPV and CFV of the amino acid substitutions at site í. The probability status of CPV for both the Active and Inactive class may take up only positive values (0 to 1). The PSH for both the Active and Inactive classes for the CPV is given by values greater than zero (>0) while the PSL is given by zero (=0). Eq. (8.3) depicted the co-occurring amino-acid substitutions that mutants [R]. Eq. (8.4) revealed the set of Rescue mutants[r] that generated PSH with the Active class (A) for all the recombinant sites (j=i+1 <=4) thus constituting the set of Strong Rescue [SR] mutants. Eq. (8.5) extracted the set of Rescue mutants[r] if there existed at least one recombinant amino acid substitution at a site (j=i+1 <=4) with the Inactive class (IA) thus constituting the set of Weak Rescue [WR] mutants. Hot spot Cancer mutants [C] from Eq. (8.6) are obtained by detecting the amino acid substitutions that revealed PSH with the Inactive class for all sites (until 4). The experimental results and the validations performed are discussed in the following section.

10 RESULTS AND DISCUSSION The experimental results are discussed as follows. Section details the data pre-processing outcome while Section summarizes the evaluation results of the supervised learning approaches on the P53 amino-acid substitution data. Section highlights the results of identifying the hot spot cancer/strong rescue/weak rescue mutants from the conditional probabilities and reports the novel rescue mutants for fourteen hot-spot cancer mutants at each site Pre-Processed P53 Amino-Acid Substitution Data The site-specific P53 mutation data records for tumour marker prediction are tabulated in Table 8.2. The 2-site, 3-site and 4-site mutation records were utilized as the training data for the subsequent prediction techniques. Table 8.2 P53 mutation sites sample training samples 1-site 2-site 3-site 4-site 5- site 6- site Class a119e X x X x x inactive a161t X x X x x inactive c135y X x X x x inactive c141y X x X x x inactive c141y d228e n235k n239l y236n x active c141y i232v h233r y234f n235k n239l active c141y n235k x X x x active c141y n235k n239y X x x active c141y n235k s240t X x x active c176f x x x x x inactive c176y x x x x x inactive The rows containing 2-site, 3-site and 4-site mutations were segregated to obtain the P53 mutation training data from which the genetic

11 markers could be extracted at the respective site-wise recombinations. Table 8.3 holds the P53 amino-acid substitutions at 2-site. 144 Table 8.3 Sample P53 mutation 2-site training samples 1- site 2-site Class a119e l125p inactive c135y e285m inactive c135y e285v inactive The total number of records spanning the P53 amino-acid substitutions at all sites summed up to 16,669 records of which nearly 16, 553 records constituted the 2-site data. Table 8.4 Sample P53 mutation 3-site training samples 1- site 2-site 3-site Class a119e r283k a353v inactive c135y n235k n239y inactive c141y n235k n239y active c141y n235k s240t active c176f n235k n239y inactive The 3-site data constituted 112 records while the 4-site mutation data held 31 records, the samples of which are shown in Table 8.4 and Table 8.5 respectively. Table 8.5 Sample P53 mutation 4-site training samples 1-site 2-site 3-site 4-site Class c141y d228a n235k n239m inactive g245s i232m n239i s240k inactive g245s m237l s240w s241a inactive r249m n235k n239y s227t active r249s k139r h168r n239y active r249s v122i c124s h168r inactive Once the data was pre-processed, a comparison was drawn among the predictor methods to identify the most optimal method to detect the

12 oncomutations. The performance of the classifier models is discussed in the next section Classifier Performance on P53 Amino-Acid Substitution Data Five classification methods namely Naïve Bayes, Nearest-Neighbor, Decision Tree, Random Forest and Adaboost learning methods were utilized for comparison based on their reported optimal performance on biological datasets. The results are tabulated in Table 8.6 and Table 8.7. Table 8.6. Classifier performance on P53 amino acid substitution data Classification Algorithms ACC (%) MCC 2-site 3-site 4-site 2-site 3-site 4-site Naïve Bayes Nearest Neighbor Decision Tree Random Forest Adaboost In order to ensure the efficiency of the Naïve Bayes algorithm in P53 functionality prediction, the AUC and computation time of the algorithms were also investigated. Table 8.7. Area Under ROC Curve of classification algorithms on P53 mutation data S.No Classification AUC Computation Time Algorithms 2-site 3-site 4-site 2-site 3-site 4-site 1. Naïve Bayes Nearest Neighbor Decision Tree Random Forest Adaboost

13 146 The Naïve Bayes algorithm was found to be most suitable for the prediction of P53 mutant status based on the MCC, AUC and the computation time. The related sensitivity (True Positive Rate-TPR) and False Positive Rate (FPR) values for ROC analysis are tabulated in Table 8.8. Table 8.8. Sensitivity and specificity values for ROC curve analysis Algorithm 2-Site 3-Site 4-Site TPR FPR TPR FPR TPR FPR Naïve Bayes Nearest-Neighbor Decision Tree Random Forest Adaboost FPR is measured as 1- Specificity. Although the Random Forest and SVM classifiers also showed comparable accuracy and MCC in prediction, the execution time of the algorithms was heavy compared to Naïve Bayes learning. The predicted P53 cancer/rescue mutants are discussed in the next section Prediction of Rescue Markers from P53 Amino-Acid Substitution Data The P53 cancer mutations and their corresponding rescue mutants obtained via wet-lab assay experiments have been reported in previous studies. The 2-site suppressor mutations are tabulated in Table 8.9. Rescue markers previously reported by Brachmann were brought out by the proposed GMME technique too: C141Y - I232V - H233R - Y234F - N235K - N239L and R272L - D228Q - N235K - N239Y - S240R - C229W.

14 147 Table 8.9 P53 suppressor mutations at 2-site recombination S.No Cancer Mutant 2-Site Rescue Mutants 1. C141Y N235K 2. G245S S240Y, T231Y 3. P152L D207E,H115D,H115E,K101E,L114P,L137M,Q100A,T118Y, T123A,T123G,T123P,T123S 4 R273H C242R,S240N,S240Q 5. V272M N235K,N239Y The results were tabulated according to the sites at which the mutants were rescued. The graphical representation of the cancer-rescue mutant relationship is depicted in Figure 8.3. Figure 8.3 P53 cancer-rescue mutants at 2-site recombination The cancer mutants are depicted in green color in Figure 8.3, Figure 8.4 and Figure 8.5. The cancer mutants are depicted as diamonds while the rescue mutants are portrayed as discs. E286K is weakly rescued by N235K at the second site but later inactivated P53 at codon 239 re-combining as N239W and N239Y at the 3rd

15 148 site.t123a is a weak active mutant that turns P53 inactive due to combination with H168R/S240R in the second site. Hence, they were not included in the list of rescue mutants. Table 8.10 lists the P53 suppressor mutations at the 3-site recombination. Table 8.10 P53 suppressor mutations at 3-site recombination S.No Cancer 2-Site-3- Site Rescue Recombination Mutant 1. C141Y N235K-N239Y 2. G245C N235K-N239Y 3. G245S H115A-T123P,H115D-T123P,H115P-T123P,H115R- T123P,H115S-T123P,H115T-T123P,H233C-N239F,N235K- N239Y,N239F-S240N,N239Y-S99H,N239Y-T102S,N239Y- T230S,Y234F-S240Y 4. R158L D228E-N239R,H233L-N239F,H233Y-N239L,N235K- N239Y, N235T-N239R,S227F-N239Y 5 R249M N235K-N239Y 6. R273C D281G-E285G 7. R273H G226D-S240R,N239R-S240N 8. V157F N235K-N239Y,N239R-N235K 9. V173L N239Y-S240T,S227T-N239Y,N235K-N239Y 10. V272M N235K-N239Y 11. Y205C D207V-N239W,D228E-N239Y,N235K-N239Y 12. Y220C D228E-N239Y,N235K-N239L,N235K-N239Y,T230C- N239Y,Y234F-N239L The proposed GMME methodology extracted the hot-spot cancer, strong rescue and weak rescue mutants by relating the conditional probabilities and classification function values at the site-wise recombination beginning at the 2- site. The rescue mutants were thus identified corresponding to their active functionality at the respective site. The rescue mutants at the 3-site recombination are graphically represented in Figure 8.4 along with the cancer mutants they rescue.

16 149 Figure 8.4 P53 cancer-rescue mutants at 3-site recombination The 4-site P53 suppressor mutations are tabulated in Table It is to be noted that the major number of rescue mutations exist in the surface regions spanning the 2-site, 3-site and 4-site data. Table 8.11 P53 suppressor mutations at 4-site recombination S.No Cancer Mutant 2-Site-3-Site-4-Site Rescue Recombination 1. R249S K139R - H168R - N239Y 2 V173L T231I-N235K-S240N 3. Y205C D207V-H233N-N239W The graphical representation of the rescue mutants at the 4-site recombination is depicted in Figure 8.5. Only three rescue mutants have been detected from this site. This calls for more training samples from wet-lab investigations to identify possible rescues for other cancer mutants.

17 150 Figure 8.5 P53 cancer-rescue mutants at 4-site recombination The cancer mutants that could not be rescued according to our findings are tabulated in Table These cancer mutants were not rescued by any other mutation at any higher order site recombination. Table 8.12 Hot Spot Cancer Mutants S.No Mutant S.No Mutant S.No Mutant S.No Mutant 1. A119E 11. F113L 21. H214R 31. R248W 2 A161T 12. G117Q 22. I195T 32. R280K 3. C135Y 13. G244S 23. M237I 33. R280T 4. C176F 14. G245D 24. P151S 34. R282W 5. C176Y 15. G245V 25. P278L 35. S183T 6. C238Y 16. G266E 26. P278S 36. S227P 7. C242F 17. G266R 27. R158H 37. S240R 8. C275Y 18. H179R 28. R175H 38 S241F 9. E258K 19. H179Y 29. R248L 39. V172M 10. E285K 20. H193R 30. R248Q 40. Y234C Such mutants need further clinical investigations and stronger applications of permissible external forces to induce possible mutations for rescue. The proposed GMME methodology directly targeted the individual P53 cancer mutants that have been validated by yeast assays and have revealed

18 151 rescue markers for fourteen cancer mutants. No existing feature selection algorithm could be utilized for categorizing the mutants since all the features (1- site to 4-site mutations) need to be considered to categorize and predict the mutants as hot-spot cancer, strong rescue and weak rescue mutants. In addition, no existing classification algorithm could categorize the mutants as the only information that was available on their status was whether they were active or inactive. The proposed GMME methodology targeted this drawback and attempted to detect the Bayesian probabilities and classification function values of the mutants in the respective categories (active/inactive), then related them using the site-wise recombination, and then categorized them as hot spot cancer, weak rescue and strong rescue mutants. 8.4 SUMMARY This chapter details the proposed Genetic Mutant Marker Extraction (GMME) technique to extract and categorize the hot spot cancer, strong rescue and weak rescue mutants at the respective mutation sites from P53 amino-acid substitutions. The importance of oncoprotein activity motivated further research on utilizing data mining techniques to predict novel oncoprotein interaction patterns. This revealed the downsides of the existing approaches that were restricted in their application to supervised datasets. The next chapter details the proposed Association Rule Mining based approach to detect new oncoprotein interaction patterns from available HIV1-human protein-protein interacting pairs to predict yet unknown viral-human protein pair interactions through unsupervised learning.