Bioinformatics pipeline for detection of immunogenic cancer ...

Bioinformatics pipeline for detection of immunogenic cancer ...

Bioinformatics Methods for Diagnosis and Treatment of Human Diseases Jorge Duitama Dissertation Defense for the Degree of Doctorate in Philosophy Computer Science & Engineering Department University of Connecticut Outline Introduction Analysis pipeline for immunotherapy Strategies for mRNA reads mapping SNV detection and genotyping Single individual haplotyping Results on detection of immunogenic cancer mutations Conclusions Future work: RCCX sequencing Introduction Research efforts during the last two decades have provided a huge amount of genomic information for almost every form of life

Much effort is focused on refining methods for diagnosis and treatment of human diseases The focus of this research is on developing computational methods and software tools for diagnosis and treatment of human diseases Immunology Background J.W. Yedell, E Reits and J Neefjes. Making sense of mass destruction: quantitating MHC class I antigen presentation. Nature Reviews Immunology, 3:952-961, 2003 Cancer Immunotherapy Tumor mRNA Sequencing Tumor Specific Epitopes Discovery Peptides Synthesis CTCAATTGATGAAATTGTTCTGAAACT GCAGAGATAGCTAAAGGATACCGGGTT CCGGTATCCTTTAGCTATCTCTGCCTC CTGACACCATCTGTGTGGGCTACCATG

SYFPEITHI ISETDLSLL CALRRNESL AGGCAAGCTCATGGCCAAATCATGAGA Immune System Training Mouse Image Source: http://www.clker.com/clipart-simple-cartoon-mouse-2.html Tumor Remission Analysis Pipeline CCDS Mapping CCDS mapped reads

Tumor mRNA reads Read Merging Genome Mapping Tumorspecific SNVs Haplotyping Primers Design Primers for Sanger Sequencing Genome mapped reads

Close SNV Haplotypes Mapped reads SNVs Detection Epitopes Prediction Tumor specific epitopes Analysis Pipeline CCDS Mapping CCDS mapped reads Tumor mRNA reads

Read Merging Genome Mapping Tumorspecific SNVs Haplotyping Primers Design Primers for Sanger Sequencing Genome mapped reads Close SNV Haplotypes Mapped reads

SNVs Detection Epitopes Prediction Tumor specific epitopes SNP Calling from Genomic DNA Reads Read sequences & quality scores Reference genome sequence @HWI-EAS299_2:2:1:1536:631 GGGATGTCAGGATTCACAATGACAGTGCTGGATGAG +HWI-EAS299_2:2:1:1536:631 ::::::::::::::::::::::::::::::222220 @HWI-EAS299_2:2:1:771:94 ATTACACCACCTTCAGCCCAGGTGGTTGGAGTACTC +HWI-EAS299_2:2:1:771:94 :::::::::::::::::::::::::::2::222220

Read Mapping SNP calling 1 1 1 1 1 1 4764558 4767621 4767623 4767633 4767643 4767656 G C T T A T

T A A A C C 2 2 2 2 4 7 1 1 1 1 2 1 >ref|NT_082868.6|Mm19_82865_37:1-3688105 Mus musculus chromosome 19 genomic contig, strain

C57BL/6J GATCATACTCCTCATGCTGGACATTCTGGTTCCTAG TATATCTGGAGAGTTAAGATGGGGAATTATGTCA ACTTTCCCTCTTCCTATGCCAGTTATGCATAATGCA CAAATATTTCCACGCTTTTTCACTACAGATAAAG AACTGGGACTTGCTTATTTACCTTTAGATGAACAGA TTCAGGCTCTGCAAGAAAATAGAATTTTCTTCAT ACAGGGAAGCCTGTGCTTTGTACTAATTTCTTCATT ACAAGATAAGAGTCAATGCATATCCTTGTATAAT Mapping mRNA Reads http://en.wikipedia.org/wiki/File:RNA-Seq-alignment.png Read Merging Genome CCDS Agree? Hard Merge Soft Merge

Unique Unique Yes Keep Keep Unique Unique No Throw Throw Unique Multiple

No Throw Keep Unique Not Mapped No Keep Keep Multiple Unique No Throw

Keep Multiple Multiple No Throw Throw Multiple Not Mapped No Throw Throw Not mapped

Unique No Keep Keep Not mapped Multiple No Throw Throw Not mapped Not Mapped Yes

Throw Throw Analysis Pipeline CCDS Mapping CCDS mapped reads Tumor mRNA reads Read Merging Genome Mapping Tumorspecific SNVs

Haplotyping Primers Design Primers for Sanger Sequencing Genome mapped reads Close SNV Haplotypes Mapped reads SNVs Detection Epitopes Prediction Tumor specific epitopes

SNV Detection and Genotyping Locus i Reference Ri AACGCGGCCAGCCGGCTTCTGTCGGCCAGCAGCCAGGAATCTGGAAACAATGGCTACAGCGTGC AACGCGGCCAGCCGGCTTCTGTCGGCCAGCCGGCAG CGCGGCCAGCCGGCTTCTGTCGGCCAGCAGCCCGGA GCGGCCAGCCGGCTTCTGTCGGCCAGCCGGCAGGGA GCCAGCCGGCTTCTGTCGGCCAGCAGCCAGGAATCT GCCGGCTTCTGTCGGCCAGCAGCCAGGAATCTGGAA CTTCTGTCGGCCAGCCGGCAGGAATCTGGAAACAAT CGGCCAGCAGCCAGGAATCTGGAAACAATGGCTACA CCAGCAGCCAGGAATCTGGAAACAATGGCTACAGCG CAAGCAGCCAGGAATCTGGAAACAATGGCTACAGCG GCAGCCAGGAATCTGGAAACAATGGCTACAGCGTGC r(i) : Base call of read r at locus i r(i) : Probability of error reading base call r(i) Gi : Genotype at locus i SNV Detection and Genotyping Use Bayes rule to calculate posterior

probabilities and pick the genotype with the largest one SNV Detection and Genotyping Calculate conditional probabilities by multiplying contributions of individual reads Accuracy Assessment of Variants Detection 113 million Illumina mRNA reads generated from blood cell tissue of Hapmap individual NA12878 (NCBI SRA database accession numbers SRX000565 and SRX000566) We tested genotype calling using as gold standard 3.4 million SNPs with known genotypes for NA12878 available in the database of the Hapmap project True positive: called variant for which Hapmap genotype coincides False positive: called variant for which Hapmap genotype does not coincide Comparison of Mapping Strategies 4500 4000

True Positives 3500 Transcripts 3000 Genome SoftMerge 2500 HardMerge 2000 1500 0 20 40

60 False Positives 80 100 120 Comparison of Variant Calling Strategies 25000 True Positives 20000 15000 SNVQ SOAPsnp 10000 Maq

5000 0 0 200 400 600 800 1000 1200 1400 1600 1800 2000 False Positives Data Filtering 45% 40% Transcripts 35% Genome 30% Hard Merge SoftMerge

25% 20% 15% 10% 5% 0% 1 3 5 7 9 11 13 15 17

19 21 23 25 27 29 31 33 Data Filtering Allow just x reads per start locus to eliminate PCR amplification artifacts Chepelev et. al. algorithm: For each locus groups starting reads with 0, 1 and 2 mismatches Choose at random one read of each group

Comparison of Data Filtering Strategies 18500 16500 True Positives 14500 12500 None 10500 Alignment Trimming 8500 Three Reads Per Start Locus 6500 One Read Per Start Locus 4500

2500 0 50 100 150 200 False Positives 250 300 350 400 Accuracy per RPKM bins 100.00%

90.00% 80.00% 70.00% 60.00% 50.00% 40.00% 30.00% 20.00% 10.00% 0.00% 1 Homozygous Missing 5 10 Heterozygous Missing 50 100

False Positives >100 True Positives Analysis Pipeline CCDS Mapping CCDS mapped reads Tumor mRNA reads Read Merging Genome Mapping Tumorspecific SNVs

Haplotyping Primers Design Primers for Sanger Sequencing Genome mapped reads Close SNV Haplotypes Mapped reads SNVs Detection Epitopes Prediction Tumor specific epitopes

ReFHap: A Reliable and Fast Algorithm for Single Individual Haplotyping Jorge Duitama1,2, Thomas Huebsch2, Gayle McEwen2, Eun-Kyung Suk2, Margret R. Hoehe2 1. Department of Computer Science and Engineering University of Connecticut, Storrs, CT, USA 2. Max Planck Institute for Molecular Genetics, Berlin, Germany Haplotyping Human somatic cells are diploid, containing two sets of nearly identical chromosomes, one set derived from each parent. ACGTTACATTGCCACTCAATC--TGGA ACGTCACATTG-CACTCGATCGCTGGA Heterozygous variants Haplotyping Locu s 1 2 3 4

Event Alleles Hap 1 Alleles Hap 2 SNV Deletion SNV Insertio n T C,T C C,A A,G --,GC C

G GC The process of grouping alleles that are present together on the same chromosome copy of an individual is called haplotyping Haplotyping enables improved predictions of changes in protein structure and increase power for genome-wide association studies Current Approaches Source Information Populaton genotypes or haplotypes Parental genotypes Evidence of coocurrance of alleles Approach Statistical Phasing Trio Phasing Single Individual Haplotyping New experimental approaches are now able to deliver input data for whole genome Single Individual Haplotyping We propose a new formulation and an algorithm for this problem

Problem Formulation Alleles for each locus are encoded with 0 and 1 Fragment: Segment showing coocurrance of two or more alleles in the same chromosome copy Locus 1 2 3 4 5 6 7 8

9 ... f - 0 1 1 - 1 - 0 0

... Problem Formulation Input: Matrix M of m fragments covering n loci Locus 1 2 3 4 5 ... n f1 1 1

0 - 1 - f2 - 0 1 0 0 1 f3

- 0 0 0 1 - - - - - 1 0

... fm Problem Formulation Input: Matrix M of m fragments covering n loci Locus 1 2 3 4 5 ... n f1 1 1

0 - 1 - f2 - 0 1 0 0 1 f3

- 0 0 0 1 - - - - - 1 0

... fm Problem Formulation Input: Matrix M of m fragments covering n loci Locus 1 2 3 4 5 ... n f1 1

1 0 - 1 - f2 - 0 1 0 0 1

f3 - 0 0 0 1 - - - - - 1

0 ... fm Problem Formulation Input: Matrix M of m fragments covering n loci Locus 1 2 3 4 5 ... n f1 1

1 0 - 1 - f2 - 0 1 0 0 1

f3 - 0 0 0 1 - - - - - 1

0 ... fm Problem Formulation For two alleles a1, a2 For two rows i1, i2 of M f1 - 0 1 1 0 f2 1 1 1 -

Score 0 1 -1 0 1 1 s(M,1,2) = 1 Problem Formulation For a cut I of rows of M Complexity MFC is NP-Complete 2 4 1 3 0

1 - 0 1 - 0 1 Algorithm Reduce the problem to Max-Cut. Solve Max-Cut Build haplotypes according with the cut Locus 1 2 3 4 5 f1 - f2 1 1 0 - f3

1 - f4 - 1 0 1 1 0 - 1 0 - 0 0 - 1 h1 00110 h2 11001 -1

3 1 1 4 3 2 -1 Heuristic for Max-Cut 1. 2. 3. 4. Build G=(V,E,w) from M Sort E from largest to smallest weight Init I with a random subset of V For each e in the first k edges

a) I GreedyInit(G,e) b) I GreedyImprovement(G,I) c) If s(M, I) < s(M, I) then I I Total complexity: O(k(m2k1k2 + mk12k22)) Greedy Init 1 2 4 1 2 3 5 4 3

5 Complexity: O(m2k1k2) Local Optimization Classical greedy algorithm 1 4 1 4 3 2 2 3 Complexity: O(mk1k2)

Local Optimization Edge flipping 1 2 2 1 3 4 3 4 Complexity: O(mk12k22) Simulations Setup We generated random instances varying: Number of loci n Number of fragments f

Mean fragment length l Error rate e Gap rate g For each experiment we fixed all parameters and generated 100 random instances 2 0,4 1,5 0,2 0 -0,2 -0,4 -0,6 -0,8 1 0,5 0 -0,5

-1 -1,5 -2 -1 6 7 8 Coverage Time Difference (Seconds) 0,6 Switch Error Difference MEC Difference

ReFHap vs HapCUT 9 10 6 7 8 9 Coverage Number of loci: 200 Mean fragment length: 6 Error rate: 0.05 Gap rate: 0.1 Number of Fragments between 222 and 370 10

20 18 16 14 12 10 8 6 4 2 0 6 7 8 Coverage 9 10 ReFHap vs HapCUT

Analysis Pipeline CCDS Mapping CCDS mapped reads Tumor mRNA reads Read Merging Genome Mapping Tumorspecific SNVs Haplotyping Primers Design

Primers for Sanger Sequencing Genome mapped reads Close SNV Haplotypes Mapped reads SNVs Detection Epitopes Prediction Tumor specific epitopes Epitopes Prediction Predictions include MHC binding, TAP transport efficiency, and proteasomal cleavage

C. Lundegaard et al. MHC Class I Epitope Binding Prediction Trained on Small Data Sets. In Lecture Notes in Computer Science, 3239:217-225, 2004 NetMHC vs. SYFPEITHI H2-Kd 30 25 SYF PEIT HI Scor e 20 15 W ea k Bi St n

ro d ng er sBi n d er s 10 5 0 -20 -15 -10 -5 0

NetMHC Score 5 10 15 20 Results on Tumor Reads Validation Results Mutations reported by [Noguchi et al 94] were found by this pipeline Confirmed with Sanger sequencing 18 out of 20 mutations for MethA and 26 out of 28 mutations for CMS5 NetMHC Scores Distribution of Mutated Peptides 9000 8000 7000

6000 5000 4000 3000 2000 1000 0 6 7 8 9 10 11 12 13

14 15 16 17 18 19 Distribution of NetMHC Score Differences Between Mutated and Reference Peptides 8000 7000 6000 5000 4000

3000 2000 1000 0 -8 -6 -4 -2 0 2 4 6

8 10 12 14 16 18 20 22 Conclusions We presented a bioinformatics pipeline for detection of immunogenic cancer mutations from high throughput mRNA sequencing data We contributed new techniques and strategies for: Mapping of mRNA reads SNV detection and genotyping Single individual Haplotyping

We discovered hundreds of candidate epitopes for two cancer cell lines and four spontaneous tumors Current Status PrimerHunter paper published in NAR journal Jorge Duitama, Dipu M. Kumar, Edward Hemphill, Mazhar Khan, Ion I. Mandoiu and Craig E. Nelson. PrimerHunter: a primer design tool for PCR-based virus subtype identification. Nucleic Acids Research, 37(8):2483-2492,2009 ReFHap paper published in ACM BCB proceedings Jorge Duitama, Thomas Huebsch, Gayle McEwen, Eun-Kyung Suk, and Margret R. Hoehe. ReFHap: A reliable and fast algorithm for single individual haplotyping. In Proceedings of the First ACM international Conference on Bioinformatics and Computational Biology (Niagara Falls, New York, August 02 - 04, 2010). BCB '10. ACM, New York, NY, 160-169, 2010 GeneSeq paper to appear in BMC Bioinformatics Jorge Duitama, Justin Kennedy, Sanjiv Dinakar, Yozen Hernandez, Yufeng Wu and Ion I. Mandoiu. Linkage Disequilibrium Based Genotype Calling from Low-Coverage Shotgun Sequencing Reads. BMC

Bioinformatics (to appear), 2011 Papers to be submitted SNV detection on mRNA reads to NAR Whole genome haplotyping from fosmid pools to Nature Major Histocompatibility Complex (MHC) J. A. Traherne. Human MHC architecture and evolution: implications for disease association studies. International Journal of Immunogenetics, 35:179-192, 2008 Fosmid Based Sequencing Fosmid Detection Algorithm 1. Assign each read to a single 1kb long bin. Select bins with more than 5 reads 2. Perform allele calls for each heterozygous SNP. Mark bins with heterozygous calls 3. Cluster adjacent bins as belonging to the same fosmid if: i. The gap distance between them is less than 10kb and ii. There are no bins with heterozygous SNPs between them 4. Keep fosmids with lengths between 3kb and 60kb MHC Phasing: Preliminary Results

Number of blocks: 8 N50 block length: 793 kb Maximum block length: 1.6 MB Total extent of all blocks: 3.8 MB Fraction of MHC phased into haplotype blocks: 95% Number of heterozygous SNPs: 8030 SNPs Fraction of SNPs phased: 86% RCCX CNV Reconstruction J. A. Traherne. Human MHC architecture and evolution: implications for disease association studies. International Journal of Immunogenetics, 35:179-192, 2008 Acknowledgments

Ion Mandoiu, Yufeng Wu and Sanguthevar Rajasekaran Mazhar Khan, Dipu Kumar (Pathobiology & Vet. Science) Craig Nelson and Edward Hemphill (MCB) Pramod Srivastava, Brent Graveley and Duan Fei (UCHC) Margret Hoehe, Thomas Huebsch, Gayle McEwen and Eun-Kyung Suk (MPIMG) Fiona Hyland and Dumitru Brinza (Life Technologies) NSF awards IIS-0546457, IIS-0916948, and DBI-0543365 UCONN Research Foundation UCIG grant PrimerHunter: A Primer Design Tool for PCR-Based Virus Subtype Identification Jorge Duitama1, Dipu Kumar2, Edward Hemphill3, Mazhar Khan2, Ion Mandoiu1, and Craig Nelson3 Department of Computer Sciences & Engineering 2

Department of Pathobiology & Veterinary Science 3 Department of Molecular & Cell Biology 1 Avian Influenza C.W.Lee and Y.M. Saif. Avian influenza virus. Comparative Immunology, Microbiology & Infectious Diseases, 32:301-310, 2009 Polymerase Chain Reaction (PCR) http://www.obgynacademy.com/basicsciences/fetology/genetics/ Primer3 PRIMER PICKING RESULTS FOR gi|13260565|gb|AF250358 No mispriming library specified Using 1-based sequence positions OLIGO start len tm LEFT PRIMER 484 25 59.94

RIGHT PRIMER 621 25 59.95 SEQUENCE SIZE: 1410 INCLUDED REGION SIZE: 1410 gc% 56.00 52.00 any 5.00 3.00 3' seq 3.00 CCTGTTGGTGAAGCTCCCTCTCCAT 2.00 TTTCAATACAGCCACTGCCCCGTTG PRODUCT SIZE: 138, PAIR ANY COMPL: 4.00, PAIR 3' COMPL: 1.00 481 TGTCCTGTTGGTGAAGCTCCCTCTCCATACAATTCAAGGTTTGAGTCGGTTGCTTGGTCA >>>>>>>>>>>>>>>>>>>>>>>>> 541 GCAAGTGCTTGCCATGATGGCATTAGTTGGTTGACAATTGGTATTTCCGGGCCAGACAAC

<<<< 601 GGGGCAGTGGCTGTATTGAAATACAATGGTATAATAACAGACACTATCAAGAGTTGGAGA <<<<<<<<<<<<<<<<<<<<< Tools Comparison Notations s(l,i): subsequence of length l ending at position i (i.e., s(i,l) = si-l+1 si-1si) Given a 5 3 sequence p and a 3 5 sequence s, |p| = |s|, the melting temperature T(p,s) is the temperature at which 50% of the possible p-s duplexes are in hybridized state Given two 5 3 sequences p, t and a position i, T(p,t,i): Melting temperature T(p,t(|p|,i)) Notations (Cont) Given two 5 3 sequences p and s, |p| = |s|, and a 0-1 mask M, p matches s according to M if pi = si for every i {1,,|s|} for which Mi = 1 AATATAATCTCCATAT CTTTAGCCCTTCAGAT

0000000000011011 I(p,t,M): Set of positions i for which p matches t(|p|, i) according to M Discriminative Primer Selection Problem (DPSP) Given Sets TARGETS and NONTARGETS of target/non-target DNA sequences in 5 3 orientation, 0-1 mask M, temperature thresholds Tmin_target and Tmax_nontarget Find All primers p satisfying that for every t TARGETS, exists i I(p,t,M) s.t. T(p,t,i) Tmin_target for every t NONTARGETS T(p,t,i) Tmax_nontarget for every i {|p| |t|} Nearest Neighbor Model Given an alignment x: H H (x) Tm (x) = S (x) + 0.368*N/2*ln(Na+) + Rln(C) where C is c1-c2/2 if c1c2 and (c1+c2)/4 if c1=c2

H (x) and S (x) are calculated by adding contributions of each pair of neighbor base pairs in x Problem: Find the alignment x maximizing Tm (x) Fractional Programming Given a finite set S, and two functions f,g:SR, if g>0, t*= maxxS(f(x) / g(x)) can be approximated by the Dinkelbach algorithm: 1. Choose t1 t*; i 1 2. Find xi S maximizing F(x) = f(x) ti g(x) 3. If F(xi) for some tolerance output > 0, output ti 4. Else, ti+1 (f(xi) / g(xi)) and i i +1 and then go to step 2 Fractional Programming Applied to Tm Calculation Use dynamic programming to maximize: ti(S (x) + 0.368*N/2*ln(Na+) + Rln(C)) - H (x) = -G (x) G (x) is the free energy of the alignment x at temperature ti Melting Temperature Calculation Results Design

forward primers Design reverse primers Iterate over targets to build a hash table of occurances of seed patterns H according with mask M Build candidates as suitable length substrings of one or more target sequences Test each candidate p Make pairs filtering by product length, cross dymerization and Tm Test GC Content, GC Clamp, single base repeat and self complementarity For each target t use H to

build I(p,t,M) and test if T(p,t,i) Tmin_target For each non target t test on every i if T(p,t,i) < Tmax_nontarget Design Success Rate FP: Forward Primers; RP: Reverse Primers; PP: Primer Pairs Primers Validation Primers Validation Primers Design Parameters 1. 2. 3. 4. 5. 6. 7. 8. 9.

Primer length between 20 and 25 Amplicon length between 75 and 200 GC content between 25% and 75% Maximum mononucleotide repeat of 5 3-end perfect match mask M = 11 No required 3 GC clamp Primer concentration of 0.8MM Salt concentration of 50mM Tmin_target =Tmax_nontarget = 40o C NA Phylogenetic Tree Current Status Paper published in Nucleic Acids Research in March 2009 Web server, and open source code available at http://dna.engr.uconn.edu/software/PrimerHunter/ Successful primers design for 287 submissions since publication 2nd Generation Sequencing Technologies

Massively parallel, orders of magnitude higher throughput compared to classic Sanger sequencing Roche/454 FLX Titanium ~1M reads 400bp avg. 400-600Mb / run (10h) ABI SOLiD 3 plus ~500M reads/pairs 35-50bp 25-60Gb / run (3.5-14 days) Illumina Genome Analyzer IIx ~100-300M reads/pairs 35-100bp 4.5-33 Gb / run (2-10 days) Helicos HeliScope 25-55bp reads >1Gb/day Current Status Presented as a poster in ISBRA 2009 and as a

talk at Genome Informatics in CSHL Over a hundred of candidate epitopes are currently under experimental validation Results with Real Data Instance on chromosome 22 with 13,905 fragments spanning 32,347 SNPs Number of blocks: 102 ReFHap HapCUT (1 It) %MEC 6.32% 6.26% Time 73.04s 0.99H HapCUT (50 It) 6.24% 50.4H Predicted switch error rate: 1.86% Results with Real Data

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