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Towards Personalized Genomics-Guided Cancer
ImmunotherapyIon Mandoiu
Department of Computer Science & Engineering
Joint work with
Sahar Al Seesi (CSE)
Jorge Duitama (CIAT)
Fei Duan, Tatiana Blanchard, Pramod K. Srivastava (UCHC)
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2
Mandoiu LabMain Research Areas:• Bioinformatics Algorithms• Development of Computational Methods for Next-Gen Sequencing Data AnalysisOngoing Projects• RNA-Seq Analysis (NSF, NIH, Life Technologies)
- Novel transcript reconstruction- Allele-specific isoform expression
• Viral quasispecies reconstruction (USDA)- IBV evolution and vaccine optimization
• Genome assembly and scaffolding, LD-based genotype calling, local ancestry inference, metabolomics, … - More info & software at http://dna.engr.uconn.edu
- Computational deconvolution of heterogeneous samples
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Genomics-Guided Cancer Immunotherapy
CTCAATTGATGAAATTGTTCTGAAACTGCAGAGATAGCTAAAGGATACCGGGTTCCGGTATCCTTTAGCTATCTCTGCCTCCTGACACCATCTGTGTGGGCTACCATG
…
AGGCAAGCTCATGGCCAAATCATGAGA
mRNA Sequencing
SYFPEITHIISETDLSLLCALRRNESL
…
Tumor Specific Epitopes
PeptideSynthesis
Immune System Stimulation
Mouse Image Source: http://www.clker.com/clipart-simple-cartoon-mouse-2.html
TumorRemission
T-CellResponse
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Bioinformatics Pipeline
Read Alignment
•Hybrid alignment strategy (HardMerge)
Data Clean
ing
•Clipping alignments & removal of PCR artifacts
Variant
Detection
•Bayesian model based on quality scores (SNVQ)
Haplotypi
ng
•Max-Cut algorithm (RefHap)
Epitope
Prediction
•PWM and ANN algorithms (NetMHC)
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Hybrid Read Alignment Approach
http://en.wikipedia.org/wiki/File:RNA-Seq-alignment.png
mRNA reads
Transcript Library
Mapping
Genome Mapping
Read Merging
Transcript mapped reads
Genome mapped reads
Mapped reads
• More efficient compared to spliced alignment onto genome
• Stringent filtering: reads with multiple alignments are discarded
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Clipping Alignments
1 3 5 7 9 11 13 15 17 19 21 23 25 27 29 31 33 35 37 39 41 43 45 47 49 51 53 55 57 59 61 63 65 67 69 710
0.5
1
1.5
2
2.5
Lane 1 Lane 2
Lane 3
Read position
Perc
enta
ge o
f rea
ds w
ith m
ism
atch
es
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Removal of PCR Artifacts
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Variant Detection and Genotyping
AACGCGGCCAGCCGGCTTCTGTCGGCCAGCAGCCAGGAATCTGGAAACAATGGCTACAGCGTGCAACGCGGCCAGCCGGCTTCTGTCGGCCAGCCGGCAG CGCGGCCAGCCGGCTTCTGTCGGCCAGCAGCCCGGA GCGGCCAGCCGGCTTCTGTCGGCCAGCCGGCAGGGA GCCAGCCGGCTTCTGTCGGCCAGCAGCCAGGAATCT GCCGGCTTCTGTCGGCCAGCAGCCAGGAATCTGGAA CTTCTGTCGGCCAGCCGGCAGGAATCTGGAAACAAT CGGCCAGCAGCCAGGAATCTGGAAACAATGGCTACA CCAGCAGCCAGGAATCTGGAAACAATGGCTACAGCG CAAGCAGCCAGGAATCTGGAAACAATGGCTACAGCG GCAGCCAGGAATCTGGAAACAATGGCTACAGCGTGC
Referencegenome
Locus i
Ri
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Variant Detection and Genotyping• Pick genotype with the largest posterior probability
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Haplotyping• Somatic cells are diploid, containing two nearly identical copies of
each autosomal chromosome– Novel mutations are present on only one chromosome copy– For epitope prediction we need to know if nearby mutations appear in
phase
Locus Mutation Alleles
1 SNV C,T
2 Deletion C,-
3 SNV A,G
4 Insertion -,GC
Locus Mutation Haplotype 1
Haplotype 2
1 SNV T C
2 Deletion C -
3 SNV A G
4 Insertion - GC
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RefHap Algorithm
• Reduce the problem to Max-Cut• Solve Max-Cut• Build haplotypes according with the cut
Locus 1 2 3 4 5
f1 * 0 1 1 0
f2 1 1 0 * 1
f3 1 * * 0 *
f4 * 0 0 * 1
3f1
1
1 -1
-1f4
f2
f3
h1 00110h2 11001
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Epitope Prediction
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
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
Profile weight matrix (PWM) model
-20 -15 -10 -5 0 5 10 15 20
NetMHC Score
SYFP
EITH
I Sc
ore
H2-Kd
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Results on Tumor DataTumor Type MethA CMS5RNA-Seq Reads (Million) 105.8 23.4
Genome Mapped 75% 54%Transcriptome Mapped 83% 59%HardMerge Mapped 50% 36%HardMerge Mapped Bases (Gb) 3.18 0.41
High-Quality Heterozygous SNVs in CCDS Exons 1,504 232 Non-synonymous 1,160 182 Missense 1,096 178 Nonsense 63 4 No-stop 1 -
NetMHC Predicted Epitopes 836 142
0 10 20 30 400
5
10
15
Tnpo3
0 10 20 30 400
5
10
15
Naive
Mea
n T
um
or
Dia
met
er
(mm
)
Days after tumor challengeA
UC
(m
m2 )
Naive
Tnpo3
0
200
400
600
800 P < 0.0001
• Tumor rejection potential of identified epitopes currently evaluated experimentally in the Srivastava lab
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Ongoing Work
• Sequencing of spontaneous tumors (TRAMP mice)
• Detecting other forms of variation: indels, gene fusions, novel transcripts
• Incorporating predictions of TAP transport efficiency and proteasomal cleavage in epitope prediction
• Integration of mass-spectrometry data
• Monitoring immune response by TCR sequencing