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)

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

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

Bioinformatics Pipeline

Read Alignment •Hybrid alignment strategy (HardMerge)

Data Cleaning •Clipping alignments & removal of PCR artifacts

Variant Detection •Bayesian model based on quality scores (SNVQ)

Haplotyping•Max-Cut algorithm (RefHap)

Epitope Prediction •PWM and ANN algorithms (NetMHC)

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

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

Removal of PCR Artifacts

Variant Detection and Genotyping

AACGCGGCCAGCCGGCTTCTGTCGGCCAGCAGCCAGGAATCTGGAAACAATGGCTACAGCGTGCAACGCGGCCAGCCGGCTTCTGTCGGCCAGCCGGCAG CGCGGCCAGCCGGCTTCTGTCGGCCAGCAGCCCGGA GCGGCCAGCCGGCTTCTGTCGGCCAGCCGGCAGGGA GCCAGCCGGCTTCTGTCGGCCAGCAGCCAGGAATCT GCCGGCTTCTGTCGGCCAGCAGCCAGGAATCTGGAA CTTCTGTCGGCCAGCCGGCAGGAATCTGGAAACAAT CGGCCAGCAGCCAGGAATCTGGAAACAATGGCTACA CCAGCAGCCAGGAATCTGGAAACAATGGCTACAGCG CAAGCAGCCAGGAATCTGGAAACAATGGCTACAGCG GCAGCCAGGAATCTGGAAACAATGGCTACAGCGTGC

Referencegenome

Locus i

Ri

Variant Detection and Genotyping• Pick genotype with the largest posterior probability

Accuracy as Function of Coverage

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

RefHap Algorithm

• Reduce the problem to Max-Cut• Solve Max-Cut• Build haplotypes according with the cut

Locus 1 2 3 4 5f1 * 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

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

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 Tu

mor

D

iam

eter

(m

m)

Days after tumor challengeA

UC

(mm

2 )Naiv

eTnpo3

0

200

400

600

800 P < 0.0001

• Tumor rejection potential of identified epitopes currently evaluated experimentally in the Srivastava lab

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