Software for Tumor Neoantigen Prediction and Vaccine DesignTim O’DonnellMount Sinai, Hammer Lab

Nov 15, 2016Neoantigen Summit

Hammer Lab■Bringing emerging

software development and data science technologies into cancer immunotherapy

■Tools developed at github.com/hammerlab under an Apache license

Personalized genome vaccine (PGV)

NCT02721043: PHASE I, OPEN LABEL, STUDY OF PGV001: A MULTI-PEPTIDE

THERAPEUTIC VACCINE PLATFORM FOR USE IN THE TREATMENT OF SOLID TUMORS IN

THE ADJUVANT SETTING

Nina Bhardwaj

Personalized Genomic Vaccine

■Solid tumors patients (H&N, NSCLC, Breast, Ovarian, Urothelial, SCC) without evidence of metastatic disease

■Vaccine: ten 25-mer peptides containing predicted Class I MHC mutated ligands

■Adjuvant: Poly-ICLC■No checkpoint blockade (unfortunately)■Endpoint: safety and feasibility

Tumor neoepitope selection

Sequencing

■150x normal■300x tumor■Sureselect XT■~150 million mRNA reads

Pipeline

Tools developed for the trial

Available at github.com/hammerlab

varcode Variant effect prediction including indel coding sequence

isovar Determine mutant coding sequence from RNA-seqvaxrank Neoantigen vaccine selectionepidisco Workflow to generate vaccine peptide report from

FASTQsmhctools Standard interface to pMHC binding predictorspyensembl Python interface to Ensembl reference genome

annotations

Coding sequence prediction (varcode)Code Value

variant = varcode.Variant( "3", 36779850, ref="C", alt="", ensembl='grch37')

Variant( contig='3', start=36779850, ref='C', alt='', reference_name='GRCh37')

effect = variant .effects() .top_priority_effect()

FrameShift( variant=chr3 g.36779850_36779850delC, transcript_name=DCLK3-001, transcript_id=ENST00000416516, effect_description=p.E101fs)

effect.mutant_protein_sequence MGKEPLTLKSIQVAVEELYPNKARALTLAQHSRAPSPRLRSRLFSKALKGDHRCGETETPKSCSEVAGCKAAMRHQGKIPEELSLDDRARTQKKWGRGKWSQNPVASPPGKPLWKRGTQGERSILGWRLKRPRVKLSDARSARERGSSSRAWSVRGFLWGPVSWIWGRAQCMMWRSW

GGCGACTGTCCGGCTTTGAGCCAGGTGCCTC

Intron

Phasing and transcript selection (isovar)

TGTCCGGCTACTTGTCATGGCGACTGTCCGGCT

TGGCGACTGTCCAGCTCGACTGTCCAGCT

TGTCATGGCGACTGTCCAGCT

Somatic mutation Germline mut.RNA Read 1RNA Read 2RNA Read 3

RNA Read 5

RNA Read 4

TTGAGCCAGGAGCCTCTTGAGCCATTGAGCCAGGAGCCTCTTGTGCCAGGAGCCTCTTGTGCCAGGA

Exon 1 Exon 2

Selected coding sequence includes germline mutation:

Vaccine generation (vaxrank) vaxrank--vcf mutect.vcf --vcf strelka.vcf --bam tumor-rna.bam --vaccine-peptide-length 25 --mhc-predictor netmhcpan --mhc-alleles-file alleles.txt

Mutational burden can be limitingPatient #1 Patient #2 Patient #3 Patient #4 Patient #5

Variants 501 888 591 663 912(Non-silent)

Coding Variants 180 253 173 231 305Frame Shifts 4 8 1 3 1

Peptides in Report 11 9 17 32 22

Peptides with

Predicted MHC

ligands of affinity <=

100nM

4 3 8 10 9

First patient

■Oct 5, 2016 - samples acquired■Oct 10 - pathology deposits samples in Genomics

Core■Oct 17 - sequencing data delivered■Oct 19 - vaccine pipeline completes

■9 credible neoepitope-generating non-synonymous mutations identified

■Oct 20 - Histogenetics (HLA types) report arrives■Concordant with seq2hla except for one HLA-C allele

MHC Binding Affinity Prediction

■This trial uses netMHCpan for peptide/MHC binding prediction

■We think there is room for improvement over netMHC

■We are developing a new predictor called MHCflurry

Motivation for MHC binding prediction

■Identifying T cell antigens is required for vaccine design

■MHC binding is the most restrictive step in antigen processing

■Each MHC allele is capable of binding a distinct set of peptides

■There are thousands of MHC alleles in human population

Binding motifs

■Introduced: A. Sette 1989■Scan for occurrences of a “master sequence”,

e.g. the 6-mer sequence VHAAHA■Allow a certain number of substitutions between

similar amino acids

Position specific scoring matrices (PSSM)

■Introduced: Parker 1994

■At each position in the peptide, specify a value for each amino acid

■To predict whether a peptide binds, sum the values for each amino acid

Source: Bjoern Peters

Nonlinear effects

■Suppose a positively charged residue is required at exactly one of two positions in a peptide to bind an MHC allele

■This cannot be represented with binding motifs or PSSMs

Output

Hidden Layers

Input

Neural Networks

Neural networks for pMHC prediction■ Allele-specific

■ Train a model for each MHC allele■ Input to the model: peptide sequence

■ Pan-allele■ Train a model across alleles■ Input to the model:

(peptide sequence, MHC sequence)

netMHC neural networks■ Allele-specific

■ netMHC - M. Nielsen 2003■ Best choice for alleles with the most training

data ■ Pan-allele

■ netMHCpan - M. Nielsen 2007■ Best choice for alleles with less training data

MHCflurry

■Hybrid between allele-specific and pan-allele■Uses imputation (matrix completion) to “fill in”

missing training data for alleles with little data■Allele-specific predictors are trained on the

imputed data■As the training progresses, the imputed data is down-

weighted in favor of the real data

Imputation algorithmsAlgorithm AUC F1 scoreknnImpute (k=1)

0.8088 0.6906

knnImpute (k=3) 0.8202 0.6054

knnImpute (k=5) 0.8164 0.5884

meanFill 0.6590 0.0677

MICE (20 imputations) 0.8675 0.6292

similarityWeightedAveraging

0.8259 0.6162

softImpute (lambda =1,0) 0.8296 0.5126

softImpute (lambda=10,0) 0.7903 0.3835

softImpute (lambda=5,0) 0.8266 0.4930

svdImpute (rank=10) 0.8283 0.6201

Architecture

Performance

“Predicting Peptide-MHC Binding Affinities With Imputed Training Data.” Alex Rubinsteyn, Timothy O'Donnell, Nandita Damaraju, Jeffrey Hammerbacher. ICML 2016: Computational Biology Workshop. doi: http://dx.doi.org/10.1101/054775

Performance

“Predicting Peptide-MHC Binding Affinities With Imputed Training Data.” Alex Rubinsteyn, Timothy O'Donnell, Nandita Damaraju, Jeffrey Hammerbacher. ICML 2016: Computational Biology Workshop. doi: http://dx.doi.org/10.1101/054775

Weekly contest performance

http://tools.immuneepitope.org/auto_bench/mhci/weekly/

Using MHCflurryInstall $ pip install mhcflurry

$ mhcflurry-downloads fetch

Run$ mhcflurry-predict \ --alleles HLA-A0201 \ --peptides SIINFEKL SIINFEKD

MHCflurry

■ Allele-specific class I predictors may be downloaded from http://github.com/hammerlab/mhcflurry

■Working on■Expanding our training data by including less precise assays■Better handling of non-9mers■Class II prediction■Pan-allele prediction

■Patients■Jeff Hammerbacher & Hammer Lab■Nina Bhardwaj and the PGV team■Nikesh Kotecha and the PICI Bioinformatics

team■OSS bug reporters!

Thanks

End

Backup slides

Related work■Academic

■UCSC ProTECT■WashU pVAC-Seq■DTU MuPeXi

■Commercial■PGD ImmunoSelect-R■Personalis ACE ImmunoID■Immatics XPRESIDENT■MedGenome OncoPept

Does chemotherapy create neoantigens?

Question

■Mutations and neoantigens detectable from bulk-sequencing may be a biomarker for response to checkpoint blockade

■Chemotherapy induces mutations

Does chemotherapy meaningfully impact detectable mutation and neoantigen burden in high grade serous ovarian cancer?

Australian Ovarian Cancer Study (AOCS)

AOCS Cohort

■High grade serous ovarian carcinoma■WGS + RNA on 114 samples from 92 patients

■79 chemotherapy-naive primary samples■30 relapse samples taken after surgery and adjuvant-chemo■5 primary samples taken after neoadjuvant-chemo

Our analysis

■ Predict neoantigens and look for expression■Connect neoantigens to chemotherapy-

associated mutational signatures

Two contexts for chemotherapy

■Neoadjuvant: given before surgery to try to shrink tumors to be operable

■Adjuvant: given after surgery, when there are usually no clinical signs of disease

After adjuvant chemo and relapse, detectable mutation and neoantigen burden nearly double

81% increase124% increase

But not after neoadjuvant chemo

No changeNo change

In fact, neoadjuvant-treated samples trend toward fewer expressed neoantigens (p=0.09)

44 → 16

What part of the increase in neoantigens at relapse is due to chemo?

■As adjuvant chemo is standard of care, there are no patients who receive surgery but not adjuvant chemo to answer this question

■Alternative: mutational signatures (Alexandrov 2013)

Source: http://cancer.sanger.ac.uk/cosmic/signatures

Source: http://cancer.sanger.ac.uk/cosmic/signatures

Animal studies: cisplatin, cyclophosphamide, etoposide

Extracted signatures from G. Gallus experiments

Cyclophosphamide signature enriched in samples treated with cyclophosphamide (4/10 treated and 4/104 non-treated, p=0.001)

Focus on mutations introduced during treatment

■14 samples from 12 patients with paired pre- and post-treatment samples

■Extract unique-to-treated mutations: 0 variant reads and more than 30 depth in pre-treatment samples

■93,986 / 206,766 (45%) SNVs satisfy this filter■Perform deconvolution on these mutations

Deconvolution of unique-to-treated mutations

▶ Cisplatin detected only in the two cisplatin-treated samples

▶ Cyc detected in 3/6 cyc-treated. Unexpectedly, in 6/8 non-cyc treated samples

Chemo contributes at most 16% of neoantigens in adjuvant-treated relapse samples

Conclusions■ Ovarian tumors at relapse harbor nearly double the predicted

expressed neoantigen burden as primary chemo-naive samples■ Mutagenesis from standard chemotherapy regimes contribute a

small but detectable part of this effect■ Processes already operative in the primaries, including COSMIC

signatures (3) BRCA disruption and (8) Unknown etiology continue to contribute most mutations

■ Cisplatin-derived mutational signatures may not generalize to carboplatin

■ Some evidence that neoadjuvant treatment may decrease neoantigen expression, but larger cohorts are required to assess