BIOINF 3360 Computa1onal Immunomics

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1 BIOINF 3360 Computa1onal Immunomics Oliver Kohlbacher Summer Personalized Cancer Vaccines Therapeu1c Cancer Vaccines Vaccines are one of the big success stories of modern medicine Edward Jenner and Luis Pasteur laid the foundaaons of prophylac1c vaccines Therapeu1c (cancer) vaccines Goal: raise an immune response against specific (e.g., cancer- associated) anagens Administered aier surgery/chemotherapy TargeAng of anagens either shared by many cancer of the same type, unique to the to a specific paaent (individualized) 2 Edward Jenner ( ) Luis Pasteur ( ) Cancer Mechanisms Cancer is a term used for diseases in which abnormal cells divide without control and are able to invade other Assues. (Na$onal Cancer Ins$tute) Abnormal cells Typically caused by genomic damage (cancer is someames called a genomic disease ) MutaAons, structural variaaons, chromosomal aberraaons can all be causes Mechanism Damage in a single cell suffices to start a cancer DysregulaAon: break the otherwise Aght cell cycle control Immune system failed to detect these aberrant cells Cells can proliferate without control 3 1

2 Cancer Immunology Immune system should be able to detect these aberraaons Existence of a tumor: immune system failed or immune response was not strong enough Once a tumor reaches a certain size, it becomes virtually impossible for immune cells to remove it Immune therapy: acavate the immune system to increase its aggressiveness towards a cancer 4 Heterogeneity & Spreading Cancers are oien not geneacally stable GeneAc makeup changes while the tumor grows Heterogeneous mix of cell types (with different genomic background) Spreading Cancers oien start spreading to other organs/assues From primary tumor to metastases: intravasaaon into bloodstream, extravasaaon into another Assue OIen also metastases in lymph nodes Micrometastases: metastases too small to be visible, thus not surgically resectable 5 Metastasis 6 2

3 Classical Cancer Treatment Surgery ResecAon of the affected Assue and metastases Radia1on therapy High- energy radiaaon can kill cancer cells in a locally well- defined area X- ray, gamma rays, or ion beams applied externally or radioacave compounds administered internally Chemotherapy CytostaAc drugs damage rapidly dividing cells more than other cells Targeted therapies (kinase inhibitors, anabodies) 7 Cancer Immunotherapy Idea Immune system provides best protecaon from cancer AcAvate immune system and direct its aeenaon to the cancer Problems Obviously the immune system failed to aeack the cancer in the first place Only works aier resecaon of the primary tumor/metastases or another classical therapy; immune system cannot infiltrate large tumors Tumor cells are modified normal cells: avoid acavaang an immune response against normal Assues Cancer is a personalized disease each cancer is different! 8 Cancer An1gens Two types of cancer anagens Over- expressed proteins Proteins not normally expressed in the Assue or only at low levels Copy number variaaons (CNV) or dysregulaaon leads to increased expression in the tumor Assue Increased copy number of MHC ligands being presented Mutated proteins Different protein sequence caused by somaac mutaaons (SNVs, single nucleoade variants, or InDels) or structural variaaon (e.g., fusion proteins) Different sequence causes novel presented MHC ligands 9 3

4 Cancer An1gens Over- expressed proteins OIen not uniquely expressed in the cancer Assue Danger of autoimmune reacaon Can be easily idenafied by expression analysis Some of these have been found to be characterisac for certain types of cancers Mutated proteins Specific to the cancer Assue (only occurring in cells derived from the iniaal mutaaon) Harder to detect: mutaaons are most of the Ame different for every paaent 10 Epitope- Based Cancer Vaccines Idea IdenAfy cancer an1gens specific for a cancer- type or a paaent Construct an epitope- based vaccine from these anagen sequences Vaccinate the paaent Immune system will find and remove micrometastases and prevent relapse Problems How to idenafy the relevant cancer anagens? How to construct the vaccine? 11 Finding Over- Expressed An1gens Omics technologies Transcriptomics Determine mrna expression levels using microarrays/rna- Seq Compare mrna expression between tumor and normal Proteomics Extract proteins, quanafy and idenafy using mass spectrometry (HPLC- MS) Less comprehensive, but reveals what is really there MHC Ligandomics Pull down MHC- pepade complexes from tumor/normal Assue, idenafy pepades using mass spectrometry Even less sensiave, but reveals what the immune system really sees of the cancer 12 4

5 Microarrays for Transcriptomics Fluorescence Readout Laser excites fluorescence dyes on chip CCD detects fluorescence signal of the whole chip Different dyes = different colors/ channels QuanAfy fluorescence per probe/ spot from the image From Transcriptomes to Pep1des Compare expression levels between normal Assue and tumor for a specific type of cancer IdenAfy proteins that are specifically over- expressed Consistently high expression in the majority of paaents Very low or not detectable expression in normal Assue Compare to expression data on other Assues! IdenAfy good epitopes for these anagens Compare to naturally processed ligands for this anagens (databases) Predict epitopes for relevant MHC class I alleles Experimental validaaon 15 5

6 Epitope- Based Vaccines 16 Purcell, McCluskey & Rossjohn, Nature Reviews Drug Discovery 6, (2007) Epitope- Based Cancer Vaccines Epitope- based vaccines have been shown to be effecave in cancer Prostate cancer: delayed progression in 4/19 paaents (Feyerabend, Prostate, 69:917, 2009) MetastaAc melanoma: increase survival, for immune responders (Becker et al., Cancer Immunol Immunother, 61:2091, 2012) Becker et al., Cancer Immunol Immunother, 61:2091, IMMATICS 6

7 IMMATICS - IMA 901 Walter et al., Nature Med, 18:1245, IMMATICS - IMA 901 Walter et al., Nature Med, 18:1245, IMMATICS - IMA 901 Walter et al., Nature Med, 18:1245,

8 IMMATICS - IMA 901 Walter et al., Nature Med, 18:1245, IMMATICS - IMA 901 IMA901 for treatment of kidney cancer (renal cell carcinoma) Nine pepades are presented on HLA- A*02 treatment for A2+ paaents only One addiaonal pepade presented on DRB PepAde mix was administered to paaents repeatedly 20 out of 27 paaents developed a T cell response to at least one of the pepade 8 paaents responded to mulaple pepades Survival increases with the number of immungenic pep1des Walter et al., Nature Med, 18:1245, Personalized Vaccines Personalized vaccines are a special case of a populaaon- opamized vaccine PopulaAon of one: HLA allele distribuaon is the paaent's HLA type Other aspects like anagen processing, anagen and allele coverage remain idenacal Sequence conservaaon is typically not an issue Personalized epitope selec1on problem: IdenAfy the best set of epitopes for a paaent's tumor specific expression and mutaaon paeern 24 8

9 Personalized Cancer Vaccines Epitope- based vaccines are an promising strategy PepAdes can be selected for each paaent individually Immune system can be acavated to target tumor- specific anagens PepAde synthesis can be done with moderate effort even under GMP condiaons Problems MHC is polymorphic: epitopes differ from paaent to paaent and from tumor to tumor Avoid side effects Regulatory issues 25 Science (2001), 291 (5507) Nature (2001), 409 (6822) Shotgun Sequencing DNA Shearing Size Selection Paired Reads (Mates) Cloning Sequencing Vector 9

10 Sanger Sequencing Sequencing Cost Comparison of costs for generating DNA sequence and their computational analysis (based on Moore's law) over the past decade. Note the logarithmic scale on the y-axis. (source: NHGRI, Sequencing by Synthesis Sequencing by synthesis produces massive amounts of data by parallelization Drawback: reduced quality of reads, read length 10

11 Illumina Metzker et al.,nat Rev Genetics (2010), 11:31 31 Illumina Sequencers Read Mapping Read mapping Alignment of short reads to a reference genome Typical problem size: mio. reads of 100 bp Variant calling Alignment needs to be inexact to permit genomic variants There are about mio. polymorphic sites in the genome Most have no effect, but some are disease- associated Difficult: disanguish variants from sequencing errors 11

12 ivac Integrated plauorm for personalized vaccine design Several individualized clinical trials for various cancers in progress Algorithmic core based on OpATope, addiaonal bioinformaacs infrastructure is needed A wealth of (high- throughput) data needs to be integrated to have as much informaaon available as possible Process ought to be automated: clinician sends appropriate tumor samples to pathology receives the synthesized vaccine no later than four weeks aier surgery Sample Collection HT Data Generation Data Analysis Synthesis & Vaccination 34 Pa1ent- Specific Variants Sequence only the exome (all exons) of the tumor and normal Assue Map reads to the human reference genome Variants can either be present in both Assues (SNPs, single nucleoade polymorphisms present in the germline) or only in the tumor (SNVs, somaac mutaaons) Variant calling has to be staasacally sound: disanguish sequencing errors from true variants Confirm idenafied variants with addiaonal (e.g., Sanger) sequencing run 35 From Reads to Variants Exome sequencing (Illumina GA IIx) Key steps Read mapping Variant calling AnnotaAon IntegraAon into an efficient pipeline for automated processing MutaAons in tumor MutaAons in normal Assue Tumor- specific muta1ons 36 12

13 From Reads to Epitopes Tumor (T) Reference Genome Normal (N) AA Seq (N T) Tumor-specific Peptides SNP C C C T C C SNV A A G G G IRQVKGDL[A V]FLNFQNNL IRQVKGDLV RQVKGDLVF QVKGDLVFL VKGDLVFLN KGDLVFLNF GDLVFLNFQ DLVFLNFQN LVFLNFQNN VFLNFQNNL Read mapping Variant calling SNP removal & translation Epitope prediction Predicted Epitopes QVKGDLVFL VFLNFQNNL 37 MHC Ligandome Analysis ExtracAon of differenaal pepades (tumor vs. normal) yields confirmed TUMAPs however at a low sensiavity MHC ligands are analyzed using LC- ESI- MS/MS (Orbitrap XL) MHC ligandomics differs from proteomics PepAde ID relies on databases SNVs need to be integrated! Search engines are opamized for trypac pepades MHC pepades are non- trypac Consensus idenaficaaon MulAple search engines, imputaaon of missing scores Increases idenaficaaon rates drasacally Nahnsen et al., J. Proteome Res., 2011, 10(8): Junker et al., J. Proteome Res., 2012, 11(7): ivac Workflow Samples (Tumor & Normal) DNA Extraction RNA Extraction MHC Ligand Shedding Exome Seq RNA Seq LC-ESI-MS/MS Read Mapping Read Mapping Quantitation HLA Typing Variant Calling Variant Calling Variant Integration & Annotation Identification Epitope Prediction Optimal Epitope Selection Peptide Synthesis and Vaccination 39 13

14 ivac Infrastructure Metadata Web Portal Other Labs openbis Database Workflow System NGS Lab Lab Storage MS Lab Lab Storage Project Storage Archival Storage Compute Cluster 40 Vaccina1on Web Portal Immunomonitoring data Interdisciplinary Tumor Board Vaccination GMP Facility 41 Ini1al ivac Trials 42 14

15 IndividuaLIVER 10 paaents suffering from surgically resectable liver cancer (cholangiocarcinoma or hepatocellular carcinoma) classified as pt1- pt4 Tumors were resected, blood samples taken as normal Assue; tumor contents: % Whole- exome seq, RNA- Seq, and MHC ligandome analysis Tumor- specific variants non- synonymous variants per paaent About 25% of the variants were found to be expressed in the tumor For every paaent we could idenafy between 1 and 9 tumor- specific pepades LC- MS could not confirm any of the variants seen on the genome level, but confirmed pepades of overexpressed Ags 43 Outlook Epitope-based vaccines are a compelling approach for personalized cancer therapy Immunological issues Optimize vaccination schemes Combination with chemotherapy Regulatory issues with personalized therapies Computational challenges Prediction of T-cell reactivity Modeling interplay with chemotherapy Efficient algorithms for fast turn-over times Computational design of personalized vaccines needs Smart data analysis algorithms Software systems able to handle high-throughput data efficiently Tight integration with clinical processes 44 15