Clinical-Genomics HL7 SIG The Tissue Typing Use Case Amnon Shabo1, Shosh Israel2, Guy Karlebach1 1IBM Research Lab in Haifa, 2Hadassah University Hospital Presented by Amnon Shabo SHAMAN = Secured Health and Medical Access Network IMR = Integrated Medical Records Middleware In collaboration with the Hadassah University Hospital in Jerusalem Haifa Labs Integration of multiple sources of data; transformation to standards; full-text indexation Watson/Yorktown Labs Processing of personal genomic and proteomic data

Types of Genomic Data DNA Sequences Personal SNPs (Single Nucleotide Polymorphism) Programmatic / manual annotation (e.g., SNPs combination x could possibly lead to mutation y) Gene expression levels Proteomic (proteins translated w/SNPs)

The Case for Clinical-Genomics Clinical-Genomics: the use of information obtained from DNA sequencing, patterns of gene expression & resulted proteins for healthcare purposes Personalized Medicine Detect sensitivities/allergies beforehand Drug Selection by clinicians Pharmacogenomics Improve drug development based on clinical-genomics correlations Personal customization of drugs Preventive Care

Gene Expression in Cancer Differences between normal tissue vs. premalignant lesion vs. neoplastic tissue markers of diagnostic value targets for drug research evolution of cancer Differences between responders vs. non-responders for a standard therapy Development of drug-resistance Correlation of gene expression patterns with presentation or evolution: long vs. short survivors metastatic vs. non-metastatic clinical or pathological grades

Differential Display Difference between banding patterns of cDNA from tumor tissue and normal tissue on polyacrylamide gel can point to a protein that could potentially be the target of a therapeutic antibody. DNA microarrays are also employed to examine the genetic expression of thousands of potential antigens and determine which are present in abnormal (tumor) tissue but not in normal tissue.

Using Databases Vast databases of genetic information contribute to genomic research Search for potential antigens can be as easy as an online search HLA Database example: (part of the IMGT - international immunogentics project) http://www.ebi.ac.uk/imgt/hla/

Clinical-Genomics Interrelations Bi-directional relationships: Genomics  Clinical Personal SNPs could be interpreted as mutations and thus indicate possible diseases/sensitivities Clinical  Genomics Patient & family history leads to genetic testing order Crosschecking of genomics results

SNPs Interpretation SNPs as known mutations (might imply the develop. of diseases) Unknown SNPs: in significant segments of the gene (possibly imply individual differences) in gene segments that translate to inactive parts of the proteins (thought to be insignificant) SNPs as normal polymorphisms

CG Uses: From Clinical to Forensic These pictures describes paternity casework autoRADS - the left picture shows a case of paternity exclusion and the right one a case of paternity inclusion. Paternity Testing Taken from the site of Genelex, a company which offers, among other genomic services, paternity testing (see http://www.genelex.com/).

Variety of Methods STR (short tandem repeats ) STR’s are short sequences that are easy to detect and its specific pattern of repetitions could identify a gene without needing to sequence the entire gene.

HL7 Specs for Clinical-Genomics Create a DIM for Clinical-Genomics Derive R-MIMs and message types Clinical-Genomic Documents (CDA L3!) Review / Utilize the following emerging bio-informatics standards BSML (Bioinformatic Sequence Markup Language) MAGE-ML (Microarray and GeneExpression Markup Language) Problem: These standards are not necessarily patient-based.

BSML: Sequencing Markup <Sequence id="_2" db-source="GMS" length="51" representation="raw" molecule="dna" topology="linear" alignment-sequence="_"> <Feature-tables> <Feature-table>- <Feature title="gms:sequence">  <Interval-loc startpos="1" endpos="51" />   </Feature> <Feature title="gms:new_fragment">  <Interval-loc startpos="1" endpos="51" /> </Feature>  <Feature title="gms:annotation" value="possible somatic mutation cell line #4 end-11thxml" />   <Feature title="/gms:new_fragment" />   <Feature title="/gms:sequence"/>   </Feature-table>  </Feature-tables>  <Seqdata> AGGAATCAGAAAGGACACTCTGGACTTCAGCCAACAGGATACCTGAGCTGA </Seq-data>   </Sequence>

MAGE-ML: Gene Expression Gene Description: <reporter id="1051_g_at">  <rep_des V="Source: Human melanoma antigen recognized by T-cells (MART-1) mRNA." />   </reporter> Gene Expression Levels: <reporter id="32847_at" accession="U48959"> <NormalizedIntensity value="0.235" />   <Control value="230.972" />   <Raw value="54.3" />   <T-testPValue value="no replicates" />   <PresentAbsentCall value="A" /> </reporter>

Analogy to Imaging Integration HL7DICOM relationship: existing standards IMAGING DICOM GENOMICS BSML; MAGE; I3C Efforts Mass data Summarized data Pixels Radiologist- Report Sequences; Gene- Expression; Proteins Genomicist-

Current Experimentations at IBM Research A clinical point of view Bone-marrow transplantation center in Israel Donor-recipient matching: tissue typing Reporting to international BMT registry A research point of view Research center in Canada Focusing on heart&lung diseases Trying to find clinical-genomic interrelations Using clinical data from patient records compared with healthy people Using genomic data, mainly gene expression levels and proteins

Collaboration with Hadassah Information exchange Report to international registries (IBMTR) Standardization Transform to HL7-CDA documents (L.13) Indexing Index all data including semi-structured data Annotation Integrating the personal genomic data Visualization Visualizing the integrated BMT documents …agctgaa… SNPs

The BMT Procedure Pre-BMT BMT Post-BMT Matching a donor or autologous transplant Conditioning Irradiation Chemotherapy GVHD (Graft vs. Host Disease) Prophylaxis BMT Substance donated Bone-marrow Peripheral blood stem cells Cord blood stem cells Donor lymphocytes -Transplant Post-BMT Control of GVHD and other complications Hematopoietic Reconstitution Engraftment and Chimerism

Mini-allografts (mini-transplantations) New Trends in BMT Mini-allografts (mini-transplantations) Immunosuppression instead of total conditioning (destroying the entire immune system) Infusing donor lymphocytes to attack tumors, cancerous cells, autoimmune artifacts and infectious pathogens Stopping the donor lymphocytes once they’re done with the patient disease source, so that they won’t attack the patient normal cells using ‘suicide genes’ Striking a balance between to 2 immune systems

The HLA-Typing Use Case HLA = Human Leucocytes Antigens; determine the personal fingerprint distinguishing between self and non-self HLA-Typing methods move from serology (antibodies) to molecular (DNA) and recently to DNA sequencing yielding higher levels of typing resolution Common Triggers: donor-recipient matching, familial relationships, disease association

Donor Matching HLA (Human Leukocytes Antigens) HLA Typing DNA typing About 6 important loci, each can have dozens of different antigens (alleles) Haplotype – common set of antigens Relatives versus unrelated donation Donor banks Search engines Lack of donors to minorities

HLA Alleles in the Family

Differences in Antigens Allelic polymorphism is concentrated in the peptide (antigen) binding site: Class II Variables exons: 2 Class I: Variables exons: 2,3,4

The HLA-Typing Triggers Donor-Recipient Matching Bone-Marrow transplant Full match (identical twin) Avoid GVHD and Promote GVM  Precise and personal match rather than full match Organ transplant (cross-match: antibodies) Living donor: also HLA typing before transplant Select the best treatment for the individual patient-donor matching HLA-typing is done for post-transplant Info. Forensic Scenarios Paternity disputes Crime suspects (HLA is one component of known genetic markers)

Personal Rather than Full Match Personal match could be beneficial to to new trends in BMT: HLA - A & B versus C: When there is a match in HLA A & B: Mismatch in HLA-C might promote GVL (Graft vs. Leukemia) Mini-transplants: Avoid full-match (even when identical twin is available)

Data of Interest Class I allele sequences (all cells): HLA-A HLA-B HLA-C Class II allele sequences (certain cells from the immune system): HLA-DR (most important) HLA-DQ (the contribution is not proven but can verify the DR match since there there is strong linkage) HLA-DP (usually is not being typed) might sequence only the polymorphic segments (e.g., exon 2 in class II and exon 2-4 in class I), each exon is about a 300 nucleotides, because SNPs in other segments are not important to the matching

New Naming Convention DOB*01010101 Letter designates the membrane locus Full allele name: eight digits First 2 digits defining the allele family and where possible corresponding to the serological family Third and fourth digits describing coding variation Fifth and sixth digits describing synonymous variation Seventh and eighth digits describing variation in introns DOB*01010101

Sequencing Data Example: Generic Meta Data: Local Names: DRB1*110101 IMGT/HLA No: HLA00756 Class: II Assigned: 01-AUG-1989 Last Aligned: 17-OCT-2002 Component Entries: AF029281 AJ297587 Cell Sequence Derived From: 34A2, FPAF Known Ethnic Origin of Cells: Caucasoid Length: 801 bps

Sequencing Data Example: DRB1*110101 IMGT-HLA SEQUENCE DATABASE.htm SNPs

Sequencing Data Example: SNP-Resulted Protein Sequence IMGT-HLA SEQUENCE DATABASE.htm

Sequencing Data Example: DRB1*110401 IMGT-HLA SEQUENCE DATABASE2.htm SNP

Sequencing Data Example: SNP-Resulted Protein Sequence IMGT-HLA SEQUENCE DATABASE2.htm

Testing Kit Output Example - Sample ID - Kit Name - Name - Kit Lot Number - Ethnic Group - Kit Expires - Donor/Patient - DNA Extraction - Purpose of Test - DNA Quality - Test Date - DNA Concentration - Test By - Review Date - Comments - Reviewed By Serology Results: HLA A: B: C: DR: DQ: Positive Lanes: Kit-specific data

Tissue Typing Report Recipient Subject Specific Alleles Record Number Molecular Sample Date Disease Patient Result Specific Alleles Possible combinations Siblings Unrelated Donors

Search for Unrelated Donor Banks of potential donors (volunteers) Each donor was tested only for HLA Class I When a patient needs a donor: The transplant facility searches the donor banks to find a donor (direct access to the donor banks databases) The search is based on Class I matching If appropriate donors are found – then the searching transplant facility initiates a request to the respective donor banks, asking for Class II typing Each approached donor bank is moving the request to the tissue typing lab where the DNA samples reside Class II matching results are returned to the searching facility and if the donor with the best match in both class I & II is approached

Search for Unrelated Donor Donor Banks Transplant Center (TC) searches for donors Patient Class I HLA Donor Banks Class I Matching donors TC chooses potential donors Donor Bank Request for HLA class II typing TC chooses best donor Class II Matching donors Tissue Typing Lab Class II Typing

Genomic Data in a Clinical Docs A DNA Testing Device – raw DNA sequences Reports from service units, e.g., tissue typing, should answer questions such as patient-donor matching, fatherhood, etc. Embedding annotated results received from a DNA lab in a CDA document Linking genomic annotations and clinical data (external links?)

Matching Option Notations Different notations for coarse-grain results: possibilities from the A24 antigen family could be represented differently by different kits on the same patient DNA tested: A*2402101-06/08-11N/13-15/17/18/20-23/25-36N A*2402101-06/08-11N/13-15/17/18/20-23/25-31 Pair combinations (inherited alleles): DRB1*0402 AND DRB1*0408 or DRB1*0404/44 AND DRB1*0414 Kit A: Exact combination Kit B: two possible combinations or

Report Example – Unrelated Donors The Patient Unrelated Donor 2 Unrelated Donor 1 Unrelated Donor 3

Class I vs. Class II Antigens A 4-digit resolution level is common in class II antigens as they have been discovered more lately It’s desired that class I antigens will report in 4 –digits as well as they are more crucial to BMT success 4-digits reporting requires molecular and sequencing procedures 4-digits reporting still not common in class I

Clinical-Genomic Data in CDA? What should go into a clinical document (extent of detail)? Programmatic and manual annotation at different levels? The users of such integrated documents: clinicians? genomicists? patients? Medico-ethical issues! HL7-Association semantics that represents the interrelations of clinical-genomics

First Attempts using CDA… GMS Genetic Messaging System From the computational biology center in IBM Watson Example: integrating the genomic annotation and analysis of the personal DNA sequences, into the clinical document (CDA format) <levelone> <clinical_document_header> <!--header structures per CDA--> </clinical_document_header> <body> <!--clinical content per CDA--> <!--GMS merges genomic data here--> <gms:dna sequence="2" base="802" locus="1"> <gms:annotation> possible somatic mutation cell line #4 end-11th </gms:annotation> AGGAATCAGAAAGGACACTCTGGACTTCAGCCAACAGGATACCTGAGCTGA... <gms:automated_annotation> </body> </levelone> CDA L1

And the Work Just Begins… Use Cases in Detail & Taxonomy High-Level CG Model and  HL7-DIM Messages Documents Prototyping info. Exchange using specs Thanks You!