1. 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

2. 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)

3. 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

4. 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

5. 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.

6. 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)

7. 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

8. 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

9. 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

10. 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.

11. 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.

12. 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>

13. 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=" " />   <Raw value="54.3" />   <T-testPValue value="no replicates" />   <PresentAbsentCall value="A" /> </reporter>

14. 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-

15. 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

16. 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

17. 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

18. 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

19. 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

20. 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

21. HLA Alleles in the Family

22. 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

23. 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)

24. 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)

25. 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

26. 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*

27. 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: AF AJ297587
Cell Sequence Derived From: 34A2, FPAF
Known Ethnic Origin of Cells: Caucasoid
Length: bps

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

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

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

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

32. 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

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

34. 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

35. 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

36. 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?)

37. 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* /08-11N/13-15/17/18/20-23/25-36N
A* /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

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

39. 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

40. 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

41. 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

42. 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!