Transcript Clinical-Genomics-Tissue-Typing-San-Antonio

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 =
IMR =
Secured Health and Medical Access Network
Integrated Medical Records Middleware
Haifa Labs
Integration of multiple sources
of data; transformation to
standards; full-text indexation
Watson/Yorktown
Labs
Processing of personal genomic
and proteomic data
In collaboration with the Hadassah University Hospital in Jerusalem
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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)
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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
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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. nonresponders 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
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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.
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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/
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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
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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
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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.
Taken from the site of Genelex, a company which offers, among other genomic services,
paternity testing (see http://www.genelex.com/).
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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.
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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.
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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>
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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>
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Analogy to Imaging Integration
HL7DICOM relationship:
existing standards
IMAGING
DICOM
Mass data
Pixels
Summarized data
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GENOMICS
BSML;
MAGE;
I3C Efforts
Sequences;
GeneExpression;
Proteins
Radiologist- GenomicistReport
Report
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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
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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
…agctgaa…
SNPs
– Integrating the personal genomic data
• Visualization
– Visualizing the integrated BMT documents
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The BMT Procedure
–Matching a donor or autologous transplant
–Conditioning
Pre-BMT
•Irradiation
•Chemotherapy
•GVHD (Graft vs. Host Disease) Prophylaxis
–Substance donated
BMT
-Transplant
•Bone-marrow
•Peripheral blood stem cells
•Cord blood stem cells
•Donor lymphocytes
–Control of GVHD and other complications
Post-BMT
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–Hematopoietic Reconstitution
–Engraftment and Chimerism
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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
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The HLA-Typing Use Case
• HLA = Human Leucocytes Antigens;
determine the personal fingerprint
distinguishing between self and nonself
• 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
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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
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HLA Alleles in the Family
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Differences in Antigens
Allelic polymorphism is concentrated in
the peptide (antigen) binding site:
Class I:
Class II
Variables exons: 2,3,4
Variables exons: 2
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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)
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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)
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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
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New Naming Convention
• 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
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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:
– Known Ethnic
Origin of Cells:
– Length:
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34A2, FPAF
Caucasoid
801 bps
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Sequencing Data Example:
DRB1*110101
IMGT-HLA SEQUENCE DATABASE.htm
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SNPs
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Sequencing Data Example:
SNP-Resulted Protein Sequence
IMGT-HLA SEQUENCE DATABASE.htm
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Sequencing Data Example:
DRB1*110401
IMGT-HLA SEQUENCE DATABASE2.htm
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SNP
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Sequencing Data Example:
SNP-Resulted Protein Sequence
IMGT-HLA SEQUENCE DATABASE2.htm
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Testing Kit Output Example
- Sample ID
- Name
- Ethnic Group
- Donor/Patient
- Purpose of Test - Test Date
- Test By
- Comments
Serology Results:
HLA A:
B:
C:
Kit-specific
data
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Kit Name
Kit Lot Number
Kit Expires
DNA Extraction
DNA Quality
DNA Concentration
Review Date
Reviewed By
DR:
DQ:
Positive Lanes:
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Tissue Typing Report
- Recipient
- Subject
- Specific
Alleles
- Record Number
- Molecular
Sample
- Date
- Disease
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- Patient
Result
- Specific
Alleles
- Possible
combinations
- Siblings
- Unrelated
Donors
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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
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Search for Unrelated Donor
Transplant Center
(TC) searches for
donors
TC
chooses
potential
donors
TC
chooses
best donor
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Patien
t
Class
I HLA
Donor
Donor
Banks
Banks
Class I
Matchin
g donors
Request
for HLA
class II
typing
Donor
Bank
Class II
Matchin
g donors
Tissue Typing Lab
Class II Typing
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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?)
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Matching Option Notations
• Different notations for coarsegrain 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
Kit B:
or
two
DRB1*0404/44
possible
combinations
or
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Kit A:
Exact combination
AND DRB1*0414
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Report Example – Unrelated Donors
The Patient
Unrelated Donor 2
Unrelated Donor 1
Unrelated Donor 3
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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
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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
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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>
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And the Work Just Begins…
• Use Cases in Detail & Taxonomy
• High-Level CG Model and  HL7-DIM
• Messages
• Documents
• Prototyping info. Exchange using specs
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