Functionalizing Genomic Data for Clinical Applications Kartiki V

Functionalizing Genomic Data for Clinical Applications
Kartiki V. Desai
National Institute of Biomedical Genomics, Kalyani
Indo-Global Healthcare Summit & Expo 2014, Hyderabad
June 21st, 2014
Collaborators
Antara Biswas, Sugandha Basu, Tajnoor Fatima, Yifang Lee, Xiu
Bin Chan
Priyanka Pandey, Lance Miller, Ed Liu, Krishna Karuturi
Funding
DBT BioCare Program, NIBMG intramural
Consultant Oncostem Diagnostics, Bangalore
Aspiring Students(http://www.nibmg.ac.in/NIBMG-DOCTORAL-RESEARCH-2014.pdf)
Last date for online application is June 20th 2014
Genomics and Cancer
Platforms to Patterns in disease
1. Classification of tumors leading to patient stratification
(Microarray)
2. Genetic susceptibility to disease and response to
treatment(GWAS)
3. Chromosomal abnormalities (Karyotyping and aCGH)
4. Epigenetic modifications (Methylation arrays, Histone
modifications (ChIP)
5. All (Next-generation sequencing) including point
mutations, chromosomal aberrations etc
THAT CANCER IS CLONAL AND IT EVOLVES
M Greaves, CC Maley - Nature, 2012 481: 306-313
COMPARE PRIMARY AND METS AND MODELS
Primary tumor
Met
Xenograft
L Ding et al. Nature 464, 999-1005 (2010) doi:10.1038/nature08989
Classification of breast cancer samples, treatment, response
Breast Cancer
ER positive
Luminal B
Luminal A
Tamoxifen
ER negative
TNBCs
Erbb2+
Chemo
Normal like
Ab
TWO PROBLEMS IN ALL CANCERs- RESISTANCE
ER/PR+
Hormone therapy
NR
Drug
Treatment
Options for
Breast
Cancer
R
Drug
Resistance
HER2+
Immunotherapy
Triplenegative
Chemotherapy
Toxic
TWO PROBLEMS IN ALL CANCERs-METASTASIS
Stage at Diagnosis
Stage
Distribution (%)
5-year
Relative Survival (%)
Localized (confined to
primary site)
60
98.6
Regional (spread to
regional lymphnodes)
33
83.8
Distant (cancer has
metastasized)
5
23.3
Unknown (unstaged)
2
52.4
OBJECTIVE
USE OUTPUT FROM INTEGRATIVE ANALYSIS OF GENOMIC
DATA TO FIND BIOMARKERS, THERAPEUTIC TARGETS IN
ADVANCED BREAST CANCER
BREAST, OVARIAN cancer
STRATEGY
• RE-MINE DATA USING STATISTICAL METHODS and CLINICAL
ANNOTATION
• HIGHTHROUGHPUT SCREEN FOR CANCER PHENOTYPES
• DETAILED FUNCTIONAL VALIDATION
GENE EXPRESSION DATA
14 cohorts 2027 patients…
(Lance Miller, Krishna Karuturi et al)
Gene ontology
Receptors
Secretory Proteins
Growth factors
Enzymes
Cox proportional
Hazards
Disease-Metastasis free
(DFMS)
32 candidates
Primary screen :
SiRNA based 96 well plate assay
3 independent siRNAs per gene/target, 2 cell lines
1. Change in proliferation-WST assay
2. Apoptosis assay- change in caspase 3 activation and PARP cleavage
3. Soft agar colony formation assay
Secondary screen
1.
2.
Cell motility and invasion-Boyden Chamber assay
3T3 transformation assay
Gain of function screen
Over-express candidate to revert the phenotypes observed
Mouse xenograft/tail vein assay
UPSTREAM REGULATORS OF CANCER CIRCUITRY
SPINK1
JMJD6
JMJD6
TGF-β, TFs
JMJD6
PMCH
CCNE, ER
SPINK1
Casp3, Bcl2
Hannahan and Weinberg, 2011
JMJD6- Jumanji Domain Containing Protein 6
Annotated as Phosphotidylserine receptor (PTDSR)
Single JMJC domain at the C-terminal
Histone arginine demethylase, hydroxylase, interacts
with U2AF65 and may influence alternate splicing
Recently shown to bind SSRNA
JMJD6 and TUMORIGENESIS ?
JMJD6 associates with high grade, poorly differentiated tumors
JMJD6 and Cell Proliferation
JMJD6 increases proliferation of BrCa cells
Over-Expression
SiRNA mediated knock-down
JMJD6 increases cell motility but not cell invasion
JMJD6 induces cyclin E1, suppresses the TGF-β axis
JMJD6
ATF2
p38MAPK
Cyclin E
TGF-β pathway
TGF-β1,TGF-β2,
Smad 2, Smad 4
Type IIR
Lee et al, Breast Cancer Research, 2012, 14(3):R85.
Cellular relationship could be extrapolated to patient samples
CCNE
TGF-β2
JMJD6 (avg of: 212723_at, 212722_s_at)
Signal Intensity (log2)
12
p= 5 x 10E-39
10
8
6
4
p=5.2x10-08
2
6
7
8
9
10
11
TGFB2 (avg of: 209909_s_at, 220407_s_at, 209908_s_at)
Signal Intensity (log2)
J6
J6
12
JMJD6 displays higher levels of expression in aggressive subtypes
Poor prognosis
ER+, especially LumAs, typically have better survival
due to SERMs
Does J6 predicts poor prognosis in ER+ patients?
ER+
LUMINAL A
TAMOXIFEN
Gene Set Enrichment Analysis
Gene Set Name
CHARAFE BREAST_CANCER_LUM_VS_BASAL_UP
CREIGHTON ENDOCRINE THERAPY RESIST_1
CREIGHTON_ENDOCRINE_THERAPY_RESIST_4
CREIGHTON_ENDOCRINE_THERAPY_RESIST_5
DOANE_BREAST_CANCER_ESR1_UP
FARMER_BREAST_CANCER_APOCRINE VS LUM
FARMER_BREAST_CANCER_BASAL_VS_LUM
FARMER_BREAST_CANCER_BASAL_VS_LUM
GOZGIT_ESR1_TARGETS_DN
GOZGIT_ESR1_TARGETS_DN
MASSARWEH_TAMOXIFEN_RESISTANCE_DN
MASSARWEH_TAMOXIFEN_RESISTANCE_UP
SMID_BREAST_CANCER_BASAL_DN
TGCCTTA,MIR-124A
TTGCACT,MIR-130A,MIR-301,MIR-130B
TTTGCAC,MIR-19A,MIR-19B
Total
383
526
308
482
114
329
335
335
776
776
253
579
713
482
341
448
Overlap
39
50
36
43
21
41
35
38
83
94
37
52
65
42
43
46
Description
UP luminal-like VS basal-like cells
ER+ acquired TAMR
ER+ acquired TAMR
ER+ acquired TAMR
UP ER positive vs ER negative
Discriminate ESR1- AR+ Vs ESR1+ AR+
Discriminate ESR1- AR Vs ESR1+ AR+
Discriminate ESR1- AR Vs ESR1+ AR+
DN in ER-TMX2-28 cells
DN in ER-TMX2-28 cells
DN in MCF-7 xenografts TamR
UP MCF-7 xenografts TamR
DN basal subtype
Targets of MIR-124A
Targets of MIR-130A,MIR-301,MIR-130B
Targets MIR-19A,MIR-19B
J E2
Tam
9/18 ↑TamR
NRIP1
TPBG
Propose this model
ER+
low JMJD6
ER+
High JMJD6
TAM
Cyclin D high,
low TGF-b
X
Cell proliferation
Cyclin D/E high,
low TGF-b
X
Cell proliferation
FUTURE…..
1. We have generated an assay system and a screen for potential
inhibitors of JMJD6: cell-based loss in cell proliferation and a
biochemical assay to measure secreted TGF-beta.
2. Collaborating- Stapled peptides (BII, Singapore)/small
molecules (Indian Consortium IICB, JNACSR, Bose Institute)
HOPE
We may be able to treat TamR patients
THANK YOU