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Applications of
Next Generation Sequencing in
Healthcare
-Dr. Raman Govindarajan Genotypic- Conference on Applying NGS:
Basic research, Agriculture & Healthcare Bangalore, 11 September, 2014
Human disease technology needs
Human Diseases Genes and environment
• How does environment affect the genes.
Medical implication
• Genetic diseases
– Unigenic
– Polygenic
• Environment
• Inherited Diseases
• Acquired Diseases
• Multisystem Diseases
– Ageing
– Degenerative diseases
– Cancer
Medical need Molecular correlates of disease
Diagnosis
Prognosis
Response prediction
Therapy monitoring
Toxicity prediction
Disease progression
New target discovery
Therapies with minimal side effects Small molecule Large molecule siRNA Aptamers Stem cells
Delivery technologies
Targeted
Optimal residence time
No accumulation
Preferably reversible
Technology
Nucleic acids:- S/N blot, RAPD, RFLP, Microarray (Expression, CGH, SNP), Sequencing, NGS Proteins :- WB, IHC, Proteomics, Sequencing Lipids Gravimetric quantification
methods Staining methods Nile Red/BODIPY Staining
Nile Red modifications Colorimetric SPV method TD-NMR method TLC/HPLC method GS-MS, LC-MS, Nano-ESI-FTMS
Carbohydrate analysis methods Chemical Methods
Condensation reactions Reducing power
Enzymatic Methods Physical Methods
Polarimetry, Refractive Index, Density, Infrared
Immunoassays Chromatographic Methods:-
HPLC, TLC, GC Metabalome – LC MS
Identification and antimicrobial choice
Molecular approach to diseases
Mutational disease
Heterogeneous
How do we
Diagnose cancer
Monitor cancer
Predict cancer
Restrain cancer
Prevent cancer
Cancer
Fundamental problem
Energy utilization block
Three key organs responsible
Liver
Muscle
Adipose tissue
Multiple organs affected
Do organs have different genomes??
Diabetes
Detecting fetal abnormalities in maternal blood
Targets in degenerative diseases
Neonatal/pediatrics/geriatric
Pathogen detection
Sustaining Proliferative
Signaling Insensitivity to anti-growth
signals
Evading apoptosis
Limitless replicative potential
Sustained angiogenesis
Tissue invasion and metastasis
Reprogramming of energy
metabolism
Evading immune
destruction
Genome Instability &
Mutation
Tumor promoting
Inflammation
Hallmarks of Cancer
We
ak
ER
sta
inin
g
M
od
era
te E
R s
tain
ing
Str
on
g E
R s
tain
ing
ER staining in Breast cancer
Large-scale, structural changes in DNA (such as amplification and deletion of large blocks of DNA) probably occur early in tumour development, in punctuated bursts of evolution, whereas point mutations may accumulate more gradually, generating extensive subclonal diversity EDWARD J. F OX & LAWRENCE A . LOEB 1 4 AU G U S T 2 0 1 4 | VO L 5 1 2 | N AT U R E | 1 4 3
Each cell is different YongWang et.al 1 4 AU G U S T 2 0 1 4 | V O L 5 1 2 | N AT U R E | 1 5 5
Major clinical changes are probably not due to SNVs but due to larger changes in genome
We are using one gene product to decide therapy and predict course!!! In a multi-genic/multifactorial, constantly mutating and evolving disease
Cancer gene panels
Association is not equal to causality/prognosis/ responsivity
Ho
tsp
ot
gen
es •ABL1AKT1
•ALK
•APC
•BRAF
•CDH1
•CTNNB1
•EGFR
•FBXW7
•FGFR2
•GNAQ
•GNAS
•KIT
•KRAS
•MET
•NRAS
•PDGFRA
•PIK3CA
•PTEN
•SMAD4
•SRC
50
Illu
min
a ca
nce
r ge
ne
pan
el •ALK
•APC
•ATM
•CDH1
•CDKN2A
•EGFR
•EZH2
•HNF1A
•HRAS
•KIT
•MET
•MLH1
•PTEN
•RB1
•RET
•SMAD4
•SMARCB1
•STK11
•TP53
•VHL
•AIP
94
CO
SMIC
- C
ance
r ge
ne
ce
nsu
s •ABI1
•ABL1
•ABL2
•ACSL3
•AF15Q14
•AF1Q
•AF3p21
•AF5q31
•AKAP9
•AKT1
•AKT2
•ALDH2
•ALK
•ALO17
•AMER1
•APC
•ARHGEF12
•ARHH
•ARID1A
•ARID2
•ARNT
522
TP53 ATM CDKN2A CSF1R ERBB2 ERBB4 EZH2 FGFR1 FGFR3 FLT3 GNA11 HNF1A HRAS IDH1 IDH2 JAK2 JAK3 KDR MLH1 MPL NOTCH1 NPM1
RET SMARCB1 SMO VHL PTPN11 RB1
BAP1 BLM BMPR1A BRCA1 BRCA2 BRIP1 BUB1B CDC73 CDK4 CDKN1C CEBPA CEP57 CHEK2 CYLD DDB2 DICER1 DIS3L2 EPCAM ERCC2 ERCC3 ERCC4 ERCC5 EXT1
EXT2 FANCA FANCB FANCC FANCD2 FANCE FANCF FANCG FANCI FANCL FANCM FH FLCN GATA2 GPC3 MAX MEN1 MSH2 MSH6 MUTYH NBN NF1 NF2
NSD1 PALB2 PHOX2B PMS1 PMS2 PRF1 PRKAR1A PTCH1 RAD51C RAD51D RECQL4 RHBDF2 RUNX1 SBDS SDHAF2 SDHB SDHC SDHD SLX4 SUFU TMEM127 TSC1 TSC2
WRN WT1 XPA XPC
ASPSCR1 ASXL1 ATF1 ATIC ATM ATP1A1 ATP2B3 ATRX AXIN1 BAP1 BCL10 BCL11A BCL11B BCL2 BCL3 BCL5 BCL6 BCL7A BCL9 BCOR BCR BHD
BIRC3 BLM BMPR1A BRAF BRCA1 BRCA2 BRD3 BRD4 BRIP1 BTG1 BUB1B C12orf9 C15orf21 C15orf55 C16orf75 C2orf44 CACNA1D CALR . . . .
Clinical Tests to guide diagnosis and therapy Flowcytometry- 43 Lineage markers available
FISH
Hemotologic Tumors Acute Lymphocytic Leukemia (ALL) B-ALL PEDIATRIC/ADULT 11q23 (MLL-Break Apart) t(9;22) (BCR/ABL/ASS) 17p13 (TP53) t(12;21) (ETV6/RUNX1) 9p21 (CDKN2A[p16]) CEP4,10, 17 Acute Lymphocytic Leukemia (ALL) T-ALL 14q11 (TCR-Alpha/Delta Break Apart) Acute Myeloid Leukemia (AML) 11q23 (MLL-Break Apart) t(8;21) (ETO/AML1) [M2] t(15;17) (PML/RARA) [M3] inv(16) (CBFB-Break Apart) [M4, Eos] Anaplastic Large Cell Lymphoma (ALCL) 2p23 (ALK-Break Apart) BM Transplant Monitoring CEP X/Y
Chronic Lymphocytic Leukemia (CLL) 11q22.3 (ATM)/17p13 (TP53) CEP12/13q14 (D13S319)/13q34 CEP6/6q23 (c-MYB) t(11;14)(CCND1/IGH) Chronic Myelogenous Leukemia (CML) t(9;22) (BCR/ABL/ASS) ervical) CML in blast crisis t(9;22) (BCR/ABL/ASS) 17p13 (TP53) CEP8 Multiple Myeloma (MM) with purified plasma cells (PPC) 13q14/13q34 17/17p13 (TP53) 1p/1q D5S23/D5S72/CEP9/CEP15 t(4;14) (FGFR3/IGH) t(11;14) (CCND1/IGH) t(14;16) (lGH/MAF) Also Available: IGH-Break Apart CEP7/CEP11 t(6;14) (CCND3/IGH) t(14;20) (IGH/MAFB)
Myelodysplastic Syndrome (MDS) 5q15.2/5q31 CEP7/7q31 CEP8 20q12 11q23 (MLL-Break Apart) Myeloproliferative Disease (MPD) 4q12 (FIP1L1/CHIC2/PDGFRA) 5q33 (PDGFRB-Break Apart) BCR/ABL (BCR/ABL/ASS) CEP8/CEP9 Non-Hodgkin’s Lymphoma (NHL) Burkitt: t(8;14) (MYC/IGH) DLBCL: 3q27 (BCL6-Break Apart) Follicular: t(14;18) (IGH/BCL2) Mantle: t(11;14) (CCND1/IGH) MALT Lymphoma: MALT1-Break Apart Solid Tumors ALK-Break Apart (NSCLC) PathVysion® (HER2/neu)(Breast) UroVysion® (Bladder) FHACT™
Clinical Tests to guide diagnosis and therapy
Hematologic Tumors- • ABL Kinase Domain Mutation Analysis (CML)
• B-Cell Clonality (IGH) (Lymphoma)-
• BCR/ABL Qualitative (CML)
• BCR/ABL Quantitative Major(p210) & Minor(p190) (CML)
• c-KIT Mutation Analysis (Exon 8 and 17) (AML)
• c-KIT Mutation Analysis (System Mastocytosis) (D816) (MPN)
• CALR Mutation Analysis (ET, PMF)
• CEBPA Mutation Analysis (AML)
• FLT3 Mutation Analysis (AML)
• IGHV Mutation Analysis (CLL)
• JAK2 V617F Mutation Analysis (MPN)
• JAK2 Exon 12 Mutation Analysis (MPN)
• MatBA®-CLL/SLL Array-CGH (CLL, SLL)
• MatBA®-DLBCL Array-CGH (DLBCL)
• MatBA®-FL Array-CGH (FL)
• MatBA®-MCL Array-CGH (MCL)
• MPL 515/505 Mutation Analysis (MPN)
• MYD88 Mutation Analysis (Lymphoma)
• NOTCH 1 Mutation Analysis (CLL)
• NPM1 Mutation Analysis (AML)
• SF3B1 Mutation Analysis (CLL)
• T-Cell Clonality (TCRβ) (Lymphoma)
• T-Cell Clonality (TCRγ) (Lymphoma)
• TP53 Mutation Analysis (CLL, DLBCL)
Solid Tumor- • BRAF Mutation Analysis (CRC) • EGFR Mutation Analysis (NSCLC) • KRAS Mutation Analysis (CRC, NSCLC) • NRAS Mutation Analysis (CRC, Melanoma, Thyroid cancer) • UroGenRA™-Kidney Array CGH (Kidney Cancer)
Array-CGH- • MatBA®-CLL/SLL Array-CGH (CLL, SLL) • MatBA®-DLBCL Array-CGH (DLBCL) • MatBA®-FL Array-CGH (FL) • MatBA®-MCL Array-CGH (MCL) • UroGenRA™-Kidney Array-CGH (Kidney Cancer)
5. Molecular Diagnostics
PCR to detect deletion, translocation, fusion and point mutations
Resistance mechanisms to chemotherapy Cytotoxic
agent Cancer type Target
Resistance mechanism
Antimetabolites (5-FU, methotrexate, gemcitabine and cytarabine)
Breast cancer, colorectal cancer, pancreatic cancer, gastric cancer, head and neck cancer, ovarian cancer, lymphoma and leukaemia
Thymidylate synthase and DNA synthesis
Increased target expression (thymidylate synthase)
MLH1 hypermethylation
Activation of survival pathways (for ex, ERBB signalling pathways)
Increased expression of anti-apoptotic proteins (for example, FLIP, BCL-2 or MCL1)
Platinum compounds (cisplatin and oxaliplatin)
Ovarian cancer, testicular cancer, sarcoma, lymphoma and small-cell lung carcinoma
DNA Reduced cellular uptake
Increased efflux
Increased DNA repair
MLH1 hypermethylation
Topoisomerase I inhibitors (irinotecan)
Colorectal cancer and small-cell lung carcinoma
Topoisomerase I
Drug efflux
Reduced target expression
Topoisomerase I mutations
Suppression of apoptosis
Activation of survival pathways (for ex., ERBB signalling pathways)
Topoisomerase II inhibitors (doxorubicin and etoposide)
Kaposi’s sarcoma, Ewing’s sarcoma, lung cancer, testicular cancer, lymphoma, leukaemia and glioblastoma
Topoisomerase II
MDR1 overexpression
Mutation or decreased expression of topoisomerase II
Decreased apoptosis due to mutation of p53
Microtubule poisons (paclitaxel and vinorelbine)
Lung cancer, ovarian cancer, breast cancer, head and neck cancer, Kaposi’s sarcoma
Tubulin Tubulin mutations
MDR1 overexpression
Chromosomal instability
Nature Reviews Cancer 13, 714–726 (2013) doi:10.1038/nrc3599
Resistance mechanisms to targeted therapies in cancer
Targeted therapy
Cancer type Target Resistance mechanism
Imatinib CML, ALL and GIST
BCR–ABL1, KIT and PDGFRα
Mutations of the target (for example,T315 in ABL1, T670I in KIT and T674I in PDGFRα, Elevated MDR1 expression
Dasatinib ALL and CML BCR–ABL1 T315 mutation in ABL1
Nilotinib CML BCR–ABL1 BCR–ABL1 up regulation, T315 mutation in ABL1
Trastuzumab ERBB2 +ve breast cancer
ERBB2 PTEN loss, Truncation of ERBB2, Activating mutations of PIK3CA, p95HER2
Activation of alternative signalling pathways (such as IGF1 and ERBB3),
Gefitinib
NSCLC EGFR Primary resistance
EGFR exon 20 insertion, BIM deletion, EGFR T790M
KRAS Mutation in exon 2 (codon 12–13)
Down-regulation of PTEN expression
Acquired resistance
Activation of the FGF2-FGFR1 autocrine pathway
FAS-NFkB activation, HGF overexpression
EGFR point mutation T854A in exon 21
EGFR point mutation D761Y in exon 19
EGFR point mutation L747S in exon 19
CRKL amplification
MET gene amplification, PIK3CA mutation, IGF-1R
hypophosphorylation
IGFBP3 downregulation, ERBB3 activation
Alternative pathway
activation
BRAF, CRKL, DAPK, FGF, HER2 , JAK2 , MED12 , NF-κB, PUMA, ROR1
VEGF
Histologic
transformation
Acquisition of stem cell properties
EMT (AXL, Notch-1 or TGF-β activation)
Small cell lung cancer transformation
Targeted therapy
Cancer type Target Resistance mechanism
Cetuximab
Head and neck cancer and colorectal cancer
EGFR Increased PTEN instability, Akt activation, Upregulation of EGFR, Dysregulation of EGFR internalization/ degradation and subsequent EGFR-dependent activation of HER3, Oncogenic shift- EGFR-dependent activation of HER2, HER3 and cMet ERBB2 amplification, EGFR-S492R mutation inhibits cetuximab binding, KRAS mutation
Vemurafenib Melanoma BRAF-V600E Elevated BRAF-V600E expression, Acquired mutations in KRAS, NRAS or MEK1,
Activation of EGFR, IGF1R and PDGFRβ pathways
Crizotinib NSCLC EML4–ALK Secondary EML4–ALK mutations or rearrangement, COT-mediated MAPK
reactivation, CD74–ROS1 rearrangement
Bortezomib
Multiple myeloma and mantle cell lymphoma
Proteasome Mutation in the binding site for bortezomib, Anti-apoptotic mechanisms
Bevacizumab
Colorectal cancer, NSCLC, glioblastoma and renal cell carcinoma
VEGF Activation of alternative signalling pathways (such as IGF1R, PDGFR, FGFR or MET),
Hypoxia-induced autophagy
Induction of tumour dormancy or an increase in the cancer stem cell niche
Resistance mechanisms to targeted therapies in cancer
The Metastasis phenomenon
Why metastasis – cancer and evolution; role of heterogeneity; why heterogeneity happens
Metastatic niche, role of bone marrow
Metastatic destiny – what determines organ of metastasis and when
Does fusion of cancer cell with white cell lead to metastasis
Serial progression and parallel progression – do they both exist or is one preferred – if so what determines this phenomenon
What are the molecular mechanisms of metastasis??
Cancer is a moving target
Changes with progression and treatment
EMT and two way switching
Heterogeneity and its role in progression
Mutations and heterogeneity in somatic cells, stem cells and stroma
Mechanisms of heterogeneity
Metastatic destiny
Natural selection and/ vs genetic drift
Equilibrium and its maintenance
Molecular mechanisms??
More Questions… New ways to diagnose and tailor
Patterns in cancer
– if all cancers evolve in roughly the same way, are there a fixed number of patterns for each stage of progression
– there is hope in the midst of chaos
Why certain parts of the genome are more susceptible?
– Analogies between evolution and cancer
– Chromosomal localization and reasons/effects
Detection of metastasis from blood
• Circulating Tumor Cells
• Ciruculating tumor DNA
Clonal evolution of cancer
• Small versus larger changes – when is it significant to biological behaviour
Role of subpopulations of cells in cancer- Endothelial cells, Stromal cells, Cancer stem cells, Epithelial cells
DNA functionality/ Exome in different cells, Transcriptome over time, Methylome, miRNA
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