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

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ER

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inin

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ing

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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