Personalized Medicine and Precision Diagnostics in Oncology

Robert D. Daber PhD, DABMG Director of Cancer Genomics Director of Research and Development

Personalized Medicine in the Spotlight

Traditionally, treatment of disease has been based upon broad standards and classifications

What is Personalized Medicine?

However, we know that not everyone responds the same:

By testing for Biomarkers we can better treat patients

Genetics and Cancer

• Cancer results from mutations in our DNA that cause the normal processes to misbehave (ie. cells grow and divide when they shouldn’t, or they do not die when they are supposed to)

– Some people are born with a mutation that predisposes them to developing cancer (Risk screening)

– Others develop cancer due to random events that happen in their DNA over time

• Since acquired DNA mutations are responsible for Disease, we can study the DNA of a tumor to understand why the tumor is misbehaving

Evolution of Pathology of Lung Cancer

• Before technologies existed to let us look at the DNA inside a tumor, we used less precise tests

• Lung Cancer is diagnosed by a pathologist reviewing a specimen collected from the lung under the microscope

– A series of immunostains, which are developed and validated by the pathologist, are used to determine the histologic type

– The pathologist then stages the tumor to assess the degree with which the cancer spread

Histological Type

Immunostain

Squamous-Cell Carcinoma

CK5/6 Positive CK7 Negative

Adenocarcinoma

CK7 Positive TTF-1 Positive

Large-Cell Carcinoma

TTF-1 Negative

Small-Cell Carcinoma

TTF-1 Positive CD56 Positive Chromogranin Positive Synaptophysin Positive

Evolution of Pathology of Lung Cancer • Based on the staging, a prognosis (survival prediction) is

provided.

• Treatment is decided based on National Guidelines

Five year survival

Clinical Stage NSCLC Small cell

IA 50 38

IB 47 21

IIA 36 38

IIB 26 18

IIIA 19 13

IIIB 7 9

IV 2 1

How are these guidelines determined?

By a team of Experts across the community

Next Generation Pathology of Lung Cancer • Through innovative assays in molecular pathology we now

know that Lung Cancer patient stratification and treatment based on several broad (common) histologic types is not sufficient

Lung Cancer Small cell Adenocarcinoma Squamous Cell Carcinoma Large Cell

KRAS Mutated EGFR Mutated ALK Mutated Ros1 Mutated

Increasing Resolution (Diagnostic Precision)

Personalized Medicine Precision Diagnostics

Test ALK Positive Likely to Respond

Test Negative Unresponsive to therapy

Test EGFR Positive Likely to Respond

Crizotinib

Afatinib

Standard Therapy

Precision Diagnostics Enables Personalized Medicine

NSC Lung Cancer

Precision Diagnostics results in Better Medicine

• Patients with ALK positive NSCLC have improved survival when treated with Crizotinib

• Patients without ALK mutations show no benefit

• Crizotinib can cost $200k/year J Thorac Dis. 2013 Oct;5 Suppl 5:S579-92.

Cancer not so Common After All?

AKT1 1%

ALK 3%

BRAF 2% DDR2

3%

EGFR 23%

FGFR1 16%

HER2 2%

KRAS 23%

MEK1 1%

METa 2%

NRAS 1%

PIK3CA 2%

PTEN 3%

RET 1%

ROS1 a 1%

unkown 16% Through innovation in

molecular testing we now know there are over 15 different molecular subtypes of Lung Cancer

Molecular Subtypes in MDS

• Multiple studies have found that approximately 50%-89.5% of MDS cases have recurrent mutations in at least one of ~18-20 different genes

• Mutations in these 18-20 genes represent 80-90% of all MDS cases with mutations

• 60-70% of cytogenetically normal MDS patients have a recurrent MDS mutation

Prevalence of Mutations in MDS

Mutational Incidences in Various Cancer

Disease State Common Mutations and Incidence Lung Cancer EGFR (10-35%), KRAS(15-25%), BRAF (1-3%), ALK (3-7%), MET (2-4%), ROS1 (1%), DDR2 (4%),

FGFR1 (20%), MAP2K1 (1%), AKT1 (1%), PIK3CA (1-3%), RET (1-3%), NRAS (1%)

Colorectal Cancer KRAS (36-40%), BRAF (8-15%), NRAS (1-6%), AKT1 (1-6%), PIK3CA (10-30%)

Breast Cancer ESR1 (73-75%), ERBB2 (18-20%), FGFR1 (10-13%), FGFR2 (1-2%), AKT1 (4%), PIK3CA (26%)

Melanoma BRAF (50%), KIT (2-6%), CTNNB1 (2-3%), GNA11 (34%), GNAQ (50%), MAP2K1 (6%), NRAS (13-25%)

MDS ZRSR2 (6.8%), TET2 (18.7%), DNMT3A (17.1%), ETV6 (1.3-4.2%), EZH2 (5.8%), RUNX1 (8.9%), U2AF1 (6.2%), TP53 (9%), ASXL1 (15.8%), SF3B1 (19.9%), SRSF2 (7.4-12%), BCOR (2.8-4.2%)

MPNs JAK2 (95% PV, 50-60% ET and PMF), CALR (20-30%), MPL (3-7%), SRSF2 (17%)

AML FLT3 (24.3%), IDH1 (6.4%), IDH2 (9.1%), KIT (8%), RUNX1 (5-12%), DNMT3A (7.8%)

Cancer is Rare at the Molecular Level

Number of unique mutations seen in analysis of ~1200 tumors

Unique Mutations Detected in our lab

# of

Var

iant

s

0 100 200 300 400 500 600 700 800

NCCN Recommended Biomarkers

MDS MPN AML LUNG CRC MELANOMA

ABL1 ASXL1 CALR CBL

CSF3R DNMT3A

EZH2 IDH1 IDH2 JAK2 MPL

SETBP1 SRSF2

ASXL1 CBL

DNMT3A ETV6 EZH2 JAK2 KRAS NRAS

PTPN11 RUNX1 SETBP1 SF3B1 SRSF2 TET2 TP53

U2AF1 ZRSR2

FLT3 KIT

NPM1 CEBPA

DNMT3A IDH1 IDH2 KRAS NRAS

RUNX1 TET2

EGFR KRAS ALK

ERBB2 ROS1 MET RET

BRAF KRAS NRAS

BRAF KIT

Blue text = recommended Black text = discussed as a consideration or emerging target

NCCN BIOMARKERS BY DISEASE TYPE

Genes important in Therapy Selection

There is an explosion of therapies guided by molecular targets

This requires rapid innovation in testing

Mutations in AML important in Prognosis

17

Reduction in intermediate (unclear) risk patients from 63% to 35% with addition of NGS panel

Testing

Molecular Testing is Transforming Clinical Trials

Clinical Trials are now widely based on

molecular profiles, and NOT histological types

NGS Molecular Testing is equally Complicated

Tissue Procurement Tissue Review DNA Isolation DNA Quality

Control Library

Preparation Sequencing ClearView Analytics

Report Generation Pathology

Portal

OnkoSight Portal

Population Analytics

High quality Molecular testing requires expertise at every step in the process

Evolving landscape in MDS

Concluding Remarks

• In oncology, the roles that mutations play in disease continue to emerge at a historic rate

• Each of these discoveries is fueled by technological testing advances

• Improved patient care requires an equivalent pace of innovation in diagnostics