Determinants and Outcomes and... · christinaa.nguyen harvard&college&’15 mentors:...

CHRISTINA A. NGUYENHARVARD COLLEGE ’15

MENTORS: PETER GROENEVELD, MD, MSANDREW EPSTEIN, PHD, MPPMIRAR BRISTOL-‐DEMETER, MA

Determinants and OutcomesOF MUTATION TESTING

in Cancer Treatment

August 15, 2013

Roadmap

2

‣ Significance

‣ Overall aims

‣ Background

‣ Cancer in Pennsylvania

‣ Soma8c muta8on tests

‣ Overview of projects

‣ Project descrip8ons

‣ Aims, methods, findings, my role

‣ Policy implica8ons

‣ Lessons learned

Signi:icance

3

‣ Frequency of lung, colon, and breast cancers by county in Pennsylvania

Philadelphia, PA lung cancer (n = 9,958) colon cancer (n = 5,406) breast cancer (n = 10,737)

1. Pennsylvania Cancer Registry, Dept of Health, 2004-‐2011

Signi:icance

4

Es8mated Deaths in United States in 2013 by Cancer Type1%# 2%#2%# 3%#

4%#5%#

6%#

7%#

9%#

10%#12%#

39%#

Thyroid#

Endometrial#

Melanoma#

Kidney#(Renal#Cell)#Cancer#

Bladder#

NonCHodgkin#Lymphoma#

Leukemia#

Prostate#

PancreaLc#

Breast#

Colorectal#

Lung#

3. American Cancer Society, Cancer Facts and Figures, 2013

1) Lung

2)Colorectal

3)Breast

= 61% of all deaths from cancer

Signi:icance

5

* Rates are age-‐adjusted to the corresponding year’s US standard popula8on

2. Centers for Disease Control and Preven8on, US Cancer Sta8s8cs, 1999-‐2009

Lung Cancer

Incidence and Mortality Rates*

0"

10"

20"

30"

40"

50"

60"

70"

1999" 2000" 2001" 2002" 2003" 2004" 2005" 2006" 2007" 2008" 2009"

Rate%per%100,000%

Colorectal%Cancer%%Incidence%and%Mortality%Rates*%

PA"Incidence"Rates"

US"Incidence"Rates"

PA"Mortality"Rates"

US"Mortality"Rates"

Colorectal Cancer

0"

10"

20"

30"

40"

50"

60"

70"

80"

1999" 2000" 2001" 2002" 2003" 2004" 2005" 2006" 2007" 2008" 2009"

Rate%per%100,000%

0"

10"

20"

30"

40"

50"

60"

70"

80"

1999" 2000" 2001" 2002" 2003" 2004" 2005" 2006" 2007" 2008" 2009"

Rate%per%100,000%

Overall Aims

For lung, colon, and breast cancer:______________________

6

Advanced StageCancer

Muta8on Tes8ng

No Tes8ng

Outcomes‣ Chemotherapy‣ Costs‣ Survival‣ Dispari<es

Race

Technical Challenges

PCR

PDS / IBC / Medicare & Medicaid

1) PCR = Pennsylvania Cancer Registry2) PDS = Penn Data Store3) IBC = Independence Blue Cross4) Medicare & Medicaid

Background

7

‣ 3 soma8c muta8on tests used to help determine poten<al effec<veness of treatment

Epidermal Growth Factor Receptor

(EGFR)

✓Lung Cancer

KRAS

✓Lung Cancer✓Colon Cancer

Oncotype DX

✓Breast Cancer

Background

8

0 4 8 12 16 20 24 0.0

0.2

0.4

0.6

0.8

1.0

Prob

abili

ty o

f pr

ogre

ssio

n-fr

ee s

urvi

val

Months

0 4 8 12 16 20 24 0.0

0.2

0.4

0.6

0.8

1.0

Prob

abili

ty o

f pr

ogre

ssio

n-fr

ee s

urvi

val

EGFR muta8on posi8ve(261/437 = 59.7%)

Carbopla8n / paclitaxel (n=129)

Gefi8nib (n=132)

Gefi8nib (n=91)

Carbopla8n / paclitaxel (n=85)

EGFR muta8on nega8ve

Progression-‐Free Survival in EGFR Muta8on +/-‐ Pa8ents

Prob

abili

ty o

f pr

ogre

ssio

n-fr

ee s

urvi

val

Months

Prob

abili

ty o

f pr

ogre

ssio

n-fr

ee s

urvi

val

Months

Hazard Ra8o (95% CI) = 0.48 (0.36, 0.64)p < 0.0001

Hazard Ra8o (95% CI) = 2.85 (2.05, 3.98)p < 0.0001

4. Mok et al., N Engl J Med, 2009

Background

9

KRAS muta8on posi8ve(167/394 = 42.3%)

Prob

abili

ty o

f pr

ogre

ssio

n-fr

ee s

urvi

val

Months

Cetuximab plus best suppor8ve care (n=75)

Best suppor8ve care alone (n=76)

0 2 4 80.0

0.2

0.4

0.6

0.8

1.0

Prob

abili

ty o

f pr

ogre

ssio

n-fr

ee s

urvi

val

Months0

0.0

0.2

0.4

0.6

0.8

1.0

2 4 6 8 10

Cetuximab plus best suppor8ve care (n=110)

Best suppor8ve care alone (n=105)

KRAS muta8on nega8ve

Hazard Ra8o (95% CI) = 0.99 (0.73, 1.35)p = 0.96

Hazard Ra8o (95% CI) = 0.40 (0.30, 0.54)p < 0.001

Progression-‐Free Survival in KRAS Muta8on +/-‐ Pa8ents

5. Karape8s et al., N Engl J Med, 2008

Overview of Projects

10

1)Valida8on of claims data for soma<c muta<on tes<ng

2)Systema8c review of the suitability of using cytological specimens in soma<c muta<on tes<ng for lung cancer pa<ents

Claims Validation Systematic Review

Claims Validation

Methods

Findings

My Role

Aims

11

Epic ?


Claims Validation

Methods

Findings

My Role

AimsThrough a valida8on of claims data for soma8c muta8on tes8ng using data obtained from the Penn Data Store, verify that

1. eligible pa<ents are receiving muta<on tes<ng

2. muta<on tes<ng can be iden<fied in both the medical and administra<ve data records

12


Claims Validation

Methods

Findings

My Role

Aims

13

‣ Targeted population:

- 40 years of age or older

- diagnosed between 2007 and 2010

- invasive lung, colorectal, or breast cancer in Pennsylvania

- diagnosed or treated at Penn Med

Systematic ReviewClaims Validation

Claims Validation

Findings

My Role

Methods

Aims

14

Case Report Form


Claims Validation

Findings

My Role

Methods

Aims

15Source: Penn KnowledgeLink Epic EMR Training

EPIC Chart and Administra8ve Data


Claims Validation

My Role

Aims

T

Findings

Methods

16

Lung CA pa8ents at risk in PCR & PDS*

(n = 2,719)

Diagnosed in 2010(n = 834)

Had IBC Insurance(n = 1,164)

Screened(n = 365)

Treated or Diagnosed at Penn(n = 111)

Received Tes8ng(n = 28)

* Diagnosed between 2007 and 2010


Claims Validation

My Role

Aims

T

Findings

Methods

17

Colon CA pa8ents at risk in PCR & PDS*

(n = 574)

Diagnosed in 2010(n = 92)

Had IBC Insurance(n = 223)

Screened(n = 34)

Treated or Diagnosed at Penn(n = 10)

Received Tes8ng(n = 2)

* Diagnosed between 2007 and 2010


Claims Validation

My Role

Aims

T

Findings

Methods

18

30/121 = 24.79% received mutation testing

EPIC Medical & Administra8ve Records

Claims

Yes No

Yes

No

28 83 111

2 8 10

Lung

Colon

Sensi8vity = Claim +EPIC +

P

N Specificity = Claim –EPIC –

Sensi<vityLung = P 28

Sensi<vityColon = P 2

SpecificityLung = N 83

SpecificityColon = N 8

Systematic Review

Claims Validation

Aims

T

Methods

My Role

Findings

Claims Validation

19

‣ Data synthesis, and creation of tables and figures

‣ Literature reviews of overall aims and testing

‣ IRB protocol for subproject

‣ Case Report Form in REDCap

‣ Validation of claims data for testing in EPIC

‣ Next steps:

- Cross data with claims

- Manuscript development

EMR Review Systematic Review

Systematic Review

Methods

Findings

My Role

AimsAssess the suitability of using cytological specimens to perform soma<c muta<on tests for individuals with lung cancer

20

‣ Cytological specimen obtained by EBUS-‐TBNA


Systematic Review

Methods

Findings

My Role

Aims

21

Lung Cancer

Muta8on Tes8ng

No Tes8ng

Outcomes‣ Chemotherapy‣ Costs‣ Survival‣ Dispari<es

Technical Challenges


Systematic Review

Methods

Findings

My Role

Aims

22

Mutation found in 54% (280/516) of tumors completely tested (CI 50-59%)

NO#MUTATION#DETECTED#

EML4.ALK#7%#

DOUBLE'''''''''MUTANTS'3%'

BRAF'2%'PIK3CA'2%'HER2'MET'AMP'MEK1'NRAS'AKT1'

KRAS#22%##

EGFR##17%#

AKT1NRAS

MEK1

MET AMPHER2

PIK3CA 2%BRAF 2%

DOUBLE MUTANTS 3%

Incidence of Mutations Detected in Lung Cancer Patients

6. Johnson et al., WLCC, July 20117. Kris et al., ASCO, June 2011

EMR Review

Systematic Review

Findings

My Role

Methods

Aims

Systematic Review

23

‣ Inclusion criteria: original data on the sufficiency of cytological samples and the sensitivity of mutation testing

Ar8cles included in synthesis and analysis(n = 19)

Unique ar8cles in EMBASE and PubMed(n = 134)

Full-‐text ar8cles assessed for eligibility

(n = 41)

Ar8cles screened(n = 134)

Ar8cles excluded(n = 99)

Ar8cles excluded(n = 22)

Ar8cles added through cita8on review

(n = 6)

EMR Review

Aims

Systematic Review

Systematic Review

My Role

T

Findings

Methods

Aims

24

Savic, 2013Patriella, 2013

Esterbrook, 2013San8s, 2011

Nakajima, 2011Billah, 2011

Schuurbiers, 2010Sakairi, 2010

Garcia-‐Olive, 2010Molina-‐Vila, 2008

Smith, 2007Nakajima, 2007Horiike, 2007Nomoto, 2006

Sizes of bubbles propor8onate to sample size

0"

1"

2"

3"

4"

5"

6"

7"

8"

9"

10"

11"

12"

13"

14"

15"

0" 20" 40" 60" 80" 100"

Overall'Sufficiency'(%)'

Overall'Sufficiency'of'Cytological'Specimens'

EMR Review

Aims

Systematic Review

Systematic Review

My Role

T

Findings

Methods

Aims

25

Endobronchial ultrasound-‐guided transbronchial aspira8on (EBUS-‐TBNA)

EMR Review

Aims

Systematic Review

Systematic Review

My Role

T

Findings

Methods

Aims

26

* EBUS-‐TBNA = Endobronchial ultrasound-‐guided transbronchial aspira8on

0" 100" 200" 300" 400" 500" 600" 700" 800"

Nakajima,"2007"

Garcia5Olive,"2010"

Sakairi,"2010"

Billah,"2011"

Nakajima,"2011"

San>s,"2011"

Navani,"2012"

Esterbrook,"2013"

Number'of'Procedures'

EBUS2TBNA*'Sufficiency'of'Cytological'Specimens'

Successful"Procedures"

Total"Procedures"

EMR Review

Aims

Systematic Review

Systematic Review

My Role

T

Findings

Methods

Aims

27

0" 25" 50" 75" 100" 125" 150" 175" 200" 225"

Nomoto,"2006"

Horiike,"2007"

Nakajima,"2007"

Smith,"2007"

Molina9Vila,"2008"

Schuurbiers,"2010"

Billah,"2011"

Nakajima,"2011"

SanAs,"2011"

Navani,"2012"

Esterbrook,"2013"

Patriella,"2013"

Number'of'Procedures'

Sensi1vity'of'EGFR'Detec1on'in'Cytological'Specimens'

Successful"Procedures"

Total"Procedures"


Systematic Review

Aims

T

Methods

My Role

Findings

28

‣ Searches for and collection of relevant articles

‣ Review of studies

‣ Data extraction

‣ Production of tables and figures

‣ Next steps:

- Data synthesis and analysis

- Further development of tables and figures

- Manuscript development

Policy Implications

29

‣ improvements in treatment and survival

‣ improvements in the quality of documenta<on of muta<on tes<ng and concordance with insurance claims

Other&treatment&

Insufficient&chemotherapy&

drugs&

Soma6c&muta6on&tes6ng&

$

Policy Implications

30

–"Racial"dispari,es"–"Costs"

policies"to"improve"access"of"genomic"technologies"to"minority"popula,ons"





Lessons Learned

31

This summer, I....

‣ completed an IRB protocol

‣ created a case report form in REDCap

‣ read and analyzed lab results, and chart and administra<ve records in EPIC

‣ discovered that obtaining data is a wai<ng game and uphill baPle

‣ conducted a systema<c review

‣ honed my Excel and Stata skills while synthesizing data

Special Thanks

32

‣ Peter Groeneveld, MD, MS

‣ Mirar Bristol-‐Demeter, MA

‣ Andrew Epstein, PhD, MPP

‣ Anil Vachani, MD

‣ Joanne Levy, MBA, MCP; SUMR Staff; and LDI

‣ Yu-‐Ning Wong, MD, MSCE

‣ Lin Yang, MS

References

33

1. Pennsylvania Department of Health (2004-‐2011). Pennsylvania Cancer Registry.

2. Centers for Disease Control and Preven8on (1999-‐2009). United States Cancer Sta8s8cs. Na*onal Program of Cancer Registries.

3. American Cancer Society: Cancer Facts and Figures (2013). American Cancer Society.

4. Mok TS, Wu YL, Thongprasert S, Yang CH, Chu DT, Saijo N, et al. (2009). Gefi8nib or carbopla8n–paclitaxel in pulmonary adenocarcinoma. New England Journal of Medicine, 361(10):947-‐957.

5. Karape8s CS, Khambata-‐Ford S, Jonker DJ, O'Callaghan CJ, Tu D, Tebbul NC, et al. (2008). K-‐ras muta8ons and benefit from cetuximab in advanced colorectal cancer. New England Journal of Medicine, 359(17):1757-‐1765.

6. Johnson et al. on behalf of LCMC inves8gators (July 2011). WLCC, Abstract #O16.01.

7. Kris et al. on behalf of LCMC inves8gators (June 2011). ASCO, Abstract #CRA7506.

34

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