Aging, Frailty, and Medical Therapy for Colorectal Cancer: Who Should Be Treated and How?

Martine Extermann M.D., Ph.D.Moffit Cancer Center

Daniel Sargent Ph.D.Mayo Clinic Cancer Center

Richard M. Goldberg M.D.University of North Carolina

Lineberger Comprehensive Cancer Center

Elderly and/ or frail patients with colorectal cancer - a clinician's approach

Richard M. GoldbergUniversity of North Carolina

Lineberger Comprehensive Cancer Center

Disclosures: Consulting and/or Research Support

• Abbott • Amgen• Astra Zeneca• BMS• Enzon• Genentech

• GHI• ImClone• Myriad• NCI• Poniard• sanofi-aventis

Statistics

• 2/3 of pts with mCRC are >65 years old• 40% > 75 years old

Edwards , Cancer 94: 2766-92, 2002And http:seer.cancer.gov

<20 20-34 35-44 45-54 55-64 65-74 75-84 >8505

1015202530

% of cases by age

Additional Expected Years of Life By Age/Gender

60 70 800

5

10

15

20

25

30

womenmen

Kohne, Oncologist , 13:390, 2008

2

3.7 43.3

2.41.9 1.7

1.3 1.2 1.10.6 0.7 0.5 0.5 0.2 0.3

4.7

109.4

7.7

5.6

4.5

3.3 3.1

1.9 1.5 1.3 1.1 0.8 0.8 0.6 0.30

2

4

6

8

10

12

0 0.5 1 1.5 2 2.5 3 3.5 4 4.5 5 5.5 6 6.5 7 7.5 8

Year

Rec

urre

nce

Rat

e

Stage 2Stage 3

Stage 2: 67% of recurrences occur

by 3 years

Stage 3: 75% of recurrences occur by 3

years

Colon Cancer Stage II vs Stage IIIRecurrence Rate by 6 mo intervals

Sargent, JCO 2005

STEPP analysis – Oxaliplatin-based therapyage cutoff validation for observed lack of added treatment effect

Age 68.5

3 Ye

ar D

FS

39 44 48 51 54 55 58 60 62 64 66 68 70 73 74

020

4060

8010

0

(n=547) (n=509) (n=584) (n=606) (n=556) (n=544) (n=523) (n=633) (n=512) (n=518) (n=513) (n=503) (n=543) (n=502) (n=338)(n=547) (n=509) (n=584) (n=606) (n=556) (n=544) (n=523) (n=633) (n=512) (n=518) (n=513) (n=503) (n=543) (n=502) (n=338)

Subpopulations by Median Age

ControlExperimental

p-value = 0.318 Age 70

A Five Part Talk

• Surgery• Radiation• A few relevant chemotherapy/biologic reports• Comparative effectiveness research• Clinical approaches

Percent of Patients Who Died Related to Colorectal Cancer Surgery

• 6457 Dutch patients• 4.4 % overall mortality, 10% and 13% if >80

Damhuis, Int J Colorect Dis, 11:45-48,1996

<50 50-9 60-9 70-9 80-9 >900%

2%

4%

6%

8%

10%

12%

14%

Colorectal Cancer Surgery in England:Laparoscopic surgery may be better

• 29,000 patients > 75 years old operated from 1996-2007 at National Health Service Hospitals

• Hospital Episodes Database• Emergency procedures excluded• 865 laparoscopic procedures

– 12% in 2007– Postoperative mortality 3.1% – 5.4% for open colectomy, p=0.003– Range between hospitals 0-14.1%

Faiz, Colorectal Dis, epub April 19,2010

Liver Resection In >70 Year Old Patients

• 1624/7764 (21%) Patients operated on at LiverMetSurvey Registry Centers in Europe were >70– 70-74: 999 (13%)– 74-80: 468 (6%)– >80: 157 (2%)

• 6% were over 70 in 1990, 26% in 2007

Adam, Br J Surg 97:366-76, 2010

Liver Resection In >70 Year Old Patients

• 1624/7764 (21%) Patients operated on at LiverMetSurvey Registry Centers in Europe were >70– 70-74: 999 (13%)– 74-80: 468 (6%)– >80: 157 (2%)

• 6% were over 70 in 1990, 26% in 2007

Adam, Br J Surg 97:366-76, 2010

<2035-44

55-6475-84

0102030

% of cases by age

Outcomes: > 70 versus <70 years old

• 60 day perioperative mortality p< 0.001– 3.8% versus 1.6%

• 60 day perioperative morbidity– 32% versus 29%

• 3-Year overall survival 57% vs 60% p <0.001• Median overall survival

– 43 months versus 47 months

5-Year Overall Survival<70 Years old > 70 years old P-value

> 3 lesions 24% 12% <0.001

Unilateral 58% 73% 0.001

Synchronous 57% 42% <0.001

Perioperative Ctx 44% 34% <0.001

Liver Resection In >70 Year Old Patients

• Preoperative Chemotherapy– No survival difference between those who did or

did not have preoperative chemotherapy– Morbidity was higher 38% vs 32% p=0.03

• Postoperative chemotherapy– An independent predictor of survival HR 1.79,

p <0.001

Radiation for Rectal Cancer

• Meta-analysis of 22 randomized trials of surgery +/- RT – Preop: 6350 patients in 14 trials– Postop: 2157 patients in 8 trials

Colorectal Cancer Collaborative Group, Lancet 356:968-74, 2000.

Rectal Cancer Meta-AnalysisOverall survival

Ro resection Local recurrence

Dead, Rectal Ca

Dead, other

Surgery 62% 86% 50% 4%

Surgery + RT 63% 45% 8%

Preop 85% 46% lower

Postop 37% lower

P-value 0.06 NS 0.002 0.003 0.0001

Age and Death Rates:Rectal Cancer and “Other”

Age Dead of Rectal CA Dead of Other Cause

Surgery Surgery + RT Surgery Surgery + RT

<55 48% 31% 5% 6%

55-64 40% 38% 6% 9%

65-74 43% 32% 13% 21%

>75 40% 32% 26% 34%

Dutch TME Trial Elderly Analysis

• All patients received a total mesorectal excision

• All patients given RT received 5 fractions of 500 cGy preoperatively

• 1356 patients– 17% older than age 75

Rutten, Eur J Cancer, 43: 2295-3000, 2007

TME Study Statistics< 75 >75 P-value

Overall Survival 70% 43% <0.0001

Spared Sphincter 66% 60% <0.001

1-month death rate 2.5% 7.8% <0.0001

6-month death rate 3.3% 14% <0.0001

Outcomes By Age and Study Arm< 75 >75

Surgery Surgery + RT

P-value Surgery Surgery + RT

P-value

Overall Survival 72% 72% 0.3 43% 48% 0.27

Local Failure 11% 5.2% 0.001 14% 5.4% 0.02

DFS 70% 81% 0.44 66% 81% 0.03

Conclusions

• Older patients benefit from RT + surgery more than younger patients

• Older patients have a significantly higher complication and early death rate

• Patients should be in optimal shape before surgery

• If life expectancy exceeds 1 year combined modality therapy is best in the older patient

Bevacizumab Pooled Analysis

• Three 1st line and one 2nd line trial with 5-FU, irinotecan and oxaliplatin

• 1,142/3007 (38%) > 65 years old• 24% > 70 years old

Age\PFS + Bev - Bev P-value< 65 9.5 mos 6.7 <0.0001> 65 9.3 6.9 <0.0001> 70 9.2 6.4 <0.0001

Cassidy, J Cancer Res Clin Oncol 136:737-43, 2010

Overall Survival With and Without Bevacizumab

Age\ Median OS + Bev - Bev P-value

< 65 19.9 mos 16.5 <0.0001

>65 17.9 15 <0.015

>70 17.4 14.1 =0.005

Toxicity As a Function of Age

Sources of Additional Data

• Limited numbers of patients >75 are accrued to Phase II or III studies

• These patients are carefully selected• There is only so much data out there• We need other sources of information

Comparative Effectiveness Research

Health Care Quality

• IOM:– The degree to which health services for individuals

and populations increase the likelihood of desired health outcomes and are consistent with current professional knowledge.

– Underuse, overuse, misuse

Outcomes – Comparative Effectiveness

• Comparative effectiveness research (CER) is the generation and synthesis of evidence that compares the benefits and harms of alternative methods to prevent, diagnose, treat, and monitor a clinical condition or to improve the delivery of care.

• The purpose of CER is to assist consumers, clinicians, purchasers, and policy makers to make informed decisions that will improve health care at both the individual and population levels.

(IOM, 2009)

Federal Funding Mechanisms

• AHRQ:– Agency for Health Research and Quality Quality

• improvement and patient safety.• Outcomes and effectiveness of care.• Clinical practice and technology assessment.• Health care organization and delivery systems. • Primary care (including preventive services).• Health care costs and sources of payment.

AHRQ Cancer Comparative Effectiveness

• Medicare Modernization Act: – “…conduct research to improve the quality, effectiveness, and efficiency of

Medicare, Medicaid, and State Children Health Insurance (SCHIP) programs.”

• Increasing emphasis on patient-level attributes (rather than “the average patient”) that may modify the balance of benefits or harms can lead to more personalized medicine, reducing the pressure to try alternatives found to be ineffective in similar subgroups.

• DEcIDE : Decisions about effectiveness research : – Expeditiously develop valid scientific evidence about the outcomes,

comparative clinical effectiveness, safety, and appropriateness of health care items and services

AHRQ Cancer DEcIDE Comparative Effectiveness

• Clinical trials– Relatively homogeneous population

• Younger, healthier, more likely Caucasian– Randomization to control for unmeasured (and

unmeasurable) heterogeneity

• CER– Examinations prioritizing the context of heterogeneity

• Better representativeness• Examination of subpopulations

ExamplesStage II/III Colorectal cancer Chemotherapy

Trials– NEJM, 2004– JCO, 2007

vs.

SEER:

II III IVMean Age 71.4 69.0 67.7

Race White 83.7% 81.2% 79.3% Black 9.0% 10.6% 13.5% Other/Unk 7.4% 8.3% 7.2%

Gender Female 52% 52% 51% Male 48% 48% 49%

*Source: SEER, 2004-2005 data.

Stage at Diagnosis

Generalizable, results by sub-population

NCDB study: n=86,000; hospitals=560(Jessup et al, JAMA, 2005)

CER and Outcomes Research

• CER has value, maximizing our understanding with observational data– Fast– Inexpensive– Can be very large databases

• CER will not replace clinical trials• Larger future of outcomes research: Moving

from studies of “what” to understanding “why”

CER and Outcomes Research:Emerging Directions

• Application of advanced methods using secondary data, including the development of new methods– AHRQ-sponsored White papers:

• “Registries for Evaluating Patient Outcomes”– July 2009 DEcIDE RFTO (~$500,000 x 1 year):

• “Methods to Study the Heterogeneity of Treatment Effects in Comparative Effectiveness Research”

– Fall 2009: 10 x $10 million awards• Clinical and Health Outcomes Initiative in Comparative

Effectiveness

CER and Outcomes Research:Emerging Directions

• Development and application of advanced data– Developing new data sources

• Retrospective studies• Prospective studies

– Fall 2009: $48 million• New Registries for CER

• Data needs:– Sample size, generalizability of claims-based studies– Richness, depth of measures of survey / interview-based

studies– Clinical detail, follow-up of registries

Why new data and models?A prevailing model of cancer, comorbidity, and outcomes:

• Current models are linear, simply specified, and fairly simple• Randomization controls for many relevant factors• Intent-to-treat is dominant

Source: Geraci, JM, et al. (2005). “Comorbid Disease and Cancer: The Need for More Relevant Conceptual Models in Health Services Research.” Journal of Clinical Oncology. 23(30):7399-404.

Why new data and models?

• Because its just not that simple• We are increasingly interested in other outcomes,

including Patient Reported Outcomes (PROS)• With observational data, you can’t randomize-out

confounders and effect modifiers– Before we can make assumptions with them, we need to study

them• Need new data that allow rich characterization of factors

at multiple levels, with a substantial sample size for generalizability of findings and sub-population analysis

What Data Sources Might We Use to Evaluate Effectiveness?

1. SEER-Medicare

2. NYSCR/CCR-Medicaid

3. CanCORS

4. NCCN

– SEER • For purposes of anchoring comparison for overall mortality

Conclusions

• Elderly colorectal cancer patients are underrepresented in clinical trials

• Surgery (including hepatic resections) and RT for rectal cancers can be done safely and effectively but leads to higher early death rates

• CER can help answer some of the questions in this subpopulation