can we predict who lives long (and well) with ovarian cancer? · limiting predictive value for a...

Can we predict who lives long (and

well) with ovarian cancer?

Michael A Bookman MD

Kaiser Permanente, San Francisco CA, USA

Can we predict who lives long (and well) with ovarian cancer?

“How long will I live?” Understanding the question...Pati

ent

and F

am

ily

Seeking a balance between information-seeking

behavior vs information-avoidance

Understanding the relationship between aggregate

statistical data, individualized outcomes, and

changes over time

Intense focus on technology and terminology:

including CA125, genetics, pathology reports, and

imaging modalities/reports

Expecting to receive a specific number

Oncolo

gis

t Appreciation for personal context: age, family

lifecycle events, goals, comorbidities, social

situation, and evolving information needs

Tendency to focus on treatment interventions,

rather than long-term outcomes (goals and toxicity) Crystal Ball v1.0

Breast Mortality

Uterus Mortality

Lung Mortality

Ovary Mortality

Ovary 5 Y OS

Ovary Incidence


Has disease-specific mortality improved? What are the facts...

Cisplatin Phase II

(GOG26)

Cisplatin-Paclitaxel

(GOG111 and OV10)

Carboplatin-Paclitaxel

(GOG158 and AGO)

IP Cisplatin

(GOG172) Olaparib Front-Line

Maint (SOLO1)

Paclitaxel Phase IICytoreductive

Surgery

Neoadjuvant Chemotherapy

(EORTC and CHORUS)

Discovery of BRCA1

and BRCA2

Dose-Dense

Paclitaxel (JGOG)

Bevacizumab

(GOG218 and ICON7)

National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention

Surveillance, Epidemiology, and End Results (SEER), National Cancer Institute

Cancer Statistics Center, American Cancer Society



• Trends in 5-Y OS are multifactorial, including

improved access to care, more accurate

diagnostic imaging, better supportive care,

and effective primary/recurrent treatment

interventions (surgical and medical)

Brady MS. GOG-SDC, 2011



National Center for Health Statistics (NCHS), Centers for Disease Control and Prevention

Surveillance, Epidemiology, and End Results (SEER), National Cancer Institute

Cancer Statistics Center, American Cancer Society

Ovary Incidence

Ovary Mortality

• A modest reduction in mortality may reflect a

decline in the use of hormonal therapy,

documentation of high-risk families, and

implementation of risk-reducing surgery (all

associated with lower incidence)

There is not yet any convincing evidence that

modern treatment has impacted mortality or cure

• Trends in 5-Y OS are multifactorial, including

improved access to care, more accurate

diagnostic imaging, better supportive care,

and effective primary/recurrent treatment

interventions (surgical and medical)


Limitations of Predictive Models

• Ovarian cancer demonstrates clinical and molecular heterogeneity, but models are weighted

toward dominant factors (stage, residual disease, and histology), which are highly clustered,

limiting predictive value for a typical patient

• Limitations are most apparent at initial diagnosis (The 80% Rule):

>80% of patients are Stage IIIC-IV at diagnosis

>80% of advanced-stage tumors are HGSC

~80% of surgical procedures achieve optimal (< 1cm) cytoreduction

~80% of patients achieve clinical complete remission (surgery and chemotherapy)

~80% of patients will recur within 3 years

• Published models at initial diagnosis incorporate surgical findings, without molecular factors,

functional imaging, or treatment outcomes, and do not have an impact on treatment selection

• Models post-recurrence are potentially more informative, incorporating information on treatment,

outcomes (PFI), tumor biology, comorbidities, and other factors


5 Year OS at Diagnosis (After Primary Cytoreductive Surgery)

Barlin JN, et al. Gynecol Oncol. 2012; 125:25-30

Tertiary cancer care center (n = 478), MSKCC NYC

Value Points

Age 65 y

ASA 1

Histology HGSC

FIGO Stage III

Residual <1cm

Albumin 3.5

Family Hx None

Value Points

Age 65 y 25

ASA 1 0

Histology HGSC 0

FIGO Stage III 73

Residual <1cm 3

Albumin 3.5 58

Family Hx None 12

Total Points 171

5 y OS 57%


5 Year OS at Diagnosis (After Primary Cytoreductive Surgery)

Barlin JN, et al. Gynecol Oncol. 2012; 125:25-30

• Includes non-serous early-stage disease

• endometrioid histology as higher risk, contrary to GOG and GCIG

meta-analysis

• No allowance for disease score, surgical complexity, or NACT

• Heavily weighted toward stage and albumin

• Does not differentiate LGSC from HGSC

• Includes Family Hx without actual genetics profile

• Urban tertiary cancer care center environment

• Includes non-serous early-stage disease

• Ranks endometrioid histology as higher risk, contrary to GOG and

GCIG meta-analysis

Winter WE, et al J Clin Oncol 2007; 25:3621-7


Predictors at Initial Diagnosis: 5 Y OS, 5 Y PFS

Gerestein CG, et al. BJOG. 2009; 116:372-80

Nomogram for 5 Y OS Nomogram for 5 Y PFS

• Weighted toward Hgb and Plts (surrogate markers, cytokines)

• Small series, but subsequently validated

• 118 Pts (Stage IIB-IV), all with Primary Cytoreductive Surgery

• Three academic medical centers, Netherlands


Tumor Biology: Pre-Operative Disease Score and Long-Term Outcomes

Horowitz NS, et al. J Clin Oncol 10.1200/JCO.2014.56.3106

• Data from GOG0182 FIGO Stage III-IV (n = 2,655)

• Analyzed according to R0 (microscopic) or MR (macroscopic) residual,

and pre-operative DS (disease score)

• Achieving R0 status generally associated with better outcomes

• However, outcomes with R0 not improved with high PRE-operative DS

• Value of aggressive cytoreduction not resolved in extensive disease

• We await prospective randomized data from TRUST


Multiple Predictors at Initial Diagnosis: 5 Y OS, 3 Y PFS, Platinum-Sensitivity

Kim SI, et al. Cancer Res Treat. 2018

• 866 pts, Two tertiary hospitals (2007 to 2016), Korea

• Separate nomograms tailored for OS, PFS, and platinum sensitivity

• Incorporates detailed tumor distribution, biologic markers, and treatment (NACT-ICS vs PCS)


Multiple Predictors at Initial Diagnosis: 5 Y OS, 3 Y PFS, Platinum-Sensitivity

Kim SI, et al. Cancer Res Treat. 2018

• 866 pts, Two tertiary hospitals (2007 to 2016), Korea

• Separate nomograms tailored for OS, PFS, and platinum sensitivity

• Incorporates detailed tumor distribution, biologic markers, and treatment (NACT-ICS vs PCS)

Nomogram for 3 Y PFS Nomogram for 5 Y OS


Predicting Long-Term Survival > 10 Y

Hamilton CA, et al. Gynecol Oncol. 2018; 148:275-280

• Long term OS associated with PS, endometrioid histology, stage III, absence of ascites, preoperative

disease score, R0 cytoreduction, lower surgical complexity (p<0.01)

• Prediction model fell short of pre-defined clinical utility, “molecular profiles are needed”

• Retrospective analysis, Pts enrolled on GOG0182

• 3010 evaluable Pts with 195 surviving > 10 Y (6.5%)

CovariateP

valueComparison OR (95% CI)

Age 0.50 10 year increase 0.95 (0.83 to 1.10)

Ascites 0.02 Yes ref: No 0.66 (0.47 to 0.94)

log(CA125) <0.01 Continuous 0.78 (0.69 to 0.89)

Residual

Disease<0.01 Micro ref: (Opt+Sub) 2.54 (1.80 to 3.58)

Performance

Status0.20 Asympt ref: Sympt 0.81 (0.59 to 1.11)

Stage <0.01 4 ref: 3 0.35 (0.17 to 0.73)

Surgical

Complexity0.03

CS-High ref: CS-Mod 0.70 (0.42 to 1.16)

CS-Low vs CS-Mod 0.59 (0.38 to 0.91)


Predicting Surgical Outcomes with Functional Imaging (PET-CT)

Shim SH, et al. Gynecol Oncol. 2015;136:30-6

• Detailed analysis of tumor distribution, tailored to individual surgeon “aggressiveness”

• Could supplement clinical assessments (comorbidities, obvious unresectable disease), but

would benefit from prospective evaluation of utility

• 343 Pts divided between model development and validation sets, single institution series, Korea


Survival After First Recurrence (Patients with HGSC enrolled on Phase III GOG trials)

0%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

0

5

10

15

20

25

30

35

40

45

50

<6 6-9 9-12 12-15 15-18 18-21 21-24 24-27 27-30 30-33 33-36 >36

Cu

mu

alative Rate o

f Recu

rrence

Ove

rall

Surv

ival

Po

st-R

ecu

rren

ce (

mo

nth

s)

Primary Treatment-Free Interval (months)

Cumulative

Recurrence

OS Post-Recurrence

(Median +/- 95% CI)

Rose PG, et al. Obstet Gynecol. 2019; 133:245-254

OS Post-Recurrence

Median = 21.4 months

The relationship between the primary treatment-free interval and overall

survival is a continuous variable, without a transition point at 6 months


Survival After First Recurrence (Patients with HGSC enrolled on Phase III GOG trials)

Rose PG, et al. Obstet Gynecol. 2019; 133:245-254

Value Points

Age 65 y

PS 1

Histology HGSC

FIGO Stage III

Residual <1cm

TFI 12

Value Points

Age 65 y 26

PS 1 10

Histology HGSC 0

FIGO Stage III 0

Residual <1cm 15

TFI 12 80

Total Points 131

Median OS 14 m

• Informative model, large number of patients

(n=4739), uniform protocol-based management

• Heavily weighted toward TFI

• Does not incorporate NACT, maintenance

therapy, genetic risk factors, molecular profile,

LGSC, or emerging treatments (PARPi, anti-

VEGF, Immune CPI)


Survival After Multiple Recurrences

Bookman MA, et al. Gynecol Oncol. 2017;146:58-63.

• Primary PFI >6 m correlated with improved OS through third-line therapy

• Patients receiving fourth-line therapy had similar OS, regardless of Primary PFI

• Retrospective analysis, community-based population (n = 750)

• Overall survival measured from the start of each line of therapy, segregated by Primary PFI

(n=274)

(n=188)

(n=199)

(n=271)

(n=118)

(n=157) (n=101)

(n=63)

What is the best way to share prognostic and predictive information with patients?

• Models can encourage dialogue with patients regarding expected outcomes (response,

remission duration, survival) but should be tailored according to individual

characteristics and personal information tolerance, emphasizing a range of outcomes,

rather than specific values

• Current models at initial diagnosis are not sufficiently accurate or precise to justify

changes in primary therapy, and provide information that is comparable to survival

curves segregated by stage, NACT vs PCS, and extent of residual disease.

• Future models incorporating surgical assessment, functional imaging, and molecular

markers have the potential to change treatments and provide more detailed outcomes.

• Current models are more informative after disease recurrence, and help to refine the

conversation with patients, through multiple lines of therapy, as new information

becomes available (progression-free interval, response to treatment, BRCA status,

molecular profiles, vital organ complications)


What can be accomplished: Patient Interactions

• Non-Randomized Phase II studies with “nomogram controls” to efficiently screen new

regimens prior to randomized trials

• Identify potentially modifiable clinical factors with a meaningful impact on long-term

outcomes, such as obesity, weight-based dosing, chemotherapy-induced neutropenia,

relative dose intensity, and physical activity, to optimize care for each patient

• Facilitate the selection of specific treatment strategies (such as aggressive primary

surgery, NACT, dose-dense chemotherapy, anti-VEGF, PARPi, maintenance). Design

prospective Phase IV studies to address these questions

• Future models should integrate more detailed molecular and clinical characteristics,

with updating to reflect current treatment paridigms


What can be accomplished: Research Opportunities

Phase II Non-Randomized

Investigational Therapy

(Enroll ~40 Pts)

As clinical endpoints accumulate

(RR, CA125, PFS), compare each

patient with expected outcomes

according to updated nomograms

Specify criteria for selection

of promising regimens

(randomized validation) and

rejection of inactive regimens

Thank You!

This house has “lived long”

(about 1000 years)

The White House

Canyon de Chelly

Arizona

can we predict who lives long (and well) with ovarian cancer? · limiting predictive value for a...

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