NCI Workshop on the Biology, Prevention NCI Workshop on the Biology, Prevention and Treatment of Relapse after Allo-HCT and Treatment of Relapse after Allo-HCT Subcommittee III Report: Epidemiology Subcommittee III Report: Epidemiology

and Natural History of Relapseand Natural History of Relapse

Co-Chairs:Co-Chairs: Steven Pavletic, NCI; Daniel Weisdorf, U. MinnesotaSteven Pavletic, NCI; Daniel Weisdorf, U. Minnesota

Committee Members:Committee Members: James M. Foran, U. AlabamaJames M. Foran, U. Alabama

Shaji Kumar, Mayo ClinicShaji Kumar, Mayo ClinicMarcos de Lima, MDACC Marcos de Lima, MDACC

Mohamad Mohty, U.NantesMohamad Mohty, U.NantesMarcelo Pasquini, CIBMTRMarcelo Pasquini, CIBMTR

Mei-Jie Zhang, CIBMTRMei-Jie Zhang, CIBMTR

Update November 2, 2009Update November 2, 2009

Epidemiology Subcommittee

• Committee Charge:• Review available data on relapse after allo-HCT • a) incidence• b) risk factors• c) survival after relapse• d) statistical methods

Too many topics

• AML • MDS• CML• ALL • Myeloma• Lymphoma• CLL• GVHD/GVL• Common Risk Factors for relapse• Statistics

Epidemiology of Relapse

Moderators: Pavletic and Weisdorf

1) Myeloid malignancies and ALL (Mohty, de Lima, Weisdorf)

2) Lymphoma, CLL (Foran, Pavletic)

3) Myeloma (Kumar)

4) GVHD/GVL (Pasquini, Pavletic, Weisdorf,)

5) Statistics (Zhang)

6) Discussion

Epidemiology and Natural History

of Relapse following Allogeneic

Cell Transplantation:

AML, MDS, CML and ALL

Mohamad Mohty, Marcos de Lima

and Daniel Weisdorf

NCI Workshop on Biology, Prevention and Treatment of Relapse After Allogeneic HCT

Subcommittee III: Epidemiology and Natural History of Relapse

Leukemia relapse is the major cause of treatment failure after allo-HCT in AML

AML in CR1: 10-40%Advanced disease stage: >40-50%After RIC allo-HCT: 18-50%

AML: Relapse Incidence post Allo-HCT

Martino et al, Blood 2006

AML: Risk Factors for Relapse post Allo-HCT

- Transplant beyond first CR

- Poor risk cytogenetics, FLT3-ITD mutations

- Secondary AML (prior chemo/radiotherapy; prior MDS)

- Age >60 years and comorbidities

- HLA-matching

- Single CB transplantation

- Reduced intensity and non-myeloablative conditioning

- In vitro and in vivo T cell depletion

- Gender donor/recipient combinations other than FM

- Specific KIR haplotypes

AML: Outcome post Relapse after Allo-HCT

Generally poor long-term survival and limited DLI response

Risk factors influencing outcome

- Patient age

- Remission duration

- Use of DLI

- Favorable cytogenetics

- Presence of comorbidities

- Disease stage at relapse (e.g. lower tumor burden at relapse)

Probability of relapse: 20% - 60%

(outcomes frequently reported ‘mixed’ with AML patients)

Trends that may confound comparisons with historic data:

- new treatments (ie, hypomethylating agents,

lenalidomide )

- treatment of older patients

- use of reduced-intensity regimens

- length of follow-up

MDS: Relapse Incidence post Allo-HCT

Not controversial

- disease stage (driven mostly by blasts and poor prognosis chromosomal abnormalities

- T cell depleted grafts

Controversial

- Secondary MDS

- Preparative regimen intensity

- Development of chronic GVHD

- Mixed chimerism

- Stem cell source

Unlikely/no evidence

- Recipient age / Donor age

- Acute GVHD / Related versus unrelated donor

MDS: Risk Factors for Relapse post Allo-HCT

Poor (0-40% long-term survival)

Major determinants for survival :

- remission duration

- MDS stage at relapse

Covariates that may influence outcome:

- comorbidities, age, patient preference, ongoing GVHD

MDS: Outcome post Relapse after Allo-HCT

Areas needing study

- Impact of novel therapies

- Impact of new molecular

classifications

- Incidence of relapse after

haplo and CBT

- Preferred timing in light of

availability of haplo and

UCB

- Role of maintenance

therapies

- Role of MRD detection in

predicting relapse.

- Relapse rates after hypomet. agents- Impact of new MDS class.- Incidence of relapse after haplo and CBT- Preferred timing in light of availability of haplo and UCB- Hypomethylating agents, angiogenesis inhibitors and other medications as maintenance therapy-Role of MRD detection in predicting relapse

AML MDS

Risk Factors Relapse Incidence

Outcome Risk factors influencing

outcome

- Age

- Disease stage

- Time interval from

diagnosis to transplant

- Donor type

- Unclear if

relapse rate is

higher with

older age

- 20% (CP) to

65% (BP)

- >1-2 years

worse results

Disease stage

= major

determinant of

survival:

CP: 30-60%

AP: 10-40%

BC: 0-10%

- PS

- Presence of

comorbidities

- Disease stage

upon relapse

- Remission

duration

- Sensitivity to

TKIs

Influence of BCR-ABL kinase mutations on HSCT outcomes ?

Impact of new TKI in predicting peri-HCT outcomes ?

Will pts who failed TKI pre-HCT respond following post-HCT relapse ?

Relapse rates after transplants from alternative donors ?

CML: Relapse after Allo-HCT

Leukemia relapse is the major cause of treatment failure after allo-HCT in ALL

25-50% in CR1 40-60% in CR2

ALL: Relapse Incidence post Allo-HCT

Goldstone, Blood, 2008

ALL: Risk Factors and outcome after Relapse

Short survival and limited DLI response

Early relapse worse; no other data

Sib vs URD: ALL Adjusted Multivariate risk of Relapse

Ringden et al, Blood, 2009

ALL RR 95% CI P

URD vs Sib 0.95 0.68-1.33 .78

Acute GVHD 0.78 0.57-1.07 .13

Chronic GVHD

0.69 0.47-1.01 .058

ALL: Areas needing study

Who will relapse?

- Detailed epidemiology

- Age/WBC/cytogenetics/time to HCT

- Duration of CR1

- MAC vs. RIC

Impact of MRD pre-allo-HCT:

- What assay? Does it predict relapse?

- Can intervention prevent relapse?

James Foran & Steven PavleticJames Foran & Steven Pavletic

NCI Workshop on Biology, Prevention and Treatment of Relapse After Allogeneic HCT

Subcommittee III: Epidemiology and Natural History of Relapse

Epidemiology and Natural History of Relapse following Allogeneic Cell

Transplantation for Lymphoma & CLL

Issues in Studying Epidemiology of Lymphoma Relapse After Allo-HCT

• Lymphoma most common hematologic malignancy

• No prospective studies incorporate Allo-HCT into initial treatment planning (*MCL – exception)

• Small retrospective studies with limited power

• Heterogeneous studies

– Divergent histologies

– Mix of refractory & sensitive patients

– Multiple lines of prior therapy including Auto-SCT

– Heterogeneity of transplant regimens

• GVL effect may have variable potency

GVL in NHL

• Similar relapse rate in allogeneic vs. syngeneic HCT CIBMTR/EBMT analysis

• Graft-versus-lymphoma effect in question, particularly for intermediate-grade histology

Bierman, J Clin Oncol 21:3744, 2003

• Emerging evidence of GVL in HL; seen with RIC

• Lower relapse risk for indolent, T-cell, mantle-cell lymphoma

CIR CIR SyngeneicSyngeneicvs. Allo-HCT & vs. Allo-HCT & Auto-SCT (p=ns) Auto-SCT (p=ns) HG

Int. Grade

LG

Relapse Risk & Histology

Corradini, Leukemia 21:2316, 2007

•Greatest relapse risk with aggressive histology (DLBCL)

& Hodgkin Lymphoma•Lower relapse risk with Follicular lymphoma, Mantle-Cell (esp. after RIC/NMA), & PTCL

•RIC/NMA: Relapse rate related to histology & disease status:

Stage n RelapseRate per PY

Low Risk CLL CR 7 0.00 NHL, low grade Not CR 34 0.15 NHL, low grade CR 9 0.18 Waldenström ‘s Advanced 9 0.19 NHL, mantle cell CR 16 0.19 NHL, mantle cell Not CR 25 0.20 NHL, high grade CR 26 0.23

Standard Risk CLL Not CR 75 0.26

High Risk NHL, high grade Not CR 36 0.57 HD CR 13 0.62 HD Not CR 38 0.72

Adapted from: Kahl, Blood 110:2744, 2007

NMA: Relapse Rates by Diagnosis & Disease Stage Groups

Allo-HCT after Auto-SCT

• Very high relapse rate after salvage MA Allo-HCT following prior Auto-SCT (n=114)– Disease progression 45% at 1yr, 70% at 5yrs

• Increase risk of progression if: – no TBI for NHL RR 3.02 (95% CI 1.71-5.32)– Chemoresistant RR 2.90 (95% CI 1.38-6.08) – 10 Induction Failure, untreated RR 3.45 (95% CI 1.74-6.87)

Freytes, Blood 104:3797, 2004

Hodgkin Lymphoma

• Refractory disease and after failure of Auto-SCT • Relapse greatest contributor to treatment failure1,

more common with bulky disease

• 60% disease progression at 3-5 yrs– Disease status at Allo-HCT (presence of CR &

chemosensitivity) only factor significantly associated with relapse after MA2

(n= 167, 42% chemoresistant)

1Peggs, Br J Haematol 143:468, 2008 2Peniket, BMT 31:667, 2003

Hodgkin Lymphoma – EBMT RIC Series (n=285)

• Disease progression: 41% at 1yr, 58% at 5yrs1

• Increase disease progression:Refractory RR 2.1 (95% CI 1.5-2.9)>3 lines prior therapy RR 1.7 (95% CI 1.2-2.5)Donor ♀: recipient ♂ RR 1.5 (95% CI 1.0-2.2)

• Similar rate progression/relapse after URD in CIBMTR2

• Suggestion of lower relapse risk with haploidentical donor3

Related HR 0.35 (95% CI 0.2-0.8), p=0.01Unrelated HR 0.43 (95% CI 0.2-0.9), p=0.03

• DLI for HL (n=64)1

13/41 evaluable achieved CRMedian survival 20 months after DLI

1Robinson, Haematologica 94:230, 2009 2Devetten, BBMT 15:109, 20093Burroughs, BBMT 14:1279, 2008

Hodgkin Lymphoma – RIC vs. MA

• ↑ relapse rate RIC (57%) vs. MA (30%)1

RIC RR 2.48 (95% CI 0.77-2.79)Bulk disease @ Dx RR 3.10 (95% CI 1.32-7.24)Refractory Disease RR 1.51 (95% CI 0.95-2.39)

• Low-dose TBI in RIC RR 3.10 (95% CI 1.32-7.24)• Children: ↑ relapse with RIC vs. MA (RR 4.4, p=0.05)3

• No adverse effect of alemtuzumab in RIC regimens2

1Sureda, J Clin Oncol 26:455, 20082Peggs, Br J Haematol 139:70, 20073Claviez, Blood 114:2060, 2009

•* cGVHD- lower risk relapse1 →

Aggressive NHL

• Disease sensitivity predicts relapse risk• Relapse at 3 yrs: refractory 70% vs. chemosensitive 30%2,8

RR ~3 if progression within 12 months initial therapy>3 prior regimens associated with ↑ relapse risk5

‘Stable’ refractory better than ‘progressive’3

• No clear benefit of rituximab pre-Allo-HCT6

• No clear impact of GVHD on relapse7 • Responses to withdrawal immunosuppression or DLI

reported4

1Doocey, Br J Haematol 131:223, 2005 2Aksentijevich BBMT 12:965, 20063Hamadani, BBMT 15:547, 2009 4Bishop, Ann Oncol 19:1935, 20085Law, BBMT 12:703, 2006 6Ratanatharathorn, Br J Haematol 145:816, 20097Ramadan, BMT 42:601, 20088Corradini, Leukemia 21:2316, 2007

Aggressive NHL – RIC & NMA

• Generally less chemo-resistant patients • Increase relapse rate NMA (36%) vs. MA (11%), p<0.011

NMA RR relapse 3.3 (95% CI 1.2-9.2), p=0.03

• In NMA – risk of relapse2:Age HR ↑ 0.68 (p=0.08) each decadeChemosensitive HR 0.2 (p=0.009)

• In RIC – relapse rate 33%3

• *Lower relapse rate with Mantle-Cell lymphoma & PTCL4-8

– Chemo-refractory AITL ↑ relapse (28% vs. 15% at 3 yrs), p=0.041Tomblyn, BBMT14:538, 2008 2Rezvani, Br J Haematol 143:395, 20083Thomson, J Clin Oncol 27:426, 2009 4Khouri, J Clin Oncol 21:4407, 20035Kyriakou, J Clin Oncol 27:3951, 2009 6Ganti, Ann Oncol 16:618, 20057Kim, Blood 108:382, 2006 8Baron, J Clin Oncol 24:4150, 2006

Indolent Lymphoma

• Less relapse than aggressive NHL or HL, generally <20%• Significantly lower after Allo-HCT than Auto-SCT1-3

(*not compared with syngeneic)

• Increase risk of relapse with MA:Chemo-resistant RR 1.69 (95% CI 1.27-2.23)Abnormal LDH RR 1.51 (95% CI 1.21-1.87)KPS<90% RR 1.31 (95% CI 1.07-1.61)BM+ RR 1.31 (95% CI 1.04-1.65)>2 yrs from dx RR 1.40 (95% CI 1.06-1.84)

1van Besien, Blood 92:1832, 1998 2van Besien, Blood 102:3521, 20023Ingram, Br J Haematol 141:235, 2008

Indolent Lymphoma RIC/NMA

• Relapse risk after NMA1:Transformed disease HR 4.85 (95% CI 1.5-15)Tandem Auto-Allo HR 5.47 (95% CI 1.5-21)Chemo-refractory HR 5.37 (95% CI 1.7-17)

• Increase risk relapse after RIC vs. MA, RR ~3 (p=0.04)2,5

• Impact of mixed chimerism on relapse unclear3

• Increase relapse risk for MRD by PCR (Bcl-2 or IgH) after RIC (40% vs. 0% if PCR-, p=0.010)4

1Rezvani, J Clin Oncol 26:211, 2008 2Vigouroux, Haematologica 92:627, 20073Khouri, Blood 111:5530, 2008 4Corradini, Leukemia 21:2316, 20075Hari, BBMT 14:236, 2008

CLL: Progression after Allo-HCT

Relapse % PFS %

MA1,2 2-32 30-65

RIC3 7-48 34-47

TCD4 68 24

Twins5 50 45

1Delgado, Blood 114:2581, 20092Montserrat, Blood, 107:1276, 20063Ben-Bassat, BMT, 39:441, 20074Gribben, Blood, 106:4389, 20055Pavletic, Leukemia, 21:2452, 2007

-MA data older than RIC-CLL notorious for slow disappearance of malignant clone – kinetics of response! - Late relapses 5%

CLL & Relapse

• Risk factors for relapse or progression • Chemorefractory, >CR/PR (consistently reported)

• Lymphadenopathy > 5 cm• >3 lines of chemotherapy• T-cell depletion in vivo or ex vivo • Slow donor T-cell chimerism• MRD positive post transplant

• No known impact:• 17p deletion or ZAP-70 status• URD versus MSD

CLL: Epidemiology of Relapse after Allo-HCT: Future ResearchFuture Research

• Few data on outcomes after relapse• DLI reported responses 15-50%• No data on risk factors or other interventions

• Other questions• Outcomes of Allo-HCT vs. conventional therapy• Optimal timing of transplant • Role of MRD pre and post alloHCT

• High Priority Studies• Retrospective, single center: CLL relapse index• Retrospective, community based: survival HCT vs. other• Prospective, multicenter cohort study, high detail of data

• Obstacles – lack of funding mechanisms and proposals

Lymphoma Relapse After Allo-HCT –More Data Needed!More Data Needed!

• GVL potency controversial in aggressive lymphoma & HL– more clearly identify GVL (except for MCL & PTCL)– understanding relapse in relation to TCD, T-cell

chimerism & cGVHD requires larger multicenter study

• Standardization of patient population definitions including IPI score, molecular pathology, and disease state

• Focus on chemosensitive, HCT earlier in disease course in pre-determined target patient populations

• Improved strategy for design of RIC regimens: – targeted vs. disease-specific vs. maintenance

• Alternative donors (esp. Haplo in HL)

Epidemiology and Natural History of Relapse following Allogeneic Cell

Transplantation for Myeloma

Shaji KumarShaji Kumar

NCI Workshop on Biology, Prevention and Treatment of Relapse After Allogeneic HCT

Subcommittee III: Epidemiology and Natural History of Relapse

Allo-HCT in myeloma

• Evidence to support graft vs. myeloma effect

• High treatment related mortality, historically

• Relapse along with TRM remains the principal reasons for treatment failure

• Bulk of existing data in the setting of relapsed disease

Trends

Myeloablative conditioning

Reduced intensity conditioning

Auto SCT followed by Reduced intensity conditioning

Decreasing TRM, but increased relapse risk

Auto RIC allo

Bruno et al, Blood (2009). 113 (14), 3375-3382; Rotta et al, Blood (2009). 113 (14), 3383-3391

Risk Factors for relapse• Poor patient performance status• Gender donor/recipient combination• Allo-HCT > 1 year from diagnosis• Durie stage 3 at diagnosis• Chemoresistant disease, advanced disease• Elevated B2 microglobulin, Deletion chromosome

13, deletion 17p• RIC regimens• T-cell depleted grafts, Campath or ATG use• Lack of complete response

How can we decrease risk of relapse?

• Allo-HCT early in disease course: needs better prognostic markers of the right group.

• Novel agents to obtain CR prior to allo-HCT or use of auto allo-HCT?

• Novel agents as maintenance post allo-HCT?

• Immunomodulatory drugs (lenalidomide) in conjunction with DLI?

• Use of bortezomib to separate GVM from GVH?

Outcome after relapse form allo-HCT

• Little data available, need retrospective studies

• Trials needed to explore novel approaches for post-relapse Rx

• Use of novel agents in conjunction with DLI:– To reduce tumor bulk– To enhance GVM effect– To alter GVM/ GVH ratio

• Explore novel drug combinations

GVHD/GVT and Relapse

Marcelo Pasquini, Steven Pavletic & Daniel Weisdorf

NCI Workshop on Biology, Prevention and Treatment of Relapse After Allogeneic HCT

Subcommittee III: Epidemiology and Natural History of Relapse

Donor Recipient Immune Interactions: Donor Recipient Immune Interactions: GVHD/GVT vs. Graft Rejection/RelapseGVHD/GVT vs. Graft Rejection/Relapse

GVHD/GVT

Rejection/Relapse

Donor Cells

Recipient

GVHDprophylaxis

Lower relapse rates in patients with GVHD

Horowitz, M et al. Blood 1990

Ringden, Blood 2009

Chronic, but not acute GVHD reduces relapse in leukemiaafter Sib or URD HCT

Relapse after HCT with non-myeloablative conditioning and importance of full donor chimerism

(N=322)

Baron, JCO 23:1993, 2005

Acute GVHD HR 95% CI PNo 1.0 -Grade 1 0.3 0.1 to 1.1 .07Grade 2 1.0 0.7 to 1.6 .91Grade 3-4 0.7 0.3 to 1.7 .44

Chronic GVHDNo 1.0 -Extensive 0.4 0.2 to 0.8 .006

>95% chimerismNo 1.0 -Yes 0.5 0.3 to 0.8 .002

Chronic GVHD severity does not impact Chronic GVHD severity does not impact leukemia relapseleukemia relapse

Black-no CGVHDBlack-no CGVHD

Blue-good risk CGVHD (50%),Blue-good risk CGVHD (50%), Green-intermediate (30-35%),Green-intermediate (30-35%),

Red-worst risk group (15-20%)Red-worst risk group (15-20%) Lee et al. Blood, 2002

Relapse Non-relapse Mortality DF survival

Similar leukemia relapse comparing HLA-matched BM and PBSC to HLA-

mismatched cord blood in adults

0.00

1.00

2.00

2.50

RELA

TIV

E R

ISK

P=0.006

0.60

0.42

0.86

P=0.003

0.44

0.85

0.61

CB mismatched BM matched PBSC matched

1.04

1.49

0.72

P=0.836 P=0.645

1.08

0.77

1.51

TRM TRMRelapse Relapse

Eapen M. et al, Blood 2008

Ringden, Blood 2009

No significant effect on relapse after 8/8 allele matched URD compared to MSD

Prophylaxis and RelapseMethod N Disease/

DonorRelapse p-value

Tac/Mtx vs CSA/MTX

165/164 Several/ Sibs

25% vs 22% at 2 y NS

Mtx /CSA vs CSA 59/43 AML/ Sibs

44% vs 24% at 4y 0.02

CSA/MMF vs CSA/MTX

26/67 Several/ Sibs

10%vs 21% at 2 y NS

T-cell depletion vs. CSA/Mtx

89/94 CML/URD

20% vs 7% at 2 y 0.01

Tac.Sir.MTX vs Tac/Sir

46/29 Several/Sibs

7%vs 6% at 2 y NS

Weaver C.H.et al, 1994 Ratanatharathorn V. et al, 1998

Wagner J. et al, 2005Neuman F. et al, 2005

Ho V. et al, 2009

Leukemia relapse remains unchanged in the last two decades despite improvements in supportive

care and transplant related mortality

Months

Cu

mu

lati

ve I

ncid

en

ce o

f R

ela

pse,

%

0

100

80

20

40

60

0 4836 6012 24

1995-1999, N=901

1985-1989, N=1124

1990-1994, N=1283

2000-2004, N=460

Cumulative incidence of relapse after HLA-matched Sibling Donor Transplant for AML in CR1, 1985-2004

GVHD Prophylaxis and Relapse: Impact of approaches free from chronic IS

No Chronic Immunesuppression

Luznik et al, 2008

-9 -8 -7 -6 -5 -4 -3 -2 -1 0 +1 +2 +3 +4 +5 +6

TBI

THIO

rATG

Cy

CD34+ Selected PBSC Infusion No Chronic Immunesuppression

Devine S., O’Reilly R. et al, 2009HLA-matched sibling donorsAML in CR1 and CR2

Related and unrelated donorsHigh Risk Heme Malignancies

Epidemiology of GVHD/GVT and Relapse: Open Questions

• GVT/GVHD – Impact of disease-specific phenotypes

• Chronic GVHD– Impact of different cGVHD presentations on relapse– Impact of cGVHD therapy

• Stem cell sources and alternative donors– Impact on disease-specific subsets

• GVHD prophylaxis– Impact of novel agents (sirolimus) and combinations

on relapse.– Relapse rates on approaches free from chronic IS.

Statistical Methods for Analyzing the Relapse After HCT

Mei-Jie Zhang

NCI Workshop on Biology, Prevention and Treatment of Relapse After Allogeneic HCT Subcommittee III: Epidemiology and Natural

History of Relapse

• Relapse and TRM (treatment-related mortality) are two competing risks:

HCT

Relapse

TRM

hR(t)

hTRM(t)

Proper Summary Curve for Competing Risks Data

• In literature, “1 – Kaplan-Meier” has been used to compute relapse rate (treating TRM as censored)

• “1-KM” OVERESTIMATES the relapse incidence rate in the presence of competing risk of TRM

• Cumulative incidence function (CIF) is probability of relapse rate in the presence of competing risk of TRM.

• CIF is a proper summary curve for analyzing competing risks data

• Computing CIF is available in SAS & R

HCT example

• To illustrate, we consider a subset of data from a CIBMTR study (Szydlo et al. JCO, 1997, 15:1767-1777):HLA-identical Sibling (N=1224)Matched Unrelated (N=383)

0 12 24 36 48 60

05

1015

2025

30

Months

Pro

ba

bili

ty (

%)

Relapse

CIF1 - KM

0 12 24 36 48 60

05

1015

2025

30

Months

Pro

ba

bili

ty (

%)

TRM

CIF1 - KM

0 12 24 36 48 60

010

2030

4050

MonthsP

rob

ab

ility

(%

)

'REL+TRM' = '1 - KM (LFS)'

CIF of Relapse

CIF of TRM

1 - KM (LFS)

Cumulative Incidence Function (CIF) versus (1 - Kaplan-Meier (KM))

Compare Cumulative Incidence Functions

• In practice, Log-Rank test is commonly used/reported along with CIF curves by Trt groups

• Log-Rank test compares cause-specific hazards of relapse. BUT, CIF of relapse is determined by both cause-specific hazards of relapse and TRM

• In some studies, Log-Rank test may lead to a different conclusion compared to reported CIFs

Comparing Cumulative Incidence Functions

• Recently, Gray developed a test, which directly compares the cumulative incidence curves.

• Gray’s test should be used when we are interested in comparing the CIF of relapse (not cause-specific hazard of relapse)

• Gray’s test is available in R(R-cmprsk package: R-cuminc function)

0 12 24 36 48 60

01

02

03

0

Months

Pro

ba

bili

ty (

%)

HLA-Identical Sibling (N=1224)

Matched Unrelated (N=383)

Log-Rank Test: P=0.47

Gray's Test: P=0.12

5 Yr Pointwise Test: P=0.0179HLA-Id Sib: At Risk = 113, 25 (22-27)%MUD: At Risk = 10, 18 (13-23)%

Comparing Cumulative Incidence Functions of Relapse

Multivariate Analysis• In medical studies, we need to

assess covariate effect on the relapse rate OR compare relapse rates adjusting other risk factors

• BMT Example: Risk factors are not balanced

• Disease (AML/ALL/CML): SIB: 20/37/43% vs MUD: 16/18/65%; p<0.0001

• Stage (Early/InterMed/Adv): SIB: 66/20/14% vs MUD: 47/33/20%; p<0.0001

• Karnofsky (<90/≥90): SIB: 19/82% vs MUD: 24/76%; p=0.0138

Multivariate Analysis

• Commonly, we use Cox model which models cause-specific hazards of Relapse

• CIF of Relapse is determined by both cause-specific hazards of Relapse and TRM

• We need to directly model CIF: Subdistribution hazards (Fine&Gray R-cmprsk) Inverse Weighting (Scheike&Zhang; R-timereg) Pseudovalues Technique (Klein et al, SAS & R)

Example: Multivariate Analysis

• Adjusting risk factors of Disease, Disease Stage and Karnofsky:

• Cox Model: MUD vs Sib:

β = 0.10 (se=0.15); RR=1.01; (P=0.967)

Not significant and opposite effect direction!• Fine & Gray’s Model: MUD vs Sib:

β = -0.33 (se=0.16); RR=0.72; (P=0.039)

Significant and CORRECT effect direction!

• Other directly modeling approaches gives similar results

Adjusted Cumulative Incidence Curve

• For non-competing risks data, we often plot ADJUSTED SURVIVAL CURVES, which estimate likelihood of outcomes in populations with similar prognostic factors based on regression model

• For RELAPSE, treating TRM as censored event [1- Adjusted Survival Curves] overestimate relapse rate

• Unadjusted CIF curves do not adjust potentially unbalanced treatment cohorts

• One can plot adjusted CIF curves

0 12 24 36 48 60

010

2030

Months

Pro

babi

lity

(%)

1 - Adjusted Survival of Relapse(Treating TRM as Censored)

HLA-Id Sibling (N=1224)Matched Unrelated (N=383)

0 12 24 36 48 60

010

2030

Months

Pro

babi

lity

(%)

Un-Ajdusted CIF of Relapse

HLA-Id Sibling (N=1224)Matched Unrelated (N=383)

Gray's Test: P=0.12

0 12 24 36 48 60

010

2030

Months

Pro

babi

lity

(%)

Ajdusted CIF of Relapse

HLA-Id Sibling (N=1224)Matched Unrelated (N=383)

Fine&Gray's Model:

P=0.039

Adjusted Cumulative Incidence Functions of Relapse

Study GVL (Graft-versus-leukemia) Effect

• At time of TX, it is unknown who will develop GVHD

• GVHD should be treated as time-dependent covariate when analyzing GVL effect of fitting a Cox model

• Directly modeling CIF with time-dependent covariate needs to be developed

Epidemiology of RelapseConclusions

• Relapse a problem, no improvements in decades• AlloHCT can overcome high risk disease biology • Developing strategies to identify best transplant

candidates while malignancy is in more treatable stage – a high priority research

• RIC vs. MAC to be evaluated in concurrent cohorts • Few data on risks and outcomes after relapse• Few data on impact of new targeted therapies• Few data on relapse and management in cGVHD

Epidemiology of RelapseConclusions cont.

• Better understanding of MRD impact pre and post transplant

• Larger data sets needed with higher detail of information, retrospective and prospective multi-center collaboration

• Enhance and refine statistical analyses in application and research

• Publication bias! • Opportunities multiple! – discipline in careful

studying is needed