Myelodysplastic syndromes...Lionel Adès Hopital Saint Louis , Paris Diderot University French MDS group Disease characteristics MDS • Clonal myeloid disorders –Ineffective hematopoiesis

Myelodysplastic syndromes

Lionel Adès Hopital Saint Louis , Paris Diderot University

French MDS group

Disease characteristics

MDS

• Clonal myeloid disorders

– Ineffective hematopoiesis leading to cytopenias

– Progression to AML in 30-35% of cases

• Predominance in the elderly

• Etiology generally unknown

Incidence and prevalence of myelodysplastic syndromes

• Crude incidence rate - 4.15/100,000/year • Point prevalence - 7 per 100,000 persons • Incidence and prevalence of MDS higher in men than women • Increased sharply with increasing age.

Data from the Düsseldorf MDS-registry. (Neukirchen J, 2011)

morphologic abnormalities of myelodysplasia

Cazolla, Blood 2013 122:4021-4034

MDS Classificiation – A long History

6

1976 - FAB Classification

2 groups of MDS - RAEB - CMML

1982 - FAB Classification

5 groups of MDS - RA - RARS - RAEB - RAEBt - CMML

2001 - WHO Classification

10 groups of MDS - RA - MDS with del(5q) - RCMD - RCMD-RS - RARS - RAEB - RAEB1 - RAEB2 - MDS-U - tMDS - RAEBt AML - CMML SMD/SMP

MDS Classificiation – A long History


10 groups of MDS - RA - MDS with del(5q) - RCMD - RCMD-RS - RARS - RAEB1 - RAEB2 - MDS-U - tMDS


11 groups of MDS - RA - R. Neutropenia - R. Thrombocytopenia - MDS with del(5q) - RCMD - RCMD-RS - RARS - RAEB1 - RAEB2 - MDS-U - tMDS


Many changes (too many??) - RA - R. Neutropenia - R. Thrombocytopenia - MDS with single lineage dysplasia - MDS with del(5q) - RCMD - MDS with multi lineage dysplasia - MDS with multi lineage dysplasia-RS - RARS - RAEB1 MDS-EB1 - RAEB2 MDS-EB2 - MDS-U - tMDS

Etiology of MDS

Exogenous factors in MDS (15% of the cases)

• Chemotherapy

– Alkylating agents

– Purine analogs

– Cisplatin

• Radiotherapy

• Environmental factors

– Benzene

– Tobacco smoke

Kiran Tawana and Jude Fitzgibbon, Blood 2016

Inherited factors in MDS (2-3% of adult cases)

HMs alone

associated bone marrow failure syndromes

Cytopenias and/or platelet dysfunction

DDX41 exemple

• 289 Families with familial Myeloid malignacies

– DDX41 Mutations in 3%

• DDX41 mutation screening (NGS)

Maya Lewinsohn et coll., Blood Février 2016

median age of AML/MDS : 62 years

Genetic pathophysiology of MDS

Haase, D. et al. Blood 2007;110:4385-4395

Cytogenetic landscape

Survival AML Evolution 2,902 patients

Chromosomal abnormalities

Many oncogenetic events

111 genes in 838 patients

Mutations observed in 80% of the cases

Elli Papaemmanuil, Blood 2013

Several Pathways

16

MDS

JAK2

NRAS CBL

PTPN11

KRAS

BRAF RUNX1

ETV6

GATA2

SF3B1 U2AF1

SRSF2 ZRSF2

DNMT3A

TET2 ASXL1

IDH1 et 2

EZH2

SETBP1 UTX

TP53

NPM1

BCOR

WT1

EPIGENETIC REGULATION SPLICING

TYROSINE KINASE TRANSCRIPTION OTHERS

Importance of epigenetic modifications

One

DNA

Two phenotypes

Santini with permission

SF3B1 mutations in myelodysplastic syndromes

For personal use only. at INSERM DISC on October 14, 2011. bloodjournal.hematologylibrary.orgFrom

Malcovati et al, Blood on line 2011

Significant association of SF3B1

mutations with the presence of

ring sideroblasts.

SF3B1 mutations were found to

be independently associated

with better overall survival

(HR=0.15, P=.025) and lower

risk of evolution into AML

(HR=0.33, P=.049).

WES in normal population, without HM

Mutation, but…

NEJM 2014; 371:2488-98

• 17 000 patients

• 160 genes

➜ Frequency of the mutation increased with age

Mutation, but…

NEJM 2014; 371:2488-98

Mutation, but…

NEJM 2014; 371:2488-98

International Prognostic Scoring System

Prognostic Variable (points)

0

0.5

1

1.5

2

Bone marrow blasts (%)

< 5%

5-10 %

- 11- 20%

21- 30%

Cytopenias : - platelets < 100.10⁹ /L - Hemoglobin < 10 g/dL - ANC < 1.8.10⁹ /L

0/1

2/3

Cytogenetic

Good: - Normal - -Y - del(5q) - del(20q)

Intermediate: - other abnorm

Poor: - Complex ≥ 3 abnorm - Chr 7 abnorm

Greenberg Blood 1997

4 categories

2 categories

3 categories 7 Subgroups

International Prognostic Scoring System

Greenberg Blood 1997

Survival AML Evolution

Revised IPSS (IPSS-R)

points

0

0.5

1

1.5

2

3

4

blasts ( %) ≦2%

-

2-4%

-

5-10%

>10%

Hemoglobin >10 g/dl 8-10 g/dl <8 g/dl

ANC >0.8 G/l <0/8 G/l

Platelet >100 50-100 <50

Cytogenetics

Very Good -Y del(11q)

Good Normal der(1;7) del(5q) del(20q) del(12p) Double incl del(5q)

Intermed -7/7q +8 Iso(17q) +19 +21 other double inclusions

Poor: der3q(21) der3q(26) Complex Double inclusion 7q/7

Very Poor Complex >3

2 categories

5 categories 16 subgroups

P. Greenberg et al, Blood 2012

3 categories

4 categories

3 categories

IPSS-R

Survie Transformation LAM

Treatment of Lower Risk MDS

26

Treatment : Based on IPSS

Malcovati et al, Blood 2013

Quality of Life is correlated to Hemoglobin levels

Hb level (g/dl)

Qu

alit

y o

f Li

fe (

LASA

, mm

)

45

50

55

60

65

7 8 9 10 11 12 13 14

Crawford et al. Cancer 2002; 95: 888–95

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 20 40 60 80 100 120 140 160 180

0.0

0.1

0.2

0.3

0.4

0.5

0.6

0.7

0.8

0.9

1.0

0 20 40 60 80 100 120 140 160 180

– 0 U PRBC/4 week

– 1 U PRBC/4 week

– 2 U PRBC/4 week

– 3 U PRBC/4 week

– 4 U PRBC/4 week

Time (months) Time (months)

Cu

mu

lati

ve s

urv

ival

Cu

mu

lati

ve s

urv

ival

Overall survival

(HR = 1.36;p < 0.001)

Leukaemia-free survival

(HR = 1.40;p < 0.001)

Malcovati L. et al, Haematologica 2006

Influence of RBC transfusion on OS

How to treat anemia in MDS ?

• First line treatment

– ESAs (EPO and darbepoetin)

– Lenalidomide ( if del 5q)

• Second line treatments

– Immunosuppression (ATG+/- ciclo)

– Lenalidomide (non del 5q)

– Hypomethylating agents

– TGFbeta inhibitors









Response rate to ESA (n=419)

Park et al. Blood, 2008

63

41

22

49

0

10

20

30

40

50

60

70

IWG2000 Criteria

IWG2006 Criteria

Res

po

nse

rat

e (%

)

ORR Major Minor ORR

EPO treated versus IMRAW untreated cohort : Time to AML progression

Comparison between IMRAW and French-EPO cohort restricted to IPSS LOW INT1 patients without unfavorable karyotype

(IMRAW n=447 patients, French-EPO= 284)

progression to AML , p= NS

Park , Blood, 2008 Years since Diagnosis/EPO

100%

50%

0%

0 1 2 3 4 5

ESA treated Untreated

EPO treated versus IMRAW untreated cohort : Overall Survival

Park , Blood, 2008

P<0.0001

Years since diagnosis or rEPO treatment

0 12 24 36 48 60 72 84 96 108 1200%

10%

20%

30%

40%

50%

60%

70%

80%

90%

100%

IMRAW

EPO response

EPO no response

p<0.0001

years

Overa

ll s

urv

ival

0 1 2 3 4 5 6 7 8 9 10

P<0.0001

EPO responders

EPO non-responders

IMRAW

Years since Diagnosis/EPO

O

vera

ll Su

rviv

al

Years since Diagnosis/EPO

ESA treated Untreated

O

vera

ll Su

rviv

al ESA Responders

ESA non Responders

Untreated

A simplified validated decision model for treatment of the anemia in MDS with EPO

Hellstrom-Lindberg, et al, Br J Haem, 2003

74

23

7

0

10

20

30

40

50

60

70

80

Probability of Response

Variable Value Score Value Score

Transfusion need

<2/ mo 0 >=2/ mo 1

Serum EPO <500 U/I

0 >=500 UI/l 1

Score 0 1 2

• IPSS low/int-1 • EPO<500 • <=4 RBCs/8 Week

EPO-a 40-80.000 vs

PLACEBO

Darbepoetin 500 q3w vs

PLACEBO

ESA Registration trials in MDS

1

2









MDS-004 study

*Patients stratified by IPSS score and cytogenetic complexity prior to randomization.

**Bone marrow assessments were performed at baseline, 12 weeks, and every 24 weeks thereafter.

LEN, orally5 mg/day for 28 days of each 28-day cycle

Placebo

Responders (at least minor

erythroid response at week 16):

Continued double-blind treatment

for up to 52 weeks, relapse or

progression

Non responders:

Discontinued double-blind

treatment and entered open-label

treatment or withdrew from study

S

T

R

A

T

I

F

Y

R

E

S

P

O

N

S

E

R

A

N

D

O

M

I Z

E

D

LEN, orally 10 mg/day for 21 days of each 28-day cycle

Double-blind phase**

Planned enrollment

(n = 205)

Week 0 4 8 12 16 52

*

Fenaux et al. Blood. 2011 Oct 6;118(14):3765-76

MDS-004 study

Protocol defined

(≥ 26 weeks)

IWG

(≥ 8 weeks)

*P < 0.001 vs placebo

Bars represent 95% CI

*

56

8

*

50

*

61


Placebo

(n = 51)

LEN 5 mg

(n = 46)

LEN 10 mg

(n = 41)

Cytogenetic response, %

Complete response (CR)

Partial response (PR)

CR + PR

0

0

0

10.9*

6.5

17.4**

24.4**

17.1

41.5**

*P = 0.01 vs placebo

**P < 0.001 vs placebo

MDS004 : Cytogenetic response


MDS-004: Side effects

Grade 3 or 4 adverse events

Placebo

(n = 67)

LEN 5 mg

(n = 69)

LEN 10 mg

(n = 69)

Patients with ≥ 1 event, n (%)

Neutropenia

Thrombocytopenia

Leucopenia

Anemia

Deep vein thrombosis

29 (43)

10 (15)

1 (2)

0 (0)

6 (9)

1 (2)

62 (90)

51 (74)

23 (33)

9 (13)

4 (6)

1 (1)

65 (94)

52 (75)

28 (41)

6 (9)

2 (3)

4 (6)

Adverse events leading to, n (%)

Discontinuation

Dose reduction

Dose interruption

3 (5)

0 (0)

4 (6)

11 (16)

36 (52)

19 (28)

6 (9)

40 (58)

28 (41)


Time (years)

0 1 2 3 4 5 6

1.0

0.8

0.6

0.4

0.2

0

Cu

mu

lati

ve

in

cid

en

ce

of

AM

L p

rog

res

sio

n

LEN-treated Untreated

2-year cumulative incidence 7% 12%

5-year cumulative incidence 23% 20%

Median time to AML progression Not reached Not reached

Untreated

LEN-treated

Kuendgen, Leukemia 2013

Risk of AML Evolution

Role of TP53 in del(5q) patients

• TP53 mutations with a median clone size of 11% were detected in 18% at an early phase of the disease.

• Associated with evolution to acute myeloid leukemia.

Jadersten M et al. , JCO 2011

IDH mutation

44

IDH 1 & 2 are metabolic

enzyme that interconvert

isocitrate and a-ketoglutarate

(aKG) while reducing NADP to

NADPH

Effect of mutation : Gain of

Function

Phase 1/2 Trial : AG-221, a Potent Inhibitor of Mutant IDH2 in myeloid malignancies

Eytan M. Stein, ASH 2015

RR-AML (n = 159)

Untreated AML

(n = 24) MDS

(n = 14) All

(N = 209)

Overall Response (CR, CRp, CRi, mCR, PR)

59 (37%)

10 (42%)

7 (50%)

79 (38%)

CR 29 (18%) 4 (17%)

3 (21%)

37 (18%)

CRp 1 (1%) 1 (4%) 1 (7%) 3 (1%)

CRi 3 (2%) 0 0 3 (1%)

mCR 9 (6%) 1 (4%) 3 (21%) 14 (7%)

PR 17 (11%) 4 (17%) 0 22 (11%)

SD 72 (45%) 9 (38%) 6 (43%) 96 (46%)

PD 10 (6%) 1 (4%) 0 11 (5%)

Not evaluable 18 (11%) 4 (17%) 1 (7%) 23 (11%)

Higher Risk MDS

46

Treatment : Based on IPSS

47 Malcovati et al, Blood 2013

Azacitidine 75mg/m2/day x 7d q.28d

CCR

Randomisation

BSC was included with each arm

Treatment continued until unacceptable toxicity, progression to AML or disease progression

• BSC only

• LDAC, 20mg/m2/day x 14d q.28–42d

• Intensive chemotherapy (7+3)

Screening/central

pathology review

Investigator CCR

Tx selection

AZA-001: randomised, phase III survival study of azacitidine versus CCR in MDS < 30%

Lancet Oncol 2009;10:223–32

Azacitidine significantly improved OS versus CCR

• Median OS was significantly longer with azacitidine versus CCR (24.5 vs 15 months [difference 9.4 months]; HR=0.58; p=0.0001)

• 2-year survival rate was significantly greater with azacitidine versus CCR (50.8 vs 26.2%; p<0.0001)

Time from randomisation (months)

Pro

po

rtio

n o

f p

ati

en

ts s

urv

ivin

g

CCR (n=179)

Azacitidine (n=179)

0 5 10 15 20 25 30 35 40

0

0.2

0.4

0.6

0.8

1.0


Secondary endpoints (IWG 2000)

Azacitidine

n=179 (%)

CCR

n=179 (%)

p value azacitidine versus CCR

Overall (CR + PR) 29 12 0.0001

CR 17 8 0.015

PR 12 4 0.0094

IWG HI

Major + minor 49 29 <0.0001

HI-E major 40 11 <0.0001

HI-P major 33 14 0.0003

HI-N major 19 18 0.87


IWG = International Working Group; E = erythroid; P = platelet; N = neutrophil

Silverman LR, et al. cancer 2011

Cu

mu

lati

ve p

rob

abili

ty

Time (cycles)

50%, 2 cycles

87%, 6 cycles

Range: 1–22 cycles

Number of cycles of AZA to first response

Gore Haematologica 2013

Time from randomisation (months)

Pro

po

rtio

n o

f p

atie

nts

su

rviv

ing

0 5 10 15 20 25 30 35 40 0

0.2

0.4

0.6

0.8

1.0

HI

PR

CR

CCR

71.7%

78.4%

Survival Benefit without CR

OS According to HR (IC 95%) p=

Performance Status ≥ 2 2,0 [1,4-2,9] <10-4

IPSS cytogenetic group

Intermediate

High

1,4 [0,8-2,3]

3,0 [2,0-4,3]

<10-4

Transfusion ≥ 4 CGR/8 w 1,9 [1,4-2,6] <10-4

Peripheral circulating blasts 2,0 [1,5-2,7] <10-4

Median Follow up: 26 months Median Overall Survival: 13,5 months

Factors influencing OS (n=282 pts)

Itzykson & Thépot, Blood, 2011

Des specificités pour les Séniors?

Factors influencing OS in MDS/AML

% of Bone Marrow blast

Cytogenetics

Cytopenia

Mutations

Age

Co-morbidities

Social issues

? General Condition

OS according to Age Irrespective of the treatment in AML

Swedish Acute Leukemia Registry

Juliusson G, Blood. 2009 Apr 30;113(18):4179-87

Some long term survivors

The effect of age is not constant throughout a patient's course of

treatment

Cumulative probability of deaths attributed to the primary cancer

CANCER | 2008 / Volume 112 / Number 6

Probability of death from primary cancer and comorbidity by Age

CANCER | 2008 / Volume 112 / Number 6

59

Itzykson ASH 2009

•45 patients, median age 83 years.

•More frequent dose reduction (49% vs 33%, p=0.04)

• Early discontinuation 29% unchanged • ORR 34% vs 39% (p=0.6) • Median OS was 12.1 months (unchanged)

0

,2

,4

,6

,8

1

Survie

Cum

.

0 5 10 15 20 25 30 35 40 45

Temps

Age ≥ 80 years

Age < 80 years

Time (months) O

S

Azacitidine in higher-risk MDS patients older than 80 years

Older adults with AML Evaluation of geriatric assesment

Figure 2.

Proportion of older adults with acute myelogenous leukemia (AML) with impairments in

geriatric assessment measures among the overall cohort and the subset with good oncology

performance status (Eastern Cooperative Oncology Group (ECOG) Performance Status

score ≤1). Figure displays results for participants with available ECOG scores; N = 52 for

overall and N = 38 for ECOG ≤1 subsets.

Klepin et al. Page 14

J Am Geriatr Soc . Author manuscript; available in PMC 2013 September 24.

NIH

-PA

Au

tho

r Man

uscrip

tN

IH-P

A A

uth

or M

an

uscrip

tN

IH-P

A A

uth

or M

an

uscrip

t

Heidi D. Klepin J Am Geriatr Soc | 2011

Geriatric assement inAML/MDS

• 195 pts > 60y with AML or MDS

• Treated with

– Best supportive care (n=47)

– Hypomethylating agents (n=73)

– Intensive Chemotherpay (n=75)

• Geriatric assesement

• Observational study

Deschler Haematolgica | 2013

Geriatric assement in AML/MDS


pendent patient-related prognostic parameters suited todeveloping a prognostic model. In the multivariate analy-sis of overall survival in 107 patients, only impairments inperformance status, in activities of daily living (ADL) andthe symptom item ‘fatigue’ from the EORTC QOL-C30were retained as independent prognostic factors of overallsurvival, in addition to the know n MDS/AML-related riskfactors poor risk cytogenetics/IPSS and bone marrow

blasts of 20% or over. Therefore, the basic informationreflecting a patient's functionality (KI, ADL) and QOLstrongly indicate vulnerability and complement the keyclinical parameters that have until now influenced treat-ment decision-making (i.e. numerical age, percentage ofblast or cytogenetics). Impairments in the more sophisti-cated parameters (IADL, MMSE, ‘Get-Up-and-Go Test’)may on the contrary represent a distinct individual state

B. Deschler et al.

212 haematologica | 2013; 98(2)

Figure 1. Overall survival (OS) of non-intensively treated patients according to the geriatric assessment results for activities of daily living(ADL) (A,B), performance status (Karnofsky Index <80) (C,D), and ‘fatigue’ <50 (E,F). (A). Patients receiving best supportive care only. (B).Patients receiving hypomethylating agents. (C). Patients receiving best supportive care only. (D). Patients receiving hypomethylating agents.(E). Patients receiving best supportive care only. (F). Patients receiving hypomethylating agents.

A

C D

FE

BPatients receiving best supportive care onlyPatients receiving hypomethylating agents

Patients receiving hypomethylating agents


P=0.0059

P<0.0001

P<0.0001

0 6 12 18 24 30 36 42

# Patients at risk Months from start of therapy18 8 3 0 0 0 0 0

23 12 10 2 0 0 0 0


44 28 16 11 5 2 2 0


13 10 7 1 0 0 0 0


41 27 17 11 5 2 2 0


23 10 5 1 0 0 0 0


39 15 9 6 1 0 0 0

0 6 12 18 24 30 36 420 6 12 18 24 30 36 42

0 6 12 18 24 30 36 42 0 6 12 18 24 30 36 42

ADL

Karnofsky Index

Fatigue Fatigue

Ove

rall

Surv

ival

Ove

rall

Surv

ival

Ove

rall

Surv

ival

<100=100

<100=100

<80>=80

<80>=80

<50>=50

<50>=50

<100=100

<80>=80

<80>=80

<50>=50

<50>=50

P=0.065

P=0.0001

P=0.007

Patients receiving best supportive care only


1.0

0.8

0.6

0.4

0.2

0.0

1.0

0.8

0.6

0.4

0.2

0.0

1.0

0.8

0.6

0.4

0.2

0.0

ADL1.0

0.8

0.6

0.4

0.2

0.0

0 6 12 18 24 30 36 42

Ove

rall

Surv

ival

<100=100

Karnofsky Index1.0

0.8

0.6

0.4

0.2

0.0

1.0

0.8

0.6

0.4

0.2

0.0

Ove

rall

Surv

ival

Ove

rall

Surv

ival

pendent patient-related prognostic parameters suited todeveloping a prognostic model. In the multivariate analy-sis of overall survival in 107 patients, only impairments inperformance status, in activities of daily living (ADL) andthe symptom item ‘fatigue’ from the EORTC QOL-C30were retained as independent prognostic factors of overallsurvival, in addition to the known MDS/AML-related riskfactors poor risk cytogenetics/IPSS and bone marrow

blasts of 20% or over. Therefore, the basic informationreflecting a patient's functionality (KI, ADL) and QOLstrongly indicate vulnerability and complement the keyclinical parameters that have until now influenced treat-ment decision-making (i.e. numerical age, percentage ofblast or cytogenetics). Impairments in the more sophisti-cated parameters (IADL, MMSE, ‘Get-Up-and-Go Test’)may on the contrary represent a distinct individual state

B. Deschler et al.


Figure 1. Overall survival (OS) of non-intensively treated patients according to the geriatric assessment results for activities of daily living(ADL) (A,B), performance status (Karnofsky Index <80) (C,D), and ‘fatigue’ <50 (E,F). (A). Patients receiving best supportive care only. (B).Patients receiving hypomethylating agents. (C). Patients receiving best supportive care only. (D). Patients receiving hypomethylating agents.(E). Patients receiving best supportive care only. (F). Patients receiving hypomethylating agents.

A

C D

FE

BPatients receiving best supportive care onlyPatients receiving hypomethylating agents



P=0.0059

P<0.0001

P<0.0001

0 6 12 18 24 30 36 42


23 12 10 2 0 0 0 0


44 28 16 11 5 2 2 0


13 10 7 1 0 0 0 0


41 27 17 11 5 2 2 0


23 10 5 1 0 0 0 0


39 15 9 6 1 0 0 0

0 6 12 18 24 30 36 420 6 12 18 24 30 36 42

0 6 12 18 24 30 36 42 0 6 12 18 24 30 36 42

ADL

Karnofsky Index

Fatigue Fatigue

Ove

rall

Surv

ival

Ove

rall

Surv

ival

Ove

rall

Surv

ival

<100=100

<100=100

<80>=80

<80>=80

<50>=50

<50>=50

<100=100

<80>=80

<80>=80

<50>=50

<50>=50

P=0.065

P=0.0001

P=0.007



1.0

0.8

0.6

0.4

0.2

0.0

1.0

0.8

0.6

0.4

0.2

0.0

1.0

0.8

0.6

0.4

0.2

0.0

ADL1.0

0.8

0.6

0.4

0.2

0.0

0 6 12 18 24 30 36 42

Ove

rall

Surv

ival

<100=100

Karnofsky Index1.0

0.8

0.6

0.4

0.2

0.0

1.0

0.8

0.6

0.4

0.2

0.0

Ove

rall

Surv

ival

Ove

rall

Surv

ival

©Fer

rata

Sto

rti F

ound

ation

BEST SUPPORTIVE CARE HYPOMETHYLATING AGENT

Geriatric assement in AML/MDS


estimate and communicate degrees of dependence,emerges in this study as having additional value in objec-tifying decision-making processes. In agreement w ith this,a recent investigation on the impact of a geriatric assess-ment in treatment decision-making in elderly patientsrevealed that the ADL’s value correlates w ith treatmentallocation (non-intensive care vs. intensive treatmentefforts).37

Several studies have shown baseline QOL parameters tobe independent prognostic factors in different malignan-cies42-45 underscoring the assumption that QOL scales addprognostic information to clinical measures and predictsurvival.46 Patient ratings of physical symptoms (i.e.‘fatigue’), physical functioning and global healthstatus/QOL have repeatedly been the best predictors ofsurvival.45,47 In this context, Oliva et al. reported a study onelderly AML patients in which QOL physical functioningwas of prognostic relevance yet, somewhat surprisingly,did not correlate to the physician-assessed ECOG per-formance status.48 While the item ‘fatigue’ has beenshown to be prognostically relevant in several differentmalignant diseases,47,49-51 so far only hypotheses to explainthe mechanisms underlying the association betw eenreported data on patient health status and duration of sur-vival have been proposed.52 ‘Fatigue’ is a patient-reported

outcome and multi-faceted concept including both mentaland physical components whose critical domains have notbeen sufficiently standardized and for which several scaleshave been developed.53 Despite these shortcomings, webelieve that further investigation of this extremely debili-tating symptom observed in many if not all cancerpatients is useful for optimizing patient care.

When comparing our score to established risk assess-ment scores (i.e. comorbidity score by Sorror, risk indexby Wheatley), we found that, despite some associations,independent and complementary information could beobtained. We, therefore, suggest that the scores do actual-ly measure different aspects of patient- and disease-specif-ic factors. Possibly, the estimation of functionality mightdisplay an increasing relevance in patients treated non-intensively who are, on average, older, while parameterscalculated in the established scores may be even more rel-evant in younger, intensively treated patients. Future stud-ies may reveal whether the scores can complement eachother.

Our study has several limitations. First, the assessmentswere all performed by a small number of trained physi-cians raising the possibility that a bias could have beenintroduced. However, the instruments were, wheneverpossible, patient self-administered. Second, our patient

B. Deschler et al.


Figure 2. Overall survival (OS) according to frailty score risk groups and treatment (evaluable patients) (A). All patients treated non-intensively(n=107). (B). Patients receiving best supportive care only (n=41). (C). Patients receiving hypomethylating agents (n=66). (D). Patients receiv-ing induction chemotherapy/hematopoietic cell transplantation (n=75).

A B

C D

All patients treated non-intensively (n=107)

Patients receiving hypomethylating agents (n=66)

Patients receiving best supportive care only (n=41)

Patients receiving induction chemotherapy/hematopoietic

cell transplantation (n=75)

0 6 12 18 24 30 36 420 6 12 18 24 30 36 42

0 6 12 18 24 30 36 420 6 12 18 24 30 36 42

1.0

0.8

0.6

0.4

0.2

0.0

1.0

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rall

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ival

Ove

rall

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ival

Score value

HR:1

HR:1

HR:2.23

HR:6.0

HR:1

HR:2467

HR:2.62HR:12.82

P<0.0001 P=0.0043

P=0.61

P<0.0001

HR:9432

01-23

Score value 01-23

01-23

Score value Score value01-23

# Patients at risk Months from start of therapy

30 21 14 6 4 2 2 0

46 24 15 7 1 0 0 0

31 6 2 0 0 0 0 0


11 8 7 1 0 0 0 0

15 7 4 1 0 0 0 0

5 5 2 0 0 0 0 0

# Patients at risk Months from start of therapy19 13 7 5 4 2 2 031 17 11 6 1 0 0 016 1 0 0 0 0 0 0


01-2

3

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01-2

3

01-2

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ival

©Fer

rata

Sto

rti F

ound

ation

HYPOMETHYLATING AGENT

estimate and communicate degrees of dependence,emerges in this study as having additional value in objec-tifying decision-making processes. In agreement w ith this,a recent investigation on the impact of a geriatric assess-ment in treatment decision-making in elderly patientsrevealed that the ADL’s value correlates w ith treatmentallocation (non-intensive care vs. intensive treatmentefforts).37

Several studies have shown baseline QOL parameters tobe independent prognostic factors in different malignan-cies42-45 underscoring the assumption that QOL scales addprognostic information to clinical measures and predictsurvival.46 Patient ratings of physical symptoms (i.e.‘fatigue’), physical functioning and global healthstatus/QOL have repeatedly been the best predictors ofsurvival.45,47 In this context, Oliva et al. reported a study onelderly AML patients in which QOL physical functioningwas of prognostic relevance yet, somewhat surprisingly,did not correlate to the physician-assessed ECOG per-formance status.48 While the item ‘fatigue’ has beenshown to be prognostically relevant in several differentmalignant diseases,47,49-51 so far only hypotheses to explainthe mechanisms underlying the association betw eenreported data on patient health status and duration of sur-vival have been proposed.52 ‘Fatigue’ is a patient-reported

outcome and multi-faceted concept including both mentaland physical components whose critical domains have notbeen sufficiently standardized and for which several scaleshave been developed.53 Despite these shortcomings, webelieve that further investigation of this extremely debili-tating symptom observed in many if not all cancerpatients is useful for optimizing patient care.

When comparing our score to established risk assess-ment scores (i.e. comorbidity score by Sorror, risk indexby Wheatley), we found that, despite some associations,independent and complementary information could beobtained. We, therefore, suggest that the scores do actual-ly measure different aspects of patient- and disease-specif-ic factors. Possibly, the estimation of functionality mightdisplay an increasing relevance in patients treated non-intensively who are, on average, older, while parameterscalculated in the established scores may be even more rel-evant in younger, intensively treated patients. Future stud-ies may reveal whether the scores can complement eachother.

Our study has several limitations. First, the assessmentswere all performed by a small number of trained physi-cians raising the possibility that a bias could have beenintroduced. However, the instruments were, wheneverpossible, patient self-administered. Second, our patient

B. Deschler et al.


Figure 2. Overall survival (OS) according to frailty score risk groups and treatment (evaluable patients) (A). All patients treated non-intensively(n=107). (B). Patients receiving best supportive care only (n=41). (C). Patients receiving hypomethylating agents (n=66). (D). Patients receiv-ing induction chemotherapy/hematopoietic cell transplantation (n=75).

A B

C D

All patients treated non-intensively (n=107)

Patients receiving hypomethylating agents (n=66)

Patients receiving best supportive care only (n=41)

Patients receiving induction chemotherapy/hematopoietic

cell transplantation (n=75)

0 6 12 18 24 30 36 420 6 12 18 24 30 36 42

0 6 12 18 24 30 36 420 6 12 18 24 30 36 42

1.0

0.8

0.6

0.4

0.2

0.0

1.0

0.8

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0.4

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0.0

1.0

0.8

0.6

0.4

0.2

0.0

Ove

rall

Surv

ival

Ove

rall

Surv

ival

Score value

HR:1

HR:1

HR:2.23

HR:6.0

HR:1

HR:2467

HR:2.62HR:12.82

P<0.0001 P=0.0043

P=0.61

P<0.0001

HR:9432

01-23

Score value 01-23

01-23

Score value Score value01-23


30 21 14 6 4 2 2 0

46 24 15 7 1 0 0 0

31 6 2 0 0 0 0 0


11 8 7 1 0 0 0 0

15 7 4 1 0 0 0 0

5 5 2 0 0 0 0 0



01-2

3

01-2

3

01-2

3

01-2

3

Ove

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©Fer

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ound

ation

Intensive treatement

Combination of performance status activities of daily living’ (ADL)

QOL/fatigue

The decision to treat intensively

may be primarily influenced by the

patient's assumed ability to tolerate

treatment

600 MDS – Score ACE-27

Socio-economical issues in AML

Townsend Index is a

measure of material

deprivation based on:

• unemployment

• car ownership,

• home ownership

• and overcrowding.

BMC Cancer. 2009 Jul 26;9:252.

Socio-economical issues

Kristinsson SY J Clin Oncol. 2009 Apr 20;27(12):2073-80

Relative Risk of Death in AML

9,165 AML patients, in Sweden

Groupe Francophone des Myélodysplasies

• Activates clinical trials in MDS

35 centres in France and Belgium, Switzerland and Tunisia

• Website: www.gfmgroup.org

• Online registry of French MDS cases

• Close cooperation with – a patient support group

– the International MDS Foundation

– the European Leukaemia Net

Documents

Myelodysplastic syndromes...Lionel Adès Hopital Saint Louis , Paris Diderot University French MDS group Disease characteristics MDS • Clonal myeloid disorders –Ineffective hematopoiesis