Leucemia Linfoblastica Acuta dellet pediatrica A.Biondi and - - PowerPoint PPT Presentation

Leucemia Linfoblastica Acuta dell’età pediatrica

A.Biondi and G.Cazzaniga Department of Pediatrics and Centro Ricerca Tettamanti University of Milano-Bicocca

• S. Gerardo Hospital / Fondazione

MBBM Monza, Italy

Hunger SP and Mullighan CG N Engl J Med 2015;373: 1541-52 Teachey DT and Pui CH Lancet Oncolo.2019: 20(30):142-154

Outcome of contemporary trials involving children and adolescents with ALL

Contents

MRD response: still the best proxy but with limitations; Impact of genomics; Genetic predisposition of childhood ALL? The AIEOP-BFM ALL 2017: integrate genetics in MRD-based strategies.

MRD evaluation as a marker of in vivo early response

• Close association between the

quality of the molecular remission and the final outcome, independently on the applied treatments in childhood and adult ALL ;

• It is still unknown why the exposure

to drugs during the early phases of treatment (induction or consolidation) discloses different in vivo chemosensitivities which influence the final treatment

• utcome

Cazzaniga G. et al Br J Haematol, 2011; Cazzaniga G and Biondi A, Hematology, 2013

Relapses in BCP-ALL by MRD risk groups AIEOP-BFM ALL 2000

1348 61 6.0%(0.8) 7.2%(1.2) 1647 266 21.0%(1.2) 22.3%(1.4) 189 60 34.9%(3.8) 38.5%(5.0) SR IR HR N.pts

• N. rel.

5-yrs CI 7-yrs CI p-value<0.001

• Cum. Incidence

0.0 0.2 0.4 0.6 0.8 1.0

Years from diagnosis

1 2 3 4 5 6 7

Conter V et al Blood 2010; 115: 3206

69% of all relapses in IR pts

Relapses in T-ALL by MRD risk groups AIEOP-BFM ALL 2000

75 5 7.6%(3.3) 292 51 17.6%(2.2) 97 36 37.7%(5.0) SR IR HR N.pts

• N. rel.

7-yrs CI p-value: overall<0.001; SR vs MR=0.02; MR vs HR<0.001

• Cum. Incidence

0.0 0.2 0.4 0.6 0.8 1.0

Years from diagnosis

1 2 3 4 5 6 7

Schrappe et al. Blood 2011; 118: 2077

55% of all relapses in IR pts

Improving MRD Detection by Next Generation/High Throughput Sequencing (HTS)

• HTS of clonotypic Ig/TCR

rearrangements detects MRD at ~1/1,000,000

• Pilot study of ~300 pts from

AALL0331 showed that 20% had no detectable residual clonal sequence at any level at day 29

• These HTS neg pts had a 5-

yr EFS of 98.1% and OS 100%

• Includes pts with and

without favorable genetics

Wu et al, Clin Cancer Res 2014 Kirsch, SIOP 2016 and Wood, ASH 2016

The CyTOF instrument is similar to a traditional flow cytometer because it can screen numerous parameters

• n individual cells. However, it does

so with the use of metal particle conjugated antibodies instead of the traditional antibody conjugated fluorophores that conventional flow cytometers uses.

To view BCP-ALL through the lens of normal development, we classified individual leukemia cells to the closest normal stage of B-cell development

Workflow experiments Developmental Classification

Good Z and Sarno J, Nat Med 2018

DDPR synergize with current risk-stratification methods

NCI-Rome Criteria NCI-Rome + DDPR Final risk Final risk + DDPR

Good Z and Sarno J, Nat Med 2018

Developmentally Dependent Predictor of Relapse (DDPR) identifies critical features of pro-BII to pre-BI developmental transition that effectively predict relapse in childhood BCP-ALL based on diagnostic samples

High basal pSYK, pCREB, and prpS6 in pre-BI cells not responsive to ex-vivo stimulation High basal prpS6 and p4EBP1 in pro-BII cells

Estimated frequency of specific genotypes in childhood ALL

Hyperdiploid >50; 25,0% TEL-AML1; 20,0% E2A-PBX1; 4,0% MLL-AF4; 2,0% BCR-ABL1; 2,0% BCR-ABL1-like; 9.0% CRLF2; 4,0% IKZF1; 12,0% ERG; 3,0% dic(9;20); 2,0% iAMP21; 2,0% E2A-HLF; 0,5% Hipodiploid; 0,5% Other MLL-R; 4,0% Other BCP-ALL; 4,5% TAL1; 7,0% TLX3; 2,3% LYL1; 1,4% TLX1; 3,0% MLL-ENL; 3,0% ETP; 2.0% Others T-ALL; 1.7%

Modified from Pui et al, Blood. 2012;120:1165

T-ALL BCP-ALL

+8 +15 +78

BCR/ABL TEL/AML1 TCF3/PBX1 MLL/AF4 TCF3/HLF RQ-PCR Final MRD-risk

By FCM by RNA by DNA

DNAindex

TKI If HR

FCM MRD

JAK inhibitor?

1) TK fusions 2) JAK/STAT 3) MLL-other

HR-blocks

Experimental arm

Integrated diagnostic algorithm for childhood ALL

Current analyses Next analyses

IKZF1plus dMLPA NGS IG/TR

Targeted NGS

Padova Monza

RNA-seq? ERG-PCR ?

≥5x10-4 and ‘B-other’

+50

(random)

RQ-PCR Early MRD-risk

+33

CRLF2+ NG2+ CLL1 +

alert ≥10% and ‘B-other’

1- Identification of new targetable chromosomal traslocations

RT-PCR

A novel TCF3-HLF fusion in ALL with a t(17;19)(q22;p13)

Panagopoulos I. et al., Cancer Genet. 2012;205:669-72

Multiplex RT-PCR (x6)

IKZF1plus

• Is present in 6 – 7% of patients
• Is a very poor prognostic marker in intermediate

and high-risk BCP ALL

• Identifies 25% of relapses occurring in

intermediate and high-risk BCP ALL

• Acts as a prototype for a transition from

prognostic markers prognostic patterns

(Deletion of IKZF1 and: PAX5 and/or CDKN2A and/or CDKN2B and/or CRLF2 and negativity for ERG deletion)

Stanulla M et al J Clin Oncol 2017

IKZF1 plus identification

Digital MLPA ALL (MRC-Holland, kit and software not commercially available yet ):

• 320 probes, targeting 56 key target genes,

with at least 3 probes per gene.

• all ‘B-others’ ALL (75%), @d33.
• one run any 2 weeks (pooling of 10

samples).

• 86 diagnosis ALL samples analyzed with

MLPA P335 and digital MLPA- ALL kit.

• 98.8% of concordance on IKZF1-plus

identification between two methods.

• Depending on samples availability, dMLPA

can be spiked-in with IG/TR screening (once a week), no extra sequencing cost.

Stanulla M. et al, J Clin Oncol. 2018 Mar 2

‘Ikaros-plus’: any IKZF1del + del of PAX5 or CRLF2 or CDKN2, but ERGwt

MRD standard risk (SR) MRD intermediate risk (MR) MRD high risk (HR)

IKZF1plus – cumulative relapse incidence in MRD- based risk groups SR = 6% MR = 62% HR = 55%

Class of Kinase rearrangements and therapeutic targets in Ph-like ALL

Roberts KG & Mullighan CG, Nat Rev Clin Onc 2015 CHILDREN ADOLESCENTS YOUNG ADULTS

EBF1-PDGFRB (Ph-like) responds to TKI

Weston BW et al, J Clin Oncol. 2013;31:e413-6 Lengline E et al, Haematologica. 2013;98:e146-8

days

Cario G et al Haematologica, 2020 Cario G et al Haematologica, 2019

MISEQ ILLUMINA RNA CAPTURED LIBRARY

• ILLUMINA TRUSIGHT RNA PANCANCER PANEL
• NUGEN OVATION TARGET ENRICHMENT SYSTEM

RNA target capture in the diagnostic flowchart

IDENTIFICATION OF FUSION GENES BIOINFORMATICS (TopHat and RNASeq Alignment analysis)

• pooling of 8 samples (MRD TP1 +33>5X10-4)
• once any 3-4 weeks

Prospective analysis of B-ALL cases in years 2017-2018

2017 2018 - nowadays BCP-ALL TP1(D33) > 5 X 10^-4 56 52 MRD SER ( TP2 D78 < 5 X 10^-4 ) 16 28 (5 on going) MRD HR ( TP2 D78 > 5 X 10^-4 ) 15 8 (1 on going) MRD- nonHR 25 16 (7 on going)

1.Schwab, Blood, 2016 May 5; 127(18):2214-8 2.Kawamata N, Oncogene. 2011;31(8):966-77

• 3. Panagopoulos, Cancer Genet. 2012 Dec;205(12):669-72

4.Andersson AK, Nat Genet. 2015 Apr;47(4):330-7 5.Russell L., Genes Chromosomes Cancer. 2017 May;56(5):363-372 6.Hirabhayashi, Haematologica, 2017 Jan; 102(1): 118–129 MRD-SER (N=39) 2017: N=15 negative

N=7 not available N=4 with known traslocations

1 chr12-chr19 ETV6/JAK3 1 chrX-chrX USP9X/DDX3X (ref.5) 1 chr12-chr22 ZNF384EP300 (ref.6) 2 chr19-chr19 DOT1/OAZ1 2 chrX-chrX P2RY87CRLF2 1 chr12-chr22 ZNF384/TCF3 1 chr9-chr12 PAX5/FBRLS1 1 chr19-chr19 BRD4/KLF2 1 chrX-chrX ASMTL/P2RY8 1 chr5-chr5 ZNF608/PDGFRB 1 chr16-chr16 ZNF276/FANCA MRD-non HR (N=34) 2017: N=16 negative

N=4 not available N=5 with known traslocations

1 chrX-chrX P2RY8/CRLF2 1 del(5) EBF1/PDGFRB(ref.1) 2 chr8-chr8 NDRG1/ST3GAL1 1 chrX-chrX P2RY8/ZEBD1 1 chr12-chr12 BCL7A/NCOR2 1 chrX-chrX GPR128/TGF 1 chr8-chr8 NDRG1/ST3GAL1 1 chrX-chrX GPR128/TGF

chr19-chr19 DOT1/OAZ1

MRD-HR (N=22) 2017: N=14 negative

N=1 with known traslocations

1 chr1-chr1 HDAC1/MARCKSL1 chr9-chr20 PAX5/C20orf112 (ref.2) 1 chr17-chr19 TCF3/HLF (ref.3) 1 chr11-chr11 MLL/USP2 (ref.4) 1 chr19-chr19 DOT1/OAZ1 1 del(5) EBF1/PDGFRB (ref.1) 1 chr17-chr19 TCF3-HLF (ref.3)

Challenges in clinical development of “targeted therapies”in childhood ALL

• In vitro and in vivo evaluation of new agents in

ALL model systems: lenghty and

• ften

imperfect;

• Single-agent biologic activity in murine models

and translational into clinical activity in patients, and improper drug sequencing can result in untoward clinical outcomes;

• Complex

dymamics, with highly variable patterns of genetic diversity and resulting clonal architecture.

Selective pressures on branching clonal architecture of clonal evolution in leukemia

Greaves M, Nature 2012;481:306

Genetic predisposition to acute lymphoblastic leukemia

Pui CH, Nichols KE, Yang JJ. Somatic and germline genomics in paediatric acute lymphoblastic leukaemia. Nat Rev Clin Oncol. 2019 Apr;16(4):227-240.

Background

• Causes of ALL remain largely unknown, and ALL has

been considered for long time as not inheritable.

• It is estimated that at least 5-10% of cases diagnosed

with cancer harbor constitutional genetic variants that increase their lifetime cancer risk.

• Recently, both low and high impact genetic risk

factors for familial and non-familial childhood leukemia have been identified.

1.1 1.5 3.0 20.0

Effect Size

Low High Medium Moderate

Allele-Frequency

Very rare Rare Less common Common

0.001 0.005 0.05

Rare ALL risk variants

Rare high-risk Rare non high- risk

Rare ALL risk variants

Rare non high-risk variants associated with ALL

Li Fraumeni syndrome

TP53

DNA repair syndromes

ATM NBS1 BLM MLH1, MSH2, MSH6, PMS2

Chromosome 21 Impaired transcription PAX5 IKZF1

RUNX1 PHF6 EZH2 NSD1 CREBBP EP300

Impaired signaling

PTPN11

SH2B3 NF1 BRAF

Impact of predisposing variants

Impact of predisposing variants

• Genetic predisposition for childhood cancer is under

diagnosed.

• Identifying these patients may lead to therapy

adjustments in case of increased toxicity or resistant disease

• Screening programs may lead to early detection of a

further independent malignancy.

• Cancer surveillance might also be warranted for

affected relatives and detection of a genetic mutation can allow for reproductive counseling.

AIEO EOP-BFM ALL 2017 ALL 2017

International collaborative treatment protocol for children and adolescents with acute lymphoblastic leukemia

Stratification and Treatment Questions

• A. Biondi (AIEOP), J. Starý (CPH), S. Elitzur (INS),
• A. Kolenova (SPHOS), G. Mann (BFM-A), D. Barbaric (ANZCHOG),
• F. Niggli (BFM-CH), M. Schrappe (BFM-G)

Sponsor: University Medical Center Schleswig-Holstein (Kiel, Germany)

• Improving stratification by incorporating newly defined high-

risk subgroups.

• The randomized treatment intensification should be

implemented in an early post-induction phase.

• Late interventions (mainly in MR) failed to reduce relapse rates.
• MRD negativity before consolidation makes a profound difference in

prognosis in biological subgroup(s).

• Genotoxicity of cyclophosphamide may induce genetic damage in the

leukemic cells potentially increasing the risk of evolving resistant clones.

• Revision of MRD-based risk stratification: differentiation between

early and final stratification

• Introducing novel treatment approaches instead of increasing

the intensity of conventional chemotherapy.

Aims ms

AIEO EOP-BFM A ALL 2017 2017

Ri Risk stratif ific icatio ion

T-ALL (15%) precB-ALL (85%)

All eligible patients with ALL (100%)

AIEO EOP-BFM A ALL 2017 2017

Ri Risk stratif ific icatio ion

T-ALL (15%) precB-ALL (85%) Day 8 (Prednisone Response) T-ALL/PGR (9%) T-ALL/PPR (6%)

All eligible patients with ALL (100%)

AIEO EOP-BFM A ALL 2017 2017

Ri Risk stratif ific icatio ion

T-ALL (15%) precB-ALL (85%) Day 8 (Prednisone Response) T-ALL/PGR (9%) T-ALL/PPR (6%) Stratification time point 1 (end of induction) Pred PR, or FCM-MRD d15 ≥10%, or early HR Non-Remission d33 MRD at TP1 pos → non-SR

All eligible patients with ALL (100%)

AIEO EOP-BFM A ALL 2017 2017

Ri Risk stratif ific icatio ion

early SR (3%) early non-SR (12%) T-ALL (15%) precB-ALL (85%) Day 8 (Prednisone Response) T-ALL/PGR (9%) T-ALL/PPR (6%) Stratification time point 1 (end of induction)

All eligible patients with ALL (100%)

AIEO EOP-BFM A ALL 2017 2017

Ri Risk stratif ific icatio ion

early SR (3%) early non-SR (12%) T-ALL (15%) early non-HR (65%) early HR (20%) precB-ALL (85%) Day 8 (Prednisone Response) T-ALL/PGR (9%) T-ALL/PPR (6%) Stratification time point 1 (end of induction)

All eligible patients with ALL (100%)

early non-HR (65%) early HR (20%)

AIEO EOP-BFM A ALL 2017 2017

Ri Risk stratif ific icatio ion

early SR (3%) early non-SR (12%) T-ALL (15%) precB-ALL (85%) Day 8 (Prednisone Response) T-ALL/PGR (9%) T-ALL/PPR (6%) Stratification time point 1 (end of induction)

precB-ALL: early HR (20%)

• no complete remission on day 33, or
• positivity for KMT2A-AFF1 (MLL-AF4), or
• hypodiploidy <45 chromosomes, or
• FCM-MRD in BM on day 15 ≥ 10%, and not ETV6-RUNX1 positive, or
• positivity for TCF3-HLF (E2A-HLF), or
• IKZF1plus and PCR-MRD at TP1 positive, or
• PCR-MRD at TP1 ≥ 5x10-4, or
• age < 1 year and any KMT2A (MLL) rearrangement

All eligible patients with ALL (100%)

Definition: IKZF1plus

Deletion of IKZF1 and: − PAX5 and/or − CDKN2A and/or − CDKN2B and/or − CRLF2 (PAR) and − Negativity for ERG deletion

AIEO EOP-BFM A ALL 2017 2017

Ri Risk stratif ific icatio ion

early SR (3%) early non-SR (12%) T-ALL (15%) early non-HR (65%) early HR (20%) precB-ALL (85%) Day 8 (Prednisone Response) T-ALL/PGR (9%) T-ALL/PPR (6%) Stratification time point 1 (end of induction) Stratification time point 2 (after consolidation)

All eligible patients with ALL (100%)

AIEO EOP-BFM A ALL 2017 2017

Ri Risk stratif ific icatio ion

early SR (3%) early non-SR (12%) T-ALL (15%) early non-HR (65%) early HR (20%)

All eligible patients with ALL (100%)

precB-ALL (85%) Day 8 (Prednisone Response) T-ALL/PGR (9%) T-ALL/PPR (6%) Stratification time point 1 (end of induction) Stratification time point 2 (after consolidation) “early HR criteria“? MRD at TP1 and TP2?

AIEO EOP-BFM A ALL 2017 2017

Ri Risk stratif ific icatio ion

early SR (3%) non-HR (8%) early non-SR (12%) HR (7%) T-ALL (15%) early non-HR (65%) early HR (20%) precB-ALL (85%) Stratification time point 2 (after consolidation) Day 8 (Prednisone Response) T-ALL/PGR (9%) T-ALL/PPR (6%) Stratification time point 1 (end of induction)

All eligible patients with ALL (100%)

AIEO EOP-BFM A ALL 2017 2017

Ri Risk stratif ific icatio ion

early SR (3%) non-HR (8%) early non-SR (12%) HR (7%) T-ALL (15%) early non-HR (65%) MR (35%) early HR (20%) HR (16%) SR (34%) precB-ALL (85%) Stratification time point 2 (after consolidation) Day 8 (Prednisone Response) T-ALL/PGR (9%) T-ALL/PPR (6%) Stratification time point 1 (end of induction)

All eligible patients with ALL (100%)

AIEO EOP-BFM A ALL 2017 2017

Ri Risk stratif ific icatio ion pB-ALL

Stand ndard Ri Risk (SR) R), M Mediu dium Ri Risk (MR) R), and nd High gh Ri Risk (HR) R)

MR (35%) No SR and no HR criteria Stratification time point 2 (after consolidation)

HR (16%)

• no complete remission on day 33, or
• positivity for KMT2A-AFF1 (MLL-AF4), or
• positivity for TCF3-HLF, or
• hypodiploidy <45 chromosomes, or
• FCM-MRD in BM on day 15 ≥ 10% and not ETV6-RUNX1 positive, or
• IKZF1plus and PCR-MRD at TP1 positive or inconclusive and not positive for ETV6-

RUNX1, TCF3-PBX1 or KMT2A rearr. other than KMT2A-AFF1, or

• PCR-MRD at TP1 ≥ 5x10-4 and positive < 5x10-4 at TP2 (PCR-MRD SER), or
• PCR-MRD at TP2 ≥ 5x10-4 (PCR-MRD-HR), or
• age < 1 year and any KMT2A (MLL) rearrangement

SR (34%) No early HR criteria, and MRD-PCR TP1 & TP2 neg.

AIEO EOP-BFM A ALL 2017 2017

Ri Risk stratif ific icatio ion and nd rando domizations ns

early SR (3%) non-HR (8%) early non-SR (12%) HR (7%) T-ALL (15%) early non-HR (65%) MR (35%) early HR (20%) HR (16%) SR (34%) precB-ALL (85%) Stratification time point 2 (after consolidation) Random R-T Random R-eHR Random R-MR Random R-HR Day 8 (Prednisone Response) T-ALL/PGR (9%) T-ALL/PPR (6%) Stratification time point 1 (end of induction)

All eligible patients with ALL (100%)

Conclusions

• Depth and timing of leukemia remission after initial

chemotherapy, as measured by MRD testing, remain critical determinants of long-term outcomes;

• Advance genomics has facilitated the identification of

additional high-risk subtypes in de novo ALL and in relapsed patients;

• The trend to reduce subgroups further sustain the need

for continuous collaboration among researchers, clinical

• ncologists, government agencies, and pharmaceutical

industries;

• Finally, the impact of strategies based on immune tools

and immunoregulation it is likely will have great impact in clinical trials design.

Acknowledgments

Cytomorph./Immunoph. Oscar Maglia Simona Sala Giuseppe Gaipa Cytogenetics Giovanni Giudici Silvia Bungaro Marta Galbiati Molecular Biology Federica Mottadelli Federica Colnaghi Arianna Colombo Statisticians Daniela Silvestri Maria Grazia Valsecchi Emanuela Giarin Katia Polato Maddalena Paganin Barbara Buldini Truus te Kronnie Giuseppe Basso Clinica Ped. Univ. Padova

Grants by EU(FP7), Fondazione Cariplo, AIRC and Comitato M.L.Verga

BFM Germany BFM Austria BFM Suisse

Jacques van Dongen

MRD group Lilia Corral Simona Songia Tiziana Villa Eugenia Mella Sabrina Morfeo Andrea Garofalo

Chiara Palmi Grazia Fazio Giovanni Cazzaniga Valentino Conter

Mel Greaves Martin Schrappe Martin Stanulla