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The New Genetics of Acute Lymphoblastic Leukemia

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The New Genetics of Acute Lymphoblastic Leukemia The new genetics of acute lymphoblastic leukemia Charles Mullighan Department of Pathology Hematological Malignancies Program St. Jude Children’s Research Hospital International Conference of the Korean Society of Hematology March 16th 2019 Transcriptomic classification of 1988 ALL cases Classification of ALL: pre genomics Gene expression profiling of ALL • Many patients do not have a gross chromosomal alteration or translocation • Leukemic cell gene expression profile is a key “readout” of ALL biology Adapted from Gu et al, Nat Genet, 2019;51:296 Zhang J, et al. Nat Genet 2016;48:1481 Roberts KG. N Engl J Med 2014;371:1005; Yasuda T. Nat Genet 2016;48:569; Lilljebjörn H. Nat Commun 2016;7:11790; Suzuki K. J Clin Oncol 2016;34:3451; Zhang J. Nat Genet 2016;48:1481; Gocho Y. Leukemia 2015;29:2445; Liu YF. EBioMedicine 2016;8:173; Gu Z. Nat Commun 2016;7:13331. RNA-seq gene expression classification of ALL • Center for Precision Medicine in Leukemia Ph (BCR-ABL1) Other • St Jude: PCGP Ph-like • COG: TARGET • JAK/STAT • ECOG-ACRIN PAX5 • ABL-class • Alliance – CALGB P80R • SWOG iAMP21 • MDACC PAX5 alt KMT2A BCL2/MYC Hyperdiploid NUTM1 Near haploid MEF2D HLF Low hypodiploid ETV6-RUNX1 TCF3-PBX1 IKZF1 N159Y • Total/mRNA sequencing • Batch correction • Fusion calling ZNF384 • Mutation calling • Aneuploidy inference • Hierarchical clustering • PC analysis DUX4 • PAM prediction • tSNE analysis tSNE analysis of 1988 ALL transcriptomes Gu Z, et al. Nat Genet, 2019 1402 childhood, 208 young adult, 378 adult Current molecular classification Aneuploid Transcription factor TF other Kinase rearrangement Z Gu et al, Nat Genet, 2019 Founding alterations are powerful predictors of outcome Z Gu et al, Nat Genet, 2019 Why were subtypes not identified? • Rearrangements cryptic • Diverse rearrangement partners to a single gene • Founding alterations mutations • All of the above • Phenocopies of subtypes with key fusions MEF2D-rearranged B-ALL: cryptic and diverse rearrangements Chr 1q copy number profiles MEF2D BCL9 CD20 CD38 CD10 CD19 MEF2D ALL Non-MEF2D ALL Ex vivo xenografts Gu Z, et al. Nat Commun 2016;7:13331. From Roberts et al NEJM 2014 DUX4/ERG ALL: Cryptic + sequential transcription factor deregulation • ~7% ALL cases have a highly distinct gene expression profile, lack rearrangement on karyotyping • ~55% cases have ERG deletion En IGH DUX4 Expression of truncated DUX4 DUX4 DUX4 DUX4 Binding of DUX4 at ERG intron 6 DUX4 1 5 6 7 8 9 10 Focal ERG deletions in 60% Zhang J, et al. Nat Genet 2016;48:1481–9; Harvey RC, et al. Blood 2010;116:4874–84; Clappier E, et al. Leukemia 2014;28:70–7. Favorable outcome of DUX4 ALL Leukemia transcending immunophenotype: ZNF384 • Multiple partners with full coding region of ZNF384: SWI/SNF and histone acetyl transferases • Historically: B with myeloid features, case reports of MPAL, lineage switch GATA3 LYL1 FLT3 RUNX2 ETV6 CEBPA NFKBIZ TCL1A DTX1 Martini A, et al Cancer Res 2002;62:5408–12; Zhong CH, et al, Leukemia 2008;22:723–9; Gu Z, et al. Nat Commun 2016;7:13331; Alexander T etFrom al Nature Roberts 2018 562, et 373al NEJM-379 2014 ZNF384-rearrangements define a subset of B-ALL and B/myeloid MPAL CD3 MPO CD19 MPO Alexander TB, et al. Nature 2018;562: From Roberts et al NEJM 2014 Phenotypic plasticity is independent of genetic variegation WES and/or WGS sequencing for sorted subpopulations - 50 cases - 134 sorted subpopulations SNP array for copy number analysis for sorted subpopulations - 29 cases – 81 sorted subpopulations Phenotypic plasticity is intrinsic B/M MPAL, EP300-ZNF384-rearranged CD34+ CD19+ CD33+ CD34- CD19+ CD33+ CD34- CD19+ CD33- Sort of progenitor/blast populations for WGA and targeted sequencing Model of MPAL leukemogenesis Ph-like ALL: diverse fusions, mutations, structural alterations • Gene expression profile similar to Ph+ ALL • Frequent alteration of IKZF1 • Poor outcome Ph+ ALL den Boer Lancet Oncol 2009; Mullighan NEJM 2009; Roberts NEJM 2014; Roberts J Clin Oncol 2017 The growing diversity of kinase fusions in Ph-like ALL Partners Kinase TKI (N) 5’ genes ABL1 Dasatinib 12 CENPC, ETV6, FOXP1, LSM14A, NUP214, NUP153, RCSD1, RANBP2, SFPQ, SNX2, SPTAN1, ZMIZ1 ABL2 Dasatinib 3 PAG1, RCSD1, ZC3HAV1 CSF1R Dasatinib 3 SSBP2, MEF2D, TBL1XR1 PDGFRA Dasatinib 1 FIP1L1 PDGFRB Dasatinib 7 ATF7IP, EBF1, ETV6, SSBP2, TNIP1, ZEB2, ZMYND8 LYN Dasatinib 1 GATAD2A CRLF2 JAK2 inhibitor 2 IGH, P2RY8 JAK2 JAK2 inhibitor 20 ATF7IP, BCR, EBF1, ETV6, GOLGA5, HMBOX1, PAX5, PCM1, PPFIBP1, RFX3, SMU1, SNX29, SSBP2, STRN3, TERF2, TPR, USP25, WDR37, ZNF274, ZNF340 TYK2 TYK2 inhibitor 3 MYB, SMARCA4, ZNF340 EPOR JAK2 inhibitor 4 IGH, IGK, LAIR, THADA IL2RB JAK1/JAK3 inhibitor 1 MYH9 NTRK3 TRK inhibitor 1 ETV6 FLT3 FLT3 inhibitor 1 ZMYM2 FGFR1 FGFR1 inhibitor 2 BCR, MYO18A BLNK SYK/MEKi 1 DNTT Signaling pathways in Ph-like ALL: III Other TSLP EPO ITD ZMYM2 KD TMEM2 Y ETV6 Y Y DTT2 Y Y FLT3 Y Y CRLF2 IL-7Rα ins Truncated F232C EPOR KD KD KD KD KD KD KD TYK2 KD KD CSF1R FLT3 JAK2 NTRK3 PTK2B ABL1/2 PDGFRB/A BLNK JAK-STAT Signaling ABL Signaling Other Signaling JAKi BCL2i MAPK/ STAT ABLi STAT STAT PI3Ki ERK mTORi PI3K/ PI3K/ mTOR mTOR TRKi FLT3i Transcription MEKi JAKi X FAKi PI3Ki IKZF1/EBF1/ mTORi PAX5/CDKN2A/B Targeting ABL-class alterations: importance of combinatorial therapy KD KD KD CSF1R ABL1/2 PDGFRB/A ABL Signaling ABLi PI3Ki STAT mTORi PI3K/ pCRKL mTOR Roberts Cancer Cell 2012; Roberts NEJM 2014; Blood Adv 2017 Exceptional responders: ETV6-NTRK3 in Ph-like ALL Pre-B cells CD19 CRE Etv6 NTRK3 YFP Roberts Blood 2018; 132, 861 PAX5 P80R ALL: mutations as initiating events Ph (BCR-ABL1) Other Ph-like PAX5 P80R iAMP21 PAX5 alt KMT2A BCL2/MYC Hyperdiploid NUTM1 Near haploid MEF2D HLF Low hypodiploid ETV6-RUNX1 TCF3-PBX1 IKZF1 N159Y ZNF384 DUX4 tSNE analysis of 1988 ALL transcriptomes Gu Z, et al, Nat Genet 2019 1402 childhood, 208 young adult, 378 adult Mullighan, Nature 2007 PAX5 P80R ALL: mutations as initiating events Genetic mutations in PAX5 P80R mutated ALL Modeling of PAX5 P80R mutant ALL CRISPR/Cas9 zygote targeting PAX5alt ALL: diversity to rival Ph-like ALL Ph (BCR-ABL1) Other Ph-like PAX5 P80R iAMP21 PAX5 alt KMT2A BCL2/MYC Hyperdiploid NUTM1 Near haploid MEF2D HLF Low hypodiploid ETV6-RUNX1 TCF3-PBX1 IKZF1 N159Y ZNF384 DUX4 tSNE analysis of 1988 ALL transcriptomes Gu Z, et al, Nat Genet 2019 1402 childhood, 208 young adult, 378 adult Genomic alterations in PAX5alt ALL PAX5 internal tandem duplications in PAX5alt ALL Scott Newman, Ilaria Iacobucci Phenocopies Ph (BCR-ABL1) Other Ph-like(JAK/STAT) PAX5 P80R iAMP21 PAX5 alt KMT2A BCL2/MYC Hyperdiploid NUTM1 Near haploid MEF2D HLF Low hypodiploid ETV6-RUNX1 TCF3-PBX1 IKZF1 N159Y ZNF384 DUX4 tSNE analysis of 1988 ALL transcriptomes Gu Z, et al. under review 1402 childhood, 208 young adult, 378 adult Many unclassified cases are phenocopies of existing subtypes Lilljebjörn H, et al. Nat Commun 2016;7:11790 • CASC15-ETV6 • ETV6-AMPH • ETV6-ELMO1 • ETV6-EXTL1 • ETV6-LHFPL3-AS2 • ETV6-RNU6-19P_locA • ETV6-SLC30A7 • ETV6-SRRM1 • ETV6-STYK1 • FUS-ERG • IKZF1-CLNK • IKZF1-ETV6 • IKZF1-ZPBP • STIM2-IKZF1 • TCF3-FLI1 Gu Z, et al. Nat Genet, in press. Clinical implications of new ALL subtypes Subtype Risk Therapy Hyperdiploid Good Standard ETV6-RUNX1 Good Standard Hypodiploid Poor Intensive, transplant KMT2r Poor Intensive, transplant Ph+ (BCR-ABL1) Poor Kinase inhibitors Ph-like Poor Kinase inhibitors MEF2D-rearranged Intermediate HDAC inhibitors ZNF384-rearranged Intermediate FLT3 inhibitors DUX4-rearranged Very good Reduced intensity NUTM1-rearranged Good BRD inhibitors BCL2/MYC Very poor IKZF1 N159 Intermediate FAK inhibitors Conclusions • Genomic sequencing has revised the molecular taxonomy of ALL • Transcriptomic profiling remains a key marker of ALL subtype • Underlying genetic drivers are heterogeneous in type and spectrum • Sequencing-based approaches are essential for accurate risk stratification and targeted therapy. Diagnostic utility of RNA-seq ETV6-RUNX1 Aneuploidy assignment MLL-like Mutant allele frequency 61,XX,+X,+3,+4,+5,+6,+10,+11,+12,del(12)(p11.2),+14,+15,+16,+17,+18,+21,+21 (14/70%) 62,idem,+mar (3/15%) 46,XX (3/15%). Scott Newman, Zhaohui Gu Acknowledgments Mullighan Lab Contributors and Andrew Bryant collaborators David Cervi Taosheng Chen Yunchao Chang Michael Edmonson Michelle Churchman Laura Janke Kirsten Dickerson Wenwei Lin Qingsong Gao Jon Lowe Pankaj Ghate Junmin Peng Zhaohui Gu Ying Shao Ashley Hill Haiyan Tan Bryan Huber Xin Zhou Ilaria Iacobucci Joy Nakitandwe Debbie Payne-Turner Jim Downing Kathryn Roberts Chunxu Qu External Hiroki Yoshihara John Dick Yaqi Zhao Steph Dobson Stephen Hunger St Jude Roland Kuiper William Evans Mignon Loh https://www.stjuderesearch.org/site/lab/mullighan Kohei Hagiwara Esme Waanders Laura Janke Mary Relling Jun Yang Jinghui Zhang .
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