Genetic Basis of MPAL PDF

HHS Public Access Author manuscript Nature. Author manuscript; available in PMC 2019 April 01. Author Manuscript Published in final edited form as: Nature. 2018 October ; 562(7727): 373–379. d...

HHS Public Access Author manuscript Nature. Author manuscript; available in PMC 2019 April 01. Author Manuscript Published in final edited form as: Nature. 2018 October ; 562(7727): 373–379. doi:10.1038/s41586-018-0436-0. The genetic basis and cell of origin of mixed phenotype acute leukaemia A full list of authors and affiliations appears at the end of the article. Abstract Mixed phenotype acute leukaemia (MPAL) is a high-risk subtype of leukaemia with myeloid and Author Manuscript lymphoid features, limited genetic characterization, and a lack of consensus regarding appropriate therapy. Here we show that the two principal subtypes of MPAL, T/myeloid (T/M) and B/myeloid (B/M), are genetically distinct. Rearrangement of ZNF384 is common in B/M MPAL, and biallelic WT1 alterations are common in T/M MPAL, which shares genomic features with early T-cell precursor acute lymphoblastic leukaemia. We show that the intratumoral immunophenotypic heterogeneity characteristic of MPAL is independent of somatic genetic variation, that founding lesions arise in primitive haematopoietic progenitors, and that individual phenotypic subpopulations can reconstitute the immunophenotypic diversity in vivo. These findings indicate that the cell of origin and founding lesions, rather than an accumulation of distinct genomic alterations, prime tumour cells for lineage promiscuity. Moreover, these findings position MPAL in the spectrum of immature leukaemias and provide a genetically informed framework for future clinical trials of potential treatments for MPAL. Author Manuscript Acute leukaemia of ambiguous lineage (ALAL) comprises a collection of high-risk leukaemias defined by immunophenotype, including MPAL and acute undifferentiated leukaemia (AUL). MPAL demonstrates features of acute lymphoblastic leukaemia (ALL) and acute myeloid leukaemia (AML), while AUL lacks lineage-defining features. MPAL Reprints and permissions information is available at www.nature.com/reprints. * Correspondence and requests for materials should be addressed to C.M. or H.I. [email protected]; [email protected]. Author contributions T.B.A.: study design, flow analysis and sorting, data analysis, and manuscript writing. Z.G.: genomic data analysis. I.I.: genomic and mouse experiments, data analysis, data interpretation and manuscript preparation. K.D.: ZNF384r modeling. J.K.C.: central review of immunophenotype. B.X.: ChIP–seq and RNA-seq data analysis. D.P.-T and H.Y.: performed experiments. M.L.L. and S.P.H.: led and contributed to Children’s Oncology Group ALL studies and the ALL TARGET project. M.B. Author Manuscript and B.W.: reviewed flow cytometry. M.D., N.A.H., and A.C.: provided clinical data. J.H., E.O., B.B, G.B., S.E., V.d.H., C.M.Z., A.Y., D.R., D.T., N.K., T.L., B.D.M., D.C., H.H., A.M., A.S.M., O.H., K.E.N., J.R.D., and J.Z.: patient samples and clinical data. S.M.: data for comparison cohort. Y.-L.Y.: flow analysis. M.A.S., T.M.D., L.C.H., P.G., M.A.M., Y.M., A.J.M., R.A.M., S.J.M.J., and J.M.G.A.: genomic sequencing, analysis, and support. M.V.: performed FISH. L.J.J.: necropsy and histology on xenograft models. J.E.R. and C.- H.P.: patient samples and clinical data. D.S.G.: support for genomic analysis and manuscript editing. L.D. and Y.L.: genomic analysis. X.C., L.S., S.P. and D.P.: statistical analysis. S.N.: somatic and germline variant analysis. H.I.: acquisition of patient samples and clinical data. C.G.M.: designed and oversaw the study, analysed data and wrote the manuscript. Online content Any Methods, including any statements of data availability and Nature Research reporting summaries, along with any additional references and Source Data files, are available in the online version of the paper at Reviewer information Nature thanks R. Levine and the other anonymous reviewer(s) for their contribution to the peer review of this work. Competing interests: The authors declare no competing interests. Publisher's Disclaimer: Publisher’s note: Springer Nature remains neutral with regard to jurisdictional claims in published maps and institutional affiliations.  Alexander et al. Page 2 represents 2–3% of cases of childhood acute leukaemia, whereas AUL is rare1,2. Survival Author Manuscript rates for children and adults with MPAL are 47–75% and 20–40%, respectively, and there is no consensus regarding the optimal (AML- or ALL-directed) therapeutic regimen1–3. Up to 15% of patients with MPAL have rearrangements of KMT2A (also known as MLL; rearrangements referred to as KMT2Ar) or a BCR–ABL1 fusion gene, but the genetic basis of most cases of MPAL remains unknown. As the lineage ‘aberrancy’ or ‘promiscuity’ of T/M MPAL shares features with early T-cell precursor (ETP) ALL4,5, we sought to define the genetic basis of MPAL, to compare its genomic landscape to those of other leukaemia subtypes, and to determine the genetic basis of the intratumoral phenotypic heterogeneity that is characteristic of this disorder. Genomic characterization of ALAL We performed a central review of 159 potential paediatric cases of ALAL by repeating (n = Author Manuscript 138) or reviewing flow cytometry data (n = 21); 115 fulfilled WHO (World Health Organization) criteria for the diagnosis of ALAL6 (Extended Data Fig. 1). There was a male predominance of ALAL (1.6:1), which was diagnosed at similar frequency throughout childhood, except for cases with KMT2Ar, which were common in infants (Supplementary Tables 1, 2). The cohort included 49 cases of T/M MPAL, 35 B/M MPAL, 16 KMT2Ar MPAL and 2 BCR–ABL1 MPAL, 8 MPAL not otherwise specified (NOS), and 5 AUL. There was extensive immunophenotypic heterogeneity, with bilineal patterns (multiple immunophenotypic subpopulations), biphenotypic patterns (coexpression of lymphoid and myeloid antigens), or both (Extended Data Fig. 2a–g). There was no difference in five-year overall survival between T/M MPAL and B/M MPAL (56.7%+/−10.8% (95% confidence interval) and 59.7%+/−11.4%. respectively); outcome for patients with KMT2Ar was poor (five-year overall survival 21.2% ± 10.8%) (Extended Data Fig. 2h–o). Author Manuscript Genomic alterations were examined by exome (n = 92), transcriptome (n = 95), and/or whole-genome (n = 47) sequencing, and single nucleotide polymorphism (SNP) array analysis (n = 95) (Supplementary Tables 3, 4). We identified 158 recurrently altered genes, of which 81 were mutated in at least three cases. Commonly mutated genes included those recurrent in AML, such as FLT3 (n = 31), RUNX1 (n = 15), CUX1 (n = 7) and CEBPA (n = 5); those recurrent in ALL, including CDKN2A or CDKN2B (n = 22), ETV6 (n = 23), and VPREB1 (n = 15); and those recurrent in both AML and ALL, including WT1 (n = 28) and KMT2A (n = 26) (Fig. 1a, Extended Data Figs. 3, 4 and Supplementary Tables 5–13). We analysed associations between genomic alterations and age at diagnosis, sex and disease subtype, and between pathway alterations and outcome (Supplementary Tables 14, 15 and Supplementary Note). We analysed germline samples for potential pathogenic variants in Author Manuscript recurrently somatically mutated genes, and identified few putatively deleterious variants7 (Supplementary Table 16 and Supplementary Note). Distinct profiles of MPAL subtypes The three most common subtypes of MPAL (T/M, B/M and KMT2Ar) had distinct patterns of genomic alterations (Fig. 1a–c, Supplementary Table 13). As in infant ALL, KMT2Ar MPAL had a low mutation burden (median 1 (range 0–3) copy number alterations (CNAs) Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 3 and 4 (0–12) single nucleotide variants (SNVs) or insertions/deletions (indels) per case), Author Manuscript whereas mutation burden was higher for T/M MPAL (4.5 (0–35) CNAs, 8 (2–29) SNVs or indels) and B/M MPAL (3.5 (0–29) CNAs, 9 (0–167) SNVs or indels) (Extended Data Fig. 3b). Alterations in genes encoding transcriptional regulators were detected in 100% of cases of T/M MPAL, with mutually exclusive alterations in WT1, ETV6, RUNX1 and CEBPA in 82% of cases (Fig. 1b, Extended Data Fig. 5a, b); and in 94% of cases of B/M MPAL, with the B-lineage transcriptional regulators PAX5 and IKZF1 altered in 40% of cases (Fig. 1b). Alterations in signalling pathways were observed in 88% of cases of T/M MPAL, 74% of cases of B/M MPAL and 63% of cases of KMT2Ar MPAL. Alterations in JAK–STAT signalling were more common in T/M MPAL (57%) than B/M MPAL (23%) or KMT2Ar MPAL (19%) (Fig. 1c), and we observed a negative correlation between alterations in FLT3 (43%) and the Ras pathway (33%) in T/M MPAL (P = 0.002) (Fig. 1c, Supplementary Table 15). Ras pathway alterations were common in B/M MPAL (63%, most commonly NRAS Author Manuscript and PTPN11). Genes encoding epigenetic regulators were mutated in 69% of cases of T/M MPAL, including inactivating mutations in EZH25 (16%) and PHF6 (16%), and in 63% of cases of B/M MPAL, most commonly in MLLT3 (17%), KDM6A (in one-third of ZNF384- rearranged cases), EP300 and CREBBP (Supplementary Table 13). Transcriptome sequencing identified chimaeric in-frame fusions in 15 of 40 cases of T/M MPAL: ZEB2–BCL11B (n = 3), ETV6–NCOA2 (n = 2), ETV6–ARNT (n = 2) and single cases of ETV6–FOXO1, ETV6–MAML3, NUP214–ABL1, PICALM–MLLT10 and PCM1– FGFR1 (Supplementary Tables 17–20). KMT2Ar MPAL had a B/M phenotype in 15 out of 16 cases and a T/M phenotype in one case, and involved AFF1 (also known as AF4) in seven cases, MLLT3 (also known as AF9) in three cases and MLLT1 (also known as ENL) in two cases. KMT2Ar was also found in two of five cases of AUL. Author Manuscript ZNF384 rearrangement in leukaemia Rearrangement of ZNF384 (ZNF384r) was present in 48% of cases of B/M MPAL, involving TCF3 (n = 8), EP300 (n = 5), TAF15 (n = 1) and CREBBP (n = 1) (Extended Data Fig. 5c). The chimaeric fusions involved the entire ZNF384 coding region, loss of the C termini of the partner genes, and translation of both wild-type ZNF384 and chimaeric fusion proteins. The mutational burden of ZNF384r B/M MPAL (median of 4 (1–29) CNAs and 8 (3–39) SNVs or indels) was similar to those of other MPAL subtypes (Extended Data Fig. 3b), with no variation in mutations between immunophenotypic subpopulations in ten cases examined (Extended Data Fig. 5d). ZNF384r, most commonly with TCF3, is also observed in B cell ALL (B-ALL), in which aberrant expression of myeloid markers that do not fulfil the diagnostic criteria for B/M MPAL is common8. The genomic landscape of childhood Author Manuscript ZNF384r B-ALL (n = 19, Supplementary Tables 21, 22) was similar to that of ZNF384r MPAL with the exception of KDM6A alterations, which were observed only in ZNF384r MPAL (Fig. 2a). Analysis of a diverse range of acute leukaemias, including AML (Supplementary Tables 23, 24), showed that the gene expression profiles (GEPs) of ZNF384r B/M MPAL and B-ALL were indistinguishable (Fig. 2b, Extended Data Fig. 5e and Supplementary Table 25). Patients with ZNF384r exhibited higher FLT3 expression those with other types of B/M or T/M MPAL (Extended Data Fig. 5f). Cases of B/M MPAL Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 4 that exhibited genomic features of other subtypes of B-ALL, such as hyperdiploidy or a Ph- Author Manuscript like GEP, clustered with those subtypes of B-ALL (Fig. 2b). Gene set enrichment analysis suggested that ZNF384r B/M MPAL was arrested at a more mature stage of development than other types of B/M MPAL (Extended Data Fig 6a, Supplementary Tables 26, 27). However, compared with B-ALL, ZNF384r leukaemia showed enrichment of stem cell pathways and genes upregulated in ETP-ALL (Extended Data Fig. 6b, Supplementary Tables 27–29). Serial sampling of a case of ZNF384r B/M MPAL showed acquisition of a focal heterozygous IKZF1 deletion at first relapse, and a focal homozygous deletion of CDKN2A and CDKN2B at second relapse, with a shift from a myeloid to a lymphoid immunophenotype. Thus, ZNF384r defines a distinct subtype of acute leukaemia with a variable immunophenotype ranging from B-ALL to B/M MPAL. To further investigate the role of ZNF384 rearrangement in leukemogenesis, we expressed haemagglutinin (HA)-tagged ZNF384, TAF15–ZNF384 and TCF3–ZNF384 in mouse Arf Author Manuscript −/− pre-B cells9 (Extended Data Fig. 6c). Chromatin immunoprecipitation with sequencing (ChIP–seq) identified 2,298 peaks with new or increased binding of the fusion proteins compared to wild-type ZNF384, and 495 peaks with reduced binding (Extended Data Fig. 6d). Gained or increased peaks contained the core ZNF384 binding motif, and were enriched at promoters of genes important for immune system development and transcriptional regulation (Supplementary Tables 30, 31). Increased promoter binding was associated with increased gene expression (Extended Data Fig. 6e and Supplementary Table 32), with similarity between the GEPs of mouse pre-B cells expressing ZNF384 fusions and human ZNF384r leukaemia cells (Extended Data Fig. 6f and Supplementary Table 28). Thus, chimaeric ZNF384 oncoproteins exhibit perturbed binding and drive transcriptional deregulation in human ZNF384r leukaemia. Author Manuscript The driver alterations and GEPs of non-ZNF384r MPAL and AUL were heterogeneous (Supplementary Table 33). Three cases were Ph-like (EBF1–PDGFRB, IGH–CRLF2 and a case lacking an identified kinase lesion), and two were hyperdiploid. Eight cases were KMT2A-like with HOXA9 deregulation, and six of these had genetic alterations associated with HOXA overexpression: MLLT10 rearrangement (n = 2), SET–NUP214 (n = 2), KMT2A partial tandem duplication and MNX1–ETV6 (n = 1 each). Similarity between T/M MPAL and ETP-ALL ETP-ALL exhibits aberrant expression of stem cell and myeloid markers (with the exception of myeloperoxidase, which would classify the disease as AML or MPAL)10. ETP-ALL is characterized by mutations in regulators of haematopoietic development, signalling, and chromatin remodelling, and a GEP suggesting the cell of origin to be a haematopoietic stem Author Manuscript cell (HSC) or progenitor, rather than a T-cell precursor5. Because T/M MPAL and ETP-ALL are defined by a phenotype that includes lymphoid and myeloid features6,10, we hypothesized that they might share molecular features. We compared the genomic features of T/M MPAL with those of childhood T cell ALL (T-ALL; n = 245)11, ETP-ALL (n = 19)11 and AML (n = 197)12 (Supplementary Tables 34, 35). Transcription factor gene alterations were common in each but varied between subtypes (Extended Data Fig. 6g). The core transcription factors driving T-ALL (TAL1, TAL2, TLX1, TLX3, LMO1, LMO2, Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 5 NKX2–1, HOXA10 and LYL1) were less frequently altered in T/M MPAL and ETP-ALL Author Manuscript (63% versus 16% and 26%, respectively; P < 0.001). Alterations that deregulated TAL1, which were present in 31% of cases of T-ALL, were never observed in T/M MPAL, including the 15 cases for which whole-genome sequencing (WGS) was examined for noncoding enhancer mutations13. Other alterations that are common in T-ALL, such as MYB amplification, LEF1 deletion, CDKN2A and CDKN2B deletions, and amplification of the NOTCH1-driven MYC enhancer, were rare in T/M MPAL and ETP-ALL. By contrast, WT1 alterations were common in T/M MPAL (41%) and ETP-ALL (42%), but not in T- ALL (9%; P < 0.001). Alterations of CEBPA and CUX1 were common in T/M MPAL but not ETP-ALL or T-ALL. Conversely, NOTCH1 mutations were uncommon in T/M MPAL and AML. Signalling pathway mutations were also associated with specific subtypes, with Ras and JAK–STAT pathway mutations being common in T/M MPAL and ETP-ALL, and phosphotidylinositol 3-kinase (PI3K) signalling pathway mutations being common in T- Author Manuscript ALL (Extended Data Fig. 6h). Several genes were mutated at similar frequencies in T/M MPAL and ETP-ALL, including ETV6, EZH2, WT1 and FLT3 (Fig. 2c), and the GEPs of T/M MPAL and ETP-ALL were similar (Fig. 2b). Thus, T/M MPAL and ETP-ALL are similar entities in the spectrum of immature leukaemias. Analysis of intratumoral variegation Elucidating whether the intra-sample immunophenotypic heterogeneity is determined by genetic variegation or by genomic priming of a haematopoietic progenitor has important implications for therapy. Accordingly, we sequenced 2–4 subpopulations from 50 cases of MPAL (Supplementary Table 36). In 41 cases, the non-silent mutations were present in each separate population (Fig. 3a, b and Extended Data Fig. 7a). In nine cases, multiple mutations were detected in a single gene (WT1 in five cases) with at least one of the mutations Author Manuscript detected in all subpopulations in all cases. In two cases, the second mutation called from the same gene was not present in each subpopulation sequenced (WHSC1 in T/M case SJMPAL016447 and CREBBP in T/M case SJMPAL017976). In five cases, a subpopulation-restricted mutation occurred in a signalling pathway, either as gain of function (PTPN11, FLT3) or loss of function (NF1, CBL) (Supplementary Table 36), consistent with previous studies of diagnosis and relapse pairs showing frequent subclonal signalling alterations14. By contrast, mutations in the most commonly altered transcription factor in T/M MPAL, WT1, were consistently present in the major clone in each case. These observations support the notion that transcription factor gene alterations arise early in leukemogenesis, and alterations that drive signalling alterations are secondary events. Similarly, analysis of the DNA methylation profiles of 27 cases of MPAL (11 with multiple Author Manuscript subpopulations), 74 non-MPAL leukaemias and 17 normal progenitor samples showed distinct methylation profiles between leukaemia subtypes, but not between MPAL subclones (Extended Data Fig 7b–e, Supplementary Table 37). Thus, cytosine methylation does not drive immunophenotypic heterogeneity in MPAL. Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 6 Phenotypic plasticity of MPAL Author Manuscript To further examine the basis of lineage plasticity in MPAL, we used xenograft models in which immunophenotypic subpopulations were purified and transplanted into immunocompromised NOD-SCID IL2Rγ-null-3/GM/SF (NSG-SGM3) mice. Sorted subpopulations of cells from a patient with T/M MPAL (Fig. 3c, Extended Data Fig. 8a), the ZNF384r B/M JIH-5 cell line15 (Extended Data Fig. 8b, c), and a patient with KMT2Ar MPAL (Extended Data Fig. 8d), when transplanted into multiple independent NSG-SGM3 mice, propagated the immunophenotypic diversity of the primary samples. Moreover, we observed a phenotype shift in a sample from a patient with T/M MPAL during passaging of the bulk tumour sample, with engraftment of either a B/M or T/M leukemia phenotype (Extended Data Fig. 8e–h). These data demonstrate the multilineage potential of phenotypic subpopulations in MPAL, and phenotypic evolution even in the absence of therapeutic pressure. Author Manuscript Collectively, our genomic data and in vivo lineage plasticity data suggest that intra-sample lineage diversification in MPAL is driven by constellations of genomic alterations acquired in a haematopoietic stem or progenitor cell with multilineage potential. To test this idea, we purified progenitor cell and blast populations and normal mature lymphocytes from samples from a patient with ZNF384r B/M MPAL and two patients with WT1-altered T/M MPAL (Fig. 4a and Extended Data Fig. 9a, b). Alterations identified in the unfractionated samples (for example, TCF3–ZNF384 and mutations in MYCN, NTSD2 and DNAH17 in the ZNF384r sample) were identified in the purified blast populations but not in non-leukaemic T or natural killer (NK) cells. Each alteration was also present in multiple haematopoietic progenitor populations with myeloid and lymphoid potential, and a subset of HSCs (Fig. 4b and Extended Data Fig. 9c). Analogous results were detected in two cases of T/M MPAL Author Manuscript with WT1 alterations (data not shown); these contrast with Ph-like B-ALL, in which founding lesions are detectable in a primitive progenitor with the capacity for myelo- lymphoid differentiation, but not in HSCs16. These data support the notion that mutations are acquired in a haematopoietic stem cell that is primed for lineage aberrancy. To gain further insight into the relative roles of founding genomic lesions, acquired genetic alterations and the role of therapy in dictating MPAL phenotype, we analysed sequential samples obtained at initial diagnosis and disease recurrence in nine patients. The immunophenotypes of five cases (three T/M MPAL, one B/M MPAL, and one MPAL NOS with T/B phenotype) were stable from diagnosis and relapse, but changed in four cases. Two were ALL (one B-ALL, one ETP-ALL) at diagnosis and relapsed as MPAL, and two were MPAL at diagnosis (one T/M, one B/M) and subsequently relapsed as AML and ALL, Author Manuscript respectively (Extended Data Fig. 10). In the five cases with immunophenotypic stability, mutations in the predominant clone were lost (PTPN11, CCND3, NOTCH1, and RPL22) or emerged (TP53, IKZF1, NF1, NCOR1, and SUZ12). Despite this genomic evolution, the lineage ambiguity remained, further supporting the notion that MPAL leukaemia-initiating cells are primed for multi-lineage potential. In all four cases with phenotype shifts, the initial therapy correlated with the type of phenotype shift: patients who received ALL-directed therapy relapsed with myeloid leukaemia and one patient who received AML-directed therapy relapsed with lymphoid leukaemia. In two cases, immunophenotype at relapse was Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 7 also correlated with a mutation characteristic of leukaemia subtype: CEBPA for AML and Author Manuscript CDKN2A or CDKN2B for B-ALL. Together, these nine cases with serial samples support the theory that early genomic lesions prime progenitors for lineage aberrancy, which may remain stable or change over time, and that phenotype is influenced by therapeutic pressure and/or genomic evolution. Discussion This study provides a comprehensive genomic analysis of paediatric MPAL, providing insights into the genomic relationships between immunophenotypically defined subtypes of acute leukaemia. We propose an update to the WHO classification of acute leukaemia that includes new subtypes of ZNF384-rearranged acute leukaemia (either B-ALL or MPAL), WT1-mutant T/M MPAL, and Ph-like B/M MPAL (Extended Data Fig. 1c). Author Manuscript The ALL-like genomic landscape of B/M MPAL and the similarity in genomic alterations between ZNF384r B/M MPAL and B-ALL supports the use of ALL-directed therapy for patients with B/M MPAL. Furthermore, the overexpression of FLT3 and responsiveness to FLT3 inhibition in ZNF384 leukaemia17 suggest that such targeted therapy should be considered in this form of leukaemia. Non-ZNF384r cases of B/M MPAL should be carefully evaluated for other kinase-activating alterations that may be amenable to kinase inhibition, as shown in Ph-like ALL18. Our data show that ETP-ALL5 and T/M MPAL are genomically and epigenomically similar, and suggest that FLT3 and/or JAK inhibition should be evaluated further4. T/M MPAL exhibits infrequent alteration of core T-ALL transcription factor genes and few mutations in CDKN2A, CDKN2B, NOTCH1 and FBXW7; frequent FLT3-activating mutations; and a GEP that overlaps with that of AML, consistent with the notion that the pathogenesis of T/M Author Manuscript MPAL is distinct from that of T-ALL. However, contemporary paediatric ALL trials have demonstrated remarkable success in treating ETP-ALL, which is similar to T/M MPAL, so ALL-directed therapy may also be appropriate for T/M MPAL19. In contrast to the notion that subclonal genomic variation drives clonal evolution during disease progression in ALL14, our analysis of phenotypically distinct subpopulations within individual patients with MPAL revealed that mutational variegation did not determine phenotypic diversification. Rather, the common genomic features of ZNF384r B-ALL and MPAL, limited mutational variegation between subclones, multi-lineage potential of subclones in xenograft models, lineage plasticity in serial patient samples, and identification of leukaemia-initiating alterations in early haematopoietic progenitors indicate that the ambiguous phenotype of MPAL is the result of the acquisition of alterations in immature Author Manuscript haematopoietic progenitors (Fig. 4c, d). These data also support a model of haematopoiesis in which progenitors retaining multilineage potential undergo terminal differentiation into a single lineage only relatively late in haematopoiesis20. By demonstrating the genomic similarity of phenotypically distinct malignant populations, and by identifying the potential clinical importance of ZNF384 fusions, these results emphasize the limitations of morphology and immunophenotype alone in diagnostic Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 8 evaluation. As has been demonstrated in AML, ETP-ALL, MDS, and Ph-like ALL5,11,18,21,22, accurate MPAL sub-classification requires careful genomic analysis to Author Manuscript optimally guide diagnosis, risk-stratification and tailoring of therapy. Together, these findings have implications for disease classification and therapeutic decisions, while also clarifying the pathogenesis of this high-risk subtype of acute leukaemia. METHODS Patients and samples Diagnosis and remission samples were obtained from St. Jude Children’s Research Hospital (SJCRH), the Children’s Oncology Group, the European Organization for Research and Treatment of Cancer—Children’s Leukaemia Group, the Belgian Society for Paediatric Hematology–Oncology, the Dutch Children’s Oncology Group, the Italian Association of Paediatric Hematology and Oncology, the Japanese Association of Childhood Leukaemia Author Manuscript Study, the Tokyo Children’s Cancer Study Group, the I-BFM Study Group, the Queensland Children’s Tumour Bank, The Children’s Hospital at Westmead, Schneider Children’s Medical Center, Yong Loo Lin School of Medicine in Singapore, and the United Kingdom Childhood Leukaemia Cell Bank. After central review of pathology and immunophenotyping of 159 cases, 115 patients diagnosed with ALAL were included in this analysis, including 80 with germline samples. We examined leukaemia samples from 115 patients with ALAL (Supplementary Table 2–4) using whole-exome sequencing (WES) or WGS, transcriptome sequencing (RNA-seq), SNP microarray, and methylation array analysis. Samples collected on tumour banking protocols were used. Samples were not prospectively collected. The study was approved by the SJCRH Institutional Review Board. No statistical methods were used to predetermine sample size. The experiments were not Author Manuscript randomized and investigators were not blinded to allocation during experiments and outcome assessment. Tissue Non-tumour DNA was extracted from remission bone marrow or peripheral blood samples, flow-sorted normal lymphocytes, or cultured fibroblasts using phenol-chloroform organic extraction. Tumour DNA was extracted using phenol-chloroform organic extraction. Tumour RNA was extracted using a TRIzol (Life Technologies). Whole genome/exome and transcriptome sequencing WGS for 44 cases and RNA-seq for 45 cases were performed by the British Columbia Cancer Agency’s Michael Smith Genome Sciences Centre (BCGSC); WGS for 3 cases, Author Manuscript WES for 92 cases and RNA-seq for 77 cases were performed at SJCRH. For WGS at BCGSC, methods for DNA preparation, sequencing, and quality control are available at https://ocg.cancer.gov/programs/target/target-methods. For WES at SJCRH, library construction used DNA tagmentation (fragmentation and adaptor attachment) performed using the reagent provided in the Illumina Nextera rapid exome kit, and was performed using the Caliper Biosciences (Perking Elmer) Sciclone G3. First-round PCR (10 cycles) was performed using Illumina Nextera kit reagents, and clean-up steps employ BC/ Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 9 Agencourt AMPure XP beads. Target capture used Illumina Nextera rapid capture exome kit Author Manuscript and supplied hybridization and associated reagents. The pre-hybridization pool size was 12 samples, and second round PCR (10 cycles) performed with Nextera kit reagents. Library quality control was performed using a Victor fluorescence plate reader with Quant-it dsDNA reagents for pre-pool quantitation, and Agilent Bio-analyzer 2200 for final library quantitation. Paired-end sequencing was performed using Illumina HiSeq 2500 with read length 100 bp. Methods for RNA preparation, sequencing, and quality control at BCGSC are available at https://ocg.cancer.gov/programs/target/target-methods. At SJCRH, total RNA quality and quantity were assessed on Agilent RNA6000 chips (Agilent Technologies) and Qubit (Life Technologies). RNA-seq libraries were prepared from 500 ng of total RNA for each sample following Illumina RNA-seq protocols, including DNase treatment and phenol purification, cDNA conversion, fragmentation by Covaris Ultrasonicator, end repair, deoxyadenosine Author Manuscript tailing, adaptor ligation and PCR amplification (ten cycles). Libraries with a 10 pM concentration were clustered on an Illumina cBot, and each flow cell was loaded onto a HiSeq instrument for sequencing using the Illumina 2×100 bp sequencing kit. RNA-seq was not performed on flow sorted subpopulations due to the deleterious effects on RNA integrity of cellular fixation/permeabilization performed to enable staining for intracellular markers. Sequencing read alignment Paired-end WGS and WES data were aligned to the human reference genome GRCh37 by BWA23 (version 0.7.12). Samtools24 (version 1.3.1) was used to generate chromosomal coordinate-sorted and indexed bam files, and then Picard (http://broadinstitute.github.io/ picard/, version 1.129) MarkDuplicates module was used for marking PCR duplication. Afterwards, the reads were realigned around potential indel regions by GATK25 (version Author Manuscript 3.5) IndelRealigner module following the recommended pipeline. Sequencing depth and coverage was evaluated based on coding regions defined by refSeq genes from UCSC, with the length around 34 Mb. SNV/indel calling and filter workflow The GATK UnifiedGenotyper module was used to identify SNVs and indels from leukaemia and germline samples, which were filtered by a homemade pipeline, excluding: 1) reported common SNPs/indels from UCSC dbSNP v142; 2) germline mutations detected from matched germline control samples. All the non-silent SNVs/indels yield from the filtering pipeline were manually reviewed and only the highly reliable somatic ones were reported. Meanwhile, adjacent nucleotide changes on the same allele were merged into a single mutation. For patients with flow sorted subpopulations of leukaemia cells sequenced, the Author Manuscript mutation calling for each population was performed de novo. Mutations detected from some/one of the samples were checked across the other samples from the same patient. In these cases, we applied a threshold of at least 3 mutant allele reads and variant allele frequency of at least 1% to report a mutation. For cases without germline samples, a germline sample was picked with highest sequence depth as a pseudo-germline sample to run through the filtering pipeline. In cases in which flow sorted subpopulations were sequenced, WES or WGS of the unfractionated samples were not performed. Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 10 Structure variant detection Author Manuscript Structural variants in the tumours were identified by CREST26 using tumour vs germline mode, with pseudo-germline data applied for tumours without germline samples. Candidate variants were manually reviewed and the mapping uniqueness was re-evaluated by running BLAT27 mapping and the confident calls were considered as the final structural variant set. RNA-seq data analysis for patient samples Paired-end reads were mapped to the GRCh37 human genome reference by STAR28 (version 2.5.1b) through the recommended two pass mapping pipeline with default parameters and the Picard MarkDuplicates module was used to mark the duplication rate. Gene annotation files were downloaded from Ensembl (http://www.ensembl.org/) and used for STAR mapping and subsequent gene expression level evaluation. CICERO18 and FusionCatcher29 were used to detect fusions from mapped BAM files and raw FASTQ files, Author Manuscript respectively. The reported fusion contigs were remapped by BLAT to check the reliability of mapping quality, the breakpoints were manually reviewed from the aligned reads and the highly confident fusions were reported. To evaluate GEP, reads count for annotated genes was called by HTSeq30 (version 0.6.0) and processed by DESeq2 R package31 to normalize gene expression into regularized log2 values (rlog). Six cases without DNA sequence data were screened for SNVs/indels by following the GATK Best Practices for Variant Calling on RNAseq (https://gatkforums.broadinstitute.org/gatk/discussion/3892/the-gatk-best-practices- for-variant-calling-on-rnaseq-in-full-detail). The filtering process is the same as germline variant analysis described below. Gene set enrichment and pathway analysis Read counts from RNA-seq data were imported to DESeq232 R package for differential gene Author Manuscript expression analysis. To perform gene set enrichment analysis (GSEA)33, all the genes were ranked according to the fold-change and significance from differential analysis. GSEA was performed using mSigDB C2 genes and curated gene sets from in house analyses. Cell line transcriptome analysis Total RNA was isolated from green fluorescent protein (GFP)-positive, sorted cells using the RNeasy Mini Kit (Qiagen). RNA quality was checked using 2100 Bioanalyzer RNA 6000 Nanoassay (Agilent) or LabChip RNA Pico Sensitivity assay (PerkinElmer) before library generation. Libraries were prepared from total RNA with the TruSeq Stranded Total RNA Library Prep Kit (Illumina). Libraries were quantified using the Quant-iT PicoGreen dsDNA assay (Life Technologies) Kapa Library Quantification kit (Kapa Biosystems) or low pass sequencing on a MiSeq Nano v2 run (Illumina). One hundred cycle paired end sequencing Author Manuscript was performed on an Illumina HiSeq 2500, HiSeq 4000, or NovaSeq 6000. RNA isolation, library preparation, and sequencing were performed on three biological replicates. RNA-seq data were mapped as described previously18 and HTSeq30 (version 0.6.1p1) were used to get gene-level count and estimated FPKM based on GENCODE (vM9)34. Voom35 was used for gene differential expression analysis after trimmed mean normalization. Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 11 CNA and loss of heterozygosity (LOH) Author Manuscript DNA from leukaemia and matched germline samples was prepared for hybridization to Illumina Infinium Omni2.5 Exome-8 SNP arrays according to the manufacturer’s protocol. The raw intensity data (*.idat files) were analysed by the Genotyping Module of Illumina Genome Studio software version 2.0.3. Normalized log R ratio (LRR) and B allele frequency (BAF) for all the available probes in each sample were extracted. For ZNF384r B- ALL cases, data acquired from Affymetrix Genome-Wide Human SNP Array 6.0 was also converted to LRR and BAF value following the pipeline described by PennCNV36 (http:// penncnv.openbioinformatics.org/en/latest/user-guide/affy/). With the input of LRR and BAF, somatic genomic alterations in paired or unpaired samples were called by OncoSNP version 2.137. To verify the reliability of CNAs and LOHs, all the reported alterations were plotted based on LRR and BAF in ShinyCNV (https://github.com/gzhmat/ShinyCNV) and visually checked38. Only somatic alterations meeting the criteria proposed by OncoSNP and Author Manuscript PennCNV were kept for further analysis. DNA methylation assay and data analysis We examined DNA methylation profiles in 27 MPAL cases (11 with 2–4 subpopulations), 15 AML, 29 B-ALL, 30 T-ALL, and 17 normal lymphocyte samples from 4 healthy donors. Raw data from the Infinium MethylationEPIC BeadChip Kit (Illumina Inc.) were analysed using the ChAMP39 R package. In general, the raw *.idat files were imported through ‘minfi’ method40 and then the following filters were applied to exclude the probes: 1) with detection P value above 0.01 in one or more samples; 2) with beadcount 5%, mice Author Manuscript were euthanized, and blood, bone marrow, and spleen samples were analysed to determine the leukaemia phenotype, using morphology, flow cytometry, and histopathologic analysis. Immunohistochemistry (IHC) was performed on formalin-fixed paraffin-embedded tissues sectioned at 4 μm. Assays for CD19 (AbDserotec, MCA2454T; 1:100), CD34 (Ventana, 790–2927; ready to use), CD45 (Ventana, 760–2505; ready to use) and myeloperoxidase (MPO, DAKO A398; 1:500) were performed on the Ventana Benchmark. The assay for CD33 (Leica Biosystems, NCL-L-CD33; 1:200) was performed on the Dako Omnis. Reporting summary Further information on experimental design is available in the Nature Research Reporting Summary linked to this paper. Author Manuscript Data availability Sequencing, SNP, and methylation data are available at the NCI Genomics Data Commons (GDC, gdc.cancer.giv) and analysed data may be accessed at the TARGET website at https:// ocg.cancer.gov/programs/target/data-matrix or https://gdc.cancer.gov/about-data/ publications/TARGET-ALAL-2018. Murine RNA-seq and ChIP–seq data have been deposited in the GEO database under accession ID GSE112561. For T-ALL and ETP-ALL, RNA sequencing for comparison comprised previously published data11. B-ALL RNA- sequencing data for comparison comprised previously published data and recently sequenced samples that will be made available through St Jude’s Children’s Ressearch Hospital11,18,48,49,63. T-ALL, ETP-ALL, and AML data for mutation comparison comprised previously published data11,12. The genomic landscape reported in this study can be Author Manuscript explored at the St. Jude PeCan Data Portal, http://pecan.stjude.org/proteinpaint/study/ pediatric-mpal. Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 18 Extended Data Author Manuscript Author Manuscript Author Manuscript Extended Data Fig. 1 |. Criteria for diagnosis of ALAL. Author Manuscript a, Subtypes of ALAL according to the WHO 2008 criteria and consistent with minor revisions of WHO 2016 criteria6. b, Antigen requirements for lineage assignment for MPAL according to WHO 2008 criteria. The 2016 revisions to the WHO classification for ALAL did not change the above categories or requirements. Rather, the revision emphasized that care should be taken before making a diagnosis of B/M MPAL when low-intensity myeloperoxidase is the only myeloid-associated feature. Additionally, the revision emphasized that in cases in which it is possible to resolve two distinct blast populations, it is Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 19 not necessary that the specific markers be present, but only that each population would meet the criteria for B, T, or myeloid leukaemia64. c, Proposed update to WHO ALAL subtypes Author Manuscript incorporating critical newer genomic information (new subtypes in red). d, Flow chart of ALAL cohort showing reasons for exclusion and initial diagnosis in cases for which initial ALAL diagnosis occurring at relapse. Author Manuscript Author Manuscript Author Manuscript Extended Data Fig. 2 |. Illustrative immunophenotype and overall survival. a–e, Representative flow cytometry pseudocolour dot plots and contour plots for five different MPAL cases gated on blast area from CD45 and side scatter area (SSC-A). There Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 20 are a wide variety of immunophenotypic patterns, including classic bilineal phenotype (a), Author Manuscript classic biphenotypic case (b), myeloid predominance (c), lymphoid predominance (d) and complex phenotype with more than two immunophenotypic clones (e). f, g, Morphology of cells from two patients with MPAL showing both lymphoid (orange arrow) and myeloid (black arrow) morphology. f, Bone marrow aspirate stained with myeloperoxidase from a patient with T/M MPAL showing multiple blasts with moderate MPO positivity along with one normal granulocyte. g, Peripheral blood haematoxylin and eosin stain from a patient with B/M MPAL. h–o, Kaplan–Meier survival curves with overall survival (OS) distributions of patients whose initial diagnosis was MPAL or AUL compared using log-rank tests. At risk numbers for each analysis are provided in the figures. Outcome associations were analysed with the log-rank test. OS according to WHO 2016 subtype (h), initial therapy (i), WT1 status within the T/M MPAL cohort (j), ZNF384 status within the B/M MPAL cohort (k), RAS pathway alteration within the entire cohort (l) and FLT3 alteration Author Manuscript within the entire cohort (m). n, OS according to initial therapy for patients with B/M MPAL with ZNF384r. o, OS according to initial therapy for patients with B/M MPAL without ZNF384r. Patients included in this cohort were collected from a range of treatment eras, treatment locations, treatment regimens, and include a range of ages and genomic subtype, limiting the conclusions that may be drawn from these analyses. Author Manuscript Author Manuscript Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 21 Author Manuscript Author Manuscript Author Manuscript Extended Data Fig. 3 |. Copy number alterations and mutation burden in ALAL. a, Map showing spectrum of CNAs, visually recapitulating the data shown in Supplementary Table 10. Twenty-seven patients had SNP arrays for multiple subpopulations, annotated by stars. b, CNA and non-silent SNVs or indels in ALAL subtypes according the WHO 2016 classification. (CNA, T/M MPAL n = 36, B/M MPAL n = 34, KMT2Ar MPAL n = 15, MPAL NOS n = 7, AUL n = 5, Ph+ MPAL n = 1; SNV/indel, T/M MPAL n = 46, B/M Author Manuscript MPAL n = 35, KMT2Ar MPAL n = 15, MPAL NOS n = 7, AUL n = 5, Ph+ MPAL n = 1) Patients with KMT2Ar MPAL have a lower mutation burden than those with T/M MPAL or B/M MPAL. c, CNAs and non-silent SNVs or indels in our proposed updated classification system. (CNA, T/M MPAL NOS n = 24, T/M MPAL with WT1 alteration, n = 12, B/M MPAL NOS n = 17, B/M MPAL with ZNF384r n = 15, KMT2Ar MPAL/AUL n = 17, MPAL/AUL NOS n = 9, Ph+/Ph-like MPAL/AUL n = 4; SNV/indel, T/M MPAL NOS n = 27, T/M MPAL with WT1 alteration, n = 19, B/M MPAL NOS n = 18, B/M MPAL with Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 22 ZNF384r n = 15, KMT2Ar MPAL/AUL n = 17, MPAL/AUL NOS n = 9, Ph+/Ph-like Author Manuscript MPAL/AUL n = 4) Data shown as median ± 95% confidence interval. Comparisons assessed by two-sided unpaired t-test. One data point is outside the SNV/indel graph for the B/M NOS subtype (1 patient with 167 SNV/indels). SNV/indels per case shown for cases with DNA sequencing completed. Author Manuscript Author Manuscript Author Manuscript Extended Data Fig. 4 |. Complete ALAL mutation oncoprint. Mutation spectrum of ALAL. Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 23 Author Manuscript Author Manuscript Author Manuscript Extended Data Fig. 5 |. Features of MPAL genomic analysis. a, WT1 alterations were observed in 28 patients, commonly as frameshift mutations (31/47 mutations) in exon 7 (29/47 mutations) and were frequently biallelic. In 16 patients, two Author Manuscript clonal alterations were detected, and in 9 patients the locations of the alteration were encompassed by the same sequencing read, providing definitive demonstration that the mutations were in trans. Additionally, one patient (SJMPAL043773) had a frameshift mutation and copy number loss of the second allele, while another had a frameshift mutation with copy-neutral loss of heterozygosity (SJMPAL040036). Data are shown for two representative patients with MPAL, showing double-hit mutations on WT1. The read alignment view was generated by Samtools24. The reference human genome is on the first Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 24 row and sequence reads are aligned below, with matched nucleotides as dots (forward strand Author Manuscript match) and commas (reverse strand match) and mismatched ones showing the differences. Alignment gaps are shown as asterisks. Adjacent mutations are shown on different sequence reads, indicating that the mutations are on different alleles. b, Frequency of alteration by pathway analysis and MPAL subtype. The similarity of somatic alteration prevalence in different leukaemia subtypes was evaluated by two-sided Fisher’s exact test (n = 100 biologically independent cases). See also Supplementary Tables 12, 13 for numbers and P values for each gene and pathway. c, Schematic representation of ZNF384r observed in B/M MPAL. NLS, nuclear localization signal; TAZ1, transcriptional adaptor zinc-binding; LZ, leucine rich domain; QA, glycine/alanine repeat. d, Fluorescence-activated sorting schema in a representative case with a ZNF384r, and variant allele frequency of SNVs/indels present in the respective sorted subpopulations, demonstrating genomic similarity of the sorted populations. e, tSNE plot of RNA-seq gene expression of all patients with ZNF384r show no Author Manuscript clear segregation of B/M MPAL and B-ALL cases. f, FLT3 gene expression in subtypes of ALAL showing that patients with ZNF384r B/M MPAL have high levels of FLT3 expression. As in patients with KMT2Ar, this occurs in the absence of FLT3 alteration in most cases. By contrast, high levels of FLT3 expression in T/M MPAL appears to be driven by FLT3 alterations. Data shown as median ± 95% confidence interval. Comparisons assessed by unpaired t-test, two sided. T/M MPAL FLT3 wild type n = 18, B/M MPAL NOS n = 10, T/M MPAL with FLT3 alteration n = 16, B/M MPAL NOS n = 17, B/M MPAL with ZNF384r n = 15, KMT2Ar MPAL/AUL n = 11, MPAL/AUL NOS n = 7, Ph+/Ph-like MPAL/AUL n = 5, KMT2A-like MPAL/AUL n = 8. Author Manuscript Author Manuscript Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 25 Author Manuscript Author Manuscript Author Manuscript Extended Data Fig. 6 |. ZNF384r leukaemia analysis and T/M MPAL mutation comparisons. a, GSEA of ZNF384r B/M MPAL versus non-ZNF384r B/M MPAL. HSC gene sets are negatively enriched, supporting the proposed update to MPAL subtypes in which ZNF384r leukaemia has distinct biology compared with other B/M MPAL cases20,65,66. b, GSEA of all ZNF384r cases versus other B-ALL cases indicates immaturity of this subtype compared to B-ALL, with positive enrichment for genes upregulated in ETP-ALL (a stem cell leukaemia), and negative enrichment for genes upregulated in Ph-like ALL in other B-ALL Author Manuscript cases. ZNF384r acute leukaemia is also enriched for genes upregulated in patients with detectable minimal residual disease at end of induction10,51,67. c, Western blot analysis to validate expression of ZNF384, TAF15–ZNF384, and TCF3–ZNF384 in transduced Arf−/− pre-B cells. Proteins contain an HA epitope tag and are detected by anti-HA antibody. d, Heatmap showing the ChIP–seq signal, centred on ZNF384 peaks, of wild-type (WT) ZNF384 compared to TAF15–ZNF384 and TCF3–ZNF384. Middle, peaks with increased binding of fusion proteins compared to wild-type. Bottom, peaks with decreased binding of the fusion proteins compared to wild-type. e, GSEA showing enrichment of genes whose Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 26 promoters exhibit increased binding by ZNF384 fusions in the GEP of ZNF384r versus WT Author Manuscript pre-B cells. f, GSEA showing similarity of the GEP of mouse pre-B cells expressing ZNF384r to the GEP of human ZNF384r leukaemia cells, supporting the notion that perturbation of ZNF384 binding contributes to deregulated gene expression in human ZNF384r leukaemia. g, Oncoprint of mutations in transcription factor genes across T/M MPAL (n = 49), ETP-ALL (n = 19) and T-ALL (other) (n = 245), showing lack of TAL1 alterations in T/M MPAL and few core T-ALL transcription factor alterations in T/M MPAL or ETP-ALL. The association of leuekmia subtype with individual transcription factor alterations was evaluated using two-sided Fisher exact test. Act, activating mutation; LoF, loss-of-function mutation. h, Gene pathway analyses showing similarity of ETP-ALL and T/M MPAL, specifically in frequency of mutations in pathways regulating cell cycle or apoptosis, transcriptional regulation, and signalling pathways. The similarity of somatic alteration prevalence in different leukaemia subtypes was evaluated by two sided Fisher’s Author Manuscript exact tests in these four subtypes (T/M MPAL n = 49, ETP-ALL n = 19, non-ETP T-ALL n = 245, AML n = 197). Author Manuscript Author Manuscript Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 27 Author Manuscript Author Manuscript Author Manuscript Extended Data Fig. 7 |. MPAL subpopulation analysis and methylation analysis. Author Manuscript a, Results of genomic analysis of the 50 patients with sorted subpopulations with WGS or WES results. Listed here are all genes with mutations that were either recurrent in the ALAL cohort or were in known cancer consensus genes68. *CNA results also available for sorted subpopulations in these cases. b–d, Methylation analysis of MPAL, comparison with acute leukaemia and normal lymphocytes. The top 5,000 probes with highest mean absolute deviation were used to assess the clustering through a 2D t-SNE plot and heatmap with Pearson correlation clustering. See Supplementary Table 37 for sample details. b, Heatmap of all samples used for methylation analysis showing the general alignment of samples by Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 28 leukaemia phenotype with B/M cases clustering with B-ALL, T/M MPAL, ETP-ALL cases Author Manuscript together, and AML cases clustering separately. c, tSNE analysis of the same samples as in the top heatmap, showing general alignment by leukaemia phenotype with B/M cases clustering with B-ALL, T/M MPAL, ETP-ALL cases together, and AML cases clustering separately. d, Heatmap of all MPAL cases, again showing some clustering by phenotype between B/M and T/M cases. Subpopulations sorted by distinct immunophenotype in MPAL cases clustered tightly with samples from the same patient, rather than with samples with similar phenotype from a different patient. e, Methylation analysis of sorted subpopulations from 11 patients with MPAL, demonstrating that methylation profiles cluster by patient and not by immunophenotype lineage. Author Manuscript Author Manuscript Author Manuscript Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 29 Author Manuscript Author Manuscript Author Manuscript Extended Data Fig. 8 |. Xenograft analysis. Author Manuscript a, Flow cytometry analysis of bulk leukaemic cells from patient SJMPAL011911 before sorting, and cytospins from bone marrow samples from representative primary recipient mice transplanted with different leukaemia subpopulations or bulk, confirming the presence of leukaemic blasts from each engrafted population. Scale bars, 10 μm. b, Phenotypic subpopulations from JIH-5 cells in the first column were sorted and injected into NSG- SGM3 mice. Remaining plots show the immunophenotypes of engrafted leukaemia propagated from each sorted subpopulation, demonstrating recapitulation of biphenotypic leukaemia from each. c, Flow cytometry analysis of bulk JIH-5 cells prior to sorting (left) Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 30 and haematoxylin and eosin staining and IHC labelling for human CD45, CD19, CD33, Author Manuscript MPO, CD34 and CD3 in sternum samples from representative primary recipient mice transplanted with different leukaemia subpopulations or bulk. Scale bars, 20 μm. d, Phenotypic subpopulations from patient SJMPAL012424 were sorted (left) and injected into irradiated NSG-SGM3 mice. Remaining plots show the immunophenotypes of engrafted leukaemia from each starting subpopulation, demonstrating recapitulation of mixed phenotype leukaemia from two sorted subpopulations. e, Flow cytometry analyses of bone marrow cells from an engrafted primary mouse transplanted with leukaemia cells from a patient with T/M MPAL (SJMPAL040036). f, g, Flow cytometry analyses of representative engrafted secondary recipient mice transplanted with leukaemia cells from the mouse in e showing lineage plasticity with mice developing an emerging CD19+CD33+ population (f) and other mice recapitulating the immunophenotype in the primary recipient (g). h, IHC labelling for human CD45, CD19, CD33, MPO and CD34 from harvested and fixed spleen Author Manuscript cells from a representative secondary recipient mouse showing high expression of CD19 and CD33 and thus confirming the leukaemic lineage plasticity. Scale bars are 20 μm. Author Manuscript Author Manuscript Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 31 Author Manuscript Author Manuscript Author Manuscript Extended Data Fig. 9 |. Haematopoietic progenitor cell analysis. a, Progenitor cell sorting scheme for diagnosis sample from patient SJMPAL040028. Progenitor populations were all gated on CD19–CD33−CD34+ and sorted into HSC (CD38−CD34+CD90+CD45RA−; 2 replicates: HSC_1 and HSC_2); MPP (CD38−CD34+CD90–CD45RA−); MLP (CD38−CD34+CD45RA+); megakaryocyte erythroid progenitors/common myeloid progenitors (CD38+CD34+CD7−CD10−CD45RA−); Author Manuscript and granulocyte monocyte progenitor (CD38+CD34+CD7−CD10−CD45RA+) populations. b, Blast cell sorting scheme for diagnosis sample from patient SJMPAL040028. Cells were gated on CD45dim and sorted into four different immunophenotypic populations (CD33+CD19+CD10–; CD33+CD19modCD10–; CD33+CD19−CD10−; and CD33−CD19−). c, Sanger sequencing electropherograms for the mutational status of DNAH17, NDST2 and MYCN and for the fusion TCF3–ZNF384 in isolated progenitor and blast populations from patient SJMPAL040028 at diagnosis. The identification of somatic missense mutations and Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 32 TCF3–ZNF384 fusion in early haematopoietic progenitors indicate that the ambiguous Author Manuscript phenotype of MPAL is the result of the acquisition of alterations within an immature haematopoietic progenitor cells. Author Manuscript Author Manuscript Author Manuscript Extended Data Fig. 10 |. Phenotypic and genotypic evolution from diagnosis to relapse. Patients for which diagnosis and relapse pairs with matching non-tumour controls are available show recapitulation of the diagnostic multilineage phenotype in some cases and phenotype plasticity in others. The first column shows the case ID, the leukaemia subtype at diagnosis and then subsequent relapse, the in-frame fusion if present, and initial therapy Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 33 received by the patient. Flow plots are shown of cells gated on CD45dim versus SSC-Alow. Author Manuscript The diagram depicts the inferred clonal evolution based on WES and/or WGS and SNP array data (where available). Mutated genes (either recurrent in ALAL cohort or known cancer consensus genes68) are listed. The genes beside the initial diagnostic cell cluster remained present at relapse. The grey cells represent clones that were extinguished with therapy. The genes in the relapse column represent mutations that were gained at relapse. Supplementary Material Refer to Web version on PubMed Central for supplementary material. Authors Thomas B. Alexander1,2,37, Zhaohui Gu3,37, Ilaria Iacobucci3,37, Kirsten Dickerson3, Author Manuscript John K. Choi3, Beisi Xu4, Debbie Payne-Turner3, Hiroki Yoshihara3, Mignon L. Loh5, John Horan6, Barbara Buldini7, Giuseppe Basso7, Sarah Elitzur8, Valerie de Haas9, C. Michel Zwaan10, Allen Yeoh11, Dirk Reinhardt12, Daisuke Tomizawa13, Nobutaka Kiyokawa14, Tim Lammens15, Barbara De Moerloose15, Daniel Catchpoole16, Hiroki Hori17, Anthony Moorman18, Andrew S. Moore19, Ondrej Hrusak20, Soheil Meshinchi21,22, Etan Orgel23, Meenakshi Devidas24, Michael Borowitz25, Brent Wood26, Nyla A. Heerema27, Andrew Carrol28, Yung-Li Yang29, Malcolm A. Smith30, Tanja M. Davidsen31, Leandro C. Hermida32, Patee Gesuwan32, Marco A. Marra33, Yussanne Ma33, Andrew J. Mungall33, Richard A. Moore33, Steven J. M. Jones33, Marcus Valentine34, Laura J. Janke3, Jeffrey E. Rubnitz1, Ching-Hon Pui1, Liang Ding4, Yu Liu4, Jinghui Zhang4, Kim E. Nichols1, James R. Downing3, Xueyuan Cao35, Lei Shi35, Stanley Pounds35, Scott Newman4, Deqing Pei4, Jaime M. Guidry Auvil32, Daniela S. Gerhard32, Stephen P. Hunger36, Hiroto Inaba1,*, and Charles G. Author Manuscript Mullighan3,* Affiliations 1Department of Oncology, St. Jude Children’s Research Hospital, Memphis, TN, USA. 2Department of Pediatrics, University of North Carolina, Chapel Hill, NC, USA. 3Department of Pathology, St. Jude Children’s Research Hospital, Memphis, TN, USA. 4Department of Computational Biology, St. Jude Children’s Research Hospital, Memphis, TN, USA. 5Department of Pediatrics, Benioff Children’s Hospital and the Helen Diller Family Comprehensive Cancer Center, University of California at San Francisco, San Francisco, CA, USA. 6Aflac Cancer and Blood Disorders Center, Children’s Healthcare of Atlanta and Emory University School of Medicine, Department of Pediatrics, Atlanta, GA, USA. 7Department of Women and Child Author Manuscript Health, Hemato-Oncology Division, University of Padova, Padova, Italy. 8Pediatric Hematology-Oncology, Schneider Children’s Medical Center, Sackler Faculty of Medicine, Tel Aviv University, Israel. 9Prinses Maxima Centre, Dutch Childhood Oncology Group Laboratory, Utrecht, The Netherlands. 10Department of Pediatric Oncology, Erasmus MC-Sophia, Rotterdam and Princess Máxima Center, Utrecht, The Netherlands. 11Department of Paediatrics, Yong Loo Lin School of Medicine, National University of Singapore, Singapore, Singapore. 12Universitäts-Klinikum, Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 34 Author Manuscript Essen, Germany. 13Division of Leukemia and Lymphoma, Children’s Cancer Center, National Center for Child Health and Development, Tokyo, Japan. 14Department of Pediatric Hematology and Oncology Research, National Research Institute for Child Health and Development, Tokyo, Japan. 15Department of Pediatric Hematology- Oncology and Stem Cell Transplantation, Ghent University Hospital, Ghent, Belgium. 16The Tumour Bank CCRU, The Kids Research Institute, The Children’s Hospital at Westmead, Westmead, NSW, Australia 17Department of Pediatrics, Mie University, Tsu, Japan. 18Wolfson Childhood Cancer Centre, Northern Institute for Cancer Research, Newcastle University, Newcastle-upon-Tyne, UK. 19The University of Queensland Diamantina Institute & Children’s Health, Brisbane, Queensland, Australia. 20Department of Paediatric Haematology and Oncology, 2nd Faculty of Medicine, Charles University and University Hospital Motol, Prague, Author Manuscript Czech Republic. 21Fred Hutchinson Cancer Research Center, Clinical Research Division, Seattle, WA, USA. 22Children’s Oncology Group, Arcadia, CA, USA. 23Children’s Center for Cancer and Blood Disease, Children’s Hospital Los Angeles, Los Angeles, CA, USA. 24University of Florida, Gainesville, FL, USA. 25Johns Hopkins Medical Institutions, Baltimore, MD, USA. 26University of Washington, Seattle, WA, USA. 27The Ohio State University School of Medicine, Columbus, OH, USA. 28University of Alabama at Birmingham, Birmingham, AL, USA. 29Department of Laboratory Medicine and Pediatrics, National Taiwan University Hospital, College of Medicine, National Taiwan University, Taipei, Taiwan. 30Cancer Therapy Evaluation Program, National Cancer Institute, Bethesda, MD, USA. 31Center for Biomedical Informatics and Information Technology, National Cancer Institute, Rockville, MD, USA. 32Office of Cancer Genomics, National Cancer Institute, Author Manuscript Bethesda, MD, USA. 33Michael Smith Genome Sciences Centre, BC Cancer Agency, Vancouver, British Columbia, Canada. 34Cytogenetics Shared Resource, St. Jude Children’s Research Hospital, Memphis, TN, USA. 35Department of Biostatistics, St. Jude Children’s Research Hospital, Memphis, TN, USA. 36Division of Oncology and Center for Childhood Cancer Research, Children’s Hospital of Philadelphia and the Perelman School of Medicine at the University of Pennsylvania, Philadelphia, PA, USA. 37These authors contributed equally: Thomas B. Alexander, Zhaohui Gu, Ilaria Iacobucci. Acknowledgements We thank the Biorepository, the Genome Sequencing Facility of the Hartwell Center for Bioinformatics and Biotechnology, and the Flow Cytometry and Cell Sorting core facility and Cytogenetics core facility of St. Jude Author Manuscript Children’s Research Hospital (SJCRH). This work was supported in part by the American Lebanese Syrian Associated Charities of SJCRH, Cookies for Kids Cancer (to H.I.), St. Baldrick’s Foundation Robert J. Arceci Innovation Award and Henry Schueler 41&9 Foundation (to C.G.M.), SJCRH Physician Scientist Training Program Fellowship (to T.B.A.), the National Cancer Institute grants P30 CA021765 (SJCRH Cancer Center Support Grant), Chair’s grant and supplement to support the COG ALL TARGET project), U10 CA98413 (to the COG Statistical Center), U24 CA114766 (to COG; Specimen Banking), and Outstanding Investigator Award R35 CA197695 (to C.G.M.). The results published here are in part based upon data generated by the Therapeutically Applicable Research to Generate Effective Treatments initiative of the NCI (http://ocg.cancer.gov/programs/target). This project has been funded in part with Federal funds from the National Cancer Institute, National Institutes of Health, under contract No. HHSN261200800001E (to C.G.M. and Michael Smith Genome Sciences Centre). The content of this publication does not necessarily reflect the views or policies of the Department of Health and Human Nature. Author manuscript; available in PMC 2019 April 01.  Alexander et al. Page 35 Services, nor does mention of trade names, commercial products, or organizations imply endorsement by the US Government. We acknowledge Canada’s Michael Smith Genome Sciences Centre, Vancouver, Canada for library Author Manuscript construction and sequencing. 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