The AML Salad Bowl

Cancer Cell

Previews be highly relevant to clinical resistance to conventional cytotoxic chemotherapy (Ni Chonghaile et al., 2011). Moreover, transplantation of human T-ALL into mice is associated with clonal evolution that closely mimics the evolution observed in human patients between diagnosis and relapse (Clappier et al., 2011), highlighting the relevance of this approach to treatment resistance in humans. The findings of Blackburn et al. (2014) have clear potential translational implications, because they suggest that AKT activation in T-ALL cells with LPC potential may be poised to mediate treatment resistance and relapse. The authors show that these cells can be effectively targeted by the combination of dexamethasone and AKT inhibitors, potentially due to reversal of AKTmediated dexamethasone resistance (Piovan et al., 2013), thus providing an additional rationale for clinical trials

testing such a strategy in high-risk T-ALL.

King, B., Trimarchi, T., Reavie, L., Xu, L., Mullenders, J., Ntziachristos, P., Aranda-Orgilles, B., Perez-Garcia, A., Shi, J., Vakoc, C., et al. (2013). Cell 153, 1552–1566.

ACKNOWLEDGMENTS

Lapidot, T., Sirard, C., Vormoor, J., Murdoch, B., Hoang, T., Caceres-Cortes, J., Minden, M., Paterson, B., Caligiuri, M.A., and Dick, J.E. (1994). Nature 367, 645–648.

We apologize to the authors of the many relevant papers whose work we were unable to cite due to space constraints.

REFERENCES

Ni Chonghaile, T., Sarosiek, K.A., Vo, T.T., Ryan, J.A., Tammareddi, A., Moore, Vdel.G., Deng, J., Anderson, K.C., Richardson, P., Tai, Y.T., et al. (2011). Science 334, 1129–1133.

Blackburn, J.S., Liu, S., Wilder, J.L., Dobrinski, K.P., Lobbardi, R., Moore, F.E., Martinez, S.A., Chen, E.Y., Lee, C., and Langenau, D.M. (2014). Cancer Cell 25, this issue, 366–378.

Piovan, E., Yu, J., Tosello, V., Herranz, D., Ambesi-Impiombato, A., Da Silva, A.C., SanchezMartin, M., Perez-Garcia, A., Rigo, I., Castillo, M., et al. (2013). Cancer Cell 24, 766–776.

Chaffer, C.L., Brueckmann, I., Scheel, C., Kaestli, A.J., Wiggins, P.A., Rodrigues, L.O., Brooks, M., Reinhardt, F., Su, Y., Polyak, K., et al. (2011). Proc. Natl. Acad. Sci. USA 108, 7950– 7955.

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Chiu, P.P., Jiang, H., and Dick, J.E. (2010). Blood 116, 5268–5279.

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Clappier, E., Gerby, B., Sigaux, F., Delord, M., Touzri, F., Hernandez, L., Ballerini, P., Baruchel, A., Pflumio, F., and Soulier, J. (2011). J. Exp. Med. 208, 653–661.

Taussig, D.C., Miraki-Moud, F., Anjos-Afonso, F., Pearce, D.J., Allen, K., Ridler, C., Lillington, D., Oakervee, H., Cavenagh, J., Agrawal, S.G., et al. (2008). Blood 112, 568–575.

The AML Salad Bowl Jesu´s Duque-Afonso1 and Michael L. Cleary1,* 1Department of Pathology, Stanford University School of Medicine, Stanford, CA 94305, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.ccr.2014.03.002

Tumors arise from single cells but become genetically heterogeneous through continuous acquisition of somatic mutations as they progress. In this issue of Cancer Cell, Klco and colleagues used whole genome sequence analysis to demonstrate the correlation of genetic clonal architecture with functional heterogeneity in acute myeloid leukemia. A major tenet of cancer biology is that tumors are clonal reflecting their origins from single cells. This is best illustrated by early seminal cytogenetic studies demonstrating that all cells of a patient’s leukemia, for example, may harbor a specific chromosomal aberration. However, it has also been recognized for decades that leukemias and solid tumors are heterogeneous in their genetic composition such that, despite sharing a specific chromosomal aberration, not all cells of a given cancer demonstrate a completely identical cytogenetic profile. This intratumoral genetic heterogeneity extends to

the level of individual genes and DNA mutations as shown by next-generation sequencing technologies and is fully expected based on the fact that tumor (and normal) cells acquire new mutations with each cell division. At a practical level, somatically acquired mutations that accumulate at defined frequencies can distinguish individual cells or tumor subclones and serve as a clock to mark and track their divergence from a common ancestor cell. The complexity of clonal architecture has been shown in hematological malignancies, including acute lymphoblastic leukemia (ALL)

(Anderson et al., 2011) and acute myeloid leukemia (AML) (Ding et al., 2012) as well as other cancer types such as breast carcinoma (Shah et al., 2009), and is likely a universal feature of all cancers. It is also known that subpopulations of cells in an individual tumor can be morphologically or functionally distinct, e.g., display sensitivity or resistance to therapeutic agents. However, the relationship between intratumoral genetic heterogeneity and cancer cell function has not been well defined. Nevertheless, clonal evolution has major implications for understanding the cellular hierarchies and

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Cancer Cell

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Figure 1. Functional and Genetic Heterogeneity of Primary AML A founding AML clone arises with accumulation of a set of acquired ‘‘signature’’ mutations. During tumor progression, the founding clone evolves into genetically distinct subclones through the acquisition of new mutations. The complete spectrum of mutations defines morphological and phenotypic properties of the subclones that correlate with distinct functional characteristics, such as in vitro growth, engraftment in immune-deficient mice, or relapse in patients.

interrelationships in tumors, as well as the development and application of targeted therapies in the rapidly unfolding era of personalized medicine. In this issue of Cancer Cell, Klco et al. (2014) explored the correlation of clonal architecture with functional heterogeneity in AML. Rather than a melting pot blend of operational and genomic diversity, the data support that AML comprises a salad bowl of distinct subclones whose functional differences may be genetically determined (Figure 1). Whole genome (and capture-based targeted) sequences were analyzed to determine the somatic mutations present in unfractionated bone marrow cells of patients at presentation with de novo AML encompassing a range of morphological and genetic subtypes. The spectrum of mutations and their fractional representation was used to define the founding clone, from which all leukemic cells were descended, and also identify leukemic cell subpopulations possessing the ‘‘signature’’ variants of the founding clone as well as additional subclonal sequence variants that arose during tumor evolution. Sequence analysis of single cells purified by cell sorting in

several AMLs verified the identity of subclonal genotypes and the allele fractions deduced from unfractionated bone marrow samples. The genetically defined subclones were evaluated under various biological and experimental conditions. The clonal architecture present in the bone marrow was consistently detected in the peripheral blood, indicating no major differences in trafficking properties among different AML subclones, unlike the regional intratumoral and metastatic variation reported in solid tumors (Navin et al., 2011). Mutations found in AML blast cells were often present in morphologically more mature myelomonocytic cells, demonstrating maintenance of at least minimal differentiation potential despite the presence of AML driver genes that otherwise antagonize maturation. Somatic mutations in rare peripheral blood B and T lymphocytes suggested the acquisition of some mutations in leukemic multi-potential hematopoietic stem-progenitor cells or even in preleukemic hematopoietic stem cells consistent with recent observations (Shlush et al., 2014). In some cases, morphologic or phenotypic features, as well as in vitro

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growth properties, correlated with distinct subclones, suggesting functional variation in differentiation potential that may be genetically determined. The in vivo functional heterogeneity of cells comprising leukemia samples at disease presentation was interrogated by transplantation into immune-compromized mice. Unexpectedly, none of the resulting xenografts displayed a clonal architecture that was identical to that of the transplanted AML. Rather, subclones showed variable engraftment potential, and single subclones generally predominated in the engrafted mice despite the presence of multiple subclones in the injected sample. Relapsing AML subclones were not predicted by engraftment outcome or by the presence of recurring AML mutations. Thus, in many cases, there was no apparent relationship between the engrafting cells and the evolutionary hierarchy of the leukemia subclones in the patient. However, these results should be interpreted with caution. Although the functional heterogeneity among AML clones was clearly demonstrated, the engrafted subclone in some cases was dictated by the recipient mouse strain used. This underscores that xeno-engraftment can be affected by a variety of technical factors that were not optimized, such as mouse strain and preconditioning, route of injection, number of injected cells, and time for engraftment/ disease assessment. The application of next-generation sequencing techniques in future studies should illuminate the relative influence of these various factors on the clonal compositions and clinical significance of engrafting leukemia cells. AML cells capable of engrafting in xenograft assays, and thus establishing disease in mice, are operationally defined as leukemia-initiating cells or leukemia stem cells (LSCs) (Lapidot et al., 1994). Previous studies have shown that LSCs defined by this experimental approach may be phenotypically heterogeneous (Goardon et al., 2011). The studies of Klco et al. (2014) extend this to suggest that LSCs may also be genetically heterogeneous. Importantly, the founding AML clone defined genetically may not necessarily be the same as the LSC clone defined functionally by xenotransplantation. This likely reflects that xeno-transplant models exert selective growth pressures through the mouse

Cancer Cell

Previews microenvironment or lack of immune system that are not equivalent to those encountered by leukemia cells in the patient. The authors’ results highlight the clonal and functional diversity of LSCs and suggest that future studies should include an integration of genetic and functional data as part of their characterization. The authors correlated the presence of specific subclones marked by their respective mutational spectra with functional readouts, but the mutations that mechanistically account for the observed functional differences are unknown. It will be important to define the genetic (or epigenetic) determinants that underlie the observed biology, particularly the genes or pathways that may promote engraftment in various immune-compromised mouse models and relapse in patients. Xenograft models also serve an instrumental role in the preclinical stages of cancer therapeutics development. Indeed, almost every FDA-approved anticancer drug in the modern era has been tested in such models. However, they are known to have limitations for predicting clinical responses in solid

tumors (Sharpless and Depinho, 2006), and the results of Klco et al. (2014) underscore potential constraints of the approach to evaluate drug efficacy in AML, although previous studies have demonstrated their value in predicting early relapse in ALL (Meyer et al., 2011). Clonal analysis through whole genome sequencing provides a powerful approach for assessing the fidelity of xenografts in support of ongoing efforts to devise novel targeted therapeutics directed at the pathways and mutant factors that sustain critical functions of founding clones and/ or cancer stem cells.

M.A., Lamprecht, T., McLellan, M.D., et al. (2012). Nature 481, 506–510. Goardon, N., Marchi, E., Atzberger, A., Quek, L., Schuh, A., Soneji, S., Woll, P., Mead, A., Alford, K.A., Rout, R., et al. (2011). Cancer Cell 19, 138–152. Klco, J.M., Spencer, D.H., Miller, A.M., Griffith, M., Lamprecht, T.L., O’Laughlin, M., Fronick, C., Magrini, V., Demeter, R.T., Fulton, R.S., et al. (2014). Cancer Cell 25, this issue, 379–392. Lapidot, T., Sirard, C., Vormoor, J., Murdoch, B., Hoang, T., Caceres-Cortes, J., Minden, M., Paterson, B., Caligiuri, M.A., and Dick, J.E. (1994). Nature 367, 645–648. Meyer, L.H., Eckhoff, S.M., Queudeville, M., Kraus, J.M., Giordan, M., Stursberg, J., Zangrando, A., Vendramini, E., Mo¨ricke, A., Zimmermann, M., et al. (2011). Cancer Cell 19, 206–217.

ACKNOWLEDGMENTS J. D.-A. is supported by the German Research Foundation (DFG, reference DU 1287/2-1). M.L.C. is supported by grants from the National Cancer Institute and by the Lucile Packard Foundation for Children’s Health. REFERENCES Anderson, K., Lutz, C., van Delft, F.W., Bateman, C.M., Guo, Y., Colman, S.M., Kempski, H., Moorman, A.V., Titley, I., Swansbury, J., et al. (2011). Nature 469, 356–361. Ding, L., Ley, T.J., Larson, D.E., Miller, C.A., Koboldt, D.C., Welch, J.S., Ritchey, J.K., Young,

Navin, N., Kendall, J., Troge, J., Andrews, P., Rodgers, L., McIndoo, J., Cook, K., Stepansky, A., Levy, D., Esposito, D., et al. (2011). Nature 472, 90–94. Shah, S.P., Morin, R.D., Khattra, J., Prentice, L., Pugh, T., Burleigh, A., Delaney, A., Gelmon, K., Guliany, R., Senz, J., et al. (2009). Nature 461, 809–813. Sharpless, N.E., and Depinho, R.A. (2006). Nat. Rev. Drug Discov. 5, 741–754. Shlush, L.I., Zandi, S., Mitchell, A., Chen, W.C., Brandwein, J.M., Gupta, V., Kennedy, J.A., Schimmer, A.D., Schuh, A.C., Yee, K.W., et al.; HALT Pan-Leukemia Gene Panel Consortium (2014). Nature 506, 328–333.

Therapeutic Opportunities for Medulloblastoma Come of Age James M. Olson1,* 1Fred Hutchinson Cancer Research Center and Seattle Children’s Hospital, Seattle WA 98109, USA *Correspondence: [email protected] http://dx.doi.org/10.1016/j.ccr.2014.03.003

In this issue of Cancer Cell, Kool and colleagues reveal clear genetically defined subclasses of the sonic hedgehog (SHH) subclass of medulloblastoma. This molecular dissection of the SHH subclass is not simply a cutting-edge advance; the data have profound impact on clinical trial design and decision-making. Twenty years ago, the world health organization guidelines and revered neuropathologists encouraged us to ‘‘lump’’ any small round blue cell tumor in the brain into a single category for the purpose of clinical trials and therapeutics. The only way to improve survival in the ‘‘age of the lumpers’’ was to intensify therapy, so

we collectively brought the kids to the very edge of life and death. This resulted in improved survival rates at the expenses of both short- and long-term toxicity (Jakacki et al., 2012; Packer et al., 2006). It is now the age of the ‘‘splitters’’. In the wake of a half dozen outstanding medulloblastoma genomics papers in

2012, Kool et al. (2014, in this issue of Cancer Cell) now further show us that there are at least three distinct molecular subclasses within the sonic hedgehog (SHH)-driven subclass of medulloblastoma. These data create a double-edged sword, establishing clear responder hypotheses while revealing that, as we

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