The challenge of a complex disease
Acute myeloid leukemia (AML) is an aggressive cancer where functionally immature cells derived from the myeloid compartment undergo unchecked proliferation (Döhner et al., 2015). Adept at survival, AML cells accumulate in the bone marrow, blood, and peripheral tissues, where they inhibit the normal function of the hematopoietic system (Saultz and Garzon, 2016). While AML is rare in people under the age of 45 (Howlader et al., 2016), disease outcome is poor in patients over the age of 60 with only 5-15% of patients achieving a cure for their disease (Döhner et al., 2015). Indeed, in patients unable to tolerate aggressive chemotherapy, the median survival time is only 5-10 months (Döhner et al., 2015). While many patients are responsive to chemotherapy, disease relapse is a pervasive barrier to treatment success (Kubal and Lancet, 2013).
A fundamental challenge in treating AML is the inherent heterogeneity of the diseased cells within each patient (Levine, 2013), rendering targeted treatment difficult. AML is a dynamic illness with many mutant clones and subclones that expand differentially within a single cancer (Papaemmanuil et al., 2016). In the continued quest to develop therapeutic strategies, deep coverage genome and exome sequencing using Next Generation Sequencing (NGS) platforms represent a promising tool (Levine, 2013; Townsend et al., 2016). As an example, a recent study identified 5234 driver mutations in 76 genes from a cohort of over 1500 AML patients (Papaemmanuil et al., 2016). Characterization of these mutations and how they affect the biology of AML is underway. To this end, models that recapitulate the complex heterogeneity of AML in vivo have become powerful tools for preclinical studies.
Improved modeling in support of clinical advancement
Historically, patient-derived xenografts (PDX) of AML have been particularly challenging to establish in host mouse strains (Wunderlich et al., 2010). However, the development of highly immunodeficient mouse strains, including NSG (NOD.Cg-Prkdcscid Il2rgtm1Wjl/SzJ; stock# 005557), ushered in significant improvements in AML PDX modeling (Sanchez et al., 2009). NSG-SGM3 mice (NOD.Cg-Prkdcscid Il2rgtm1Wjl Tg(CMV-IL3,CSF2,KITLG)1Eav/MloySzJ; stock# 013062) express transgenes for human IL3, GM-CSF, and KITLG (Billerbeck et al., 2011) that promote myeloid lineage development, leading to increased proliferation and survival of engrafted AML cells and decreased disease latency (Wunderlich et al., 2010). In one report, AML homing to the bone marrow niche 24h post-tail vein engraftment was roughly 2.5 times higher in NSG and NSG-SGM3 mice compared to NOD.CB17-Prkdcscid (Wunderlich et al., 2010). Moreover, one-month following tail vein injection, AML engraftment in the bone marrow and peripheral blood was sustained in a manner indicating that NSG-SGM3 is superior at supporting engrafted AML cells (Wunderlich et al., 2010). In parallel, findings by Klco and colleagues demonstrated that engraftment of AML in NSG-SGM3 mice was similar to or greater than that in NSG mice (Klco et al., 2014). Taken together, these data indicate that a highly-immunodeficient mouse background and the additional support of exogenous human cytokines enable more robust engraftment of AML.
Xenografting tumors is not a new strategy in cancer research. However, improvements in immunodeficient host mice to better engraft PDX models has allowed broader adoption of this approach. In parallel, the mainstream use of deep sequencing technologies has facilitated higher resolution of the genomic landscape of human cancers. Taken together, these technologies may lay the framework for modeling cancer molecular heterogeneity in a way that is more reflective of real-world disease variability (Aparicio et al., 2015). Indeed, a team at Dana-Farber Cancer Institute has demonstrated that a well-powered study using PDX models of hematological cancer can function analogously to a phase-II clinical trial, specifically by capturing disease heterogeneity of well-characterized experimental models (Townsend et al., 2016). The Jackson Laboratory has worked with this group to increase the portfolio of AML PDX models available in order to facilitate the broader access by the research community to faithful models in support of the shared quest to improve human health.
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