Integrated genetic and pharmacologic interrogation of rare cancers

Abstract

Identifying therapeutic targets in rare cancers remains challenging due to the paucity of established models to perform preclinical studies. As a proof-of-concept, we developed a patient-derived cancer cell line, CLF-PED-015-T, from a paediatric patient with a rare undifferentiated sarcoma. Here, we confirm that this cell line recapitulates the histology and harbours the majority of the somatic genetic alterations found in a metastatic lesion isolated at first relapse. We then perform pooled CRISPR-Cas9 and RNAi loss-of-function screens and a small-molecule screen focused on druggable cancer targets. Integrating these three complementary and orthogonal methods, we identify CDK4 and XPO1 as potential therapeutic targets in this cancer, which has no known alterations in these genes. These observations establish an approach that integrates new patient-derived models, functional genomics and chemical screens to facilitate the discovery of targets in rare cancers.

Physiologic Medium Rewires Cellular Metabolism and Reveals Uric Acid as an Endogenous Inhibitor of UMP Synthase

“…Similarly, HPLM +dIFS caused dramatic elevations in fructose-6-phosphate/glucose-1-phosphate (F6P/G1P) only in K562, P12-Ichikawa, and SUDHL4 cells, and modest reductions in oxoglutarate only in KMS12BM cells. Culture in HPLM +dIFS also induced widespread and largely similar alterations to the metabolomes of several other cell types, including a variety of epithelial cancer cell lines (786-0, A549, MCF7, and SW620), an early passage undifferentiated sarcoma cell line (CLF-PED-015T (Hong et al, 2016)), a normal foreskin fibroblast cell line (BJ), and a primary acute myeloid leukemia sample (Figure S2A, Table S3). …”

Emerging Opportunities for Target Discovery in Rare Cancers

“…Soon after the discovery of the DNAJB1-PRKACA fusion transcript driving fibrolamellar hepatocellular carcinoma (Honeyman et al, 2014), a transplantable tumor line of this rare cancer was developed (Oikawa et al, 2015). The recent development of a cell-line model from a rare undifferentiated pediatric sarcoma demonstrated how the derivation of a single rare cancer model can be used in high-throughput screens to drive new therapeutic discoveries (Hong et al, 2016). In other applications, cell culture models were generated from individual lung cancer patients that had progressed after EGFR- or ALK-targeted therapies, or from the circulating tumor cells of advanced breast cancer patients, and were subjected to drug testing (Crystal et al, 2014; Yu et al, 2014).…”

“…The recent emergence of clustered regularly inter-spaced short palindromic repeats (CRISPR)/Cas9-based technologies, which mediate gene inactivation (reviewed in Howard et al, 2016), now provide an exciting complementary approach (Wang et al, 2015). The joint application of both RNAi-based and CRISPR/Cas9-based technologies to discover rare cancer dependencies that are technology agnostic is now possible (Hong et al, 2016). …”

“…Now, each rare cancer small-molecule dependency assessment can be compared against these large reference datasets (Figure 4) to prioritize dependencies that share a common cellular feature or are highly specific to the rare cancer under study, thereby deprioritizing compounds whose effects are pan-lethal. For example, these types of comparative analyses have recently been used to identify small-molecule dependencies in models of medulloblastoma (Hanaford et al, 2016) and pediatric undifferentiated sarcoma (Hong et al, 2016). …”

“…Now, demonstration efforts have shown basic feasibility of using these technologies to assess cancer dependencies in novel patient-derived cell lines (Hong et al, 2016; Crystal et al, 2014; Yu et al, 2014), organoids (van de Wetering et al, 2015), and xenografts (Bruna et al, 2016; Townsend et al, 2016; Gao et al, 2015; Bossi et al, 2016). Leveraging multiple technologies in unison, it should soon become increasingly feasible to assess dependencies of a given rare cancer type (even with a limited number of models), compare results to large reference datasets, and home in on therapeutically actionable dependencies that are induced by specific, shared cellular features (Figure 4).…”