High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response.

Profiling candidate therapeutics with limited cancer models during preclinical development hinders predictions of clinical efficacy and identifying factors that underlie heterogeneous patient responses for patient-selection strategies. We established ∼1,000 patient-derived tumor xenograft models (PDXs) with a diverse set of driver mutations. With these PDXs, we performed in vivo compound screens using a 1 × 1 × 1 experimental design (PDX clinical trial or PCT) to assess the population responses to 62 treatments across six indications. We demonstrate both the reproducibility and the clinical translatability of this approach by identifying associations between a genotype and drug response, and established mechanisms of resistance. In addition, our results suggest that PCTs may represent a more accurate approach than cell line models for assessing the clinical potential of some therapeutic modalities. We therefore propose that this experimental paradigm could potentially improve preclinical evaluation of treatment modalities and enhance our ability to predict clinical trial responses.

Gene Expression Ratios Lead to Accurate and Translatable Predictors of DR5 Agonism across Multiple Tumor Lineages.

Death Receptor 5 (DR5) agonists demonstrate anti-tumor activity in preclinical models but have yet to demonstrate robust clinical responses. A key limitation may be the lack of patient selection strategies to identify those most likely to respond to treatment. To overcome this limitation, we screened a DR5 agonist Nanobody across >600 cell lines representing 21 tumor lineages and assessed molecular features associated with response. High expression of DR5 and Casp8 were significantly associated with sensitivity, but their expression thresholds were difficult to translate due to low dynamic ranges. To address the translational challenge of establishing thresholds of gene expression, we developed a classifier based on ratios of genes that predicted response across lineages. The ratio classifier outperformed the DR5+Casp8 classifier, as well as standard approaches for feature selection and classification using genes, instead of ratios. This classifier was independently validated using 11 primary patient-derived pancreatic xenograft models showing perfect predictions as well as a striking linearity between prediction probability and anti-tumor response. A network analysis of the genes in the ratio classifier captured important biological relationships mediating drug response, specifically identifying key positive and negative regulators of DR5 mediated apoptosis, including DR5, CASP8, BID, cFLIP, XIAP and PEA15. Importantly, the ratio classifier shows translatability across gene expression platforms (from Affymetrix microarrays to RNA-seq) and across model systems (in vitro to in vivo). Our approach of using gene expression ratios presents a robust and novel method for constructing translatable biomarkers of compound response, which can also probe the underlying biology of treatment response.

FGFR-Mediated Reactivation of MAPK Signaling Attenuates Antitumor Effects of Imatinib in Gastrointestinal Stromal Tumors.

UNLABELLED: Activating mutations in either KIT or PDGFRA are present in approximately 90% of gastrointestinal stromal tumors (GIST). Although treatment with the KIT and PDGFR inhibitor imatinib can control advanced disease in about 80% of GIST patients, the beneficial effect is not durable. Here, we report that ligands from the FGF family reduced the effectiveness of imatinib in GIST cells, and FGF2 and FGFR1 are highly expressed in all primary GIST samples examined. The combination of KIT and FGFR inhibition showed increased growth inhibition in imatinib-sensitive GIST cell lines and improved efficacy in patient-derived GIST xenografts. In addition, inhibition of MAPK signaling by imatinib was not sustained in GIST cells. An ERK rebound occurred through activation of FGF signaling, and was repressed by FGFR1 inhibition. Downregulation of Sprouty proteins played a role in the imatinib-induced feedback activation of FGF signaling in GIST cells. SIGNIFICANCE: We here show that FGFR-mediated reactivation of the MAPK pathway attenuates the antiproliferation effects of imatinib in GISTs. The imatinib-induced ERK rebound can be repressed by the FGFR inhibitor BGJ398, and combined KIT and FGFR inhibition leads to increased efficacy in vitro and in patient-derived xenografts.

An F876L mutation in androgen receptor confers genetic and phenotypic resistance to MDV3100 (enzalutamide).

UNLABELLED: Castration-resistant prostate cancer (CRPC) is the most aggressive, incurable form of prostate cancer. MDV3100 (enzalutamide), an antagonist of the androgen receptor (AR), was approved for clinical use in men with metastatic CRPC. Although this compound showed clinical efficacy, many initial responders later developed resistance. To uncover relevant resistant mechanisms, we developed a model of spontaneous resistance to MDV3100 in LNCaP prostate cancer cells. Detailed characterization revealed that emergence of an F876L mutation in AR correlated with blunted AR response to MDV3100 and sustained proliferation during treatment. Functional studies confirmed that AR(F876L) confers an antagonist-to-agonist switch that drives phenotypic resistance. Finally, treatment with distinct antiandrogens or cyclin-dependent kinase (CDK)4/6 inhibitors effectively antagonized AR(F876L) function. Together, these findings suggest that emergence of F876L may (i) serve as a novel biomarker for prediction of drug sensitivity, (ii) predict a "withdrawal" response to MDV3100, and (iii) be suitably targeted with other antiandrogens or CDK4/6 inhibitors. SIGNIFICANCE: We uncovered an F876L agonist-switch mutation in AR that confers genetic and phenotypic resistance to the antiandrogen drug MDV3100. On the basis of this fi nding, we propose new therapeutic strategies to treat patients with prostate cancer presenting with this AR mutation.

FGFR genetic alterations predict for sensitivity to NVP-BGJ398, a selective pan-FGFR inhibitor.

UNLABELLED: Patient stratification biomarkers that enable the translation of cancer genetic knowledge into clinical use are essential for the successful and rapid development of emerging targeted anticancer therapeutics. Here, we describe the identification of patient stratification biomarkers for NVP-BGJ398, a novel and selective fibroblast growth factor receptor (FGFR) inhibitor. By intersecting genome-wide gene expression and genomic alteration data with cell line-sensitivity data across an annotated collection of cancer cell lines called the Cancer Cell Line Encyclopedia, we show that genetic alterations for FGFR family members predict for sensitivity to NVP-BGJ398. For the first time, we report oncogenic FGFR1 amplification in osteosarcoma as a potential patient selection biomarker. Furthermore, we show that cancer cell lines harboring FGF19 copy number gain at the 11q13 amplicon are sensitive to NVP-BGJ398 only when concomitant expression of ß-klotho occurs. Thus, our findings provide the rationale for the clinical development of FGFR inhibitors in selected patients with cancer harboring tumors with the identified predictors of sensitivity. SIGNIFICANCE: The success of a personalized medicine approach using targeted therapies ultimately depends on being able to identify the patients who will benefit the most from any given drug. To this end, we have integrated the molecular profiles for more than 500 cancer cell lines with sensitivity data for the novel anticancer drug NVP-BGJ398 and showed that FGFR genetic alterations are the most significant predictors for sensitivity. This work has ultimately endorsed the incorporation of specific patient selection biomakers in the clinical trials for NVP-BGJ398.

The Cancer Cell Line Encyclopedia enables predictive modelling of anticancer drug sensitivity.

The systematic translation of cancer genomic data into knowledge of tumour biology and therapeutic possibilities remains challenging. Such efforts should be greatly aided by robust preclinical model systems that reflect the genomic diversity of human cancers and for which detailed genetic and pharmacological annotation is available. Here we describe the Cancer Cell Line Encyclopedia (CCLE): a compilation of gene expression, chromosomal copy number and massively parallel sequencing data from 947 human cancer cell lines. When coupled with pharmacological profiles for 24 anticancer drugs across 479 of the cell lines, this collection allowed identification of genetic, lineage, and gene-expression-based predictors of drug sensitivity. In addition to known predictors, we found that plasma cell lineage correlated with sensitivity to IGF1 receptor inhibitors; AHR expression was associated with MEK inhibitor efficacy in NRAS-mutant lines; and SLFN11 expression predicted sensitivity to topoisomerase inhibitors. Together, our results indicate that large, annotated cell-line collections may help to enable preclinical stratification schemata for anticancer agents. The generation of genetic predictions of drug response in the preclinical setting and their incorporation into cancer clinical trial design could speed the emergence of 'personalized' therapeutic regimens.

A drug resistance screen using a selective MET inhibitor reveals a spectrum of mutations that partially overlap with activating mutations found in cancer patients.

The emergence of drug resistance is a primary concern in any cancer treatment, including with targeted kinase inhibitors as exemplified by the appearance of Bcr-Abl point mutations in chronic myeloid leukemia (CML) patients treated with imatinib. In vitro approaches to identify resistance mutations in Bcr-Abl have yielded mutation spectra that faithfully recapitulated clinical observations. To predict resistance mutations in the receptor tyrosine kinase MET that could emerge during inhibitor treatment in patients, we conducted a resistance screen in BaF3 TPR-MET cells using the novel selective MET inhibitor NVP-BVU972. The observed spectrum of mutations in resistant cells was dominated by substitutions of tyrosine 1230 but also included other missense mutations and partially overlapped with activating MET mutations that were previously described in cancer patients. Cocrystallization of the MET kinase domain in complex with NVP-BVU972 revealed a key role for Y1230 in binding of NVP-BVU972, as previously reported for multiple other selective MET inhibitors. A second resistance screen in the same format with the MET inhibitor AMG 458 yielded a distinct spectrum of mutations rich in F1200 alterations, which is consistent with a different predicted binding mode. Our findings suggest that amino acid substitutions in the MET kinase domain of cancer patients need to be carefully monitored before and during treatment with MET inhibitors, as resistance may preexist or emerge. Compounds binding in the same manner as NVP-BVU972 might be particularly susceptible to the development of resistance through mutations in Y1230, a condition that may be addressed by MET inhibitors with alternative binding modes.

Interfering with resistance to smoothened antagonists by inhibition of the PI3K pathway in medulloblastoma.

The malignant brain cancer medulloblastoma is characterized by mutations in Hedgehog (Hh) signaling pathway genes, which lead to constitutive activation of the G protein (heterotrimeric guanosine triphosphate-binding protein)-coupled receptor Smoothened (Smo). The Smo antagonist NVP-LDE225 inhibits Hh signaling and induces tumor regression in animal models of medulloblastoma. However, evidence of resistance was observed during the course of treatment. Molecular analysis of resistant tumors revealed several resistance mechanisms. We noted chromosomal amplification of Gli2, a downstream effector of Hh signaling, and, more rarely, point mutations in Smo that led to reactivated Hh signaling and restored tumor growth. Analysis of pathway gene expression signatures also, unexpectedly, identified up-regulation of phosphatidylinositol 3-kinase (PI3K) signaling in resistant tumors as another potential mechanism of resistance. Probing the relevance of increased PI3K signaling, we demonstrated that addition of the PI3K inhibitor NVP-BKM120 or the dual PI3K-mTOR (mammalian target of rapamycin) inhibitor NVP-BEZ235 to the initial treatment with the Smo antagonist markedly delayed the development of resistance. Our findings may be useful in informing treatment strategies for medulloblastoma.