Audrey Kauffmann

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.

Validated prediction of clinical outcome in sarcomas and multiple types of cancer on the basis of a gene expression signature related to genome complexity

Sarcomas are heterogeneous and aggressive mesenchymal tumors. Histological grading has so far been the best predictor for metastasis-free survival, but it has several limitations, such as moderate reproducibility and poor prognostic value for some histological types. To improve patient grading, we performed genomic and expression profiling in a training set of 183 sarcomas and established a prognostic gene expression signature, complexity index in sarcomas (CINSARC), composed of 67 genes related to mitosis and chromosome management. In a multivariate analysis, CINSARC predicts metastasis outcome in the training set and in an independent 127 sarcomas validation set. It is superior to the Fédération Francaise des Centres de Lutte Contre le Cancer grading system in determining metastatic outcome for sarcoma patients. Furthermore, it also predicts outcome for gastrointestinal stromal tumors (GISTs), breast carcinomas and lymphomas. Application of the signature will permit more selective use of adjuvant therapies for people with sarcomas, leading to decreased iatrogenic morbidity and improved outcomes for such individuals.

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 β-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.

Pharmacological and genomic profiling identifies NF-κB–targeted treatment strategies for mantle cell lymphoma

Mantle cell lymphoma (MCL) is an aggressive malignancy that is characterized by poor prognosis. Large-scale pharmacological profiling across more than 100 hematological cell line models identified a subset of MCL cell lines that are highly sensitive to the B cell receptor (BCR) signaling inhibitors ibrutinib and sotrastaurin. Sensitive MCL models exhibited chronic activation of the BCR-driven classical nuclear factor-κB (NF-κB) pathway, whereas insensitive cell lines displayed activation of the alternative NF-κB pathway. Transcriptome sequencing revealed genetic lesions in alternative NF-κB pathway signaling components in ibrutinib-insensitive cell lines, and sequencing of 165 samples from patients with MCL identified recurrent mutations in TRAF2 or BIRC3 in 15% of these individuals. Although they are associated with insensitivity to ibrutinib, lesions in the alternative NF-κB pathway conferred dependence on the protein kinase NIK (also called mitogen-activated protein 3 kinase 14 or MAP3K14) both in vitro and in vivo. Thus, NIK is a new therapeutic target for MCL treatment, particularly for lymphomas that are refractory to BCR pathway inhibitors. Our findings reveal a pattern of mutually exclusive activation of the BCR–NF-κB or NIK–NF-κB pathways in MCL and provide critical insights into patient stratification strategies for NF-κB pathway–targeted agents.

Characterization of the novel and specific PI3Kα inhibitor NVP-BYL719 and development of the patient stratification strategy for clinical trials.

Somatic PIK3CA mutations are frequently found in solid tumors, raising the hypothesis that selective inhibition of PI3Kα may have robust efficacy in PIK3CA-mutant cancers while sparing patients the side-effects associated with broader inhibition of the class I phosphoinositide 3-kinase (PI3K) family. Here, we report the biologic properties of the 2-aminothiazole derivative NVP-BYL719, a selective inhibitor of PI3Kα and its most common oncogenic mutant forms. The compound selectivity combined with excellent drug-like properties translates to dose- and time-dependent inhibition of PI3Kα signaling in vivo, resulting in robust therapeutic efficacy and tolerability in PIK3CA-dependent tumors. Novel targeted therapeutics such as NVP-BYL719, designed to modulate aberrant functions elicited by cancer-specific genetic alterations upon which the disease depends, require well-defined patient stratification strategies in order to maximize their therapeutic impact and benefit for the patients. Here, we also describe the application of the Cancer Cell Line Encyclopedia as a preclinical platform to refine the patient stratification strategy for NVP-BYL719 and found that PIK3CA mutation was the foremost positive predictor of sensitivity while revealing additional positive and negative associations such as PIK3CA amplification and PTEN mutation, respectively. These patient selection determinants are being assayed in the ongoing NVP-BYL719 clinical trials. Mol Cancer Ther; 13(5); 1117–29. ©2014 AACR.

CRISPR Screens Provide a Comprehensive Assessment of Cancer Vulnerabilities but Generate False-Positive Hits for Highly Amplified Genomic Regions

UNLABELLED CRISPR/Cas9 has emerged as a powerful new tool to systematically probe gene function. We compared the performance of CRISPR to RNAi-based loss-of-function screens for the identification of cancer dependencies across multiple cancer cell lines. CRISPR dropout screens consistently identified more lethal genes than RNAi, implying that the identification of many cellular dependencies may require full gene inactivation. However, in two aneuploid cancer models, we found that all genes within highly amplified regions, including nonexpressed genes, scored as lethal by CRISPR, revealing an unanticipated class of false-positive hits. In addition, using a CRISPR tiling screen, we found that sgRNAs targeting essential domains generate the strongest lethality phenotypes and thus provide a strategy to rapidly define the protein domains required for cancer dependence. Collectively, these findings not only demonstrate the utility of CRISPR screens in the identification of cancer-essential genes, but also reveal the need to carefully control for false-positive results in chromosomally unstable cancer lines. SIGNIFICANCE We show in this study that CRISPR-based screens have a significantly lower false-negative rate compared with RNAi-based screens, but have specific liabilities particularly in the interrogation of regions of genome amplification. Therefore, this study provides critical insights for applying CRISPR-based screens toward the systematic identification of new cancer targets. Cancer Discov; 6(8); 900-13. ©2016 AACR.See related commentary by Sheel and Xue, p. 824See related article by Aguirre et al., p. 914This article is highlighted in the In This Issue feature, p. 803.

MITOTIC CHECKPOINTS AND CHROMOSOME INSTABILITY ARE STRONG PREDICTORS OF CLINICAL OUTCOME IN GASTROINTESTINAL STROMAL TUMORS.

Purpose: The importance of KIT and PDGFRA mutations in the oncogenesis of gastrointestinal stromal tumors (GIST) is well established, but the genetic basis of GIST metastasis is poorly understood. We recently published a 67 gene expression prognostic signature related to genome complexity (CINSARC for Complexity INdex in SARComas) and asked whether it could predict outcome in GISTs. Experimental Design: We carried out genome and expression profiling on 67 primary untreated GISTs. Results: We show and validate here that it can predict metastasis in a new data set of 67 primary untreated GISTs. The gene whose expression was most strongly associated with metastasis was AURKA, but the AURKA locus was not amplified. Instead, we identified deletion of the p16 (CDKN2A) and retinoblastoma (RB1) genes as likely causal events leading to increased AURKA and CINSARC gene expression, to chromosome rearrangement, and ultimately to metastasis. On the basis of these findings, we established a Genomic Index that integrates the number and type of DNA copy number alterations. This index is a strong prognostic factor in GISTs. We show that CINSARC class, AURKA expression, and Genomic Index all outperform the Armed Forces Institute of Pathology (AFIP) grading system in determining the prognosis of patients with GISTs. Interestingly, these signatures can identify poor prognosis patients in the group classified as intermediate-risk by the AFIP classification. Conclusions: We propose that a high Genomic Index determined by comparative genomic hybridization from formalin-fixed, paraffin-embedded samples could be used to identify AFIP intermediate-risk patients who would benefit from imatinib therapy. Clin Cancer Res; 18(3); 826–38. ©2011 AACR.

Characterization of the Mechanism of Action of the Pan Class I PI3K Inhibitor NVP-BKM120 across a Broad Range of Concentrations

The pan-phosphoinositide 3-kinase (PI3K) inhibitor BKM120 was found, at high concentrations, to cause cell death in various cellular systems, irrespective of their level of PI3K addiction. Transcriptional and biochemical profiling studies were used to identify the origin of these unexpected and apparently PI3K-independent effects. At 5- to 10-fold, the concentration needed to half-maximally inhibit PI3K signaling. BKM120 treatment caused changes in expression of mitotic genes and the induction of a robust G2–M arrest. Tubulin polymerization assays and nuclear magnetic resonance-binding studies revealed that BKM120 inhibited microtubule dynamics upon direct binding to tubulin. To assess the contribution of this off-target activity vis-à-vis the antitumor activity of BKM120 in PI3K-dependent tumors, we used a mechanistic PI3K-α–dependent model. We observed that, in vivo, daily treatment of mice with doses of BKM120 up to 40 mg/kg led to tumor regressions with no increase in the mitotic index. Thus, strong antitumor activity can be achieved in PI3K-dependent models at exposures that are below those necessary to engage the off-target activity. In comparison, the clinical data indicate that it is unlikely that BKM120 will achieve exposures sufficient to significantly engage the off-target activity at tolerated doses and schedules. However, in preclinical settings, the consequences of the off-target activity start to manifest themselves at concentrations above 1 μmol/L in vitro and doses above 50 mg/kg in efficacy studies using subcutaneous tumor–bearing mice. Hence, careful concentration and dose range selection is required to ensure that any observation can be correctly attributed to BKM120 inhibition of PI3K. Mol Cancer Ther; 11(8); 1747–57. ©2012 AACR.

Pharmacogenomic agreement between two cancer cell line data sets

Large cancer cell line collections broadly capture the genomic diversity of human cancers and provide valuable insight into anti-cancer drug response. Here we show substantial agreement and biological consilience between drug sensitivity measurements and their associated genomic predictors from two publicly available large-scale pharmacogenomics resources: The Cancer Cell Line Encyclopedia and the Genomics of Drug Sensitivity in Cancer databases.

Pan-PIM Kinase Inhibition Provides a Novel Therapy for Treating Hematologic Cancers

Purpose: PIM kinases have been shown to act as oncogenes in mice, with each family member being able to drive progression of hematologic cancers. Consistent with this, we found that PIMs are highly expressed in human hematologic cancers and show that each isoform has a distinct expression pattern among disease subtypes. This suggests that inhibitors of all three PIMs would be effective in treating multiple hematologic malignancies. Experimental Design: Pan-PIM inhibitors have proven difficult to develop because PIM2 has a low Km for ATP and, thus, requires a very potent inhibitor to effectively block the kinase activity at the ATP levels in cells. We developed a potent and specific pan-PIM inhibitor, LGB321, which is active on PIM2 in the cellular context. Results: LGB321 is active on PIM2-dependent multiple myeloma cell lines, where it inhibits proliferation, mTOR-C1 signaling and phosphorylation of BAD. Broad cancer cell line profiling of LGB321 demonstrates limited activity in cell lines derived from solid tumors. In contrast, significant activity in cell lines derived from diverse hematological lineages was observed, including acute lymphoblastic leukemia (ALL), acute myelogenous leukemia (AML), multiple myeloma and non-Hodgkin lymphoma (NHL). Furthermore, we demonstrate LGB321 activity in the KG-1 AML xenograft model, in which modulation of pharmacodynamics markers is predictive of efficacy. Finally, we demonstrate that LGB321 synergizes with cytarabine in this model. Conclusions: We have developed a potent and selective pan-PIM inhibitor with single-agent antiproliferative activity and show that it synergizes with cytarabine in an AML xenograft model. Our results strongly support the development of Pan-PIM inhibitors to treat hematologic malignancies. Clin Cancer Res; 20(7); 1834–45. ©2014 AACR.