论文信息 - The Public Repository of Xenografts Enables Discovery and Randomized Phase II-like Trials in Mice.

The Public Repository of Xenografts Enables Discovery and Randomized Phase II-like Trials in Mice.

More than 90% of drugs with preclinical activity fail in human trials, largely due to insufficient efficacy. We hypothesized that adequately powered trials of patient-derived xenografts (PDX) in mice could efficiently define therapeutic activity across heterogeneous tumors. To address this hypothesis, we established a large, publicly available repository of well-characterized leukemia and lymphoma PDXs that undergo orthotopic engraftment, called the Public Repository of Xenografts (PRoXe). PRoXe includes all de-identified information relevant to the primary specimens and the PDXs derived from them. Using this repository, we demonstrate that large studies of acute leukemia PDXs that mimic human randomized clinical trials can characterize drug efficacy and generate transcriptional, functional, and proteomic biomarkers in both treatment-naive and relapsed/refractory disease.

[1] M. Wiesmann,et al. Inhibition of Wild-Type p53-Expressing AML by the Novel Small Molecule HDM2 Inhibitor CGM097 , 2015, Molecular Cancer Therapeutics.

[2] P. Meltzer,et al. The exomes of the NCI-60 panel: a genomic resource for cancer biology and systems pharmacology. , 2013, Cancer research.

[3] Christopher A. Miller,et al. Functional heterogeneity of genetically defined subclones in acute myeloid leukemia. , 2014, Cancer cell.

[4] Andrew L. Kung,et al. Examining the utility of patient-derived xenograft mouse models , 2015, Nature Reviews Cancer.

[5] J. Byrd,et al. Chronic lymphocytic leukemia/small lymphocytic lymphoma, version 1.2015. , 2015, Journal of the National Comprehensive Cancer Network : JNCCN.

[6] Michael Becker,et al. Establishment of Patient-Derived Non–Small Cell Lung Cancer Xenografts as Models for the Identification of Predictive Biomarkers , 2008, Clinical Cancer Research.

[7] Philippe Dessen,et al. Characterization of a Large Panel of Patient-Derived Tumor Xenografts Representing the Clinical Heterogeneity of Human Colorectal Cancer , 2012, Clinical Cancer Research.

[8] Sven Rahmann,et al. Snakemake--a scalable bioinformatics workflow engine. , 2012, Bioinformatics.

[9] Giovanni Parmigiani,et al. A primary xenograft model of small-cell lung cancer reveals irreversible changes in gene expression imposed by culture in vitro. , 2009, Cancer research.

[10] Manuel Hidalgo,et al. Patient-derived xenograft models: an emerging platform for translational cancer research. , 2014, Cancer discovery.

[11] Anne Vincent-Salomon,et al. Molecular profiling of patient-derived breast cancer xenografts , 2012, Breast Cancer Research.

[12] Matthew E. Ritchie,et al. limma powers differential expression analyses for RNA-sequencing and microarray studies , 2015, Nucleic acids research.

[13] John D. Storey,et al. Capturing Heterogeneity in Gene Expression Studies by Surrogate Variable Analysis , 2007, PLoS genetics.

[14] I. Kola,et al. Can the pharmaceutical industry reduce attrition rates? , 2004, Nature Reviews Drug Discovery.

[15] Eduardo Sontag,et al. Transcriptional control of human p53-regulated genes , 2008, Nature Reviews Molecular Cell Biology.

[16] K. Cibulskis,et al. Comprehensive analysis of cancer-associated somatic mutations in class I HLA genes , 2015, Nature Biotechnology.

[17] Pengcheng Lv,et al. Recent advances of p53-MDM2 small molecule inhibitors (2011-present). , 2015, Current medicinal chemistry.

[18] Hans Bitter,et al. A distinct p53 target gene set predicts for response to the selective p53–HDM2 inhibitor NVP-CGM097 , 2015, eLife.

[19] Eric Vangrevelinghe,et al. Genetic resistance to JAK2 enzymatic inhibitors is overcome by HSP90 inhibition , 2011, The Journal of experimental medicine.

[20] Adam A. Margolin,et al. The Cancer Cell Line Encyclopedia enables predictive modeling of anticancer drug sensitivity , 2012, Nature.

[21] Heidi Ledford,et al. Translational research: 4 ways to fix the clinical trial , 2011, Nature.

[22] Jean-Pierre Gillet,et al. Redefining the relevance of established cancer cell lines to the study of mechanisms of clinical anti-cancer drug resistance , 2011, Proceedings of the National Academy of Sciences.

[23] Christopher J. Ott,et al. BET bromodomain inhibition targets both c-Myc and IL7R in high-risk acute lymphoblastic leukemia. , 2012, Blood.

[24] Joshua M. Korn,et al. High-throughput screening using patient-derived tumor xenografts to predict clinical trial drug response , 2015, Nature Medicine.

[25] Chad A Shaw,et al. A renewable tissue resource of phenotypically stable, biologically and ethnically diverse, patient-derived human breast cancer xenograft models. , 2013, Cancer research.

[26] R. Brenner,et al. Phenotypic instability of Saos-2 cells in long-term culture. , 2005, Biochemical and biophysical research communications.

[27] N. Harris,et al. Classification of lymphoid neoplasms: the microscope as a tool for disease discovery. , 2008, Blood.

[28] N. Harris,et al. A targeted mutational landscape of angioimmunoblastic T-cell lymphoma. , 2012, Blood.

[29] Davide Corà,et al. A molecularly annotated platform of patient-derived xenografts ("xenopatients") identifies HER2 as an effective therapeutic target in cetuximab-resistant colorectal cancer. , 2011, Cancer discovery.

[30] M. Konopleva,et al. Role of Microenvironment in Resistance to Therapy in AML , 2015, Current Hematologic Malignancy Reports.

[31] Thomas M. Keane,et al. The mutational landscapes of genetic and chemical models of Kras-driven lung cancer , 2014, Nature.

[32] P. Vyas,et al. Results of the Phase I Trial of RG7112, a Small-Molecule MDM2 Antagonist in Leukemia , 2015, Clinical Cancer Research.

[33] R. Lock,et al. Characterization of childhood acute lymphoblastic leukemia xenograft models for the preclinical evaluation of new therapies. , 2004, Blood.

[34] Karen Cichowski,et al. Drug-Induced Death Signaling Strategy Rapidly Predicts Cancer Response to Chemotherapy , 2015, Cell.

[35] M. Ghert,et al. Lost in translation: animal models and clinical trials in cancer treatment. , 2014, American journal of translational research.