Proxe
The Public Repository of Xenografts
May 23, 2020
To the PRoXe community,
PRoXe has now migrated to the cBioPortal platform. We implemented this change to enable important functionality, scalability, and integration with other clinical-genomic data sets.
To see the most current PRoXe data, register for a cBioPortal account here. Note that a Gmail account is required.
The legacy PRoXe Shiny app will remain accessible until May 1, 2025 but will not receive updates.
Thank you for your support, and we look forward to seeing you at cBioPortal.
Sincerely,
The PRoXe Team
THE PUBLIC REPOSITORY OF XENOGRAFTS
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PRoXe | Weinstock Laboratory
PRoXe (Public Repository of Xenografts) is an open-source website to disseminate information relevant to patient-derived xenografts (PDXs), particularly PDXs generated from patients with leukemia or lymphoma.
To view the data portal, sign up here. We will review your request and send you an invitation to a second signup for the app-hosting platform,, after which you will be able to access the app here. For a step-by-step tutorial on how to navigate PRoXe, click here.
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Patient-derived xenografting
Renal capsule implantation surgery
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Cancer Cell. Author manuscript; available in PMC 2016 Dec lished in final edited form as:PMCID: PMC5177991NIHMSID: NIHMS794414Elizabeth C. Townsend, 1, * Mark A. Murakami, 1, * Alexandra Christodoulou, 1 Amanda L. Christie, 1 Johannes Köster, 1, 2 Tiffany A. DeSouza, 1 Elizabeth A. Morgan, 3 Scott P. Kallgren, 4 Huiyun Liu, 1 Shuo-Chieh Wu, 1 Olivia Plana, 1 Joan Montero, 1 Kristen E. Stevenson, 5 Prakash Rao, 2 Raga Vadhi, 2 Michael Andreeff, 19 Philippe Armand, 1 Karen K. Ballen, 6 Patrizia Barzaghi-Rinaudo, 8 Sarah Cahill, 1 Rachael A. Clark, 7 Vesselina G. Cooke, 8 Matthew S. Davids, 1 Daniel J. DeAngelo, 1 David M. Dorfman, 3 Hilary Eaton, 9 Benjamin L. Ebert, 10 Julia Etchin, 21 Brant Firestone, 8 David C. Fisher, 1 Arnold S. Freedman, 1 Ilene A. Galinsky, 1 Hui Gao, 8 Jacqueline S. Garcia, 1 Francine Garnache-Ottou, 11 Timothy A. Graubert, 6 Alejandro Gutierrez, 12, 21 Ensar Halilovic, 8 Marian H. Harris, 27 Zachary T. Herbert, 13 Steven M. Horwitz, 14 Giorgio Inghirami, 15 Andrew M. Intlekoffer, 14 Moriko Ito, 8 Shai Izraeli, 16 Eric D. Jacobsen, 1 Caron A. Jacobson, 1 Sébastien Jeay, 8 Irmela Jeremias, 17 Michelle A. Kelliher, 18 Raphael Koch, 1 Marina Konopleva, 19 Nadja Kopp, 1 Steven M. Kornblau, 19 Andrew L. Kung, 20 Thomas S. Kupper, 7 Nicole LaBoeuf, 7 Ann S. LaCasce, 1 Emma Lees, 8 Loretta S. Li, 21 A. Thomas Look, 21 Masato Murakami, 8 Markus Muschen, 22 Donna Neuberg, 5 Samuel Y. Ng, 1 Oreofe O. Odejide, 1 Stuart H. Orkin, 21 Rachel R. Paquette, 25 Andrew E. Place, 21 Justine E. Roderick, 18 Jeremy A. Ryan, 1 Stephen E. Sallan, 21 Brent Shoji, 23 Lewis B. Silverman, 21 Robert J. Soiffer, 1 David P. Steensma, 1 Kimberly Stegmaier, 21 Richard M. Stone, 1 Jerome Tamburini, 24 Aaron R. Thorner, 25 Paul van Hummelen, 25 Martha Wadleigh, 1 Marion Wiesmann, 8 Andrew P. Weng, 26 Jens U. Wuerthner, 8 David A. Williams, 20 Bruce M. Wollison, 25 Andrew A. Lane, 1 Anthony Letai, 1 Monica M. Bertagnolli, 22 Jerome Ritz, 1 Myles Brown, 1, 2 Henry Long, 1, 2 Jon C. Aster, 3 Margaret A. Shipp, 1 James D. Griffin, 1 and David M. Weinstock1, 28The publisher’s final edited version of this article is available at Cancer CellSee other articles in PMC that cite the published mmaryOver 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, publically 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-naïve and relapsed/refractory troductionEssentially all oncology therapeutic candidates tested in humans have preclinical activity yet over 90% of these agents fail during clinical development (Kola and Landis, 2004; Ledford, 2011; Mak et al., 2014). This lamentable status quo results largely from a lack of efficacy in clinical trials. These trials, which generally evaluate agents in unselected patient populations with relapsed and refractory disease, are large, expensive and empiric. Thus, there is a desperate need for efficient and broadly applicable methods for preclinical assessment that have improved predictive value for human testing (Mak et al., 2014) cell lines have significant limitations in their ability to model the biology and therapeutic responsiveness of cancers in their native microenvironment (Abaan et al., 2013; Gillet et al., 2011; Hausser and Brenner, 2005). The diversity of cancer, based on extensive genomic and transcriptional studies, is remarkably underrepresented by the number of available cell lines. This is even more problematic for transgenic murine models, which exist for a very small number of genetically-defined cancer subtypes. For example, there are over 100 different diagnostic subtypes of hematologic malignancies alone and almost all of these encompass multiple distinct genetic entities based on the presence of well-defined chromosomal rearrangements, aneuploidies and/or single/oligonucleotide sequence alterations (Jaffe et al., 2008). Among the cell lines that do exist, adaptation to in vitro culture and passaging for hundreds or even thousands of generations exerts substantial selective pressure that is not reverted by subcutaneous or even orthotopic xenografting (Daniel et al., 2009; Gillet et al., 2011; Hausser and Brenner, 2005). Nearly all cell lines are derived from patients who were previously untreated and/or from sites (e. g. pleural effusions) that are very uncommonly involved by their tumor types. For these reasons, the available cell lines are not representative of either the genetic abnormalities or treatment status of most patient tumors that will receive treatment in early phase trials. Finally, trials of in vivo therapeutics performed by subcutaneous xenografting of cell lines into the mouse flank fail to capture microenvironmental interactions that can modulate therapeutic efficacy (Aparicio et al., 2015). Patient-derived xenografts (PDXs) established within highly immunocompromised mice overcome many of these shortcomings (Bertotti et al., 2011; Fichtner et al., 2008; Hidalgo et al., 2014; Julien et al., 2012; Reyal et al., 2012; Zhang et al., 2013). PDXs are passaged only in vivo and thereby avoid the selective pressures from ex vivo culture. They can be collected from patients with typical presentations of disease, either upfront or in the relapsed/refractory setting. Because engraftment rates are high for a variety of tumor types, very large repositories can be established to more broadly capture the range of human cancer. For example, a bank of over 1, 000 solid tumor PDXs (mostly treatment-naïve) was recently reported (Gao et al., 2015). Large therapeutic studies of small molecule inhibitors in these PDXs recapitulated population-based response frequencies that were observed in clinical trials. In addition, the synergy identified between IGF1R inhibitors and multiple agents in cell lines was not observed in PDXs (Gao et al., 2015), a proof-of-principle that in vivo studies with PDXs may challenge results from cell some settings, primary cancers can be orthotopically xenografted to recapitulate microenvironmental interactions within patients. The study by Gao et al. utilized subcutaneous flank xenografts of solid tumors, and as such, therapeutic efficacy was based on reduced growth or regression relative to vehicle-treated animals (Gao et al., 2015). In contrast, acute leukemias and other bone marrow-resident disorders readily undergo orthotopic engraftment after tail-vein or intra-osseous injection (Liem et al., 2004). As a result, therapeutic trials in mice engrafted with these diseases can utilize endpoints like overall survival or time to disease progression, just as in human trials. Mice can be treated until they progress on therapy, which allows for the development of acquired resistance. Samples can be taken from the peripheral blood or by sacrificing sentinel animals at multiple timepoints to establish biomarkers of response and resistance. Passaging in adequate numbers of animals also generates essentially unlimited numbers of primary cells for agnostic and targeted discovery central concern over PDXs is that they may fail to capture phenotypic, transcriptional, genetic and other characteristics of the tumors from which they were derived (Aparicio et al., 2015; Klco et al., 2014). Despite this concern, multiple entities now offer xenografting of patient tumors followed by in vivo drug testing, which they market as predictive of clinical response. We do not consider that each PDX is directly representative of the clinical sample from which it was derived. Instead, we hypothesize that using PDXs to conduct statistically powered, randomized trials in mice can efficiently define therapeutic activity across a broad range of genetically distinct tumor xenografts and inform the design and execution of human trials. At the same time, these preclinical trials can be used to establish biomarkers predictive of response, to generate models of drug resistance after in vivo exposure, and to interrogate aspects of in vivo biology, including tropism. In order to address this hypothesis, we aimed to establish a large repository of well-characterized PDXs of hematologic malignancies and implement these PDXs in comprehensive preclinical studies of candidate therapeutics in sultsEstablishment of Patient Derived XenograftsPrimary bone marrow and blood specimens from patients with leukemia and lymphoma were xenografted into
AML with MDS-related changes116 AML with inv(16) CBFB-MYH1110 AML with t(9;11) MLLT3-MLL11 AML with MLL rearrangement (details N/A)22 Therapy-related AML11 AML with recurrent gene mutations168
FLT3 ITD+/−TKD and NPM1 mutated96 FLT3 ITD only, NPM1 unknown21 FLT3 ITD only, NPM1 negative11 NPM1 mutated only30 CEBPA mutated only10 AML NOS1610
Acute myelomonocytic leukemia73 Acute monocytic leukemia11 No specific subcategory86 Blastic plasmacytoid dendritic cell neoplasm98 Subclassification pending annotation144
Myelodysplastic syndrome11
Refractory cytopenia with multilineage dysplasia11
Acute lymphoid leukemia14474
B-ALL NOS5328 B-ALL with t(12;21) TEL-AML1 (ETV6-RUNX1)173 B-ALL with t(9;22) BCR-ABL1166 B-ALL with t(v;11q23) MLL rearranged127 B-ALL with t(1;19) E2A-PBX1 (TCF3-PBX1)31 B-ALL with hypodiploidy61 B-ALL with hyperdiploidy40 T-ALL3228 Subclassification pending annotation10
Acute leukemia of ambiguous lineage43
Mixed phenotype acute leukemia with t(v;11q23) MLL rearranged22 Mixed phenotype acute leukemia with t(9;22) BCR-ABL110 B/myeloid acute leukemia11
Mature B-cell neoplasms1713
Mantle cell lymphoma64 DLBCL NOS44 Follicular lymphoma32 B-cell lymphoma, unclassifiable, with features intermediate between DLBCL and BL22 Extranodal marginal zone lymphoma11 Subclassification pending annotation10
Mature T- and NK-cell neoplasms97
Angioimmunoblastic T-cell lymphoma32 Anaplastic large cell lymphoma, ALK positive22 Anaplastic large cell lymphoma, ALK negative11 Adult T-cell leukemia/lymphoma11 Extranodal NK/T-cell lymphoma11 Primary cutaneous CD30 positive T-cell lymphoproliferative disorder10
Hodgkin Lymphoma10
Subclassification pending annotation10
All Diagnoses248138Multiple models demonstrated tropism within recipient mice that mimicked the clinical disease. For example, Sézary Syndrome is a form of cutaneous T-cell lymphoma with peripheral blood involvement. We injected peripheral blood from a patient with Sézary Syndrome into the tail-vein of NSG mice and the cells trafficked to and involved the mouse skin (Figure S1H), establishing a unique model of human lymphoma epidermotropism. Similarly, a diffuse large B-cell lymphoma (DLBCL) from a patient who did not have central nervous system (CNS) involvement engrafted under the renal capsule in P0. That tumor was dissociated into a single-cell suspension and injected by tail-vein into P1 mice. All 5 (100%) injected mice succumbed with CNS involvement by DLBCL (Figure S1I–K). Despite the difficulty of developing low-grade lymphoma models in vivo, a marginal zone lymphoma engrafted as a low-grade, CD20+ lymphoma after implantation under the renal capsule (Figure S1L) and a follicular lymphoma engrafted with small BCL2-positive, CD20-positive lymphocytes. Similarly, two different mantle cell lymphomas (MCLs) that involved blood and bone marrow in patients also involved the blood, bone marrow and spleen of xenografted mice (Figure S1M). Even more remarkably, MCL obtained from the blood of a patient with blood, nodal and gastrointestinal involvement trafficked to each of these sites after tail vein injection (Movie S1) early passage, some PDXs maintained human, non-malignant cell populations that were present in the original tumor. For example, the PTCL-NOS engrafted in P0 with populations of non-malignant CD4+ and CD8+ T-cells as well as a rare population of EBV-positive B-cells (Figure S1E). Some PTCLs harbor a small subset of EBV-positive B-cells (Jaffe et al., 2008) but the role of these cells in disease development and persistence has not been anscriptional, genetic and phenotype characterization of PDXs confirms subtype fidelityWe confirmed lineage fidelity in 157 PDXs by flow cytometry for markers including human CD3 (T cell), CD19 (B cell), CD33 (early myeloid), CD34 (hematopoietic progenitor) and CD45 (pan-hematopoietic). Whole transcriptome sequencing (RNAseq) was performed on 107 PDXs in addition to targeted DNA sequencing of all exons of 205 hematologic malignancy-associated genes (Odejide et al., 2014) (Table S1, S2). Analysis of RNAseq data was implemented as a Snakemake (Koster and Rahmann, 2012) workflow. To permit similar analyses by other users, the complete workflow documenting all utilized parameters and tools is available at:. Unsupervised hierarchical clustering based on disease type (AML, T-ALL, B-ALL, BPDCN, or lymphoma (Figure 1, Table S3) correctly clustered 105 (98%) of the 107 PDXs. A broad diversity of genetic alterations is captured within the PDXs, including targetable lesions (e. mutations of IDH1, IDH2 or JAK2, rearrangements involving MLL or ABL). Integrative analysis of leukemia and lymphoma PDXs(A) Unsupervised hierarchical clustering over expression of 1000 genes with the greatest variance-to-mean ratios among 107 PDXs representing all WHO diagnostic categories encompassed by our repository. (B) Key clinical characteristics of patients and their tumors from which PDXs were derived. Patient age in years reflects the time when the xenografted tumor specimen was obtained. Phases of treatment are defined as: Untreated, prior to therapy directed at the xenografted tumor (n. b., does not include therapy directed at prior malignancies); Primary refractory, failed to respond to all tumor-directed therapy to date; Relapse, recurred by standard disease-specific criteria after achievement of a complete remission; Refractory, disease that is progressing during or shortly after the administration of salvage therapy for relapsed disease; Progression, specific to lymphomas progressing by clinical or radiographic criteria after a period of stable disease or partial remission. (C) Binary matrix of prior therapies to which patient was exposed prior to sampling of the referenced tumor, if known. (D) Selected cytogenetic features of patient tumors from which PDXs were derived. (E) OncoPrint of selected mutations detected in PDXs by targeted exon sequencing of a panel of 205 genes (Odejide et al., 2014). See also Figure S2 and Tables S1– B-ALL PDXs, interrogation of RNAseq data for gene fusions rediscovered 28 (96. 6%) of 29 fusions identified by clinical cytogenetics analysis of the patient samples, including BCR-ABL1, MLL, ETV6-AML1, and TCF3-PBX1 rearrangements (Figure S2A). In addition, previously unrecognized fusions involving PAX5, JAK2, KDM6A, RUNX1, CRLF2 and SPI1 were recovered from the PDXs, with fusion transcripts from both derivative chromosomes identified in multiple PDXs (Figure S2A). Interrogation of the transcriptomes from AML PDXs similarly identified both known and unrecognized fusions (Figure S2B), we performed unsupervised clustering of transcriptomes from B-ALL PDXs in combination with transcriptomes of primary B-ALLs and B-ALL cell lines from the Cancer Cell Line Encyclopedia (Figure 2, Table S4) (Barretina et al., 2012). To permit analysis of the aggregated data, quantile normalization (Limma 3. 26. 1 (Ritchie et al., 2015)) was first performed to adjust for library depth and platform-specific differences. Principle components analysis revealed that the primary source of post-normalization variation derived from batch effects. These batch effects within each general diagnostic category (AML, B-ALL, T-ALL, lymphoma) were successfully removed using the ComBat approach from SVA 3. 18. 00 (Leek and Storey, 2007) agnostic of additional clinical and molecular covariates. Hierarchical clustering with Ward’s linkage and Euclidean distance over the 1000 genes with the greatest variance-to-mean ratio was performed over all diagnostic categories (PDX dataset only) and for each diagnostic category separately (all three datasets) (see Supplemental Experimental Procedures for details). Integrative analysis of B-ALL PDXs, primary samples and cell lines(A) Unsupervised hierarchical clustering of RNA expression profiles among 60 B-ALL PDXs (PDX cohort), 19 primary pre-B ALL samples (SRP058414), and 10 B-cell leukemia cell lines (CCLE), using the same methods as for Figure 1. (B) Key clinical characteristics of cell lines and primary samples, including those from which PDXs were derived. (D) Selected cytogenetic features of cell lines and primary samples, including those from which PDXs were derived. (E) OncoPrint of selected mutations detected in PDXs by targeted exon sequencing, or reported in cell lines by ATCC or DSMZ. Mutation data for the primary samples (SRP058414) were not available. See also Table mpared with cell lines, PDXs spanned a broader diversity of WHO diagnostic categories, patient demographic characteristics, phases of treatment, prior therapies, cytogenetic profiles, and genotypes (Figure 2). At present, there are 28 B-cell leukemia cell lines available through the German cell line repository DSMZ, of which only 10 have RNA expression data available through the CCLE. In contrast, we have generated 115 B-ALL PDX lines, of which 82 have RNA expression data already available. Unsupervised clustering underscored the biologic influence of canonical fusion genes such as those involving MLL, BCR-ABL1, TEL-AML1, and E2A-PBX1. Although fewer cases were analyzed, separate clustering of AML, T-ALL and lymphomas identified distinct clades in each disease (Figures S2C–E). Lymphoma samples segregated by cell lineage (B vs. T), with canonical fusion genes correlating with a number of derivative clades, including CCND1-IGH+ MCL, NPM1-ALK+ ALCL, and DLBCLs with concurrent rearrangements of MYC and BCL2 (Figure S2E) A resource to facilitate studies of leukemia and lymphoma PDXsTo massively expand the widespread use of these models and the data derived from them, we established an open source web portal called the Public Repository of Xenografts (PRoXe;). We created PRoXe using Shiny, a web application library for R that permits the fluid integration and analysis of heterogeneous data types in a graphical user interface. All underlying code for PRoXe is freely available on GitHub () provides extensive patient, tumor, PDX, and germline administrative information for each line. Any of the 56 characteristics that are categorical or quantitative can be visualized interactively via histograms, barplots, scatterplots, and boxplots, allowing the user to dynamically interrogate individual variables as well as interactions between these variables. Flow cytometry plots, full-color immunohistochemistry images, detailed pathology reports, and class I HLA alleles inferred from RNAseq (Shukla et al., 2015) are available for a subset of lines. RNA expression in the form of log-transformed RPKM derived from RNAseq can be visualized as bar plots or heat maps for individual genes or panels of genes for selected PDX lines. Curated mutations among panels of genes identified by targeted exon sequencing can be visualized in matrix form using a publically available OncoPrint script (). RPKMs and mutation calls are also being uploaded to the cBio portal (). Randomized phase II-like trial in PDXsWe tested the utility of our large PDX repository by designing and performing a phase II-like preclinical trial following the same approaches utilized in human trials. Small molecule inhibitors that disrupt the MDM2-p53 interaction have proven effective in a subset of tumor models and patient tumors with wild-type p53 (Andreeff et al., 2015; Jeay et al., 2015; Lv et al., 2015; Weisberg et al., 2015). In contrast, tumors with pathogenic TP53 mutations or biallelic deletions are typically resistant to MDM2 inhibition. The MDM2 inhibitor CGM097 is currently in phase I clinical trials for patients with solid tumors that have wild-type TP53 (Jeay et al., 2015; Weisberg et al., 2015). Approximately 85% of B-ALLs lack TP53 alterations (Pui et al., 2004), suggesting that this agent may have broad activity across a heterogeneous panel of significant benefit to performing trials in PDXs (compared with human trials) is that each xenograft can be injected into multiple animals, which allows for direct comparison between drug and control in the same cancer. To test the reproducibility of using only one vehicle-treated and one CMG097-treated mouse per PDX, we performed a pilot study on eight PDXs, in which six mice were injected with each PDX. Of the six, three mice were randomized to receive vehicle and three to receive CGM097 100 mg/kg by oral gavage beginning upon engraftment (defined as ≥2% peripheral blood hCD45+ cells). One of the three from each cohort was randomly selected prior to beginning therapy and considered a distinct cohort. Survival from the 3 vs. 3 study and the 1 vs. 1 study showed outstanding correlation for both drug-treated and vehicle-treated arms (Figure 3A). This strongly supports a 1 mouse/cohort study design in B-ALL MDM2 inhibitor CGM097 extends survival of mice engrafted with TP53-wild-type B-ALL PDXs in a randomized phase II-like trial(A) Kaplan-Meier survival analysis for overall survival of mice engrafted with 8 B-ALL PDXs, with 3 animals per treatment arm or 1 randomly selected animal from each arm. (B) Kaplan-Meier survival analysis for overall survival of vehicle- and CGM097 treated cohorts of mice engrafted with TP53 wild-type (n=20) and TP53 mutant (n=4) B-ALL PDXs. (C) Time to sacrifice after start of treatment for vehicle and CGM097 treated animals for each PDX. (D) Kaplan-Meier analysis of PDXs derived from patients who had relapsed or progressed on any form of therapy (relapsed/progressed) versus patients who had not received treatment (untreated). See also Figure primary pre-defined endpoint of the full phase II-like study was overall survival in animals treated with CGM097 daily compared to animals treated with vehicle. The trial was designed to detect a 40% difference in survival at 50 days of treatment assuming 60% alive in the treated group versus 20% alive in the untreated group using a one-sided Fisher exact test at the 0. 05 level with 84% power. These estimates were based on the 8 PDX pilot study (Figure 3A). Pre-defined subset analyses included comparisons of median survival between vehicle and CGM097 treatment of leukemias with wild-type TP53 from patients who are: age <18 years old, age ≥18 years old, male sex, female sex, untreated for B-ALL, or have relapsed/refractory disease after injected 29 PDXs into four mice each and monitored animals weekly for engraftment (Figure S3A). Two PDXs were not included in the final analysis due to failure to engraft, one PDX was excluded because of death of one animal prior to engraftment, and two additional PDXs were censored from the survival analysis due to failure of the vehicle-treated mouse to meet criteria for sacrifice by day +120 of treatment (Figure S3A). Thus, the final cohort included 24 different PDXs, including 4 that harbored TP53 mutations (Table 2). Engraftment across the four mice for each PDX was very similar (Figure S3B). Upon engraftment, two mice from each PDX were randomly selected to receive vehicle and two mice received CGM097. After 26 hours (two hours after the second dose), one animal from each cohort was sacrificed to assess pharmacodynamic (PD) markers and the remaining two animals were followed until they met criteria for 2Patient characteristics and survival data of PDXs in CGM097 survival tient characteristicsSurvival Data
PDXTP53 genotypeSex (m/f)Age (years)WHOPhase of TreatmentVehicle (days)CGM097 (days)86438WTf48MLLrPersistent, post-induction108329894WTf5NOSUntreated1911188779WTf5NOSUntreated137587017WTf8NOSRelapsed83647294WTm50NOSRelapsed, post-allogeneic transplant208055918WT1MLLrUntreated5515679275WTf12NOSUntreated247391898WTf83NOSUntreated215382812WTf5NOSUntreated4410813601WTf59Ph+Relapsed, post-allogeneic transplant286562876WTm36Ph+Relapsed112593213WT0. 3MLLrUntreated326744464WTm3hypodiploidUntreated417770486WTm15NOSRelapsed7011097113WTf54MLLrUntreated375673571WTm26NOSUntreated375666654WTm30MLLrUntreated8813260588WTf12NOSRelapsed283974952WTf44Ph+Untreated354734953WTf20B/myeloidUntreated868688178N239Dm15MLLrUntreated242456198Y220Cm71hypodiploidUntreated907656336D61fsm13hypodiploidUntreated201697613C275Gf2hypodiploidUntreated272797626WTm6NOSUntreated>120>12044038WTm59NOSUntreated>120>120CGM097 extends survival across a diverse panel of B-ALL PDXs with wild-type TP53CGM097 conferred no survival benefit in the 4 PDXs with TP53 mutations (Figure 3B–C, Table 2). In contrast, CGM097 conferred a survival benefit of ≥10 days in 19 of 20 PDXs with wild-type TP53 (Figure 3C). For the one exception (PDX 34953), both animals required sacrifice at day 86 of treatment due to dermatitis and lethargy but neither had splenomegaly or significant numbers of B-ALL cells upon necropsy, suggesting that mortality was unrelated to leukemia progression. The pre-defined primary endpoint (overall survival at day 50 of treatment) was statistically in favor of CGM097 treatment (80% versus 20% among the 20 TP53 wild-type PDXs; p=0. 0004 by two-sided Fisher’s exact test). The difference remained statistically significant (p=0. 0012) if all 24 PDXs were the 20 PDXs with wild-type TP53, median survival was improved by a median value of 44 days in the CGM097 cohort (Figure 3B). The gradual downward slope of the survival curve for the 20 mice treated with vehicle demonstrates the heterogeneity in disease progression across the B-ALL PDXs. Similarly, the gradual separation between the vehicle-treated and CGM097-treated curves for the 20 PDXs demonstrates heterogeneity in the extent of disease control by CGM097. This data is also captured in a “randomized Swimmer’s plot” in Figure 3C, which further shows the broad variability in both absolute and relative survival benefit conferred by CGM097 treatment across subset analyses demonstrated statistical superiority for the CGM097 treatment arm in 5 of 6 cohorts with wild-type TP53: age <18 years old, male sex, female sex, (Figure S3C) previously untreated, and relapsed/refractory (Figure 3D). In fact, treatment with CGM097 improved survival of mice engrafted with PDXs from relapsed/refractory B-ALL by 45 days (65 vs. 20 days for vehicle) (Figure 3D). This provides strong preclinical evidence for testing CGM097 in patients who have received extensive treatment for B-ALL but maintain wild-type TP53, including studies allow for the development of pharmacodynamic endpoints and biomarkers predictive of responseTo assess transcriptional correlates of CGM097 activity, we designed a custom NanoString panel that included p53 targets and modulators of p53 activity (Table S5). We applied this assay to purified PDX cells obtained from mice treated with either vehicle or CGM097 at both the PD and survival (SUR) timepoints. Nine genes were differentially expressed in CGM097-treated PDXs with wild-type TP53 at the PD timepoint (≥1. 2-fold over vehicle-treated, false discovery rate (FDR) adjusted p value <0. 05) (Table S6). In addition, all vehicle-treated and CGM097-treated samples at both timepoints were subjected to agglomerative clustering on expression of a curated set of p53 target genes (Figure S4A) (GenePattern) (Riley et al., 2008). Visual inspection of the resulting heatmap revealed a cluster of genes strongly expressed in 10 CGM097-treated PD samples, 3 CGM097-treated SUR samples and 1 vehicle-treated SUR sample. The genes in this cluster included GADD45A, BBC3, MDM2, ACTA2, FAS, BAX, DDB2, CDKN1A, FDXR, TNFRSF10B, PRKAB1, CCNG1, SESN1 RPS27L and RRM2B. This p53 target cluster included 7 of 9 genes identified solely by differential expression analysis in PD samples, as well as 10 of 13 genes identified in a signature that predicted response to CGM097 in cell lines and solid tumor xenografts (Jeay et al., 2015). Combining the two analyses generated an 18 gene set of CGM097-dependent p53 target genes (Figure S4B, Figure 4A). The 18 genes included all 7 genes (BAX, BBC3, CDKN1A, FDXR, MDM2, TNFRSF10B and ZMAT3) that were previously reported to increase expression in patient tumors after treatment with the MDM2 inhibitor RG7112 (Andreeff et al., 2015). This overlap is remarkable, as the previous signatures (Andreeff et al., 2015; Jeay et al., 2015) were established with untreated specimens (i. e. as predictive biomarkers) while our gene set was established on treated leukemias (i. as a pharmacodynamic biomarker). As expected, expression of the 18 gene set did not differ between vehicle- and CGM097-treated samples from PDXs with TP53 mutations (Figure 4A). Transcriptional, protein-based and functional biomarkers for response and resistance to CGM097 in B-ALL PDXs(A) Agglomerative clustering by gene expression of CGM097-induced genes in each treatment group. Samples from censored animals indicated by ^. PDX samples with TP53 mutations are indicated by *. Samples used for immunoblotting in Figure 4F are indicated with lower-case letters (a–g). (B) Immunoblotting performed on purified splenic B-ALL cells at the 26 hour timepoint. Transcript levels of CDKN1A (encodes p21) were determined on the same samples. (C) Immunohistochemistry for p53 and p21 in vehicle and CGM097-treated spleens collected on day 6 of treatment. Scale bars depict 0. 1mm. (D) Δ% priming in 26 hour vehicle- and CGM097-treated groups was determined by dynamic BH3 profiling using a PUMA BH3 peptide in 11 PDXs. The 4 “non-responder” models harbor TP53 mutations and had no improvement in survival between CGM097- and vehicle-treated mice. Error bars represent standard error of the mean (S. E. M. ) (E) R. O. C. curve analysis for dynamic BH3 profiling using the PUMA BH3 peptide. (F) Immunoblotting for p53 and p21 in CGM097- and vehicle-treated splenic B-ALL cells collected at the SUR timepoint. See also Figure S4, Tables S5–duction of both p53 and p21 protein expression by CGM097 in TP53 wild-type PDXs was confirmed by immunoblotting and IHC in spleen (Figure 4B–C). Expression levels of p21 protein closely paralleled the normalized levels of CDKN1A (which encodes p21) transcript (Figure 4B) Letai laboratory previously demonstrated that dynamic BH3 profiling can predict the response of tumor cells to both chemotherapy and targeted agents in vitro and in vivo (Montero et al., 2015). To determine the accuracy of dynamic BH3 profiling as a predi
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