Abcg2 Overexpression Represents a Novel Mechanism for Acquired Resistance to the Multi-Kinase Inhibitor Danusertib in BCR-ABL-Positive Cells In Vitro

The success of Imatinib (IM) therapy in chronic myeloid leukemia (CML) is compromised by the development of IM resistance and by a limited IM effect on hematopoietic stem cells. Danusertib (formerly PHA-739358) is a potent pan-aurora and ABL kinase inhibitor with activity against known BCR-ABL mutations, including T315I. Here, the individual contribution of both signaling pathways to the therapeutic effect of Danusertib as well as mechanisms underlying the development of resistance and, as a consequence, strategies to overcome resistance to Danusertib were investigated. Starting at low concentrations, a dose-dependent inhibition of BCR-ABL activity was observed, whereas inhibition of aurora kinase activity required higher concentrations, pointing to a therapeutic window between the two effects. Interestingly, the emergence of resistant clones during Danusertib exposure in vitro occurred considerably less frequently than with comparable concentrations of IM. In addition, Danusertib-resistant clones had no mutations in BCR-ABL or aurora kinase domains and remained IM-sensitive. Overexpression of Abcg2 efflux transporter was identified and functionally validated as the predominant mechanism of acquired Danusertib resistance in vitro. Finally, the combined treatment with IM and Danusertib significantly reduced the emergence of drug resistance in vitro, raising hope that this drug combination may also achieve more durable disease control in vivo

Sensitivity to Entrectinib Associated with a Novel LMNA-NTRK1 Gene Fusion in Metastatic Colorectal Cancer

In metastatic colorectal cancer (CRC), actionable genetic lesions represent potential clinical opportunities. NTRK1, 2, and 3 gene rearrangements encode oncogenic fusions of the tropomyosin-receptor kinase (TRK) family of receptor tyrosine kinases in different tumor types. The TPM3-NTRK1 rearrangement is a recurring event in CRC that renders tumors sensitive to TRKA kinase inhibitors in preclinical models. We identified abnormal expression of the TRKA protein in tumor and liver metastases of a CRC patient refractory to standard therapy. Molecular characterization unveiled a novel LMNA-NTRK1 rearrangement within chromosome 1 with oncogenic potential, and the patient was treated with the pan-TRK inhibitor entrectinib, achieving partial response with decrease in hepatic target lesions from 6.8 and 8.2cm in longest diameter to 4.7 and 4.3cm, respectively. To our knowledge, this is the first clinical evidence of efficacy for therapeutic inhibition of TRKA in a solid tumor, illuminating a genomic-driven strategy to identify CRCs reliant on this oncogene to be clinically targeted with entrectinib

Cross platform microarray analysis for robust identification of differentially expressed genes

<p>Abstract</p> <p>Background</p> <p>Microarrays have been widely used for the analysis of gene expression and several commercial platforms are available. The combined use of multiple platforms can overcome the inherent biases of each approach, and may represent an alternative that is complementary to RT-PCR for identification of the more robust changes in gene expression profiles.</p> <p>In this paper, we combined statistical and functional analysis for the cross platform validation of two oligonucleotide-based technologies, Affymetrix (AFFX) and Applied Biosystems (ABI), and for the identification of differentially expressed genes.</p> <p>Results</p> <p>In this study, we analysed differentially expressed genes after treatment of an ovarian carcinoma cell line with a cell cycle inhibitor. Treated versus control RNA was analysed for expression of 16425 genes represented on both platforms.</p> <p>We assessed reproducibility between replicates for each platform using CAT plots, and we found it high for both, with better scores for AFFX. We then applied integrative correlation analysis to assess reproducibility of gene expression patterns across studies, bypassing the need for normalizing expression measurements across platforms. We identified 930 genes as differentially expressed on AFFX and 908 on ABI, with ~80% common to both platforms. Despite the different absolute values, the range of intensities of the differentially expressed genes detected by each platform was similar. ABI showed a slightly higher dynamic range in FC values, which might be associated with its detection system. 62/66 genes identified as differentially expressed by Microarray were confirmed by RT-PCR.</p> <p>Conclusion</p> <p>In this study we present a cross-platform validation of two oligonucleotide-based technologies, AFFX and ABI. We found good reproducibility between replicates, and showed that both platforms can be used to select differentially expressed genes with substantial agreement. Pathway analysis of the affected functions identified themes well in agreement with those expected for a cell cycle inhibitor, suggesting that this procedure is appropriate to facilitate the identification of biologically relevant signatures associated with compound treatment. The high rate of confirmation found for both common and platform-specific genes suggests that the combination of platforms may overcome biases related to probe design and technical features, thereby accelerating the identification of trustworthy differentially expressed genes.</p

Strumenti bioinformatici a supporto dell'identificazione di bersagli molecolari per lo sviluppo di nuovi farmaci antitumorali: applicazioni alla famiglia delle chinasi

L'attività di ricerca descritta in questa tesi è stata condotta presso Nerviano Medical Sciences (NMS), un’azienda farmaceutica dedicata alla ricerca e allo sviluppo di farmaci antitumorali “mirati” o “target-therapy”. Con il termine terapia “mirata” si intende un farmaco in grado di bloccare la crescita del tumore interferendo con molecule specifiche (“target”) coinvolte nello sviluppo del tumore. In genere un “target” è una proteina espressa esclusivamente o preferenzialmente nelle cellule tumorali. Le chinasi, una famiglia di circa 500 enzimi con ruoli chiave in diverse funzioni cellulari, sono state trovate alterate in diversi tipi di tumore. Sono caratterizzate dalla presenza di una “tasca” altamente conservata in grado di legare l’ATP. Questa “tasca” può essere sfruttata per bloccare l’attività catalitica dell’enzima attraverso il legame con piccole molecole chimiche che spiazzano l’ATP. Nei tumori le chinasi sono attivate a seguito di mutazioni, overespressione o riarrangiamenti che risultano nella fusione del dominio catalitico della chinasi con un altro gene. Quest’ultimo gene, tipicamente espresso costitutivamente, è il responsabile dell’espressione anomala della chinasi in un tessuto dove normalmente non è presente. In questa tesi è descritta l’implementazione di un nuovo tool, KAOS (Kinase Automatic Outliers Search), sviluppato per identificare nuovi target chinasici. KAOS consente l’identificazione di chinasi con un profilo di espressione anomalo (“outlier”) se paragonato a quello osservato in altri campioni dello stesso tipo di tumore. Il software richiede come input dati di espressione genica ed utilizza l’espressione anormalmente alta di una chinasi come indicatore della presenza di un potenziale evento di fusione. In questa tesi è descritta inoltre l’implementazione di un sistema per la valutazione dell’espressione genica dell’intero chinoma umano, comprensivo sia una parte sperimentale, sia di un sistema dedicato all’analisi dei dati. La piattaforma, chiamata KING-REX (KINase Gene RNA EXpression), consente di analizzare la sola porzione di genoma relativa alle chinasi, con una riduzione di tempi di analisi, e di identificare potenziali eventi di fusione a carico delle chinasi. La piattaforma si basa su un approccio custom di RNAseq mirato, l’ Illumina TruSeq Targeted RNA expression kit (TREx). Il sequenziamento viene effettuato su sequenziatori Illumina di piccolo/media scala, richiedendo così risorse computazionali ridotte sia in termini di storage dei dati, sia di processamento. In parallelo allo sviluppo di nuovi farmaci ”mirati”, è importante l’implementazione di metodi di screening e di validazione che consentano la selezione dei pazienti, portatori della chinasi bersaglio del farmaco, da sottoporre a trattamento. Con le nuove tecnologie di sequenziamento, l’identificazione di specifiche fusioni geniche può beneficiare dell’altissima sensibilità degli approcci di RNAseq “mirati”, che vengono quindi sempre più proposti anche come metodi diagnostici. Uno di questi sistemi è l’Anchored Multiplex PCR (AMP) (ArcherDx, Inc.), un sistema basato sulla tecnologia NGS che consente l’identificazione di riarrangiamenti che coinvolgono una o poche chinasi, senza la necessità di conoscere il partner di fusione. In questa tesi, viene descritto l’utilizzo di questa tecnologia per l’analisi di campioni clinici di tumore colorettale (CRC) e l’identificazione di pazienti affetti da CRC portatori di due nuovi riarrangiamenti genici delle chinasi NTRK e ALK. Tumori con riarrangiamenti di NTRK e ALK sono responsivi al trattamento con entrectinib, un farmaco originariamente sviluppato presso NMS per colpire in modo specifico queste chinasi bersaglio.The research activity described in this thesis has been conducted at Nerviano Medical Sciences (NMS), a research-based company dedicated to the discovery and development of innovative target drugs for the treatment of cancer. A target therapy is a drug able to block cancer growth by interfering with specific molecules (target), involved in cancer development. Typically, a target is a protein specifically or at least preferentially expressed in cancer but not in normal cells. Kinases are a family of about 500 enzymes involved in several key cellular functions, which have been often found deregulated in cancer. They are characterized by a conserved ATP-binding pocket, which can be exploited for the binding of small molecules, blocking the catalytic activity of the enzyme, thus representing ideal targets for drug development. Kinases in tumors are activated by gene mutations or by overexpression, as a consequence of copy number alteration or more complex genomic rearrangements, like gene fusions, which are rare events resulting in the overexpression of the driver kinase. In this thesis, in order to identify potential new targets for the development of novel drugs, the implementation of a tool, called KAOS (Kinase Automatic Outliers Search) is described. KAOS was specifically developed for the identification of kinases showing an outlier gene expression profile, when compared to other samples from the same tumor subtype. The tool requires in input gene expression data and uses the anomalous overexpression of a kinase as readout of the presence of a gene fusion event. In addition, the implementation of a comprehensive whole kinome expression screening, called KING-REX (KINase Gene RNA EXpression), is also described, enabling the analysis of the kinase only portion of the transcriptome, with reduced time and costs. The platform allows investigating kinase expression and identifying potential gene fusion events using a customized Illumina RNAseq targeted NGS approach (Illumina TruSeq Targeted RNA expression kit, TREx), together with an ad hoc analysis pipeline. KING-REX has been conceived for the profiling of the human “kinome” on small/medium scale Illumina sequencers, requiring reduced computational resources in terms of storage space and data processing, thus representing a rapid and cost effective kinome investigation tool in the field of kinase target identification, for applications in cancer biology. In parallel, with the development of new drugs targeting specific kinase rearrangements, it is important the development of screening and validation methods, allowing the selection of the patient population harboring a specific driver gene, for treatment prescription. With the advent of Next Generation Sequencing (NGS), the detection of specific gene fusions can benefit from the high sensitivity of target-RNAseq approaches and has been proposed as diagnostic platforms. One of these methods is the Anchored Multiplex PCR (AMP) (ArcherDx, Inc.), an NGS-based system allowing the detection of rearrangements for a selected number of kinases, without requiring the knowledge of the rearrangement partner. In this thesis the use of AMP technology is described for the analysis of colorectal cancer (CRC) clinical specimens. The use of this test allowed the identification of patients harboring novel rearrangements of the kinases NTRK and ALK in CRC patients, responsive to the treatment with entrectinib, a drug initially developed at NMS specifically targeting these kinases

Additional file 1: of KAOS: a new automated computational method for the identification of overexpressed genes

Protein expression of ZAP70 in DU4475 breast cancer cell line. Characterization by Western Blot analysis of ZAP-70 protein. Total cell lysated were subjected to Western Blot analysis using anti-ZAP70 (sc-1526) goat polyclonal antibody raised against a peptide mapping at the C-terminus of ZAP-70. 1) DU4475 (20 ng); 2) MCF7 (20 ng); 3) HisGST-ZAP70 recombinant protein (15 ng), positive control. (PNG 68 kb

Cross platform microarray analysis for robust identification of differentially expressed genes-2

<p><b>Copyright information:</b></p><p>Taken from "Cross platform microarray analysis for robust identification of differentially expressed genes"</p><p>http://www.biomedcentral.com/1471-2105/8/S1/S5</p><p>BMC Bioinformatics 2007;8(Suppl 1):S5-S5.</p><p>Published online 8 Mar 2007</p><p>PMCID:PMC1885857.</p><p></p>xpressed genes

Cross platform microarray analysis for robust identification of differentially expressed genes-0

<p><b>Copyright information:</b></p><p>Taken from "Cross platform microarray analysis for robust identification of differentially expressed genes"</p><p>http://www.biomedcentral.com/1471-2105/8/S1/S5</p><p>BMC Bioinformatics 2007;8(Suppl 1):S5-S5.</p><p>Published online 8 Mar 2007</p><p>PMCID:PMC1885857.</p><p></p>BrdU was added 30 min before harvesting and samples were processed for cell cycle analysis and BrdU incorporation analysis

Cross platform microarray analysis for robust identification of differentially expressed genes-4

<p><b>Copyright information:</b></p><p>Taken from "Cross platform microarray analysis for robust identification of differentially expressed genes"</p><p>http://www.biomedcentral.com/1471-2105/8/S1/S5</p><p>BMC Bioinformatics 2007;8(Suppl 1):S5-S5.</p><p>Published online 8 Mar 2007</p><p>PMCID:PMC1885857.</p><p></p>s passing SAM statistical validation only in ABI, C) Set of 204 genes passing SAM statistical validation only in AFFX. AFFX data are reported in red, ABI in black. The lack of statistical significance in one of the platforms (B, C) is also associated with a limited log(Fold Change) variation. On the other hand, log(Fold Change) variation for genes passing the statistical validation in both platforms (A) is quite similar, although ABI seems to have wider log(Fold Change) dynamic range

Cross platform microarray analysis for robust identification of differentially expressed genes-3

<p><b>Copyright information:</b></p><p>Taken from "Cross platform microarray analysis for robust identification of differentially expressed genes"</p><p>http://www.biomedcentral.com/1471-2105/8/S1/S5</p><p>BMC Bioinformatics 2007;8(Suppl 1):S5-S5.</p><p>Published online 8 Mar 2007</p><p>PMCID:PMC1885857.</p><p></p>greement. The Correspondence at the top was evaluated for AFFX and ABI using the full set of genes after the IQR filtering step (black line) or after the IC filtering step (red line). For each of the two platforms averag

Cross platform microarray analysis for robust identification of differentially expressed genes-1

<p><b>Copyright information:</b></p><p>Taken from "Cross platform microarray analysis for robust identification of differentially expressed genes"</p><p>http://www.biomedcentral.com/1471-2105/8/S1/S5</p><p>BMC Bioinformatics 2007;8(Suppl 1):S5-S5.</p><p>Published online 8 Mar 2007</p><p>PMCID:PMC1885857.</p><p></p>ray precision within each microarray platform for the three replicates. CAT Plots describe the proportion of genes in common between replicates as function of list size. To generate CAT Plots on treated samples we used the lists of genes ranked b