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Prostate cancer: A personalised approach through the development of patient-derived xenografts
EACR24 Poster Sessions / European Journal of Cancer 61, Suppl. 1 (2016) S9–S218 using the Ventana automated system and automated image analysis with Definiens software. Results: 1217 men diagnosed with prostate cancer between 1994 and 2008 were identified and included in the database. They had a median age of 71.5 years (range 45 to 99) at diagnosis. 507 (41.7%) had Gleason score 6 or less, 288 (23.7%) Gleason 7, 304 (25%) Gleason 8−10, and 118 (9.7%) Gleason score unknown. 254 (20.9%) were T1 at diagnosis, 395 (32.5%) stage T2, 357 (29.3%) T3, 181 (14.9%) T4, and 30 (2.5%) Tx. 249 (20.5%) were N0, 57 (4.7%) N1, and 877 (72.1%) Nx at diagnosis. 453 (37.2%) were M0, 145 (11.9%) M1, and 498 (40.9%) Mx at diagnosis. Using the model TMA patients high/medium EphA2 score correlated with a lower overall survival (median overall survival of 87 months vs. 136 months; p = 0.0024). EphA2 expression correlated with Gleason score related overall survival; p 0.0001. Interestingly patients with Gleason score 6 with a high/medium EphA2 score have a median overall survival of 72 months, similar to low EphA2 expressing Gleason score 8 patients (73 months). Validation of these findings with the test TMA will be discussed. Conclusions: EphA2 has potential as a prognostic marker in prostate cancer and it is able to differentiate between good and poor outcomes in patients with a low Gleason score. No conflict of interest. 809 Prostate cancer: A personalised approach through the development of patient-derived xenografts A. Cannistraci1 , M. Parry1 , M. Smith1 , V. Ramani2 , M. Lau2 , J. Shanks3 , N. Daisuke3 , N. Clarke4 , N. Dhomen1 , E. Baena5 , R. Marais1 . 1 Cancer Research UK Manchester Institute, Molecular Oncology, Manchester, United Kingdom, 2 The Christie NHS Foundation Trust, Urology − Surgery, Manchester, United Kingdom, 3 The Christie NHS Foundation Trust, Histopathology, Manchester, United Kingdom, 4 The Christie NHS Foundation Trust, Urology, Manchester, United Kingdom, 5 Cancer Research UK Manchester Institute, Prostate Oncobiology, Manchester, United Kingdom Background: Prostate Cancer (PCa) is a complex disease that affects more than 1 million men worldwide. Recent efforts in characterising the PCa genomic landscape resulted in the generation of a large volume of data, which, unfortunately, did not single out specific molecular profiles that could stratify patients based on their histopathological characteristics and risk of progression. Tumour heterogeneity and paucity of reliable experimental models, such as cell lines and genetic-engineered mouse models, are the two major obstacles that hamper further progress in the field. In this study, we aim to generate patient-derived xenografts (PDXs) using multiple tumour cores from PCa patients who undergo radical prostatectomy. Tumorigenic capabilities of these samples (tumour growth rate, latency) will be tested in vivo, as well as their responsiveness to systemic therapies. Parallel genomic analysis will be performed to identify prognostic biomarkers for PCa progression and new druggable targets. Material and Method: Tissue sampling was performed as described by Warren in 2013. Briefly, radical prostatectomy specimens were transferred to the laboratory immediately after surgical resection. Leaving the margins intact, multiple punch biopsies were removed, and the sites were marked on a map diagram to allow retrospective pathological identification. Half of each core was frozen for DNA/RNA extraction, and the other half was implanted subcutaneously into immunocompromised mice. Results and Discussion: A total of 25 prostates have been processed. To maximise our chances of successfully generating PDX models, we selected 5 high-risk patients, according to the D’Amico classification, for implantation. To provide a functional microenvironment, we co-implanted each core with bonemarrow-derived mesenchymal stem cells. Hormonal stimuli were provided using both dihydrotestosterone (DHT)-releasing minipumps and DHT-food pellets. We observed tumour growth 1 month after the initial implantation. In particular for patients FM96 and FM97, we were able to detect palpable tumour masses derived from 6/8 and 5/10 cores respectively. Different tumour sizes suggest different growth capabilities however further analysis is ongoing. Conclusion: The “multicore” approach allows the functional analysis of tumour heterogeneity, which can be translated into clinical benefit for PCa patients. No conflict of interest. 810 An open source R package for Droplet Digital PCR analysis A. Chiu1 , G. Brady1 , M. Ayub1 , C. Dive1 , C. Miller2 . 1 CRUK Manchester Institute, Clinical and Experimental Pharmacology Group, Manchester, United Kingdom, 2 CRUK Manchester Institute, RNA Biology Group, Manchester, United Kingdom Droplet Digital PCR (ddPCR) is a technology used to quantify the number of nucleic acid molecules amplified by polymerase chain reactions. A major application of ddPCR is the detection of low abundance mutations within a population of DNA molecules making it particularly well suited to tumour characterisation using circulating free DNA extracted from patients’ peripheral blood. While traditional PCR performs one reaction per sample, ddPCR
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partitions the sample into 1000s of droplets in which individual qPCR reactions are carried out. By establishing the presence or absence of qPCR products in each droplet, the absolute number of targets of interest can be estimated. Although the ddPCR system from Bio-Rad includes analysis software, it is provided as a stand-alone implementation, making it harder to integrate into analysis pipelines, and difficult to develop alternative analysis techniques to those offered by the manufacturer. Here we present an alternative open source R package for mutation calling and quantitation that can be incorporated into the ddPCR workflow. This package integrates with the popular R/Bioconductor packages used widely within the biosciences community, allowing data to be more easily integrated into statistical analyses and incorporated into transparent and reproducible bioinformatics pipelines. Data from the Bio-Rad system was used as a starting point. Since the main feature of ddPCR is the partitioning of the sample into equal volume droplets, the distribution of molecules across droplets is expected to be Poisson distributed. We therefore use a Poisson model to capture this relationship, and from this, estimate the input number of target molecules. Data were then compared to the output of the Bio-Rad software. The new R package takes droplet data and computes a variety of metrics, including the number of mutant and wild type molecules present per ml or total sample input and their ratio. There was a high correlation with the output from the commercial software although the R package we present here appeared to show increased accuracy under certain conditions. Since R and Bioconductor are widely used by the bioinformatics community, the development of a ddPCR package enables researchers to incorporate data produced from ddPCR experiments into existing workflows. The openness of the package is important because complex pipelines may depend on ddPCR data and the understanding of each step of a calculation is paramount. With frameworks like Shiny, the ddPCR package will also be accessible to researchers with no knowledge of R. No conflict of interest. 811 PATZ1 is a new prognostic marker of diffuse large B cell lymphomas R. Franco1 , G. Scognamiglio2 , E. Valentino2 , M. Vitiello3 , L. Panico4 , A. Pinto5 , G. Botti2 , A. De Chiara2 , L. Cerchia3 , M. Fedele3 . 1 Seconda Universita` di Napoli, Pathology Unit, Napoli, Italy, 2 Istituto dei Tumori di Napoli- IRCCS- ‘Fondazione G. Pascale’, Pathology Unit, Napoli, Italy, 3 Istituto di Oncologia e Endocrinologia Sperimentale IEOS del CNR, Molecular Oncology, Napoli, Italy, 4 Ospedale ‘S.G. Moscati’, Pathology Unit, Avellino, Italy, 5 Istituto dei Tumori di Napoli- IRCCS- ‘Fondazione G. Pascale’, Haematology-Oncology and Stem Cell Transplantation Unit, Napoli, Italy Background: Diffuse Large B cell Lymphomas (DLBCLs) are among the most aggressive and frequently observed Non-Hodgkin lymphomas (NHLs). They are clinically and molecularly heterogeneous and have been further subdivided in three sub-types according to different cell of origin, mechanisms of oncogenesis and clinical outcome. Among them, the germinal center B-cell like (GCB) derives from the germinal center and expresses the BCL6 oncogene. The BTB/POZ domain transcription factor PATZ1 is a crucial negative regulator of BCL6 and Patz1-knockout mice develop B cell neoplasias expressing BCL6. Materials and Methods: The expression of PATZ1 and BCL6 has been studied by immunohistochemical staining of a Tissue-Micro-Array, including 70 Follicular lymphomas (FLs) and 100 DLBCLs. The Pearson’s chi2 test was used, where appropriate, to establish whether there were any relationships between the frequencies of PATZ1 expression in nuclear compartment and BCL6, histotypes and specific subtypes in DLBCL category. To assess the impact of PATZ1 expression on patients’ outcome, we analysed overall (OS) and progression-free (PFS) survival of DLBCL patients treated with Rituximab plus Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone (R-CHOP) chemotherapy. Kaplan–Meier survival curves were used to analyze OS and PFS. Statistical significance was assessed by the logrank test. Results and Discussion: We found that PATZ1 nuclear expression is significantly downregulated in FLs and DLBCLs, supporting a tumor suppressor role in these neoplasias. Moreover, consistent with previous results showing a direct downregulation of BCL6 transcription by PATZ1, we show that low PATZ1 nuclear expression significantly correlates with high BCL6 expression, mainly in DLBCLs. Finally, a significant correlation between low levels of PATZ1 and a worst outcome in DLBCLs patients treated with R-CHOP was observed. By analyzing the clinical and protein expression data available for the patients in which low expression of PATZ1 was correlated with a worse PFS following R-CHOP therapy, we found that most of them (84%) have high expression of BCL6 protein, supporting in these patients the negative regulation of BCL6 by PATZ1, but no evident correlation was observed with IPI code and cell of origin. These data suggest that, independently from some major known prognostic markers, such as IPI and cell of origin, PATZ1 expression could be evaluated as an additional marker to predict clinical outcome following R-CHOP therapy.
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partitions the sample into 1000s of droplets in which individual qPCR reactions are carried out. By establishing the presence or absence of qPCR products in each droplet, the absolute number of targets of interest can be estimated. Although the ddPCR system from Bio-Rad includes analysis software, it is provided as a stand-alone implementation, making it harder to integrate into analysis pipelines, and difficult to develop alternative analysis techniques to those offered by the manufacturer. Here we present an alternative open source R package for mutation calling and quantitation that can be incorporated into the ddPCR workflow. This package integrates with the popular R/Bioconductor packages used widely within the biosciences community, allowing data to be more easily integrated into statistical analyses and incorporated into transparent and reproducible bioinformatics pipelines. Data from the Bio-Rad system was used as a starting point. Since the main feature of ddPCR is the partitioning of the sample into equal volume droplets, the distribution of molecules across droplets is expected to be Poisson distributed. We therefore use a Poisson model to capture this relationship, and from this, estimate the input number of target molecules. Data were then compared to the output of the Bio-Rad software. The new R package takes droplet data and computes a variety of metrics, including the number of mutant and wild type molecules present per ml or total sample input and their ratio. There was a high correlation with the output from the commercial software although the R package we present here appeared to show increased accuracy under certain conditions. Since R and Bioconductor are widely used by the bioinformatics community, the development of a ddPCR package enables researchers to incorporate data produced from ddPCR experiments into existing workflows. The openness of the package is important because complex pipelines may depend on ddPCR data and the understanding of each step of a calculation is paramount. With frameworks like Shiny, the ddPCR package will also be accessible to researchers with no knowledge of R. No conflict of interest. 811 PATZ1 is a new prognostic marker of diffuse large B cell lymphomas R. Franco1 , G. Scognamiglio2 , E. Valentino2 , M. Vitiello3 , L. Panico4 , A. Pinto5 , G. Botti2 , A. De Chiara2 , L. Cerchia3 , M. Fedele3 . 1 Seconda Universita` di Napoli, Pathology Unit, Napoli, Italy, 2 Istituto dei Tumori di Napoli- IRCCS- ‘Fondazione G. Pascale’, Pathology Unit, Napoli, Italy, 3 Istituto di Oncologia e Endocrinologia Sperimentale IEOS del CNR, Molecular Oncology, Napoli, Italy, 4 Ospedale ‘S.G. Moscati’, Pathology Unit, Avellino, Italy, 5 Istituto dei Tumori di Napoli- IRCCS- ‘Fondazione G. Pascale’, Haematology-Oncology and Stem Cell Transplantation Unit, Napoli, Italy Background: Diffuse Large B cell Lymphomas (DLBCLs) are among the most aggressive and frequently observed Non-Hodgkin lymphomas (NHLs). They are clinically and molecularly heterogeneous and have been further subdivided in three sub-types according to different cell of origin, mechanisms of oncogenesis and clinical outcome. Among them, the germinal center B-cell like (GCB) derives from the germinal center and expresses the BCL6 oncogene. The BTB/POZ domain transcription factor PATZ1 is a crucial negative regulator of BCL6 and Patz1-knockout mice develop B cell neoplasias expressing BCL6. Materials and Methods: The expression of PATZ1 and BCL6 has been studied by immunohistochemical staining of a Tissue-Micro-Array, including 70 Follicular lymphomas (FLs) and 100 DLBCLs. The Pearson’s chi2 test was used, where appropriate, to establish whether there were any relationships between the frequencies of PATZ1 expression in nuclear compartment and BCL6, histotypes and specific subtypes in DLBCL category. To assess the impact of PATZ1 expression on patients’ outcome, we analysed overall (OS) and progression-free (PFS) survival of DLBCL patients treated with Rituximab plus Cyclophosphamide, Doxorubicin, Vincristine, and Prednisone (R-CHOP) chemotherapy. Kaplan–Meier survival curves were used to analyze OS and PFS. Statistical significance was assessed by the logrank test. Results and Discussion: We found that PATZ1 nuclear expression is significantly downregulated in FLs and DLBCLs, supporting a tumor suppressor role in these neoplasias. Moreover, consistent with previous results showing a direct downregulation of BCL6 transcription by PATZ1, we show that low PATZ1 nuclear expression significantly correlates with high BCL6 expression, mainly in DLBCLs. Finally, a significant correlation between low levels of PATZ1 and a worst outcome in DLBCLs patients treated with R-CHOP was observed. By analyzing the clinical and protein expression data available for the patients in which low expression of PATZ1 was correlated with a worse PFS following R-CHOP therapy, we found that most of them (84%) have high expression of BCL6 protein, supporting in these patients the negative regulation of BCL6 by PATZ1, but no evident correlation was observed with IPI code and cell of origin. These data suggest that, independently from some major known prognostic markers, such as IPI and cell of origin, PATZ1 expression could be evaluated as an additional marker to predict clinical outcome following R-CHOP therapy.