Yes / Background The HOX genes are a family of homeodomain-containing transcription factors that determine cellular identity during development and which are dys-regulated in some cancers. In this study we examined the expression and oncogenic function of HOX genes in mesothelioma, a cancer arising from the pleura or peritoneum which is associated with exposure to asbestos. Methods We tested the sensitivity of the mesothelioma-derived lines MSTO-211H, NCI-H28, NCI-H2052, and NCI-H226 to HXR9, a peptide antagonist of HOX protein binding to its PBX co-factor. Apoptosis was measured using a FACS-based assay with Annexin, and HOX gene expression profiles were established using RT-QPCR on RNA extracted from cell lines and primary mesotheliomas. The in vivo efficacy of HXR9 was tested in a mouse MSTO-211H flank tumor xenograft model. Results We show that HOX genes are significantly dysregulated in malignant mesothelioma. Targeting HOX genes with HXR9 caused apoptotic cell death in all of the mesothelioma-derived cell lines, and prevented the growth of mesothelioma tumors in a mouse xenograft model. Furthermore, the sensitivity of these lines to HXR9 correlated with the relative expression of HOX genes that have either an oncogenic or tumor suppressive function in cancer. The analysis of HOX expression in primary mesothelioma tumors indicated that these cells could also be sensitive to the disruption of HOX activity by HXR9, and that the expression of HOXB4 is strongly associated with overall survival. Conclusion HOX genes are a potential therapeutic target in mesothelioma, and HOXB4 expression correlates with overall survival. / The authors gratefully acknowledge the support of the British Lung Foundation, grant number ICAPPG10-1. KJH acknowledges support from the ICR/RM NIHR Biomedical Research Centre.

Thulani Nyathi, Student no: 0413256X, MSc thesis, Physics, Faculty of science. 2006. Supervisor: Prof D van der Merwe. / This study aims to calculate tumour control probabilities (TCP) and normal tissue complication probabilities (NTCP) using radiobiological models and correlate these probabilities with clinically observed treatment outcome from follow-up records. These radiobiological calculations were applied retrospectively to thirty-nine paediatric patients who were treated with radiation at Johannesburg Hospital during the period January 1990 to December 2000 and had histologically proven rhabdomyosarcoma. Computer software, BIOPLAN, was used to calculate the TCP and NTCP arising from the dose distribution calculated by the treatment planning system and characterized by dosevolume histograms (DVHs). There was a weak correlation between the calculated TCP and the observed 5-year overall survival status. Furthermore, potential prognostic factors for survival were examined. Statistical analysis was performed using the Cox proportional hazards regression model. The 5-year overall survival rate was 55 %. The findings of this study are a yardstick against which more aggressive radiotherapy fractionation regimes can be compared.

L'analyse de survie constitue bien souvent l'objectif principal des études cliniques en cancérologie. Les données de survie découlent d'un événement subi par les sujets de l'étude, événement qui correspond par exemple au décès pour la survie globale et à la progression tumorale pour la survie sans progression. Les méthodes non-paramétriques de Kaplan-Meier et semi-paramétriques de Cox représentent les modèles standards les plus utilisés pour modéliser ces données de survie, mais ne s'appliquent que dans le cas d'un seul événement temporel. La survie globale est considérée comme le critère clinique optimal pour juger de l'efficacité d'un traitement. La survie sans progression est un critère intermédiaire, qui représente un critère potentiel de substitution pour la survie globale. Depuis plusieurs années, un intérêt croissant s'est porté sur la validation statistique de critères intermédiaires. Cette validation passe par la mesure de la corrélation entre le critère clinique principal et le critère intermédiaire. Ainsi, une modélisation bivariée apparait intéressante afin de décrire la structure de dépendance entre les survies sans progression et globale. L'objectif de cette thèse concerne la modélisation de la structure d'association entre les survies sans progression et globale ainsi que la quantification de cette association via des mesures de dépendance. Pour cela, nous étudions en premier lieu les extensions du modèle de Cox qui peuvent traiter la dépendance statistique entre les données. Nous proposons ensuite une nouvelle modélisation paramétrique de la survie globale basée sur une distribution conditionnelle et sur les survies sans progression et post-progression. De plus, nous examinons différents modèles paramétriques de survie bivariée en termes de mesures de corrélation. Ces modèles sont fondés sur deux approches : les distributions marginales et l'indépendance conditionnelle. Enfin, nous appliquons et comparons les modèles étudiés en utilisant les données d'un essai clinique randomisé de phase III, impliquant des patients atteints de cancer du poumon non à petites cellules localement avancé. / Analysis of survival often represents the main aim in cancer clinical studies. Survival data arise from an event experienced by the study subjects. This event corresponds for example to the death for overall survival and to tumor progression for progression-free survival. The Kaplan-Meier nonparametric estimator and the Cox semiparametric model are the most used standard methods for modeling survival data, although they are applied only in the case of unique temporal event. Overall survival is the optimal clinical endpoint for assessing the efficiency of treatment. Progression-free survival is an intermediate endpoint considered as a potential surrogate of overall survival. For the past few years, we observed an increasing focus on statistical validation of intermediate endpoints and this through measurement of the correlation between the principal clinical endpoint and the intermediate one. Thus, bivariate modeling could be of interest for describing the dependence structure between progression-free survival and overall survival. The aim of this thesis is the modeling of the structure of association between progression-free survival and overall survival as well as the quantification of this association using dependence measures. For this, we study at first extensions of Cox model able to address the topic concerning the statistical dependence between data. Next, we propose a new parametric modeling of overall survival based on two survival times : progression-free survival and post-progression survival, assumed to be linked by a conditional distribution. Moreover, we examine different parametric models for bivariate survival data concerning correlation measurement. These models are based on the marginal distributions and the conditional independence. Finally, we apply and compare these models using data from a phase III randomized clinical trial, involving patients with locally advanced non-small cell lung cancer.

京都大学 / 0048 / 新制・課程博士 / 博士(医学) / 甲第22739号 / 医博第4657号 / 新制||医||1046(附属図書館) / 京都大学大学院医学研究科医学専攻 / (主査)教授 武藤 学, 教授 佐藤 俊哉, 教授 椛島 健治 / 学位規則第4条第1項該当 / Doctor of Medical Science / Kyoto University / DGAM

This thesis deals with a classification of glioma grade in high and low aggressive tumours and overall survival prediction based on magnetic resonance imaging. Data used in this work is from BRATS challenge 2019 and each set contains information from 4 weighting sequences of MRI. Thesis is implemented in PYTHON programming language and Jupyter Notebooks environment. Software PyRadiomics is used for calculation of image features. Goal of this work is to determine best tumour region and weighting sequence for calculation of image features and consequently select set of features that are the best ones for classification of tumour grade and survival prediction. Part of thesis is dedicated to survival prediction using set of statistical tests, specifically Cox regression

Background: Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor with poor clinical outcome. Identification and development of new markers could be beneficial for the diagnosis and prognosis of GBM patients. Deregulation of microRNAs (miRNAs or miRs) is involved in GBM. Therefore, we attempted to identify and develop specific miRNAs as prognostic and predictive markers for GBM patient survival.Methods: Expression profiles of miRNAs and genes and the corresponding clinical information of 480 GBM samples from The Cancer Genome Atlas (TCGA) dataset were downloaded and interested miRNAs were identified. Patients' overall survival (OS) and progression-free survival (PFS) associated with interested miRNAs and miRNA-interactions were performed by Kaplan-Meier survival analysis. The impacts of miRNA expressions and miRNA-interactions on survival were evaluated by Cox proportional hazard regression model. Biological processes and network of putative and validated targets of miRNAs were analyzed by bioinformatics.Results: In this study, 6 interested miRNAs were identified. Survival analysis showed that high levels of miR-326/miR-130a and low levels of miR-323/miR-329/miR-155/miR-210 were significantly associated with long OS of GBM patients, and also showed that high miR-326/miR-130a and low miR-155/miR-210 were related with extended PFS. Moreover, miRNA-323 and miRNA-329 were found to be increased in patients with no-recurrence or long time to progression (TTP). More notably, our analysis revealed miRNA-interactions were more specific and accurate to discriminate and predict OS and PFS. This interaction stratified OS and PFS related with different miRNA levels more detailed, and could obtain longer span of mean survival in comparison to that of one single miRNA. Moreover, miR-326, miR-130a, miR-155, miR-210 and 4 miRNA-interactions were confirmed for the first time as independent predictors for survival by Cox regression model together with clinicopathological factors: Age, Gender and Recurrence. Plus, the availability and rationality of the miRNA-interaction as predictors for survival were further supported by analysis of network, biological processes, KEGG pathway and correlation analysis with gene markers.Conclusions: Our results demonstrates that miR-326, miR-130a, miR-155, miR-210 and the 4 miRNA-interactions could serve as prognostic and predictive markers for survival of GBM patients, suggesting a potential application in improvement of prognostic tools and treatments.

Background: Glioblastoma multiforme (GBM) is the most common and aggressive brain tumor with poor clinical outcome. Identification and development of new markers could be beneficial for the diagnosis and prognosis of GBM patients. Deregulation of microRNAs (miRNAs or miRs) is involved in GBM. Therefore, we attempted to identify and develop specific miRNAs as prognostic and predictive markers for GBM patient survival.Methods: Expression profiles of miRNAs and genes and the corresponding clinical information of 480 GBM samples from The Cancer Genome Atlas (TCGA) dataset were downloaded and interested miRNAs were identified. Patients' overall survival (OS) and progression-free survival (PFS) associated with interested miRNAs and miRNA-interactions were performed by Kaplan-Meier survival analysis. The impacts of miRNA expressions and miRNA-interactions on survival were evaluated by Cox proportional hazard regression model. Biological processes and network of putative and validated targets of miRNAs were analyzed by bioinformatics.Results: In this study, 6 interested miRNAs were identified. Survival analysis showed that high levels of miR-326/miR-130a and low levels of miR-323/miR-329/miR-155/miR-210 were significantly associated with long OS of GBM patients, and also showed that high miR-326/miR-130a and low miR-155/miR-210 were related with extended PFS. Moreover, miRNA-323 and miRNA-329 were found to be increased in patients with no-recurrence or long time to progression (TTP). More notably, our analysis revealed miRNA-interactions were more specific and accurate to discriminate and predict OS and PFS. This interaction stratified OS and PFS related with different miRNA levels more detailed, and could obtain longer span of mean survival in comparison to that of one single miRNA. Moreover, miR-326, miR-130a, miR-155, miR-210 and 4 miRNA-interactions were confirmed for the first time as independent predictors for survival by Cox regression model together with clinicopathological factors: Age, Gender and Recurrence. Plus, the availability and rationality of the miRNA-interaction as predictors for survival were further supported by analysis of network, biological processes, KEGG pathway and correlation analysis with gene markers.Conclusions: Our results demonstrates that miR-326, miR-130a, miR-155, miR-210 and the 4 miRNA-interactions could serve as prognostic and predictive markers for survival of GBM patients, suggesting a potential application in improvement of prognostic tools and treatments.

No description available.

Introduction Aspirin has been associated with a reduction in mortality in patients diagnosed with colorectal cancer (CRC). A possible mechanism for this is related to the programmed cell death 1 (PD-1) immune checkpoint pathway. Aspirin may have a synergistic effect with PD-1 inhibitors via inhibition of prostaglandin E2 (PGE2) production, which can reverse the ability of tumor cells to evade the immune system. This appears to be strongest in cancers that express PI3 kinase (PI3K) signaling activity, which aspirin downregulates. However, the benefit of pre-diagnosis aspirin use on CRC overall survival (OS) and cancer-specific survival is still controversial, and most studies have been performed in racially homogenous populations. Our study examines the effect of pre-diagnosis aspirin therapy on OS in a racially diverse group of patients with CRC. Methods This is a retrospective chart review of 782 patients diagnosed with CRC from January 2007 to December 2020. Kaplan-Meier curve was created to study the association of aspirin exposure compared to no exposure on OS. In addition, univariate and multivariate binary logistic regression analyses were done to investigate potential predictors of survival. Results Of the 782 patients with CRC, 55.1% were males, 22.2% whites, 58.5% Asians, and 17.7% Pacific-Islanders. Moreover, 38.4% of the patients had a history of aspirin use, 79% of them used it for more than one year. There were more patients with hypertension (HTN), hyperlipidemia (HLD), diabetes mellitus (DM), and chronic kidney disease (CKD) among those with a history of aspirin use. There was no difference in one, three, and five-year OS among aspirin users compared to non-users, p-value = 0.63. Age, grade, and stage were potential predictors of worsened OS. However, treatment with chemotherapy and CKD were potential predictors of worsened OS on univariate analysis only. No significant association was noticed with gender, tumor location, or other associated comorbidities. Conclusion The effect of pre-diagnosis aspirin use on CRC survival is not clear. In this retrospective analysis of a racially diverse population of CRC patients, we found that aspirin use was not associated with improved OS. Therefore, physicians should be careful about using aspirin as adjuvant therapy in CRC patients until high-quality prospective data are available, given the potential associated complications.

No description available.