Sequential first-line therapy in metastatic colorectal cancer with Capecitabine/FUFA, Irinotecan and Bevacizumab
Ontology highlight
ABSTRACT:
DISEASE(S): Metastasiertes Kolorektales Karzinom,Metastatic Colorectal Cancer
PROVIDER: 2544319 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
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Project description:Purpose: Accurate prediction of clinical response is the prerequisite for individualized therapy in chronic lymphocytic leukemia (CLL). We hypothesized that sequential assessment of gene expression changes early during therapy may well reflect behaviour of the leukemic clone in response to specific drugs. Patients and Methods: Gene expression profiles (GEP) were determined in CD19+ selected B-cells from 20 patients treated with fludarabine and cyclophosphamide (FC) (N=10) or FC plus rituximab (FCR) (N=10). Samples were collected in the first cycle before and within 48hours after initiation of treatment. GEP analysis was stratified by clinical response 3 months after start of therapy. Results: GEP before treatment detected high expression of 34 genes correlated with response and 32 genes correlated with resistance to therapy. These genes were related to regulation of apoptosis, cell cycle, cell adhesion, and signal transduction. Different results were obtained with sequential GEP: Sixteen genes were up-regulated after rituximab infusion in non-responders. Rituximab therapy resulted in down-regulation of AKT1 indicating involvement of the PI3-kinase pathway in CD20-signaling. Up-regulation of 24 genes after FC (including ITPKB (inositol 1,4,5-trisphosphate 3-kinase) and CD44) and of 36 genes after FCR (including CD49d) was associated with resistance. Down-regulation of CTLA4 correlated with poor response to FC. CD44, CD49d and the PI3-kinase signaling pathway were confirmed as potential therapeutic targets to overcome resistance by (protein analysis) or functional experiments. Conclusion Sequential GEP provides rapid and relevant information for prediction of response and resistance. This approach could be used to guide and adapt individualized therapy in CLL. 50 Samples from CD19 selected B-cells, three treatments (rituximab, FC and RFC), 10 replicates for each treatment, 20 replicates for control condition
2011-03-01 | E-GEOD-15490 | biostudies-arrayexpress
Project description:In the present investigation, we have exploited the opportunity provided by neoadjuvant treatment of a group of postmenopausal women with large operable or locally advanced breast cancer (in which therapy is given with the primary tumour remaining within the breast) to take sequential biopsies of the same cancers before and after 10-14 days treatment with letrozole. RNA extracted from the biopsies has been subjected to Affymetrix microarray analysis and the data from paired biopsies interrogated to discover genes whose expression is most influenced by oestrogen deprivation. Experiment Overall Design: biopsies were taken from the same subjects both pretreatment and after 10-14 days Letrozol, 2.5 mg/day, oral
2008-06-13 | E-GEOD-5462 | biostudies-arrayexpress
Project description:Mixed immunotherapy and chemotherapy of tumors: feedback design and model updating schemes.
Chareyron S1, Alamir M.
Author information
Abstract
In this paper, a recently developed model governing the cancer growth on a cell population level with combination of immune and chemotherapy is used to develop a reactive (feedback) mixed treatment strategy. The feedback design proposed here is based on nonlinear constrained model predictive control together with an adaptation scheme that enables the effects of unavoidable modeling uncertainties to be compensated. The effectiveness of the proposed strategy is shown under realistic human data showing the advantage of treatment in feedback form as well as the relevance of the adaptation strategy in handling uncertainties and modeling errors. A new treatment strategy defined by an original optimal control problem formulation is also proposed. This new formulation shows particularly interesting possibilities since it may lead to tumor regression under better health indicator profile.
2020-01-16 | MODEL2001160002 | BioModels
Project description:Purpose: Accurate prediction of clinical response is the prerequisite for individualized therapy in chronic lymphocytic leukemia (CLL). We hypothesized that sequential assessment of gene expression changes early during therapy may well reflect behaviour of the leukemic clone in response to specific drugs. Patients and Methods: Gene expression profiles (GEP) were determined in CD19+ selected B-cells from 20 patients treated with fludarabine and cyclophosphamide (FC) (N=10) or FC plus rituximab (FCR) (N=10). Samples were collected in the first cycle before and within 48hours after initiation of treatment. GEP analysis was stratified by clinical response 3 months after start of therapy. Results: GEP before treatment detected high expression of 34 genes correlated with response and 32 genes correlated with resistance to therapy. These genes were related to regulation of apoptosis, cell cycle, cell adhesion, and signal transduction. Different results were obtained with sequential GEP: Sixteen genes were up-regulated after rituximab infusion in non-responders. Rituximab therapy resulted in down-regulation of AKT1 indicating involvement of the PI3-kinase pathway in CD20-signaling. Up-regulation of 24 genes after FC (including ITPKB (inositol 1,4,5-trisphosphate 3-kinase) and CD44) and of 36 genes after FCR (including CD49d) was associated with resistance. Down-regulation of CTLA4 correlated with poor response to FC. CD44, CD49d and the PI3-kinase signaling pathway were confirmed as potential therapeutic targets to overcome resistance by (protein analysis) or functional experiments. Conclusion Sequential GEP provides rapid and relevant information for prediction of response and resistance. This approach could be used to guide and adapt individualized therapy in CLL.
2011-03-01 | GSE15490 | GEO
Project description:The feasibility of longitudinal metastatic biopsies for gene expression profiling in breast cancer is unexplored. Dynamic changes in gene expression can potentially predict efficacy of targeted cancer drugs. Patients enrolled in a phase III trial of metastatic breast cancer with sunitinib combined with docetaxel (SU+DOC) versus docetaxel alone (DOC) were offered to participate in a translational substudy comprising longitudinal fine needle aspiration biopsies (FNAB) and positron emission imaging before (T1) and two weeks after start of treatment (T2). Aspirated tumor material was used for microarray analysis, and treatment-induced changes (T2 versus T1) in gene expression and standardized uptake values were investigated. Twenty-one patients were included in the docetaxel ± sunitinib trial at Karolinska and 18 of them agreed to participate in the substudy. Of the 18 women enrolled, 8 were randomly assigned to SU+DOC and 10 to DOC. Metastatic FNAB were carried out in 17 of the 18 patients at both time points with no complications reported. Representative tumor material, sufficient for RNA extraction was obtained in 15 patients at T1 and 14 patients at T2. Matched samples both at T1 and T2 were obtained for 14 subjects, 7 in each arm. The main objective was to determine whether gene expression profiling is feasible using sequential, intra-patient FNAB. Secondary objectives were to identify potential biomarkers of early response by gene expression and/or functional imaging and to explore drug action in vivo by changes in gene expression. A Karolinska substudy of the docetaxel ± sunitinib phase III clinical trial utilising sequential metastatic fine needle aspiration biopsies (FNAB) and 18-F-2-fluoro-2-deoxyglucose positron emission tomography/computed tomography (FDG PET/CT). In brief, the randomized docetaxel ± sunitinib trial compared sunitinib combined with docetaxel (SU+DOC) versus docetaxel alone (DOC), as first line therapy in patients with HER2 negative metastatic breast cancer. Patients in the exploratory substudy were subjected to baseline FDG PET/CT assessment, followed by FNAB of one tumor lesion prior to start of treatment (Time point T1). FDG PET/CT and FNAB were repeated at Day 14 ± 1 (Time point T2) when sunitinib has achieved steady state.
2015-04-14 | E-GEOD-54323 | biostudies-arrayexpress
Project description:Clinical drug resistance is one major concern in the treatment of human cancers, most of them being found resistant to therapy at the time of drug presentation. Deciphering the molecular mechanisms that contribute to such innate drug resistance should improve both the prediction of treatment failure and the development of new strategies to overcome resistance. Our study reports for the first time a systems approach toward understanding the in vivo cellular states of clinical samples collected from colorectal cancer (CRC) patients prior to their exposure to combined chemotherapy of folinic acid (FA), 5-fluorouracil (5-FU) and irinotecan (CPT-11). Vigilant experimental design, power simulations and robust statistics were used to restrain the rates of false negative and false positive hybridizations, allowing successful discrimination between drug resistance and sensitivity states with restricted sampling. A list of 679 genes was established which intrinsically differentiate, for the first time prior to drug exposure, subsequently diagnosed chemo-sensitive and resistant patients. Independent biological validation performed through quantitative-PCR confirmed the expression pattern on two additional patients. Careful annotation of interconnected functional networks provided a unique representation of the cellular states underlying drug responses. Molecular interaction networks were described that provide a solid foundation on which to anchor working hypotheses about mechanisms underlying in vivo innate tumor drug responses. These broad-spectrum cellular signatures represent a starting point from which by-pass chemotherapy schemes, targeting simultaneously several of the molecular mechanisms involved, may be developed for critical therapeutic intervention in CRC patients. The demonstrated power of this research strategy makes it generally applicable to other physiological and pathological situations. Keywords: Drug response
2006-03-01 | GSE3964 | GEO
Project description:Stability Analysis of a Mathematical Model for Glioma-Immune Interaction under Optimal Therapy
Subhas Khajanchi
Abstract
We investigate a mathematical model using a system of coupled ordinary differential equations, which describes the interplay of malignant glioma cells, macrophages, glioma specific CD8+T cells and the immunotherapeutic drug Adoptive Cellular Immunotherapy (ACI). To better understand under what circumstances the glioma cells can be eliminated, we employ the theory of optimal control. We investigate the dynamics of the system by observing biologically feasible equilibrium points and their stability analysis before administration of the external therapy ACI. We solve an optimal control problem with an objective functional which minimizes the glioma cell burden as well as the side effects of the treatment. We characterize our optimal control in terms of the solutions to the optimality system, in which the state system coupled with the adjoint system. Our model simulation demonstrates that the strength of treatment u1(t) plays an important role to eliminate the glioma cells. Finally, we derive an optimal treatment strategy and then solve it numerically.
Keywords: malignant gliomas; stability analysis; optimal control; adoptive cellular immunotherapy
2020-01-03 | BIOMD0000000891 | BioModels
Project description:SEEKING BANG-BANG SOLUTIONS OF MIXED IMMUNO-CHEMOTHERAPY OF TUMORS
LISETTE G. DE PILLIS, K. RENEE FISTER, WEIQING GU,
CRAIG COLLINS, MICHAEL DAUB, DAVID GROSS, JAMES MOORE, BEN PRESKILL*
Abstract.
It is known that a beneficial cancer treatment approach for a single patient often involves the administration of more than one type of therapy. The question of how best to combine multiple cancer therapies, however, is still open. In this study, we investigate the theoretical interaction of three treatment types (two biological therapies and one chemotherapy) with a growing cancer, and present an analysis of an optimal control strategy for administering all three therapies in combination. In the situations with controls introduced linearly, we find that there are conditions on which the controls exist singularly. Although bang-bang controls (on-off) reflect the drug treatment approach that is often implemented clinically, we have demonstrated, in the context of our mathematical model, that there can exist regions on which this may not be the best strategy for minimizing a tumor burden. We characterize the controls in singular regions by taking time derivatives of the switching functions. We will examine these representations and the conditions necessary for the controls to be minimizing in the singular region. We begin by assuming only one of the controls is singular on a given interval. Then we analyze the conditions on which a pair and then all three controls are singular
2020-03-06 | MODEL2003060001 | BioModels
Project description:Background
Cancer is one of the leading causes for the morbidity and mortality worldwide. Although substantial studies have been conducted theoretically and experimentally in recent years, it is still a challenge to explore the mechanisms of cancer initiation and progression. The investigation for these problems is very important for the diagnosis of cancer diseases and development of treatment schemes.
Results
To accurately describe the process of cancer initiation, we propose a new concept of gene initial mutation rate based on our recently designed mathematical model using the non-constant mutation rate. Unlike the widely-used average gene mutation rate that depends on the number of mutations, the gene initial mutation rate can be used to describe the initiation process of a single patient. In addition, we propose the instantaneous tumour doubling time that is a continuous function of time based on the non-constant mutation rate. Our proposed concepts are supported by the clinic data of seven patients with advanced pancreatic cancer. The regression results suggest that, compared with the average mutation rate, the estimated initial mutation rate has a larger value of correlation coefficient with the patient survival time. We also provide the estimated tumour size of these seven patients over time.
Conclusions
The proposed concepts can be used to describe the cancer initiation and progression for different patients more accurately. Since a quantitative understanding of cancer progression is important for clinical treatment, our proposed model and calculated results may provide insights into the development of treatment schemes and also have other clinic implications.
2020-02-28 | BIOMD0000000915 | BioModels
Project description:Endocrine therapy is the most used treatment for hormone receptor positive breast cancers. Despite the clear benefit of endocrine therapy for patients with ER+ breast cancer, resistance to treatment is a critical clinical issue affecting a large number of patients. While many studies have shown that genetics is an important factor in therapy resistance, recent publications have also reported that epigenetics might play a major role in the acquisition of resistance to endocrine therapies. This role is exploited both at the DNA methylation level, with activation of oncogenes and silencing of tumor suppressor genes, and at the histone modification level, with changes in chromatin accessibility. DNA methylation exerts its role in resistance both by targeting the ER itself, by inducing promoter methylation, and genome wide, by changing the methylation levels of estrogen response elements. To better understand resistance acquisition in an in vitro setting, researchers developed cellular models of endocrine therapy resistance. The prevalent strategy to identify mechanisms of resistance development and novel targets involved in the process has been to study differences between resistant and sensitive cells. Here, we performed time-resolved DNA methylation profiling of development of resistance to tamoxifen treatment and to estrogen deprivation in the T47D luminal A breast cancer cell line with the aim to identify novel drivers.
2020-07-17 | GSE154626 | GEO