Homo sapiens
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ABSTRACT:
PROVIDER: PRJNA174168 | ENA |
REPOSITORIES: ENA
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Project description:Background: Validated markers to predict outcome in rectal cancer patients treated with multimodal therapy remain elusive. Identifying molecular profiles for disease prognosis would be required for the design of clinical trials aimed at optimizing risk-adapted therapies. We have therefore used whole genome expression profiling of tumors of a large cohort of patients enrolled in the multicenter trials of the German Rectal Cancer Study Group (GRCSG) to identify molecular profiles for individualized therapy. Methods: We prospectively collected pretherapeutic biopsies from patients (n=300) treated according to the GRCSG trial guidelines from seven different German surgical departments using rigid quality controls. These samples were profiled by global gene expression analysis and a classifier developed to predict postoperative lymph node status and Disease Free Survival (DFS). The performance of the classifier was validated with an independent, prospectively collected set of samples. Findings: The final training and test set included 198 patients. Analyzes for postoperative nodal status and DFS revealed 69 and 674 differentially regulated genes, respectively. Depending on the classifier the accuracy to predict lymph node status ranged from 64% to 69% with negative predictive values between 72% and 74%. Stratification according to DFS resulted in a good (n=99), bad (n=96) and a small very bad prognosis group (n=3). Based on linear discriminant analysis the classifier for positive lymph node status was validated in 47 independent patients and revealed an accuracy of 72% and a positive predictive value of 100%. Thereby, prediction based on molecular profiling is superior to prediction based on conventional clinical markers. Interpretation: Whole genome expression analysis of pretherapeutical biopsies resulted in a molecular classifier of disease prognosis. This classifier was successfully validated. The high positive predictive value for post therapeutic lymph node status allows identification of patients requiring alternative or intensified treatment protocols. These data are currently validated for their clinical applicability and should be taken as basis for future molecular driven clinical trials to develop risk-adapted treatments.
2023-03-31 | GSE40492 | GEO
Project description:Comprehensive Molecular Characterization of Human Colon and Rectal Cancer
| PRJNA174201 | ENA
Project description:Background: Neoadjuvant radiotherapy (neo-RT) is widely used in locally advanced rectal cancer (LARC) as a component of radical treatment. Despite the advantages of neo-RT, which typically improves outcomes in LARC patients, the lack of reliable biomarkers that predict response and monitor the efficacy of therapy, can result in the application of unnecessary aggressive therapy affecting patients’ quality of life. Hence, the search for molecular biomarkers for assessing the radio responsiveness of this cancer represents a relevant issue. Methods: Here, we combined proteomic and metabolomic approaches to identify molecular signatures, which could discriminate LARC tumors with good and poor responses to neo-RT. Results: The integration of data on differentially accumulated proteins and metabolites made it possible to identify disrupted metabolic pathways and signaling processes connected with response to irradiation, including ketone bodies synthesis and degradation, purine metabolism, energy metabolism, degradation of fatty acid, amino acid metabolism, and focal adhesion. Moreover, we proposed multi-component panels of proteins and metabolites which could serve as a solid base to develop biomarkers for monitoring and predicting the efficacy of preoperative RT in rectal cancer patients. Conclusions: We proved that an integrated multi-omic approach presents a valid look at the analysis of the global response to cancer treatment from the perspective of metabolomic reprogramming.
2024-05-22 | PXD048647 | Pride
Project description:Locally advanced rectal cancer (LARC) patients have been treated with a pre-operative multimodal regimen based on 5-fluorouracil and radiotherapy (nCRT) followed by surgery. Patients that achieve pathological complete response (pCR) present better overall survival and lower rates of recurrence. Substantial evidence indicate that these patients could be spared of surgery, while near 70% of cases present incomplete response (pIR), varying from partial response to stable-disease/progression. Markers capable of distinguishing pIR from pCR have the potential to address alternative treatment strategies. Herein, we evaluated the ability of predicting response to nCRT by DNA methylation analysis in pre-treatment biopsies of LARC.
2021-06-08 | GSE132668 | GEO
Project description:ChIP-seq on primary breast cancer tumor samples for Era, H3K4me3, H3K27me3. Patients had a poor or good outcome after aromatase inhibitor treatment. Differential binding patterns between good and poor outcome patients were identified for each marker, predicting response to treatment comparing ER, H3K4me3 and H3K27me3 patterns between two breast cancer patient populations
2013-12-05 | E-GEOD-40867 | biostudies-arrayexpress
Project description:Background Clinically useful molecular markers predicting the clinical course of patients diagnosed with non-muscle invasive bladder cancer are needed to improve treatment outcome. Methods We used custom designed oligonucleotide microarrays to validate four previously reported gene expression signatures for molecular diagnosis of disease stage and carcinoma in situ, and for predicting disease recurrence and progression. We analyzed tumors from 404 patients diagnosed with bladder cancer in hospitals in Denmark, Sweden, England, Spain and France. Molecular classifications were compared to pathological diagnosis and clinical outcome. The median follow-up time for the patients was 3.5 years. Results Classification of disease stage using a 52-gene classifier was found to be highly significantly correlated with pathological stage (P<0.001). Furthermore, the classifier added information regarding future disease progression of Ta or T1 tumors (P<0.001). The molecular 77-gene progression classifier was highly significantly correlated with progression free survival (P<0.001) and cancer specific survival (P=0.001). Furthermore, multivariate Cox´s regression analysis showed the progression classifier to be an independently significant variable associated with disease progression after adjustment for age, sex, stage, grade and treatment (hazard ratio 2.4, P=0.005). The diagnosis of carcinoma in situ (CIS) using a 68-gene classifier showed a highly significant correlation with histopathological CIS diagnosis (odds ratio 5.8, P<0.001) in multivariate logistic regression analysis. Conclusions We conclude that this multicenter validation study confirms the clinical utility of molecular classifiers to guide treatment decisions for patients initially diagnosed with non-muscle invasive bladder cancer. Keywords: Multi center validation study of gene expression signatures
2007-06-15 | GSE5479 | GEO
Project description:ChIP-seq on primary breast cancer tumor samples for Era, H3K4me3, H3K27me3. Patients had a poor or good outcome after aromatase inhibitor treatment. Differential binding patterns between good and poor outcome patients were identified for each marker, predicting response to treatment
2013-12-05 | GSE40867 | GEO
Project description:A Cartes dM-^RIdentite des Tumeurs (CIT) project from the french Ligue Nationale Contre le Cancer (http://cit.ligue-cancer.net/). This study aims to determine candidate genes and chromosomal imbalances capable of predicting occurrence of metastasis in patients with rectal cancer.
2014-03-28 | E-MTAB-2387 | biostudies-arrayexpress
Project description:This ordinary differential equation model of cancer virotherapy dynamics is described in the publication:
Salma M. Al-Tuwairqi, Najwa O. Al-Johani, Eman A. Simbawa,
"Modeling dynamics of cancer radiovirotherapy",
Journal of Theoretical Biology, Volume 506, 2020, 110405, ISSN 0022-5193,
DOI: 10.1016/j.jtbi.2020.110405.
Comment:
This model is represented by the equations in system (2) of the publication manuscript and describes the cancer-virus interactions for the Phase I virotherapy treatment.
Abstract:
Advances in genetic engineering have paved the way for a new therapy for cancer, which is called virotherapy. This treatment uses genetically engineered viruses which selectively infect, replicate in, and destroy cancer cells without damaging normal cells. Furthermore, current research and clinical trials have indicated that these viruses can be delivered as single agents or in combination with other therapies. In this paper, we propose systems of ordinary differential equations for modeling the dynamics of aggressive tumor growth under radiovirotherapy treatment. We divide the treatment period into two phases; consequently, we present two mathematical models. First, we formulate the virotherapy model as Phase I of the treatment. Then we extend the model to include radiotherapy in combination with virotherapy as Phase II of the treatment. Comprehensive qualitative analyses of both models are conducted. Furthermore, numerical experiments are performed in order to support the analytical results. An analysis of the parameters is also carried out to investigate their effects on the outcome of the treatment. Overall, the analytical results reveal that radiovirotherapy is more effective than, and a good alternative to, virotherapy, as it is capable of eradicating tumors completely.
2024-09-02 | BIOMD0000001031 | BioModels
Project description:This ordinary differential equation model of cancer radiovirotherapy dynamics is described in the publication:
Salma M. Al-Tuwairqi, Najwa O. Al-Johani, Eman A. Simbawa,
"Modeling dynamics of cancer radiovirotherapy",
Journal of Theoretical Biology, Volume 506, 2020, 110405, ISSN 0022-5193,
DOI: 10.1016/j.jtbi.2020.110405.
Comment:
This model is represented by the equations in system (14) of the publication manuscript and describes the cancer-virus interactions for the Phase II radiovirotherapy treatment.
Abstract:
Advances in genetic engineering have paved the way for a new therapy for cancer, which is called virotherapy. This treatment uses genetically engineered viruses which selectively infect, replicate in, and destroy cancer cells without damaging normal cells. Furthermore, current research and clinical trials have indicated that these viruses can be delivered as single agents or in combination with other therapies. In this paper, we propose systems of ordinary differential equations for modeling the dynamics of aggressive tumor growth under radiovirotherapy treatment. We divide the treatment period into two phases; consequently, we present two mathematical models. First, we formulate the virotherapy model as Phase I of the treatment. Then we extend the model to include radiotherapy in combination with virotherapy as Phase II of the treatment. Comprehensive qualitative analyses of both models are conducted. Furthermore, numerical experiments are performed in order to support the analytical results. An analysis of the parameters is also carried out to investigate their effects on the outcome of the treatment. Overall, the analytical results reveal that radiovirotherapy is more effective than, and a good alternative to, virotherapy, as it is capable of eradicating tumors completely.
2024-09-02 | BIOMD0000001032 | BioModels