Study to Investigate Outcome of Individualized Treatment in Patients With Metastatic Colorectal Cancer
Ontology highlight
ABSTRACT:
DISEASE(S): Metastatic Colorectal Cancer,Colorectal Neoplasms
PROVIDER: 8119 | ecrin-mdr-crc |
REPOSITORIES: ECRIN MDR
Similar Datasets
Project description:The purpose of this study was to improve prediction of patients at high-risk for metastatic disease utilizing a nested case-control design that uniquely enables enrichment for relevant phenotypes. We identified all women diagnosed with primary breast cancer from January 1, 1997, to December 31, 2005, in the Stockholm health care region. Patients developing distant metastatic disease (cases) were selected and controls (free from distant disease) were randomly matched by adjuvant therapy, age and calendar period at diagnosis. The nested case-control study included 768 study subjects with clinical information and gene expression arrays (Human Cancer G110). Study subjects were randomly and equally divided into training set (discovery) or testing (validation) set. Metastatic onset prediction was then compared including either clinical variables only or combining clinical and genetic information. Differentially expressed genes and pathways between cases and controls included a wide-spectrum of well known as well as candidate regulators of the metastatic cascade.
2015-07-08 | GSE48091 | GEO
Project description:Bordel2018 - GSMM for Human Metabolic
Reactions (HMR database)
This model is described in the article:
Constraint based modeling of
metabolism allows finding metabolic cancer hallmarks and
identifying personalized therapeutic windows
Sergio Bordel
Oncotarget. 2018; 9:19716-19729
Abstract:
In order to choose optimal personalized anticancer
treatments, transcriptomic data should be analyzed within the
frame of biological networks. The best known human biological
network (in terms of the interactions between its different
components) is metabolism. Cancer cells have been known to have
specific metabolic features for a long time and currently there
is a growing interest in characterizing new cancer specific
metabolic hallmarks. In this article it is presented a method
to find personalized therapeutic windows using RNA-seq data and
Genome Scale Metabolic Models. This method is implemented in
the python library, pyTARG. Our predictions showed that the
most anticancer selective (affecting 27 out of 34 considered
cancer cell lines and only 1 out of 6 healthy mesenchymal stem
cell lines) single metabolic reactions are those involved in
cholesterol biosynthesis. Excluding cholesterol biosynthesis,
all the considered cell lines can be selectively affected by
targeting different combinations (from 1 to 5 reactions) of
only 18 metabolic reactions, which suggests that a small subset
of drugs or siRNAs combined in patient specific manners could
be at the core of metabolism based personalized treatments.
This model is hosted on
BioModels Database
and identified by:
MODEL1707250000.
To cite BioModels Database, please use:
Chelliah V et al. BioModels: ten-year
anniversary. Nucl. Acids Res. 2015, 43(Database
issue):D542-8.
To the extent possible under law, all copyright and related or
neighbouring rights to this encoded model have been dedicated to
the public domain worldwide. Please refer to
CC0
Public Domain Dedication for more information.
| MODEL1707250000 | BioModels
Project description:This series contains the gene expression data from urothelial bladder cancer samples from Swedish patients that were used to analyze metastatic sites. Included patients have a recurrence or distant metastasis before or after treatment with chemotherapy. Patients with only lymph-node metastases are not included. A previous series (GSE169455) contains data from all patients that recieved two or more cycles of neoadjuvant chemotherapy with curative intent. Patients in that series that developed distant recurrence are re-analyzed here. A few samples from a previous cystectomy series (GSE83586) are also included as re-analysis. In addition, the current series contains data from patients treated with palliative first-line chemotherapy, curative adjuvant chemotherapy, or < 2 cycles of neoadjuvant chemotherapy. Raw data should be adjusted in data processing for batch variables: Labeling batch and Labeling kit.
2023-08-24 | GSE222073 | GEO
Project description:Colorectal cancer is characterized by an extremely high mortality rate, mainly caused by the high metastatic potential of this type of cancer. To date chemotherapy remains the backbone of the treatment of metastatic colorectal cancer. Three main chemotherapeutic drugs used for treatment of metastatic colorectal cancer are 5-FU, oxaliplatin and irinotecan which is metabolized to an active compound SN-38. The main goal of this study was to find the genes connected to the resistance to the aforementioned drugs and to construct predictive gene expression-based classifier to separate responders and non-responders. In this study we used 7 established colorectal cancer organoids to conduct correlational analysis of the expression and drug resistance. We also included in the study publicly available datasets of colorectal cancer cell lines. The obtained results demonstrated that the expression of just a small group of genes consistently correlate with resistance to standard chemotherapeutic drugs in different datasets. Some of these genes have been previously connected to prognosis or response to anticancer drugs, but some of them were linked to the drug resistance for the first time. The classifier constructed on the expression levels of some of these genes allows to discriminate between patients with favorable and unfavorable 5-year OS prognosis and can be further validated in clinic to improve the results of the treatment.
2025-01-08 | GSE251958 | GEO
Project description:The purpose of this study was to improve prediction of patients at high-risk for metastatic disease utilizing a nested case-control design that uniquely enables enrichment for relevant phenotypes. We identified all women diagnosed with primary breast cancer from January 1, 1997, to December 31, 2005, in the Stockholm health care region. Patients developing distant metastatic disease (cases) were selected and controls (free from distant disease) were randomly matched by adjuvant therapy, age and calendar period at diagnosis. The nested case-control study included 768 study subjects with clinical information and gene expression arrays (Human Cancer G110). Study subjects were randomly and equally divided into training set (discovery) or testing (validation) set. Metastatic onset prediction was then compared including either clinical variables only or combining clinical and genetic information. Differentially expressed genes and pathways between cases and controls included a wide-spectrum of well known as well as candidate regulators of the metastatic cascade. The nested case-control study included 768 study subjects corresponding to 623 primary tumor samples. Details concerning case-control status are given in the samples section. Each case and its' matching controls form risk sets, indicated by the setnr variable.
2014-11-03 | E-GEOD-48091 | biostudies-arrayexpress
Project description:Individualized outcome prediction classifiers were successfully constructed through expression profiling of a total of 8,644 genes in 20 pancreatic cancer cases, which had been consecutively operated.
2024-11-02 | GSE246880 | GEO
Project description:Individualized outcome prediction classifiers were successfully constructed through expression profiling of a total of 8,644 genes in 50 non-small cell lung cancer (NSCLC) cases, which had been consecutively operated on within a defined short period of time and followed up more than five years. The resultant classifier of NSCLCs yielded 82% accuracy for forecasting survival or death five years after surgery of a given patient. In addition, since two major histologic classes may differ in terms of outcome-related expression signatures, histologic type-specific outcome classifiers were also constructed. The resultant highly predictive classifiers, designed specifically for non-squamous cell carcinomas, showed a prediction accuracy of more than 90% independent of disease stage. In addition to the presence of heterogeneities in adenocarcinomas, our unsupervised hierarchical clustering analysis revealed for the first time the existence of clinicopathologically relevant subclasses of squamous cell carcinomas with marked differences in their invasive growth and prognosis. This finding clearly suggests that NSCLCs comprise distinct subclasses with considerable heterogeneities even within one histologic type. Overall, these findings should advance not only our understanding of the biology of lung cancer but also our ability to individualize post-operative therapies based on the predicted outcome. Keywords: cell type comparison and prognosis prediction
2007-01-20 | GSE4716 | GEO
Project description:Individualized outcome prediction classifiers were successfully constructed through expression profiling of 91 up-regulated and 67 down-regulated miRNAs in 5 autism spectrum disorder (ASD) cases and 5 controls.
2015-04-17 | GSE67979 | GEO
Project description:Individualized outcome prediction classifiers were successfully constructed through expression profiling of 91 up-regulated and 67 down-regulated miRNAs in 5 autism spectrum disorder (ASD) cases and 5 controls. In the study presented here, a well-defined cohort of 5 autism spectrum disorder cases and 5 controls was used to acquire expression profiles of 91 up-regulated and 67 down-regulated miRNAs, leading to the first global miRNA expression profile of ASD in China.
2015-04-17 | E-GEOD-67979 | biostudies-arrayexpress
Project description:Neoadjuvant immune checkpoint inhibitors (ICIs) improve event-free survival in metastatic melanoma, yet resistance and recurrence remain common. Here we evaluated pepinemab, a Semaphorin 4D (SEMA4D)–blocking antibody, in combination with anti-PD1 nivolumab and/or anti-CTLA4 ipilimumab in a pilot biomarker-driven neoadjuvant trial (NCT03769155) for patients with resectable metastatic melanoma. The primary endpoint was to determine if pepinemab combined with ICIs modulates phenotypic properties within the tumor microenvironment in a manner that enhances T effector cell infiltration. Secondary endpoints included safety and tolerability, pathological response rates and radiographical response. Patients received two neoadjuvant doses followed by curative-intent surgery and one year of adjuvant nivolumab. Pepinemab-containing regimens demonstrated manageable toxicity profiles and did not delay or preclude curative-intent surgical resection in the 38 patients treated. The trial met its primary endpoint, as enrichment of transcripts associated with activated dendritic cells and immune recruitment pathways was observed in tumors from patients receiving pepinemab-containing regimens. Further, immune remodeling was evident by formation of mature lymphoid aggregates resembling tertiary lymphoid structures that contained clonally expanded T cell receptor repertoires. Together, these findings support further evaluation of SEMA4D blockade as a strategy to modulate the tumor microenvironment and potentially augment immunotherapy in melanoma and other solid malignancies.
2026-09-17 | GSE347762 | GEO