UMCU colon cancer - Biopsies Of Surgical Specimens (BOSS)
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE85043 | GEO | 2017/08/01
SECONDARY ACCESSION(S): PRJNA336014
REPOSITORIES: GEO
Similar Datasets
Project description:Although preoperative chemoradiotherapy (CRT) and surgical mesorectal resection is the standard of care for locally advanced rectal carcinomas, it is still difficult to predict which patients will respond to treatment. We explored how differential stromal transcriptomic profiles from microdissected pretreatment rectal biopsies can be used to define an immunohistochemical score based on two CAF-specific proteins for predicting neoadjuvant treatment response. The analysis of differentially expressed genes (DEGs) of stroma and tumour glands from responder and non-responder patients shows that most changes were associated with the stromal compartment. Gene ontology analysis revealed that the DEGs codify mainly for extracellular matrix and ribosomal components. We built a CAF-specific classifier with genes showing monotonic changes in expression according to the tumour regression grade (coefficient >1; FN1, COL3A1, COL1A1, MMP2 and IGFBP5). These are the genes that display the biggest a priori differences in expression between non-responder and responder stroma. We translated these five genes at the protein level by means of immunohistochemical staining in a cohort of 38 patients. For predictive purposes we used a leave-one-out cross-validated (LOOCV) model with a positive predictive value (PPV) of 80%. Classifier optimisation with Random Forest identified FN1 and COL3A1 as the best predictors. Rebuilding the LOOCV regression model improved the classification performance with a PPV of 89.5% and a negative predictive value (NPV) of 73.7%. An independent cohort of 36 patients was used to validate the classifier performance, which had a PPV of 84.2% and an NPV of 70.6%. In a multivariate analysis the two-protein classifier proved to be the only independent predictor of response (HR=2.58; P=0.003). We also explored a pharmacogenomic approach to gather information about possible therapeutic strategies for non-responder patients. In conclusion, we developed a two-protein immunohistochemical classifier that performs well at predicting the absence of response to neoadjuvant treatment in rectal cancer.
2017-01-11 | GSE93375 | GEO
Project description:Immunotherapy has improved the prognosis of patients with advanced non-small cell lung
cancer (NSCLC), but only a small subset of patients achieved clinical benefit. The purpose of our study was to integrate multidimensional data using a machine learning method to predict the therapeutic efficacy of immune checkpoint inhibitors (ICIs) monotherapy in patients with advanced NSCLC.The authors retrospectively enrolled 112 patients with stage IIIB-IV NSCLC receiving ICIs monotherapy. The random forest (RF) algorithm was used to establish efficacy prediction models based on five different input datasets, including precontrast computed tomography (CT) radiomic data, postcontrast CT radiomic data, combination of the two CT radiomic data, clinical data, and a combination of radiomic and clinical data. The 5-fold cross-validation was used to train and test the random forest classifier. The performance of the models was assessed according to the area under the curve (AUC) in the receiver operating characteristic (ROC) curve. Among these models(RF MLP LR XGBoost), our reproduced onnx models have better performance, especially for random forest. The response variable with a value (1/0) indicates the (efficacy/inefficacy) of PD-1/PD-L1 monotherapy in patients with advanced NSCLC
2023-07-11 | BIOMD0000001074 | BioModels
Project description:This work presents a novel Consensus Molecular Subtypes (CMS) classifier for colorectal cancer (CRC), optimized for degraded RNA stemming from clinical formalin-fixed paraffin-embedded tissue samples.
2024-05-22 | GSE267010 | GEO
Project description:Both sepsis and acute respiratory distress syndrome (ARDS) rely on imprecise clinical definitions leading to heterogeneity, which has contributed to negative trials. Because circulating protein/DNA complexes have been implicated in sepsis and ARDS, we aimed to develop a proteomic signature of DNA-bound proteins to discriminate between children with sepsis with and without ARDS. We performed a prospective case-control study in 12 children with sepsis with ARDS matched to 12 children with sepsis without ARDS on age, severity of illness score, and source of infection. We performed co-immunoprecipitation and downstream proteomics in plasma collected ≤ 24 h of intensive care unit admission. Expression profiles were generated, and a random forest classifier was used on differentially expressed proteins to develop a signature which discriminated ARDS. The classifier was tested in six independent blinded samples. Neutrophil and nucleosome proteins were over-represented in ARDS, including two S100A proteins, superoxide dismutase (SOD), and three histones. Random forest produced a 10-protein signature that accurately discriminated between children with sepsis with and without ARDS. This classifier perfectly assigned six independent blinded samples as having ARDS or not. We validated higher expression of the most informative discriminating protein, galectin-3-binding protein, in children with ARDS. Our methodology has applicability to isolation of DNA-bound proteins from plasma. Our results support the premise of a molecular definition of ARDS, and give preliminary insight into why some children with sepsis, but not others, develop ARDS.
2024-02-14 | PXD049377 | Pride
Project description:Background: The development of a new drug from candidate to market is a complex process requiring vast resources of time, money and personnel. The rate of failure in the development pipeline is enormous, leading to wasted resources that could have been better employed on alternative candidates. The requirement for early stage prediction of toxicity is, then, of paramount importance to expedite the introduction of new therapies to clinical practice. To date, most transcriptomics efforts to solve this problem have applied Support Vector Machine techniques to data derived from in vivo studies in rats. Results: We applied a toxicogenomics approach to determine whether known renal toxicants could be identified as such from their effects on the transcriptome of the human renal proximal tubular epithelial cell line, HK-2. Based on clustering of differentially expressed genes, we identified 3 toxicity groups within the set of compounds. We used Random Forest to generate a classifier to accurately place compounds in groups. The classifier is based on a signature biomarker comprising 21 genes identified by Random Forest and could differentiate between the groups with high accuracy. Furthermore, we could correctly classify external samples from a dataset exhibiting a marked ‘batch effect’. Conclusions: No toxicity-associated gene expression alterations could be identified across a set of toxic compounds. Random Forest is a suitable technique for the classification of compounds into toxicity groups. Using a measure of differential expression rather than expression level per se generates a robust classifier that can potentially be applied in a platform-independent manner.
2015-12-31 | GSE27196 | GEO
Project description:Background: The development of a new drug from candidate to market is a complex process requiring vast resources of time, money and personnel. The rate of failure in the development pipeline is enormous, leading to wasted resources that could have been better employed on alternative candidates. The requirement for early stage prediction of toxicity is, then, of paramount importance to expedite the introduction of new therapies to clinical practice. To date, most transcriptomics efforts to solve this problem have applied Support Vector Machine techniques to data derived from in vivo studies in rats. Results: We applied a toxicogenomics approach to determine whether known renal toxicants could be identified as such from their effects on the transcriptome of the human renal proximal tubular epithelial cell line, HK-2. Based on clustering of differentially expressed genes, we identified 3 toxicity groups within the set of compounds. We used Random Forest to generate a classifier to accurately place compounds in groups. The classifier is based on a signature biomarker comprising 21 genes identified by Random Forest and could differentiate between the groups with high accuracy. Furthermore, we could correctly classify external samples from a dataset exhibiting a marked ‘batch effect’. Conclusions: No toxicity-associated gene expression alterations could be identified across a set of toxic compounds. Random Forest is a suitable technique for the classification of compounds into toxicity groups. Using a measure of differential expression rather than expression level per se generates a robust classifier that can potentially be applied in a platform-independent manner.
2015-12-31 | GSE27192 | GEO
Project description:Background: The development of a new drug from candidate to market is a complex process requiring vast resources of time, money and personnel. The rate of failure in the development pipeline is enormous, leading to wasted resources that could have been better employed on alternative candidates. The requirement for early stage prediction of toxicity is, then, of paramount importance to expedite the introduction of new therapies to clinical practice. To date, most transcriptomics efforts to solve this problem have applied Support Vector Machine techniques to data derived from in vivo studies in rats. Results: We applied a toxicogenomics approach to determine whether known renal toxicants could be identified as such from their effects on the transcriptome of the human renal proximal tubular epithelial cell line, HK-2. Based on clustering of differentially expressed genes, we identified 3 toxicity groups within the set of compounds. We used Random Forest to generate a classifier to accurately place compounds in groups. The classifier is based on a signature biomarker comprising 21 genes identified by Random Forest and could differentiate between the groups with high accuracy. Furthermore, we could correctly classify external samples from a dataset exhibiting a marked ‘batch effect’. Conclusions: No toxicity-associated gene expression alterations could be identified across a set of toxic compounds. Random Forest is a suitable technique for the classification of compounds into toxicity groups. Using a measure of differential expression rather than expression level per se generates a robust classifier that can potentially be applied in a platform-independent manner.
2015-12-31 | GSE27191 | GEO
Project description:Background: The development of a new drug from candidate to market is a complex process requiring vast resources of time, money and personnel. The rate of failure in the development pipeline is enormous, leading to wasted resources that could have been better employed on alternative candidates. The requirement for early stage prediction of toxicity is, then, of paramount importance to expedite the introduction of new therapies to clinical practice. To date, most transcriptomics efforts to solve this problem have applied Support Vector Machine techniques to data derived from in vivo studies in rats. Results: We applied a toxicogenomics approach to determine whether known renal toxicants could be identified as such from their effects on the transcriptome of the human renal proximal tubular epithelial cell line, HK-2. Based on clustering of differentially expressed genes, we identified 3 toxicity groups within the set of compounds. We used Random Forest to generate a classifier to accurately place compounds in groups. The classifier is based on a signature biomarker comprising 21 genes identified by Random Forest and could differentiate between the groups with high accuracy. Furthermore, we could correctly classify external samples from a dataset exhibiting a marked ‘batch effect’. Conclusions: No toxicity-associated gene expression alterations could be identified across a set of toxic compounds. Random Forest is a suitable technique for the classification of compounds into toxicity groups. Using a measure of differential expression rather than expression level per se generates a robust classifier that can potentially be applied in a platform-independent manner.
2015-12-31 | GSE27168 | GEO
Project description:Background: The development of a new drug from candidate to market is a complex process requiring vast resources of time, money and personnel. The rate of failure in the development pipeline is enormous, leading to wasted resources that could have been better employed on alternative candidates. The requirement for early stage prediction of toxicity is, then, of paramount importance to expedite the introduction of new therapies to clinical practice. To date, most transcriptomics efforts to solve this problem have applied Support Vector Machine techniques to data derived from in vivo studies in rats. Results: We applied a toxicogenomics approach to determine whether known renal toxicants could be identified as such from their effects on the transcriptome of the human renal proximal tubular epithelial cell line, HK-2. Based on clustering of differentially expressed genes, we identified 3 toxicity groups within the set of compounds. We used Random Forest to generate a classifier to accurately place compounds in groups. The classifier is based on a signature biomarker comprising 21 genes identified by Random Forest and could differentiate between the groups with high accuracy. Furthermore, we could correctly classify external samples from a dataset exhibiting a marked ‘batch effect’. Conclusions: No toxicity-associated gene expression alterations could be identified across a set of toxic compounds. Random Forest is a suitable technique for the classification of compounds into toxicity groups. Using a measure of differential expression rather than expression level per se generates a robust classifier that can potentially be applied in a platform-independent manner.
2015-12-31 | GSE27167 | GEO
Project description:Background: The development of a new drug from candidate to market is a complex process requiring vast resources of time, money and personnel. The rate of failure in the development pipeline is enormous, leading to wasted resources that could have been better employed on alternative candidates. The requirement for early stage prediction of toxicity is, then, of paramount importance to expedite the introduction of new therapies to clinical practice. To date, most transcriptomics efforts to solve this problem have applied Support Vector Machine techniques to data derived from in vivo studies in rats. Results: We applied a toxicogenomics approach to determine whether known renal toxicants could be identified as such from their effects on the transcriptome of the human renal proximal tubular epithelial cell line, HK-2. Based on clustering of differentially expressed genes, we identified 3 toxicity groups within the set of compounds. We used Random Forest to generate a classifier to accurately place compounds in groups. The classifier is based on a signature biomarker comprising 21 genes identified by Random Forest and could differentiate between the groups with high accuracy. Furthermore, we could correctly classify external samples from a dataset exhibiting a marked ‘batch effect’. Conclusions: No toxicity-associated gene expression alterations could be identified across a set of toxic compounds. Random Forest is a suitable technique for the classification of compounds into toxicity groups. Using a measure of differential expression rather than expression level per se generates a robust classifier that can potentially be applied in a platform-independent manner.
2015-12-31 | GSE27096 | GEO