Radiomics Predicts High or Low Recurrence Risk and is Associated with LncRNAs in Breast Cancer
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ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE189371 | GEO | 2023/11/21
REPOSITORIES: GEO
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Project description:Background/Aims: Recurrence-free survival (RFS) following curative resection of hepatocellular carcinoma (HCC) in subjects with hepatitis C virus (HCV) infection is highly variable. Traditional clinico-pathological endpoints are recognized as weak predictors of RFS. It has been suggested that gene expression profiling of HCC and nontumoral liver tissue may improve prediction of RFS, aid in understanding of the underlying liver disease, and guide individualized therapy. The goal of this study was to create a gene expression predictor of HCC recurrence in subjects with HCV. Methods: Frozen samples of the tumors and nontumoral liver were obtained from 47 subjects with HCV-associated HCC. Additional nontumoral liver samples were obtained from HCV-free subjects with metastatic liver tumors. Gene expression profiling data was used to determine the molecular signature of HCV-associated HCC and to develop a predictor of RFS. Results: The molecular profile of the HCV-associated HCC confirmed central roles for MYC and TGF-beta1 in liver tumor development. Gene expression in tumors was found to have poor predictive power with regards to RFS, but analysis of nontumoral tissues yielded a strong predictor for RFS in late-recurring (>1 year) subjects. Importantly, nontumoral tissue-derived gene expression predictor of RFS was highly significant in both univariable and multivariable Cox proportional hazard model analyses. Conclusions: Microarray analysis of the nontumoral tissues from subjects with HCV-associated HCC delivers novel molecular signatures of RFS, especially among the late-recurrence subjects. The gene expression signature of the predictor gives important insights into the pathobiology of HCC recurrence and used in design of the individualized therapy.
2010-04-14 | GSE17856 | GEO
Project description:Background/Aims: Recurrence-free survival (RFS) following curative resection of hepatocellular carcinoma (HCC) in subjects with hepatitis C virus (HCV) infection is highly variable. Traditional clinico-pathological endpoints are recognized as weak predictors of RFS. It has been suggested that gene expression profiling of HCC and nontumoral liver tissue may improve prediction of RFS, aid in understanding of the underlying liver disease, and guide individualized therapy. The goal of this study was to create a gene expression predictor of HCC recurrence in subjects with HCV. Methods: Frozen samples of the tumors and nontumoral liver were obtained from 47 subjects with HCV-associated HCC. Additional nontumoral liver samples were obtained from HCV-free subjects with metastatic liver tumors. Gene expression profiling data was used to determine the molecular signature of HCV-associated HCC and to develop a predictor of RFS. Results: The molecular profile of the HCV-associated HCC confirmed central roles for MYC and TGF-beta1 in liver tumor development. Gene expression in tumors was found to have poor predictive power with regards to RFS, but analysis of nontumoral tissues yielded a strong predictor for RFS in late-recurring (>1 year) subjects. Importantly, nontumoral tissue-derived gene expression predictor of RFS was highly significant in both univariable and multivariable Cox proportional hazard model analyses. Conclusions: Microarray analysis of the nontumoral tissues from subjects with HCV-associated HCC delivers novel molecular signatures of RFS, especially among the late-recurrence subjects. The gene expression signature of the predictor gives important insights into the pathobiology of HCC recurrence and used in design of the individualized therapy. 43 tumor (JT) and 44 non-tumor (JNT) liver tissues surgically resected from patients with HCV-associated hepatocellular carcinoma; 8 non-tumor liver tissues (control samples, JC) surgically resected from HCV- or HBV-free patients with metastatic liver tumor. Inter-batch normalization was carried out using Distance Weighted Discrimination procedure. The supplementary file 'GSE17856_Readme.txt' contains a description of the replicates used for normalization. The 'GSE17856_US14702406_2514850*' files are the raw data files for the replicates.
2010-04-14 | E-GEOD-17856 | biostudies-arrayexpress
Project description:To develop and validate novel multigene signatures to facilitate individualized treatment of TNBC patients By integrating expression profiles of messenger RNAs (mRNAs) and long non-coding RNAs (lncRNAs). We analysed 165 TNBC samples and 33 paired normal breast tissues using transcriptome microarrays. Tumor-specific mRNAs and lncRNAs were identified and correlated with patientsâ?? recurrence-free survival (RFS). Using Cox regression model, we built two multigene signatures incorporating mRNAs and lncRNAs. The prognostic and predictive accuracy of the signatures were tested in a training set of 165 TNBC patients and validated in another 101 TNBC patients.
2016-01-04 | E-GEOD-76250 | biostudies-arrayexpress
Project description:Introduction: The I-SPY 1 TRIAL was designed to evaluate complete pathologic response and tumor volume change, measured by magnetic resonance imaging (MRI), stratified by molecular subtypes and link response to 3-year recurrence free survival (RFS).Methods: Eligible patients had T =3 cm and received neoadjuvant chemotherapy with AC plus optional taxane. Serial MRI, biopsy, and blood draws were conducted over the course of treatment, and a database of multiple molecular profiles was assembled.Results: A total of 221 patients were eligible for analysis: median tumor size was 6.0 cm and median age was 49 years. The I-SPY 1 TRIAL patients had tumors with aggressive biology: 45% were estrogen receptor (ER) negative, 31% Her2+; and 91% high risk by the Netherlands Cancer Institute (NKI) 70-gene profile. After a median of 3.9 years, RFS was 77%. HR/HER2 status improved predictability of RFS with the greatest difference between HR+/HER2- and triple-negative disease (hazard ratio 0.39). Wound healing signature activation, p53 mutation, and Risk of Relapse (ROR) score were highly correlated and also significantly improved the accuracy of RFS predictions when added to stage. The rate of pathologic complete response (pCR) varied considerably from a low of 0-2% (NKI low risk, luminal A) to a high of 43-61% (17q Amplified, HER2 enriched, HR-/HER2+). Both pCR and the more refined residual cancer burden (RCB) were significant predictors of RFS for all patients and even more predictive when analyzed within biomarker subsets. Good risk (NKI low, ROR-S low risk, Wound Healing quiescent, p53 wild type) signatures were associated with significantly higher 3-year RFS than poor risk expression signatures (ROR-S high risk, Wound Healing Activated, p53 mutation, NKI high risk).Conclusion: Importantly, in this set of biologically poor prognosis tumors, pCR predicts for better outcome, especially when analyzed within breast cancer subsets. Keywords: reference x sample reference x sample
2012-01-13 | E-GEOD-22226 | biostudies-arrayexpress
Project description:Introduction: The I-SPY 1 TRIAL was designed to evaluate complete pathologic response and tumor volume change, measured by magnetic resonance imaging (MRI), stratified by molecular subtypes and link response to 3-year recurrence free survival (RFS).Methods: Eligible patients had T =3 cm and received neoadjuvant chemotherapy with AC plus optional taxane. Serial MRI, biopsy, and blood draws were conducted over the course of treatment, and a database of multiple molecular profiles was assembled.Results: A total of 221 patients were eligible for analysis: median tumor size was 6.0 cm and median age was 49 years. The I-SPY 1 TRIAL patients had tumors with aggressive biology: 45% were estrogen receptor (ER) negative, 31% Her2+; and 91% high risk by the Netherlands Cancer Institute (NKI) 70-gene profile. After a median of 3.9 years, RFS was 77%. HR/HER2 status improved predictability of RFS with the greatest difference between HR+/HER2- and triple-negative disease (hazard ratio 0.39). Wound healing signature activation, p53 mutation, and Risk of Relapse (ROR) score were highly correlated and also significantly improved the accuracy of RFS predictions when added to stage. The rate of pathologic complete response (pCR) varied considerably from a low of 0-2% (NKI low risk, luminal A) to a high of 43-61% (17q Amplified, HER2 enriched, HR-/HER2+). Both pCR and the more refined residual cancer burden (RCB) were significant predictors of RFS for all patients and even more predictive when analyzed within biomarker subsets. Good risk (NKI low, ROR-S low risk, Wound Healing quiescent, p53 wild type) signatures were associated with significantly higher 3-year RFS than poor risk expression signatures (ROR-S high risk, Wound Healing Activated, p53 mutation, NKI high risk).Conclusion: Importantly, in this set of biologically poor prognosis tumors, pCR predicts for better outcome, especially when analyzed within breast cancer subsets. Keywords: reference x sample
2012-01-13 | GSE22226 | GEO
Project description:Molecular prognostic assays, such as Oncotype DX, are increasingly incorporated into the management of patients with invasive breast carcinoma. BreastPRS is a new molecular assay developed and validated from a meta-analysis of publically available genomic datasets. We applied the assay to matched fresh-frozen (FF) and formalin-fixed paraffin embedded (FFPE) tumor samples to translate the assay to FFPE. A linear relationship of the BreastPRS prognostic score was observed between tissue preservation formats. BreastPRS recurrence scores were compared with Oncotype DX recurrence scores from 246 patients with invasive breast carcinoma and known Oncotype DX results. Using this series, a 120-gene linear discriminant algorithm (LDA) was trained to predict Oncotype DX risk groups and then applied to series of untreated, node-negative, estrogen receptor (ER) – positive patients from previously published studies with known clinical outcomes. Correlation of recurrence score and risk group between Oncotype DX and BreastPRS was statistically significant (P<0.0001). 59 of 260 (23%) patients from four previously published studies were classified as intermediate-risk when the 120-gene LDA was applied. BreastPRS reclassified the 59 patients into binary risk groups (high vs. low-risk). 23 (39%) patients were classified as low-risk 36 (61%) as high-risk [P=0.029, HR: 3.64, 95% CI: 1.40 to 9.50]. At 10 years from diagnosis, the low-risk group had a 90% recurrence-free survival (RFS) rate, compared to 60% for the high-risk group. BreastPRS recurrence score is comparable to Oncotype DX and can reclassify Oncotype DX intermediate-risk patients into two groups with significant differences in RFS. Further studies are needed to validate these findings. Expression profiles of 246 invasive breast carcinomas.
2013-06-23 | E-GEOD-47109 | biostudies-arrayexpress
Project description:A vast portion of the mammalian genome is transcribed as long non-coding RNAs (lncRNAs) acting in the cytoplasm with largely unknown functions. Surprisingly, lncRNAs have been shown to interact with ribosomes, encode uncharacterized proteins, or act as ribosome sponges. These functions still remain mostly undetected and understudied owing to the lack of efficient tools for genome-wide simultaneous identification of ribosome-associated lncRNAs and peptide-producing lncRNAs. Here we present AHARIBO, a method for the detection of lncRNAs either untranslated, but associated with ribosomes, or encoding small peptides. Using AHARIBO in mouse embryonic stem cells during neuronal differentiation, we isolated ribosome-protected RNA fragments, translated RNAs and corresponding de novo synthesized polypeptides. Besides identifying mRNAs under active translation and associated ribosomes, we found and distinguished lncRNAs acting as ribosome sponges or encoding micropeptides, laying the ground for a better functional understanding of hundreds lncRNAs.
2021-09-09 | PXD022679 | Pride
Project description:The aim of this study was to investigate the role of lncRNAs in acute myeloid leukemia with normal cytogenetics (NC-AML). To this end, we used RNA-sequencing approach on forty NC-AML to evaluate the lncRNAs expression profile.
2016-10-01 | GSE62852 | GEO
Project description:Molecular prognostic assays, such as Oncotype DX, are increasingly incorporated into the management of patients with invasive breast carcinoma. BreastPRS is a new molecular assay developed and validated from a meta-analysis of publically available genomic datasets. We applied the assay to matched fresh-frozen (FF) and formalin-fixed paraffin embedded (FFPE) tumor samples to translate the assay to FFPE. A linear relationship of the BreastPRS prognostic score was observed between tissue preservation formats. BreastPRS recurrence scores were compared with Oncotype DX recurrence scores from 246 patients with invasive breast carcinoma and known Oncotype DX results. Using this series, a 120-gene linear discriminant algorithm (LDA) was trained to predict Oncotype DX risk groups and then applied to series of untreated, node-negative, estrogen receptor (ER) – positive patients from previously published studies with known clinical outcomes. Correlation of recurrence score and risk group between Oncotype DX and BreastPRS was statistically significant (P<0.0001). 59 of 260 (23%) patients from four previously published studies were classified as intermediate-risk when the 120-gene LDA was applied. BreastPRS reclassified the 59 patients into binary risk groups (high vs. low-risk). 23 (39%) patients were classified as low-risk 36 (61%) as high-risk [P=0.029, HR: 3.64, 95% CI: 1.40 to 9.50]. At 10 years from diagnosis, the low-risk group had a 90% recurrence-free survival (RFS) rate, compared to 60% for the high-risk group. BreastPRS recurrence score is comparable to Oncotype DX and can reclassify Oncotype DX intermediate-risk patients into two groups with significant differences in RFS. Further studies are needed to validate these findings.
2013-06-23 | GSE47109 | GEO
Project description:PAPER 1:"Identification of novel subgroups of high-risk pediatric precursor B acute lymphoblastic leukemia (B-ALL) by unsupervised microarray analysis: clinical correlates and therapeutic implications. A Children's Oncology Group (COG) study." ABSTRACT We examined gene expression profiles of pre-treatment specimens from 207 patients from the COG P9906 study to identify signatures of children with high risk B-precursor acute lymphoblastic leukemia (ALL) and to determine whether the resulting clusters are associated with either specific clinical features or treatment response characteristics. Four unsupervised clustering methods were utilized to classify patients into similar groups. The different clustering algorithms showed significant overlap in cluster membership. Two clusters contained all cases with either t(1;19)(q23;p13) translocations or MLL rearrangements. The other six clusters were novel and had no recurring chromosomal abnormalities or distinctive clinical features. Members of two of these novel clusters had significant survival differences when compared to the overall 4-year relapse-free survival (RFS) of 61%. These included clusters of patients with either significantly better (94.7%) or worse (21.0%) RFS at 4 years. Children of Hispanic/Latino ethnicity were disproportionately present in the poor outcome cluster. The poor outcome cluster represents a novel biologically distinctive subset of B-precursor ALL that may occur at least as frequently as BCR/ABL. Further molecular characterization of this cluster may lead to the discovery of genomic abnormalities that can be targeted to improve the currently dismal outcome for children with this gene signature. The Sample data have also been used in another study: PAPER 2: "Gene expression classifiers for minimal residual disease and relapse free survival improve outcome prediction and risk classification in children with high risk acute lymphoblastic leukemia. A Children's Oncology Group study". ABSTRACT Background. Nearly 25% of children with B-precursor ALL present with "high-risk" disease (HR-ALL) that is resistant to current therapies. Gene expression profiling may yield molecular classifiers for outcome prediction that can be used to improve risk classification and therapeutic targeting. Methods. Expression profiles were obtained in pre-treatment leukemic samples from 207 uniformly treated children with HR-ALL. Relapse free survival (RFS) was 61% at 4 years and flow cytometric measures of minimal residual disease (MRD) at the end of induction (day 29) were predictive of outcome (P<0.001). Molecular classifiers predictive of RFS and MRD were developed using extensive cross-validation procedures. Results. A 38 gene molecular risk classifier predictive of RFS (MRC-RFS) distinguished two groups in HR-ALL with different relapse risks: low (4 yr RFS: 81%, n=109) vs. high (4 yr RFS: 50%, n=98) (P<0.0001). In multivariate analysis, the best predictor combined MRC-RFS and day 29 flow MRD data, classifying children into low (87% RFS), intermediate (62% RFS), or high risk (29% RFS) groups (P<0.0001). A 21 gene molecular classifier predictive of MRD could effectively substitute for day 29 flow MRD, yielding a combined classifier that similarly distinguished three risk groups at pre-treatment (low: 82% RFS; intermediate: 63% RFS; and high risk: 45% RFS) (P<0.0001). This combined molecular classifier was further validated on an independent cohort of 84 children with HR-ALL (P = 0.006). Conclusions. Molecular classifiers predictive of RFS and MRD can be used to distinguish distinct prognostic groups within HR-ALL, significantly improving risk classification schemes and the ability to prospectively identify children at diagnosis who will respond to or fail current treatment regimens. NOTE: Due to Children's Oncology Group (COG) restrictions, outcome and MRD data cannot be provided as part of the covariate data for this dataset at the present time. If you would like to arrange individual access to this data, please contact COG or the PI of this study, Dr. Cheryl Willman, at the University of New Mexico Cancer Center (cwillman@unm.edu) to arrange a collaboration. Unsupervised clustering and supervised risk classification analyses of 207 diagnostic samples and associated clinical covariate data. See the Summary for greater details. The data were analyzed using Microarray Suite version 5.0 (MAS 5.0) in the Affymetrix Gene Chip Operating Software Version 1.4. Probe masking was used (see 9906_TT207_Affymetrix_probe_mask.msk, linked below as a supplementary file). Otherwise all Affymetrix default parameter settings were used. Global scaling as the normalization method, with the default target intensity of 500, was used.
2009-06-25 | E-GEOD-11877 | biostudies-arrayexpress