Intratumor Heterogeneity and Precision of Microarray-Based Predictors of Breast Cancer Biology and Clinical Outcome
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
SUBMITTER: Barry WT
PROVIDER: S-ECPF-GEOD-23593 | biostudies-other | 2010 May
REPOSITORIES: biostudies-other
Similar Datasets
Project description:Genome-wide expression profiling was performed on 50 core needle biopsies from 18 breast cancer patients using Affymetrix GeneChip Human Genome Plus 2.0 Arrays. Global profiles of expression were characterized using unsupervised clustering methods and variance components models. Precision of predictors of breast cancer biology and clinical outcome were evaluated by interclass correlation.
2010-08-12 | E-GEOD-23593 | biostudies-arrayexpress
Project description:PurposeIdentifying sources of variation in expression microarray data and the effect of variance in gene expression measurements on complex predictive and diagnostic models is essential when translating microarray-based experimental approaches into clinical assays. The technical reproducibility of microarray platforms is well established. Here, we investigate the additional impact of intratumor heterogeneity, a largely unstudied component of variance, on the performance of several microarray-based assays in breast cancer.Patients and methodsGenome-wide expression profiling was performed on 50 core needle biopsies from 18 breast cancer patients using Affymetrix GeneChip Human Genome U133 Plus 2.0 arrays. Global profiles of expression were characterized using unsupervised clustering methods and variance components models. Array-based measures of estrogen receptor (ER) and progesterone receptor (PR) status were compared with immunohistochemistry. The precision of genomic predictors of ER pathway status, recurrence risk, and sensitivity to chemotherapeutics was evaluated by interclass correlation.ResultsGlobal patterns of gene expression demonstrated that intratumor variation was substantially less than the total variation observed across the patient population. Nevertheless, a fraction of genes exhibited significant intratumor heterogeneity in expression. A high degree of reproducibility was observed in single-gene predictors of ER (intraclass correlation coefficient [ICC] = 0.94) and PR expression (ICC = 0.90), and in a multigene predictor of ER pathway activation (ICC = 0.98) with high concordance with immunohistochemistry. Substantial agreement was also observed for multigene signatures of cancer recurrence (ICC = 0.71) and chemotherapeutic sensitivity (ICC = 0.72 and 0.64).ConclusionIntratumor heterogeneity, although present at the level of individual gene expression, does not preclude precise microarray-based predictions of tumor behavior or clinical outcome in breast cancer patients.
| S-EPMC2860437 | biostudies-literature
Project description:Genome-wide expression profiling was performed on 50 core needle biopsies from 18 breast cancer patients using Affymetrix GeneChip Human Genome Plus 2.0 Arrays.
2010-08-12 | GSE23593 | GEO
Project description:Tumors exhibit genomic and phenotypic heterogeneity, which has prognostic significance and may influence response to therapy. Imaging can quantify the spatial variation in architecture and function of individual tumors through quantifying basic biophysical parameters such as CT density or MRI signal relaxation rate; through measurements of blood flow, hypoxia, metabolism, cell death, and other phenotypic features; and through mapping the spatial distribution of biochemical pathways and cell signaling networks using PET, MRI, and other emerging molecular imaging techniques. These methods can establish whether one tumor is more or less heterogeneous than another and can identify subregions with differing biology. In this article, we review the image analysis methods currently used to quantify spatial heterogeneity within tumors. We discuss how analysis of intratumor heterogeneity can provide benefit over more simple biomarkers such as tumor size and average function. We consider how imaging methods can be integrated with genomic and pathology data, instead of being developed in isolation. Finally, we identify the challenges that must be overcome before measurements of intratumoral heterogeneity can be used routinely to guide patient care.
| S-EPMC4688961 | biostudies-literature
Project description:Targeted therapy for patients with HER2-positive (HER2+) breast cancer has improved overall survival, but many patients still suffer relapse and death from the disease. Intratumor heterogeneity of both estrogen receptor (ER) and HER2 expression has been proposed to play a key role in treatment failure, but little work has been done to comprehensively study this heterogeneity at the single-cell level. In this study, we explored the clinical impact of intratumor heterogeneity of ER protein expression, HER2 protein expression, and HER2 gene copy number alterations. Using combined immunofluorescence and in situ hybridization on tissue sections followed by a validated computational approach, we analyzed more than 13 000 single tumor cells across 37 HER2+ breast tumors. The samples were taken both before and after neoadjuvant chemotherapy plus HER2-targeted treatment, enabling us to study tumor evolution as well. We found that intratumor heterogeneity for HER2 copy number varied substantially between patient samples. Highly heterogeneous tumors were associated with significantly shorter disease-free survival and fewer long-term survivors. Patients for which HER2 characteristics did not change during treatment had a significantly worse outcome. This work shows the impact of intratumor heterogeneity in molecular diagnostics for treatment selection in HER2+ breast cancer patients and the power of computational scoring methods to evaluate in situ molecular markers in tissue biopsies.
| S-EPMC6210052 | biostudies-literature
Project description:Many studies failed to demonstrate benefit from the addition of targeted agents to current standard adjuvant FOLFOX chemotherapy in stage III colorectal cancer (CRC) patients. Intratumor heterogeneity may foster the resistant subclones and leads to cancer recurrence. Here, we built a cancer evolution model and applied machine learning analysis to identify potential therapeutic targets. Among 78 CRC cases, whole-genome (WGS) and deep targeted sequencing data generated from paired blood and primary tumor were used for phylogenetic tree reconstruction. Genetic alterations in the PI3K/AKT, and RTK oncogenic signaling pathways were commonly detected in founding clones. The dominant subclones frequently exhibited dysregulations in the TP53, FBXW7/NOTCH1 tumor suppression, and DNA repair pathways. Fourteen genetic mutations were simultaneously selected by random forest and LASSO methods. The logistic regression model had better accuracy (79%), precision (70%), and recall (65%) and area under the curve (AUC) (82%) for cancer recurrence prediction. Three genes, including MYO18A in the founding clone, FBXW7, and ATM in the dominant subclone, affected the prognosis were selected simultaneously by different feature sets. The in vitro studies, HCT-116 cells transfected with MYO18A siRNA demonstrated a significant reduction in cell migration activity by 20-40%. These results indicate that MYO18A plays a crucial role in the migration of human CRC cells. The cancer evolution model revealed the critical mutations in the founding and dominant subclones. They can be used to predict clinical outcomes and the development of novel therapeutic targets for stage III CRC.
| S-EPMC7658598 | biostudies-literature
Project description:This was a retrospective comparison study of SNP-based preimplantation genetic screening (SNP-PGS) and FISH-based preimplantation genetic diagnosis (FISH-PGD) for 575 couples in total with chromosome translocations, including 169 couples treated by SNP-PGS between October 2011 and August 2012, and 406 couples treated by FISH- PGD between January 2005 and October 2011. In total, 773 blastocysts obtained from 169 couples were biopsied and frozen, embryo transfer was carried out on the balanced embryos. The PGS results and pregnancy outcomes were compared with those of FISH-PGD for 406 translocation carriers with 3,968 embryos biopsied on day 3. Of the 773 biopsied blastocysts, reliable SNP-PGS results were obtained for 717 (92.76%). For Robertsonian translocation carriers, the rate of normal/balanced embryos, embryos with translocation-related abnormalities, and embryos with abnormalities unrelated to a translocation were 57.80%, 23.39% and 18.81%, respectively. In reciprocal translocation carriers, the rate of normal/balanced embryos, embryos with translocation-related abnormalities and embryos with abnormalities unrelated to translocation were 35.47%, 52.10% and 12.42%, respectively. There was no significant differences in patient age, basal endocrine level and the average number of retrieved oocytes and good quality day 3 embryos before biopsy in the SNP-PGS group compared with the FISH-PGD group. The number of embryos biopsied in the FISH-PGD group was higher than in the SNP-PGS group. However, the pregnancy rate with successful delivery per oocyte retrieval and the implantation rate were both lower in the FISH-PGD group than in the SNP-PGS group. The spontaneous abortion rate was higher in the FISH-PGD group than in the SNP-PGS group.
2013-03-11 | GSE44994 | GEO
Project description:Proteome and phosphoproteome analysis of 10 patient derived xenografts from one neuroblastoma tumor
2022-02-28 | PXD007901 | Pride
Project description:The hope that genomic biomarkers would dramatically and immediately improve care for common, complex diseases has been tempered by slow progress in their translation beyond bioinformatics. We propose a novel use of genomic information where the goal is to improve estimator precision in a randomized trial. We analyze the potential precision gains from the popular MammaPrint genomic signature and clinical variables in simulations of randomized trials analyzed using covariate adjustment.We apply an estimator for the average treatment effect in the trial that adjusts for prognostic baseline variables to improve precision [1]. This precision gain can be translated directly into sample size reduction and corresponding cost savings. We conduct simulation studies based on resampling genomic and clinical data of breast cancer patients from four publicly available observational studies.Separate simulation studies were conducted based on each of the four data sets, with sample sizes ranging from 115 to 307. Adjusting only for clinical variables provided gains of -1%, 5%, 6%, 17%, compared to the unadjusted estimator. Adjusting only for the MammaPrint genomic signature provided gains of 2%, 4%, 4%, 5%. Simultaneously adjusting for clinical variables and the genomic signature provided gains of 2%, 6%, 7%, 16%. The differences between precision gains from adjusting for genomic plus clinical variables, versus only clinical variables, were -1%, 0%, 1%, 3%.Adjusting only for clinical variables led to substantial precision gains (at least 5%) in three of the four data sets, with a 1% precision loss in the remaining data set. These gains were unchanged or increased when sample sizes were doubled in our simulations. The precision gains due to incorporating genomic information, beyond the gains from adjusting for clinical variables, were not substantial.
| S-EPMC5935844 | biostudies-other
Project description:BackgroundThe prognosis of adrenocortical carcinoma (ACC) is heterogeneous. Genomic studies have identified ACC subgroups characterized by specific molecular alterations, including features measured at DNA level (somatic mutations, chromosome alterations, DNA methylation), which are closely associated with outcome. The aim of this study was to evaluate intratumor heterogeneity of prognostic molecular markers at the DNA level.MethodsTwo different tissue samples (primary tumor, local recurrence or metastasis) were analyzed in 26 patients who underwent surgery for primary or recurrent ACC. DNA-related biomarkers with prognostic role were investigated in frozen and paraffin-embedded samples. Somatic mutations of p53/Rb and Wnt/β-catenin pathways were assessed using next-generation sequencing (n = 26), chromosome alteration profiles were determined using SNP arrays (n = 14) and methylation profiles were determined using four-gene bisulfite pyrosequencing (n = 12).ResultsSomatic mutations for ZNRF3, TP53, CTNN1B and CDKN2A were found in 7, 6, 6 and 4 patients, respectively, with intratumor heterogeneity in 8/26 patients (31%). Chromosome alteration profiles were 'Noisy' (numerous and anarchic alterations) in 8/14 and 'Chromosomal' (extended patterns of loss of heterozygosity) in 5/14 of the study samples. For these profiles, no intratumor heterogeneity was observed. Methylation profiles were hypermethylated in 5/12 and non-hypermethylated in 7/12 of the study samples. Intratumor heterogeneity of methylation profiles was observed in 2/12 patients (17%).ConclusionsIntratumor heterogeneity impacts DNA-related molecular markers. While somatic mutation can differ, prognostic DNA methylation and chromosome alteration profile seem rather stable and might be more robust for the prognostic assessment.
| S-EPMC7424337 | biostudies-literature