Single-cell and spatially resolved analysis uncovers cell heterogeneity of breast cancer
Ontology highlight
ABSTRACT:
ORGANISM(S): Homo sapiens
PROVIDER: GSE198745 | GEO | 2022/03/19
REPOSITORIES: GEO
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Project description:Breast cancer classification has been in the focus of numerous large worldwide efforts, analyzing the molecular basis of breast cancer subtypes, aiming to associate them with clinical outcome and improve the current diagnostic routine. Genomic and transcriptomic profiles of breast cancer have been well established, however the proteomic contribution to these profiles is yet to be elucidated. In this work, we examined inter-tumor heterogeneity by performing a mass-spectrometry (MS)-based proteomic analysis of more than 130 clinical breast samples originating from three breast cancer subtypes and healthy tissue. Unsupervised analysis identified four proteomic clusters, among them one that represents a novel luminal subtype characterized by increased cancer signaling. This subtype was further validated using an independent protein-based dataset, but not in two independent transcriptome cohorts. Altogether, our results demonstrate the importance of deep proteomic analysis, which may affect cancer treatment decision making.
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Project description:Breast cancer is a heterogeneous disease described in well-recognized biological subtypes. Particularly, gene expression profiling has revealed 5 intrinsic subtypes of breast cancer characterized by different biological and clinical features. The diversity of the intrinsic subtypes across human population has been limited described, and there is few information about the genomic architecture of breast tumors in Mexican or Hispanic populations. In this study, we performed PAM50 assay, based in Affymetrix microarray profiling of 128 fresh frozen tumors from Mexican Latino Hispanic population, to describe the overall distribution of subtypes, and characterize the relation to clinicopathologic characteristics. As well, we correlated the mRNA expression patterns with specific copy number alterations (CPA), in order to analyze their role in breast tumors. A total of 100 blood-tumor samples were assayed using Affymetrix 6.0 SNP arrays; segmentation analysis and GISTIC were performed to identify focal amplifications or deletions. The distribution of PAM50 intrinsic subtypes in our cohort was computed to be 44% luminal A, 20% luminal B, 12.0% HER2-enriched, 12% basal-like, and 12% normal-like. Study comparison with the literature mainly TCGA and METABRIC (most of the patients came from Caucasian population), as well as LACE (which describe a population study) show a similar distribution of the intrinsic subtypes within Hispanic and Caucasian populations. Interestingly, basal-like subtype is less represented than in African-American race. The sum of sensitivity and specificity between the clinicopathologic and intrinsic subtype categories across 4 groups (excluding normal-like) was 50% and 87.5%, respectively. Differentially expression profiles within the subtypes reveal a set of genes altered in each group with biological relevance to stablish the phenotypical characteristics of each subtype. Our analyses confirmed the already reported copy number data. Importantly, many of the copy number profiles correlated with mRNA subtype. With this analysis we can conclude that breast cancer intrinsic subtypes have been reproduced in Mexican population contributing to the description of the PAM50 subtypes among multiple ethnic groups based on a gene expression assay. Our observation based in the integrative genomic analysis of mRNA expression and CPA allowed us to define gene circuits and phenotypic characteristics that can explain the heterogeneity of breast cancer subtypes.
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Project description:Epithelial ovarian cancer is morphologically and clinically heterogeneous. Transcriptional profiling has revealed molecular subtypes (referred to as M-bM-^@M-^\C-signaturesM-bM-^@M-^]) that correlate to biological as well as clinical features. We aimed to determine gene expression differences between malignant, benign and borderline serous ovarian tumors, and to investigate similarities to the intrinsic molecular subtypes of breast cancer. Global gene expression profiling was performed using Illumina's HT12 Bead Arrays and applied to 59 fresh-frozen ovarian tumors. SAM analysis revealed enrichment of cell cycel processes among the malignant tumors, in line with malignant tumors being highly proliferative. The borderline tumors were split between the malignant and benign tumor clusters, indicating that borderline tumors have both malignant and benign features. Furthermore, nearest centroid classification was performed applying previously published gene profiles for the ovarian cancer C-signatures and the intrinsic breast cancer subtypes, respectively, and showed significant correlations between the malignant serous tumors and the highly aggressive C1, C2 and C4 ovarian cancer signatures, and the basal-like breast cancer subtype. The benign and borderline serous tumors together were significantly correlated to the normal-like breast cancer subtype and the ovarian cancer C3 signature. The borderline tumors, on the other hand, correlated significantly to the Luminal A breast cancer subtype. These findings remained when analyzed in a large, independent dataset. The data in this study link the transcriptional profiles of serous ovarian cancer to the intrinsic molecular subtypes of breast cancer, in line with the shared clinical and molecular features between high-grade serous ovarian cancer and basal-like breast cancer, including an aggressive phenotype, frequent TP53 mutations and a high degree of genomic instability, and suggest that biomarkers and targeted therapies may overlap between these subsets of ovarian and breast cancers. Finally, the link between benign and borderline ovarian cancer and luminal breast cancer may indicate endocrine responsiveness in a subset of ovarian cancers. Total RNA obtained from serous ovarian adenocarcinomas, adenomas and borderline tumors. Gene expression profiling using Illumina's HT12 v4 bead arrays. Application of ovarian cancer molecular subtypes and intrinsic breast cancer subtypes using nearest centroid classification. KRAS and BRAF mutation analyses in the malignant and borderline tumors.
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Project description:Background: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods: Tumor tissue from 425 primary breast cancer patients of the Oslo2 study was cut and blended before being divided into fractions for DNA-, RNA- and protein isolation, and metabolomics, allowing for representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering. Results: Based on DNA copy number data, tumors were scored into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and using integrative clustering, tumors were classified into ten subtypes. The final consensus clustering incorporating all the aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype where these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significant different outcome, and in another a trend was observed. Conclusions: The herein identified six integrated subtypes confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing the knowledge of the luminal A subtype heterogeneity may add pivotal information guiding therapeutic choices, evidently bringing us closer to more personalized treatment for this largest subgroup of breast cancer.
2017-03-01 | GSE81000 | GEO
Project description:Background: Breast cancer is a heterogeneous disease at the clinical and molecular level. In this study we integrate classifications extracted from five different molecular levels in order to identify integrated subtypes. Methods: Tumor tissue from 425 primary breast cancer patients of the Oslo2 study was cut and blended before being divided into fractions for DNA-, RNA- and protein isolation, and metabolomics, allowing for representative and comparable molecular data. Patients were stratified into groups based on their tumor characteristics from five different molecular levels, using various clustering methods. Finally, all previously identified and newly determined subgroups were combined in a multilevel classification using a “cluster-of-clusters” approach with consensus clustering. Results: Based on DNA copy number data, tumors were scored into three groups according to the complex arm aberration index. mRNA expression profiles divided tumors into five molecular subgroups according to PAM50 subtyping, and clustering based on microRNA expression revealed four subgroups. Reverse-phase protein array data divided tumors into five subgroups. Hierarchical clustering of tumor metabolic profiles revealed three clusters. Combining DNA copy number and mRNA expression classified tumors into seven clusters based on pathway activity levels, and using integrative clustering, tumors were classified into ten subtypes. The final consensus clustering incorporating all the aforementioned subtypes revealed six major groups. Five corresponded well with the mRNA subtypes, while a sixth group resulted from a split of the luminal A subtype where these tumors belonged to distinct microRNA clusters. Gain-of-function studies using MCF-7 cells showed that microRNAs differentially expressed between the luminal A clusters were important for cancer cell survival. These microRNAs were used to validate the split in luminal A tumors in four independent breast cancer cohorts. In two cohorts the microRNAs divided tumors into subgroups with significant different outcome, and in another a trend was observed. Conclusions: The herein identified six integrated subtypes confirm the heterogeneity of breast cancer and show that finer subdivisions of subtypes are evident. Increasing the knowledge of the luminal A subtype heterogeneity may add pivotal information guiding therapeutic choices, evidently bringing us closer to more personalized treatment for this largest subgroup of breast cancer.
2017-03-01 | GSE80999 | GEO