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Spatial topology of organelle is a new breast cancer cell classifier.

ABSTRACT: Genomics and proteomics have been central to identify tumor cell populations, but more accurate approaches to classify cell subtypes are still lacking. We propose a new methodology to accurately classify cancer cells based on their organelle spatial topology. Herein, we developed an organelle topology-based cell classification pipeline (OTCCP), which integrates artificial intelligence (AI) and imaging quantification to analyze organelle spatial distribution and inter-organelle topology. OTCCP was used to classify a panel of human breast cancer cells, grown as 2D monolayer or 3D tumor spheroids using early endosomes, mitochondria, and their inter-organelle contacts. Organelle topology allows for a highly precise differentiation between cell lines of different subtypes and aggressiveness. These findings lay the groundwork for using organelle topological profiling as a fast and efficient method for phenotyping breast cancer function as well as a discovery tool to advance our understanding of cancer cell biology at the subcellular level.

SUBMITTER: Wang L

PROVIDER: S-EPMC10384275 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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Spatial topology of organelle is a new breast cancer cell classifier.

Wang Ling L Goldwag Joshua J Bouyea Megan M Barra Jonathan J Matteson Kailie K Maharjan Niva N Eladdadi Amina A Embrechts Mark J MJ Intes Xavier X Kruger Uwe U Barroso Margarida M

iScience 20230628 7

Genomics and proteomics have been central to identify tumor cell populations, but more accurate approaches to classify cell subtypes are still lacking. We propose a new methodology to accurately classify cancer cells based on their organelle spatial topology. Herein, we developed an organelle topology-based cell classification pipeline (OTCCP), which integrates artificial intelligence (AI) and imaging quantification to analyze organelle spatial distribution and inter-organelle topology. OTCCP wa ...[more]

PMID: 37519903

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Project description:BackgroundAnalysis of genome-wide data is often carried out using standard methods such as differential expression analysis, clustering analysis and heatmaps. Beyond that, differential correlation analysis was suggested to identify changes in the correlation patterns between disease states. The detection of differential correlation is a demanding task, as the number of entries in the gene-by-gene correlation matrix is large. Currently, there is no gold standard for the detection of differential correlation and statistical validation.ResultsWe developed two untargeted algorithms (DCloc and DCglob) that identify differential correlation patterns by comparing the local or global topology of correlation networks. Construction of networks from correlation structures requires fixing of a correlation threshold. Instead of a single cutoff, the algorithms systematically investigate a series of correlation thresholds and permit to detect different kinds of correlation changes at the same level of significance: strong changes of a few genes and moderate changes of many genes. Comparing the correlation structure of 208 ER- breast carcinomas and 208 ER+ breast carcinomas, DCloc detected 770 differentially correlated genes with a FDR of 12.8%, while DCglob detected 630 differentially correlated genes with a FDR of 12.1%. In two-fold cross-validation, the reproducibility of the list of the top 5% differentially correlated genes in 140 ER- tumors and in 140 ER+ tumors was 49% for DCloc and 33% for DCglob.ConclusionsWe developed two correlation network topology based algorithms for the detection of differential correlations in different disease states. Clusters of differentially correlated genes could be interpreted biologically and included the marker genes hydroxyprostaglandin dehydrogenase (PGDH) and acyl-CoA synthetase medium chain 1 (ACSM1) of invasive apocrine carcinomas that were differentially correlated, but not differentially expressed. Using random subsampling and cross-validation, DCloc and DCglob were shown to identify specific and reproducible lists of differentially correlated genes.

Project description:BackgroundThe role of miRNAs in familial breast cancer (fBC) is poorly investigated as also in the BRCA-like tumors. To identify a specific miRNA expression pattern which could allow a better fBC classification not only based on clinico-pathological and immunophenotypical parameters we analyzed miRNA profile in familial and sporadic samples. Moreover since BRCA1 tumors and sporadic triple negative (TN) breast tumors share similarities regarding clinical outcomes and some histological characteristics, we focused on TN and not TN cases.MethodsThe sample set included fresh frozen tissue samples, including 39 female fBCs (19 BRCA-related and 20 BRCAX) and 12 male fBC (BRCAX). Moreover, we considered TN and non TN (NTN), 21 BRCA-related and 27 sporadic BCs. MiRNA profiling was performed through GeneChip miRNA v.1.0 Array (Affymetrix). ANOVA, hierarchical and consensus clustering analyses allowed identification of pattern of expression of miRNAs and pathway enrichment analysis, considering validated target genes, was carried out to achieve a deeper biological understanding.ResultsANOVA test led to the identification of 53 deregulated miRNAs; hierarchical and consensus clustering of female fBCs (fFBCs) and male fBCs (fMBCs) highlighted the presence of 3 sample clusters named FBC1, FBC2 and FBC3. We found a correlation between ER-status and the three sample clusters. The three clusters are distinct by a different expression of two clusters of miRNAs (CLU1 and CLU2), which resulted to be different in targeted pathways. In particular, CLU1 targets cellular pathways and CLU2 is involved in epigenetic activities. Considering TN and NTN BRCA-related and sporadic tumors, a hierarchical clustering identified two clusters of miRNAs, which were not so different from CLU1 and CLU2, both in miRNA content and targeted pathways.ConclusionsOur results highlighted the importance of miRNA regulation to better clarify similarities and differences between familial and sporadic BC groups.

Project description:In clinical trials evaluating antibody-conjugated drugs (ADCs), HER2-low breast cancer is defined through protein immunohistochemistry scoring (IHC) 1+ or 2+ without gene amplification. However, in daily practice, the accuracy of IHC is compromised by inter-observer variability. Herein, we aimed to identify HER2-low breast cancer primary tumors by leveraging gene expression profiling. A discovery approach was applied to gene expression profile of institutional INT1 (n = 125) and INT2 (n = 84) datasets. We identified differentially expressed genes (DEGs) in each specific HER2 IHC category 0, 1+, 2+ and 3+. Principal Component Analysis was used to generate a HER2-low signature whose performance was evaluated in the independent INT3 (n = 95), and in the publicly available TCGA and GSE81538 datasets. The association between the HER2-low signature and HER2 IHC categories was evaluated by Kruskal-Wallis test with post hoc pair-wise comparisons. The HER2-low signature discriminatory capability was assessed by estimating the area under the receiver operating characteristic curve (AUC). Gene Ontology and KEGG analyses were performed to evaluate the HER2-low signature genes functional enrichment. A HER2-low signature was computed based on HER2 IHC category-specific DEGs. The twenty genes included in the signature were significantly enriched with lipid and steroid metabolism pathways, peptidase regulation, and humoral immune response. The HER2-low signature values showed a bell-shaped distribution across IHC categories (low values in 0 and 3+; high values in 1+ and 2+), effectively distinguishing HER2-low from 0 (p < 0.001) to 3+ (p < 0.001). Notably, the signature values were higher in tumors scored with 1+ as compared to 0. The HER2-low signature association with IHC categories and its bell-shaped distribution was confirmed in the independent INT3, TCGA and GSE81538 datasets. In the combined INT1 and INT3 datasets, the HER2-low signature achieved an AUC value of 0.74 (95% confidence interval, CI 0.67-0.81) in distinguishing HER2-low vs. the other categories, outperforming the individual ERBB2 mRNA AUC value of 0.52 (95% CI 0.43-0.60). These results represent a proof-of-concept for an observer-independent gene-expression-based classifier of HER2-low status. The herein identified 20-gene signature shows promise in distinguishing between HER2 0 and HER2-low expressing tumors, including those scored as 1+ at IHC, and in developing a selection approach for ADCs candidates.

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Spatial topology of organelle is a new breast cancer cell classifier.

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Spatial topology of organelle is a new breast cancer cell classifier.

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