Differential Survival and Therapy Benefit of Patients with Breast Cancer Are Characterized by Distinct Epithelial and Immune Cell Microenvironments

Kester, Lennart; Seinstra, Daniëlle; Rossum, Annelot G.J. van; Vennin, Claire; Hoogstraat, Marlous; Velden, Daphne van der; Opdam, Mark; Werkhoven, Erik van; Hahn, Kerstin; Nederlof, Iris; Lips, Ester H; Mandjes, Ingrid A.M.; Leeuwen-Stok, A. Elise van; Canisius, Sander; Imholz, Alex L.T.; Portielje, Johanneke E.A.; Bos, Monique E.M.M.; Bakker, Sandra D.; Rutgers, Emiel J.; Horlings, Hugo M.; Wesseling, Jelle; Voest, Emile E.; Wessels, Lodewyk F.A.; Kok, Marleen; Oosterkamp, Hendrika M.; Oudenaarden, Alexander van; Linn, Sabine C.; Rheenen, Jacco van

doi:10.1158/1078-0432.ccr-21-1442

Cited by 8 publications

(12 citation statements)

References 41 publications

(57 reference statements)

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“…10 copper-dependent genes (Negative hits: MTF1 , GLS , CDKN2A ; Positive hits: FDX1 , LIAS , LIPT1 , DLD , DLAT , PDHA1 , PDHB ) were obtained from the study by Tsvetkov et al (2022 ). We downloaded the BC single-cell sequencing dataset GSE168410 ( Kester et al, 2022 ) from the GEO database ( https://www.ncbi.nlm.nih.gov/geo/ ). We also downloaded two databases GSE20685 ( Kao et al, 2011 ) and GSE20711 ( Dedeurwaerder et al, 2011 ) from the GEO database as the external validation cohorts to validate our model.…”

Section: Methodsmentioning

confidence: 99%

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Construction of a prognostic model related to copper dependence in breast cancer by single-cell sequencing analysis

2022

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Purpose: To explore the clinical significance of copper-dependent-related genes (CDRG) in female breast cancer (BC).Methods: CDRG were obtained by single-cell analysis of the GSE168410 dataset in the Gene Expression Omnibus (GEO) database. According to a 1:1 ratio, the Cancer Genome Atlas (TCGA) cohort was separated into a training and a test cohort randomly. Based on the training cohort, the prognostic model was built using COX and Lasso regression. The test cohort was used to validate the model. The GSE20685 dataset and GSE20711 dataset were used as two external validation cohorts to further validate the prognostic model. According to the median risk score, patients were classified as high-risk or low-risk. Survival analysis, immune microenvironment analysis, drug sensitivity analysis, and nomogram analysis were used to evaluate the clinical importance of this prognostic model.Results: 384 CDRG were obtained by single-cell analysis. According to the prognostic model, patients were classified as high-risk or low-risk in both cohorts. The high-risk group had a significantly worse prognosis. The area under the curve (AUC) of the model was around 0.7 in the four cohorts. The immunological microenvironment was examined for a possible link between risk score and immune cell infiltration. Veliparib, Selumetinib, Entinostat, and Palbociclib were found to be more sensitive medications for the high-risk group after drug sensitivity analysis.Conclusion: Our CDRG-based prognostic model can aid in the prediction of prognosis and treatment of BC patients.

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Section: Methodsmentioning

confidence: 99%

Section: Data Collectionmentioning

confidence: 99%

Construction of a prognostic model related to copper dependence in breast cancer by single-cell sequencing analysis

2022

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“…As mentioned, we demonstrate the utility of our tools in an example scenario of investigating the role of the BC TME in modulating response to NAC. A recent study by Kester et al investigated the association of cellular compositions of the TME with patient survival in BC [43], but to our knowledge, the current study is the rst to analyze the cell-typespeci c transcriptomic pro les in the BC TME to objectively quantify their association with treatment response.…”

Section: Introductionmentioning

confidence: 99%

The expression patterns of different cell types and their interactions in the tumor microenvironment are predictive of breast cancer patient response to neoadjuvant chemotherapy

Ruppin,

Dhruba,

Sahni

et al. 2024

Preprint

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The tumor microenvironment (TME) is a complex ecosystem of diverse cell types whose interactions govern tumor growth and clinical outcome. While the TME’s impact on immunotherapy has been extensively studied, its role in chemotherapy response remains less explored. To address this, we developed DECODEM (DEcoupling Cell-type-specific Outcomes using DEconvolution and Machine learning), a generic computational framework leveraging cellular deconvolution of bulk transcriptomics to associate the gene expression of individual cell types in the TME with clinical response. Employing DECODEM to analyze the gene expression of breast cancer (BC) patients treated with neoadjuvant chemotherapy, we find that the gene expression of specific immune cells (myeloid, plasmablasts, B-cells) and stromal cells (endothelial, normal epithelial, CAFs) are highly predictive of chemotherapy response, going beyond that of the malignant cells. These findings are further tested and validated in a single-cell cohort of triple negative breast cancer. To investigate the possible role of immune cell-cell interactions (CCIs) in mediating chemotherapy response, we extended DECODEM to DECODEMi to identify such CCIs, validated in single-cell data. Our findings highlight the importance of active pre-treatment immune infiltration for chemotherapy success. The tools developed here are made publicly available and are applicable for studying the role of the TME in mediating response from readily available bulk tumor expression in a wide range of cancer treatments and indications.

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“…Subpopulations of CD8-CXCL13 and CD4-CXCL13 T cells predicted atezolizumab benefit in a scRNA-seq study on 22 advanced TNBC patients [ 98 ]. Furthermore, microenvironments containing various abundances of specific myoepithelial, T cells, and epithelial cell types can confer differential treatment benefits for anthracyclines and taxanes in a scRNA-seq study of breast cancer [ 99 ].…”

Section: Introductionmentioning

confidence: 99%

Heterogeneity of triple negative breast cancer: Current advances in subtyping and treatment implications

Asleh

Riaz

Nielsen

2022

J Exp Clin Cancer Res

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As the field of translational ‘omics has progressed, refined classifiers at both genomic and proteomic levels have emerged to decipher the heterogeneity of breast cancer in a clinically-applicable way. The integration of ‘omics knowledge at the DNA, RNA and protein levels is further expanding biologic understanding of breast cancer and opportunities for customized treatment, a particularly pressing need in clinically triple negative tumors. For this group of aggressive breast cancers, work from multiple groups has now validated at least four major biologically and clinically distinct omics-based subtypes. While to date most clinical trial designs have considered triple negative breast cancers as a single group, with an expanding arsenal of targeted therapies applicable to distinct biological pathways, survival benefits may be best realized by designing and analyzing clinical trials in the context of major molecular subtypes. While RNA-based classifiers are the most developed, proteomic classifiers proposed for triple negative breast cancer based on new technologies have the potential to more directly identify the most clinically-relevant biomarkers and therapeutic targets. Phospho-proteomic data further identify targetable signalling pathways in a unique subtype-specific manner. Single cell profiling of the tumor microenvironment represents a promising way to allow a better characterization of the heterogeneity of triple negative breast cancer which could be integrated in a spatially resolved context to build an ecosystem-based patient classification. Multi-omic data further allows in silico analysis of genetic and pharmacologic screens to map therapeutic vulnerabilities in a subtype-specific context. This review describes current knowledge about molecular subtyping of triple negative breast cancer, recent advances in omics-based genomics and proteomics diagnostics addressing the diversity of this disease, key advances made through single cell analysis approaches, and developments in treatments including targeted therapeutics being tested in major clinical trials.

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Differential Survival and Therapy Benefit of Patients with Breast Cancer Are Characterized by Distinct Epithelial and Immune Cell Microenvironments

Cited by 8 publications

References 41 publications

Construction of a prognostic model related to copper dependence in breast cancer by single-cell sequencing analysis

Construction of a prognostic model related to copper dependence in breast cancer by single-cell sequencing analysis

The expression patterns of different cell types and their interactions in the tumor microenvironment are predictive of breast cancer patient response to neoadjuvant chemotherapy

Heterogeneity of triple negative breast cancer: Current advances in subtyping and treatment implications

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