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

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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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“…One such approach is deciphering cell phenotypes within bulk gene expression profile (bGEP) through deconvolution based on marker genes [17, 18] for which a wide range of methods have been developed [19]. The large number of samples with available bGEP can potentially provide the necessary statistical power for a proper analysis [20, 21]. However, traditional statistical models often encounter difficulties when dealing with complex, highly non-linear problems with intricate interactions, such as the role of cell types in pCR.…”

Section: Introductionmentioning

confidence: 99%

Explainable Machine Learning Reveals the Role of the Breast Tumor Microenvironment in Neoadjuvant Chemotherapy Outcome

Azimzade,

Haugen,

Tekpli

et al. 2023

Preprint

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Recent advancements in single-cell RNA sequencing (scRNA-seq) have enabled the identification of phenotypic diversity within breast tumor tissues. However, the contribution of these cell phenotypes to tumor biology and treatment response has remained less understood. This is primarily due to the limited number of available samples and the inherent heterogeneity of breast tumors. To address this limitation, we leverage a state-of-the-art scRNA-seq atlas and employ CIBER-SORTx to estimate cell phenotype fractions by de-convolving bulk expression profiles in more than 2000 samples from patients who have undergone Neoad-juvant Chemotherapy (NAC). We introduce a pipeline based on explainable Machine Learning (XML) to robustly explore the associations between different cell phenotype fractions and the response to NAC in the general population as well as different subtypes of breast tumors. By comparing tumor subtypes, we observe that multiple cell types exhibit a distinct association with pCR within each subtype. Specifically, Dendritic cells (DCs) exhibit a negative association with pathological Complete Response (pCR) in Estrogen Receptor positive, ER+, (Luminal A/B) tumors, while showing a positive association with pCR in ER-(Basal-like/HER2-enriched) tumors. Analysis of new spatial cyclic immunoflu-orescence data and publicly available imaging mass cytometry data showed significant differences in the spatial distribution of DCs between ER subtypes. These variations underscore disparities in the engagement of DCs within the tumor microenvironment (TME), potentially driving their divergent associations with pCR across tumor subtypes. Overall, our findings on 28 different cell types provide a comprehensive understanding of the role played by cellular compo-nents of the TME in NAC outcomes. They also highlight directions for further experimental investigations at a mechanistic level.

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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

Explainable Machine Learning Reveals the Role of the Breast Tumor Microenvironment in Neoadjuvant Chemotherapy Outcome

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