Dataset Information

Breast tumor microenvironment structures are associated with genomic features and clinical outcome.

ABSTRACT: The functions of the tumor microenvironment (TME) are orchestrated by precise spatial organization of specialized cells, yet little is known about the multicellular structures that form within the TME. Here we systematically mapped TME structures in situ using imaging mass cytometry and multitiered spatial analysis of 693 breast tumors linked to genomic and clinical data. We identified ten recurrent TME structures that varied by vascular content, stromal quiescence versus activation, and leukocyte composition. These TME structures had distinct enrichment patterns among breast cancer subtypes, and some were associated with genomic profiles indicative of immune escape. Regulatory and dysfunctional T cells co-occurred in large 'suppressed expansion' structures. These structures were characterized by high cellular diversity, proliferating cells and enrichment for BRCA1 and CASP8 mutations and predicted poor outcome in estrogen-receptor-positive disease. The multicellular structures revealed here link conserved spatial organization to local TME function and could improve patient stratification.

SUBMITTER: Danenberg E

PROVIDER: S-EPMC7612730 | biostudies-literature | 2022 May

REPOSITORIES: biostudies-literature

ACCESS DATA

Publications

Breast tumor microenvironment structures are associated with genomic features and clinical outcome.

Danenberg Esther E Bardwell Helen H Zanotelli Vito R T VRT Provenzano Elena E Chin Suet-Feung SF Rueda Oscar M OM Green Andrew A Rakha Emad E Aparicio Samuel S Ellis Ian O IO Bodenmiller Bernd B Caldas Carlos C Ali H Raza HR

Nature genetics 20220418 5

The functions of the tumor microenvironment (TME) are orchestrated by precise spatial organization of specialized cells, yet little is known about the multicellular structures that form within the TME. Here we systematically mapped TME structures in situ using imaging mass cytometry and multitiered spatial analysis of 693 breast tumors linked to genomic and clinical data. We identified ten recurrent TME structures that varied by vascular content, stromal quiescence versus activation, and leukocy ...[more]

PMID: 35437329

Similar Datasets

Project description:The breast tumor microenvironment plays an active role in tumorigenesis. Molecular alterations, including epigenetic modifications to DNA, and changes in RNA and protein expression have been identified in tumor-associated stroma; however, there is considerable debate as to whether the stroma is characterized by genomic instability or whether detection of chromosomal alterations in the breast stroma is a reflection of technological artifact rather than the true genomic content of the tumor microenvironment. Methods: Surgically-removed breast stroma specimens from 112 women undergoing reductive mammoplasty (n=15), prophylactic mastectomy (N=6) or mastectomy for a diagnosis of breast disease (n=92) were frozen in optimal cutting temperature medium. Allelic imbalance (AI) analysis was performed in 484 stromal specimens from 98 women using a panel of 52 microsatellite markers; SNP data was generated from a subset of 86 stromal specimens using 250K SNP arrays (Affymetrix). Copy number alterations were identified using Partek Genomics Suite. Results: AI was not detected in 92% (444/484) of stroma specimens. When compared to previously generated AI data from 77 formalin-fixed, paraffin-embedded stroma specimens (Ellsworth et al., Ann Surg Oncol 2004), 32 (42%) of which harbored at least one detectable AI event, the frequency of AI in the FFPE specimens (4.62%) was significantly higher (P<0.0001) than that found in frozen specimens (0.45%). Of the stroma specimens assayed using SNP arrays 95% (82/86) had no detectable alterations and the 11 copy number changes were small and not shared between specimens. Conclusions: The data presented here support a model in which the tumor microenvironment is genetically stable. The direct comparison of copy number alterations between FFPE and frozen research-grade specimens using identical methodologies suggests that past reports of significant AI in breast stroma, both adjacent to and distant from the tumor, reflects artifact in the archival specimens caused by formalin-fixation, paraffin-embedding and tissue storage. 250K STY data was generated for 86 breast stromal specimens. The tissue specimens were analyzed by paired analysis to the SNP data from matched genomic (blood) DNAs

Project description:Ferroptosis genes have recently been reported to be involved in regulating the development of cancer, but their potential role in breast cancer (BRCA) is not fully understood. The purpose of this study is to systematically study the mechanism of ferroptosis in BRCA and its relationship with this cancer's prognosis, cell infiltration, gene mutation, and other clinical features. In this study, The Cancer Genome Atlas breast cancer (TCGA-BRCA) database (UCSC Xena) was used to mine the ferroptosis genes related to BRCA patients, and the genes with prognostic value were screened by Cox regression analysis, which were then used to construct a prognostic model for scoring prognostic molecular risk. The relationships between ferroptosis score and prognosis, molecular typing, and clinical characteristics of BRCA were also analyzed. A total of 176 ferroptosis genes related to BRCA were retrieved from the database, 22 of which were found to be significantly related to BRCA prognosis after screening by single-factor Cox regression analysis (p < 0.01). Unsupervised clustering of samples was performed using factoextra, and two subgroups (ferroptosis cluster A and ferroptosis cluster B) with significant differences in prognosis were identified. Subsequently, single-factor Cox regression analysis and random forest dimensionality reduction were used to screen characteristic genes to construct a ferroptosis score model, which included a high ferroptosis score group and a low ferroptosis score group. The results showed that there were significant differences in ferroptosis scores between the ferroptosis cluster A and B groups. The prognosis of patients with low ferroptosis scores was poor, and the overall survival (OS) rate of patients with high ferroptosis scores was significantly higher, indicating that the prognosis of the sample can be well characterized based on calculated ferroptosis scores. Ferroptosis scores differed significantly according to patient age, TP53 and PIK3CA gene mutations, different PAM50 molecular types, and clinical stages. Ferroptosis activation plays a non-negligible role in tumor occurrence and development. Evaluating the ferroptosis score within BRCA will help advance our understanding of the infiltrating properties of cells in the tumor microenvironment and may guide more effective immunotherapy strategies.

Project description:BackgroundAcute myeloid leukemia (AML) is the most common malignancy of the hematological system, and there are currently a number of studies regarding abnormal alterations in energy metabolism, but fewer reports related to fatty acid metabolism (FAM) in AML. We therefore analyze the association of FAM and AML tumor development to explore targets for clinical prognosis prediction and identify those with potential therapeutic value.MethodsThe identification of AML patients with different fatty acid metabolism characteristics was based on a consensus clustering algorithm. The CIBERSORT algorithm was used to calculate the proportion of infiltrating immune cells. We used Cox regression analysis and least absolute shrinkage and selection operator (LASSO) regression analysis to construct a signature for predicting the prognosis of AML patients. The Genomics of Drug Sensitivity in Cancer database was used to predict the sensitivity of patient samples in high- and low-risk score groups to different chemotherapy drugs.ResultsThe consensus clustering approach identified three molecular subtypes of FAM that exhibited significant differences in genomic features such as immunity, metabolism, and inflammation, as well as patient prognosis. The risk-score model we constructed accurately predicted patient outcomes, with area under the receiver operating characteristic curve values of 0.870, 0.878, and 0.950 at 1, 3, and 5 years, respectively. The validation cohort also confirmed the prognostic evaluation performance of the risk score. In addition, higher risk scores were associated with stronger fatty acid metabolisms, significantly higher expression levels of immune checkpoints, and significantly increased infiltration of immunosuppressive cells. Immune functions, such as inflammation promotion, para-inflammation, and type I/II interferon responses, were also significantly activated. These results demonstrated that immunotherapy targeting immune checkpoints and immunosuppressive cells, such as myeloid-derived suppressor cells (MDSCs) and M2 macrophages, are more suitable for patients with high-risk scores. Finally, the prediction results of chemotherapeutic drugs showed that samples in the high-risk score group had greater treatment sensitivity to four chemotherapy drugs in vitro.ConclusionsThe analysis of the molecular patterns of FAM effectively predicted patient prognosis and revealed various tumor microenvironment (TME) characteristics.

Dataset Information

Breast tumor microenvironment structures are associated with genomic features and clinical outcome.

Publications

Breast tumor microenvironment structures are associated with genomic features and clinical outcome.

Similar Datasets

OmicsDI is part of the ELIXIR infrastructure

Tweets