Dataset Information

Classification of triple-negative breast cancers based on Immunogenomic profiling.

ABSTRACT: BACKGROUND:Abundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures that may facilitate the optimal stratification of TNBC patients responsive to immunotherapy. METHODS:Using four publicly available TNBC genomics datasets, we classified TNBC on the basis of the immunogenomic profiling of 29 immune signatures. Unsupervised and supervised machine learning methods were used to perform the classification. RESULTS:We identified three TNBC subtypes that we named Immunity High (Immunity_H), Immunity Medium (Immunity_M), and Immunity Low (Immunity_L) and demonstrated that this classification was reliable and predictable by analyzing multiple different datasets. Immunity_H was characterized by greater immune cell infiltration and anti-tumor immune activities, as well as better survival prognosis compared to the other subtypes. Besides the immune signatures, some cancer-associated pathways were hyperactivated in Immunity_H, including apoptosis, calcium signaling, MAPK signaling, PI3K-Akt signaling, and RAS signaling. In contrast, Immunity_L presented depressed immune signatures and increased activation of cell cycle, Hippo signaling, DNA replication, mismatch repair, cell adhesion molecule binding, spliceosome, adherens junction function, pyrimidine metabolism, glycosylphosphatidylinositol (GPI)-anchor biosynthesis, and RNA polymerase pathways. Furthermore, we identified a gene co-expression subnetwork centered around five transcription factor (TF) genes (CORO1A, STAT4, BCL11B, ZNF831, and EOMES) specifically significant in the Immunity_H subtype and a subnetwork centered around two TF genes (IRF8 and SPI1) characteristic of the Immunity_L subtype. CONCLUSIONS:The identification of TNBC subtypes based on immune signatures has potential clinical implications for TNBC treatment.

SUBMITTER: He Y

PROVIDER: S-EPMC6310928 | biostudies-literature | 2018 Dec

REPOSITORIES: biostudies-literature

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Classification of triple-negative breast cancers based on Immunogenomic profiling.

He Yin Y Jiang Zehang Z Chen Cai C Wang Xiaosheng X

Journal of experimental & clinical cancer research : CR 20181229 1

<h4>Background</h4>Abundant evidence shows that triple-negative breast cancer (TNBC) is heterogeneous, and many efforts have been devoted to identifying TNBC subtypes on the basis of genomic profiling. However, few studies have explored the classification of TNBC specifically based on immune signatures that may facilitate the optimal stratification of TNBC patients responsive to immunotherapy.<h4>Methods</h4>Using four publicly available TNBC genomics datasets, we classified TNBC on the basis of ...[more]

PMID: 30594216

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Project description:BackgroundApproximately two thirds of patients with localized triple-negative breast cancer (TNBC) harbor residual disease (RD) after neoadjuvant chemotherapy (NAC) and have a high risk-of-recurrence. Targeted therapeutic development for TNBC is of primary significance as no targeted therapies are clinically indicated for this aggressive subset. In view of this, we conducted a comprehensive molecular analysis and correlated molecular features of chemorefractory RD tumors with recurrence for the purpose of guiding downstream therapeutic development.MethodsWe assembled DNA and RNA sequencing data from RD tumors as well as pre-operative biopsies, lymphocytic infiltrate, and survival data as part of a molecular correlative to a phase II post-neoadjuvant clinical trial. Matched somatic mutation, gene expression, and lymphocytic infiltrate were assessed before and after chemotherapy to understand how tumors evolve during chemotherapy. Kaplan-Meier survival analyses were conducted categorizing cancers with TP53 mutations by the degree of loss as well as by the copy number of a locus of 18q corresponding to the SMAD2, SMAD4, and SMAD7 genes.ResultsAnalysis of matched somatic genomes pre-/post-NAC revealed chaotic acquisition of copy gains and losses including amplification of prominent oncogenes. In contrast, significant gains in deleterious point mutations and insertion/deletions were not observed. No trends between clonal evolution and recurrence were identified. Gene expression data from paired biopsies revealed enrichment of actionable regulators of stem cell-like behavior and depletion of immune signaling, which was corroborated by total lymphocytic infiltrate, but was not associated with recurrence. Novel characterization of TP53 mutation revealed prognostically relevant subgroups, which were linked to MYC-driven transcriptional amplification. Finally, somatic gains in 18q were associated with poor prognosis, likely driven by putative upregulation of TGFß signaling through the signal transducer SMAD2.ConclusionsWe conclude TNBCs are dynamic during chemotherapy, demonstrating complex plasticity in subclonal diversity, stem-like qualities, and immune depletion, but somatic alterations of TP53/MYC and TGFß signaling in RD samples are prominent drivers of recurrence, representing high-yield targets for additional interrogation.

Project description:PurposePatients with triple-negative breast cancer (TNBC) who do not achieve pathological complete response (pCR) following neoadjuvant chemotherapy have a high risk of recurrence and death. Molecular characterization may identify patients unlikely to achieve pCR. This neoadjuvant trial was conducted to determine the pCR rate with docetaxel and carboplatin and to identify molecular alterations and/or immune gene signatures predicting pCR.Experimental designPatients with clinical stages II/III TNBC received 6 cycles of docetaxel and carboplatin. The primary objective was to determine if neoadjuvant docetaxel and carboplatin would increase the pCR rate in TNBC compared to historical expectations. We performed whole-exome sequencing (WES) and immune profiling on pre-treatment tumor samples to identify alterations that may predict pCR. Thirteen matching on-treatment samples were also analyzed to assess changes in molecular profiles.ResultsFifty-eight of 127 (45.7%) patients achieved pCR. There was a non-significant trend toward higher mutation burden for patients with residual cancer burden (RCB) 0/I versus RCB II/III (median 80 versus 68 variants, p 0.88). TP53 was the most frequently mutated gene, observed in 85.7% of tumors. EGFR, RB1, RAD51AP2, SDK2, L1CAM, KPRP, PCDHA1, CACNA1S, CFAP58, COL22A1, and COL4A5 mutations were observed almost exclusively in pre-treatment samples from patients who achieved pCR. Seven mutations in PCDHA1 were observed in pre-treatment samples from patients who did not achieve pCR. Several immune gene signatures including IDO1, PD-L1, interferon gamma signaling, CTLA4, cytotoxicity, tumor inflammation signature, inflammatory chemokines, cytotoxic cells, lymphoid, PD-L2, exhausted CD8, Tregs, and immunoproteasome were upregulated in pre-treatment samples from patients who achieved pCR.ConclusionNeoadjuvant docetaxel and carboplatin resulted in a pCR of 45.7%. WES and immune profiling differentiated patients with and without pCR.Trial registrationClinical trial information: NCT02124902, Registered 24 April 2014 & NCT02547987, Registered 10 September 2015.

Project description:Background: The aim of this paper was to identify an immunotherapy-sensitive subtype for estrogen receptor-positive breast cancer (ER+ BC) patients by exploring the relationship between cancer genetic programs and antitumor immunity via multidimensional genome-scale analyses. Methods: Multidimensional ER+ BC high-throughput data (raw count data) including gene expression profiles, copy number variation (CNV) data, single-nucleotide polymorphism mutation data, and relevant clinical information were downloaded from The Cancer Genome Atlas to explore an immune subtype sensitive to immunotherapy using the Consensus Cluster Plus algorithm based on multidimensional genome-scale analyses. One ArrayExpress dataset and eight Gene Expression Omnibus (GEO) datasets (GEO-meta dataset) as well as the Molecular Taxonomy of Breast Cancer International Consortium dataset were used as validation sets to confirm the findings regarding the immune profiles, mutational features, and survival outcomes of the three identified immune subtypes. Moreover, the development trajectory of ER+ BC patients from the single-cell resolution level was also explored. Results: Through comprehensive bioinformatics analysis, three immune subtypes of ER+ BC (C1, C2, and C3, designated the immune suppressive, activation, and neutral subtypes, respectively) were identified. C2 was associated with up-regulated immune cell signatures and immune checkpoint genes. Additionally, five tumor-related pathways (transforming growth factor, epithelial-mesenchymal transition, extracellular matrix, interferon-γ, and WNT signaling) tended to be more activated in C2 than in C1 and C3. Moreover, C2 was associated with a lower tumor mutation burden, a decreased neoantigen load, and fewer CNVs. Drug sensitivity analysis further showed that C2 may be more sensitive to immunosuppressive agents. Conclusion: C2 (the immune activation subtype) may be sensitive to immunotherapy, which provides new insights into effective treatment approaches for ER+ BC.

Project description:Triple negative breast cancer (TNBC) has negative expression of ER, PR and HER-2. TNBC shows high histological grade and positive rate of lymph node metastasis, easy recurrence and distant metastasis. Molecular typing based on metabolic genes can reflect deeper characteristics of breast cancer and provide support for prognostic evaluation and individualized treatment. Metabolic subtypes of TNBC samples based on metabolic genes were determined by consensus clustering. CIBERSORT method was applied to evaluate the score distribution and differential expression of 22 immune cells in the TNBC samples. Linear discriminant analysis (LDA) established a subtype classification feature index. Kaplan-Meier (KM) and receiver operating characteristic (ROC) curves were generated to validate the performance of prognostic metabolic subtypes in different datasets. Finally, we used weighted correlation network analysis (WGCNA) to cluster the TCGA expression profile dataset and screen the co-expression modules of metabolic genes. Consensus clustering of the TCGA cohort/dataset obtained three metabolic subtypes (MC1, MC2, and MC3). The ROC analysis showed a high prognostic performance of the three clusters in different datasets. Specifically, MC1 had the optimal prognosis, MC3 had a poor prognosis, and the three metabolic subtypes had different prognosis. Consistently, the immune characteristic index established based on metabolic subtypes demonstrated that compared with the other two subtypes, MC1 had a higher IFNγ score, T cell lytic activity and lower angiogenesis score, T cell dysfunction and rejection score. TIDE analysis showed that MC1 patients were more likely to benefit from immunotherapy. MC1 patients were more sensitive to immune checkpoint inhibitors and traditional chemotherapy drugs Cisplatin, Paclitaxel, Embelin, and Sorafenib. Multiclass AUC based on RNASeq and GSE datasets were 0.85 and 0.85, respectively. Finally, based on co-expression network analysis, we screened 7 potential gene markers related to metabolic characteristic index, of which CLCA2, REEP6, SPDEF, and CRAT can be used to indicate breast cancer prognosis. Molecular classification related to TNBC metabolism was of great significance for comprehensive understanding of the molecular pathological characteristics of TNBC, contributing to the exploration of reliable markers for early diagnosis of TNBC and predicting metastasis and recurrence, improvement of the TNBC staging system, guiding individualized treatment.

Project description:BackgroundThe high heterogeneity of triple negative breast cancer (TNBC) is the main clinical challenge for individualized therapy. Considering that fatty acid metabolism (FAM) plays an indispensable role in tumorigenesis and development of TNBC, we proposed a novel FAM-based classification to characterize the tumor microenvironment immune profiles and heterogeneous for TNBC.MethodsWeighted gene correlation network analysis (WGCNA) was performed to identify FAM-related genes from 221 TNBC samples in Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) dataset. Then, non-negative matrix factorization (NMF) clustering analysis was applied to determine FAM clusters based on the prognostic FAM-related genes, which chosen from the univariate/multivariate Cox regression model and the least absolute shrinkage and selection operator (LASSO) regression algorithm. Then, a FAM scoring scheme was constructed to further quantify FAM features of individual TNBC patient based on the prognostic differentially expressed genes (DEGs) between different FAM clusters. Systematically analyses were performed to evaluate the correlation between the FAM scoring system (FS) with survival outcomes, genomic characteristics, tumor microenvironment (TME) features and immunotherapeutic response for TNBC, which were further validated in the Cancer Genome Atlas (TCGA) and GSE58812 datasets. Moreover, the expression level and clinical significancy of the selected FS gene signatures were further validated in our cohort.Results1860 FAM-genes were screened out using WGCNA. Three distinct FAM clusters were determined by NMF clustering analysis, which allowed to distinguish different groups of patients with distinct clinical outcomes and tumor microenvironment (TME) features. Then, prognostic gene signatures based on the DEGs between different FAM clusters were identified using univariate Cox regression analysis and Lasso regression algorithm. A FAM scoring scheme was constructed, which could divide TNBC patients into high and low-FS subgroups. Low FS subgroup, characterized by better prognosis and abundance with effective immune infiltration. While patients with higher FS were featured with poorer survival and lack of effective immune infiltration. In addition, two independent immunotherapy cohorts (Imvigor210 and GSE78220) confirmed that patients with lower FS demonstrated significant therapeutic advantages from anti-PD-1/PD-L1 immunotherapy and durable clinical benefits. Further analyses in our cohort found that the differential expression of CXCL13, FBP1 and PLCL2 were significantly associated with clinical outcomes of TNBC samples.ConclusionsThis study revealed FAM plays an indispensable role in formation of TNBC heterogeneity and TME diversity. The novel FAM-based classification could provide a promising prognostic predictor and guide more effective immunotherapy strategies for TNBC.

Dataset Information

Classification of triple-negative breast cancers based on Immunogenomic profiling.

Publications

Classification of triple-negative breast cancers based on Immunogenomic profiling.

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