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Survival outcomes are associated with genomic instability in luminal breast cancers.

ABSTRACT: Breast cancer is the leading cause of cancer related death among women. Breast cancers are generally diagnosed and treated based on clinical and histopathological features, along with subtype classification determined by the Prosigna Breast Cancer Prognostic Gene Signature Assay (also known as PAM50). Currently the copy number alteration (CNA) landscape of the tumour is not considered. We set out to examine the role of genomic instability (GI) in breast cancer survival since CNAs reflect GI and correlate with survival in other cancers. We focused on the 70% of breast cancers classified as luminal and carried out a comprehensive survival and association analysis using Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) data to determine whether CNA Score Quartiles derived from absolute CNA counts are associated with survival. Analysis revealed that patients diagnosed with luminal A breast cancer have a CNA landscape associated with disease specific survival, suggesting that CNA Score can provide a statistically robust prognostic factor. Furthermore, stratification of patients into subtypes based on gene expression has shown that luminal A and B cases overlap, and it is in this region we largely observe luminal A cases with reduced survival outlook. Therefore, luminal A breast cancer patients with quantitatively elevated CNA counts may benefit from more aggressive therapy. This demonstrates how individual genomic landscapes can facilitate personalisation of therapeutic interventions to optimise survival outcomes.

SUBMITTER: King L

PROVIDER: S-EPMC7857737 | biostudies-literature | 2021

REPOSITORIES: biostudies-literature

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Survival outcomes are associated with genomic instability in luminal breast cancers.

King Lydia L Flaus Andrew A Holian Emma E Golden Aaron A

PloS one 20210203 2

Breast cancer is the leading cause of cancer related death among women. Breast cancers are generally diagnosed and treated based on clinical and histopathological features, along with subtype classification determined by the Prosigna Breast Cancer Prognostic Gene Signature Assay (also known as PAM50). Currently the copy number alteration (CNA) landscape of the tumour is not considered. We set out to examine the role of genomic instability (GI) in breast cancer survival since CNAs reflect GI and ...[more]

PMID: 33534788

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Project description:Despite constituting approximately two thirds of all breast cancers, the luminal A and B tumours are poorly classified at both clinical and molecular levels. There are contradictory reports on the nature of these subtypes: some define them as intrinsic entities, others as a continuum. With the aim of addressing these uncertainties and identifying molecular signatures of patients at risk, we conducted a comprehensive transcriptomic and genomic analysis of 2,425 luminal breast cancer samples. Our results indicate that the separation between the molecular luminal A and B subtypes-per definition-is not associated with intrinsic characteristics evident in the differentiation between other subtypes. Moreover, t-SNE and MST-kNN clustering approaches based on 10,000 probes, associated with luminal tumour initiation and/or development, revealed the close connections between luminal A and B tumours, with no evidence of a clear boundary between them. Thus, we considered all luminal tumours as a single heterogeneous group for analysis purposes. We first stratified luminal tumours into two distinct groups by their HER2 gene cluster co-expression: HER2-amplified luminal and ordinary-luminal. The former group is associated with distinct transcriptomic and genomic profiles, and poor prognosis; it comprises approximately 8% of all luminal cases. For the remaining ordinary-luminal tumours we further identified the molecular signature correlated with disease outcomes, exhibiting an approximately continuous gene expression range from low to high risk. Thus, we employed four virtual quantiles to segregate the groups of patients. The clinico-pathological characteristics and ratios of genomic aberrations are concordant with the variations in gene expression profiles, hinting at a progressive staging. The comparison with the current separation into luminal A and B subtypes revealed a substantially improved survival stratification. Concluding, we suggest a review of the definition of luminal A and B subtypes. A proposition for a revisited delineation is provided in this study.

Project description:Integrating tumor heterogeneity in the drug discovery process is a key challenge to tackle breast cancer resistance. Identifying protein targets for functionally distinct tumor clones is particularly important to tailor therapy to the heterogeneous tumor subpopulations and achieve clonal theranostics. For this purpose, we performed an unsupervised, label-free, spatially resolved shotgun proteomics guided by MALDI mass spectrometry imaging (MSI) on 124 selected tumor clonal areas from early luminal breast cancers, tumor stroma, and breast cancer metastases. 2868 proteins were identified. The main protein classes found in the clonal proteome dataset were enzymes, cytoskeletal proteins, membrane-traffic, translational or scaffold proteins, or transporters. As a comparison, gene-specific transcriptional regulators, chromatin related proteins or transmembrane signal receptor were more abundant in the TCGA dataset. Moreover, 26 mutated proteins have been identified. Similarly, expanding the search to alternative proteins databases retrieved 126 alternative proteins in the clonal proteome dataset. Most of these alternative proteins were coded mainly from non-coding RNA. To fully understand the molecular information brought by our approach and its relevance to drug target discovery, the clonal proteomic dataset was further compared to the TCGA breast cancer database and two transcriptomic panels, BC360 (nanoString®) and CDx (Foundation One®). We retrieved 139 pathways in the clonal proteome dataset. Only 55% of these pathways were also present in the TCGA dataset, 68% in BC360 and 50% in CDx. Seven of these pathways have been suggested as candidate for drug targeting, 22 have been associated with breast cancer in experimental or clinical reports, the remaining 19 pathways have been understudied in breast cancer. Among the anticancer drugs, 35 drugs matched uniquely with the clonal proteome dataset, with only 7 of them already approved in breast cancer. The number of target and drug interactions with non-anticancer drugs (such as agents targeting the cardiovascular system, metabolism, the musculoskeletal or the nervous systems) was higher in the clonal proteome dataset (540 interactions) compared to TCGA (83 interactions), BC360 (419 interactions), or CDx (172 interactions). Many of the protein targets identified and drugs screened were clinically relevant to breast cancer and are in clinical trials. Thus, we described the non-redundant knowledge brought by this clone-tailored approach compared to TCGA or transcriptomic panels, the targetable proteins identified in the clonal proteome dataset, and the potential of this approach for drug discovery and repurposing through drug interactions with antineoplastic agents and non-anticancer drugs.

Project description:PurposeHuman epidermal growth factor receptor 2 (HER2)-positive luminal B type comprises estrogen receptor (ER)-positive and HER2-positive cancers, and HER2-negative luminal B type comprises ER-positive cancers showing a Ki-67 labeling index ?14% or progesterone receptor (PR) expression of <20% according to the St. Gallen consensus 2013. The current study aimed to classify intrinsic subtypes according to the St. Gallen consensus 2013 and determine the differences in clinicopathological parameters and survival outcomes among the molecular types, especially among the luminal types.MethodsAssessment of molecular types was performed for 267 invasive ductal carcinomas. The differences in clinicopathological parameters, disease-free survival (DFS), and overall survival (OS) among the molecular types were analyzed.ResultsThe luminal B type was the most prevalent, at 44.9%, followed by the luminal A, triple-negative (including basal-like type), and HER2 type, at 21.7%, 18.7%, and 14.6%, respectively. There were statistically significant differences in size (p=0.003), nodal status (p=0.046), histologic grade (p<0.001), p53 (p<0.001) and cyclooxygenase 2 (COX-2) positivity (p=0.002), recurrence (p=0.001) and death rates (p=0.036), DFS (p=0.002), and OS (p=0.039) among the molecular types. Significant differences in size (p=0.009), nodal metastasis (p=0.019), histologic grade (p<0.001), p53 positivity (p=0.001), and PR expression (p<0.001) were noted between the luminal A and B types. Among the luminal B type cancers, the distributions of ER and PR scores showed significant differences (p=0.003, p=0.003). p53 positivity in the luminal B type cancers was related to shortened DFS (p=0.034). In luminal type cancers, COX-2 positivity was related to longer DFS (p=0.026).ConclusionDifferent management guidelines should be considered for the luminal A and luminal B breast cancer types. Positive p53 expression in luminal B type cancers and negative COX-2 expression in luminal type cancers seem to be related to poor clinical outcome.

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Survival outcomes are associated with genomic instability in luminal breast cancers.

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Survival outcomes are associated with genomic instability in luminal breast cancers.

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