Differential whole-genome doubling and homologous recombination deficiencies across breast cancer subtypes from the Taiwanese population

AbstractWhole-genome doubling (WGD) is an early macro-evolutionary event in tumorigenesis, involving the doubling of an entire chromosome complement. However, its impact on breast cancer subtypes remains unclear. Here, we performed a comprehensive and quantitative analysis of WGD and its influence on breast cancer subtypes in patients from Taiwan and consequently highlight the genomic association between WGD and homologous recombination deficiency (HRD). A higher manifestation of WGD was reported in triple-negative breast cancer, conferring high chromosomal instability (CIN), while HER2 + tumors exhibited early WGD events, with widely varied CIN levels, compared to luminal-type tumors. An association of higher activity of de novo indel signature 2 with WGD and HRD in Taiwanese breast cancer patients was reported. A control test between WGD and pseudo non-WGD samples was further employed to support this finding. The study provides a better comprehension of tumorigenesis in breast cancer subtypes, thus assisting in personalized treatment.

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Expanding Roles of De Novo Lipogenesis in Breast Cancer

International Journal of Environmental Research and Public Health ◽

10.3390/ijerph18073575 ◽

2021 ◽

Vol 18 (7) ◽

pp. 3575

Author(s):

Pasquale Simeone ◽

Stefano Tacconi ◽

Serena Longo ◽

Paola Lanuti ◽

Sara Bravaccini ◽

...

Keyword(s):

Breast Cancer ◽

Fatty Acid ◽

De Novo ◽

Breast Cancer Subtypes ◽

De Novo Lipogenesis ◽

Acid Oxidation ◽

Breast Cancer Patients ◽

Membrane Composition ◽

Lipogenic Enzymes ◽

Cancer Subtypes

In recent years, lipid metabolism has gained greater attention in several diseases including cancer. Dysregulation of fatty acid metabolism is a key component in breast cancer malignant transformation. In particular, de novo lipogenesis provides the substrate required by the proliferating tumor cells to maintain their membrane composition and energetic functions during enhanced growth. However, it appears that not all breast cancer subtypes depend on de novo lipogenesis for fatty acid replenishment. Indeed, while breast cancer luminal subtypes rely on de novo lipogenesis, the basal-like receptor-negative subtype overexpresses genes involved in the utilization of exogenous-derived fatty acids, in the synthesis of triacylglycerols and lipid droplets, and fatty acid oxidation. These metabolic differences are specifically associated with genomic and proteomic changes that can perturb lipogenic enzymes and related pathways. This behavior is further supported by the observation that breast cancer patients can be stratified according to their molecular profiles. Moreover, the discovery that extracellular vesicles act as a vehicle of metabolic enzymes and oncometabolites may provide the opportunity to noninvasively define tumor metabolic signature. Here, we focus on de novo lipogenesis and the specific differences exhibited by breast cancer subtypes and examine the functional contribution of lipogenic enzymes and associated transcription factors in the regulation of tumorigenic processes.

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Clinical Significance of Annexin A2 Expression in Breast Cancer Patients

Cancers ◽

10.3390/cancers13010002 ◽

2020 ◽

Vol 13 (1) ◽

pp. 2

Author(s):

Lee D. Gibbs ◽

Kelsey Mansheim ◽

Sayantan Maji ◽

Rajesh Nandy ◽

Cheryl M. Lewis ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Clinical Significance ◽

Cell Lines ◽

Breast Cancer Subtypes ◽

Enzyme Linked Immunosorbent Assay ◽

Breast Cancer Patients ◽

Serum Samples ◽

Cancer Subtypes ◽

Tumor Tissues

Increasing evidence suggests that AnxA2 contributes to invasion and metastasis of breast cancer. However, the clinical significance of AnxA2 expression in breast cancer has not been reported. The expression of AnxA2 in cell lines, tumor tissues, and serum samples of breast cancer patients were analyzed by immunoblotting, immunohistochemistry, and enzyme-linked immunosorbent assay, respectively. We found that AnxA2 was significantly upregulated in tumor tissues and serum samples of breast cancer patients compared with normal controls. The high expression of serum AnxA2 was significantly associated with tumor grades and poor survival of the breast cancer patients. Based on molecular subtypes, AnxA2 expression was significantly elevated in tumor tissues and serum samples of triple-negative breast cancer (TNBC) patients compared with other breast cancer subtypes. Our analyses on breast cancer cell lines demonstrated that secretion of AnxA2 is associated with its tyrosine 23 (Tyr23) phosphorylation in cells. The expression of non-phosphomimetic mutant of AnxA2 in HCC1395 cells inhibits its secretion from cells compared to wild-type AnxA2, which further suggest that Tyr23 phosphorylation is a critical step for AnxA2 secretion from TNBC cells. Our analysis of AnxA2 phosphorylation in clinical samples further confirmed that the phosphorylation of AnxA2 at Tyr23 was high in tumor tissues of TNBC patients compared to matched adjacent non-tumorigenic breast tissues. Furthermore, we observed that the diagnostic value of serum AnxA2 was significantly high in TNBC compared with other breast cancer subtypes. These findings suggest that serum AnxA2 concentration could be a potential diagnostic biomarker for TNBC patients.

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PIWI-Like 1 and PIWI-Like 2 Expression in Breast Cancer

Cancers ◽

10.3390/cancers12102742 ◽

2020 ◽

Vol 12 (10) ◽

pp. 2742

Author(s):

Ramona Erber ◽

Julia Meyer ◽

Helge Taubert ◽

Peter A. Fasching ◽

Sven Wach ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Disease Free Survival ◽

Breast Cancer Subtypes ◽

Breast Cancer Patients ◽

Free Survival ◽

Cancer Subtypes ◽

Prognostic Effect ◽

Enhanced Expression ◽

Disease Free

PIWI-like 1 and PIWI-like 2 play a role in stem cell self-renewal, and enhanced expression has been reported for several tumor entities. However, few studies have investigated PIWI-like 1 and PIWI-like 2 expressions in breast cancer subtypes regarding prognosis. Therefore, we examined protein expression in a large consecutive cohort of breast cancer patients and correlated it to breast cancer subtypes and survival outcome. PIWI-like 1 and PIWI-like 2 expressions were evaluated using immunohistochemistry in a cohort of 894 breast cancer patients, of whom 363 were eligible for further analysis. Percentage and intensity of stained tumor cells were analyzed and an immunoreactive score (IRS) was calculated. The interaction of PIWI-like 1 and PIWI-like 2 showed a prognostic effect on survival. For the combination of high PIWI-like 1 and low PIWI-like 2 expressions, adjusted hazard ratios (HRs) were significantly higher with regard to overall survival (OS) (HR 2.92; 95% confidence interval (CI) 1.24, 6.90), disease-free survival (DFS) (HR 3.27; 95% CI 1.48, 7.20), and distant disease-free survival (DDFS) (HR 7.64; 95% CI 2.35, 24.82). Both proteins were significantly associated with molecular-like and PAM50 subgroups. Combining high PIWI-like 1 and low PIWI-like 2 expressions predicted poorer prognosis and both markers were associated with aggressive molecular subtypes.

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Abstract P5-08-20: Association between quantitative mammographic density and breast cancer subtypes among Chinese breast cancer patients

10.1158/1538-7445.sabcs19-p5-08-20 ◽

2020 ◽

Author(s):

Xiaohong R Yang ◽

Yuan Tian ◽

Jennifer Guida ◽

Hela Koka ◽

Ariane Chan ◽

...

Keyword(s):

Breast Cancer ◽

Cancer Patients ◽

Mammographic Density ◽

Breast Cancer Subtypes ◽

Breast Cancer Patients ◽

Cancer Subtypes

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The influence of breast cancer subtypes on response to anthracycline neoadjuvant chemotherapy in locally advanced breast cancer patients

The Breast ◽

10.1016/s0960-9776(17)30266-7 ◽

2017 ◽

Vol 32 ◽

pp. S81-S82

Author(s):

S. Susnjar ◽

L. Stamatovic ◽

D. Gavrilovic ◽

I. Minic ◽

T. Ursulovic

Keyword(s):

Breast Cancer ◽

Neoadjuvant Chemotherapy ◽

Advanced Breast Cancer ◽

Cancer Patients ◽

Locally Advanced Breast Cancer ◽

Locally Advanced ◽

Advanced Breast ◽

Breast Cancer Subtypes ◽

Breast Cancer Patients ◽

Cancer Subtypes

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The prognostic impact of age in different molecular subtypes of breast cancer: a population-based study

PeerJ ◽

10.7717/peerj.7252 ◽

2019 ◽

Vol 7 ◽

pp. e7252 ◽

Cited By ~ 3

Author(s):

Dongjun Dai ◽

Yiming Zhong ◽

Zhuo Wang ◽

Neelum Aziz Yousafzai ◽

Hongchuan Jin ◽

...

Keyword(s):

Breast Cancer ◽

Older Patients ◽

Risk Model ◽

Young Patients ◽

Competing Risk ◽

Breast Cancer Subtypes ◽

Prognostic Impact ◽

Breast Cancer Patients ◽

Competing Risk Model ◽

Cancer Subtypes

Background The aim of current study was to use competing risk model to calculate the potential differences that age played in the prognosis of different breast cancer subtypes. Methods The cohort was selected from Surveillance, Epidemiology, and End Results (SEER) program. The cumulative incidences of death (CID) was assessed for breast cancer caused deaths and other causes of mortality. The multivariate Cox proportional hazards regression model and the multivariate subdistribution hazard (SH) model were used to evaluate the prognostic value of age in different breast cancer subtypes. Results We involved 33,968 breast cancer patients into our cohort. We found older patients had worse overall survival (OS) than young patients in hormone receptor positive and human epidermal growth factor receptor 2 positive breast cancer (HR+/HER2+) (≥40 vs. <40, HR = 2.07, 95% CI [1.28–3.35], p < 0.05). However, when we used competing risk model, we found young age was an independent risk factor only for triple negative breast cancer (TNBC) (≥40 vs. <40, HR = 0.71, 95% CI [0.56–0.89], p < 0.05). No association was found in other groups. Conclusion Our research was currently the largest sample size study and the first competing risk model-based study on the prognostic association between age and different breast cancer subtypes. We found <40 years patients had worse breast cancer specific survival (BCSS) than older patients in the TNBC subtype.

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Whole Genome Doubling mitigates Muller’s Ratchet in Cancer Evolution

10.1101/513457 ◽

2019 ◽

Cited By ~ 1

Author(s):

Saioa López ◽

Emilia Lim ◽

Ariana Huebner ◽

Michelle Dietzen ◽

Thanos Mourikis ◽

...

Keyword(s):

Negative Selection ◽

Chromosome Complement ◽

Essential Genes ◽

Tumour Suppressor Genes ◽

Whole Genome ◽

Cancer Genes ◽

Cancer Evolution ◽

Genome Doubling ◽

Whole Genome Doubling ◽

Strong Negative Selection

AbstractWhole genome doubling (WGD) is a prevalent macro-evolutionary event in cancer, involving a doubling of the entire chromosome complement. However, despite its prevalence and clinical prognostic relevance, the evolutionary selection pressures for WGD have not been investigated. Here, we explored whether WGD may act to mitigate the irreversible, inexorable ratchet-like, accumulation of deleterious mutations in essential genes. Utilizing 1050 tumor regions from 816 non-small cell lung cancers (NSCLC), we temporally dissect mutations to determine their temporal acquisition in relation to WGD. We find evidence for strong negative selection against homozygous loss of essential cancer genes prior to WGD. However, mutations in essential genes occurring after duplication were not subject to significant negative selection, consistent with WGD providing a buffering effect, decreasing the likelihood of homozygous loss. Finally, we demonstrate that loss of heterozygosity and temporal dissection of mutations can be exploited to identify signals of positive selection in lung, breast, colorectal cancer and other cancer types, enabling the elucidation of novel tumour suppressor genes and a deeper characterization of known cancer genes.

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Computationally Repurposing Drugs for Breast Cancer Subtypes Using a Network-Based Approach

10.21203/rs.3.rs-655605/v1 ◽

2021 ◽

Author(s):

Forough Firoozbakht ◽

Iman Rezaeian ◽

Luis Rueda ◽

Alioune Ngom

Keyword(s):

Breast Cancer ◽

Large Scale ◽

De Novo ◽

Low Cost ◽

Drug Repurposing ◽

Breast Cancer Subtypes ◽

Therapeutic Effects ◽

Phenotypic Data ◽

Cancer Subtypes ◽

Experimental Drugs

Abstract 'De novo' drug discovery is costly, slow, and with high risk. Repurposing known drugs for treatment of other diseases offers a fast, low-cost/risk and highly-efficient method toward development of efficacious treatments. The emergence of large-scale heterogeneous biomolecular networks, molecular, chemical and bioactivity data, and genomic and phenotypic data of pharmacological compounds is enabling the development of new area of drug repurposing called 'in silico' drug repurposing, i.e., computational drug repurposing (CDR). The aim of CDR is to discover new indications for an existing drug (drug-centric) or to identify effective drugs for a disease (disease-centric). Both drug-centric and disease-centric approaches have the common challenge of either assessing the similarity or connections between drugs and diseases. However, traditional CDR is fraught with many challenges due to the underlying complex pharmacology and biology of diseases, genes, and drugs, as well as the complexity of their associations. As such, capturing highly non-linear associations among drugs, genes, diseases by most existing CDR methods has been challenging.We propose a network-based integration approach that can best capture knowledge (and complex relationships) contained within and between drugs, genes and disease data. A network-based machine learning approach is applied thereafter by using the extracted knowledge and relationships in order to identify single and pair of approved or experimental drugs with potential therapeutic effects on different breast cancer subtypes.

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