TP53 and breast cancer

Aberrant p53 immunostaining patterns in breast carcinoma of no special type strongly correlate with presence and type of TP53 mutations

Recent studies have revealed an association between TP53 mutations and endocrine resistance in hormone receptor-positive, HER2-negative breast cancer (HR + HER2 -BC). Aberrant p53 immunostaining (IHC) patterns may provide a surrogate marker for TP53 mutations. Building upon a ternary algorithm of aberrant staining patterns, this study evaluates the reliability of p53 IHC as screening tool for TP53 mutations in BC (NST). Furthermore, it describes the histopathological and molecular characteristics of TP53-mutated cases, along with the mutational status of PIK3CA. This study comprised 131 early-stage, node-negative BCs with available core biopsies and resection specimens. Cases were categorized as follows: HR + HER2 - (85 cases), HER2 + (21 cases) and triple negative (TN, 25 cases). Aberrant IHC staining patterns for p53 were defined as overexpression (OE), complete absence (CA) and cytoplasmic (CY). In addition, targeted sequencing of TP53 and PIK3CA genes was performed. TP53 mutations were identified in 53 of 126 cases (42.1%). Within HR + HER2 - cases, TP53 mutations were found in 17 of 80 cases (21.3%). IHC accurately predicted TP53 mutation in 96.2% of cases with a specificity of 100%. Additionally, there was a significant agreement between missense mutations and OE, as well as between truncating mutations and CA (κ 73% and 76%). CY was observed in two TN cases with truncating mutations within the nuclear localization signalling domain of p53. TP53-mutated cases exhibited higher grade, greater nuclear pleomorphism and higher Ki-67 proliferation index and were associated with the PIK3CA wild-type status (p < 0.001). p53 IHC may provide a useful screening tool for identifying TP53-mutated BC of NST.

Promoter profiles in plasma CfDNA exhibits a potential utility of predicting the efficacy of neoadjuvant chemotherapy in breast cancer patients

BACKGROUND

Gene expression profiles in breast tissue biopsies contain information related to chemotherapy efficacy. The promoter profiles in cell-free DNA (cfDNA) carrying gene expression information of the original tissues may be used to predict the response to neoadjuvant chemotherapy in breast cancer as a non-invasive biomarker. In this study, the feasibility of the promoter profiles in plasma cfDNA was evaluated as a novel clinical model for noninvasively predicting the efficacy of neoadjuvant chemotherapy in breast cancer.

METHOD

First of all, global chromatin (5 Mb windows), sub-compartments and promoter profiles in plasma cfDNA samples from 94 patients with breast cancer before neoadjuvant chemotherapy (pCR = 31 vs. non-pCR = 63) were analyzed, and then classifiers were developed for predicting the efficacy of neoadjuvant chemotherapy in breast cancer. Further, the promoter profile changes in sequential cfDNA samples from 30 patients (pCR = 8 vs. non-pCR = 22) during neoadjuvant chemotherapy were analyzed to explore the potential benefits of cfDNA promoter profile changes as a novel potential biomarker for predicting the treatment efficacy.

RESULTS

The results showed significantly distinct promoter profile in plasma cfDNA of pCR patients compared with non-pCR patients before neoadjuvant chemotherapy. The classifier based on promoter profiles in a Random Forest model produced the largest area under the curve of 0.980 (95% CI: 0.978-0.983). After neoadjuvant chemotherapy, 332 genes with significantly differential promoter profile changes in sequential cfDNA samples of pCR patients was observed, compared with non-pCR patients, and their functions were closely related to treatment response.

CONCLUSION

These results suggest that promoter profiles in plasma cfDNA may be a powerful, non-invasive tool for predicting the efficacy of neoadjuvant chemotherapy breast cancer patients before treatment, and the on-treatment cfDNA promoter profiles have potential benefits for predicting the treatment efficacy.

Assessment and process optimization of high throughput biofabrication of immunocompetent breast cancer model for drug screening applications

Recent advancements in 3D cancer modeling have significantly enhanced our ability to delve into the intricacies of carcinogenesis. Despite the pharmaceutical industry's substantial investment of both capital and time in the drug screening and development pipeline, a concerning trend persists: drug candidates screened on conventional cancer models exhibit a dismal success rate in clinical trials. One pivotal factor contributing to this discrepancy is the absence of drug testing on pathophysiologically biomimetic 3D cancer models during pre-clinical stages. Unfortunately, current manual methods of 3D cancer modeling, such as spheroids and organoids, suffer from limitations in reproducibility and scalability. In our study, we have meticulously developed 3D bioprinted breast cancer model utilizing decellularized adipose tissue-based hydrogel obtained via a detergent-free decellularization method. Our innovative printing techniques allows for rapid, high-throughput fabrication of 3D cancer models in a 96-well plate format, demonstrating unmatched scalability and reproducibility. Moreover, we have conducted extensive validation, showcasing the efficacy of our platform through drug screening assays involving two potent anti-cancer drugs, 5-Fluorouracil and PRIMA-1Met. Notably, our platform facilitates effortless imaging and gene expression analysis, streamlining the evaluation process. In a bid to enhance the relevance of our cancer model, we have introduced a heterogeneous cell population into the DAT-based bioink. Through meticulous optimization and characterization, we have successfully developed a biomimetic immunocompetent breast cancer model, complete with microenvironmental cues and diverse cell populations. This breakthrough paves the way for rapid multiplex drug screening and the development of personalized cancer models, marking a paradigm shift in cancer research and pharmaceutical development.

Prioritizing drug targets by perturbing biological network response functions

Therapeutic interventions are designed to perturb the function of a biological system. However, there are many types of proteins that cannot be targeted with conventional small molecule drugs. Accordingly, many identified gene-regulatory drivers and downstream effectors are currently undruggable. Drivers and effectors are often connected by druggable signaling and regulatory intermediates. Methods to identify druggable intermediates therefore have general value in expanding the set of targets available for hypothesis-driven validation. Here we identify and prioritize potential druggable intermediates by developing a network perturbation theory, termed NetPert, for response functions of biological networks. Dynamics are defined by a network structure in which vertices represent genes and proteins, and edges represent gene-regulatory interactions and protein-protein interactions. Perturbation theory for network dynamics prioritizes targets that interfere with signaling from driver to response genes. Applications to organoid models for metastatic breast cancer demonstrate the ability of this mathematical framework to identify and prioritize druggable intermediates. While the short-time limit of the perturbation theory resembles betweenness centrality, NetPert is superior in generating target rankings that correlate with previous wet-lab assays and are more robust to incomplete or noisy network data. NetPert also performs better than a related graph diffusion approach. Wet-lab assays demonstrate that drugs for targets identified by NetPert, including targets that are not themselves differentially expressed, are active in suppressing additional metastatic phenotypes.

Effect of aza-BODIPY-photodynamic therapy on the expression of carcinoma-associated genes and cell death mode

BACKGROUND

Breast cancer is the most common cancer affecting women worldwide.Photodynamic therapy(PDT) has now proven to be a promising form of cancer therapy due to its targeted and low cytotoxicity to healthy cells and tissues.PDT is a technique used to create cell death localized by light after application of a light-sensitive agent.Aza-BODIPY is a promising photosensitizer for use in PDT. Our results showed that aza-BODIPY-PDT induced apoptosis, probably through p53 and caspase3 in MCF-7 cells. Future studies should delineate the molecular mechanisms underlying aza-BODIPY-PDT-induced cell death for a better understanding of the signaling pathways modulated by the therapy so that this novel technology could be implemented in the clinic for treating breast cancer.

AIM

In this study,we aimed to determine the change in the expression levels of 88 carcinoma-associated genes induced by aza-BODIPY-PDT were analyzed so as to understand the specific pathways that are modulated by aza-BODIPY-PDT.

MATERIAL METHOD

In this study,the molecular basis of the anti-cancer activity of aza-BODIPY-PDT was investigated.Induction of apoptosis and necrosis in MCF-7 breast cancer cells after treatment with aza- BODIPY derivative with phthalonitrile substituents (aza-BODIPY) followed by light exposure was evaluated by Annexin V 7- Aminoactinomycin D (7-AAD) flow cytometry.

RESULTS

Aza-BODIPY-PDT induced cell death in MCF-7 cells treated with aza-BODIPY-PDT; flow cytometry revealed that 28 % of the cells died by apoptosis. Seven of the 88 carcinoma-associated genes that were assayed were differentially expressed -EGF, LEF1, WNT1, TCF7, and TGFBR2 were downregulated, and CASP3 and TP53 were upregulated - in cells subjected to aza-BODIPY-PDT.This made us think that the aza-BODIPY-PDT induced caspase 3 and p53-mediated apoptosis in MCF7 cells.

CONCLUSION

In our study,it was determined that the application of aza-BODIPY-PDT to MCF7 cells had a negative effect on cell connectivity and cell cycle.The fact that the same effect was not observed in control cells and MCF7 cells in the dark field of aza-BODIPY indicates that aza-BODIPY has a strong phodynamic anticancer effect.

Enhancer regulatory networks globally connect non-coding breast cancer loci to cancer genes

Genetic studies have associated thousands of enhancers with breast cancer. However, the vast majority have not been functionally characterized. Thus, it remains unclear how variant-associated enhancers contribute to cancer. Here, we perform single-cell CRISPRi screens of 3,512 regulatory elements associated with breast cancer to measure the impact of these regions on transcriptional phenotypes. Analysis of >500,000 single-cell transcriptomes in two breast cancer cell lines shows that perturbation of variant-associated enhancers disrupts breast cancer gene programs. We observe variant-associated enhancers that directly or indirectly regulate the expression of cancer genes. We also find one-to-multiple and multiple-to-one network motifs where enhancers indirectly regulate cancer genes. Notably, multiple variant-associated enhancers indirectly regulate TP53. Comparative studies illustrate sub-type specific functions between enhancers in ER+ and ER- cells. Finally, we developed the pySpade package to facilitate analysis of single-cell enhancer screens. Overall, we demonstrate that enhancers form regulatory networks that link cancer genes in the genome, providing a more comprehensive understanding of the contribution of enhancers to breast cancer development.

Multidimensional quantitative phenotypic and molecular analysis reveals neomorphic behaviors of p53 missense mutants

Mutations in the TP53 tumor suppressor gene occur in >80% of the triple-negative or basal-like breast cancer. To test whether neomorphic functions of specific TP53 missense mutations contribute to phenotypic heterogeneity, we characterized phenotypes of non-transformed MCF10A-derived cell lines expressing the ten most common missense mutant p53 proteins and observed a wide spectrum of phenotypic changes in cell survival, resistance to apoptosis and anoikis, cell migration, invasion and 3D mammosphere architecture. The p53 mutants R248W, R273C, R248Q, and Y220C are the most aggressive while G245S and Y234C are the least, which correlates with survival rates of basal-like breast cancer patients. Interestingly, a crucial amino acid difference at one position-R273C vs. R273H-has drastic changes on cellular phenotype. RNA-Seq and ChIP-Seq analyses show distinct DNA binding properties of different p53 mutants, yielding heterogeneous transcriptomics profiles, and MD simulation provided structural basis of differential DNA binding of different p53 mutants. Integrative statistical and machine-learning-based pathway analysis on gene expression profiles with phenotype vectors across the mutant cell lines identifies quantitative association of multiple pathways including the Hippo/YAP/TAZ pathway with phenotypic aggressiveness. Further, comparative analyses of large transcriptomics datasets on breast cancer cell lines and tumors suggest that dysregulation of the Hippo/YAP/TAZ pathway plays a key role in driving the cellular phenotypes towards basal-like in the presence of more aggressive p53 mutants. Overall, our study describes distinct gain-of-function impacts on protein functions, transcriptional profiles, and cellular behaviors of different p53 missense mutants, which contribute to clinical phenotypic heterogeneity of triple-negative breast tumors.

Increased Expression of miR-223-3p and miR-375-3p and Anti-Inflammatory Activity in HDL of Newly Diagnosed Women in Advanced Stages of Breast Cancer

The expression of inflammation-related miRs bound to high-density lipoproteins (HDLs), the anti-inflammatory activity of HDLs isolated from individuals with breast cancer, and controls were determined. Forty newly diagnosed women with breast cancer naïve of treatment and 10 control participants were included. Cholesterol-loaded bone-marrow-derived macrophages were incubated with HDL from both groups and challenged with lipopolysaccharide (LPS). Interleukin 6 (IL6) and tumor necrosis factor (TNF) in the medium were quantified. The miRs in HDLs were determined by RT-qPCR. Age, body mass index, menopausal status, plasma lipids, and HDL composition were similar between groups. The ability of HDL to inhibit IL6 and TNF production was higher in breast cancer compared to controls, especially in advanced stages of the disease. The miR-223-3p and 375-3p were higher in the HDLs of breast cancer independent of the histological type of the tumor and had a high discriminatory power between breast cancer and controls. The miR-375-3p was greater in the advanced stages of the disease and was inversely correlated with the secretion of inflammatory cytokines. Inflammation-related miRs and the anti-inflammatory role of HDLs may have a significant impact on breast cancer pathophysiology.

Integrated bioinformatics analyses identifying potential biomarkers for type 2 diabetes mellitus and breast cancer: In SIK1-ness and health

The bidirectional causal relationship between type 2 diabetes mellitus (T2DM) and breast cancer (BC) has been established by numerous epidemiological studies. However, the underlying molecular mechanisms are not yet fully understood. Identification of hub genes implicated in T2DM-BC molecular crosstalk may help elucidate on the causative mechanisms. For this, expression series GSE29231 (T2DM-adipose tissue), GSE70905 (BC- breast adenocarcinoma biopsies) and GSE150586 (diabetes and BC breast biopsies) were extracted from Gene Expression Omnibus (GEO) database, and analyzed to obtain differentially expressed genes (DEGs). The overlapping DEGs were determined using FunRich. Gene Ontology (GO), Kyoto Encyclopedia of Genes and Genomes (KEGG) and Transcription Factor (TF) analyses were performed on EnrichR software and a protein-protein interaction (PPI) network was constructed using STRING software. The network was analyzed on Cytoscape to determine hub genes and Kaplan-Meier plots were obtained. A total of 94 overlapping DEGs were identified between T2DM and BC samples. These DEGs were mainly enriched for GO terms RNA polymerase II core promoter proximal region sequence and its DNA binding, and cAMP response element binding protein, and KEGG pathways including bladder cancer, thyroid cancer and PI3K-AKT signaling. Eight hub genes were identified: interleukin 6 (IL6), tumor protein 53 (TP53), interleukin 8 (CXCL8), MYC, matrix metalloproteinase 9 (MMP9), beta-catenin 1 (CTNNB1), nitric oxide synthase 3 (NOS3) and interleukin 1 beta (IL1β). MMP9 and MYC associated unfavorably with overall survival (OS) in breast cancer patients, IL6, TP53, IL1β and CTNNB1 associated favorably, whereas NOS3 did not show any correlation with OS. Salt inducible kinase 1 (SIK1) was identified as a significant key DEG for comorbid samples when compared with BC, also dysregulated in T2DM and BC samples (adjusted p <0.05). Furthermore, four of the significant hub genes identified, including IL6, CXCL8, IL1B and MYC were also differentially expressed for comorbid samples, however at p < 0.05. Our study identifies key genes including SIK1, for comorbid state and 8 hub genes that may be implicated in T2DM-BC crosstalk. However, limitations associated with the insilico nature of this study necessitates for subsequent validation in wet lab. Hence, further investigation is crucial to study the molecular mechanisms of action underlying these genes to fully explore their potential as diagnostic and prognostic biomarkers and therapeutic targets for T2DM-BC association.

Genetic mutations in HER2-positive breast cancer: possible association with response to trastuzumab therapy

BACKGROUND

HER2-positive breast cancer occurs in 15-20% of breast cancer patients and is characterized by poor prognosis. Trastuzumab is considered the key drug for treatment of HER2-positive breast cancer patients. It improves patient survival; however, resistance to trastuzumab remains a challenge in HER2-positive breast cancer patients. Therefore, the prediction of response to trastuzumab is crucial to choose optimal treatment regimens. The aim of the study was to identify genetic variants that could predict response to anti-HER2-targeted therapy (trastuzumab) using next-generation sequencing.

METHOD

Genetic variants in the hotspot regions of 17 genes were studied in 24 Formalin-Fixed Paraffin-Embedded (FFPE) samples using Ion S5 next-generation sequencing system. FFPE samples were collected from HER2‑positive breast cancer patients previously treated with anti‑HER2‑targeted treatment (Trastuzumab). Patients were divided into two groups; trastuzumab-sensitive group and trastuzumab-resistant group based on their response to targeted therapy.

RESULTS

We identified 29 genetic variants in nine genes that only occurred in trastuzumab-resistant patients and could be associated with resistance to targeted therapy including TP53, ATM, RB1, MLH1, SMARCB1, SMO, GNAS, CDH1, and VHL. Four variants out of these 29 variants were repeated in more than one patient; two variants in TP53, one variant in ATM gene, and the last variant in RB1 gene. In addition, three genes were found to be mutated only in resistant patients; MLH1, SMARCB1 and SMO genes. Moreover, one novel allele (c.407A > G, p. Gln136Arg) was detected within exon 4 of TP53 gene in one resistant patient.

CONCLUSION

NGS sequencing is a useful tool to detect genetic variants that could predict response to trastuzumab therapy.

Mutational signatures for breast cancer diagnosis using artificial intelligence

BACKGROUND

Breast cancer is the most common female cancer worldwide. Its diagnosis and prognosis remain scanty, imprecise, and poorly documented. Previous studies have indicated that some genetic mutational signatures are suspected to lead to progression of various breast cancer scenarios. There is paucity of data on the role of AI tools in delineating breast cancer mutational signatures. This study sought to investigate the relationship between breast cancer genetic mutational profiles using artificial intelligence models with a view to developing an accurate prognostic prediction based on breast cancer genetic signatures. Prior research on breast cancer has been based on symptoms, origin, and tumor size. It has not been investigated whether diagnosis of breast cancer can be made utilizing AI platforms like Cytoscape, Phenolyzer, and Geneshot with potential for better prognostic power. This is the first ever attempt for a combinatorial approach to breast cancer diagnosis using different AI platforms.

METHOD

Artificial intelligence (AI) are mathematical algorithms that simulate human cognitive abilities and solve difficult healthcare issues such as complicated biological abnormalities like those experienced in breast cancer scenarios. The current models aimed to predict outcomes and prognosis by correlating imaging phenotypes with genetic mutations, tumor profiles, and hormone receptor status and development of imaging biomarkers that combine tumor and patient-specific features. Geneshotsav 2021, Cytoscape 3.9.1, and Phenolyzer Nature Methods, 12:841-843 (2015) tools, were used to mine breast cancer-associated mutational signatures and provided useful alternative computational tools for discerning pathways and enriched networks of genes of similarity with the overall goal of providing a systematic view of the variety of mutational processes that lead to breast cancer development. The development of novel-tailored pharmaceuticals, as well as the distribution of prospective treatment alternatives, would be aided by the collection of massive datasets and the use of such tools as diagnostic markers.

RESULTS

Specific DNA-maintenance defects, endogenous or environmental exposures, and cancer genomic signatures are connected. The PubMed database (Geneshot) search for the keywords yielded a total of 21,921 genes associated with breast cancer. Then, based on their propensity to result in gene mutations, the genes were screened using the Phenolyzer software. These platforms lend credence to the fact that breast cancer diagnosis using Cytoscape 3.9.1, Phenolyzer, and Geneshot 2021 reveals high profile of the following mutational signatures: BRCA1, BRCA2, TP53, CHEK2, PTEN, CDH1, BRIP1, RAD51C, CASP3, CREBBP, and SMAD3.

Multimodality MRI radiomics analysis of TP53 mutations in triple negative breast cancer

Objectives

To explore the value of T1-weighted imaging (T1WI), T2-weighted imaging (T2WI) and diffusion-weighted imaging (DWI) radiomics features reflecting TP53 mutations in patients with triple negative breast cancer (TNBC).

Study design

This retrospective study enrolled 91 patients with TNBC with TP53 testing (64 patients in the training cohort and 27 patients in the validation cohort). A total of 2832 radiomics features were extracted from the first phase of dynamic contrast-enhanced T1WI, T2WI and ADC maps. Analysis of variance (ANOVA) and the Kruskal-Wallis-test were used for feature selection. Then, linear discriminant analysis (LDA), multilayer perceptron (MLP), logistic regression (LR), LR with LASSO, decision tree (DT), naïve Bayes (NB), random forest (RF), and support vector machine (SVM) models were used for classification.

Results

The validation AUCs of the eight classifiers ranged from 0.74 (NB) to 0.85 (SVM). SVM attained the highest AUC (0.85) and diagnostic accuracy (0.82) of all tested models. The top 3 ranking features in the SVM model were T1-square-first order-skewness (coefficient: 1.735), T2-wavelet-LHH-GLCM-joint energy, and T2-wavelet-LHH-GLCM-inverse difference moment (coefficient: -0.654, -0.634).

Conclusions

Radiomics-based analysis with the SVM model is recommended for the detection of TP53 mutations in TNBC. Furthermore, T1WI- and T2WI-related features could be used as noninvasive biomarkers for predicting TP53 mutations.

The prognostic value of tumor mutation burden (TMB) and its relationship with immune infiltration in breast cancer patients

OBJECTIVE

Although the tumor mutation burden (TMB) was reported as a biomarker for immunotherapy of various cancers, whether it can effectively predict the survival prognosis in breast cancer patients remains unclear. In this study, the prognostic value of TMB and its correlation with immune infiltration were explored by using multigroup studies.

METHODS

The somatic mutation data of 986 breast cancer patients were obtained from TCGA database. Breast cancer patients were divided into a low-TMB group and a high-TMB group according to the quartile of TMB scores. The differentially expressed genes (DEGs) were identified by the "limma" R program. The CIBERSORT algorithm was utilized to estimate the immune cell fraction of each sample. The TIMER database was utilized to evaluate the association between CNVs of immune genes and tumor immune cell infiltration and the prognostic value of the immune cells in breast cancer.

RESULTS

In breast cancer, TP53, PIK3CA, TTN, CDH1 and other genes were the most important mutated genes. Higher survival rate of patients was found in the low-TMB group. Among the top 10 DEGs, three of them belong to the KRT gene family. GSEA enrichment analysis showed that MAPK, Hedgehog, mTOR, TGF-bate and GnRH signaling pathways were enriched in the low-TMB group. The infiltration levels of the most of immune cells were higher in the low-TMB group (P < 0.01). Higher expression of CCL18 and TRGC1 was correlated with poor prognosis. Breast cancer patients with CCL18 copy number variations, especially arm-level gains, showed significantly decreased immune cell infiltration. In the low B cell infiltration group, the survival prognosis of breast cancer patients was poor.

CONCLUSIONS

TMB is a potential prognosis marker in breast cancer. Immune-related gene CCL18 and TRGC1 are biomarkers of poor prognosis while immune (B cell) infiltration is a biomarker of good prognosis.

Chromosomal Instability as Enabling Feature and Central Hallmark of Breast Cancer

Chromosomal instability (CIN) has become a topic of great interest in recent years, not only for its implications in cancer diagnosis and prognosis but also for its role as an enabling feature and central hallmark of cancer. CIN describes cell-to-cell variation in the number or structure of chromosomes in a tumor population. Although extensive research in recent decades has identified some associations between CIN with response to therapy, specific associations with other hallmarks of cancer have not been fully evidenced. Such associations place CIN as an enabling feature of the other hallmarks of cancer and highlight the importance of deepening its knowledge to improve the outcome in cancer. In addition, studies conducted to date have shown paradoxical findings about the implications of CIN for therapeutic response, with some studies showing associations between high CIN and better therapeutic response, and others showing the opposite: associations between high CIN and therapeutic resistance. This evidences the complex relationships between CIN with the prognosis and response to treatment in cancer. Considering the above, this review focuses on recent studies on the role of CIN in cancer, the cellular mechanisms leading to CIN, its relationship with other hallmarks of cancer, and the emerging therapeutic approaches that are being developed to target such instability, with a primary focus on breast cancer. Further understanding of the complexity of CIN and its association with other hallmarks of cancer could provide a better understanding of the cellular and molecular mechanisms involved in prognosis and response to treatment in cancer and potentially lead to new drug targets.

Breast Cancer Dataset, Classification and Detection Using Deep Learning

Incorporating scientific research into clinical practice via clinical informatics, which includes genomics, proteomics, bioinformatics, and biostatistics, improves patients' treatment. Computational pathology is a growing subspecialty with the potential to integrate whole slide images, multi-omics data, and health informatics. Pathology and laboratory medicine are critical to diagnosing cancer. This work will review existing computational and digital pathology methods for breast cancer diagnosis with a special focus on deep learning. The paper starts by reviewing public datasets related to breast cancer diagnosis. Additionally, existing deep learning methods for breast cancer diagnosis are reviewed. The publicly available code repositories are introduced as well. The paper is closed by highlighting challenges and future works for deep learning-based diagnosis.

The correlation of leukocyte-specific protein 1 (LSP1) rs3817198(T>C) polymorphism with breast cancer: A meta-analysis

BACKGROUND

Multiple studies have investigated the correlation of single nucleotide polymorphisms (SNPs) in leukocyte-specific protein 1 (LSP1) with susceptibility to breast cancer (BC) and have yielded inconsistent conclusions, particularly rs3817198(T > C). Consequently, we performed a meta-analysis to estimate this relationship more comprehensively.

METHODS

Four databases were utilized to locate eligible publications: PubMed, Embase, Web of Science, and China National Knowledge Infrastructure. This meta-analysis included 14 studies, including 22 reports of 33194 cases and 36661 controls. The relationship of rs3817198 polymorphism with breast cancer was estimated using odds ratios (ORs) with 95% confidence intervals (CIs). The LSP1 co-expression network was constructed by STRING, and Gene Ontology (GO) and Kyoto Encyclopedia of Genes and Genomes (KEGG) enrichment analyses were performed using DAVIDE. Download TCGA breast cancer mRNA-seq data and analyze the relationship between LSP1 expression and breast cancer chemotherapy sensitivity.

RESULTS

The results indicated that rs3817198(T > C) was positively correlated to with breast malignancy (dominant model: OR = 1.11, 95%CI = 1.06-1.17; recessive model: OR = 1.10, 95%CI = 1.04-1.15; heterozygous model: OR = 1.09, 95%CI = 1.04-1.15; homozygous model: OR = 1.18, 95%CI = 1.09-1.28; additive model: OR = 1.09, 95%CI = 1.05-1.13), among Caucasians and Asians. However, rs3817198(T > C) may reduce the risk of breast carcinoma in Africans. Rs3817198(T > C) might result in breast carcinoma in individuals with BRCA1 and BRCA2 variants and can contribute to estrogen receptor (ER)-positive breast carcinoma. The expression of LSP1 was inversely correlated with the IC50 of doxorubicin (P = 8.91e-15, Cor = -0.23), 5-fluorouracil (P = 1.18e-22, Cor = -0.29), and cisplatin (P = 1.35e-42, Cor = -0.40).

CONCLUSION

Our study identified that LSP1 rs3817198 polymorphism might result in breast malignancy, particularly among Caucasians and Asians, but lower breast cancer susceptibility in African populations. The expression of LSP1 was negatively correlated with the IC50 of doxorubicin, 5-fluorouracil, and cisplatin.

A case report of the sustained and rapid response of bevacizumab in a TP53-positive breast cancer and liver metastatic patient through personalized medicine

HER2-positive metastatic breast cancer is much less frequent than other subgroups of breast cancer. Treatment options for this cancer are mostly limited to systemic chemotherapy, which leads to moderate improvements. Targeted therapy against malignant breast cancer requires the identification of reliable biomarkers for personalized medicine to obtain the maximum benefit of this therapy. Any mutations in the TP53 signaling pathway can be considered as a significant causative factor of breast cancer, for which the identification of target genes plays an important role in selecting the appropriate treatment. The use of personalized gene expression profiling could be valuable to find the direct target of the treatment in this case. The present study assessed the genetic profile of an HER2-positive metastatic breast cancer patient (with a liver metastasis) and figured out a complete and sustained response to bevacizumab. According to the results of next-generation sequencing (NGS) analysis, the patient’s genetic profile showed an increased expression of p4EBP1 and PTEN and the activation of the mTOR signaling pathway with a mutation in the TP53 gene. Based on the common treatment of similar profiling, we administrated bevacizumab/Taxol/Gemzar chemotherapy up to six courses. Accordingly, as the response to treatment was revealed by reducing the volume of the liver metastasis from 4 to 1.4 cm, metastasectomy was performed as a complementary treatment. Hence, personalized gene expression profiling not only is useful for targeted therapy but also could be recommended to avoid prescription of non-responsive drugs.

Impact on breast cancer susceptibility and clinicopathological traits of common genetic polymorphisms in TP53, MDM2 and ATM genes in Sardinian women

Common variants of genes involved in DNA damage correction [tumor protein p53 (TP53), murine double 2 homolog oncoprotein (MDM2) and ataxia-telengiectasia mutated (ATM)] may serve a role in cancer predisposition. The purpose of the present study was to investigate the association of five variants in these genes with breast cancer risk and clinicopathological traits in a cohort of 261 women from northern Sardinia. Polymorphic variants in TP53 (rs17878362, rs1042522 and rs1625895), MDM2 (rs2279744) and ATM (rs1799757) were determined by PCR and TaqMan single nucleotide polymorphism assay in patients with breast cancer (n=136) and healthy controls (n=125). Association with clinicopathological (e.g., age at diagnosis, lymph node involvement, clinical stage) and lifestyle factors (e.g., smoking status, alcohol intake, contraceptive use) was also evaluated. TP53 rs17878362 and rs1625895 polymorphisms were associated with decreased risk of BC diagnosis in patients older than 50 years (codominant and recessive models) and post-menopause (recessive model). Furthermore, there was a significant association between lymph node status (positive vs. negative) and ATM rs1799757-delT in dominant and additive models and between MDM2 rs2279744-allele and use of oral contraceptives. This analysis suggested that TP53 rs17878362 and rs1625895 may affect age of onset of breast cancer and ATM rs1799757 and MDM2 rs2279744 may be associated with lymph node status and prolonged use of oral contraceptives, respectively.

Prognostic value of baseline genetic features and newly identified TP53 mutations in advanced breast cancer

Approximately 30% of breast cancer (BC) patients suffer from disease relapse after definitive treatment. Monitoring BC at baseline and disease progression using comprehensive genomic profiling would facilitate the prediction of prognosis. We retrospectively studied 101 BC patients ultimately experiencing relapse and/or metastases. The baseline and circulating tumor DNA‐monitoring cohorts included patients with baseline tumor tissue and serial plasma samples, respectively. Samples were analyzed with targeted next‐generation sequencing of 425 cancer‐relevant genes. Of 35 patients in the baseline cohort, patients with TP53 mutations (P < 0.01), or CTCF/GNAS mutations (P < 0.01) displayed inferior disease‐free survival, and patients harboring TP53 (P = 0.06) or NOTCH1 (P = 0.06) mutations showed relatively poor overall survival (OS), compared to patients with wild‐type counterparts. Of the 59 patients with serial plasma samples, 11 patients who were newly detected with TP53 mutations had worse OS than patients whose TP53 mutational status remained negative (P < 0.01). These results indicate that an inferior prognosis of advanced breast cancer was potentially associated with baseline TP53, CTCF, and NOTCH1 alterations. Newly identified TP53 mutations after relapse and/or metastasis was another potential prognostic biomarker of poor prognosis.

Platinum-based adjuvant therapy was efficient for triple-negative breast cancer: a meta-analysis from randomized controlled trials

Triple-negative breast cancer (TNBC) is the most aggressive breast cancer. Neoadjuvant chemotherapy was widely accepted for treating TNBC. This systematic review and meta-analysis aimed to evaluate the efficacy, safety, and survival benefit of platinum-based adjuvant therapy (PBAT) in treating TNBC. The keywords were searched in Medline, Embase, Pubmed, and Cochrane Library database up to July 24, 2022. All the randomized control trials (RCTs) comparing PBAT and non-PBAT in treating TNBC were included in our study. The pathological complete remission (pCR) and complications were compared by odds ratio (OR) and 95% confidence intervals (CIs). The overall survival (OS) and relapse-free survival (RFS) were compared by hazard ratio (HR) and 95% CIs. A total of 19 RCTs were included in our meta-analysis, among which 2,501 patients were treated with PBAT and 2,290 with non-PBAT. The patients treated with PBAT combined a significantly higher pCR rate compared to those patients treated with non-PBAT (49.8% versus 36.4%, OR = 1.27, 95%CI = 1.14-1.43, P < 0.001). Besides, patients treated with PBAT had a significantly better RFS (HR = 0.78, 95%CI = 0.63-0.95, P = 0.016), but not in OS (HR = 0.84, P = 0.304). Although the occurrence of neutropenia and nausea were slightly different between the PBAT group (51.5% and 24.4%) and the non-PBAT group (47.0% and 29.4%), the complications were acceptable in the two treatments groups. Our results demonstrated that TNBC patients treated with PBAT could achieve a higher pCR rate and better RFS benefit without a higher complication rate.Highlights Platinum-based adjuvant therapy provided a higher pCR rate for TNBC.Platinum-based adjuvant therapy prolonged the RFS but without prolongingthe OS.Neutropenia and nausea rate was different between group PBAT and non-PBAT.

Association between TP53 mutation and high 21-gene recurrence score in estrogen receptor-positive/HER2-negative breast cancer

We investigated the association between TP53 mutation and 21-gene recurrence score (RS) in ER-positive/HER2-negative breast cancer (BC) using data from 141 patients who underwent TP53 sequencing and Oncotype DX^® tests. We detected TP53 mutations in 18 (12.8%) patients. Most patients with TP53 mutation had a high 21-gene RS (≥26). The average 21-gene RS was higher in TP53 mutant tumors. Multivariate analysis showed that mutated TP53 is an independent factor for a high 21-gene RS. Mutated TP53 remained closely associated with high 21-gene RS in patients with low pathological risk (n = 103). In the ER⁺/PR⁺/HER2-negative subset (n = 356) of The Cancer Genome Atlas, the non-luminal A intrinsic subtype was more prevalent in the group with mutant TP53. mRNA levels of p53-regulated senescence gatekeeper and cell cycle-related genes were increased in BC with mutated TP53. Mutational analysis of TP53 helped identify endocrine-resistant tumors.

Rho GTPase gene expression and breast cancer risk: a Mendelian randomization analysis

The Rho GTPase family consists of 20 genes encoding intracellular signalling proteins that influence cytoskeletal dynamics, cell migration and cell cycle progression. They are implicated in breast cancer progression but their role in breast cancer aetiology is unknown. As aberrant Rho GTPase activity could be associated with breast cancer, we aimed to determine the potential for a causal role of Rho GTPase gene expression in breast cancer risk, using two-sample Mendelian randomization (MR). MR was undertaken in 122,977 breast cancer cases and 105,974 controls, including 69,501 estrogen receptor positive (ER+) cases and 105,974 controls, and 21,468 ER negative (ER-) cases and 105,974 controls. Single nucleotide polymorphisms (SNPs) underlying expression quantitative trait loci (eQTLs) obtained from normal breast tissue, breast cancer tissue and blood were used as genetic instruments for Rho GTPase expression. As a sensitivity analysis, we undertook co-localisation to examine whether findings reflected shared causal variants or genomic confounding. We identified genetic instruments for 14 of the 20 human Rho GTPases. Using eQTLs obtained from normal breast tissue and normal blood, we identified evidence of a causal role of RHOD in overall and ER+ breast cancers (overall breast cancer: odds ratio (OR) per standard deviation (SD) increase in expression level 1.06; (95% confidence interval (CI) 1.03, 1.09; P = 5.65 × 10-5) and OR 1.22 (95% CI 1.11, 1.35; P = 5.22 × 10-5) in normal breast tissue and blood respectively). There was a consistent direction of association for ER- breast cancer, although the effect-estimate was imprecisely estimated. Using eQTLs from breast cancer tissue and normal blood there was some evidence that CDC42 was negatively associated with overall and ER + breast cancer risk. The evidence from colocalization analyses strongly supported our MR results particularly for RHOD. Our study suggests a potential causal role of increased RHOD gene expression, and, although the evidence is weaker, a potential protective role for CDC42 gene expression, in overall and ER+ breast cancers. These finding warrant validation in independent samples and further biological investigation to assess whether they may be suitable targets for drug targeting.

Predicting clinical outcomes of cancer patients with a p53 deficiency gene signature

The tumor suppressor p53, encoded by the TP53 gene, is mutated or nullified in nearly 50% of human cancers. It has long been debated whether TP53 mutations can be utilized as a biomarker to predict clinical outcomes of cancer patients. In this study, we applied computational methods to calculate p53 deficiency scores (PDSs) that reflect the inactivation of the p53 pathway, instead of TP53 mutation status. Compared to TP53 mutation status, the p53 deficiency gene signature is a powerful predictor of overall survival and drug sensitivity in a variety of cancer types and treatments. Interestingly, the PDSs predicted clinical outcomes more accurately than drug sensitivity in cell lines, suggesting that tumor heterogeneity and/or tumor microenvironment may play an important role in predicting clinical outcomes using p53 deficiency gene signatures.

Construction of a genome instability-derived lncRNA-based risk scoring system for the prognosis of hepatocellular carcinoma

Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, genome instability-associated lncRNAs (GILncRNAs) and their performance in clinical prognostic significance in hepatocellular carcinoma (HCC) are rarely reported. Our study constructed a computational framework integrating somatic mutation information and lncRNA expression profiles of HCC genome and we identified 88 GILncRNAs of HCC. Function enrichment analysis revealed that GILncRNAs were involved in various metabolism processes and genome instability of cancer. A genome instability-derived lncRNA-based gene signature (GILncSig) was constructed using training set data. The performance of GILncSig for outcome prediction was validated in testing set and The Cancer Genome Atlas (TCGA) set. The multivariate cox regression analysis and stratification analysis demonstrated GILncSig could serve as an independent prognostic factor for the overall survival of HCC patients. The time-dependent Receiver Operating Characteristic (ROC) curve illustrated GILncSig outperformed two recently published lncRNA signatures for overall survival prediction. The combination of GILncSig and tumor protein p53 (TP53) mutation status exhibited better prognostic performance in survival evaluation compared to TP53 mutation status alone. AC145343.1 was further validated to be a risk factor for HCC in vitro among GILncSig. Overall, our study provided a novel approach for identification of genome instability-associated lncRNAs and established an independent risk score system for outcome prediction of HCC patients, which provided a new insight for exploring in-depth mechanism and potential therapy strategy.

Construction of a genome instability-derived lncRNA-based risk scoring system for the prognosis of hepatocellular carcinoma

Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, genome instability-associated lncRNAs (GILncRNAs) and their performance in clinical prognostic significance in hepatocellular carcinoma (HCC) are rarely reported. Our study constructed a computational framework integrating somatic mutation information and lncRNA expression profiles of HCC genome and we identified 88 GILncRNAs of HCC. Function enrichment analysis revealed that GILncRNAs were involved in various metabolism processes and genome instability of cancer. A genome instability-derived lncRNA-based gene signature (GILncSig) was constructed using training set data. The performance of GILncSig for outcome prediction was validated in testing set and The Cancer Genome Atlas (TCGA) set. The multivariate cox regression analysis and stratification analysis demonstrated GILncSig could serve as an independent prognostic factor for the overall survival of HCC patients. The time-dependent Receiver Operating Characteristic (ROC) curve illustrated GILncSig outperformed two recently published lncRNA signatures for overall survival prediction. The combination of GILncSig and tumor protein p53 (TP53) mutation status exhibited better prognostic performance in survival evaluation compared to TP53 mutation status alone. AC145343.1 was further validated to be a risk factor for HCC in vitro among GILncSig. Overall, our study provided a novel approach for identification of genome instability-associated lncRNAs and established an independent risk score system for outcome prediction of HCC patients, which provided a new insight for exploring in-depth mechanism and potential therapy strategy.

Genetic mutation and biological pathway prediction based on whole slide images in breast carcinoma using deep learning

Breast carcinoma is the most common cancer among women worldwide that consists of a heterogeneous group of subtype diseases. The whole-slide images (WSIs) can capture the cell-level heterogeneity, and are routinely used for cancer diagnosis by pathologists. However, key driver genetic mutations related to targeted therapies are identified by genomic analysis like high-throughput molecular profiling. In this study, we develop a deep-learning model to predict the genetic mutations and biological pathway activities directly from WSIs. Our study offers unique insights into WSI visual interactions between mutation and its related pathway, enabling a head-to-head comparison to reinforce our major findings. Using the histopathology images from the Genomic Data Commons Database, our model can predict the point mutations of six important genes (AUC 0.68-0.85) and copy number alteration of another six genes (AUC 0.69-0.79). Additionally, the trained models can predict the activities of three out of ten canonical pathways (AUC 0.65-0.79). Next, we visualized the weight maps of tumor tiles in WSI to understand the decision-making process of deep-learning models via a self-attention mechanism. We further validated our models on liver and lung cancers that are related to metastatic breast cancer. Our results provide insights into the association between pathological image features, molecular outcomes, and targeted therapies for breast cancer patients.

A Case Report of Targeted Therapy with Anlotinib in a Patient with Advanced Breast Metaplastic Carcinoma

Metaplastic breast carcinoma (MBC) is a rare malignant breast tumor, and no effective chemotherapy unique to metaplastic carcinoma exists. As MBC is typically “triple negative”, endocrine therapy and molecular therapy targeted to Her2 might not be favorable, resulting in a poor prognosis. Anlotinib is currently being tested in patients with breast or cancer. Here, we report a successful case in which anlotinib was used to treat MBC. A 54-year-old female patient visited the hospital after the discovery of a left breast tumor 10 months prior, and tumor redness and swelling had lasted for more than one month. After admission, relevant examinations were performed. After left breast tumor puncture revealed left emulsified biological cancer, the tumor significantly increased in size, and bleeding was obvious after 2 cycles of the “EC” chemotherapy regimen. The curative effect was evaluated as progressive disease (PD). After two cycles of chemotherapy with the “PCb” regimen, the efficacy was still PD. The Karnofsky performance status (KPS) score of the patient after 4 cycles of chemotherapy was 60 points, with severe anemia, and she could not tolerate chemotherapy. The patient was given radiotherapy to stop bleeding, and the tumor further increased in size during radiotherapy. The curative effect was evaluated as PD. After a multidisciplinary consultation in our hospital, we initiated oral anlotinib (12 mg; 2 weeks on, 1 week off). The tumor significantly decreased in size after taking anlotinib, and the efficacy was evaluated as PR. Adverse reactions during treatment were controlled, and progression-free survival (PFS) reached up to 25+ months. The follow-up is ongoing. The patient has provided written informed consent for the case details and images to be published, and at the same time institutional approval was required to publish the case details, we report this case.

Breast Cancer-Epidemiology, Risk Factors, Classification, Prognostic Markers, and Current Treatment Strategies-An Updated Review

Simple Summary

Breast cancer is the most common cancer among women. It is estimated that 2.3 million new cases of BC are diagnosed globally each year. Based on mRNA gene expression levels, BC can be divided into molecular subtypes that provide insights into new treatment strategies and patient stratifications that impact the management of BC patients. This review addresses the overview on the BC epidemiology, risk factors, classification with an emphasis on molecular types, prognostic biomarkers, as well as possible treatment modalities.

Abstract

Breast cancer (BC) is the most frequently diagnosed cancer in women worldwide with more than 2 million new cases in 2020. Its incidence and death rates have increased over the last three decades due to the change in risk factor profiles, better cancer registration, and cancer detection. The number of risk factors of BC is significant and includes both the modifiable factors and non-modifiable factors. Currently, about 80% of patients with BC are individuals aged >50. Survival depends on both stage and molecular subtype. Invasive BCs comprise wide spectrum tumors that show a variation concerning their clinical presentation, behavior, and morphology. Based on mRNA gene expression levels, BC can be divided into molecular subtypes (Luminal A, Luminal B, HER2-enriched, and basal-like). The molecular subtypes provide insights into new treatment strategies and patient stratifications that impact the management of BC patients. The eighth edition of TNM classification outlines a new staging system for BC that, in addition to anatomical features, acknowledges biological factors. Treatment of breast cancer is complex and involves a combination of different modalities including surgery, radiotherapy, chemotherapy, hormonal therapy, or biological therapies delivered in diverse sequences.

VarSAn: associating pathways with a set of genomic variants using network analysis

There is a pressing need today to mechanistically interpret sets of genomic variants associated with diseases. Here we present a tool called ‘VarSAn’ that uses a network analysis algorithm to identify pathways relevant to a given set of variants. VarSAn analyzes a configurable network whose nodes represent variants, genes and pathways, using a Random Walk with Restarts algorithm to rank pathways for relevance to the given variants, and reports P-values for pathway relevance. It treats non-coding and coding variants differently, properly accounts for the number of pathways impacted by each variant and identifies relevant pathways even if many variants do not directly impact genes of the pathway. We use VarSAn to identify pathways relevant to variants related to cancer and several other diseases, as well as drug response variation. We find VarSAn's pathway ranking to be complementary to the standard approach of enrichment tests on genes related to the query set. We adopt a novel benchmarking strategy to quantify its advantage over this baseline approach. Finally, we use VarSAn to discover key pathways, including the VEGFA-VEGFR2 pathway, related to de novo variants in patients of Hypoplastic Left Heart Syndrome, a rare and severe congenital heart defect.

TP53 signature diagnostic system using multiplex reverse transcription-polymerase chain reaction system enables prediction of prognosis of breast cancer patients

Background

TP53 status based on TP53 signature, a gene expression profile to determine the presence or absence of TP53 mutation, is an independent prognostic factor of breast cancer. The purpose of this study was to develop a simple diagnostic system for TP53 signature status.

Methods

We developed a multiplex reverse transcription–polymerase chain reaction system to determine TP53 status. Based on this system, prospectively collected 189 patients with stage I and II breast cancer were determined to have TP53 mutant signature or TP53 wild-type signature. The prognostic significance of the TP53 signature by the diagnostic system was analyzed.

Results

The diagnostic accuracy of TP53 status and reproducibility of this diagnosis system was confirmed. Using the diagnostic system, 89 patients were classified as TP53 mutant signature and the remaining 100 cases were classified as TP53 wild-type signature. Recurrence-free survival (RFS) among patients with TP53 mutant signature was significantly shorter than that among those with TP53 wild-type signature. On univariate and multivariate analyses, the TP53 signature status was an independent predictor of RFS. RFS among patients with TP53 mutant signature was significantly shorter than that among those with TP53 wild-type signature in a cohort of estrogen receptor-positive breast cancer. Although a difference was not significant, no recurrent cases was observed in TP53 wild-type signature group in triple negative breast cancer.

Conclusion

This simple and precise diagnostic system to determine TP53 signature status may help in prognostic assessment, therapeutic decision-making, and treatment optimization in patients with breast cancer.

Supplementary Information

The online version contains supplementary material available at 10.1007/s12282-021-01250-z.

Antimicrobial peptides as potential therapeutics for breast cancer

Breast cancer is the most common and deadliest cancer in women worldwide. Although notable advances have been achieved in the treatment of breast cancer, the overall survival rate of metastatic breast cancer patients is still considerably low due to the development of resistance to breast cancer chemotherapeutic agents and the non-optimal specificity of the current generation of cancer medications. Hence, there is a growing interest in the search for alternative therapeutics with novel anticancer mechanisms. Recently, antimicrobial peptides (AMPs) have gained much attention due to their cost-effectiveness, high specificity of action, and robust efficacy. However, there are no clinical data available about their efficacy. This warrants the increasing need for clinical trials to be conducted to assess the efficacy of this new class of drugs. Here, we will focus on the recent progress in the use of AMPs for breast cancer therapy and will highlight their modes of action. Finally, we will discuss the combination of AMP-based therapeutics with other breast cancer therapy strategies, including nanotherapy and chemotherapy, which may provide a potential avenue for overcoming drug resistance.

Breast Cancer Predisposition Genes and Synthetic Lethality

BRCA1 and BRCA2 are tumor suppressor genes with pivotal roles in the development of breast and ovarian cancers. These genes are essential for DNA double-strand break repair via homologous recombination (HR), which is a virtually error-free DNA repair mechanism. Following BRCA1 or BRCA2 mutations, HR is compromised, forcing cells to adopt alternative error-prone repair pathways that often result in tumorigenesis. Synthetic lethality refers to cell death caused by simultaneous perturbations of two genes while change of any one of them alone is nonlethal. Therefore, synthetic lethality can be instrumental in identifying new therapeutic targets for BRCA1/2 mutations. PARP is an established synthetic lethal partner of the BRCA genes. Its role is imperative in the single-strand break DNA repair system. Recently, Olaparib (a PARP inhibitor) was approved for treatment of BRCA1/2 breast and ovarian cancer as the first successful synthetic lethality-based therapy, showing considerable success in the development of effective targeted cancer therapeutics. Nevertheless, the possibility of drug resistance to targeted cancer therapy based on synthetic lethality necessitates the development of additional therapeutic options. This literature review addresses cancer predisposition genes, including BRCA1, BRCA2, and PALB2, synthetic lethality in the context of DNA repair machinery, as well as available treatment options.

Utility of urinary ctDNA to monitoring minimal residual disease in early breast cancer patients

BACKGROUND

Cancer recurrence for patients with early breast cancer is significant. Patients will benefit from more non-invasive modes of monitoring and we aim to study the feasibility of urinary circulating tumor DNA (ctDNA) to monitor for residual disease (MRD).

METHODS

In this longitudinal study, 300 early breast cancer patients were recruited prospectively. Measurements were taken prior to treatment and at different time points thereafter for a total of 8 measurements. Comparisons were made with healthy volunteers and patients without detectable mutations in urine specimens. Disease free relapse were correlated to both urinary DNA quantity and ctDNA concentration.

RESULTS

Baseline index measurements showed 38% of patients with detectable mutations. The concordance with biopsy tissues was 97.3%. Overall, breast cancer patients had higher urinary DNA compared with healthy volunteers. Over time, fluctuations in urinary DNA was negligible in healthy volunteers, indicating the stability of the marker. Among the patients with detectable mutations, we observed that higher urinary DNA quantity measurements at 6-month and patients with positive mutations were associated with greater risk of relapse. Hazard ratios for patients in this category was 1.65 (95% CI 1.26-2.16) and 1.98 (95% CI 1.48-2.63) respectively.

CONCLUSION

Urinary DNA offers non-invasive probing and real-time monitoring of breast cancer relapse. Our results demonstrated clear clinical relevance in breast cancer and significant risk profiling of early breast cancer patients. This potentially aids to complement current cancer relapse monitoring and may help in early intervention.

Up-regulation of L Antigen Family Member 3 Associates With Aggressive Progression of Breast Cancer

The role of L Antigen Family Member 3 (LAGE3) in breast cancer (BC) has not been sufficiently studied. In this study, we explored the clinical value and biological functions of LAGE3 in BC. Comprehensive analysis of LAGE3 was carried out on The Cancer Genome Atlas, Molecular Taxonomy of Breast Cancer International Consortium and Gene Expression Omnibus datasets. Results showed that LAGE3 expression was higher in BC tissues than in normal breast tissues of public datasets and our local cohort. Moreover, its expression was higher in BC patients with larger tumor size, significant lymph node metastasis, higher tumor grade, and more advanced disease stage. High expression of LAGE3 was correlated with poor prognosis, and LAGE3 could independently predict survival of BC patients. Functional enrichment analysis revealed a correlation between LAGE3 expression and biochemical metabolism and immune-related terms and cancer-related pathways. Analysis of tumor microenvironment indicated that LAGE3 expression was associated with the immune cell infiltration and anti-cancer immunity cycle. LAGE3 expression was higher in triple-negative breast cancer (TNBC) compared to hormone receptor-positive BC, but not HER2-positive subtype. Suppression of LAGE3 expression inhibited the proliferation and induced apoptosis of TNBC cell lines. Besides, the down-regulation of LAGE3 attenuated the migration and invasion but reduced the expression level of epithelial-mesenchymal-transition related proteins in TNBC cell lines. In conclusion, this study demonstrated for the first time that LAGE3 promotes the progression of BC. Therefore, it may be a potential diagnostic and prognostic biomarker, as well as a treatment target for BC.

A Novel Framework to Predict Breast Cancer Prognosis Using Immune-Associated LncRNAs

Background: Breast cancer (BC) is one of the most frequently diagnosed malignancies among females. As a huge heterogeneity of malignant tumor, it is important to seek reliable molecular biomarkers to carry out the stratification for patients with BC. We surveyed immune- associated lncRNAs that may be used as potential therapeutic targets in BC.

Methods: LncRNA expression data and clinical information of BC patients were downloaded from the TCGA database for a comprehensive analysis of candidate genes. A model consisting of immune-related lncRNAs enriched in BC cancerous tissues was established using the univariate Cox regression analysis and the iterative Lasso Cox regression analysis. The prognostic performance of this model was validated in two independent cohorts (GSE21653 and BC-KR), and compared with known prognostic biomarkers. A nomogram that integrated the immune-related lncRNA signature and clinicopathological factors was constructed to accurately assess the prognostic value of this signature. The correlation between the signature and immune cell infiltration in BC was also analyzed.

Results: The Kaplan-Meier analysis showed that the OS of Patients in the low-risk group had significantly better survival than those in the high-risk group, Clinical subgroup analysis showed that the predictive ability was independent of clinicopathological factors. Univariate/multivariate Cox regression analysis showed immune lncRNA signature is an important prognostic factor and an independent prognostic marker. In addition, GSEA and GSVA analysis as well as comprehensive analysis of immune cells showed that the signature was significantly correlated with the infiltration of immune cells.

Conclusion: We successfully constructed an immune-associated lncRNA signature that can accurately predict BC prognosis.

Circular RNA hsa_circ_0000376 Participates in Tumorigenesis of Breast Cancer by Targeting miR-1285-3p

This study was designed to identify novel circular RNAs and the related regulatory axis to provide research targets for the diagnosis and treatment of breast cancer. The circular RNA expression microarray “GSE101123” related to breast cancer was downloaded from the Gene Expression Omnibus database. The differentially expressed circular RNAs between tumor and normal samples were screened using Limma package. The targeted microRNAs of the differentially expressed circular RNAs and the targeted messenger RNAs of the microRNAs were predicted using miRanda and miRWalk, respectively, and a circular RNAs–microRNAs–messenger RNAs network was constructed. Then, functional enrichment analysis, protein–protein interaction network construction, and drug–gene interaction analysis were conducted for the messenger RNAs. A total of 11 differentially expressed circular RNAs were identified between the breast cancer and normal samples, of which 3 were upregulated, while 8 were downregulated. The circular RNA–microRNA–messenger RNA network contained 1 circular RNA (hsa_circ_0000376), 2 microRNAs (miR-1285-3p and miR-1286), and 353 messenger RNAs. The protein–protein interaction network contained 150 nodes and 240 interactions. The hub genes in the protein–protein interaction network were all targeted messenger RNAs of miR-1285-3p that were significantly enriched in the ubiquitin–proteasome system, apoptosis, cell cycle arrest–related pathways, and cancer-related pathways involving SMAD specific E3 ubiquitin protein ligase 1, β-transducin repeat containing E3 ubiquitin protein ligase, tumor protein P53 among others. Twenty-two drugs were predicted to target 4 messenger RNAs, including tumor protein P53. A novel circular RNA, hsa_circ_0000376, was identified in breast cancer that may act as a sponge targeting miR-1285-3p expression which through its target genes, SMURF1, BTRC, and TP53, may further regulate tumorigenesis.

The 40bp Indel Polymorphism rs150550023 in the MDM2 Promoter is Associated with Intriguing Shifts in Gene Expression in the p53-MDM2 Regulatory Hub

Most low-penetrance genetic risk factors for cancer are located in noncoding regions, presumably altering the regulation of neighboring genes. The poorly characterized Indel polymorphism rs150550023 (rs3730485; del1518) in the promoter of MDM2 (human homolog of mouse double minute 2) is a biologically plausible candidate genetic risk factor, which might influence the expression of MDM2, a key negative regulator of the central tumor suppressor p53. Here, we genotyped rs150550023 in a Central European hospital-based case-control study of 407 breast cancer patients and 254 female controls. mRNA levels of MDM2, p53, and the p53 target genes p21, BAX, and PERP were quantified with qRT-PCR, and p53 protein was assessed with immune histochemistry in ≈100 primary breast tumors with ascertained rs150550023 genotype. We found no evidence for an association of rs150550023 with the risk, age at onset, or prognosis of breast cancer. A possible synergism was observed with SNP309 in promoter P2 of MDM2. Mean mRNA levels of MDM2, p53, p21, and BAX were ≈1.5-3 fold elevated in TP53 wildtype tumors with the minor homozygous Del/Del genotype. However, systematic shifts in p53 protein levels or mutation rates were not observed, suggesting that the elevated p53 mRNA levels are due to regulatory feedback loops that compensate for the effects of rs150550023 on MDM2 expression.

Inter-tumor genomic heterogeneity of breast cancers: comprehensive genomic profile of primary early breast cancers and relapses

BACKGROUND

The breast cancer genome dynamically evolves during malignant progression and recurrence. We investigated the genomic profiles of primary early-stage breast cancers and matched relapses to elucidate the molecular underpinnings of the metastatic process, focusing on potentially actionable alterations in the recurrences.

METHODS

A mono-institutional cohort of 128 patients with breast cancers (n = 68 luminal B HER2, n = 6 luminal B HER2+, n = 1 HER2+ non-luminal, n = 56 triple negative) and at least one recurrence in a timeframe of 17 years was evaluated. Next-generation sequencing comprehensive genomic profiling was performed on 289 formalin-fixed paraffin-embedded (FFPE) samples, including primary tumors and matched relapses. Correlations of genomic aberrations with clinicopathologic factors and time to breast cancer relapse were analyzed.

RESULTS

Genomic data were available for 188 of 289 FFPE samples that achieved the sequencing quality parameters (failure rate 34.9%), including 106 primary tumors and 82 relapses. All primary and relapse samples harbored at least one genomic alteration, with a median number of six alterations per sample (range 1-16). The most frequent somatic genomic alterations were mutations of TP53 (primary tumors = 49%, relapses = 49%) and PIK3CA (primary tumors = 33%, relapses = 30%). Distinctive genomic alterations of primary tumors were significantly associated with molecular subtypes. TP53, PIK3R1, and NF1 somatic alterations were more frequently detected in triple negative tumors (p value < 0.05); CCND1, FGF3, and FGFR1 copy number gains were recurrently identified in luminal cases (p value < 0.05). Moreover, TP53 mutations and MYC amplification were significantly and independently associated with a shorter time to relapse (p value < 0.05). Molecular subtype changes between primary tumors and relapses were seen in 10 of 128 (7.8%) cases. Most driver genomic alterations (55.8%) were shared between primary tumors and matched recurrences. However, in 39 of 61 cases (63.9%), additional private alterations were detected in the relapse samples only, including 12 patients with potentially actionable aberrations.

CONCLUSIONS

Specific genomic aberrations of primary breast cancers were associated with time to relapse. Primary tumors and matched recurrences showed a core of shared driver genomic aberrations but private actionable alterations have been identified in the relapses.

An Overview of Promising Biomarkers in Cancer Screening and Detection

Applications of biomarkers have been proved in oncology screening, diagnosis, predicting response to treatment as well as monitoring the progress of the disease. Considering the crucial role played by them during different disease stages, it is extremely important to evaluate, validate, and assess them to incorporate them into routine clinical care. In this review, the role of few most promising and successfully used biomarkers in cancer detection, i.e. PD-L1, E-Cadherin, TP53, Exosomes, cfDNA, EGFR, mTOR with regard to their structure, mode of action, and reports signifying their pathological significance, are addressed. Also, an overview of some successfully used biomarkers for cancer medicine has been presented. The study also summarizes biomarker-driven personalized cancer therapy i.e., approved targets and indications, as per the US FDA. The review also highlights the increasingly prominent role of biomarkers in drug development at all stages, with particular reference to clinical trials. The increasing utility of biomarkers in clinical trials is clearly evident from the trend shown, wherein ~55 percent of all oncology clinical trials in 2019 were seen to involve biomarkers, as opposed to ~ 15 percent in 2001, which clearly proves the essence and applicability of biomarkers for synergizing clinical information with tumor progression. Still, there are significant challenges in the implementation of these possibilities with strong evidence in cost-- effective manner.

Recurrence/Regrowth in Grade I Meningioma: How to Predict?

The HLA-G and HLA-E molecules, Ki67, progesterone (PR), estrogen (ER) and androgen receptors (AR), p53, COX-2, and HER2 were studied to assess whether the biological behavior of grade I meningiomas is related to their expression. Tissue samples from 96 patients with grade I intracranial meningiomas were analyzed by immunohistochemistry on tissue microarray blocks (TMA) using antibodies specific for HLA-G, HLA-E, Ki67, PR, ER, AR, p53, COX-2, and HER2. Meningiomas were classified as small (≤2 cm, 1.0%), medium (>2 and ≤4 cm, 32.3%), and large (>4 cm, 66.7%). Tumor size was not related to recurrence/regrowth (p = 0.486), but was significantly correlated with peritumoral edema (p = 0.031) and intratumoral calcifications (p = 0.018). Recurrent meningiomas were observed in 14.6% of cases. Immunostaining for each marker was: HLA-G 100%; HLA-E 95.6%; PR 62%; ER 2.1%; AR 6.5%; p53 92.6%; COX-2 100%; HER2 0%; Ki67, mean 2.61 ± 2.29%, median 2.1%. Primary and recurrent meningiomas showed no significant relation with HLA-E and hormone receptors (p > 0.05), except for Ki67, where a higher median was observed in recurrent tumors than in primary (p = 0.014). The larger the tumor, the more severe the peritumoral edema, and the greater the presence of calcifications. Ki67 appears to be a good biomarker of recurrence/regrowth in grade I meningiomas.

Expression of Ki67 and p53 Proteins: Breast Cancer Aggressivity Markers in Brazilian Young Patients

Background: The increase in breast cancer (BC) cases in young women is of great importance since the tumor behavior in this group is generally more aggressive than in their older counterparts, and strategies for early diagnosis and prognostication are needed. Therefore, this work sought to investigate prognostic markers associated with young (<44 years old) BC patients. Methods: Two hundred thirty-six primary tumor tissues from 232 BC patients, of which 44 had less than 44 years at diagnosis were evaluated regarding the expression of estrogen and progesterone receptors (ER and PR), human epidermal growth factor receptor 2 (HER2), Ki67, and p53 (used as an indicator of p53 mutations) through immunohistochemistry. Also, data regarding tumor size, histopathological grade (HG), lymph node metastasis disease stage, and patients' survival status were collected. Results: Early age tumors had higher Ki67 expression and p53 mutations, and these markers were positively correlated with each other and associated worse prognosis parameters, such as negativity for ER and PR and positivity for HER2, and with higher HG, tumor size, and disease stage. In young patients, Ki67 correlated with ER, PR, and HG, whereas p53 correlated with HER2 and disease stage. Also, Ki67 associated with BC death independently of time from diagnosis, patients age, tumor size, and disease stage, and showed a trend toward a positive correlation with death in young patients, but not in the older group. Conclusion: Young BC patients were more likely to have intensely proliferative tumors with p53 mutations and these markers may hold prognostic relevance in BC, especially in this subgroup of patients.

Pathology of triple negative breast cancer

Triple negative breast cancer (TNBC) is a subtype of breast tumor lacking hormone receptors expression and HER2 gene amplification and represents 24 % of newly diagnosed breast neoplasms. In this review, pathological aspects of triple-negative breast cancer are illustrated, with particular attention to the seminal studies that defined this subtype of breast cancer by a molecular point of view. This paper also focuses on practical issues raised in clinical routine by the introduction of genetic expression breast cancer profiling and the innovative prognostic and predictive impact on triple-negative breast cancer pathology. Moreover, histopathological aspects of triple-negative neoplasms are also mentioned, underlying the importance of histologic diagnosis of particular cancer subtypes with decisive impact on clinical outcome. Importantly, focus on new therapeutic frontier represented by immunotherapy is illustrated, with particular mention of immune checkpoint inhibitors introduction in TNBC therapy and their impact on future treatments.

TP53 Arg72Pro polymorphism and neuroblastoma susceptibility in eastern Chinese children: a three-center case-control study

TP53 is a tumor suppressor gene that regulates cell growth, apoptosis and DNA repair. Previous studies have reported the contribution of TP53 Arg72Pro (rs1042522 C>G) polymorphism to pathogenesis of multiple tumors. Hence, we evaluated the association between this polymorphism and neuroblastoma susceptibility in eastern Chinese children. The Taqman genotyping assay was performed in 373 patients and 762 controls. Odds ratios (ORs) with 95% confidence intervals (CIs) were calculated to assess the strength of the association. No significant association was found between the TP53 gene rs1042522 C>G polymorphism and neuroblastoma susceptibility in the overall analysis (CG vs. CC: adjusted OR = 0.92, 95% CI = 0.70-1.22, P=0.567; GG vs. CC: adjusted OR = 0.99, 95% CI = 0.69-1.42, P=0.947; CG/GG vs. CC: adjusted OR = 0.94, 95% CI = 0.72-1.23, P=0.639; or GG vs. CC/CG: adjusted OR = 1.04, 95% CI = 0.75-1.43, P=0.814) and stratified analysis by age, gender, sites of origin, and clinical stages. The TP53 gene rs1042522 C>G polymorphism may not be a risk factor for neuroblastoma in eastern Chinese children. Future studies are needed to confirm this negative result and to reveal additional functional TP53 variants predisposing to neuroblastoma.

HRness in Breast and Ovarian Cancers

Ovarian and breast cancers are currently defined by the main pathways involved in the tumorigenesis. The majority are carcinomas, originating from epithelial cells that are in constant division and subjected to cyclical variations of the estrogen stimulus during the female hormonal cycle, therefore being vulnerable to DNA damage. A portion of breast and ovarian carcinomas arises in the context of DNA repair defects, in which genetic instability is the backdrop for cancer initiation and progression. For these tumors, DNA repair deficiency is now increasingly recognized as a target for therapeutics. In hereditary breast/ovarian cancers (HBOC), tumors with BRCA1/2 mutations present an impairment of DNA repair by homologous recombination (HR). For many years, BRCA1/2 mutations were only screened on germline DNA, but now they are also searched at the tumor level to personalize treatment. The reason of the inactivation of this pathway remains uncertain for most cases, even in the presence of a HR-deficient signature. Evidence indicates that identifying the mechanism of HR inactivation should improve both genetic counseling and therapeutic response, since they can be useful as new biomarkers of response.

Clinical spectrum and prognostic value of TP53 mutations in circulating tumor DNA from breast cancer patients in China

Background

TP53 mutations are common in breast cancer. There is currently no large‐scale cohort study to investigate the TP53 landscape in breast cancer patients from China. The predictive value of TP53 mutations for the efficacy of human epidermal growth factor receptor 2 (HER2)‐targeted therapy in breast cancer remains controversial. In the present study, we aimed to analyze the clinical spectrum and prognostic value of TP53 mutations in circulating tumor DNA (ctDNA) from breast cancer patients in China.

Methods

We retrospectively analyzed the clinical data and TP53 mutation features in ctDNA samples from 804 patients with metastatic breast cancer. TP53 mutations were detected by target region capture‐based next‐generation sequencing. The relationship between TP53 mutation status and disease‐free survival (DFS) was analyzed in 444 patients with metastatic breast cancer. Moreover, the relationship between TP53 mutation status and progression‐free survival (PFS) was analyzed in 55 HER2‐positive patients treated with first‐line trastuzumab‐based therapy. Kaplan‐Meier analysis was performed to estimate the survival curves of the different subgroups, and the log‐rank test was used to compare the curves. A Cox regression model was used to estimate multivariable‐adjusted hazard ratios and their 95% confidence intervals (CIs) associated with the DFS and PFS.

Results

Among the 804 investigated patients, 431 (53.6%) patients harbored TP53 mutations. TP53 mutations were differentially distributed among different molecular subtypes of breast cancer (P < 0.05). Patients with TP53 mutations had a shorter DFS than those with wild‐type TP53 (hazard ratio = 1.32, 95% CI = 1.09‐1.61, P = 0.005). TP53 mutations in exons 5‐8 were associated with worse outcome (hazard ratio = 1.50, 95% CI = 1.11‐2.03, P = 0.009). However, TP53 mutation status was not significantly associated with PFS in HER2‐positive patients who received first‐line trastuzumab‐based therapy (P = 0.966). Interestingly, in the taxane combination group, patients with TP53 mutations exhibited longer PFS than those without TP53 mutations (hazard ratio = 0.08, 95% CI = 0.02‐0.30, P < 0.001). However, in the non‐taxane combination group, patients with TP53 mutations displayed shorter PFS than those with wild‐type TP53 (hazard ratio = 4.84, 95% CI = 1.60‐14.66, P = 0.005).

Conclusions

TP53 mutations in exons 5‐8 may be an independent prognostic marker for short DFS in patients with metastatic breast cancer. TP53 mutations had opposite effects on trastuzumab‐treated patients treated with and without taxanes.

p53 and BLC2 Immunohistochemical Expression Across Molecular Subtypes in 1099 Early Breast Cancer Patients With Long-Term Follow-up: An Observational Study

INTRODUCTION

p53 and antiapoptotic B-cell leukemia/lymphoma 2 (BLC2) have been proposed as prognostic markers for early breast cancer (BC), although their relationship with conventional parameters and patient prognosis, as well as their distribution within the molecular BC subtypes remains uncertain.

PATIENTS AND METHODS

In this observational study, we analyzed the immunohistochemical expression of p53 and BLC2 in 1099 early BC patients surgically treated between 2000 and 2006 and followed for at least 5 years, also considering their association with pathologic factors and molecular subtypes, as well as their influence on disease-free survival.

RESULTS

p53 and BLC2 are distributed differently across molecular subtypes (P < .0001); in particular, p53 positivity and BLC2 negativity seems to be associated with more aggressive conventional tumor phenotypes. Moreover, BLC2 negativity seems to be a significant discriminating factor for disease-free survival (P = .003) according to Kaplan-Meier analysis, while p53 seems to have no discriminating effect. Among patients with discordant p53/BLC2 phenotype, the combination p53⁺BLC2^- seems to be associated with the worst outcomes (P = .007) and significantly influenced the clinical course of node-negative patients treated only with hormone therapy (P = .004).

CONCLUSION

These two biomarkers, in addition to conventional pathologic factors and molecular subtype, could help define the risk and outcome of BC.

Investigation on the Genomic Characterization of Uterine Sarcoma for rAd-p53 Combined with Chemotherapy Treatment

The aim is to investigate the genomic characterization of uterine sarcoma for rAd-p53 (Gendicine^®) combined with chemotherapy treatment. We recently published an article on 12 cases of uterine sarcomas, which were treated with rAd-p53 combined with chemotherapy. We found that rAd-p53 combined with chemotherapy is effective for various uterine sarcomas. Pretreatment pathological specimens of four uterine sarcoma patients were collected from the above recent clinical research and numbered 1-4A/B. Tumor samples were subjected to targeted sequencing by using a 416 genes panel. We profiled the mutation spectrum and tumor mutation burden in the tumors, identified mutated genes, and explored their gene function. We also verified the p53 protein expression using immunohistochemistry. We identified a total of 30 mutated genes that were found from the next-generation sequencing test results. The average number of mutated genes was up to seven in the five samples. TP53 gene was mutated in two of the four patients, No. 1 and No. 4B. They are c.C833G (p.P278R) missense mutation and a point mutation (C141*) that result in a premature stop codon. We did not find a mutated TP53 gene in the other two cases, but we identified mutated genes, including CREBBP, LYN, CDKN2A, and JAK2, which were located upstream of the TP53 gene; they may have an impact on TP53. We also identified 11 additional genes which are involved in p53-related signaling pathways or have interaction with p53. Compared to solid tumor mutational burden (TMB) distribution, none of their TMB was ranking in the top 25%. Mutant p53 protein expression was positive in two specimens. Our results demonstrated that the TP53 signaling pathway plays an important role in uterine sarcoma tumorigenesis. TP53 and the upstream genes such as CREBBP, LYN, CDKN2A, and JAK2 may be involved in the genomic characterization for rAd-p53 (Gendicine) combined with chemotherapy in uterine sarcoma. Besides, the average amount of mutated genes from every patient is large.

Recommendations for Advancing the Diagnosis and Management of Hereditary Breast and Ovarian Cancer in Brazil

PURPOSE

The objective of this review was to address the barriers limiting access to genetic cancer risk assessment and genetic testing for individuals with suspected hereditary breast and ovarian cancer (HBOC) through a review of the diagnosis and management steps of HBOC.

METHODS

A selected panel of Brazilian experts in fields related to HBOC was provided with a series of relevant questions to address before the multiday conference. During this conference, each narrative was discussed and edited by the entire group, through numerous drafts and rounds of discussion, until a consensus was achieved.

RESULTS

The authors propose specific and realistic recommendations for improving access to early diagnosis, risk management, and cancer care of HBOC specific to Brazil. Moreover, in creating these recommendations, the authors strived to address all the barriers and impediments mentioned in this article.

CONCLUSION

There is a great need to expand hereditary cancer testing and counseling in Brazil, and changing current policies is essential to accomplishing this goal. Increased knowledge and awareness, together with regulatory actions to increase access to this technology, have the potential to improve patient care and prevention and treatment efforts for patients with cancer across the country.

Identification of Breast Cancer Subtype Specific MicroRNAs Using Survival Analysis to Find Their Role in Transcriptomic Regulation

The microRNA (miRNA) biomolecules have a significant role in the development of breast cancer, and their expression profiles are different in each subtype of breast cancer. Thus, our goal is to use the Next Generation Sequencing provided high-throughput miRNA expression and clinical data in an integrated fashion to perform survival analysis in order to identify breast cancer subtype specific miRNAs, and analyze associated genes and transcription factors. We select top 100 miRNAs for each of the four subtypes, based on the value of hazard ratio and p-value, thereafter, identify 44 miRNAs that are related to all four subtypes, which we call as four-star miRNAs. Moreover, 12, 14, 9, and 15 subtype specific, viz. one-star miRNAs, are also identified. The resulting miRNAs are validated by using machine learning methods to differentiate tumor cases from controls (for four-star miRNAs), and subtypes (for one-star miRNAs). The four-star miRNAs provide 95% average accuracy, while in case of one-star miRNAs 81% accuracy is achieved for HER2-Enriched. Differences in expression of miRNAs between cancer stages is also analyzed, and a subset of eight miRNAs is found, for which expression is increased in stage II relative to stage I, including hsa-miR-10b-5p, which contributes to breast cancer metastasis. Subsequently we prepare regulatory networks in order to identify the interactions among miRNAs, their targeted genes and transcription factors (TFs), that are targeting those miRNAs. In this way, key regulatory circuits are identified, where genes such as TP53, ESR1, BRCA1, MYC, and others, that are known to be important genetic factors for the cause of breast cancer, produce transcription factors that target the same genes as well as interact with the selected miRNAs. To provide further biological validation the Protein-Protein Interaction (PPI) networks are prepared and Kyoto Encyclopedia of Genes and Genomes pathway and gene ontology (GO) enrichment analysis are performed. Among the enriched pathways many are breast cancer-related, such as PI3K-Akt or p53 signaling pathways, and contain proteins such as TP53, also present in the regulatory networks. Moreover, we find that the genes are enriched in GO terms associated with breast cancer. Our results provide detailed analysis of selected miRNAs and their regulatory networks.

Computational identification of mutator-derived lncRNA signatures of genome instability for improving the clinical outcome of cancers: a case study in breast cancer

Emerging evidence revealed the critical roles of long non-coding RNAs (lncRNAs) in maintaining genomic instability. However, identification of genome instability-associated lncRNAs and their clinical significance in cancers remain largely unexplored. Here, we developed a mutator hypothesis-derived computational frame combining lncRNA expression profiles and somatic mutation profiles in a tumor genome and identified 128 novel genomic instability-associated lncRNAs in breast cancer as a case study. We then identified a genome instability-derived two lncRNA-based gene signature (GILncSig) that stratified patients into high- and low-risk groups with significantly different outcome and was further validated in multiple independent patient cohorts. Furthermore, the GILncSig correlated with genomic mutation rate in both ovarian cancer and breast cancer, indicating its potential as a measurement of the degree of genome instability. The GILncSig was able to divide TP53 wide-type patients into two risk groups, with the low-risk group showing significantly improved outcome and the high-risk group showing no significant difference compared with those with TP53 mutation. In summary, this study provided a critical approach and resource for further studies examining the role of lncRNAs in genome instability and introduced a potential new avenue for identifying genomic instability-associated cancer biomarkers.