Gene-expression profiles in hereditary breast cancer

Druggable Molecular Networks in BRCA1/BRCA2-Mutated Breast Cancer

Mutations in the tumor suppressor genes BRCA1 and BRCA2 are associated with the triple-negative breast cancer phenotype, particularly aggressive and hard-to-treat tumors lacking estrogen, progesterone, and human epidermal growth factor receptor 2. This research aimed to understand the metabolic and genetic links behind BRCA1 and BRCA2 mutations and investigate their relationship with effective therapies. Using the Cytoscape software, two networks were generated through a bibliographic analysis of articles retrieved from the PubMed-NCBI database. We identified 98 genes deregulated by BRCA mutations, and 24 were modulated by therapies. In particular, BIRC5, SIRT1, MYC, EZH2, and CSN2 are influenced by BRCA1, while BCL2, BAX, and BRIP1 are influenced by BRCA2 mutation. Moreover, the study evaluated the efficacy of several promising therapies, targeting only BRCA1/BRCA2-mutated cells. In this context, CDDO-Imidazolide was shown to increase ROS levels and induce DNA damage. Similarly, resveratrol decreased the expression of the anti-apoptotic gene BIRC5 while it increased SIRT1 both in vitro and in vivo. Other specific drugs were found to induce apoptosis selectively in BRCA-mutated cells or block cell growth when the mutation occurs, i.e., 3-deazaneplanocin A, genistein or daidzein, and PARP inhibitors. Finally, over-representation analysis on the genes highlights ferroptosis and proteoglycan pathways as potential drug targets for more effective treatments.

Metabolomics-Driven Biomarker Discovery for Breast Cancer Prognosis and Diagnosis

Breast cancer is a cancer with global prevalence and a surge in the number of cases with each passing year. With the advancement in science and technology, significant progress has been achieved in the prevention and treatment of breast cancer to make ends meet. The scientific intradisciplinary subject of "metabolomics" examines every metabolite found in a cell, tissue, system, or organism from different sources of samples. In the case of breast cancer, little is known about the regulatory pathways that could be resolved through metabolic reprogramming. Evidence related to the significant changes taking place during the onset and prognosis of breast cancer can be obtained using metabolomics. Innovative metabolomics approaches identify metabolites that lead to the discovery of biomarkers for breast cancer therapy, diagnosis, and early detection. The use of diverse analytical methods and instruments for metabolomics includes Magnetic Resonance Spectroscopy, LC/MS, UPLC/MS, etc., which, along with their high-throughput analysis, give insights into the metabolites and the molecular pathways involved. For instance, metabolome research has led to the discovery of the glutamate-to-glutamate ratio and aerobic glycolysis as biomarkers in breast cancer. The present review comprehends the updates in metabolomic research and its processes that contribute to breast cancer prognosis and metastasis. The metabolome holds a future, and this review is an attempt to amalgamate the present relevant literature that might yield crucial insights for creating innovative therapeutic strategies aimed at addressing metastatic breast cancer.

Emerging methods and techniques for cancer biomarker discovery

Modern cancer research depends heavily on the identification and validation of biomarkers because they provide important information about the diagnosis, prognosis, and response to treatment of the cancer. This review will provide a comprehensive overview of cancer biomarkers, including their development phases and recent breakthroughs in transcriptomics and computational techniques for detecting these biomarkers. Blood-based biomarkers have great potential for non-invasive tumor dynamics and treatment response monitoring. These include circulating tumor DNA, exosomes, and microRNAs. Comprehensive molecular profiles are provided by multi-omic technologies, which combine proteomics, metabolomics, and genomes to support the identification of biomarkers and the targeting of therapeutic interventions. Genetic changes are detected by next-generation sequencing, and patterns of protein expression are found by protein arrays and mass spectrometry. Tumor heterogeneity and clonal evolution can be understood using metabolic profiling and single-cell studies. It is projected that the use of several biomarkers-genetic, protein, mRNA, microRNA, and DNA profiles, among others-will rise, enabling multi-biomarker analysis and improving individualised treatment plans. Biomarker identification and patient outcome prediction are further improved by developments in AI algorithms and imaging techniques. Robust biomarker validation and reproducibility require cooperation between industry, academia, and doctors. Biomarkers can provide individualized care, meet unmet clinical needs, and enhance patient outcomes despite some obstacles. Precision medicine will continue to take shape as scientific research advances and the integration of biomarkers with cutting-edge technologies continues to offer a more promising future for personalized cancer care.

Breast cancer prognosis through the use of multi-modal classifiers: current state of the art and the way forward

We present a survey of the current state-of-the-art in breast cancer detection and prognosis. We analyze the evolution of Artificial Intelligence-based approaches from using just uni-modal information to multi-modality for detection and how such paradigm shift facilitates the efficacy of detection, consistent with clinical observations. We conclude that interpretable AI-based predictions and ability to handle class imbalance should be considered priority.

Pathologic complete response after neoadjuvant systemic therapy for breast cancer in BRCA mutation carriers and noncarriers

BRCA1 and BRCA2 pathogenic variant carriers develop breast cancers with distinct pathological characteristics and mutational signatures that may result in differential response to chemotherapy. We compared rates of pathologic complete response (pCR) after NAC between BRCA1/2 variant carriers and noncarriers in a cohort of 1426 women (92 [6.5%] BRCA1 and 73 [5.1%] BRCA2) with clinical stage I-III breast cancer treated with NAC followed by surgery from 11/2013 to 01/2022 at Memorial Sloan Kettering Cancer Center. The majority received doxorubicin/cyclophosphamide/paclitaxel therapy (93%); BRCA1/2 carriers were more likely to receive carboplatin (p < 0.001). Overall, pCR was achieved in 42% of BRCA1 carriers, 21% of BRCA2 carriers, and 26% of noncarriers (p = 0.001). Among clinically node-positive (cN+) patients, nodal pCR was more frequent in BRCA1/2 carriers compared to noncarriers (53/96 [55%] vs. 371/856 [43%], p = 0.015). This difference was seen in HR+/HER2- (36% vs. 20% of noncarriers; p = 0.027) and TN subtypes (79% vs. 45% of noncarriers; p < 0.001). In a multivariable analysis of the overall cohort, BRCA1 status, and TN and HER2+ subtypes were independently associated with pCR. These data indicate that BRCA1 carriers may be more likely to achieve overall and nodal pCR in response to NAC compared with BRCA2 carriers and patients with sporadic disease. Further studies with a larger cohort of BRCA1/2 mutation carriers are needed, as a small sample size may have a restricted ability to detect a significant association between mutational status and pCR in sensitivity analyses stratified by subtype and adjusted for clinically relevant factors.

Pesticide exposure and increased breast cancer risk in women population studies

Pesticide exposure is emerging as a risk factor for various human diseases. Breast cancer (BC) is a multifactorial disease with known genetic and non-genetic risk factors. Most BC cases are attibutable to non-genetic risk factors, with a history of adverse environmental exposures playing a significant role. Pesticide exposure can occur at higher levels in female populations participating in rural activities such as spraying of pesticides in the field, unprotected handling of pesticides at home, and washing of contaminated clothes. Exposure can also be significant in the drinking water of certain populations. Here, we reviewed the literature on women's exposure to pesticides and the risk of BC. We summarize the main links between pesticide exposure and BC and discuss the role of dose and exposure context, as well as potential mechanisms of toxicity. Overall, reports reviewed here have documented stronger associations between higher levels of exposure and BC risk, including documenting direct and acute pesticide exposure in certain female populations. However, discrepancies among studies regarding dose and mode of exposure may result in misunderstandings about the risks posed by pesticide exposure. Plausible mechanisms linking pesticides to breast cancer risk include their impacts as endocrine disruptors, as well as their roles as genotoxic agents, and modulators of the epigenome. Besides establishing links between pesticide exposure and breast cancer, the literature also highlights the critical need to understand the routes and doses of women's exposure to pesticides and the specific associations and mechanisms that are determinants of disease etiology and prognosis.

Benefits and Harms of Mammography Screening in 75 + Women to Inform Shared Decision-making: a Simulation Modeling Study

BACKGROUND

Guidelines recommend shared decision-making (SDM) around mammography screening for women ≥ 75 years old.

OBJECTIVE

To use microsimulation modeling to estimate the lifetime benefits and harms of screening women aged 75, 80, and 85 years based on their individual risk factors (family history, breast density, prior biopsy) and comorbidity level to support SDM in clinical practice.

DESIGN, SETTING, AND PARTICIPANTS

We adapted two established Cancer Intervention and Surveillance Modeling Network (CISNET) models to evaluate the remaining lifetime benefits and harms of screening U.S. women born in 1940, at decision ages 75, 80, and 85 years considering their individual risk factors and comorbidity levels. Results were summarized for average- and higher-risk women (defined as having breast cancer family history, heterogeneously dense breasts, and no prior biopsy, 5% of the population).

MAIN OUTCOMES AND MEASURES

Remaining lifetime breast cancers detected, deaths (breast cancer/other causes), false positives, and overdiagnoses for average- and higher-risk women by age and comorbidity level for screening (one or five screens) vs. no screening per 1000 women.

RESULTS

Compared to stopping, one additional screen at 75 years old resulted in six and eight more breast cancers detected (10% overdiagnoses), one and two fewer breast cancer deaths, and 52 and 59 false positives per 1000 average- and higher-risk women without comorbidities, respectively. Five additional screens over 10 years led to 23 and 31 additional breast cancer cases (29-31% overdiagnoses), four and 15 breast cancer deaths avoided, and 238 and 268 false positives per 1000 average- and higher-risk screened women without comorbidities, respectively. Screening women at older ages (80 and 85 years old) and high comorbidity levels led to fewer breast cancer deaths and a higher percentage of overdiagnoses.

CONCLUSIONS

Simulation models show that continuing screening in women ≥ 75 years old results in fewer breast cancer deaths but more false positive tests and overdiagnoses. Together, clinicians and 75 + women may use model output to weigh the benefits and harms of continued screening.

Gender-Specific Genetic Predisposition to Breast Cancer: BRCA Genes and Beyond

Among neoplastic diseases, breast cancer (BC) is one of the most influenced by gender. Despite common misconceptions associating BC as a women-only disease, BC can also occur in men. Additionally, transgender individuals may also experience BC. Genetic risk factors play a relevant role in BC predisposition, with important implications in precision prevention and treatment. The genetic architecture of BC susceptibility is similar in women and men, with high-, moderate-, and low-penetrance risk variants; however, some sex-specific features have emerged. Inherited high-penetrance pathogenic variants (PVs) in BRCA1 and BRCA2 genes are the strongest BC genetic risk factor. BRCA1 and BRCA2 PVs are more commonly associated with increased risk of female and male BC, respectively. Notably, BRCA-associated BCs are characterized by sex-specific pathologic features. Recently, next-generation sequencing technologies have helped to provide more insights on the role of moderate-penetrance BC risk variants, particularly in PALB2, CHEK2, and ATM genes, while international collaborative genome-wide association studies have contributed evidence on common low-penetrance BC risk variants, on their combined effect in polygenic models, and on their role as risk modulators in BRCA1/2 PV carriers. Overall, all these studies suggested that the genetic basis of male BC, although similar, may differ from female BC. Evaluating the genetic component of male BC as a distinct entity from female BC is the first step to improve both personalized risk assessment and therapeutic choices of patients of both sexes in order to reach gender equality in BC care. In this review, we summarize the latest research in the field of BC genetic predisposition with a particular focus on similarities and differences in male and female BC, and we also discuss the implications, challenges, and open issues that surround the establishment of a gender-oriented clinical management for BC.

Biological and clinical significance of the glypican-3 gene in human lung adenocarcinoma: An in silico analysis

Glypican-3 (GPC3), a membrane-bound heparan sulfate proteoglycan, has long been found to be dysregulated in human lung adenocarcinomas (LUADs). Nevertheless, the function, mutational profile, epigenetic regulation, co-expression profile, and clinicopathological significance of the GPC3 gene in LUAD progression are not well understood. In this study, we analyzed cancer microarray datasets from publicly available databases using bioinformatics tools to elucidate the above parameters. We observed significant downregulation of GPC3 in LUAD tissues compared to their normal counterparts, and this downregulation was associated with shorter overall survival (OS) and relapse-free survival (RFS). Nevertheless, no significant differences in the methylation pattern of GPC3 were observed between LUAD and normal tissues, although lower promoter methylation was observed in male patients. GPC3 expression was also found to correlate significantly with infiltration of B cells, CD8+, CD4+, macrophages, neutrophils, and dendritic cells in LUAD. In addition, a total of 11 missense mutations were identified in LUAD patients, and ~1.4% to 2.2% of LUAD patients had copy number amplifications in GPC3. Seventeen genes, mainly involved in dopamine receptor-mediated signaling pathways, were frequently co-expressed with GPC3. We also found 11 TFs and 7 miRNAs interacting with GPC3 and contributing to disease progression. Finally, we identified 3 potential inhibitors of GPC3 in human LUAD, namely heparitin, gemcitabine and arbutin. In conclusion, GPC3 may play an important role in the development of LUAD and could serve as a promising biomarker in LUAD.

CBDT-Oglyc: Prediction of O-glycosylation sites using ChiMIC-based balanced decision table and feature selection

O-glycosylation (Oglyc) plays an important role in various biological processes. The key to understanding the mechanisms of Oglyc is identifying the corresponding glycosylation sites. Two critical steps, feature selection and classifier design, greatly affect the accuracy of computational methods for predicting Oglyc sites. Based on an efficient feature selection algorithm and a classifier capable of handling imbalanced datasets, a new computational method, ChiMIC-based balanced decision table O-glycosylation (CBDT-Oglyc), is proposed. ChiMIC-based balanced decision table for O-glycosylation (CBDT-Oglyc), is proposed to predict Oglyc sites in proteins. Sequence characterization is performed by combining amino acid composition (AAC), undirected composition of [Formula: see text]-spaced amino acid pairs (undirected-CKSAAP) and pseudo-position-specific scoring matrix (PsePSSM). Chi-MIC-share algorithm is used for feature selection, which simplifies the model and improves predictive accuracy. For imbalanced classification, a backtracking method based on local chi-square test is designed, and then cost-sensitive learning is incorporated to construct a novel classifier named ChiMIC-based balanced decision table (CBDT). Based on a 1:49 (positives:negatives) training set, the CBDT classifier achieves significantly better prediction performance than traditional classifiers. Moreover, the independent test results on separate human and mouse glycoproteins show that CBDT-Oglyc outperforms previous methods in global accuracy. CBDT-Oglyc shows great promise in predicting Oglyc sites and is expected to facilitate further experimental studies on protein glycosylation.

Thresholding Gini variable importance with a single-trained random forest: An empirical Bayes approach

Random forests (RFs) are a widely used modelling tool capable of feature selection via a variable importance measure (VIM), however, a threshold is needed to control for false positives. In the absence of a good understanding of the characteristics of VIMs, many current approaches attempt to select features associated to the response by training multiple RFs to generate statistical power via a permutation null, by employing recursive feature elimination, or through a combination of both. However, for high-dimensional datasets these approaches become computationally infeasible. In this paper, we present RFlocalfdr, a statistical approach, built on the empirical Bayes argument of Efron, for thresholding mean decrease in impurity (MDI) importances. It identifies features significantly associated with the response while controlling the false positive rate. Using synthetic data and real-world data in health, we demonstrate that RFlocalfdr has equivalent accuracy to currently published approaches, while being orders of magnitude faster. We show that RFlocalfdr can successfully threshold a dataset of 106 datapoints, establishing its usability for large-scale datasets, like genomics. Furthermore, RFlocalfdr is compatible with any RF implementation that returns a VIM and counts, making it a versatile feature selection tool that reduces false discoveries.

Distinct breast cancer phenotypes in BRCA 1/2 carriers based on ER status

PURPOSE

BRCA1/2 genes are the two main genes associated with hereditary breast cancers (BC). In the present study, we explore clinical and molecular characteristics of BRCA-associated BC in relation to estrogen receptor (ER) status.

METHODS

Three BC databases (DB) were evaluated: (i) Hadassah oncogenetics (n = 4826); (ii) METABRIC (n = 1980), and (iii) Nick-Zainal (n = 560). We evaluated age at diagnosis in BRCA positive (BP) and BRCA negative (BN) patients, and tested for mutational signature differences in cohort iii. mRNA differential expression analysis (DEA) and pathway analysis were performed in cohort ii.

RESULTS

Age at diagnosis was lower in BP vs. BN tumors in all cohorts in the ER- group, and only in cohort i for the ER + group. Signature 3 was universal in BP BC, whereas several signatures were associated with ER status. Pathway analysis was performed between BP&BN, and was significant only in ER- tumors: the major activated pathways involved cancer-related processes and were highly significant. The most significant pathway was estrogen-mediated S-phase entry and the most activated upstream regulator was ERBB2.

CONCLUSION

Signature 3 was universal for all BP BC, while other signatures were associated with ER status. ER + BP& BN show similar genomic characteristics, ER- BP differed markedly from BN. This suggests that the initial carcinogenic process is universal for all BRCA carriers, but further insults lead to the development of two genomically distinct subtypes ER- and ER + . This may shed light on possible mechanisms involved in BP and carry preventive and therapeutic implications.

Reassessing the Benefits and Harms of Risk-Reducing Medication Considering the Persistent Risk of Breast Cancer Mortality in Estrogen Receptor-Positive Breast Cancer

PURPOSE

Recent studies, including a meta-analysis of 88 trials, have shown higher than expected rates of recurrence and death in hormone receptor-positive breast cancer. These new findings suggest a need to re-evaluate the use of risk-reducing medication to avoid invasive breast cancer and breast cancer death in high-risk women.

METHODS

We adapted an established Cancer Intervention and Surveillance Modeling Network model to evaluate the lifetime benefits and harms of risk-reducing medication in women with a ≥ 3% 5-year risk of developing breast cancer according to the Breast Cancer Surveillance Consortium risk calculator. Model input parameters were derived from meta-analyses, clinical trials, and large observational data. We evaluated the effects of 5 years of risk-reducing medication (tamoxifen/aromatase inhibitors) with annual screening mammography ± magnetic resonance imaging (MRI) compared with no screening, MRI, or risk-reducing medication. The modeled outcomes included invasive breast cancer, breast cancer death, side effects, false positives, and overdiagnosis. We conducted subgroup analyses for individual risk factors such as age, family history, and prior biopsy.

RESULTS

Risk-reducing tamoxifen with annual screening (± MRI) decreased the risk of invasive breast cancer by 40% and breast cancer death by 57%, compared with no tamoxifen or screening. This is equivalent to an absolute reduction of 95 invasive breast cancers, and 42 breast cancer deaths per 1,000 high-risk women. However, these drugs are associated with side effects. For example, tamoxifen could increase the number of endometrial cancers up to 11 per 1,000 high-risk women. Benefits and harms varied by individual characteristics.

CONCLUSION

The addition of risk-reducing medication to screening could further decrease the risk of breast cancer death. Clinical guidelines for high-risk women should consider integrating shared decision making for risk-reducing medication and screening on the basis of individual risk factors.

Effectiveness of Secondary Risk-Reducing Strategies in Patients With Unilateral Breast Cancer With Pathogenic Variants of BRCA1 and BRCA2 Subjected to Breast-Conserving Surgery: Evidence-Based Simulation Study

BACKGROUND

Approximately 62% of patients with breast cancer with a pathogenic variant (BRCA1 or BRCA2) undergo primary breast-conserving therapy.

OBJECTIVE

The study aims to develop a personalized risk management decision support tool for carriers of a pathogenic variant (BRCA1 or BRCA2) who underwent breast-conserving therapy for unilateral early-stage breast cancer.

METHODS

We developed a Bayesian network model of a hypothetical cohort of carriers of BRCA1 or BRCA2 diagnosed with stage I/II unilateral breast cancer and treated with breast-conserving treatment who underwent subsequent second primary cancer risk-reducing strategies. Using event dependencies structured according to expert knowledge and conditional probabilities obtained from published evidence, we predicted the 40-year overall survival rate of different risk-reducing strategies for 144 cohorts of women defined by the type of pathogenic variants (BRCA1 or BRCA2), age at primary breast cancer diagnosis, breast cancer subtype, stage of primary breast cancer, and presence or absence of adjuvant chemotherapy.

RESULTS

Absence of adjuvant chemotherapy was the most powerful factor that was linked to a dramatic decline in survival. There was a negligible decline in the mortality in patients with triple-negative breast cancer, who received no chemotherapy and underwent any secondary risk-reducing strategy, compared with surveillance. The potential survival benefit from any risk-reducing strategy was more modest in patients with triple-negative breast cancer who received chemotherapy compared with patients with luminal breast cancer. However, most patients with triple-negative breast cancer in stage I benefited from bilateral risk-reducing mastectomy and risk-reducing salpingo-oophorectomy or just risk-reducing salpingo-oophorectomy. Most patients with luminal stage I/II unilateral breast cancer benefited from bilateral risk-reducing mastectomy and risk-reducing salpingo-oophorectomy. The impact of risk-reducing salpingo-oophorectomy in patients with luminal breast cancer in stage I/II increased with age. Most older patients with the BRCA1 and BRCA2 pathogenic variants in exons 12-24/25 with luminal breast cancer may gain a similar survival benefit from other risk-reducing strategies or surveillance.

CONCLUSIONS

Our study showed that it is mandatory to consider the complex interplay between the types of BRCA1 and BRCA2 pathogenic variants, age at primary breast cancer diagnosis, breast cancer subtype and stage, and received systemic treatment. As no prospective study results are available at the moment, our simulation model, which will integrate a decision support system in the near future, could facilitate the conversation between the health care provider and patient and help to weigh all the options for risk-reducing strategies leading to a more balanced decision.

Nondestructive protein sampling with electroporation facilitates profiling of spatial differential protein expression in breast tumors in vivo

Excision tissue biopsy, while central to cancer treatment and precision medicine, presents risks to the patient and does not provide a sufficiently broad and faithful representation of the heterogeneity of solid tumors. Here we introduce e-biopsy—a novel concept for molecular profiling of solid tumors using molecular sampling with electroporation. As e-biopsy provides access to the molecular composition of a solid tumor by permeabilization of the cell membrane, it facilitates tumor diagnostics without tissue resection. Furthermore, thanks to its non tissue destructive characteristics, e-biopsy enables probing the solid tumor multiple times in several distinct locations in the same procedure, thereby enabling the spatial profiling of tumor molecular heterogeneity.We demonstrate e-biopsy in vivo, using the 4T1 breast cancer model in mice to assess its performance, as well as the inferred spatial differential protein expression. In particular, we show that proteomic profiles obtained via e-biopsy in vivo distinguish the tumors from healthy breast tissue and reflect spatial tumor differential protein expression. E-biopsy provides a completely new molecular sampling modality for solid tumors molecular cartography, providing information that potentially enables more rapid and sensitive detection at lesser risk, as well as more precise personalized medicine.

Metabolomics of Breast Cancer: A Review

Breast cancer is the most commonly diagnosed cancer in women worldwide. Major advances have been made towards breast cancer prevention and treatment. Unfortunately, the incidence of breast cancer is still increasing globally. Metabolomics is the field of science which studies all the metabolites in a cell, tissue, system, or organism. Metabolomics can provide information on dynamic changes occurring during cancer development and progression. The metabolites identified using cutting-edge metabolomics techniques will result in the identification of biomarkers for the early detection, diagnosis, and treatment of cancers. This review briefly introduces the metabolic changes in cancer with particular focus on breast cancer.

Epigenetic Factors as Etiological Agents, Diagnostic Markers, and Therapeutic Targets for Luminal Breast Cancer

Luminal breast cancer, an etiologically heterogeneous disease, is characterized by high steroid hormone receptor activity and aberrant gene expression profiles. Endocrine therapy and chemotherapy are promising therapeutic approaches to mitigate breast cancer proliferation and recurrence. However, the treatment of therapy-resistant breast cancer is a major challenge. Recent studies on breast cancer etiology have revealed the critical roles of epigenetic factors in luminal breast cancer tumorigenesis and drug resistance. Tumorigenic epigenetic factor-induced aberrant chromatin dynamics dysregulate the onset of gene expression and consequently promote tumorigenesis and metastasis. Epigenetic dysregulation, a type of somatic mutation, is a high-risk factor for breast cancer progression and therapy resistance. Therefore, epigenetic modulators alone or in combination with other therapies are potential therapeutic agents for breast cancer. Several clinical trials have analyzed the therapeutic efficacy of potential epi-drugs for breast cancer and reported beneficial clinical outcomes, including inhibition of tumor cell adhesion and invasiveness and mitigation of endocrine therapy resistance. This review focuses on recent findings on the mechanisms of epigenetic factors in the progression of luminal breast cancer. Additionally, recent findings on the potential of epigenetic factors as diagnostic biomarkers and therapeutic targets for breast cancer are discussed.

The extended concurrent genes signature for disease-free survival in breast cancer

BACKGROUND/PURPOSE

Previously we had identified concurrent genes, which highlighted the interplay between copy number variation (CNV) and differential gene expression (GE) for Han Chinese breast cancers. The merit of the approach is to discovery biomarkers not identifiable by conventional GE only data, for which phenotype-correlation or gene variability is the criteria of gene selection.

MATERIALS AND METHODS

Thirty-one comparative genomic hybridization (CGH) and 83 GE microarrays were performed, with 29 breast cancers assayed from both platforms. Potential targets were revealed by Genomic Identification of Significant Targets in Cancer (GISTIC) from CGH arrays. Concurrent genes and genes with significant GISTIC scores were used to derive the extended concurrent genes signature, which was consensus from leading edge analysis across all studies and a supervised partial least square (PLS) regression predictive model of disease-free survival was constructed.

RESULTS

There were 1584 concurrent genes from 29 samples with both CGH and GE microarrays. Enriched concurrent genes sets for disease-free survival were identified independently from 83 GE arrays and another one with Han Chinese origin as well as three studies of Western origin. For five studies with disease-free survival follow up, prognostic discrepancy was observed between predicted high-risk and low-risk group patients.

CONCLUSION

We concluded that through parallel analyses of CGH and GE microarrays, the proposed extended concurrent gene expression signature can identify biomarkers with prognostic values.

Eukaryotic translation initiation factor 3 subunit B could serve as a potential prognostic predictor for breast cancer

The EIF3 gene family is essential in controlling translation initiation during the cell cycle. The significance of the EIF3 subunits as prognostic markers and therapeutic targets in breast cancer is not yet clear. We analyzed the expression of EIF3 subunits in breast cancer on the GEPIA and Oncomine databases and compared their expression in breast cancer and normal tissues using BRCA data downloaded from TCGA. Then we performed clinical survival analysis on the Kaplan–Meier Plotter database and clinicopathologic analysis on the bc-genexMiner v4.1 database. And EIF3B was chosen for mutation analysis via the Cancer SEA online tool. Meanwhile, we performed the immunohistochemical assay, real-time RT-PCR, and Western blotting to analyze EIF3B expression levels in breast cancer. An EIF3B knockdown and a negative control cell line were conducted for MTT assay and cell cycle analysis to assess cell growth. Specifically, the results of TCGA and online databases demonstrated that upregulated EIF3B was associated with poorer overall and advanced tumor progression. We also confirmed that EIF3B was more highly expressed in breast cancer cells and tissues than normal and correlated with a worse outcome. And knockdown of EIF3B expression inhibited the cell cycle and proliferation. Furthermore, EIF3B was highly mutated in breast cancer. Collectively, our results suggested EIF3B as a potential prognostic marker and therapeutic target for breast cancer.

Racial Disparities in Adverse Cardiovascular Outcomes After a Myocardial Infarction in Young or Middle-Aged Patients

Background

Black patients tend to develop coronary artery disease at a younger age than other groups. Previous data on racial disparities in outcomes of myocardial infarction (MI) have been inconsistent and limited to older populations. Our objective was to investigate racial differences in the outcome of MI among young and middle‐aged patients and the role played by socioeconomic, psychosocial, and clinical differences.

Methods and Results

We studied 313 participants (65% non‐Hispanic Black) <61 years old hospitalized for confirmed type 1 MI at Emory‐affiliated hospitals and followed them for 5 years. We used Cox proportional‐hazard models to estimate the association of race with a composite end point of recurrent MI, stroke, heart failure, or cardiovascular death after adjusting for demographic, socioeceonomic status, psychological, and clinical risk factors. The mean age was 50 years, and 50% were women. Compared with non‐Black patients, Black patients had lower socioeconomic status and more clinical and psychosocial risk factors but less angiographic coronary artery disease. The 5‐year incidence of cardiovascular events was higher in Black (35%) compared to non‐Black patients (19%): hazard ratio (HR) 2.1, 95% CI, 1.3 to 3.6. Adjustment for socioeconomic status weakened the association (HR 1.3, 95% CI, 0.8–2.4) more than adjustment for clinical and psychological risk factors. A lower income explained 46% of the race‐related disparity in outcome.

Conclusions

Among young and middle‐aged adult survivors of an MI, Black patients have a 2‐fold higher risk of adverse outcomes, which is largely driven by upstream socioeconomic factors rather than downstream psychological and clinical risk factors.

Gene Expression-Assisted Cancer Prediction Techniques

Cancer is one of the deadliest diseases and with its growing number, its detection and treatment become essential. Researchers have developed various methods based on gene expression. Gene expression is a process that is used to convert deoxyribose nucleic acid (DNA) to ribose nucleic acid (RNA) and then RNA to protein. This protein serves so many purposes, such as creating cells, drugs for cancer, and even hybrid species. As genes carry genetic information from one generation to another, some gene deformity is also transferred to the next generation. Therefore, the deformity needs to be detected. There are many techniques available in the literature to predict cancerous and noncancerous genes from gene expression data. This is an important development from the point of diagnostics and giving a prognosis for the condition. This paper will present a review of some of those techniques from the literature; details about the various datasets on which these techniques are implemented and the advantages and disadvantages.

Molecular subtyping of breast cancer intrinsic taxonomy with oligonucleotide microarray and NanoString nCounter

Breast cancer intrinsic subtypes have been identified based on the transcription of a predefined gene expression (GE) profiles and algorithm (PAM50). This study compared molecular subtyping with oligonucleotide microarray and NanoString nCounter assay. A total of 109 Taiwanese breast cancers (24 with adjacent normal breast tissues) were assayed with Affymetrix Human Genome U133 plus 2.0 microarrays and 144 were assayed with the NanoString nCounter while 64 patients were assayed for both platforms. Subtyping with the nearest centroid (single sample prediction) was performed, and 16 out of 24 (67%) matched normal breasts were categorized as the normal breast-like subtype. For 64 breast cancers assayed for both platforms, 41 (65%, one unclassified by microarray) were predicted with an identical subtype, resulting in a fair Kappa statistic of 0.60. Taking nCounter subtyping as the gold standard, prediction accuracy was 43% (3/7), 81% (13/16), 25% (5/20), and 100% (20/20) for basal-like, HER2-enriched, luminal A and luminal B subtype predicted from microarray GE profiles. Microarray identified more luminal B cases from luminal A subtype predicted by nCounter. It's not uncommon to use microarray for breast cancer molecular subtyping for research. Our study showed that fundamental discrepancy existed between distinct GE assays, and cross platform equivalence should be carefully appraised when molecular subtyping was conducted with oligonucleotide microarray.

Increased gene expression variability in BRCA1-associated and basal-like breast tumours

Purpose

Inherited variants in the cancer susceptibility genes, BRCA1 and BRCA2 account for up to 5% of breast cancers. Multiple gene expression studies have analysed gene expression patterns that maybe associated with BRCA12 pathogenic variant status; however, results from these studies lack consensus. These studies have focused on the differences in population means to identified genes associated with BRCA1/2-carriers with little consideration for gene expression variability, which is also under genetic control and is a feature of cellular function.

Methods

We measured differential gene expression variability in three of the largest familial breast cancer datasets and a 2116 breast cancer meta-cohort. Additionally, we used RNA in situ hybridisation to confirm expression variability of EN1 in an independent cohort of more than 500 breast tumours.

Results

BRCA1-associated breast tumours exhibited a 22.8% (95% CI 22.3–23.2) increase in transcriptome-wide gene expression variability compared to BRCAx tumours. Additionally, 40 genes were associated with BRCA1-related breast cancers that had ChIP-seq data suggestive of enriched EZH2 binding. Of these, two genes (EN1 and IGF2BP3) were significantly variable in both BRCA1-associated and basal-like breast tumours. RNA in situ analysis of EN1 supported a significant (p = 6.3 × 10⁻⁰⁴) increase in expression variability in BRCA1-associated breast tumours.

Conclusion

Our novel results describe a state of increased gene expression variability in BRCA1-related and basal-like breast tumours. Furthermore, genes with increased variability may be driven by changes in DNA occupancy of epigenetic effectors. The variation in gene expression is replicable and led to the identification of novel associations between genes and disease phenotypes.

Supplementary Information

The online version contains supplementary material available at 10.1007/s10549-021-06328-y.

Feature Selection for Colon Cancer Detection Using K-Means Clustering and Modified Harmony Search Algorithm

This paper proposes a feature selection method that is effective in distinguishing colorectal cancer patients from normal individuals using K-means clustering and the modified harmony search algorithm. As the genetic cause of colorectal cancer originates from mutations in genes, it is important to classify the presence or absence of colorectal cancer through gene information. The proposed methodology consists of four steps. First, the original data are Z-normalized by data preprocessing. Candidate genes are then selected using the Fisher score. Next, one representative gene is selected from each cluster after candidate genes are clustered using K-means clustering. Finally, feature selection is carried out using the modified harmony search algorithm. The gene combination created by feature selection is then applied to the classification model and verified using 5-fold cross-validation. The proposed model obtained a classification accuracy of up to 94.36%. Furthermore, on comparing the proposed method with other methods, we prove that the proposed method performs well in classifying colorectal cancer. Moreover, we believe that the proposed model can be applied not only to colorectal cancer but also to other gene-related diseases.

General ways to improve false coverage rate-adjusted selective confidence intervals

Post-selection inference on thousands of parameters has attracted considerable research interest in recent years. Specifically, Benjamini & Yekutieli (2005) considered constructing confidence intervals after selection. They proposed adjusting the confidence levels of marginal confidence intervals for the selected parameters to ensure control of the false coverage-statement rate. However, although Benjamini–Yekutieli confidence intervals are widely used, they are uniformly inflated. In this article, two methods for narrowing the Benjamini–Yekutieli confidence intervals are proposed. The first improves the confidence intervals by incorporating the selection event into the calculation. The second method further narrows those confidence intervals in which some parameters are selected with very small probabilities, which results in underutilization of the target level for control of the false coverage-statement rate. A breast cancer dataset is analysed to compare the methods.

MicroRNA-638 induces apoptosis and autophagy in human liver cancer cells by targeting enhancer of zeste homolog 2 (EZH2)

Liver cancer is of the devastating human cancers and its incidence is increasing at an alarming rate. The clinical outcomes are far from descent due to lack of efficient therapeutic targets and chemotherapeutic agents. Studies have revealed the therapeutic implications of microRNAs in the management of different human cancers. This study was designed to explore the role and therapeutic potential of miR-638 in liver cancer via modulation of zeste homolog 2 (EZH2). The results revealed significant (P < 0.05) downregulation of miR-638 in human liver cancer tissues and cell lines. Overexpression of miR-638 led to a significant (P < 0.05) decline in liver cancer cell proliferation. Nonetheless, inhibition of miR-638 could promote the proliferation of the human liver cancer cells. The DAPI and annexin V/PI staining assays revealed that miR-638 induces apoptosis in human liver cancer cells which was accompanied by enhancement of Bax and depletion of Bcl-2 expression. Furthermore, miR-638 overexpression also leads to a significant (P < 0.05) increase of autophagosomes and autolysosomes in liver cancer cells suggestive of autophagy. The induction of autophagy was further confirmed by increase and decrease in expression of LC3B-II and Beclin-1 proteins, respectively. In contrary, inhibition of miR-638 prevented both apoptosis and autophagy of the liver cancer cells. In silico analysis and the dual luciferase assay revealed EZH2 as the molecular target of miR-638 at post-transcriptional level. The qRT-PCR showed that EZH2 to be significantly (P < 0.05) upregulated in the human liver cancer tissues and cell lines. However, the expression of EZH2 was considerably suppressed upon miR-638 overexpression in SNU-423 cells. Taken together, these findings suggest the tumor-suppressive role of miR-638/EZH2 axis liver cancer and point towards the potential of miR-638 as therapeutic target in the treatment of liver cancer.

Transcriptome Patterns of BRCA1- and BRCA2- Mutated Breast and Ovarian Cancers

Mutations in the BRCA1 and BRCA2 genes are known risk factors and drivers of breast and ovarian cancers. So far, few studies have been focused on understanding the differences in transcriptome and functional landscapes associated with the disease (breast vs. ovarian cancers), gene (BRCA1 vs. BRCA2), and mutation type (germline vs. somatic). In this study, we were aimed at systemic evaluation of the association of BRCA1 and BRCA2 germline and somatic mutations with gene expression, disease clinical features, outcome, and treatment. We performed BRCA1/2 mutation centered RNA-seq data analysis of breast and ovarian cancers from the TCGA repository using transcriptome and phenotype "portrayal" with multi-layer self-organizing maps and functional annotation. The results revealed considerable differences in BRCA1- and BRCA2-dependent transcriptome landscapes in the studied cancers. Furthermore, our data indicated that somatic and germline mutations for both genes are characterized by deregulation of different biological functions and differential associations with phenotype characteristics and poly(ADP-ribose) polymerase (PARP)-inhibitor gene signatures. Overall, this study demonstrates considerable variation in transcriptomic landscapes of breast and ovarian cancers associated with the affected gene (BRCA1 vs. BRCA2), as well as the mutation type (somatic vs. germline). These results warrant further investigations with larger groups of mutation carriers aimed at refining the understanding of molecular mechanisms of breast and ovarian cancers.

Validity of whole genomes sequencing results in neoplasms in precision medicine

OBJECTIVE

To compare the whole genomes sequencing (WGS) results in the 100K Genomes project with the results of routine molecular diagnostics in precision medicine.

MATERIALS AND METHODS

We analysed 374 cancers including a high tumour mutational burden (TMB-high) subgroup, defined as >10 non-synonymous single nucleotide variations per megabase. Colon cancers were evaluated for microsatellite instability (MSI), mismatch repair (MMR) genes and NRAS, KRAS and BRAF mutations using routine molecular diagnostics. Fluorescence in-situ hybridisation/immunohistochemistry was used to evaluate the Her2Neu status in breast cancers.

RESULTS

There was high correlation between WGS and routine diagnostic testing results irrespective of TMB status in colon cancers. Her2Neu status was discordant in 3 out of the 5 TMB-high breast cancers (p=0.049). The presence of ductal carcinoma in-situ correlated significantly with discordance (p=0.04). There were 3 (5%) discordant colorectal cases, all in the KRAS gene, 2 of which were from the non-invasive adenomatous component (p=0.0058). Of the 374 cases we identified 24 tumours with a TMB >10; comprising (colorectal carcinomas (CRCs) n=16, breast carcinomas n=5, bladder urothelial cell cancers n=3). Of the 16 TMB-high colorectal adenocarcinomas, 13 had MSI-high status. The same 13 had defective MMR protein expression. TMB-high colorectal cancers had 100% concordant results between WGS and NGS testing for KRAS, BRAF and NRAS (16/16).

CONCLUSION

The microsatellite and mutational status of colorectal cancers evaluated by WGS seem to correlate well with the routine diagnostic testing if it is ensured that the invasive component is sequenced. Evaluation of WGS results need to be carefully correlated with histomorphology, as tumour heterogeneity/contamination with pre-malignant components needs to be taken into account.

Genomic Diversity in Sporadic Breast Cancer in a Latin American Population

Among Latin American women, breast cancer incidences vary across populations. Uruguay and Argentina have the highest rates in South America, which are mainly attributed to strong, genetic European contributions. Most genetic variants associated with breast cancer were described in European populations. However, the vast majority of genetic contributors to breast cancer risk remain unknown. Here, we report the results of a candidate gene association study of sporadic breast cancer in 176 cases and 183 controls in the Uruguayan population. We analyzed 141 variants from 98 loci that have been associated with overall breast cancer risk in European populations. We found weak evidence for the association of risk variants rs294174 (ESR1), rs16886165 (MAP3K1), rs2214681 (CNTNAP2), rs4237855 (VDR), rs9594579 (RANKL), rs8183919 (PTGIS), rs2981582 (FGFR2), and rs1799950 (BRCA1) with sporadic breast cancer. These results provide useful insight into the genetic susceptibility to sporadic breast cancer in the Uruguayan population and support the use of genetic risk scores for individualized screening and prevention.

Distributions associated with simultaneous multiple hypothesis testing

We develop the distribution for the number of hypotheses found to be statistically significant using the rule from Simes (Biometrika 73: 751–754, 1986) for controlling the family-wise error rate (FWER). We find the distribution of the number of statistically significant p-values under the null hypothesis and show this follows a normal distribution under the alternative. We propose a parametric distribution ΨI(·) to model the marginal distribution of p-values sampled from a mixture of null uniform and non-uniform distributions under different alternative hypotheses. The ΨI distribution is useful when there are many different alternative hypotheses and these are not individually well understood. We fit ΨI to data from three cancer studies and use it to illustrate the distribution of the number of notable hypotheses observed in these examples. We model dependence in sampled p-values using a latent variable. These methods can be combined to illustrate a power analysis in planning a larger study on the basis of a smaller pilot experiment.

Genome-Wide Gene Expression Analyses of BRCA1- and BRCA2-Associated Breast and Ovarian Tumours

Germline pathogenic variants in BRCA1 and BRCA2 increase cumulative lifetime risk up to 75% for breast cancer and 76% for ovarian cancer. Genetic testing for BRCA1 and BRCA2 pathogenic variants has become an important part of clinical practice for cancer risk assessment and for reducing individual risk of developing cancer. Genetic testing can produce three outcomes: positive (a pathogenic variant), uninformative (no pathogenic variant) and uncertain significance (a variant of unknown clinical significance). More than one third of BRCA1 and BRCA2 variants identified have been classified as variants of uncertain significance, presenting a challenge for clinicians. To address this important clinical challenge, a number of studies have been undertaken to establish a gene expression phenotype for pathogenic BRCA1 and BRCA2 variant carriers in several diseased and normal tissues. However, the consistency of gene expression phenotypes described in studies has been poor. To determine if gene expression analysis has been a successful approach for variant classification, we describe the design and comparability of 23 published gene expression studies that have profiled cells from BRCA1 and BRCA2 pathogenic variant carriers. We show the impact of advancements in expression-based technologies, the importance of developing larger study cohorts and the necessity to better understand variables affecting gene expression profiles across different tissue types.

The role of TP53 pathogenic variants in early-onset HER2-positive breast cancer

Breast cancer is the most frequent event in Li-Fraumeni syndrome associated with germline TP53 variants. Some studies have shown that breast cancers in women with Li-Fraumeni syndrome are commonly HER2-positive, suggesting that HER2 amplification or over-expression in a young woman may be a useful criterion to test for germline variants in the TP53 gene. We assessed the prevalence of germline TP53 variants by Sanger sequencing or next-generation sequencing in 149 women with HER2-positive breast cancer diagnosed until age 40. The pattern of HER2 amplification was evaluated with dual-probe FISH in a subset of breast carcinomas from patients with germline TP53 variants as compared with those of noncarriers. Among 149 women tested, three presented a deleterious TP53 germline variant (2%), with one patient diagnosed at age 31 and the other two with bilateral breast cancer at ages 29/33 and 28/32, respectively. Three of the 36 patients (8.3%) with the first breast cancer diagnosed at age 31 or younger presented a pathogenic TP53 variant. Additionally, all TP53 deleterious variant carriers had a first degree relative diagnosed with different early-onset cancers (frequently not belonging to the Li-Fraumeni syndrome tumor spectrum) diagnosed at age 45 or younger. Higher levels of HER2 amplification were found in breast carcinomas of TP53 pathogenic variant carriers than in those of noncarriers. Deleterious germline TP53 variants account for a small proportion of early-onset HER2-positive breast cancers, but these seem to have higher HER2 amplification ratios. All TP53 pathogenic variant carriers found in this study had the first breast carcinoma diagnosed at age 31 or younger and a first-degree relative with early-onset cancer. Further studies are needed to clarify if HER2 status in early-onset breast cancer patients, in combination with other personal and/or familial cancer history, is useful to update the TP53 testing criteria.

Effects of germline and somatic events in candidate BRCA-like genes on breast-tumor signatures

Mutations in BRCA1 and BRCA2 cause deficiencies in homologous recombination repair (HR), resulting in repair of DNA double-strand breaks by the alternative non-homologous end-joining pathway, which is more error prone. HR deficiency of breast tumors is important because it is associated with better responses to platinum salt therapies and PARP inhibitors. Among other consequences of HR deficiency are characteristic somatic-mutation signatures and gene-expression patterns. The term "BRCA-like" (or "BRCAness") describes tumors that harbor an HR defect but have no detectable germline mutation in BRCA1 or BRCA2. A better understanding of the genes and molecular events associated with tumors being BRCA-like could provide mechanistic insights and guide development of targeted treatments. Using data from The Cancer Genome Atlas (TCGA) for 1101 breast-cancer patients, we identified individuals with a germline mutation, somatic mutation, homozygous deletion, and/or hypermethylation event in BRCA1, BRCA2, and 59 other cancer-predisposition genes. Based on the assumption that BRCA-like events would have similar downstream effects on tumor biology as BRCA1/BRCA2 germline mutations, we quantified these effects based on somatic-mutation signatures and gene-expression profiles. We reduced the dimensionality of the somatic-mutation signatures and expression data and used a statistical resampling approach to quantify similarities among patients who had a BRCA1/BRCA2 germline mutation, another type of aberration in BRCA1 or BRCA2, or any type of aberration in one of the other genes. Somatic-mutation signatures of tumors having a non-germline aberration in BRCA1/BRCA2 (n = 80) were generally similar to each other and to tumors from BRCA1/BRCA2 germline carriers (n = 44). Additionally, somatic-mutation signatures of tumors with germline or somatic events in ATR (n = 16) and BARD1 (n = 8) showed high similarity to tumors from BRCA1/BRCA2 carriers. Other genes (CDKN2A, CTNNA1, PALB2, PALLD, PRSS1, SDHC) also showed high similarity but only for a small number of events or for a single event type. Tumors with germline mutations or hypermethylation of BRCA1 had relatively similar gene-expression profiles and overlapped considerably with the Basal-like subtype; but the transcriptional effects of the other events lacked consistency. Our findings confirm previously known relationships between molecular signatures and germline or somatic events in BRCA1/BRCA2. Our methodology represents an objective way to identify genes that have similar downstream effects on molecular signatures when mutated, deleted, or hypermethylated.

Lessons learned from understanding chemotherapy resistance in epithelial tubo-ovarian carcinoma from BRCA1and BRCA2mutation carriers

BRCA1 and BRCA2 are multi-functional proteins and key factors for maintaining genomic stability through their roles in DNA double strand break repair by homologous recombination, rescuing stalled or damaged DNA replication forks, and regulation of cell cycle DNA damage checkpoints. Impairment of any of these critical roles results in genomic instability, a phenotypic hallmark of many cancers including breast and epithelial ovarian carcinomas (EOC). Damaging, usually loss of function germline and somatic variants in BRCA1 and BRCA2, are important drivers of the development, progression, and management of high-grade serous tubo-ovarian carcinoma (HGSOC). However, mutations in these genes render patients particularly sensitive to platinum-based chemotherapy, and to the more innovative targeted therapies with poly-(ADP-ribose) polymerase inhibitors (PARPis) that are targeted to BRCA1/BRCA2 mutation carriers. Here, we reviewed the literature on the responsiveness of BRCA1/2-associated HGSOC to platinum-based chemotherapy and PARPis, and propose mechanisms underlying the frequent development of resistance to these therapeutic agents.

Gene Expression Classification of Lung Adenocarcinoma into Molecular Subtypes

As one of the most common malignancies in the world, lung adenocarcinoma (LUAD) is currently difficult to cure. However, the advent of precision medicine provides an opportunity to improve the treatment of lung cancer. Subtyping lung cancer plays an important role in performing a specific treatment. Here, we developed a framework that combines k-means clustering, t-test, sensitivity analysis, self-organizing map (SOM) neural network, and hierarchical clustering methods to classify LUAD into four subtypes. We determined that 24 differentially expressed genes could be used as therapeutic targets, and five genes (i.e., RTKN2, ADAM6, SPINK1, COL3A1, and COL1A2) could be potential novel markers for LUAD. Multivariate analysis showed that the four subtypes could serve as prognostic subtypes. Representative genes of each subtype were also identified, which could be potentially targetable markers for the different subtypes. The function and pathway enrichment analyses of these representative genes showed that the four subtypes have different pathological mechanisms. Mutations associated with the subtypes, e.g., epidermal growth factor receptor (EGFR) mutations in subtype 4 and tumor protein p53 (TP53) mutations in subtypes 1 and 2, could serve as potential markers for drug development. The four subtypes provide a foundation for subtype-specific therapy of LUAD.

Uncovering Potential Roles of Differentially Expressed Genes, Upstream Regulators, and Canonical Pathways in Endometriosis Using an In Silico Genomics Approach

Endometriosis is characterized by ectopic endometrial tissue implantation, mostly within the peritoneum, and affects women in their reproductive age. Studies have been done to clarify its etiology, but the precise molecular mechanisms and pathophysiology remain unclear. We downloaded genome-wide mRNA expression and clinicopathological data of endometriosis patients and controls from NCBI’s Gene Expression Omnibus, after a systematic search of multiple independent studies comprising 156 endometriosis patients and 118 controls to identify causative genes, risk factors, and potential diagnostic/therapeutic biomarkers. Comprehensive gene expression meta-analysis, pathway analysis, and gene ontology analysis was done using a bioinformatics-based approach. We identified 1590 unique differentially expressed genes (129 upregulated and 1461 downregulated) mapped by IPA as biologically relevant. The top upregulated genes were FOS, EGR1, ZFP36, JUNB, APOD, CST1, GPX3, and PER1, and the top downregulated ones were DIO2, CPM, OLFM4, PALLD, BAG5, TOP2A, PKP4, CDC20B, and SNTN. The most perturbed canonical pathways were mitotic roles of Polo-like kinase, role of Checkpoint kinase proteins in cell cycle checkpoint control, and ATM signaling. Protein–protein interaction analysis showed a strong network association among FOS, EGR1, ZFP36, and JUNB. These findings provide a thorough understanding of the molecular mechanism of endometriosis, identified biomarkers, and represent a step towards the future development of novel diagnostic and therapeutic options.

Advances in quantitative immunohistochemistry and their contribution to breast cancer

Introduction: Automated image analysis provides an objective, quantitative, and reproducible method of measurement of biomarkers. Image quantification is particularly well suited for the analysis of tissue microarrays which has played a major pivotal role in the rapid assessment of molecular biomarkers. Data acquired from grinding up bulk tissue samples miss spatial information regarding cellular localization; therefore, methods that allow for spatial cell phenotyping at high resolution have proven to be valuable in many biomarker discovery assays. Here, we focus our attention on breast cancer as an example of a tumor type that has benefited from quantitative biomarker studies using tissue microarray format.Areas covered: The history of immunofluorescence and immunohistochemistry and the current status of these techniques, including multiplexing technologies (spectral and non-spectral) and image analysis software will be addressed. Finally, we will turn our attention to studies that have provided proof-of-principle evidence that have been impacted from the use of these techniques.Expert opinion: Assessment of prognostic and predictive biomarkers on tissue sections and TMA using Quantitative immunohistochemistry is an important advancement in the investigation of biologic markers. The challenges in standardization of quantitative technologies for accurate assessment are required for adoption into routine clinical practice.

Establishment and Characterization of a Brca1-/-, p53-/- Mouse Mammary Tumor Cell Line

Breast cancer is the most commonly occurring cancer in women and the second most common cancer overall. By the age of 80, the estimated risk for breast cancer for women with germline BRCA1 or BRCA2 mutations is around 80%. Genetically engineered BRCA1-deficient mouse models offer a unique opportunity to study the pathogenesis and therapy of triple negative breast cancer. Here we present a newly established Brca1^−/−, p53^−/− mouse mammary tumor cell line, designated as CST. CST shows prominent features of BRCA1-mutated triple-negative breast cancers including increased motility, high proliferation rate, genome instability and sensitivity to platinum chemotherapy and PARP inhibitors (olaparib, veliparib, rucaparib and talazoparib). Genomic instability of CST cells was confirmed by whole genome sequencing, which also revealed the presence of COSMIC (Catalogue of Somatic Mutations in Cancer) mutation signatures 3 and 8 associated with homologous recombination (HR) deficiency. In vitro sensitivity of CST cells was tested against 11 chemotherapy agents. Tumors derived from orthotopically injected CST-mCherry cells in FVB-GFP mice showed sensitivity to cisplatin, providing a new model to study the cooperation of BRCA1-KO, mCherry-positive tumor cells and the GFP-expressing stromal compartment in therapy resistance and metastasis formation. In summary, we have established CST cells as a new model recapitulating major characteristics of BRCA1-negative breast cancers.

The changing role of pathologists from morphologists to molecular pathologists in the era of precision medicine

Pathology is the study of human illness. Throughout centuries of scientific discoveries, pathologic examination of tissue samples has been the gold standard for diagnosis and pathologists have been involved in the elucidation of aetiology, assessment of the biology, clinicopathologic correlation and prediction of prognosis. The advances in science and technology and focused interest in breast cancer research have provided ample opportunities for pathologists to participate in better understanding of the basic fundamental cascade of events leading to tumorigenesis in breast cancer. They also partnered with their clinical colleagues and scientists to find more effective therapeutic options. This change has been possible with recognition of the fact that morphology alone may not be sufficient to tell the entire story of clinical behaviour of all breast cancer patients. In addition, the realization of heterogeneity of breast cancer and the differences in the expression of various biomarkers and the observed differences in response to therapy have resulted in extensive efforts to better define the characters of each breast cancer subtype. It is now generally agreed that breast cancer is not a single disease and not all patients with breast cancer can benefit from the same therapy. These changes have brought new challenges for pathologists. Pathologist are now required to not only provide diagnosis, but also study the precise molecular characterization of each individual breast cancer case and play a significant role in the treatment planning of breast cancer patients. This remarkable change in the role of the pathologist require his/her involvement in the modern taxonomy of this disease and to rise to the challenge of genomic medicine and molecular diagnostics, which are the fastest growing areas of medicine. Emphasis should also been placed to create a new morphomolecular pathology and train our young pathologist to expand beyond morphology and to embrace the power of molecular diagnostics, in order to be able to effectively practise in the era of precision medicine.

Molecular harvesting with electroporation for tissue profiling

Recent developments in personalized medicine are based on molecular measurement steps that guide personally adjusted medical decisions. A central approach to molecular profiling consists of measuring DNA, RNA, and/or proteins in tissue samples, most notably in and around tumors. This measurement yields molecular biomarkers that are potentially predictive of response and of tumor type. Current methods in cancer therapy mostly use tissue biopsy as the starting point of molecular profiling. Tissue biopsies involve a physical resection of a small tissue sample, leading to localized tissue injury, bleeding, inflammation and stress, as well as to an increased risk of metastasis. Here we developed a technology for harvesting biomolecules from tissues using electroporation. We show that tissue electroporation, achieved using a combination of high-voltage short pulses, 50 pulses 500 V cm-1, 30 µs, 1 Hz, with low-voltage long pulses 50 pulses 50 V cm-1, 10 ms, delivered at 1 Hz, allows for tissue-specific extraction of RNA and proteins. We specifically tested RNA and protein extraction from excised kidney and liver samples and from excised HepG2 tumors in mice. Further in vivo development of extraction methods based on electroporation can drive novel approaches to the molecular profiling of tumors and of tumor environment and to related diagnosis practices.

Heterogeneity of breast cancer: The importance of interaction between different tumor cell populations

INTRODUCTION

Breast cancer is the most common cancer and the second leading cause of cancer-related death in women worldwide. Despite the early detection of breast cancer and increasing knowledge of its biology and chemo-resistance, metastatic breast cancer is largely incurable disease. We provide a review of the intertumor and intratumor heterogeneity, explain the differences between triple-negative breast cancer subtypes. Also, we describe the interaction of breast tumor cells with their microenvironment cells and explain how this interaction contributes to the tumor progression, metastasis formation and resistance to the treatment.

DISCUSSION

One of the main causes that complicate the treatment is tumor heterogeneity. It is observed among patients (intertumor heterogeneity) and in each individual tumor (intratumor heterogeneity). In the case of intratumor heterogeneity, the tumor consists of different phenotypical cell populations. During breast cancer subtype identification, a small piece of solid tumor tissue does not necessarily represent all the tumor composition. Breast tumor cell phenotypical differences may appear due to cell localization in different tumor sites, unique response to the treatment, cell interaction with tumor microenvironment or tumor cell interaction with each other. This heterogeneity may lead to breast cancer aggressiveness and challenging treatment.

CONCLUSION

Understanding the molecular and cellular mechanisms of tumor heterogeneity that are relevant to the development of treatment resistance is a major area of research. Identification of differences between populations and their response to anticancer drugs would help to predict the potential resistance to chemotherapy and thus would help to select the most suitable anticancer treatment.

Clinicopathological Significance of Decreased Expression of the Tumor Inhibitor Gene PDCD5 in Osteoclastoma

Background: The gene programmed cell death 5 (PDCD5) has recently been characterized as a tumor suppressor gene and is believed to be an important prognostic cancer marker; it is frequently involved in neoplastic transformation and apoptosis of tumor cells. Several studies have demonstrated a decrease or loss of expression of PDCD5 in certain tumors. However, the relevance of PDCD5 expression in human osteoclastoma and its clinicopathological significance have not been extensively studied. Methods: The aim of this study was to explore the relative transcriptional and translational expression levels of PDCD5 in 79 osteoclastoma samples using multi-modal methods of analysis. Results: Our findings showed that 52% (15/29) of osteoclastoma cases exhibited reduced PDCD5 expression at the transcriptional level, and 56% (44/79) exhibited lower PDCD5 expression at the protein level, when compared with nontumor tissue. In addition, the statistical significance of the altered PDCD5 protein expression was examined using the Campanacci grading system for osteoclastoma. More importantly, the decreased expression at the translational level was observed to have a negative association with the Ki-67 staining index. Conclusion: Based on these findings, abnormal PDCD5 expression might be an important biomarker in human osteoclastoma and may contribute to tumor progression and malignant cell proliferation.

C-HMOSHSSA: Gene selection for cancer classification using multi-objective meta-heuristic and machine learning methods

BACKGROUND AND OBJECTIVE

Over the last two decades, DNA microarray technology has emerged as a powerful tool for early cancer detection and prevention. It helps to provide a detailed overview of disease complex microenvironment. Moreover, online availability of thousands of gene expression assays made microarray data classification an active research area. A common goal is to find a minimum subset of genes and maximizing the classification accuracy.

METHODS

In pursuit of a similar objective, we have proposed framework (C-HMOSHSSA) for gene selection using multi-objective spotted hyena optimizer (MOSHO) and salp swarm algorithm (SSA). The real-life optimization problems with more than one objective usually face the challenge to maintain convergence and diversity. Salp Swarm Algorithm (SSA) maintains diversity but, suffers from the overhead of maintaining the necessary information. On the other hand, the calculation of MOSHO requires low computational efforts hence is used for maintaining the necessary information. Therefore, the proposed algorithm is a hybrid algorithm that utilizes the features of both SSA and MOSHO to facilitate its exploration and exploitation capability.

RESULTS

Four different classifiers are trained on seven high-dimensional datasets using a subset of features (genes), which are obtained after applying the proposed hybrid gene selection algorithm. The results show that the proposed technique significantly outperforms existing state-of-the-art techniques.

CONCLUSION

It is also shown that the new sets of informative and biologically relevant genes are successfully identified by the proposed technique. The proposed approach can also be applied to other problem domains of interest which involve feature selection.

Biomarkers for Homologous Recombination Deficiency in Cancer

Defective DNA repair is a common hallmark of cancer. Homologous recombination is a DNA repair pathway of clinical interest due to the sensitivity of homologous recombination-deficient cells to poly-ADP ribose polymerase (PARP) inhibitors. The measurement of homologous recombination deficiency (HRD) in cancer is therefore vital to the appropriate design of clinical trials incorporating PARP inhibitors. However, methods to identify HRD in tumors are varied and controversial. Understanding existing and new methods to measure HRD is important to their appropriate use in clinical trials and practice. The aim of this review is to summarize the biology and clinical validation of current methods to measure HRD, to aid decision-making for patient stratification and translational research in PARP inhibitor trials. We discuss the current clinical development of PARP inhibitors, along with established indicators for HRD such as germline BRCA1/2 mutation status and clinical response to platinum-based therapy. We then examine newer assays undergoing clinical validation, including 1) somatic mutations in homologous recombination genes, 2) "genomic scar" assays using array-based comparative genomic hybridization (aCGH), single nucleotide polymorphism (SNP) analysis or mutational signatures derived from next-generation sequencing, 3) transcriptional profiles of HRD, and 4) phenotypic or functional assays of protein expression and localization. We highlight the strengths and weaknesses of each of these assays, for consideration during the design of studies involving PARP inhibitors.

Myriads: P-value-based multiple testing correction

Motivation

There are many multiple testing correction methods. Some of them are robust to various dependencies in the data while others are not. Some of the implementations have problems for managing high dimensional list of P-values as currently demanded by microarray and other omic technologies.

Results

The program Myriads, formerly SGoF+, provides some of the most important P-value-based correction methods jointly with a test of dependency and a P-value simulator. Myriads easily manage hundreds of thousands of P-values.

Availability and implementation

http://myriads.webs.uvigo.es.

Contact

myriads@uvigo.es.

Supplementary information

Supplementary data are available at Bioinformatics online.