Insight into the heterogeneity of breast cancer through next-generation sequencing

Phage display screening in breast cancer: From peptide discovery to clinical applications

Breast cancer is known as the most common type of cancer found in women and a leading cause of cancer death in women, with the global incidence only increasing. Breast cancer in Malaysia is also unfortunately the most prevalent in Malaysian women. Many treatment options are available for breast cancer, but there is increasing resistance developed against treatment and increased recurrence risk, emphasizing the need for new treatment options. This review will focus on the applications of phage display screening in the context of breast cancer. Phage display screening can facilitate the drug discovery process by providing rapid screening and isolation of peptides that bind to targets of interest with high specificity. Peptides derived from phage display target various types of proteins involved in breast cancer, including HER2, C5AR1, p53 and PRDM14, either for therapeutic or diagnostic purposes. Different approaches were employed as well to produce potential peptides using radiolabelling and conjugation techniques. Promising results were reported for in vitro and in vivo studies utilizing peptides derived from phage display screening. Further optimization of the protocols and factors to consider are required to mitigate the challenges involved with phage display screening of peptides for breast cancer diagnosis and treatment.

Immunohistochemical Evaluation of Cathepsin B, L, and S Expression in Breast Cancer Patients

PURPOSE

Cysteine cathepsins are proteases that play a role in normal cellular physiology and neoplastic transformation. Elevated expression and enzymatic activity of cathepsins in breast cancer (BCa) indicates their potential as a target for tumor imaging. In particular cathepsin B (CTSB), L (CTSL), and S (CTSS) are used as targets for near-infrared (NIR) fluorescence imaging (FI), a technique that allows real-time intraoperative tumor visualization and resection margin assessment. Therefore, this immunohistochemical study explores CTSB, CTSL, and CTSS expression levels in a large breast cancer patient cohort, to investigate in which BCa patients the use of cathepsin-targeted NIR FI may have added value.

PROCEDURES

Protein expression was analyzed in tumor tissue microarrays (TMA) of BCa patients using immunohistochemistry and quantified as a total immunostaining score (TIS), ranging from 0-12. In total, the tissues of 557 BCa patients were included in the TMA.

RESULTS

CTSB, CTSL, and CTSS were successfully scored in respectively 340, 373 and 252 tumors. All tumors showed CTSB, CTSL, and/or CTSS expression to some extent (TIS > 0). CTSB, CTSL, and CTSS expression was scored as high (TIS > 6) in respectively 28%, 80%, and 18% of tumors. In 89% of the tumors scored for all three cathepsins, the expression level of one or more of these proteases was scored as high (TIS > 6). Tumors showed significantly higher cathepsin expression levels with advancing Bloom-Richardson grade (p < 0.05). Cathepsin expression was highest in estrogen receptor (ER)-negative, human epidermal growth factor receptor 2(HER2)-positive and triple-negative (TN) tumors. There was no significant difference in cathepsin expression between tumors that were treated with neoadjuvant systemic therapy and tumors that were not.

CONCLUSIONS

The expression of at least one of the cysteine cathepsins B, L and S in all breast tumor tissues tested suggests that cathepsin-activatable imaging agents with broad reactivity for these three proteases will likely be effective in the vast majority of breast cancer patients, regardless of molecular subtype and treatment status. Patients with high grade ER-negative, HER2-positive, or TN tumors might show higher imaging signals.

[18F]F-Poly(ADP-Ribose) Polymerase Inhibitor Radiotracers for Imaging PARP Expression and Their Potential Clinical Applications in Oncology

Including poly(ADP-ribose) polymerase (PARP) inhibitors in managing patients with inoperable tumors has significantly improved outcomes. The PARP inhibitors hamper single-strand deoxyribonucleic acid (DNA) repair by trapping poly(ADP-ribose)polymerase (PARP) at sites of DNA damage, forming a non-functional "PARP enzyme-inhibitor complex" leading to cell cytotoxicity. The effect is more pronounced in the presence of PARP upregulation and homologous recombination (HR) deficiencies such as breast cancer-associated gene (BRCA1/2). Hence, identifying HR-deficiencies by genomic analysis-for instance, BRCA1/2 used in triple-negative breast cancer-should be a part of the selection process for PARP inhibitor therapy. Published data suggest BRCA1/2 germline mutations do not consistently predict favorable responses to PARP inhibitors, suggesting that other factors beyond tumor mutation status may be at play. A variety of factors, including tumor heterogeneity in PARP expression and intrinsic and/or acquired resistance to PARP inhibitors, may be contributing factors. This justifies the use of an additional tool for appropriate patient selection, which is noninvasive, and capable of assessing whole-body in vivo PARP expression and evaluating PARP inhibitor pharmacokinetics as complementary to the currently available BRCA1/2 analysis. In this review, we discuss [18F]Fluorine PARP inhibitor radiotracers and their potential in the imaging of PARP expression and PARP inhibitor pharmacokinetics. To provide context we also briefly discuss possible causes of PARP inhibitor resistance or ineffectiveness. The discussion focuses on TNBC, which is a tumor type where PARP inhibitors are used as part of the standard-of-care treatment strategy.

Comprehensive genomic profiling and therapeutic implications for Taiwanese patients with treatment-naïve breast cancer

BACKGROUND

Breast cancer is a heterogeneous disease categorized based on molecular characteristics, including hormone receptor (HR) and human epidermal growth factor receptor 2 (HER2) expression levels. The emergence of profiling technology has revealed multiple driver genomic alterations within each breast cancer subtype, serving as biomarkers to predict treatment outcomes. This study aimed to explore the genomic landscape of breast cancer in the Taiwanese population through comprehensive genomic profiling (CGP) and identify diagnostic and predictive biomarkers.

METHODS

Targeted next-generation sequencing-based CGP was performed on 116 archived Taiwanese breast cancer specimens, assessing genomic alterations (GAs), including single nucleotide variants, copy number variants, fusion genes, tumor mutation burden (TMB), and microsatellite instability (MSI) status. Predictive variants for FDA-approved therapies were evaluated within each subtype.

RESULTS

In the cohort, frequent mutations included PIK3CA (39.7%), TP53 (36.2%), KMT2C (9.5%), GATA3 (8.6%), and SF3B1 (6.9%). All subtypes had low TMB, with no MSI-H tumors. Among HR + HER2- patients, 42% (27/65) harbored activating PIK3CA mutations, implying potential sensitivity to PI3K inhibitors and resistance to endocrine therapies. HR + HER2- patients exhibited intrinsic hormonal resistance via FGFR1 gene gain/amplification (15%), exclusive of PI3K/AKT pathway alterations. Aberrations in the PI3K/AKT/mTOR and FGFR pathways were implicated in chemoresistance, with a 52.9% involvement in triple-negative breast cancer. In HER2+ tumors, 50% harbored GAs potentially conferring resistance to anti-HER2 therapies, including PIK3CA mutations (32%), MAP3K1 (2.9%), NF1 (2.9%), and copy number gain/amplification of FGFR1 (18%), FGFR3 (2.9%), EGFR (2.9%), and AKT2 (2.9%).

CONCLUSION

This study presents CGP findings for treatment-naïve Taiwanese breast cancer, emphasizing its value in routine breast cancer management, disease classification, and treatment selection.

Evolution of chromosome-arm aberrations in breast cancer through genetic network rewiring

The basal breast cancer subtype is enriched for triple-negative breast cancer (TNBC) and displays consistent large chromosomal deletions. Here, we characterize evolution and maintenance of chromosome 4p (chr4p) loss in basal breast cancer. Analysis of The Cancer Genome Atlas data shows recurrent deletion of chr4p in basal breast cancer. Phylogenetic analysis of a panel of 23 primary tumor/patient-derived xenograft basal breast cancers reveals early evolution of chr4p deletion. Mechanistically we show that chr4p loss is associated with enhanced proliferation. Gene function studies identify an unknown gene, C4orf19, within chr4p, which suppresses proliferation when overexpressed-a member of the PDCD10-GCKIII kinase module we name PGCKA1. Genome-wide pooled overexpression screens using a barcoded library of human open reading frames identify chromosomal regions, including chr4p, that suppress proliferation when overexpressed in a context-dependent manner, implicating network interactions. Together, these results shed light on the early emergence of complex aneuploid karyotypes involving chr4p and adaptive landscapes shaping breast cancer genomes.

18F-FDG Dedicated Breast PET Complementary to Breast MRI for Evaluating Early Response to Neoadjuvant Chemotherapy

Purpose To compare quantitative measures of tumor metabolism and perfusion using fluorine 18 (18F) fluorodeoxyglucose (FDG) dedicated breast PET (dbPET) and breast dynamic contrast-enhanced (DCE) MRI during early treatment with neoadjuvant chemotherapy (NAC). Materials and Methods Prospectively collected DCE MRI and 18F-FDG dbPET examinations were analyzed at baseline (T0) and after 3 weeks (T1) of NAC in 20 participants with 22 invasive breast cancers. FDG dbPET-derived standardized uptake value (SUV), metabolic tumor volume, and total lesion glycolysis (TLG) and MRI-derived percent enhancement (PE), signal enhancement ratio (SER), and functional tumor volume (FTV) were calculated at both time points. Differences between FDG dbPET and MRI parameters were evaluated after stratifying by receptor status, Ki-67 index, and residual cancer burden. Parameters were compared using Wilcoxon signed rank and Mann-Whitney U tests. Results High Ki-67 tumors had higher baseline SUVmean (difference, 5.1; P = .01) and SUVpeak (difference, 5.5; P = .04). At T1, decreases were observed in FDG dbPET measures (pseudo-median difference T0 minus T1 value [95% CI]) of SUVmax (-6.2 [-10.2, -2.6]; P < .001), SUVmean (-2.6 [-4.9, -1.3]; P < .001), SUVpeak (-4.2 [-6.9, -2.3]; P < .001), and TLG (-29.1 mL3 [-71.4, -6.8]; P = .005) and MRI measures of SERpeak (-1.0 [-1.3, -0.2]; P = .02) and FTV (-11.6 mL3 [-22.2, -1.7]; P = .009). Relative to nonresponsive tumors, responsive tumors showed a difference (95% CI) in percent change in SUVmax of -34.3% (-55.9%, 1.5%; P = .06) and in PEpeak of -42.4% (95% CI: -110.5%, 8.5%; P = .08). Conclusion 18F-FDG dbPET was sensitive to early changes during NAC and provided complementary information to DCE MRI that may be useful for treatment response evaluation. Keywords: Breast, PET, Dynamic Contrast-enhanced MRI Clinical trial registration no. NCT01042379 Supplemental material is available for this article. © RSNA, 2024.

A Bayesian framework to study tumor subclone-specific expression by combining bulk DNA and single-cell RNA sequencing data

Genetic and gene expression heterogeneity is an essential hallmark of many tumors, allowing the cancer to evolve and to develop resistance to treatment. Currently, the most commonly used data types for studying such heterogeneity are bulk tumor/normal whole-genome or whole-exome sequencing (WGS, WES); and single-cell RNA sequencing (scRNA-seq), respectively. However, tools are currently lacking to link genomic tumor subclonality with transcriptomic heterogeneity by integrating genomic and single-cell transcriptomic data collected from the same tumor. To address this gap, we developed scBayes, a Bayesian probabilistic framework that uses tumor subclonal structure inferred from bulk DNA sequencing data to determine the subclonal identity of cells from single-cell gene expression (scRNA-seq) measurements. Grouping together cells representing the same genetically defined tumor subclones allows comparison of gene expression across different subclones, or investigation of gene expression changes within the same subclone across time (i.e., progression, treatment response, or relapse) or space (i.e., at multiple metastatic sites and organs). We used simulated data sets, in silico synthetic data sets, as well as biological data sets generated from cancer samples to extensively characterize and validate the performance of our method, as well as to show improvements over existing methods. We show the validity and utility of our approach by applying it to published data sets and recapitulating the findings, as well as arriving at novel insights into cancer subclonal expression behavior in our own data sets. We further show that our method is applicable to a wide range of single-cell sequencing technologies including single-cell DNA sequencing as well as Smart-seq and 10x Genomics scRNA-seq protocols.

Development of Integrated Bioorthogonal Self-Catalyzed NO Donor/Platinum(IV) Prodrugs for Synergistical Intervention against Triple-Negative Breast Cancer

The platinum(IV) prodrug strategy is attractive for the synergistic antitumor effect. High levels (>400 nM) of nitric oxide (NO) exert promising cancer inhibition effects via multiple mechanisms. Herein, we designed and synthesized a new group of integrated bioorthogonal self-catalyzed NO donor/Pt(IV) prodrugs bearing long alkyl chains to enhance the stability in circulation, while the cytoplasmic reductants trigger cascade activation to release Pt and NO in tumor cells. Specifically, compound 10c exhibited an improved stability, favorable pharmacokinetic properties (AUC(0-t) of 2210.10 h*ng/mL), potent anti-triple-negative breast cancer (TNBC) effects (71.08% tumor growth inhibition (TGI) against the MDA-MB-231 xenograft model), potent in vivo anti-TNBC lung metastasis activity, and acceptable low toxicity. Importantly, NO released from 10c leads to the S-nitrosation of metal transporters Atox1&ATP7a in TNBC cells, which increases the Pt retention and inhibits lysyl oxidase, generating synergistic tumoricidal and antimetastatic activity. These results may inspire further study on the synergistical therapy of Pt and NO for the treatment of TNBC.

Omics Technologies Improving Breast Cancer Research and Diagnostics

Breast cancer (BC) has yielded approximately 2.26 million new cases and has caused nearly 685,000 deaths worldwide in the last two years, making it the most common diagnosed cancer type in the world. BC is an intricate ecosystem formed by both the tumor microenvironment and malignant cells, and its heterogeneity impacts the response to treatment. Biomedical research has entered the era of massive omics data thanks to the high-throughput sequencing revolution, quick progress and widespread adoption. These technologies-liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics and artificial intelligence imaging-could help researchers and clinicians to better understand the formation and evolution of BC. This review focuses on the findings of recent multi-omics-based research that has been applied to BC research, with an introduction to every omics technique and their applications for the different BC phenotypes, biomarkers, target therapies, diagnosis, treatment and prognosis, to provide a comprehensive overview of the possibilities of BC research.

Breast Cancer Statistics in Korea, 2019

This article provides an annual update of Korean breast cancer statistics, including the incidence, tumor stage, type of surgical treatment, and mortality. The data was collected from the Korean Breast Cancer Society registry system and Korean Central Cancer Registry. In 2019, 29,729 women were newly diagnosed with breast cancer. Breast cancer has continued to increase in incidence since 2002 and been the most common cancer in Korean women since 2019. Of the newly diagnosed cases in 2019, 24,820 (83.5%) were of invasive carcinomas, and 4,909 (16.5%) were of carcinoma in situ. The median age of women with breast cancer was 52.8 years, and breast cancer was most commonly diagnosed in the age group of 40-49 years. The number of patients who have undergone breast conserving surgery has continued to increase since 2016, with 68.6% of patients undergoing breast conserving surgery in 2019. The incidence of early-stage breast cancer continues to increase, with stage 0 or I breast cancer accounting for 61.6% of cases. The most common subtype of breast cancer is the hormone receptor-positive human epidermal growth factor receptor 2-negative subtype (63.1%). The 5-year relative survival rate of patients with breast cancer from 2015 to 2019 was 93.6%, with an increase of 14.3% compared to that from 1993 to 1995. This report improves our understanding of breast cancer characteristics in South Korea.

A Pilot Study on Biochemical Profile of Follicular Fluid in Breast Cancer Patients

Breast cancer (BC) is the most common type of cancer among women in almost all countries worldwide and is one of the oncological pathologies for which is indicated fertility preservation, a type of procedure used to help keep a person's ability to have children. Follicular fluid (FF) is a major component of oocyte microenvironment, which is involved in oocyte growth, follicular maturation, and in communication between germ and somatic cells; furthermore, it accumulates all metabolites during oocytes growth. To obtain information about changes on fertility due to cancer, we aimed at investigating potential biomarkers to discriminate between FF samples obtained from 16 BC patients and 10 healthy women undergoing in vitro fertilization treatments. An NMR-based metabolomics approach was performed to investigate the FF metabolic profiles; ELISA and western blotting assays were used to investigate protein markers of oxidative and inflammatory stress, which are processes closely related to cancer. Our results seem to suggest that FFs of BC women display some significant metabolic alterations in comparison to healthy controls, and these variations are also related with tumor staging.

Combination treatment with hENT1 and miR-143 reverses gemcitabine resistance in triple-negative breast cancer

Background

Triple-negative breast cancer (TNBC) is an aggressive subtype of breast cancer and is susceptible to develop gemcitabine (GEM) resistance. Decreased expression of human equilibrative nucleoside transporter 1 (hENT1) accompanied by compensatory increase of glycolysis is strongly associated with GEM resistance in TNBC. In this study, we investigated the treatment feasibility of combined hENT1 upregulation and miR-143-mediated inhibition of glycolysis for reversing GEM resistance in TNBC.

Methods

Experiments were performed in vitro and in vivo to compare the efficacy of GEM therapies. In this study, we established stable drug-resistant cell line, GEM-R cells, from parental cells (MDA-MB-231) through exposure to GEM following a stepwise incremental dosing strategy. Then GEM-R cells were transfected by lentiviral plasmids and GEM-R cells overexpressing hENT1 (GEM-R-hENT1) were established. The viability and apoptosis of wild-type (MDA-MB-231), GEM-R, and GEM-R-hENT1 cells treated with GEM or GEM + miR-143 were analyzed by CCK8 assay and flow cytometry. The RNA expression and protein expression were measured by RT-PCR and western blotting respectively. GEM uptake was determined by multiple reaction monitoring (MRM) analysis. Glycolysis was measured by glucose assay and ¹⁸F-FDG uptake. The antitumor effect was assessed in vivo in a tumor xenograft model by evaluating toxicity, tumor volume, and maximum standardized uptake value in ¹⁸F-FDG PET. Immunohistochemistry and fluorescence photography were taken in tumor samples. Pairwise comparisons were performed using Student’s t-test.

Results

Our results represented that overexpression of hENT1 reversed GEM resistance in GEM-R cells by showing lower IC₅₀ and higher rate of apoptosis. MiR-143 suppressed glycolysis in GEM-R cells and enhanced the effect of reversing GEM resistance in GEM-R-hENT1 cells. The therapeutic efficacy was validated using a xenograft mouse model. Combination treatment decreased tumor growth rate and maximum standardized uptake value in ¹⁸F-FDG PET more effectively.

Conclusions

Combined therapy of exogenous upregulation of hENT1 expression and miR-143 mimic administration was effective in reversing GEM resistance, providing a promising strategy for treating GEM-resistant TNBC.

Rational Approach to Finding Genes Encoding Molecular Biomarkers: Focus on Breast Cancer

Early detection of cancer facilitates treatment and improves patient survival. We hypothesized that molecular biomarkers of cancer could be rationally predicted based on even partial knowledge of transcriptional regulation, functional pathways and gene co-expression networks. To test our data mining approach, we focused on breast cancer, as one of the best-studied models of this disease. We were particularly interested to check whether such a ‘guilt by association’ approach would lead to pan-cancer markers generally known in the field or whether molecular subtype-specific ‘seed’ markers will yield subtype-specific extended sets of breast cancer markers. The key challenge of this investigation was to utilize a small number of well-characterized, largely intracellular, breast cancer-related proteins to uncover similarly regulated and functionally related genes and proteins with the view to predicting a much-expanded range of disease markers, especially that of extracellular molecular markers, potentially suitable for the early non-invasive detection of the disease. We selected 23 previously characterized proteins specific to three major molecular subtypes of breast cancer and analyzed their established transcription factor networks, their known metabolic and functional pathways and the existing experimentally derived protein co-expression data. Having started with largely intracellular and transmembrane marker ‘seeds’ we predicted the existence of as many as 150 novel biomarker genes to be associated with the selected three major molecular sub-types of breast cancer all coding for extracellularly targeted or secreted proteins and therefore being potentially most suitable for molecular diagnosis of the disease. Of the 150 such predicted protein markers, 114 were predicted to be linked through the combination of regulatory networks to basal breast cancer, 48 to luminal and 7 to Her2-positive breast cancer. The reported approach to mining molecular markers is not limited to breast cancer and therefore offers a widely applicable strategy of biomarker mining.

Fate decisions of breast cancer stem cells in cancer progression

Breast cancer has a marked recurrence and metastatic trait and is one of the most prevalent malignancies affecting women’s health worldwide. Tumor initiation and progression begin after the cell goes from a quiescent to an activated state and requires different mechanisms to act in concert to regulate t a specific set of spectral genes for expression. Cancer stem cells (CSCs) have been proven to initiate and drive tumorigenesis due to their capability of self-renew and differentiate. In addition, CSCs are believed to be capable of causing resistance to anti-tumor drugs, recurrence and metastasis. Therefore, exploring the origin, regulatory mechanisms and ultimate fate decision of CSCs in breast cancer outcomes has far-reaching clinical implications for the development of breast cancer stem cell (BCSC)-targeted therapeutic strategies. In this review, we will highlight the contribution of BCSCs to breast cancer and explore the internal and external factors that regulate the fate of BCSCs.

High levels of unfolded protein response component CHAC1 associates with cancer progression signatures in malignant breast cancer tissues

PURPOSE

The aberrant mRNA expression of a UPR component Cation transport regulator homolog 1 (CHAC1) has been reported to be associated with poor survival in breast and ovarian cancer patients, however, the expression of CHAC1 at protein levels in malignant breast tissues is underreported. The following study aimed at analyzing CHAC1 protein expression in malignant breast cancer tissues.

METHODS

Evaluation of CHAC1 expression in invasive ductal carcinomas (IDCs) with known ER, PR, and HER2 status was carried out using immunohistochemistry (IHC) with CHAC1 specific antibody. The Human breast cancer tissue microarray (TMA, cat# BR1503f, US Biomax, Inc., Rockville, MD) was used to determine CHAC1 expression. The analysis of CHAC1 IHC was done to determine its expression in terms of molecular subtypes of breast cancer, lymph node status, and proliferation index using Qu-Path software. Survival analysis was studied with a Kaplan-Meier plotter.

RESULTS

Immunohistochemical analysis of CHAC1 in breast cancer tissues showed significant up-regulation of CHAC1 as compared to the adjacent normal and benign tissues. Interestingly, CHAC1 immunostaining revealed high expression in tumor tissues with high proliferation and positive lymph node metastasis suggesting that CHAC1 might have an important role to play in breast cancer progression. Furthermore, high CHAC1 expression is associated with poor overall survival (OS) in large breast cancer patient cohorts.

CONCLUSION

As a higher expression of CHAC1 was observed in tissue cores with high Ki67 index and positive lymph node metastasis it may be concluded that enhanced CHAC1 expression correlates with proliferation and metastasis. The further analysis of breast cancer patients' survival data through KM plot indicated that high CHAC1 expression is associated with a bad prognosis hinting that CHAC1 may have a possible prognostic significance in breast cancer.

A Novel YTHDF3-Based Model to Predict Prognosis and Therapeutic Response in Breast Cancer

Background: Due to high tumor heterogeneity, breast cancer (BC) patients still suffer poor survival outcomes. YTHDF3 plays a critical role in the prognosis of BC patients. Hence, we aimed to construct a YTHDF3-based model for the prediction of the overall survival (OS) and the sensitivity of therapeutic agents in BC patients.

Methods: Based on The Cancer Genome Atlas (TCGA, https://portal.gdc.cancer.gov/) database, we obtained BC patients’ data (n = 999) with YTHDF3 expression profiles. The association between YTHDF3 expression and 5-year OS was determined via Cox proportional hazards regression (CPHR) analysis. By integrating the variables, we established a prognostic nomogram. The model was estimated via discrimination, calibration ability, and decision curve analysis (DCA). The performance of the model was compared with the TNM stage system through receiver operating characteristic (ROC) curves and DCA. By means of the Genomics of Drug Sensitivity in Cancer (GDSC) database (https://www.cancerrxgene.org/), the therapeutic agents’ response was estimated. Gene set enrichment analysis (GSEA) demonstrated possible biological mechanisms related to YTHDF3. TIMER and CIBERSORTx were employed to analyze the association between YTHDF3 and tumor-infiltrating immune cells.

Results: The high YTHDF3 expression was significantly correlated with poor 5-year OS in BC patients. Through multivariate CPHR, four independent prognostic variables (age, TNM stage, YTHDF3 expression, and molecular subtype) were determined. On the basis of the four factors, a YTHDF3-based nomogram was built. The area under the curve (AUC) of the ROC curve for the model surpassed that of the TNM stage system (0.72 vs. 0.63, p = 0.00028). The model predictions showed close consistency with the actual observations via the calibration plot. Therapeutic response prediction was conducted in high- and low-risk groups and compared with each other. The BC patients with higher risk scores showed more therapeutic resistance than those with a lower risk score.

Conclusion: YTHDF3 was verified as a prognostic biomarker of BC, and a novel YTHDF3-based model was constructed to predict the 5-year OS of BC patients. Our model could be applied to effectively predict the therapeutic response of commonly used agents for BC patients.

Global Increase in Breast Cancer Incidence: Risk Factors and Preventive Measures

Breast cancer is a global cause for concern owing to its high incidence around the world. The alarming increase in breast cancer cases emphasizes the management of disease at multiple levels. The management should start from the beginning that includes stringent cancer screening or cancer registry to effective diagnostic and treatment strategies. Breast cancer is highly heterogeneous at morphology as well as molecular levels and needs different therapeutic regimens based on the molecular subtype. Breast cancer patients with respective subtype have different clinical outcome prognoses. Breast cancer heterogeneity emphasizes the advanced molecular testing that will help on-time diagnosis and improved survival. Emerging fields such as liquid biopsy and artificial intelligence would help to under the complexity of breast cancer disease and decide the therapeutic regimen that helps in breast cancer management. In this review, we have discussed various risk factors and advanced technology available for breast cancer diagnosis to combat the worst breast cancer status and areas that need to be focused for the better management of breast cancer.

MOCA for Integrated Analysis of Gene Expression and Genetic Variation in Single Cells

In cancer, somatic mutations occur continuously, causing cell populations to evolve. These somatic mutations result in the evolution of cellular gene expression patterns that can also change due to epigenetic modifications and environmental changes. By exploring the concordance of gene expression changes with molecular evolutionary trajectories of cells, we can examine the role of somatic variation on the evolution of gene expression patterns. We present Multi-Omics Concordance Analysis (MOCA) software to jointly analyze gene expressions and genetic variations from single-cell RNA sequencing profiles. MOCA outputs cells and genes showing convergent and divergent gene expression patterns in functional genomics.

Transcriptional Factor Repertoire of Breast Cancer in 3D Cell Culture Models

Simple Summary

Knowledge of the transcriptional regulation of breast cancer tumorigenesis is largely based on studies performed in two-dimensional (2D) monolayer culture models, which lack tissue architecture and therefore fail to represent tumor heterogeneity. However, three-dimensional (3D) cell culture models are better at mimicking in vivo tumor microenvironment, which is critical in regulating cellular behavior. Hence, 3D cell culture models hold great promise for translational breast cancer research.

Abstract

Intratumor heterogeneity of breast cancer is driven by extrinsic factors from the tumor microenvironment (TME) as well as tumor cell–intrinsic parameters including genetic, epigenetic, and transcriptomic traits. The extracellular matrix (ECM), a major structural component of the TME, impacts every stage of tumorigenesis by providing necessary biochemical and biomechanical cues that are major regulators of cell shape/architecture, stiffness, cell proliferation, survival, invasion, and migration. Moreover, ECM and tissue architecture have a profound impact on chromatin structure, thereby altering gene expression. Considering the significant contribution of ECM to cellular behavior, a large body of work underlined that traditional two-dimensional (2D) cultures depriving cell–cell and cell–ECM interactions as well as spatial cellular distribution and organization of solid tumors fail to recapitulate in vivo properties of tumor cells residing in the complex TME. Thus, three-dimensional (3D) culture models are increasingly employed in cancer research, as these culture systems better mimic the physiological microenvironment and shape the cellular responses according to the microenvironmental cues that will regulate critical cell functions such as cell shape/architecture, survival, proliferation, differentiation, and drug response as well as gene expression. Therefore, 3D cell culture models that better resemble the patient transcriptome are critical in defining physiologically relevant transcriptional changes. This review will present the transcriptional factor (TF) repertoire of breast cancer in 3D culture models in the context of mammary tissue architecture, epithelial-to-mesenchymal transition and metastasis, cell death mechanisms, cancer therapy resistance and differential drug response, and stemness and will discuss the impact of culture dimensionality on breast cancer research.

An adaptive method of defining negative mutation status for multi-sample comparison using next-generation sequencing

BACKGROUND

Multi-sample comparison is commonly used in cancer genomics studies. By using next-generation sequencing (NGS), a mutation's status in a specific sample can be measured by the number of reads supporting mutant or wildtype alleles. When no mutant reads are detected, it could represent either a true negative mutation status or a false negative due to an insufficient number of reads, so-called "coverage". To minimize the chance of false-negative, we should consider the mutation status as "unknown" instead of "negative" when the coverage is inadequately low. There is no established method for determining the coverage threshold between negative and unknown statuses. A common solution is to apply a universal minimum coverage (UMC). However, this method relies on an arbitrarily chosen threshold, and it does not take into account the mutations' relative abundances, which can vary dramatically by the type of mutations. The result could be misclassification between negative and unknown statuses.

METHODS

We propose an adaptive mutation-specific negative (MSN) method to improve the discrimination between negative and unknown mutation statuses. For a specific mutation, a non-positive sample is compared with every known positive sample to test the null hypothesis that they may contain the same frequency of mutant reads. The non-positive sample can only be claimed as "negative" when this null hypothesis is rejected with all known positive samples; otherwise, the status would be "unknown".

RESULTS

We first compared the performance of MSN and UMC methods in a simulated dataset containing varying tumor cell fractions. Only the MSN methods appropriately assigned negative statuses for samples with both high- and low-tumor cell fractions. When evaluated on a real dual-platform single-cell sequencing dataset, the MSN method not only provided more accurate assessments of negative statuses but also yielded three times more available data after excluding the "unknown" statuses, compared with the UMC method.

CONCLUSIONS

We developed a new adaptive method for distinguishing unknown from negative statuses in multi-sample comparison NGS data. The method can provide more accurate negative statuses than the conventional UMC method and generate a remarkably higher amount of available data by reducing unnecessary "unknown" calls.

FUNMarker: Fusion Network-Based Method to Identify Prognostic and Heterogeneous Breast Cancer Biomarkers

Breast cancer is a heterogeneous disease with many clinically distinguishable molecular subtypes each corresponding to a cluster of patients. Identification of prognostic and heterogeneous biomarkers for breast cancer is to detect cluster-specific gene biomarkers which can be used for accurate survival prediction of breast cancer outcomes. In this study, we proposed a FUsion Network-based method (FUNMarker) to identify prognostic and heterogeneous breast cancer biomarkers by considering the heterogeneity of patient samples and biological information from multiple sources. To reduce the affect of heterogeneity of patients, samples were first clustered using the K-means algorithm based on the principal components of gene expression. For each cluster, to comprehensively evaluate the influence of genes on breast cancer, genes were weighted from three aspects: biological function, prognostic ability and correlation with known disease genes. Then they were ranked via a label propagation model on a fusion network that combined physical protein interactions from seven types of networks and thus could reduce the impact of incompleteness of interactome. We compared FUNMarker with three state-of-the-art methods and the results showed that biomarkers identified by FUNMarker were biological interpretable and had stronger discriminative power than the existing methods in differentiating patients with different prognostic outcomes.

Response prediction of neoadjuvant chemoradiation therapy in locally advanced rectal cancer using CT-based fractal dimension analysis

OBJECTIVES

There are individual variations in neo-adjuvant chemoradiation therapy (nCRT) in patients with locally advanced rectal cancer (LARC). No reliable modality currently exists that can predict the efficacy of nCRT. The purpose of this study is to assess if CT-based fractal dimension and filtration-histogram texture analysis can predict therapeutic response to nCRT in patients with LARC.

METHODS

In this retrospective study, 215 patients (average age: 57 years (18-87 years)) who received nCRT for LARC between June 2005 and December 2016 and underwent a staging diagnostic portal venous phase CT were identified. The patients were randomly divided into two datasets: a training set (n = 170), and a validation set (n = 45). Tumor heterogeneity was assessed on the CT images using fractal dimension (FD) and filtration-histogram texture analysis. In the training set, the patients with pCR and non-pCR were compared in univariate analysis. Logistic regression analysis was applied to identify the predictive value of efficacy of nCRT and receiver operating characteristic analysis determined optimal cutoff value. Subsequently, the most significant parameter was assessed in the validation set.

RESULTS

Out of the 215 patients evaluated, pCR was reached in 20.9% (n = 45/215) patients. In the training set, 7 out of 37 texture parameters showed significant difference comparing between the pCR and non-pCR groups and logistic multivariable regression analysis incorporating clinical and 7 texture parameters showed that only FD was associated with pCR (p = 0.001). The area under the curve of FD was 0.76. In the validation set, we applied FD for predicting pCR and sensitivity, specificity, and accuracy were 60%, 89%, and 82%, respectively.

CONCLUSION

FD on pretreatment CT is a promising parameter for predicting pCR to nCRT in patients with LARC and could be used to help make treatment decisions.

KEY POINTS

• Fractal dimension analysis on pretreatment CT was associated with response to neo-adjuvant chemoradiation in patients with locally advanced rectal cancer. • Fractal dimension is a promising biomarker for predicting pCR to nCRT and may potentially select patients for individualized therapy.

Exosomal hsa-miR-21-5p is a biomarker for breast cancer diagnosis

Purpose

Breast cancer (BC) is characterized by concealed onset, delayed diagnosis, and high fatality rates making it particularly dangerous to patients' health. The purpose of this study was to use comprehensive bioinformatics analysis and experimental verification to find a new biomarker for BC diagnosis.

Methods

We comprehensively analyzed microRNA (miRNA) and mRNA expression profiles from the Gene Expression Omnibus (GEO) and screened out differentially-expressed (DE) miRNAs and mRNAs. We used the miRNet website to predict potential DE-miRNA target genes. Using the Database for Annotation, Visualization and Integrated Discovery (DAVID), we performed Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) analyses on overlapping potential target genes and DE-mRNAs. The protein-protein interaction (PPI) network was then established. The miRNA-mRNA regulatory network was constructed using Cytoscape and the analysis results were visualized. We verified the expression of the most up-regulated DE-miRNA using reverse transcription and a quantitative polymerase chain reaction in BC tissue. The diagnostic value of the most up-regulated DE-miRNA was further explored across three levels: plasma-derived exosomes, cells, and cell exosomes.

Results

Our comprehensive bioinformatics analysis and experimental results showed that hsa-miR-21-5p was significantly up-regulated in BC tissue, cells, and exosomes. Our results also revealed that tumor-derived hsa-miR-21-5p could be packaged in exosomes and released into peripheral blood. Additionally, when evaluating the diagnostic value of plasma exosomal hsa-miR-21-5p, we found that it was significantly up-regulated in BC patients. Receiver operating characteristic (ROC) analysis also confirmed that hsa-miR-21-5p could effectively distinguish healthy people from BC patients. The sensitivity and specificity were 86.7% and 93.3%, respectively.

Conclusion

This study's results showed that plasma exosomal hsa-miR-21-5p could be used as a biomarker for BC diagnosis.

Breast cancer heterogeneity through the lens of single-cell analysis and spatial pathologies

Breast cancer ecosystems are composed of complex cell types, including tumor, stromal and immune cells, each of which can assume diverse phenotypes. Both the heterogeneous composition and spatially distinct tumor microenvironment impact breast cancer progression, treatment response and therapeutic resistance. Thus, a deeper understanding of breast cancer heterogeneity may help facilitate the development of novel therapies and improve outcomes for patients. The advent of paradigm shifting single-cell analysis and spatial pathologies allows for a comprehensive analysis of the tumor ecosystem as well as the interactions between its components at unprecedented resolution. In this review, we discuss the insights gained through single-cell analysis and spatial pathologies on breast cancer heterogeneity.

Deregulation of Transcriptional Enhancers in Cancer

Simple Summary

One of the major challenges in cancer treatments is the dynamic adaptation of tumor cells to cancer therapies. In this regard, tumor cells can modify their response to environmental cues without altering their DNA sequence. This cell plasticity enables cells to undergo morphological and functional changes, for example, during the process of tumour metastasis or when acquiring resistance to cancer therapies. Central to cell plasticity, are the dynamic changes in gene expression that are controlled by a set of molecular switches called enhancers. Enhancers are DNA elements that determine when, where and to what extent genes should be switched on and off. Thus, defects in enhancer function can disrupt the gene expression program and can lead to tumour formation. Here, we review how enhancers control the activity of cancer-associated genes and how defects in these regulatory elements contribute to cell plasticity in cancer. Understanding enhancer (de)regulation can provide new strategies for modulating cell plasticity in tumour cells and can open new research avenues for cancer therapy.

Abstract

Epigenetic regulations can shape a cell’s identity by reversible modifications of the chromatin that ultimately control gene expression in response to internal and external cues. In this review, we first discuss the concept of cell plasticity in cancer, a process that is directly controlled by epigenetic mechanisms, with a particular focus on transcriptional enhancers as the cornerstone of epigenetic regulation. In the second part, we discuss mechanisms of enhancer deregulation in adult stem cells and epithelial-to-mesenchymal transition (EMT), as two paradigms of cell plasticity that are dependent on epigenetic regulation and serve as major sources of tumour heterogeneity. Finally, we review how genetic variations at enhancers and their epigenetic modifiers contribute to tumourigenesis, and we highlight examples of cancer drugs that target epigenetic modifications at enhancers.

Dhaka: variational autoencoder for unmasking tumor heterogeneity from single cell genomic data

MOTIVATION

Intra-tumor heterogeneity is one of the key confounding factors in deciphering tumor evolution. Malignant cells exhibit variations in their gene expression, copy numbers and mutation even when originating from a single progenitor cell. Single cell sequencing of tumor cells has recently emerged as a viable option for unmasking the underlying tumor heterogeneity. However, extracting features from single cell genomic data in order to infer their evolutionary trajectory remains computationally challenging due to the extremely noisy and sparse nature of the data.

RESULTS

Here we describe 'Dhaka', a variational autoencoder method which transforms single cell genomic data to a reduced dimension feature space that is more efficient in differentiating between (hidden) tumor subpopulations. Our method is general and can be applied to several different types of genomic data including copy number variation from scDNA-Seq and gene expression from scRNA-Seq experiments. We tested the method on synthetic and six single cell cancer datasets where the number of cells ranges from 250 to 6000 for each sample. Analysis of the resulting feature space revealed subpopulations of cells and their marker genes. The features are also able to infer the lineage and/or differentiation trajectory between cells greatly improving upon prior methods suggested for feature extraction and dimensionality reduction of such data.

AVAILABILITY AND IMPLEMENTATION

All the datasets used in the paper are publicly available and developed software package and supporting info is available on Github https://github.com/MicrosoftGenomics/Dhaka.

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

A BAYESIAN NONPARAMETRIC MODEL FOR INFERRING SUBCLONAL POPULATIONS FROM STRUCTURED DNA SEQUENCING DATA

There are distinguishing features or "hallmarks" of cancer that are found across tumors, individuals, and types of cancer, and these hallmarks can be driven by specific genetic mutations. Yet, within a single tumor there is often extensive genetic heterogeneity as evidenced by single-cell and bulk DNA sequencing data. The goal of this work is to jointly infer the underlying genotypes of tumor subpopulations and the distribution of those subpopulations in individual tumors by integrating single-cell and bulk sequencing data. Understanding the genetic composition of the tumor at the time of treatment is important in the personalized design of targeted therapeutic combinations and monitoring for possible recurrence after treatment. We propose a hierarchical Dirichlet process mixture model that incorporates the correlation structure induced by a structured sampling arrangement and we show that this model improves the quality of inference. We develop a representation of the hierarchical Dirichlet process prior as a Gamma-Poisson hierarchy and we use this representation to derive a fast Gibbs sampling inference algorithm using the augment-and-marginalize method. Experiments with simulation data show that our model outperforms standard numerical and statistical methods for decomposing admixed count data. Analyses of real acute lymphoblastic leukemia cancer sequencing dataset shows that our model improves upon state-of-the-art bioinformatic methods. An interpretation of the results of our model on this real dataset reveals co-mutated loci across samples.

Mapping Spatial Genetic Landscapes in Tissue Sections through Microscale Integration of Sampling Methodology into Genomic Workflows

In cancer research, genomic profiles are often extracted from homogenized macrodissections of tissues, with the histological context lost and a large fraction of material underutilized. Pertinently, the spatial genomic landscape provides critical complementary information in deciphering disease heterogeneity and progression. Microscale sampling methods such as microdissection to obtain such information are often destructive to a sizeable fraction of the biopsy sample, thus showing limited multiplexability and adaptability to different assays. A modular microfluidic technology is here implemented to recover cells at the microscale from tumor tissue sections, with minimal disruption of unsampled areas and tailored to interface with genome profiling workflows, which is directed here toward evaluating intratumoral genomic heterogeneity. The integrated workflow-GeneScape-is used to evaluate heterogeneity in a metastatic mammary carcinoma, showing distinct single nucleotide variants and copy number variations in different tumor tissue regions, suggesting the polyclonal origin of the metastasis as well as development driven by multiple location-specific drivers.

Intratumor heterogeneity of breast cancer detected by epialleles shows association with hypoxic microenvironment

Rationale: In breast cancer, high intratumor DNA methylation heterogeneity can lead to drug-resistant, metastasis and poor prognosis of tumors, which increases the complexity of cancer diagnosis and treatment. However, most studies are limited to average DNA methylation level of individual CpGs and ignore heterogeneous DNA methylation patterns of cell subpopulations within the tumor. Thus, quantifying the variability in DNA methylation pattern in sequencing reads is valuable for understanding intratumor heterogeneity. Methods: We performed Reduced Representation Bisulfite Sequencing and RNA sequencing for tumor core and tumor periphery regions within one breast tumor. By developing a method named "epialleJS" based on Jensen-Shannon divergence, we detected the differential epialleles between tumor core and tumor periphery (CPDEs). We then explored the correlation between intratumor methylation heterogeneity and hypoxic microenvironment in TCGA breast cancer cohort. Results: More than 70% of CPDEs had higher epipolymorphism in tumor core than tumor periphery, and these CPDEs had lower methylation in tumor core. The CPDEs with lower methylation in tumor core may associate with hypoxic tumor microenvironment. Moreover, we identified a signature of five hypoxia-related DNA methylation markers which can predict the prognosis of breast cancer patients, including a CpG site cg15190451 in gene SLC16A5. Furthermore, immunohistochemical analysis confirmed that the expression of SLC16A5 was associated with clinicopathological characteristics and survival of breast cancer patients. Conclusions: The analysis of intratumor DNA methylation heterogeneity based on epialleles reveals that disordered methylation patterns in tumor core are associated with hypoxic microenvironment, which provides a framework for understanding biological heterogeneous behavior and guidance for developing effective treatment schemes for breast cancer patients.

Genome-Wide Estrogen Receptor Activity in Breast Cancer

The largest subtype of breast cancer is characterized by the expression and activity of the estrogen receptor alpha (ERalpha/ER). Although several effective therapies have significantly improved survival, the adaptability of cancer cells means that patients frequently stop responding or develop resistance to endocrine treatment. ER does not function in isolation and multiple associating factors have been reported to play a role in regulating the estrogen-driven transcriptional program. This review focuses on the dynamic interplay between some of these factors which co-occupy ER-bound regulatory elements, their contribution to estrogen signaling, and their possible therapeutic applications. Furthermore, the review illustrates how some ER association partners can influence and reprogram the genomic distribution of the estrogen receptor. As this dynamic ER activity enables cancer cell adaptability and impacts the clinical outcome, defining how this plasticity is determined is fundamental to our understanding of the mechanisms of disease progression.

Trailblazing perspectives on targeting breast cancer stem cells

Breast cancer (BCa) is one of the most prevalent malignant tumors affecting women's health worldwide. The recurrence and metastasis of BCa have made it a long-standing challenge to achieve remission-persistent or disease-undetectable clinical outcomes. Cancer stem cells (CSCs) possess the ability to self-renew and generate heterogeneous tumor bulk. The existence of CSCs has been found to be vital in the initiation, metastasis, therapy resistance, and recurrence of tumors across cancer types. Because CSCs grow slowly in their dormant state, they are insensitive to conventional chemotherapies; however, when CSCs emerge from their dormant state and become clinically evident, they usually acquire genetic traits that make them resistant to existing therapies. Moreover, CSCs also show evidence of acquired drug resistance in synchrony with tumor relapses. The concept of CSCs provides a new treatment strategy for BCa. In this review, we highlight the recent advances in research on breast CSCs and their association with epithelial-mesenchymal transition (EMT), circulating tumor cells (CTCs), plasticity of tumor cells, tumor microenvironment (TME), T-cell modulatory protein PD-L1, and non-coding RNAs. On the basis that CSCs are associated with multiple dysregulated biological processes, we envisage that increased understanding of disease sub-classification, selected combination of conventional treatment, molecular aberration directed therapy, immunotherapy, and CSC targeting/sensitizing strategy might improve the treatment outcome of patients with advanced BCa. We also discuss novel perspectives on new drugs and therapeutics purposing the potent and selective expunging of CSCs.

Breast cancer stem cells, heterogeneity, targeting therapies and therapeutic implications

Both hereditary and sporadic breast cancer are suggested to develop from a stem cell subcomponent retaining most key stem cell properties but with dysregulation of self-renewal pathways, which drives tumorigenic differentiation and cellular heterogeneity. Cancer stem cells (CSCs), characterized by their self-renewal and differentiation potential, have been reported to contribute to chemo-/radio-resistance and tumor initiation and to be the main reason for the failure of current therapies in breast cancer and other CSC-bearing cancers. Thus, CSC-targeted therapies, such as those inducing CSC apoptosis and differentiation, inhibiting CSC self-renewal and division, and targeting the CSC niche to combat CSC activity, are needed and may become an important component of multimodal treatment. To date, the understanding of breast cancer has been extended by advances in CSC biology, providing more accurate prognostic and predictive information upon diagnosis. Recent improvements have enhanced the prospect of targeting breast cancer stem cells (BCSCs), which has shown promise for increasing the breast cancer remission rate. However, targeted therapy for breast cancer remains challenging due to tumor heterogeneity. One major challenge is determining the CSC properties that can be exploited as therapeutic targets. Another challenge is identifying suitable BCSC biomarkers to assess the efficacy of novel BCSC-targeted therapies. This review focuses mainly on the characteristics of BCSCs and the roles of BCSCs in the formation, maintenance and recurrence of breast cancer; self-renewal signaling pathways in BCSCs; the BCSC microenvironment; potential therapeutic targets related to BCSCs; and current therapies and clinical trials targeting BCSCs.

Current State of Radiolabeled Heterobivalent Peptidic Ligands in Tumor Imaging and Therapy

Over the past few years, an approach emerged that combines different receptor-specific peptide radioligands able to bind different target structures on tumor cells concomitantly or separately. The reason for the growing interest in this special field of radiopharmaceutical development is rooted in the fact that bispecific peptide heterodimers can exhibit a strongly increased target cell avidity and specificity compared to their corresponding monospecific counterparts by being able to bind to two different target structures that are overexpressed on the cell surface of several malignancies. This increase of avidity is most pronounced in the case of concomitant binding of both peptides to their respective targets but is also observed in cases of heterogeneously expressed receptors within a tumor entity. Furthermore, the application of a radiolabeled heterobivalent agent can solve the ubiquitous problem of limited tumor visualization sensitivity caused by differential receptor expression on different tumor lesions. In this article, the concept of heterobivalent targeting and the general advantages of using radiolabeled bispecific peptidic ligands for tumor imaging or therapy as well as the influence of molecular design and the receptors on the tumor cell surface are explained, and an overview is given of the radiolabeled heterobivalent peptides described thus far.

Characterization and comparison of genomic profiles between primary cancer cell lines and parent atypical meningioma tumors

Background

Meningiomas are the second most common primary tumors of the central nervous system. However, there is a paucity of data on meningioma biology due to the lack of suitable preclinical in vitro and in vivo models. In this study, we report the establishment and characterization of patient-derived, spontaneously immortalized cancer cell lines derived from World Health Organization (WHO) grade I and atypical WHO grade II meningiomas.

Methods

We evaluated high-resolution 3T MRI neuroimaging findings in meningioma patients which were followed by histological analysis. RT-qPCR and immunostaining analyses were performed to determine the expression levels of meningioma-related factors. Additionally, flow cytometry and sorting assays were conducted to investigate and isolate the CD133 and CD44 positive cells from primary atypical meningioma cells. Further, we compared the gene expression profiles of meningiomas and cell lines derived from them by performing whole-exome sequencing of the blood and tumor samples from the patients, and the primary cancer cell lines established from the meningioma tumor.

Results

Our results were consistent with earlier studies that reported mutations in NF2, SMO, and AKT1 genes in atypical meningiomas, and we also observed mutations in MYBL2, a gene that was recently discovered. Significantly, the genomic signature was consistent between the atypical meningioma cancer cell lines and the tumor and blood samples from the patient.

Conclusion

Our results lead us to conclude that established meningioma cell lines with a genomic signature identical to tumors might be a valuable tool for understanding meningioma tumor biology, and for screening therapeutic agents to treat recurrent meningiomas.

Metastatic breast cancer patient perceptions of somatic tumor genomic testing

BACKGROUND

To assess metastatic breast cancer (MBC) patient psychological factors, perceptions, and comprehension of tumor genomic testing.

METHODS

In a prospective, single institution, single-arm trial, patients with MBC underwent next-generation sequencing at study entry with sequencing results released at progression. Patients who completed surveys before undergoing sequencing were included in the present secondary analysis (n = 58). We administered four validated psychosocial measures: Center for Epidemiologic Studies Depression Scale, Beck Anxiety Inventory, Trust in Physician Scale, and Communication and Attitudinal Self-Efficacy scale for Cancer. Genetic comprehension was assessed using 7-question objective and 6-question subjective measures. Longitudinal data were assessed (n = 40) using paired Wilcoxon signed rank and McNemar's test of agreement.

RESULTS

There were no significant differences between the beginning and end of study in depression, anxiety, physician trust, or self-efficacy (median time on study: 7.6 months). Depression and anxiety were positively associated with each other and both negatively associated with self-efficacy. Self-efficacy decreased from pre- to post-genomic testing (p = 0.05). Objective genetics comprehension did not significantly change from pre- to post-genomic testing, but patients expressed increased confidence in their ability to teach others about genetics (p = 0.04). Objective comprehension was significantly lower in non-white patients (p = 0.02) and patients with lower income (p = 0.04).

CONCLUSIONS

This is the only study, to our knowledge, to longitudinally evaluate multiple psychological metrics in MBC as patients undergo tumor genomic testing. Overall, psychological dimensions remained stable over the duration of tumor genomic testing. Among patients with MBC, depression and anxiety metrics were negatively correlated with patient self-efficacy. Patients undergoing somatic genomic testing had limited genomic knowledge, which varied by demographic groups and may warrant additional educational intervention.

CLINICAL TRIAL INFORMATION

NCT01987726, registered November 13, 2013.

Can NF-κB Be Considered a Valid Drug Target in Neoplastic Diseases? Our Point of View

Multidrug resistance (MDR), of the innate and acquired types, is one of major problems in treating tumor diseases with a good chance of success. In this review, we examine the key role of nuclear factor-kappa B (NF-κB) to induce MDR in three tumor models characterized precisely by innate or acquired MDR, in particular triple negative breast cancer (TNBC), hepatocellular carcinoma (HCC), and acute myeloid leukemia (AML). We also present different pharmacological approaches that our group have employed to reduce the expression/activation of this transcriptional factor and thus to restore chemo-sensitivity. Finally, we examine the latest scientific evidence found by other groups, the most significant clinical trials regarding NF-κB, and new perspectives on the possibility to consider this transcriptional factor a valid drug target in neoplastic diseases.

Hepatic resection for breast cancer liver metastases: Impact of intrinsic subtypes

INTRODUCTION

The role of surgery for breast cancer liver metastases (BCLM) remains controversial. This study aimed to analyze survival in patients treated with hepatectomy plus systemic therapy or systemic therapy alone for BCLM and to determine selection factors to guide surgical therapy.

MATERIALS AND METHODS

Patients who underwent hepatectomy plus systemic therapy (n = 136) and systemic therapy alone for isolated BCLM (n = 763) were compared. Overall survival (OS) was analyzed after propensity score matching. Intrinsic subtypes were defined as: luminal A (estrogen receptor [ER]+ and/or progesterone receptor positive [PR]+, human epidermal growth factor receptor 2 [HER2]-), luminal B (ER and/or PR+, HER2+), HER2-enriched (ER and PR-, HER2+), and basal-like (ER, PR, HER2-).

RESULTS

After hepatectomy, independent predictors of poor OS were number and size of liver metastases, and intrinsic subtype (hazard ratios, 1.11, 1.16, and 4.28, respectively). Median OS was 75 and 81 months among patients with luminal B and HER2-enriched subtypes, compared with 17 and 53 months among patients with basal-like and luminal A subtypes (P < .001). Median progression-free survival (PFS) was 60 months with the HER2-enriched subtype, compared with 17, 16, and 5 months with luminal A, luminal B, and basal-like subtypes, respectively (P < .001). After propensity score matching, 5-year OS rates were 56% vs. 40% in the surgery vs. systemic therapy alone groups (P = .018).

CONCLUSION

Surgical resection of BCLM yielded higher OS compared with systemic therapy alone and prolonged PFS among patients with the HER2-enriched subtype. These findings support the use of surgical therapy in appropriately selected patients, based on intrinsic subtypes.

Breast Cancer: A Molecularly Heterogenous Disease Needing Subtype-Specific Treatments

Breast cancer is the most commonly occurring cancer in women. There were over two-million new cases in world in 2018. It is the second leading cause of death from cancer in western countries. At the molecular level, breast cancer is a heterogeneous disease, which is characterized by high genomic instability evidenced by somatic gene mutations, copy number alterations, and chromosome structural rearrangements. The genomic instability is caused by defects in DNA damage repair, transcription, DNA replication, telomere maintenance and mitotic chromosome segregation. According to molecular features, breast cancers are subdivided in subtypes, according to activation of hormone receptors (estrogen receptor and progesterone receptor), of human epidermal growth factors receptor 2 (HER2), and or BRCA mutations. In-depth analyses of the molecular features of primary and metastatic breast cancer have shown the great heterogeneity of genetic alterations and their clonal evolution during disease development. These studies have contributed to identify a repertoire of numerous disease-causing genes that are altered through different mutational processes. While early-stage breast cancer is a curable disease in about 70% of patients, advanced breast cancer is largely incurable. However, molecular studies have contributed to develop new therapeutic approaches targeting HER2, CDK4/6, PI3K, or involving poly(ADP-ribose) polymerase inhibitors for BRCA mutation carriers and immunotherapy.

Autophagy Inhibition Induces the Secretion of Macrophage Migration Inhibitory Factor (MIF) with Autocrine and Paracrine Effects on the Promotion of Malignancy in Breast Cancer

Breast cancer is the main cause of cancer-related death in women in the world. Because autophagy is a known survival pathway for cancer cells, its inhibition is currently being explored in clinical trials for treating several types of malignancies. In breast cancer, autophagy has been shown to be necessary for the survival of cancer cells from the triple negative subtype (TNBC), which has the worst prognosis among breast cancers and currently has limited therapeutic options. Autophagy has also been involved in the regulation of protein secretion and, of importance for this work, the inhibition of autophagy is known to promote the secretion of proinflammatory cytokines from distinct cell types. We found that the inhibition of autophagy in TNBC cell lines induced the secretion of the macrophage migration inhibitory factor (MIF), a pro-tumorigenic cytokine involved in breast cancer invasion and immunomodulation. MIF secretion was dependent on an increase in reactive oxygen species (ROS) induced by the inhibition of autophagy. Importantly, MIF secreted from autophagy-deficient cells increased the migration of cells not treated with autophagy inhibitors, indicating that autophagy inhibition in cancer cells promoted malignancy in neighboring cells through the release of secreted factors, and that a combinatorial approach should be evaluated for cancer therapy.

Bioinformatics and Computational Tools for Next-Generation Sequencing Analysis in Clinical Genetics

Clinical genetics has an important role in the healthcare system to provide a definitive diagnosis for many rare syndromes. It also can have an influence over genetics prevention, disease prognosis and assisting the selection of the best options of care/treatment for patients. Next-generation sequencing (NGS) has transformed clinical genetics making possible to analyze hundreds of genes at an unprecedented speed and at a lower price when comparing to conventional Sanger sequencing. Despite the growing literature concerning NGS in a clinical setting, this review aims to fill the gap that exists among (bio)informaticians, molecular geneticists and clinicians, by presenting a general overview of the NGS technology and workflow. First, we will review the current NGS platforms, focusing on the two main platforms Illumina and Ion Torrent, and discussing the major strong points and weaknesses intrinsic to each platform. Next, the NGS analytical bioinformatic pipelines are dissected, giving some emphasis to the algorithms commonly used to generate process data and to analyze sequence variants. Finally, the main challenges around NGS bioinformatics are placed in perspective for future developments. Even with the huge achievements made in NGS technology and bioinformatics, further improvements in bioinformatic algorithms are still required to deal with complex and genetically heterogeneous disorders.

Concordance of Genomic Variants in Matched Primary Breast Cancer, Metastatic Tumor, and Circulating Tumor DNA: The MIRROR Study

PURPOSE

Genetic heterogeneity between primary tumors and their metastatic lesions has been documented in several breast cancer studies. However, the selection of therapy for patients with metastatic breast cancer and the search for biomarkers for targeted therapy are often based on findings from the primary tumor, mainly because of the difficulty of distant metastasis core biopsies. New methods for monitoring genomic changes in metastatic breast cancer are needed (ie, circulating tumor DNA [ctDNA] genomic analysis). The objectives of this study were to assess the concordance of genomic variants between primary and metastatic tumor tissues and the sensitivity of plasma ctDNA analysis to identify variants detected in tumor biopsies.

PATIENTS AND METHODS

Next-generation sequencing technology was used to assess the genomic mutation profile of a panel of 54 cancer genes in matched samples of primary tumor, metastatic tumor, and plasma from 40 patients with metastatic breast cancer.

RESULTS

Using Ion Torrent technology (ThermoFisher Scientific, Waltham, MA), we identified 110 variants that were common to the primary and metastatic tumors. ctDNA analysis had a sensitivity of 0.972 in detecting variants present in both primary and metastatic tissues. In addition, we identified 13 variants in metastatic tissue and ctDNA not present in primary tumor.

CONCLUSION

We identified genomic variants present in metastatic biopsies and plasma ctDNA that were not present in the primary tumor. Deep sequencing of plasma ctDNA detected most DNA variants previously identified in matched primary and metastatic tissues. ctDNA might aid in therapy selection and in the search for biomarkers for drug development in metastatic breast cancer.

A pipeline for identification and validation of tumor-specific antigens in a mouse model of metastatic breast cancer

Cancer immunotherapy continues to make headway as a treatment for advanced stage tumors, revealing an urgent need to understand the fundamentals of anti-tumor immune responses. Noteworthy is a scarcity of data pertaining to the breadth and specificity of tumor-specific T cell responses in metastatic breast cancer. Autochthonous transgenic models of breast cancer display spontaneous metastasis in the FVB/NJ mouse strain, yet a lack of knowledge regarding tumor-bound MHC/peptide immune epitopes in this mouse model limits the characterization of tumor-specific T cell responses, and the mechanisms that regulate T cell responses in the metastatic setting. We recently generated the NetH2pan prediction tool for murine class I MHC ligands by building an FVB/NJ H-2q ligand database and combining it with public information from six other murine MHC alleles. Here, we deployed NetH2pan in combination with an advanced proteomics workflow to identify immunogenic T cell epitopes in the MMTV-PyMT transgenic model for metastatic breast cancer. Five unique MHC I/PyMT epitopes were identified. These tumor-specific epitopes were confirmed to be presented by the class I MHC of primary MMTV-PyMT tumors and their T cell immunogenicity was validated. Vaccination using a DNA construct encoding a truncated PyMT protein generated CD8 + T cell responses to these MHC class I/peptide complexes and prevented tumor development. In sum, we have established an MHC-ligand discovery pipeline in FVB/NJ mice, identified and tracked H-2D^q/PyMT neoantigen-specific T cells, and developed a vaccine that prevents tumor development in this metastatic model of breast cancer.

Clonal Expansion and Diversification of Cancer-Associated Mutations in Endometriosis and Normal Endometrium

Endometriosis is characterized by ectopic endometrial-like epithelium and stroma, of which molecular characteristics remain to be fully elucidated. We sequenced 107 ovarian endometriotic and 82 normal uterine endometrial epithelium samples isolated by laser microdissection. In both endometriotic and normal epithelium samples, numerous somatic mutations were identified within genes frequently mutated in endometriosis-associated ovarian cancers. KRAS is frequently mutated in endometriotic epithelium, with a higher mutant allele frequency (MAF) accompanied by arm-level allelic imbalances. Analyses of MAF, combined with multiregional sequencing, illuminated spatiotemporal evolution of the endometriosis and uterine endometrium genomes. We sequenced 109 single endometrial glands and found that each gland carried distinct cancer-associated mutations, demonstrating the heterogeneity of the genomic architecture of endometrial epithelium. Remarkable increases in MAF of mutations in cancer-associated genes in endometriotic epithelium suggest retrograde flow of endometrial cells already harboring cancer-associated mutations, with selective advantages at ectopic sites, leading to the development of endometriosis.

Paclitaxel Response Can Be Predicted With Interpretable Multi-Variate Classifiers Exploiting DNA-Methylation and miRNA Data

To address the problem of resistance to paclitaxel treatment, we have investigated to which extent is possible to predict Breast Cancer (BC) patient response to this drug. We carried out a large-scale tumor-based prediction analysis using data from the US National Cancer Institute’s Genomic Data Commons. These data sets comprise the responses of BC patients to paclitaxel along with six molecular profiles of their tumors. We assessed 10 Machine Learning (ML) algorithms on each of these profiles and evaluated the resulting 60 classifiers on the same BC patients. DNA methylation and miRNA profiles were the most informative overall. In combination with these two profiles, ML algorithms selecting the smallest subset of molecular features generated the most predictive classifiers: a complexity-optimized XGBoost classifier based on CpG island methylation extracted a subset of molecular factors relevant to predict paclitaxel response (AUC = 0.74). A CpG site methylation-based Decision Tree (DT) combining only 2 of the 22,941 considered CpG sites (AUC = 0.89) and a miRNA expression-based DT employing just 4 of the 337 analyzed mature miRNAs (AUC = 0.72) reveal the molecular types associated to paclitaxel-sensitive and resistant BC tumors. A literature review shows that features selected by these three classifiers have been individually linked to the cytotoxic-drug sensitivities and prognosis of BC patients. Our work leads to several molecular signatures, unearthed from methylome and miRNome, able to anticipate to some extent which BC tumors respond or not to paclitaxel. These results may provide insights to optimize paclitaxel-therapies in clinical practice.

The Detection and Morphological Analysis of Circulating Tumor and Host Cells in Breast Cancer Xenograft Models

Hematogenous dissemination may occur early in breast cancer (BC). Experimental models could clarify mechanisms, but in their development, the heterogeneity of this neoplasia must be considered. Here, we describe circulating tumor cells (CTCs) and the metastatic behavior of several BC cell lines in xenografts. MDA-MB-231, BT-474, MDA-MB-453 and MDA-MB-468 cells were injected at the orthotopic level in immunocompromised mice. CTCs were isolated using a size-based method and identified by cytomorphological criteria. Metastases were detected by COX IV immunohistochemistry. CTCs were detected in 90% of animals in each model. In MDA-MB-231, CTCs were observed after 5 weeks from the injection and step wisely increased at later time points. In animals injected with less aggressive cell lines, the load of single CTCs (mean ± SD CTCs/mL: 1.8 ± 1.3 in BT-474, 122.2 ± 278.5 in MDA-MB-453, 3.4 ± 2.5 in MDA-MB468) and the frequency of CTC clusters (overall 38%) were lower compared to MDA-MB231 (946.9 ± 2882.1; 73%). All models had lung metastases, MDA-MB-453 and MDA-MB468 had ovarian foci too, whereas lymph nodal involvement was observed in MDA-MB231 and MDA-MB-468 only. Interestingly, CTCs showed morphological heterogeneity and were rarely associated to host cells. Orthotopic xenograft of BC cell lines offers valid models of hematogenous dissemination and a possible experimental setting to study CTC-blood microenvironment interactions.

Initial experience of dedicated breast PET imaging of ER+ breast cancers using [F-18]fluoroestradiol

Dedicated breast positron emission tomography (dbPET) is an emerging technology with high sensitivity and spatial resolution that enables detection of sub-centimeter lesions and depiction of intratumoral heterogeneity. In this study, we report our initial experience with dbPET using [F-18]fluoroestradiol (FES) in assessing ER+ primary breast cancers. Six patients with >90% ER+ and HER2- breast cancers were imaged with dbPET and breast MRI. Two patients had ILC, three had IDC, and one had an unknown primary tumor. One ILC patient was treated with letrozole, and another patient with IDC was treated with neoadjuvant chemotherapy without endocrine treatment. In this small cohort, we observed FES uptake in ER+ primary breast tumors with specificity to ER demonstrated in a case with tamoxifen blockade. FES uptake in ILC had a diffused pattern compared to the distinct circumscribed pattern in IDC. In evaluating treatment response, the reduction of SUVmax was observed with residual disease in an ILC patient treated with letrozole, and an IDC patient treated with chemotherapy. Future study is critical to understand the change in FES SUVmax after endocrine therapy and to consider other tracer uptake metrics with SUVmax to describe ER-rich breast cancer. Limitations include variations of FES uptake in different ER+ breast cancer diseases and exclusion of posterior tissues and axillary regions. However, FES-dbPET has a high potential for clinical utility, especially in measuring response to neoadjuvant endocrine treatment. Further development to improve the field of view and studies with a larger cohort of ER+ breast cancer patients are warranted.

Recent advances in the molecular landscape of lung neuroendocrine tumors

INTRODUCTION

Neuroendocrine tumors of the lung (Lung-NETs) make up a heterogenous family of neoplasms showing neuroendocrine differentiation and encompass carcinoids and neuroendocrine carcinomas. On molecular grounds, they considered two completely distinct and separate tumor groups with no overlap of molecular alterations nor common developmental mechanisms. Areas covered: Two perspectives were evaluated based on an extensive review and rethinking of literature: (1) the current classification as an instrument to obtaining clinical and molecular insights into the context of Lung-NETs; and (2) an alternative and innovative interpretation of these tumors, proposing a tripartite separation into early aggressive primary high-grade neuroendocrine tumors (HGNET), differentiating or secondary HGNET, and indolent NET. Expert opinion: We herein provide an alternative outlook on Lung-NETs, which is a paradigm shift to current pathogenesis models and expands the understanding of these tumors.