A new genome-driven integrated classification of breast cancer and its implications. EMBO J. 2013;32:617-628. [PMID: 23395906 DOI: 10.1038/emboj.2013.19]

The amino acid transporter SLC7A11 expression in breast cancer

Breast cancer (BC), characterized by its diverse molecular profiles and clinical outcomes, presents a significant challenge in the development of effective therapeutic strategies. Metabolic reprogramming, a defining characteristic of cancer, has emerged as a promising target for novel therapies. SLC7A11, an amino acid transporter that facilitates cysteine uptake in exchange for glutamate, plays a crucial role in sustaining the altered metabolism of cancer cells. This study delves into the comprehensive analysis of SLC7A11 at the genomic, transcriptomic, and protein levels in extensive BC datasets to elucidate its potential role in different BC subtypes. SLC7A11 gene copy number and mRNA expression were evaluated using the Molecular Taxonomy of Breast Cancer International Consortium (METABRIC) cohort (n = 1,980) and Breast Cancer Gene Expression Miner (n = 4,712). SLC7A11 protein was assessed using immunohistochemistry in a large BC cohort (n = 1,981). Additionally, The Cancer Genome Atlas (TCGA) dataset was used to explore SLC7A11 DNA methylation patterns using MethSurv (n = 782) and association of SLC7A11 mRNA expression with immune infiltrates using TIMER (n = 1,100). High SLC7A11 mRNA and SLC7A11 protein expression were significantly associated with high tumor grade (p ≤ .02), indicating a potential role in cancer progression. Interestingly, SLC7A11 copy number gain was observed in HER2+ tumors (p = .01), suggesting a subtype-specific association. In contrast, SLC7A11 mRNA expression was higher in the basal-like/triple-negative (TN; p < .001) and luminal B tumors (p = .02), highlighting its differential expression across BC subtypes. Notably, high SLC7A11 protein expression was predominantly observed in Estrogen Receptor (ER)-negative and Triple Negative (TN) BC, suggesting a role in these aggressive subtypes. Further analysis revealed that SLC7A11 was positively correlated with other amino acid transporters and enzymes associated with glutamine metabolism, implying a coordinated role in metabolic regulation. Additionally, SLC7A11 gene expression was positively associated with neutrophil and macrophage infiltration, suggesting a potential link between SLC7A11 and tumor immunity. Our findings suggest that SLC7A11 plays a significant role in BC metabolism, demonstrating differential expression across subtypes and associations with poor patient outcomes. Further functional studies are warranted to elucidate the precise mechanisms by which SLC7A11 contributes to BC progression and to explore its potential as a therapeutic target.

Hypoxia-induced epigenetic regulation of breast cancer progression and the tumour microenvironment

The events that control breast cancer progression and metastasis are complex and intertwined. Hypoxia plays a key role both in oncogenic transformation and in fueling the metastatic potential of breast cancer cells. Here we review the impact of hypoxia on epigenetic regulation of breast cancer, by interfering with multiple aspects of the tumour microenvironment. The co-dependent relationship between oxygen depletion and metabolic shift to aerobic glycolysis impacts on a range of enzymes and metabolites available in the cell, promoting posttranslational modifications of histones and chromatin, and changing the gene expression landscape to facilitate tumour development. Hormone signalling, particularly through ERα, is also tightly regulated by hypoxic exposure, with HIF-1α expression being a prognostic marker for therapeutic resistance in ER+ breast cancers. This highlights the strong need to understand the hypoxia-endocrine signalling axis and exploit it as a therapeutic target. Furthermore, hypoxia has been shown to enhance metastasis in TNBC cells, as well as promoting resistance to taxanes, radiotherapy and even immunotherapy through microRNA regulation and changes in histone packaging. Finally, several other mediators of the hypoxic response are discussed. We highlight a link between ionic dysregulation and hypoxia signalling, indicating a potential connection between HIF-1α and tumoural Na+ accumulation which would be worth further exploration; we present the role of Ca2+ in mediating hypoxic adaptation via chromatin remodelling, transcription factor recruitment and changes in signalling pathways; and we briefly summarise some of the findings regarding vesicle secretion and paracrine induced epigenetic reprogramming upon hypoxic exposure in breast cancer. By summarising these observations, this article highlights the heterogeneity of breast cancers, presenting a series of pathways with potential for therapeutic applications.

Development and validation of a reliable DNA copy-number-based machine learning algorithm (CopyClust) for breast cancer integrative cluster classification

The Integrative Cluster subtypes (IntClusts) provide a framework for the classification of breast cancer tumors into 10 distinct groups based on copy number and gene expression, each with unique biological drivers of disease and clinical prognoses. Gene expression data is often lacking, and accurate classification of samples into IntClusts with copy number data alone is essential. Current classification methods achieve low accuracy when gene expression data are absent, warranting the development of new approaches to IntClust classification. Copy number data from 1980 breast cancer samples from METABRIC was used to train multiclass XGBoost machine learning algorithms (CopyClust). A piecewise constant fit was applied to the average copy number profile of each IntClust and unique breakpoints across the 10 profiles were identified and converted into ~ 500 genomic regions used as features for CopyClust. These models consisted of two approaches: a 10-class model with the final IntClust label predicted by a single multiclass model and a 6-class model with binary reclassification in which four pairs of IntClusts were combined for initial multiclass classification. Performance was validated on the TCGA dataset, with copy number data generated from both SNP arrays and WES platforms. CopyClust achieved 81% and 79% overall accuracy with the TCGA SNP and WES datasets, respectively, a nine-percentage point or greater improvement in overall IntClust subtype classification accuracy. CopyClust achieves a significant improvement over current methods in classification accuracy of IntClust subtypes for samples without available gene expression data and is an easily implementable algorithm for IntClust classification of breast cancer samples with copy number data.

Tripartite Motif-Containing 2, a Glutamine Metabolism-Associated Protein, Predicts Poor Patient Outcome in Triple-Negative Breast Cancer Treated with Chemotherapy

BACKGROUND

Breast cancer (BC) remains heterogeneous in terms of prognosis and response to treatment. Metabolic reprogramming is a critical part of oncogenesis and a potential therapeutic target. Glutaminase (GLS), which generates glutamate from glutamine, plays a role in triple-negative breast cancer (TNBC). However, targeting GLS directly may be difficult, as it is essential for normal cell function. This study aimed to determine potential targets in BC associated with glutamine metabolism and evaluate their prognostic value in BC.

METHODS

The iNET model was used to identify genes in BC that are associated with GLS using RNA-sequencing data. The prognostic significance of tripartite motif-containing 2 (TRIM2) mRNA was assessed in BC transcriptomic data (n = 16,575), and TRIM2 protein expression was evaluated using immunohistochemistry (n = 749) in patients with early-stage invasive breast cancer with long-term follow-up. The associations between TRIM2 expression and clinicopathological features and patient outcomes were evaluated.

RESULTS

Pathway analysis identified TRIM2 expression as an important gene co-expressed with high GLS expression in BC. High TRIM2 mRNA and TRIM2 protein expression were associated with TNBC (p < 0.01). TRIM2 was a predictor of poor distant metastasis-free survival (DMFS) in TNBC (p < 0.01), and this was independent of established prognostic factors (p < 0.05), particularly in those who received chemotherapy (p < 0.05). In addition, TRIM2 was a predictor of shorter DMFS in TNBC treated with chemotherapy (p < 0.01).

CONCLUSIONS

This study provides evidence of an association between TRIM2 and poor patient outcomes in TNBC, especially those treated with chemotherapy. The molecular mechanisms and functional behaviour of TRIM2 and the functional link with GLS in BC warrant further exploration using in vitro models.

Luminal breast epithelial cells from wildtype and BRCA mutation carriers harbor copy number alterations commonly associated with breast cancer

Cancer-associated mutations have been documented in normal tissues, but the prevalence and nature of somatic copy number alterations and their role in tumor initiation and evolution is not well understood. Here, using single cell DNA sequencing, we describe the landscape of CNAs in >42,000 breast epithelial cells from women with normal or high risk of developing breast cancer. Accumulation of individual cells with one or two of a specific subset of CNAs (e.g. 1q gain and 16q, 22q, 7q, and 10q loss) is detectable in almost all breast tissues and, in those from BRCA1 or BRCA2 mutations carriers, occurs prior to loss of heterozygosity (LOH) of the wildtype alleles. These CNAs, which are among the most common associated with ductal carcinoma in situ (DCIS) and malignant breast tumors, are enriched almost exclusively in luminal cells not basal myoepithelial cells. Allele-specific analysis of the enriched CNAs reveals that each allele was independently altered, demonstrating convergent evolution of these CNAs in an individual breast. Tissues from BRCA1 or BRCA2 mutation carriers contain a small percentage of cells with extreme aneuploidy, featuring loss of TP53 , LOH of BRCA1 or BRCA2 , and multiple breast cancer-associated CNAs in addition to one or more of the common CNAs in 1q, 10q or 16q. Notably, cells with intermediate levels of CNAs are not detected, arguing against a stepwise gradual accumulation of CNAs. Overall, our findings demonstrate that chromosomal alterations in normal breast epithelium partially mirror those of established cancer genomes and are chromosome- and cell lineage-specific.

Potential Value of HSP90α in Prognosis of Triple-Negative Breast Cancer

BACKGROUND Triple-negative breast cancer (TNBC) is a distinct subtype of breast cancer, accounting for 12-18% of all breast cancer cases. It exhibits high heterogeneity and aggressiveness, resulting in a poorer prognosis with a high risk of early recurrence and metastasis. Due to the lack of expression of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2), as well as insensitivity to endocrine therapy, determining a standard treatment for TNBC is challenging. The identification of potential prognostic biomarkers is crucial for developing personalized treatment strategies for patients. MATERIAL AND METHODS Our study investigated the potential value of HSP90a in TNBC prognosis. A retrospective analysis was conducted on 127 TNBC patients and 127 Healthy controls from March 1, 2019 to July 31, 2022. Venous blood was collected and tested for HSP90alpha, CEA, CA199, and CA125, and we recorded the clinical characteristics of the patients, including age, BMI, alcohol consumption status, surgical history, CEA level, CA199 level, CA125 level, HSP90alpha level, tumor size, distant metastases, lymph node metastasis, and TNM stage. Univariate and multivariate methods were used to screen independent risk factors for progression-free survival (PFS) and overall survival (OS). RESULTS HSP90alpha is not only upregulated in TNBC but is also highly correlated with lymph node metastasis and TNM stage. The results of multivariate analysis showed that distant metastasis, TNM stage and HSP90a level were independent factors associated with PFS. BMI, tumor size, TNM stage, surgical history, and HSP90a level were independent factors influencing OS. CONCLUSIONS Our research findings demonstrate a significant association between high HSP90alpha expression and adverse clinical features, suggesting a poorer prognosis for TNBC patients.

Identifying and targeting key driver genes for collagen production within the 11q13/14 breast cancer amplicon

Genetic studies indicate that breast cancer can be divided into several basic molecular groups. One of these groups, termed IntClust-2, is characterized by amplification of a small portion of chromosome 11 and has a median survival of only five years. Several cancer-relevant genes occupy this portion of chromosome 11, and it is thought that overexpression of a combination of driver genes in this region is responsible for the poor outcome of women in this group. In this study we used a gene editing method to knock out, one by one, each of 198 genes that are located within the amplified region of chromosome 11 and determined how much each of these genes contributed to the survival of breast cancer cells. In addition to well-known drivers such as CCND1 and PAK1 , we identified two different genes ( SERPINH1 and P4HA3 ), that encode proteins involved in collagen synthesis and organization. Using both in vitro and in vivo functional analyses, we determined that P4HA3 and/or SERPINH1 provide a critical driver function on IntClust-2 basic processes, such as viability, proliferation, and migration. Inhibiting these enzymes via genetic or pharmacologic means reduced collagen synthesis and impeded oncogenic signaling transduction in cell culture models, and a small-molecule inhibitor of P4HA3 was effective in treating 11q13 tumor growth in an animal model. As collagen has a well-known association with tissue stiffness and aggressive forms of breast cancer, we believe that the two genes we identified provide an opportunity for a new therapeutic strategy in IntClust-2 breast cancers.

Exploring the Synergistic Effect of Sildenafil and Green Tea Polyphenols on Breast Cancer Stem Cell-like Cells and their Parental Cells: A Potential Novel Therapeutic Approach

BACKGROUND

Many cancer studies have intensely focused on the role of diet, among other factors involved in cancer establishment. The positive effect of green tea polyphenols (GTP) on controlling breast cancer cells has been reported in several studies. Cancer stem cell-like cells (CSC-LCs) possessing self-renewal, metastatic, and drug-resistant capacities are considered prominent therapeutic targets. In many tumors, inducible nitric oxide synthase (iNOS) expression levels are high; however, they have a dual effect on breast cancer pathogenesis.

OBJECTIVE

This study aimed to investigate the cytotoxicity of the iNOS agonist (Sildenafil) and antagonist (LNAME), both alone and in combination with GTP, on MDA-MB-231, CD44+/CD24- CSC-LCs, and their parental cells (MCF-7).

METHODS

The cell viability assay has been studied using the MTT assay. To analyze drug-drug combinations, CompuSyn and Combenefit software were used. The cytotoxicity mechanism was determined using flow cytometric analysis.

RESULTS

L-NAME and GTP showed a synergistic effect on MDA-MB-231 and CSC-LCs. Such an effect was not observed on MCF-7. Sildenafil and GTP, on the other hand, showed synergistic cytotoxicity in all the cells mentioned above. Flow cytometric tests resulted in more than 70% apoptosis in MDA-MB-231 and MCF-7. Also, sub-G1 arrest among MCF-7 cells and a considerable decrease in ROS production by MDA-MB-231 cells following treatment with Sildenafil and GTP were observed.

CONCLUSION

Sildenafil, in combination with flavonoids, may be considered a novel strategy for cancer treatment.

Breast Cancer Circulating Tumor Cells: Current Clinical Applications and Future Prospects

BACKGROUND

Identification and characterization of circulating tumor markers, designated as "liquid biopsies," have greatly impacted the care of cancer patients. Although more recently referring to circulating tumor DNA (ctDNA), the term liquid biopsy initially was coined to refer to any blood-borne biomarker related to malignancy, including circulating tumor cells (CTCs) in blood. In this manuscript, we review the specific state of the art of CTCs in breast cancer.

CONTENT

Liquid biopsies might play a clinical role across the entire spectrum of breast cancer, from risk assessment, prevention, screening, and treatment. CTC counts have been shown to carry clear, independent prognostic information in the latter situation. However, the clinical utility of CTCs in breast cancer remains to be determined. Nonetheless, in addition to CTC enumeration, analyses of CTCs provide tumor molecular information representing the entire, often-heterogeneous disease, relatively noninvasively and longitudinally. Technological advances have allowed the interrogation of CTC-derived information, providing renewed hope for a clinical role in disease monitoring and precision oncology.

SUMMARY

This narrative review examines CTCs, their clinical validity, and current prospects of clinical utility in breast cancer with the goal of improving patient outcomes.

Lysine metabolism is a novel metabolic tumor suppressor pathway in breast cancer

The International Agency for Research on Cancer determined that obesity is the primary preventable cause of breast cancer. The nuclear receptor peroxisome proliferator activated receptor γ (PPARγ) binds inflammatory mediators in obesity and its expression is reduced in human breast cancer. We created a new model to better understand how the obese microenvironment alters nuclear receptor function in breast cancer. The obesity related cancer phenotype was PPARγ dependent; deletion of PPARγ in mammary epithelium which is a tumor suppressor in lean mice unexpectedly increased tumor latency, reduced the luminal progenitor (LP) tumor cell fraction, and increased autophagic and senescent cells. Loss of PPARγ expression in mammary epithelium of obese mice increased expression of 2-aminoadipate semialdehyde synthase (AASS) which regulates lysine catabolism to acetoacetate. PPARγ-associated co-repressors and activators regulated AASS expression via a canonical response element. AASS expression was significantly reduced in human breast cancer, and AASS overexpression or acetoacetate treatment inhibited proliferation and induced autophagy and senescence in human breast cancer cell lines. Genetic or pharmacologic HDAC inhibition promoted autophagy and senescence in mammary tumor cells in vitro and in vivo. We concluded that lysine metabolism is a novel metabolic tumor suppressor pathway in breast cancer.

Chromatin Organization and Transcriptional Programming of Breast Cancer Cell Identity

The advent of sequencing technologies for assessing chromosome conformations has provided a wealth of information on the organization of the 3-dimensional genome and its role in cancer progression. It is now known that changes in chromatin folding and accessibility can promote aberrant activation or repression of transcriptional programs that can drive tumorigenesis and progression in diverse cancers. This includes breast cancer, which comprises several distinct subtypes defined by their unique transcriptomes that dictate treatment response and patient outcomes. Of these, basal-like breast cancer is an aggressive subtype controlled by a pluripotency-enforcing transcriptome. Meanwhile, the more differentiated luminal subtype of breast cancer is driven by an estrogen receptor-dominated transcriptome that underlies its responsiveness to antihormone therapies and conveys improved patient outcomes. Despite the clear differences in molecular signatures, the genesis of each subtype from normal mammary epithelial cells remains unclear. Recent technical advances have revealed key distinctions in chromatin folding and organization between subtypes that could underlie their transcriptomic and, hence, phenotypic differences. These studies also suggest that proteins controlling particular chromatin states may be useful targets for treating aggressive disease. In this review, we explore the current state of understanding of chromatin architecture in breast cancer subtypes and its potential role in defining their phenotypic characteristics.

Omics-Based Investigations of Breast Cancer

Breast cancer (BC) is characterized by an extensive genotypic and phenotypic heterogeneity. In-depth investigations into the molecular bases of BC phenotypes, carcinogenesis, progression, and metastasis are necessary for accurate diagnoses, prognoses, and therapy assessments in predictive, precision, and personalized oncology. This review discusses both classic as well as several novel omics fields that are involved or should be used in modern BC investigations, which may be integrated as a holistic term, onco-breastomics. Rapid and recent advances in molecular profiling strategies and analytical techniques based on high-throughput sequencing and mass spectrometry (MS) development have generated large-scale multi-omics datasets, mainly emerging from the three "big omics", based on the central dogma of molecular biology: genomics, transcriptomics, and proteomics. Metabolomics-based approaches also reflect the dynamic response of BC cells to genetic modifications. Interactomics promotes a holistic view in BC research by constructing and characterizing protein-protein interaction (PPI) networks that provide a novel hypothesis for the pathophysiological processes involved in BC progression and subtyping. The emergence of new omics- and epiomics-based multidimensional approaches provide opportunities to gain insights into BC heterogeneity and its underlying mechanisms. The three main epiomics fields (epigenomics, epitranscriptomics, and epiproteomics) are focused on the epigenetic DNA changes, RNAs modifications, and posttranslational modifications (PTMs) affecting protein functions for an in-depth understanding of cancer cell proliferation, migration, and invasion. Novel omics fields, such as epichaperomics or epimetabolomics, could investigate the modifications in the interactome induced by stressors and provide PPI changes, as well as in metabolites, as drivers of BC-causing phenotypes. Over the last years, several proteomics-derived omics, such as matrisomics, exosomics, secretomics, kinomics, phosphoproteomics, or immunomics, provided valuable data for a deep understanding of dysregulated pathways in BC cells and their tumor microenvironment (TME) or tumor immune microenvironment (TIMW). Most of these omics datasets are still assessed individually using distinct approches and do not generate the desired and expected global-integrative knowledge with applications in clinical diagnostics. However, several hyphenated omics approaches, such as proteo-genomics, proteo-transcriptomics, and phosphoproteomics-exosomics are useful for the identification of putative BC biomarkers and therapeutic targets. To develop non-invasive diagnostic tests and to discover new biomarkers for BC, classic and novel omics-based strategies allow for significant advances in blood/plasma-based omics. Salivaomics, urinomics, and milkomics appear as integrative omics that may develop a high potential for early and non-invasive diagnoses in BC. Thus, the analysis of the tumor circulome is considered a novel frontier in liquid biopsy. Omics-based investigations have applications in BC modeling, as well as accurate BC classification and subtype characterization. The future in omics-based investigations of BC may be also focused on multi-omics single-cell analyses.

Real world evidence of adjuvant trastuzumab in HER2 positive early breast cancer

Adjuvant trastuzumab in HER2+ breast cancer reduces recurrence and mortality, and has been the standard treatment since 2006. The objective was to analyze health outcomes in the real world. Observational, retrospective study of patients with HER2+ breast cancer, stages I-III, treated with adjuvant trastuzumab in the past 15 years in only one center and for the first time in Spain. Survival was analyzed according to the number of cycles and cardiotoxicity. Two hundred and seventy-five HER2positive patients (18.60%) out of 1479 received adjuvant (73%) or neoadjuvant/adjuvant (26%) trastuzumab, concomitantly (90%) or sequentially (10%) with chemotherapy. The probability of overall and disease-free survival (OS and DFS) at 5 years was 0.93 (95% CI 0.89-0.96), and 0.88 (95% CI 0.83-0.92). The number of cases with a significant and asymptomatic decrease in ventricular ejection fraction and heart failure were 54 (19.64%) and 12 (4.36%), respectively. Sixty-eight patients (24.70%) received 16 or fewer cycles, especially those older than 65 (OR 0.371, 95% CI 0.152-0.903; p = 0.029) and with cardiotoxicity (OR 15.02, 95% CI 7.437-30.335; p < 0.001). The risk of cardiotoxicity was associated with having received radiotherapy (OR 0.0362, 95% CI 0.139-0.938; p = 0.037). Arterial hypertension (HR 0.361, 95% CI 0.151-0.863, p = 0.022), neoadjuvant treatment (HR 0.314, 95% CI 0.132-0.750, p = 0.009) and cardiotoxicity (HR 2.755, 95% CI 1.235-6.143, p = 0.013) maintained significant association with OS. Only neoadjuvant treatment maintained a significant association with DFS (HR 0.437, 95% CI 0.213-0.899, p = 0.024). The effectiveness of neoadjuvant and adjuvant trastuzumab can be considered comparable to those of clinical trials. In the real world, factors such as age, hypertension, radiotherapy, neoadjuvant treatment, and cardiotoxicity should be taken into consideration to optimize outcomes.

A Rapid, Shallow Whole Genome Sequencing Workflow Applicable to Limiting Amounts of Cell-Free DNA

BACKGROUND

Somatic copy number alterations (sCNAs) acquired during the evolution of breast cancer provide valuable prognostic and therapeutic information. Here we present a workflow for screening sCNAs using picogram amounts of cell-free DNA (cfDNA) and single circulating tumor cells (CTCs).

METHODS

We repurposed the Ion ReproSeq PGS™ preimplantation genetic testing kit to perform shallow whole genome sequencing on 178 cfDNA samples (300 pg) and individual CTCs from 10 MBC patients with metastatic breast cancer (MBC) recovered by CellSearch®/DEPArray™. Results were analyzed using a tailored ichorCNA workflow.

RESULTS

sCNAs were detected in cfDNA of 41/105 (39%) patients with MBC and 3/23 (13%) primary breast cancers on follow-up (PBC FU), all of whom subsequently relapsed. In 8 of 10 MBCs, individual CTCs had a higher copy number count than matched cfDNA. The median tumor fraction detected by ichorCNA was 0.34 (range 0.17-0.58) for MBC and 0.36 (range 0.31-0.37) for PBC FU. Patients with detectable tumor fraction (≥ 0.1) and TFx and OncomineTM variants had significantly lower overall survival rates (P values P = 0.002 and P < 0.0001 for the log-rank test, respectively).

CONCLUSIONS

The ReproSeq PGS assay is rapid, at approximately $120 per sample, providing both a sCNA profile and estimation of the tumor DNA fraction from limiting cfDNA template (300pg) and individual CTCs. The approach could be used to examine the copy number landscape over time to guide treatment decisions, support future trial designs, and be applied to low volume blood spot samples enabling remote monitoring.

Total flavonoids of Litchi seed attenuate stem cell-like properties in breast cancer by regulating Notch3 signaling pathway

ETHNOPHARMACOLOGICAL RELEVANCE

Breast cancer has been the most commonly-diagnosed cancer worldwide, and the treatment and prognosis of which are often limited by breast cancer stem cells (BCSCs). Litchi seeds have shown good anti-cancer activity in various cancers including prostate cancer, lung cancer and breast cancer. However, the activity and underlying mechanism of Litchi seeds against BCSCs remain unknown.

AIM OF THE STUDY

To investigate the activity and mechanism of total flavonoids of litchi seed (TFLS) against BCSCs in vitro and in vivo.

MATERIALS AND METHODS

Two orthotopic xenograft mouse models were established using HCC1806 cells pretreated or untreated with TFLS to determine whether TFLS could target BCSCs in vivo. Mammosphere formation and flow cytometry assays were employed to evaluate the effect of TFLS on BCSCs in vitro. The underlying mechanism was investigated using RT-qPCR, Western blot, immunohistochemistry and immunofluorescence experiments.

RESULTS

TFLS could significantly inhibit the viability of HCC1806, MCF-7 and HCC1937 cells in vitro and suppress the growth of HCC1806 cells in vivo. TFLS attenuated stem cell-like properties of breast cancer through reducing the percentage of CD44⁺CD24^-/low cells, inhibiting the mammospheres formation and down-regulating the mRNA and protein levels of cancer stem cells related markers (Oct4, Nanog, Sox2) in MCF-7 and HCC1806 cells. Meanwhile, TFLS suppressed the tumor-initiating ability of BCSCs via reducing the percentage of CD44⁺CD24^-/low cells in tumor and lowering tumor incidence rate in orthotopic xenograft mice. In addition, TFLS treatments restricted the expression and nuclear translocation of Notch3, subsequently down-regulated Hes1 and Runx2 expressions.

CONCLUSIONS

TFLS could suppress the growth of breast cancer and eliminate breast cancer stem cells by inhibiting the Notch3 signaling pathway.

Genomic characterisation of hormone receptor-positive breast cancer arising in very young women

BACKGROUND

Very young premenopausal women diagnosed with hormone receptor-positive, human epidermal growth factor receptor 2-negative (HR+HER2-) early breast cancer (EBC) have higher rates of recurrence and death for reasons that remain largely unexplained.

PATIENTS AND METHODS

Genomic sequencing was applied to HR+HER2- tumours from patients enrolled in the Suppression of Ovarian Function Trial (SOFT) to determine genomic drivers that are enriched in young premenopausal women. Genomic alterations were characterised using next-generation sequencing from a subset of 1276 patients (deep targeted sequencing, n = 1258; whole-exome sequencing in a young-age, case-control subsample, n = 82). We defined copy number (CN) subgroups and assessed for features suggestive of homologous recombination deficiency (HRD). Genomic alteration frequencies were compared between young premenopausal women (<40 years) and older premenopausal women (≥40 years), and assessed for associations with distant recurrence-free interval (DRFI) and overall survival (OS).

RESULTS

Younger women (<40 years, n = 359) compared with older women (≥40 years, n = 917) had significantly higher frequencies of mutations in GATA3 (19% versus 16%) and CN amplifications (CNAs) (47% versus 26%), but significantly lower frequencies of mutations in PIK3CA (32% versus 47%), CDH1 (3% versus 9%), and MAP3K1 (7% versus 12%). Additionally, they had significantly higher frequencies of features suggestive of HRD (27% versus 21%) and a higher proportion of PIK3CA mutations with concurrent CNAs (23% versus 11%). Genomic features suggestive of HRD, PIK3CA mutations with CNAs, and CNAs were associated with significantly worse DRFI and OS compared with those without these features. These poor prognostic features were enriched in younger patients: present in 72% of patients aged <35 years, 54% aged 35-39 years, and 40% aged ≥40 years. Poor prognostic features [n = 584 (46%)] versus none [n = 692 (54%)] had an 8-year DRFI of 84% versus 94% and OS of 88% versus 96%. Younger women (<40 years) had the poorest outcomes: 8-year DRFI 74% versus 85% and OS 80% versus 93%, respectively.

CONCLUSION

These results provide insights into genomic alterations that are enriched in young women with HR+HER2- EBC, provide rationale for genomic subgrouping, and highlight priority molecular targets for future clinical trials.

The RUNX/CBFβ Complex in Breast Cancer: A Conundrum of Context

Dissecting and identifying the major actors and pathways in the genesis, progression and aggressive advancement of breast cancer is challenging, in part because neoplasms arising in this tissue represent distinct diseases and in part because the tumors themselves evolve. This review attempts to illustrate the complexity of this mutational landscape as it pertains to the RUNX genes and their transcription co-factor CBFβ. Large-scale genomic studies that characterize genetic alterations across a disease subtype are a useful starting point and as such have identified recurring alterations in CBFB and in the RUNX genes (particularly RUNX1). Intriguingly, the functional output of these mutations is often context dependent with regards to the estrogen receptor (ER) status of the breast cancer. Therefore, such studies need to be integrated with an in-depth understanding of both the normal and corrupted function in mammary cells to begin to tease out how loss or gain of function can alter the cell phenotype and contribute to disease progression. We review how alterations to RUNX/CBFβ function contextually ascribe to breast cancer subtypes and discuss how the in vitro analyses and mouse model systems have contributed to our current understanding of these proteins in the pathogenesis of this complex set of diseases.

RUNX1 Is Regulated by Androgen Receptor to Promote Cancer Stem Markers and Chemotherapy Resistance in Triple Negative Breast Cancer

Triple negative breast cancer (TNBC) is an aggressive breast cancer subtype for which no effective targeted therapies are available. Growing evidence suggests that chemotherapy-resistant cancer cells with stem-like properties (CSC) may repopulate the tumor. The androgen receptor (AR) is expressed in up to 50% of TNBCs, and AR inhibition decreases CSC and tumor initiation. Runt-related transcription factor 1 (RUNX1) correlates with poor prognosis in TNBC and is regulated by the AR in prostate cancer. Our group has shown that RUNX1 promotes TNBC cell migration and regulates tumor gene expression. We hypothesized that RUNX1 is regulated by the AR and that both may work together in TNBC CSC to promote disease recurrence following chemotherapy. Chromatin immunoprecipitation sequencing (ChIP-seq) experiments in MDA-MB-453 revealed AR binding to RUNX1 regulatory regions. RUNX1 expression is upregulated by dihydrotestosterone (DHT) in MDA-MB-453 and in an AR⁺-TNBC HCI-009 patient-derived xenograft (PDX) tumors (p < 0.05). RUNX1 is increased in a CSC-like experimental model in MDA-MB-453 and SUM-159PT cells (p < 0.05). Inhibition of RUNX1 transcriptional activity reduced the expression of CSC markers. Interestingly, RUNX1 inhibition reduced cell viability and enhanced paclitaxel and enzalutamide sensitivity. Targeting RUNX1 may be an attractive strategy to potentiate the anti-tumor effects of AR inhibition, specifically in the slow-growing CSC-like populations that resist chemotherapy which lead to metastatic disease.

Construction of a fatty acid metabolism-related gene signature for predicting prognosis and immune response in breast cancer

Background: Breast cancer has the highest incidence among malignant tumors in women, and its prevalence ranks first in global cancer morbidity. Aim: This study aimed to explore the feasibility of a prognostic model for patients with breast cancer based on the differential expression of genes related to fatty acid metabolism. Methods: The mRNA expression matrix of breast cancer and paracancer tissues was downloaded from The Cancer Genome Atlas database. The differentially expressed genes related to fatty acid metabolism were screened in R language. The TRRUST database was used to predict transcriptional regulators related to hub genes and construct an mRNA-transcription factor interaction network. A consensus clustering approach was used to identify different fatty acid regulatory patterns. In combination with patient survival data, Lasso and multivariate Cox proportional risk regression models were used to establish polygenic prognostic models based on fatty acid metabolism. The median risk score was used to categorize patients into high- and low-risk groups. Kaplan-Meier survival curves were used to analyze the survival differences between both groups. The Cox regression analysis included risk score and clinicopathological factors to determine whether risk score was an independent risk factor. Models based on genes associated with fatty acid metabolism were evaluated using receiver operating characteristic curves. A comparison was made between risk score levels and the fatty acid metabolism-associated genes in different subtypes of breast cancer. The differential gene sets of the Kyoto Encyclopedia of Genes and Genomes for screening high- and low-risk populations were compared using a gene set enrichment analysis. Furthermore, we utilized CIBERSORT to examine the abundance of immune cells in breast cancer in different clustering models. Results: High expression levels of ALDH1A1 and UBE2L6 prevented breast cancer, whereas high RDH16 expression levels increased its risk. Our comprehensive assessment of the association between prognostic risk scoring models and tumor microenvironment characteristics showed significant differences in the abundance of various immune cells between high- and low-risk breast cancer patients. Conclusions: By assessing fatty acid metabolism patterns, we gained a better understanding of the infiltration characteristics of the tumor microenvironment. Our findings are valuable for prognosis prediction and treatment of patients with breast cancer based on their clinicopathological characteristics.

A Bioinformatics View on Acute Myeloid Leukemia Surface Molecules by Combined Bayesian and ABC Analysis

“Big omics data” provoke the challenge of extracting meaningful information with clinical benefit. Here, we propose a two-step approach, an initial unsupervised inspection of the structure of the high dimensional data followed by supervised analysis of gene expression levels, to reconstruct the surface patterns on different subtypes of acute myeloid leukemia (AML). First, Bayesian methodology was used, focusing on surface molecules encoded by cluster of differentiation (CD) genes to assess whether AML is a homogeneous group or segregates into clusters. Gene expressions of 390 patient samples measured using microarray technology and 150 samples measured via RNA-Seq were compared. Beyond acute promyelocytic leukemia (APL), a well-known AML subentity, the remaining AML samples were separated into two distinct subgroups. Next, we investigated which CD molecules would best distinguish each AML subgroup against APL, and validated discriminative molecules of both datasets by searching the scientific literature. Surprisingly, a comparison of both omics analyses revealed that CD339 was the only overlapping gene differentially regulated in APL and other AML subtypes. In summary, our two-step approach for gene expression analysis revealed two previously unknown subgroup distinctions in AML based on surface molecule expression, which may guide the differentiation of subentities in a given clinical–diagnostic context.

Co-expression effect of LLGL2 and SLC7A5 to predict prognosis in ERα-positive breast cancer

Lethal giant larvae homolog 2 (LLGL2) and solute carrier family 7 member 5 (SLC7A5) have been reported to be involved in resistance to endocrine therapy. This study aimed to assess the effects of LLGL2/SLC7A5 co-expression in predicting prognosis and response to tamoxifen therapy in ERα-positive breast cancer patients according to LLGL2/SLC7A5 mRNA and protein expression in long-term follow-up invasive breast cancer tissues. We identified that low LLGL2/SLC7A5 mRNA co-expression (LLGL2^low/SLC7A5^low) was associated with disease-free survival (DFS) compared with other combination groups in all breast cancer patients. In ERα-positive breast cancer patients, LLGL2^low/SLC7A5^low showed longer DFS and overall survival (OS) compared with LLGL2^high/SLC7A5^high and a positive trend of longer survival compared with the other combination groups. We also observed that LLGL2^low/SLC7A5^low showed longer survival compared with LLGL2^high/SLC7A5^high in ERα-positive breast cancer patients receiving adjuvant tamoxifen therapy. Multivariate analysis demonstrated that LLGL2^low/SLC7A5^low was an independent favorable prognostic factor of both DFS and OS, not only in all breast cancer patients, but also in ERα-positive breast cancer patients. High co-expression of LLGL2 and SLC7A5 protein showed a positive trend of shorter survival. Our study showed that co-expression of LLGL2 and SLC7A5 mRNA is a promising candidate biomarker in early breast cancer patients.

Identification of a novel signature with prognostic value in triple-negative breast cancer through clinico-transcriptomic analysis

Background

Although perceived as a highly aggressive disease, triple-negative breast cancer (TNBC) constitutes heterogeneous features with various outcomes. In this study, we aimed to establish a prognostic signature for patients with TNBC to improve risk stratification.

Methods

Gene expression data were obtained from The Cancer Genome Atlas (TCGA). Differentially expressed genes (DEGs) were detected pairwise between TNBC and other subtypes of samples. Then, TNBC-correlated modules were determined using coexpression network analysis. A gene signature was established based on the prognostic genes in the intersection between DEGs and selected gene modules using least absolute shrinkage and selection operator (LASSO) Cox regression. Finally, a clinico-transcriptomic signature was developed to predict overall survival (OS). Model performance was quantified, and the bootstrap resampling method was used for validation.

Results

The gene signature included 6 messenger RNAs (mRNAs) and a clinical score indicating an increased likelihood of death when used as continuous or categorical predictors. A nomogram was built by integrating the pathological stage and gene signature to predict 2-, 3-, and 5-year OS. The addition of pathological stage increased the concordance index (C-index) compared with pathological stage alone and the gene signature alone. Bootstrap resampling revealed a stable performance of the nomogram.

Conclusions

A 6-mRNA signature was established to inform prognosis for patients with TNBC. Its combination with pathological stage can contribute to improving performance and provide additional supporting evidence for clinical decision-making.

The role of HER2 alterations in clinicopathological and molecular characteristics of breast cancer and HER2-targeted therapies: a comprehensive review

Breast cancer (BC) is the most common malignancy in women and one of the leading causes of cancer mortality, despite significant treatment advancements over the last decades. Human epidermal growth factor receptor-2 (HER2) is a member of the ERBB family of receptor tyrosine kinases which have long been known to mediate cancer cell growth and invasion through constitutive activation of oncogenic downstream signaling, such as PI3K/Akt/mTOR and MAPK. Overexpression/amplification of HER2 in various tumors, especially BC, offers the possible therapeutic potential for target therapies. HER2-targeted therapies, either with a combination of chemotherapy or through multi-anti-HER2 therapies without chemotherapy, have significantly improved the prognosis of HER2-positive tumors. In recent years, novel anti-HER2 agents and combination therapies have garnered much attention, especially for heavily treated advanced or metastatic BCs. HER2-positive BC is biologically a heterogeneous group depending on HER2 activation mechanisms, hormone receptor status, genome variations, tumor heterogeneity, and treatment resistance, which affect the treatment benefit and patients' outcomes. This review will discuss HER2 alternations (gene amplification or receptor overexpression) in BC, their correlation with clinicopathological characteristics and molecular characteristics, and HER2-based therapies in tumors with HER2 overexpression/amplification.

EpICC: A Bayesian neural network model with uncertainty correction for a more accurate classification of cancer

Accurate classification of cancers into their types and subtypes holds the key for choosing the right treatment strategy and can greatly impact patient well-being. However, existence of large-scale variations in the molecular processes driving even a single type of cancer can make accurate classification a challenging problem. Therefore, improved and robust methods for classification are absolutely critical. Although deep learning-based methods for cancer classification have been proposed earlier, they all provide point estimates for predictions without any measure of confidence and thus, can fall short in real-world applications where key decisions are to be made based on the predictions of the classifier. Here we report a Bayesian neural network-based model for classification of cancer types as well as sub-types from transcriptomic data. This model reported a measure of confidence with each prediction through analysis of epistemic uncertainty. We incorporated an uncertainty correction step with the Bayesian network-based model to greatly enhance prediction accuracy of cancer types (> 97% accuracy) and sub-types (> 80%). Our work suggests that reporting uncertainty measure with each classification can enable more accurate and informed decision-making that can be highly valuable in clinical settings.

Integrative Bioinformatics Analysis Reveals CHEK1 and UBE2C as Luminal A Breast Cancer Subtype Biomarkers

In light of the limited number of targetable oncogenic drivers in breast cancer (BRCA), it is important to identify effective and druggable gene targets for the treatment of this devastating disease. Herein, the GSE102484 dataset containing expression profiling data from 683 BRCA patients was re-analyzed using weighted gene co-expression network analysis (WGCNA). The yellow module with the highest correlation to BRCA progression was screened out, followed by functional enrichment analysis and establishment of a protein–protein interaction (PPI) network. After further validation through survival analysis and expression evaluation, CHEK1 and UBE2C were finally identified as hub genes related to the progression of BRCA, especially the luminal A breast cancer subtype. Notably, both hub genes were found to be dysregulated in multiple types of immune cells and closely correlated with tumor infiltration, as revealed by Tumor Immune Estimation Resource (TIMER) along with other bioinformatic tools. Construction of transcription factors (TF)-hub gene network further confirmed the existence of 11 TFs which could regulate both hub genes simultaneously. Our present study may facilitate the invention of targeted therapeutic drugs and provide novel insights into the understanding of the mechanism beneath the progression of BRCA.

ENDORSE: a prognostic model for endocrine therapy in estrogen-receptor-positive breast cancers

Advanced and metastatic estrogen receptor-positive (ER+ ) breast cancers are often endocrine resistant. However, endocrine therapy remains the primary treatment for all advanced ER+ breast cancers. Treatment options that may benefit resistant cancers, such as add-on drugs that target resistance pathways or switching to chemotherapy, are only available after progression on endocrine therapy. Here we developed an endocrine therapy prognostic model for early and advanced ER+ breast cancers. The endocrine resistance (ENDORSE) model is composed of two components, each based on the empirical cumulative distribution function of ranked expression of gene signatures. These signatures include a feature set associated with long-term survival outcomes on endocrine therapy selected using lasso-regularized Cox regression and a pathway-based curated set of genes expressed in response to estrogen. We extensively validated ENDORSE in multiple ER+ clinical trial datasets and demonstrated superior and consistent performance of the model over clinical covariates, proliferation markers, and multiple published signatures. Finally, genomic and pathway analyses in patient data revealed possible mechanisms that may help develop rational stratification strategies for endocrine-resistant ER+ breast cancer patients.

Computational Screening of Anti-Cancer Drugs Identifies a New BRCA Independent Gene Expression Signature to Predict Breast Cancer Sensitivity to Cisplatin

Simple Summary

Using a collection of publicly available drug screening resources, we identified different partners of genes associated with either sensitivity or resistance to 90 anti-cancer therapies. When subsequently applying these signatures to multiple datasets, we found that these predictive models could predict a large range of drug responses in patient samples. In particular, we discovered a new gene signature to identify breast cancer tumors that are likely to respond to cisplatin in the absence of BRCA1 mutations. This work constitutes an important advance to accelerate the application of platinum-based therapies in patient groups that are not routinely treated with these drugs. In the future, this approach may help to guide the choice of drugs based on the molecular profile of the tumors.

Abstract

The development of therapies that target specific disease subtypes has dramatically improved outcomes for patients with breast cancer. However, survival gains have not been uniform across patients, even within a given molecular subtype. Large collections of publicly available drug screening data matched with transcriptomic measurements have facilitated the development of computational models that predict response to therapy. Here, we generated a series of predictive gene signatures to estimate the sensitivity of breast cancer samples to 90 drugs, comprising FDA-approved drugs or compounds in early development. To achieve this, we used a cell line-based drug screen with matched transcriptomic data to derive in silico models that we validated in large independent datasets obtained from cell lines and patient-derived xenograft (PDX) models. Robust computational signatures were obtained for 28 drugs and used to predict drug efficacy in a set of PDX models. We found that our signature for cisplatin can be used to identify tumors that are likely to respond to this drug, even in absence of the BRCA-1 mutation routinely used to select patients for platinum-based therapies. This clinically relevant observation was confirmed in multiple PDXs. Our study foreshadows an effective delivery approach for precision medicine.

High-throughput single-сell sequencing in cancer research

With advances in sequencing and instrument technology, bioinformatics analysis is being applied to batches of massive cells at single-cell resolution. High-throughput single-cell sequencing can be utilized for multi-omics characterization of tumor cells, stromal cells or infiltrated immune cells to evaluate tumor progression, responses to environmental perturbations, heterogeneous composition of the tumor microenvironment, and complex intercellular interactions between these factors. Particularly, single-cell sequencing of T cell receptors, alone or in combination with single-cell RNA sequencing, is useful in the fields of tumor immunology and immunotherapy. Clinical insights obtained from single-cell analysis are critically important for exploring the biomarkers of disease progression or antitumor treatment, as well as for guiding precise clinical decision-making for patients with malignant tumors. In this review, we summarize the clinical applications of single-cell sequencing in the fields of tumor cell evolution, tumor immunology, and tumor immunotherapy. Additionally, we analyze the tumor cell response to antitumor treatment, heterogeneity of the tumor microenvironment, and response or resistance to immune checkpoint immunotherapy. The limitations of single-cell analysis in cancer research are also discussed.

Early Breast Cancer Evolution by Autosomal Broad Copy Number Alterations

The availability of comprehensive genomic datasets across patient populations enables the application of novel methods for reconstructing tumor evolution within individual patients. To this end, we propose studying autosomal broad copy number alterations (CNAs) as a framework to better understand early tumor evolution. We compared the broad CNAs and somatic mutations of patients with 1 to 10 autosomal broad CNAs against the full set of patients, using data from The Cancer Genome Atlas breast cancer project. We reveal here that the frequency of a chromosome arm obtaining a broad CNA and a genome acquiring somatic mutations changes as autosomal broad CNAs accumulate. Therefore, we propose that the number of autosomal broad CNAs is an important characteristic of breast tumors that needs to be taken into consideration when studying breast tumors. To investigate this idea more in-depth, we next studied the frequency that specific chromosome arms acquire broad CNAs in patients with 1 to 10 broad CNAs. With this process, we identified the broad CNAs that exhibit the fastest rates of accumulation across all patients. This finding suggests a likely order of occurrence of these alterations in patients, which is apparent when we consider a subset of patients with few broad CNAs. Here, we lay the foundation for future studies to build upon our findings and use autosomal broad CNAs as a method to monitor breast tumor progression in vivo to further our understanding of how early tumor evolution unfolds.

Nucleoporin-93 reveals a common feature of aggressive breast cancers: robust nucleocytoplasmic transport of transcription factors

By establishing multi-omics pipelines, we uncover overexpression and gene copy-number alterations of nucleoporin-93 (NUP93), a nuclear pore component, in aggressive human mammary tumors. NUP93 overexpression enhances transendothelial migration and matrix invasion in vitro, along with tumor growth and metastasis in animal models. These findings are supported by analyses of two sets of naturally occurring mutations: rare oncogenic mutations and inactivating familial nephrotic syndrome mutations. Mechanistically, NUP93 binds with importins, boosts nuclear transport of importins' cargoes, such as β-catenin, and activates MYC. Likewise, NUP93 overexpression enhances the ultimate nuclear transport step shared by additional signaling pathways, including TGF-β/SMAD and EGF/ERK. The emerging addiction to nuclear transport exposes vulnerabilities of NUP93-overexpressing tumors. Congruently, myristoylated peptides corresponding to the nuclear translocation signals of SMAD and ERK can inhibit tumor growth and metastasis. Our study sheds light on an emerging hallmark of advanced tumors, which derive benefit from robust nucleocytoplasmic transport.

Multi-omic machine learning predictor of breast cancer therapy response

Breast cancers are complex ecosystems of malignant cells and the tumour microenvironment1. The composition of these tumour ecosystems and interactions within them contribute to responses to cytotoxic therapy2. Efforts to build response predictors have not incorporated this knowledge. We collected clinical, digital pathology, genomic and transcriptomic profiles of pre-treatment biopsies of breast tumours from 168 patients treated with chemotherapy with or without HER2 (encoded by ERBB2)-targeted therapy before surgery. Pathology end points (complete response or residual disease) at surgery3 were then correlated with multi-omic features in these diagnostic biopsies. Here we show that response to treatment is modulated by the pre-treated tumour ecosystem, and its multi-omics landscape can be integrated in predictive models using machine learning. The degree of residual disease following therapy is monotonically associated with pre-therapy features, including tumour mutational and copy number landscapes, tumour proliferation, immune infiltration and T cell dysfunction and exclusion. Combining these features into a multi-omic machine learning model predicted a pathological complete response in an external validation cohort (75 patients) with an area under the curve of 0.87. In conclusion, response to therapy is determined by the baseline characteristics of the totality of the tumour ecosystem captured through data integration and machine learning. This approach could be used to develop predictors for other cancers.

Detection of subtype-specific breast cancer surface protein biomarkers via a novel transcriptomics approach

BACKGROUND

Cell-surface proteins have been widely used as diagnostic and prognostic markers in cancer research and as targets for the development of anticancer agents. So far, very few attempts have been made to characterize the surfaceome of patients with breast cancer, particularly in relation with the current molecular breast cancer (BRCA) classification. In this view, we developed a new computational method to infer cell-surface protein activities from transcriptomics data, termed 'SURFACER'.

METHODS

Gene expression data from GTEx were used to build a normal breast network model as input to infer differential cell-surface proteins activity in BRCA tissue samples retrieved from TCGA versus normal samples. Data were stratified according to the PAM50 transcriptional subtypes (Luminal A, Luminal B, HER2 and Basal), while unsupervised clustering techniques were applied to define BRCA subtypes according to cell-surface proteins activity.

RESULTS

Our approach led to the identification of 213 PAM50 subtypes-specific deregulated surface genes and the definition of five BRCA subtypes, whose prognostic value was assessed by survival analysis, identifying a cell-surface activity configuration at increased risk. The value of the SURFACER method in BRCA genotyping was tested by evaluating the performance of 11 different machine learning classification algorithms.

CONCLUSIONS

BRCA patients can be stratified into five surface activity-specific groups having the potential to identify subtype-specific actionable targets to design tailored targeted therapies or for diagnostic purposes. SURFACER-defined subtypes show also a prognostic value, identifying surface-activity profiles at higher risk.

How Lineage Tracing Studies Can Unveil Tumor Heterogeneity in Breast Cancer

Lineage tracing studies have become a well-suited approach to reveal cellular hierarchies and tumor heterogeneity. Cellular heterogeneity, particularly in breast cancer, is still one of the main concerns regarding tumor progression and resistance to anti-cancer therapies. Here, we review the current knowledge about lineage tracing analyses that have contributed to an improved comprehension of the complexity of mammary tumors, highlighting how targeting different mammary epithelial cells and tracing their progeny can be useful to explore the intra- and inter-heterogeneity observed in breast cancer. In addition, we examine the strategies used to identify the cell of origin in different breast cancer subtypes and summarize how cellular plasticity plays an important role during tumorigenesis. Finally, we evaluate the clinical implications of lineage tracing studies and the challenges remaining to address tumor heterogeneity in breast cancer.

Validation of HER2 Status in Whole Genome Sequencing Data of Breast Cancers with the Ploidy-Corrected Copy Number Approach

BACKGROUND AND OBJECTIVE

Human epidermal growth factor receptor 2 (HER2) protein overexpression is one of the most significant biomarkers for breast cancer diagnostics, treatment prediction, and prognostics. The high accessibility of HER2 inhibitors in routine clinical practice directly translates into the diagnostic need for precise and robust marker identification. Even though multigene next-generation sequencing methodologies have slowly taken over the field of single-biomarker molecular tests, the copy number alterations such as amplification of the HER2-coding ERBB2 gene are hard to validate on next-generation sequencing platforms as they are characterized by chromosomal structural heterogeneity, polysomy, and genomic context of ploidy. In our study, we tested the approach of using whole genome sequencing instead of next-generation sequencing panels to determine HER2 status in the clinical set-up.

METHODS

We used a large dataset of 876 patients with breast cancer whole genomes with curated clinical data and an additional set of 551 patients' external genomic data. We used the decision-tree-based algorithm for optimization of the diagnostic tool for HER2 status assessment by whole genome sequencing.

RESULTS

The most efficient approach to assess HER2 status in whole genome sequencing data was the ploidy-corrected copy number, utilizing ERBB2 copy number and mean tumor ploidy. The classifier achieved sensitivity of 91.18% and specificity of 98.69% on the internal validation dataset and 89.86% and 96.06% on the external data, which is similar to other next-generation sequencing methods, currently tested in the clinic.

CONCLUSIONS

We provide evidence that the HER2 status may be reliably determined by whole genome sequencing and is applicable across different laboratory protocols and pipelines. We suggest using the ploidy-corrected copy number for diagnostic purposes.

Quantitative single-cell analysis of immunofluorescence protein multiplex images illustrates biomarker spatial heterogeneity within breast cancer subtypes

BACKGROUND

The extent of cellular heterogeneity in breast cancer could have potential impact on diagnosis and long-term outcome. However, pathology evaluation is limited to biomarker immunohistochemical staining and morphology of the bulk cancer. Inter-cellular heterogeneity of biomarkers is not usually assessed. As an initial evaluation of the extent of breast cancer cellular heterogeneity, we conducted quantitative and spatial imaging of Estrogen Receptor (ER), Progesterone Receptor (PR), Epidermal Growth Factor Receptor-2 (HER2), Ki67, TP53, CDKN1A (P21/WAF1), CDKN2A (P16INK4A), CD8 and CD20 of a tissue microarray (TMA) representing subtypes defined by St. Gallen surrogate classification.

METHODS

Quantitative, single cell-based imaging was conducted using an Immunofluorescence protein multiplexing platform (MxIF) to study protein co-expression signatures and their spatial localization patterns. The range of MxIF intensity values of each protein marker was compared to the respective IHC score for the TMA core. Extent of heterogeneity in spatial neighborhoods was analyzed using co-occurrence matrix and Diversity Index measures.

RESULTS

On the 101 cores from 59 cases studied, diverse expression levels and distributions were observed in MxIF measures of ER and PR among the hormonal receptor-positive tumor cores. As expected, Luminal A-like cancers exhibit higher proportions of cell groups that co-express ER and PR, while Luminal B-like (HER2-negative) cancers were composed of ER+, PR- groups. Proliferating cells defined by Ki67 positivity were mainly found in groups with PR-negative cells. Triple-Negative Breast Cancer (TNBC) exhibited the highest proliferative fraction and incidence of abnormal P53 and P16 expression. Among the tumors exhibiting P53 overexpression by immunohistochemistry, a group of TNBC was found with much higher MxIF-measured P53 signal intensity compared to HER2+, Luminal B-like and other TNBC cases. Densities of CD8 and CD20 cells were highest in HER2+ cancers. Spatial analysis demonstrated variability in heterogeneity in cellular neighborhoods in the cancer and the tumor microenvironment.

CONCLUSIONS

Protein marker multiplexing and quantitative image analysis demonstrated marked heterogeneity in protein co-expression signatures and cellular arrangement within each breast cancer subtype. These refined descriptors of biomarker expressions and spatial patterns could be valuable in the development of more informative tools to guide diagnosis and treatment.

Current advances in prognostic and diagnostic biomarkers for solid cancers: Detection techniques and future challenges

Solid cancers are one of the leading causes of cancer related deaths, characterized by rapid growth of tumour, and local and distant metastases. Current advances on multimodality care have substantially improved local control and metastasis-free survival of patients by resection of primary tumour. The major concern in disease prognosis is the timely detection of resectable or metastatic tumour, thus reinforcing the need for identification of biomarkers for premalignant lesions of solid cancer. This ultimately improves the outcome for the patients. Therefore, the purpose of this review is to update the recent advancements on prognostic and diagnostic biomarkers to enhance early detection of common solid cancers including, breast, lung, colorectal, prostate and stomach cancer. We also provide an insight into Food and Drug Administration (FDA)-approved solid cancers biomarkers; various conventional techniques used for detection of prognostic and diagnostic biomarkers and discuss approaches to turn challenges in this field into opportunities.

Whole-genome analysis of Nigerian patients with breast cancer reveals ethnic-driven somatic evolution and distinct genomic subtypes

Black women across the African diaspora experience more aggressive breast cancer with higher mortality rates than white women of European ancestry. Although inter-ethnic germline variation is known, differential somatic evolution has not been investigated in detail. Analysis of deep whole genomes of 97 breast cancers, with RNA-seq in a subset, from women in Nigeria in comparison with The Cancer Genome Atlas (n = 76) reveal a higher rate of genomic instability and increased intra-tumoral heterogeneity as well as a unique genomic subtype defined by early clonal GATA3 mutations with a 10.5-year younger age at diagnosis. We also find non-coding mutations in bona fide drivers (ZNF217 and SYPL1) and a previously unreported INDEL signature strongly associated with African ancestry proportion, underscoring the need to expand inclusion of diverse populations in biomedical research. Finally, we demonstrate that characterizing tumors for homologous recombination deficiency has significant clinical relevance in stratifying patients for potentially life-saving therapies.

Breast cancer heterogeneity and tumour evolutionary trajectories remain largely unknown among women of African ancestry. Here, the authors perform whole genome and transcriptome sequencing of Nigerian breast cancer patients and identify unique evolutionary phenomena.

MicroRNAs in Molecular Classification and Pathogenesis of Breast Tumors

The current clinical practice of breast tumor classification relies on the routine immunohistochemistry-based expression analysis of hormone receptors, which is inadequate in addressing breast tumor heterogeneity and drug resistance. MicroRNA expression profiling in tumor tissue and in the circulation is an efficient alternative to intrinsic molecular subtyping that enables precise molecular classification of breast tumor variants, the prediction of tumor progression, risk stratification and also identifies critical regulators of the tumor microenvironment. This review integrates data from protein, gene and miRNA expression studies to elaborate on a unique miRNA-based 10-subtype taxonomy, which we propose as the current gold standard to allow appropriate classification and separation of breast cancer into a targetable strategy for therapy.

Detection of pancreatic cancer by convolutional-neural-network-assisted spontaneous Raman spectroscopy with critical feature visualization

Pancreatic cancer is the deadliest cancer type with a five-year survival rate of less than 9%. Detection of tumor margins plays an essential role in the success of surgical resection. However, histopathological assessment is time-consuming, expensive, and labor-intensive. We constructed a lab-designed, hand-held Raman spectroscopic system that could enable intraoperative tissue diagnosis using convolutional neural network (CNN) models to efficiently distinguish between cancerous and normal pancreatic tissue. To our best knowledge, this is the first reported effort to diagnose pancreatic cancer by CNN-aided spontaneous Raman scattering with a lab-developed system designed for intraoperative applications. Classification based on the original one-dimensional (1D) Raman, two-dimensional (2D) Raman images, and the first principal component (PC1) from the principal component analysis on the 2D image, could all achieve high performance: the testing sensitivity, specificity, and accuracy were over 95%, and the area under the curve approached 0.99. Although CNN models often show great success in classification, it has always been challenging to visualize the CNN features in these models, which has never been achieved in the Raman spectroscopy application in cancer diagnosis. By studying individual Raman regions and by extracting and visualizing CNN features from max-pooling layers, we identified critical Raman peaks that could aid in the classification of cancerous and noncancerous tissues. 2D Raman PC1 yielded more critical peaks for pancreatic cancer identification than that of 1D Raman, as the Raman intensity was amplified by 2D Raman PC1. To our best knowledge, the feature visualization was achieved for the first time in the field of CNN-aided spontaneous Raman spectroscopy for cancer diagnosis. Based on these CNN feature peaks and their frequency at specific wavenumbers, pancreatic cancerous tissue was found to contain more biochemical components related to the protein contents (particularly collagen), whereas normal pancreatic tissue was found to contain more lipids and nucleic acid (particularly deoxyribonucleic acid/ribonucleic acid). Overall, the CNN model in combination with Raman spectroscopy could serve as a useful tool for the extraction of key features that can help differentiate pancreatic cancer from a normal pancreas.

The Breast Tumor Microenvironment: A Key Player in Metastatic Spread

The tumor microenvironment plays a pivotal role in the tumorigenesis, progression, and metastatic spread of many cancers including breast. There is now increasing evidence to support the observations that a bidirectional interplay between breast cancer cells and stromal cells exists within the tumor and the tumor microenvironment both at the primary tumor site and at the metastatic site. This interaction occurs through direct cell to cell contact, or by the release of autocrine or paracrine factors which can activate pro-tumor signaling pathways and modulate tumor behavior. In this review, we will highlight recent advances in our current knowledge about the multiple interactions between breast cancer cells and neighboring cells (fibroblasts, endothelial cells, adipocytes, innate and adaptive immune cells) in the tumor microenvironment that coordinate to regulate metastasis. We also highlight the role of exosomes and circulating tumor cells in facilitating breast cancer metastasis. We discuss some key markers associated with stromal cells in the breast tumor environment and their potential to predict patient survival and guide treatment. Finally, we will provide some brief perspectives on how current technologies may lead to the development of more effective therapies for the clinical management of breast cancer patients.

Single allele loss-of-function mutations select and sculpt conditional cooperative networks in breast cancer

The most common events in breast cancer (BC) involve chromosome arm losses and gains. Here we describe identification of 1089 gene-centric common insertion sites (gCIS) from transposon-based screens in 8 mouse models of BC. Some gCIS are driver-specific, others driver non-specific, and still others associated with tumor histology. Processes affected by driver-specific and histology-specific mutations include well-known cancer pathways. Driver non-specific gCIS target the Mediator complex, Ca++ signaling, Cyclin D turnover, RNA-metabolism among other processes. Most gCIS show single allele disruption and many map to genomic regions showing high-frequency hemizygous loss in human BC. Two gCIS, Nf1 and Trps1, show synthetic haploinsufficient tumor suppressor activity. Many gCIS act on the same pathway responsible for tumor initiation, thereby selecting and sculpting just enough and just right signaling. These data highlight ~1000 genes with predicted conditional haploinsufficient tumor suppressor function and the potential to promote chromosome arm loss in BC.

The Biological and Clinical Significance of Glutaminase in Luminal Breast Cancer

Simple Summary

Certain nutrients are needed by cancers to grow. Some breast cancers need the nutrient glutamine to grow and without it they don’t grow as quickly. In this study, we wanted to know the role of an enzyme, glutaminase, which is a substance produced by the body that breaks down glutamine so it can be used by cancers to grow. This enzyme occurs as two different types but we don’t know what their roles are in breast cancer. We therefore looked at the two types of enzyme in over 7000 breast cancers. We found that patients with high amounts of enzyme in early forms of breast cancer died earlier. Therefore, this enzyme has an important role in breast cancer and could be used to identify cancers which will get worse. We also think that using a drug to stop this enzyme will stop cancers growing. More studies are needed to confirm this.

Abstract

The glutamine metabolism has a key role in the regulation of uncontrolled tumour growth. This study aimed to evaluate the expression and prognostic significance of glutaminase in luminal breast cancer (BC). The glutaminase isoforms (GLS/GLS2) were assessed at genomic/transcriptomic levels, using METABRIC (n = 1398) and GeneMiner datasets (n = 4712), and protein using immunohistochemistry in well-characterised cohorts of Oestrogen receptor-positive/HER2-negative BC patients: ductal carcinoma in situ (DCIS; n = 206) and invasive breast cancer (IBC; n = 717). Glutaminase expression was associated with clinicopathological features, patient outcome and glutamine-metabolism-related genes. In DCIS, GLS alone and GLS+/GLS2- expression were risk factors for shorter local recurrence-free interval (p < 0.0001 and p = 0.001, respectively) and remained prognostic factors independent of tumour size, grade and comedo necrosis (p = 0.0008 and p = 0.003, respectively). In IBC, GLS gene copy number gain with high mRNA expression was associated with poor patient outcome (p = 0.011), whereas high GLS2 protein was predictive of a longer disease-free survival (p = 0.006). Glutaminase plays a role in the biological function of luminal BC, particularly GLS in the early non-invasive stage, which could be used as a potential biomarker to predict disease progression and a target for inhibition. Further validation is required to confirm these observations, and functional assessments are needed to explore their specific roles.

Stem cell therapy: A paradigm shift in breast cancer treatment

As per the latest Globocan statistics, the high prevalence rate of breast cancer in low- and middle-income countries has led to it becoming the most common cancer to be diagnosed, hence posing a major public health challenge. As per this data, more than 11.7% of the estimated new cancer cases in 2020 were due to breast cancer. A small but significant subpopulation of cells with self- renewing ability are present in the tumor stroma and have been given the nomenclature of cancer stem cells (CSCs). These cells display a high degree of plasticity owing to their ability to transition from the slowly cycling quiescent phase to the actively proliferating phenotype. This attribute of CSCs allows them to differentiate into various cell types having diverse functions. Breast CSCs have a pivotal role in development, metastasis, treatment resistance and relapse of breast cancers. This review focuses on the pathways regulating breast CSC maintenance and the current strategies that are being explored for directing the development of novel, targeted, therapeutic approaches for limiting and eradicating this aberrant stem cell population.

CELLector: Genomics-Guided Selection of Cancer In Vitro Models

Selecting appropriate cancer models is a key prerequisite for maximizing translational potential and clinical relevance of in vitro oncology studies. We developed CELLector: an R package and R Shiny application allowing researchers to select the most relevant cancer cell lines in a patient-genomic-guided fashion. CELLector leverages tumor genomics to identify recurrent subtypes with associated genomic signatures. It then evaluates these signatures in cancer cell lines to prioritize their selection. This enables users to choose appropriate in vitro models for inclusion or exclusion in retrospective analyses and future studies. Moreover, this allows bridging outcomes from cancer cell line screens to precisely defined sub-cohorts of primary tumors. Here, we demonstrate the usefulness and applicability of CELLector, showing how it can aid prioritization of in vitro models for future development and unveil patient-derived multivariate prognostic and therapeutic markers. CELLector is freely available at https://ot-cellector.shinyapps.io/CELLector_App/ (code at https://github.com/francescojm/CELLector and https://github.com/francescojm/CELLector_App).

Systematic dissection of transcriptional regulatory networks by genome-scale and single-cell CRISPR screens

Millions of putative transcriptional regulatory elements (TREs) have been cataloged in the human genome, yet their functional relevance in specific pathophysiological settings remains to be determined. This is critical to understand how oncogenic transcription factors (TFs) engage specific TREs to impose transcriptional programs underlying malignant phenotypes. Here, we combine cutting edge CRISPR screens and epigenomic profiling to functionally survey ≈15,000 TREs engaged by estrogen receptor (ER). We show that ER exerts its oncogenic role in breast cancer by engaging TREs enriched in GATA3, TFAP2C, and H3K27Ac signal. These TREs control critical downstream TFs, among which TFAP2C plays an essential role in ER-driven cell proliferation. Together, our work reveals novel insights into a critical oncogenic transcription program and provides a framework to map regulatory networks, enabling to dissect the function of the noncoding genome of cancer cells.

TSHZ2 is an EGF-regulated tumor suppressor that binds to the cytokinesis regulator PRC1 and inhibits metastasis

Unlike early transcriptional responses to mitogens, later events are less well-characterized. Here, we identified delayed down-regulated genes (DDGs) in mammary cells after prolonged treatment with epidermal growth factor (EGF). The expression of these DDGs was low in mammary tumors and correlated with prognosis. The proteins encoded by several DDGs directly bind to and inactivate oncoproteins and might therefore act as tumor suppressors. The transcription factor teashirt zinc finger homeobox 2 (TSHZ2) is encoded by a DDG, and we found that overexpression of TSHZ2 inhibited tumor growth and metastasis and accelerated mammary gland development in mice. Although the gene TSHZ2 localizes to a locus (20q13.2) that is frequently amplified in breast cancer, we found that hypermethylation of its promoter correlated with down-regulation of TSHZ2 expression in patients. Yeast two-hybrid screens and protein-fragment complementation assays in mammalian cells indicated that TSHZ2 nucleated a multiprotein complex containing PRC1/Ase1, cyclin B1, and additional proteins that regulate cytokinesis. TSHZ2 increased the inhibitory phosphorylation of PRC1, a key driver of mitosis, mediated by cyclin-dependent kinases. Furthermore, similar to the tumor suppressive transcription factor p53, TSHZ2 inhibited transcription from the PRC1 promoter. By recognizing DDGs as a distinct group in the transcriptional response to EGF, our findings uncover a group of tumor suppressors and reveal a role for TSHZ2 in cell cycle regulation.

Circulating immune cell populations related to primary breast cancer, surgical removal, and radiotherapy revealed by flow cytometry analysis

BACKGROUND

Advanced breast cancer (BC) impact immune cells in the blood but whether such effects may reflect the presence of early BC and its therapeutic management remains elusive.

METHODS

To address this question, we used multiparametric flow cytometry to analyze circulating leukocytes in patients with early BC (n = 13) at the time of diagnosis, after surgery, and after adjuvant radiotherapy, compared to healthy individuals. Data were analyzed using a minimally supervised approach based on FlowSOM algorithm and validated manually.

RESULTS

At the time of diagnosis, BC patients have an increased frequency of CD117⁺CD11b⁺ granulocytes, which was significantly reduced after tumor removal. Adjuvant radiotherapy increased the frequency of CD45RO⁺ memory CD4⁺ T cells and CD4⁺ regulatory T cells. FlowSOM algorithm analysis revealed several unanticipated populations, including cells negative for all markers tested, CD11b⁺CD15^low, CD3⁺CD4^-CD8^-, CD3⁺CD4⁺CD8⁺, and CD3⁺CD8⁺CD127⁺CD45RO⁺ cells, associated with BC or radiotherapy.

CONCLUSIONS

This study revealed changes in blood leukocytes associated with primary BC, surgical removal, and adjuvant radiotherapy. Specifically, it identified increased levels of CD117⁺ granulocytes, memory, and regulatory CD4⁺ T cells as potential biomarkers of BC and radiotherapy, respectively. Importantly, the study demonstrates the value of unsupervised analysis of complex flow cytometry data to unravel new cell populations of potential clinical relevance.