Oestrogen-regulated protein SLC39A6: a biomarker of good prognosis in luminal breast cancer

Endocrine persistence in ER+ breast cancer is accompanied by metabolic vulnerability in oxidative phosphorylation

Despite adjuvant treatment with endocrine therapies, estrogen receptor-positive (ER+) breast cancers recur in a significant proportion of patients. Recurrences are attributable to clinically undetectable endocrine-tolerant persister cancer cells that retain tumor-forming potential. Therefore, strategies targeting such persister cells may prevent recurrent disease. Using CRISPR-Cas9 genome-wide knockout screening in ER+ breast cancer cells, we identified a survival mechanism involving metabolic reprogramming with reliance upon mitochondrial respiration in endocrine-tolerant persister cells. Quantitative proteomic profiling showed reduced levels of glycolytic proteins in persisters. Metabolic tracing of glucose revealed an energy-depleted state in persisters where oxidative phosphorylation was required to generate ATP. A phase II clinical trial was conducted to evaluate changes in mitochondrial markers in primary ER+/HER2-breast tumors induced by neoadjuvant endocrine therapy ( NCT04568616 ). In an analysis of tumor specimens from 32 patients, tumors exhibiting residual cell proliferation after aromatase inhibitor-induced estrogen deprivation with letrozole showed increased mitochondrial content. Genetic profiling and barcode lineage tracing showed that endocrine-tolerant persistence occurred stochastically without genetic predisposition. Mice bearing cell line- and patient-derived xenografts were used to measure the anti-tumor effects of mitochondrial complex I inhibition in the context of endocrine therapy. Pharmacological inhibition of complex I suppressed the tumor-forming potential of persisters and synergized with the anti-estrogen fulvestrant to induce regression of patient-derived xenografts. These findings indicate that mitochondrial metabolism is essential in endocrine-tolerant persister ER+ breast cancer cells and warrant the development of treatment strategies to leverage this vulnerability in the context of endocrine-sensitive disease.

Statement of Significance

Endocrine-tolerant persister cancer cells that survive endocrine therapy can cause recurrent disease. Persister cells exhibit increased energetic dependence upon mitochondria for survival and tumor re-growth potential.

Anoikis-related genes in breast cancer patients: reliable biomarker of prognosis

BACKGROUND

Breast cancer (BC) is the most common cancer in women, and its progression is closely related to the phenomenon of anoikis. Anoikis, the specific programmed death resulting from a lack of contact between cells and the extracellular matrix, has recently been recognized as playing a critical role in tumor initiation, maintenance, and treatment. The ability of cancer cells to resist anoikis leads to cancer progression and metastatic colonization. However, the impact of anoikis on the prognosis of BC patients remains unclear.

METHOD

This study utilized data from the Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases to collect transcriptome and clinical data of BC patients. Anoikis-related genes (ARGs) were classified into subtypes A and B through consensus clustering. Subsequently, survival prognosis analysis, immune cell infiltration analysis, and functional enrichment analysis were performed for both subtypes. Using the Least Absolute Shrinkage and Selection Operator (LASSO) regression analysis, a set of 10 ARGs related to prognosis was identified. Immune cell infiltration and tumor microenvironment analyses were conducted on these 10 ARGs to develop a prognostic model. Furthermore, single-cell data analysis and real-time polymerase chain reaction (RT-PCR) analysis were employed to study the expression of the 10 identified prognostic ARGs in BC cells.

RESULTS

One hundred thirty-five ARGs were identified as differentially expressed genes in the TCGA and GEO databases, with 42 of them associated with the survival prognosis of BC patients. Analyses involving Principal Component Analysis (PCA), t-Distributed Stochastic Neighbor Embedding (t-SNE), and Uniform Manifold Approximation and Projection (UMAP) revealed distinct expression patterns of ARGs between types A and B. Patients in type A exhibited worse survival prognosis and lower immune cell infiltration compared to type B. Subsequent analyses identified 10 key ARGs (YAP1, PIK3R1, BAK1, PHLDA2, EDA2R, LAMB3, CD24, SLC2A1, CDC25C, and SLC39A6) relevant to BC prognosis. Kaplan-Meier analysis indicated that high-risk patients based on these ARGs had a poorer BC prognosis. Additionally, Cox regression analysis established gender, age, T (tumor), N (nodes), and risk score as predictive factors in a nomogram model for BC. The model demonstrated diagnostic value for BC patients at 1, 3, and 5 years. Decision curve analysis (DCA) verified the risk score as a reliable predictor of BC patient survival rates. Moreover, RT-PCR results confirmed differential expressions of YAP1, PIK3R1, BAK1, PHLDA2, CD24, SLC2A1, and CDC25C in BC cells, with SLC39A6, EDA2R, and LAMB3 showing low expression levels.

CONCLUSION

ARGs markers can be used as BC biomarkers for risk stratification and survival prediction in BC patients. Besides, ARGs can be used as stratification factors for individualized and precise treatment of BC patients.

Cellular zinc metabolism and zinc signaling: from biological functions to diseases and therapeutic targets

Zinc metabolism at the cellular level is critical for many biological processes in the body. A key observation is the disruption of cellular homeostasis, often coinciding with disease progression. As an essential factor in maintaining cellular equilibrium, cellular zinc has been increasingly spotlighted in the context of disease development. Extensive research suggests zinc's involvement in promoting malignancy and invasion in cancer cells, despite its low tissue concentration. This has led to a growing body of literature investigating zinc's cellular metabolism, particularly the functions of zinc transporters and storage mechanisms during cancer progression. Zinc transportation is under the control of two major transporter families: SLC30 (ZnT) for the excretion of zinc and SLC39 (ZIP) for the zinc intake. Additionally, the storage of this essential element is predominantly mediated by metallothioneins (MTs). This review consolidates knowledge on the critical functions of cellular zinc signaling and underscores potential molecular pathways linking zinc metabolism to disease progression, with a special focus on cancer. We also compile a summary of clinical trials involving zinc ions. Given the main localization of zinc transporters at the cell membrane, the potential for targeted therapies, including small molecules and monoclonal antibodies, offers promising avenues for future exploration.

Transcriptional intra-tumour heterogeneity predicted by deep learning in routine breast histopathology slides provides independent prognostic information

BACKGROUND

Intra-tumour heterogeneity (ITH) causes diagnostic challenges and increases the risk for disease recurrence. Quantification of ITH is challenging and has not been demonstrated in large studies. It has previously been shown that deep learning can enable spatially resolved prediction of molecular phenotypes from digital histopathology whole slide images (WSIs). Here we propose a novel method (Deep-ITH) to predict and measure ITH, and we evaluate its prognostic performance in breast cancer.

METHODS

Deep convolutional neural networks were used to spatially predict gene-expression (PAM50 set) from WSIs. For each predicted transcript, 12 measures of heterogeneity were extracted in the training data set (N = 931). A prognostic score to dichotomise patients into Deep-ITH low- and high-risk groups was established using an elastic-net regularised Cox proportional hazards model (recurrence-free survival). Prognostic performance was evaluated in two independent data sets: SöS-BC-1 (N = 1358) and SCAN-B-Lund (N = 1262).

RESULTS

We observed an increase in risk of recurrence in the high-risk group with hazard ratio (HR) 2.11 (95%CI:1.22-3.60; p = 0.007) using nested cross-validation. Subgroup analyses confirmed the prognostic performance in oestrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative, grade 3, and large tumour subgroups. The prognostic value was confirmed in the independent SöS-BC-1 cohort (HR=1.84; 95%CI:1.03-3.3; p = 3.99 ×10-2). In the other external cohort, significant HR was observed in the subgroup of histological grade 2 patients, as well as in the subgroup of patients with small tumours (<20 mm).

CONCLUSION

We developed a novel method for an automated, scalable, and cost-efficient measure of ITH from WSIs that provides independent prognostic value for breast cancer.

SIGNIFICANCE

Transcriptional ITH predicted by deep learning models enables prediction of patient survival from routine histopathology WSIs in breast cancer.

Prognostic and Predictive Value of LIV1 Expression in Early Breast Cancer and by Molecular Subtype

Background: LIV1 is a transmembrane protein that may become a new therapeutic target through the development of antibody–drug conjugates (ADCs). Few studies are available regarding the assessment of LIV1 expression in clinical breast cancer (BC) samples. Methods: We analyzed LIV1 mRNA expression in 8982 primary BC. We searched for correlations between LIV1 expression and clinicopathological data, including disease-free survival (DFS), overall survival (OS), pathological complete response to chemotherapy (pCR), and potential vulnerability and actionability to anti-cancer drugs used or under development in BC. Analyses were performed in the whole population and each molecular subtype separately. Results: LIV1 expression was associated with good-prognosis features and with longer DFS and OS in multivariate analysis. However, patients with high LIV1 expression displayed a lower pCR rate than patients with low expression after anthracycline-based neoadjuvant chemotherapy, including in multivariate analysis adjusted on grade and molecular subtypes. LIV1-high tumors were associated with higher probabilities of sensitivity to hormone therapy and CDK4/6 inhibitors and lower probabilities of sensitivity to immune-checkpoint inhibitors and PARP inhibitors. These observations were different according to the molecular subtypes when analyzed separately. Conclusions: These results may provide novel insights into the clinical development and use of LIV1-targeted ADCs by identifying prognostic and predictive value of LIV1 expression in each molecular subtype and associated vulnerability to other systemic therapies.

Identification of a gene expression signature associated with breast cancer survival and risk that improves clinical genomic platforms

Motivation

Modern genomic technologies allow us to perform genome-wide analysis to find gene markers associated with the risk and survival in cancer patients. Accurate risk prediction and patient stratification based on robust gene signatures is a key path forward in personalized treatment and precision medicine. Several authors have proposed the identification of gene signatures to assign risk in patients with breast cancer (BRCA), and some of these signatures have been implemented within commercial platforms in the clinic, such as Oncotype and Prosigna. However, these platforms are black boxes in which the influence of selected genes as survival markers is unclear and where the risk scores provided cannot be clearly related to the standard clinicopathological tumor markers obtained by immunohistochemistry (IHC), which guide clinical and therapeutic decisions in breast cancer.

Results

Here, we present a framework to discover a robust list of gene expression markers associated with survival that can be biologically interpreted in terms of the three main biomolecular factors (IHC clinical markers: ER, PR and HER2) that define clinical outcome in BRCA. To test and ensure the reproducibility of the results, we compiled and analyzed two independent datasets with a large number of tumor samples (1024 and 879) that include full genome-wide expression profiles and survival data. Using these two cohorts, we obtained a robust subset of gene survival markers that correlate well with the major IHC clinical markers used in breast cancer. The geneset of survival markers that we identify (which includes 34 genes) significantly improves the risk prediction provided by the genesets included in the commercial platforms: Oncotype (16 genes) and Prosigna (50 genes, i.e. PAM50). Furthermore, some of the genes identified have recently been proposed in the literature as new prognostic markers and may deserve more attention in current clinical trials to improve breast cancer risk prediction.

Availability and implementation

All data integrated and analyzed in this research will be available on GitHub (https://github.com/jdelasrivas-lab/breastcancersurvsign), including the R scripts and protocols used for the analyses.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

The role of cuproptosis-related gene in the classification and prognosis of melanoma

Background

Melanoma, as one of the most aggressive and malignant cancers, ranks first in the lethality rate of skin cancers. Cuproptosis has been shown to paly a role in tumorigenesis, However, the role of cuproptosis in melanoma metastasis are not clear. Studying the correlation beteen the molecular subtypes of cuproptosis-related genes (CRGs) and metastasis of melanoma may provide some guidance for the prognosis of melanoma.

Methods

We collected 1085 melanoma samples in The Cancer Genome Atlas(TCGA) and Gene Expression Omnibus(GEO) databases, constructed CRGs molecular subtypes and gene subtypes according to clinical characteristics, and investigated the role of CRGs in melanoma metastasis. We randomly divide the samples into train set and validation set according to the ratio of 1:1. A prognostic model was constructed using data from the train set and then validated on the validation set. We performed tumor microenvironment analysis and drug sensitivity analyses for high and low risk groups based on the outcome of the prognostic model risk score. Finally, we established a metastatic model of melanoma.

Results

According to the expression levels of 12 cuproptosis-related genes, we obtained three subtypes of A₁, B₁, and C₁. Among them, C₁ subtype had the best survival outcome. Based on the differentially expressed genes shared by A₁, B₁, and C₁ genotypes, we obtained the results of three gene subtypes of A₂, B₂, and C₂. Among them, the B₂ group had the best survival outcome. Then, we constructed a prognostic model consisting of 6 key variable genes, which could more accurately predict the 1-, 3-, and 5-year overall survival rates of melanoma patients. Besides, 98 drugs were screened out. Finally, we explored the role of cuproptosis-related genes in melanoma metastasis and established a metastasis model using seven key genes.

Conclusions

In conclusion, CRGs play a role in the metastasis and prognosis of melanoma, and also provide new insights into the underlying pathogenesis of melanoma.

Zinc transporter LIV1: A promising cell surface target for triple negative breast cancer

Breast cancer is one of the leading causes contributing to the global cancer burden. The triple negative breast cancer (TNBC) molecular subtype accounts for the most aggressive type. Despite progression in therapeutic options and prognosis in breast cancer treatment options, there remains a high rate of distant relapse. With advancements in understanding the role of zinc and zinc carriers in the prognosis and treatment of the disease, the scope of precision treatment/targeted therapy has been expanded. Zinc levels and zinc transporters play a vital role in maintaining cellular homeostasis, tumor surveillance, apoptosis, and immune function. This review focuses on the zinc transporter, LIV1, as an essential target for breast cancer prognosis and emerging treatment options. Previous studies give an insight into the role of LIV1 in fulfilling the most important hallmarks of cancer such as apoptosis, metastasis, invasion, and evading the immune system. Normal tissue expression of LIV1 is limited. Higher expression of LIV1 has been linked to Epithelial-Mesenchymal Transition, histological grade of cancer, and early node metastasis. LIV1 was found to be one of the attractive targets in the therapeutic hunt for TNBCs. TNBCs are an immunogenic breast cancer subtype. As zinc transporters are known to serve as the metabolic gatekeepers of immune cells, this review bridges tumor infiltrating lymphocytes, TNBC and LIV1. In addition, the suitability of LIV1 as an antibody-drug conjugate (Seattle genetics [SGN]-LIV1A) target in TNBC, represents a promising strategy for patients. Early clinical trial results reveal that this novel agent reduces tumor burden by inducing mitotic arrest, immunomodulation, and immunogenic cell death, warranting further investigation of SGN-LIV1A in combination with immuno-oncology agents. Priming the patient's immune response in combination with SGN-LIV1A could eventually change the landscape for the TNBC patient population.