High Expression of Three-Gene Signature Improves Prediction of Relapse-Free Survival in Estrogen Receptor-Positive and Node-Positive Breast Tumors

Transmembrane Amino Acid Transporters in Shaping the Metabolic Profile of Breast Cancer Cell Lines: The Focus on Molecular Biological Subtype

Amino acid metabolism in breast cancer cells is unique for each molecular biological subtype of breast cancer. In this review, the features of breast cancer cell metabolism are considered in terms of changes in the amino acid composition due to the activity of transmembrane amino acid transporters. In addition to the main signaling pathway PI3K/Akt/mTOR, the activity of the oncogene c-Myc, HIF, p53, GATA2, NF-kB and MAT2A have a direct effect on the amino acid metabolism of cancer cells, their growth and proliferation, as well as the maintenance of homeostatic equilibrium. A distinctive feature of luminal subtypes of breast cancer from TNBC is the ability to perform gluconeogenesis. Breast cancers with a positive expression of the HER2 receptor, in contrast to TNBC and luminal A subtype, have a distinctive active synthesis and consumption of fatty acids. It is interesting to note that amino acid transporters exhibit their activity depending on the pH level inside the cell. In the most aggressive forms of breast cancer or with the gradual progression of the disease, pH will also change, which will directly affect the metabolism of amino acids. Using the cell lines presented in this review, we can trace the characteristic features inherent in each of the molecular biological subtypes of breast cancer and develop the most optimal therapeutic targets.

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.

Proteomics-Derived Biomarker Panel Facilitates Distinguishing Primary Lung Adenocarcinomas With Intestinal or Mucinous Differentiation From Lung Metastatic Colorectal Cancer

The diagnosis of primary lung adenocarcinomas with intestinal or mucinous differentiation (PAIM) remains challenging due to the overlapping histomorphological, immunohistochemical (IHC), and genetic characteristics with lung metastatic colorectal cancer (lmCRC). This study aimed to explore the protein biomarkers that could distinguish between PAIM and lmCRC. To uncover differences between the two diseases, we used tandem mass tagging-based shotgun proteomics to characterize proteomes of formalin-fixed, paraffin-embedded tumor samples of PAIM (n = 22) and lmCRC (n = 17).Then three machine learning algorithms, namely support vector machine (SVM), random forest, and the Least Absolute Shrinkage and Selection Operator, were utilized to select protein features with diagnostic significance. These candidate proteins were further validated in an independent cohort (PAIM, n = 11; lmCRC, n = 19) by IHC to confirm their diagnostic performance. In total, 105 proteins out of 7871 proteins were significantly dysregulated between PAIM and lmCRC samples and well-separated two groups by Uniform Manifold Approximation and Projection. The upregulated proteins in PAIM were involved in actin cytoskeleton organization, platelet degranulation, and regulation of leukocyte chemotaxis, while downregulated ones were involved in mitochondrial transmembrane transport, vasculature development, and stem cell proliferation. A set of ten candidate proteins (high-level expression in lmCRC: CDH17, ATP1B3, GLB1, OXNAD1, LYST, FABP1; high-level expression in PAIM: CK7 (an established marker), NARR, MLPH, S100A14) was ultimately selected to distinguish PAIM from lmCRC by machine learning algorithms. We further confirmed using IHC that the five protein biomarkers including CDH17, CK7, MLPH, FABP1 and NARR were effective biomarkers for distinguishing PAIM from lmCRC. Our study depicts PAIM-specific proteomic characteristics and demonstrates the potential utility of new protein biomarkers for the differential diagnosis of PAIM and lmCRC. These findings may contribute to improving the diagnostic accuracy and guide appropriate treatments for these patients.

Amino acids and their roles in tumor immunotherapy of breast cancer

Breast cancer is the most commonly diagnosed cancer among women. The primary treatment options include surgery, radiotherapy, chemotherapy, targeted therapy and hormone therapy. The effectiveness of breast cancer therapy varies depending on the stage and aggressiveness of the cancer, as well as individual factors. Advances in early detection and improved treatments have significantly increased survival rates for breast cancer patients. Nevertheless, specific subtypes of breast cancer, particularly triple-negative breast cancer, still lack effective treatment strategies. Thus, novel and effective therapeutic targets for breast cancer need to be explored. As substrates of protein synthesis, amino acids are important sources of energy and nutrition, only secondly to glucose. The rich supply of amino acids enables the tumor to maintain its proliferative competence through participation in energy generation, nucleoside synthesis and maintenance of cellular redox balance. Amino acids also play an important role in immune-suppressive microenvironment formation. Thus, the biological effects of amino acids may change unexpectedly in tumor-specific or oncogene-dependent manners. In recent years, there has been significant progress in the study of amino acid metabolism, particularly in their potential application as therapeutic targets in breast cancer. In this review, we provide an update on amino acid metabolism and discuss the therapeutic implications of amino acids in breast cancer.

INO80 function is required for mouse mammary gland development, but mutation alone may be insufficient for breast cancer

The aberrant function of ATP-dependent chromatin remodeler INO80 has been implicated in multiple types of cancers by altering chromatin architecture and gene expression; however, the underlying mechanism of the functional involvement of INO80 mutation in cancer etiology, especially in breast cancer, remains unclear. In the present study, we have performed a weighted gene co-expression network analysis (WCGNA) to investigate links between INO80 expression and breast cancer sub-classification and progression. Our analysis revealed that INO80 repression is associated with differential responsiveness of estrogen receptors (ERs) depending upon breast cancer subtype, ER networks, and increased risk of breast carcinogenesis. To determine whether INO80 loss induces breast tumors, a conditional INO80-knockout (INO80 cKO) mouse model was generated using the Cre-loxP system. Phenotypic characterization revealed that INO80 cKO led to reduced branching and length of the mammary ducts at all stages. However, the INO80 cKO mouse model had unaltered lumen morphology and failed to spontaneously induce tumorigenesis in mammary gland tissue. Therefore, our study suggests that the aberrant function of INO80 is potentially associated with breast cancer by modulating gene expression. INO80 mutation alone is insufficient for breast tumorigenesis.

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.

Comparative Analysis of Transcriptomic Changes including mRNA and microRNA Expression Induced by the Xenoestrogens Zearalenone and Bisphenol A in Human Ovarian Cells

Xenoestrogens are natural or synthetic compounds that mimic the effect of endogenous estrogens and might cause cancer. We aimed to compare the global transcriptomic response to zearalenone (ZEA; mycotoxin) and bisphenol A (BPA; plastic additive) with the effect of physiological estradiol (E2) in the PEO1 human ovarian cell line by mRNA and microRNA sequencing. Estrogen exposure induced remarkable transcriptomic changes: 308, 288 and 63 genes were upregulated (log₂FC > 1); 292, 260 and 45 genes were downregulated (log₂FC < -1) in response to E2 (10 nM), ZEA (10 nM) and BPA (100 nM), respectively. Furthermore, the expression of 13, 11 and 10 miRNAs changed significantly (log₂FC > 1, or log₂FC < -1) after exposure to E2, ZEA and BPA, respectively. Functional enrichment analysis of the significantly differentially expressed genes and miRNAs revealed several pathways related to the regulation of cell proliferation and migration. The effect of E2 and ZEA was highly comparable: 407 genes were coregulated by these molecules. We could identify 83 genes that were regulated by all three treatments that might have a significant role in the estrogen response of ovarian cells. Furthermore, the downregulation of several miRNAs (miR-501-5p, let-7a-2-3p, miR-26a-2-3p, miR-197-5p and miR-582-3p) was confirmed by qPCR, which might support the proliferative effect of estrogens in ovarian cells.

Identification and verification of microRNA signature and key genes in the development of osteosarcoma with lung metastasis

BACKGROUND

Osteosarcoma (OS) is a heterogeneous malignant spindle cell tumor in children under the age of 20. This study aims to research the association between Solute Carrier Family 7 Member 8 (SLC7A8) as well as related genes and OS.

METHOD

OS and normal samples (GSE38698 and GSE85537) were downloaded from Gene Expression Omnibus dataset. The bioinformatics analysis was performed to distinguish 2 differentially expressed genes, prognostic candidate genes and functional enrichment pathway. Immunohistochemistry and quantitative real-time PCR were utilized for further study.

RESULTS

There were 5 DEMs and 10 differentially expressed genes in cancer tissues compared to normal tissues. According to the km-plot software, ARHGEF3, BSN, PQLC3, and SLC7A8 were significantly related to the overall survival of patients with OS. Furthermore, Multivariate analysis included that SLC7A8 was independent risk factors for OS patients. Furthermore, immunohistochemistry and quantitative real-time PCR outcomes indicated that the expression level of SLC7A8 and hsa-miR-506 was differentially expressed in lung metastasis OS tissues and non-metastasis tissues.

CONCLUSION

The prognostic model based on the miRNA-mRNA network could provide predictive significance for prognosis of OS patients, which would be worthy of clinical application. Our results concluded that SLC7A8 may play a key role in the development of OS.

SLC7A8 coding for LAT2 is associated with early disease progression in osteosarcoma and transports doxorubicin

Background: Despite (neo) adjuvant chemotherapy with cisplatin, doxorubicin and methotrexate, some patients with primary osteosarcoma progress during first-line systemic treatment and have a poor prognosis. In this study, we investigated whether patients with early disease progression (EDP), are characterized by a distinctive pharmacogenetic profile.

Methods and Findings: Germline DNA from 287 Dutch high-grade osteosarcoma patients was genotyped using the DMET Plus array (containing 1,936 genetic markers in 231 drug metabolism and transporter genes). Associations between genetic variants and EDP were assessed using logistic regression models and associated variants (p <0.05) were validated in independent cohorts of 146 (Spain and United Kingdom) and 28 patients (Australia). In the association analyses, EDP was significantly associated with an SLC7A8 locus and was independently validated (meta-analysis validation cohorts: OR 0.19 [0.06–0.55], p = 0.002). The functional relevance of the top hits was explored by immunohistochemistry staining and an in vitro transport models. SLC7A8 encodes for the L-type amino acid transporter 2 (LAT2). Transport assays in HEK293 cells overexpressing LAT2 showed that doxorubicin, but not cisplatin and methotrexate, is a substrate for LAT2 (p < 0.0001). Finally, SLC7A8 mRNA expression analysis and LAT2 immunohistochemistry of osteosarcoma tissue showed that the lack of LAT2 expression is a prognostic factor of poor prognosis and reduced overall survival in patients without metastases (p = 0.0099 and p = 0.14, resp.).

Conclusion: This study identified a novel locus in SLC7A8 to be associated with EDP in osteosarcoma. Functional studies indicate LAT2-mediates uptake of doxorubicin, which could give new opportunities to personalize treatment of osteosarcoma patients.

Value of Proline, Glutamic Acid, and Leucine-Rich Protein 1 and GATA Binding Protein 3 Expression in Breast Cancer: An Immunohistochemical study

GATA binding protein 3 was more sensitive than traditional markers such as gross cystic disease fluid protein 15 and mammaglobin for identifying primary and metastatic breast carcinomas, but its significance decreased in triple-negative breast cancer. Recent studies showed a high expression rate of proline glutamic acid and leucine-rich protein in breast cancer and their superiority over GATA3 in triple-negative breast cancer. Our study provided new insights into the diagnostic and prognostic roles of PELP1 and GATA3 in primary and metastatic breast cancer. An immunohistochemical assay was carried out using PELP1 and GATA3 in 60 cases of primary breast cancer and 15 metastatic. Invasive carcinoma of no special type was the predominant type (80%). The majority of cases were grade 3 (68.3%). GATA3 expression was 83.3% positive in primary breast carcinomas and 73.5% positive in metastatic breast carcinomas. In comparison, PELP1 had a 96.7% positive expression rate in primary breast carcinomas and an 86.7% positive expression rate in metastasis. There was a statistically significant agreement between GATA3 and PELP1 in the diagnosis of the cases. PELP1 is a significantly higher proportion of both primary and metastatic breast carcinomas than GATA3. In breast cancer, there was a strong association between favorable prognostic factors and GATA3 expression, with evidence of an inverse association with Ki-67 overexpression.

The role of the glutamine transporter ASCT2 in antineoplastic therapy

Cancer cells are metabolically reprogrammed to support their high rates of proliferation, continuous growth, survival, invasion, metastasis, and resistance to cancer treatments. Among changes in cancer cell bioenergetics, the role of glutamine metabolism has been receiving increasing attention. Increased glutaminolysis in cancer cells is associated with increased expression of membrane transporters that mediate the cellular uptake of glutamine. ASCT2 (Alanine, Serine, Cysteine Transporter 2) is a Na⁺-dependent transmembrane transporter overexpressed in cancer cells and considered to be the primary transporter for glutamine in these cells. The possibility of inhibiting ASCT2 for antineoplastic therapy is currently under investigation. In this article, we will present the pharmacological agents currently known to act on ASCT2, which have been attracting attention in antineoplastic therapy research. We will also address the impact of ASCT2 inhibition on the prognosis of some cancers. We conclude that ASCT2 inhibition and combination of ASCT2 inhibitors with other anti-tumor therapies may be a promising antineoplastic strategy. However, more research is needed in this area.

Identification of a Transcriptomic Prognostic Signature by Machine Learning Using a Combination of Small Cohorts of Prostate Cancer

Determining which treatment to provide to men with prostate cancer (PCa) is a major challenge for clinicians. Currently, the clinical risk-stratification for PCa is based on clinico-pathological variables such as Gleason grade, stage and prostate specific antigen (PSA) levels. But transcriptomic data have the potential to enable the development of more precise approaches to predict evolution of the disease. However, high quality RNA sequencing (RNA-seq) datasets along with clinical data with long follow-up allowing discovery of biochemical recurrence (BCR) biomarkers are small and rare. In this study, we propose a machine learning approach that is robust to batch effect and enables the discovery of highly predictive signatures despite using small datasets. Gene expression data were extracted from three RNA-Seq datasets cumulating a total of 171 PCa patients. Data were re-analyzed using a unique pipeline to ensure uniformity. Using a machine learning approach, a total of 14 classifiers were tested with various parameters to identify the best model and gene signature to predict BCR. Using a random forest model, we have identified a signature composed of only three genes (JUN, HES4, PPDPF) predicting BCR with better accuracy [74.2%, balanced error rate (BER) = 27%] than the clinico-pathological variables (69.2%, BER = 32%) currently in use to predict PCa evolution. This score is in the range of the studies that predicted BCR in single-cohort with a higher number of patients. We showed that it is possible to merge and analyze different small and heterogeneous datasets altogether to obtain a better signature than if they were analyzed individually, thus reducing the need for very large cohorts. This study demonstrates the feasibility to regroup different small datasets in one larger to identify a predictive genomic signature that would benefit PCa patients.

iMOKA: k-mer based software to analyze large collections of sequencing data

iMOKA (interactive multi-objective k-mer analysis) is a software that enables comprehensive analysis of sequencing data from large cohorts to generate robust classification models or explore specific genetic elements associated with disease etiology. iMOKA uses a fast and accurate feature reduction step that combines a Naïve Bayes classifier augmented by an adaptive entropy filter and a graph-based filter to rapidly reduce the search space. By using a flexible file format and distributed indexing, iMOKA can easily integrate data from multiple experiments and also reduces disk space requirements and identifies changes in transcript levels and single nucleotide variants. iMOKA is available at https://github.com/RitchieLabIGH/iMOKA and Zenodo https://doi.org/10.5281/zenodo.4008947 .

Integrative Network Fusion: A Multi-Omics Approach in Molecular Profiling

Recent technological advances and international efforts, such as The Cancer Genome Atlas (TCGA), have made available several pan-cancer datasets encompassing multiple omics layers with detailed clinical information in large collection of samples. The need has thus arisen for the development of computational methods aimed at improving cancer subtyping and biomarker identification from multi-modal data. Here we apply the Integrative Network Fusion (INF) pipeline, which combines multiple omics layers exploiting Similarity Network Fusion (SNF) within a machine learning predictive framework. INF includes a feature ranking scheme (rSNF) on SNF-integrated features, used by a classifier over juxtaposed multi-omics features (juXT). In particular, we show instances of INF implementing Random Forest (RF) and linear Support Vector Machine (LSVM) as the classifier, and two baseline RF and LSVM models are also trained on juXT. A compact RF model, called rSNFi, trained on the intersection of top-ranked biomarkers from the two approaches juXT and rSNF is finally derived. All the classifiers are run in a 10x5-fold cross-validation schema to warrant reproducibility, following the guidelines for an unbiased Data Analysis Plan by the US FDA-led initiatives MAQC/SEQC. INF is demonstrated on four classification tasks on three multi-modal TCGA oncogenomics datasets. Gene expression, protein expression and copy number variants are used to predict estrogen receptor status (BRCA-ER, N = 381) and breast invasive carcinoma subtypes (BRCA-subtypes, N = 305), while gene expression, miRNA expression and methylation data is used as predictor layers for acute myeloid leukemia and renal clear cell carcinoma survival (AML-OS, N = 157; KIRC-OS, N = 181). In test, INF achieved similar Matthews Correlation Coefficient (MCC) values and 97% to 83% smaller feature sizes (FS), compared with juXT for BRCA-ER (MCC: 0.83 vs. 0.80; FS: 56 vs. 1801) and BRCA-subtypes (0.84 vs. 0.80; 302 vs. 1801), improving KIRC-OS performance (0.38 vs. 0.31; 111 vs. 2319). INF predictions are generally more accurate in test than one-dimensional omics models, with smaller signatures too, where transcriptomics consistently play the leading role. Overall, the INF framework effectively integrates multiple data levels in oncogenomics classification tasks, improving over the performance of single layers alone and naive juxtaposition, and provides compact signature sizes.

The solute carrier SLC7A8 is a marker of favourable prognosis in ER-positive low proliferative invasive breast cancer

PURPOSE

Breast cancer (BC) is a heterogeneous disease consisting of various subtypes, with different prognostic and therapeutic outcomes. The amino acid transporter, SLC7A8, is overexpressed in oestrogen receptor-positive BC. However, the consequence of this overexpression, in terms of disease prognosis, is still obscure. This study aimed to evaluate the biological and prognostic value of SLC7A8 in BC with emphasis on the intrinsic molecular subtypes.

METHODS

SLC7A8 was assessed at the genomic, using METABRIC data (n = 1980), and proteomic, using immunohistochemistry and TMA (n = 1562), levels in well-characterised primary BC cohorts. SLC7A8 expression was examined with clinicopathological parameters, molecular subtypes, and patient outcome.

RESULTS

SLC7A8 mRNA and SLC7A8 protein expression were strongly associated with good prognostic features, including small tumour size, low tumour grade, and good Nottingham Prognostic Index (NPI) (all P < 0.05). Expression of SLC7A8 mRNA was higher in luminal tumours compared to other subtypes (P < 0.001). High expression of SLC7A8 mRNA and SLC7A8 protein was associated with good patient outcome (P ≤ 0.001) but only in the low proliferative ER+/luminal A tumours (P = 0.01). In multivariate analysis, SLC7A8 mRNA and SLC7A8 protein were independent factors for longer breast cancer specific survival (P = 0.01 and P = 0.03), respectively.

CONCLUSION

SLC7A8 appears to play a role in BC and is a marker for favourable prognosis in the most predominant, ER+ low proliferative/luminal A, BC subtype. Functional assessment is necessary to reveal the specific role played by SLC7A8 in ER+ BC.

A STAT3 of Addiction: Adipose Tissue, Adipocytokine Signalling and STAT3 as Mediators of Metabolic Remodelling in the Tumour Microenvironment

Metabolic remodelling of the tumour microenvironment is a major mechanism by which cancer cells survive and resist treatment. The pro-oncogenic inflammatory cascade released by adipose tissue promotes oncogenic transformation, proliferation, angiogenesis, metastasis and evasion of apoptosis. STAT3 has emerged as an important mediator of metabolic remodelling. As a downstream effector of adipocytokines and cytokines, its canonical and non-canonical activities affect mitochondrial functioning and cancer metabolism. In this review, we examine the central role played by the crosstalk between the transcriptional and mitochondrial roles of STAT3 to promote survival and further oncogenesis within the tumour microenvironment with a particular focus on adipose-breast cancer interactions.

The Expression, Clinicopathologic Characteristics, and Prognostic Value of Androgen Receptor in Breast Cancer: A Bioinformatics Analysis Using Public Databases

The role of androgen receptor (AR) in breast cancer has been unveiled in succession for the past few years. In this study, we conducted a comprehensive analysis based on four online public databases of data from many previous studies. We found that the expression of AR is significantly related to age, histological grade, and subtype but not to lymph node status. The low expression level of AR is strongly associated with poor recurrence-free survival, especially with poor distance metastasis-free survival in luminal A patients, but inverse in HER2 (human epidermal growth factor receptor-2) enriched patients. AR might be a biomarker of chemosensitivity in the basal subtype. Besides, the expression of melanophilin (MLPH) is distinctly in accordance with that of AR. AR could play diverse roles in different subtypes of breast cancer.

Neuroevolution as a tool for microarray gene expression pattern identification in cancer research

Microarrays are still one of the major techniques employed to study cancer biology. However, the identification of expression patterns from microarray datasets is still a significant challenge to overcome. In this work, a new approach using Neuroevolution, a machine learning field that combines neural networks and evolutionary computation, provides aid in this challenge by simultaneously classifying microarray data and selecting the subset of more relevant genes. The main algorithm, FS-NEAT, was adapted by the addition of new structural operators designed for this high dimensional data. In addition, a rigorous filtering and preprocessing protocol was employed to select quality microarray datasets for the proposed method, selecting 13 datasets from three different cancer types. The results show that Neuroevolution was able to successfully classify microarray samples when compared with other methods in the literature, while also finding subsets of genes that can be generalized for other algorithms and carry relevant biological information. This approach detected 177 genes, and 82 were validated as already being associated to their respective cancer types and 44 were associated to other types of cancer, becoming potential targets to be explored as cancer biomarkers. Five long non-coding RNAs were also detected, from which four don't have described functions yet. The expression patterns found are intrinsically related to extracellular matrix, exosomes and cell proliferation. The results obtained in this work could aid in unraveling the molecular mechanisms underlying the tumoral process and describe new potential targets to be explored in future works.

LAT2 regulates glutamine-dependent mTOR activation to promote glycolysis and chemoresistance in pancreatic cancer

BACKGROUND

Reprogrammed energy metabolism has become an emerging hallmark of cancer in recent years. Transporters have been reported to be amino acid sensors involved in controlling mTOR recruitment and activation, which is crucial for the growth of both normal and tumor cells. L-type amino acid transporter 2 (LAT2), encoded by the SLC7A8 gene, is a Na⁺-independent neutral amino acid transporter and is responsible for transporting neutral amino acids, including glutamine, which can activate mTOR. Previous studies have shown that LAT2 was overexpressed in gemcitabine-resistant pancreatic cancer cells. However, the role of LAT2 in chemoresistance in pancreatic cancer remains uncertain and elusive.

METHODS

The effects of LAT2 on biological behaviors were analyzed. LAT2 and LDHB levels in tissues were detected, and the clinical value was evaluated.

RESULTS

We demonstrated that LAT2 emerged as an oncogenic protein and could decrease the gemcitabine sensitivity of pancreatic cancer cells in vitro and in vivo. The results of a survival analysis indicated that high expression levels of both LAT2 and LDHB predicted a poor prognosis in patients with pancreatic cancer. Furthermore, we found that LAT2 could promote proliferation, inhibit apoptosis, activate glycolysis and alter glutamine metabolism to activate mTOR in vitro and in vivo. Next, we found that gemcitabine combined with an mTOR inhibitor (RAD001) could reverse the decrease in chemosensitivity caused by LAT2 overexpression in pancreatic cancer cells. Mechanistically, we demonstrated that LAT2 could regulate two glutamine-dependent positive feedback loops (the LAT2/p-mTOR^Ser2448 loop and the glutamine/p-mTOR^Ser2448/glutamine synthetase loop) to promote glycolysis and decrease gemcitabine (GEM) sensitivity in pancreatic cancer.

CONCLUSION

Taken together, our data reveal that LAT2 functions as an oncogenic protein and could regulate glutamine-dependent mTOR activation to promote glycolysis and decrease GEM sensitivity in pancreatic cancer. The LAT2-mTOR-LDHB pathway might be a promising therapeutic target in pancreatic cancer.

NF1 deficiency correlates with estrogen receptor signaling and diminished survival in breast cancer

The key negative regulatory gene of the RAS pathway, NF1, is mutated or deleted in numerous cancer types and is associated with increased cancer risk and drug resistance. Even though women with neurofibromatosis (germline NF1 mutations) have a substantially increased breast cancer risk at a young age and NF1 is commonly mutated in sporadic breast cancers, we have a limited understanding of the role of NF1 in breast cancer. We utilized CRISPR-Cas9 gene editing to create Nf1 rat models to evaluate the effect of Nf1 deficiency on tumorigenesis. The resulting Nf1 indels induced highly penetrant, aggressive mammary adenocarcinomas that express estrogen receptor (ER) and progesterone receptor (PR). We identified distinct Nf1 mRNA and protein isoforms that were altered during tumorigenesis. To evaluate NF1 in human breast cancer, we analyzed genomic changes in a data set of 2000 clinically annotated breast cancers. We found NF1 shallow deletions in 25% of sporadic breast cancers, which correlated with poor clinical outcome. To identify biological networks impacted by NF1 deficiency, we constructed gene co-expression networks using weighted gene correlation network analysis (WGCNA) and identified a network connected to ESR1 (estrogen receptor). Moreover, NF1-deficient cancers correlated with established RAS activation signatures. Estrogen-dependence was verified by estrogen-ablation in Nf1 rats where rapid tumor regression was observed. Additionally, Nf1 deficiency correlated with increased estrogen receptor phosphorylation in mammary adenocarcinomas. These results demonstrate a significant role for NF1 in both NF1-related breast cancer and sporadic breast cancer, and highlight a potential functional link between neurofibromin and the estrogen receptor.

Robust identification of target genes and outliers in triple-negative breast cancer data

Correct classification of breast cancer subtypes is of high importance as it directly affects the therapeutic options. We focus on triple-negative breast cancer which has the worst prognosis among breast cancer types. Using cutting edge methods from the field of robust statistics, we analyze Breast Invasive Carcinoma transcriptomic data publicly available from The Cancer Genome Atlas data portal. Our analysis identifies statistical outliers that may correspond to misdiagnosed patients. Furthermore, it is illustrated that classical statistical methods may fail to identify outliers due to their heavy influence, prompting the need for robust statistics. Using robust sparse logistic regression we obtain 36 relevant genes, of which ca. 60% have been previously reported as biologically relevant to triple-negative breast cancer, reinforcing the validity of the method. The remaining 14 genes identified are new potential biomarkers for triple-negative breast cancer. Out of these, JAM3, SFT2D2, and PAPSS1 were previously associated to breast tumors or other types of cancer. The relevance of these genes is confirmed by the new DetectDeviatingCells outlier detection technique. A comparison of gene networks on the selected genes showed significant differences between triple-negative breast cancer and non-triple-negative breast cancer data. The individual role of FOXA1 in triple-negative breast cancer and non-triple-negative breast cancer, and the strong FOXA1-AGR2 connection in triple-negative breast cancer stand out. The goal of our paper is to contribute to the breast cancer/triple-negative breast cancer understanding and management. At the same time it demonstrates that robust regression and outlier detection constitute key strategies to cope with high-dimensional clinical data such as omics data.

Amino Acid Transporters and Glutamine Metabolism in Breast Cancer

Amino acid transporters are membrane transport proteins, most of which are members of the solute carrier families. Amino acids are essential for the survival of all types of cells, including tumor cells, which have an increased demand for nutrients to facilitate proliferation and cancer progression. Breast cancer is the most common malignancy in women worldwide and is still associated with high mortality rates, despite improved treatment strategies. Recent studies have demonstrated that the amino acid metabolic pathway is altered in breast cancer and that amino acid transporters affect tumor growth and progression. In breast cancer, glutamine is one of the key nutrients, and glutamine metabolism is closely related to the amino acid transporters. In this review, we focus on amino acid transporters and their roles in breast cancer. We also highlight the different subsets of upregulated amino acid transporters in breast cancer and discuss their potential applications as treatment targets, cancer imaging tracers, and drug delivery components. Glutamine metabolism as well as its regulation and therapeutic implication in breast cancer are also discussed.

Transcriptional activation of Nlp by estrogen-ERα in breast cancer

Estrogen Receptor-α (ERα) is the key transcription factor that regulates cell proliferation and homeostasis. In this pathway, estrogen plays an important role in genomic instability and cell cycle regulation processes and the mechanisms of its action are multifaceted. In this study, we showed that estrogen regulates genomic instability through promoting the expression of Nlp, a BRCA1-associated centrosomal protein which is involved in microtubule nucleation, spindle formation, chromosomal missegregation and abnormal cytokinesis. We demonstrated that the expression of Nlp is strongly associated with ERα and FOXA1 level in clinical breast cancer samples with poor clinical outcomes to breast cancer patients. Addition of estrogen in the ER-positive breast cancer cells resulted in elevation of NLP mRNA. Significantly, we identified that estrogen-ERα is capable of regulating Nlp expression through specifically binding ERα to the proximal region and the Estrogen Responsive Elements (ERE) enhancer in the distal region of NLP gene. Reporter assays demonstrated that estrogen directly activated Nlp promoter. ChIP assay results showed that E2-ERα directly bound to the EREs of Nlp. Therefore, overexpression of Nlp in breast cancer exhibits a hormone-dependent pattern, and estrogen participates in the regulation of genome instability and cell cycle in breast cancer cells partially through transcriptional activation of NLP gene. Overexpression of Nlp enhances the malignant progression of ERα-positive breast cancer cells in vitro, whereas knockdown of Nlp suppresses this biological effects in ERα-positive breast cancer cells. ERα/Nlp axis may serve as a promising target against breast cancer.

Prognostic and clinicopathological value of GATA binding protein 3 in breast cancer: A systematic review and meta-analysis

The potential prognostic value of GATA binding protein 3 (GATA3) in breast cancer has recently increased, although the evidence is inconclusive. This meta-analysis of 10 articles involving 5,080 breast cancer patients explored the prognostic and clinicopathological value of GATA3 in breast cancer. Time to tumor progression (TTP) and overall survival (OS) were primary endpoints. Pooled hazard ratio (HR), pooled risk ratio (RR), and 95% confidence interval (CI) were calculated to evaluate the association between GATA3, prognosis, and clinicopathological parameters. High GATA3 expression predicts breast cancer, with a HR (HR = 0.671; 95% CI = 0.475–0.947; P = 0.023) of TTP, but is not associated with OS (HR = 0.889; 95% CI = 0.789–1.001; P = 0.052). GATA3 overexpression is associated with positive ER (RR = 3.155; 95% CI = 1.680–5.923; P = 0.000), positive PR (RR = 3.949; 95% CI = 1.567–9.954, P = 0.004), lower nuclear grade (RR = 0.435; 95% CI = 0.369–0.514; P = 0.000), and smaller tumor size (RR = 0.816; 95% CI = 0.709–0.940; P = 0.005). High GATA3 expression may predict TTP in breast cancer, and such patients may show better clinicopathological features.

Prognostic Value of microRNA Signature in Patients with Gastric Cancers

The occurrence of lymph node metastases (LNM) after endoscopic submucosal dissection (ESD) in patients with gastric cancer (GC) leads to poor prognosis. However, few biomarkers are available to predict LNM in GC patients. Thus, we measured expression of 6 cancer-related miRNAs using real-time RT-PCR in 102 GC samples that were randomized into a training set and a testing set (each, 51 cases). Using logistic regression, we identified 4-miRNA (miR-27b, miR-128, miR-100 and miR-214) signatures for predicting LNM in GC patients. Patients with high-risk scores for the 4-miRNA signature tended to have higher LNM than those with low-risk scores. Meanwhile, the ROC curve of the 4-miRNA signature was better for predicting LNM in GC patients. In addition, Cox regression analysis indicated that a 2-miRNA signature (miR-27b and miR-214) or a miR-214/N stage signature was predictive of survival for GC patients. This work describes a previously unrecognized 4-miRNA signature involved in LNM and a 2-miRNA signature or miR-214/N stage signature related to GC patients’ survival.