Although significant variations in the metabolic profiles exist among different cells, little is understood in terms of genetic regulations of such cell type-specific metabolic phenotypes and ...nutrient requirements. While many cancer cells depend on exogenous glutamine for survival to justify the therapeutic targeting of glutamine metabolism, the mechanisms of glutamine dependence and likely response and resistance of such glutamine-targeting strategies among cancers are largely unknown. In this study, we have found a systematic variation in the glutamine dependence among breast tumor subtypes associated with mammary differentiation: basal- but not luminal-type breast cells are more glutamine-dependent and may be susceptible to glutamine-targeting therapeutics. Glutamine independence of luminal-type cells is associated mechanistically with lineage-specific expression of glutamine synthetase (GS). Luminal cells can also rescue basal cells in co-culture without glutamine, indicating a potential for glutamine symbiosis within breast ducts. The luminal-specific expression of GS is directly induced by GATA3 and represses glutaminase expression. Such distinct glutamine dependency and metabolic symbiosis is coupled with the acquisition of the GS and glutamine independence during the mammary differentiation program. Understanding the genetic circuitry governing distinct metabolic patterns is relevant to many symbiotic relationships among different cells and organisms. In addition, the ability of GS to predict patterns of glutamine metabolism and dependency among tumors is also crucial in the rational design and application of glutamine and other metabolic pathway targeted therapies.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Purpose
To determine whether a multivariate machine learning-based model using computer-extracted features of pre-treatment dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) can predict ...pathologic complete response (pCR) to neoadjuvant therapy (NAT) in breast cancer patients.
Methods
Institutional review board approval was obtained for this retrospective study of 288 breast cancer patients at our institution who received NAT and had a pre-treatment breast MRI. A comprehensive set of 529 radiomic features was extracted from each patient’s pre-treatment MRI. The patients were divided into equal groups to form a training set and an independent test set. Two multivariate machine learning models (logistic regression and a support vector machine) based on imaging features were trained to predict pCR in (a) all patients with NAT, (b) patients with neoadjuvant chemotherapy (NACT), and (c) triple-negative or human epidermal growth factor receptor 2-positive (TN/HER2+) patients who had NAT. The multivariate models were tested using the independent test set, and the area under the receiver operating characteristics (ROC) curve (AUC) was calculated.
Results
Out of the 288 patients, 64 achieved pCR. The AUC values for predicting pCR in TN/HER+ patients who received NAT were significant (0.707, 95% CI 0.582–0.833,
p
< 0.002).
Conclusions
The multivariate models based on pre-treatment MRI features were able to predict pCR in TN/HER2+ patients.
As a class of endogenous noncoding RNAs, circular RNAs (circRNAs) have been recently identified to regulate tumourigenesis and progression in multiple malignancies. However, the expression profiles ...and function of circRNAs in breast cancer metastasis are largely unknown. Here, we determined that the expression of a novel circRNA, which we named circIRAK3, was increased in metastatic breast cancer (BC) cells and predictive of BC recurrence. Gain-of-function and loss-of-function studies in BC cells demonstrated that circIRAK3 promoted cell migration, invasion and metastasis in vitro and in vivo but did not affect cell proliferation, colony formation or cell cycle progression. Using circIRAK3 in vivo precipitation and luciferase reporter assays, we identified miR-3607 as a circIRAK3-associated miRNA. Furthermore, RNA sequencing and bioinformatics analysis showed that forkhead box C1 (FOXC1), the target of miR-3607, was downregulated in circIRAK3-silenced cells and mediated circIRAK3-induced BC cell migration. Intriguingly, FOXC1 could, in turn, bind to the IRAK3 promoter, triggering a positive-feedback loop that perpetuated the circIRAK3/miR-3607/FOXC1 signaling axis. Collectively, our findings indicated that circIRAK3 may exert regulatory roles in BC metastasis and may be a potential target for metastatic BC therapy.
•High circIRAK3 expression is correlated with breast cancer (BC) recurrence.•CircIRAK3 facilitates BC metastasis via sponging miR-3607.•CircIRAK3 regulates FOXC1 and its target genes expression to promote BC metastasis.•FOXC1 binds to IRAK3 promoter and perpetuates circIRAK3/miR-3607/FOXC1 signaling.
The Hippo pathway plays essential roles in organ size control and cancer prevention via restricting its downstream effector, Yes‐associated protein (YAP). Previous studies have revealed an oncogenic ...function of YAP in reprogramming glucose metabolism, while the underlying mechanism remains to be fully clarified. Accumulating evidence suggests long noncoding RNAs (lncRNAs) as attractive therapeutic targets, given their roles in modulating various cancer‐related signaling pathways. In this study, we report that lncRNA breast cancer anti‐estrogen resistance 4 (BCAR4) is required for YAP‐dependent glycolysis. Mechanistically, YAP promotes the expression of BCAR4, which subsequently coordinates the Hedgehog signaling to enhance the transcription of glycolysis activators HK2 and PFKFB3. Therapeutic delivery of locked nucleic acids (LNAs) targeting BCAR4 attenuated YAP‐dependent glycolysis and tumor growth. The expression levels of BCAR4 and YAP are positively correlated in tissue samples from breast cancer patients, where high expression of both BCAR4 and YAP is associated with poor patient survival outcome. Taken together, our study not only reveals the mechanism by which YAP reprograms glucose metabolism, but also highlights the therapeutic potential of targeting YAP‐BCAR4‐glycolysis axis for breast cancer treatment.
Synopsis
Yes‐associated protein promotes cancer formation by reprogramming glucose metabolism. A long noncoding RNA BCAR4 is a key downstream effector of YAP, in regulation of glycolysis and tumorigenesis via GLI2‐mediated expression of key glycolytic enzymes.
BCAR4 is a direct transcriptional target of YAP.
BCAR4 promotes glycolysis by increasing the expression of HK2 and PFKFB3.
GLI2 activation is required for the expression of glycolytic enzymes downstream of BCAR4
High YAP and BCAR4 expression levels positively correlate in breast cancer patient samples and are linked to poor clinical outcomes.
Inhibition of BCAR4 via Locked Nucleic Acids (LNAs) attenuated YAP‐dependent glycolysis and tumor growth.
Yes‐associated protein activation triggers transcription of long noncoding RNA BCAR4, leading to GLI2‐mediated expression of key glycolytic enzymes.
Would you like your children to grow up bilingual, even if you aren’t yet? Then speak to your kids in Spanish as you learn the language along with them. Becoming a Bilingual Family gives ...English-speaking parents the tools to start speaking Spanish with their kids in their earliest years, when children are most receptive to learning languages. It teaches the vocabulary and idioms for speaking to children in Spanish and offers practical, proven ways to create a language-learning environment at home. The first part of the book introduces parents to many resources—books, audio books, music, television, computer programs, childcare workers, school, and friends—that can help you establish a home environment conducive to the acquisition of Spanish. The second part is a Spanish phrasebook that takes you through all the typical activities that parents and children share, from getting up in the morning to going to bed at night. Few, if any, other Spanish study aids provide this much vocabulary and guidance for talking to small children about common daily activities. The authors also include a quick course in Spanish pronunciation and enough grammar to get a parent started. Spanish-language resources, kids’ names in Spanish, and an easy-to-use index and glossary complete the book. Take the Markses’ advice and start talking to your kids in Spanish, even if it’s not perfect. You’ll learn the language together and share the excitement of discovering the peoples and cultures that make up the Spanish-speaking world.
The development of an oncogenic state is a complex process involving the accumulation of multiple independent mutations that lead to deregulation of cell signalling pathways central to the control of ...cell growth and cell fate. The ability to define cancer subtypes, recurrence of disease and response to specific therapies using DNA microarray-based gene expression signatures has been demonstrated in multiple studies. Various studies have also demonstrated the potential for using gene expression profiles for the analysis of oncogenic pathways. Here we show that gene expression signatures can be identified that reflect the activation status of several oncogenic pathways. When evaluated in several large collections of human cancers, these gene expression signatures identify patterns of pathway deregulation in tumours and clinically relevant associations with disease outcomes. Combining signature-based predictions across several pathways identifies coordinated patterns of pathway deregulation that distinguish between specific cancers and tumour subtypes. Clustering tumours based on pathway signatures further defines prognosis in respective patient subsets, demonstrating that patterns of oncogenic pathway deregulation underlie the development of the oncogenic phenotype and reflect the biology and outcome of specific cancers. Predictions of pathway deregulation in cancer cell lines are also shown to predict the sensitivity to therapeutic agents that target components of the pathway. Linking pathway deregulation with sensitivity to therapeutics that target components of the pathway provides an opportunity to make use of these oncogenic pathway signatures to guide the use of targeted therapeutics.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
Breast cancer arising in young women is correlated with inferior survival and higher incidence of negative clinicopathologic features. The biology driving this aggressive disease has yet to be ...defined.
Clinically annotated, microarray data from 784 early-stage breast cancers were identified, and prospectively defined, age-specific cohorts (young: </= 45 years, n = 200; older: >/= 65 years, n = 211) were compared by prognosis, clinicopathologic variables, mRNA expression values, single-gene analysis, and gene set enrichment analysis (GSEA). Univariate and multivariate analyses were performed.
Using clinicopathologic variables, young women illustrated lower estrogen receptor (ER) positivity (immunohistochemistry IHC, P = .027), larger tumors (P = .012), higher human epidermal growth factor receptor 2 (HER-2) overexpression (IHC, P = .075), lymph node positivity (P = .008), higher grade tumors (P < .0001), and trends toward inferior disease-free survival (DFS; hazard ratio = 1.32; P = .094). Using genomic expression analysis, tumors arising in young women had significantly lower ERalpha mRNA (P < .0001), ERbeta (P = .02), and progesterone receptor (PR) expression (P < .0001), but higher HER-2 (P < .0001) and epidermal growth factor receptor (EGFR) expression (P < .0001). Exploratory analysis (GSEA) revealed 367 biologically relevant gene sets significantly distinguishing breast tumors arising in young women. Combining clinicopathologic and genomic variables among tumors arising in young women demonstrated that younger age and lower ERbeta and higher EGFR mRNA expression were significant predictors of inferior DFS.
This large-scale genomic analysis illustrates that breast cancer arising in young women is a unique biologic entity driven by unifying oncogenic signaling pathways, is characterized by less hormone sensitivity and higher HER-2/EGFR expression, and warrants further study to offer this poor-prognosis group of women better preventative and therapeutic options.
Ductal carcinoma in situ (DCIS) is a pre-invasive lesion that is thought to be a precursor to invasive breast cancer (IBC). To understand the changes in the tumor microenvironment (TME) accompanying ...transition to IBC, we used multiplexed ion beam imaging by time of flight (MIBI-TOF) and a 37-plex antibody staining panel to interrogate 79 clinically annotated surgical resections using machine learning tools for cell segmentation, pixel-based clustering, and object morphometrics. Comparison of normal breast with patient-matched DCIS and IBC revealed coordinated transitions between four TME states that were delineated based on the location and function of myoepithelium, fibroblasts, and immune cells. Surprisingly, myoepithelial disruption was more advanced in DCIS patients that did not develop IBC, suggesting this process could be protective against recurrence. Taken together, this HTAN Breast PreCancer Atlas study offers insight into drivers of IBC relapse and emphasizes the importance of the TME in regulating these processes.
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•A spatial atlas of breast cancer progression using MIBI-TOF and tissue transcriptomics•Coordinated changes in the tumor microenvironment (TME) track invasive transition of DCIS•DCIS TME structure is predictive of invasive relapse within 10 years of diagnosis•Recurrence risk is heavily influenced by myoepithelial phenotype and morphology
A spatial imaging atlas of patient-matched ductal carcinoma in situ and invasive breast cancer depicts coordinated changes in the tumor microenviroment associated with invasive relapse, suggesting a potential protective role of myoepithelial disruption against invasive progression.
To assess the performance bias caused by sampling data into training and test sets in a mammography radiomics study.
Mammograms from 700 women were used to study upstaging of ductal carcinoma in ...situ. The dataset was repeatedly shuffled and split into training (n = 400) and test cases (n = 300) forty times. For each split, cross-validation was used for training, followed by an assessment of the test set. Logistic regression with regularization and support vector machine were used as the machine learning classifiers. For each split and classifier type, multiple models were created based on radiomics and/or clinical features.
Area under the curve (AUC) performances varied considerably across the different data splits (e.g., radiomics regression model: train 0.58-0.70, test 0.59-0.73). Performances for regression models showed a tradeoff where better training led to worse testing and vice versa. Cross-validation over all cases reduced this variability, but required samples of 500+ cases to yield representative estimates of performance.
In medical imaging, clinical datasets are often limited to relatively small size. Models built from different training sets may not be representative of the whole dataset. Depending on the selected data split and model, performance bias could lead to inappropriate conclusions that might influence the clinical significance of the findings.
Performance bias can result from model testing when using limited datasets. Optimal strategies for test set selection should be developed to ensure study conclusions are appropriate.
Celotno besedilo
Dostopno za:
DOBA, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SIK, UILJ, UKNU, UL, UM, UPUK
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