Copy number variations in endometrial cancer: from biological significance to clinical utility.

The molecular basis of endometrial cancer, which is the most common malignancy of the female reproductive organs, relies not only on onset of mutations but also on copy number variations, the latter consisting of gene gains or losses. In this review, we introduce copy number variations and discuss their involvement in endometrial cancer to determine the perspectives of clinical applicability. We performed a literature analysis on PubMed of publications over the past 30 years and annotated clinical information, including histological and molecular subtypes, adopted molecular techniques for identification of copy number variations, their locations, and the genes involved. We highlight correlations between the presence of some specific copy number variations and myometrial invasion, lymph node metastasis, advanced International Federation of Gynecology and Obstetrics (FIGO) stage, high grade, drug response, and cancer progression. In particular, type I endometrial cancer cells have few copy number variations and are mainly located in 8q and 1q, while type II, high grade, and advanced FIGO stage endometrial cancer cells are aneuploid and have a greater number of copy number variations. As expected, the higher the number of copy number variations the worse the prognosis, especially if they amplify CCNE1, ERBB2, KRAS, MYC, and PIK3CA oncogenes. Great variability in copy number and location among patients with the same endometrial cancer histological or molecular subtype emerged, making them interesting candidates to be explored for the improvement of patient stratification. Copy number variations have a role in endometrial cancer progression, and therefore their detection may be useful for more accurate prediction of prognosis. Unfortunately, only a few studies have been carried out on the role of copy number variations according to the molecular classification of endometrial cancer, and even fewer have explored the correlation with drugs. For these reasons, further studies, also using single cell RNA sequencing, are needed before reaching a clinical application.

Effects of Eribulin on the RNA Content of Extracellular Vesicles Released by Metastatic Breast Cancer Cells.

Extracellular vesicles (EVs) are small lipid particles secreted by almost all human cells into the extracellular space. They perform the essential function of cell-to-cell communication, and their role in promoting breast cancer progression has been well demonstrated. It is known that EVs released by triple-negative and highly aggressive MDA-MB-231 breast cancer cells treated with paclitaxel, a microtubule-targeting agent (MTA), promoted chemoresistance in EV-recipient cells. Here, we studied the RNA content of EVs produced by the same MDA-MB-231 breast cancer cells treated with another MTA, eribulin mesylate. In particular, we analyzed the expression of different RNA species, including mRNAs, lncRNAs, miRNAs, snoRNAs, piRNAs and tRNA fragments by RNA-seq. Then, we performed differential expression analysis, weighted gene co-expression network analysis (WGCNA), functional enrichment analysis, and miRNA-target identification. Our findings demonstrate the possible involvement of EVs from eribulin-treated cells in the spread of chemoresistance, prompting the design of strategies that selectively target tumor EVs.

Keratinocytes Exposed to Blue or Red Light: Proteomic Characterization Showed Cytoplasmic Thioredoxin Reductase 1 and Aldo-Keto Reductase Family 1 Member C3 Triggered Expression.

Several cell-signaling mechanisms are activated by visible light radiation in human keratinocytes, but the key regulatory proteins involved in this specific cellular response have not yet been identified. Human keratinocytes (HaCaT cells) were exposed to blue or red light at low or high irradiance for 3 days in cycles of 12 h of light and 12 h of dark. The cell viability, apoptotic rate and cell cycle progression were analyzed in all experimental conditions. The proteomic profile, oxidative stress and mitochondrial morphology were additionally evaluated in the HaCaT cells following exposure to high-irradiance blue or red light. Low-irradiance blue or red light exposure did not show an alteration in the cell viability, cell death or cell cycle progression. High-irradiance blue or red light reduced the cell viability, induced cell death and cell cycle G2/M arrest, increased the reactive oxygen species (ROS) and altered the mitochondrial density and morphology. The proteomic profile revealed a pivotal role of Cytoplasmic thioredoxin reductase 1 (TXNRD1) and Aldo-keto reductase family 1 member C3 (AKR1C3) in the response of the HaCaT cells to high-irradiance blue or red light exposure. Blue or red light exposure affected the viability of keratinocytes, activating a specific oxidative stress response and inducing mitochondrial dysfunction. Our results can help to address the targets for the therapeutic use of light and to develop adequate preventive strategies for skin damage. This in vitro study supports further in vivo investigations of the biological effects of light on human keratinocytes.

Three-dimensional nuclear architecture distinguishes thyroid cancer histotypes.

Molecular markers can serve as diagnostic tools to support pathological analysis in thyroid neoplasms. However, because the same markers can be observed in some benign thyroid lesions, additional approaches are necessary to differentiate thyroid tumor subtypes, prevent overtreatment and tailor specific clinical management. This applies particularly to the recently described variant of thyroid cancer referred to as noninvasive follicular thyroid neoplasm with papillary-like nuclear features (NIFTP). This variant has an estimated prevalence of 4.4% to 9.1% of all papillary thyroid carcinomas worldwide. We studied 60 thyroid lesions: 20 classical papillary thyroid carcinoma (CPTC), 20 follicular variant of PTC (FVPTC) and 20 NIFTP. We examined morphological and molecular features to identify parameters that can differentiate NIFTP from the other PTC subtypes. When blindly investigating the nuclear architecture of thyroid neoplasms, we observed that NIFTP has significantly longer telomeres than CPTC and FVPTC. Super-resolved 3D-structured illumination microscopy demonstrated that NIFTP is heterogeneous and that its nuclei contain more densely packed DNA and smaller interchromatin spaces than CPTC and FVPTC, a pattern that resembles normal thyroid tissue. These data are consistent with the observed indolent biological behavior and favorable prognosis associated with NIFTP, which lacks BRAFV600E mutations. Of note, next-generation thyroid oncopanel sequencing was unable to distinguish the thyroid cancer histotypes in our study cohort. In summary, our data suggest that 3D nuclear architecture can be a powerful analytical tool to diagnose and guide clinical management of NIFTP.

Effects of extremely low-frequency magnetic fields on human MDA-MB-231 breast cancer cells: proteomic characterization.

Extremely low-frequency electromagnetic fields (ELF-MF) can modify the cell viability and regulatory processes of some cell types, including breast cancer cells. Breast cancer is a multifactorial disease where a role for ELF-MF cannot be excluded. ELF-MF may influence the biological properties of breast cells through molecular mechanisms and signaling pathways that are still unclear. This study analyzed the changes in the cell viability, cellular morphology, oxidative stress response and alteration of proteomic profile in breast cancer cells (MDA-MB-231) exposed to ELF-MF (50 Hz, 1 mT for 4 h). Non-tumorigenic human breast cells (MCF-10A) were used as control cells. Exposed MDA-MB-231 breast cancer cells increased their viability and live cell number and showed a higher density and length of filopodia compared with the unexposed cells. In addition, ELF-MF induced an increase of the mitochondrial ROS levels and an alteration of mitochondrial morphology. Proteomic data analysis showed that ELF-MF altered the expression of 328 proteins in MDA-MB-231 cells and of 242 proteins in MCF-10A cells. Gene Ontology term enrichment analysis demonstrated that in both cell lines ELF-MF exposure up-regulated the genes enriched in "focal adhesion" and "mitochondrion". The ELF-MF exposure decreased the adhesive properties of MDA-MB-231 cells and increased the migration and invasion cell abilities. At the same time, proteomic analysis, confirmed by Real Time PCR, revealed that transcription factors associated with cellular reprogramming were upregulated in MDA-MB-231 cells and downregulated in MCF-10A cells after ELF-MF exposure. MDA-MB-231 breast cancer cells exposed to 1 mT 50 Hz ELF-MF showed modifications in proteomic profile together with changes in cell viability, cellular morphology, oxidative stress response, adhesion, migration and invasion cell abilities. The main signaling pathways involved were relative to focal adhesion, mitochondrion and cellular reprogramming.

Accurate Analysis of Lipid Building Blocks Using the Tool LipidOne.

LC/MS-based analysis techniques combined with specialized lipid platforms allow the qualitative and quantitative determination of thousands of lipid molecules. Each individual molecule can be considered as an assembly of smaller parts, often called building blocks that are the result of a myriad of biochemical synthesis and transformation processes. LipidOne is a new lipidomic tool that automatically highlights all qualitative and quantitative changes in lipid building blocks both among all detected lipid classes and between experimental groups. Thanks to LipidOne, the discovered differences among lipid building blocks can be easily linked to the activity of specific enzymes.

Copy Number Variations in Pancreatic Cancer: From Biological Significance to Clinical Utility.

Pancreatic ductal adenocarcinoma (PDAC) is the most common type of pancreatic cancer, characterized by high tumor heterogeneity and a poor prognosis. Inter- and intra-tumoral heterogeneity in PDAC is a major obstacle to effective PDAC treatment; therefore, it is highly desirable to explore the tumor heterogeneity and underlying mechanisms for the improvement of PDAC prognosis. Gene copy number variations (CNVs) are increasingly recognized as a common and heritable source of inter-individual variation in genomic sequence. In this review, we outline the origin, main characteristics, and pathological aspects of CNVs. We then describe the occurrence of CNVs in PDAC, including those that have been clearly shown to have a pathogenic role, and further highlight some key examples of their involvement in tumor development and progression. The ability to efficiently identify and analyze CNVs in tumor samples is important to support translational research and foster precision oncology, as copy number variants can be utilized to guide clinical decisions. We provide insights into understanding the CNV landscapes and the role of both somatic and germline CNVs in PDAC, which could lead to significant advances in diagnosis, prognosis, and treatment. Although there has been significant progress in this field, understanding the full contribution of CNVs to the genetic basis of PDAC will require further research, with more accurate CNV assays such as single-cell techniques and larger cohorts than have been performed to date.

Concurrent Endometrial Cancer in Women with Atypical Endometrial Hyperplasia: What Is the Predictive Value of Patient Characteristics?

BACKGROUND: The rate of concurrent endometrial cancer (EC) in atypical endometrial hyperplasia (AEH) can be as high as 40%. Some patient characteristics showed associations with this occurrence. However, their real predictive power with related validation has yet to be discovered. The present study aimed to assess the performance of various models based on patient characteristics in predicting EC in women with AEH. METHODS: This is a retrospective multi-institutional study including women with AEH undergoing definitive surgery. The women were divided according to the final histology (EC vs. no-EC). The available cases were divided into a training and validation set. Using k-fold cross-validation, we built many predictive models, including regressions and artificial neural networks (ANN). RESULTS: A total of 193/629 women (30.7%) showed EC at hysterectomy. A total of 26/193 (13.4%) women showed high-risk EC. Regression and ANN models showed a prediction performance with a mean area under the curve of 0.65 and 0.75 on the validation set, respectively. Among the best prediction models, the most recurrent patient characteristics were age, body mass index, Lynch syndrome, diabetes, and previous breast cancer. None of these independent variables showed associations with high-risk diseases in women with EC. CONCLUSIONS: Patient characteristics did not show satisfactory performance in predicting EC in AEH. Risk stratification in AEH based mainly on patient characteristics may be clinically unsuitable.

Effects of the Exposure of Human Non-Tumour Cells to Sera of Pancreatic Cancer Patients.

Pancreatic ductal adenocarcinoma (PDAC) has high metastatic potential. The "genometastasis" theory proposes that the blood of some cancer patients contains elements able to transform healthy cells by transferring oncogenes. Since findings on genometastasis in PDAC are still scarce, we sought supporting evidence by treating non-tumour HEK293T and hTERT-HPNE human cell lines with sera of PDAC patients. Here, we showed that HEK293T cells have undergone malignant transformation, increased the migration and invasion abilities, and acquired a partial chemoresistance, whereas hTERT-HPNE cells were almost refractory to transformation by patients' sera. Next-generation sequencing showed that transformed HEK293T cells gained and lost several genomic regions, harbouring genes involved in many cancer-associated processes. Our results support the genometastasis theory, but further studies are needed for the identification of the circulating transforming elements. Such elements could also be useful biomarkers in liquid biopsy assays.

Estrogen Related Receptor Alpha (ERRα) a Bridge between Metabolism and Adrenocortical Cancer Progression.

The aim of this study was to investigate the metabolic changes that occur in adrenocortical cancer (ACC) cells in response to the modulation of Estrogen Related Receptor (ERR)α expression and the impact on ACC progression. Proteomics analysis and metabolic profiling highlighted an important role for ERRα in the regulation of ACC metabolism. Stable ERRα overexpression in H295R cells promoted a better mitochondrial fitness and prompted toward a more aggressive phenotype characterized by higher Vimentin expression, enhanced cell migration and spheroids formation. By contrast, a decrease in ERRα protein levels, by molecular (short hairpin RNA) and pharmacological (inverse agonist XCT790) approaches modified the energetic status toward a low energy profile and reduced Vimentin expression and ability to form spheroids. XCT790 produced similar effects on two additional ACC cell lines, SW13 and mitotane-resistant MUC-1 cells. Our findings show that ERRα is able to modulate the metabolic profile of ACC cells, and its inhibition can strongly prevent the growth of mitotane-resistant ACC cells and the progression of ACC cell models to a highly migratory phenotype. Consequently, ERRα can be considered an important target for the design of new therapeutic strategies to fight ACC progression.

LipidOne: user-friendly lipidomic data analysis tool for a deeper interpretation in a systems biology scenario.

SUMMARY: LC/MS-based analysis techniques combined with specialized lipid tool allow for the qualitative and quantitative determination of thousands of lipid molecules. Some recent bioinformatics tools have been developed to study changes in the lipid profile in case-control experiments and correlate these changes to different enzyme activity or gene expression. However, the existing tools have the limitation to treat only the assembled lipid molecules. In reality, each individual molecule can be considered as an assembly of smaller parts, often called building blocks. These are the result of a myriad of biochemical synthesis and transformation processes that, from a systems biology perspective, should not be ignored. Here, we present LipidOne, a new lipidomic tool which highlights all qualitative and quantitative changes in lipid building blocks both among all detected lipid classes and among experimental groups. Thanks to LipidOne, even differences in lipid building blocks can now be linked to the activity of specific classes of enzymes, transcripts and genes. AVAILABILITY AND IMPLEMENTATION: LipidOne software is freely available at www.dcbb.unipg.it/LipidOne and https://github.com/matteogiulietti/LipidOne. CONTACT: roberto.pellegrino@unipg.it. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.

Effects of CXCL12 isoforms in a pancreatic pre-tumour cellular model: Microarray analysis.

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is the fourth leading cause of death among cancers, it is characterized by poor prognosis and strong chemoresistance. In the PDAC microenvironment, stromal cells release different extracellular components, including CXCL12. The CXCL12 is a chemokine promoting the communication between tumour and stromal cells. Six different splicing isoforms of CXCL12 are known (α, ß, γ, δ, ε, θ) but their role in PDAC has not yet been characterized. AIM: To investigate the specific role of α, ß, and γ CXCL12 isoforms in PDAC onset. METHODS: We used hTERT-HPNE E6/E7/KRasG12D (Human Pancreatic Nestin-Expressing) cell line as a pancreatic pre-tumour model and exposed it to the α, ß, and γ CXCL12 isoforms. The altered expression profiles were assessed by microarray analyses and confirmed by Real-Time polymerase chain reaction. The functional enrichment analyses have been performed by Enrichr tool to highlight Gene Ontology enriched terms. In addition, wound healing assays have been carried out to assess the phenotypic changes, in terms of migration ability, induced by the α, ß, and γ CXCL12 isoforms. RESULTS: Microarray analysis of hTERT-HPNE cells treated with the three different CXCL12 isoforms highlighted that the expression of only a few genes was altered. Moreover, the α and ß isoforms showed an alteration in expression of different genes, whereas γ isoform affected the expression of genes also common with α and ß isoforms. The ß isoform altered the expression of genes mainly involved in cell cycle regulation. In addition, all isoforms affected the expression of genes associated to cell migration, adhesion and cytoskeleton. In vitro cell migration assay confirmed that CXCL12 enhanced the migration ability of hTERT-HPNE cells. Among the CXCL12 splicing isoforms, the γ isoform showed higher induction of migration than α and ß isoforms. CONCLUSION: Our data suggests an involvement and different roles of CXCL12 isoforms in PDAC onset. However, more investigations are needed to confirm these preliminary observations.

The Role of Artificial Intelligence in the Diagnosis and Prognosis of Renal Cell Tumors.

The increasing availability of molecular data provided by next-generation sequencing (NGS) techniques is allowing improvement in the possibilities of diagnosis and prognosis in renal cancer. Reliable and accurate predictors based on selected gene panels are urgently needed for better stratification of renal cell carcinoma (RCC) patients in order to define a personalized treatment plan. Artificial intelligence (AI) algorithms are currently in development for this purpose. Here, we reviewed studies that developed predictors based on AI algorithms for diagnosis and prognosis in renal cancer and we compared them with non-AI-based predictors. Comparing study results, it emerges that the AI prediction performance is good and slightly better than non-AI-based ones. However, there have been only minor improvements in AI predictors in terms of accuracy and the area under the receiver operating curve (AUC) over the last decade and the number of genes used had little influence on these indices. Furthermore, we highlight that different studies having the same goal obtain similar performance despite the fact they use different discriminating genes. This is surprising because genes related to the diagnosis or prognosis are expected to be tumor-specific and independent of selection methods and algorithms. The performance of these predictors will be better with the improvement in the learning methods, as the number of cases increases and by using different types of input data (e.g., non-coding RNAs, proteomic and metabolic). This will allow for more precise identification, classification and staging of cancerous lesions which will be less affected by interpathologist variability.

Promising Extracellular Vesicle-Based Vaccines against Viruses, Including SARS-CoV-2.

Extracellular vesicles (EVs) are secreted from almost all human cells and mediate intercellular communication by transferring heterogeneous molecules (i.e., DNA, RNAs, proteins, and lipids). In this way, EVs participate in various biological processes, including immune responses. Viruses can hijack EV biogenesis systems for their dissemination, while EVs from infected cells can transfer viral proteins to uninfected cells and to immune cells in order to mask the infection or to trigger a response. Several studies have highlighted the role of native or engineered EVs in the induction of B cell and CD8(+) T cell reactions against viral proteins, strongly suggesting these antigen-presenting EVs as a novel strategy for vaccine design, including the emerging COVID-19. EV-based vaccines overcome some limitations of conventional vaccines and introduce novel unique characteristics useful in vaccine design, including higher bio-safety and efficiency as antigen-presenting systems and as adjuvants. Here, we review the state-of-the-art for antiviral EV-based vaccines, including the ongoing projects of some biotech companies in the development of EV-based vaccines for SARS-CoV-2. Finally, we discuss the limits for further development of this promising class of therapeutic agents.

Artificial Neural Networks as a Way to Predict Future Kidney Cancer Incidence in the United States.

INTRODUCTION: The incidence of kidney cancer is increasing; it could be counteracted with new ways to predict and detect it. We aimed to implement an artificial neural network in order to predict new cases of renal-cell carcinoma (RCC) in the population using population rate, obesity, smoking incidence, uncontrolled hypertension, and life expectancy data in the United States. PATIENTS AND METHODS: Statistics were collected on US population numbers, life expectancy, obesity, smoking, and hypertension. We used MATLAB R2018 (MathWorks) software to implement an artificial neural network. Data were repeatedly and randomly divided into training (70%) and validation (30%) subsets. RESULTS: The number of new RCC cases will grow from 44,400 (2020) to 55,400 (2050), an increase of +24.7%. Our data show that preventing hypertension would have the greatest impact on reduction of the incidence, estimated at -775 and -575 cases per year in 2020 and in 2030, respectively. The prevention of obesity and smoking would have a more limited impact, estimated at -64 and -180 cases per year in 2020 and in 2030, respectively, for obesity, and -173 and -21 cases per year in 2020 and in 2030, respectively, for smoking. CONCLUSIONS: Our predictions underline the need for accurate studies on RCC-related risk factors to reduce the incidence.

Expression and co-expression analyses of TMPRSS2, a key element in COVID-19.

The ACE2 receptor is, so far, the best-known host factor for SARS-CoV-2 entry, but another essential element, the TMPRSS2 protease, has recently been identified. Here, we have analysed TMPRSS2 expression data in the lung correlating them with age, sex, diabetes, smoking habits, exposure to pollutant and other stimuli, in order to highlight which factors might alter TMPRSS2 expression, and thus impact the susceptibility to infection and COVID-19 prognosis. Moreover, we reported TMPRSS2 polymorphisms affecting its expression and suggested the ethnic groups more prone to COVID-19. Finally, we also highlighted a gender-specific co-expression between TMPRSS2 and other genes related to SARS-CoV-2 entry, maybe explaining the higher observed susceptibility of infection in men. Our results could be useful in designing potential prevention and treatment strategies regarding the COVID-19.

Predicting future cancer burden in the United States by artificial neural networks.

Aims: To capture the complex relationships between risk factors and cancer incidences in the US and predict future cancer burden. Materials & methods: Two artificial neural network (ANN) algorithms were adopted: a multilayer feed-forward network (MLFFNN) and a nonlinear autoregressive network with eXogenous inputs (NARX). Data on the incidence of the four most common tumors (breast, colorectal, lung and prostate) from 1992 to 2016 (available from National Cancer Institute online datasets) were used for training and validation, and data until 2050 were predicted. Results: The rapid decreasing trend of prostate cancer incidence started in 2010 will continue until 2018-2019; it will then slow down and reach a plateau after 2050, with several differences among ethnicities. The incidence of breast cancer will reach a plateau in 2030, whereas colorectal cancer incidence will reach a minimum value of 35 per 100,000 in 2030. As for lung cancer, the incidence will decrease from 50 per 100,000 (2017) to 31 per 100,000 in 2030 and 26 per 100,000 in 2050. Conclusion: This up-to-date prediction of cancer burden in the US could be a crucial resource for planning and evaluation of cancer-control programs.

Exploring the Spectrum of Kidney Ciliopathies.

Ciliopathies are a group of multi-organ diseases caused by the disruption of the primary cilium. This event leads to a variety of kidney disorders, including nephronophthisis, renal cystic dysplasia, and renal cell carcinoma (RCC). Primary cilium contributes to the regulation of the cell cycle and protein homeostasis, that is, the balance between protein synthesis and degradation by acting on the ubiquitin-proteasome system, autophagy, and mTOR signaling. Many proteins are involved in renal ciliopathies. In particular, fibrocystin (PKHD1) is involved in autosomal recessive polycystic kidney disease (ARPKD), while polycystin-1 (PKD1) and polycystin-2 (PKD2) are implicated in autosomal dominant polycystic kidney disease (ADPKD). Moreover, primary cilia are associated with essential signaling pathways, such as Hedgehog, Wnt, and Platelet-Derived Growth Factor (PDGF). In this review, we focused on the ciliopathies associated with kidney diseases, exploring genes and signaling pathways associated with primary cilium and the potential role of cilia as therapeutic targets in renal disorders.

MetaTropismDB: a database of organ-specific metastasis induced by human cancer cell lines in mouse models.

The organotropism is the propensity of metastatic cancer cells to colonize preferably certain distant organs, resulting in a non-random distribution of metastases. In order to shed light on this behaviour, several studies were performed by the injection of human cancer cell lines into immunocompromised mouse models. However, the information about these experiments is spread in the literature. For each xenograft experiment reported in the literature, we annotated both the experimental conditions and outcomes, including details on inoculated human cell lines, mouse models, injection methods, sites of metastasis, organs not colonized, rate of metastasis, latency time, overall survival and the involved genes. We created MetaTropismDB, a freely available database collecting hand-curated data useful to highlight the mechanisms of organ-specific metastasis. Currently, it stores the results of 513 experiments in which injections of 219 human cell lines have been carried out in mouse models. Notably, 296 genes involved in organotropic metastases have been collected. This specialized database allows the researchers to compare the current results about organotropism and plan future experiments in order to identify which tumour molecular signatures establish if and where the metastasis will develop. Database URL:  http://www.introni.it/Metastasis/metastasis.html.

Sequential or Concomitant Inhibition of Cyclin-Dependent Kinase 4/6 Before mTOR Pathway in Hormone-Positive HER2 Negative Breast Cancer: Biological Insights and Clinical Implications.

About 75% of all breast cancers are hormone receptor-positive (HR+). However, the efficacy of endocrine therapy is limited due to the high rate of either pre-existing or acquired resistance. In this work we reconstructed the pathways around estrogen receptor (ER), mTOR, and cyclin D in order to compare the effects of CDK4/6 and PI3K/AKT/mTOR inhibitors. A positive feedback loop links mTOR and ER that support each other. We subsequently considered whether a combined or sequential inhibition of CDK4/6 and PI3K/AKT/mTOR could ensure better results. Studies indicate that inhibition of CDK4/6 activates mTOR as an escape mechanism to ensure cell proliferation. In literature, the little evidence dealing with this topic suggests that pre-treatment with mTOR pathway inhibitors could prevent or delay the onset of CDK4/6 inhibitor resistance. Additional studies are needed in order to find biomarkers that can identify patients who will develop this resistance and in whom the sensitivity to CDK4/6 inhibitors can be restored.