CDH2/N-cadherin and early diagnosis of invasion in patients with ductal carcinoma in situ.

PURPOSE: This proof-of-concept study investigates gene expression in core needle biopsies (CNB) to predict whether individuals diagnosed with ductal carcinoma in situ (DCIS) on CNB were affected by invasion at the time of diagnosis. METHODS: Using a QuantiGene Plex 2.0 assay, 14 gene expression profiling was performed in 303 breast tissue samples. Preoperative diagnostic performance of a gene was measured by area under receiver-operating characteristic curve (AUC) with 95% confidence interval (CI). The gene mRNA positivity cutoff was computed using Gaussian mixture model (GMM); protein expression was measured by immunohistochemistry; DNA methylation was evaluated by targeted bisulfite sequencing. RESULTS: mRNA from 69% (34/49) mammoplasties, 72% (75/104) CNB DCIS, and 89% (133/150) invasive breast cancers (IBC) were analyzed. Based on pre-and post-surgery DCIS chart reviews, 21 cases were categorized as DCIS synchronous with invasion and 54 DCIS were pure DCIS without pathologic evidence of invasive disease. The ectopic expression of neuronal cadherin CDH2 was probable in 0% mammoplasties, 6% pure DCIS, 29% synchronous DCIS, and 26% IBC. The CDH2 mRNA positivity in preoperative biopsies showing pure DCIS was predictive of a final diagnosis of invasion (AUC = 0.67; 95% CI 0.53-0.80; P = 0.029). Site-specific methylation of the CDH2 promoter (AUC = 0.76; 95% CI 0.54-0.97; P = 0.04) and measurements of N-cadherin, a pro-invasive cell-cell adhesion receptor encoded by CDH2 (AUC = 0.8; 95% CI 0.66-0.99; P < 0.005) had a discriminating power allowing for discernment of CDH2-positive biopsy. CONCLUSIONS: Evidence of CDH2/N-cadherin expression, predictive of invasion synchronous with DCIS, may help to clarify a diagnosis and direct the course of therapy earlier in a patient's care.

The small GTPase Rap1 promotes cell movement rather than stabilizes adhesion in epithelial cells responding to insulin-like growth factor I.

The Ras-related GTPase Rap1 promotes cell adhesion and migration. Although the significance of Rap1 contribution to cell migration is increasingly being recognized, little is known about the biochemical mechanisms driving this process. In the present study, we discovered a previously unidentified regulatory role of insulin-like growth factor type I (IGF-I) receptor (IGF-IR) in CRK Src homology 3 (SH3)-binding guanine-nucleotide-releasing protein (C3G)-Rap1-fascin-actin axis promoting cell movement. We demonstrate that a burst of Rap1 activity, rather than presumed hyperactivation, is imperative for the onset of cell movement. We show that while autophosphorylated IGF-IR signals to C3G to activate Rap1, subsequent IGF-IR internalization promotes gradual inactivation of Rap1 by putative Rap1 GTPase-activating protein (GAP). Additionally, IGF-IR signalling recruits active Rap1 at sites of cell motile protrusions. C3G depletion prevents IGF-I-induced fascin accumulation at actin microspikes and blocks protrusions. In the absence of IGF-IR activity, the wild-type (WT) Rap1 and the constitutively active V12Rap1 mutant remain in cell-cell contacts. Forced inactivation of Rap1 signalling by overexpressing dominant negative N17Rap1, Rap1GAP or by silencing C3G has a detrimental effect on filamentous (F)-actin and cell adhesion irrespective of IGF-IR signalling. We conclude that the basal levels of Rap1 activity holds up cell adhesion, whereas sequential regulation of C3G and GAP by IGF-IR reverses the labile Rap1 function from supporting adhesion to promoting migration.

Automated Classification of Breast Cancer Across the Spectrum of ERBB2 Expression Focusing on Heterogeneous Tumors With Low Human Epidermal Growth Factor Receptor 2 Expression.

PURPOSE: Although pharmaceutical companies conduct clinical trials of novel human epidermal growth factor receptor 2 (HER2)-low-directed drugs, diagnosing HER2-low cancer by immunohistochemistry (IHC) and in situ hybridization (ISH) remains challenging. This study investigates the performance of first-in-kind computerized intelligence to classify samples across gene expression levels and differentiate HER2-low tumors. MATERIALS AND METHODS: We classified 251 samples: 142 primary invasive breast cancers (IBCs), 75 ductal carcinomas in situ (DCIS), and 34 mammaplasties (reference) using mRNA expression data from the QuantiGene Plex 2.0 assay. We used g3mclass probabilistic software to assess the number of classes in the assay data, the mean and the variance in each class, diagnostic cutoffs, and the prevalence of each class in the study population. RESULTS: HER2-low (IHC score of 1+ or 2+/ISH-) accounted for 31% of IBC. First, we discovered that HER2-low tumors were represented by cases with normal ERBB2 transcript levels that were expected to produce physiologic levels of HER2 (70%) and cases with abnormally upregulated unamplified ERBB2 (30%). We termed the latter cancers ERBB2-up as they do not meet the standard definitions for ERBB2 overexpression and amplification. Second, HER2-low IBC classified as ERBB2-up had not only abnormally increased luminal growth and adhesion markers (ERBB2, ESR1, PGR, IGF1R, VAV2, VAV3, KRT8, CDH1) but also downregulated myoepithelial marker (KRT5). The vascularization (RAP1 and C3G), immune cell infiltration (VAV1), and mesenchymal transition (CDH2) markers were dysregulated. Finally, in the independent cohort of DCIS, 40% of HER2-low DCIS shared similar traits with HER2-low IBC except for rare downregulation of KRT5 and no change in C3G, VAV1, and CDH2. CONCLUSION: We demonstrated how innovative bioinformatic tools could help diagnose cancer across the spectrum of ERBB2 expression to aid decision making for HER2-low.

The g3mclass is a practical software for multiclass classification on biomarkers.

The analytes qualified as biomarkers are potent tools to diagnose various diseases, monitor therapy responses, and design therapeutic interventions. The early assessment of the diverseness of human disease is essential for the speedy and cost-efficient implementation of personalized medicine. We developed g3mclass, the Gaussian mixture modeling software for molecular assay data classification. This software automates the validated multiclass classifier applicable to single analyte tests and multiplexing assays. The g3mclass achieves automation using the original semi-constrained expectation-maximization (EM) algorithm that allows inference from the test, control, and query data that human experts cannot interpret. In this study, we used real-world clinical data and gene expression datasets (ERBB2, ESR1, PGR) to provide examples of how g3mclass may help overcome the problems of over-/underdiagnosis and equivocal results in diagnostic tests for breast cancer. We showed the g3mclass output's accuracy, robustness, scalability, and interpretability. The user-friendly interface and free dissemination of this multi-platform software aim to ease its use by research laboratories, biomedical pharma, companion diagnostic developers, and healthcare regulators. Furthermore, the g3mclass automatic extracting information through probabilistic modeling is adaptable for blending with machine learning and artificial intelligence.

Ras-related protein 1 and the insulin-like growth factor type I receptor are associated with risk of progression in patients diagnosed with carcinoma in situ.

Currently, there are no applied molecular markers to aid in predicting risk of carcinoma in situ (CIS) progression to invasive cancer, and therefore, all women diagnosed with CIS undergo surgery. Standard assessment of protein expression in fixed tissue by immunohistochemistry (IHC) is not quantitative and hence is not well suited for measuring biomarkers. In this study, we developed an original analytical method for IHC quantification. Using our novel image-based uniplex (IBU) method, quantitative protein profiling was performed on 90 samples of the breast (17 histologically normal tissues, 16 benign lesions, 15 CIS, and 42 invasive carcinomas). Differences between groups were assessed using analysis of variance (ANOVA) and mixed effects models. Measuring protein expression on a continuous scale revealed a significant increase in Ras-related protein 1 (Rap1) and the insulin-like growth factor type I receptor (IGF-IR) in conjunction with the presence of cancer invasion. Women with invasive cancers were four times more likely to have increased levels of Rap1 [odds ratio (OR) = 3.91; P = 0.0002] and IGF-IR (OR=4.33; P<0.0001) than women with non-invasive lesions. Furthermore, expression of both proteins was also increased significantly in CIS adjacent to invasive tumors compared with non-cancerous tissue. These novel findings of a significant up-regulation of Rap1 and IGF-IR in CIS progressing to invasive cancers warrant further investigation of Rap1 and IGF-IR together as a dual biomarker to aid in predicting risk of progression and ultimately providing non-surgical treatment options to those at lower risk.

Improving patient classification and biomarker assessment using Gaussian Mixture Models and Bayes' rule.

In clinical research, determining cutoff values for continuous variables in test results remains challenging, particularly when considering candidate biomarkers or therapeutic targets for disease. Distribution of a continuous variable into two populations is known as dichotomization and has been commonly used in clinical studies. We recently reported a new method for determining multiple cutoffs for continuous variables. The development of this original approach was based on fitting Gaussian Mixture Models (GMM) onto real-world clinical data. We also explored how to leverage Bayesian probability to minimize uncertainty while classifying individual patients into respective subpopulations. In addition, we investigated the performance of the proposed method for the distribution of classical prognostic markers in breast cancer. Finally, we applied the proposed method to analyze a candidate marker and a target for cancer therapy. Here, we present an overview of this method and our prospects for its implementation in biomedical and clinical research.

Gaussian Mixture Models for Probabilistic Classification of Breast Cancer.

In the era of omics-driven research, it remains a common dilemma to stratify individual patients based on the molecular characteristics of their tumors. To improve molecular stratification of patients with breast cancer, we developed the Gaussian mixture model (GMM)-based classifier. This probabilistic classifier was built on mRNA expression data from more than 300 clinical samples of breast cancer and healthy tissue and was validated on datasets of ESR1, PGR, and ERBB2, which encode standard clinical markers and therapeutic targets. To demonstrate how a GMM approach could be exploited for multiclass classification using data from a candidate marker, we analyzed the insulin-like growth factor I receptor (IGF1R), a promising target, but a marker of uncertain importance in breast cancer. The GMM defined subclasses with downregulated (40%), unchanged (39%), upregulated (19%), and overexpressed (2%) IGF1R levels; inter- and intrapatient analyses of IGF1R transcript and protein levels supported these predictions. Overexpressed IGF1R was observed in a small percentage of tumors. Samples with unchanged and upregulated IGF1R were differentiated tumors, and downregulation of IGF1R correlated with poorly differentiated, high-risk hormone receptor-negative and HER2-positive tumors. A similar correlation was found in the independent cohort of carcinoma in situ, suggesting that loss or low expression of IGF1R is a marker of aggressiveness in subsets of preinvasive and invasive breast cancer. These results demonstrate the importance of probabilistic modeling that delves deeper into molecular data and aims to improve diagnostic classification, prognostic assessment, and treatment selection. SIGNIFICANCE: A GMM classifier demonstrates potential use for clinical validation of markers and determination of target populations, particularly when availability of specimens for marker development is low.

Insulin-like growth factor I controls adhesion strength mediated by alpha5beta1 integrins in motile carcinoma cells.

One of the intriguing questions regarding cell motility concerns the mechanism that makes stationary cells move. Here, we provide the first physical evidence that the onset of breast cancer cell motility in response to insulin-like growth factor I (IGF-I) correlates with lowering of adhesion strength from 2.52 +/- 0.20 to 1.52 +/- 0.13 microdynes/microm2 in cells attached to fibronectin via alpha5beta1 integrin. The adhesion strength depends on the dose of IGF-I and time of IGF-I treatment. Weakening of cell-matrix adhesion is blocked significantly (p < 0.01) by the catalytically inactive IGF-I receptor (IGF-IR) and the phosphoinositide 3-kinase (PI-3 kinase) inhibitor LY-294002, but it is unaffected by mitogen-activated protein kinase kinase inhibitor UO-126 and Src kinase inhibitor PP2. Sustained blockade of Rho-associated kinase (ROCK) with Y-27632 down-regulates adhesion strength in stationary, but not in IGF-I-treated, cells. Jasplakinolide, a drug that prevents actin filament disassembly, counteracts the effect of IGF-I on integrin-mediated cell adhesion. In the absence of growth factor signaling, ROCK supports a strong adhesion via alpha5beta1 integrin, whereas activation of the IGF-IR kinase reduces cell-matrix adhesion through a PI-3K-dependent, but ROCK-independent, mechanism. We propose that disassembly of the actin filaments via PI-3 kinase pathway contributes to weakening of adhesion strength and induction of cell movement. Understanding how cell adhesion and migration are coordinated has an important application in cancer research, developmental biology, and tissue bioengineering.

Insulin-like growth factors control cell migration in health and disease.

Insulin-like growth factors I and II (IGF-I and IGF-II) have an ancient origin and play essential roles in fundamental biological processes. Although IGFs are principally known for their roles in regulating cell growth and survival, their ability to influence cell motility is just as significant. In the past 20 years, research has provided indisputable evidence for the regulatory role of IGFs in the migration of various cell types. Cell migration is crucial for reproduction, development, and tissue regeneration; IGFs play an important role in coordinating these processes. Moreover, studies continue to uncover the IGFs' role in stimulating cancer cell migration, invasion and metastasis. This review surveys current knowledge on the cell migration-modulating properties of IGFs and the biochemical pathways by which these peptides regulate cell movement in both physiological and pathological conditions.

Induction of fascin spikes in breast cancer cells by activation of the insulin-like growth factor-I receptor.

Insulin-like growth factor-I receptor (IGF-IR) signaling contributes to the formation of mammary carcinomas and has chiefly been studied with regard to the proliferative and anti-apoptotic effects of IGF-IR signaling. However, IGF-IR activation also affects the actin cytoskeleton and alterations in cell migratory behavior are of known importance for the malignant conversion and metastasis of epithelial cells. The actin-binding protein fascin is found in cell projections and spikes that are involved in the locomotion of mesenchymal cells. Fascin expression is typically low in normal epithelial cells, but is markedly upregulated in several types of carcinomas. Here, we also demonstrate increased fascin expression in breast carcinoma cell lines and adopt MCF-7 human mammary carcinoma cells that over-express wild-type or kinase-inactivated forms of the IGF-IR as a model system to test the hypothesis that IGF-IR activation induces fascin projections. We show that the time-dependent dissociation of cell colonies that occurs upon receptor activation by IGF-I involves the formation of dynamic, fascin-containing lateral cell projections that co-localize with ruffling membranes in association with protrusive activity and cell migratory phenotype. The molecular mechanism of these effects is completely dependent on IGF-IR tyrosine kinase activity and is mediated by a phosphatidylinositol (PI) 3-kinase-dependent process. In demonstrating transduction of fascin spike assembly by activation of a peptide growth factor receptor, these novel data reveal a wide role for fascin spikes in cell motility and provide new insight into the complex effects of IGF-IR signaling on actin cytoskeletal organization.

Vav2 protein overexpression marks and may predict the aggressive subtype of ductal carcinoma in situ.

BACKGROUND: A subset of patients with ductal carcinoma in situ (DCIS) will develop invasive breast cancer (IBC). To date, there are no effective predictive biomarkers for identifying this subset with worse prognosis whose lesions are essentially indistinguishable histologically from those with favorable outcomes. We hypothesized that measurable parameters that discriminate DCIS from DCIS with concurrent invasion may serve as diagnostic biomarkers (BM) of progressive cancer in situ (CIS). RESULTS: Using a novel imaging-based method of tissue testing, we measured the relative expression levels of three candidate BM proteins specifically implicated in IBC progression - the insulin-like growth factor I receptor (IGF-IR), Ras-related protein 1 (Rap1), and Vav2 oncoprotein. Protein profiles were compared in 42 histologically normal mammary epithelial samples, 71 CIS (35 without/36 with invasion either on diagnostic biopsy or final surgical excision), and 98 IBC of known estrogen receptor (ER), progesterone receptor (PR) and human epidermal growth factor receptor 2 (HER2) status. The levels of the IGF-IR and Rap1 protein expression were significantly elevated in ER-positive (ER+/PR+/-/HER2 -) DCIS relative to normal epithelium (P <0.0001). The IGF-IR protein expression was also significantly up regulated in HER2-positive (ER+/-/PR+/-/HER2+) DCIS relative to normal epithelium (P = 0.0002). IGF-IR and Rap1 protein expression levels were similar among DCIS patients without or with concurrent invasion. Vav2 upregulation in DCIS relative to normal group was not associated with steroid hormone receptor and HER2 status, but was associated with the presence of concurrent invasion, including microinvasion (invasive foci of less than 1 mm). DCIS with high Vav2 were more than twice as likely to progress to invasive cancers as DCIS with low Vav2 (odds ratio, 2.42; 95% CI, 1.26-4-65; P =0.008). Furthermore, a receiver operating characteristic curve analysis revealed moderate ability of Vav2 protein expression measurements in DCIS to predict the existence of invasion concurrent with DCIS (area under the curve, 0.71; 95% CI, 0.59- 0.84). CONCLUSIONS: Our novel findings hold promise for utilizing Vav2 protein as a predictive BM for differentiating progressive from non-progressive DCIS.

Rap2A Is Upregulated in Invasive Cells Dissected from Follicular Thyroid Cancer.

The development of molecular biomarkers (BMs) of follicular thyroid carcinoma is aimed at advancing diagnosis of follicular neoplasm, as histological examination of those tumors does not lend itself to definitive diagnosis of carcinoma. We assessed the relative levels of expression of 6 genes: CCND2, PCSK2, PLAB, RAP2A, TSHR, and IGF-1R in archived thyroid tissue. The quantitative real-time PCR analysis revealed a significant change in 3 genes: PSCK2 (a 22.4-fold decrease, P = 2.81E - 2), PLAB (an 8.3-fold increase, P = 9.81E - 12), and RAP2A (a 6.3-fold increase, P = 9.13E - 10) in carcinoma compared with adenoma. Expression of PCSK2 was equally low, PLAB was equally high, whereas RAP2A expression was significantly higher (25.9-fold, P = 0.039) in microdissected carcinoma cells that have invaded through the thyroid capsule and entered blood vessels than in thyroid tumor cells growing under the capsule. Thus, RAP2A appeared as a unique and worthy of further evaluation candidate BM associated with invasion of thyroid follicular cells.

Functional role of alpha-actinin, PI 3-kinase and MEK1/2 in insulin-like growth factor I receptor kinase regulated motility of human breast carcinoma cells.

Within epithelial tissue, cells are held together by specialized lateral junctions. At particular stages of development and in pathological processes such as metastasis, cells break down the intercellular junctions, separate from the epithelial sheet and migrate individually. Despite the importance of these processes, little is understood about the regulatory mechanisms of active cell separation. In view of the effects of insulin-like growth factor I (IGF-I) on mammary gland development and cancer, we developed a model using MCF-7 human breast cancer cells in which the process of cell separation can be induced by IGF-I. The separation was enhanced in MCF-7 cells overexpressing the IGF-IR and blocked in the cells expressing a dead-kinase mutant of this receptor. Activation of the IGF-IR resulted in a rapid formation of motile actin microspikes at the regions of cell-cell contacts, disorganization of mature adherens junctions and the onset of cell migration. In cell separation, the signaling between the IGF-IR kinase and actin required phosphatidylinositol 3 (PI 3)-kinase-generated phospholipids but not MAP kinases and was mediated by alpha-actinin. The activity of MEK1/2 kinases was needed for consecutive cell migration. This work also defined a new function for alpha-actinin. Upon IGF-IR activation, green fluorescence protein (GFP)-labeled alpha-actinin concentrated at the base of actin microspikes. Deletion of the N-terminal actin-binding domain of alpha-actinin prevented this redistribution, indicating that this domain is necessary. Detection of the C-terminal tail of alpha-actinin reduced the number of microspikes, showing that alpha-actinin has a role in the development of microspikes and is not passively reorganized with filamentous actin. We suggest that the signaling pathway from the IGF-IR kinase through the PI-3 kinase to alpha-actinin participates in the rapid organization of actin into microspikes at the cell-cell junctions and leads to active cell separation, whereas signaling through ERK1/2 MAP kinases controls cell migration following cell separation.