Type I gamma phosphatidylinositol phosphate kinase modulates adherens junction and E-cadherin trafficking via a direct interaction with mu 1B adaptin.

Assembly of E-cadherin-based adherens junctions (AJ) is obligatory for establishment of polarized epithelia and plays a key role in repressing the invasiveness of many carcinomas. Here we show that type Igamma phosphatidylinositol phosphate kinase (PIPKIgamma) directly binds to E-cadherin and modulates E-cadherin trafficking. PIPKIgamma also interacts with the mu subunits of clathrin adaptor protein (AP) complexes and acts as a signalling scaffold that links AP complexes to E-cadherin. Depletion of PIPKIgamma or disruption of PIPKIgamma binding to either E-cadherin or AP complexes results in defects in E-cadherin transport and blocks AJ assembly. An E-cadherin germline mutation that loses PIPKIgamma binding and shows disrupted basolateral membrane targeting no longer forms AJs and leads to hereditary gastric cancers. These combined results reveal a novel mechanism where PIPKIgamma serves as both a scaffold, which links E-cadherin to AP complexes and the trafficking machinery, and a regulator of trafficking events via the spatial generation of phosphatidylinositol-4,5-bisphosphate.

Subcellular localization of activated AKT in estrogen receptor- and progesterone receptor-expressing breast cancers: potential clinical implications.

Activated v-AKT murine thymoma viral oncogene homolog 1 (AKT)/protein kinase B (PKB) kinase (pAKT) is localized to the plasma membrane, cytoplasm, and/or nucleus in 50% of cancers. The clinical importance of pAKT localization and the mechanism(s) controlling this compartmentalization are unknown. In this study, we examined nuclear and cytoplasmic phospho-AKT (pAKT) expression by immunohistochemistry in a breast cancer tissue microarray (n = 377) with approximately 15 years follow-up and integrated these data with the expression of estrogen receptor (ER)alpha, progesterone receptor (PR), and FOXA1. Nuclear localization of pAKT (nuclear-pAKT) was associated with long-term survival (P = 0.004). Within the ERalpha+/PR+ subgroup, patients with nuclear-pAKT positivity had better survival than nuclear-pAKT-negative patients (P < or = 0.05). The association of nuclear-pAKT with the ERalpha+/PR+ subgroup was validated in an independent cohort (n = 145). TCL1 family proteins regulate nuclear transport and/or activation of AKT. TCL1B is overexpressed in ERalpha-positive compared with ERalpha-negative breast cancers and in lung metastasis-free breast cancers. Therefore, we examined the possible control of TCL1 family member(s) expression by the estrogen:ERalpha pathway. Estradiol increased TCL1B expression and increased nuclear-pAKT levels in breast cancer cells; short- interfering RNA against TCL1B reduced nuclear-pAKT. Overexpression of nuclear-targeted AKT1 in MCF-7 cells increased cell proliferation without compromising sensitivity to the anti-estrogen, tamoxifen. These results suggest that subcellular localization of activated AKT plays a significant role in determining its function in breast cancer, which in part is dependent on TCL1B expression.

Biomarker expression in pelvic high-grade serous carcinoma: comparison of ovarian and omental sites.

Neoadjuvant therapy has an emerging role in the treatment of high-stage ovarian carcinoma. Some ovarian carcinoma subtypes do not respond well to standard chemotherapy, making accurate subtype diagnosis before starting therapy important. This diagnosis is frequently based on omental biopsy specimens. In particular, with very small biopsies, immunostaining for diagnostic biomarkers may be needed. To assess intratumoral heterogeneity of biomarker expression in pelvic high-grade serous carcinoma, we compared the expression of a set of 10 biomarkers between ovarian and omental sites. Tissue microarrays were constructed from 123 high-grade serous carcinomas with paired ovarian and omental tumor samples. These samples were stained with biomarkers that have been used in ovarian carcinoma subtype diagnosis (WT1, TP53/p53, MUC16/CA125, CDKN2A/p16), and with biomarkers of the tumor microenvironment (CD8, CD163, SPARC, PDGFRB), cell adhesion (CDH1/E-Cadherin), and proliferation (Ki67) as well. Expression frequencies in samples from the 2 sites were compared, as was concordance at the 2 sites for individual tumors. The 2 markers of desmoplastic stromal response (PDGFRB, SPARC) were more frequently expressed in the omentum compared with the ovary (P<0.001; McNemar test). The other 8 markers did not show a significant difference in the frequency of expression between sites. Within individual cases, some markers such as Ki67 and CDKN2A showed variability, indicating that these markers are affected by intratumoral heterogeneity. The intratumoral variability for MUC16, TP53, and WT1 was modest. Commonly used diagnostic markers, such as TP53 and WT1, show little variability between ovarian and omental sites, suggesting that they can be successfully used in small biopsy specimens from extraovarian sites. In contrast, markers of host stromal response do vary between sites, suggesting a biologic difference of the microenvironment at different sites that should be taken into account when tissue-based research is carried out.

Type I gamma phosphatidylinositol phosphate kinase modulates invasion and proliferation and its expression correlates with poor prognosis in breast cancer.

INTRODUCTION: The loss of E-cadherin based cell-cell contacts and tumor cell migration to the vasculature and lymphatic system are hallmarks of metastasis of epithelial cancers. Type I gamma phosphatidylinositol phosphate kinase (PIPKIgamma), an enzyme that generates phosphatidylinositol 4,5-bisphosphate (PI4,5P2) a lipid messenger and precursor to many additional second messengers, was found to regulate E-cadherin cell-cell contacts and growth factor-stimulated directional cell migration, indicating that PIPKIgamma regulates key steps in metastasis. Here, we assess the expression of PIPKIgamma in breast cancers and have shown that expression correlated with disease progression and outcome. METHODS: Using a tissue microarray, we analyzed 438 breast carcinomas for the levels of PIPKIgamma and investigated the correlation of PIPKIgamma expression with patient survival via Kaplan-Meier survival analysis. Moreover, via knockdown of the expression of PIPKIgamma in cultured breast cancer cells with siRNA, the roles of PIPKIgamma in breast cancer migration, invasion, and proliferation were examined. RESULTS: Tissue microarray data shows that approximately 18% of the cohort immunostained showed high expression of PIPKIgamma. The Kaplan-Meier survival analysis revealed a significant inverse correlation between strong PIPKIgamma expression and overall patient survival. Expression of PIPKIgamma correlated positively with epidermal growth factor receptor (EGFR) expression, which regulates breast cancer progression and metastasis. In cultured breast cancer cells, PIPKIgamma is required for growth factor stimulated migration, invasion, and proliferation of cells. CONCLUSIONS: The results reveal a significant correlation between PIPKIgamma expression and the progression of breast cancer. This is consistent with PIPKIgamma 's role in breast cancer cell migration, invasion, and proliferation.

Relaxin drives Wnt signaling through upregulation of PCDHY in prostate cancer.

BACKGROUND: Relaxin, a potent peptide hormone of the insulin-like family normally produced and secreted by the human prostate, is upregulated in castrate resistant prostate cancer progression. In various tissues, relaxin increases angiogenesis and cell motility through upregulation of vascular endothelial growth factor, matrix metalloproteases, and nitric oxide, and therefore maybe an attractive target for cancer therapeutics. METHODS: To examine the role of relaxin in prostate cancer progression, LNCaP cells stably transfected with relaxin (LNCaP(RLN)) were used to form xenograft tumors, and microarray expression analysis was subsequently performed to determine novel pathways regulated by relaxin. Prostate cancer tissue microarrays from patient samples were stained by immunohistochemistry for further validation and correlation of the findings. RESULTS: Expression analysis identified novel relaxin regulated pathways, including the ProtocadherinY (PCDHY)/Wnt pathway. PCDHY, which upregulates Wnt11, has previously been shown to stabilize beta-catenin, causing beta-catenin to translocate from the cytoplasmic membrane to the nucleus and initiate TCF-mediated signaling. LNCaP(RLN) xenografts exhibit increased PCDHY expression and increased cytoplasmic localization of beta-catenin, suggesting relaxin directs Wnt11 overexpression through PCDHY upregulation. Similarly, prostate cancer samples from patients who have undergone androgen ablation have increased Wnt11 expression, which is further upregulated in castrate resistant tissues. Like relaxin, Wnt11, and PCDHY are negatively regulated by androgens, and further analysis indicated that the overexpression of relaxin results in dysregulation of androgen-regulated genes. CONCLUSIONS: These data suggest that prostate cancer cell motility and altered androgen receptor activity attributed to relaxin may be mediated in part by Wnt11.

Differential subcellular expression of protein kinase C betaII in breast cancer: correlation with breast cancer subtypes.

Protein kinase C betaII (PKCßII) represents a novel potential target for anticancer therapies in breast cancer. In order to identify patient subgroups which might benefit from PKC-targeting therapies, we investigated the expression of PKCßII in human breast cancer cell lines and in a tissue microarray (TMA). We first screened breast cancer cell line representatives of breast cancer subtypes for PKCßII expression at the mRNA and at the protein levels. We analyzed a TMA comprising of tumors from 438 patients with a median followup of 15.4 years for PKCßII expression by immunohistochemistry along with other prognostic factors in breast cancer. Among a panel of human breast cancer cell lines, only MDA-MB-436, a triple negative basal cell line, showed overexpression for PKCßII both at the mRNA and at the protein levels. In breast cancer patients, cytoplasmic expression of PKCßII correlated positively with human epidermal growth factor receptor-2 (HER-2; P = 0.01) and Ki-67 (P = 0.016), while nuclear PKCßII correlated positively with estrogen receptor (ER; P = 0.016). The positive correlation of CK5/6 with cytoplasmic PKCßII (P = 0.033) lost statistical significance after adjusting for multiple comparisons (P = 0.198). Cytoplasmic PKCßII did not correlate with cyclooxygenase (COX-2; P = 0.925) and vascular endothelial growth factor (P = 1). There was no significant association between PKCßII staining and overall survival. Cytoplasmic PKCßII correlates with HER-2 and Ki-67, while nuclear PKCßII correlates with ER in breast cancer. Our study suggests the necessity for assessing the subcellular localization of PKCßII in breast cancer subtypes when evaluating the possible effectiveness of PKCßII-targeting agents.

Predictors of Outcome in Mammary Adenoid Cystic Carcinoma: A Multi-Institutional Study.

Mammary adenoid cystic carcinoma (ACC) is a rare subtype of breast cancer with a favorable prognosis. Here we report on predictors of outcome based on a detailed morphologic review and analysis of 108 mammary ACC. Sixty-four tumors (59.2%) were pure conventional ACC, 23 (21.3%) were pure basaloid ACC. Follow-up was available for 87 patients (median: 51 mo). Eighteen patients (20.7%) developed recurrence: 7 (8%) had local recurrence and 14 (16%) had distant metastasis. Two patients died of disease, 1 died of an unrelated cause, 14 were alive with disease (including 8 in palliative care), and 70 (80.5%) were alive with no evidence of disease. Of 90 patients with known lymph node (LN) status 9 (10%) had nodal involvement (all with basaloid ACC). Distant metastases in patients with predominantly basaloid ACC compared with pure conventional ACC were more common (40% vs. 7.7%) and occurred earlier (22 vs. 84 mo). The following factors were found to be predictive of recurrence-free survival: positive margin, Nottingham grade, neovascularization, basaloid component, perineural invasion, lymphovascular invasion, >30% solid growth, necrosis and LN involvement; the first 3 remained statistically significant on multivariate analysis. Factors predictive of distant disease-free survival were neovascularization, Nottingham grade, lymphovascular invasion, solid component >50%, LN involvement, basaloid component >50%, tumor necrosis, perineural invasion, and final margin. Only neovascularization remained statistically significant on multivariate analysis. Basaloid ACC is an aggressive variant of mammary ACC with more frequent nodal involvement and higher incidence of distant spread. LN staging should be performed for all mammary basaloid ACC.

AlphaB-crystallin is a novel oncoprotein that predicts poor clinical outcome in breast cancer.

Recent gene profiling studies have identified a new breast cancer subtype, the basal-like group, which expresses genes characteristic of basal epithelial cells and is associated with poor clinical outcomes. However, the genes responsible for the aggressive behavior observed in this group are largely unknown. Here we report that the small heat shock protein alpha-basic-crystallin (alphaB-crystallin) was commonly expressed in basal-like tumors and predicted poor survival in breast cancer patients independently of other prognostic markers. We also demonstrate that overexpression of alphaB-crystallin transformed immortalized human mammary epithelial cells (MECs). In 3D basement membrane culture, alphaB-crystallin overexpression induced luminal filling and other neoplastic-like changes in mammary acini, while silencing alphaB-crystallin by RNA interference inhibited these abnormalities. alphaB-Crystallin overexpression also induced EGF- and anchorage-independent growth, increased cell migration and invasion, and constitutively activated the MAPK kinase/ERK (MEK/ERK) pathway. Moreover, the transformed phenotype conferred by alphaB-crystallin was suppressed by MEK inhibitors. In addition, immortalized human MECs overexpressing alphaB-crystallin formed invasive mammary carcinomas in nude mice that recapitulated aspects of human basal-like breast tumors. Collectively, our results indicate that alphaB-crystallin is a novel oncoprotein expressed in basal-like breast carcinomas that independently predicts shorter survival. Our data also implicate the MEK/ERK pathway as a potential therapeutic target for these tumors.

A panel of antibodies to determine site of origin and malignancy in smooth muscle tumors.

Leiomyosarcomas are malignant smooth muscle tumors that occur most commonly in the gynecologic tract and soft tissue. There are different diagnostic criteria of malignancy for smooth muscle tumors arising at gynecologic and soft tissue sites and they may be managed differently but determining the primary site of a smooth muscle tumor can be difficult in some cases. In addition, the distinction between malignant and benign gynecologic tract smooth muscle tumors on morphologic grounds can be challenging. Using a series of tissue microarrays that contain 245 cases of leiomyosarcomas (102 gynecologic) with survival data, and 49 cases of uterine leiomyoma, we examined the ability of selected immune-markers (estrogen receptor (ER) and WT1) to distinguish between leiomyosarcomas of gynecologic and nongynecologic origin. In addition, we examined whether immunostains for p16, p53 and Ki-67 could distinguish between malignant and benign gynecologic smooth muscle tumors. ER nuclear positivity was observed in 3 and 50% of the nongynecologic and gynecologic leiomyosarcomas, respectively (P<0.001). Nuclear WT1 positivity was seen in 0 and 8% of the nongynecologic and gynecologic leiomyosarcomas, respectively (P<0.001). 87% of primary gynecologic leiomyosarcomas and 2% of uterine leiomyomas showed diffuse (>or=50% of cells) p16 staining (P<0.001). 23% of gynecologic leiomyosarcomas showed p53 immunopositivity (>or=50% of cells) whereas none of the leiomyomas were positive for p53 (P<0.001). 65% of the gynecologic leiomyosarcomas and 0% of the leiomyomas exhibited >10% Ki-67 proliferation index (P<0.001). Diffuse p16 and p53 immunopositivity and high Ki-67 proliferation index, singly or in combination, yielded an overall sensitivity of 92% and specificity of 98% for distinguishing between gynecologic leiomyosarcomas and leiomyomas and can be used as indicators of malignancy for gynecologic smooth muscle tumors. Although ER positivity can be used to support the gynecologic origin of a leiomyosarcomas, nuclear WT1 immunostaining is of little use.

Intraepithelial T cells and prognosis in ovarian carcinoma: novel associations with stage, tumor type, and BRCA1 loss.

Intraepithelial tumor-infiltrating T cells have been correlated with improved outcomes in ovarian carcinoma, however, it is not known whether there is an association with disease stage, histological subtype, or BRCA mutation/expression. Two case series of ovarian carcinomas were included in the study; a retrospective series of 500 patients, and 40 prospectively collected cases fully characterized for BRCA1 mutation status and expression. Intraepithelial immune cells were assessed as present or absent by immunohistochemical staining of tissue microarrays. In the retrospective case series, the presence of intraepithelial CD8(+) T-cells correlated with improved disease-specific survival (P=0.027), whereas intraepithelial CD3(+) T cells did not (P=0.49). For serous ovarian carcinomas, the presence of intraepithelial CD3(+) and CD8(+) T-cells correlated with improved disease-specific survival (P=0.0016 and P

Inter-observer reproducibility of HER2 immunohistochemical assessment and concordance with fluorescent in situ hybridization (FISH): pathologist assessment compared to quantitative image analysis.

BACKGROUND: In breast cancer patients, HER2 overexpression is routinely assessed by immunohistochemistry (IHC) and equivocal cases are subject to fluorescent in situ hybridization (FISH). Our study compares HER2 scoring by histopathologists with automated quantitation of staining, and determines the concordance of IHC scores with FISH results. METHODS: A tissue microarray was constructed from 1,212 invasive breast carcinoma cases with linked treatment and outcome information. IHC slides were semi-quantitatively scored by two independent pathologists on a range of 0 to 3+, and also analyzed with an Ariol automated system by two operators. 616 cases were scorable by both IHC and FISH. RESULTS: Using data from unequivocal positive (3+) or negative (0, 1+) results, both visual and automated scores were highly consistent: there was excellent concordance between two pathologists (kappa = 1.000, 95% CI: 1-1), between two machines (kappa = 1.000, 95% CI: 1-1), and between both visual and both machine scores (kappa = 0.898, 95% CI: 0.775-0.979). Two pathologists successfully distinguished negative, positive and equivocal cases (kappa = 0.929, 95% CI: 0.909-0.946), with excellent agreement with machine 1 scores (kappa = 0.835, 95% CI: 0.806-0.862; kappa = 0.837, 95% CI: 0.81-0.862), and good agreement with machine 2 scores (kappa = 0.698, 95% CI: 0.6723-0.723; kappa = 0.709, 95% CI: 0.684-0.732), whereas the two machines showed good agreement (kappa = 0.806, 95% CI: 0.785-0.826). When comparing categorized IHC scores and FISH results, the agreement was excellent for visual 1 (kappa = 0.814, 95% CI: 0.768-0.856), good for visual 2 (kappa = 0.763, 95% CI: 0.712-0.81) and machine 1 (kappa = 0.665, 95% CI: 0.609-0.718), and moderate for machine 2 (kappa = 0.535, 95% CI: 0.485-0.584). CONCLUSION: A fully automated image analysis system run by an experienced operator can provide results consistent with visual HER2 scoring. Further development of such systems will likely improve the accuracy of detection and categorization of membranous staining, making this technique suitable for use in quality assurance programs and eventually in clinical practice.

Comparison of Artificial Intelligence Techniques to Evaluate Performance of a Classifier for Automatic Grading of Prostate Cancer From Digitized Histopathologic Images.

Importance: Proper evaluation of the performance of artificial intelligence techniques in the analysis of digitized medical images is paramount for the adoption of such techniques by the medical community and regulatory agencies. Objectives: To compare several cross-validation (CV) approaches to evaluate the performance of a classifier for automatic grading of prostate cancer in digitized histopathologic images and compare the performance of the classifier when trained using data from 1 expert and multiple experts. Design, Setting, and Participants: This quality improvement study used tissue microarray data (333 cores) from 231 patients who underwent radical prostatectomy at the Vancouver General Hospital between June 27, 1997, and June 7, 2011. Digitized images of tissue cores were annotated by 6 pathologists for 4 classes (benign and Gleason grades 3, 4, and 5) between December 12, 2016, and October 5, 2017. Patches of 192 µm2 were extracted from these images. There was no overlap between patches. A deep learning classifier based on convolutional neural networks was trained to predict a class label from among the 4 classes (benign and Gleason grades 3, 4, and 5) for each image patch. The classification performance was evaluated in leave-patches-out CV, leave-cores-out CV, and leave-patients-out 20-fold CV. The analysis was performed between November 15, 2018, and January 1, 2019. Main Outcomes and Measures: The classifier performance was evaluated by its accuracy, sensitivity, and specificity in detection of cancer (benign vs cancer) and in low-grade vs high-grade differentiation (Gleason grade 3 vs grades 4-5). The statistical significance analysis was performed using the McNemar test. The agreement level between pathologists and the classifier was quantified using a quadratic-weighted κ statistic. Results: On 333 tissue microarray cores from 231 participants with prostate cancer (mean [SD] age, 63.2 [6.3] years), 20-fold leave-patches-out CV resulted in mean (SD) accuracy of 97.8% (1.2%), sensitivity of 98.5% (1.0%), and specificity of 97.5% (1.2%) for classifying benign patches vs cancerous patches. By contrast, 20-fold leave-patients-out CV resulted in mean (SD) accuracy of 85.8% (4.3%), sensitivity of 86.3% (4.1%), and specificity of 85.5% (7.2%). Similarly, 20-fold leave-cores-out CV resulted in mean (SD) accuracy of 86.7% (3.7%), sensitivity of 87.2% (4.0%), and specificity of 87.7% (5.5%). Results of McNemar tests showed that the leave-patches-out CV accuracy, sensitivity, and specificity were significantly higher than those for both leave-patients-out CV and leave-cores-out CV. Similar results were observed for classifying low-grade cancer vs high-grade cancer. When trained on a single expert, the overall agreement in grading between pathologists and the classifier ranged from 0.38 to 0.58; when trained using the majority vote among all experts, it was 0.60. Conclusions and Relevance: Results of this study suggest that in prostate cancer classification from histopathologic images, patch-wise CV and single-expert training and evaluation may lead to a biased estimation of classifier's performance. To allow reproducibility and facilitate comparison between automatic classification methods, studies in the field should evaluate their performance using patient-based CV and multiexpert data. Some of these conclusions may be generalizable to other histopathologic applications and to other applications of machine learning in medicine.

Redefining prognostic factors for breast cancer: YB-1 is a stronger predictor of relapse and disease-specific survival than estrogen receptor or HER-2 across all tumor subtypes.

INTRODUCTION: Gene expression analysis is used to subtype breast cancers such that the most aggressive tumors are identified, but translating this into clinical practice can be cumbersome. Our goal is to develop a universal biomarker that distinguishes patients at high risk across all breast cancer subtypes. We previously reported that Y-box binding protein-1 (YB-1), a transcription/translation factor, was a marker of poor prognosis in a cohort of 490 patients with breast cancer, but the study was not large enough to subtype the cancers. We therefore investigated whether YB-1 identifies patients at risk for either reduced relapse free survival or decreased r breast cancer specific survival (BCSS) across all tumor subtypes by evaluating 4,049 cases. METHODS: Tumor tissue microarrays, representing 4,049 cases of invasive breast cancers with 20 years of follow up, were subtyped by the expression profiles of estrogen receptor, progesterone receptor, or HER-2. We then addressed whether YB-1 expression identified patients at higher risk for relapse and/or lower BCSS. RESULTS: We found YB-1 to be a highly predictive biomarker of relapse (P < 2.5 x 10(-20)) and poor survival (P < 7.3 x 10(-26)) in the entire cohort and across all breast cancer subtypes. Patients with node-positive or node-negative cancer were more likely to die from the disease if YB-1 was expressed. This was further substantiated using a Cox regression model, which revealed that it was significantly associated with relapse and poor survival in a subtype independent manner (relapse patients, hazard ratio = 1.28, P < 8 x 10(-3); all patients, hazard ratio = 1.45, P < 6.7 x 10(-7)). Moreover, YB-1 was superior to estrogen receptor and HER-2 as a prognostic marker for relapse and survival. For a subset of patients who were originally considered low risk and were therefore not given chemotherapy, YB-1 was indicative of poor survival (P < 7.1 x 10 (-17)). Likewise, YB-1 was predictive of decreased BCSS in tamoxifen-treated patients (P = 0.001); in this setting a Cox regression model once again demonstrated it to be an independent biomarker indicating poor survival (hazard ratio = 1.70, P = 0.022). CONCLUSIONS: Expression of YB-1 universally identifies patients at high risk across all breast cancer subtypes and in situations where more aggressive treatment may be needed. We therefore propose that YB-1 may re-define high-risk breast cancer and thereby create opportunities for individualized therapy.

Stromal mast cells in invasive breast cancer are a marker of favourable prognosis: a study of 4,444 cases.

PURPOSE: We have previously demonstrated in a pilot study of 348 invasive breast cancers that mast cell (MC) infiltrates within primary breast cancers are associated with a good prognosis. Our aim was to verify this finding in a larger cohort of invasive breast cancer patients and examine the relationship between the presence of MCs and other clinical and pathological features. EXPERIMENTAL DESIGN: Clinically annotated tissue microarrays (TMAs) containing 4,444 cases were constructed and stained with c-Kit (CD-117) using standard immunoperoxidase techniques to identify and quantify MCs. For statistical analysis, we applied a split-sample validation technique. Breast cancer specific survival was analyzed by Kaplan-Meier [KM] method and log rank test was used to compare survival curves. RESULTS: Survival analysis by KM method showed that the presence of stromal MCs was a favourable prognostic factor in the training set (P = 0.001), and the validation set group (P = 0.006). X-tile plot generated to define the optimal number of MCs showed that the presence of any number of stromal MCs predicted good prognosis. Multivariate analysis showed that the MC effect in the training set (Hazard ratio [HR] = 0.804, 95% Confidence interval [CI], 0.653-0.991, P = 0.041) and validation set analysis (HR = 0.846, 95% CI, 0.683-1.049, P = 0.128) was independent of age, tumor grade, tumor size, lymph node, ER and Her2 status. CONCLUSIONS: This study concludes that stromal MC infiltration in invasive breast cancer is an independent good prognostic marker and reiterates the critical role of local inflammatory responses in breast cancer progression.

Clinicopathologic factors and nuclear morphometry as independent prognosticators in KIT-positive gastrointestinal stromal tumors.

Gastrointestinal stromal tumors (GISTs) are mesenchymal neoplasms found in the gastrointestinal tract. The purpose of this study was to evaluate whether morphometric measurements could complement tumor size and mitotic activity in risk evaluation. Nuclear roundness and ellipse axis ratio were found to correlate with tumor size, mitotic activity, nuclear atypia, and hemorrhage. Morphometric variables in 422 GISTs were significant for overall survival in univariate analyses but did not retain independent significance in multivariate analyses incorporating mitotic count and tumor size. Traditional variables, together with sex, location of primary tumor, and nuclear atypia, seem to be the best parameters for prognostic evaluation.

FOXA1 expression in breast cancer--correlation with luminal subtype A and survival.

PURPOSE: FOXA1, a forkhead family transcription factor, is essential for optimum expression of approximately 50% of estrogen receptor alpha (ERalpha):estrogen responsive genes. FOXA1 is expressed in breast cancer cells. It segregates with genes that characterize the luminal subtypes in DNA microarray analyses. The utility of FOXA1 as a possible independent prognostic factor has not been determined in breast cancers. MATERIALS AND METHODS: A tissue microarray comprising tumors from 438 patients with 15.4 years median follow-up was analyzed for FOXA1 expression by immunohistochemistry. Interpretable FOXA1 expression obtained in 404 patients was analyzed along with other prognostic factors like tumor grade, size, nodal status, ER, progesterone receptor (PR), and HER2/neu. RESULTS: FOXA1 expression (score >3) was seen in 300 of 404 breast cancers and it correlated with ER (P = 0.000001), PR (P = 0.00001), and luminal A subtype (P = 0.000001). Loss of expression was noted with worsening tumor grade (P = 0.001). Univariate analysis showed nodal status (P = 0.0000012), tumor size (P = 0.00001), FOXA1 (P = 0.0004), and ER (P = 0.012) to be predictors of breast cancer-specific survival. Multivariate analysis showed only nodal status (P = 0.001) and tumor size (P = 0.039) to be significant prognostic factors, whereas FOXA1 (P = 0.060) and ER (P = 0.131) were not significant. In luminal subtype A patient subgroup, FOXA1 expression was associated with better cancer-specific survival (P = 0.024) and in ER-positive subgroup, it was better predictor of cancer-specific survival (P = 0.009) than PR (P = 0.213). CONCLUSION: FOXA1 expression correlates with luminal subtype A breast cancer and it is significant predictor of cancer-specific survival in patients with ER-positive tumors. Prognostic ability of FOXA1 in these low-risk breast cancers may prove to be useful in clinical treatment decisions.

Disruption of the Y-box binding protein-1 results in suppression of the epidermal growth factor receptor and HER-2.

The overexpression of the epidermal growth factor receptor (EGFR) and HER-2 underpin the growth of aggressive breast cancer; still, it is unclear what governs the regulation of these receptors. Our laboratories recently determined that the Y-box binding protein-1 (YB-1), an oncogenic transcription/translation factor, induced breast tumor cell growth in monolayer and in soft agar. Importantly, mutating YB-1 at Ser(102), which resides in the DNA-binding domain, prevented growth induction. We reasoned that the underlying cause for growth attenuation by YB-1(Ser(102)) is through the regulation of EGFR and/or HER-2. The initial link between YB-1 and these receptors was sought by screening primary tumor tissue microarrays. We determined that YB-1 (n = 389 cases) was positively associated with EGFR (P < 0.001, r = 0.213), HER-2 (P = 0.008, r = 0.157), and Ki67 (P < 0.0002, r = 0.219). It was inversely linked to the estrogen receptor (P < 0.001, r = -0.291). Overexpression of YB-1 in a breast cancer cell line increased HER-2 and EGFR. Alternatively, mutation of YB-1 at Ser(102) > Ala(102) prevented the induction of these receptors and rendered the cells less responsive to EGF. The mutant YB-1 protein was also unable to optimally bind to the EGFR and HER-2 promoters based on chromatin immunoprecipitation. Furthermore, knocking down YB-1 with small interfering RNA suppressed the expression of EGFR and HER-2. This was coupled with a decrease in tumor cell growth. In conclusion, YB-1(Ser(102)) is a point of molecular vulnerability for maintaining the expression of EGFR and HER-2. Targeting YB-1 or more specifically YB-1(Ser(102)) are novel approaches to inhibiting the expression of these receptors to ultimately suppress tumor cell growth.

Automatic grading of prostate cancer in digitized histopathology images: Learning from multiple experts.

Prostate cancer (PCa) is a heterogeneous disease that is manifested in a diverse range of histologic patterns and its grading is therefore associated with an inter-observer variability among pathologists, which may lead to an under- or over-treatment of patients. In this work, we develop a computer aided diagnosis system for automatic grading of PCa in digitized histopathology images using supervised learning methods. Our pipeline comprises extraction of multi-scale features that include glandular, cellular, and image-based features. A number of novel features are proposed based on intra- and inter-nuclei properties; these features are shown to be among the most important ones for classification. We train our classifiers on 333 tissue microarray (TMA) cores that were sampled from 231 radical prostatectomy patients and annotated in detail by six pathologists for different Gleason grades. We also demonstrate the TMA-trained classifier's performance on additional 230 whole-mount slides of 56 patients, independent of the training dataset, by examining the automatic grading on manually marked lesions and randomly sampled 10% of the benign tissue. For the first time, we incorporate a probabilistic approach for supervised learning by multiple experts to account for the inter-observer grading variability. Through cross-validation experiments, the overall grading agreement of the classifier with the pathologists was found to be an unweighted kappa of 0.51, while the overall agreements between each pathologist and the others ranged from 0.45 to 0.62. These results suggest that our classifier's performance is within the inter-observer grading variability levels across the pathologists in our study, which are also consistent with those reported in the literature.

Tissue microarray analysis of connexin expression and its prognostic significance in human breast cancer.

Breast cancer accounts for approximately 15% of all cancer deaths. Currently, axillary nodal status is the most reliable prognostic indicator for breast cancer. Tumor size and histological grade are used to stage breast cancer. Estrogen receptor/progesterone receptor (ER/PR) and HER-2/neu status are useful in predicting patient survival and relapse. Ki67, an indicator of proliferative activity, also correlates well with prognosis. Connexin proteins form gap junction channels, permitting intercellular exchange of ions and small molecules. Reduced connexin protein levels and impaired gap junctional intercellular communication are associated with tumor phenotypes. This study investigated the prognostic value of connexin proteins as breast cancer markers. Tissue microarrays, containing 438 cases of invasive breast carcinoma, were stained with Cx26, Cx32, and Cx43 antibodies. The degree of connexin immunoreactivity was determined and then correlated with patient outcome, tumor grade, tumor size, lymph node status, and immunohistochemical markers, such as p53, ER/PR status, Ki67 and c-erbB-2 expression. Cx26, Cx32, or Cx43 did not correlate well with tumor grade, tumor size, p53 or c-erbB-2 status. There was an inverse correlation between Cx32 and lymph node status (P <0.05) and a positive correlation between Cx43 and PR status (P <0.01). Cx32 and Cx43 correlated positively with ER status (P <0.01). Cx43 correlated negatively with Ki67 expression (P <0.01). Cx26, Cx32, and Cx43 did not correlate with patient outcome. Based on our observations in this study, connexin proteins do not appear to be reliable indicators of breast cancer prognosis.

Akt phosphorylates the Y-box binding protein 1 at Ser102 located in the cold shock domain and affects the anchorage-independent growth of breast cancer cells.

Akt/PKB is a serine/threonine kinase that promotes tumor cell growth by phosphorylating transcription factors and cell cycle proteins. There is particular interest in finding tumor-specific substrates for Akt to understand how this protein functions in cancer and to provide new avenues for therapeutic targeting. Our laboratory sought to identify novel Akt substrates that are expressed in breast cancer. In this study, we determined that activated Akt is positively correlated with the protein expression of the transcription/translation factor Y-box binding protein-1 (YB-1) in primary breast cancer by screening tumor tissue microarrays. We therefore questioned whether Akt and YB-1 might be functionally linked. Herein, we illustrate that activated Akt binds to and phosphorylates the YB-1 cold shock domain at Ser102. We then addressed the functional significance of disrupting Ser102 by mutating it to Ala102. Following the stable expression of Flag:YB-1 and Flag:YB-1 (Ala102) in MCF-7 cells, we observed that disruption of the Akt phosphorylation site on YB-1 suppressed tumor cell growth in soft agar and in monolayer. This correlated with an inhibition of nuclear translocation by the YB-1(Ala102) mutant. In conclusion, YB-1 is a new Akt substrate and disruption of this specific site inhibits tumor cell growth.