Search

Phosphatase and tensin homologue deleted on chromosome 10 (PTEN) is a dual-function phosphatase with tumor suppressor function compromised in a wide spectrum of cancers. Because tissue polarity and architecture are crucial modulators of normal and malignant behavior, we postulated that PTEN may play a role in maintenance of tissue integrity. We used two nonmalignant human mammary epithelial cell lines that form polarized, growth-arrested structures (acini) when cultured in three-dimensional laminin-rich extracellular matrix gels (lrECM). As acini begin to form, PTEN accumulates both in the cytoplasm and at cell-cell contacts where it colocalizes with the E-cadherin/β-catenin complex. Reduction of PTEN levels by shRNA in lrECM prevents formation of organized breast acini and disrupts growth arrest. Importantly, disruption of acinar polarity and cell-cell contact by E-cadherin function–blocking antibodies reduces endogenous PTEN protein levels and inhibits its accumulation at cell-cell contacts. Conversely, in Skbr-3 breast cancer cells lacking endogenous E-cadherin expression, exogenous introduction of E-cadherin gene causes induction of PTEN expression and its accumulation at sites of cell interactions. These studies provide evidence that E-cadherin regulates both the PTEN protein levels and its recruitment to cell-cell junctions in three-dimensional lrECM, indicating a dynamic reciprocity between architectural integrity and the levels and localization of PTEN. This interaction thus seems to be a critical integrator of proliferative and morphogenetic signaling in breast epithelial cells. [Cancer Res 2009;69(10):4545–52]

Supplementary Tables 1-6 from Gene Expression Signature in Organized and Growth-Arrested Mammary Acini Predicts Good Outcome in Breast Cancer

Supplementary Figure 1 from Interaction of E-cadherin and PTEN Regulates Morphogenesis and Growth Arrest in Human Mammary Epithelial Cells

Nonmalignant human mammary epithelial cells (HMEC) seeded in laminin-rich extracellular matrix (lrECM) form polarized acini and, in doing so, transit from a disorganized proliferating state to an organized growth-arrested state. We hypothesized that the gene expression pattern of organized and growth-arrested HMECs would share similarities with breast tumors with good prognoses. Using Affymetrix HG-U133A microarrays, we analyzed the expression of 22,283 gene transcripts in 184 (finite life span) and HMT3522 S1 (immortal nonmalignant) HMECs on successive days after seeding in a lrECM assay. Both HMECs underwent growth arrest in G0-G1 and differentiated into polarized acini between days 5 and 7. We identified gene expression changes with the same temporal pattern in both lines and examined the expression of these genes in a previously published panel of microarray data for 295 breast cancer samples. We show that genes that are significantly lower in the organized, growth-arrested HMEC than in their proliferating counterparts can be used to classify breast cancer patients into poor and good prognosis groups with high accuracy. This study represents a novel unsupervised approach to identifying breast cancer markers that may be of use clinically. (Cancer Res 2006; 66(14): 7095-102)

Supplementary Figure Legend from Interaction of E-cadherin and PTEN Regulates Morphogenesis and Growth Arrest in Human Mammary Epithelial Cells

We developed a test to predict which patients will achieve pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) and which will have residual disease (RD). Gene expression data from pretreatment biopsies of patients with all breast cancer subtypes were combined into a 519-patient cohort containing 177 TNBC patients. Two RNA classifiers of 16 genes each were sequentially applied to the total cohort, classifying patients into 3 distinct classes. The test performance was further validated in an independent 304-patient cohort. The test accurately identified 70.5% (79/112) of pCR and 83.5% (340/407) of RD patients in the total population, and 75.0% (45/60) of pCR and 75.2% (88/117) of RD patients in the TNBC subset. For the independent cohort, the test identified 91.5% RD patients in the total population and 86.2% RD patients in the TNBC subset. However, the identification of pCR in both total and TNBC population are as low as 21.1% and 30%, respectively. The TNBC RD patients were subdivided by our classifiers, with one class showing significantly higher levels of Ki67 expression and having significantly poorer survival rates than the other classes. This stratification of patients may allow predicted residual disease classes to be assigned an alternative therapy.

Clinically relevant biomarkers are useful to determine cancer patients' prognosis and treatments. To discover new putative biomarkers, we performed in silico analysis of a 325-gene panel previously associated with breast epithelial cell biology and clinical outcomes. Sixteen public datasets of microarray samples representing 8 cancer types and a total of 3,663 patients' samples were used for the analyses. Feature selection was used to identify the best subsets of the 325 genes for each classification, and linear discriminant analysis was used to quantify the accuracy of the classifications. A subset of 102 of the 325 genes were found to be housekeeping (HK) genes, and the classifications were repeated using only the 102 HK subset. The 325-gene panel and 102 HK subset were able to distinguish colon, gastric, lung, ovarian, pancreatic, and prostate tumors and leukemia from normal adjacent tissue, and classify disease subtypes of breast and lung cancers and leukemia with 70% or higher accuracy. HK genes have been overlooked as potential biomarkers due to their relative stability. This study describes a set of HK genes as putative biomarkers applicable to multiple cancer types worth following in subsequent validation studies.

Pathological complete response (pCR) to neoadjuvant chemotherapy (NAC) is correlated with better outcomes for breast cancer, especially for triple negative breast cancer (TNBC). We developed RNA expression classifiers from a model of breast epithelial cell organization to predict which patients will achieve pCR to NAC, and which will have residual disease (RD). An exclusive collection of retrospective formalin-fixed, paraffin-embedded (FFPE) pretreatment biopsies from 222 multi-institutional breast cancer patients treated with NAC, including 90 TNBC patients, were processed using standard procedures. A novel strategy using machine learning algorithms and statistical cross-validation were used to develop predictive classifiers based on AmpliSeq differential gene expression analysis of patient samples. Two RNA expression classifiers of 18 genes and 15 genes applied sequentially to the total cohort, classified patients into three distinct classes which accurately identified 83.75% of pCR and 86.62% of RD patients in the total population, and 92.10% of pCR and 80.77% of RD patients in the TNBC subset. This new approach identified a subset of TNBC patients predicted to have RD showing significantly higher levels of Ki-67 expression and having significantly poorer survival rates than the other TNBC patients. Stratification of patients may allow identification of TNBC patients with the worst prognosis prior to NAC, allowing for personalized treatments with the potential to improve patient outcomes.Statement of SignificanceStratification of TNBC patients by prognosis prior to NAC, may allow for more personalized treatment approaches with the potential to improve patient outcomes and reduce toxicity.

The objective of the present study was to evaluate the epigenetic changes occurring in early stages of breast cancer. The present study investigated the methylation profile of the ATM, p14ARF and p16INK4a promoters in total blood and plasma cell-free DNA (cfDNA) from women with impalpable breast lesions compared with in total blood of a control cohort of women without breast lesions. The samples were evaluated using the methylation-specific PCR method. The Fisher's exact test was used to evaluate statistical significance between the methylation and clinical variables. A total of 111 women were evaluated, including 56 women with impalpable breast cancer (39/56 also had paired plasma cfDNA) and 55 women in the control cohort (55 blood DNA). For blood DNA from women with malignant impalpable breast lesions, p16INK4a exhibited the greatest percentage of methylation (48%), followed by ATM (37.5%) and p14ARF (27%) promoters, regardless of age variation. For plasma cfDNA, the methylation rates for ATM, p14ARF and p16INK4a were 26, 26 and 10%, respectively. The methylation rates for the blood DNA of controls were the lowest for ATM (9%), p14ARF (7%) and p16INK4a (7%). The women with impalpable breast lesions (benign and malignant lesions) exhibited the highest methylation rate, regardless of age, compared with the paired plasma cfDNA and controls. This epigenetic change was statistically significant for the promoters of ATM (P=0.009) and p16INK4a (P=0.001) (impalpable breast lesions vs. control). The present study demonstrated that epigenetic changes occurring in the ATM and CDKN2A genes detectable in liquid biopsy were associated with the development of impalpable breast lesions.

Prediction of pathological complete response (pCR) for neoadjuvant treatment is an area of unmet clinical need, especially for triple negative breast cancer (TNBC) as pCR is correlated with better outcomes. Predicting which patients will have residual disease (RD) provides an opportunity to improve treatment planning. We developed a test to predict which patients are likely to achieve pCR or RD to the standard of care (taxane-based) neoadjuvant chemotherapy using gene expression profiling of 325 previously identified novel biomarkers. Three microarray datasets were used (GSE22226, GSE25055, and GSE25065) including a total of 594 stage II-III breast cancer patients of which 125 (21%) achieved pCR, and 469 (79%) RD. ER+ tumors were present in 57% of the patients and 52% were PGR+. Almost 90% of the patients were Her2-. Of 231 TNBC, 78 (33.8%) achieved pCR, while 153 (66.2%) RD. Of 303 ER+Her- patients 26 (8.6%) achieved pCR while 277 (91.4%) RD. The cohort was divided into balanced populations with 476 patients used for training (80%) and test (20%) rounds of model development, while 118 patients were reserved as a validation set. Combining a “winnowing” process to remove genes with least predictive power, and hundreds of thousands of step-wise runs, followed by ranking genes based on conditional probabilities, we developed a 17-gene cassette (BA100) which was locked-down in the validation set with ROC (AUC) = 0.818. With a cut-off of 83% sensitivity and 68% specificity (PPV 0.4; NPV 0.94), BA100 achieved a 16% true positive rate (true pCR) and 55% true negative rate (true RD) identifying 76% of the patients who achieved pCR, and 69% of the patients with RD. In TNBC, BA100 classified 29% as true positives (TP), 36% as false positive (FP), 30% true negative (TN), and 4.8% false negative (FN). Kaplan Meier (KM) curves showed a significant difference in 5-year disease-free survival (5Y DFS) between TP and TN (p=0.00453) or FP (p=2.09E-06). However, FP had even worse outcomes than TN patients. To improve the TP rate, additional genes expressed in TNBC plus the original 325 genes were subjected to a second round of gene selection to discriminate between TP and FP, resulting in a 16-gene cassette (BA100.1). With a cut-off of 95% sensitivity and 73% specificity (PPV 0.7; NPV 0.95), applying BA100.1 reduced the FP rates from 24% to 9%, while correctly identifying 88% of RD in the validation set. KM curves showed no significant difference in 5Y DFS between 124 TNBC (53.7%) classified as TN versus 29 TNBC (12.6%) classified as FP, while a significant difference in survival rate was found between TNBC classified as TN vs TP (Cox Proportional Harzard p=8.42e-05). Taken together, we developed a predictive test consisting of two gene cassettes that accurately identified 71% (88/104) of pCR, and 88% (417/469) of RD patients. Gene cassettes include several transcriptional repressors, PI3K signal transduction, components of telomerase, DNA repair genes, fatty acid metabolism and estrogen-independent proliferation. The test stratified TNBC with differential response to chemotherapy and survival rates so that novel approaches can be used without delay. Further validation will confirm the test utility. Citation Format: Fournier MV, Chen J, Obenauer J, Goodwin EC, Tannenbaum SH, Brufsky AM. A predictive test for neoadjuvant chemotherapy in breast cancer identifies a subset of triple negative patients with resistant disease and the poorest prognosis [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P2-10-08.

The aim of this study was to identify genes with higher expression in solid tumor cells by comparing human tumor biopsies with healthy blood samples using both in silico statistical analysis and experimental validations. This approach resulted in a novel panel of 80 RNA biomarkers with high discrimination power to detect circulating tumor cells in blood samples. To identify the 80 RNA biomarkers, Affymetrix HG-U133 plus 2.0 microarrays datasets were used to compare breast tumor tissue biopsies and breast cancer cell lines with blood samples from patients with conditions other than cancer. A total of 859 samples were analyzed at the discovery stage, consisting of 417 mammary tumors, 41 breast lines, and 401 control samples. To confirm this discovery, external datasets of eight types of tumors were used, and experimental validation studies (NanoString n-counter gene expression assay) were performed, totaling 5028 samples analyzed. In these analyses, the 80 biomarkers showed higher expression in all solid tumors analyzed relative to healthy blood samples. Experimental validation studies using NanoString assay confirmed the results were not dependent of the gene expression platform. A panel of 80 RNA biomarkers was described here, with the potential to detect solid tumor cells present in the blood of multiple tumor types.

The objective of the present study was to evaluate the epigenetic changes occurring in early stages of breast cancer. The present study investigated the methylation profile of the ATM, p14(ARF) and p16(INK4a) promoters in total blood and plasma cell-free DNA (cfDNA) from women with impalpable breast lesions compared with in total blood of a control cohort of women without breast lesions. The samples were evaluated using the methylation-specific PCR method. The Fisher's exact test was used to evaluate statistical significance between the methylation and clinical variables. A total of 111 women were evaluated, including 56 women with impalpable breast cancer (39/56 also had paired plasma cfDNA) and 55 women in the control cohort (55 blood DNA). For blood DNA from women with malignant impalpable breast lesions, p16(INK4a) exhibited the greatest percentage of methylation (48%), followed by ATM (37.5%) and p14(ARF) (27%) promoters, regardless of age variation. For plasma cfDNA, the methylation rates for ATM, p14(ARF) and p16(INK4a) were 26, 26 and 10%, respectively. The methylation rates for the blood DNA of controls were the lowest for ATM (9%), p14(ARF) (7%) and p16(INK4a) (7%). The women with impalpable breast lesions (benign and malignant lesions) exhibited the highest methylation rate, regardless of age, compared with the paired plasma cfDNA and controls. This epigenetic change was statistically significant for the promoters of ATM (P=0.009) and p16(INK4a) (P=0.001) (impalpable breast lesions vs. control). The present study demonstrated that epigenetic changes occurring in the ATM and CDKN2A genes detectable in liquid biopsy were associated with the development of impalpable breast lesions.

Background. With the development of new drug combinations and targeted treatments for multiple types of cancer, the ability to stratify categories of patient populations and to develop companion diagnostics has become increasingly important. A panel of 325 RNA biomarkers was selected based on cancer-related biological processes of healthy cells and gene expression changes over time during nonmalignant epithelial cell organization. This “cancer in reverse” approach resulted in a panel of biomarkers relevant for at least 7 cancer types, providing gene expression profiles representing key cellular signaling pathways beyond mutations in “driver genes.” Objective. To further investigate this biomarker panel, the objective of the current study is to (1) validate the assay reproducibility for the 325 RNA biomarkers and (2) compare gene expression profiles side by side using two technology platforms. Methods and Results. We have mapped the 325 RNA transcripts and in a custom NanoString nCounter expression panel to be compared to all potential probe sets in the Affymetrix Human Genome U133 Plus 2.0. The experiments were conducted with 10 unique biological formalin-fixed paraffin-embedded (FFPE) breast tumor samples. Each site extracted RNA from four sections of 10-micron thick FFPE tissue over three different days by two different operators using an optimized standard operating procedure and quality control criteria. Samples were analyzed using mas5 in BioConductor and NanoStringNorm in R. Pearson correlation showed reproducibility between sites for all 60 samples with r=0.995 for Affymetrix and r=0.999 for NanoString. Correlation in multiple days and multiple users was for Affymetrix r=0.962−0.999 and for NanoString r=0.982−0.991. Conclusion. The 325 RNA biomarkers showed reproducibility in two technology platforms with moderate to high concordance. Future directions include performing clinical validation studies and generating rationale for patient selection in clinical trials using the technically validated assay.

Breast impalpable lesions have become a clinical dilemma because they are small, presenting a heterogeneous cellular phenotype. The aim of this study was to evaluate the mutational profile of the PIK3CA, TP53, and CDKN2A genes, comparing the mammary tissue with the respective circulating free DNA (cfDNA). The PIK3CA, TP53, and CDKN2A genes were sequenced (PCR-Sanger) in 58 women with impalpable lesions (49 malignant and 9 benign) with the respective cfDNA. The chi-square or Fisher's exact test was used to evaluate statistical significance between the clinical variables and mutational profile. A total of 51 out of 58 samples generated successful mutation profiles in both breast lesion and cfDNA. Of the 37 mutations detected, 10 (27%) and 16 (43%) mutations were detected in benign and malignant breast lesions, respectively, while 2 (5%) and 9 (24%) were found in cfDNA of women with benign and malignant lesions, respectively. The lymph node involvement with mutations in the PIK3CA in malignant lesions (P = 0.001), and the relationship between mutations in PIK3CA, comparing ductal tumors with benign lesions (P = 0.05), were statistically significant. This study detected different mutations in PIK3CA, TP53, and CDKN2A genes, which represent, in part, the heterogeneity of impalpable lesions. The results confirm that more studies should be conducted on the functional role of cfDNA in the impalpable lesions.

Magnetic resonance imaging (MRI) captures the three dimensional way in which tumors are organized in the breast, defined as imaging phenotypes in the I-SPY 1 trial. We developed a gene set based on the way in which breast epithelial cells aggregate and organize in three dimensional cultures. We investigated whether these organizational genes correspond to the imaging phenotypes. MRI phenotypes have been shown to correspond to the degree of response to neoadjuvant chemotherapy, and are used to predict the ability to achieve breast conservation treatment (Mukhtar et al, Ann Surg Oncol, 20: 3823–3830, 2013). We hypothesized that the molecular profile accompanying phenotypic changes occurring during the organization process of non-malignant acini may explain the molecular basis of MRI tumor phenotypes. We have developed prediction models for MRI phenotypes based on expression profiles identified during the organization process of non-malignant breast epithelial cells in three-dimensional laminin-rich extracellular matrix (Fournier et al. Cancer Res, 66, 7095-102, 2006). We analyzed a subset of 324 organizational genes in 147 samples from stage II-III breast cancer in the I-SPY 1 trial cohort with Agilent microarrays and MRI annotation (CALGB 150007/150012; ACRIN 6657). MRI phenotype of the index lesion was assessed by the site radiologists using the following radiographic criteria: A) well defined unicentric mass; B) well-defined multilobulated mass; C) area enhancement with nodularity; D) area enhancement without nodularity; E) Septal spreading. The distribution of phenotypes in I-SPY 1 was: A: 16%, B: 33%, C: 30%, D: 15%, E: 7%. We developed predictors for MRI phenotypes dichotomized as either "well-defined" (A and B) or "non-well-defined" (C, D and E). Using patent-pending algorithms we selected a subset of the organizational genes with the greatest predictive power to identify MRI "well-defined" phenotypes in the I-SPY 1 cohort. Then prediction models were developed using the 50 top ranking genes and logistic regression methods. We followed Monte-Carlo cross validation method to make sure that the performance of a model is not a result of over-fitting the model to the sample data using separate datasets to support the modeling. The samples were randomly partitioned 10,000 times in a 85% training, and 15% test ratio to test models performance. The resulting performance of predictive models in the test set had an average classification accuracy of greater than 80% (ROC statistics AUC>0.8) using gene expression measurements from between 16 to 20 genes. For random lists of 16-20 genes, the accuracy was approximately 50% (ROC AUC∼0.5). Amongst the MRI models were several genes that are known to regulate key cellular pathways such as cell division, metabolism, and migration using MetaCore pathway analysis. Taken together, the results suggest that the MRI phenotypes may be a manifestation of the organization genes that determine behavior in three dimensional culture. Future research includes confirming these results using an independent dataset, defining the potential drivers of MRI phenotypes, and determining if putative drivers provide a key contribution to tumor subtypes. Citation Format: Marcia V Fournier, Alan Derr, Alex Margulis, Kevin Reid, Sara Brumbaugh, Richard Anderson, W Fraser Symmans, Laura Esserman, Nola Hylton. Gene expression profiles accompanying phenotypic changes during non-malignant breast epithelial cells acini formation to explain MRI phenotypes [abstract]. In: Proceedings of the Thirty-Seventh Annual CTRC-AACR San Antonio Breast Cancer Symposium: 2014 Dec 9-13; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2015;75(9 Suppl):Abstract nr P3-06-19.

Combining multiple data sets from the Gene Expression Omnibus (GEO) or other data repositories for an integrated analysis requires appropriate batch correction. ComBat, an empirical Bayesian method for batch correction of microarray data, is widely used and has been reported to be the best correction method. We combined cancer data from 16 public studies representing 8 tissue types and a total of 3,563 samples, used the R “sva” package and ComBat for batch correction, and examined 6 gene sets representing positive and negative controls. As positive controls, we extracted 4 gene sets from the Human Protein Atlas that were found to be expressed at least 5-fold higher in one tissue than in any of 35 other tissues, and we matched these genes to their Affymetrix U133A probesets. This resulted in 16 probesets specific for stomach, 18 for lung, 37 for pancreas, and 27 for prostate. A fifth positive control is a group of 85 genes called BA80 that we have found to be expressed much lower in blood than in solid tissues. As a negative control that we do not expect to change much between tissues, we used a list of 3,804 housekeeping (HK) genes that were reported to show less than a four-fold expression change across 16 tissue types. We compared the ComBat results to a new method we call equal medians. The equal medians method assumes that the 22,277 genes measured on the Affymetrix U133A microarrays can vary widely between tissues and batches, but that the median of the 22,277 genes is the same for every sample. We created boxplots of each gene set across the 16 studies before and after each method of batch correction. The reduction in batch effects was scored using the change in standard deviation of the HK genes. The preservation of biological variability was scored using the fold change of the positive controls, comparing the target tissue’s median to the nearest alternate tissue’s median. We used two GEO studies as independent representatives of each tissue type, so the two fold changes were averaged to create a single measure. The results using the HK genes showed that ComBat removed 99.90% of the batch effects visible in the raw data, while equal medians removed 61.58%. However, equal medians did the best at preserving biological variability, with a fold change of 4.8 for stomach, 13.1 for lung, 42.3 for pancreas, 12.0 for prostate, and 3.9 for blood. The corresponding fold changes for ComBat were 1.4, 1.1, 2.2, 1.0, and 1.0. We conclude that ComBat was best at removing batch effects, but at the undesirable cost of minimizing biological variation. We believe this is due to known and unknown sources of variability that are confounded with batches, which is one of ComBat’s known risks. Equal medians showed the opposite performance, preserving biological variation better while partially removing batch effects. We offer the equal medians method as an alternative batch correction method in cases where ComBat shows evidence of overcorrection. Citation Format: John C. Obenauer, Thomas P. Stockfisch, Marcia V. Fournier. Overcorrection of batch effects by ComBat can be avoided by using an equal medians method [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 1659.

Screening and prevention is critical to reducing breast cancer deaths, and mammography is not an ideal tool. Mammography has not reduced invasive breast cancer incidence, it has high numbers of false positives and it detects many non-invasive cancers that are not life threatening. Metastatic breast cancer is the second leading cause of cancer death for women. The development of a new blood test for breast cancer screening could offer significant improvements with the potential to be more accurate, less costly, less intimidating, not subject to age differences, and able to discriminate between cancers subtypes. Detection of breast cancer cells (BCC) in blood samples offers a valuable opportunity for screening. However, BCC in blood are rare and must be identified against a background of millions of other blood cells. Although many technologies have been developed for the isolation and detection of circulating tumor cells using EpCAM and cytokeratins, these tests do not provide levels of sensitivity and specificity required for a cancer screening tool. To address this critical unmet need, we developed a panel of cell surface markers whose expression patterns specifically distinguish breast cancer cells in blood samples. These genes were selected based on a discovery platform tailored to detect early stage breast cancers and include 20 novel putative cell surface molecules that are candidates for breast cancer cell detection and isolation. We prioritized these molecules by specificity of expression in tumor cells including: 1) Ratio >25:1 average breast tumor biopsy expression versus average normal blood expression, 2) High expression relative (>3 fold) in >20% of tumor biopsy samples, 3) High expression (>3 fold) in >40% of breast cancer cell lines, and 4) Expression significantly associated with 10-year outcomes (Kaplan Meier analysis, p Citation Format: Lucas Delmonico, Edward C. Goodwin, Marcia Vasconcellos Fournier. Isolation and detection of breast cancer cells in blood samples using antibody cocktails selected by large-scale genome-wide screening [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr 5576.

From over two hundred driver mutations identified to date, only about a dozen are FDA-approved biomarkers, and there is an unmet need to discover novel suitable biomarkers. We selected novel biomarkers based on non-malignant breast epithelial cell phenotypes and identified 325 genes (BA325) showing 32 significant oncology drug associations. A total of 251 genes out of 325 are unique and not found in any of 9 other oncology panels investigated, suggesting that BA325 may yield novel insights regarding tumor biology, clinical outcomes, and novel therapeutic targets, not covered by current tools. While prior work has validated the utility of BA325 in breast cancer, the current study investigates BA325 expression in other tumor types beyond breast. We tested BA325 expression in 8 tumor types (breast, colon, lung, ovarian, prostate, pancreatic, gastric cancers, and leukemia), using two independent public data sets for each, totaling 3,563 samples in 16 datasets. All used Affymetrix HG-U133A or Plus 2.0 microarrays. Samples were normalized within each study using RMA and batch-corrected across studies. BA325 expression levels were investigated, showing at least 324 of the 325 genes expressed above background levels (log2 > 4) in all cases tested. The expression profile of BA325 differed in magnitude and pattern across tumor types from 1,000 randomly generated sets of 325 genes. Surprisingly, 102 of the BA325 genes were among 3,804 published housekeeping (HK) genes, with 7 of them present in a predictive signature developed for breast cancer chemotherapy response. The results suggest HK genes may play a role in the underlying biology of how cancer cells respond to treatments. A set of 119 genes showed tissue-specific expression (10 breast, 2 colon, 7 lung, 3 ovarian, and 42 prostate), while 87 genes showed the least variation across tumors (4-fold change or less), including the genes MPRIP, MUS81, and AKT1. Twenty seven (27) of the tissue-specific genes and 48 of the least-varying genes are also reported to be HK genes. Differential expression was tested by unsupervised clustering in 14 of the 16 studies. BA325 genes successfully distinguished tumor and normal samples in gastric and ovarian cancers, and subtypes of lung and breast cancers, with implications for treatment responses. We conclude that BA325 expression profiles in all datasets examined include both tissue-specific genes and genes with similar expression across tissues. Preliminary results indicate BA325 genes may have utility as biomarkers in a surprisingly wide variety of tumor types (including leukemia) in addition to breast cancer, with discriminatory power in at least gastric, ovarian, lung and breast cancers. Thus, BA325 can greatly increase the biomarker repertoire beyond oncogenes or other driver genes and may provide relevant insight in novel oncology therapeutic targets. Citation Format: Marcia V. Fournier, John C. Obenauer, Andreia Maer, Edward C. Goodwin. Genes involved in non-malignant breast phenotypes are widely expressed in multiple cancers and provide novel biomarkers of clinical outcomes and therapeutic response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-217.

Phosphatase and tensin homologue deleted on chromosome 10 (PTEN) is a dual-function phosphatase with tumor suppressor function compromised in a wide spectrum of cancers. Because tissue polarity and architecture are crucial modulators of normal and malignant behavior, we postulated that PTEN may play a role in maintenance of tissue integrity. We used two nonmalignant human mammary epithelial cell lines that form polarized, growth-arrested structures (acini) when cultured in three-dimensional laminin-rich extracellular matrix gels (lrECM). As acini begin to form, PTEN accumulates both in the cytoplasm and at cell-cell contacts where it colocalizes with the E-cadherin/β-catenin complex. Reduction of PTEN levels by shRNA in lrECM prevents formation of organized breast acini and disrupts growth arrest. Importantly, disruption of acinar polarity and cell-cell contact by E-cadherin function–blocking antibodies reduces endogenous PTEN protein levels and inhibits its accumulation at cell-cell contacts. Conversely, in Skbr-3 breast cancer cells lacking endogenous E-cadherin expression, exogenous introduction of E-cadherin gene causes induction of PTEN expression and its accumulation at sites of cell interactions. These studies provide evidence that E-cadherin regulates both the PTEN protein levels and its recruitment to cell-cell junctions in three-dimensional lrECM, indicating a dynamic reciprocity between architectural integrity and the levels and localization of PTEN. This interaction thus seems to be a critical integrator of proliferative and morphogenetic signaling in breast epithelial cells. [Cancer Res 2009;69(10):4545–52]

Nonmalignant human mammary epithelial cells (HMEC) seeded in laminin-rich extracellular matrix (lrECM) form polarized acini and, in doing so, transit from a disorganized proliferating state to an organized growth-arrested state. We hypothesized that the gene expression pattern of organized and growth-arrested HMECs would share similarities with breast tumors with good prognoses. Using Affymetrix HG-U133A microarrays, we analyzed the expression of 22,283 gene transcripts in 184 (finite life span) and HMT3522 S1 (immortal nonmalignant) HMECs on successive days after seeding in a lrECM assay. Both HMECs underwent growth arrest in G0-G1 and differentiated into polarized acini between days 5 and 7. We identified gene expression changes with the same temporal pattern in both lines and examined the expression of these genes in a previously published panel of microarray data for 295 breast cancer samples. We show that genes that are significantly lower in the organized, growth-arrested HMEC than in their proliferating counterparts can be used to classify breast cancer patients into poor and good prognosis groups with high accuracy. This study represents a novel unsupervised approach to identifying breast cancer markers that may be of use clinically. (Cancer Res 2006; 66(14): 7095-102)

The more severe form of dengue virus infection, dengue hemorrhagic fever, is characterized by plasma leakage and derangements in hemostasis. As elevated interleukin-8 (IL-8) levels have been observed in sera from patients with more severe disease manifestations, a study was initiated to look at the effect of dengue virus infection in vitro on proinflammatory cytokine secretion and expression. A significant increase in IL-8 levels in the culture supernatant of primary human monocytes infected with dengue 2 virus (D2V) New Guinea C (NGC) was found by enzyme-linked immunosorbent assay. Additionally, by reverse transcriptase PCR, the mRNA was also augmented. Among the proinflammatory cytokines and their mRNAs measured (IL-6, IL-1β, IL-8, and tumor necrosis factor alpha), IL-8 showed the greatest change following D2V infection. Similarly, two cell lines, 293T (a human epithelial cell line) and ECV304 (an endothelial cell line), were permissive to D2V NGC and responded to the infection by increasing the synthesis of IL-8. Nuclear factor kappa B (NF-κB) and nuclear factor IL-6 (NFIL-6) are primary mediators of IL-8 expression. We studied the transcriptional regulation of IL-8 in the ECV304 and 293T cell lines and found that the induction of IL-8 gene expression involved the activation of NF-κB (P= 0.001) and, to a lesser extent, the activation of NFIL-6 in ECV304 cells only. We next observed by the chromatin immunoprecipitation procedure in vivo acetylation of core histones bound to the IL-8 promoter after D2V infection. IL-8 produced by infected monocytes and also IL-8 that may be produced by endothelial or other epithelial cells is associated with the hyperacetylation of histones bound to the IL-8 promoter in addition to the activation of transcription by NF-κB. We hypothesize that the overall increase in IL-8 synthesis observed in this in vitro study may play a role in the pathogenesis of the plasma leakage seen in dengue hemorrhagic fever and dengue shock syndrome.

Background: We developed an algorithm based on the gene expression of tumor biopsies to identify the best combination of biomarkers to reliably predict a patient’s response to relevant cancer treatments. This algorithm is derived from 325 genes whose expression showed significant changes during differentiation of non-malignant human mammary epithelial cells cultured in laminin-rich extracellular matrix. Of these 325 genes, 251 are novel and not present in 9 other cancer based gene expression panels such as FoundationOne or PAM50. These differentiated cells formed multicellular structures with defined lumens and tight junctions and with specific localizations of cadherin and integrins. In contrast, cells from breast cancers displayed a general loss of structure. Previous work showed that different sets of these 325 biomarkers accurately predicted overall breast cancer patients' survival or response to neoadjuvant therapy in multiple independent studies. Objective: Predict cell-signaling pathways, drug associations, and disease associations for the 325 biomarkers (BA325) in contrast to other cancer gene panels. Methods and Results: The Qiagen Ingenuity program was used to identify pathways and disease states containing significant overlap with BA325. Multiple cell signaling pathways including cell proliferation, migration, invasion, and metabolism were found in BA325, while most other cancer biomarker panels were highly concentrated in cell proliferation. Examples of significant pathway associations relevant for oncology drug discovery and targeted treatments include Cell Cycle Control of Chromosomal Replication (p=8.1E-14), Polo-like-kinase and HSP90 complex (p=6.3E-07), G2/M DNA Damage Checkpoint (p=6.4E-07), Integrin Signaling (p=3E-05), Integrin Linked Kinase Signaling (p=4.51E-05), BRCA1 DNA Damage Response (p=1.75E-04), Estrogen Mediated S-phase entry (p=4.24E-04), Regulation of Actin Based Motility by RHO (p=4.55E-04), Adherins Junction Remodeling (p=5.6E-04), Actin Cytoskeleton Signaling (p=7.25E-04), Mismatch Repair (p=1.6E-03), RAN Signaling (p=1.64E-03), Protein Ubiquitination (p=1.78E-03) and Cholesterol Biosynthesis (p=4.8E-03). This wide coverage of cellular signaling pathways provides an advantage in developing predictive tests for treatments targeting highly complex cell signaling networks. Surprisingly, even though the BA325 panel was obtained during non-malignant differentiation, the most significant disease state associated with these genes was mammary tumor, demonstrating the validity of this approach to probe breast cancer biology. Conclusion: This analysis demonstrates that the BA325 panel is useful both in understanding non-malignant mammary epithelial differentiation and breast cancer tumors. Citation Format: Edward C. Goodwin, Said Attiya, Marcia Fournier. A novel panel of 325 biomarkers is part of a large interconnected network representing multiple cell signaling pathways and allowing development of predictive tests for oncology drugs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 405. doi:10.1158/1538-7445.AM2017-405

Background: With new developments of oncology drug combinations and targeted treatments, the ability to stratify categories of patient populations and to develop companion diagnostics has become increasingly important. A panel of 325 RNA-biomarkers was selected based on cancer-related biological processes of healthy cells and gene expression changes over time during non-malignant epithelial cell organization. This "cancer in reverse" approach in combination with empirically derived algorithms resulted in a panel of biomarkers having little overlap with 9 other widely-used commercial gene panels analyzed to date (e.g., overlap with FoundationOne was 2% (lowest) and with OncotypeDX was 14% (highest)), providing a more in-depth and comprehensive view of gene expression profiles and key cellular signaling pathways beyond mutations in "driver-genes", and drug associations including chemotherapies, immunotherapies, and targeted-therapies. Objective: In order to technically validate an assay for the 325 RNA-biomarkers we compared gene expression profiles side-by-side using two technology platforms to address the reproducibility of the assay. Methods and Results: We have mapped the 325 RNA transcripts and 7 housekeeping genes in a custom NanoString n-Counter expression panel to be compared to all potential probe sets in the Affymetrix Human Genome U133 Plus 2.0 microarray. The experiments were conducted with 10 unique biological formalin-fixed paraffin-embedded (FFPE) breast tumor samples. Each site extracted RNA from four sections of 10-microns thick FFPE tissue over three different days by three different operators using an optimized standard operating procedure and quality control criteria. Samples were analyzed using mas5 in BioConductor and NanoStringNorm in R. Pearson correlation showed reproducibility between sites for all 60 samples with r=0.995 for Affymetrix and r=0.999 for NanoString. Correlation in multiple days and multiple users were for Affymetrix r=(0.962-0.999) and for NanoString r=(0.982-0.991). The platforms were compared using relative expression fold changes using linear regression (lm). Concordant genes were defined to have gene expression levels within +/- 2 standard deviation (sd) of each other at 95% confidence interval, or to have greater than +/- 2 sd but have changes in the same direction in both platforms. The discordant genes were defined to have opposite direction of changes. By this definition, approximately 90% of the genes fell into the concordant category. Conclusion: The 325 RNA-biomarkers showed reproducibility in two technology platforms with high concordance. With five predictive tests under development for breast (3), lung and pancreatic cancers future directions include performing clinical validation studies and generate rationale for patient selection in clinical trials using the technically validated assays. Citation Format: Marcia V. Fournier, Said Attiya, Joan Chen, John Obenauer, Anup Madan, Patricia Smith. Technical validation of novel 325 RNA predictive biomarkers using gene expression data generated by Nanostring n-counter and Affymetrix microarray [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr 1785. doi:10.1158/1538-7445.AM2017-1785

Efforts in metastasis research have centered on the phenotypic and genetic differences between primary site and metastatic site tumors. However, genes that may be used as molecular markers of metastasis in circulating tumor cells remain unidentified. Genes regulating the dissemination and survival of solid tumor cells in the blood, as well as their adaptation to new environments, could be candidates for unique metastatic tumor markers. Differential display (DD) was conducted to compare the blood of tumor-free individuals with the blood of patients with lung, breast, and colon cancers. Twenty-one up-expressed genes in the tumor patient blood samples but none in the tumor-free donor blood samples were identified. Nine of these samples were isolated, amplified, and directly sequenced. A gene AB-1 homologous to a Bcl-2 family member, which might function as an apoptosis inhibitor, was identified. The overexpression of an apoptosis inhibitor in blood from patients with metastatic tumors might be correlated with the capability of solid tumor cells to survive in peripheral blood. This is the first demonstration of the usefulness of comparing control and patient blood samples by DD to find novel potential genetic markers identifying metastasis in the blood. http://link.springer-ny. com/link/service/journals/00020/bibs/5n5p313.html

Open innovation and collaborative research including through international consortium institutions are generating important issues in life science fields. There are special difficulties in addressing the current science and technology issues by reinforcing the existing therapeutics and diagnostics development system. In case of cancer research, the tumor sample is limited, while predictability and validation of preclinical research require scientists to allocate additional attentions both to scientific research and operations. In this context, we discuss whether it is wise for the science community to treat positive paper retractions, such as those caused by scientific reproducibility issues the same as those caused by misconduct. It is possible that overly aggressive retractions of papers are removing important evidence from knowledge accumulation. Connectivity and recoupling are sources of knowledge creation that have a potential for generating new hypotheses, mechanisms and discoveries. In this paper, we focus on peer-reviewed science paper publications. We set up a research question exploring whether cancer research retractions are following the general pattern of peer reviewed paper retractions. A prior paper (Fang, et. al. 2012) showed that the majority of peer reviewed science paper retractions in the field of biomedical and life science were caused by misconduct. In contrast to them, we found several pieces of evidence that cancer and cancer biomarker research are not following the general trends. There are several authors who retracted their papers proactively because of reproducibility issues. Among them, several could have failed even if they followed the scientific methodology and practices with the best knowledge at the timings of their submissions. Treating such papers the same as retractions rooted in misconduct will lead to the removal of important scientific evidence from the field where knowledge networks and recoupling are crucial. On the side of scientific journals, several initiatives have started. For example, the Reproducibility Initiative makes it a rule to have models confirmed by other independent teams. At the same time, we propose for the science community to rethink the positive retraction issue and keep open the possibility that some papers with reproducibility issues might be rescued from hatchet-like retraction policies.

Cancer continues to be a major cause of mortality despite decades of effort and expense. The problem reviewed here is that before many cancers are discovered they have already progressed to become drug resistant or metastatic. Biomarkers found in blood or other body fluids could supplement current clinical indicators to permit earlier detection and thereby reduce cancer mortality. Cancer Res; 70(13); 5203–6. ©2010 AACR.

Background One of the major tenets in breast cancer research is that early detection is vital for patient survival by increasing treatment options. To that end, we have previously used a novel unsupervised approach to identify a set of genes whose expression predicts prognosis of breast cancer patients. The predictive genes were selected in a well-defined three dimensional (3D) cell culture model of non-malignant human mammary epithelial cell morphogenesis as down-regulated during breast epithelial cell acinar formation and cell cycle arrest. Here we examine the ability of this gene signature (3D-signature) to predict prognosis in three independent breast cancer microarray datasets having 295, 286, and 118 samples, respectively. Methods and Findings Our results show that the 3D-signature accurately predicts prognosis in three unrelated patient datasets. At 10 years, the probability of positive outcome was 52, 51, and 47 percent in the group with a poor-prognosis signature and 91, 75, and 71 percent in the group with a good-prognosis signature for the three datasets, respectively (Kaplan-Meier survival analysis, p

More and more, array platforms are being used to assess gene expression in a wide range of biological and clinical models. Technologies using arrays have proven to be reliable and affordable for most of the scientific community worldwide. By typing microarrays or proteomics into a search engine such as PubMed, thousands of references can be viewed. Nevertheless, almost everyone in life science research has a story to tell about array experiments that were expensive, did not generate reproducible data, or generated meaningless data. Because considerable resources are required for any experiment using arrays, it is desirable to evaluate the best method and the best design to ask a certain question. Multiple levels of technical problems, such as sample preparation, array spotting, signal acquisition, dye intensity bias, normalization, or sample-contamination, can generate inconsistent results or misleading conclusions. Technical recommendations that offer alternatives and solutions for the most common problems have been discussed extensively in previous work. Less often discussed is the experimental design. A poor design can make array data analysis difficult, even if there are no technical problems. This chapter focuses on experimental design choices in terms of controls such as replicates and comparisons for microarray and proteomics. It also covers data validation and provides examples of studies using diverse experimental designs. The overall emphasis is on design efficiency. Though perhaps obvious, we also emphasize that design choices should be made so that biological questions are answered by clear data analysis.

Early detection has been one of the most effective strategies to control the growing cancer burden. The power of earlier detection has been demonstrated by the impact of pap-smear, mammography, and PSA tests on cancer patient treatment and survival. These tests benefit patients independent of their genetic background or race. However, in many cases, we are still losing the battle against cancer because patients that initially presented with low-grade disease progress rapidly to aggressive forms of the disease. As of yet, we have limited means to predict a particular patient's fate or to specifically treat subtypes of cancer. A combination of earlier detection and targeted therapy, based on information from transcriptome analysis, could be a powerful ally in this battle. The theme of this review article is to briefly summarize innovative strategies using three-dimensional (3D) cell cultures of human mammary epithelial cells to predict clinical outcome in breast cancer. This strategy has the potential to further enhance our understanding of breast cancer biology and to contribute to the identification of biologically significant bio-markers that are also useful drug targets.

It has been suggested that a switch in chemokine receptor expression underlies Langerhans cell migration from skin to lymphoid tissue. Activated cells are thought to down-regulate CCR6, whose ligand macrophage inflammatory protein-3 alpha (MIP-3 alpha)/CCL20 is expressed in skin, and up-regulate CCR7, whose ligands are in lymphoid tissues. In Langerhans cell histiocytosis (LCH), pathologic Langerhans cells (LCs) accumulate in several tissues, including skin, bone, and lymphoid organs. We have examined 24 LCH cases and find that pathologic LCs expressed CCR6 and CCR7 coincidentally in all cases. Furthermore, MIP-3 alpha/CCL20 is expressed by keratinocytes in involved skin and by macrophages and osteoblasts in involved bone. Expression of CCR6 by pathologic LCs may contribute to their accumulation in nonlymphoid organs such as skin and bone, whereas CCR7 expression may direct them to lymphoid tissue. Histiocytes in Rosai-Dorfman disease and hemophagocytic syndrome also coexpressed CCR6 and CCR7, suggesting that this may be a general attribute of abnormal histiocytes.

Early detection is an effective means of reducing cancer mortality. Here, we describe a highly sensitive high-throughput screen that can identify panels of markers for the early detection of solid tumor cells disseminated in peripheral blood. The method is a two-step combination of differential display and high-sensitivity cDNA arrays. In a primary screen, differential display identified 170 candidate marker genes differentially expressed between breast tumor cells and normal breast epithelial cells. In a secondary screen, high-sensitivity arrays assessed expression levels of these genes in 48 blood samples, 22 from healthy volunteers and 26 from breast cancer patients. Cluster analysis identified a group of 12 genes that were elevated in the blood of cancer patients. Permutation analysis of individual genes defined five core genes ( P ≤ 0.05, permax test). As a group, the 12 genes generally distinguished accurately between healthy volunteers and patients with breast cancer. Mean expression levels of the 12 genes were elevated in 77% (10 of 13) untreated invasive cancer patients, whereas cluster analysis correctly classified volunteers and patients ( P = 0.0022, Fisher's exact test). Quantitative real-time PCR confirmed array results and indicated that the sensitivity of the assay (1:2 × 10 8 transcripts) was sufficient to detect disseminated solid tumor cells in blood. Expression-based blood assays developed with the screening approach described here have the potential to detect and classify solid tumor cells originating from virtually any primary site in the body.

Statement of Purpose: To test the utility of a novel, multifaceted 22-gene signature to predict response to chemotherapy in different molecular subtypes of breast cancer. Background: Only 15-25% of breast cancer patients from all histopathological subtypes currently respond to primary chemotherapy with a complete pathological response (pCR) and there is currently no test in widespread clinical use to predict whether a patient will respond. Here we present results describing a 22-gene signature that can identify breast cancer patients who will respond to taxol-combination chemotherapy in a wide range of molecular subtypes. The signature was identified using a biology-driven approach. We hypothesized that genes involved in normal human mammary acinar morphogenesis represent biomarkers capable of stratifying breast cancers. Methods: To determine whether genes with differential expression during acinar morphogenesis predict response to chemotherapy, we have analyzed gene expression microarray results. Three preexisting microarray datasets were used. 1) The dataset of Fournier et al (Cancer Res, 2006. 66:7095) obtained from a time course of two different non-malignant human mammary epithelial cell types grown in a laminin-rich extracellular matrix culture (lrECM). 2) The dataset of Hess et al (J Clin Oncol, 2006. 24:4236) including 133 patients with stage I-III breast cancer obtained before primary taxol-combination chemotherapy (TFAC). 3) The dataset of Popovici et al (Breast Cancer Res, 2010. 12:R5) including 278 patient samples that overlaps with the previous dataset. The assignment of molecular subtype of tumor samples was performed using the intrinsic gene set of 300 genes (Hu et al., BMC Genomics, 2006.7:96). Results: The majority of genes (12 out 22) down regulated upon growth arrest and polarity formation during acinar morphogenesis in lrECM were significantly over expressed in patients who responded to chemotherapy with pCR (p Conclusions: This study is the first example of a single gene signature to predict chemotherapy response in different molecular subtypes of breast cancer. Its predictive value in ER-negative and basal-like breast cancers is notable. Patients identified as non-responsive have the potential to benefit from adding an alternative treatment early in their care or from improved quality of life in forgoing an ineffective treatment. Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 102nd Annual Meeting of the American Association for Cancer Research; 2011 Apr 2-6; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2011;71(8 Suppl):Abstract nr 5065. doi:10.1158/1538-7445.AM2011-5065

The purpose of this study is to evaluate a novel 22-gene signature as a predictor of response to chemotherapy in breast cancer. The extent of residual viable tumor following chemotherapy is the most important predictor of long-term outcome in breast cancer. It is essential to identify patients who will not respond to chemotherapy to avoid ineffective therapies as well as unnecessary side effects. The rate of complete pathologic response to chemotherapy is relatively low, ranging from 3% to 27% of patients. Much work has been performed to predict chemotherapy responsiveness, but, as of yet, no test is effective and widely used in the clinic. Gene expression signatures are a multifaceted approach to characterizing disease states. They offer the potential to tailor a chemotherapy regimen to an individual patient. Here we compare the ability of multiple gene signatures, including the 70-gene prognostic signature (MammaPrint) and the 22-gene signature among others, to accurately predict response to multiple different regimens of primary chemotherapy in breast cancer. Predictive ability is compared in independent microarray datasets totaling approximately 157 breast cancer patients treated with primary chemotherapy. Sensitivity, specificity, and overall accuracy were evaluated by receiver operating characteristics (ROC) graphs, a technique for visualizing, organizing, and selecting classifiers based on their performance. Results show that the 22-gene signature accurately predicts response to multiple primary chemotherapies (p Citation Format: {Authors}. {Abstract title} [abstract]. In: Proceedings of the 101st Annual Meeting of the American Association for Cancer Research; 2010 Apr 17-21; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2010;70(8 Suppl):Abstract nr 1767.