Comparison of gene expression profiles in core biopsies and corresponding surgical breast cancer samples

Pre-operative prediction of BCR-free survival with mRNA variables in prostate cancer

Technological innovation yielded opportunities to obtain mRNA expression data for prostate cancer (PCa) patients even prior to biopsy, which can be used in a precision medicine approach to treatment decision-making. This can apply in particular to predict the risk of, and time to biochemical recurrence (BCR). Most mRNA-based models currently proposed to this end are designed for risk classification and post-operative prediction. Effective pre-operative prediction would facilitate early treatment decision-making, in particular by indicating more appropriate therapeutic pathways for patient profiles who would likely not benefit from a systematic prostatectomy regime. The aim of this study is to investigate the possibility to leverage mRNA information pre-operatively for BCR-free survival prediction. To do this, we considered time-to-event machine learning (ML) methodologies, rather than classification models at a specific survival horizon. We retrospectively analysed a cohort of 135 patients with clinical follow-up data and mRNA information comprising over 26,000 features (data accessible at NCBI GEO database, accession GSE21032). The performance of ML models including random survival forest, boosted and regularised Cox models were assessed, in terms of model discrimination, calibration, and predictive accuracy for overall, 3-year and 5-year survival, aligning with common clinical endpoints. Results showed that the inclusion of mRNA information could yield a gain in performance for pre-operative BCR prediction. ML-based time-to-event models significantly outperformed reference nomograms that used only routine clinical information with respect to all metrics considered. We believe this is the first study proposing pre-operative transcriptomics models for BCR prediction in PCa. External validation of these findings, including confirmation of the mRNA variables identified as potential key predictors in this study, could pave the way for pre-operative precision nomograms to facilitate timely personalised clinical decision-making.

Analysis of Carcinogenic Involvement of MicroRNA Pattern in Peripheral Non-Cancerous Tissues and Chronic Viral Liver Injury

Risk factors for hepatocarcinogenesis include chronic inflammation due to viral infection, liver fibrosis, and aging. In this study, we separated carcinogenic and non-carcinogenic cases due to hepatitis C virus (HCV) infection, aiming to comprehensively analyze miRNA expression in liver tissues by age, and identify factors that contribute to carcinogenesis. Total RNA was extracted from 360 chronic hepatitis C (CH), 43 HCV infected hepatocellular carcinoma (HCC), and surrounding non-tumor (SNT) tissues. MicroRNA (miRNA) expression patterns were analyzed using microarray. Using machine learning, we extracted characteristic miRNA expression patterns for each disease and age. There were no age-dependent changes in miRNA expression in the disease-specific comparisons; however, miRNA expression differed among the age groups of 50, 60, and 70 years of age between CH and SNT. The expression of miRNA was different between SNT and HCC only in patients in their 70s. Of the 55 miRNAs with significant differences in expression between CH and SNT, 34 miRNAs showed significant differences in expression even in the degree of liver fibrosis. The observation that miRNAs involved in hepatocarcinogenesis differ at different ages suggests that the mechanisms of carcinogenesis differ by age group as well. We also found that many miRNAs whose expression did not affect liver fibrosis were involved in carcinogenesis. These findings are expected to define biomarkers for detection of HCC at early stage, and develop novel therapeutic targets for HCC.

Development of Prediction Model Including MicroRNA Expression for Sentinel Lymph Node Metastasis in ER-Positive and HER2-Negative Breast Cancer

BACKGROUND

The aim of our study is to find microRNAs (miRNAs) associated with sentinel lymph node metastasis (SLNM) and to develop a prediction model for SLNM in ER-positive and HER2-negative (ER+/HER2-) breast cancer.

PATIENTS AND METHODS

In the present study, only ER+/HER2- primary breast cancer was considered. The discovery set for SLNM-associated miRNAs included 10 tumors with and 10 tumors without SLNM. The training and validation sets both included 100 tumors. miRNA expression in tumors was examined comprehensively by miRNA microarray in the discovery set and by droplet digital PCR in the training and validation sets.

RESULTS

In the discovery set, miR-98, miR-22, and miR-223 were found to be significantly (P < 0.001, fold-change > 2.5) associated with SLNM. In the training set, we constructed the prediction model for SLNM using miR-98, tumor size, and lymphovascular invasion (LVI) with high accuracy (AUC, 0.877). The accuracy of this prediction model was confirmed in the validation set (AUC, 0.883), and it outperformed the conventional Memorial Sloan Kettering Cancer Center nomogram. In situ hybridization revealed the localization of miR-98 expression in tumor cells.

CONCLUSIONS

We developed a prediction model consisting of miR-98, tumor size, and LVI for SLNM with high accuracy in ER+/HER2- breast cancer. This model might help decide the indication for SLN biopsy in this subtype.

Efficacy of an RNA-based multigene assay with core needle biopsy samples for risk evaluation in hormone-positive early breast cancer

BACKGROUND

Gene expression profiling provides key information for prognosis of breast cancer to establish treatment strategy. However, the genetic assessment should be available before induction of treatment to be useful for clinical practice. To evaluate the reliability of using needle biopsy samples for gene assays, we compared gene-expression profiling results between core needle biopsy (CNB) samples and surgical specimens in breast cancer.

METHODS

Thirty-one paired, formalin-fixed, paraffin-embedded CNB and surgical specimen samples were selected from patients with hormone receptor-positive breast cancer. Total RNA was extracted from the samples and the risk classifications based on GenesWell BCT scores were compared.

RESULTS

The BCT scores correlated between CNB samples and surgical specimens of hormone receptor-positive breast cancer (Pearson r = 0.66). The overall concordance rate of risk classification (high/low risk) was 83.9%. However, when the breast cancer does not contain intratumoral microcalcification, the concordance rate increased as 92.0%. And, when the breast cancer formed a solitary nodule (non-multifocal), the concordance rate increased up to 95.8%.

CONCLUSION

Risk classification using the GenesWell BCT multigene kit with CNB samples could be considered reliable, when the breast cancer is a solitary nodule without intratumoral microcalcification. Such genetic profiling results should be helpful for establishing a treatment plan for hormone receptor-positive breast cancer before treatment induction.

Concordance of DNA methylation profiles between breast core biopsy and surgical excision specimens containing ductal carcinoma in situ (DCIS)

The utility and reliability of assessing molecular biomarkers for translational applications on pre-operative core biopsy specimens assume consistency of molecular profiles with larger surgical specimens. Whether DNA methylation in ductal carcinoma in situ (DCIS), measured in core biopsy and surgical specimens are similar, remains unclear. Here, we compared genome-scale DNA methylation measured in matched core biopsy and surgical specimens from DCIS, including specific DNA methylation biomarkers of subsequent invasive cancer. DNA was extracted from guided 2mm cores of formalin fixed paraffin embedded (FFPE) specimens, bisulfite-modified, and measured on the Illumina HumanMethylation450 BeadChip. DNA methylation profiles of core biopsies exhibited high concordance with matched surgical specimens. Within-subject variability in DNA methylation was significantly lower than between-subject variability (all P<2.20E-16). In 641 CpGs whose methylation was related with increased hazard of invasive breast cancer, lower within-subject than between-subject variability was observed in 92.3% of the study participants (P<0.05). Between patient-matched core biopsy and surgical specimens, <0.6% of CpGs measured had changes in median DNA methylation >15%, and a pathway analysis of these CpGs indicated enrichment for genes related with wound healing. Our results indicate that DNA methylation measured in core biopsies are representative of the matched surgical specimens and suggest that DCIS biomarkers measured in core biopsies can inform clinical decision-making.

Comparison of the RNA-based EndoPredict multigene test between core biopsies and corresponding surgical breast cancer sections

Aim

This study compared the perfomance of the RNA-based EndoPredict multigene test on core biopsies and surgical breast cancer specimens and analysed the influence of biopsy-induced tissue injuries on the test result.

Methods

80 formalin-fixed paraffin-embedded samples comprising paired biopsies and surgical specimens from 40 ER-positive, HER2-negative patients were evaluated. Total RNA was extracted and the EndoPredict score was determined.

Results

RNA yield was considerably lower in core biopsies, but sufficient to measure the assay in all samples. The EndoPredict score was highly correlated between paired samples (Pearson r=0.92), with an excellent concordance of classification into a low or high risk of metastasis (overall agreement 95%).

Conclusions

The measurements are comparable between core biopsies and surgical sections, which suggest that the EndoPredict assay can be performed on core biopsy tissue. Inflammatory changes induced by presurgical biopsies had no significant effect on the RNA-based risk assessment in surgical specimens.

Hormone replacement therapy dependent changes in breast cancer-related gene expression in breast tissue of healthy postmenopausal women

Risk assessment of future breast cancer risk through exposure to sex steroids currently relies on clinical scorings such as mammographic density. Knowledge about the gene expression patterns in existing breast cancer tumors may be used to identify risk factors in the breast tissue of women still free of cancer. The differential effects of estradiol, estradiol together with gestagens, or tibolone on breast cancer-related gene expression in normal breast tissue samples taken from postmenopausal women may be used to identify gene expression profiles associated with a higher breast cancer risk. Breast tissue samples were taken from 33 healthy postmenopausal women both before and after a six month treatment with either 2mg micronized estradiol [E2], 2mg micronized estradiol and 1mg norethisterone acetate [E2+NETA], 2.5mg tibolone [T] or [no HRT]. Except for [E2], which was only given to women after hysterectomy, the allocation to each of the three groups was randomized. The expression of 102 mRNAs and 46 microRNAs putatively involved in breast cancer was prospectively determined in the biopsies of 6 women receiving [no HRT], 5 women receiving [E2], 5 women receiving [E2+NETA], and 6 receiving [T]. Using epithelial and endothelial markers genes, non-representative biopsies from 11 women were eliminated. Treatment of postmenopausal women with [E2+NETA] resulted in the highest number of differentially (p<0.05) regulated genes (16.2%) compared to baseline, followed by [E2] (10.1%) and [T] (4.7%). Among genes that were significantly down-regulated by [E2+NETA] ranked estrogen-receptor-1 (ESR1, p=0.019) and androgen receptor (AR, p=0.019), whereas CYP1B1, a gene encoding an estrogen-metabolizing enzyme, was significantly up-regulated (p=0.016). Mammary cells triggered by [E2+NETA] and [E2] adjust for steroidogenic up-regulation through down-regulation of the estrogen-receptor pathway. In this prospective study, prolonged administration of [E2+NETA] and to a lesser extent of [E2] but not [T] were associated in otherwise healthy breast tissue with a change in the expression of genes putatively involved in breast cancer. Our data suggest that normal mammary cells triggered by [E2+NETA] adjust for steroidogenic up-regulation through down-regulation of the estrogen-receptor pathway. This feasibility study provides the basis for whole genome analyses to identify novel markers involved in increased breast cancer risk.

Monitoring the response of circulating epithelial tumor cells to adjuvant chemotherapy in breast cancer allows detection of patients at risk of early relapse

PURPOSE

To demonstrate that it is possible to monitor the response to adjuvant therapy by repeated analysis of circulating epithelial tumor cells (CETCs) and to detect patients early who are at risk of relapse.

PATIENTS AND METHODS

In 91 nonmetastatic primary breast cancer patients, CETCs were quantified using laser scanning cytometry of anti-epithelial cell adhesion molecule-stained epithelial cells from whole unseparated blood before and during adjuvant chemotherapy.

RESULTS

Numbers of CETCs were analyzed before therapy, before each new cycle, and at the end of chemotherapy. The following three typical patterns of response were observed: (1) decrease in cell numbers (> 10-fold); (2) marginal changes in cell numbers (< 10-fold); and (3) an (sometimes saw-toothed) increase or an initial decrease with subsequent reincrease (> 10-fold) in numbers of CETCs. Twenty relapses (22%) were observed within the accrual time of 40 months, including one of 28 patients from response group 1, five of 30 patients from response group 2, and 14 of 33 patients from response group 3. The difference in relapse-free survival was highly significant for CETC (hazard ratio = 4.407; 95% CI, 1.739 to 9.418; P < .001) between patients with decreasing cell numbers and those with marginal changes and between patients with marginal changes and those with an increase of more than 10-fold (linear Cox regression model).

CONCLUSION

These results show that peripherally circulating tumor cells are influenced by systemic chemotherapy and that an increase (even after initial response to therapy) of 10-fold or more at the end of therapy is a strong predictor of relapse and a surrogate marker for the aggressiveness of the tumor cells.

Low E2F1 transcript levels are a strong determinant of favorable breast cancer outcome

Introduction

We investigated whether mRNA levels of E2F1, a key transcription factor involved in proliferation, differentiation and apoptosis, could be used as a surrogate marker for the determination of breast cancer outcome.

Methods

E2F1 and other proliferation markers were measured by quantitative RT-PCR in 317 primary breast cancer patients from the Stiftung Tumorbank Basel. Correlations to one another as well as to the estrogen receptor and ERBB2 status and clinical outcome were investigated. Results were validated and further compared with expression-based prognostic profiles using The Netherlands Cancer Institute microarray data set reported by Fan and colleagues.

Results

E2F1 mRNA expression levels correlated strongly with the expression of other proliferation markers, and low values were mainly found in estrogen receptor-positive and ERBB2-negative phenotypes. Patients with low E2F1-expressing tumors were associated with favorable outcome (hazard ratio = 4.3 (95% confidence interval = 1.8–9.9), P = 0.001). These results were consistent in univariate and multivariate Cox analyses, and were successfully validated in The Netherlands Cancer Institute data set. Furthermore, E2F1 expression levels correlated well with the 70-gene signature displaying the ability of selecting a common subset of patients at good prognosis. Breast cancer patients' outcome was comparably predictable by E2F1 levels, by the 70-gene signature, by the intrinsic subtype gene classification, by the wound response signature and by the recurrence score.

Conclusion

Assessment of E2F1 at the mRNA level in primary breast cancer is a strong determinant of breast cancer patient outcome. E2F1 expression identified patients at low risk of metastasis irrespective of the estrogen receptor and ERBB2 status, and demonstrated similar prognostic performance to different gene expression-based predictors.

Quantitative detection of circulating epithelial cells by Q-RT-PCR

INTRODUCTION

It has been shown that the quantity of circulating tumor cells (CTCs) in breast cancer patients is an independent predictor of survival and treatment response. Real time quantitative reverse transcriptase PCR (Q-RT-PCR) is a sensitive technique for detection of CTCs. Our aim was to investigate whether the technique can be used also to quantitate these CTCs.

METHODS

We tested cytokeratin 19 (CK19), maspin, mammaglobin, GAPDH and RPL19 genes for their level of expression and linearity of amplification in serial dilutions of RNA extracted from the MDA-MB-231, UACC-812, T47D and HS578T breast cancer cell lines. To simulate CTCs, serial dilutions of cultured T47D and HS578T cells were added to peripheral blood from healthy volunteers. The samples were subjected to enrichment, RNA extraction and Q-RT-PCR.

RESULTS

CK19 was reliably expressed in all four cell lines with a linear relationship between the quantity of added cells and the amount of CK19 RNA. The lower limit of reliable detection was 5 cells per sample, which corresponds to a concentration of 0.7 cell/ml in 7.5 ml of blood or would translate to a lower CTC concentration in a larger volume of blood.

CONCLUSION

This technique may prove useful for high throughput comparative quantification of CTCs in individual patients during treatment and subsequent follow up for research and clinical management purposes.