Analytical validation of a 12-gene molecular test for the prediction of distant recurrence in breast cancer.
Ontology highlight
ABSTRACT: Aim
To validate the analytical performance of a 12-gene molecular assay that predicts distant recurrence for early-stage ER+/HER2- invasive breast cancer as run within a central reference laboratory.Materials & methods
Formalin-fixed paraffin-embedded breast resections were evaluated by quantitative reverse transcription polymerase chain reaction for the expression of eight target genes, three housekeeper genes and one control gene to assess for DNA contamination.Results
The assay results were highly correlated with a validated reference laboratory. The assay had a broad linear range for input RNA, with similar amplicon efficiencies for target and housekeeper genes. The assay test was highly reproducible, with comparable inter- and intrabatch precision to the reference laboratory.Conclusion
These studies demonstrate that the 12-gene molecular assay is highly robust and accurate.
SUBMITTER: Warf MB
PROVIDER: S-EPMC5583651 | biostudies-literature | 2017 Aug
REPOSITORIES: biostudies-literature
Publications
Analytical validation of a 12-gene molecular test for the prediction of distant recurrence in breast cancer.
Warf M Bryan MB Rajamani Saradha S Krappmann Kristin K Doedt Jennifer J Cassiano Jared J Brown Krystal K Reid Julia E JE Kronenwett Ralf R Roa Benjamin B BB
Future science OA 20170605 3
<h4>Aim</h4>To validate the analytical performance of a 12-gene molecular assay that predicts distant recurrence for early-stage ER+/HER2- invasive breast cancer as run within a central reference laboratory.<h4>Materials & methods</h4>Formalin-fixed paraffin-embedded breast resections were evaluated by quantitative reverse transcription polymerase chain reaction for the expression of eight target genes, three housekeeper genes and one control gene to assess for DNA contamination.<h4>Results</h4> ...[more]
Similar Datasets
Project description:CanAssist-Breast (CAB) is an immunohistochemistry (IHC)-based prognostic test for early-stage Hormone Receptor (HR+)-positive breast cancer patients. CAB uses a Support Vector Machine (SVM) trained algorithm which utilizes expression levels of five biomarkers (CD44, ABCC4, ABCC11, N-Cadherin, and Pan-Cadherin) and three clinical parameters such as tumor size, grade, and node status as inputs to generate a risk score and categorizes patients as low- or high-risk for distant recurrence within 5 years of diagnosis. In this study, we present clinical validation of CAB. CAB was validated using a retrospective cohort of 857 patients. All patients were treated either with endocrine therapy or chemoendocrine therapy. Risk categorization by CAB was analyzed by calculating Distant Metastasis-Free Survival (DMFS) and recurrence rates using Kaplan-Meier survival curves. Multivariate analysis was performed to calculate Hazard ratios (HR) for CAB high-risk vs low-risk patients. The results showed that Distant Metastasis-Free Survival (DMFS) was significantly different (P-0.002) between low- (DMFS: 95%) and high-risk (DMFS: 80%) categories in the endocrine therapy treated alone subgroup (n = 195) as well as in the total cohort (n = 857, low-risk DMFS: 95%, high-risk DMFS: 84%, P < 0.0001). In addition, the segregation of the risk categories was significant (P = 0.0005) in node-positive patients, with a difference in DMFS of 12%. In multivariate analysis, CAB risk score was the most significant predictor of distant recurrence with hazard ratio of 3.2048 (P < 0.0001). CAB stratified patients into discrete risk categories with high statistical significance compared to Ki-67 and IHC4 score-based stratification. CAB stratified a higher percentage of the cohort (82%) as low-risk than IHC4 score (41.6%) and could re-stratify >74% of high Ki-67 and IHC4 score intermediate-risk zone patients into low-risk category. Overall the data suggest that CAB can effectively predict risk of distant recurrence with clear dichotomous high- or low-risk categorization.
| S-EPMC6488210 | biostudies-literature
Project description:Efficient prediction of cancer recurrence in advance may help to recruit high risk breast cancer patients for clinical trial on-time and can guide a proper treatment plan. Several machine learning approaches have been developed for recurrence prediction in previous studies, but most of them use only structured electronic health records and only a small training dataset, with limited success in clinical application. While free-text clinic notes may offer the greatest nuance and detail about a patient's clinical status, they are largely excluded in previous predictive models due to the increase in processing complexity and need for a complex modeling framework. In this study, we developed a weak-supervision framework for breast cancer recurrence prediction in which we trained a deep learning model on a large sample of free-text clinic notes by utilizing a combination of manually curated labels and NLP-generated non-perfect recurrence labels. The model was trained jointly on manually curated data from 670 patients and NLP-curated data of 8062 patients. It was validated on manually annotated data from 224 patients with recurrence and achieved 0.94 AUROC. This weak supervision approach allowed us to learn from a larger dataset using imperfect labels and ultimately provided greater accuracy compared to a smaller hand-curated dataset, with less manual effort invested in curation.
| S-EPMC8096809 | biostudies-literature
Project description:BackgroundEndoPredict (EP) is a clinically validated multianalyte gene expression test to predict distant metastasis in ER-positive, HER2-negative breast cancer treated with endocrine therapy alone. The test is based on the combined analysis of 12 genes in formalin-fixed, paraffin-embedded (FFPE) tissue by reverse transcription-quantitative real-time PCR (RT-qPCR). Recently, it was shown that EP is feasible for reliable decentralized assessment of gene expression. The aim of this study was the analytical validation of the performance characteristics of the assay and its verification in a molecular-pathological routine laboratory.MethodsGene expression values to calculate the EP score were assayed by one-step RT-qPCR using RNA from FFPE tumor tissue. Limit of blank, limit of detection, linear range, and PCR efficiency were assessed for each of the 12 PCR assays using serial samples dilutions. Different breast cancer samples were used to evaluate RNA input range, precision and inter-laboratory variability.ResultsPCR assays were linear up to Cq values between 35.1 and 37.2. Amplification efficiencies ranged from 75% to 101%. The RNA input range without considerable change of the EP score was between 0.16 and 18.5 ng/μl. Analysis of precision (variation of day, day time, instrument, operator, reagent lots) resulted in a total noise (standard deviation) of 0.16 EP score units on a scale from 0 to 15. The major part of the total noise (SD 0.14) was caused by the replicate-to-replicate noise of the PCR assays (repeatability) and was not associated with different operating conditions (reproducibility). Performance characteristics established in the manufacturer's laboratory were verified in a routine molecular pathology laboratory. Comparison of 10 tumor samples analyzed in two different laboratories showed a Pearson coefficient of 0.995 and a mean deviation of 0.15 score units.ConclusionsThe EP test showed reproducible performance characteristics with good precision and negligible laboratory-to-laboratory variation. This study provides further evidence that the EP test is suitable for decentralized testing in specialized molecular pathological laboratories instead of a reference laboratory. This is a unique feature and a technical advance in comparison with existing RNA-based prognostic multigene expression tests.
| S-EPMC3534340 | biostudies-literature
Project description:Previous studies have demonstrated the potential value of gene expression signatures in assessing the risk of post-surgical breast cancer recurrence, however, many of these predictive models have been derived using simple computational algorithms and validated internally or using one-way validation on a single dataset. We have recently developed a new feature selection algorithm that overcomes some limitations inherent to high-dimensional data analysis. In this study, we applied this algorithm to two publicly available gene expression datasets obtained from over 400 patients with breast cancer to investigate whether we could derive more accurate prognostic signatures and reveal common predictive factors across independent datasets. We compared the performance of three advanced computational algorithms using a robust two-way validation method, where one dataset was used for training and to establish a prediction model that was then blindly tested on the other dataset. The experiment was then repeated in the reverse direction. Analyses identified prognostic signatures that while comprised of only 10-13 genes, significantly outperformed previously reported signatures for breast cancer evaluation. The cross-validation approach revealed CEGP1 and PRAME as major candidates for breast cancer biomarker development.
| S-EPMC2844120 | biostudies-literature
Project description:Recent COVID-19 pandemic guidelines recommend genomic assessment of core biopsies to help guide treatment decisions in estrogen receptor (ER)-positive early-stage breast cancer. Herein we characterize biopsy and excisional breast cancer specimens submitted for 21-gene testing. US samples submitted to Genomic Health for 21-gene testing (01/2004-04/2020) were assessed by pathologists and analyzed by a standardized quantitative reverse transcription-polymerase chain reaction. Predefined cutoffs were: ESR1 (positive ≥6.5), PGR (positive ≥5.5), and ERBB2 (negative <10.7). ER status by immunohistochemistry (IHC) and lymph node status were determined locally. Median and interquartile range were reported for continuous variables, and total and percent for categorical variables. Distributions were assessed overall, by age, and by nodal involvement. Of 919 701 samples analyzed, 13% were biopsies and 87% were excisions. Initial assay success rates were 94.5% (biopsies) and 97.3% (excisions). ER IHC concordance with central ESR1 was 96.8% (biopsies) and 97.6% (excisions). Biopsy and excisional medians were: Recurrence Score results 16 (each); ESR1 10.2 (each); PGR 7.7 and 7.6; ERBB2 9.4 and 9.2, respectively. Biopsy submissions for 21-gene testing are common and consistently generate results that are very similar to the experience with excisions. The 21-gene test can be performed reliably on core biopsies.
| S-EPMC7496790 | biostudies-literature
Project description:BackgroundBreast cancer subtypes are known to have different metastatic recurrence sites. Distant metastases are often observed during the post-operative course in patients with human epidermal growth factor receptor 2 (HER2)-enriched breast cancer and triple-negative breast cancer, but are relatively rare in those with hormone receptor-positive and HER2-negative (HR+/HER2-) breast cancer. Tumor-infiltrating lymphocytes (TILs) serve as an index to monitor tumor immune microenvironment and may possibly predict the prognosis and therapeutic effect in breast cancer. This study aimed to investigate the correlation between TIL density and recurrence site in HR+/HER2- breast cancer.MethodsIn stages I-II of HR+/HER2- breast cancer patients who underwent surgery as the first treatment and received adjuvant endocrine therapy (except adjuvant chemotherapy), forty-two patients relapsed after surgery. TILs were evaluated using needle biopsy specimens for the diagnosis of breast cancer. Morphological assessment was conducted using conventional hematoxylin and eosin staining.ResultsSix patients had no TILs density. In them, local recurrence was significantly less (p = 0.022), while distant metastases were significantly more (p = 0.015) compared to those in patients with TIL density. Therefore, for the prediction of distant metastases in HR+/HER2- breast cancer without chemotherapy, TILs could be used as predictors in univariate analysis (p = 0.015, odds ratio [OR] = 0.127), although not as independent factors (p = 0.285, OR = 0.144).ConclusionsOur findings indicate that TILs may predict distant metastatic recurrence in stages I-II of HR+/HER2- breast cancer in patients who do not undergo chemotherapy.
| S-EPMC8164786 | biostudies-literature
Project description:BackgroundCanAssist-Breast is an immunohistochemistry based test that predicts risk of distant recurrence in early-stage hormone receptor positive breast cancer patients within first five years of diagnosis. Immunohistochemistry gradings for 5 biomarkers (CD44, ABCC4, ABCC11, N-Cadherin and pan-Cadherins) and 3 clinical parameters (tumor size, tumor grade and node status) of 298 patient cohort were used to develop a machine learning based statistical algorithm. The algorithm generates a risk score based on which patients are stratified into two groups, low- or high-risk for recurrence. The aim of the current study is to demonstrate the analytical performance with respect to repeatability and reproducibility of CanAssist-Breast.MethodsAll potential sources of variation in CanAssist-Breast testing involving operator, run and observer that could affect the immunohistochemistry performance were tested using appropriate statistical analysis methods for each of the CanAssist-Breast biomarkers using a total 309 samples. The cumulative effect of these variations in the immunohistochemistry gradings on the generation of CanAssist-Breast risk score and risk category were also evaluated. Intra-class Correlation Coefficient, Bland Altman plots and pair-wise agreement were performed to establish concordance on IHC gradings, risk score and risk categorization respectively.ResultsCanAssist-Breast test exhibited high levels of concordance on immunohistochemistry gradings for all biomarkers with Intra-class Correlation Coefficient of ≥0.75 across all reproducibility and repeatability experiments. Bland-Altman plots demonstrated that agreement on risk scores between the comparators was within acceptable limits. We also observed > 90% agreement on risk categorization (low- or high-risk) across all variables tested.ConclusionsThe extensive analytical validation data for the CanAssist-Breast test, evaluating immunohistochemistry performance, risk score generation and risk categorization showed excellent agreement across variables, demonstrating that the test is robust.
| S-EPMC6425559 | biostudies-literature
Project description:PurposeIBTR! 2.0 nomogram is web-based nomogram that predicts ipsilateral breast tumor recurrence (IBTR). We aimed to validate the IBTR! 2.0 using an external data set. Materials and.MethodsThe cohort consisted of 2,206 patients, who received breast conserving surgery and radiation therapy from 1992 to 2012 at our institution, where wide surgical excision is been routinely performed. Discrimination and calibration were used for assessing model performance. Patients with predicted 10-year IBTR risk based on an IBTR! 2.0 nomogram score of <3%, 3%-5%, 5%-10%, and >10% were assigned to groups 1, 2, 3, and 4, respectively. We also plotted calibration values to observe the actual IBTR rate against the nomogram-derived 10-year IBTR probabilities.ResultsThe median follow-up period was 73 months (range, 6 to 277 months). The area under the receiver operating characteristic curve was 0.607, showing poor accordance between the estimated and observed recurrence rate. Calibration plot confirmed that the IBTR! 2.0 nomogram predicted the 10-year IBTR risk higher than the observed IBTR rates in all groups. High discrepancies between nomogram IBTR predictions and observed IBTR rates were observed in overall risk groups. Compared with the original development dataset, our patients had fewer high grade tumors, less margin positivity, and less lymphovascular invasion, and more use of modern systemic therapies. Conclusions: IBTR! 2.0 nomogram seems to have the moderate discriminative ability with a tendency to over-estimating risk rate. Continued efforts are needed to ensure external applicability of published nomograms by validating the program using an external patient population.
| S-EPMC6074074 | biostudies-literature
Project description:BACKGROUND:Biomarkers to improve the risk-benefit of extended adjuvant endocrine therapy for late recurrence in patients with oestrogen-receptor-positive breast cancer would be clinically valuable. We compared the prognostic ability of the breast-cancer index (BCI) assay, 21-gene recurrence score (Oncotype DX), and an immunohistochemical prognostic model (IHC4) for both early and late recurrence in patients with oestrogen-receptor-positive, node-negative (N0) disease who took part in the Arimidex, Tamoxifen, Alone or in Combination (ATAC) clinical trial. METHODS:In this prospective comparison study, we obtained archival tumour blocks from the TransATAC tissue bank from all postmenopausal patients with oestrogen-receptor-positive breast cancer from whom the 21-gene recurrence score and IHC4 values had already been derived. We did BCI analysis in matched samples with sufficient residual RNA using two BCI models-cubic (BCI-C) and linear (BCI-L)-using previously validated cutoffs. We assessed prognostic ability of BCI for distant recurrence over 10 years (the primary endpoint) and compared it with that of the 21-gene recurrence score and IHC4. We also tested the ability of the assays to predict early (0-5 years) and late (5-10 years) distant recurrence. To assess the ability of the biomarkers to predict recurrence beyond standard clinicopathological variables, we calculated the change in the likelihood-ratio ?(2) (LR-??(2)) from Cox proportional hazards models. FINDINGS:Suitable tissue was available from 665 patients with oestrogen-receptor-positive, N0 breast cancer for BCI analysis. The primary analysis showed significant differences in risk of distant recurrence over 10 years in the categorical BCI-C risk groups (p<0·0001) with 6·8% (95% CI 4·4-10·0) of patients in the low-risk group, 17·3% (12·0-24·7) in the intermediate group, and 22·2% (15·3-31·5) in the high-risk group having distant recurrence. The secondary analysis showed that BCI-L was a much stronger predictor for overall (0-10 year) distant recurrence compared with BCI-C (interquartile HR 2·30 [95% CI 1·62-3·27]; LR-??(2)=22·69; p<0·0001). When compared with BCI-L, the 21-gene recurrence score was less predictive (HR 1·48 [95% CI 1·22-1·78]; LR-??(2)=13·68; p=0·0002) and IHC4 was similar (HR 1·69 [95% CI 1·51-2·56]; LR-??(2)=22·83; p<0·0001). All further analyses were done with the BCI-L model. In a multivariable analysis, all assays had significant prognostic ability for early distant recurrence (BCI-L HR 2·77 [95% CI 1·63-4·70], LR-??(2)=15·42, p<0·0001; 21-gene recurrence score HR 1·80 [1·42-2·29], LR-??(2)=18·48, p<0·0001; IHC4 HR 2·90 [2·01-4·18], LR-??(2)=29·14, p<0·0001); however, only BCI-L was significant for late distant recurrence (BCI-L HR 1·95 [95% CI 1·22-3·14], LR-??(2)=7·97, p=0·0048; 21-gene recurrence score HR 1·13 [0·82-1·56], LR-??(2)=0·48, p=0·47; IHC4 HR 1·30 [0·88-1·94], LR-??(2)=1·59, p=0·20). INTERPRETATION:BCI-L was the only significant prognostic test for risk of both early and late distant recurrence and identified two risk populations for each timeframe. It could help to identify patients at high risk for late distant recurrence who might benefit from extended endocrine or other therapy. FUNDING:Avon Foundation, National Institutes of Health, Breast Cancer Foundation, US Department of Defense Breast Cancer Research Program, Susan G Komen for the Cure, Breakthrough Breast Cancer through the Mary-Jean Mitchell Green Foundation, AstraZeneca, Cancer Research UK, and the National Institute for Health Research Biomedical Research Centre at the Royal Marsden (London, UK).
| S-EPMC3918681 | biostudies-literature
Project description:PURPOSE:The purpose of the study was to define quantitative measures of intra-tumor heterogeneity in breast cancer based on histopathology data gathered from multiple samples on individual patients and determine their association with distant recurrence-free survival (DRFS). METHODS:We collected data from 971 invasive breast cancers, from 1st January 2000 to 23rd March 2014, that underwent repeat tumor sampling at our institution. We defined and calculated 31 measures of intra-tumor heterogeneity including ER, PR, and HER2 immunohistochemistry (IHC), proliferation, EGFR IHC, grade, and histology. For each heterogeneity measure, Cox proportional hazards models were used to determine whether patients with heterogeneous disease had different distant recurrence-free survival (DRFS) than those with homogeneous disease. RESULTS:The presence of heterogeneity in ER percentage staining was prognostic of reduced DRFS with a hazard ratio of 4.26 (95% CI 2.22-8.18, p?<?0.00002). It remained significant after controlling for the ER status itself (p?<?0.00062) and for patients that had chemotherapy (p?<?0.00032). Most of the heterogeneity measures did not show any association with DRFS despite the considerable sample size. CONCLUSIONS:Intra-tumor heterogeneity of ER receptor status may be a predictor of patient DRFS. Histopathologic data from multiple tissue samples may offer a view of tumor heterogeneity and assess recurrence risk.
| S-EPMC6588400 | biostudies-literature