Dataset Information

Evaluation of multiple transcriptomic gene risk signatures in male breast cancer.

ABSTRACT: Male breast cancer (BCa) is a rare disease accounting for less than 1% of all breast cancers and 1% of all cancers in males. The clinical management is largely extrapolated from female BCa. Several multigene assays are increasingly used to guide clinical treatment decisions in female BCa, however, there are limited data on the utility of these tests in male BCa. Here we present the gene expression results of 381 M0, ER+ve, HER2-ve male BCa patients enrolled in the Part 1 (retrospective analysis) of the International Male Breast Cancer Program. Using a custom NanoString™ panel comprised of the genes from the commercial risk tests Prosigna®, OncotypeDX®, and MammaPrint®, risk scores and intrinsic subtyping data were generated to recapitulate the commercial tests as described by us previously. We also examined the prognostic value of other risk scores such as the Genomic Grade Index (GGI), IHC4-mRNA and our prognostic 95-gene signature. In this sample set of male BCa, we demonstrated prognostic utility on univariate analysis. Across all signatures, patients whose samples were identified as low-risk experienced better outcomes than intermediate-risk, with those classed as high risk experiencing the poorest outcomes. As seen with female BCa, the concordance between tests was poor, with C-index values ranging from 40.3% to 78.2% and Kappa values ranging from 0.17 to 0.58. To our knowledge, this is the largest study of male breast cancers assayed to generate risk scores of the current commercial and academic risk tests demonstrating comparable clinical utility to female BCa.

SUBMITTER: Bayani J

PROVIDER: S-EPMC8313692 | biostudies-literature | 2021 Jul

REPOSITORIES: biostudies-literature

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Evaluation of multiple transcriptomic gene risk signatures in male breast cancer.

Bayani Jane J Poncet Coralie C Crozier Cheryl C Neven Anouk A Piper Tammy T Cunningham Carrie C Sobol Monika M Aebi Stefan S Benstead Kim K Bogler Oliver O Dal Lago Lissandra L Fraser Judith J Hilbers Florentine F Hedenfalk Ingrid I Korde Larissa L Linderholm Barbro B Martens John J Middleton Lavinia L Murray Melissa M Kelly Catherine C Nilsson Cecilia C Nowaczyk Monika M Peeters Stephanie S Peric Aleksandra A Porter Peggy P Schröder Carolien C Rubio Isabel T IT Ruddy Kathryn J KJ van Asperen Christi C Van Den Weyngaert Danielle D van Deurzen Carolien C van Leeuwen-Stok Elise E Vermeij Joanna J Winer Eric E Giordano Sharon H SH Cardoso Fatima F Bartlett John M S JMS

NPJ breast cancer 20210726 1

Male breast cancer (BCa) is a rare disease accounting for less than 1% of all breast cancers and 1% of all cancers in males. The clinical management is largely extrapolated from female BCa. Several multigene assays are increasingly used to guide clinical treatment decisions in female BCa, however, there are limited data on the utility of these tests in male BCa. Here we present the gene expression results of 381 M0, ER+ve, HER2-ve male BCa patients enrolled in the Part 1 (retrospective analysis) ...[more]

PMID: 34312396

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Project description:ImportanceThere has been little consideration of genomic risk of recurrence by breast cancer subtype despite evidence of racial disparities in breast cancer outcomes.ObjectiveTo evaluate associations between clinical trial end points, namely pathologic complete response (pCR) and distant recurrence-free survival (DRFS), and race and examine whether gene expression signatures are associated with outcomes by race.Design, setting, and participantsThis retrospective cohort study used data from the Investigation of Serial Studies to Predict Your Therapeutic Response With Imaging and Molecular Analysis 2 (I-SPY 2) multicenter clinical trial of neoadjuvant chemotherapy with novel agents and combinations for patients with previously untreated stage II/III breast cancer. Analyses were conducted of associations between race and short- and long-term outcomes, overall and by receptor subtypes, and their association with 28 expression biomarkers. The trial enrolled 990 female patients between March 30, 2010, and November 5, 2016, with a primary tumor size of 2.5 cm or greater and clinical or molecular high risk based on MammaPrint or hormone receptor (HR)-negative/ERBB2 (formerly HER2 or HER2/neu)-positive subtyping across 9 arms. This data analysis was performed between June 10, 2021, and October 20, 2022.ExposureRace, tumor receptor subtypes, and genomic biomarker expression of early breast cancer.Main outcomes and measuresThe primary outcomes were pCR and DRFS assessed by race, overall, and by tumor subtype using logistic regression and Cox proportional hazards regression models. The interaction between 28 expression biomarkers and race, considering pCR and DRFS overall and within subtypes, was also evaluated.ResultsThe analytic sample included 974 participants (excluding 16 self-reporting as American Indian or Alaska Native, Native Hawaiian or Other Pacific Islander, or multiple races due to small sample sizes), including 68 Asian (7%), 120 Black (12%), and 786 White (81%) patients. Median (range) age at diagnosis was 47 (25-71) years for Asian, 49 (25-77) for Black, and 49 (23-73) years for White patients. The pCR rates were 32% (n = 22) for Asian, 30% for Black (n = 36), and 32% for White (n = 255) patients (P = .87). Black patients with HR-positive/ERBB2-negative tumors not achieving pCR had significantly worse DRFS than their White counterparts (hazard ratio, 2.28; 95% CI, 1.24-4.21; P = .01), with 5-year DRFS rates of 55% (n = 32) and 77% (n = 247), respectively. Black patients with HR-positive/ERBB2-negative tumors, compared with White patients, had higher expression of an interferon signature (mean [SD], 0.39 [0.87] and -0.10 [0.99]; P = .007) and, compared with Asian patients, had a higher mitotic score (mean [SD], 0.07 [1.08] and -0.69 [1.06]; P = .01) and lower estrogen receptor/progesterone receptor signature (mean [SD], 0.31 [0.90] and 1.08 [0.95]; P = .008). A transforming growth factor β signature had a significant association with race relative to pCR and DRFS, with a higher signature associated with lower pCR and worse DRFS outcomes among Black patients only.Conclusions and relevanceThe findings show that women with early high-risk breast cancer who achieve pCR have similarly good outcomes regardless of race, but Black women with HR-positive/ERBB2-negative tumors without pCR may have worse DRFS than White women, highlighting the need to develop and test novel biomarker-informed therapies in diverse populations.

Project description:RationaleMany blood-based transcriptional gene signatures for tuberculosis (TB) have been developed with potential use to diagnose disease, predict risk of progression from infection to disease, and monitor TB treatment outcomes. However, an unresolved issue is whether gene set enrichment analysis (GSEA) of the signature transcripts alone is sufficient for prediction and differentiation, or whether it is necessary to use the original statistical model created when the signature was derived. Intra-method comparison is complicated by the unavailability of original training data, missing details about the original trained model, and inadequate publicly-available software tools or source code implementing models. To facilitate these signatures' replicability and appropriate utilization in TB research, comprehensive comparisons between gene set scoring methods with cross-data validation of original model implementations are needed.ObjectivesWe compared the performance of 19 TB gene signatures across 24 transcriptomic datasets using both re-rebuilt original models and gene set scoring methods to evaluate whether gene set scoring is a reasonable proxy to the performance of the original trained model. We have provided an open-access software implementation of the original models for all 19 signatures for future use.MethodsWe considered existing gene set scoring and machine learning methods, including ssGSEA, GSVA, PLAGE, Singscore, and Zscore, as alternative approaches to profile gene signature performance. The sample-size-weighted mean area under the curve (AUC) value was computed to measure each signature's performance across datasets. Correlation analysis and Wilcoxon paired tests were used to analyze the performance of enrichment methods with the original models.Measurement and main resultsFor many signatures, the predictions from gene set scoring methods were highly correlated and statistically equivalent to the results given by the original diagnostic models. PLAGE outperformed all other gene scoring methods. In some cases, PLAGE outperformed the original models when considering signatures' weighted mean AUC values and the AUC results within individual studies.ConclusionGene set enrichment scoring of existing blood-based biomarker gene sets can distinguish patients with active TB disease from latent TB infection and other clinical conditions with equivalent or improved accuracy compared to the original methods and models. These data justify using gene set scoring methods of published TB gene signatures for predicting TB risk and treatment outcomes, especially when original models are difficult to apply or implement.

Project description:Prostate cancer (PCa) remains a significant global health challenge, with approximately 1.6 million new cases and 366,000 deaths annually. Despite high survival rates for localized prostate cancer, recurrence poses a substantial risk due to inherent biological factors and residual disease. Early detection and intervention are essential for enhancing patient outcomes and reducing mortality. However, traditional diagnostics such as PSA tests, digital rectal examinations, and biopsies often lack specificity resulting in overdiagnosis. There is a pressing need for novel biomarkers to enhance precision medicine approaches for PCa. This study employs a machine learning approach to identify DNA methylation and RNA expression biomarkers predictive of PCa recurrence using datasets from The Cancer Genome Atlas (TCGA). We analyzed 49,133 genes, identifying 684 differentially methylated genes (DMGs) and 691 differentially expressed genes (DEGs) between recurrence and non-recurrence groups. Ten genes (TNNI2, SPIN2, COL5A3, RNF169, CCND1, FGFR1, SLC17A2, FAMM71F2, RREB1, AOX1) were found to have significant correlations between methylation and expression, forming the basis for our predictive model. A support vector machine (SVM) model was developed using these ten genes, achieving an area under the curve (AUC) of 0.773, demonstrating robust predictive capability. Multivariate regression analysis confirmed the SVM score as an independent predictor of recurrence (HR = 0.45; 95% CI 0.28-0.69, P < 0.001). The analysis of recurrence-free survival suggested that patients with low-risk scores experienced significantly better outcomes compared to those with high-risk scores. Functional enrichment analyses of DMGs revealed significant involvement in biological processes such as transcription regulation, signal transduction, and immune response, highlighting the potential mechanistic pathways of these biomarkers. Validation using real-time PCR confirmed differential expression and methylation patterns of the identified genes in prostate cancer (PC3) and non-cancerous cell lines (PNT2). In conclusion, our study hihglights the DNA methylation biomarkers linked to PCa recurrence and introduces a promising SVM model for early prediction, potentially improving treatment outcomes. Further research is needed to explore the biological roles of these genes in PCa aiming to refine therapeutic approaches.

Dataset Information

Evaluation of multiple transcriptomic gene risk signatures in male breast cancer.

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Evaluation of multiple transcriptomic gene risk signatures in male breast cancer.

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