Delineating transcriptional networks of prognostic gene signatures refines treatment recommendations for lymph node-negative breast cancer patients

Downregulation of IGFBP5 contributes to replicative senescence via ERK2 activation in mouse embryonic fibroblasts

Insulin-like growth factor (IGF)-binding proteins (IGFBPs) are secretory proteins that regulate IGF signaling. In this study, we investigated the role of IGFBP5 in replicative senescence in embryonic mouse fibroblasts (MEFs). During passages according to the 3T3 method, MEFs underwent senescence after the 5th passage (P5) based on cell growth arrest, an increase in the number of cells positive for senescence-associated β-galactosidase (SA-β-GAL) staining, and upregulation of p16 and p19. In P8 MEFs, IGFBP5 mRNA level was markedly reduced compared with that in P2 MEFs. Downregulation of IGFBP5 via siRNA in P2 MEFs increased the number of SA-β-GAL-positive cells, upregulated p16 and p19, and inhibited cell growth. Incubation of MEFs with IGFBP5 during serial passage increased the cumulative population doubling and decreased SA-β-GAL positivity compared with those in vehicle-treated cells. IGFBP5 knockdown in P2 MEFs increased phosphorylation levels of ERK1 and ERK2. Silencing of ERK2, but not that of ERK1, blocked the increase in the number of SA-β-GAL-positive cells in IGFBP5-knockdown cells. The reduction in the cell number and upregulation of p16 and p21 in IGFBP5-knockdown cells were attenuated by ERK2 knockdown. Our results suggest that downregulation of IGFBP5 during serial passage contributes to replicative senescence via ERK2 in MEFs.

The OncoMasTR test predicts distant recurrence in estrogen receptor-positive, HER2-negative early-stage breast cancer: A validation study in ABCSG Trial 8

PURPOSE

To validate the clinical performance of the OncoMasTR Risk Score in the biomarker cohort of ABCSG Trial 8.

EXPERIMENTAL DESIGN

We evaluated the OncoMasTR test in 1200 formalin-fixed, paraffin-embedded surgical specimens from postmenopausal women with estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative primary breast cancer with 0-3 involved lymph nodes in the prospective, randomized ABCSG Trial 8. Time to distant recurrence (DR) was analyzed by Cox models.

RESULTS

The OncoMasTR Risk Score categorized 850 of 1087 (78.2%) evaluable patients as "low risk". At 10 years, the DR rate for patients in the low-risk group was 5.8% versus 21.1% for patients in the high-risk group (P<0.0001, absolute risk reduction 15.3%). The OncoMasTR Risk Score was highly prognostic for prediction of DR in years 0-10 in all patients (hazard ratio (HR) 1.91, 95% confidence interval (CI) 1.62 to 2.26, P<0.0001; C-index 0.73), in node-negative patients (HR 1.79, 95% CI 1.43 to 2.24, P<0.0001; C-index 0.72), and in patients with 1-3 involved lymph nodes (HR 1.93, 95% CI 1.44 to 2.58, P<0.0001; C-index 0.71). The OncoMasTR Risk Score provided significant additional prognostic information beyond clinical parameters, Ki67, Nottingham Prognostic Index, and Clinical Treatment Score.

CONCLUSIONS

OncoMasTR Risk Score is highly prognostic for DR in postmenopausal women with ER-positive, HER2-negative primary breast cancer with 0-3 involved lymph nodes. In combination with prior validation studies, this fully independent validation in ABCSG Trial 8 provides level 1B evidence for the prognostic capability of the OncoMasTR Risk Score.

Future of biomarker evaluation in the realm of artificial intelligence algorithms: application in improved therapeutic stratification of patients with breast and prostate cancer

Clinical workflows in oncology depend on predictive and prognostic biomarkers. However, the growing number of complex biomarkers contributes to costly and delayed decision-making in routine oncology care and treatment. As cancer is expected to rank as the leading cause of death and the single most important barrier to increasing life expectancy in the 21st century, there is a major emphasis on precision medicine, particularly individualisation of treatment through better prediction of patient outcome. Over the past few years, both surgical and pathology specialties have suffered cutbacks and a low uptake of pathology specialists means a solution is required to enable high-throughput screening and personalised treatment in this area to alleviate bottlenecks. Digital imaging in pathology has undergone an exponential period of growth. Deep-learning (DL) platforms for hematoxylin and eosin (H&E) image analysis, with preliminary artificial intelligence (AI)-based grading capabilities of specimens, can evaluate image characteristics which may not be visually apparent to a pathologist and offer new possibilities for better modelling of disease appearance and possibly improve the prediction of disease stage and patient outcome. Although digital pathology and AI are still emerging areas, they are the critical components for advancing personalised medicine. Integration of transcriptomic analysis, clinical information and AI-based image analysis is yet an uncultivated field by which healthcare professionals can make improved treatment decisions in cancer. This short review describes the potential application of integrative AI in offering better detection, quantification, classification, prognosis and prediction of breast and prostate cancer and also highlights the utilisation of machine learning systems in biomarker evaluation.

Prognostic value of the 6-gene OncoMasTR test in hormone receptor-positive HER2-negative early-stage breast cancer: Comparative analysis with standard clinicopathological factors

AIM

The aim of the study was to assess the prognostic performance of a 6-gene molecular score (OncoMasTR Molecular Score [OMm]) and a composite risk score (OncoMasTR Risk Score [OM]) and to conduct a within-patient comparison against four routinely used molecular and clinicopathological risk assessment tools: Oncotype DX Recurrence Score, Ki67, Nottingham Prognostic Index and Clinical Risk Category, based on the modified Adjuvant! Online definition and three risk factors: patient age, tumour size and grade.

METHODS

Biospecimens and clinicopathological information for 404 Irish women also previously enrolled in the Trial Assigning Individualized Options for Treatment [Rx] were provided by 11 participating hospitals, as the primary objective of an independent translational study. Gene expression measured via RT-qPCR was used to calculate OMm and OM. The prognostic value for distant recurrence-free survival (DRFS) and invasive disease-free survival (IDFS) was assessed using Cox proportional hazards models and Kaplan-Meier analysis. All statistical tests were two-sided ones.

RESULTS

OMm and OM (both with likelihood ratio statistic [LRS] P < 0.001; C indexes = 0.84 and 0.85, respectively) were more prognostic for DRFS and provided significant additional prognostic information to all other assessment tools/factors assessed (all LRS P ≤ 0.002). In addition, the OM correctly classified more patients with distant recurrences (DRs) into the high-risk category than other risk classification tools. Similar results were observed for IDFS.

DISCUSSION

Both OncoMasTR scores were significantly prognostic for DRFS and IDFS and provided additional prognostic information to the molecular and clinicopathological risk factors/tools assessed. OM was also the most accurate risk classification tool for identifying DR. A concise 6-gene signature with superior risk stratification was shown to increase prognosis reliability, which may help clinicians optimise treatment decisions.

Inferring latent temporal progression and regulatory networks from cross-sectional transcriptomic data of cancer samples

Unraveling molecular regulatory networks underlying disease progression is critically important for understanding disease mechanisms and identifying drug targets. The existing methods for inferring gene regulatory networks (GRNs) rely mainly on time-course gene expression data. However, most available omics data from cross-sectional studies of cancer patients often lack sufficient temporal information, leading to a key challenge for GRN inference. Through quantifying the latent progression using random walks-based manifold distance, we propose a latent-temporal progression-based Bayesian method, PROB, for inferring GRNs from the cross-sectional transcriptomic data of tumor samples. The robustness of PROB to the measurement variabilities in the data is mathematically proved and numerically verified. Performance evaluation on real data indicates that PROB outperforms other methods in both pseudotime inference and GRN inference. Applications to bladder cancer and breast cancer demonstrate that our method is effective to identify key regulators of cancer progression or drug targets. The identified ACSS1 is experimentally validated to promote epithelial-to-mesenchymal transition of bladder cancer cells, and the predicted FOXM1-targets interactions are verified and are predictive of relapse in breast cancer. Our study suggests new effective ways to clinical transcriptomic data modeling for characterizing cancer progression and facilitates the translation of regulatory network-based approaches into precision medicine.

Multi-Gene Prognostic Signatures and Prediction of Pathological Complete Response to Neoadjuvant Chemotherapy in ER-positive, HER2-negative Breast Cancer Patients

Determining which patients with early-stage breast cancer should receive chemotherapy is an important clinical issue. Chemotherapy has several adverse side effects, impacting on quality of life, along with significant economic consequences. There are a number of multi-gene prognostic signatures for breast cancer recurrence but there is less evidence that these prognostic signatures are predictive of therapy benefit. Biomarkers that can predict patient response to chemotherapy can help avoid ineffective over-treatment. The aim of this work was to assess if the OncoMasTR prognostic signature can predict pathological complete response (pCR) to neoadjuvant chemotherapy, and to compare its predictive value with other prognostic signatures: EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes. Gene expression datasets from ER-positive, HER2-negative breast cancer patients that had pre-treatment biopsies, received neoadjuvant chemotherapy and an assessment of pCR were obtained from the Gene Expression Omnibus repository. A total of 813 patients with 66 pCR events were included in the analysis. OncoMasTR, EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes numeric risk scores were approximated by applying the gene coefficients to the corresponding mean probe expression values. OncoMasTR, EndoPredict and Oncotype DX prognostic scores were moderately well correlated according to the Pearson's correlation coefficient. Association with pCR was estimated using logistic regression. The odds ratio for a 1 standard deviation increase in risk score, adjusted for cohort, were similar in magnitude for all four signatures. Additionally, the four signatures were significant predictors of pCR. OncoMasTR added significant predictive value to EndoPredict, Oncotype DX and Tumor Infiltrating Leukocytes signatures as determined by bivariable and trivariable analysis. In this in silico analysis, OncoMasTR, EndoPredict, Oncotype DX, and Tumor Infiltrating Leukocytes were significantly predictive of pCR to neoadjuvant chemotherapy in ER-positive and HER2-negative breast cancer patients.

Master Transcriptional Regulators in Cancer: Discovery via Reverse Engineering Approaches and Subsequent Validation

Reverse engineering of transcriptional networks using gene expression data enables identification of genes that underpin the development and progression of different cancers. Methods to this end have been available for over a decade and, with a critical mass of transcriptomic data in the oncology arena having been reached, they are ever more applicable. Extensive and complex networks can be distilled into a small set of key master transcriptional regulators (MTR), genes that are very highly connected and have been shown to be involved in processes of known importance in disease. Interpreting and validating the results of standardized bioinformatic methods is of crucial importance in determining the inherent value of MTRs. In this review, we briefly describe how MTRs are identified and focus on providing an overview of how MTRs can and have been validated for use in clinical decision making in malignant diseases, along with serving as tractable therapeutic targets. Cancer Res; 77(9); 2186-90. ©2017 AACR.