Characterization of molecular scores and gene expression signatures in primary breast cancer, local recurrences and brain metastases

Integration and validation of host transcript signatures, including a novel 3-transcript tuberculosis signature, to enable one-step multiclass diagnosis of childhood febrile disease

BACKGROUND

Whole blood host transcript signatures show great potential for diagnosis of infectious and inflammatory illness, with most published signatures performing binary classification tasks. Barriers to clinical implementation include validation studies, and development of strategies that enable simultaneous, multiclass diagnosis of febrile illness based on gene expression.

METHODS

We validated five distinct diagnostic signatures for paediatric infectious diseases in parallel using a single NanoString nCounter® experiment. We included a novel 3-transcript signature for childhood tuberculosis, and four published signatures which differentiate bacterial infection, viral infection, or Kawasaki disease from other febrile illnesses. Signature performance was assessed using receiver operating characteristic curve statistics. We also explored conceptual frameworks for multiclass diagnostic signatures, including additional transcripts found to be significantly differentially expressed in previous studies. Relaxed, regularised logistic regression models were used to derive two novel multiclass signatures: a mixed One-vs-All model (MOVA), running multiple binomial models in parallel, and a full-multiclass model. In-sample performance of these models was compared using radar-plots and confusion matrix statistics.

RESULTS

Samples from 91 children were included in the study: 23 bacterial infections (DB), 20 viral infections (DV), 14 Kawasaki disease (KD), 18 tuberculosis disease (TB), and 16 healthy controls. The five signatures tested demonstrated cross-platform performance similar to their primary discovery-validation cohorts. The signatures could differentiate: KD from other diseases with area under ROC curve (AUC) of 0.897 [95% confidence interval: 0.822-0.972]; DB from DV with AUC of 0.825 [0.691-0.959] (signature-1) and 0.867 [0.753-0.982] (signature-2); TB from other diseases with AUC of 0.882 [0.787-0.977] (novel signature); TB from healthy children with AUC of 0.910 [0.808-1.000]. Application of signatures outside of their designed context reduced performance. In-sample error rates for the multiclass models were 13.3% for the MOVA model and 0.0% for the full-multiclass model. The MOVA model misclassified DB cases most frequently (18.7%) and TB cases least (2.7%).

CONCLUSIONS

Our study demonstrates the feasibility of NanoString technology for cross-platform validation of multiple transcriptomic signatures in parallel. This external cohort validated performance of all five signatures, including a novel sparse TB signature. Two exploratory multi-class models showed high potential accuracy across four distinct diagnostic groups.

Recent advances in breast cancer metastasis with special emphasis on metastasis to the brain

Understanding the underlying mechanisms of breast cancer metastasis is of vital importance for developing treatment approaches. This review emphasizes contemporary breakthrough studies with special focus on breast cancer brain metastasis. Acquired mutational changes in metastatic lesions are often distinct from the primary tumor, suggesting altered mutagenesis pathways. The concept of micrometastases and heterogeneity within the tumors unravels novel therapeutic targets at genomic and molecular levels through epigenetic and proteomic profiling. Several pre-clinical studies have identified mechanisms involving the immune system, where tumor associated macrophages are key players. Expression of cell proteins like Syndecan1, fatty acid-binding protein 7 and tropomyosin kinase receptor B have been implicated in aiding the transmigration of breast cancer cells to the brain. Changes in the proteomic landscape of the blood-brain-barrier show altered permeability characteristics, supporting entry of cancer cells. Findings from laboratory studies pave the path for the emergence of new biomarkers, especially blood-based miRNA and circulating tumor cell markers for prognostic staging. The constantly evolving therapeutics call for clinical trials backing supportive evidence of efficacies of both novel and existing approaches. The challenge lying ahead is discovering innovative techniques to replace use of human samples and optimize small-scale patient recruitment in trials.

Selecting postoperative adjuvant systemic therapy for early-stage breast cancer: An updated assessment and systematic review of leading commercially available gene expression assays

Gene expression assays (GEAs) can guide treatment for early-stage breast cancer. Several large prospective randomized clinical trials, and numerous additional studies, now provide new information for selecting an appropriate GEA. This systematic review builds upon prior reviews, with a focus on five widely commercialized GEAs (Breast Cancer Index®, EndoPredict®, MammaPrint®, Oncotype DX®, and Prosigna®). The comprehensive dataset available provides a contemporary opportunity to assess each GEA's utility as a prognosticator and/or predictor of adjuvant therapy benefit.

Retrospective study of transcriptomic profiling identifies Thai triple-negative breast cancer patients who may benefit from immune checkpoint and PARP inhibitors

Background

Triple-negative breast cancer (TNBC) is a rare and aggressive breast cancer subtype. Unlike the estrogen receptor-positive subtype, whose recurrence risk can be predicted by gene expression-based signature, TNBC is more heterogeneous, with diverse drug sensitivity levels to standard regimens. This study explored the benefit of gene expression-based profiling for classifying the molecular subtypes of Thai TNBC patients.

Methods

The nCounter-based Breast 360 gene expression was used to classify Thai TNBC retrospective cohort subgroups. Their expression profiles were then compared against the previously established TNBC classification system. The differential characteristics of the tumor microenvironment and DNA damage repair signatures across subgroups were also explored.

Results

Thai TNBC cohort could be classified into four main subgroups, corresponding to the LAR, BL-2, and M subtypes based on Lehmann's TNBC classification. The PAM50 gene set classified most samples as basal-like subtypes except for Group 1. Group 1 exhibited similar enrichment of the metabolic and hormone response pathways to the LAR subtype. Group 2 shared pathway activation with the BL-2 subtype. Group 3 showed an increase in the EMT pathway, similar to the M subtype. Group 4 showed no correlation with Lehmann's TNBC. The tumor microenvironment (TME) analysis showed high TME cell abundance with increased expression of immune blockade genes in Group 2. Group 4 exhibited low TME cell abundance and reduced immune blockade gene expressions. We also observed distinct signatures of the DNA double-strand break repair genes in Group 1.

Conclusions

Our study reported unique characteristics between the four TNBC subgroups and showed the potential use of immune checkpoint and PARP inhibitors in subsets of Thai TNBC patients. Our findings warrant further clinical investigation to validate TNBC's sensitivity to these regimens.

[uPA/PAI-1 and EPClin®: Comparison of their impact on the management of intermediate-prognosis breast cancers]

OBJECTIVE

The uPA/PAI-1 assay and the EPClin® test are useful tools that add to clinico-anatomical characteristics to determine the indication of adjuvant chemotherapy in case of intermediate-prognosis invasive breast cancer. The principal purpose of our study was to analyze the concordance of uPA/PAI-1 and EPClin® in classification of patients into two groups: low and high risk of relapse.

METHODS

We prospectively included 63 patients treated for intermediate-prognosis invasive breast cancer. All of these patients received a uPA/PAI-1 assay and an EPClin® test.

RESULTS

The uPA/PAI-1 assay and EPClin® test were consistent for 56.2% and inconsistent for 43.8%. In the event of a discrepancy, the treatment decision was based in 95.2% of patients on the EPClin® test result. In total, 38 patients were selected for adjuvant chemotherapy after achievement of the two tests. The mean time to report results after surgery was 9 days for the uPA/PAI-1 assay and 35 days for the EPClin® test. No cases of recurrence or death were found, with an average follow-up of 32 months.

CONCLUSION

The EPClin® test resulted in more chemotherapy prescriptions than indicated by uPA/PAI-1. However, we can't conclude to the superiority of one of these two tests, survival data and the effectiveness of our study being insufficient. In general, studies comparing different signatures useful to the therapeutic decision of intermediate prognosis breast cancers should be encouraged.

Diverse Distribution and Gene Expression on the 21-Gene Recurrence Assay in Breast Cancer Patients with Locoregional Recurrence Versus Distant Metastasis

Background

It remains uncertain whether the 21-gene recurrence score (RS) of a primary tumor has selective prognostic value for locoregional recurrence (LRR) or distant metastasis (DM). The current study aimed to compare the distribution and single-gene expression on the RS panel in breast cancer patients with LRR versus DM.

Methods

Consecutive early breast cancer patients who had been operated on at the Comprehensive Breast Health Center, Ruijin Hospital from January 2009 to December 2016 were retrospectively reviewed. Patients were divided into LRR, DM, and no-recurrence groups according to the first reported recurrent event. Comparison and subgroup analysis of 21-gene RS, RS category, and single-gene expression on the RS panel were conducted among patients with different recurrence status.

Results

A total of 1,287 patients were included, with median follow-up of 61.5 months, and 27, 47, and 1,213 patients were classified as LRR, DM, and no recurrence groups, respectively. RS was significantly diversely distributed among the three groups (P<0.001). No-recurrence patients (median 22) presented much lower RS than LRR (median 39, P<0.001) and DM (median 30, P<0.001) patients. LRR patients had lower PR (P<0.001), BCL2 (P=0.010), and CEGP1 (P<0.001) expression, and DM patients had higher STMY3 (P=0.019) expression than no-recurrence patients. Moreover, CEGP1 expression was significantly lower in the LRR group than the DM one (P=0.028).

Conclusion

RS was differently distributed between recurrent and nonrecurrent patients. PR, BCL2, CEGP1, and STMY3 expression was associated with LRR and DM, while CEGP1 was lower in the LRR group than DM patients, warranting further clinical evaluation.

Prediction of Late Recurrence and Distant Metastasis in Early-stage Breast Cancer: Overview of Current and Emerging Biomarkers

Recently, a significant number of breast cancer (BC) patients have been diagnosed at an early stage. It is therefore critical to accurately predict the risk of recurrence and distant metastasis for better management of BC in this setting. Clinicopathologic patterns, particularly lymph node status, tumor size, and hormonal receptor status are routinely used to identify women at increased risk of recurrence. However, these factors have limitations regarding their predictive ability for late metastasis risk in patients with early BC. Emerging molecular signatures using gene expression-based approaches have improved the prognostic and predictive accuracy for this indication. However, the use of their based-scores for risk assessment has provided contradictory findings. Therefore, developing and using newly emerged alternative predictive and prognostic biomarkers for identifying patients at high- and low-risk is of great importance. The present review discusses some serum biomarkers and multigene profiling scores for predicting late recurrence and distant metastasis in early-stage BC based on recently published studies and clinical trials.

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.

Development and validation for research assessment of Oncotype DX® Breast Recurrence Score, EndoPredict® and Prosigna®

Multi-gene prognostic signatures including the Oncotype® DX Recurrence Score (RS), EndoPredict® (EP) and Prosigna® (Risk Of Recurrence, ROR) are widely used to predict the likelihood of distant recurrence in patients with oestrogen-receptor-positive (ER+), HER2-negative breast cancer. Here, we describe the development and validation of methods to recapitulate RS, EP and ROR scores from NanoString expression data. RNA was available from 107 tumours from postmenopausal women with early-stage, ER+, HER2- breast cancer from the translational Arimidex, Tamoxifen, Alone or in Combination study (TransATAC) where previously these signatures had been assessed with commercial methodology. Gene expression was measured using NanoString nCounter. For RS and EP, conversion factors to adjust for cross-platform variation were estimated using linear regression. For ROR, the steps to perform subgroup-specific normalisation of the gene expression data and calibration factors to calculate the 46-gene ROR score were assessed and verified. Training with bootstrapping (n = 59) was followed by validation (n = 48) using adjusted, research use only (RUO) NanoString-based algorithms. In the validation set, there was excellent concordance between the RUO scores and their commercial counterparts (rc(RS) = 0.96, 95% CI 0.93-0.97 with level of agreement (LoA) of -7.69 to 8.12; rc(EP) = 0.97, 95% CI 0.96-0.98 with LoA of -0.64 to 1.26 and rc(ROR) = 0.97 (95% CI 0.94-0.98) with LoA of -8.65 to 10.54). There was also a strong agreement in risk stratification: (RS: κ = 0.86, p < 0.0001; EP: κ = 0.87, p < 0.0001; ROR: κ = 0.92, p < 0.001). In conclusion, the calibrated algorithms recapitulate the commercial RS and EP scores on individual biopsies and ROR scores on samples based on subgroup-centreing method using NanoString expression data.

Dose and Dose-Rate Effects in a Mouse Model of Internal Exposure from 137Cs. Part 2: Integration of Gamma-H2AX and Gene Expression Biomarkers for Retrospective Radiation Biodosimetry

Inhalation and ingestion of 137Cs and other long-lived radionuclides can occur after large-scale accidental or malicious radioactive contamination incidents, resulting in a complex temporal pattern of radiation dose/dose rate, influenced by radionuclide pharmacokinetics and chemical properties. High-throughput radiation biodosimetry techniques for such internal exposure are needed to assess potential risks of short-term toxicity and delayed effects (e.g., carcinogenesis) for exposed individuals. Previously, we used γ-H2AX to reconstruct injected 137Cs activity in experimentally-exposed mice, and converted activity values into radiation doses based on time since injection and 137Cs-elimination kinetics. In the current study, we sought to assess the feasibility and possible advantages of combining γ-H2AX with transcriptomics to improve 137Cs activity reconstructions. We selected five genes (Atf5, Hist2h2aa2, Olfr358, Psrc1, Hist2h2ac) with strong statistically-significant Spearman's correlations with injected activity and stable expression over time after 137Cs injection. The geometric mean of log-transformed signals of these five genes, combined with γ-H2AX fluorescence, were used as predictors in a nonlinear model for reconstructing injected 137Cs activity. The coefficient of determination (R2) comparing actual and reconstructed activities was 0.91 and root mean squared error (RMSE) was 0.95 MBq. These metrics remained stable when the model was fitted to a randomly-selected half of the data and tested on the other half, repeated 100 times. Model performance was significantly better when compared to our previous analysis using γ-H2AX alone, and when compared to an analysis where genes are used without γ-H2AX, suggesting that integrating γ-H2AX with gene expression provides an important advantage. Our findings show a proof of principle that integration of radiation-responsive biomarkers from different fields is promising for radiation biodosimetry of internal emitters.