Why Hasn't Genomic Testing Changed the Landscape in Clinical Oncology?

Novel 2 Gene Signatures Associated With Breast Cancer Proliferation: Insights From Predictive Differential Gene Expression Analysis

The use of proliferation markers provides valuable information about the rate of tumor growth, which can guide treatment decisions. However, there is still a lack of consensus regarding the optimal molecular markers or tests to use in clinical practice. Integrating gene expression data with clinical and histopathologic parameters enhances our understanding of disease processes, facilitates the identification of precise prognostic predictors, and supports the development of effective therapeutic strategies. The purpose of this study was to apply an integrated approach that combines morphologic, clinical, and bioinformatic data to reveal effective regulators of proliferation. Whole-slide images generated from hematoxylin-and-eosin-stained sections of The Cancer Genome Atlas (TCGA) breast cancer (BC) database (n = 1053) alongside their transcriptomic and clinical data were used to identify genes differentially expressed between tumors with high and low mitotic scores. Genes enriched in the cell-cycle pathway were used to predict the protein-protein interaction (PPI) network. Ten hub genes (ORC6, SKP2, SMC1B, CDKN2A, CDC25B, E2F1, E2F2, ORC1, PTTG1, and CDC25A) were identified using CytoHubba a Cytoscape plugin. In a multivariate Cox regression model, ORC6 and SKP2 were predictors of survival independent of existing methods of proliferation assessment including mitotic score and Ki67. The prognostic ability of these genes was validated using the Molecular Taxonomy of Breast Cancer International Consortium, Nottingham cohort, Uppsala cohort, and a combined multicentric cohort. The protein expression of these 2 genes was investigated on a large cohort of BC cases, and they were significantly associated with poor prognosis and patient outcome. A positive correlation between ORC6 and SKP2 mRNA and protein expression was observed. Our study has identified 2 gene signatures, ORC6 and SKP2, which play a significant role in BC proliferation. These genes surpassed both mitotic scores and Ki67 in multivariate analysis. Their identification provides potential opportunities for the development of targeted treatments for patients with BC.

SHON expression predicts response and relapse risk of breast cancer patients after anthracycline-based combination chemotherapy or tamoxifen treatment

Background

SHON nuclear expression (SHON-Nuc⁺) was previously reported to predict clinical outcomes to tamoxifen therapy in ERα⁺ breast cancer (BC). Herein we determined if SHON expression detected by specific monoclonal antibodies could provide a more accurate prediction and serve as a biomarker for anthracycline-based combination chemotherapy (ACT).

Methods

SHON expression was determined by immunohistochemistry in the Nottingham early-stage-BC cohort (n = 1,650) who, if eligible, received adjuvant tamoxifen; the Nottingham ERα⁻ early-stage-BC (n = 697) patients who received adjuvant ACT; and the Nottingham locally advanced-BC cohort who received pre-operative ACT with/without taxanes (Neo-ACT, n = 120) and if eligible, 5-year adjuvant tamoxifen treatment. Prognostic significance of SHON and its relationship with the clinical outcome of treatments were analysed.

Results

As previously reported, SHON-Nuc⁺ in high risk/ERα⁺ patients was significantly associated with a 48% death risk reduction after exclusive adjuvant tamoxifen treatment compared with SHON-Nuc⁻ [HR (95% CI) = 0.52 (0.34–0.78), p = 0.002]. Meanwhile, in ERα⁻ patients treated with adjuvant ACT, SHON cytoplasmic expression (SHON-Cyto⁺) was significantly associated with a 50% death risk reduction compared with SHON-Cyto⁻ [HR (95% CI) = 0.50 (0.34–0.73), p = 0.0003]. Moreover, in patients received Neo-ACT, SHON-Nuc⁻ or SHON-Cyto⁺ was associated with an increased pathological complete response (pCR) compared with SHON-Nuc⁺ [21 vs 4%; OR (95% CI) = 5.88 (1.28–27.03), p = 0.012], or SHON-Cyto⁻ [20.5 vs. 4.5%; OR (95% CI) = 5.43 (1.18–25.03), p = 0.017], respectively. After receiving Neo-ACT, patients with SHON-Nuc⁺ had a significantly lower distant relapse risk compared to those with SHON-Nuc⁻ [HR (95% CI) = 0.41 (0.19–0.87), p = 0.038], whereas SHON-Cyto⁺ patients had a significantly higher distant relapse risk compared to SHON-Cyto⁻ patients [HR (95% CI) = 4.63 (1.05–20.39), p = 0.043]. Furthermore, multivariate Cox regression analyses revealed that SHON-Cyto⁺ was independently associated with a higher risk of distant relapse after Neo-ACT and 5-year tamoxifen treatment [HR (95% CI) = 5.08 (1.13–44.52), p = 0.037]. The interaction term between ERα status and SHON-Nuc⁺ (p = 0.005), and between SHON-Nuc⁺ and tamoxifen therapy (p = 0.007), were both statistically significant.

Conclusion

SHON-Nuce⁺ in tumours predicts response to tamoxifen in ERα⁺ BC while SHON-Cyto⁺ predicts response to ACT.

Clinical evidence supporting genomic tests in early breast cancer: Do all genomic tests provide the same information?

Breast cancer (BC) has historically been treated as a single disease entity; however, in the last decade, insights into its molecular heterogeneity have underpinned the development/commercialisation of several genomic tools whose goal is to guide patient management in early BC. These include the Oncotype DX^® Breast Recurrence Score™ assay, MammaPrint^®, Prosigna^®, and EndoPredict^®. Although these assays are similar in that they are all multigene assays reflecting risk of recurrence, they differ substantially in the technological platform used to measure gene expression; the number and identity of genes assessed; the patient populations used for development and validation; and the level of evidence supporting clinical utility. They also differ in the amount of evidence demonstrating their impact on treatment decisions and cost effectiveness in different countries. This review discusses these 4 assays, highlighting the clinical evidence that supports each of them, while focussing on the Recurrence Score assay. This assay has the greatest body of evidence supporting its clinical utility and decision impact/effectiveness, and currently is the only one validated as a predictor of response to adjuvant chemotherapy in hormone-receptor positive early BC patients treated with endocrine therapy and to be included as such in international/national BC treatment guidelines. The review also discusses ongoing prospective trials investigating the 4 assays, recent outcome studies, as well as analyses comparing different assays on the same tumour blocks.

SPAG5 as a prognostic biomarker and chemotherapy sensitivity predictor in breast cancer: a retrospective, integrated genomic, transcriptomic, and protein analysis

BACKGROUND

Proliferation markers and profiles have been recommended for guiding the choice of systemic treatments for breast cancer. However, the best molecular marker or test to use has not yet been identified. We did this study to identify factors that drive proliferation and its associated features in breast cancer and assess their association with clinical outcomes and response to chemotherapy.

METHODS

We applied an artificial neural network-based integrative data mining approach to data from three cohorts of patients with breast cancer (the Nottingham discovery cohort (n=171), Uppsala cohort (n=249), and Molecular Taxonomy of Breast Cancer International Consortium [METABRIC] cohort; n=1980). We then identified the genes with the most effect on other genes in the resulting interactome map. Sperm-associated antigen 5 (SPAG5) featured prominently in our interactome map of proliferation and we chose to take it forward in our analysis on the basis of its fundamental role in the function and dynamic regulation of mitotic spindles, mitotic progression, and chromosome segregation fidelity. We investigated the clinicopathological relevance of SPAG5 gene copy number aberrations, mRNA transcript expression, and protein expression and analysed the associations of SPAG5 copy number aberrations, transcript expression, and protein expression with breast cancer-specific survival, disease-free survival, distant relapse-free survival, pathological complete response, and residual cancer burden in the Nottingham discovery cohort, Uppsala cohort, METABRIC cohort, a pooled untreated lymph node-negative cohort (n=684), a multicentre combined cohort (n=5439), the Nottingham historical early stage breast cancer cohort (Nottingham-HES; n=1650), Nottingham early stage oestrogen receptor-negative breast cancer adjuvant chemotherapy cohort (Nottingham-oestrogen receptor-negative-ACT; n=697), the Nottingham anthracycline neoadjuvant chemotherapy cohort (Nottingham-NeoACT; n=200), the MD Anderson taxane plus anthracycline-based neoadjuvant chemotherapy cohort (MD Anderson-NeoACT; n=508), and the multicentre phase 2 neoadjuvant clinical trial cohort (phase 2 NeoACT; NCT00455533; n=253).

FINDINGS

In the METABRIC cohort, we detected SPAG5 gene gain or amplification at the Ch17q11.2 locus in 206 (10%) of 1980 patients overall, 46 (19%) of 237 patients with a PAM50-HER2 phenotype, and 87 (18%) of 488 patients with PAM50-LumB phenotype. Copy number aberration leading to SPAG5 gain or amplification and high SPAG5 transcript and SPAG5 protein concentrations were associated with shorter overall breast cancer-specific survival (METABRIC cohort [copy number aberration]: hazard ratio [HR] 1·50, 95% CI 1·18-1·92, p=0·00010; METABRIC cohort [transcript]: 1·68, 1·40-2·01, p<0·0001; and Nottingham-HES-breast cancer cohort [protein]: 1·68, 1·32-2·12, p<0·0001). In multivariable analysis, high SPAG5 transcript and SPAG5 protein expression were associated with reduced breast cancer-specific survival at 10 years compared with lower concentrations (Uppsala: HR 1·62, 95% CI 1·03-2·53, p=0·036; METABRIC: 1·27, 1·02-1·58, p=0·034; untreated lymph node-negative cohort: 2·34, 1·24-4·42, p=0·0090; and Nottingham-HES: 1·73, 1·23-2·46, p=0·0020). In patients with oestrogen receptor-negative breast cancer with high SPAG5 protein expression, anthracycline-based adjuvant chemotherapy increased breast cancer-specific survival overall compared with that for patients who did not receive chemotherapy (Nottingham-oestrogen receptor-negative-ACT cohort: HR 0·37, 95% CI 0·20-0·60, p=0·0010). Multivariable analysis showed high SPAG5 transcript concentrations to be independently associated with longer distant relapse-free survival after receiving taxane plus anthracycline neoadjuvant chemotherapy (MD Anderson-NeoACT: HR 0·68, 95% CI 0·48-0·97, p=0·031). In multivariable analysis, both high SPAG5 transcript and high SPAG5 protein concentrations were independent predictors for a higher proportion of patients achieving a pathological complete response after combination cytotoxic chemotherapy (MD Anderson-NeoACT: OR 1·71, 95% CI, 1·07-2·74, p=0·024; Nottingham-ACT: 8·75, 2·42-31·62, p=0·0010).

INTERPRETATION

SPAG5 is a novel amplified gene on Ch17q11.2 in breast cancer. The transcript and protein products of SPAG5 are independent prognostic and predictive biomarkers that might have clinical utility as biomarkers for combination cytotoxic chemotherapy sensitivity, especially in oestrogen receptor-negative breast cancer.

FUNDING

Nottingham Hospitals Charity and the John and Lucille van Geest Foundation.

The clinical utility of molecular genetic cancer profiling

INTRODUCTION

Next-Generation-Sequencing (NGS) has enabled gene mutation profiling - cataloguing sequence variants and modifications in clinical assays encompassing tens to thousands of genes in tumors and in germlines. The clinical benefit of applying multi-gene NGS to diverse applications in various malignancies remains to be demonstrated.

AREAS COVERED

Applications of gene mutation profiling in oncology include screening cancer-prone families, classification of malignancies, treatment selection, and monitoring the response to treatment of solid tumors (the 'liquid biopsy'). Google Scholar was used to search PubMed for the period 2011-2016 using combinations of the following search terms: 'clinical utility', NGS, 'molecular diagnostics'. Expert commentary: Clinical studies are in progress pairing mutation profiling with streamlined new trial designs to speed identification of promising drug-target combinations and to see if genotype-informed treatment selection will improve outcome across a spectrum of histologies. The analytical advantages and falling cost of NGS make focused gene panels likely to become the dominant modality in molecular diagnostic testing even if trials eventually discourage use of large panels to test all malignancies.

Planning clinically relevant biomarker validation studies using the "number needed to treat" concept

Purpose

Despite an explosion of translational research to exploit biomarkers in diagnosis, prediction and prognosis, the impact of biomarkers on clinical practice has been limited. The elusiveness of clinical utility may partly originate when validation studies are planned, from a failure to articulate precisely how the biomarker, if successful, will improve clinical decision-making for patients. Clarifying what performance would suffice if the test is to improve medical care makes it possible to design meaningful validation studies. But methods for tackling this part of validation study design are undeveloped, because it demands uncomfortable judgments about the relative values of good and bad outcomes resulting from a medical decision.

Methods

An unconventional use of “number needed to treat” (NNT) can structure communication for the trial design team, to elicit purely value-based outcome tradeoffs, conveyed as the endpoints of an NNT “discomfort range”. The study biostatistician can convert the endpoints into desired predictive values, providing criteria for designing a prospective validation study. Next, a novel “contra-Bayes” theorem converts those predictive values into target sensitivity and specificity criteria, to guide design of a retrospective validation study. Several examples demonstrate the approach.

Conclusion

In practice, NNT-guided dialogues have contributed to validation study planning by tying it closely to specific patient-oriented translational goals. The ultimate payoff comes when the report of the completed study includes motivation in the form of a biomarker test framework directly reflecting the clinical decision challenge to be solved. Then readers will understand better what the biomarker test has to offer patients.

Electronic supplementary material

The online version of this article (doi:10.1186/s12967-016-0862-4) contains supplementary material, which is available to authorized users.

Closing the gap between knowledge and clinical application: challenges for genomic translation

Despite early predictions and rapid progress in research, the introduction of personal genomics into clinical practice has been slow. Several factors contribute to this translational gap between knowledge and clinical application. The evidence available to support genetic test use is often limited, and implementation of new testing programs can be challenging. In addition, the heterogeneity of genomic risk information points to the need for strategies to select and deliver the information most appropriate for particular clinical needs. Accomplishing these tasks also requires recognition that some expectations for personal genomics are unrealistic, notably expectations concerning the clinical utility of genomic risk assessment for common complex diseases. Efforts are needed to improve the body of evidence addressing clinical outcomes for genomics, apply implementation science to personal genomics, and develop realistic goals for genomic risk assessment. In addition, translational research should emphasize the broader benefits of genomic knowledge, including applications of genomic research that provide clinical benefit outside the context of personal genomic risk.

What Tumor Dynamics Modeling Can Teach us About Exploiting the Stem-Cell View for Better Cancer Treatment

The cancer stem cell hypothesis is that in human solid cancers, only a small proportion of the cells, the cancer stem cells (CSCs), are self-renewing; the vast majority of the cancer cells are unable to sustain tumor growth indefinitely on their own. In recent years, discoveries have led to the concentration, if not isolation, of putative CSCs. The evidence has mounted that CSCs do exist and are important. This knowledge may promote better understanding of treatment resistance, create opportunities to test agents against CSCs, and open up promise for a fresh approach to cancer treatment. The first clinical trials of new anti-CSC agents are completed, and many others follow. Excitement is mounting that this knowledge will lead to major improvements, even breakthroughs, in treating cancer. However, exploitation of this phenomenon may be more successful if informed by insights into the population dynamics of tumor development. We revive some ideas in tumor dynamics modeling to extract some guidance in designing anti-CSC treatment regimens and the clinical trials that test them.