Gene-expression signatures in breast cancer

Copper Chaperone for Superoxide Dismutase Subtypes as a Prognostic Marker in Luminal B Breast Cancer

Background

Copper chaperone for superoxide dismutase (CCS) is an essential component of the oxidation-reduction system. In breast cancer cells, CCS expression is highly up-regulated, which contributes to cellular proliferation and migration. Breast cancer is a multifaceted disease with different tumor prognoses and responses to clinical treatments, which may be associated with multiple molecular subtypes of CCS.

Methods

The CCS expression patterns in breast cancer were investigated by TNMplot, cBioPortal, and HPA network database. The correlation of CCS expression with clinicopathological parameters was analyzed using the UALCAN database. The Cancer Genome Atlas (TCGA) data set was used to analyze the Clinical characteristics of CCS in luminal B patients. The bc-GenExMiner database was used to analyze the effects of BReast-CAncer susceptibility gene (BRCA)1/2, TP53 mutation status, estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor (HER) expression on CCS expression. The survival curves and prognostic value of CCS in luminal B breast cancer were performed through Kaplan-Meier curves, univariate and multivariate Cox regression using the PrognoScan, bc-GenExMiner, and Clinical bioinformatics analysis platform.

Results

We found that CCS expression was associated with patient age, race, ER, and PR status. We also discovered that BRCA1/2 mutations had an effect on CCS expression. The luminal B subtype had the highest CCS expression, which was linked to poor survival compared with other subtypes. In addition, Kaplan-Meier curve analysis showed that luminal B patients with high CCS mRNA expression showed a poor survival and the CCS gene is an independent predictor of outcome in patients with luminal B breast cancer by univariate and multivariate Cox regression.

Conclusions

Our findings emphasize the significant expression of CCS in luminal B breast cancer and its potential as an autonomous prognostic determinant for this specific molecular subtype. These findings suggest that CCS holds promise as a prospective marker for the treatment of luminal B breast cancer.

Performance Goals for an Adjunct Diagnostic Test to Reduce Unnecessary Biopsies After Screening Mammography: Analysis of Costs, Benefits, and Consequences

PURPOSE

Because benign biopsies resulting from false-positive mammographic findings are a known harm of breast cancer screening, physicians and test manufacturers are searching for ways to reduce their frequency. The aim of this study was to estimate potential costs and consequences associated with using an adjunct diagnostic test for triaging women with suspicious mammographic findings before biopsy.

METHODS

A decision model was developed to compare the use of an adjunct test before biopsy to the current standard of care for suspicious mammographic findings. The decision analysis was performed from the perspective of a national health payer, with a 1-year time horizon among women representative of the US screening population aged 40 to 79 years. Three primary outcomes were assessed: (1) incremental costs, (2) number of benign biopsies avoided, and (3) number of missed opportunities for diagnosing cancer per million women screened. Input parameters were obtained from the medical literature and expert opinion. Sensitivity analyses were performed to evaluate the effects of uncertainty in parameter estimates.

RESULTS

The base-case analysis demonstrated that the use of an adjunct diagnostic test with 95% sensitivity, 75% specificity, and a cost of $1,000 would eliminate 8,127 unnecessary breast biopsies per million women screened. However, this would cost the US health care system an additional $6,462,977 and result in 255 missed opportunities for diagnosing cancer per million women screened.

CONCLUSIONS

The addition of an adjunct test for triaging women for breast biopsy after abnormal findings on screening mammography would likely eliminate many unnecessary biopsies but also increase overall health care costs. This exploratory analysis highlights the fact that mammography remains a relatively inexpensive and effective breast cancer screening and diagnostic modality.

The curative effects of radiotherapy-based therapies for human epidermal growth factor receptor 2-positive breast cancer: A meta-analysis

AIM

This meta-analysis was designed to fully assess the curative effects of radiotherapy-based therapies for human epidermal growth factor receptor 2-positive (HER2+) breast cancer (BC).

METHODS

English articles were retrieved through searching Cochrane library, PubMed, and Embase databases updated to February 2017. Studies were selected based on the inclusion and exclusion criteria. The curative effects of radiotherapy-based therapies forHER2+ BC patients were assessed using hazard rates (HRs) or odds ratios (ORs), as well as their 95% confidence intervals (CIs). In addition, Egger test was used to assess publication bias, followed by sensitivity analysis. All statistic methods were conducted using R 3.12 software.

RESULTS

A total of 9 eligible studies were included into this meta-analysis, which involved 2236 HER2+ BC patients. Egger test showed that the eligible studies had no publication bias (t = 2.198, P = .05918). Sensitivity analysis demonstrated that the results were stable. HER2+ BC patients in radiotherapy group had lower locoregional recurrences than those in other groups. Moreover, meta-analysis showed that no significant difference was found between HER2+ BC patients in radiotherapy group and other groups on the 1-year overall survival (P = 0.5263, I = 65.4%), 3-year overall survival (P = 0.4591, I = 0), and 5-year overall survival (P = 0.06277, I = 0).

CONCLUSION

Radiotherapy-based therapies might have certain advantages in treating HER2+ BC patients.

Prediction of cancer outcome using DNA microarray technology: past, present and future

BACKGROUND

The use of DNA microarray technology to predict cancer outcome already has a history of almost a decade. Although many breakthroughs have been made, the promise of individualized therapy is still not fulfilled. In addition, new technologies are emerging that also show promise in outcome prediction of cancer patients.

OBJECTIVE

The impact of DNA microarray and other 'omics' technologies on the outcome prediction of cancer patients was investigated. Whether integration of omics data results in better predictions was also examined.

METHODS

DNA microarray technology was focused on as a starting point because this technology is considered to be the most mature technology from all omics technologies. Next, emerging technologies that may accomplish the same goals but have been less extensively studied are described.

CONCLUSION

Besides DNA microarray technology, other omics technologies have shown promise in predicting the cancer outcome or have potential to replace microarray technology in the near future. Moreover, it is shown that integration of multiple omics data can result in better predictions of cancer outcome; but, owing to the lack of comprehensive studies, validation studies are required to verify which omics has the most information and whether a combination of multiple omics data improves predictive performance.

Do two machine-learning based prognostic signatures for breast cancer capture the same biological processes?

The fact that there is very little if any overlap between the genes of different prognostic signatures for early-discovery breast cancer is well documented. The reasons for this apparent discrepancy have been explained by the limits of simple machine-learning identification and ranking techniques, and the biological relevance and meaning of the prognostic gene lists was questioned. Subsequently, proponents of the prognostic gene lists claimed that different lists do capture similar underlying biological processes and pathways. The present study places under scrutiny the validity of this claim, for two important gene lists that are at the focus of current large-scale validation efforts. We performed careful enrichment analysis, controlling the effects of multiple testing in a manner which takes into account the nested dependent structure of gene ontologies. In contradiction to several previous publications, we find that the only biological process or pathway for which statistically significant concordance can be claimed is cell proliferation, a process whose relevance and prognostic value was well known long before gene expression profiling. We found that the claims reported by others, of wider concordance between the biological processes captured by the two prognostic signatures studied, were found either to be lacking statistical rigor or were in fact based on addressing some other question.

Genetic prognostic and predictive markers in colorectal cancer

Despite many studies of the likely survival outcome of individual patients with colorectal cancer, our knowledge of this subject remains poor. Until recently, we had virtually no understanding of individual responses to therapy, but the discovery of the KRAS mutation as a marker of probable failure of epidermal growth factor receptor (EGFR)-targeted therapy is a first step in the tailoring of treatment to the individual. With the application of molecular analyses, as well as the ability to perform high-throughput screens, there has been an explosive increase in the number of markers thought to be associated with prognosis and treatment outcome in this disease. In this Review, we attempt to summarize the sometimes confusing findings, and critically assess those markers already in the public domain.

Review of gene-expression profiling and its clinical use in breast cancer

Despite advances in the treatment of early-stage breast cancer, physicians still lack the ability to accurately predict which individual patients will relapse and would benefit from adjuvant chemotherapy. Traditional clinicopathologic factors are important in helping to determine risk of relapse, but do not fully account for the biologic complexity of breast cancer. Gene-expression profiling has provided us with insight into the heterogeneity of breast cancer and led to the development of prognostic and predictive molecular gene signature models designed to aid in clinical decision-making. However, it remains to be determined how much refinement in prognosis genomic models provide over standard clinicopathologic features and whether these refinements translate into improvements in clinical practice. On-going large prospective multi-center clinical trials will provide us with information regarding the clinical utility of two of these assays, but for now, implementation of these models into widespread clinical practice remains limited.

How to improve reliability and efficiency of research about molecular markers: roles of phases, guidelines, and study design

BACKGROUND AND OBJECTIVE

The search for molecular markers for cancer, using "discovery-based" techniques, has resulted in claims of a very high degree of discrimination both for cancer diagnosis (e.g., serum proteomics patterns) and prognosis (e.g., RNA expression genomic signatures). However, many promising initial results have been found to be unreliable or not reproducible, and the larger process of discovery can seem slow and inefficient. To improve the process to develop molecular markers, proposals to use "phases" and "guidelines" have been made, based on experience with the process of drug development and randomized controlled clinical trials. The objective is to help improve the reliability and efficiency of development of molecular markers for cancer diagnosis.

STUDY DESIGN AND SETTING

The literature was searched to identify important current problems (in serum proteomics for cancer diagnosis and RNA expression genomics for cancer prognosis) are identified, and the roles of tools ("phases," "guidelines," and "study design") to address those problems are considered. Based on lessons learned, approaches for the future are discussed, some of which may seem "radical" compared with drug development.

RESULTS

Phases identify and organize questions to be addressed by individual studies. Guidelines identify features of design and conduct to be reported so that each study's reliability can be judged. Study design involves the myriad details and choices involved in actual planning and conduct of a study. Study design is most important in the sense of determining whether a study is reliable or not. Studies that are unreliable, because of problems from chance and bias, constitute a major current problem leading to inflated expectations, wasted effort, and inefficiency in the larger process of development. By considering fundamental principles, it may be possible to identify approaches that are different than those used in drug development, while preserving reliability and efficiency.

CONCLUSION

Phases and guidelines have important roles, but issues in study design address the fundamental problems that compromise reliability and efficiency. Tools to study markers are underdeveloped and will evolve over time, perhaps to include seemingly radical approaches.

Gene-expression signatures in oncology diagnostics

The advent of DNA microarray technologies has enabled the development of gene expression signatures that can be used for prognostic and predictive purposes. This new information can change the paradigm of how medicine is practiced, coupling physical examination, pathology and clinical tests with new molecular information. However, many unanswered questions regarding sample acquisition, platform development, signature validation and clinical trial design will need to be addressed before this new medical content will have an impact on the clinical setting. This article will examine some of these issues in greater detail, focusing on tissue type, platform comparison, biospecimen collection and signature validation.

Intratumoral DNA stem-line heterogeneity in superficial spreading melanoma

BACKGROUND

In primary melanomas, data on the degree of intratumoral heterogeneity to date have been lacking.

OBJECTIVE

Our purpose was to investigate intratumoral DNA stem-line heterogeneity in superficial spreading melanoma (SSM).

METHODS

Multiple measuring fields of 54 SSMs (tumor thickness median 1.60 mm) were studied by DNA image cytometry to obtain data on the number of DNA stem lines per tumor, their ploidy characteristics, and intratumoral distribution. Results were compared with standard histopathological criteria.

RESULTS

Twenty-three of 54 SSMs were found to have two or three distinct proliferating tumor cell stem lines (1.46 +/- 0.57 per tumor). Stem lines appeared spatially separated in 22 of 23 SMMs. At least 3 measuring fields per tumor were necessary to identify all stem lines with a likelihood of 95%. DNA heterogeneity correlated with tumor thickness, but occurred in 5 of 19 cases of pT1 melanoma.

CONCLUSIONS

Primary SSMs can be regarded as potentially clonally unstable with a tendency for spatial separation of tumor cell stem lines.

Lessons from controversy: ovarian cancer screening and serum proteomics

In 2002 a study reported that a blood test, based on pattern-recognition proteomics mass spectroscopy analysis of serum, was nearly 100% sensitive and specific to detect ovarian cancer. Plans to introduce a commercial screening test by early 2004 were delayed amid concerns about whether the approach was reproducible and reliable. In this issue of JNCI, two commentaries discuss whether the initial results are reproducible and whether bias may account for results. This essay describes how threats to validity from chance and bias may cause erroneous results and inflated expectations in the kind of observational research being conducted in several "-omics" fields to assess molecular markers for diagnosis and prognosis of cancer. To address such threats and to realize the potential of new -omics technology will require application of appropriate rules of evidence in the design, conduct, and interpretation of clinical research about molecular markers.

Bias as a threat to the validity of cancer molecular-marker research

Claims that molecular markers can accurately diagnose cancer have recently been disputed; some prominent results have not been reproduced and bias has been proposed to explain the original observations. As new '-omics' fields are explored to assess molecular markers for cancer, bias will increasingly be recognized as the most important 'threat to validity' that must be addressed in the design, conduct and interpretation of such research.