High-throughput techniques in breast cancer: A clinical perspective

Discovery of a 29-gene panel in peripheral blood mononuclear cells for the detection of colorectal cancer and adenomas using high throughput real-time PCR

Colorectal cancer (CRC) is the second leading cause of cancer-related death in developed countries. Early detection of CRC leads to decreased CRC mortality. A blood-based CRC screening test is highly desirable due to limited invasiveness and high acceptance rate among patients compared to currently used fecal occult blood testing and colonoscopy. Here we describe the discovery and validation of a 29-gene panel in peripheral blood mononuclear cells (PBMC) for the detection of CRC and adenomatous polyps (AP). Blood samples were prospectively collected from a multicenter, case-control clinical study. First, we profiled 93 samples with 667 candidate and 3 reference genes by high throughput real-time PCR (OpenArray system). After analysis, 160 genes were retained and tested again on 51 additional samples. Low expressed and unstable genes were discarded resulting in a final dataset of 144 samples profiled with 140 genes. To define which genes, alone or in combinations had the highest potential to discriminate AP and/or CRC from controls, data were analyzed by a combination of univariate and multivariate methods. A list of 29 potentially discriminant genes was compiled and evaluated for its predictive accuracy by penalized logistic regression and bootstrap. This method discriminated AP >1cm and CRC from controls with a sensitivity of 59% and 75%, respectively, with 91% specificity. The behavior of the 29-gene panel was validated with a LightCycler 480 real-time PCR platform, commonly adopted by clinical laboratories. In this work we identified a 29-gene panel expressed in PBMC that can be used for developing a novel minimally-invasive test for accurate detection of AP and CRC using a standard real-time PCR platform.

Microarray methods to identify factors determining breast cancer progression: potentials, limitations, and challenges

65-80% of the patients with breast cancer might not benefit from the adjuvant therapy they receive based on 'classical' markers used for the selection for adjuvant therapy. Therefore it is necessary to develop new markers that are able to tailor treatment for an individual patient. A number of microarray methods have been developed in recent years to accommodate this search for new factors that determine breast cancer progression. We give an overview of the most commonly used microarray methods to identify tumour progression markers (oligo- or cDNA arrays, CGH arrays, PCR arrays, and tissue microarrays). Their applications will be illustrated using the most influential examples from literature. The potentials, limitations and the related statistical analyses of each method are discussed. We conclude that microarray studies have led to an increase in the understanding of the complexity and diversity of breast carcinoma and have provided clinical relevant subgroups of breast cancer that may benefit from patient tailored treatment. Still, more extensive external validation and long-term follow-up will be necessary before such assays can be implemented into routine clinical practice. Most likely, these novel prognostic indicators will be complementary to the already available classical prognostic factors.

Can clinically relevant prognostic subsets of breast cancer patients with four or more involved axillary lymph nodes be identified through immunohistochemical biomarkers? A tissue microarray feasibility study

Introduction

Primary breast cancer involving four or more axillary lymph nodes carries a poor prognosis. We hypothesized that use of an immunohistochemical biomarker scoring system could allow for identification of variable risk subgroups.

Methods

Patients with four or more positive axillary nodes were identified from a clinically annotated tissue microarray of formalin-fixed paraffin-embedded primary breast cancers and randomized into a 'test set' and a 'validation set'. A prospectively defined prognostic scoring model was developed in the test set and was further assessed in the validation set combining expression for eight biomarkers by immunohistochemistry, including estrogen receptor, human epidermal growth factor receptors 1 and 2, carbonic anhydrase IX, cytokeratin 5/6, progesterone receptor, p53 and Ki-67. Survival outcomes were analyzed by the Kaplan–Meier method, log rank tests and Cox proportional-hazards models.

Results

A total of 313 eligible patients were identified in the test set for whom 10-year relapse-free survival was 38.3% (SEM 2.9%), with complete immunohistochemical data available for 227. Tumor size, percentage of positive axillary nodes and expression status for the progesterone receptor, Ki-67 and carbonic anhydrase IX demonstrated independent prognostic significance with respect to relapse-free survival. Our combined biomarker scoring system defined three subgroups in the test set with mean 10-year relapse-free survivals of 75.4% (SEM 7.0%), 35.3% (SEM 4.1%) and 19.3% (SEM 7.0%). In the validation set, differences in relapse-free survival for these subgroups remained statistically significant but less marked.

Conclusion

Biomarkers assessed here carry independent prognostic value for breast cancer with four or more positive axillary nodes and identified clinically relevant prognostic subgroups. This approach requires refinement and validation of methodology.

International Agency for Research on Cancer workshop on 'Expression array analyses in breast cancer taxonomy'

In May 2006, a workshop on Expression array analyses in breast cancer taxonomy was held at the International Agency for Research on Cancer (IARC). The workshop covered an array of topics from the validity of the currently defined breast tumor subtypes and other expression profile-based signatures to the technical limitations of expression analysis and the types of platforms on which these omics results will eventually reach clinical practice. Overall, the workshop participants believed firmly that tumor taxonomy is likely to yield improved prognostic and predictive markers. Even so, further standardization and validation are required before clinical trials are set in motion.

Molecular screening for breast cancer prevention, early detection, and treatment planning: combining biomarkers from DNA, RNA, and protein

The completion of the human genome project, along with the ancillary technologies derived from this effort, provides the ability to comprehensively analyze patient tumors as well as the individual patient's own genetic make-up at the DNA, RNA, and protein level. As a result, novel molecular screening techniques have the potential to push the boundaries of detection to even smaller tumors and also to allow accurate risk assessment, cancer prevention, and treatment planning in individual women. This review focuses on advances over the past 2 years in the use of molecular signatures and circulating tumor cells for early breast cancer detection and for prediction of response to therapy.

A dolphin peripheral blood leukocyte cDNA microarray for studies of immune function and stress reactions

A microarray focused on stress response and immune function genes of the bottlenosed dolphin has been developed. Random expressed sequence tags (ESTs) were isolated and sequenced from two dolphin peripheral blood leukocyte (PBL) cDNA libraries biased towards T- and B-cell gene expression by stimulation with IL-2 and LPS, respectively. A total of 2784 clones were sequenced and contig analysis yielded 1343 unigenes (archived and annotated at ). In addition, 52 dolphin genes known to be important in innate and adaptive immune function and stress responses of terrestrial mammals were specifically targeted, cloned and added to the unigene collection. The set of dolphin sequences printed on a cDNA microarray comprised the 1343 unigenes, the 52 targeted genes and 2305 randomly selected (but unsequenced) EST clones. This set was printed in duplicate spots, side by side, and in two replicates per slide, such that the total number of features per microarray slide was 19,200, including controls. The dolphin arrays were validated and transcriptomic profiles were generated using PBL from a wild dolphin, a captive dolphin and dolphin skin cells. The results demonstrate that the array is a reproducible and informative tool for assessing differential gene expression in dolphin PBL and in other tissues.

MALDI-TOF serum protein profiling for the detection of breast cancer

BACKGROUND

Proteomic expression profiling has been suggested as a potential tool for the early diagnosis of cancer and other diseases. The objective of our study was to assess the feasibility of this approach for the detection of breast cancer.

MATERIALS AND METHODS

In a randomized block design pre-operative serum samples obtained from 78 breast cancer patients and 29 controls were used to generate high-resolution MALDI-TOF protein profiles. The spectra generated using C8 magnetic beads assisted mass spectrometry were smoothed, binned and normalized after baseline correction. Linear discriminant analysis with double cross-validation, based on principal component analysis, was used to classify the protein profiles.

RESULTS

A total recognition rate of 99%, a sensitivity of 100%, and a specificity of 97.0% for the detection of breast cancer were shown. The area under the curve of the classifier was 98.3%, which demonstrates the separation power of the classifier. The first 2 principal components account for most of the between- group separation.

CONCLUSIONS

Double cross-validation showed that classification could be attributed to actual information in the protein profiles rather than to chance. Although preliminary, the high sensitivity and specificity indicate the potential usefulness of serum protein profiles for the detection of breast cancer.