Controlling technical variation amongst 6693 patient microarrays of the randomized MINDACT trial.

Gene expression data obtained in large studies hold great promises for discovering disease signatures or subtypes through data analysis. It is also prone to technical variation, whose removal is essential to avoid spurious discoveries. Because this variation is not always known and can be confounded with biological signals, its removal is a challenging task.

Performance Characteristics of the BluePrint® Breast Cancer Diagnostic Test.

The analytical performance of a multi-gene diagnostic signature depends on many parameters, including precision, repeatability, reproducibility and intra-tumor heterogeneity. Here we study the analytical performance of the BluePrint 80-gene breast cancer molecular subtyping test through determination of these performance characteristics. BluePrint measures the expression of 80 genes that assess functional pathways which determine the intrinsic breast cancer molecular subtypes (i.

Decentralization of Next-Generation RNA Sequencing-Based MammaPrint® and BluePrint® Kit at University Hospitals Leuven and Curie Institute Paris.

A previously developed and centrally validated MammaPrint® (MP) and BluePrint® (BP) targeted RNA next-generation sequencing (NGS) kit was implemented and validated in two large academic European hospitals. Additionally, breast cancer molecular subtypes by MP and BP RNA sequencing were compared with immunohistochemistry (IHC). Patients with early breast cancer diagnosed at University Hospitals Leuven and Curie Institute Paris were prospectively included between September 2017 and January 2018.

MammaPrint and BluePrint Molecular Diagnostics Using Targeted RNA Next-Generation Sequencing Technology.

Next-generation DNA sequencing is rapidly becoming an indispensable tool for genome-directed cancer diagnostics, but next-generation RNA sequencing (RNA-seq) is currently not standardly used in clinical diagnostics for expression assessment. However, multigene RNA diagnostic assays are used increasingly in the routine diagnosis of early-stage breast cancer. Two of the most widely used tests are currently available only as a central laboratory service, which limits their clinical use.

A Computational Workflow Translates a 58-Gene Signature to a Formalin-Fixed, Paraffin-Embedded Sample-Based Companion Diagnostic for Personalized Treatment of the BRAF-Mutation-Like Subtype of Colorectal Cancers.

Colorectal cancer patients with the (p.V600E) mutation have poor prognosis in metastatic setting. Personalized treatment options and companion diagnostics are needed to better treat these patients.

A breast cancer gene signature for indolent disease.

Purpose: Early-stage hormone-receptor positive breast cancer is treated with endocrine therapy and the recommended duration of these treatments has increased over time. While endocrine therapy is considered less of a burden to patients compared to chemotherapy, long-term adherence may be low due to potential adverse side effects as well as compliance fatigue. It is of high clinical utility to identify subgroups of breast cancer patients who may have excellent long-term survival without or with limited duration of endocrine therapy to aid in personalizing endocrine treatment.

Equivalence of MammaPrint array types in clinical trials and diagnostics.

MammaPrint is an FDA-cleared microarray-based test that uses expression levels of the 70 MammaPrint genes to assess distant recurrence risk in early-stage breast cancer. The prospective RASTER study proved that MammaPrint Low Risk patients can safely forgo chemotherapy, which is further subject of the prospective randomized MINDACT trial. While MammaPrint diagnostic results are obtained from mini-arrays, clinical trials may be performed on whole-genome arrays.

MammaPrint molecular diagnostics on formalin-fixed, paraffin-embedded tissue.

MammaPrint, a prognostic 70-gene profile for early-stage breast cancer, has been available for fresh tissue. Improvements in RNA processing have enabled microarray diagnostics for formalin-fixed, paraffin-embedded (FFPE) tissue. Here, we describe method optimization, validation, and performance of MammaPrint using analyte from FFPE tissue.

Performance characteristics of the MammaPrint breast cancer diagnostic gene signature.

Background: The analytical performance of multigene signatures depends on many parameters, including precision, repeatability, reproducibility and intratumor heterogeneity. Indicators such as sensitivity, specificity, positive predictive value and negative predictive value are typically used to define the clinical performance of a diagnostic test.

Aim: Here we study these performance characteristics of the MammaPrint (Agendia NV, Amsterdam, The Netherlands) 70-gene signature using the US FDA-recommended guidelines, as well as predetermined acceptance criteria.

A gene expression profile for detection of sufficient tumour cells in breast tumour tissue: microarray diagnosis eligibility.

Background: Microarray diagnostics of tumour samples is based on measurement of prognostic and/or predictive gene expression profiles. Typically, diagnostic profiles have been developed using bulk tumour samples with a sufficient amount of tumour cells (usually >50%). Consequentially, a diagnostic results depends on the minimal percentage of tumour cells within a sample.

Comparison of gene expression profiles predicting progression in breast cancer patients treated with tamoxifen.

Background: Molecular signatures that predict outcome in tamoxifen treated breast cancer patients have been identified. For the first time, we compared these response profiles in an independent cohort of (neo)adjuvant systemic treatment naïve breast cancer patients treated with first-line tamoxifen for metastatic disease.

Methods: From a consecutive series of 246 estrogen receptor (ER) positive primary tumors, gene expression profiling was performed on available frozen tumors using 44K oligoarrays (n = 69).

Gene-expression and immunohistochemical study of specific T-cell subsets and accessory cell types in the transformation and prognosis of follicular lymphoma.

Purpose: Despite the generally favorable clinical course in follicular lymphoma (FL), a minority of patients have a poor prognosis-with death within 3 years of diagnosis-most often due to transformation to aggressive disease.

Patients And Methods: In this study, we analyzed the potential of predicting early transformation on the basis of gene expression and immunologic parameters in FL biopsy samples taken at diagnosis.

Results: At the gene-expression level, FL is a highly uniform disease at the time of diagnosis, precluding the detection of sufficiently validated prognostic gene-expression profiles suitable for a clinical setting.

Converting a breast cancer microarray signature into a high-throughput diagnostic test.

Background: A 70-gene tumor expression profile was established as a powerful predictor of disease outcome in young breast cancer patients. This profile, however, was generated on microarrays containing 25,000 60-mer oligonucleotides that are not designed for processing of many samples on a routine basis.

Results: To facilitate its use in a diagnostic setting, the 70-gene prognosis profile was translated into a customized microarray (MammaPrint) containing a reduced set of 1,900 probes suitable for high throughput processing.

Gene expression profiling in follicular lymphoma to assess clinical aggressiveness and to guide the choice of treatment.

Follicular lymphoma (FL) is a disease characterized by a long clinical course marked by frequent relapses that vary in clinical aggressiveness over time. Therefore, the main dilemma at each relapse is the choice for the most effective treatment for optimal disease control and failure-free survival while at the same time avoiding overtreatment and harmful side effects. The selection for more aggressive treatment is currently based on histologic grading and clinical criteria; however, in up to 30% of all cases these methods prove to be insufficient.

A gene-expression signature as a predictor of survival in breast cancer.

Background: A more accurate means of prognostication in breast cancer will improve the selection of patients for adjuvant systemic therapy.

Methods: Using microarray analysis to evaluate our previously established 70-gene prognosis profile, we classified a series of 295 consecutive patients with primary breast carcinomas as having a gene-expression signature associated with either a poor prognosis or a good prognosis. All patients had stage I or II breast cancer and were younger than 53 years old; 151 had lymph-node-negative disease, and 144 had lymph-node-positive disease.