DNA histogram analysis for node-negative breast cancer

Quantitative Assessment of Heterogeneity in Bladder Tumor MRI Diffusivity: Can Response be Predicted Prior to Neoadjuvant Chemotherapy?

Background:

It is a critical unmet need to predict chemosensitivity in muscle-invasive bladder cancer patients who receive neoadjuvant chemotherapy (NAC). Quantification of tumor heterogeneity has been shown to be useful in the assessment of therapeutic response. Apparent diffusion coefficient (ADC) is derived from diffusion weighted MRI (DWI) to quantify the water diffusivity which characterizes micro-cellularity in tumor tissues.

Objective:

The aim of this study is to assess if a quantitative measurement of ADC heterogeneity in bladder tumors can be a predictor of therapeutic response to NAC.

Materials and Methods:

Twenty patients with pT2 bladder cancer have been included in this study. Patient MRI was performed on a 3T system with DWI prior to NAC. Regions of interest (ROIs) were placed over the whole tumor volume on ADC maps to acquire a data matrix of voxel-wise ADC values for each patient. We performed histogram analysis on each ADC data matrix to calculate uniformity (U) and entropy (E). These quantities were subsequently correlated with the patient’s response to chemotherapy. Statistical significance was found with P < 0.05.

Results:

Fifteen patients were categorized as responders, and five as non-responders. The data showed that tumors of responders were significantly higher in U (P = 0.01) and lower in E (P < 0.01) than non-responders. This finding indicates that resistant tumors were more heterogeneous in their spatial distribution of ADC values. While this difference in ADC heterogeneity was not always visually recognizable, it could be quantified by the data analytics.

Conclusions:

This study demonstrates that the quantitative readout of tumor heterogeneity in micro-cellularity is associated with the patient’s defined response to chemotherapy. Quantification of tumor ADC heterogeneity may provide useful information to enable the prediction of chemotherapeutic response prior to the treatment to improve patient outcomes.

Prognostic relevance of DNA flow cytometry in breast cancer revisited: The 25-year experience of the Portuguese Institute of Oncology of Lisbon

The potential prognostic significance of DNA flow cytometric measurements (DNA ploidy and S-phase fraction) in breast cancer remains in dispute. Inconclusive data, primarily due to the lack of consistent standardization and quality control programs, have limited its translation into clinical practice. The aim of the present review, based on the 25-year experience of the Portuguese Institute of Oncology of Lisbon, is to assess the clinical relevance and application of DNA flow cytometry for the prognosis of breast cancer. Overall, data from Portuguese Institute of Oncology of Lisbon indicate that DNA flow cytometry provides significant prognostic information that is biologically relevant and clinically useful for the management of patients with breast cancer. Furthermore, this data has demonstrated the independent value of DNA aneuploidy as a prognostic indicator of poor clinical outcome in various subgroups of patients with early or locally advanced breast cancer at short- and long-term follow-up. Notably, aneuploidy identifies subsets of patients with grade (G)1 or G2 tumours who exhibit a poor clinical outcome. These patients may benefit from adjuvant chemotherapy, particularly those with luminal A and luminal B/human epidermal growth factor-2-negative endocrine-responsive breast cancer. In conclusion, data from Portuguese Institute of Oncology of Lisbon reinforces the clinical importance and utility of DNA flow cytometric analysis, particularly DNA ploidy, in the prognostic assessment and therapeutic planning for patients with breast cancer.

Computational analysis of high-dimensional flow cytometric data for diagnosis and discovery

Recent technological advancements have enabled the flow cytometric measurement of tens of parameters on millions of cells. Conventional manual data analysis and bioinformatics tools cannot provide a complete analysis of these datasets due to this complexity. In this chapter we will provide an overview of a general data analysis pipeline both for automatic identification of cell populations of known importance (e.g., diagnosis by identification of predefined cell population) and for exploratory analysis of cohorts of flow cytometry assays (e.g., discovery of new correlates of a malignancy). We provide three real-world examples of how unsupervised discovery has been used in basic and clinical research. We also discuss challenges for evaluation of the algorithms developed for (1) identification of cell populations using clustering, (2) identification of specific cell populations, and (3) supervised analysis for discriminating between patient subgroups.

QUAliFiER: an automated pipeline for quality assessment of gated flow cytometry data

Background

Effective quality assessment is an important part of any high-throughput flow cytometry data analysis pipeline, especially when considering the complex designs of the typical flow experiments applied in clinical trials. Technical issues like instrument variation, problematic antibody staining, or reagent lot changes can lead to biases in the extracted cell subpopulation statistics. These biases can manifest themselves in non–obvious ways that can be difficult to detect without leveraging information about the study design or other experimental metadata. Consequently, a systematic and integrated approach to quality assessment of flow cytometry data is necessary to effectively identify technical errors that impact multiple samples over time. Gated cell populations and their statistics must be monitored within the context of the experimental run, assay, and the overall study.

Results

We have developed two new packages, flowWorkspace and QUAliFiER to construct a pipeline for quality assessment of gated flow cytometry data. flowWorkspace makes manually gated data accessible to BioConductor’s computational flow tools by importing pre–processed and gated data from the widely used manual gating tool, FlowJo (Tree Star Inc, Ashland OR). The QUAliFiER package takes advantage of the manual gates to perform an extensive series of statistical quality assessment checks on the gated cell sub–populations while taking into account the structure of the data and the study design to monitor the consistency of population statistics across staining panels, subject, aliquots, channels, or other experimental variables. QUAliFiER implements SVG–based interactive visualization methods, allowing investigators to examine quality assessment results across different views of the data, and it has a flexible interface allowing users to tailor quality checks and outlier detection routines to suit their data analysis needs.

Conclusion

We present a pipeline constructed from two new R packages for importing manually gated flow cytometry data and performing flexible and robust quality assessment checks. The pipeline addresses the increasing demand for tools capable of performing quality checks on large flow data sets generated in typical clinical trials. The QUAliFiER tool objectively, efficiently, and reproducibly identifies outlier samples in an automated manner by monitoring cell population statistics from gated or ungated flow data conditioned on experiment–level metadata.

B cells with high side scatter parameter by flow cytometry correlate with inferior survival in diffuse large B-cell lymphoma

Despite advances in the understanding of diffuse large B-cell lymphoma (DLBCL) biology, only the clinically based International Prognostic Index (IPI) is used routinely for risk stratification at diagnosis. To find novel prognostic markers, we analyzed flow cytometric data from 229 diagnostic DLBCL samples using an automated multiparameter data analysis approach developed in our laboratory. By using the developed automated data analysis pipeline, we identified 71 of 229 cases as having more than 35% B cells with a high side scatter (SSC) profile, a parameter reflecting internal cellular complexity. This high SSC B-cell feature was associated with inferior overall and progression-free survival (P = .001 and P = .01, respectively) and remained a significant predictor of overall survival in multivariate Cox regression analysis (IPI, P = .001; high SSC, P = .004; rituximab, P = .53). This study suggests that high SSC among B cells may serve as a useful biomarker to identify patients with DLBCL at high risk for relapse. This is of particular interest because this biomarker is readily available in most clinical laboratories without significant alteration to existing routine diagnostic strategies or incurring additional costs.

Single-color multitarget flow cytometry using monoclonal antibodies labeled with different intensities of the same fluorochrome

Background

We developed a single-color multitarget flow cytometry (SM-FC) assay, a single-tube assay with graded mean fluorescence intensities (MFIs). We evaluated the repeatability of SM-FC, and its correlation with multicolor flow cytometry (MFC), to assess its application as a routine FC assay.

Methods

We selected CD19, CD3, CD4, and CD8 as antigen targets to analyze a lymphocyte subset. MFIs were graded by adjusting monoclonal antibody (mAb) volumes to detect several cell populations. Dimly labeled mAb was prepared by decreasing mAb volume and the optimum diluted volume was determined by serial dilution. SM-FC repeatability was analyzed 10 times in 2 normal controls. The correlation between SM-FC and MFC was evaluated in 20 normal and 23 patient samples.

Results

CV values (0.8-5.0% and 1.3-4.1% in samples 1 and 2, respectively) acquired by SM-FC with CD3-fluorescein α-isothyocyanate (FITC)_dim+CD4-FITC_bright and with CD19-FITC_dim+CD3-FITC_bright showed good repeatability, comparable to that acquired by MFC (1.6-3.7% and 1.0-4.8% in samples 1 and 2, respectively). Excellent correlation was observed between the 2 methods in the 20 normal samples (B cells, T cells, non-T_helper cells, and T_helper cells; r²=0.87, 0.97, 0.97, and 0.98, respectively; P<0.05). There were also linear relationships between SM-FC with CD19-FITC_dim+CD3-FITC_bright and CD8-PE_dim+CD4-PE_bright, and MFC, in the 23 patient samples (B cells, T cells, T_cytotoxic cells, and T_helper cells; r²≥0.98, 0.99, 0.99, and 0.99, respectively; P<0.05).

Conclusions

The multicolor, single-tube SM-FC technique is a potential alternative tool for identifying a lymphocyte subset.

Panel development for multicolor flow-cytometry testing of proliferation and immunophenotype in hMSCs

Adult human mesenchymal stem cells (hMSC) are rare fibroblast-like cells capable of differentiation into a variety of cell tissues which include bone, cartilage, muscle, ligament, tendon, and adipose. Normal adult bone marrow and adipose tissue are the most common sources of these cells. The International Society for Cellular Therapy (ISCT) has proposed a set of standards to define hMSC for laboratory investigations and preclinical studies: adherence to plastic in standard culture conditions; in vitro differentiation into osteoblasts, adipocytes, and chondroblasts; and specific surface antigen expression. Direct measurement of proliferation combined with simultaneous detection of the ISCT-consensus immunophenotypic profile provides data that is used to determine the differentiation status and health of the cells. Flow cytometry provides a powerful technology that is routinely used to simultaneously and rapidly measure multiple parameters in a single sample. This chapter describes a flow cytometric panel for the simultaneous detection of immunophenotypic profile, proliferative capacity, and DNA content measurement in hMSC. Because a relatively small number of cells are needed with this approach, measurements can be made with minimal impact on expansion potential. The ability to assess antigen expression and proliferative status enables the investigator to make informed decisions on expansion and harvesting.

A pipeline for automated analysis of flow cytometry data: preliminary results on lymphoma sub-type diagnosis

Flow cytometry (FCM) is widely used in health research and is a technique to measure cell properties such as phenotype, cytokine expression, etc., for up to millions of cells from a sample. FCM data analysis is a highly tedious, subjective and manually time-consuming (to the level of impracticality for some data) process that is based on intuition rather than standardized statistical inference. This study proposes a pipeline for automatic analysis of FCM data. The proposed pipeline identifies biomarkers that correlate with physiological/pathological conditions and classifies the samples to specific pathological/physiological entities. The pipeline utilizes a model-based clustering approach to identify cell populations that share similar biological functions. Support vector machine (SVM) and random forest (RF) classifiers were then used to classify the samples and identify biomarkers associated with disease status. The performance of the proposed data analysis pipeline has been evaluated on lymphoma patients. Preliminary results show more than 90% accuracy in differentiating between some sub-types of lymphoma. The proposed pipeline also finds biologically meaningful biomarkers that differ between lymphoma subtypes.

A survey of flow cytometry data analysis methods

Flow cytometry (FCM) is widely used in health research and in treatment for a variety of tasks, such as in the diagnosis and monitoring of leukemia and lymphoma patients, providing the counts of helper-T lymphocytes needed to monitor the course and treatment of HIV infection, the evaluation of peripheral blood hematopoietic stem cell grafts, and many other diseases. In practice, FCM data analysis is performed manually, a process that requires an inordinate amount of time and is error-prone, nonreproducible, nonstandardized, and not open for re-evaluation, making it the most limiting aspect of this technology. This paper reviews state-of-the-art FCM data analysis approaches using a framework introduced to report each of the components in a data analysis pipeline. Current challenges and possible future directions in developing fully automated FCM data analysis tools are also outlined.

flowClust: a Bioconductor package for automated gating of flow cytometry data

Background

As a high-throughput technology that offers rapid quantification of multidimensional characteristics for millions of cells, flow cytometry (FCM) is widely used in health research, medical diagnosis and treatment, and vaccine development. Nevertheless, there is an increasing concern about the lack of appropriate software tools to provide an automated analysis platform to parallelize the high-throughput data-generation platform. Currently, to a large extent, FCM data analysis relies on the manual selection of sequential regions in 2-D graphical projections to extract the cell populations of interest. This is a time-consuming task that ignores the high-dimensionality of FCM data.

Results

In view of the aforementioned issues, we have developed an R package called flowClust to automate FCM analysis. flowClust implements a robust model-based clustering approach based on multivariate t mixture models with the Box-Cox transformation. The package provides the functionality to identify cell populations whilst simultaneously handling the commonly encountered issues of outlier identification and data transformation. It offers various tools to summarize and visualize a wealth of features of the clustering results. In addition, to ensure its convenience of use, flowClust has been adapted for the current FCM data format, and integrated with existing Bioconductor packages dedicated to FCM analysis.

Conclusion

flowClust addresses the issue of a dearth of software that helps automate FCM analysis with a sound theoretical foundation. It tends to give reproducible results, and helps reduce the significant subjectivity and human time cost encountered in FCM analysis. The package contributes to the cytometry community by offering an efficient, automated analysis platform which facilitates the active, ongoing technological advancement.

Automated gating of flow cytometry data via robust model-based clustering

The capability of flow cytometry to offer rapid quantification of multidimensional characteristics for millions of cells has made this technology indispensable for health research, medical diagnosis, and treatment. However, the lack of statistical and bioinformatics tools to parallel recent high-throughput technological advancements has hindered this technology from reaching its full potential. We propose a flexible statistical model-based clustering approach for identifying cell populations in flow cytometry data based on t-mixture models with a Box-Cox transformation. This approach generalizes the popular Gaussian mixture models to account for outliers and allow for nonelliptical clusters. We describe an Expectation-Maximization (EM) algorithm to simultaneously handle parameter estimation and transformation selection. Using two publicly available datasets, we demonstrate that our proposed methodology provides enough flexibility and robustness to mimic manual gating results performed by an expert researcher. In addition, we present results from a simulation study, which show that this new clustering framework gives better results in terms of robustness to model misspecification and estimation of the number of clusters, compared to the popular mixture models. The proposed clustering methodology is well adapted to automated analysis of flow cytometry data. It tends to give more reproducible results, and helps reduce the significant subjectivity and human time cost encountered in manual gating analysis.

S-phase fraction determined on fine needle aspirates is an independent prognostic factor in breast cancer - a multivariate study of 770 patients

BACKGROUND

The prognostic significance of DNA ploidy and the S-phase fraction (SPF) have been extensively studied in breast cancer, but their clinical utility remains controversial. The type of tumour material can substantially influence flow cytometric DNA measurements. Material obtained by fine needle aspiration (FNA) biopsy is very suitable for flow cytometric DNA analysis because it contains a low proportion of non-tumour cells and less debris than tissue samples.

METHODS

The prognostic significance of DNA ploidy and SPF, determined on FNA samples, was analysed in 770 breast cancer patients, diagnosed between 1992 and 1997. DNA ploidy and SPF were determined at the time of diagnosis as part of the diagnostic work-up. The median follow-up was 90 months. Survival analysis included overall cancer specific survival (OS), disease free survival (DFS) and survival after recurrence (SAR). Other variables included in survival analyses were age, histological grade, histological type, lymph node status and tumour size. Disease free interval and the site of recurrence were also included in SAR analysis.

RESULTS

DNA ploidy and SPF correlated with tumour type, size, lymph node involvement and, especially, tumour grade. In a univariate analysis, both aneuploidy and high SPF were associated with shorter OS, DFS and SAR, but only SPF retained its independent prognostic significance in multivariate analyses. Independent prognostic variables for OS were node status, histological grade, SPF and tumour size. Node status, histological grade and SPF were independent predictors of DFS, while the site of recurrence, SPF, histological grade, disease free interval and age were independent predictors of SAR.

CONCLUSIONS

DNA ploidy and SPF can be efficiently and routinely determined on FNA samples. High SPF is independently associated with a worse clinical outcome of patients with breast cancer. Although SPF and histological grade share prognostic information to some degree, SPF provides additional, less subjective prognostic information. The prognostic value of SPF determined on FNA samples could be even more relevant in neoadjuvant settings and for patients not amenable for surgical treatment, when histological grade cannot be assessed.

A standardized ZAP-70 assay—Lessons learned in the trenches

BACKGROUND

The disease of chronic lymphocytic leukemia (CLL) has been shown to exhibit varying clinical outcomes based on reported laboratory parameters. One of these parameters involves the measurement of the protein levels of zeta-associated protein (ZAP-70) in CLL cells. A standardized assay has not yet reached consensus in the clinical cytometry community.

METHODS

We developed a system using the 8-peak Rainbow beads as our fluorescence calibrator along with a fixed cell control. Using a panel of CD19-PE, CD5-FITC, and ZAP-70-Alexa 647, we stained normal whole blood, and blood and bone marrow from patients with CLL to determine the level of ZAP-70 expression in T-cell, B-cell, and CLL-cell populations. ZAP-70 expression was reported in molecules equivalent fluorescence (MEFL) based on the calibration of the flow cytometer with the 8-peak Rainbow beads.

RESULTS

Daily assay performance as well as operating MEFL defined ranges for ZAP-70 detection in CLL were developed. A rank-order, nonparametric approach to reference ranges was used to assign a cutoff for "negative" as well as ranges for "intermediate" and "positive" staining using T and B cells from a pool of 50 normal subjects, and CLL cells from 395 patients. The assay was validated in a multi-institutional study and has demonstrated correlation with published techniques. Since its initial development, the assay has been implemented at two additional laboratory sites and has been shown to produce reproducible, correlated data at all sites.

CONCLUSIONS

Strict adherence to standardization can yield an assay that is predictable, reliable, and reproducible as well as capable of multisite implementation. The Rainbow beads provide a common platform for system calibration. The fixed cell culture controls provide a common target to test antibody. The final level of control tests the sensitivity of ZAP-70 detection in a normal peripheral blood sample stained along with the submitted CLL patients. The acceptance/rejection of test results must meet all three levels of control before patient results are reported.