Algorithms for quantitation of protein expression variation in normal versus tumor tissue as a prognostic factor in cancer: Met oncogene expression, and breast cancer as a model

Abstract

Background Immunohistochemistry and immunofluorescence (IF) assays frequently rely on subjective observer evaluation for grading. The aim of our study was to develop an objective quantitative index based on confocal laser scanning microscopy (CLSM) and image analysis of an IF assay to determine alteration in protein expression levels in normal versus tumor tissue. The relative levels of Met expression, a prognostic factor in breast cancer, were used as a model for evaluating image analysis algorithms. Methods Primary human breast cancer biopsies were collected. Sections containing tumor and adjacent uninvolved normal regions were immunostained for Met and digital images were acquired by CLSM. Subsequently, the digital data were manipulated using several different algorithms to calculate prognostic indexes. The results were correlated with the clinical outcome to determine the prognostic value of these indexes. Results Different algorithms were used to obtain quantitative indexes to evaluate the relative levels of Met expression. We report a statistical correlation between patient prognosis and relative Met level in normal versus tumor tissue as determined by three distinct algorithms using Kaplan‐Meier analysis (log‐rank): calculations based on intensity levels differences DV (P = 0.002), DIntensity (P = 0.014), and entropy divergence (Dentropy; P = 0.0023). Conclusions Using adjacent normal tissue as an internal reference, a quantitative index of tumor Met level divergence can be objectively determined to have a prognostic value. Moreover, this methodology can be used for other proteins in a variety of different diseases. Cytometry 41:155–165, 2000 © 2000 Wiley‐Liss, Inc.

Met Kinetic Signature Derived from the Response to HGF/SF in a Cellular Model Predicts Breast Cancer Patient Survival

“…In contrast to Met protein levels [8], [9], the mRNA levels of Met alone and those of the Met canonical pathway genes did not correlate with patient survival in all three data sets (Figure S1 B, C).…”

“…Activated Met mutation or Met and/or HGF/SF overexpression are associated with increased angiogenesis, tumorigenesis, invasiveness and metastasis in numerous human solid tumors (www.vai.org/metandcancer) [6], [7]. Overexpression of HGF/SF and Met in breast carcinoma [8]–[10] correlates with triple-negative and basal type tumors [11], [12], and are strong independent predictors of decreased survival [9], [13]–[15], including stage-I patients [16]–[19]. Met overexpression is found in approximately 20% of breast cancer patients [9], [14].…”

Differential expression of c‐Met, its ligand HGF/SF and HER2/neu in DCIS and adjacent normal breast tissue

“…These may differ from events leading to invasive potential and c-Met may be the important factor promoting breast cancer progression and prognosis. 5,[15][16][17] We have shown in 39 cases of DCIS of the breast that high c-Met expression of the tumour tissue, assessed by confocal immunofluorescent analysis, is correlated with unfavourable histopathological factors such as VN Grade 3. DCIS cases classified as VN3 are considered high-grade lesions and are associated with a greater risk of recurrence.…”

“…13,14 Thus, c-Met is an important factor in breast cancer progression and prognosis. 5,[15][16][17] Jin et al observed significant stronger staining for c-Met and HGF ⁄ SF in ductal carcinoma in situ (DCIS) than in benign hyperplasia, yet lower levels than in infiltrating carcinoma. 18 Her2 ⁄ neu is involved in regulation of normal breast development and growth, but also plays a role in malignant transformation and tumorigenesis.…”

Quantifying cell scattering: The blob algorithm revisited

“…The lack of objective, quantitative methodology has been cited as a significant challenge in the emerging era of molecular epidemiology (10). To this end, we previously described quantitative analysis of gene expression ratios in breast cancer patients (11) and a comparison of a variety of quantitative parameters applicable to quantitative analysis of breast cancer specimens (12). In this report, this tool set is expanded to quantify cell clustering or scattering as a quantitative parameter.…”