Quantitative single-cell analysis of immunofluorescence protein multiplex images illustrates biomarker spatial heterogeneity within breast cancer subtypes.

BACKGROUND: The extent of cellular heterogeneity in breast cancer could have potential impact on diagnosis and long-term outcome. However, pathology evaluation is limited to biomarker immunohistochemical staining and morphology of the bulk cancer. Inter-cellular heterogeneity of biomarkers is not usually assessed. As an initial evaluation of the extent of breast cancer cellular heterogeneity, we conducted quantitative and spatial imaging of Estrogen Receptor (ER), Progesterone Receptor (PR), Epidermal Growth Factor Receptor-2 (HER2), Ki67, TP53, CDKN1A (P21/WAF1), CDKN2A (P16INK4A), CD8 and CD20 of a tissue microarray (TMA) representing subtypes defined by St. Gallen surrogate classification. METHODS: Quantitative, single cell-based imaging was conducted using an Immunofluorescence protein multiplexing platform (MxIF) to study protein co-expression signatures and their spatial localization patterns. The range of MxIF intensity values of each protein marker was compared to the respective IHC score for the TMA core. Extent of heterogeneity in spatial neighborhoods was analyzed using co-occurrence matrix and Diversity Index measures. RESULTS: On the 101 cores from 59 cases studied, diverse expression levels and distributions were observed in MxIF measures of ER and PR among the hormonal receptor-positive tumor cores. As expected, Luminal A-like cancers exhibit higher proportions of cell groups that co-express ER and PR, while Luminal B-like (HER2-negative) cancers were composed of ER+, PR- groups. Proliferating cells defined by Ki67 positivity were mainly found in groups with PR-negative cells. Triple-Negative Breast Cancer (TNBC) exhibited the highest proliferative fraction and incidence of abnormal P53 and P16 expression. Among the tumors exhibiting P53 overexpression by immunohistochemistry, a group of TNBC was found with much higher MxIF-measured P53 signal intensity compared to HER2+, Luminal B-like and other TNBC cases. Densities of CD8 and CD20 cells were highest in HER2+ cancers. Spatial analysis demonstrated variability in heterogeneity in cellular neighborhoods in the cancer and the tumor microenvironment. CONCLUSIONS: Protein marker multiplexing and quantitative image analysis demonstrated marked heterogeneity in protein co-expression signatures and cellular arrangement within each breast cancer subtype. These refined descriptors of biomarker expressions and spatial patterns could be valuable in the development of more informative tools to guide diagnosis and treatment.

Morphometric Analysis in the Diagnosis of Low-Grade Ductal and Lobular Carcinoma in Situ of the Breast.

OBJECTIVE: To identify quantitative histological features that can differentiate between low grade ductal carcinoma in situ (LG-DCIS) and classic type lobular carcinoma in situ (C-LCIS). STUDY DESIGN: Regions of solid intraductal proliferation from scanned hematoxylin and eosin images of LG-DCIS (20 cases) and C-LCIS (25 cases) were analyzed using Mercator Version 1.0 (ExploraNova, La Rochelle, France). The system detects structures based on optical density. Multiple variables were measured, including estimators of size (mean nuclear area), shape (form factor), and staining (grayscale value). We also calculated the percentage that nuclei, cytoplasm, and white space represent in the total gland surface area. RESULTS: C-LCIS showed higher mean nuclear area, higher percentage of nuclear area, higher nuclear and cytoplasmic grayscale values, and lower percentage of cytoplasm area as compared to LG-DCIS. Combination of these variables using a random forest learning algorithm classified correctly 80% of LG-DCIS cases and 69% of C-LCIS cases. CONCLUSION: Morphometric analysis is potentially a useful ancillary tool in the distinction between low-grade ductal and lobular in situ proliferations of the breast.

Selecting and assessing quantitative early ultrasound texture measures for their association with cerebral palsy.

Cerebral palsy (CP) develops as a consequence of white matter damage (WMD) in approximately one out of every 10 very preterm infants. Ultrasound (US) is widely used to screen for a variety of brain injuries in this patient population, but early US often fails to detect WMD. We hypothesized that quantitative texture measures on US images obtained within one week of birth are associated with the subsequent development of CP. In this retrospective study, using images from a variety of US machines, we extracted unique texture measures by means of adaptive processing and high resolution feature enhancement. We did not standardize the images, but used patients as their own controls. We did not remove speckle, as it may contain information. To test our hypothesis, we used the "random forest" algorithm to create a model. The random forest classifier achieved a 72% match to the health outcome of the patients (CP versus no CP), whereas designating all patients as having CP would have resulted in 53% error. This suggests that quantitative early texture measures contain diagnostic information relevant to the development of CP.

Technology review: the use of electrical impedance scanning in the detection of breast cancer.

The present paper focuses on electrical impedance scanning. The basic science behind the new modality, measurements of breast tissue impedance in vivo and in vitro, and the studies performed with a newly available commercial machine are discussed. Electrical impedance scanning has been generating interest for several reasons, including comfort to the patient, the relatively low cost, and studies suggest that it may be effective in detecting disease in mammographically dense breasts.