AngioMap is a novel image analysis algorithm for assessment of plasma cell distribution within bone marrow vascular niche.

The ability to characterize distribution of neoplastic hematopoietic cells and their progenitors in their native microenvironment is emerging as an important challenge and potential therapeutic target in many disease areas, including multiple myeloma. In multiple myeloma, bone marrow (BM) angiogenesis is typically increased and microvessel density is a known indicator of poor prognosis. However, the difficulty of consistently measuring 3D vessels from 2D cut sections has previously limited the study of spatial distribution of plasma cells (PC) and their interaction with BM microenvironment. The aim of the study is to report a novel method to study myeloma cells spatial distribution within their hematopoietic niche context using readily available tissue sections and standard histology approaches. We utilized a novel whole-tissue image analysis approach to identify vessels, and then applied computational grown regions extended out from each vessel at 15, 35, 55, 75, and 100 µm to identify the spatial distribution of PC on CD34/CD138 double-stained core biopsy slides. Percent PC to total cells (TC) was significantly higher at <15 µm distance compared with those at 16 to 35, 36 to 55, 56 to 75, and 76 to 100 µm distance (P=0.0001). Similarly, PC/TC at <35 µm region was significantly higher compared with 36 to 55 (P=0.0001), 56 to 75 (P≤0.0001), and 76 to 100 (P=0.0002) µm distances. The mean PC/TC differences in the spatial gradient of 36 to 55, 56 to 75, and 76 to 100 µm distance regions were not significant. Our findings suggest possible preferential advantage to neoplastic PC in the proximity of blood vessels compared with other hematopoietic marrow cells. We demonstrate the feasibility of analyzing the spatial distribution of PC, and possibly other hematopoietic/stem cells in their microenvironment, as characterized by the distance to vessels in BM using a novel image analysis approach.

Evaluating tumor heterogeneity in immunohistochemistry-stained breast cancer tissue.

Quantitative clinical measurement of heterogeneity in immunohistochemistry staining would be useful in evaluating patient therapeutic response and in identifying underlying issues in histopathology laboratory quality control. A heterogeneity scoring approach (HetMap) was designed to visualize a individual patient's immunohistochemistry heterogeneity in the context of a patient population. HER2 semiquantitative analysis was combined with ecology diversity statistics to evaluate cell-level heterogeneity (consistency of protein expression within neighboring cells in a tumor nest) and tumor-level heterogeneity (differences of protein expression across a tumor as represented by a tissue section). This approach was evaluated on HER2 immunohistochemistry-stained breast cancer samples using 200 specimens across two different laboratories with three pathologists per laboratory, each outlining regions of tumor for scoring by automatic cell-based image analysis. HetMap was evaluated using three different scoring schemes: HER2 scoring according to American Society of Clinical Oncology and College of American Pathologists (ASCO/CAP) guidelines, H-score, and a new continuous HER2 score (HER2(cont)). Two definitions of heterogeneity, cell-level and tumor-level, provided useful independent measures of heterogeneity. Cases where pathologists had disagreement over reads in the area of clinical importance (+1 and +2) had statistically significantly higher levels of tumor-level heterogeneity. Cell-level heterogeneity, reported either as an average or the maximum area of heterogeneity across a slide, had low levels of dependency on the pathologist choice of region, while tumor-level heterogeneity measurements had more dependence on the pathologist choice of regions. HetMap is a measure of heterogeneity, by which pathologists, oncologists, and drug development organizations can view cell-level and tumor-level heterogeneity for a patient for a given marker in the context of an entire patient cohort. Heterogeneity analysis can be used to identify tumors with differing degrees of heterogeneity, or to highlight slides that should be rechecked for QC issues. Tumor heterogeneity plays a significant role in disconcordant reads between pathologists.