AngioMap is a Novel Image Analysis Algorithm for Assessment of Plasma Cell Distribution Within Bone Marrow Vascular Niche

Evaluation of an open-source machine-learning tool to quantify bone marrow plasma cells

AIMS

The objective of this study was to develop and validate an open-source digital pathology tool, QuPath, to automatically quantify CD138-positive bone marrow plasma cells (BMPCs).

METHODS

We analysed CD138-scanned slides in QuPath. In the initial training phase, manual positive and negative cell counts were performed in representative areas of 10 bone marrow biopsies. Values from the manual counts were used to fine-tune parameters to detect BMPCs, using the positive cell detection and neural network (NN) classifier functions. In the testing phase, whole-slide images in an additional 40 cases were analysed. Output from the NN classifier was compared with two pathologist's estimates of BMPC percentage.

RESULTS

The training set included manual counts ranging from 2403 to 17 287 cells per slide, with a median BMPC percentage of 13% (range: 3.1%-80.7%). In the testing phase, the quantification of plasma cells by image analysis correlated well with manual counting, particularly when restricted to BMPC percentages of <30% (Pearson's r=0.96, p<0.001). Concordance between the NN classifier and the pathologist whole-slide estimates was similarly good, with an intraclass correlation of 0.83 and a weighted kappa for the NN classifier of 0.80 with the first rater and 0.90 with the second rater. This was similar to the weighted kappa between the two human raters (0.81).

CONCLUSIONS

This represents a validated digital pathology tool to assist in automatically and reliably counting BMPC percentage on CD138-stained slides with an acceptable error rate.

Development of a minimal physiologically-based pharmacokinetic/pharmacodynamic model to characterize target cell depletion and cytokine release for T cell-redirecting bispecific agents in humans

T cell-redirecting bispecific antibodies (bsAbs) are highly potent tumor-killing molecules. Following bsAb mediated engagement with target cells, T cells get activated and kill target cells while inducing cytokine release, which at higher levels may lead to life-threatening cytokine release syndrome (CRS). Clinical evidence suggests that CRS can be mitigated by implementing a stepwise dosing strategy. Here, we developed a mechanism-based minimal physiologically-based pharmacokinetic/pharmacodynamic (mPBPK/PD) model using reported preclinical and clinical data from blinatumomab. The mPBPK/PD model reasonably captured blinatumomab PK and B cell depletion profiles in blood and in various tissue sites of action (i.e., red marrow perivascular niche, spleen, and lymph nodes) in patients with non-Hodgkin's lymphoma (NHL) and acute lymphoblastic leukemia (ALL). Using interleukin 6 (IL-6) as an example, our model quantitatively characterized the mitigation of cytokine release by a blinatumomab 5-15-60 µg/m²/day stepwise dosing regimen comparing to a 60 µg/m²/day flat dose in NHL patients. Furthermore, by only modifying the system parameters specific for ALL patients, the mPBPK/PD model successfully predicted the mitigation of IL-6 release by a blinatumomab 5-15 µg/m²/day stepwise dosing regimen comparing to a 15 µg/m²/day flat dose. Our work provided a case example to show how mPBPK/PD model can be used to support the discovery and clinical development of T cell-redirecting bsAbs.

Automated enumeration of lymphoid and plasma cells in bone marrow to establish normal reference ranges

Aims

The number of precursor and mature lymphoid cells and plasma cells in normal bone marrow trephine (BMT) biopsies throughout the human lifespan is unknown. Reference ranges have been established from aspirated marrow, but due to haemodilution errors, these do not accurately reflect the native marrow milieu. We aimed to define age-specific, normal reference ranges for lymphoid and plasma cells in BMT biopsy specimens using a combined immunophenotyping and digital enumeration approach.

Methods

Morphologically normal BMT biopsy specimens (n=483) were obtained from patients aged 1 month to 90 years of age. Immunohistochemistry was performed to identify lymphoid progenitors , T-lymphocytes (CD3), B-lymphocytes (CD20) and plasma cells (CD138 and MUM1). Positive cells were counted using digital enumeration software, and the percent positivity for each antigen was determined per case. Mean values were generated for specific age groups, and age-defined reference ranges were determined for each antigen using normalised data.

Results

A mean of 16 609 cells (range: 7210–34 097) were counted per biopsy. Infant marrows showed a predominance of immature lymphoid progenitors and B cells. With increasing age, an increase in mean T cell and plasma cell numbers were observed. The results showed the same trends to flow cytometry references for aspirate material although the absolute values differed.

Conclusions

Combined immunohistochemistry and automated enumeration gives an accurate, reproducible number of antigen-positive cells and has generated normal reference ranges for these cell types in BMT biopsies. The method and ranges we have established have the potential to be applied in routine clinical practice.