Immune Profiling and Quantitative Analysis Decipher the Clinical Role of Immune-Checkpoint Expression in the Tumor Immune Microenvironment of DLBCL.

PD-1/L1 and CTLA-4 blockade immunotherapies have been approved for 13 types of cancers and are being studied in diffuse large B-cell lymphoma (DLBCL), the most common aggressive B-cell lymphoma. However, whether both PD-1 and CTLA-4 checkpoints are active and clinically significant in DLBCL is unknown. Whether PD-1 ligands expressed by tumor cells or by the microenvironment of DLBCL are critical for the PD-1 immune checkpoint is unclear. We performed immunophenotypic profiling for 405 patients with de novo DLBCL using a MultiOmyx immunofluorescence platform and simultaneously quantitated expression/coexpression of 13 immune markers to identify prognostic determinants. In both training and validation cohorts, results demonstrated a central role of the tumor immune microenvironment, and when its functionality was impaired by deficiency in tumor-infiltrating T cells and/or natural killer cells, high PD-1 expression (but not CTLA-4) on CD8+ T cells, or PD-L1 expression on T cells and macrophages, patients had significantly poorer survival after rituximab-CHOP (cyclophosphamide, doxorubicin, vincristine, and prednisone) immunochemotherapy. In contrast, tumor-cell PD-L2 expression was associated with superior survival, as well as PD-L1+CD20+ cells proximal (indicates interaction) to PD-1 + CD8+ T cells in patients with low PD-1 + percentage of CD8+ T cells. Gene-expression profiling results suggested the reversibility of T-cell exhaustion in PD-1+/PD-L1+ patients with unfavorable prognosis and implication of LILRA/B, IDO1, CHI3L1, and SOD2 upregulation in the microenvironment dysfunction with PD-L1 expression. This study comprehensively characterized the DLBCL immune landscape, deciphered the differential roles of various checkpoint components in rituximab-CHOP resistance in DLBCL patients, and suggests targets for PD-1/PD-L1 blockade and combination immunotherapies.

An active learning framework for enhancing identification of non-artifactual intracranial pressure waveforms.

OBJECTIVE: Intracranial pressure (ICP) is an important and established clinical measurement that is used in the management of severe acute brain injury. ICP waveforms are usually triphasic and are susceptible to artifact because of transient catheter malfunction or routine patient care. Existing methods for artifact detection include threshold-based, stability-based, or template matching, and result in higher false positives (when there is variability in the ICP waveforms) or higher false negatives (when the ICP waveforms lack complete triphasic components but are valid). APPROACH: We hypothesized that artifact labeling of ICP waveforms can be optimized by an active learning approach which includes interactive querying of domain experts to identify a manageable number of informative training examples. MAIN RESULTS: The resulting active learning based framework identified non-artifactual ICP pulses with a superior AUC of 0.96 + 0.012, compared to existing methods: template matching (AUC: 0.71 + 0.04), ICP stability (AUC: 0.51 + 0.036) and threshold-based (AUC: 0.5 + 0.02). SIGNIFICANCE: The proposed active learning framework will support real-time ICP-derived analytics by improving precision of artifact-labelling.

Quantitative 3-D analysis of GFAP labeled astrocytes from fluorescence confocal images.

BACKGROUND: There is a need for effective computational methods for quantifying the three-dimensional (3-D) spatial distribution, cellular arbor morphologies, and the morphological diversity of brain astrocytes to support quantitative studies of astrocytes in health, injury, and disease. NEW METHOD: Confocal fluorescence microscopy of multiplex-labeled (GFAP, DAPI) brain tissue is used to perform imaging of astrocytes in their tissue context. The proposed computational method identifies the astrocyte cell nuclei, and reconstructs their arbors using a local priority based parallel (LPP) tracing algorithm. Quantitative arbor measurements are extracted using Scorcioni's L-measure, and profiled by unsupervised harmonic co-clustering to reveal the morphological diversity. RESULTS: The proposed method identifies astrocyte nuclei, generates 3-D reconstructions of their arbors, and extracts quantitative arbor measurements, enabling a morphological grouping of the cell population. COMPARISON WITH EXISTING METHODS: Our method enables comprehensive spatial and morphological profiling of astrocyte populations in brain tissue for the first time, and overcomes limitations of prior methods. Visual proofreading of the results indicate a >95% accuracy in identifying astrocyte nuclei. The arbor reconstructions exhibited 3.2% fewer erroneous jumps in tracing, and 17.7% fewer false segments compared to the widely used fast-marching method that resulted in 9% jumps and 20.8% false segments. CONCLUSIONS: The proposed method can be used for large-scale quantitative studies of brain astrocyte distribution and morphology.

Large-scale automated image analysis for computational profiling of brain tissue surrounding implanted neuroprosthetic devices using Python.

In this article, we describe the use of Python for large-scale automated server-based bio-image analysis in FARSIGHT, a free and open-source toolkit of image analysis methods for quantitative studies of complex and dynamic tissue microenvironments imaged by modern optical microscopes, including confocal, multi-spectral, multi-photon, and time-lapse systems. The core FARSIGHT modules for image segmentation, feature extraction, tracking, and machine learning are written in C++, leveraging widely used libraries including ITK, VTK, Boost, and Qt. For solving complex image analysis tasks, these modules must be combined into scripts using Python. As a concrete example, we consider the problem of analyzing 3-D multi-spectral images of brain tissue surrounding implanted neuroprosthetic devices, acquired using high-throughput multi-spectral spinning disk step-and-repeat confocal microscopy. The resulting images typically contain 5 fluorescent channels. Each channel consists of 6000 × 10,000 × 500 voxels with 16 bits/voxel, implying image sizes exceeding 250 GB. These images must be mosaicked, pre-processed to overcome imaging artifacts, and segmented to enable cellular-scale feature extraction. The features are used to identify cell types, and perform large-scale analysis for identifying spatial distributions of specific cell types relative to the device. Python was used to build a server-based script (Dell 910 PowerEdge servers with 4 sockets/server with 10 cores each, 2 threads per core and 1TB of RAM running on Red Hat Enterprise Linux linked to a RAID 5 SAN) capable of routinely handling image datasets at this scale and performing all these processing steps in a collaborative multi-user multi-platform environment. Our Python script enables efficient data storage and movement between computers and storage servers, logs all the processing steps, and performs full multi-threaded execution of all codes, including open and closed-source third party libraries.

An active learning approach for rapid characterization of endothelial cells in human tumors.

Currently, no available pathological or molecular measures of tumor angiogenesis predict response to antiangiogenic therapies used in clinical practice. Recognizing that tumor endothelial cells (EC) and EC activation and survival signaling are the direct targets of these therapies, we sought to develop an automated platform for quantifying activity of critical signaling pathways and other biological events in EC of patient tumors by histopathology. Computer image analysis of EC in highly heterogeneous human tumors by a statistical classifier trained using examples selected by human experts performed poorly due to subjectivity and selection bias. We hypothesized that the analysis can be optimized by a more active process to aid experts in identifying informative training examples. To test this hypothesis, we incorporated a novel active learning (AL) algorithm into FARSIGHT image analysis software that aids the expert by seeking out informative examples for the operator to label. The resulting FARSIGHT-AL system identified EC with specificity and sensitivity consistently greater than 0.9 and outperformed traditional supervised classification algorithms. The system modeled individual operator preferences and generated reproducible results. Using the results of EC classification, we also quantified proliferation (Ki67) and activity in important signal transduction pathways (MAP kinase, STAT3) in immunostained human clear cell renal cell carcinoma and other tumors. FARSIGHT-AL enables characterization of EC in conventionally preserved human tumors in a more automated process suitable for testing and validating in clinical trials. The results of our study support a unique opportunity for quantifying angiogenesis in a manner that can now be tested for its ability to identify novel predictive and response biomarkers.

Quantifying subcellular distribution of fluorescent fusion proteins in cells migrating within tissues.

The movement of proteins within cells can provide dynamic indications of cell signaling and cell polarity, but methods are needed to track and quantify subcellular protein movement within tissue environments. Here we present a semiautomated approach to quantify subcellular protein location for hundreds of migrating cells within intact living tissue using retrovirally expressed fluorescent fusion proteins and time-lapse two-photon microscopy of intact thymic lobes. We have validated the method using GFP-PKCζ, a marker for cell polarity, and LAT-GFP, a marker for T-cell receptor signaling, and have related the asymmetric distribution of these proteins to the direction and speed of cell migration. These approaches could be readily adapted to other fluorescent fusion proteins, tissues and biological questions.