M. Khalid Khan Niazi

Impact of Diffusion Barriers to Small Cytotoxic Molecules on the Efficacy of Immunotherapy in Breast Cancer

Molecular-focused cancer therapies, e.g., molecularly targeted therapy and immunotherapy, so far demonstrate only limited efficacy in cancer patients. We hypothesize that underestimating the role of biophysical factors that impact the delivery of drugs or cytotoxic cells to the target sites (for associated preferential cytotoxicity or cell signaling modulation) may be responsible for the poor clinical outcome. Therefore, instead of focusing exclusively on the investigation of molecular mechanisms in cancer cells, convection-diffusion of cytotoxic molecules and migration of cancer-killing cells within tumor tissue should be taken into account to improve therapeutic effectiveness. To test this hypothesis, we have developed a mathematical model of the interstitial diffusion and uptake of small cytotoxic molecules secreted by T-cells, which is capable of predicting breast cancer growth inhibition as measured both in vitro and in vivo. Our analysis shows that diffusion barriers of cytotoxic molecules conspire with γδ T-cell scarcity in tissue to limit the inhibitory effects of γδ T-cells on cancer cells. This may increase the necessary ratios of γδ T-cells to cancer cells within tissue to unrealistic values for having an intended therapeutic effect, and decrease the effectiveness of the immunotherapeutic treatment.

Histopathological image analysis for centroblasts classification through dimensionality reduction approaches

We present two novel automated image analysis methods to differentiate centroblast (CB) cells from noncentroblast (non‐CB) cells in digital images of H&E‐stained tissues of follicular lymphoma. CB cells are often confused by similar looking cells within the tissue, therefore a system to help their classification is necessary. Our methods extract the discriminatory features of cells by approximating the intrinsic dimensionality from the subspace spanned by CB and non‐CB cells. In the first method, discriminatory features are approximated with the help of singular value decomposition (SVD), whereas in the second method they are extracted using Laplacian Eigenmaps. Five hundred high‐power field images were extracted from 17 slides, which are then used to compose a database of 213 CB and 234 non‐CB region of interest images. The recall, precision, and overall accuracy rates of the developed methods were measured and compared with existing classification methods. Moreover, the reproducibility of both classification methods was also examined. The average values of the overall accuracy were 99.22% ± 0.75% and 99.07% ± 1.53% for COB and CLEM, respectively. The experimental results demonstrate that both proposed methods provide better classification accuracy of CB/non‐CB in comparison with the state of the art methods.

An automated method for counting cytotoxic T-cells from CD8 stained images of renal biopsies

Studying inflammatory cell subsets in transplant biopsies can be important for diagnosis and to understand pathogenesis. Counting the different subsets of lymphocytes and macrophages in the immunostained renal biopsy is often considered as the only way to characterize the inflammatory infiltrate. Counting each subset of cells in each biopsy under a light microscope can be extremely tedious, time consuming and subject to inter- and intra-personal variability. This paper presents a new method to automatically count the number of CD8 positive cytotoxic t-cells on scanned images of immunostained slides of renal allograft biopsies. The method uses normalized multi-scale difference of Gaussian to detect the potential cytotoxic t-cell candidates regions, both in the color channel and the intensity channel. Then, it fuses the information from both channels’ candidate regions to detect the individual cells within cell clumps. The evaluation of the proposed method shows that there is a strong consensus between the proposed method’s markings with the pathologist’s markings (94.4%).

Genetically diverse mice are novel and valuable models of age-associated susceptibility to Mycobacterium tuberculosis

BackgroundTuberculosis, the disease due to Mycobacterium tuberculosis, is an important cause of morbidity and mortality in the elderly. Use of mouse models may accelerate insight into the disease and tests of therapies since mice age thirty times faster than humans. However, the majority of TB research relies on inbred mouse strains, and these results might not extrapolate well to the genetically diverse human population. We report here the first tests of M. tuberculosis infection in genetically heterogeneous aging mice, testing if old mice benefit from rapamycin.FindingsWe find that genetically diverse aging mice are much more susceptible than young mice to M. tuberculosis, as are aging human beings. We also find that rapamycin boosts immune responses during primary infection but fails to increase survival.ConclusionsGenetically diverse mouse models provide a valuable resource to study how age influences responses and susceptibility to pathogens and to test interventions. Additionally, surrogate markers such as immune measures may not predict whether interventions improve survival.

Entropy based quantification of Ki-67 positive cell images and its evaluation by a reader study

Presence of Ki-67, a nuclear protein, is typically used to measure cell proliferation. The quantification of the Ki-67 proliferation index is performed visually by the pathologist; however, this is subject to inter- and intra-reader variability. Automated techniques utilizing digital image analysis by computers have emerged. The large variations in specimen preparation, staining, and imaging as well as true biological heterogeneity of tumor tissue often results in variable intensities in Ki-67 stained images. These variations affect the performance of currently developed methods. To optimize the segmentation of Ki-67 stained cells, one should define a data dependent transformation that will account for these color variations instead of defining a fixed linear transformation to separate different hues. To address these issues in images of tissue stained with Ki-67, we propose a methodology that exploits the intrinsic properties of CIE L∗a∗b∗ color space to translate this complex problem into an automatic entropy based thresholding problem. The developed method was evaluated through two reader studies with pathology residents and expert hematopathologists. Agreement between the proposed method and the expert pathologists was good (CCC = 0.80).

Perceptual clustering for automatic hotspot detection from Ki‐67‐stained neuroendocrine tumour images

Hotspot detection plays a crucial role in grading of neuroendocrine tumours of the digestive system. Hotspots are often detected manually from Ki‐67‐stained images, a practice which is tedious, irreproducible and error prone. We report a new method to segment Ki‐67‐positive nuclei from Ki‐67‐stained slides of neuroendocrine tumours. The method combines minimal graph cuts along with the multistate difference of Gaussians to detect the individual cells from images of Ki‐67‐stained slides. It, then, automatically defines the composite function, which is used to determine hotspots in neuroendocrine tumour slide images. We combine modified particle swarm optimization with message passing clustering to mimic the thought process of the pathologist during hotspot detection in neuroendocrine tumour slide images. The proposed method was tested on 55 images of size 10 × 5 K and resulted in an accuracy of 94.60%. The developed methodology can also be part of the workflow for other diseases such as breast cancer and glioblastomas.

Detecting and characterizing cellular responses to Mycobacterium tuberculosis from histology slides

Infection with Mycobacterium tuberculosis (M.tb) results in immune cell recruitment to the lungs, forming macrophage‐rich regions (granulomas) and lymphocyte‐rich regions (lymphocytic cuffs). The objective of this study was to accurately identify and characterize these regions from hematoxylin and eosin (H&E)‐stained tissue slides. The two target regions (granulomas and lymphocytic cuffs) can be identified by their morphological characteristics. Their most differentiating characteristic on H&E slides is cell density. We developed a computational framework, called DeHiDe, to detect and classify high cell‐density regions in histology slides. DeHiDe employed a novel internuclei geodesic distance calculation and Dulmange Mendelsohn permutation to detect and classify high cell‐density regions. Lung tissue slides of mice experimentally infected with M.tb were stained with H&E and digitized. A total of 21 digital slides were used to develop and train the computational framework. The performance of the framework was evaluated using two main outcome measures: correct detection of potential regions, and correct classification of potential regions into granulomas and lymphocytic cuffs. DeHiDe provided a detection accuracy of 99.39% while it correctly classified 90.87% of the detected regions for the images where the expert pathologist produced the same ground truth during the first and second round of annotations. We showed that DeHiDe could detect high cell‐density regions in a heterogeneous cell environment with non‐convex tissue shapes.

Visually Meaningful Histopathological Features for Automatic Grading of Prostate Cancer

Histopathologic features, particularly Gleason grading system, have contributed significantly to the diagnosis, treatment, and prognosis of prostate cancer for decades. However, prostate cancer demonstrates enormous heterogeneity in biological behavior, thus establishing improved prognostic and predictive markers is particularly important to personalize therapy of men with clinically localized and newly diagnosed malignancy. Many automated grading systems have been developed for Gleason grading but acceptance in the medical community has been lacking due to poor interpretability. To overcome this problem, we developed a set of visually meaningful features to differentiate between low- and high-grade prostate cancer. The visually meaningful feature set consists of luminal and architectural features. For luminal features, we compute: 1) the shortest path from the nuclei to their closest luminal spaces; 2) ratio of the epithelial nuclei to the total number of nuclei. A nucleus is considered an epithelial nucleus if the shortest path between it and the luminal space does not contain any other nucleus; 3) average shortest distance of all nuclei to their closest luminal spaces. For architectural features, we compute directional changes in stroma and nuclei using directional filter banks. These features are utilized to create two subspaces; one for prostate images histopathologically assessed as low grade and the other for high grade. The grade associated with a subspace, which results in the minimum reconstruction error is considered as the prediction for the test image. For training, we utilized 43 regions of interest (ROI) images, which were extracted from 25 prostate whole slide images of The Cancer Genome Atlas (TCGA) database. For testing, we utilized an independent dataset of 88 ROIs extracted from 30 prostate whole slide images. The method resulted in 93.0% and 97.6% training and testing accuracies, respectively, for the spectrum of cases considered. The application of visually meaningful features provided promising levels of accuracy and consistency for grading prostate cancer.

Hot spot detection for breast cancer in Ki-67 stained slides: Image dependent filtering approach

We present a new method to detect hot spots from breast cancer slides stained for Ki67 expression. It is common practice to use centroid of a nucleus as a surrogate representation of a cell. This often requires the detection of individual nuclei. Once all the nuclei are detected, the hot spots are detected by clustering the centroids. For large size images, nuclei detection is computationally demanding. Instead of detecting the individual nuclei and treating hot spot detection as a clustering problem, we considered hot spot detection as an image filtering problem where positively stained pixels are used to detect hot spots in breast cancer images. The method first segments the Ki-67 positive pixels using the visually meaningful segmentation (VMS) method that we developed earlier. Then, it automatically generates an image dependent filter to generate a density map from the segmented image. The smoothness of the density image simplifies the detection of local maxima. The number of local maxima directly corresponds to the number of hot spots in the breast cancer image. The method was tested on 23 different regions of interest images extracted from 10 different breast cancer slides stained with Ki67. To determine the intra-reader variability, each image was annotated twice for hot spots by a boardcertified pathologist with a two-week interval in between her two readings. A computer-generated hot spot region was considered a true-positive if it agrees with either one of the two annotation sets provided by the pathologist. While the intra-reader variability was 57%, our proposed method can correctly detect hot spots with 81% precision.

Characterizing primary refractory neuroblastoma: prediction of outcome by microscopic image analysis

Neuroblastoma is a childhood cancer that starts in very early forms of nerve cells found in an embryo or fetus. It is a highly lethal cancer of sympathetic nervous system that commonly affects children of age five or younger. It accounts for a disproportionate number of childhood cancer deaths and remains a difficult cancer to eradicate despite intensive treatment that includes chemotherapy, surgery, hematopoietic stem cell transplantation, radiation therapy and immunotherapy. A poorly characterized group of patients are the 15% with primary refractory neuroblastoma (PRN) which is uniformly lethal due to de novo chemotherapy resistance. The lack of response to therapy is currently assessed after multiple months of cytotoxic therapy, driving the critical need to develop pretreatment clinic-biological biomarkers that can guide precise and effective therapeutic strategies. Therefore, our guiding hypothesis is that PRN has distinct biological features present at diagnosis that can be identified for prediction modeling. During a visual analysis of PRN slides, stained with hematoxylin and eosin, we observed that patients who survived for less than three years contained large eosin-stained structures as compared to those who survived for greater than three years. So, our hypothesis is that the size of eosin stained structures can be used as a differentiating feature to characterize recurrence in neuroblastoma. To test this hypothesis, we developed an image analysis method that performs stain separation, followed by the detection of large structures stained with Eosin. On a set of 21 PRN slides, stained with hematoxylin and eosin, our image analysis method predicted the outcome with 85.7% accuracy.