Apoptosis inhibition or inflammation: the role of NAIP protein expression in Hodgkin and non-Hodgkin lymphomas compared to non-neoplastic lymph node

<p>Abstract</p> <p>Background</p> <p>Inhibitors of Apoptosis (IAP) family play a critical role in apoptosis and inflammatory response. Neuronal Apoptosis Inhibitory Protein (NAIP), as a member of both IAPs and NLR families (NOD-Like Receptor), is a unique IAP harboring NOD (Nucleotide Oligomerization Domain) and LLR (Leucine Rich Repeat) motifs. Considering these motifs in NAIP, it has been suggested that the main function of NAIP is distinct from other members of IAPs. As a member of NLR, NAIP mediates the assembly of 'Inflammasome' for inflammatory caspase activation. Pathologic expression of NAIP has been reported not only in some infectious and inflammatory diseases but also in some malignancies. However, there is no report to elucidate NAIP expression in lymphomatic malignancies.</p> <p>Methods</p> <p>In this study, we examined <it>NAIP </it>protein expression in 101 Formalin-Fixed Paraffin-Embedded blocks including samples from 39 Hodgkin Lymphoma and 23 Non Hodgkin Lymphoma cases in comparison with 39 control samples (30 normal and 9 Reactive Lymphoid Hyperplasia (RLH) lymph nodes) using semi-quantitative immuno-flourecent Staining.</p> <p>Results</p> <p>NAIP expression was not statistically different in lymphoma samples neither in HL nor in NHL cases comparing to normal samples. However, we evaluated NAIP expression in normal and RLH lymph nodes. Surprisingly, we have found a statistically significant-difference between the NAIP expression in RLH (M.R of NAIP/GAPDH expression = 0.6365 ± 0.017) and normal lymph node samples (M.R of NAIP/GAPDH expression = 0.5882 ± 0.047) (<it>P </it>< 0.01).</p> <p>Conclusions</p> <p>These findings show that the regulation of apoptosis could not be the main function of NAIP in the cell, so the pathologic expression of NAIP is not involved in lymphoma. But, we concluded that the over expression of NAIP has more effective role in the inflammatory response. Also, this study clarifies the NAIP expression level in lymphoma which is required for IAPs profiling in order to be used in potential translational applications of IAPs.</p

MR Imaging Texture Analysis in the Abdomen and Pelvis

Texture analysis (TA) is a form of radiomics and refers to quantitative measurements of the histogram, distribution and/or relationship of pixel intensities or gray scales within a region of interest on an image. TA can be applied to MRI of the abdomen and pelvis, with the main strength being quantitative analysis of pixel intensities and heterogeneity rather than subjective/qualitative analysis. There are multiple limitations of MR texture analysis (MRTA) including a dependency on image acquisition and reconstruction parameters, non-standardized approaches without or with image filtration, diverse software methods and applications, and statistical challenges relating numerous texture analysis results to clinical outcomes in retrospective pilot studies with small sample sizes. Despite these limitations, there is a growing body of literature supporting MRTA. In this review, the application of MRTA to the abdomen and pelvis will be discussed, including tissue or tumor characterization and response evaluation or prediction of outcomes in various tumors

Ambient ionization - mass spectrometry: Advances toward intrasurgical cancer detection

My dissertation research has focused on the development of ambient ionization – mass spectrometry (MS) for clinical measurements, specifically intrasurgical cancer detection. The molecular differences between normal and cancerous tissue were detected via direct tissue analysis in vitro by touch spray ionization (TS) or by analyzing sectioned or smeared tissue using desorption electrospray ionization (DESI). The physical form of the tissue, e.g. in vitro sampling, sectioned, or smeared, was inconsequential in differentiating normal from cancerous tissue; however, the spectra acquired by TS and DESI differed due to differences in ionization processes. We envision that TS-MS and DESI-MS could impact diagnostic medicine, for example in providing surgeons with rapid, near real-time information as to tissue disease state, i.e. normal or tumor. Disease state information provided to surgeons about discrete pathologically ambiguous areas, ideally intrasurgically via TS or DESI-MS smear analysis, could improve the completeness of tumor resection while minimizing unintended damage to adjacent tissue. Touch spray ionization was developed for intrasurgical detection of cancer; TS greatly simplifies MS analysis by using the same device for in vivo sampling and subsequent ionization. Frozen tissue sections were sampled and analyzed by TS-MS providing the ability to differentiate normal from human prostate cancer, via lipid profiles, using multivariate statistics. The next proof-of-concept step for TS-MS was the analysis human kidney cancer specimens in vitro, immediately following resection. TS-MS analysis of untreated kidney tissue emulated intrasurgical use, e.g. the presence and co-sampling of biofluids such as blood. Regardless, normal renal tissue and kidney cancer was differentiated using lipid profiles and multivariate statistics. Desorption electrospray ionization (DESI) - MS imaging of tissue sections differentiated normal from tumor in all cancers studied. DESI-MS imaging of human prostate and human kidney tissue sections were performed to corroborate TS-MS results. Human brain cancer, a major focus of my dissertation research, was studied by imaging tissue sections using DESI-MS to establish the characteristic chemical features, e.g. lipid and metabolite profiles, that distinguish normal brain parenchyma from gliomas and different brain tumors. It was found that information in the negative ion mode lipid profile, positive ion mode lipid profile, and negative ion mode metabolite profile is able to discriminate brain parenchyma (grey and white matter) and gliomas, the most common form of malignant brain tumor. Further, the negative mode lipid and metabolite profiles also proved capable of discriminating different types of brain tumors (gliomas, meningiomas, and pituitary tumors) which account for ~80% of all central nervous system tumors. DESI-MS imaging of effaced or otherwise pathologically ambiguous frozen tissue sections offered the ability to determine the underlying brain parenchyma in cancerous samples – something that traditional morphologic evaluation was not able to determine. Further, DESI-MS was able to detect molecular changes resulting from varying amounts of glioma tumor cells present within infiltrated tissues. The tumor cell percentage of these samples was predicted using N-acetyl-aspartic acid, a neurometabolite which was found to decrease in cancerous tissue, and matched well with histopathologic evaluation. The transition from DESI-MS imaging of sectioned tissue to DESI-MS analysis of tissue smears was driven by the time restriction of intrasurgical application. The potential of DESI-MS analysis of smears was first demonstrated upon canine non-Hodgkin’s lymphoma fine-needle aspirate smears which provided similar sensitivity and specificity values to that of tissue section imaging but is technically less demanding and decreased analysis time. DESI-MS imaging of tissue sections established that MS profiles contained the sufficient information for diagnosis; whereas DESI-MS analysis of tissue smears made the intrasurgical analysis of human brain tumors feasible. The observed lipid or metabolite profiles were not significantly altered by the physical act of smearing and their signal intensities were comparable to those of tissue sections. Further, the chemical information obtained from tissue smears was equivalent to those of tissue sections as determined by canonical component analysis. The culmination of my dissertation research was the creation and implementation of an intrasurgical DESI-MS tissue smear analysis method for human gliomas. Preliminary results from the initial intrasurgical cases analyzed using the developed DESI-MS method are discussed

NHL pathological image classification based on hierarchical local information and GoogLeNet-based representations

Background. Accurate classification for different non-Hodgkin lymphomas (NHL) is one of the main challenges in clinical pathological diagnosis due to its intrinsic complexity. Therefore, this paper proposes an effective classification model for three types of NHL pathological images, including mantle cell lymphoma (MCL), follicular lymphoma (FL), and chronic lymphocytic leukemia (CLL). Methods. There are three main parts with respect to our model. First, NHL pathological images stained by hematoxylin and eosin (H&E) are transferred into blue ratio (BR) and Lab spaces, respectively. Then specific patch-level textural and statistical features are extracted from BR images and color features are obtained from Lab images both using a hierarchical way, yielding a set of hand-crafted representations corresponding to different image spaces. A random forest classifier is subsequently trained for patch-level classification. Second, H&E images are cropped and fed into a pretrained google inception net (GoogLeNet) for learning high-level representations and a softmax classifier is used for patch-level classification. Finally, three image-level classification strategies based on patch-level results are discussed including a novel method for calculating the weighted sum of patch results. Different classification results are fused at both feature 1 and image levels to obtain a more satisfactory result. Results. The proposed model is evaluated on a public IICBU Malignant Lymphoma Dataset and achieves an improved overall accuracy of 0.991 and area under the receiver operating characteristic curve of 0.998. Conclusion. The experimentations demonstrate the significantly increased classification performance of the proposed model, indicating that it is a suitable classification approach for NHL pathological images

Immunoglobulin heavy-chain fluorescence in situ hybridization-chromogenic in situhybridization DNA probe split signal in the clonality assessment oflymphoproliferative processes on cytological samples.

BACKGROUND: The human immunoglobulin heavy-chain (IGH) locus at chromosome 14q32 is frequently involved in different translocations of non-Hodgkin lymphoma (NHL), and the detection of any breakage involving the IGH locus should identify a B-cell NHL. The split-signal IGH fluorescence in situ hybridization-chromogenic in situ hybridization (FISH-CISH) DNA probe is a mixture of 2 fluorochrome-labeled DNAs: a green one that binds the telomeric segment and a red one that binds the centromeric segment, both on the IGH breakpoint. In the current study, the authors tested the capability of the IGH FISH-CISH DNA probe to detect IGH translocations and diagnose B-cell lymphoproliferative processes on cytological samples. METHODS: Fifty cytological specimens from cases of lymphoproliferative processes were tested using the split-signal IGH FISH-CISH DNA probe and the results were compared with light-chain assessment by flow cytometry AQ2 (FC), IGH status was tested by polymerase chain reaction (PCR), and clinicohistological data. RESULTS: The signal score produced comparable results on FISH and CISH analysis and detected 29 positive, 15 negative, and 6 inadequate cases; there were 29 true-positive cases (66%), 9 true-negative cases (20%), 6 false-negative cases (14%), and no false-positive cases (0%). Comparing the sensitivity of the IGH FISH-CISH DNA split probe with FC and PCR, the highest sensitivity was obtained by FC, followed by FISH-CISH and PCR. CONCLUSIONS: The split-signal IGH FISH-CISH DNA probe is effective in detecting any translocation involving the IGH locus. This probe can be used on different samples from different B-cell lymphoproliferative processes, although it is not useful for classifying specific entities

APPLICATION OF BIODYNAMIC IMAGING FOR PERSONALIZED CHEMOTHERAPY IN CANINE LYMPHOMA

Biodynamic imaging (BDI) is a novel phenotypic cancer profiling technology which characterizes changes in cellular and subcellular motion in living tumor tissue samples following in vitro or ex vivo treatment with chemotherapeutics. The ability of BDI to predict clinical response to single-agent doxorubicin chemotherapy was tested in ten dogs with naturally-occurring non-Hodgkin’s lymphomas (NHL). Pre-treatment tumor biopsy samples were obtained from all dogs and treated with doxorubicin (10 M) ex vivo. BDI captured cellular and subcellular motility measures on all biopsy samples at baseline and at regular intervals for 9 hours following drug application. All dogs subsequently received treatment with a standard single-agent doxorubicin protocol. Objective response (OR) to doxorubicin and progression-free survival time (PFST) following chemotherapy were recorded for all dogs. The dynamic biomarkers measured by BDI were entered into a multivariate logistic model to determine the extent to which BDI predicted OR and PFST following doxorubicin therapy. The model showed that the sensitivity, specificity, and accuracy of BDI for predicting treatment outcome were 95%, 91%, and 93%, respectively. To account for possible over-fitting of data to the predictive model, cross-validation with a one-left-out analysis was performed, and the adjusted sensitivity, specificity, and accuracy following this analysis were 93%, 87%, and 91%, respectively. These findings suggest that BDI can predict, with high accuracy, treatment outcome following single-agent doxorubicin chemotherapy in a relevant spontaneous canine cancer model, and is a promising novel technology for advancing personalized cancer medicine

Lymphoma

Lymphoma is a group of malignant diseases caused by the clonal proliferation of lymphocytes. Current treatment options include chemotherapy, radiotherapy, and bone marrow/stem cell transplantation. Development of new treatment options for cancer medications include small molecules and monoclonal antibodies for immunotherapy. In addition, the discovery of new phytochemical agents used in complementary and alternative medicine adds perspective to the treatment of lymphoma. This book highlights recent developments in the treatment of lymphoma. Chapters discuss different types of lymphomas, such as follicular lymphoma, gastrointestinal lymphoma, splenic B-cell lymphoma, and others, as well as the available treatment options for each