OPTIMIZATION OF REAL TIME IMAGE SEGMENTATION USING EFFICIENT THRESHOLDING TECHNIQUE

The process of image segmentation is how to divide images into regions with similar properties. Threshold-based image segmentation is a multidimensional optimization problem that has been highlighted as one of the most significant image pre-processing approaches. This paper proposes an efficient technique for optimizing real time image segmentation. The approach of image thresholding may be regarded an optimization objective, and it will be discovered by using Otsu's technique in conjunction with Particle Swarm Optimization basics (PSO). For real-time validation, the suggested technique was tested on several images in real time using the PSO algorithm. The simulation results showed that, when compared to Otsu's approach, the PSO algorithm gives the most efficient outcomes in real-time applications with an improved execution time

Patients with TNF Receptor Associated Periodic Syndrome (TRAPS) are hypersensitive to Toll‐like receptor 9 stimulation

Tumour necrosis factor receptor‐associated periodic syndrome (TRAPS) is an hereditary autoinflammatory disorder characterised by recurrent episodes of fever and inflammation. It is associated with autosomal dominant mutations in TNFRSF1A, which encodes tumour necrosis factor receptor‐1 (TNFR1). Our aim was to understand the influence of TRAPS mutations on the response to stimulation of the pattern recognition receptor TLR9. Peripheral blood mononuclear cells (PBMCs) and serum were isolated from TRAPS patients and healthy controls: Serum levels of fifteen pro‐inflammatory cytokines were measured to assess the initial inflammatory status. IL‐1β, IL‐6, IL‐8, IL17, IL22, TNF‐α, VEGF, IFN‐γ, MCP‐1 and TGF‐β were significantly elevated in TRAPS patients sera, consistent with constitutive inflammation. Stimulation of PBMCs with TLR9 ligand (ODN2006) triggered significantly greater upregulation of pro‐inflammatory signalling intermediates (TRAF3, IRAK2, TOLLIP, TRAF6, pTAK, TAB2, pTAB2, IRF7, RIP, NF‐kB p65, pNF‐κB p65, and MEK1/2) in TRAPS patients’ PBMCs. This upregulation of proinflammatory signalling intermediates and raised serum cytokines occurred despite concurrent anakinra treatment and no overt clinical symptoms at time of sampling. These novel findings further demonstrate the wide‐ranging nature of the dysregulation of innate immune responses underlying the pathology of TRAPS and highlights the need for novel pathway‐specific therapeutic treatments for this disease

Prognostic significance of TRAIL death receptors in Middle Eastern colorectal carcinomas and their correlation to oncogenic KRAS alterations

<p>Abstract</p> <p>Background</p> <p>Tumour necrosis factor-related apoptosis-inducing ligand (TRAIL) is a member of the tumour necrosis factor cytokine family that induces apoptosis upon binding to its death domain containing receptors, TRAIL receptor 1 (DR4) and TRAIL receptor 2 (DR5). Expression of TRAIL receptors is higher in colorectal carcinoma (CRC) as compared to normal colorectal mucosa and targeted therapy with TRAIL leads to preferential killing of tumor cells sparing normal cells.</p> <p>Methods</p> <p>We investigated the expression of TRAIL and its receptors in a tissue microarray cohort of 448 Middle Eastern CRC. We also studied the correlation between TRAIL receptors and various clinico-pathological features including key molecular alterations and overall survival.</p> <p>Results</p> <p>CRC subset with TRAIL-R1 expression was associated with a less aggressive phenotype characterized by early stage (p = 0.0251) and a histology subtype of adenocarcinomas (p = 0.0355). Similarly CRC subset with TRAIL-R2 expression was associated with a well-differentiated tumors (p < 0.0001), histology subtype of adenocarcinomas (p = 0.0010) and tumors in left colon (p = 0.0009). Over expression of pro apoptotic markers: p27^KIP1and KRAS4A isoforms was significantly higher in CRC subset with TRAIL-R1 and TRAIL-R2 expression; TRAIL-R1 expression was also associated with cleaved caspase-3(p = 0.0011). Interestingly, TRAIL-R2 expression was associated with a microsatellite stable (MS--S/L) phenotype (p = 0.0003) and with absence of KRAS mutations (p = 0.0481).</p> <p>Conclusion</p> <p>TRAIL-R1 expression was an independent prognostic marker for better survival in all CRC samples and even in the CRC group that received adjuvant therapy. The biological effects of TRAIL in CRC models, its enhancement of chemosensitivity towards standard chemotherapeutic agents and the effect of endogenous TRAIL receptor levels on survival make TRAIL an extremely attractive therapeutic target.</p

Spatial interplay of lymphocytes and fibroblasts in estrogen receptor-positive HER2-negative breast cancer

In estrogen-receptor-positive, HER2-negative (ER+HER2−) breast cancer, higher levels of tumor infiltrating lymphocytes (TILs) are often associated with a poor prognosis and this phenomenon is still poorly understood. Fibroblasts represent one of the most frequent cells in breast cancer and harbor immunomodulatory capabilities. Here, we evaluate the molecular and clinical impact of the spatial patterns of TILs and fibroblast in ER+HER2− breast cancer. We used a deep neural network to locate and identify tumor, TILs, and fibroblasts on hematoxylin and eosin-stained slides from 179 ER+HER2− breast tumors (ICGC cohort) together with a new density estimation analysis to measure the spatial patterns. We clustered tumors based on their spatial patterns and gene set enrichment analysis was performed to study their molecular characteristics. We independently assessed the spatial patterns in a second cohort of ER+HER2− breast cancer (N = 630, METABRIC) and studied their prognostic value. The spatial integration of fibroblasts, TILs, and tumor cells leads to a new reproducible spatial classification of ER+HER2− breast cancer and is linked to inflammation, fibroblast meddling, or immunosuppression. ER+HER2− patients with high TIL did not have a significant improved overall survival (HR = 0.76, P = 0.212), except when they had received chemotherapy (HR = 0.447). A poorer survival was observed for patients with high fibroblasts that did not show a high level of TILs (HR = 1.661, P = 0.0303). Especially spatial mixing of fibroblasts and TILs was associated with a good prognosis (HR = 0.464, P = 0.013). Our findings demonstrate a reproducible pipeline for the spatial profiling of TILs and fibroblasts in ER+HER2− breast cancer and suggest that this spatial interplay holds a decisive role in their cancer-immune interactions

Expression of CDK7, cyclin H and MAT1 is elevated in breast cancer and is prognostic in estrogen receptor- positive breast cancer

Purpose: CDK-activation kinase (CAK) is required for the regulation of the cell-cycle and is a trimeric complex consisting of Cyclin Dependent Kinase 7 (CDK7), Cyclin H and the accessory protein, MAT1. CDK7 also plays a critical role in regulating transcription, primarily by phosphorylating RNA polymerase II, as well as transcription factors such as estrogen receptor-α (ER). Deregulation of cell cycle and transcriptional control are general features of tumor cells, highlighting the potential for the use of CDK7 inhibitors as novel cancer therapeutics. Experimental Design: mRNA and protein expression of CDK7 and its essential co-factors cyclinH and MAT1, were evaluated in breast cancer samples to determine if their levels are altered in cancer. Immunohistochemical staining of >900 breast cancers was used to determine the association with clinicopathological features and patient outcome. Results: We show that expression of CDK7, cyclinH and MAT1 are all closely linked at the mRNA and protein level and their expression is elevated in breast cancer compared with the normal breast tissue. Intriguingly, CDK7 expression was inversely proportional to tumour grade and size and outcome analysis showed an association between CAK levels and better outcome. Moreover, CDK7 expression was positively associated with ER expression and in particular with phosphorylation of ER at serine 118, a site important for ER transcriptional activity. Conclusions: Expression of components of the CAK complex, CDK7, MAT1 and Cyclin H are elevated in breast cancer and correlates with ER. Like ERα , CDK7 expression is inversely proportional to poor prognostic factors and survival

The T cell differentiation landscape is shaped by tumour mutations in lung cancer

Tumour mutational burden (TMB) predicts immunotherapy outcome in non-small cell lung cancer (NSCLC), consistent with immune recognition of tumour neoantigens. However, persistent antigen exposure is detrimental for T cell function. How TMB affects CD4 and CD8 T cell differentiation in untreated tumours and whether this affects patient outcomes is unknown. Here, we paired high-dimensional flow cytometry, exome, single-cell and bulk RNA sequencing from patients with resected, untreated NSCLC to examine these relationships. TMB was associated with compartment-wide T cell differentiation skewing, characterized by loss of TCF7-expressing progenitor-like CD4 T cells, and an increased abundance of dysfunctional CD8 and CD4 T cell subsets with strong phenotypic and transcriptional similarity to neoantigen-reactive CD8 T cells. A gene signature of redistribution from progenitor-like to dysfunctional states was associated with poor survival in lung and other cancer cohorts. Single-cell characterization of these populations informs potential strategies for therapeutic manipulation in NSCLC

Pitfalls in machine learning-based assessment of tumor-infiltrating lymphocytes in breast cancer: a report of the international immuno-oncology biomarker working group

The clinical significance of the tumor-immune interaction in breast cancer is now established, and tumor-infiltrating lymphocytes (TILs) have emerged as predictive and prognostic biomarkers for patients with triple-negative (estrogen receptor, progesterone receptor, and HER2-negative) breast cancer and HER2-positive breast cancer. How computational assessments of TILs might complement manual TIL assessment in trial and daily practices is currently debated. Recent efforts to use machine learning (ML) to automatically evaluate TILs have shown promising results. We review state-of-the-art approaches and identify pitfalls and challenges of automated TIL evaluation by studying the root cause of ML discordances in comparison to manual TIL quantification. We categorize our findings into four main topics: (1) technical slide issues, (2) ML and image analysis aspects, (3) data challenges, and (4) validation issues. The main reason for discordant assessments is the inclusion of false-positive areas or cells identified by performance on certain tissue patterns or design choices in the computational implementation. To aid the adoption of ML for TIL assessment, we provide an in-depth discussion of ML and image analysis, including validation issues that need to be considered before reliable computational reporting of TILs can be incorporated into the trial and routine clinical management of patients with triple-negative breast cancer. © 2023 The Authors. The Journal of Pathology published by John Wiley & Sons Ltd on behalf of The Pathological Society of Great Britain and Ireland