Characterization Of Tumor Microenvironment Research Articles

Multi-omics Analysis of Ferroptosis Regulation Patterns and Characterization of Tumor Microenvironment in Patients with Oral Squamous Cell Carcinoma.

Ferroptosis is a newly recognized mechanism of regulated cell death. It was reported to be highly associated with immune therapy and chemotherapy. However, its mechanism of regulation in the tumor microenvironment (TME) and influence on oral squamous cell carcinoma (OSCC) therapy are unknown. We identified a ferroptosis-specific gene-expression signature, an FPscore, developed by a principal component analysis (PCA) algorithm to evaluate the ferroptosis regulation patterns of individual tumor. Multi-omics analysis of ferroptosis regulation patterns was conducted. Three distinct ferroptosis regulation subtypes, which linked to outcomes and the clinical relevance of each patient, were established. A high FPscore of patients with OSCC was associated with a favorable prognosis, a ferroptosis-related immune-activation phenotype, potential sensitivities to the chemotherapy and immunotherapy. Importantly, a high FPscore correlated with a low gene copy number burden and high immune checkpoint expressions. We validated the prognostic value of the FPscore using independent immunotherapy and pan-cancer cohorts. Comprehensive evaluation of individual tumors with distinct ferroptosis regulation patterns provides new mechanistic insights, which may be clinically relevant for the application of combination therapies in OSCC.

Characterization of the Tumor Microenvironment of Neurofibromatosis Type I Plexiform Neurofibromas

Background/Objective: 
 Neurofibromatosis type 1 (NF1) is a cancer predisposition syndrome caused by mutations in the NF1 tumor suppressor gene. Patients with NF1 develop tumors of the peripheral nervous system called plexiform neurofibromas (PNs). These histopathologically complex tumors are composed of various immune and inflammatory cells. Mast cells have previously been identified as one key immune cell lineage underpinning PN initiation and progression, however new technologies leveraging RNA-sequencing (RNAseq) allow for the broad and systematic characterization of the PN tumor microenvironment. Here we utilized these tools to delineate PN cellular composition. 
 Methods: 
 RNA seq was performed on murine wild type (n=6) and PN (n=6) tissues. We utilized CIBERSORT to profile the cellular constituents of the PN microenvironment. CIBERSORT is a deconvolution method that uses a reference matrix to estimate the relative proportions of various cell types. Statistical analyses were performed on cell lineage subtypes delineated by CIBERSORT. We further performed a Gene Set Enrichment Analysis (GSEA) to identify which pathways and cytokines might be upregulated in PNs. 
 Results: 
 Using a murine reference matrix, the macrophage lineage, M0 (p = 0.072), M1 (p = 0.1), were upregulated in PNs (n=6) compared to WT (n=6). A human reference matrix showed M2 (p=0.025) to be upregulated in PNs. GSEA showed IL-1, IL-6, IL-8, TNF and Type I IFN and cytokine secretion to be upregulated in PNs compared to WT. 
 Conclusion: 
 Macrophages were among the most upregulated components of the NF1 tumor microenvironment and upregulation of IL-1, IL-6, IL-8, TNF and Type I IFN production may be contributing to inflammation that is critical in the initiation and progression of PNs. 
 Scientific/Clinical/Policy Impact and Implications: 
 Pharmacotherapies that can target the macrophage lineage and/or aforementioned cytokines may have utility in the treatment of PNs. Further studies are necessary to evaluate this hypothesis.

Molecular characterization, biological function, tumor microenvironment association and clinical significance of m6A regulators in lung adenocarcinoma.

N6-methyladenosine (m6A) modification can regulate a variety of biological processes. However, the implications of m6A modification in lung adenocarcinoma (LUAD) remain largely unknown. Here, we systematically evaluated the m6A modification features in more than 2400 LUAD samples by analyzing the multi-omics features of 23 m6A regulators. We depicted the genetic variation features of m6A regulators, and found mutations of FTO and YTHDF3 were linked to worse overall survival. Many m6A regulators were aberrantly expressed in tumors, among which FTO, IGF2BP3, YTHDF1 and RBM15 showed consistent alteration features across 11 independent cohorts. Besides, the regulator-pathway interaction network demonstrated that m6A modification was associated with various biological pathways, including immune-related pathways. The correlation between m6A regulators and tumor microenvironment was also assessed. We found that LRPPRC was negatively correlated with most tumor-infiltrating immune cells. On the other hand, we established a scoring tool named m6Sig, which was positively correlated with PD-L1 expression and could reflect both the tumor microenvironment characterization and prognosis of LUAD patients. Comparison of CNV between high and low m6Sig groups revealed differences on chromosome 7. Application of m6Sig on an anti-PD-L1 immunotherapy cohort confirmed that the high m6Sig group demonstrated therapeutic advantages and clinical benefits. Our study indicated that m6A modification is involved in many aspects of LUAD and contributes to tumor microenvironment formation. A better understanding of m6A modification will provide more insights into the molecular mechanisms of LUAD and facilitate developing more effective personalized treatment strategies. A web application was built along with this study (http://www.bioinfo-zs.com/luadexpress/).

Tumor microenvironment characterization in head and neck squamous carcinoma reveals distinct genomic alterations and clinical outcomes

Tumor microenvironment characterization in head and neck squamous carcinoma reveals distinct genomic alterations and clinical outcomes

Current Trends of Artificial Intelligence for Colorectal Cancer Pathology Image Analysis: A Systematic Review.

Colorectal cancer (CRC) is one of the most common cancers requiring early pathologic diagnosis using colonoscopy biopsy samples. Recently, artificial intelligence (AI) has made significant progress and shown promising results in the field of medicine despite several limitations. We performed a systematic review of AI use in CRC pathology image analysis to visualize the state-of-the-art. Studies published between January 2000 and January 2020 were searched in major online databases including MEDLINE (PubMed, Cochrane Library, and EMBASE). Query terms included “colorectal neoplasm,” “histology,” and “artificial intelligence.” Of 9000 identified studies, only 30 studies consisting of 40 models were selected for review. The algorithm features of the models were gland segmentation (n = 25, 62%), tumor classification (n = 8, 20%), tumor microenvironment characterization (n = 4, 10%), and prognosis prediction (n = 3, 8%). Only 20 gland segmentation models met the criteria for quantitative analysis, and the model proposed by Ding et al. (2019) performed the best. Studies with other features were in the elementary stage, although most showed impressive results. Overall, the state-of-the-art is promising for CRC pathological analysis. However, datasets in most studies had relatively limited scale and quality for clinical application of this technique. Future studies with larger datasets and high-quality annotations are required for routine practice-level validation.

Tumor microenvironment characterization in head and neck cancer identifies prognostic and immunotherapeutically relevant gene signatures

The tumor microenvironment (TME) is of great clinical significance for predicting the therapeutic effect of tumors. Nonetheless, there was no systematic analysis of cellular interactions in the TME of head and neck cancer (HNSC). This study used gene expression data from 816 patients with HNSC to analyze the scores of 22 immune cells. On this basis, we have established a novel TMEscore-based prognostic risk model. The relationship between TMEscore and clinical and genomic characteristics was analyzed. The sample was divided into risk-H and risk-L groups based on the prognosis risk model of TMEscore, with significant differences in overall survival between the two groups (log rank p < 0.001). In terms of clinical features, the TMEscore is closely related to the T staging, Grade, and HPV. As for genomic characteristics, the genomic features of the Risk-H samples are a low expression of immune-related genes and high-frequency mutations of TP53 and CEP152. This model was validated in an external test set, in which the prognosis for Risk-H group and Risk-L group was also significantly different (log rank p = 0.017). A quantitative method of TME infiltration pattern is established, which may be a potential predictor of HNSC prognosis.

Gastric Cancer Tumor Microenvironment Characterization Reveals Stromal-Related Gene Signatures Associated With Macrophage Infiltration.

The tumor microenvironment (TME) has attracted attention owing to its essential role in tumor initiation, progression, and metastasis. With the emergence of immunotherapies for various cancers, and their high efficacy, an understanding of the TME in gastric cancer (GC) is critical. The aim of this study was to investigate the effect of various components within the GC TME, and to identify mechanisms that exhibit potential as therapeutic targets. The ESTIMATE algorithm was used to quantify immune and stromal components in GC samples, whose clinicopathological significance and relationship with predicted outcomes were explored. Low tumor mutational burden and high M2 macrophage infiltration, which are considered immune suppressive characteristics and may be responsible for unfavorable prognoses in GC, were observed in the high stromal group (HR = 1.585; 95% CI, 1.112–2.259; P = 0.009). Furthermore, weighted correlation network, differential expression, and univariate Cox analyses were used, along with machine learning methods (LASSO and SVM-RFE), to reveal genome-wide immune phenotypic correlations. Eight stromal-relevant genes cluster (FSTL1, RAB31, FBN1, ANTXR1, LRRC32, CTSK, COL5A2, and ENG) were identified as adverse prognostic factors in GC. Finally, using a combination of TIMER database and single-sample gene set enrichment analyses, we found that the identified genes potentially contribute to macrophage recruitment and polarization of tumor-associated macrophages. These findings provide a different perspective into the immune microenvironment and indicate potential prognostic and therapeutic targets for GC immunotherapies.

Tumor microenvironment characterization identifies two lung adenocarcinoma subtypes with specific immune and metabolic state.

The tumor microenvironment (TME) is a vital component of tumor tissue. Increasing evidence suggests their significance in predicting outcomes and guiding therapies. However, no studies have reported a systematic analysis of the clinicopathologic significance of TME in lung adenocarcinoma (LUAD). Here, we inferred tumor stromal cells in 1184 LUAD patients using computational algorithms based on bulk tumor expression data, and evaluated the clinicopathologic significance of stromal cells. We found LUAD patients showed heterogeneous abundance in stromal cells. Infiltration of stromal cells was influenced by clinicopathologic features, such as age, gender, smoking, and TNM stage. By clustering stromal cells, we identified 2 clinically and molecularly distinct LUAD subtypes with immune active and immune repressed features. The immune active subtype is characterized by repressed metabolism and repressed proliferation of tumor cells, while the immune repressed subtype is characterized by active metabolism and active proliferation of tumor cells. Differentially expressed gene analysis of the two LUAD subtypes identified an immune activation signature. To diagnose TME subtypes practically, we constructed a TME score using principal component analysis based on the immune activation signature. The TME score predicted TME subtypes effectively in 3 independent datasets with areas under the receiver operating characteristic curves of 0.960, 0.812, and 0.819, respectively. In conclusion, we proposed 2 clinically and molecularly distinct LUAD subtypes based on tumor microenvironment that could be valuable in predicting clinical outcome and guiding immunotherapy.

Abstract I27: Pancreatic cancer immunotherapy: Moving forward

Abstract As immunotherapy becomes a pillar of therapy for some of our patients with solid tumors, the role in pancreatic cancer remains limited despite a multitude of studies testing combinations of novel agents. The landscape of effective systemic therapies to date is limited to combination chemotherapy and PARP inhibitors in a small subset of patients with germline BRCA mutations. We also currently await the phase 3 data testing PEGPH20, a recombinant hyaluronidase, in combination with gemcitabine and nab-paclitaxel. These more recent studies were developed from studying and targeting the unique characteristics of pancreatic cancer based on the genomics, chemotherapy sensitivity, and tumor microenvironment characterization. Research in combination strategies that include immunotherapy agents is continuing to build on what we are actively learning about the genomics, epigenetics, RNA expression, and tumor microenvironment characterization specific to pancreatic cancer biology. Historically, studies involving single-agent immunotherapy approaches to pancreatic cancer have yielded responses in 0 or 1 patient. More recent studies with combination immunotherapy approaches with and without chemotherapy have increased the number of responders, but responses still occur in only a handful of patients except in studies where the agents are combined with chemotherapy, which may be the driver of the responses. Further characterization of the responders may help us to identify predictive biomarkers, and further understanding of the nonresponders can help drive future directions. This lecture will review the current strategies that have made it into clinical trials or strategies being proposed for further study and preliminary data that are available to support continued development. Advances in increasing T-cell delivery such as cell-based or neoantigen vaccines, modified T cells, viral and bacterial vectors, or bispecific antibodies may increase immune cell infiltration into tumors while new agonist agents promoting immune cell activation or antagonist targeting negative checkpoints or immune cell populations can increase the probability of success. The combination of increasing the number of responders and better understanding those subsets will eventually lead to additional treatment options for our patients. Citation Format: Dung T. Le. Pancreatic cancer immunotherapy: Moving forward [abstract]. In: Proceedings of the AACR Special Conference on Pancreatic Cancer: Advances in Science and Clinical Care; 2019 Sept 6-9; Boston, MA. Philadelphia (PA): AACR; Cancer Res 2019;79(24 Suppl):Abstract nr I27.

Characterisation of tumour microenvironment and immune checkpoints in primary central nervous system diffuse large B cell lymphomas.

Primary central nervous system diffuse large B cell lymphoma (PCNS-DLBCL) is a rare and aggressive entity of diffuse large B cell lymphoma (DLBCL). Elements of the tumour microenvironment (TME) including tumour-infiltrating lymphocytes (TILs) and tumour-associated macrophages (TAMs) have been associated with survival in DLBCL but their composition and prognostic impact in PCNS-DLBCL are unknown. Programmed cell death-1 (PD1)/programmed death-ligand 1 (PD-L1) immune checkpoint may represent a therapeutic option. Here, we aimed to characterise PD1/PDL1 immune checkpoints and the composition of the TME in PCNS-DLBCL. We collected tumour tissue and clinical data from 57 PCNS-DLBCL and used immunohistochemistry to examine TAMs (CD68, CD163), TILs (CD3, CD4, CD8, PD1) and tumour B cells (PAX5/PDL1 double stains, PDL1). The PDL1 gene was evaluated by fluorescence in situ hybridization (FISH). PAX5/PDL1 identified PDL1 expression by tumour B cells in 10/57 cases (17.5%). PDL1 gene translocation was a recurrent cytogenetic alteration in PNCS-DLBCL (8/47.17%) and was correlated with PDL1 positive expression in tumour B cells. The TME consisted predominantly of CD163 (+) M2 TAMs and CD8 (+) TILs. Most TAMs expressed PDL1 and most TILs expressed PD1. The density of TAMs and TILs did not associate with outcome. We showed that expression of PD1 on TILs and PDL1 on TAMs, but not the expression of PDL1 on tumour B cells was correlated with better prognosis. These findings support a significant role of TME composition and PD1/PDL1 crosstalk in PCNS-DLBCL pathogenesis and bring new insights to the targeted therapy of this aggressive lymphoma.

Artificial Intelligence in Lung Cancer Pathology Image Analysis.

Objective: Accurate diagnosis and prognosis are essential in lung cancer treatment selection and planning. With the rapid advance of medical imaging technology, whole slide imaging (WSI) in pathology is becoming a routine clinical procedure. An interplay of needs and challenges exists for computer-aided diagnosis based on accurate and efficient analysis of pathology images. Recently, artificial intelligence, especially deep learning, has shown great potential in pathology image analysis tasks such as tumor region identification, prognosis prediction, tumor microenvironment characterization, and metastasis detection. Materials and Methods: In this review, we aim to provide an overview of current and potential applications for AI methods in pathology image analysis, with an emphasis on lung cancer. Results: We outlined the current challenges and opportunities in lung cancer pathology image analysis, discussed the recent deep learning developments that could potentially impact digital pathology in lung cancer, and summarized the existing applications of deep learning algorithms in lung cancer diagnosis and prognosis. Discussion and Conclusion: With the advance of technology, digital pathology could have great potential impacts in lung cancer patient care. We point out some promising future directions for lung cancer pathology image analysis, including multi-task learning, transfer learning, and model interpretation.

Tumor Microenvironment Characterization Reveals Distinct Immune Subtypes in Lung Adenocarcinoma

Background: Tumor microenvironment (TME) cells are vital components of tumor tissue. Increasing evidence suggest their significance in predicting outcomes and guiding therapies. However, no studies have reported a systematic analysis of clinicopathological significance of TME cells in lung adenocarcinoma (LUAD). Methods: The TME cells of 1,184 LUAD patients were inferenced using computational algorithms based on bulk tumor expression data. TME subtypes were determined using a consensus clustering method. Signature genes between TME subtypes were obtained using random forest classification algorithm. TME score was obtained using principal component analysis algorithms based on signature gene expression. The Kaplan-Meier method was applied to generate survival curves. Findings: LUAD patients showed heterogeneous abundance in TME cells. Infiltration of TME cells was influenced by clinicopathological features, such as age, gender, smoking and TNM stage. By clustering TME cells, we identified two clinically and molecularly distinct LUAD subtypes with immune active and immune repressed features. The immune active subtype showed active chemokine expression and repressed metabolism expression, while the immune repressed subtype showed repressed chemokine expression and active metabolism expression. The TME score facilitated the correct classification of LUAD patients and the prediction of their prognosis. What's more, TME score predicted TME phenotype in TCGA Pan-Cancer cohort. Interpretation: There is a heterogeneous infiltration of TME cells in LUAD patients, which was shaped by clinicopathological features. Based on TME cell pattern, we proposed two clinically and molecularly distinct LUAD subtypes that may be valuable in predicting clinical outcome and guiding immunotherapy. Funding Statement: This work was supported by the National Key Research and Development Program of China (2016YFC1303201, 2016YFC0901400), the National Natural Science Foundation of China (81802299, 81502514), the CAMS Innovation Fund for Medical Sciences (2016-I2M-1-001, 2017-I2M-1-005), the Fundamental Research Funds for the Central Universities (3332018070), and the National Key Basic Research Development Plan (2018YFC1312105). Declaration of Interests: The authors declare that they have no competing interests. Ethical Approval Statement: Not required.

Tumor Microenvironment Characterization in Gastric Cancer Identifies Prognostic and Immunotherapeutically Relevant Gene Signatures.

Tumor microenvironment (TME) cells constitute a vital element of tumor tissue. Increasing evidence has elucidated their clinicopathologic significance in predicting outcomes and therapeutic efficacy. Nonetheless, no studies have reported a systematic analysis of cellular interactions in the TME. In this study, we comprehensively estimated the TME infiltration patterns of 1,524 gastric cancer patients and systematically correlated the TME phenotypes with genomic characteristics and clinicopathologic features of gastric cancer using two proposed computational algorithms. Three TME phenotypes were defined, and the TMEscore was constructed using principal component analysis algorithms. The high TMEscore subtype was characterized by immune activation and response to virus and IFNγ. Activation of transforming growth factor β, epithelial-mesenchymal transition, and angiogenesis pathways were observed in the low TMEscore subtype, which are considered T-cell suppressive and may be responsible for significantly worse prognosis in gastric cancer [hazard ratio (HR), 0.42; 95% confidence interval (CI), 0.33-0.54; P < 0.001]. Multivariate analysis revealed that the TMEscore was an independent prognostic biomarker, and its value in predicting immunotherapeutic outcomes was also confirmed (IMvigor210 cohort: HR, 0.63; 95% CI, 0.46-0.89; P = 0.008; GSE78220 cohort: HR, 0.25; 95% CI, 0.07-0.89; P = 0.021). Depicting a comprehensive landscape of the TME characteristics of gastric cancer may, therefore, help to interpret the responses of gastric tumors to immunotherapies and provide new strategies for the treatment of cancers.

Ex vivo performance comparison of three percutaneous biopsy needle systems.

The aim of this study is to identify the micro-mechanical characteristics that define biopsy performance in normal ex vivo animal organs. Three biopsy systems with differences of needle external diameter, tray height and effective tray length were assessed. Sampling was performed in porcine liver and kidneys with commercially labelled 14G, 16G and 18G, using 2-cm throw needle systems. Five samples were obtained per needle size and per organ, and the experiment was repeated twice for a total of 90 biopsy cores. Samples were analysed and compared in terms of macroscopic aspect, sample length, weight and tissue architecture. The system with the longest effective needle tray (system A) has shown significant superiority (p < 0.001) versus the other systems regarding the mean weight of tissue obtained for all needle sizes. Furthermore, the 14G needle of system A has shown superiority regarding the number of portal spaces and the 16G regarding the length of kidney fragments obtained. The outcomes obtained with the different biopsy systems were not equal. The micro-mechanical characteristic that appears to influence the quantity and quality of the obtained tissue is the effective needle tray length and not the needle external diameter or the needle tray height. This information should be taken into account in the future design of biopsy needle systems, particularly when potentially used in the assessment of biomarkers and the characterisation of tumour micro-environment. • The amount of obtained tissue mass is not the same among percutaneous biopsy needle systems. • There are different micro-mechanical characteristics that condition the amount of obtained tissue. • The micro-mechanical characteristic that offers more tissue mass for the same calibre is the effective length of the needle tray.

&lt;i&gt;PBRM1&lt;/i&gt; Mutation Recruits More Mast Cells by Upregulating CCL5 in a Clear Cell Renal Cell Carcinoma Tumor Microenvironment

Background: PBRM1 (Polybromo-1) is the second most frequently mutated gene in ccRCC (clear cell renal cell carcinoma), and loss of PBRM1 was correlated with advanced tumor and tyrosine kinase inhibitor or immune checkpoint inhibitor clinical therapy in previously reported studies. However, the mechanisms of PBRM1 mutation in tumor microenvironment characterization are poorly understood. Methods: We investigated the tumor microenvironment in ccRCC by estimating immune cells in malignant tumors using CIBERSORT and estimating stromal and immune cells in malignant tumors using an expression data (ESTIMATE) algorithm. Gene set enrichment analysis hierarchical clustering and weighted correlation network analysis were used to analyze the characteristics and clinicopathologic features of ccRCC. Mast cell recruitment assay, colony formation assay, cell cycle assay, ELISA, and tube formation assay were applied to test PBRM1 function in vitro. Findings: We discovered that ccRCC patients with PBRM1 mutations were associated with inhibited CD8 T-cell infiltration but more resting mast cells in the tumor microenvironment. Silenced PBRM1 improved mast cell recruitment, angiogenesis, and cell proliferation among ccRCC cells. The molecular mechanism involved the upregulation ofCCL5 levels through PBRM1 mutations. CCL5 evoked immune-related pathways [including interferon-inflammatory signaling, interferon-gamma signaling, and IL-6-JAK- STAT3 (interleukin-6-Janus kinase-signal transducers and activators of transcription 3)] and recruited more mast cells to the tumor microenvironment. Furthermore, we confirmed that vascular endothelial growth factor family genes are overexpressed and that hypoxia-inducible factor-related processes are activated. Interpretation: These results demonstrated that PBRM1 mutation-CCL5-mast cell infiltration plays a key role in ccRCC clinical therapy. Funding Statement: This study was supported by the National Natural Science Foundation of China (NSFC No. 81602244 to Shan Xu) and Natural Science Basic Research Plan in Shaanxi Province of China (Program No. 2017JM8018 to Shan Xu). Declaration of Interests: The authors stated: None. Ethics Approval Statement: Not required.

Abstract LB-379: Targeted single-cell RNA expression profiling for cellular characterization of tumor microenvironment

Abstract New rapid and robust transcriptome-based methods for cellular characterization of the tumor microenvironment and biomarker discovery are required to improve prognosis and treatment of cancer and other diseases. However, challenges with current approaches for the above applications include high sample requirements, poor sensitivity, low dynamic range, and limited throughput. To address these limitations, we have developed the DriverMap™ targeted RNA expression profiling assay using a genome-wide set of 19,000 validated primer pairs that leverages the sensitivity of multiplex RT-PCR with the throughput and digital readout depth of next-generation sequencing (NGS). Starting from just 10pg (single-cell) to 100ng (10,000 cells) of total RNA is sufficient to quantify over 5 orders of magnitude variation in gene expression levels with performance similar to conventional qRT-PCR. Further, the use of gene-specific primers enables direct analysis of total RNA isolate and obviates the need for globin and rRNA depletion from whole blood samples. And, using a subset of primers empirically selected from the DriverMap™ assay, we have developed a novel single-cell targeted RNA expression (scTRex) profiling assay compatible with conventional oligo dT-molecular indexing, single- cell barcoding strategies. In this study, we present the performance of the assay to analyze the level of immune cell infiltration in tumor samples, and identify active pathways in tumor, xenograft samples and cell lines treated with small molecules. Preliminary studies demonstrate the assay's unparalleled specificity and sensitivity resulting in better detection of low abundance mRNA transcripts as well as an improved cost-effectiveness for high-throughput clinical applications. Citation Format: Alex Chenchik, Michael Makhanov, Costa Frangou, Paul Diehl. Targeted single-cell RNA expression profiling for cellular characterization of tumor microenvironment [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2018; 2018 Apr 14-18; Chicago, IL. Philadelphia (PA): AACR; Cancer Res 2018;78(13 Suppl):Abstract nr LB-379.

Characterization of tumor microenvironment in extraskeletal myxoid chondrosarcoma (EMC).

11561Background: EMC is a rare sarcoma mostly originating from soft tissues. EMC carries a specific translocation, involving NR4A3, which is fused more often with EWSR1 and less frequently with oth...

Unsupervised morphological segmentation of tissue compartments in histopathological images.

Algorithmic segmentation of histologically relevant regions of tissues in digitized histopathological images is a critical step towards computer-assisted diagnosis and analysis. For example, automatic identification of epithelial and stromal tissues in images is important for spatial localisation and guidance in the analysis and characterisation of tumour micro-environment. Current segmentation approaches are based on supervised methods, which require extensive training data from high quality, manually annotated images. This is often difficult and costly to obtain. This paper presents an alternative data-independent framework based on unsupervised segmentation of oropharyngeal cancer tissue micro-arrays (TMAs). An automated segmentation algorithm based on mathematical morphology is first applied to light microscopy images stained with haematoxylin and eosin. This partitions the image into multiple binary ‘virtual-cells’, each enclosing a potential ‘nucleus’ (dark basins in the haematoxylin absorbance image). Colour and morphology measurements obtained from these virtual-cells as well as their enclosed nuclei are input into an advanced unsupervised learning model for the identification of epithelium and stromal tissues. Here we exploit two Consensus Clustering (CC) algorithms for the unsupervised recognition of tissue compartments, that consider the consensual opinion of a group of individual clustering algorithms. Unlike most unsupervised segmentation analyses, which depend on a single clustering method, the CC learning models allow for more robust and stable detection of tissue regions. The proposed framework performance has been evaluated on fifty-five hand-annotated tissue images of oropharyngeal tissues. Qualitative and quantitative results of the proposed segmentation algorithm compare favourably with eight popular tissue segmentation strategies. Furthermore, the unsupervised results obtained here outperform those obtained with individual clustering algorithms.

Comparison of DCE-MRI kinetic parameters and FMISO-PET uptake parameters in head and neck cancer patients.

PurposeTumor hypoxia is a major cause of radiation resistance, often present in various solid tumors. Dynamic [18F]‐fluoromisonidazole (FMISO) PET imaging is able to reliably assess tumor hypoxia. Comprehensive characterization of tumor microenvironment through FMISO‐PET and dynamic contrast enhanced (DCE) MR multimodality imaging might be a valuable alternative to the dynamic FMISO‐PET acquisition. The aim of this work was to explore the correlation between the FMISO‐PET and DCE‐MRI kinetic parameters.MethodsThis study was done on head and neck cancer patients (N = 6), who were imaged dynamically with FMISO‐PET and DCE‐MRI on the same day. Images were registered and analyzed for kinetics on a voxel basis. FMISO‐PET images were analyzed with the two‐tissue compartment three rate‐constant model. Additionally, tumor‐to‐muscle ratio (TMR) maps were evaluated. DCE‐MRI was analyzed with the extended Tofts model. Voxel‐wise Pearson's coefficients were calculated for each patient to assess pairwise parameter correlations.ResultsMedian correlations between FMISO uptake parameters and DCE‐MRI kinetic parameters varied across the parameter pairs in the range from −0.05 to 0.71. The highest median correlation of r = 0.71 was observed for the pair V b−v p, while the K 1−K trans median correlation was r = 0.45. Median correlation coefficients for the K 1−v p and the K i−K trans pairs were r = 0.42 and r = 0.32, respectively. Correlations between FMISO uptake rate parameter K i and DCE‐MRI kinetic parameters varied substantially across the patients, whereas correlations between the FMISO and DCE‐MRI vascular parameters were consistently high. Median TMR‐K 1 and TMR‐K trans correlations were r = 0.52 and r = 0.46, respectively, but varied substantially across the patients.ConclusionsBased on this clinical evidence, we can conclude that the vascular fraction parameters obtained through DCE‐MRI kinetic analysis or FMISO kinetic analysis measure the same biological property, while other kinetic parameters are unrelated. These results might be useful in the design of future clinical trials involving FMISO‐PET/DCE‐MR multimodality imaging for the assessment of tumor microenvironment.

Organ-Tumor-on-a-Chip for Chemosensitivity Assay: A Critical Review.

With a mortality rate over 580,000 per year, cancer is still one of the leading causes of death worldwide. However, the emerging field of microfluidics can potentially shed light on this puzzling disease. Unique characteristics of microfluidic chips (also known as micro-total analysis system) make them excellent candidates for biological applications. The ex vivo approach of tumor-on-a-chip is becoming an indispensable part of personalized medicine and can replace in vivo animal testing as well as conventional in vitro methods. In tumor-on-a-chip, the complex three-dimensional (3D) nature of malignant tumor is co-cultured on a microfluidic chip and high throughput screening tools to evaluate the efficacy of anticancer drugs are integrated on the same chip. In this article, we critically review the cutting edge advances in this field and mainly categorize each tumor-on-a-chip work based on its primary organ. Specifically, design, fabrication and characterization of tumor microenvironment; cell culture technique; transferring mechanism of cultured cells into the microchip; concentration gradient generators for drug delivery; in vitro screening assays of drug efficacy; and pros and cons of each microfluidic platform used in the recent literature will be discussed separately for the tumor of following organs: (1) Lung; (2) Bone marrow; (3) Brain; (4) Breast; (5) Urinary system (kidney, bladder and prostate); (6) Intestine; and (7) Liver. By comparing these microchips, we intend to demonstrate the unique design considerations of each tumor-on-a-chip based on primary organ, e.g., how microfluidic platform of lung-tumor-on-a-chip may differ from liver-tumor-on-a-chip. In addition, the importance of heart–liver–intestine co-culture with microvasculature in tumor-on-a-chip devices for in vitro chemosensitivity assay will be discussed. Such system would be able to completely evaluate the absorption, distribution, metabolism, excretion and toxicity (ADMET) of anticancer drugs and more realistically recapitulate tumor in vivo-like microenvironment.