Intratumoral Regions Research Articles

Abstract 2150: CD103+ tumor infiltrating lymphocytes predict a favorable prognosis in human colorectal cancer

Abstract Background: Given that immune system maintains homeostasis by excluding tumors with mutations, in recent years has been proposed the "cancer-immunoediting" hypothesis, which divides immune responses into three phases, i.e., exclusion, equilibrium, and escape. This equilibrium phase takes likely several decades in human life, during the occurrence of mutations and growth of tumors. Recent studies have emerged that the resident memory T cells (Trm) exclusively survey the transformation of normal tissues in equilibrium phase, though reported were other two memory T subsets in extra-epithelial organs, such as central memory T (Tcm) and effector memory T (Tem) cells. Notably, Trm does not exit into blood circulation, instead exerts powerful ant-tumor functions as tumor infiltrating T cells (TIL), by the expression of unique molecules such as CD69 and CD103; CD69 restricts residence in epithelial tissues, and CD103 bounds to a cognate ligand, epithelial E-cadherin, to stimulate antitumor effect. Purpose: Although previous reports suggested Trm involvement in several tumors, the significance of Trm in predictive patient prognosis of gastrointestinal cancer remains to be investigated fully. Here we study the significance of Trm in colorectal cancer. Materials and Methods: All available patients of hundred-twenty patients with colorectal cancer in Osaka University Hospital were enrolled in this study during 2012 to 2013, under the agreement of written informed consents; the study was performed according to the approval of institutional ethical committee. Immunohistochemistry by staining tissue samples from surgically resected specimens was performed to identify the cellular distribution, number and expression intensity of CD103+/CD69+ in CD8+ lymphocytes, infiltrated in colorectal cancer tissues. Kaplan-Meier analysis and Cox proportional hazards regression models were applied to predict overall and recurrence-free survival in all patients with colorectal cancer examined. Result: The staining of Trm, i.e., intratumor CD103+/CD69+/CD8+ TILs represented a favorable prognostic predictor of overall and recurrence-free survival (p<0.05). Indeed, CD103+ TILs were positively associated with the expression of E-cadherin in intratumor regions of colorectal cancer tissues. Conclusions: The present study suggests that Trm has a significant role in tumor immunity by expressing CD103 in intratumor regions of colorectal cancer tissues, indicating that CD103+ TILs will be useful for the prediction of patient' survival after surgery of colorectal cancer. Citation Format: Masatoshi Kitakaze, Masamitsu Konno, Taroh Satoh, Tsunekazu Mizushima, Yuichiro Doki, Hideshi Ishii. CD103+ tumor infiltrating lymphocytes predict a favorable prognosis in human colorectal cancer [abstract]. In: Proceedings of the Annual Meeting of the American Association for Cancer Research 2020; 2020 Apr 27-28 and Jun 22-24. Philadelphia (PA): AACR; Cancer Res 2020;80(16 Suppl):Abstract nr 2150.

Prognostic significance of spatial immune profiles in human solid cancers.

Immune‐based tumor characteristics in the context of tumor heterogeneity are associated with suppression as well as promotion of cancer progression in various tumor types. As immunity typically functions based on intercellular contacts and short‐distance cytokine communications, the location and spatial relationships of the tumor immune microenvironment can provide a framework to understand the biology and potential predictive biomarkers related to disease outcomes. Immune spatial analysis is a newly emerging form of cancer research based on recent methodological advances in in situ single‐cell analysis, where cell‐cell interaction and the tissue architecture can be analyzed in relation to phenotyping the tumor immune heterogeneity. Spatial characteristics of tumors can be stratified into the tissue architecture level and the single‐cell level. At the tissue architecture level, the prognostic significance of the density of immune cell lineages, particularly T cells, is leveraged by understanding longitudinal changes in cell distribution in the tissue architecture such as intra‐tumoral and peri‐tumoral regions, and invasive margins. At the single‐cell level, the proximity of the tumor to the immune cells correlates with disease aggressiveness and therapeutic resistance, providing evidence to understand biological interactions and characteristics of the tumor immune microenvironment. In this review, we summarize recent findings regarding spatial information of the tumor immune microenvironment and review advances and challenges in spatial single‐cell analysis toward developing tissue‐based biomarkers rooted in the immune spatial landscape.

Development of a Deep Learning Model to Identify Lymph Node Metastasis on Magnetic Resonance Imaging in Patients With Cervical Cancer

Accurate identification of lymph node metastasis preoperatively and noninvasively in patients with cervical cancer can avoid unnecessary surgical intervention and benefit treatment planning. To develop a deep learning model using preoperative magnetic resonance imaging for prediction of lymph node metastasis in cervical cancer. This diagnostic study developed an end-to-end deep learning model to identify lymph node metastasis in cervical cancer using magnetic resonance imaging (MRI). A total of 894 patients with stage IB to IIB cervical cancer who underwent radical hysterectomy and pelvic lymphadenectomy were reviewed. All patients underwent radical hysterectomy and pelvic lymphadenectomy, received pelvic MRI within 2 weeks before the operations, had no concurrent cancers, and received no preoperative treatment. To achieve the optimal model, the diagnostic value of 3 MRI sequences was compared, and the outcomes in the intratumoral and peritumoral regions were explored. To mine tumor information from both image and clinicopathologic levels, a hybrid model was built and its prognostic value was assessed by Kaplan-Meier analysis. The deep learning model and hybrid model were developed on a primary cohort consisting of 338 patients (218 patients from Sun Yat-sen University Cancer Center, Guangzhou, China, between January 2011 and December 2017 and 120 patients from Henan Provincial People's Hospital, Zhengzhou, China, between December 2016 and June 2018). The models then were evaluated on an independent validation cohort consisting of 141 patients from Yunnan Cancer Hospital, Kunming, China, between January 2011 and December 2017. The primary diagnostic outcome was lymph node metastasis status, with the pathologic characteristics diagnosed by lymphadenectomy. The secondary primary clinical outcome was survival. The primary diagnostic outcome was assessed by receiver operating characteristic (area under the curve [AUC]) analysis; the primary clinical outcome was assessed by Kaplan-Meier survival analysis. A total of 479 patients (mean [SD] age, 49.1 [9.7] years) fulfilled the eligibility criteria and were enrolled in the primary (n = 338) and validation (n = 141) cohorts. A total of 71 patients (21.0%) in the primary cohort and 32 patients (22.7%) in the validation cohort had lymph node metastais confirmed by lymphadenectomy. Among the 3 image sequences, the deep learning model that used both intratumoral and peritumoral regions on contrast-enhanced T1-weighted imaging showed the best performance (AUC, 0.844; 95% CI, 0.780-0.907). These results were further improved in a hybrid model that combined tumor image information mined by deep learning model and MRI-reported lymph node status (AUC, 0.933; 95% CI, 0.887-0.979). Moreover, the hybrid model was significantly associated with disease-free survival from cervical cancer (hazard ratio, 4.59; 95% CI, 2.04-10.31; P < .001). The findings of this study suggest that deep learning can be used as a preoperative noninvasive tool to diagnose lymph node metastasis in cervical cancer.

Correction: Preclinical Assessment of Carboplatin Treatment Efficacy in Lung Cancer by 18F-ICMT-11-Positron Emission Tomography.

[This corrects the article DOI: 10.1371/journal.pone.0091694.].

Integrated single-cell spatial multi-omics of intratumor heterogeneity in renal cell carcinoma.

e17106 Background: Clear cell renal cell carcinoma (ccRCC) exhibits conspicuous intratumor heterogeneity (ITH) - a driver of tumor evolution and metastasis. ITH in RCC has been studied extensively with bulk tumor DNA sequencing, which lacks the ability to integrate single cell resolution data, spatial architecture, and microenvironment composition. Therefore, we analyzed primary ccRCC tumors at multiple biopsy sites with CyTOF, multiplex immunofluorescence (MxIF), whole exome sequencing (WES), RNA sequencing (RNA-seq), single nuclei RNA-seq (snRNA-seq), and whole genome bisulfite sequencing (WGBS). Methods: Primary ccRCC tumors collected from 6 patients (pts) were biopsied at multiple locations and subjected to CyTOF (n = 21 sites, 6 pts), MxIF (20 markers, n = 8 sites, 3 pts), WES (n = 8 sites, 3 pts), RNA-seq (n = 8 sites, 3 pts), snRNA-seq (n = 8 sites, 3 pts), and WGBS (n = 8 sites, 3 pts), enabling integrated multi-omics analysis. MxIF, CyTOF, and genomic/transcriptomic analyses were performed by BostonGene. Results: Genomic intratumor (IT) evolution of ccRCC cells was tracked with WES, and subclonal distribution of SETD2, STAG2, TSC2 and PBRM1 mutations was observed in different IT regions. Different regions of the same tumor were similar, whereas individual patient tumors were distinct according to tumor microenvironment cellular composition measured by CyTOF or deconvoluted from RNA-seq. The cellular deconvolution of the ccRCC tumors reconstructed from RNA-seq correlated with CyTOF, snRNA-seq and WGBS, showing high concordance among the methods. The promoter CpG island methylation levels, averaged across all genes, positively correlated with ccRCC grade. MxIF revealed spatial IT heterogeneity in the distribution of immune infiltrate components. Macrophages and T cells dispersed among malignant cells; whereas, T cells formed clusters at unique tumor margins. Conclusions: The utilization of multi-omics methods produced a high-resolution portrait of the ccRCC tumor composition and identified differential ITH among regions within the primary tumors or among individual primary tumors. This study demonstrated strong concordance among the different technologies, suggesting that tumor deconvolution by bulk RNA-seq might be clinically applicable for ccRCC tumors. MxIF analysis enabled a fine elucidation of the spatial relationships among the tumor and the immune and stromal cells, missed by common omic platforms. Integrated single cell multi-omics could render specific pathobiological and therapeutic insights that impact treatment decisions.

The relationship between tumor-infiltrating lymphocytes, PD-L1 expression, and clinical outcomes of Asian melanoma patients treated with anti-PD-1 antibody.

e22010 Background: Clinical feature of Asian melanoma patients are distinct from those of Western patients. Acral and mucosal melanomas, which are common in Asian populations, predominantly occur in sun-protected areas. Immune-checkpoint inhibitors are standard treatment for inoperable melanoma, however, they are less effective against acral and mucosal melanomas. The aim of our study is to investigate the expression, clinical significance and association of programmed death-ligand 1 (PD-L1) with tumor-infiltrating lymphocytes (TIL) in Asian melanoma patient treated with anti-PD-1 therapy. Methods: Patients with unresectable stage III or metastatic stage IV melanoma who began anti-PD-1 antibody therapy between February 2016 and September 2018 were enrolled. Immunohistochemistry was used to evaluate PD-L1 expression and stromal CD8+ TIL, and PD-1+ TIL densities within the intratumoral regions and associated stromal regions. Baseline characteristics and blood parameters including neutrophil to lymphocyte ratio (NLR) were assessed, and outcome and adverse events were evaluated according to subtypes. Results: Of the 57 cases, the most common type was acral melanoma (n = 28, 49.1%) followed by mucosal melanoma (n = 19, 33.3%) and cutaneous (n = 10, 17.5%). The overall response was 8.8 % and disease control rate was 33.3%. The median progression-free survival (PFS) was 18.0 months (95% confidence interval, 12.57 to 23.43), and there was no difference in PFS according to subtypes. Overall, 29.8% (n = 17) showed PD-L1 expression in tumor cells and 26.3% in immune cells. PD-L1, CD8+TIL, LDH and BRAFV600 statues were not associated with response or survival. NLR (≥ 5) were independent poor prognostic factors by multivariate analysis. Conclusions: In our study, anti-PD-1 therapy showed less effective compared to western patients. High NLR (≥ 5) could predict response of anti-PD-1 therapy in patients with Asian melanoma. Considering the differences in genetic backgrounds and therapeutic efficacy of immunotherapy, specialized therapeutic strategies for Asian melanoma should be established.

An Automated Segmentation Pipeline for Intratumoural Regions in Animal Xenografts Using Machine Learning and Saturation Transfer MRI

Saturation transfer MRI can be useful in the characterization of different tumour types. It is sensitive to tumour metabolism, microstructure, and microenvironment. This study aimed to use saturation transfer to differentiate between intratumoural regions, demarcate tumour boundaries, and reduce data acquisition times by identifying the imaging scheme with the most impact on segmentation accuracy. Saturation transfer-weighted images were acquired over a wide range of saturation amplitudes and frequency offsets along with T1 and T2 maps for 34 tumour xenografts in mice. Independent component analysis and Gaussian mixture modelling were used to segment the images and identify intratumoural regions. Comparison between the segmented regions and histopathology indicated five distinct clusters: three corresponding to intratumoural regions (active tumour, necrosis/apoptosis, and blood/edema) and two extratumoural (muscle and a mix of muscle and connective tissue). The fraction of tumour voxels segmented as necrosis/apoptosis quantitatively matched those calculated from TUNEL histopathological assays. An optimal protocol was identified providing reasonable qualitative agreement between MRI and histopathology and consisting of T1 and T2 maps and 22 magnetization transfer (MT)-weighted images. A three-image subset was identified that resulted in a greater than 90% match in positive and negative predictive value of tumour voxels compared to those found using the entire 24-image dataset. The proposed algorithm can potentially be used to develop a robust intratumoural segmentation method.

PD-1 topographically defines distinct T cell subpopulations in urothelial cell carcinoma of the bladder and predicts patient survival

IntroductionThe pre-existing tumor-infiltrating T cell landscape in urothelial cell carcinoma of the bladder (UCB) may obtain prognostic significance and guide treatment decisions, particularly regarding immunotherapy. However, the current studies typically lead to inconsistent conclusions due to the extreme heterogeneity of T cells in cancer. Herein, we investigated the heterogeneity, distribution and clinical significance of tumor-infiltrating T cells based on PD-1 expression, their spatial organization, and the balance between subsets in a series of UCB patients. MethodsFlow cytometry for PD-1, CD4, and CD8 was performed in 6 UBC patients and 5 healthy donors. A series of 155 UBC patients with tissue slides were stained for triple color immunofluorescence. Stromal and intratumoral regions of the cancer tissue were respectively evaluated. Features derived from triple staining were analyzed for their correlations with clinical characteristics and patient prognosis. ResultsFlow cytometric analysis showed PD-1+ T cells were more frequently accumulated at the tumor site than in blood (p < 0.001). The proportion of PD-1+ T cells within CD4+ and/or CD8+ T cells is higher in the intratumoral region, as compared with the stroma by immunofluorescence evaluation (all p < 0.001, n = 155). Moreover, a high proportion of PD-1+ T cells within T cells in the intratumoral region, but not in the stroma, was predictive of a poorer overall survival (p = 0.0075) and recurrence-free survival (p = 0.0062), and was positively associated with aggressive clinical features (all p < 0.05). However, a low CD4/CD8 ratio among the PD-1+ T cells in the tumor stroma, but not in the intratumoral region, was significantly associated with shorter overall survival (p = 0.0164) and recurrence-free survival (p = 0.0016), which emerged as an independent predictor in multivariate analysis for UCB patients. ConclusionsTaken together, our results emphasize that PD-1 expression in T cell subsets, based on their topographic micro-localizations, provides valuable prognostic information for UCB patients.

FLT4 antagonist can function as an effective blocker in metastasis of melanoma cells by suppression of lymphangiogenesis

Background & Aim Background & Aim Tumor recurrence is a serious concern in the treatment of melanoma. Treatment failures, including relapse, are prompted by metastasis. Malignant melanoma has a cutaneous origin, but it is easily found in visceral organs, such as lung, after metastasis. Like angiogenesis, tumor-induced lymphangiogenesis is also regarded as an important cause of relapse. VEGF-C/FLT4 axis plays a pivotal role in lymphangiogenesis. Especially, distant metastasis is strongly involved in FLT4 activation and it is emerging that importance of lymphatic vessels as a potential therapeutic target for anti-metastatic therapy. Thus, we investigated whether suppression of lymphatic vessel by inhibition of FLT4 phosphorylation can decrease melanoma metastasis in animal model. Methods, Results & Conclusion Methods To this end, B16-F10 cells with or without FLT4 antagonist was intravenously injected to engraft tumor, then tissues was isolated at 3 weeks post cell transplantation. To confirm roles of FLT4 antagonist in tumor metastasis, real-time PCR and immunohistochemistry were performed using tumor bearing mice. Results & Conclusion Gross examination and melan A cells in lung was clearly showed that significant reduction of metastatic progress was detected by FLT4 antagonist. (in modules diameter, 4.27-fold; in number of tumor modules, 6.01-fold). In tumor regions, maker genes for lymphatic endothelial cells were significantly increased, compared to that of wild mice. Immunohistochemistry of lung tissues were revealed that FLT4 antagonist exhibited decreased lymphatic capillaries, compared to that of melanoma-bearing mice (wild & melanoma, 3.98-fold, melanoma & FLT4 antagonist, 3.02-fold). Proliferative lymphatic capillaries in the intratumoral region were also suppressed by inhibition of FLT4 molecule. Collectively, we found that tumor clusters gradually increased by growth of lymphatic vessels and decreased by its blocking, suggesting that the importance of FLT4 in tumor metastasis. These data provided a platform for advanced therapeutic strategies by targeting lymphatic vessels as a microenvironmental factor and we will next establish plan for clinic application by using safety materials such as peptide and monoclonal antibodies in targeting FLT4.

Noninvasive imaging evaluation of tumor immune microenvironment to predict outcomes in gastric cancer

The tumor immune microenvironment can provide prognostic and predictive information. A previously validated ImmunoScore of Gastric Cancer (ISGC) evaluates both lymphoid and myeloid cells in the tumor core and invasive margin with immunohistochemical staining of surgical specimens. We aimed to develop a noninvasive radiomics-based predictor of ISGC. In this retrospective study including four independent cohorts of 1778 patients, we extracted 584 quantitative features from the intratumoral and peritumoral regions on contrast-enhanced computed tomography images. A radiomic signature [radiomics ImmunoScore (RIS)] was constructed to predict ISGC using regularized logistic regression. We further evaluated its association with prognosis and chemotherapy response. A 13-feature radiomic signature for ISGC was developed and validated in three independent cohorts (area under the curve= 0.786, 0.745, and 0.766). The RIS signature was significantly associated with both disease-free and overall survival in the training and all validation cohorts [hazard ratio (HR) range: 0.296-0.487, all P < 0.001]. In multivariable analysis, the RIS remained an independent prognostic factor adjusting for clinicopathologic variables (adjusted HR range: 0.339-0.605, all P < 0.003). For stage II and stage III disease, patients with a high RIS derived survival benefit from adjuvant chemotherapy {HR= 0.436 [95% confidence interval (CI) 0.253-0.753], P= 0.002; HR= 0.591 (95% CI 0.428-0.818), P < 0.001, respectively}, whereas those with a low RIS did not. The RIS is a reliable tool for evaluation of immunoscore and retains the prognostic significance in gastric cancer. Future prospective studies are required to confirm its potential to predict treatment response and select patients who will benefit from chemotherapy.

Intratumoral and Peritumoral Radiomics of Contrast-Enhanced CT for Prediction of Disease-Free Survival and Chemotherapy Response in Stage II/III Gastric Cancer

Background: We evaluated the ability of radiomic analysis from intratumoral and peritumoral regions on preoperative gastric cancer (GC) contrast-enhanced CT imaging to predict disease-free survival (DFS) and chemotherapy response in stage II/III GC. Methods: This study consisted of 739 consecutive patients with stage II/III GC. Within the intratumoral and peritumoral regions of CT images, a total of 584 radiomic features were computed at portal venous-phase. A radiomics signature was generated by using support vector machine (SVM)–based methods. Univariate and multivariate Cox proportional hazards model and Kaplan–Meier analysis were used to determine the association of the radiomics signature and clinicopathological variables with DFS. A radiomics nomogram combining the radiomics signature and clinicopathological findings was constructed for individualized DFS estimation. Results: The radiomics signature consisted of 26 features and were significantly associated with DFS in both the training and validation sets (both P <0.0001). Multivariate analysis demonstrated that the radiomics signature was an independent prognostic factor. The signature had higher predictive accuracy than TNM stage, single radiomics feature and clinicopathological factors. Further analysis revealed that stage II and III GC patients with high scores were likely to benefit from adjuvant chemotherapy. Conclusions: The newly developed radiomics signature is a powerful predictor of DFS, and it may predict which patients with stage II and III GC benefit from chemotherapy. Funding Statement: This study was supported by the project of Medical Service Capacity Improvement of Henan provinical (2019), and Youth project of National Natural Science Foundation of China (No.81802094). Declaration of Interests: The authors have no conflicts of interest to declare. Ethics Approval Statement: Ethical approval was obtained for this retrospective analysis at the institutional review board of the hospital, and the informed consent requirement was waived.

Deep Learning vs. Radiomics for Predicting Axillary Lymph Node Metastasis of Breast Cancer Using Ultrasound Images: Don't Forget the Peritumoral Region.

Objective: Axillary lymph node (ALN) metastasis status is important in guiding treatment in breast cancer. The aims were to assess how deep convolutional neural network (CNN) performed compared with radiomics analysis in predicting ALN metastasis using breast ultrasound, and to investigate the value of both intratumoral and peritumoral regions in ALN metastasis prediction.Methods: We retrospectively enrolled 479 breast cancer patients with 2,395 breast ultrasound images. Based on the intratumoral, peritumoral, and combined intra- and peritumoral regions, three CNNs were built using DenseNet, and three radiomics models were built using random forest, respectively. By combining the molecular subtype, another three CNNs and three radiomics models were built. All models were built on training cohort (343 patients 1,715 images) and evaluated on testing cohort (136 patients 680 images) with ROC analysis. Another prospective cohort of 16 patients was enrolled to further test the models.Results: AUCs of image-only CNNs in both training/testing cohorts were 0.957/0.912 for combined region, 0.944/0.775 for peritumoral region, and 0.937/0.748 for intratumoral region, which were numerically higher than their corresponding radiomics models with AUCs of 0.940/0.886, 0.920/0.724, and 0.913/0.693. The overall performance of image-molecular CNNs in terms of AUCs on training/testing cohorts slightly increased to 0.962/0.933, 0.951/0.813, and 0.931/0.794, respectively. AUCs of both CNNs and radiomics models built on combined region were significantly better than those on either intratumoral or peritumoral region on the testing cohort (p < 0.05). In the prospective study, the CNN model built on combined region achieved the highest AUC of 0.95 among all image-only models.Conclusions: CNNs showed numerically better overall performance compared with radiomics models in predicting ALN metastasis in breast cancer. For both CNNs and radiomics models, combining intratumoral, and peritumoral regions achieved significantly better performance.

Analyses of VEGFC/VEGF-D expressions, density and endothelial lymphatic proliferation in salivary gland neoplasms

ObjectiveThe aim of this study was to assess vascular endothelial growth factor C (VEGF-C) and VEGF-D expressions, tumor lymphatic density (D2–40) and endothelial lymphatic proliferation (D2–40/Ki-67 double labeling) in a series of salivary gland neoplasm cases. Materials and methodsTwenty pleomorphic adenomas (PA), 20 adenoid cystic carcinomas (ACC) and 20 mucoepidermoid carcinomas (MEC) were assessed, as well as 10 normal minor salivary gland (SG) tissues for comparison. ResultsAll cases presented positive VEGF-C expression in the peritumoral and intratumoral regions, and no differences in immunoexpression were detected between groups. However, the ACC group presented a significant difference in VEGF-C immunoexpression according to the predominant histological pattern. Most cases presented poor VEGF-D labeling in the peritumoral and intratumoral regions. Concerning peritumoral, intratumoral and total lymphatic endothelial density, the assessed groups revealed an increasing gradient, with lower values for PA, followed by MEC and ACC. ConclusionNo correlation was detected between VEGF-C and VEGF-D immunoexpression in relation to lymphatic tumor density and endothelial lymphatic proliferation. Western blotting (WB) revealed no difference between VEGF-C and VEGF-D expression among the lesions, corroborating the immunohistochemistry findings.

Transparent tumor microenvironment: Are liposomal nanoparticles sufficient for drug delivery to hypoxic regions and clonogenic cells?

Passive targeting of nanoparticles (NPs) mediated by Enhanced Permeability and Retention effect is essential to NP delivery to tumors. However, clinically, only limited success has been found due to inadequate intratumoral delivery. Herein, for the first time, we present direct a demonstration visualizing the spatial distribution of liposomal nanoparticles (NP1: Lip-Ru (∼126 nm), NP2: Lip-Ru (∼164 nm) and NP3: Lip-Ru (∼234 nm)) in relation to hypoxic (HIF1α) and clonogenic (CD44) cells in intact transparent tumor tissues. Quantitative verification indicated that NP1, NP2, and NP3 did not diffuse into the intratumoral region over 100 μm away from the blood vessel. However, the liposomal nanoparticles represented slight difference in the three dimensional distribution within 100 μm around the blood vessel. Spatial distribution of HIF1α cells in tumors ranged from 62 to ∼460 μm. By contrast, CD44 expressing cells were observed in regions adjacent and away from the vessels. However, the maximum distribution of CD44 was observed ∼300 μm from the nearest vessels. Thus, the transparent tumor model reveals the penetration limits of Lip-Ru, and beyond the maximum diffusion of Lip-Ru, tumors contain hypoxic and clonogenic cells that are viable.

Deep Learning Improves Diagnostic Performance of Lymph Node Metastasis in Cervical Cancer by Using Magnetic Resonance Imaging

Background: Accurate identification of lymph node metastasis (LNM) preoperatively in patients with cervical cancer (CC) can avoid unnecessary surgical intervention and benefit treatment planning. In this study, we aimed to develop a deep learning (DL) model using magnetic resonance imaging (MRI) for non-invasive and preoperative LNM prediction in CC. Methods: This research retrospectively enrolled 479 patients with CC from three centres, and divided patients into a primary cohort (n = 338 from two centres) and an independent validation cohort (n = 141 from another centre). We proposed an end-to-end DL model to identify LNM in CC using MRI, compared the performance among three MRI sequences, and explored the effect of intratumoural and peritumoural regions. Moreover, we built a hybrid model to combine the DL model and clinical information, and assessed its prognostic value in predicting disease-free survival (DFS) of CC by Kaplan-Meier analysis. Findings: Among the three sequences, the DL model which used contrast-enhanced T1-weighted imaging and combined both intratumoural and peritumoural regions (defined as CET1WItumour+peri) showed the best performance (AUC = 0.844 in the validation cohort, P < 0.0001 between the positive and negative LNM patients). These results were further improved in the hybrid model that combined CET1WItumour+peri; and clinical lymph node status (AUC = 0.933 in the validation cohort). Moreover, the H-score from the hybrid model was significantly associated with DFS of CC. Interpretation: DL improves the diagnostic performance of LNM in patients with CC. The hybrid model can be served as an easy-to-use tool to diagnose LNM preoperatively and non-invasively. Funding Statement: This paper is supported by the National Natural Science Foundation of China [Grant No. 81922040, 81930053, 81227901, 61702087]; the Beijing Natural Science Foundation [Grant No. 7182109]; the National Key RD the Strategic Priority Research Program of Chinese Academy of Sciences [Grant No. XDB32030200, XDB01030200]; the Youth Innovation Promotion Association CAS [Grant No. 2019136]. Declaration of Interests: The authors declare no conflicts of interest. Ethics Approval Statement: Ethical approval was obtained for this retrospective study at every participating centre, and the informed consent from patients was waived.

Different methods of cell quantification can lead to different results: a comparison of digital methods using a pilot study of dendritic cells in HIV-positive patients.

Background Although new digital pathology tools have improved the positive cell quantification, there is a heterogeneity of the quantification methods in the literature. The aim of this study was to evaluate and propose a novel dendritic cells quantification method in squamous cell carcinoma comparing it with a conventional quantification method. Material and Methods Twenty-six squamous cell carcinomas HIV-positive cases affecting the oropharynx, lips and oral cavity were selected. Immunohistochemistry for CD1a, CD83, and CD207 was performed. The immunohistochemical stains were evaluated by automated examination using a positive pixel count algorithm. A conventional quantification method (unspecific area method; UA) and a novel method (specific area method; SA) were performed obtaining the corresponding density of positive dendritic cells for the intratumoral and peritumoral regions. The Mann-Whitney U test was used to verify the influence of the quantification methods on the positive cell counting according to the evaluated regions. Data were subjected to the ANOVA and Student’s t-test to verify the influence of the tumour location, stage, histological grade, and amount of inflammation on the dendritic cells density counting. Results The cell quantification method affected the dendritic cells counting independently of the evaluated region (P-value <0.05). Significant differences between methods were also observed according to the tumour features evaluations. Conclusions The positive cell quantification method influences the dendritic cells density results. Unlike the conventional method (UA method), the novel SA method avoids non-target areas included in the hotspots improving the reliability and reproducibility of the density cell quantification. Key words:Cell counts, immunohistochemistry, dendritic cells, HIV, head and neck neoplasms.

Glioma Grade Prediction Using Wavelet Scattering-Based Radiomics

Glioma grading before surgery is very critical for the prognosis prediction and treatment plan making. We present a novel wavelet scattering-based radiomic method to predict noninvasively and accurately the glioma grades. The method consists of wavelet scattering feature extraction, dimensionality reduction, and glioma grade prediction. The dimensionality reduction was achieved using partial least squares (PLS) regression and the glioma grade prediction using support vector machine (SVM), logistic regression (LR) and random forest (RF). The prediction obtained on multimodal magnetic resonance images of 285 patients with well-labeled intratumoral and peritumoral regions showed that the area under the receiver operating characteristic curve (AUC) of glioma grade prediction was increased up to 0.99 when considering both intratumoral and peritumoral features in multimodal images, which represents an increase of about 13% compared to traditional radiomics. In addition, the features extracted from peritumoral regions further increase the accuracy of glioma grading.

A three phase model to investigate the effects of dead material on the growth of avascular tumours

In vivo tumours are highly heterogeneous entities which often comprise intratumoural regions of hypoxia and widespread necrosis. In this paper, we develop a new three phase model of nutrient-limited, avascular tumour growth to investigate how dead material within the tumour may influence the tumour’s growth dynamics. We model the tumour as a mixture of tumour cells, dead cellular material and extracellular fluid. The model equations are derived using mass and momentum balances for each phase along with appropriate constitutive equations. The tumour cells are viewed as a viscous fluid pressure, while the extracellular fluid phase is viewed as inviscid. The physical properties of the dead material are intermediate between those of the tumour cells and extracellular fluid, and are characterised by three key parameters. Through numerical simulation of the model equations, we reproduce spatial structures and dynamics typical of those associated with the growth of avascular tumour spheroids. We also characterise novel, non-monotonic behaviours which are driven by the internal dynamics of the dead material within the tumour. Investigations of the parameter sub-space describing the properties of the dead material reveal that the way in which non-viable tumour cells are modelled may significantly influence the qualitative tumour growth dynamics.

Immune Cell Infiltration in the Microenvironment of Liver Oligometastasis from Colorectal Cancer: Intratumoural CD8/CD3 Ratio Is a Valuable Prognostic Index for Patients Undergoing Liver Metastasectomy.

Background: A comprehensive investigation into immune cell infiltration provides more accurate and reliable prognostic information for patients with colorectal liver oligometastases (CLO) after liver metastasectomy. Methods: Simultaneous detection of the immune constituents CD3+, CD8+, Foxp3+ T, and α-SMA+ cells in the liver oligometastasis of 133 patients was conducted using a four-colour immunohistochemical multiplex technique. Immune cells were quantified, and tumour-infiltrating lymphocyte (TIL) ratios were subsequently calculated. Correlation analysis was performed using Pearson’s correlation. Recurrence-free survival (RFS) and overall survival (OS) for TIL ratios were analysed using the Kaplan–Meier method and Cox regression models. Results: Significantly fewer CD3+, CD8+, and Foxp3+ T cells were observed in the intratumoural region than in the peritumoural region of liver metastases. CD3+, CD8+, Foxp3+ T, and α-SMA+ cells showed significantly positive correlations with each other both in the intratumoural and peritumoural regions of liver metastases. Only the CD8/CD3 TIL ratio demonstrated a positive correlation between intratumoural and peritumoural regions of liver metastases (r = 0.541, p < 0.001). Patients with high intratumoural CD8/CD3 ratios had significantly longer 3-year RFS (59.0% vs. 47.4%, p = 0.035) and 3-year OS rates (83.3% vs. 65.8%, p = 0.007) than those with low intratumoural CD8/CD3 ratios. Multivariate analyses revealed that the intratumoural CD8/CD3 ratio was independently associated with RFS (HR = 0.593; 95% CI = 0.357–0.985; p = 0.043) and OS (HR = 0.391; 95% CI = 0.193–0.794; p = 0.009). Conclusion: These findings offer a better understanding of the prognostic value of immune cell infiltration on liver oligometastasis from colorectal cancer.

Phosphoinositide 3-Kinase Signaling Can Modulate MHC Class I and II Expression

Molecular events activating the PI3K pathway are frequently detected in human tumors and the activation of PI3K signaling alters numerous cellular processes including tumor cell proliferation, survival, and motility. More recent studies have highlighted the impact of PI3K signaling on the cellular response to interferons and other immunologic processes relevant to antitumor immunity. Given the ability of IFNγ to regulate antigen processing and presentation and the pivotal role of MHC class I (MHCI) and II (MHCII) expression in T-cell-mediated antitumor immunity, we sought to determine the impact of PI3K signaling on MHCI and MHCII induction by IFNγ. We found that the induction of cell surface MHCI and MHCII molecules by IFNγ is enhanced by the clinical grade PI3K inhibitors dactolisib and pictilisib. We also found that PI3K inhibition increases STAT1 protein levels following IFNγ treatment and increases accessibility at genomic STAT1-binding motifs. Conversely, we found that pharmacologic activation of PI3K signaling can repress the induction of MHCI and MHCII molecules by IFNγ, and likewise, the loss of PTEN attenuates the induction of MHCI, MHCII, and STAT1 by IFNγ. Consistent with these in vitro studies, we found that within human head and neck squamous cell carcinomas, intratumoral regions with high phospho-AKT IHC staining had reduced MHCI IHC staining. IMPLICATIONS: Collectively, these findings demonstrate that MHC expression can be modulated by PI3K signaling and suggest that activation of PI3K signaling may promote immune escape via effects on antigen presentation.