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Background. Insulinoma-associated protein 1 (INSM1) has been identified as a nuclear marker of neuroendocrine tumors. Although INSM1 appears to be a subtle and specific biomarker for neuroendocrine tumor, its expression and clinicopathological significance in mesenchymal tumors remain unclear. Methods. We analyzed INSM1 mRNA level in GEO database and conducted immunohistological staining to detect the expression of INSM1 on 576 mesenchymal tumors from pathology department of Tongji Hospital. Results. At transcription level, INSM1 expression in AITL (angioimmunoblastic T-cell lymphoma) was higher than their adjacent normal tissues as well as Hodgkin’s lymphoma. Moreover, INSM1 expression in well-differentiated liposarcoma (WDLPS) was significantly higher than normal fat ( P = 0.014 ) and dedifferentiated liposarcoma (DDLPS) ( P = 0.0248 ). At protein level, the positive rate of INSM1 in AITL was 18/48 (47.4%), while in DDLPS was 9/20 (45%). INSM1 expression in AITL was significantly higher than Hodgkin’s lymphoma ( P = 0.008 ). And INSM1 expression in WDLPS was significantly lower than DDLPS ( P = 0.015 ). Conclusion. The combination of GEO data and immunohistochemistry data indicated that the expression level of INSM1 was higher in AITL compared with normal control, suggesting that INSM1 may be involved in pathogenesis of AITL. The abnormal expression of INSM1 was found in WDLPS, and the positive rate of INSM1 was higher in DDLPS than in WDLPS. INSM1 may be involved in the regulation of liposarcoma development. There were significant differences in the expression of INSM1 between AITL and Hodgkin’s lymphoma and WDLPS and DDLPS. These findings may assist in the differential diagnosis of these tumors when common markers are difficult to identify, enriching the diagnostic index system of mesenchymal tumors.

No effect of TFEB on RCC tumor growth in nude mice

Purpose:Despite the FDA approval of mTOR inhibitors (mTORi) for the treatment of renal cell carcinoma (RCC), the benefits are relatively modest and the few responders usually develop resistance. We investigated whether the resistance to mTORi is due to upregulation of PD-L1 and the underlying molecular mechanism.Experimental Design:The effects of transcription factor EB (TFEB) on RCC proliferation, apoptosis, and migration were evaluated. Correlation of TFEB with PD-L1 expression, as well as effects of mTOR inhibition on TFEB and PD-L1 expression, was assessed in human primary clear cell RCCs. The regulation of TFEB on PD-L1 was assessed by chromatin immunoprecipitation and luciferase reporter assay. The therapeutic efficacies of mTORi plus PD-L1 blockade were evaluated in a mouse model. The function of tumor-infiltrating CD8+ T cells was analyzed by flow cytometry.Results:TFEB did not affect tumor cell proliferation, apoptosis, and migration. We found a positive correlation between TFEB and PD-L1 expression in RCC tumor tissues, primary tumor cells, and RCC cells. TFEB bound to PD-L1 promoter in RCCs and inhibition of mTOR led to enhanced TFEB nuclear translocation and PD-L1 expression. Simultaneous inhibition of mTOR and blockade of PD-L1 enhanced CD8+ cytolytic function and tumor suppression in a xenografted mouse model of RCC.Conclusions:These data revealed that TFEB mediates resistance to mTOR inhibition via induction of PD-L1 in human primary RCC tumors, RCC cells, and murine xenograft model. Our data provide a strong rationale to target mTOR and PD-L1 jointly as a novel immunotherapeutic approach for RCC treatment.

Patient characteristics

Background To date, there is no approved blood‐based biomarker for breast cancer detection. Herein, we aimed to assess semaphorin 4C (SEMA4C), a pivotal protein involved in breast cancer progression, as a serum diagnostic biomarker. Methods We included 6,213 consecutive inpatients from Tongji Hospital, Qilu Hospital, and Hubei Cancer Hospital. Training cohort and two validation cohorts were introduced for diagnostic exploration and validation. A pan‐cancer cohort was used to independently explore the diagnostic potential of SEMA4C among solid tumors. Breast cancer patients who underwent mass excision prior to modified radical mastectomy were also analyzed. We hypothesized that increased pre‐treatment serum SEMA4C levels, measured using optimized in‐house enzyme‐linked immunosorbent assay kits, could detect breast cancer. The endpoints were diagnostic performance, including area under the receiver operating characteristic curve (AUC), sensitivity, and specificity. Post‐surgery pathological diagnosis was the reference standard and breast cancer staging followed the TNM classification. There was no restriction on disease stage for eligibilities. Results We included 2667 inpatients with breast lesions, 2378 patients with other solid tumors, and 1168 healthy participants. Specifically, 118 patients with breast cancer were diagnosed with stage 0 (5.71%), 620 with stage I (30.00%), 966 with stage II (46.73%), 217 with stage III (10.50%), and 8 with stage IV (0.39%). Patients with breast cancer had significantly higher serum SEMA4C levels than benign breast tumor patients and normal controls (P < 0.001). Elevated serum SEMA4C levels had AUC of 0.920 (95% confidence interval [CI]: 0.900–0.941) and 0.932 (95%CI: 0.911–0.953) for breast cancer detection in the two validation cohorts. The AUCs for detecting early‐stage breast cancer (n = 366) and ductal carcinoma in situ (n = 85) were 0.931 (95%CI: 0.916–0.946) and 0.879 (95%CI: 0.832–0.925), respectively. Serum SEMA4C levels significantly decreased after surgery, and the reduction was more striking after modified radical mastectomy, compared with mass excision (P < 0.001). The positive rate of enhanced serum SEMA4C levels was 84.77% for breast cancer and below 20.75% for the other 14 solid tumors. Conclusions Serum SEMA4C demonstrated promising potential as a candidate biomarker for breast cancer diagnosis. However, validation in prospective settings and by other study groups is warranted., Breast cancer patients had significantly higher serum SEMA4C levels than patients with benign breast tumor and normal controls. Serum SEMA4C is considered as a candidate diagnostic biomarker for the early detection and diagnosis of breast cancer.

Background: Inflammatory myofibroblastic tumor (IMT) is a distinctive tumor composed of spindle cells accompanied by mixed inflammatory cells, and immunohistochemical positivity for ALK (anaplastic lymphoma kinase protein) can be detected in half of IMTs. The diagnosis of ALK-negative IMT could be a challenge. Recently, the fusions of some kinase genes, such as RET, NTRK1, ROS1, etc., are revealed in ALK-negative IMT. Case presentation: A 19-year-old woman presented with swelling of the left upper arm. Magnetic resonance imaging (MRI) scan revealed a tumor in the left postbrachium extended to the left axillary, serratus anterior muscle, and latissimus dorsi muscle. Histopathologically, the irregular-circumscribed tumor was composed of dense spindle-shaped cells with eosinophilic abundant cytoplasm and hyalinized mesenchyme in an inflammatory background. Immunohistochemically (IHC), tumor cells were positive for SMA, MDM2, and p16; the cells were negative for desmin, MyoD1, Myogenin, pan-cytokeratin, S100, SOX10, HMB45, Malen-A, CD34, CD31, CD99, and ALK. By RNA-based NGS, a novel fusion between TPD52L2 3’ end of exon 1-4 and ROS1 5’ end of exon 36-43 was revealed. ROS1 IHC staining was negative. The final diagnosis of IMT with TPD52L2-ROS1fusion was made.Subsequently, the patient experienced a good clinical response to Crizotinib, and clinical follow-up showed stable disease after 9 months. Conclusion: This report expands the spectrum of ROS1 gene rearrangements in the IMT and highlights the importance of molecular analysis of IMT for getting a diagnostic clue and determining potential therapeutic strategies.

Objectives To investigate the diagnostic value of contrast‐enhanced computed tomography (CECT) histogram analysis in predicting the World Health Organization (WHO) grade of rectal neuroendocrine tumors (R‐NETs). Materials and Methods A total of 61 (35 G1, 12 G2, 10 G3, and 4 NECs) patients who underwent preoperative CECT and treated with surgery to be confirmed as R‐NETs were included in this study from January 2014 to May 2019. We depicted ROIs and measured the CECT texture parameters (mean, median, 10th, 25th, 75th, 90th percentiles, skewness, kurtosis, and entropy) from arterial phase (AP) and venous phase (VP) images by two radiologists. We calculated intraclass correlation coefficient (ICC) and compared the histogram parameters between low‐grade (G1) and higher grade (HG) (G2/G3/NECs) by applying appropriate statistical method. We obtained the optimal parameters to identify G1 from HG using receiver operating characteristic (ROC) curves. Results The capability of AP and VP histogram parameters for differentiating G1 from HG was similar in several histogram parameters (mean, median, 10th, 25th, 75th, and 90th percentiles) (all p, CECT histogram parameters, including arterial phase and venous phase, can be used as excellent indicators for predicting G1 and HG of rectal neuroendocrine tumors, and the size of the tumor is also an important independent predictor.

The efficacy of checkpoint immunotherapy to non-small cell lung cancer (NSCLC) largely depends on the tumor microenvironment (TME). Here, we demonstrate that CCL7 facilitates anti-PD-1 therapy for the KrasLSL−G12D/+Tp53fl/fl (KP) and the KrasLSL−G12D/+Lkb1fl/fl (KL) NSCLC mouse models by recruiting conventional DC 1 (cDC1) into the TME to promote T cell expansion. CCL7 exhibits high expression in NSCLC tumor tissues and is positively correlated with the infiltration of cDC1 in the TME and the overall survival of NSCLC patients. CCL7 deficiency impairs the infiltration of cDC1 in the TME and the subsequent expansion of CD8+ and CD4+ T cells in bronchial draining lymph nodes and TME, thereby promoting tumor development in the KP mouse model. Administration of CCL7 into lungs alone or in combination with anti-PD-1 significantly inhibits tumor development and prolongs the survival of KP and KL mice. These findings suggest that CCL7 potentially serves as a biomarker and adjuvant for checkpoint immunotherapy of NSCLC., Only a limited proportion of patients with non-small cell lung cancer respond to anti-PD-1/PD-L1 immunotherapy. Here, the authors show that in autochthonous models of KRAS-mutated lung cancer, CCL7 promotes cDC1 infiltration into the lungs, sustaining antitumor immune responses and potentiating anti-PD1 treatment efficacy.

Significance Although the overwhelming majority of COVID-19-related deaths are due to respiratory failure, little is known about the host response to SARS-CoV-2 in lung parenchyma. By profiling the lung and colon transcriptome and lung proteome of nine patients who died of COVID-19 during the first wave of the pandemic in Wuhan, China, we obtained molecular insights into the host response to severe SARS-CoV-2 infection. Interestingly, all samples had a low viral burden, a finding that suggests the patients’ deaths may be due to uncontrolled host inflammatory processes rather than an active viral infection. Taken together, our findings shed light on COVID-19 pathophysiology and offer potential therapeutic targets for severe COVID-19 disease., Coronavirus disease 2019 (COVID-19), the global pandemic caused by SARS-CoV-2, has resulted thus far in greater than 933,000 deaths worldwide; yet disease pathogenesis remains unclear. Clinical and immunological features of patients with COVID-19 have highlighted a potential role for changes in immune activity in regulating disease severity. However, little is known about the responses in human lung tissue, the primary site of infection. Here we show that pathways related to neutrophil activation and pulmonary fibrosis are among the major up-regulated transcriptional signatures in lung tissue obtained from patients who died of COVID-19 in Wuhan, China. Strikingly, the viral burden was low in all samples, which suggests that the patient deaths may be related to the host response rather than an active fulminant infection. Examination of the colonic transcriptome of these patients suggested that SARS-CoV-2 impacted host responses even at a site with no obvious pathogenesis. Further proteomics analysis validated our transcriptome findings and identified several key proteins, such as the SARS-CoV-2 entry-associated protease cathepsins B and L and the inflammatory response modulator S100A8/A9, that are highly expressed in fatal cases, revealing potential drug targets for COVID-19.

SOX11 is a critical biomarker for mantle cell lymphoma (MCL) diagnosis; however, its role remains unclear in MCL. Here, clinical-pathological analysis showed Ki67 index was negatively relevant to SOX11 expression only in CD43 positive cases. Coexpression of SOX11/CD43 indicated longer overall survival. In vitro, knockout/overexpression of SOX11 or CD43 promoted/inhibited cell proliferation respectively. CD43 overexpression reversed tumor proliferation induced by SOX11 knockdown. Furthermore, overexpressing/silencing the SOX11/CD43 gene affects phosphorylation of p38-MAPK while p38 inhibitor reversed proliferation induced by si-SOX11 or si-CD43, respectively. In CAM-DR model, both SOX11 and CD43 in MCL cells were elevated when co-cultured with M2-10B4 bone marrow fibroblasts or fibronectin. Knockdown/overexpression of SOX11 decreased/increased cell adhesion, respectively, and the effect induced by silencing SOX11 was reversed by overexpression of CD43. Collectively, SOX11 could inhibit tumor proliferation and promote CAM-DR in a CD43 dependent manner.

Purpose: Despite the FDA approval of mTOR inhibitors (mTORi) for the treatment of renal cell carcinoma (RCC), the benefits are relatively modest and the few responders usually develop resistance. We investigated whether the resistance to mTORi is due to upregulation of PD-L1 and the underlying molecular mechanism. Experimental Design: The effects of transcription factor EB (TFEB) on RCC proliferation, apoptosis, and migration were evaluated. Correlation of TFEB with PD-L1 expression, as well as effects of mTOR inhibition on TFEB and PD-L1 expression, was assessed in human primary clear cell RCCs. The regulation of TFEB on PD-L1 was assessed by chromatin immunoprecipitation and luciferase reporter assay. The therapeutic efficacies of mTORi plus PD-L1 blockade were evaluated in a mouse model. The function of tumor-infiltrating CD8+ T cells was analyzed by flow cytometry. Results: TFEB did not affect tumor cell proliferation, apoptosis, and migration. We found a positive correlation between TFEB and PD-L1 expression in RCC tumor tissues, primary tumor cells, and RCC cells. TFEB bound to PD-L1 promoter in RCCs and inhibition of mTOR led to enhanced TFEB nuclear translocation and PD-L1 expression. Simultaneous inhibition of mTOR and blockade of PD-L1 enhanced CD8+ cytolytic function and tumor suppression in a xenografted mouse model of RCC. Conclusions: These data revealed that TFEB mediates resistance to mTOR inhibition via induction of PD-L1 in human primary RCC tumors, RCC cells, and murine xenograft model. Our data provide a strong rationale to target mTOR and PD-L1 jointly as a novel immunotherapeutic approach for RCC treatment.

Point cloud upsampling aims to generate dense point clouds from given sparse ones, which is a challenging task due to the irregular and unordered nature of point sets. To address this issue, we present a novel deep learning-based model, called PU-Flow, which incorporates normalizing flows and weight prediction techniques to produce dense points uniformly distributed on the underlying surface. Specifically, we exploit the invertible characteristics of normalizing flows to transform points between Euclidean and latent spaces and formulate the upsampling process as ensemble of neighbouring points in a latent space, where the ensemble weights are adaptively learned from local geometric context. Extensive experiments show that our method is competitive and, in most test cases, it outperforms state-of-the-art methods in terms of reconstruction quality, proximity-to-surface accuracy, and computation efficiency. The source code will be publicly available at https://github.com/unknownue/pu-flow.