B7-H3 is associated with the armored-cold phenotype and predicts poor immune checkpoint blockade response in melanoma.

Melanoma is the most suitable tumor type for immunotherapy, but not all melanoma patients could respond to immunotherapy. B7 homolog 3 (B7-H3) belongs to the B7 family and is overexpressed in a number of malignant tumors, but the expression pattern of B7-H3 in melanoma has not been well summarized. The expression of B7-H3 was investigated in melanoma and its correlations with features of the tumor microenvironment (TME) by using various public databases, including the Cancer Genome Atlas (TCGA), the GEPIA, and the Human Protein Atlas databases. In addition, the in-house melanoma tissue microarray was applied to validate the results from public databases. Based on the public and in-house cohorts, we found that B7-H3 was overexpressed in melanoma tumor tissues and high B7-H3 expression was related to poor clinical outcome. Moreover, B7-H3 was negatively correlated with levels of tumor-infiltrating lymphocytes (TILs) and positively correlated with collagen infiltration. With clinical translational value, the predictive value of B7-H3 for conventional immunotherapy was detected using the Kaplan-Meier plotter tool, and the results showed that melanoma patients with high B7-H3 expression were insensitive to anti-PD-1 and anti-CTLA-4 immunotherapy. In conclusion, we first investigate the expression of B7-H3 in melanoma and its correlations with the TME features, and indicate B7-H3 as a promising therapeutic target in melanoma patients that are insensitive to conventional immunotherapy.

Conserved immuno-collagenic subtypes predict response to immune checkpoint blockade.

BACKGROUND: Immune checkpoint blockade (ICB) has revolutionized the treatment of various cancer types. Despite significant preclinical advancements in understanding mechanisms, identifying the molecular basis and predictive biomarkers for clinical ICB responses remains challenging. Recent evidence, both preclinical and clinical, underscores the pivotal role of the extracellular matrix (ECM) in modulating immune cell infiltration and behaviors. This study aimed to create an innovative classifier that leverages ECM characteristics to enhance the effectiveness of ICB therapy. METHODS: We analyzed transcriptomic collagen activity and immune signatures in 649 patients with cancer undergoing ICB therapy. This analysis led to the identification of three distinct immuno-collagenic subtypes predictive of ICB responses. We validated these subtypes using the transcriptome data from 9,363 cancer patients from The Cancer Genome Atlas (TCGA) dataset and 1,084 in-house samples. Additionally, novel therapeutic targets were identified based on these established immuno-collagenic subtypes. RESULTS: Our categorization divided tumors into three subtypes: "soft & hot" (low collagen activity and high immune infiltration), "armored & cold" (high collagen activity and low immune infiltration), and "quiescent" (low collagen activity and immune infiltration). Notably, "soft & hot" tumors exhibited the most robust response to ICB therapy across various cancer types. Mechanistically, inhibiting collagen augmented the response to ICB in preclinical models. Furthermore, these subtypes demonstrated associations with immune activity and prognostic predictive potential across multiple cancer types. Additionally, an unbiased approach identified B7 homolog 3 (B7-H3), an available drug target, as strongly expressed in "armored & cold" tumors, relating with poor prognosis. CONCLUSION: This study introduces histopathology-based universal immuno-collagenic subtypes capable of predicting ICB responses across diverse cancer types. These findings offer insights that could contribute to tailoring personalized immunotherapeutic strategies for patients with cancer.

Erratum: SECTM1 is upregulated in immuno-hot tumors and predicts immunotherapeutic efficacy in multiple cancers.

[This corrects the article DOI: 10.1016/j.isci.2023.106027.].

General anesthesia with nerve blocks for a patient with femoral fracture and essential thrombocythemia: a case report.

Essential thrombocythemia (ET) is a type of myeloproliferative neoplasm characterized by an abnormal increase in platelets. We report a female patient with a severe femoral fracture and ET who underwent the femoral intramedullary fracture fixation procedure. Her past medical history included hypertension and ET. On the second day of hospitalization, her platelet count was 922 × 109/L. In our case, general anesthesia combined with a femoral nerve block and a lateral femoral cutaneous nerve block were used when the platelet count was within normal range. After surgery, the platelet count increased to 979 × 109/L despite using anticoagulant drugs and hydroxyurea. The postoperative recovery went well after the follow-up of this patient. In this case report, we provide our experience of anesthesia management and review the progress of relevant literature to provide some reference.

Subtype-based analysis of cell-in-cell structures in non-small cell lung cancer.

Lung cancer is ranked as the leading cause of cancer-related death worldwide, and the development of novel biomarkers is helpful to improve the prognosis of non-small cell lung cancer (NSCLC). Cell-in-cell structures (CICs), a novel functional surrogate of complicated cell behaviors, have shown promise in predicting the prognosis of cancer patients. However, the CIC profiling and its prognostic value remain unclear in NSCLC. In this study, we retrospectively explored the CIC profiling in a cohort of NSCLC tissues by using the "Epithelium-Macrophage-Leukocyte" (EML) method. The distribution of CICs was examined by the Chi-square test, and univariate and multivariate analyses were performed for survival analysis. Four types of CICs were identified in lung cancer tissues, namely, tumor-in-tumor (TiT), tumor-in-macrophage (TiM), lymphocyte-in-tumor (LiT), and macrophage-in-tumor (MiT). Among them, the latter three constituted the heterotypic CICs (heCICs). Overall, CICs were more frequently present in adenocarcinoma than in squamous cell carcinoma (P = 0.009), and LiT was more common in the upper lobe of the lung compared with other lobes (P = 0.020). In univariate analysis, the presence of TiM, heCIC density, TNM stage, T stage, and N stage showed association with the overall survival (OS) of NSCLC patients. Multivariate analysis revealed that heCICs (HR = 2.6, 95% CI 1.25-5.6) and lymph node invasion (HR = 2.6, 95% CI 1.33-5.1) were independent factors associated with the OS of NSCLC. Taken together, we profiled the CIC subtypes in NSCLC for the first time and demonstrated the prognostic value of heCICs, which may serve as a type of novel functional markers along with classical pathological factors in improving prognosis prediction for patients with NSCLC.

Protocol to identify novel immunotherapy biomarkers based on transcriptomic data in human cancers.

Immune checkpoint inhibitors have transformed the management of advanced cancers, but biomarkers for the prediction of therapeutic responses have not been fully uncovered. Here, we provide a step-by-step approach for the identification of novel biomarkers from public transcriptomic datasets. We comprehensively summarize the available transcriptomic datasets containing immunotherapy information and describe the necessary procedures to evaluate the effectiveness of a novel immunotherapy biomarker, which may accelerate the identification of novel immunotherapy biomarkers. For complete details on the use and execution of this protocol, please refer to Mei et al.1.

The novel role of etoposide in inhibiting the migration and proliferation of small cell lung cancer and breast cancer via targeting Daam1.

OBJECTIVES: Daam1 (Dishevelled-associated activator of morphogenesis 1) is a Wnt/PCP signaling protein that engages in cytoskeleton reorganization and is abnormally activated in certain tumors. Daam1 is closely related to cancer metastasis, which is expected to become a target for cancer treatment. However, the natural small molecules targeting Daam1 have not been identified. MATERIALS AND METHODS: We screened several natural small molecules that may bind to Daam1 by Sybyl molecular simulation docking technique. As a first-line drug for the treatment of small cell lung cancer, etoposide was chosen for further investigation. Next, we used Micro Scale Thermophoresis (MST) to verify the interaction of etoposide and Daam1. Small cell lung cancer H446 cells and breast cancer MCF-7 cells were treated with etoposide and subjected to Western blotting to measure the Daam1 expression. The effect of etoposide on cell proliferation was determined by CCK-8 assay in vitro and by a tumor-bearing mouse model in vivo. Wound healing assay and Boyden chamber assay were used to evaluate the role of etoposide in the migration and invasion ability of tumor cells. The effect of etoposide on the microfilament assembly was visualized by immunofluorescence staining with phalloidine. Finally, the possible mechanism of down-regulation of Daam1 expression after etoposide-induced small cell lung cancer cells was detected by a half-life experiment and immunofluorescence staining with lysosomal marker LAMP1. RESULTS: Sybyl molecular modeling docking technique was performed to screen a natural chemical library for molecules that bound to the FH2 domain of Daam1 and found etoposide was virtually interacted with Daam1. MST validated etoposide directly bound to the FH2 domain of Daam1. Etoposide significantly down-regulated the expression of Daam1 in small cell lung cancer H446 cells and breast cancer MCF-7 cells. Moreover, 270 µmol/L etoposide largely inhibited the proliferation, migration, and invasion of H446 cells and MCF-7 cells. Immunofluorescence staining experiments revealed that etoposide induced the disassembly of microfilaments in H446 cells and MCF-7 cells, which were rescued by Daam1 overexpression. In nude mice transplanted with H446 cells, 5, 10, 20 mg/kg etoposide (drug/weight) injected via tail vein largely retarded the proliferation of subcutaneous tumors. Etoposide induced Daam1 to shorten its half-life and enter the lysosome degradation pathway, and eventually leading to the downregulation of Daam1 expression. CONCLUSIONS: Etoposide is a novel natural small molecule targeting Daam1. Etoposide inhibits the proliferation, migration and invasion of small cell lung cancer cells and breast cancer cells, and also suppresses tumor proliferation of small cell lung cancer in vivo.

The prognostic value of DAAM2 in lower grade glioma, liver cancer, and breast cancer.

PURPOSE: Dishevelled-associated activator of morphogenesis 2 (DAAM2) is a formin protein and has a potential role in the tumor metastasis. The prognostic value of DAAM2 in pan-cancer is investigated in this study. METHODS: TCGA and GTEx database were downloaded to perform bioinformatics analysis and ROC curves. Then we explored protein-protein interaction and GO-KEGG enrichment to figure out the protein pathways associated with DAAM2 and studied DAAM2-related immune infiltration and methylation. Fifteen pairs of BRCA clinical samples were enrolled to determine the expression and distribution of DAAM2 in BRCA sections by immunohistochemistry. Finally, BRCA cells were transfected with siRNA targeting DAAM2 and subsequently subject to cell proliferation, migration, and invasion assays. RESULTS: DAAM2 was closely related to the diagnosis and clinical characteristics of lower grade glioma (LGG), liver hepatocellular carcinoma (LIHC), and breast cancer (BRCA). Survival curve analysis demonstrated DAAM2 served as a potential prognostic indicator of LGG and LIHC (P = 0.0029 and P = 0.025, respectively). DAAM2 was mainly participated in signaling pathways mediating cytoskeleton regulation and tumor development. The correlation of DAAM2 with tumor-infiltrating immune cells (TIICs) and methylation levels was conducive to the prediction of novel biomarkers of pan-carcinoma. DAAM2 was highly expressed in BRCA tissues than that in paracancerous tissues. The proliferation, invasion, and migration of BRCA cells were inhibited by DAAM2 siRNA. CONCLUSION: DAAM2 had a specific value in foretelling the prognosis of LGG, LIHC, and BRCA. High expression level of DAAM2 has longer survival rates in LGG and LIHC. The knockdown of DAAM2 retards the proliferation, invasion, and migration of BRCA cells. This study provides a novel sight of DAAM2 into the exploration of a potential biomarker in pan-cancer.

SECTM1 is upregulated in immuno-hot tumors and predicts immunotherapeutic efficacy in multiple cancers.

Immune checkpoint inhibitors (ICIs) have transformed the management of advanced cancers. However, many patients could not benefit from ICIs therapy, and thus several biomarkers for therapeutic prediction have been uncovered. In this research, more than ten public and in-house cohorts were used to explore the predictive value and immunological correlations of secreted and transmembrane 1 (SECTM1) in cancers. SECTM1 expression was enhanced in tumors from patients with well immunotherapeutic responses in multiple cancers. In addition, SECTM1 was immuno-correlated in pan-cancer and enhanced in immuno-hot tumors. In vitro assays revealed that SECTM1 was upregulated by the IFN-γ/STAT1 signaling. Moreover, analysis of in-house immunotherapy cohorts suggested both tumor-expressed and circulating SECTM1 are promising biomarkers to predict therapeutic responses. Overall, this study reveals that SECTM1 is a biomarker of benefit to ICIs in cancer patients. Further studies including large-scale patients are needed to establish its utilization as a biomarker of benefit to ICIs.

Formin protein DIAPH1 positively regulates PD-L1 expression and predicts the therapeutic response to anti-PD-1/PD-L1 immunotherapy.

Formins are evolutionarily conserved genes and profoundly affect cancer progression. This study aims to explore the expressions, prognostic values, and immunological correlations of Formins in cancer. Specific Formins were dysregulated and immuno-biologically correlated in breast cancer (BRCA). Formins showed different expression patterns, namely some were enriched in immune cells while some were enriched in tumor cells. Among all Formins, DIAPH1 was enriched in tumor cells and associated with an inflamed tumor microenvironment (TME). DIAPH1 functioned as an oncogene in BRCA and mediated TGF-ß1-induced epithelial-mesenchymal transformation (EMT) and PD-L1 expression. Moreover, DIAPH1 was overexpressed in most cancers and functioned as a novel pan-cancer immuno-marker, which could predict the response to anti-PD-1/PD-L1 immunotherapy. Overall, DIAPH1 functions as an oncogene and is immunologically correlated, which could be utilized as an alternative biomarker for predicting the immunotherapeutic response.

Homotypic cell-in-cell structures as an adverse prognostic predictor of hepatocellular carcinoma.

Hepatocellular carcinoma (HCC) is one of the most common malignant liver tumors. A homotypic cell-in-cell structure (hoCIC) refers to one or more cells internalized into the same type as their neighbors, which predominantly occurs in multiple tumors. The objective of this study was to investigate the prognostic value of hoCICs in HCC and its relationship with other clinicopathological features. By immunostaining analysis of a panel of HCC tissues, we found that hoCICs were prevalent in tumor tissues (54/90) but not in para-tumor tissues (17/90). The presence of hoCICs in tumor tissues was closely associated with E-cadherin expression. The presence of CICs was identified as significantly associated with poor survival rates of patients with HCC, comparable to traditional clinicopathological parameters, such as histological grade [hazard ratio (HR) = 0.734, p = 0.320]. Multivariate Cox regression analysis further confirmed that CICs were an independent risk factor for poor survival (HR = 1.902, p = 0.047). In addition, hoCICs were the predominant contributor in a nomogram model constructed for survival prediction at 1, 3, and 5 years [the areas under the curve (AUCs) were 0.760, 0.733, and 0.794, respectively]. Stratification analysis indicated that hoCICs tend to selectively affect patients with high-grade disease (HR = 2.477, p = 0.009) and at the early TNM stage (HR = 2.351, p = 0.05). Thus, hoCICs predict poor survival of patients with HCC, particularly those with higher grades and at an early stage.

Angiotensin-converting enzyme 2 identifies immuno-hot tumors suggesting angiotensin-(1-7) as a sensitizer for chemotherapy and immunotherapy in breast cancer.

BACKGROUND: Angiotensin-converting enzyme 2 (ACE2) is known as a tumor suppressor and lowly expressed in most cancers. The expression pattern and role of ACE2 in breast cancer (BC) have not been deeply elucidated. METHODS: A systematic pan-cancer analysis was conducted to assess the expression pattern and immunological role of ACE2 based on RNA-sequencing (RNA-seq) data downloaded from The Cancer Genome Atlas (TCGA). The correlation of ACE2 expression and immunological characteristics in the BC tumor microenvironment (TME) was evaluated. The role of ACE2 in predicting the response to therapeutic options was estimated. Moreover, the pharmacodynamic effect of angiotensin-(1-7) (Ang-1-7), the product of ACE2, on chemotherapy and immunotherapy was evaluated on the BALB/c mouse BC model. In addition, the plasma samples from BC patients receiving neoadjuvant chemotherapy were collected and subjected to the correlation analysis of the expression level of Ang-1-7 and the response to neoadjuvant chemotherapy. RESULTS: ACE2 was lowly expressed in BC tissues compared with that in adjacent tissues. Interestingly, ACE2 was shown the highest correlation with immunomodulators, tumor-infiltrating immune cells (TIICs), cancer immunity cycles, immune checkpoints, and tumor mutation burden (TMB) in BC. In addition, a high level of ACE2 indicated a low response to endocrine therapy and a high response to chemotherapy, anti-ERBB therapy, antiangiogenic therapy and immunotherapy. In the mouse model, Ang-1-7 sensitized mouse BC to the chemotherapy and anti-PD-1 immunotherapy, which revealed its significant anti-tumor effect. Moreover, a high plasma level of Ang-1-7 was associated with a better response to neoadjuvant chemotherapy. CONCLUSIONS: ACE2 identifies immuno-hot tumors in BC, and its enzymatic product Ang-1-7 sensitizes BC to the chemotherapy and immunotherapy by remodeling the TME.

Validation of Machine Learning-Based Automated Surgical Instrument Annotation Using Publicly Available Intraoperative Video.

BACKGROUND: Intraoperative tool movement data have been demonstrated to be clinically useful in quantifying surgical performance. However, collecting this information from intraoperative video requires laborious hand annotation. The ability to automatically annotate tools in surgical video would advance surgical data science by eliminating a time-intensive step in research. OBJECTIVE: To identify whether machine learning (ML) can automatically identify surgical instruments contained within neurosurgical video. METHODS: A ML model which automatically identifies surgical instruments in frame was developed and trained on multiple publicly available surgical video data sets with instrument location annotations. A total of 39 693 frames from 4 data sets were used (endoscopic endonasal surgery [EEA] [30 015 frames], cataract surgery [4670], laparoscopic cholecystectomy [2532], and microscope-assisted brain/spine tumor removal [2476]). A second model trained only on EEA video was also developed. Intraoperative EEA videos from YouTube were used for test data (3 videos, 1239 frames). RESULTS: The YouTube data set contained 2169 total instruments. Mean average precision (mAP) for instrument detection on the YouTube data set was 0.74. The mAP for each individual video was 0.65, 0.74, and 0.89. The second model trained only on EEA video also had an overall mAP of 0.74 (0.62, 0.84, and 0.88 for individual videos). Development costs were $130 for manual video annotation and under $100 for computation. CONCLUSION: Surgical instruments contained within endoscopic endonasal intraoperative video can be detected using a fully automated ML model. The addition of disparate surgical data sets did not improve model performance, although these data sets may improve generalizability of the model in other use cases.

Expert surgeons and deep learning models can predict the outcome of surgical hemorrhage from 1 min of video.

Major vascular injury resulting in uncontrolled bleeding is a catastrophic and often fatal complication of minimally invasive surgery. At the outset of these events, surgeons do not know how much blood will be lost or whether they will successfully control the hemorrhage (achieve hemostasis). We evaluate the ability of a deep learning neural network (DNN) to predict hemostasis control ability using the first minute of surgical video and compare model performance with human experts viewing the same video. The publicly available SOCAL dataset contains 147 videos of attending and resident surgeons managing hemorrhage in a validated, high-fidelity cadaveric simulator. Videos are labeled with outcome and blood loss (mL). The first minute of 20 videos was shown to four, blinded, fellowship trained skull-base neurosurgery instructors, and to SOCALNet (a DNN trained on SOCAL videos). SOCALNet architecture included a convolutional network (ResNet) identifying spatial features and a recurrent network identifying temporal features (LSTM). Experts independently assessed surgeon skill, predicted outcome and blood loss (mL). Outcome and blood loss predictions were compared with SOCALNet. Expert inter-rater reliability was 0.95. Experts correctly predicted 14/20 trials (Sensitivity: 82%, Specificity: 55%, Positive Predictive Value (PPV): 69%, Negative Predictive Value (NPV): 71%). SOCALNet correctly predicted 17/20 trials (Sensitivity 100%, Specificity 66%, PPV 79%, NPV 100%) and correctly identified all successful attempts. Expert predictions of the highest and lowest skill surgeons and expert predictions reported with maximum confidence were more accurate. Experts systematically underestimated blood loss (mean error - 131 mL, RMSE 350 mL, R2 0.70) and fewer than half of expert predictions identified blood loss > 500 mL (47.5%, 19/40). SOCALNet had superior performance (mean error - 57 mL, RMSE 295 mL, R2 0.74) and detected most episodes of blood loss > 500 mL (80%, 8/10). In validation experiments, SOCALNet evaluation of a critical on-screen surgical maneuver and high/low-skill composite videos were concordant with expert evaluation. Using only the first minute of video, experts and SOCALNet can predict outcome and blood loss during surgical hemorrhage. Experts systematically underestimated blood loss, and SOCALNet had no false negatives. DNNs can provide accurate, meaningful assessments of surgical video. We call for the creation of datasets of surgical adverse events for quality improvement research.

Senescence as a dictator of patient outcomes and therapeutic efficacies in human gastric cancer.

Senescence is believed to be a pivotal player in the onset and progression of tumors as well as cancer therapy. However, the guiding roles of senescence in clinical outcomes and therapy selection for patients with cancer remain obscure, largely due to the absence of a feasible senescence signature. Here, by integrative analysis of single cell and bulk transcriptome data from multiple datasets of gastric cancer patients, we uncovered senescence as a veiled tumor feature characterized by senescence gene signature enriched, unexpectedly, in the noncancerous cells, and further identified two distinct senescence-associated subtypes based on the unsupervised clustering. Patients with the senescence subtype had higher tumor mutation loads and better prognosis as compared with the aggressive subtype. By the machine learning, we constructed a scoring system termed as senescore based on six signature genes: ADH1B, IL1A, SERPINE1, SPARC, EZH2, and TNFAIP2. Higher senescore demonstrated robustly predictive capability for longer overall and recurrence-free survival in 2290 gastric cancer samples, which was independently validated by the multiplex staining analysis of gastric cancer samples on the tissue microarray. Remarkably, the senescore signature served as a reliable predictor of chemotherapeutic and immunotherapeutic efficacies, with high-senescore patients benefited from immunotherapy, while low-senescore patients were responsive to chemotherapy. Collectively, we report senescence as a heretofore unrecognized hallmark of gastric cancer that impacts patient outcomes and therapeutic efficacy.

Use of surgical video-based automated performance metrics to predict blood loss and success of simulated vascular injury control in neurosurgery: a pilot study.

OBJECTIVE: Experts can assess surgeon skill using surgical video, but a limited number of expert surgeons are available. Automated performance metrics (APMs) are a promising alternative but have not been created from operative videos in neurosurgery to date. The authors aimed to evaluate whether video-based APMs can predict task success and blood loss during endonasal endoscopic surgery in a validated cadaveric simulator of vascular injury of the internal carotid artery. METHODS: Videos of cadaveric simulation trials by 73 neurosurgeons and otorhinolaryngologists were analyzed and manually annotated with bounding boxes to identify the surgical instruments in the frame. APMs in five domains were defined-instrument usage, time-to-phase, instrument disappearance, instrument movement, and instrument interactions-on the basis of expert analysis and task-specific surgical progressions. Bounding-box data of instrument position were then used to generate APMs for each trial. Multivariate linear regression was used to test for the associations between APMs and blood loss and task success (hemorrhage control in less than 5 minutes). The APMs of 93 successful trials were compared with the APMs of 49 unsuccessful trials. RESULTS: In total, 29,151 frames of surgical video were annotated. Successful simulation trials had superior APMs in each domain, including proportionately more time spent with the key instruments in view (p < 0.001) and less time without hemorrhage control (p = 0.002). APMs in all domains improved in subsequent trials after the participants received personalized expert instruction. Attending surgeons had superior instrument usage, time-to-phase, and instrument disappearance metrics compared with resident surgeons (p < 0.01). APMs predicted surgeon performance better than surgeon training level or prior experience. A regression model that included APMs predicted blood loss with an R2 value of 0.87 (p < 0.001). CONCLUSIONS: Video-based APMs were superior predictors of simulation trial success and blood loss than surgeon characteristics such as case volume and attending status. Surgeon educators can use APMs to assess competency, quantify performance, and provide actionable, structured feedback in order to improve patient outcomes. Validation of APMs provides a benchmark for further development of fully automated video assessment pipelines that utilize machine learning and computer vision.