We previously showed microRNAs (miRNAs) in plasma are potential biomarkers for colorectal cancer detection. Here, we aimed to develop specific blood-based miRNA assay for breast cancer detection.
...TaqMan-based miRNA profiling was performed in tumor, adjacent non-tumor, corresponding plasma from breast cancer patients, and plasma from matched healthy controls. All putative markers identified were verified in a training set of breast cancer patients. Selected markers were validated in a case-control cohort of 170 breast cancer patients, 100 controls, and 95 other types of cancers and then blindly validated in an independent set of 70 breast cancer patients and 50 healthy controls. Profiling results showed 8 miRNAs were concordantly up-regulated and 1 miRNA was concordantly down-regulated in both plasma and tumor tissue of breast cancer patients. Of the 8 up-regulated miRNAs, only 3 were significantly elevated (p<0.0001) before surgery and reduced after surgery in the training set. Results from the validation cohort showed that a combination of miR-145 and miR-451 was the best biomarker (p<0.0001) in discriminating breast cancer from healthy controls and all other types of cancers. In the blind validation, these plasma markers yielded Receiver Operating Characteristic (ROC) curve area of 0.931. The positive predictive value was 88% and the negative predictive value was 92%. Altered levels of these miRNAs in plasma have been detected not only in advanced stages but also early stages of tumors. The positive predictive value for ductal carcinoma in situ (DCIS) cases was 96%.
These results suggested that these circulating miRNAs could be a potential specific biomarker for breast cancer screening.
Summary
Background
The risk of gastric cancer after Helicobacter pylori (H. pylori) eradication remains unknown.
Aim
To evaluate the performances of seven different machine learning models in ...predicting gastric cancer risk after H. pylori eradication.
Methods
We identified H. pylori‐infected patients who had received clarithromycin‐based triple therapy between 2003 and 2014 in Hong Kong. Patients were divided into training (n = 64 238) and validation sets (n = 25 330), according to period of eradication therapy. The data were used to construct seven machine learning models to predict risk of gastric cancer development within 5 years after H. pylori eradication. A total of 26 clinical variables were input into these models. The performances were measured by the area under receiver operating characteristic curve (AUC) analysis.
Results
During a mean follow‐up of 4.7 years, 0.21% of H. pylori‐eradicated patients developed gastric cancer. Of the seven machine learning models, extreme gradient boosting (XGBoost) had the best performance in predicting cancer development (AUC 0.97, 95%CI 0.96‐0.98), and was superior to conventional logistic regression (AUC 0.90, 95% CI 0.84‐0.92). With the XGBoost model, the number of patients considered at high risk of gastric cancer was 6.6%, with miss rate of 1.9%. Patient age, presence of intestinal metaplasia, and gastric ulcer were the heavily weighted factors used by the XGBoost.
Conclusion
Based on simple baseline patient information, machine learning model can accurately predict the risk of post‐eradication gastric cancer. This model could substantially reduce the number of patients who require endoscopic surveillance.
•Deep learning provides efficient and accurate prediction of treatment response.•Transfer learning can be used in radiological task with insufficient image datasets.•Tumor microenvironment and ...signaling pathways are linked with radiological prediction.
Deep learning is promising to predict treatment response. We aimed to evaluate and validate the predictive performance of the CT-based model using deep learning features for predicting pathologic complete response to neoadjuvant chemoradiotherapy (nCRT) in esophageal squamous cell carcinoma (ESCC).
Patients were retrospectively enrolled between April 2007 and December 2018 from two institutions. We extracted deep learning features of six pre-trained convolutional neural networks, respectively, from pretreatment CT images in the training cohort (n = 161). Support vector machine was adopted as the classifier. Validation was performed in an external testing cohort (n = 70). We assessed the performance using the area under the receiver operating characteristics curve (AUC) and selected an optimal model, which was compared with a radiomics model developed from the training cohort. A clinical model consisting of clinical factors only was also built for baseline comparison. We further conducted a radiogenomics analysis using gene expression profiles to reveal underlying biology associated with radiological prediction.
The optimal model with features extracted from ResNet50 achieved an AUC and accuracy of 0.805 (95% CI, 0.696–0.913) and 77.1% (65.6%-86.3%) in the testing cohort, compared with 0.725 (0.605–0.846)) and 67.1% (54.9%-77.9%) for the radiomics model. All the radiological models showed better predictive performance than the clinical model. Radiogenomics analysis suggested a potential association mainly with WNT signaling pathway and tumor microenvironment.
The novel and noninvasive deep learning approach could provide efficient and accurate prediction of treatment response to nCRT in ESCC, and benefit clinical decision making of therapeutic strategy.
Local interactions between cancer cells and stroma can produce systemic effects on distant organs to govern cancer progression. Here we show that IGF2 secreted by inhibitor of differentiation ...(Id1)-overexpressing oesophageal cancer cells instigates VEGFR1-positive bone marrow cells in the tumour macroenvironment to form pre-metastatic niches at distant sites by increasing VEGF secretion from cancer-associated fibroblasts. Cancer cells are then attracted to the metastatic site via the CXCL5/CXCR2 axis. Bone marrow cells transplanted from nude mice bearing Id1-overexpressing oesophageal tumours enhance tumour growth and metastasis in recipient mice, whereas systemic administration of VEGFR1 antibody abrogates these effects. Mechanistically, IGF2 regulates VEGF in fibroblasts via miR-29c in a p53-dependent manner. Analysis of patient serum samples showed that concurrent elevation of IGF2 and VEGF levels may serve as a prognostic biomarker for oesophageal cancer. These findings suggest that the Id1/IGF2/VEGF/VEGFR1 cascade plays a critical role in tumour-driven pathophysiological processes underlying cancer progression.
This study compared the efficacy of PF-based and CROSS-based neoadjuvant chemoradiotherapy for ESCC.
PF-based regimen has been a standard regimen for ESCC, but it has been replaced by the CROSS ...regimen in the past few years, despite no prospective head-to-head comparative study has been performed.
This is a single center retrospective study. Records of all ESCC patients who have received neoadjuvant PF with 40 Gy radiotherapy in 20 daily fractions (PFRT Group) or CROSS with 41.4 Gy radiotherapy in 23 daily fractions (CROSS Group) during the period 2002 to 2019 were retrieved. Propensity score matching (1:1) was performed to minimize baseline differences. The primary and secondary endpoints were overall survival and clinicopathological response. Subgroup analysis ("CROSS Eligibility") was performed based on tumor length, cT-stage, cM-stage, age, and performance status.
One hundred (out of 109) patients (CROSS group) and propensity score matched 100 (out of 210) patients (PFRT group) were included. Esophagectomy rates in CROSS and PFRT group were 69% and 76%, respectively (P = 0.268). R0 resection rates were 85.5% and 81.6% (P = 0.525) and the pathological complete remission rates were 24.6% and 35.5% (P = 0.154). By intention-to-treat, the median survival was 16.7 and 32.7 months (P = 0.083). For "CROSS Eligible subgroup," the median survival of the CROSS and PFRT group was 21.6 versus 44.9 months (P = 0.093).
There is no statistically difference in survival or clinicopathological outcome between both groups, but the trend favors PFRT. Prospective head-to-head comparison and novel strategies to improve the outcomes in resectable ESCC are warranted.
5‐Fluorouracil (5‐FU) is a chemotherapeutic agent commonly used to treat esophageal squamous cell carcinoma (ESCC), but acquisition of chemoresistance frequently occurs and the underlying mechanisms ...are not fully understood. We found that microRNA (miR)‐338‐5p was underexpressed in ESCC cells with acquired 5‐FU chemoresistance. Forced expression of miR‐338‐5p in these cells resulted in downregulation of Id‐1, and restoration of both in vitro and in vivo sensitivity to 5‐FU treatment. The effects were abolished by reexpression of Id‐1. In contrast, miR‐338‐5p knockdown induced 5‐FU resistance in chemosensitive esophageal cell lines, and knockdown of both miR‐338‐5p and Id‐1 resensitized the cells to 5‐FU. In addition, miR‐338‐5p had suppressive effects on migration and invasion of ESCC cells. Luciferase reporter assay confirmed a direct interaction between miR‐338‐5p and the 3′‐UTR of Id‐1. We also found that miR‐338‐5p was significantly downregulated in tumor tissue and serum samples of patients with ESCC. Notably, low serum miR‐338‐5p expression level was associated with poorer survival and poor response to 5‐FU/cisplatin‐based neoadjuvant chemoradiotherapy. In summary, we found that miR‐338‐5p can modulate 5‐FU chemoresistance and inhibit invasion‐related functions in ESCC by negatively regulating Id‐1, and that serum miR‐338‐5p could be a novel noninvasive prognostic and predictive biomarker in ESCC.
We found that microRNA (miR)‐338‐5p was underexpressed in esophageal squamous cell carcinoma cells with acquired 5‐fluorouracil (5‐FU) chemoresistance, and that reexpression of miR‐338‐5p could resensitize them to 5‐FU treatment through targeting Id‐1. MicroRNA‐338‐5p was significantly downregulated in tumor tissue and serum of patients with esophageal squamous cell carcinoma. Low serum miR‐338‐5p was predictive of poor response to 5‐FU/cisplatin‐based neoadjuvant chemoradiotherapy.
Increasing appreciation of tumor heterogeneity and the tumor-host interaction has stimulated interest in developing novel therapies that target both tumor cells and tumor microenvironment. Bone ...marrow derived cells (BMDCs) constitute important components of the tumor microenvironment. In this study, we aim to investigate the significance of VEGFR1- and VEGFR2-expressing non-tumor cells, including BMDCs, in esophageal cancer (EC) progression and in VEGFR1/VEGFR2-targeted therapies. Here we report that VEGFR1 or VEGFR2 blockade can significantly attenuate VEGF-induced Src and Erk signaling, as well as the proliferation and migration of VEGFR1⁺ and VEGFR2⁺ bone marrow cells and their pro-invasive effect on cancer cells. Importantly, our in vivo data show for the first time that systemic blockade of VEGFR1⁺ or VEGFR2⁺ non-tumor cells with neutralizing antibodies is sufficient to significantly suppress esophageal tumor growth, angiogenesis and metastasis in mice. Moreover, our tissue microarray study of human EC clinical specimens showed the clinicopathological significance of VEGFR1 and VEGFR2 in EC, which suggest that anti-VEGFR1/VEGFR2 therapies may be particularly beneficial for patients with aggressive EC. In conclusion, this study demonstrates the important contributions of VEGFR1⁺ and VEGFR2⁺ non-tumor cells in esophageal cancer progression, and substantiates the validity of these receptors as therapeutic targets for this deadly disease.
Neferine, a bisbenzylisoquinoline alkaloid isolated from the green seed embryos of Lotus (
Gaertn), has been previously shown to have various anti-cancer effects. In the present study, we evaluated ...the effect of neferine in terms of P-glycoprotein (P-gp) inhibition via
cytotoxicity assays, R123 uptake assays in drug-resistant cancer cells,
molecular docking analysis on human P-gp and
absorption, distribution, metabolism, and excretion (ADME), quantitative structure activity relationships (QSAR) and toxicity analyses. Lipinski rule of five were mainly considered for the ADME evaluation and the preset descriptors including number of hydrogen bond donor, acceptor, hERG IC
, logp, logD were considered for the QSAR analyses. Neferine revealed higher toxicity toward paclitaxel- and doxorubicin-resistant breast, lung or colon cancer cells, implying collateral sensitivity of these cells toward neferine. Increased R123 uptake was observed in a comparable manner to the control P-gp inhibitor, verapamil. Molecular docking analyses revealed that neferine still interacts with P-gp, even if R123 was pre-bound. Bioinformatical ADME and toxicity analyses revealed that neferine possesses the druggability parameters with no predicted toxicity. In conclusion, neferine may allocate the P-gp drug-binding pocket and prevent R123 binding in agreement with P-gp inhibition experiments, where neferine increased R123 uptake.
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