Purpose: This study assessed the safety and efficacy of SHR-1210 (anti-PD-1 antibody) and apatinib (VEGFR2 inhibitor) as combination therapy in patients with advanced hepatocellular carcinoma (HCC), gastric, or esophagogastric junction cancer (GC/EGJC). Patients and Methods: This was an open-label, doseescalation (phase Ia) and expansion study (phase Ib). In phase Ia, patients (n ¼ 15) received SHR-1210 200 mg every 2 weeks and apatinib 125-500 mg once daily until unacceptable toxicity or disease progression. In phase Ib, patients (n ¼ 28) received apatinib at the phase Ia-identified recommended phase II dose (RP2D) plus SHR-1210. The primary objectives were safety and tolerability and RP2D determination. Results: At data cutoff, 43 patients were enrolled. In phase Ia, four dose-limiting toxicity events were observed (26.7%): one grade 3 lipase elevation (6.7%) in the apatinib 250 mg cohort and three grade 3 pneumonitis events (20%) in the apatinib 500 mg cohort. The maximum tolerated RP2D for apatinib was 250 mg. Of the 33 patients treated with the R2PD combination, 20 (60.6%) experienced a grade !3 treatmentrelated adverse event; adverse events in !10% of patients were hypertension (15.2%) and increased aspartate aminotransferase (15.2%). The objective response rate in 39 evaluable patients was 30.8% (95% CI: 17.0%-47.6%). Eight of 16 evaluable HCC patients achieved a partial response (50.0%, 95% CI: 24.7%-75.4%). Conclusions: SHR-1210 and apatinib combination therapy demonstrated manageable toxicity in patients with HCC and GC/EGJC at recommended single-agent doses of both drugs. The RP2D for apatinib as combination therapy was 250 mg, which showed encouraging clinical activity in patients with advanced HCC.

Long non-coding RNAs (lncRNAs) play key roles in human cancers. Here, FEZF1-AS1, a highly overexpressed lncRNA in colorectal cancer, was identified by lncRNA microarrays. We aimed to explore the roles and possible molecular mechanisms of FEZF1-AS1 in colorectal cancer. LncRNA expression in colorectal cancer tissues was measured by lncRNA microarray and qRT-PCR. The functional roles of FEZF1-AS1 in colorectal cancer were demonstrated by a series of and experiments. RNA pull-down, RNA immunoprecipitation and luciferase analyses were used to demonstrate the potential mechanisms of FEZF1-AS1. We identified a series of differentially expressed lncRNAs in colorectal cancer using lncRNA microarrays, and revealed that FEZF1-AS1 is one of the most overexpressed. Further validation in two expanded colorectal cancer cohorts confirmed the upregulation of FEZF1-AS1 in colorectal cancer, and revealed that increased FEZF1-AS1 expression is associated with poor survival. Functional assays revealed that FEZF1-AS1 promotes colorectal cancer cell proliferation and metastasis. Mechanistically, FEZF1-AS1 could bind and increase the stability of the pyruvate kinase 2 (PKM2) protein, resulting in increased cytoplasmic and nuclear PKM2 levels. Increased cytoplasmic PKM2 promoted pyruvate kinase activity and lactate production (aerobic glycolysis), whereas FEZF1-AS1-induced nuclear PKM2 upregulation further activated STAT3 signaling. In addition, PKM2 was upregulated in colorectal cancer tissues and correlated with FEZF1-AS1 expression and patient survival. Together, these data provide mechanistic insights into the regulation of FEZF1-AS1 on both STAT3 signaling and glycolysis by binding PKM2 and increasing its stability. .

Tumor-associated macrophages (TAMs) are frequently associated with poor prognosis in human cancers. However, the effects of TAMs in colorectal cancer are contradictory. We therefore investigated the functions, mechanisms, and clinical significance of TAMs in colorectal cancer. We measured the macrophage infiltration (CD68), P-gp, and Bcl2 expression in colorectal cancer tissues using IHC staining. Coculture of TAMs and colorectal cancer cells both and models was used to evaluate the effects of TAMs on colorectal cancer chemoresistance. Cytokine antibody arrays, ELISA, neutralizing antibody, and luciferase reporter assay were performed to uncover the underlying mechanism. TAM infiltration was associated with chemoresistance in patients with colorectal cancer. Colorectal cancer-conditioned macrophages increased colorectal cancer chemoresistance and reduced drug-induced apoptosis by secreting IL6, which could be blocked by a neutralizing anti-IL6 antibody. Macrophage-derived IL6 activated the IL6R/STAT3 pathway in colorectal cancer cells, and activated STAT3 transcriptionally inhibited the tumor suppressor miR-204-5p. Rescue experiment confirmed that miR-204-5p is a functional target mediating the TAM-induced colorectal cancer chemoresistance. miR-155-5p, a key miRNA regulating C/EBPβ, was frequently downregulated in TAMs, resulting in increased C/EBPβ expression. C/EBPβ transcriptionally activated IL6 in TAMs, and TAM-secreted IL6 then induced chemoresistance by activating the IL6R/STAT3/miR-204-5p pathway in colorectal cancer cells. Our data indicate that the maladjusted miR-155-5p/C/EBPβ/IL6 signaling in TAMs could induce chemoresistance in colorectal cancer cells by regulating the IL6R/STAT3/miR-204-5p axis, revealing a new cross-talk between immune cells and tumor cells in colorectal cancer microenvironment. .

High-frequency data observed on the prices of financial assets are commonly modeled by diffusion processes with micro-structure noise, and realized volatility-based methods are often used to estimate integrated volatility. For problems involving a large number of assets, the estimation objects we face are volatility matrices of large size. The existing volatility estimators work well for a small number of assets but perform poorly when the number of assets is very large. In fact, they are inconsistent when both the number, $p$, of the assets and the average sample size, $n$, of the price data on the $p$ assets go to infinity. This paper proposes a new type of estimators for the integrated volatility matrix and establishes asymptotic theory for the proposed estimators in the framework that allows both $n$ and $p$ to approach to infinity. The theory shows that the proposed estimators achieve high convergence rates under a sparsity assumption on the integrated volatility matrix. The numerical studies demonstrate that the proposed estimators perform well for large $p$ and complex price and volatility models. The proposed method is applied to real high-frequency financial data.Comment: Published in at http://dx.doi.org/10.1214/09-AOS730 the Annals of Statistics (http://www.imstat.org/aos/) by the Institute of Mathematical Statistics (http://www.imstat.org

Objectives/Hypothesis: To develop a deep-learning-based computer-aided diagnosis system for distinguishing laryngeal neoplasms (benign, precancerous lesions, and cancer) and improve the clinician-based accuracy of diagnostic assessments of laryngoscopy findings. Study Design: Retrospective study. Methods: A total of 24,667 laryngoscopy images (normal, vocal nodule, polyps, leukoplakia and malignancy) were collected to develop and test a convolutional neural network (CNN)-based classifier. A comparison between the proposed CNNbased classifier and the clinical visual assessments (CVAs) by 12 otolaryngologists was conducted. Results: In the independent testing dataset, an overall accuracy of 96.24% was achieved; for leukoplakia, benign, malignancy, normal, and vocal nodule, the sensitivity and specificity were 92.8% vs. 98.9%, 97% vs. 99.7%, 89% vs. 99.3%, 99.0% vs. 99.4%, and 97.2% vs. 99.1%, respectively. Furthermore, when compared with CVAs on the randomly selected test dataset, the CNN-based classifier outperformed physicians for most laryngeal conditions, with striking improvements in the ability to distinguish nodules (98% vs. 45%, P < .001), polyps (91% vs. 86%, P < .001), leukoplakia (91% vs. 65%, P < .001), and malignancy (90% vs. 54%, P < .001). Conclusions: The CNN-based classifier can provide a valuable reference for the diagnosis of laryngeal neoplasms during laryngoscopy, especially for distinguishing benign, precancerous, and cancer lesions.

It is increasingly important in financial economics to estimate volatilities of asset returns. However most the available methods are not directly applicable when the number of assets involved is large, due to the lack of accuracy in estimating high dimensional matrices. Therefore it is pertinent to reduce the effective size of volatility matrices in order to produce adequate estimates and forecasts. Furthermore, since high-frequency financial data for different assets are typically not recorded at the same time points, conventional dimension-reduction techniques are not directly applicable. To overcome those difficulties we explore a novel approach that combines high-frequency volatility matrix estimation together with low-frequency dynamic models. The proposed methodology consists of three steps: (i) estimate daily realized co-volatility matrices directly based on high-frequency data, (ii) fit a matrix factor model to the estimated daily covolatility matrices, and (iii) fit a vector autoregressive (VAR) model to the estimated volatility factors. We establish the asymptotic theory for the proposed methodology in the framework that allows sample size, number of assets, and number of days go to infinity together. Our theory shows that the relevant eigenvalues and eigenvectors can be consistently estimated. We illustrate the methodology with the high-frequency price data on several hundreds of stocks traded in Shenzhen and Shanghai Stock Exchanges over a period of 177 days in 2003. Our approach pools together the strengths of modeling and estimation at both intradaily (high-frequency) and interdaily (low-frequency) levels.

Purpose: Capecitabine plus oxaliplatin (CAPOX) is one of the standard first-line treatments for unresectable, advanced, or metastatic gastric or gastroesophageal junction (G/GEJ) adenocarcinoma. Camrelizumab shows promising antitumor activity in advanced or metastatic G/GEJ adenocarcinoma in a phase I study. We reported the outcomes of cohort 1 in a multicenter, open-label, phase II trial, which assessed camrelizumab in combination with CAPOX followed by camrelizumab plus apatinib as a first-line combination regimen for advanced or metastatic G/GEJ adenocarcinoma. Patients and Methods: Systemic treatment-naïve patients with EGFR2-negative advanced or metastatic G/GEJ adenocarcinoma received initial camrelizumab plus CAPOX for 4–6 cycles, and patients without progressive disease were administrated subsequent camrelizumab plus apatinib. Primary endpoint was objective response rate (ORR). Results: All 48 enrolled patients comprised the efficacy and safety analysis population. The ORR was 58.3% [95% confidence interval (CI), 43.2–72.4] with this combination regimen. Median duration of response was 5.7 months (95% CI, 4.4–8.3). Median overall survival was 14.9 months (95% CI, 13.0–18.6), and median progression-free survival was 6.8 months (95% CI, 5.6–9.5), respectively. The most common grade ≥3 treatment-related adverse events (&gt;10%) were decreased platelet count (20.8%), decreased neutrophil count (18.8%), and hypertension (14.6%). Treatment-related death occurred in 1 patient (2.1%) due to abnormal hepatic function and interstitial lung disease. Conclusions: Camrelizumab combined with CAPOX followed by camrelizumab plus apatinib demonstrated encouraging antitumor activity and manageable toxicity as first-line therapy for patients with advanced or metastatic G/GEJ adenocarcinoma.

Purpose: This phase I study assessed the safety, tolerability, MTD, pharmacokinetics, antitumor activity, and predictive biomarkers of pyrotinib, an irreversible pan-ErbB inhibitor, in combination with capecitabine in patients with HER2positive metastatic breast cancer (MBC).Patients and Methods: Patients received oral pyrotinib 160 mg, 240 mg, 320 mg, or 400 mg once daily continually plus capecitabine 1,000 mg/m 2 twice daily on days 1 to 14 of a 21-day cycle. Pharmacokinetic blood samples were collected on days 1 and 14. Next-generation sequencing was performed on circulating tumor DNA to probe for predictive biomarkers.Results: A total of 28 patients were enrolled, 22 patients were treated at the two top-level doses. Among 17 (60.7%) trastuzumab-pretreated patients, 11 received trastuzumab for metastatic disease and 6 received adjuvant trastuzumab. No dose-limited toxicity was observed. Grade 3 treatment-related adverse events (AE) occurred in 12 (42.9%) patients; anemia (14.3%) and diarrhea (10.7%) were the most common grade 3 AEs. The overall response rate (ORR) was 78.6% [95% confidence interval (CI): 59.0%-91.7%], and the clinical benefit rate was 85.7% (95% CI: 67.3%-96.0%). The median progression-free survival (PFS) was 22.1 months (95% CI: 9.0-26.2 months). ORR was 70.6% (12/17) in trastuzumabpretreated patients and 90.9% (10/11) in trastuzumab-na€ ve patients. Analysis of all genetic alterations in HER2-related signaling network in baseline blood samples suggested that multiple genetic alterations were significantly associated with poorer PFS compared with none or one genetic alteration (median, 16.8 vs. 29.9 months, P ¼ 0.006).Conclusions: In a population largely na€ ve to HER2-targeted therapy, pyrotinib in combination with capecitabine was well-tolerated and demonstrates promising antitumor activity in patients with HER2-positive MBC.