OncoTargets and Therapy 

About: OncoTargets and Therapy is an academic journal published by Dove Medical Press. The journal publishes majorly in the area(s): Cancer & Cell growth. It has an ISSN identifier of 1178-6930. It is also open access. Over the lifetime, 6181 publications have been published receiving 102979 citations.

PD-L1 expression in human cancers and its association with clinical outcomes

Abstract: PD-L1 is an immunoinhibitory molecule that suppresses the activation of T cells, leading to the progression of tumors. Overexpression of PD-L1 in cancers such as gastric cancer, hepatocellular carcinoma, renal cell carcinoma, esophageal cancer, pancreatic cancer, ovarian cancer, and bladder cancer is associated with poor clinical outcomes. In contrast, PD-L1 expression correlates with better clinical outcomes in breast cancer and merkel cell carcinoma. The prognostic value of PD-L1 expression in lung cancer, colorectal cancer, and melanoma is controversial. Blocking antibodies that target PD-1 and PD-L1 have achieved remarkable response rates in cancer patients who have PD-L1-overexpressing tumors. However, using PD-L1 as an exclusive predictive biomarker for cancer immunotherapy is questionable due to the low accuracy of PD-L1 immunohistochemistry staining. Factors that affect the accuracy of PD-L1 immunohistochemistry staining are as follows. First, antibodies used in different studies have different sensitivity. Second, in different studies, the cut-off value of PD-L1 staining positivity is different. Third, PD-L1 expression in tumors is not uniform, and sampling time and location may affect the results of PD-L1 staining. Therefore, better understanding of tumor microenvironment and use of other biomarkers such as gene marker and combined index are necessary to better identify patients who will benefit from PD-1/PD-L1 checkpoint blockade therapy.

Computer-aided classification of lung nodules on computed tomography images via deep learning technique.

Abstract: Lung cancer has a poor prognosis when not diagnosed early and unresectable lesions are present. The management of small lung nodules noted on computed tomography scan is controversial due to uncertain tumor characteristics. A conventional computer-aided diagnosis (CAD) scheme requires several image processing and pattern recognition steps to accomplish a quantitative tumor differentiation result. In such an ad hoc image analysis pipeline, every step depends heavily on the performance of the previous step. Accordingly, tuning of classification performance in a conventional CAD scheme is very complicated and arduous. Deep learning techniques, on the other hand, have the intrinsic advantage of an automatic exploitation feature and tuning of performance in a seamless fashion. In this study, we attempted to simplify the image analysis pipeline of conventional CAD with deep learning techniques. Specifically, we introduced models of a deep belief network and a convolutional neural network in the context of nodule classification in computed tomography images. Two baseline methods with feature computing steps were implemented for comparison. The experimental results suggest that deep learning methods could achieve better discriminative results and hold promise in the CAD application domain.

Uncovering the role of p53 splice variants in human malignancy: a clinical perspective

Abstract: Thirty-five years of research on p53 gave rise to more than 68,000 articles and reviews, but did not allow the uncovering of all the mysteries that this major tumor suppressor holds. How p53 handles the different signals to decide the appropriate cell fate in response to a stress and its implication in tumorigenesis and cancer progression remains unclear. Nevertheless, the uncovering of p53 isoforms has opened new perspectives in the cancer research field. Indeed, the human TP53 gene encodes not only one but at least twelve p53 protein isoforms, which are produced in normal tissues through alternative initiation of translation, usage of alternative promoters, and alternative splicing. In recent years, it became obvious that the different p53 isoforms play an important role in regulating cell fate in response to different stresses in normal cells by differentially regulating gene expression. In cancer cells, abnormal expression of p53 isoforms contributes actively to cancer formation and progression, regardless of TP53 mutation status. They can also be associated with response to treatment, depending on the cell context. The determination of p53 isoform expression and p53 mutation status helps to define different subtypes within a particular cancer type, which would have different responses to treatment. Thus, the understanding of the regulation of p53 isoform expression and their biological activities in relation to the cellular context would constitute an important step toward the improvement of the diagnostic, prognostic, and predictive values of p53 in cancer treatment. This review aims to summarize the involvement of p53 isoforms in cancer and to highlight novel potential therapeutic targets.

Role of the NFκB-signaling pathway in cancer

Abstract: Cancer is a group of cells that malignantly grow and proliferate uncontrollably. At present, treatment modes for cancer mainly comprise surgery, chemotherapy, radiotherapy, molecularly targeted therapy, gene therapy, and immunotherapy. However, the curative effects of these treatments have been limited thus far by specific characteristics of tumors. Abnormal activation of signaling pathways is involved in tumor pathogenesis and plays critical roles in growth, progression, and relapse of cancers. Targeted therapies against effectors in oncogenic signaling have improved the outcomes of cancer patients. NFκB is an important signaling pathway involved in pathogenesis and treatment of cancers. Excessive activation of the NFκB-signaling pathway has been documented in various tumor tissues, and studies on this signaling pathway for targeted cancer therapy have become a hot topic. In this review, we update current understanding of the NFκB-signaling pathway in cancer.

Chemotherapy-enhanced inflammation may lead to the failure of therapy and metastasis

Abstract: The lack of therapy and the failure of existing therapy has been a challenge for clinicians in treating various cancers. Doxorubicin, 5-fluorouracil, cisplatin, and paclitaxel are the first-line therapy in various cancers; however, toxicity, resistance, and treatment failure limit their clinical use. Their status leads us to discover and investigate more targeted therapy with more efficacy. In this article, we dissect literature from the patient perspective, the tumor biology perspective, therapy-induced metastasis, and cell data generated in the laboratory.