Hypomethylating agent-based therapies in older adults with acute myeloid leukemia - A joint review by the Young International Society of Geriatric Oncology and European Society for Blood and Marrow Transplantation Trainee Committee.

Acute myeloid leukemia (AML) is associated with poor outcomes in older adults. A major goal of treatment is to balance quality of life and functional independence with disease control. With the approval of new, more tolerable regimens, more older adults are able to receive AML-directed therapy. Among these options are hypomethylating agents (HMAs), specifically azacitidine and decitabine. HMAs have become an integral part of AML therapy over the last two decades. These agents are used either as monotherapy or nowadays more commonly in combination with other agents such as the Bcl-2 inhibitor venetoclax. Biological AML characteristics, such as molecular and cytogenetic risk factors, play crucial roles in guiding treatment decisions. In patients with high-risk AML, HMAs are increasingly used rather than intensive chemotherapy, although further trials based on a risk-adapted approach using patient- and disease-related factors are needed. Here, we review trials and evidence for the use of HMA monotherapy and combination therapy in the management of older adults with AML. Furthermore, we discuss the use of HMAs and HMA combination therapies in AML, mechanisms of action, their incorporation into hematopoietic stem cell transplantation strategies, and their use in patients with comorbidities and reduced organ function.

Gastroesophageal adenocarcinoma in older adults: A comprehensive narrative review of management by the Young International Society of Geriatric Oncology.

Gastroesophageal adenocarcinoma is a disease of older adults with very poor survival rates. Its incidence has risen dramatically across the world in recent decades. Current treatment approaches for older adults are based largely on extrapolated evidence from clinical trials conducted in younger and fitter participants than those more commonly encountered in clinical practice. Understanding how to apply available evidence to our patients in the clinic setting is essential given the high morbidity of both curative and palliative treatment. This review aims to use available data to inform the management of an older adult with gastroesophageal adenocarcinoma.

Biomarker identification of hepatocellular carcinoma using a methodical literature mining strategy.

Hepatocellular carcinoma (HCC), one of the most common causes of cancer-related deaths, carries a 5-year survival rate of 18%, underscoring the need for robust biomarkers. In spite of the increased availability of HCC related literatures, many of the promising biomarkers reported have not been validated for clinical use. To narrow down the wide range of possible biomarkers for further clinical validation, bioinformaticians need to sort them out using information provided in published works. Biomedical text mining is an automated way to obtain information of interest within the massive collection of biomedical knowledge, thus enabling extraction of data for biomarkers associated with certain diseases. This method can significantly reduce both the time and effort spent on studying important maladies such as liver diseases. Herein, we report a text mining-aided curation pipeline to identify potential biomarkers for liver cancer. The curation pipeline integrates PubMed E-Utilities to collect abstracts from PubMed and recognize several types of named entities by machine learning-based and pattern-based methods. Genes/proteins from evidential sentences were classified as candidate biomarkers using a convolutional neural network. Lastly, extracted biomarkers were ranked depending on several criteria, such as the frequency of keywords and articles and the journal impact factor, and then integrated into a meaningful list for bioinformaticians. Based on the developed pipeline, we constructed MarkerHub, which contains 2128 candidate biomarkers extracted from PubMed publications from 2008 to 2017. Database URL: http://markerhub.iis.sinica.edu.tw.