Abstract
Over a 20 year period, the journal Cancer Informatics has played an important role defining and forging a bridge between bioinformations and translational cancer research. The main focus of the journal has been to advance the prevention, diagnosis, and treatment of cancer. This involves the specialized intersection of genomics, molecular biology, data science, computer programing, statistics, communication theory, and the clinical sciences to answer important questions in the field of cancer research.
Editorial
Cancer Informatics (CiX) was first published on January 1, 2005, initially under the auspicious of LIBERTAS ACADEMIA (Auckland, New Zealand), and later acquired by Sage Publishing (Thousand Oaks, CA, USA) on September 21, 2016. 1 The year 2025 marks the 20th anniversary of the journal. In the inaugural issue, dedication was given to Sarah Amy Hill, the publisher’s daughter, who died of Acute Lymphoblastic Leukemia. The founders of CiX were very passionate about having a journal focused on the “fundamental reality of cancer as an affliction that harms every aspect of the lives of sufferers and those who love them.” Specifically, the aim for the new publication was to be a “distillation of papers that reflects, to a degree, the larger literature on bioinformatics, computational biology, and biomedical informatics that appear to have a high likelihood of promoting excellence in cancer research and clinical practice.” 2
The early period of CiX witnessed the publication of several seminal papers on cancer informatics. These included: DICOM structured reporting and cancer clinical trials results 3 ; The Bimodality index: A criterion for discovering and ranking bimodal signatures from cancer gene expression profiling data 4 ; Bimodal gene expression and biomarker discovery 5 ; Comparison of a label-free quantitative proteomic method based on peptide ion current area to the isotope coded affinity tag method 6 ; Germinal center B cell-like (GCB) and activated B cell-like (ABC) type of diffuse large B cell lymphoma (DLBCL): Analysis of molecular predictors, signatures, cell cycle state and patient survival 7 ; and Application of machine learning in cancer prediction and prognosis. 8 More recently, notable papers included: Prevalence of cervical cancer and associated factors among women attended cervical cancer screening center at Gahandi Memorial Hospital, Ethiopia 9 ; Prevalence of iron deficiency and its association with breast cancer in premenopausal compared to postmenopausal women in Al Ahsa, Saudi Arabia 10 ; Cervical transformation zone segmentation and classification based on improved inception-ResNet-V2 using colposcopy images 11 ; A comprehensive analysis of the PI3K/AKT pathway: Unveiling key proteins and therapeutic targets for cancer treatment 12 ;Alternative polyadenylation regulatory factors signature for survival prediction in kidney renal cell carcinoma 13 ; Identification of copper homeostasis-related gene signature for predicting prognosis in patients with epithelial ovarian cancer. 14
At last estimate, there are approximately 10 million cancer deaths occurring yearly throughout the world, with over 20 million newly reported cases. 15 The early detection and treatment of cancer has benefited greatly from advances in the field of “Cancer Informatics.” Many precancerous lesions can now be identified early, safely removed, and effectively treated to prevent their further spread. This has been largely possible through novel developments in cancer informatics methods, models, and computational tools, in tandem with the exponential growth of genomics, proteomics, and metabolomics. Key themes of Cancer Informatics embrace multi-omics data integration, personalized treatment strategies, and deep learning tools. The future of the field remains vibrant with emerging advances in machine learning (ML), artificial intelligence (AI), and treatment robotics.
In this “20th Anniversary Special Collection of Cancer Informatics” we seek manuscripts and narrative reviews highlighting the progress and new frontiers in this exciting and important field. Particularly, we are interested in data methods for optimizing patient selection and treatment stratification in cancer clinical trials, ML/AI tools for the early detection of cancer development and progression, informatics/data science algorithms to predict response to treatment, novel molecular/genetics methods (including liquid biopsies) to guide personalized targeted therapies, deep learning analyses of medical images for abnormal tumor growth, and “in silico” experiments to emulate cancer processes. Applications in cancer based eHealth, mHealth, digital health, and natural language processing (NLP) also are areas of interest.
