Oncology is considered as the pioneer indication for the clinical application of molecular biomarkers. Newly developed targeted anticancer therapies call for the implementation of molecular biomarker strategies but even novel cytotoxic treatments use biomarkers for the assessment of efficacy and toxicity. Biomarkers may play several roles in the progression of a drug from research to personalised medicine. In particular biomarkers are used to understand the mechanism of action of a drug, monitor the modulation of the intended target, assess efficacy and safety, adapt dosing and schedule, select patients and prognosticate the clinical outcome. Nowadays, the use of biomarkers in oncology is still challenged as only a limited number of oncology drugs on the market have a companion biomarker test to be mandatorily performed before treatment. This is in contradiction with the current major investment the pharmaceutical sector is devoting to biomarker identification and development. What are the measurable milestones and outcomes of these investments? How does biomarker development contribute to reaching the ultimate goal of finding the right molecules for the right targets at the right doses and schedules for the right patients? This review provides a critical overview of recent salient achievements in the identification and development of biomarkers.
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http://dx.doi.org/10.1517/17425255.4.11.1391 | DOI Listing |
Bull Math Biol
January 2025
Department of Mathematics, University of Manitoba, 340 UMSU University Centre, Winnipeg, MB, R3T 2N2, Canada.
The immune checkpoint inhibitor, anti-programmed death protein-1 (anti-PD-1), enhances adaptive immunity to kill tumor cells, and the oncolytic virus (OV) triggers innate immunity to clear the infected tumor cells. We create a mathematical model to investigate how the interaction between adaptive and innate immunities under OV and anti-PD-1 affects tumor reduction. For different immunity strength, we create the corresponding virtual baseline patients and cohort patients to decipher the major factors determining the treatment outcome.
View Article and Find Full Text PDFJ Cancer Res Clin Oncol
January 2025
Key Laboratory of Laboratory Medicine, Ministry of Education of China, School of Laboratory Medicine and Life Sciences, Wenzhou Medical University, Wenzhou, 325035, Zhejiang, China.
Purpose: Growing evidence suggests that the tyrosine phosphatase SHP2 is pivotal for tumor progression. Triple-negative breast cancer (TNBC) is the most lethal subtype of breast cancer, characterized by its high recurrence rate, aggressive metastasis, and resistance to chemotherapy. Understanding the mechanisms of tumorigenesis and the underlying molecular pathways in TNBC could aid in identifying new therapeutic targets.
View Article and Find Full Text PDFJ Cancer Res Clin Oncol
January 2025
Department of Pathology, Theodor Bilharz Research Institute, Giza, 12411, Egypt.
Introduction: Pancreatic ductal adenocarcinoma (PDAC) is associated with poor prognosis. The roles of the transcription factor special AT-rich binding protein-2 (SATB2) and β-catenin in PDAC have been a subject of controversy. We aimed to assess the diagnostic and prognostic impact of SATB2 and β-catenin in PDAC.
View Article and Find Full Text PDFJ Viral Hepat
March 2025
Department of Biomedical Informatics, Ajou University School of Medicine, Suwon, Republic of Korea.
Alpha-fetoprotein (AFP) level and its changes in chronic hepatitis B (CHB) may influence the risk of future hepatocellular carcinoma (HCC). This study aims to evaluate the HCC risk in CHB patients with no overt HCC but with elevated AFP level and to explore the prognostic role of longitudinal changes in AFP and liver-related laboratory values. This multicentre cohort study included 10,639 CHB patients without a history of HCC from seven medical facilities in South Korea.
View Article and Find Full Text PDFMed Phys
January 2025
Deparment of Radiation Oncology, Duke University, Durham, North Carolina, USA.
Background: Stereotactic radiosurgery (SRS) is widely used for managing brain metastases (BMs), but an adverse effect, radionecrosis, complicates post-SRS management. Differentiating radionecrosis from tumor recurrence non-invasively remains a major clinical challenge, as conventional imaging techniques often necessitate surgical biopsy for accurate diagnosis. Machine learning and deep learning models have shown potential in distinguishing radionecrosis from tumor recurrence.
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