Knowledge Hub

Acute myeloid leukemia (AML) remains one of the most genomically complex hematologic malignancies. As new biomarkers and targeted therapies continue to reshape disease classification, next-generation sequencing (NGS) plays an increasingly central role in diagnosis, prognosis, and treatment selection. In this article, adapted from Dr. Hubert Tsui’s presentation at a recent webinar, we explore how NGS fits within the AML patient journey, from first evaluation to meaningful insights.

Maslah et al. present a translational study exploring how ruxolitinib and other JAK inhibitors influence clonal evolution in myeloproliferative neoplasms (MPN). Using a SOPHiA DDM™ for Myeloid Malignancies application for evaluation and longitudinal monitoring, the study highlights how treatment pressure can select for RAS pathway mutations, ultimately impacting prognosis and disease progression. Our infographic distills […]

Explore this recent multimodal study to learn how applying machine learning to baseline multimodal data can help predict pathological complete response (pCR) status after neoadjuvant chemotherapy (NAC) for triple-negative breast cancer (TNBC) patients, leading to the proposal of more personalized treatment options.

Discover how a patient from São Paulo, Brazil, finally found answers after decades of uncertainty thanks to the dedicated efforts of the Bioma Genetics Laboratory.

See how Centre Léon Bérard scaled their cell-free DNA analysis with a tailor-made liquid biopsy solution for mutation detection across cancer types.

Gene fusions are key drivers in many myeloid malignancies. As our understanding of these complex events has evolved, so too have the technologies designed to detect them. This blog explores how SOPHiA DDM™ leverages advanced DNA-based workflows to enhance the efficiency of fusion detection, helping labs move faster from sample to insight.

Routine pharmacogenomic (PGx) testing has significant potential to improve drug efficacy and safety.

Dive into the groundbreaking efforts being made in academia to develop trustworthy AI algorithms that address challenges related to medical imaging analysis, including delineation, in stage IV non-small cell lung cancer (NSCLC) patients, a crucial step leading towards truly personalized medicine approaches.

In today’s fast-moving biopharma landscape, companies face increasing pressure to deliver the right treatment, to the right patient, at the right time and in the right sequence, faster and more efficiently. As our understanding of disease biology complexity grows and the need for targeted therapies further intensifies, legacy R&D models are increasingly strained in the quest to deliver timely and impactful innovations.

We sat down with Prof. Jacques Cadranel, International Coordinator of the DEEP-Lung-IV study and Head of the Pneumology Department at the Hospital Group University Hospitals of Eastern Paris, who shared his experience in this collaboration with SOPHiA GENETICS, and the importance of the integration of multimodal data in clinical practice to advance personalized medicine in lung cancer.