While the therapeutic promise of ADCs is clear, success in this space depends on more than just the drug itself — it relies heavily on precise and reliable diagnostic testing.

In this GenomeWeb Executive Q&A, Foresight Diagnostics’ chief operating and compliance officer Sandra Close and chief business officer John Truesdell discuss the new guidelines, how they may change the treatment journey for diffuse large B-cell lymphoma patients, and Foresight's approach to establishing the clinical utility of its tests.

This article highlights the impact of liquid biopsies, challenges in the space, and how current technologies are helping to push the boundaries of precision medicine.

The allure of faster turnaround times suggests quicker insights and potentially fewer sequencing requirements, but these disruptive solutions often overlook a critical element: the importance of high-quality, reproducible, and confident insights.

Success in this field demands investment in infrastructure, expanded education for healthcare providers, and improved access to genetic services.

The companies, who partnered to bring Genomenon’s Cancer Knowledgebase or “CKB” (formerly Clinical Knowledgebase) of curated variants to users of Illumina’s Connected Insights interpretation platform, say these efforts will enable scientists to interpret more oncogenic variants in more populations.

Foundation Medicine recently announced a companion diagnostic partnership with Syndax Pharmaceuticals to identify AML patients with NPM1 mutations with comprehensive genomic profiling, which, if approved, could be the first NGS CDx test to detect genomic alterations in hematologic neoplasms.

With advance planning and a diagnostics partner with demonstrated regulatory expertise, it is possible to navigate EU IVDR requirements with confidence and keep clinical trial timelines on track.

Explainable artificial intelligence, or XAI, is enabling Baylor Genetics to hasten and scale up clinical whole-genome sequencing while testing becomes more accessible and evidence for its utility grows.

Artificial intelligence models that can explicitly demonstrate their logic and expose potential biases, known as explainable AI, or XAI, are necessary for medical institutions to make healthcare decisions with increased confidence, according to Illumina.