Machine learning analyses of antibody somatic mutations predict immunoglobulin light chain toxicity.

In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LC) form toxic aggregates and amyloid fibrils in target organs. Prompt diagnosis is crucial to avoid permanent organ damage, but delayed diagnosis is common because symptoms usually appear only after strong organ involvement. Here we present LICTOR, a machine learning approach predicting LC toxicity in AL, based on the distribution of somatic mutations acquired during clonal selection.

Industrial Perspective on Immunotherapy.

Immunotherapy has revolutionised oncology and represents a fast-growing area of new drug products in anti-cancer therapy. Patients can now benefit from an expanded landscape of treatment options for several tumour types. The value of cancer immunotherapy is well-established thanks to the clinical success following regulatory approval of several immunomodulators and cellular immunotherapies, and both the private and the public sector are investing to provide patients with improved immune-based agents and to extend the indications of already marketed products.