The presence of SMs differentiates toxic and non-toxic LC sequences.Ī Schematic representation of the generation of LC diversity through the processes of VJ recombination and somatic hypermutation. Therefore, the development of a specific prediction tool represents a crucial step to anticipate AL diagnosis and improve patients’ prognosis. Predicting the onset of AL is highly challenging, as each patient carries a different pathogenic LC sequence resulting from a unique rearrangement of variable (V) and joining (J) immunoglobulin genes and a unique set of somatic mutations (SMs) acquired during B cell affinity maturation 9 (Fig. However, early diagnosis is still difficult since reliable diagnostic tests predicting whether MGUS patients are likely to develop AL are currently lacking 7, 8. Pre-existing monoclonal gammopathy of undetermined significance (MGUS) is a known risk factor for developing AL, with 9% of MGUS patients progressing to AL 5– 7. However, the complexity of the disease and its vague symptoms make a timely diagnosis of AL extremely challenging 3, 4. Therefore, an early diagnosis is essential to avoid irreversible organ damage.

Symptoms of AL are non-specific and usually reflect advanced organ involvement. Although LCs deposition can occur in any organ except the brain, the kidney and heart are the most affected sites, with the latter bearing the worst prognosis. LCs, mainly secreted as homodimers 2, misfold forming toxic species and amyloid fibrils which accumulate in target organs and lead to fatal organ dysfunction and death 1. Systemic light-chain amyloidosis (AL) is a monoclonal gammopathy characterized by the abnormal proliferation of a plasma cell clone producing large amounts of pathogenic immunoglobulin free light chains (LCs) 1. Therefore, LICTOR represents a promising strategy for AL diagnosis and reducing high mortality rates in AL. Furthermore, we are able to abolish the toxic phenotype of an LC by in silico reverting two germline-specific somatic mutations identified by LICTOR, and by experimentally assessing the loss of in vivo toxicity in a Caenorhabditis elegans model. Tested on an independent set of 12 LCs sequences with known clinical phenotypes, LICTOR achieves a prediction accuracy of 83%. LICTOR achieves a specificity and a sensitivity of 0.82 and 0.76, respectively, with an area under the receiver operating characteristic curve (AUC) of 0.87. Here we present LICTOR, a machine learning approach predicting LC toxicity in AL, based on the distribution of somatic mutations acquired during clonal selection. Prompt diagnosis is crucial to avoid permanent organ damage, but delayed diagnosis is common because symptoms usually appear only after strong organ involvement. In systemic light chain amyloidosis (AL), pathogenic monoclonal immunoglobulin light chains (LC) form toxic aggregates and amyloid fibrils in target organs.

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