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

An updated AL-Base reveals ranked enrichment of immunoglobulin light chain variable genes in AL amyloidosis

Background

Each monoclonal antibody light chain associated with AL amyloidosis has a unique sequence. Defining how these sequences lead to amyloid deposition could facilitate faster diagnosis and lead to new treatments.

Methods

Light chain sequences are collected in the Boston University AL-Base repository. Monoclonal sequences from AL amyloidosis, multiple myeloma and the healthy polyclonal immune repertoire were compared to identify differences in precursor gene use, mutation frequency and physicochemical properties.

Results

AL-Base now contains 2,193 monoclonal light chain sequences from plasma cell dyscrasias. Sixteen germline precursor genes were enriched in AL amyloidosis, relative to multiple myeloma and the polyclonal repertoire. Two genes, IGKV1-16 and IGLV1-36, were infrequently observed but highly enriched in AL amyloidosis. The number of mutations varied widely between light chains. AL-associated κ light chains harbored significantly more mutations compared to multiple myeloma and polyclonal sequences, whereas AL-associated λ light chains had fewer mutations. Machine learning tools designed to predict amyloid propensity were less accurate for new sequences than their original training data.

Conclusions

Rarely-observed light chain variable genes may carry a high risk of AL amyloidosis. New approaches are needed to define sequence-associated risk factors for AL amyloidosis. AL-Base is a foundational resource for such studies.

Causes of death in individuals with lifetime major depression: a comprehensive machine learning analysis from a community-based autopsy center

BACKGROUND

Depression can be associated with increased mortality and morbidity, but no studies have investigated the specific causes of death based on autopsy reports. Autopsy studies can yield valuable and detailed information on pathological ailments or underreported conditions. This study aimed to compare autopsy-confirmed causes of death (CoD) between individuals diagnosed with major depressive disorder (MDD) and matched controls. We also analyzed subgroups within our MDD sample, including late-life depression and recurrent depression. We further investigated whether machine learning (ML) algorithms could distinguish MDD and each subgroup from controls based on their CoD.

METHODS

We conducted a comprehensive analysis of CoD in individuals who died from nontraumatic causes. The diagnosis of lifetime MDD was ascertained based on the DSM-5 criteria using information from a structured interview with a knowledgeable informant. Eleven established ML algorithms were used to differentiate MDD individuals from controls by simultaneously analyzing different disease category groups to account for multiple tests. The McNemar test was further used to compare paired nominal data.

RESULTS

The initial dataset included records of 1,102 individuals, among whom 232 (21.1%) had a lifetime diagnosis of MDD. Each MDD individual was strictly paired with a control non-psychiatric counterpart. In the MDD group, the most common CoD were circulatory (67.2%), respiratory (13.4%), digestive (6.0%), and cancer (5.6%). Despite employing a range of ML models, we could not find distinctive CoD patterns that could reliably distinguish individuals with MDD from individuals in the control group (average accuracy: 50.6%; accuracy range: 39-59%). These findings were consistent even when considering factors within the MDD group, such as late-life or recurrent MDD. When comparing groups with paired nominal tests, no differences were found for circulatory (p=0.450), respiratory (p=0.790), digestive (p=1.000), or cancer (p=0.855) CoD.

CONCLUSIONS

Our analysis revealed that autopsy-confirmed CoD exhibited remarkable similarity between individuals with depression and their matched controls, underscoring the existing heterogeneity in the literature. Future research should prioritize more severe manifestations of depression and larger sample sizes, particularly in the context of CoD related to cancer.

Cardiac Amyloidosis: A Comprehensive Review of Pathophysiology, Diagnostic Approach, Applications of Artificial Intelligence, and Management Strategies

Cardiac amyloidosis (CA) is a serious and often fatal condition caused by the accumulation of amyloid fibrils in the heart, leading to progressive heart failure. It involves the misfolding of normally soluble proteins into insoluble amyloid fibrils, with transthyretin and light-chain amyloidosis being the most common forms affecting the heart. Advances in diagnostics, especially cardiac magnetic resonance imaging and non-invasive techniques, have improved early detection and disease management. Artificial intelligence has emerged as a diagnostic tool for cardiac amyloidosis, improving accuracy and enabling earlier intervention through advanced imaging analysis and pattern recognition. Management strategies include volume control, specific pharmacotherapies like tafamidis, and addressing arrhythmias and advanced heart failure. However, further research is needed for novel therapeutic approaches, the long-term effectiveness of emerging treatments, and the optimization of artificial intelligence applications in clinical practice for better patient outcomes. The article aims to provide an overview of CA, outlining its pathophysiology, diagnostic advancements, the role of artificial intelligence, management strategies, and the need for further research.

How Artificial Intelligence Can Enhance the Diagnosis of Cardiac Amyloidosis: A Review of Recent Advances and Challenges

Cardiac amyloidosis (CA) is an underdiagnosed form of infiltrative cardiomyopathy caused by abnormal amyloid fibrils deposited extracellularly in the myocardium and cardiac structures. There can be high variability in its clinical manifestations, and diagnosing CA requires expertise and often thorough evaluation; as such, the diagnosis of CA can be challenging and is often delayed. The application of artificial intelligence (AI) to different diagnostic modalities is rapidly expanding and transforming cardiovascular medicine. Advanced AI methods such as deep-learning convolutional neural networks (CNNs) may enhance the diagnostic process for CA by identifying patients at higher risk and potentially expediting the diagnosis of CA. In this review, we summarize the current state of AI applications to different diagnostic modalities used for the evaluation of CA, including their diagnostic and prognostic potential, and current challenges and limitations.

Abnormal global longitudinal strain and reduced serum inflammatory markers in cardiac AL amyloidosis patients without significant amyloid fibril deposition

Background

Cardiac dysfunction in AL amyloidosis is thought to be partly related to the direct impact of AL LCs on cardiomyocyte function, with the degree of dysfunction at diagnosis as a major determinant of clinical outcomes. Nonetheless, mechanisms underlying LC-induced myocardial toxicity are not well understood.

Methods

We identified gene expression changes correlating with human cardiac cells exposed to a cardiomyopathy-associated κAL LC. We then sought to confirm these findings in a clinical dataset by focusing on clinical parameters associated with the pathways dysregulated at the gene expression level.

Results

Upon exposure to a cardiomyopathy-associated κAL LC, cardiac cells exhibited gene expression changes related to myocardial contractile function and inflammation, leading us to hypothesize that there could be clinically detectable changes in GLS on echocardiogram and serum inflammatory markers in patients. Thus, we identified 29 patients with normal IVSd but abnormal cardiac biomarkers suggestive of LC-induced cardiac dysfunction. These patients display early cardiac biomarker staging, abnormal GLS, and significantly reduced serum inflammatory markers compared to patients with clinically evident amyloid fibril deposition.

Conclusion

Collectively, our findings highlight early molecular and functional signatures of cardiac AL amyloidosis, with potential impact for developing improved patient biomarkers and novel therapeutics.

Cardiac and renal AL amyloidosis controlled by autologous stem cell transplantation for 17 years accompanying late onset atrial fibrillation and complete atrioventricular block

Amyloid light chain (AL) amyloidosis is a rare hematologic disease that may involve multiple organs. Amongst the organs, cardiac involvement causes the greatest concern as its treatment is challenging. Diastolic dysfunction rapidly progresses to decompensated heart failure, pulseless electrical activity, and atrial standstill due to electro-mechanical dissociation resulting in death. High-dose melphalan plus autologous stem cell transplantation (HDM-ASCT) is the most radical treatment but its risk is very high and thus only less than 20% of patients can receive this therapy under criteria that can suppress treatment-related mortality. In substantial proportion of patients, levels of M protein remain elevated, and organ response cannot be achieved. Moreover, relapse may occur, making prediction of treatment response and judgement of disease eradication very difficult. Herein we report a case of AL amyloidosis who was treated with HDM-ASCT, resulting in preserved cardiac function and resolution of proteinuria for more than 17 years after HDM-ASCT ensuing atrial fibrillation and complete atrioventricular block required management by catheter ablation and pacemaker implantation 10 years and 12 years after transplantation, respectively.

Role of the mechanisms for antibody repertoire diversification in monoclonal light chain deposition disorders: when a friend becomes foe

The adaptive immune system of jawed vertebrates generates a highly diverse repertoire of antibodies to meet the antigenic challenges of a constantly evolving biological ecosystem. Most of the diversity is generated by two mechanisms: V(D)J gene recombination and somatic hypermutation (SHM). SHM introduces changes in the variable domain of antibodies, mostly in the regions that form the paratope, yielding antibodies with higher antigen binding affinity. However, antigen recognition is only possible if the antibody folds into a stable functional conformation. Therefore, a key force determining the survival of B cell clones undergoing somatic hypermutation is the ability of the mutated heavy and light chains to efficiently fold and assemble into a functional antibody. The antibody is the structural context where the selection of the somatic mutations occurs, and where both the heavy and light chains benefit from protective mechanisms that counteract the potentially deleterious impact of the changes. However, in patients with monoclonal gammopathies, the proliferating plasma cell clone may overproduce the light chain, which is then secreted into the bloodstream. This places the light chain out of the protective context provided by the quaternary structure of the antibody, increasing the risk of misfolding and aggregation due to destabilizing somatic mutations. Light chain-derived (AL) amyloidosis, light chain deposition disease (LCDD), Fanconi syndrome, and myeloma (cast) nephropathy are a diverse group of diseases derived from the pathologic aggregation of light chains, in which somatic mutations are recognized to play a role. In this review, we address the mechanisms by which somatic mutations promote the misfolding and pathological aggregation of the light chains, with an emphasis on AL amyloidosis. We also analyze the contribution of the variable domain (V_L) gene segments and somatic mutations on light chain cytotoxicity, organ tropism, and structure of the AL fibrils. Finally, we analyze the most recent advances in the development of computational algorithms to predict the role of somatic mutations in the cardiotoxicity of amyloidogenic light chains and discuss the challenges and perspectives that this approach faces.

AB-Amy: machine learning aided amyloidogenic risk prediction of therapeutic antibody light chains

Over 120 FDA-approved antibody-based therapeutics are used to treat a variety of diseases.However, many candidates could fail because of unfavorable physicochemical properties. Light-chain amyloidosis is one form of aggregation that can lead to severe safety risks in clinical development. Therefore, screening candidates with a less amyloidosis risk at the early stage can not only save the time and cost of antibody development but also improve the safety of antibody drugs. In this study, based on the dipeptide composition of 742 amyloidogenic and 712 non-amyloidogenic antibody light chains, a support vector machine-based model, AB-Amy, was trained to predict the light-chain amyloidogenic risk. The AUC of AB-Amy reaches 0.9651. The excellent performance of AB-Amy indicates that it can be a useful tool for the in silico evaluation of the light-chain amyloidogenic risk to ensure the safety of antibody therapeutics under clinical development. A web server is freely available at http://i.uestc.edu.cn/AB-Amy/.

Complete variable domain sequences of monoclonal antibody light chains identified from untargeted RNA sequencing data

Introduction

Monoclonal antibody light chain proteins secreted by clonal plasma cells cause tissue damage due to amyloid deposition and other mechanisms. The unique protein sequence associated with each case contributes to the diversity of clinical features observed in patients. Extensive work has characterized many light chains associated with multiple myeloma, light chain amyloidosis and other disorders, which we have collected in the publicly accessible database, AL-Base. However, light chain sequence diversity makes it difficult to determine the contribution of specific amino acid changes to pathology. Sequences of light chains associated with multiple myeloma provide a useful comparison to study mechanisms of light chain aggregation, but relatively few monoclonal sequences have been determined. Therefore, we sought to identify complete light chain sequences from existing high throughput sequencing data.

Methods

We developed a computational approach using the MiXCR suite of tools to extract complete rearranged IGVL-IGJL sequences from untargeted RNA sequencing data. This method was applied to whole-transcriptome RNA sequencing data from 766 newly diagnosed patients in the Multiple Myeloma Research Foundation CoMMpass study.

Results

Monoclonal IGVL-IGJL sequences were defined as those where >50% of assigned IGK or IGL reads from each sample mapped to a unique sequence. Clonal light chain sequences were identified in 705/766 samples from the CoMMpass study. Of these, 685 sequences covered the complete IGVL-IGJL region. The identity of the assigned sequences is consistent with their associated clinical data and with partial sequences previously determined from the same cohort of samples. Sequences have been deposited in AL-Base.

Discussion

Our method allows routine identification of clonal antibody sequences from RNA sequencing data collected for gene expression studies. The sequences identified represent, to our knowledge, the largest collection of multiple myeloma-associated light chains reported to date. This work substantially increases the number of monoclonal light chains known to be associated with non-amyloid plasma cell disorders and will facilitate studies of light chain pathology.

Identification of AL proteins from 10 λ-AL amyloidosis patients by mass spectrometry extracted from abdominal fat and heart tissue

BACKGROUND

Systemic AL amyloidosis arises from the misfolding of patient-specific immunoglobulin light chains (LCs). Potential drivers of LC amyloid formation are mutational changes and post-translational modifications (PTMs). However, little information is available on the exact primary structure of the AL proteins and their precursor LCs.

OBJECTIVE

We analyse the exact primary structure of AL proteins extracted from 10 λ AL amyloidosis patients and their corresponding precursor LCs.

MATERIALS AND METHODS

By cDNA sequencing of the precursor LC genes in combination with mass spectrometry of the AL proteins, the exact primary structure and PTMs were determined. This information was used to analyse their biochemical properties.

RESULTS

All AL proteins comprise the V_L and a small part of the C_L with a common C-terminal truncation region. While all AL proteins retain the conserved native disulphide bond of the V_L, we found no evidence for presence of other common PTMs. The analysis of the biochemical properties revealed that the isoelectric point of the V_L is significantly increased due to introduced mutations.

CONCLUSION

Our data imply that mutational changes influence the surface charge properties of the V_L and that common proteolytic processes are involved in the generation of the cleavage sites of AL proteins.

Report of Consensus Panel 6 from the 11 th International Workshop on Waldenström's Macroglobulinemia on Management of Waldenström's Macroglobulinemia Related Amyloidosis

Consensus Panel 6 (CP6) of the 11th International Workshop on Waldenström's Macroglobulinemia (IWWM-11) was tasked with reviewing the state of the art for diagnosis, prognosis, and therapy of AL amyloidosis associated with Waldenström macroglobulinemia (WM). Since significant advances have been made in the management of AL amyloidosis an update for this rare disease associated with WM was necessary. The key recommendations from IWWM-11 CP6 included: (1) The need to improve the diagnostic process by recognizing red flags and using biomarkers and imaging; (2) The essential tests for appropriate workup; (3) The diagnostic flowchart, including mandatory amyloid typing, that improves the differential diagnosis with transthyretin amyloidosis; (4) Criteria for therapy response assessment; (5) State of the art of the treatment including therapy of wild type transthyretin amyloidosis associated with WM.

Widespread amyloidogenicity potential of multiple myeloma patient-derived immunoglobulin light chains

BACKGROUND

In a range of human disorders such as multiple myeloma (MM), immunoglobulin light chains (IgLCs) can be produced at very high concentrations. This can lead to pathological aggregation and deposition of IgLCs in different tissues, which in turn leads to severe and potentially fatal organ damage. However, IgLCs can also be highly soluble and non-toxic. It is generally thought that the cause for this differential solubility behaviour is solely found within the IgLC amino acid sequences, and a variety of individual sequence-related biophysical properties (e.g. thermal stability, dimerisation) have been proposed in different studies as major determinants of the aggregation in vivo. Here, we investigate biophysical properties underlying IgLC amyloidogenicity.

RESULTS

We introduce a novel and systematic workflow, Thermodynamic and Aggregation Fingerprinting (ThAgg-Fip), for detailed biophysical characterisation, and apply it to nine different MM patient-derived IgLCs. Our set of pathogenic IgLCs spans the entire range of values in those parameters previously proposed to define in vivo amyloidogenicity; however, none actually forms amyloid in patients. Even more surprisingly, we were able to show that all our IgLCs are able to form amyloid fibrils readily in vitro under the influence of proteolytic cleavage by co-purified cathepsins.

CONCLUSIONS

We show that (I) in vivo aggregation behaviour is unlikely to be mechanistically linked to any single biophysical or biochemical parameter and (II) amyloidogenic potential is widespread in IgLC sequences and is not confined to those sequences that form amyloid fibrils in patients. Our findings suggest that protein sequence, environmental conditions and presence and action of proteases all determine the ability of light chains to form amyloid fibrils in patients.

Machine Learning Guides Peptide Nucleic Acid Flow Synthesis and Sequence Design

Peptide nucleic acids (PNAs) are potential antisense therapies for genetic, acquired, and viral diseases. Efficiently selecting candidate PNA sequences for synthesis and evaluation from a genome containing hundreds to thousands of options can be challenging. To facilitate this process, this work leverages machine learning (ML) algorithms and automated synthesis technology to predict PNA synthesis efficiency and guide rational PNA sequence design. The training data is collected from individual fluorenylmethyloxycarbonyl (Fmoc) deprotection reactions performed on a fully automated PNA synthesizer. The optimized ML model allows for 93% prediction accuracy and 0.97 Pearson's r. The predicted synthesis scores are validated to be correlated with the experimental high-performance liquid chromatography (HPLC) crude purities (correlation coefficient R² = 0.95). Furthermore, a general applicability of ML is demonstrated through designing synthetically accessible antisense PNA sequences from 102 315 predicted candidates targeting exon 44 of the human dystrophin gene, SARS-CoV-2, HIV, as well as selected genes associated with cardiovascular diseases, type II diabetes, and various cancers. Collectively, ML provides an accurate prediction of PNA synthesis quality and serves as a useful computational tool for informing PNA sequence design.

Understanding AL amyloidosis with a little help from in vivo models

Monoclonal immunoglobulin (Ig) light chain amyloidosis (AL) is a rare but severe disease that may occur when a B or plasma cell clone secretes an excess of free Ig light chains (LCs). Some of these LCs tend to aggregate into organized fibrils with a β-sheet structure, the so-called amyloid fibrils, and deposit into the extracellular compartment of organs, such as the heart or kidneys, causing their dysfunction. Recent findings have confirmed that the core of the amyloid fibrils is constituted by the variable (V) domain of the LCs, but the mechanisms underlying the unfolding and aggregation of this fragment and its deposition are still unclear. Moreover, in addition to the mechanical constraints exerted by the massive accumulation of amyloid fibrils in organs, the direct toxicity of these variable domain LCs, full-length light chains, or primary amyloid precursors (oligomers) seems to play a role in the pathogenesis of the disease. Many in vitro studies have focused on these topics, but the variability of this disease, in which each LC presents unique properties, and the extent and complexity of affected organs make its study in vivo very difficult. Accordingly, several groups have focused on the development of animal models for years, with some encouraging but mostly disappointing results. In this review, we discuss the experimental models that have been used to better understand the unknowns of this pathology with an emphasis on in vivo approaches. We also focus on why reliable AL amyloidosis animal models remain so difficult to obtain and what this tells us about the pathophysiology of the disease.

Machine learning prediction of antibody aggregation and viscosity for high concentration formulation development of protein therapeutics

Machine learning has been recently used to predict therapeutic antibody aggregation rates and viscosity at high concentrations (150 mg/ml). These works focused on commercially available antibodies, which may have been optimized for stability. In this study, we measured accelerated aggregation rates at 45°C and viscosity at 150 mg/ml for 20 preclinical and clinical-stage antibodies. Features obtained from molecular dynamics simulations of the full-length antibody and sequences were used for machine learning model construction. We found a k-nearest neighbors regression model with two features, spatial positive charge map on the CDRH2 and solvent-accessible surface area of hydrophobic residues on the variable fragment, gives the best performance for predicting antibody aggregation rates (r = 0.89). For the viscosity classification model, the model with the highest accuracy is a logistic regression model with two features, spatial negative charge map on the heavy chain variable region and spatial negative charge map on the light chain variable region. The accuracy and the area under precision recall curve of the classification model from validation tests are 0.86 and 0.70, respectively. In addition, we combined data from another 27 commercial mAbs to develop a viscosity predictive model. The best model is a logistic regression model with two features, number of hydrophobic residues on the light chain variable region and net charges on the light chain variable region. The accuracy and the area under precision recall curve of the classification model are 0.85 and 0.6, respectively. The aggregation rates and viscosity models can be used to predict antibody stability to facilitate pharmaceutical development.

Cu(II) Binding Increases the Soluble Toxicity of Amyloidogenic Light Chains

Light chain amyloidosis (AL) is caused by the aberrant overproduction of immunoglobulin light chains (LCs). The resulting abnormally high LC concentrations in blood lead to deposit formation in the heart and other target organs. Organ damage is caused not only by the accumulation of bulky amyloid deposits, but extensive clinical data indicate that circulating soluble LCs also exert cardiotoxic effects. The nematode C. elegans has been validated to recapitulate LC soluble toxicity in vivo, and in such a model a role for copper ions in increasing LC soluble toxicity has been reported. Here, we applied microscale thermophoresis, isothermal calorimetry and thermal melting to demonstrate the specific binding of Cu²⁺ to the variable domain of amyloidogenic H7 with a sub-micromolar affinity. Histidine residues present in the LC sequence are not involved in the binding, and yet their mutation to Ala reduces the soluble toxicity of H7. Copper ions bind to and destabilize the variable domains and induce a limited stabilization in this domain. In summary, the data reported here, elucidate the biochemical bases of the Cu²⁺-induced toxicity; moreover, they also show that copper binding is just one of the several biochemical traits contributing to LC soluble in vivo toxicity.

A Conservative Point Mutation in a Dynamic Antigen-binding Loop of Human Immunoglobulin λ6 Light Chain Promotes Pathologic Amyloid Formation

Immunoglobulin light chain (LC) amyloidosis (AL) is a life-threatening human disease wherein free mono-clonal LCs deposit in vital organs. To determine what makes some LCs amyloidogenic, we explored patient-based amyloidogenic and non-amyloidogenic recombinant LCs from the λ6 subtype prevalent in AL. Hydrogen-deuterium exchange mass spectrometry, structural stability, proteolysis, and amyloid growth studies revealed that the antigen-binding CDR1 loop is the least protected part in the variable domain of λ6 LC, particularly in the AL variant. N32T substitution in CRD1 is identified as a driver of amyloid formation. Substitution N32T increased the amyloidogenic propensity of CDR1 loop, decreased its protection in the native structure, and accelerated amyloid growth in the context of other AL substitutions. The destabilizing effects of N32T propagated across the molecule increasing its dynamics in regions ∼30 Å away from the substitution site. Such striking long-range effects of a conservative point substitution in a dynamic surface loop may be relevant to Ig function. Comparison of patient-derived and engineered proteins showed that N32T interactions with other substitution sites must contribute to amyloidosis. The results suggest that CDR1 is critical in amyloid formation by other λ6 LCs.

How I Treat AL Amyloidosis

The treatment of patients with systemic light chain (AL) amyloidosis is a challenge to hematologists. Despite its generally small size, the underlying clone causes a rapidly progressing, often devastating multiorgan dysfunction through the toxic light chains that form amyloid deposits. Clinical manifestations are deceitful and too often recognized at an irreversible stage. However, hematologists are in the unique position to diagnose AL amyloidosis at a pre-symptomatic stage checking biomarkers of amyloid organ involvement in patients with monoclonal gammopathies at higher risk to develop the disease. Adequate technology and expertise are needed for a prompt and correct diagnosis, particularly for ruling out non-AL amyloidoses that are now also treatable. Therapy should be carefully tailored based on severity of organ involvement and clonal characteristics, and early and continual monitoring of response is critical. Three recent randomized clinical trials moved AL amyloidosis to evidence-based era. Above all, the daratumumab-bortezomib combination is a new standard-of-care for newly diagnosed patients inducing rapid and deep responses that translate into high rates of organ response. The availability of new effective drugs allows to better personalize the therapy, reduce toxicity, and improve outcomes. Patients should be treated within clinical trials whenever possible.

Genetic pathogenesis of immunoglobulin light chain amyloidosis: basic characteristics and clinical applications

Immunoglobulin light chain amyloidosis (AL) is an indolent plasma cell disorder characterized by free immunoglobulin light chain (FLC) misfolding and amyloid fibril deposition. The cytogenetic pattern of AL shows profound similarity with that of other plasma cell disorders but harbors distinct features. AL can be classified into two primary subtypes: non-hyperdiploidy and hyperdiploidy. Non-hyperdiploidy usually involves immunoglobulin heavy chain translocations, and t(11;14) is the hallmark of this disease. T(11;14) is associated with low plasma cell count but high FLC level and displays distinct response outcomes to different treatment modalities. Hyperdiploidy is associated with plasmacytosis and subclone formation, and it generally confers a neutral or inferior prognostic outcome. Other chromosome abnormalities and driver gene mutations are considered as secondary cytogenetic aberrations that occur during disease evolution. These genetic aberrations contribute to the proliferation of plasma cells, which secrete excess FLC for amyloid deposition. Other genetic factors, such as specific usage of immunoglobulin light chain germline genes and light chain somatic mutations, also play an essential role in amyloid fibril deposition in AL. This paper will propose a framework of AL classification based on genetic aberrations and discuss the amyloid formation of AL from a genetic aspect.