Assessing Cardiac Amyloidosis Subtypes by Unsupervised Phenotype Clustering Analysis.

BACKGROUND: Cardiac amyloidosis (CA) is a set of amyloid diseases with usually predominant cardiac symptoms, including light-chain amyloidosis (AL), hereditary variant transthyretin amyloidosis (ATTRv), and wild-type transthyretin amyloidosis (ATTRwt). CA are characterized by high heterogeneity in phenotypes leading to diagnosis delay and worsened outcomes. OBJECTIVES: The authors used clustering analysis to identify typical clinical profiles in a large population of patients with suspected CA. METHODS: Data were collected from the French Referral Center for Cardiac Amyloidosis database (Hôpital Henri Mondor, Créteil), including 1,394 patients with suspected CA between 2010 and 2018: 345 (25%) had a diagnosis of AL, 263 (19%) ATTRv, 402 (29%) ATTRwt, and 384 (28%) no amyloidosis. Based on comprehensive clinicobiological phenotyping, unsupervised clustering analyses were performed by artificial neural network-based self-organizing maps to identify patient profiles (clusters) with similar characteristics, independent of the final diagnosis and prognosis. RESULTS: Mean age and left ventricular ejection fraction were 72 ± 13 years and 52% ± 13%, respectively. The authors identified 7 clusters of patients with contrasting profiles and prognosis. AL patients were distinctively located within a typical cluster; ATTRv patients were distributed across 4 clusters with varying clinical presentations, 1 of which overlapped with patients without amyloidosis; interestingly, ATTRwt patients spread across 3 distinct clusters with contrasting risk factors, biological profiles, and prognosis. CONCLUSIONS: Clustering analysis identified 7 clinical profiles with varying characteristics, prognosis, and associations with diagnosis. Especially in patients with ATTRwt, these results suggest key areas to improve amyloidosis diagnosis and stratify prognosis depending on associated risk factors.

Immunohistochemical typing of amyloid in fixed paraffin-embedded samples by an automatic procedure: Comparison with immunofluorescence data on fresh-frozen tissue.

Amyloidosis comprises a spectrum of disorders characterized by the extracellular deposition of amorphous material, originating from an abnormal serum protein. The typing of amyloid into its many variants represents a pivotal step for a correct patient management. Several methods are currently used, including mass spectrometry, immunofluorescence, immunohistochemistry, and immunogold labeling. The aim of the present study was to investigate the accuracy and reliability of immunohistochemistry by means of a recently developed amyloid antibody panel applicable on fixed paraffin-embedded tissues in an automated platform. Patients with clinically and pathologically proven amyloidosis were divided into two cohorts: a pilot one, which included selected amyloidosis cases from 2009 to 2018, and a retrospective one (comprising all consecutive amyloidosis cases analyzed between November 2018 and May 2020). The above-referred panel of antibodies for amyloid classification was tested in all cases using an automated immunohistochemistry platform. When fresh-frozen material was available, immunofluorescence was also performed. Among 130 patients, a total of 143 samples from different organs was investigated. They corresponded to 51 patients from the pilot cohort and 79 ones from the retrospective cohort. In 82 cases (63%), fresh-frozen tissue was tested by immunofluorescence, serving to define amyloid subtype only in 30 of them (36.6%). On the contrary, the automated immunohistochemistry procedure using the above-referred new antibodies allowed to establish the amyloid type in all 130 cases (100%). These included: ALλ (n = 60, 46.2%), ATTR (n = 29, 22.3%), AA (n = 19, 14.6%), ALκ (n = 18, 13.8%), ALys (n = 2, 1.5%), and Aß2M amyloidosis (n = 2, 1.5%). The present immunohistochemistry antibody panel represents a sensitive, reliable, fast, and low-cost method for amyloid typing. Since immunohistochemistry is available in most pathology laboratories, it may become the new gold standard for amyloidosis classification, either used alone or combined with mass spectrometry in selected cases.

A 16-year Survey of Clinicopathological Findings, Electron Microscopy, and Classification of Renal Amyloidosis.

Background: Electron microscopy (EM) is a valuable tool in the diagnosis of renal amyloidosis, particularly in the early stages of the disease. In Iran, studies on EM and the clinical features of renal amyloidosis are scarce. The objective of the present study was to survey EM investigations, pathological classifications, and clinical features of renal amyloidosis. Methods: This cross-sectional study was performed in Shiraz, Iran, during 2001-2016. Out of 2,770 kidney biopsies, 27 cases with a diagnosis of renal amyloidosis were investigated. EM investigation and six staining procedures for light microscopy (LM) were performed. Two pathological classifications based on glomerular, peritubular, perivascular, and interstitial involvement were made. Finally, the association between these classifications and the clinical features was assessed. Chi-square, Fisher's exact, Independent t test, and Multiple logistic regression analysis were used. P values<0.05 were considered statistically significant. Results: In 51.9% of the cases, the clinical diagnosis was nephrotic syndrome. Proteinuria and edema were the most prevalent clinical manifestations. The role of EM investigation for diagnosis was graded "necessary" or "supportive" in 48.2% of the patients. In the classification based on glomerular classes, variables such as the mean blood pressure (P=0.003), history of hypertension (P=0.02), creatinine >1.5 (P=0.03), and severe tubular atrophy (P=0.03) were significantly higher in class B (advanced amyloid depositions). Conclusion: EM plays an important role in the diagnosis of renal amyloidosis. EM in conjunction with LM investigation with Congo red staining is recommended, to prevent misdiagnosis of patients with a clinical suspicion of renal amyloidosis. Among different pathological features of renal amyloidosis, the severity of glomerular amyloid depositions had a clear relationship with clinical presentations.

Classification of Amyloidosis by Model-Assisted Mass Spectrometry-Based Proteomics.

Amyloidosis is a rare disease caused by the misfolding and extracellular aggregation of proteins as insoluble fibrillary deposits localized either in specific organs or systemically throughout the body. The organ targeted and the disease progression and outcome is highly dependent on the specific fibril-forming protein, and its accurate identification is essential to the choice of treatment. Mass spectrometry-based proteomics has become the method of choice for the identification of the amyloidogenic protein. Regrettably, this identification relies on manual and subjective interpretation of mass spectrometry data by an expert, which is undesirable and may bias diagnosis. To circumvent this, we developed a statistical model-assisted method for the unbiased identification of amyloid-containing biopsies and amyloidosis subtyping. Based on data from mass spectrometric analysis of amyloid-containing biopsies and corresponding controls. A Boruta method applied on a random forest classifier was applied to proteomics data obtained from the mass spectrometric analysis of 75 laser dissected Congo Red positive amyloid-containing biopsies and 78 Congo Red negative biopsies to identify novel "amyloid signature" proteins that included clusterin, fibulin-1, vitronectin complement component C9 and also three collagen proteins, as well as the well-known amyloid signature proteins apolipoprotein E, apolipoprotein A4, and serum amyloid P. A SVM learning algorithm were trained on the mass spectrometry data from the analysis of the 75 amyloid-containing biopsies and 78 amyloid-negative control biopsies. The trained algorithm performed superior in the discrimination of amyloid-containing biopsies from controls, with an accuracy of 1.0 when applied to a blinded mass spectrometry validation data set of 103 prospectively collected amyloid-containing biopsies. Moreover, our method successfully classified amyloidosis patients according to the subtype in 102 out of 103 blinded cases. Collectively, our model-assisted approach identified novel amyloid-associated proteins and demonstrated the use of mass spectrometry-based data in clinical diagnostics of disease by the unbiased and reliable model-assisted classification of amyloid deposits and of the specific amyloid subtype.

Systemic amyloidosis from A (AA) to T (ATTR): a review.

Systemic amyloidosis is a rare protein misfolding and deposition disorder leading to progressive organ failure. There are over 15 types of systemic amyloidosis, each caused by a different precursor protein which promotes amyloid formation and tissue deposition. Amyloidosis can be acquired or hereditary and can affect various organs, including the heart, kidneys, liver, nerves, gastrointestinal tract, lungs, muscles, skin and soft tissues. Symptoms are usually insidious and nonspecific resulting in diagnostic delay. The field of amyloidosis has seen significant improvements over the past decade in diagnostic accuracy, prognosis prediction and management. The advent of mass spectrometry-based shotgun proteomics has revolutionized amyloid typing and has led to the discovery of new amyloid types. Accurate typing of the precursor protein is of paramount importance as the type dictates a specific management approach. In this article, we review each type of systemic amyloidosis to provide the practitioner with practical tools to improve diagnosis and management of these rare disorders.

Distribution of amyloidosis subtypes based on tissue biopsy site - Consecutive analysis of 729 patients at a single amyloidosis center in Japan.

This study was performed to elucidate the distribution of amyloidosis subtypes based on tissue biopsy site. Samples obtained from 729 consecutive patients with amyloidosis were analyzed by immunohistochemical staining (IHC) and supplemental mass spectrometry (MS). The correlations between the type of organs from which samples were obtained and amyloidosis subtypes were investigated retrospectively. Among the patients, 95.1% were diagnosed by IHC and 4.9% were diagnosed by MS. The distribution of amyloidosis subtypes was as follows: AL, 59.1%; ATTR, 32.9%; AA, 4.0%; AH, 1.4%; Aß2M, 0.8%; and others, 0.9%. AL was the most common subtype in most organs, including the liver, lung, kidney, lower urinary tract, bone marrow, gastrointestinal tract, and skin/subcutaneous tissue. ATTR was the most common subtype in the heart, carpal tunnel, and peripheral nerves. AH was the second most common subtype in renal biopsy. Three or more amyloidosis subtypes were detected in each organ. In conclusion, AL was the most common subtype in most biopsy sites except the heart, carpal tunnel, and peripheral nerve, in which ATTR was more common. Because several types of amyloidogenic protein were detected in each organ, amyloid typing must be pursued, no matter the site from where biopsy was obtained.

Amyloid nomenclature 2020: update and recommendations by the International Society of Amyloidosis (ISA) nomenclature committee.

The ISA Nomenclature Committee met electronically before and directly after the XVII ISA International Symposium on Amyloidosis, which, unfortunately, had to be virtual in September 2020 due to the ongoing COVID-19 pandemic instead of a planned meeting in Tarragona in March. In addition to confirmation of basic nomenclature, several additional concepts were discussed, which are used in scientific amyloid literature. Among such concepts are cytotoxic oligomers, protofibrils, primary and secondary nucleation, seeding and cross-seeding, amyloid signature proteins, and amyloid plaques. Recommendations for their use are given. Definitions of amyloid and amyloidosis are confirmed. Possible novel human amyloid fibril proteins, appearing as 'classical' in vivo amyloid, were discussed. It was decided to include fibulin-like extracellular matrix protein 1 (amyloid protein: AEFEMP1), which appears as localised amyloid in portal veins. There are several possible amyloid proteins under investigation, and these are included in a new Table.

Amyloid in the Lung.

Amyloidosis is the term given to abnormal deposition of misfolded precursor proteins at single or multiple sites, leading to organ dysfunction or clinical signs and symptoms. Pulmonary manifestations are nonspecific and may be associated with several amyloid protein subtypes, commonly AL (light chain) and AA (autoimmune) amyloids. Signs or symptoms of amyloid disease may often involve more of the clinical abnormalities of other affected organs than the lungs themselves. Radiologic pulmonary findings include septal and parenchymal ground glass or nodular infiltrates, multiple nodules, cysts, and focal tracheobronchial abnormalities. Lymphadenopathy with or without calcification and pleural effusions has also been reported. Directed therapy is initiated in response to clinical signs or symptoms often as a result of systemic or secondary diseases or conditions. Long-term prognosis is more dependent on the extent of organ involvement where morbidity is often the highest in those with multisystemic disease.

Hiding in Plain Sight: Cardiac Amyloidosis, an Emerging Epidemic.

Amyloidosis is a term used to describe a group of rare heterogeneous diseases that ultimately result in the deposition and accumulation of misfolded proteins. These misfolded proteins, known as amyloids, are associated with a variety of precursor proteins that have amyloidogenic potential. Ultimately, the specific type of amyloidosis is dependent on multiple factors including genetic variability of precursor proteins and the tissue or organ in which the amyloid accumulates. Several types of amyloid have a predilection for the heart and thus contribute to cardiac amyloidosis, a major cause of restrictive cardiomyopathy. Individuals with cardiac amyloidosis present clinically with heart failure with preserved ejection fraction. Although improved diagnostics and increased awareness of cardiac amyloidosis have led to a relative increase in diagnosis, cardiac amyloidosis remains an underrecognized and underdiagnosed cause of heart failure with preserved ejection fraction. It is essential to properly identify cases of cardiac amyloidosis and determine the pathology responsible for the formation of amyloid to appropriately provide management. This review aims to encourage physician awareness of cardiac amyloidosis by focusing on clinical presentation and the distinctions between types. Furthermore, epidemiology is central to understanding the affected demographics and sometimes hereditary nature of the disease. Improved understanding of cardiac amyloidosis will ideally lead to earlier diagnosis and interventions to improve patient outcomes.

Causes of AA amyloidosis: a systematic review.

From a clinical perspective, there is a need for a reliable and comprehensive list of diseases causing AA amyloidosis. This list could guide clinicians in the evaluation of patients with AA amyloidosis in whom an obvious cause is lacking. In this systematic review, a PubMed, Embase and Web of Science literature search were performed on causes of AA amyloidosis published in the last four decades. Initially, 4066 unique titles were identified, but only 795 full-text articles and letters were finally selected for analysis. Titles were excluded because of non-AA type of amyloidosis, language, no full-text publication or irrelevance. Hundred and fifty diseases were initially reported to be associated with the development of AA amyloidosis. The presence of AA amyloid was proven in 208 articles (26% of all) of which 140 (67%) showed a strong association with an underlying disease process. Disease associations were categorized and 48 were listed as strong, 19 as weak, 23 as unclear, and 60 as unlikely. Most newly described diseases are not really unexpected because they often cause longstanding inflammation. Based on the spectrum of identified causes, a pragmatic diagnostic approach is proposed for the AA amyloidosis patient in whom an obvious underlying disease is lacking.

Pattern of renal amyloidosis in South Africa.

BACKGROUND: Kidney disease is a serious manifestation of systemic amyloidosis and a major cause of morbidity and mortality. Tuberculosis (TB) occurs up to 27 times more commonly in human immunodeficiency virus (HIV) infected patients and is also an important cause of renal amyloid; there are however no reports of renal amyloidosis in South Africa in the HIV era. METHODS: This was a retrospective record review of cases of amyloidosis diagnosed on renal biopsies at our tertiary referral hospital between January 1985 and December 2016. RESULTS: Forty-six cases of amyloidosis were identified over the study period. The calculated biopsy prevalence was 1.38 per 100 non-transplant renal biopsies (95% Confidence Interval 1.02-1.86). AL amyloidosis was identified in 26 (57%) cases and AA in 20 (43%). The median age at presentation was 51 years and 52% of cases were female. Patients with AA amyloidosis were significantly younger compared to their AL counterparts (age 42 years vs. 58 years, p = < 0.001) and were all significantly non-white. The main clinical presentation was nephrotic syndrome (85%) and 52% of cases also had a serum creatinine value of greater than 120 µmol/L. Of the 20 cases of AA amyloidosis, 12 (60%) were associated with tuberculosis. HIV infection was noted in only two (10%) of the 20 AA cases. Median survival after diagnosis was 2 months. CONCLUSION: Amyloidosis is a rare cause of kidney disease and typically presents with nephrotic syndrome. A similar number of AA and AL types were observed, and outcomes are worse in cases of AA amyloid. While TB remains the major underlying disease in this type, HIV infection was infrequent in cases of AA renal amyloidosis.

AmyCo: the amyloidoses collection.

Amyloid fibrils are formed when soluble proteins misfold into highly ordered insoluble fibrillar aggregates and affect various organs and tissues. The deposition of amyloid fibrils is the main hallmark of a group of disorders, called amyloidoses. Curiously, fibril deposition has been also recorded as a complication in a number of other pathological conditions, including well-known neurodegenerative or endocrine diseases. To date, amyloidoses are roughly classified, owing to their tremendous heterogeneity. In this work, we introduce AmyCo, a freely available collection of amyloidoses and clinical disorders related to amyloid deposition. AmyCo classifies 75 diseases associated with amyloid deposition into two distinct categories, namely 1) amyloidosis and 2) clinical conditions associated with amyloidosis. Each database entry is annotated with the major protein component (causative protein), other components of amyloid deposits and affected tissues or organs. Database entries are also supplemented with appropriate detailed annotation and are referenced to ICD-10, MeSH, OMIM, PubMed, AmyPro and UniProtKB databases. To our knowledge, AmyCo is the first attempt towards the creation of a complete and an up-to-date repository, containing information about amyloidoses and diseases related to amyloid deposition. The AmyCo web interface is available at http://bioinformatics.biol.uoa.gr/amyco .

SYSTEMIC AMYLOIDOSIS IN A POPULATION OF PRONGHORN ANTELOPE (ANTILOCAPRA AMERICANA).

Thirteen pronghorn antelope (Antilocapra americana) from a single captive herd at the Columbus Zoo and Aquarium underwent complete or partial necropsies between 1997 and 2016. Ten of the 13 animals had histologic evidence of amyloidosis resulting in a 77% prevalence. Histologic and ultrastructural changes were characterized in an attempt to determine the underlying cause of the amyloid. Amyloid detection was performed through histologic examination of hemotoxylin and eosin and Congo red-stained microscopic slides for all 13 animals. Transmission electron microscopy and mass spectrometry was performed on renal tissue from two animals. Pedigree analysis and retrospective investigation into the clinical histories was performed. Histologically, 9/10 animals had amyloid present in the kidneys, 8/10 in the liver, 9/10 in the spleen, 4/10 in the gastrointestinal tract, 3/10 in the adrenal glands, and 2/10 in the thyroid glands. Transmission electron microscopy demonstrated glomerular deposits consistent with amyloid. Mass spectrometry performed on renal specimens from two animals revealed the presence of serum amyloid A. Eight of the 10 animals diagnosed with systemic amyloidosis had a clinical history of haemonchosis (elevated fecal strongyle count), 5/10 were diagnosed with pneumonia postmortem, and 7/10 had postmortem findings consistent with negative energy balance. Serum amyloid A, and ß and γ globulin levels were evaluated in four cases of amyloidosis, and all were within normal ranges for healthy domestic cattle. It was possible that the herd's amyloidosis was associated with a hereditary defect that could be exacerbated by chronic inflammation. However, there was no significant association between the mean degree of relatedness and presence of amyloidosis. In conclusion, systemic amyloidosis in this captive population of pronghorn is common. It is likely reactive and secondary to underlying chronic inflammation caused by haemonchosis and/or pneumonia.

An unusual case of Aß2M amyloid deposition in bladder cancer in a non-dialysis patient.

ß2-microglobulin-related (Aß2M) amyloidosis (dialysis-associated amyloidosis) is a common complication in long-term dialysis patients. An increased concentration of ß2-microgloblin (ß2-m) in the serum appears to be a prerequisite for Aß2M amyloidosis, in turn causing Aß2M amyloid deposition predominantly in the osteoarticular tissue. There are few reports, however, of Aß2M amyloid deposition in non-dialysis patients. We describe an atypical case of a non-dialysis patient with Aß2M amyloid deposition in bladder cancer. A Japanese man in his 80s with no history of dialysis was admitted for transurethral resection of bladder cancer. Histopathological analysis revealed a small amount of amyloid deposition in the small-vessel wall of both the peripheral urothelial carcinoma and necrotic area. Amyloid typing by immunohistochemistry was strongly positive for anti-ß2-m antibody, and ß2-m was most frequently detected in laser microdissection-liquid chromatography tandem mass spectrometry. Although Aß2M amyloidosis was expected, contrary to this, the patient's serum ß2-m was only 4 mg/L, although his urine ß2-m level was increased at 1340 mg/L. The unique findings observed in our patient may contribute to the elucidation of the novel pathogenesis of Aß2M amyloid fibril formation that is distinct from conventional Aß2M amyloidosis.

The complementary role of histology and proteomics for diagnosis and typing of systemic amyloidosis.

The tissue diagnosis of amyloidosis and confirmation of fibril protein type, which are crucial for clinical management, have traditionally relied on Congo red (CR) staining followed by immunohistochemistry (IHC) using fibril protein specific antibodies. However, amyloid IHC is qualitative, non-standardised, requires operator expertise, and not infrequently fails to produce definitive results. More recently, laser dissection mass spectrometry (LDMS) has been developed as an alternative method to characterise amyloid in tissue sections. We sought to compare these techniques in a real world setting. During 2017, we performed LDMS on 640 formalin-fixed biopsies containing amyloid (CR+ve) comprising all 320 cases that could not be typed by IHC (IHC-ve) and 320 randomly selected CR+ve samples that had been typed (IHC+ve). In addition, we studied 60 biopsies from patients in whom there was a strong suspicion of amyloidosis, but in whom histology was non-diagnostic (CR-ve). Comprehensive clinical assessments were conducted in 532 (76%) of cases. Among the 640 CR+ve samples, 602 (94%) contained ≥2 of 3 amyloid signature proteins (ASPs) on LDMS (ASP+ve) supporting the presence of amyloid. A total of 49 of the 60 CR-ve samples were ASP-ve; 7 of 11 that were ASP+ve were glomerular. The amyloid fibril protein was identified by LDMS in 255 of 320 (80%) of the IHC-ve samples and in a total of 545 of 640 (85%) cases overall. The LDMS and IHC techniques yielded discordant results in only 7 of 320 (2%) cases. CR histology and LDMS are corroborative for diagnosis of amyloid, but LDMS is superior to IHC for confirming amyloid type.