AbstractExamination of abdominal subcutaneous fat aspirates is a practical, sensitive and specific method for the diagnosis of systemic amyloidosis. Here we describe the development and implementation of a clinical assay using mass spectrometry-based proteomics to type amyloidosis in subcutaneous fat aspirates. First, we validated the assay comparing amyloid-positive (n=43) and -negative (n=26) subcutaneous fat aspirates. The assay classified amyloidosis with 88% sensitivity and 96% specificity. We then implemented the assay as a clinical test, and analyzed 366 amyloid-positive subcutaneous fat aspirates in a 4-year period as part of routine clinical care. The assay had a sensitivity of 90%, and diverse amyloid types, including immunoglobulin light chain (74%), transthyretin (13%), serum amyloid A (%1), gelsolin (1%), and lysozyme (1%), were identified. Using bioinformatics, we identified a universal amyloid proteome signature, which has high sensitivity and specificity for amyloidosis similar to that of Congo red staining. We curated proteome databases which included variant proteins associated with systemic amyloidosis, and identified clonotypic immunoglobulin variable gene usage in immunoglobulin light chain amyloidosis, and the variant peptides in hereditary transthyretin amyloidosis. In conclusion, mass spectrometry-based proteomic analysis of subcutaneous fat aspirates offers a powerful tool for the diagnosis and typing of systemic amyloidosis. The assay reveals the underlying pathogenesis by identifying variable gene usage in immunoglobulin light chains and the variant peptides in hereditary amyloidosis.
Because systemic amyloidosis is a multiorgan disease with significant morbidity and mortality early diagnosis is critical for effective management of patients.31 The disease presents insidiously with subtle signs and symptoms, and most frequent clinical findings can mimic cardiac, renal and neurological diseases. For this reason, quick, sensitive and specific methods to screen for the possibility of systemic amyloidosis are necessary. Microscopic examination of Congo red-stained abdominal subcutaneous fat aspirates is one of the widely used methods for the screening and diagnosis of systemic amyloidosis.84 Subcutaneous fat aspiration (SFA) is simple, quick, can be performed in an outpatient setting, and has a high specificity and sensitivity for the diagnosis of systemic amyloidosis. However, although the diagnosis of amyloidosis can be established from the SFA specimen, identification of the specific amyloid type, which is essential for management of systemic amyloidosis, has not been possible until recently. We have recently developed a clinical assay using mass spectrometry (MS)-based proteomic analysis that can diagnose and classify amyloidosis in formalin-fixed paraffin-embedded (FFPE) tissues with high sensitivity and specificity.9 The assay has been used extensively for typing amyloidosis at the Mayo Clinic, and nationally through the reference laboratory services of Mayo Medical Laboratories.1910 We and others have shown the possibility of applying proteomic approaches including MS-based proteomics for typing amyloidosis in SFA specimens in a research setting.2520 In this study, we show the clinical validation and implementation of an MS-based proteomic assay for the diagnosis and typing of amyloidosis in SFA specimens in a 1988 Clinical Laboratory Improvement Amendments (CLIA) accredited laboratory setting. The assay not only provides diagnostic information but in many cases also sheds light on the underlying pathogenesis by identifying the detailed proteome of the amyloid deposits.
The study included two distinct cohorts. The first cohort, referred to as the test validation cohort, comprised 95 patients prospectively investigated for systemic amyloidosis by SFA screening. Under a protocol approved by the Mayo Foundation Institutional Review Board (MFIRB) and after informed consent, an extra specimen was obtained for MS-based proteomic analysis from the patients in this cohort. The second cohort, referred to as the clinical cohort, comprised 366 consecutive patients diagnosed with systemic amyloidosis by Congo red reactivity in SFA specimens. In the clinical cohort, MS-based proteomic analysis was performed as part of the routine clinical work–up, and the data were retrospectively reviewed for this study as part of a protocol approved by the MFIRB.
Subcutaneous fat aspiration for diagnosis of systemic amyloidosis
Specimens were obtained, stained with Congo red and interpreted according to previously established protocols described elsewhere.5 For the validation cohort one half of each specimen was used to make a smear and stained with Congo red. The other half, irrespective of whether the smear was positive for amyloid by Congo red, was processed for MS-based proteomic analysis. For the clinical cohort, one half of each specimen was used to make a smear and stained with Congo red. The other half was processed for MS-based proteomic analysis only if the first half stained positive for amyloidosis, as required by the established clinical practice and test validation criteria.
Mass spectrometry-based proteomic analysis of subcutaneous fat aspirates
The SFA specimens were processed for MS analysis using a modification of a previously established protocol.26 Briefly, the aspirate was solubilized and digested in trypsin and used for protein identification by nano-flow liquid chromatography electrospray tandem MS.9 Peptide spectra present in the raw data files were matched against a composite protein sequence database using three different search engines (Sequest27, X!Tandem28 and Mascot29). The composite database contains protein sequences obtained from the SwissProt database selected for human subspecies, known human immunoglobulin variant domains obtained from ImMunoGeneTics database,30 known amyloidogenic mutations collected from literature and common contaminants. Reversed protein sequences are appended to the database for estimating the false discovery rates of the identifications31. Resulting peptide identifications were combined, filtered, and assembled into protein sequences using Scaffold software (Proteome Software, Portland, OR, USA). Further details are provided in the Online Supplementary Methods.
Measurement of serum immunoglobulin free light chains
Serum immunoglobulin free light chain (FLC) was quantified using a serum FLC assay (Freelite; The Binding Site, San Diego, CA, USA) performed on a Dade-Behring Nephelometer (Deerfield, IL, USA) as described previously.32
The relative amounts of apolipoprotein E (APOE), apolipoprotein A1 (APOA1), apolipoprotein A4 (APOA4) and serum amyloid P-component (SAP) between the Congo red-negative/no amyloid (n=32) and Congo red-positive/amyloid (n=43) cases were compared in the validation cohort using the Mann-Whitney U test. Associations between the amount of patient’s FLC and immunoglobulin kappa or lambda spectra identified by MS-based proteomic assay were examined using Spearman rank correlation. Further details are provided in the Online Supplementary Methods.
Characteristics of the cases in the validation cohort
The validation cohort consisted of 43 females and 52 males with an average age of 62 years. From this cohort, 43 specimens were determined to be Congo red-positive and 52 were determined to be Congo red-negative (Table 1). All Congo red-positive cases had clinical features consistent with systemic amyloidosis and were, therefore, considered to be “true positive”. In this set, amyloidosis was classified based on previously established gold standard criteria that included exhaustive clinical and pathological work-up. Forty (93%) cases were classified as due to immunoglobulin light chain (AL) and three (7%) as transthyretin-related (ATTR).
The Congo red-negative group fell into two distinct clinical categories. Thirty-two had no clinical evidence of systemic amyloidosis after extensive clinical work-up and were, therefore, “true negative” cases. Of these, 46% had underlying myeloma, 15% lymphoma and 38% were being investigated for peripheral neuropathy or other suspicion for amyloidosis. In the remaining 20 Congo rednegative cases, a clinical diagnosis of systemic amyloidosis was established at other anatomic sites. These cases were included in the analysis to investigate evidence for an amyloid proteome in the absence of Congo red-positive deposits.
Mass spectrometry-based proteomic analysis of subcutaneous fat aspirates types systemic amyloidosis with high sensitivity and specificity
MS-based proteomic analysis of SFA specimens from 75 patients (43 cases with systemic amyloidosis and Congo red-positive SFA, and 32 patients with no evidence of systemic amyloidosis) was performed and analyzed blindly without knowledge of the patients’ clinical diagnosis or the results of Congo red staining of the SFA (Table 1). On average 670 proteins were identified per case. MS-based analysis was conclusive for an amyloid type in 38 cases. In 37 of these cases, the SFA specimens contained Congo red-positive amyloid, the patients had systemic amyloidosis and in each case MS-based proteomic analysis accurately predicted the amyloid type established by the clinical gold standard. One case was false positive with a proteome diagnostic of AL-kappa amyloidosis, but the SFA specimen was Congo red-negative and the patient did not have clinical evidence of systemic amyloidosis. This patient had very high levels of serum FLC (kappa, 285 mg/dL), suggesting that serum contamination may have been the cause of the false-positive result. In six cases of systemic amyloidosis with Congo red-positive aspirates, no proteome indicative of amyloidosis was detected and these cases were, therefore, false negatives by MS analysis.
In this validation cohort, the sensitivity of the MS-based proteomic assay was 88%, with a specificity of 96%, and an accuracy of 91%. Overall the positive predictive value in this cohort was determined to be 98% and the negative predictive value was determined to be 81%.
Elevated levels of serum free light chain do not interfere with mass spectrometry-based proteomic amyloid typing
Because we observed one false positive case that was attributed to high levels of serum FLC secondary to an underlying plasma cell myeloma, we next sought to determine whether there was a relationship between the serum FLC values and the immunoglobulin light chain spectra counts identified by MS-based proteomic analysis (Online Supplementary Figure S1). No correlation was identified between the lambda FLC values and lambda spectra numbers or between the kappa FLC values and kappa spectra numbers (Spearman ρ=0.45; P=0.0061).
The presence of apolipoprotein E, serum amyloid P-component and apoliprotein A4 provides a universal proteomic signature for diagnosis of amyloidosis in subcutaneous fat aspirates
We next sought to identify a universal proteomic signature that could distinguish SFA specimens involved by systemic amyloidosis from uninvolved specimens irrespective of specific amyloid type by analyzing the differential presence of four proteins (APOE, SAP, APOA1, APOA4) in Congo red-positive and -negative fat aspirates. APOE, SAP and APOA4 were differently expressed in amyloid versus non-amyloid specimens (P<0.0001) (Figure 1A–C). In contrast, apolipoprotein AI (APOA1), which served as a control protein for this analysis, was not differentially expressed (P<0.98) (Figure 1D). The odds of finding at least one of the APOE, APOA4, or SAP proteins was significantly higher (odds ratio=36.3; P<0.0001) in Congo red-positive cases than in Congo red-negative cases. The detection of at least two peptide spectra representing at least two of the three proteins (SAP, APOE and APOA4) was, therefore, considered to be the universal proteomic signature for involvement of subcutaneous fat by amyloidosis, and could serve as an excellent surrogate for the diagnosis of systemic amyloidosis with a sensitivity similar to that of Congo red staining.
The proteomic amyloid signature may be a more sensitive tool for the diagnosis of amyloidosis in fat aspirate specimens than Congo red staining
Our cohort included 20 cases in which SFA specimens were Congo red-negative but in which the patients were diagnosed with systemic amyloidosis at another anatomic site (SFA Congor red-false negative). Having established the presence of SAP, APOE and/or APOA4 proteins as a universal proteomic signature for amyloidosis, we investigated whether we could identify the proteomic signature of amyloidosis in Congo red-negative SFA specimens. Thirteen out of 20 SFA Congo red-negative specimens (65%) from patients with systemic amyloidosis showed a proteome consistent with AL (8 AL-lambda, 5 AL-kappa). In all cases, the amyloid types identified by MS were identical to the amyloid type identified by the clinical gold standard at the time. These results suggest that MS-based proteomic analysis may be a more sensitive method for diagnosis than Congo red staining and light microscopy interpretation.
Characteristics of the cases in the clinical cohort
Given the high sensitivity and specificity of the MS-based proteomic analysis for diagnosing systemic amyloidosis, we implemented the method in 2008 as a diagnostic test in a CLIA-accredited laboratory environment specifically to identify the amyloid type once the diagnosis of amyloidosis had been established in SFA specimens by conventional Congo red staining.
In a 4-year period (2008–2012), 2160 SFA specimens were obtained at the Mayo Clinic to screen for the possibility of systemic amyloidosis. Of these, 366 specimens were interpreted as Congo red-positive and were submitted for amyloid typing by MS-based proteomic analysis (Table 2). The sensitivity for successful amyloid typing on Congo red-positive specimens was 90% (330 out of 366). In 10% (36 out of 366) of the cases no specific amyloid type could be detected. Only four of these cases showed the presence of the universal amyloid proteome (APOE/SAP/APOA4) suggesting that in most cases failure of MS-based proteomic analysis were due to sampling differences. As expected, the most frequent type of amyloidosis was AL (n=272, 74%), followed by ATTR (n=46, 13%). Additionally we identified AA, AGel (gelsolin), ALys (lysozyme) and AIns (insulin). Genetic analysis showed that cases of AGel and ALys had mutations involving the respective coding genes, GSN and LYZ. All insulin cases were patients suffering from insulin-dependent diabetes mellitus, who administered abdominal subcutaneous insulin injections. Representative MS–based proteomic read-outs for each amyloid type are shown in Table 3.
In 20 cases data obtained from the previously established MS-based proteomic analysis for FFPE biopsies from sites other than subcutaneous fat aspirates were available. There was 100% concordance between the results obtained from SFA and FFPE specimens. These results show that SFA MS-based proteomic analysis for typing amyloidosis can be applied with high sensitivity and specificity in a routine clinical setting.
Mass spectrometry-based proteomic analysis can detect clonotypic peptides representing immunoglobulin light chain variable regions in AL-amyloidosis
Using the MS-based proteomic assay, the amyloid type is determined by the presence of peptides representing immunoglobulin light chain constant regions. Although identification of constant regions provides an excellent marker for typing amyloidosis, the variable region, which carries the clonotypic unique sequence, is the pathogenic part of the molecule. Because peptides from the variable regions are unique in each case, and not necessarily represented in reference databases, it may be difficult to identify them within the raw MS spectra data files. To overcome this difficulty we supplemented reference databases with sequences from our in- house databases as well as sequences from the ImMunoGeneTics database. Using the composite database we identified clonotypic peptides from immunoglobulin light chain variable regions in approximately 75% of the cases (Table 4). These findings show that MS–based proteomic analysis can determine light chain peptide sequences from clonotypic variableregion genes confirming the presence of an underlying clonal plasma cell population.
Mass spectrometry-based proteomic analysis can detect the variant peptides representing gene mutations in hereditary ATTR amyloidosis
Similar to immunoglobulin light chain variable regions, variant peptide sequences caused by pathogenic mutations in hereditary amyloidosis are not represented in reference databases and are not, therefore, readily detectable by conventional database searches. To investigate whether we could identify the presence of variant peptide sequences, we curated a customized protein database that contains all known pathogenic peptide sequence variants associated with hereditary amyloidosis including 110 mutations for ATTR. The MS data were interrogated using this database in 46 cases of ATTR. No variant TTR sequences were identified in 16 cases. The remaining cases showed a variety of amino acid substitutions, TTR Thr60Ala being the most frequent. When germline DNA sequencing information was available, it confirmed the findings in every case in which MS detected a TTR peptide sequence abnormality (Tables 5 and 6). In three cases, proteomic analysis did not detect an abnormal TTR peptide sequence whereas the germline DNA sequencing found TTR gene mutations. In one case the DNA sequencing indicated an Asp18Asn mutation which is in an area that lacks tryptic peptides of appropriate size for detection by MS and it would not, therefore, be expected to be detected. The second case revealed an Ala36Pro mutation via DNA sequencing but the mass shift that is induced matches the variable modification for oxidation that is allowed in our search strategy and was not, therefore, detected. In the third case, Val30Met was present in an area that could be identified by MS analysis; however, the case had only a very weak signal for TTR suggesting that the tissue analyzed had very little amyloid and thus the probability of detecting the mutated peptide was reduced. Overall these findings show that pathogenic alterations in TTR peptide sequences can be identified by MS-based proteomic analysis in hereditary ATTR amyloidosis. Although the MS-based proteomic assay as it is performed in the clinical laboratory may not detect some of the rare genetic variants of ATTR, it has 100% accuracy if an abnormal peptide is detected.
Congo red-stained SFA specimens provide an effective tool for screening and primary diagnosis of systemic amyloidosis. In this study, we demonstrate that SFA specimens can be used for the typing of systemic amyloidosis in a routine clinical setting in a CLIA-accredited laboratory environment with a high sensitivity and specificity. The ease of sample acquisition and preanalytic handling rivals the FFPE tissue-based methodology developed by and widely used by our laboratory.9
Our clinical test validation cohort comprised two groups; one with Congo red-positive SFA specimens (n=43) and the other with Congo red-negative specimens (n=32). In this cohort, the test performed with high sensitivity (88%), specificity (96%), and accuracy (91%). There was only one false positive case that had a proteome suggestive of AL-kappa in a patient with very high levels of kappa FLC due to an underlying multiple myeloma. This false result was attributed to serum contamination. The preanalytical parameters of the MS-based proteomic assay for SFA specimens differ significantly from the MS-based proteomic clinical assay for FFPE specimens previously developed and widely used by our laboratory. In SFA specimens, the diagnosis of amyloidosis was established by Congo red staining of one part of the specimen, and MS-based proteomic was performed on the other half. Unlike the FFPE-based assay no microdissection step was performed. For this reason the SFA test had a similar specificity to that of the FFPE-based test (98% versus 100%) but had a lower sensitivity (88% versus 100%) because of uneven distribution of amyloid material between the two halves of the sample.
Compared to other studies published on proteomic analysis of SFA specimens for the diagnosis of amyloidosis, our approach offers a number of advantages.2523 These include much simplified preanalytical processes which require very little tissue and provide a 24-hour turnaround time, a simple clinical read-out algorithm, a computational pipeline specifically customized for amyloidosis-associated variant proteins missing from reference databases, clinical validation and an operational infrastructure within a CLIA-compliant regulatory framework.
Most of the cases we studied in the test validation cohort were AL, which is associated with increased serum FLC levels. Because we encountered one false positive case, attributed to sample contamination by circulating FLC rather than amyloid-bound immunoglobulin, we investigated the relationship between serum FLC levels and immunoglobulin light chain spectra. We saw no correlation between serum FLC levels and proteomic results, indicating that the assay results were not affected by serum FLC levels systematically, and represented amyloid-bound immunoglobulin.
Although the causative protein is the main constituent of amyloid plaques, the amyloid proteome is complex and a number of other proteins are frequently co-deposited. Of these SAP is present in all amyloid fibrils and is thought to promote fibril formation and stabilization. APOE, APOA1 and APOA4 have also been consistently identified as constituents of various types of amyloidosis. We investigated the possibility of using these proteins as a universal proteomic signature for amyloidosis. This was particularly important in the case of SFA specimen analysis, as one half of the specimen was used for Congo red staining and the other half for proteomic analysis. We observed that the presence of peptide spectra representing at least two of the three proteins, APOE, SAP and/or APOA4 was an excellent surrogate biochemical marker for the presence of amyloid. In contrast, APOA1, a normal constituent of fat tissue, cannot be used as a marker for amyloidosis.
Armed with this proteome signature, we investigated 20 patients with known systemic amyloidosis but Congo red-negative SFA specimens. We found that approximately half of these cases had the universal amyloid signature consistent with amyloidosis despite a Congo red-negative SFA specimen. From our validation study showing 88% sensitivity, we would expect approximately 10% discordance between the Congo red result and a universal amyloid proteome results, most likely due to biological heterogeneity and sampling. We observed a much higher frequency of the universal amyloid proteome in patients with systemic amyloidosis with negative SFA suggesting that proteomic analysis may be a more sensitive tool than Congo red staining for the diagnosis of systemic amyloidosis in SFA specimens, and may detect submicroscopic deposits. Although it is premature to suggest the use of proteomic analysis as a primary screening tool for the diagnosis of amyloidosis, the combination of proteomic analysis with Congo red staining may offer the greatest sensitivity. The detection of a universal amyloid proteome in cases of SFA specimens that are equivocal or negative for Congo red staining could be used as a prompt for further sampling of the fat tissues or target organ biopsies, particularly when there is a strong clinical suspicion of amyloidosis.
Once the clinical test was validated and implemented, we used it routinely in our amyloidosis practice. The clinical implementation was limited to only Congo red-positive SFA specimens. The test performed with high sensitivity (90%) similar to that in the validation cohort and previous studies using Congo red staining.3385 In a small subset of the patients, for whom proteomic data from both an SFA specimen and a separate FFPE tissue specimen were available, there was 100% concordance. As expected AL and ATTR were the most common types of systemic amyloidosis in our clinical cohort. We also identified a small number of AA cases but also rare cases of unexpected hereditary amyloidosis such as AGel and ALys, which were confirmed by genetic analysis. Interestingly, we also discovered AIns in SFA specimens from three patients with insulin-dependent diabetes mellitus. Subcutaneous AIns amyloidosis is a well-recognized but infrequent complication of subcutaneous insulin injections for the management of insulin-dependent diabetes mellitus.3534 Patients often present with masses in the areas of repeated insulin injection, and the insulin amyloid remains localized to these sites. The only significant clinical consequence of such a diagnosis appears to be increasing insulin resistance due to poor diffusion of the injected insulin through the amyloid mass if the area continues to be used for repeated insulin injections.3736 However, localized AIns amyloidosis poses a risk for a misdiagnosis of systemic amyloidosis if SFA specimens are used for screening without performing amyloid subtyping on the specimen.38 Because the clinical features of insulin-dependent diabetes mellitus with renal or peripheral nerve disease may overlap with those of systemic amyloidosis, in particular AL amyloidosis, this is a major clinical concern. In this context, the availability of an MS-based proteomic assay has been invaluable for accurate diagnosis and clinical management.
In addition to identifying the specific amyloid types, the MS-based proteomic approach (so-called “shot-gun proteomics”) used in this clinical assay also identifies all other proteins and peptides that are part of the amyloid deposits, and the fat tissue microenvironment. Although we have not used this information for primary clinical diagnosis, we explored whether additional information could be gained regarding the underlying molecular pathogenesis. Initially, we focused on two areas; identification of immunoglobulin variable region usage in AL amyloidosis, and identification of pathogenic TTR peptide sequence variants in hereditary ATTR amyloidosis. Using protein databases enhanced for the variants of interest, we were able to determine the immunoglobulin light chain variable-region peptides in 75% of AL amyloidosis cases. Consistent with the published data based on the sequencing of immunoglobulin genes of clonal plasma cells in AL amyloidosis,4139 the variable gene usage was markedly biased favoring VλIII or VλVI for AL-lambda and VκI for AL-kappa. In AL amyloidosis, immunoglobulin light chain variable-region usage has been associated with organ involvement patterns and, as a consequence, clinical outcome.41 Availability of such information through proteomic analysis may be helpful in risk prediction and clinical decision-making.
We were also able to identify the amyloidogenic TTR amino acid sequence variants associated with hereditary ATTR amyloidosis. Although the most common familial ATTR peptide sequence variant is Val122Ile, found in the African-American population,42 the two most common alterations observed in our specimens were Thr60Ala43 and Val30Met43. This is likely due to the demographics of the patients from whom our specimens were derived. The other variants, each detected in one case, were Val122Ile, Pro24Ser44, Ser50Arg45, Glu54Gly46, Val30Ala47, Ser52Pro48, Leu58His49 and Val122Del50. While this method was very specific (100%) it had limited sensitivity since a number of rare mutations affecting residues not detectable by the current proteomic method were encountered. Alterations that are present in the TTR protein but are not within a tryptic peptide or mutations that do not result in a significant mass shift compared to the normal protein will not be identified. Despite this shortfall the TTR sequence variant information proved very helpful in routine clinical practice to demonstrate the physical presence of the pathogenic protein in the amyloid deposits and to direct downstream genetic screening, especially for those patients for whom hereditary ATTR amyloidosis was not initially suspected.
In summary, MS-based-proteomic analysis of SFA specimens for the screening and diagnosis of systemic amyloidosis is a powerful clinical tool. The assay provides unprecedented sensitivity and specificity for the diagnosis and typing of amyloidosis, including rare hereditary and iatrogenic variants. In addition to specific diagnostic information, the assay provides insights into underlying pathogenesis, which may be important for downstream clinical decisions.
SD was supported by the Center for Individualized Medicine, Mayo Clinic. OMM is a visiting fellow from the Centro di Ricerche di Immunopatologia e Documentazione su Malattie Rare (CMID), University of Turin, Italy.
- The online version of this article has a Supplementary Appendix.
- Authorship and Disclosures Information on authorship, contributions, and financial & other disclosures was provided by the authors and is available with the online version of this article at www.haematologica.org.
- Received December 15, 2013.
- Accepted April 14, 2014.
- Gertz MA, Kyle RA. Primary systemic amyloidosis–a diagnostic primer. Mayo Clin Proc. 1989; 64(12):1505-19. PubMedhttps://doi.org/10.1016/S0025-6196(12)65706-1Google Scholar
- Gertz MA, Lacy MQ, Dispenzieri A, Hayman SR. Amyloidosis: diagnosis and management. Clin Lymphoma Myeloma. 2005; 6(3):208-19. PubMedhttps://doi.org/10.3816/CLM.2005.n.048Google Scholar
- Merlini G, Seldin DC, Gertz MA. Amyloidosis: pathogenesis and new therapeutic options. J Clin Oncol. 2011; 29(14):1924-33. PubMedhttps://doi.org/10.1200/JCO.2010.32.2271Google Scholar
- Westermark P, Stenkvist B. A new method for the diagnosis of systemic amyloidosis. Arch Intern Med. 1973; 132(4):522-3. PubMedhttps://doi.org/10.1001/archinte.1973.03650100040007Google Scholar
- Gertz MA, Li CY, Shirahama T, Kyle RA. Utility of subcutaneous fat aspiration for the diagnosis of systemic amyloidosis (immunoglobulin light chain). Arch Intern Med. 1988; 148(4):929-33. PubMedhttps://doi.org/10.1001/archinte.1988.00380040169024Google Scholar
- Libbey CA, Skinner M, Cohen AS. Use of abdominal fat tissue aspirate in the diagnosis of systemic amyloidosis. Arch Intern Med. 1983; 143(8):1549-52. PubMedhttps://doi.org/10.1001/archinte.1983.00350080055015Google Scholar
- Duston MA, Skinner M, Meenan RF, Cohen AS. Sensitivity, specificity, and predictive value of abdominal fat aspiration for the diagnosis of amyloidosis. Arthritis Rheum. 1989; 32(1):82-5. PubMedhttps://doi.org/10.1002/anr.1780320114Google Scholar
- van Gameren II, Hazenberg BP, Bijzet J, van Rijswijk MH. Diagnostic accuracy of subcutaneous abdominal fat tissue aspiration for detecting systemic amyloidosis and its utility in clinical practice. Arthritis Rheum. 2006; 54(6):2015-21. PubMedhttps://doi.org/10.1002/art.21902Google Scholar
- Vrana JA, Gamez JD, Madden BJ, Theis JD, Bergen HR, Dogan A. Classification of amyloidosis by laser microdissection and mass spectrometry-based proteomic analysis in clinical biopsy specimens. Blood. 2009; 114(24):4957-9. PubMedhttps://doi.org/10.1182/blood-2009-07-230722Google Scholar
- Klein CJ, Vrana JA, Theis JD, Dyck PJ, Dyck PJ, Spinner RJ. Mass spectrometric-based proteomic analysis of amyloid neuropathy type in nerve tissue. Arch Neurol. 2011; 68(2):195-9. PubMedhttps://doi.org/10.1001/archneurol.2010.261Google Scholar
- Chee CE, Lacy MQ, Dogan A, Zeldenrust SR, Gertz MA. Pitfalls in the diagnosis of primary amyloidosis. Clin Lymphoma Myeloma Leuk. 2010; 10(3):177-80. PubMedhttps://doi.org/10.3816/CLML.2010.n.027Google Scholar
- Sethi S, Theis JD, Leung N, Dispenzieri A, Nasr SH, Fidler ME. Mass spectrometry-based proteomic diagnosis of renal immunoglobulin heavy chain amyloidosis. Clin J Am Soc Nephrol. 2010; 5(12):2180-7. PubMedhttps://doi.org/10.2215/CJN.02890310Google Scholar
- Satoskar AA, Efebera Y, Hasan A, Brodsky S, Nadasdy G, Dogan A. Strong transthyretin immunostaining: potential pitfall in cardiac amyloid typing. Am J Surg Pathol. 2011; 35(11):1685-90. PubMedhttps://doi.org/10.1097/PAS.0b013e3182263d74Google Scholar
- Valleix S, Gillmore JD, Bridoux F, Mangione PP, Dogan A, Nedelec B. Hereditary systemic amyloidosis due to Asp76Asn variant beta2-microglobulin. N Engl J Med. 2012; 366(24):2276-83. PubMedhttps://doi.org/10.1056/NEJMoa1201356Google Scholar
- Sethi S, Vrana JA, Theis JD, Leung N, Sethi A, Nasr SH. Laser microdissection and mass spectrometry-based proteomics aids the diagnosis and typing of renal amyloidosis. Kidney Int. 2012; 82(2):226-34. PubMedhttps://doi.org/10.1038/ki.2012.108Google Scholar
- Maleszewski JJ, Murray DL, Dispenzieri A, Grogan M, Pereira NL, Jenkins SM. Relationship between monoclonal gammopathy and cardiac amyloid type. Cardiovasc Pathol. 2013; 22(3):189-94. PubMedhttps://doi.org/10.1016/j.carpath.2012.09.001Google Scholar
- D’Souza A, Theis J, Quint P, Kyle R, Gertz M, Zeldenrust S. Exploring the amyloid proteome in immunoglobulin-derived lymph node amyloidosis using laser microdissection/tandem mass spectrometry. Am J Hematol. 2013; 88(7):577-80. PubMedhttps://doi.org/10.1002/ajh.23456Google Scholar
- Grogg KL, Aubry MC, Vrana JA, Theis JD, Dogan A. Nodular pulmonary amyloidosis is characterized by localized immunoglobulin deposition and is frequently associated with an indolent B-cell lymphoproliferative disorder. Am J Surg Pathol. 2013; 37(3):406-12. PubMedhttps://doi.org/10.1097/PAS.0b013e318272fe19Google Scholar
- Said SM, Reynolds C, Jimenez RE, Chen B, Vrana JA, Theis JD. Amyloidosis of the breast: predominantly AL type and over half have concurrent breast hematologic disorders. Mod Pathol. 2013; 26(2):232-8. PubMedhttps://doi.org/10.1038/modpathol.2012.167Google Scholar
- Westermark P, Benson L, Juul J, Sletten K. Use of subcutaneous abdominal fat biopsy specimen for detailed typing of amyloid fibril protein-AL by amino acid sequence analysis. 1989; 42(8):817-9. Google Scholar
- Hazenberg BP, Bijzet J, Limburg PC, Skinner M, Hawkins PN, Butrimiene I. Diagnostic performance of amyloid A protein quantification in fat tissue of patients with clinical AA amyloidosis. Amyloid. 2007; 14(2):133-40. PubMedhttps://doi.org/10.1080/13506120701260224Google Scholar
- Vrana JA, Zeldenrust SR, Theis JD, Gamez JD, Kurtin PJ, Dogan A. Diagnosis and classification of amyloidosis in abdominal subcutaneous fat aspiration specimens using mass spectrometry based proteomics. Blood. 2008; 112(11):937. Google Scholar
- Lavatelli F, Perlman DH, Spencer B, Prokaeva T, McComb ME, Théberge R. Amyloidogenic and associated proteins in systemic amyloidosis proteome of adipose tissue. Mol Cell Proteomics. 2008; 7(8):1570-83. PubMedhttps://doi.org/10.1074/mcp.M700545-MCP200Google Scholar
- Lavatelli F, Valentini V, Palladini G, Verga L, Russo P, Foli A. Mass spectrometry-based proteomics as a diagnostic tool when immunoelectron microscopy fails in typing amyloid deposits. Amyloid. 2011; 18(Suppl 1):64-6. PubMedhttps://doi.org/10.3109/13506129.2011.574354023Google Scholar
- Brambilla F, Lavatelli F, Di Silvestre D, Valentini V, Rossi R, Palladini G. Reliable typing of systemic amyloidoses through proteomic analysis of subcutaneous adipose tissue. Blood. 2012; 119(8):1844-7. PubMedhttps://doi.org/10.1182/blood-2011-07-365510Google Scholar
- Westermark P, Davey E, Lindbom K, Enqvist S. Subcutaneous fat tissue for diagnosis and studies of systemic amyloidosis. Acta Histochem. 2006; 108(3):209-13. PubMedhttps://doi.org/10.1016/j.acthis.2006.03.011Google Scholar
- Eng JK, McCormack AL, Yates JR. An approach to correlate tandem mass spectral data of peptides with amino acid sequences in a protein database. J Am Soc Mass Spectrom. 1994; 5(11):976-89. PubMedhttps://doi.org/10.1016/1044-0305(94)80016-2Google Scholar
- Fenyo D, Beavis RC. A method for assessing the statistical significance of mass spectrometry-based protein identifications using general scoring schemes. Anal Chem. 2003; 75(4):768-74. PubMedhttps://doi.org/10.1021/ac0258709Google Scholar
- Perkins DN, Pappin DJ, Creasy DM, Cottrell JS. Probability-based protein identification by searching sequence databases using mass spectrometry data. Electrophoresis. 1999; 20(18):3551-67. PubMedhttps://doi.org/10.1002/(SICI)1522-2683(19991201)20:18<3551::AID-ELPS3551>3.0.CO;2-2Google Scholar
- Lefranc MP, Giudicelli V, Ginestoux C, Jabado-Michaloud J, Folch G, Bellahcene F. IMGT, the international ImMunoGeneTics information system. Nucleic Acids Res. 2009; 37(Database issue):D1006-12. PubMedhttps://doi.org/10.1093/nar/gkn838Google Scholar
- Elias JE, Haas W, Faherty BK, Gygi SP. Comparative evaluation of mass spectrometry platforms used in large-scale proteomics investigations. Nat Methods. 2005; 2(9):667-75. PubMedhttps://doi.org/10.1038/nmeth785Google Scholar
- Katzmann JA, Abraham RS, Dispenzieri A, Lust JA, Kyle RA. Diagnostic performance of quantitative kappa and lambda free light chain assays in clinical practice. Clin Chem. 2005; 51(5):878-81. PubMedhttps://doi.org/10.1373/clinchem.2004.046870Google Scholar
- Andrews TR, Colon-Otero G, Calamia KT, Menke DM, Boylan KB, Kyle RA. Utility of subcutaneous fat aspiration for diagnosing amyloidosis in patients with isolated peripheral neuropathy. Mayo Clin Proc. 2002; 77(12):1287-90. PubMedhttps://doi.org/10.4065/77.12.1287Google Scholar
- Nagase T, Katsura Y, Iwaki Y, Nemoto K, Sekine H, Miwa K. The insulin ball. Lancet. 2009; 373(9658):184. PubMedhttps://doi.org/10.1016/S0140-6736(09)60041-6Google Scholar
- Yumlu S, Barany R, Eriksson M, Rocken C. Localized insulin-derived amyloidosis in patients with diabetes mellitus: a case report. Hum Pathol. 2009; 40(11):1655-60. PubMedhttps://doi.org/10.1016/j.humpath.2009.02.019Google Scholar
- Albert SG, Obadiah J, Parseghian SA, Yadira Hurley M, Mooradian AD. Severe insulin resistance associated with subcutaneous amyloid deposition. Diabetes Res Clin Pract. 2007; 75(3):374-6. PubMedhttps://doi.org/10.1016/j.diabres.2006.07.013Google Scholar
- Endo JO, Rocken C, Lamb S, Harris RM, Bowen AR. Nodular amyloidosis in a diabetic patient with frequent hypoglycemia: sequelae of repeatedly injecting insulin without site rotation. J Am Acad Dermatol. 2010; 63(6):e113-4. PubMedhttps://doi.org/10.1016/j.jaad.2010.03.001Google Scholar
- D’Souza A, Theis JD, Vrana JA, Buadi F, Dispenzieri A, Dogan A. Localized insulinderived amyloidosis: A potential pitfall in the diagnosis of systemic amyloidosis by fat aspirate. Am J Hematol. 2012; 87(11):E131-2. PubMedhttps://doi.org/10.1002/ajh.23334Google Scholar
- Comenzo RL, Zhang Y, Martinez C, Osman K, Herrera GA. The tropism of organ involvement in primary systemic amyloidosis: contributions of Ig V(L) germ line gene use and clonal plasma cell burden. Blood. 2001; 98(3):714-20. PubMedhttps://doi.org/10.1182/blood.V98.3.714Google Scholar
- Perfetti V, Casarini S, Palladini G, Vignarelli MC, Klersy C, Diegoli M. Analysis of V(lambda)-J(lambda) expression in plasma cells from primary (AL) amyloidosis and normal bone marrow identifies 3r (lambdaIII) as a new amyloid-associated germline gene segment. Blood. 2002; 100(3):948-53. PubMedhttps://doi.org/10.1182/blood-2002-01-0114Google Scholar
- Abraham RS, Geyer SM, Price-Troska TL, Allmer C, Kyle RA, Gertz MA. Immunoglobulin light chain variable (V) region genes influence clinical presentation and outcome in light chain-associated amyloidosis (AL). Blood. 2003; 101(10):3801-8. PubMedhttps://doi.org/10.1182/blood-2002-09-2707Google Scholar
- Gorevic PD, Prelli FC, Wright J, Pras M, Frangione B. Systemic senile amyloidosis. Identification of a new prealbumin (transthyretin) variant in cardiac tissue: immunologic and biochemical similarity to one form of familial amyloidotic polyneuropathy. J Clin Invest. 1989; 83(3):836-43. PubMedhttps://doi.org/10.1172/JCI113966Google Scholar
- Wallace MR, Dwulet FE, Conneally PM, Benson MD. Biochemical and molecular genetic characterization of a new variant prealbumin associated with hereditary amyloidosis. J Clin Invest. 1986; 78(1):6-12. PubMedhttps://doi.org/10.1172/JCI112573Google Scholar
- Uemichi T, Gertz MA, Benson MD. A new transthyretin variant (Ser 24) associated with familial amyloid polyneuropathy. J Med Genet. 1995; 32(4):279-81. PubMedhttps://doi.org/10.1136/jmg.32.4.279Google Scholar
- Ueno S, Uemichi T, Takahashi N, Soga F, Yorifuji S, Tarui S. Two novel variants of transthyretin identified in Japanese cases with familial amyloidotic polyneuropathy: transthyretin (Glu42 to Gly) and transthyretin (Ser50 to Arg). Biochem Biophys Res Commun. 1990; 169(3):1117-21. PubMedhttps://doi.org/10.1016/0006-291X(90)92011-NGoogle Scholar
- Reilly MM, Adams D, Booth DR, Davis MB, Said G, Laubriat-Bianchin M. Transthyretin gene analysis in European patients with suspected familial amyloid polyneuropathy. Brain. 1995; 118(Pt 4):849-56. PubMedhttps://doi.org/10.1093/brain/118.4.849Google Scholar
- Jones LA, Skare JC, Cohen AS, Harding JA, Milunsky A, Skinner M. Familial amyloidotic polyneuropathy: a new transthyretin position 30 mutation (alanine for valine) in a family of German descent. Clin Genet. 1992; 41(2):70-3. PubMedGoogle Scholar
- Stangou AJ, Hawkins PN, Heaton ND, Rela M, Monaghan M, Nihoyannopoulos P. Progressive cardiac amyloidosis following liver transplantation for familial amyloid polyneuropathy: implications for amyloid fibrillogenesis. Transplantation. 1998; 66(2):229-33. PubMedhttps://doi.org/10.1097/00007890-199807270-00016Google Scholar
- Nichols WC, Liepnieks JJ, McKusick VA, Benson MD. Direct sequencing of the gene for Maryland/German familial amyloidotic polyneuropathy type II and genotyping by allele-specific enzymatic amplification. Genomics. 1989; 5(3):535-40. PubMedhttps://doi.org/10.1016/0888-7543(89)90020-7Google Scholar
- Uemichi T, Liepnieks JJ, Benson MD. A trinucleotide deletion in the transthyretin gene (delta V 122) in a kindred with familial amyloidotic polyneuropathy. Neurology. 1997; 48(6):1667-70. https://doi.org/10.1212/WNL.48.6.1667Google Scholar