In hopes of improving outcomes of amyloidosis, a rare but serious disease caused by the buildup of abnormal amyloid deposits, The College of American Pathologists (CAP) recently released a new evidence-based guideline to help standardize diagnostics. Here, we hear from CAP representatives on what this could mean for patient care.
Meet the CAP Team
What inspired the creation of your clinical practice guidelines?
The guideline was developed in response to concerns from experts about variability in laboratory approaches to detecting amyloid in clinical specimens. At the same time, advances in clinical imaging and amyloid fibril-specific therapies have increased the importance of early, accurate detection and precise fibril characterization. There is an urgent need for standardized, consensus-based recommendations.
Which steps should be standardized to ensure reliable detection of amyloid across institutions?
As with all laboratory testing, amyloid staining and interpretation must be performed in well-controlled settings. Laboratories should maintain a well-standardized amyloid diagnostic panel that is validated across specimen types, including routine FFPE tissue and cytology specimens.
Standardized criteria for what constitutes a positive result are also essential, along with consistent use of established techniques such as light and polarized microscopy and, where appropriate, fluorescence microscopy.
How should pathologists interpret a negative surrogate biopsy, and what criteria should drive escalation to targeted organ biopsy?
When a surrogate-site biopsy, such as an abdominal fat pad biopsy, is negative, but clinical suspicion remains high, the findings should first be interpreted in a close clinical context. Fat pad biopsies have variable sensitivity depending on amyloid type, with higher sensitivity for kappa and lambda (AL) than for kappa, lambda, transthyretin (ATTR). Review of prior surgical specimens – such as tenosynovial tissue from carpal tunnel release, lumbar spinal stenosis specimens, or biopsies from other sites – may reveal previously unrecognized amyloid deposits.
If uncertainty persists, a targeted biopsy of the clinically involved organ may be warranted. In all cases, diagnostic terminology should be aligned with the clinical scenario.
What specific diagnostic limitations should pathologists keep in mind when evaluating cytology samples for amyloid?
Abdominal fat pad fine-needle aspiration (FNA) is a valuable first-line test for suspected systemic amyloidosis because it is rapid, inexpensive, and minimally invasive, requiring little to no anesthesia, suturing, or recovery time. This makes it feasible even for critically ill patients. When Congo red staining is technically optimized and carefully interpreted, the specificity for amyloid detection is very high – often approaching 100 percent in published series. A positive result provides strong evidence of amyloid deposition and may obviate the need for more invasive procedures, such as endomyocardial or organ-directed biopsies, in appropriate clinical settings.
Despite these advantages, fat pad FNA has important limitations. Sensitivity is variable and depends on amyloid type, overall disease burden, and technical factors, ranging from relatively low in some cohorts to more than 80 percent in patients with systemic AL amyloidosis and high total amyloid load. False-negative results may arise from scant or unrepresentative material, weak or pale Congo red staining, or misinterpretation under polarized light. As such, a negative result should be interpreted cautiously and does not exclude disease.
False-positive results are also possible and may result from technical issues with Congo red staining, such as overstaining or dye precipitation, as well as misinterpretation of congophilic non-amyloid structures. Collagen, elastin, normal connective tissue, bone trabeculae, and neural elements can all demonstrate white, yellow, or even green birefringence and must be carefully distinguished from true amyloid.
Overall, when performed and interpreted appropriately, fat pad FNA remains an important tool for early amyloid detection and patient management, particularly when integrated with clinical findings and imaging studies.
What pre-analytic factors most commonly cause false-negatives, and how can laboratories mitigate these issues?
Amyloid-specific dyes used in histochemical staining either directly intercalate into the β-sheet-rich domains of amyloidogenic peptides or bind to glycoside-rich moieties associated with amyloid deposits, which account for their characteristic ‘starch-like’ properties. Because staining depends on dye binding to these structures, tissue section thickness directly affects staining intensity: thicker sections contain more target material and therefore bind more dye. The CAP evidence-based guideline recommends an optimal section thickness of 8 to 10 microns.
Most laboratories now use automated staining platforms for Congo red staining, which can improve consistency but do not guarantee optimal results. Careful evaluation of the staining reaction in the control tissue – ideally placed on the same slide – is essential. False-negative results most commonly arise from overly thin sections or failed staining reactions. False positives are also an important concern and typically result from overstaining or nonspecific congophilia.
Using polarized light or fluorescence microscopy to examine the control tissue can help ‘train the eye’ when assessing patient sections. Amyloid deposits in the control section should display the same morphology and staining characteristics as any suspected deposits in the patient tissue, providing a critical reference for accurate interpretation.
Why is mass spectrometry (MS) recommended over immunohistochemistry (IHC) for amyloid typing?
Amyloid typing by IHC often shows limited diagnostic performance, particularly in real-world practice. While specialized reference laboratories using extensive antibody panels and optimized techniques have reported relatively good results, most clinical laboratories rely on smaller panels – typically ATTR and serum amyloid A (AA) – applied with standard methods. In these more representative settings, inconclusive rates exceeding 50 percent have been reported. This approach is costly and time-consuming, consumes valuable paraffin-embedded tissue, and can delay therapy or preclude further testing using alternative methods such as MS-based proteomics. More concerning, IHC-based amyloid typing may yield incorrect results, most commonly misclassifying ATTR as AL or AA, or AL as ATTR or AA.
The suboptimal specificity of amyloid IHC stems from several factors, including cross-reactivity with background immunoglobulins, epitope masking within amyloid fibrils, and high background staining. Most IHC antibodies are not optimized to recognize the abnormal conformations of amyloidogenic proteins within fibrillar deposits. In addition, the β-pleated sheet structure of amyloid fibrils can promote nonspecific antibody binding. ATTR patients with concurrent monoclonal gammopathy of undetermined significance – present in up to 40 percent of individuals with cardiac ATTR – are particularly vulnerable to misclassification as AL when IHC is used.
Sensitivity is also limited in routine practice. Although at least 42 canonical amyloid types are recognized, most IHC panels test only for the three most common: AL, ATTR, and AA. As a result, patients with rarer amyloid types cannot be accurately classified and may be incorrectly assigned to one of the tested categories.
In contrast, MS-based proteomics enables unambiguous identification of all amyloid types in a single assay, with reported sensitivity and specificity approaching 98 percent. This unbiased shotgun proteomics approach – typically using liquid chromatography-tandem mass spectrometry (LC-MS/MS) – directly analyzes all proteins present within laser-microdissected amyloid deposits from formalin-fixed, paraffin-embedded tissue. Only a very small amount of tissue is required. In addition to typing amyloid, this method can identify specific amino acid sequence variants in hereditary amyloidosis.
Quality assurance is maintained through rigorous standardization and strict QA/QC protocols. The presence of characteristic amyloid-associated proteins – such as apolipoprotein E, serum amyloid P component, and apolipoprotein A-IV – confirms that the analyzed material represents true amyloid. Final classification is determined by correlating the dominant amyloidogenic protein with clinical and morphologic features, interpreted by a pathologist experienced in amyloid proteomics.
Which technologies appear closest to practical diagnostic integration, and what evidence still needs to be established?
At this year’s Association for European Cardiovascular Pathology meeting, two presentations highlighted emerging techniques for amyloid detection and typing. Fourier transform infrared spectroscopy (FTIR) and discrete frequency infrared spectroscopy (DF-IR) both use paraffin-embedded tissue sections and show promise for identifying amyloid deposits and determining fibril type. These approaches may offer faster and more cost-effective alternatives to tandem mass spectrometry, and their development will be followed with close interest.
What are your hopes for the future of amyloidosis diagnostics?
Our hope is that this guideline will help reduce the current variability in amyloid workup across laboratories in the US and globally. Greater attention to staining techniques and interpretive tools – such as fluorescence microscopy– should improve the sensitivity and specificity of amyloid detection. Increased awareness of the clinical importance of accurate fibril typing, along with the emergence of more accessible typing technologies, may also reduce delays in identifying patients eligible for fibril type-specific therapies and improve treatment outcomes.
As in many areas of pathology, amyloid diagnostics are beginning to intersect with advances in artificial intelligence, and machine learning approaches applied to mass spectrometry data and digital pathology may further enhance diagnostic accuracy. These developments will be closely monitored as we look ahead to a potential guideline update in approximately five years.
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