Granulomas are a rather uncommon, yet diagnostically helpful finding in trephine bone marrow biopsies. Being indicative for a rather limited number of various underlying diseases (ranging from infections to autoimmunopathies and malignant tumours), the finding of granulomas in the bone marrow should precipitate further analyses to uncover the underlying condition.
by Dr Thomas Menter and Dr Alexandar Tzankov
Microscopic aggregations of epithelioid histiocytes are referred to as granulomas and the respective inflammatory process is called granulomatous. Granulomas are a well-known feature in pathology, with the first description ranging back to the 17th century. They show variable morphologic features including central areas of necrosis (necrotizing granulomas) or suppuration (microabsceding or suppurative granulomas), incorporation of foreign material or presence of giant cells due to the fusion of macrophages. Granuloma formation is usually associated with local CD4+ T-cell activation (and numeric increase of CD4+ T-cells at the site of granuloma formation, thus ‘consuming’ CD4+ T-cells and leading to skewing of the CD4/CD8 ratio in the peripheral blood in favour of CD8) and production of interleukin 2 and 12, Interferon (IFN) γ, and tumour necrosis factor α (TNF-α) [1, 2]. In general, granulomas are provoked by agents that are difficult to eradicate by the enzymes of histiocytes, for example because of the different phospholipid composition of the former (e.g. mycolic acids) or because of the specific enzymo/immunogenetic background of the host. The number of causative agents in granulomatous responses is rather smaller than in other inflammatory patterns. Therefore granulomas are considered to be ‘specific’; narrowing down the possible causative factors, their identification and subsequent search for possible underlying agents and conditions supports the establishment of more precise clinicopathological diagnoses.
Morphology of bone marrow granulomas and etiologic considerations
Granulomatous processes involving the bone marrow (BM) can be divided into three subgroups based on morphology and clinicopathological context: (1) lipogranulomas, (2) infectious epithelioid granulomas, and (3) epithelioid granulomas associated with immune dysregulation. Importantly, most inborn immunodeficiency disorders are accompanied by increased granuloma formation [3]. Examples of these disorders include Blau syndrome, CVID (common variable immune deficiency), RAG (recombination-activating genes) deficiency, XIAP (X-linked inhibitor of apoptosis) deficiency and chronic granulomatous disease. Depending on whether solely the BM or other organs are also involved, granulomatous BM processes may represent isolated findings or reflect the involvement of a systemic disorder. It is obvious that detection of granulomatous BM processes should be followed by an integrative diagnostic work-up considering the clinical history (travel and drug history) and presentation, but also applying imaging techniques and molecular detection methods including serology, in situ uncover techniques and PCR- and sequencing-based procedures.
Lipogranulomas
Lipogranulomas are found in up to 10% of BM samples. They are not thought to be significant since probably not linked to specific underlying disorders. Yet, they may be more commonly observed in patients with acute febrile illnesses. They consist of aggregates of histiocytes with variably sized lipid vacuoles (Fig. 1A), which tend to gradually disappear with time resulting in a morphological appearance of lipogranulomas indistinguishable from epithelioid granulomas. When detecting BM lipogranulomas, special attention must be given to the periodic acid Schiff (PAS)/diastase-PAS stains so as not to miss involvement by Whipple’s disease, in which the foamy histiocytes stain positively (Fig. 1B); in suspect cases, the diagnosis of Whipple’s disease can be enhanced by additional Ziehl–Neelsen and Warthin–Starry stains as well as by PCR- and sequencing-based procedures. Granulomas in Erdheim–Chester disease might sometimes resemble lipogranulomas [4].
Epithelioid granulomas
Epithelioid granulomas are found in <1% of BM samples [5]. They are more frequent in certain geographic areas and in samples from patients with immune dysregulation. They are considered significant as they are associated with various infectious (Fig. 1C), immune dysregulatory (Fig. 1D) and neoplastic disorders (Fig. 1E). After clinicopathological and molecular work-up, a specific etiology of BM epithelioid granulomas can be attributed in up to 80% of cases. Such granulomas consist of loose [particularly in severely immunocompromised patients (the lower the CD4+ counts or the membrane-bound TNF-α and the more virulent the infectious agent, the looser the granuloma, Fig. 1F)] to cohesive clusters of epithelioid histiocytes with accompanying lymphocytes, eosinophilic (Fig. 1G) and neutrophilic granulocytes, and giant cells (Fig. 1H). In patients with infectious diseases, these granulomas mostly contain organisms, which should be actively sought for and if possible visualized (e.g. mycobacteria, histoplasmata, Bartonella henselae, treponemata, Leishmania spp., toxoplasmata) using special stains such as PAS, Ziehl–Neelsen, Fite, Grocott, May–Grünwald–Giemsa, Warthin–Starry, etc. Immunohistochemistry (Fig. 1L, insert) or molecular genetic methods might be necessary for etiologic assignment. A particular CD8 predominance in the BM interstitium often accompanies virus infections [e.g. cytomegalovirus (CMV) and Epstein–Barr virus (EBV)] and might serve as an additional diagnostic hint [6].
Different granuloma morphotypes
There are different granuloma morphotypes, which should be recognized because they may give a clue to the underlying disorder [7]. Caseating granulomas (i.e. granulomas with central necrosis) are usually caused by infectious agents such as mycobacteria, histoplasmata, Francisella tularensis, Yersinia pestis or brucellaceae. Ring-form granulomas (Fig. 1I) can be observed in acute virus infections (e.g. CMV), brucellosis, leishmaniasis and those appearing as ‘doughnut rings’ in Q-fever. Foreign body granulomas are rarely seen, but can be encountered in patients after repeated BM sampling (e.g. containing displaced keratin), or in patients with degenerative and debilitating disorders, in whom subchondral epithelioid clusters may raise differential diagnostic concerns of metastatic carcinomas (Fig. 1J).
Occasionally detached giant osteoclasts in patients receiving long-term bisphosphonate therapy may be conventionally indistinguishable from foreign body giant cells, but immunohistochemistry for tartrate-resistant acid phosphatase (TRAP) can be helpful, as osteoclasts are intensively positive. Sarcoid-type granulomas (Fig. 1K) consist of compact epithelioid collections and are less specific as they can accompany genuine sarcoidosis and autoimmune disorders such as, for example, rheumatoid arthritis [8].
The role of giant cells in granulomas
Another clue to the etiology in a granulomatous inflammation might be the type of giant cells present around the epithelioid histiocytes [9]. Langhans giant cells are characteristically seen in tuberculosis, and appear with nuclei arranged in a horseshoe-like fashion at the periphery of the cell below the cell membrane. In contrast, in Touton giant cells that are typically observable in areas of fat necrosis, the nuclei form a complete ring, and the cytoplasm is rather foamy than eosinophilic. In foreign body giant cells, the nuclei do not show a particular order but are rather haphazardly distributed. Besides, the foreign material incorporated in these cells (a clue to this diagnosis) is often visible by conventional or polarized light, being either pigmented or birefringent.
A particular vascular association of granulomas should raise suspicion of vasculitis, either primary like granulomatous polyangiitis (formerly known as Wegener’s disease), or secondary/infectious such as syphilis (Fig. 1L).
Granulomas and malignancies
Importantly, detection of BM granulomas does not exclude an underlying malignant process; on the contrary, BM granulomas may accompany various lymphoproliferative processes such as Castleman’s disease (Fig. 1M), B- and T-cell (so called ‘non-Hodgkin’) as well as Hodgkin lymphomas (HL) [10, 11], but also the BM spread of solid tumours such as lobular breast cancer [12]. There is a significant association between BM granulomas and non-Hodgkin lymphoma spread to the BM. Granulomas due to IFN therapy can be encountered in lymphoma patients [especially patients suffering from splenic marginal zone B-cell lymphoma and diffuse large B-cell lymphoma (DLBCL)] treated for underlying chronic hepatitis B- or C-virus (HBV, HCV) infections – both viruses are known to increase the risk of these lymphomas (Fig. 1N). Occasionally, granulomatous reactions might obscure lymphoma. This may particularly apply to HL, in which on the one hand granulomatous reactions can occur independently of BM infiltration by HL (not worsening patients’ prognosis; indeed 5% of HL patients have BM granulomas without BM involvement by lymphoma), and on the other hand BM involvement by HL is usually granulomatous with only a handful Reed–Sternberg cells. Therefore step-sections supported by immunohistochemistry (CD15, CD30) are warranted if BM granulomas are encountered in patients suffering from HL (Fig. 1O). Patients with lymphomas are at an increased risk of developing infections, which may also lead to granulomas and should therefore raise awareness for the differential work-up for infectious agents as described above. Finally, patients suffering from sarcoidosis are at increased risk of lymphoma (odds ratio 2) and incipient lymphomas [especially Burkitt lymphomas, DLBCL, small lymphocytic B-cell lymphomas, lymphoplasmacytic lymphomas and peripheral T-cell lymphomas (PTCL)] may provoke sarcoid-like reactions summarized in the so called ‘lymphoma-sarcoidosis syndrome’. To be comprehensive, apart from IFN, several other treatment compounds such as hematopoietic growth factors like G-CSF (Fig. 1K), TNF-α blockers, BCG vaccination, allopurinol, amiodarone, antipsychotics, phenytoin, sulfonamides, etc., can lead to BM granuloma formation [13].
Further etiologies of granulomas in bone marrow biopsies
Several other neoplastic and non-neoplastic conditions [like systemic mastocytosis, (Langerhans cell) histiocytoses], genuine histiocytic and metabolic/storage disorders (like Erdheim–Chester, Rosai–Dorfman and Gaucher’s disease), sea blue histiocytoses, hemophagocytic lympho-histiocytosis, T-cell and histiocyte-rich B-cell lymphomas or lymphomatoid granulomatosis (Fig. 1P) involving the BM can morphologically mimic granulomas and should be distinguished from the latter by means of ancillary studies.
Technical considerations
A comprehensive review on technical handling of BM biopsies to obtain optimal immunohistochemical results has been published recently [14]. Trephine BM biopsies are best taken from the iliac crest. For further analysis, they should be sent to the pathology institutions in 10% buffered formalin (final formaldehyde concentration 4%) in order to prevent autolytic changes and to allow for an optimally preserved morphology. For decalcification, chelate binders such as EDTA should be used. We discourage the use of other decalcificative agents such as formic acid or mercury containing agents such as SUSA-fixation because of the alteration of proteins and destruction of DNA as well as for health issues for the laboratory staff with regard to mercury exposure. Standard special stains of BM biopsies include H&E, PAS, Giemsa and Gömöri stains. Applying the PAS and the Gömöri stain can highlight fungi, but in the case of artefacts caused by BM fibrosis, other than Gömöri silver, stains such as a Grocott stain are helpful. The Giemsa stain is useful in the context of leishmaniasis and toxoplasmosis. Other histochemical stains used in the context of infectious diseases include Fite or Ziehl–Neelsen stains (for less or more acid-fast bacteria) and the Warthin–Starry stain. Besides which, many infectious agents including parasites, bacteria and viruses can be detected using immunohistochemistry or in situ hybridization methods. In the context of lymphoma or suspicion of carcinoma, additional immunohistochemical stains are recommended including CD3, CD5, CD15, CD20, CD30 and pan-cytokeratin.
Take home messages
1. 0.5% of BM biopsies display epithelioid granulomas, up to 10% lipogranulomas.
2. 80% of such patients with epithelioid granulomas are symptomatic and in 80% of them a specific integrative diagnosis is possible, mostly infectious diseases (30–50%), achievable by applying:
• special stains
• field studies (travelling, ethnicity, clinical exam, drug exposure)
• serology
• PCR-based molecular genetic studies.
3. Detection of BM granulomas does not exclude an underlying malignant process, in fact this possibility must be actively sought for and, if needed, excluded.
4. Genuine neoplastic and non-neoplastic histiocytic disorders represent important differential diagnoses to granulomatous BM process.
References
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2. Helming L, Gordon S. Trends Cell Biol. 2009; 19(10): 514–522.
3. Rosé CD, Pans S, Casteels I, et al. Rheumatology 2014; 54: 1008–1016.
4. Kim NR, Ko YH, Choe YH, et al. Int J Surg Pathol. 2001; 9(1): 73–79.
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8. Rao DA, Dellaripa PF. Rheum Dis Clin North Am. 2013; 39(2): 277–297.
9. Brodbeck WG, Anderson JM. Curr Opin Hematol. 2009; 16(1): 53–57.
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12. Kettle P, Allen DC. J Clin Pathol. 1997; 50(2): 166–168.
13. Bhargava V, Farhi DC. Hematol Pathol. 1988; 2(1): 43–50.
14. Torlakovic EE, Brynes RK, Hyjek E, et al. Int J Lab Hematol. 2015; 37: 431–449.
The authors
Thomas Menter MD, Alexandar Tzankov* MD
Institute of Pathology, Basel, Switzerland
*Corresponding author
E-mail: alexandar.tzankov@usb.ch
Acknowledgment
This article is based on the presentation ‘Granulomatous infection (and reactions) in the bone marrow other than mycobacteria’ by Dr Tzankov at the 26th European Congress of Pathology in London, UK, 2014.
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, /in Featured Articles /by 3wmediaASD: a diagnosis to be sniffed at?
, /in Featured Articles /by 3wmediaThe complex neurodevelopmental condition autism spectrum disorder (ASD), now considered to be one of the most heritable of all neuropsychiatric conditions, is characterized by impaired communication skills and difficulties interacting socially together with limited and repetitive behaviour patterns. It has been suggested that the dramatic increase in the number of reported cases in recent decades- one out of 68 children in the US has been diagnosed with the condition- is largely the result of changes in how and when ASD is diagnosed as well as increased public awareness. Indeed a study reported in JAMA Pediatrics last year that followed 677,915 children born in Denmark from 1980 up to 1991 concluded that 60 percent of the increase in ASD prevalence could be attributed to changes in diagnostic methods, though as yet unidentified environmental risk factors could also be contributing to the rise.
So far there are no clinical lab tests available to facilitate diagnosis of ASD in spite of extensive research on the elevated levels of neurotransmitters such as 5-hydroxytryptamine and GABA, as well as the hormonal markers dopamine and oxytocin, found in many people affected. Studies have also focused on the potentially higher levels of inflammatory cytokines and autoantibodies, and the identification of target genes and epigenetic changes from gene-environment interactions in people with ASD. Neuroimaging has also identified activation deficits in certain areas of the brain. But currently diagnosis still relies on developmental screening followed by comprehensive (and costly) evaluation if indicated, a challenging approach for the healthcare workers involved since many of the symptoms mirror those found in other developmental disorders.
But ASD can be managed once the condition is diagnosed, so early and effective screening is essential. Appropriate and timely behavioural and speech therapy to improve learning, communication and social skills (as well as drug therapy in some cases) greatly improves the quality of life for those affected. And in July this year an exciting, if preliminary, study based on the sniff response to odours was published. Eighteen children with ASD and 18 matched controls were given both pleasant and unpleasant odours to smell and the changes in breathing patterns were recorded. Whereas pleasant or mild smells elicited a high-magnitude sniff and unpleasant odours one of low magnitude in the controls, the children with ASD sniffed the odours with equal magnitude. Could this simple low cost test eventually allow early effective screening for ASD or at least provide an additional testing tool in children who cannot communicate verbally?
Image analysis enables standardized and quantitative pathology
, /in Featured Articles /by 3wmediaThe analysis of histopathology slides is routinely performed in a manual, semi-quantitative manner which is open to observer variability. This article summarizes how technological advances in image analysis software allow the objective and standardized quantification of such samples while driving pathology towards a more personalized medicine.
by Dr Peter Caie
Introduction
The assessment of stained tissue sections by manual observation down a microscope has been, and still is, the steadfast manner in which histopathologists observe diseased tissue architecture in order to report on a patient’s prognosis. The tissue, for example the tumour microenvironment, is complex, highly heterogeneous and heterotypic. Although specific stains exist to aid in the identification and semi-quantification of histopathological features or biomarkers, the empirical field is subjective and therefore open to observer variability. In colorectal cancer (CRC) this can be the case for reporting items from the minimal core clinical data set such as differentiation [1] or promising histopathological features such as tumour budding [2] and lymphovascular invasion [3]. Similarly, in breast cancer discrepancies exist in the reproducibilty of manual reporting of human epidermal receptor protein-2 (HER2) by fluorescence in situ hybridization (FISH) or immunohistochemistry and the scoring of estrogen receptor (ER), both of which have predictive implications for patient treatment strategies [4]. Some reproducibility issues may be overcome through molecular pathology and the objective automated quantification of molecular biomarkers extracted from patient tissue samples. Modern methodology in quantitative pathology, spanning the classical ‘omics’ fields, has the ability to create a wealth of complex big data. Indeed, the field of molecular pathology has seen an explosion of big data specifically in translational genomics, transcriptomics and proteomics and which has the ability to map aberrant molecular pathways with direct impact on clinical decisions. The automated and standardized extraction of large data sets from tissue, has been termed ‘tissue datafication’. The automated quantification of molecular pathology, such as next-generation sequencing (NCS), gene-chip transcriptomics and reverse phase protein arrays may still suffer from reproducibility issues. These may occur from poor and small sample sizes or tissue artefacts which can stem from multiple sources: surgical ischemia, fixation and sample preparation. Standardization is therefore the key to accurate tissue datafication in order to report reproducible results which translate to the clinic. Tissue heterogeneity, both inter-patient and intra-patient, poses a very real problem for the effective personalized treatment decisions for patients. Tissue is often homogenized in order to extract the DNA, RNA or protein required for many molecular pathology techniques. In doing so the tissue heterogeneity (both subpopulation and spatial heterogeneity) is invariably lost and a single end-point is reported from the most dominant signal within the complex sample. A patient may therefore initially respond to a targeted treatment such as cetuximab in CRC but relapse within a set time period because of the existence of resistant KRAS and BRAF mutated subpopulations within the tumour [5]. Effective personalized combination therapy must rely on the capture of molecular end-points across the heterogeneous disease. Quantitative pathology must take into account the imperfection of the tissue sample as well as its heterogeneity in order to produce standardized and reproducible results. With the advent of digital pathology and associated image analysis solutions, histopathology has joined the ranks of molecular pathology with the ability to generate robust and standardized quantitative big data. Image analysis can also capture the heterogeneity across a patient sample by digitally segmenting the tumour subpopulations while extracting quantitative hierarchical morphological or biomarker data (Fig. 1). This review will discuss datafication of the tissue section through image analysis and its benefits as well as some of the challenges within the field.
Quantitative pathology through image analysis
Image analysis has been well established in order to quantify in vitro cell-based assays [6, 7] but has been slow to translate to molecular pathology and histopathology. This is in part due to the more complex and heterogeneous nature of the tissue as well as the need for extensive validation for clinical research compared with cell culture work. Advances in both whole-slide scanners and analysis software are now making the translation of image analysis to clinical research a reality. The use of standardized and automated image analysis solutions overcomes the reproducibility issues associated with manual semi-quantitative scoring of tissue as it negates observer variability. Image analysis has many uses within quantitative histopathology where it can report biomarker expression at sub-cellular resolution, quantify set histopathological features, identify heterogeneous subpopulations or the spatial heterogeneity of tumour and host interaction as well as identify novel histopathological features. Standardization is always the key to reproducible results and the field of image analysis is no different. Standardization and validation must be present throughout the entire process from tissue section cutting, mounting, labelling and digitizing. There are a growing number of whole-slide imagers on the market but it is paramount that these allow the use of identical image capture profiles and associated image quality across all the patient samples used in a study. Once the tissue is digitized in a standardized manner the image analysis algorithms themselves must be of a high enough quality in order to deal with the complex and heterogeneous tissue. Simplified algorithms have their use for basic biomarker quantification but may report false results or classifications owing to heterogeneous cell populations or inter-patient heterogeneity. Autofluorescence or non-specific staining in the sample may result in the reporting of false positives or inaccurate parameters when quantifying histopathological features in the complex tumour microenvironment. The image analysis workflow must therefore be robust enough to take into account or build in quality control steps to negate tissue labelling artefact [8].
Image analysis can quantify biomarkers
Whole-slide image analysis of molecular biomarkers labelled via antibodies or probes such as in FISH, avoids the contamination of signals from heterogeneous subpopulations that occur when the tissue is homogenized (Fig. 2A). This has advantages over destructive assays as the tissue structure, spatial orientation and sub-localization of molecules are retained [9] and heterogeneity can be compartmentalized and quantified while providing insight into cellular interactions within the tumour and its microenvironment. In order to quantify the biomarker in question the algorithm must segment the cells and nuclei within a region of interest, e.g. the tumour or stroma (Fig. 2B). This gives a further advantage to automated image analysis as morphometric and texture parameters may be captured and co-registered to the cell’s expression of the desired biomarker. This additional information can be used to identify a morphological surrogate to a biomarker or to capture a more definitive result that reduces false positives. When immunofluorescence is applied to biomarker quantification a continuous data capture across the dynamic range of intensity can be reported. The intensity of the fluorophore signal directly correlates to the level of protein expression and therefore returns a more accurate result than the classical 1+, 2+, 3+ manual scoring of chromogenic assays. This continuous data can be used to calculate robust cut-off points for positive and negative expression, or for patient categorization, in software such as X-Tile[ 10] or TMA Navigator [11].
Image analysis can quantify histopathological features
Image analysis may also be employed for the quantification of histopathological features. Observer variability occurs when manual semi-quantification of certain set histopathological features across tissue sections stained with hematoxylin and eosin (H&E) are reported [1–3]. Automated image analysis with the aid of specific labels negates observer variability and introduces standardization which is applicable across heterogeneous patient cohorts. In this manner tumour buds, lymphatic vessel density and invasion were co-registered upon the same tissue section and all quantified using the same algorithm across a CRC patient cohort [8]. This methodology allowed the computer-based algorithm to quantify small lymphatic vessels that were invaded by up to five cancer cells and which often go unreported because of their obscurity in H&E stained sections (Fig. 3). The results showed that these so called ‘occult lymphatic invasion’ events were independently predictive of poor prognosis in stage II CRC patients.
Similarly image analysis may be employed to quantify the host response to the tumour and not just the tumour itself; such as the lymphocytic infiltration within the cancer microenvironment. The immunoscore in CRC uses image analysis to quantify CD3+ and CD8+ lymphocytes at either the invasive front or the centre of the tumour section [12]. The automated quantification of lymphocytes and their spatial heterogeneity have also been shown to be prognostic in breast cancer [13].
Image analysis can identify novel features
Research pathologists apply their extensive experience to identify novel or significant prognostic features within the tissue section. Automated segmentation of digitized tissue sections now allows the quantification and standardization of complex and subtle morphological features or signatures in a continuous data capture manner. These features are extracted from every possible computer segmented object within the image. This image analysis methodology quantifies and profiles the complex phenome of the tumour’s microenvironment in an a priori ‘measure-everything big-data’ approach. Parameters extracted from single objects segmented across the digitized tissue section include morphometrics, texture and spatial heterogeneity. This is performed in an attempt to identify and quantify novel clinically relevant histopathological objects or predictive features from large exported image based multi-parametric big data sets. This emerging methodology has been termed ‘Tissue Phenomics’ by Gerd Binnig a Nobel Laureate and expert in image analysis. These objects may represent single or combinations of morphometrically quantifiable histological features, which may prove too subtle to observe by eye but which could prove prognostic or predictive. Beck et al. demonstrated this technique in breast cancer and found the stromal microenvironment to be specifically relevant to prognosis [14]. The big data created by image analysis approaches such as these needs to be distilled in order to identify the significant parameters which answer the clinical question being investigated. Bioinformatics must be applied which allows redundant parameters to be discarded and clinically relevant cut-offs to be applied to the remaining significant features. The reduced end result of a few significant parameters from potentially thousands of captured features should form a clinically translatable test which must then be validated across multiple international cohorts.
Future developments and challenges to the field
Technological advances in both image capture and analysis are beginning to see the translational of automated big data from the realm of academic research to clinical tests. Further technological advances such as co-registering of tissue sections and the ability to multiplex numerous biomarkers on a single tissue section will add greater value to the field. This multiplexed, next-generation immunohistochemistry [15] approach coupled with automated quantification may allow whole molecular pathways to be mapped at the single cell level. There are, however, challenges within the field. The automated quantification of pathology requires expensive whole-slide scanners as well as image analysis workstations alongside associated IT infrastructure to archive and keep secure the images and associated analysis. Fast Ethernet connections are also essential to recall these images in a time dependent manner. Another challenge is the acceptance of automated analysis within the clinical environment. This challenge will need to be overcome by validating the standardized and automated image analysis algorithms across multiple cohorts. The many applications of the field, such as objective, standardized and reproducible quantification of biomarkers, histopathological features and the profiling of a tumour’s heterogeneity hold advantages for both the pathologist and the patient. The negating of observer variability should increase the accuracy of patient results as should the application of clinically relevant categorical cut-offs across a continuous data set captured per patient. The capture of the molecular and histopathological prognostic and predictive signatures across heterogeneous subpopulations as the potential to turn traditional population based statistics into a more personalized one which informs the optimal treatment regimen for the individual patient.
References
1. Compton CC. Colorectal carcinoma: diagnostic, prognostic, and molecular features. Mod Pathol. 2003; 16: 376–388.
2. Puppa G, Senore C, Sheahan K, Vieth M, et al. Diagnostic reproducibility of tumour budding in colorectal cancer: a multicentre, multinational study using virtual microscopy. Histopathology 2012; 61: 562–575.
3. Harris EI, Lewin DN, Wang HL, Lauwers GY, et al. Lymphovascular invasion in colorectal cancer: an interobserver variability study. Am J Surg Pathol. 2008; 32:1816–1821.
4. Gown AM. Current issues in ER and HER2 testing by IHC in breast cancer. Mod Pathol. 2008; 21: S8–S15.
5. Baldus SE, Schaefer KL, Engers R, Hartleb D, et al. Prevalence and heterogeneity of KRAS, BRAF, and PIK3CA mutations in primary colorectal adenocarcinomas and their corresponding metastases. Clin Cancer Res. 2010; 16: 790–799.
6. Caie PD, Walls RE, Ingleston-Orme A, Daya S, et al. High-content phenotypic profiling of drug response signatures across distinct cancer cells. Mol Cancer Ther. 2010; 9: 1913–1926.
7. Gasparri F, Mariani M, Sola F, Galvani A. Quantification of the proliferation index of human dermal fibroblast cultures with the ArrayScan high-content screening reader. J Biomol Screen. 2004; 9: 232–243.
8. Caie PD, Turnbull AK, Farrington SM, Oniscu A, Harrison DJ. Quantification of tumour budding, lymphatic vessel density and invasion through image analysis in colorectal cancer. J Transl Med. 2014; 12: 156.
9. Kumar A, Rao A, Bhavani S, Newberg JY, Murphy RF. Automated analysis of immunohistochemistry images identifies candidate location biomarkers for cancers. Proc Natl Acad Sci U S A 2014; 111: 18249–18254.
10. Camp RL, Dolled-Filhart M, Rimm CL. X-tile: a new bio-informatics tool for biomarker assessment and outcome-based cut-point optimization. Clin Cancer Res. 2004; 10: 7252–7259.
11. Lubbock AL, Katz E, Harrison DJ, Overton IM. TMA Navigator: Network inference, patient stratification and survival analysis with tissue microarray data. Nucleic Acids Res. 2013; 41(Web Server issue): W562–568.
12. Galon J, Mlecnik B, Bindea G, Angell HK, et al. Towards the introduction of the Immunoscore in the classification of malignant tumors. J Pathol. 2013; 232: 199–209.
13. Yuan Y. Modelling the spatial heterogeneity and molecular correlates of lymphocytic infiltration in triple-negative breast cancer. J R Soc Interface 2015; 12: 20141153.
14. Beck AH, Sangoi AR, Leung S, Marinelli RJ, et al. Systematic analysis of breast cancer morphology uncovers stromal features associated with survival. Sci Transl Med. 2011; 3: 108ra113.
15. Rimm DL. Next-gen immunohistochemistry. Nat Methods 2014; 11: 381–383.
The author
Peter Caie PhD
School of Medicine, University of St Andrews, St Andrews KY16 9TF, UK
E-mail: Pdc5@st-andrews.ac.uk
Granulomatous infections in the bone marrow
, /in Featured Articles /by 3wmediaGranulomas are a rather uncommon, yet diagnostically helpful finding in trephine bone marrow biopsies. Being indicative for a rather limited number of various underlying diseases (ranging from infections to autoimmunopathies and malignant tumours), the finding of granulomas in the bone marrow should precipitate further analyses to uncover the underlying condition.
by Dr Thomas Menter and Dr Alexandar Tzankov
Microscopic aggregations of epithelioid histiocytes are referred to as granulomas and the respective inflammatory process is called granulomatous. Granulomas are a well-known feature in pathology, with the first description ranging back to the 17th century. They show variable morphologic features including central areas of necrosis (necrotizing granulomas) or suppuration (microabsceding or suppurative granulomas), incorporation of foreign material or presence of giant cells due to the fusion of macrophages. Granuloma formation is usually associated with local CD4+ T-cell activation (and numeric increase of CD4+ T-cells at the site of granuloma formation, thus ‘consuming’ CD4+ T-cells and leading to skewing of the CD4/CD8 ratio in the peripheral blood in favour of CD8) and production of interleukin 2 and 12, Interferon (IFN) γ, and tumour necrosis factor α (TNF-α) [1, 2]. In general, granulomas are provoked by agents that are difficult to eradicate by the enzymes of histiocytes, for example because of the different phospholipid composition of the former (e.g. mycolic acids) or because of the specific enzymo/immunogenetic background of the host. The number of causative agents in granulomatous responses is rather smaller than in other inflammatory patterns. Therefore granulomas are considered to be ‘specific’; narrowing down the possible causative factors, their identification and subsequent search for possible underlying agents and conditions supports the establishment of more precise clinicopathological diagnoses.
Morphology of bone marrow granulomas and etiologic considerations
Granulomatous processes involving the bone marrow (BM) can be divided into three subgroups based on morphology and clinicopathological context: (1) lipogranulomas, (2) infectious epithelioid granulomas, and (3) epithelioid granulomas associated with immune dysregulation. Importantly, most inborn immunodeficiency disorders are accompanied by increased granuloma formation [3]. Examples of these disorders include Blau syndrome, CVID (common variable immune deficiency), RAG (recombination-activating genes) deficiency, XIAP (X-linked inhibitor of apoptosis) deficiency and chronic granulomatous disease. Depending on whether solely the BM or other organs are also involved, granulomatous BM processes may represent isolated findings or reflect the involvement of a systemic disorder. It is obvious that detection of granulomatous BM processes should be followed by an integrative diagnostic work-up considering the clinical history (travel and drug history) and presentation, but also applying imaging techniques and molecular detection methods including serology, in situ uncover techniques and PCR- and sequencing-based procedures.
Lipogranulomas
Lipogranulomas are found in up to 10% of BM samples. They are not thought to be significant since probably not linked to specific underlying disorders. Yet, they may be more commonly observed in patients with acute febrile illnesses. They consist of aggregates of histiocytes with variably sized lipid vacuoles (Fig. 1A), which tend to gradually disappear with time resulting in a morphological appearance of lipogranulomas indistinguishable from epithelioid granulomas. When detecting BM lipogranulomas, special attention must be given to the periodic acid Schiff (PAS)/diastase-PAS stains so as not to miss involvement by Whipple’s disease, in which the foamy histiocytes stain positively (Fig. 1B); in suspect cases, the diagnosis of Whipple’s disease can be enhanced by additional Ziehl–Neelsen and Warthin–Starry stains as well as by PCR- and sequencing-based procedures. Granulomas in Erdheim–Chester disease might sometimes resemble lipogranulomas [4].
Epithelioid granulomas
Epithelioid granulomas are found in <1% of BM samples [5]. They are more frequent in certain geographic areas and in samples from patients with immune dysregulation. They are considered significant as they are associated with various infectious (Fig. 1C), immune dysregulatory (Fig. 1D) and neoplastic disorders (Fig. 1E). After clinicopathological and molecular work-up, a specific etiology of BM epithelioid granulomas can be attributed in up to 80% of cases. Such granulomas consist of loose [particularly in severely immunocompromised patients (the lower the CD4+ counts or the membrane-bound TNF-α and the more virulent the infectious agent, the looser the granuloma, Fig. 1F)] to cohesive clusters of epithelioid histiocytes with accompanying lymphocytes, eosinophilic (Fig. 1G) and neutrophilic granulocytes, and giant cells (Fig. 1H). In patients with infectious diseases, these granulomas mostly contain organisms, which should be actively sought for and if possible visualized (e.g. mycobacteria, histoplasmata, Bartonella henselae, treponemata, Leishmania spp., toxoplasmata) using special stains such as PAS, Ziehl–Neelsen, Fite, Grocott, May–Grünwald–Giemsa, Warthin–Starry, etc. Immunohistochemistry (Fig. 1L, insert) or molecular genetic methods might be necessary for etiologic assignment. A particular CD8 predominance in the BM interstitium often accompanies virus infections [e.g. cytomegalovirus (CMV) and Epstein–Barr virus (EBV)] and might serve as an additional diagnostic hint [6].
Different granuloma morphotypes
There are different granuloma morphotypes, which should be recognized because they may give a clue to the underlying disorder [7]. Caseating granulomas (i.e. granulomas with central necrosis) are usually caused by infectious agents such as mycobacteria, histoplasmata, Francisella tularensis, Yersinia pestis or brucellaceae. Ring-form granulomas (Fig. 1I) can be observed in acute virus infections (e.g. CMV), brucellosis, leishmaniasis and those appearing as ‘doughnut rings’ in Q-fever. Foreign body granulomas are rarely seen, but can be encountered in patients after repeated BM sampling (e.g. containing displaced keratin), or in patients with degenerative and debilitating disorders, in whom subchondral epithelioid clusters may raise differential diagnostic concerns of metastatic carcinomas (Fig. 1J).
Occasionally detached giant osteoclasts in patients receiving long-term bisphosphonate therapy may be conventionally indistinguishable from foreign body giant cells, but immunohistochemistry for tartrate-resistant acid phosphatase (TRAP) can be helpful, as osteoclasts are intensively positive. Sarcoid-type granulomas (Fig. 1K) consist of compact epithelioid collections and are less specific as they can accompany genuine sarcoidosis and autoimmune disorders such as, for example, rheumatoid arthritis [8].
The role of giant cells in granulomas
Another clue to the etiology in a granulomatous inflammation might be the type of giant cells present around the epithelioid histiocytes [9]. Langhans giant cells are characteristically seen in tuberculosis, and appear with nuclei arranged in a horseshoe-like fashion at the periphery of the cell below the cell membrane. In contrast, in Touton giant cells that are typically observable in areas of fat necrosis, the nuclei form a complete ring, and the cytoplasm is rather foamy than eosinophilic. In foreign body giant cells, the nuclei do not show a particular order but are rather haphazardly distributed. Besides, the foreign material incorporated in these cells (a clue to this diagnosis) is often visible by conventional or polarized light, being either pigmented or birefringent.
A particular vascular association of granulomas should raise suspicion of vasculitis, either primary like granulomatous polyangiitis (formerly known as Wegener’s disease), or secondary/infectious such as syphilis (Fig. 1L).
Granulomas and malignancies
Importantly, detection of BM granulomas does not exclude an underlying malignant process; on the contrary, BM granulomas may accompany various lymphoproliferative processes such as Castleman’s disease (Fig. 1M), B- and T-cell (so called ‘non-Hodgkin’) as well as Hodgkin lymphomas (HL) [10, 11], but also the BM spread of solid tumours such as lobular breast cancer [12]. There is a significant association between BM granulomas and non-Hodgkin lymphoma spread to the BM. Granulomas due to IFN therapy can be encountered in lymphoma patients [especially patients suffering from splenic marginal zone B-cell lymphoma and diffuse large B-cell lymphoma (DLBCL)] treated for underlying chronic hepatitis B- or C-virus (HBV, HCV) infections – both viruses are known to increase the risk of these lymphomas (Fig. 1N). Occasionally, granulomatous reactions might obscure lymphoma. This may particularly apply to HL, in which on the one hand granulomatous reactions can occur independently of BM infiltration by HL (not worsening patients’ prognosis; indeed 5% of HL patients have BM granulomas without BM involvement by lymphoma), and on the other hand BM involvement by HL is usually granulomatous with only a handful Reed–Sternberg cells. Therefore step-sections supported by immunohistochemistry (CD15, CD30) are warranted if BM granulomas are encountered in patients suffering from HL (Fig. 1O). Patients with lymphomas are at an increased risk of developing infections, which may also lead to granulomas and should therefore raise awareness for the differential work-up for infectious agents as described above. Finally, patients suffering from sarcoidosis are at increased risk of lymphoma (odds ratio 2) and incipient lymphomas [especially Burkitt lymphomas, DLBCL, small lymphocytic B-cell lymphomas, lymphoplasmacytic lymphomas and peripheral T-cell lymphomas (PTCL)] may provoke sarcoid-like reactions summarized in the so called ‘lymphoma-sarcoidosis syndrome’. To be comprehensive, apart from IFN, several other treatment compounds such as hematopoietic growth factors like G-CSF (Fig. 1K), TNF-α blockers, BCG vaccination, allopurinol, amiodarone, antipsychotics, phenytoin, sulfonamides, etc., can lead to BM granuloma formation [13].
Further etiologies of granulomas in bone marrow biopsies
Several other neoplastic and non-neoplastic conditions [like systemic mastocytosis, (Langerhans cell) histiocytoses], genuine histiocytic and metabolic/storage disorders (like Erdheim–Chester, Rosai–Dorfman and Gaucher’s disease), sea blue histiocytoses, hemophagocytic lympho-histiocytosis, T-cell and histiocyte-rich B-cell lymphomas or lymphomatoid granulomatosis (Fig. 1P) involving the BM can morphologically mimic granulomas and should be distinguished from the latter by means of ancillary studies.
Technical considerations
A comprehensive review on technical handling of BM biopsies to obtain optimal immunohistochemical results has been published recently [14]. Trephine BM biopsies are best taken from the iliac crest. For further analysis, they should be sent to the pathology institutions in 10% buffered formalin (final formaldehyde concentration 4%) in order to prevent autolytic changes and to allow for an optimally preserved morphology. For decalcification, chelate binders such as EDTA should be used. We discourage the use of other decalcificative agents such as formic acid or mercury containing agents such as SUSA-fixation because of the alteration of proteins and destruction of DNA as well as for health issues for the laboratory staff with regard to mercury exposure. Standard special stains of BM biopsies include H&E, PAS, Giemsa and Gömöri stains. Applying the PAS and the Gömöri stain can highlight fungi, but in the case of artefacts caused by BM fibrosis, other than Gömöri silver, stains such as a Grocott stain are helpful. The Giemsa stain is useful in the context of leishmaniasis and toxoplasmosis. Other histochemical stains used in the context of infectious diseases include Fite or Ziehl–Neelsen stains (for less or more acid-fast bacteria) and the Warthin–Starry stain. Besides which, many infectious agents including parasites, bacteria and viruses can be detected using immunohistochemistry or in situ hybridization methods. In the context of lymphoma or suspicion of carcinoma, additional immunohistochemical stains are recommended including CD3, CD5, CD15, CD20, CD30 and pan-cytokeratin.
Take home messages
1. 0.5% of BM biopsies display epithelioid granulomas, up to 10% lipogranulomas.
2. 80% of such patients with epithelioid granulomas are symptomatic and in 80% of them a specific integrative diagnosis is possible, mostly infectious diseases (30–50%), achievable by applying:
• special stains
• field studies (travelling, ethnicity, clinical exam, drug exposure)
• serology
• PCR-based molecular genetic studies.
3. Detection of BM granulomas does not exclude an underlying malignant process, in fact this possibility must be actively sought for and, if needed, excluded.
4. Genuine neoplastic and non-neoplastic histiocytic disorders represent important differential diagnoses to granulomatous BM process.
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The authors
Thomas Menter MD, Alexandar Tzankov* MD
Institute of Pathology, Basel, Switzerland
*Corresponding author
E-mail: alexandar.tzankov@usb.ch
Acknowledgment
This article is based on the presentation ‘Granulomatous infection (and reactions) in the bone marrow other than mycobacteria’ by Dr Tzankov at the 26th European Congress of Pathology in London, UK, 2014.