JOURNAL CLUB - liver pancreas gall bladder

Learning

1. JOURNAL CLUB 2 SEPTEMBER 2024 Presenter : Dr. Sai Jyoti Moderator : Dr. Nidhi Priya

2. HAEMATOLOGY The Expression and Secretion Profile of TRAP5 Isoforms in Gaucher Disease Margarita M. Ivanova * , Julia Dao, Neala Loynab , Sohailla Noor, Neil Kasaci , Andrew Friedman and Ozlem Goker-Alpan

3. Cells VOLUME 13 Published on : 20 April 2024 Conducted in : Lysosomal and Rare Disorders Research and Treatment Center, Fairfax, USA

4. Why I chose this article…? The article highlights the significance of tartrate-resistant acid phosphatase type 5 (TRAP5) isoforms in Gaucher disease. It improves understanding of disease’s effects on bone health and immune activation, offering potential for better diagnostics and treatment strategies.

5. INTRODUCTION Gaucher disease (GD) is a genetic lysosomal storage disorder caused by mutations in the glucocerebrosidase (GBA1) gene , leading to enzyme deficiency. Two main forms: 1. Non-neuronopathic - Type I or the chronic non neuronopathic form ( M/C ) 2. Neuronopathic- Type II or acute neuronopathic Gaucher disease - is the infantile acute cerebral pattern A third pattern, type III, is intermediate between types I and II.

6. Pathogenesis of lysosomal storage diseases. In the example shown, a complex substrate is normally degraded by a series of lysosomal enzymes (A, B, and C) into soluble end products. If there is a deficiency or malfunction of one of the enzymes (e.g., B), catabolism is incomplete and insoluble intermediates accumulate in the lysosomes. REVISION OF LYSOSOMAL STORAGE DISEASES

7. Gaucher disease involving the bone marrow Gaucher cells are plump macrophages that characteristically have the appearance in the cytoplasm of crumpled tissue paper due to accumulation of glucocerebroside. ( A Wright stain B Hematoxylin and eosin) often enlarged ( up to 100 μ m in diameter) have one or more dark eccentrically placed nuclei. PAS staining is usually intensely positive.

8. Gaucher cells with abundant lipid-laden granular cytoplasm.

9. MATERIALS AND METHODS Subjects Under IRB-approved protocols, 39 participants (29 women, 10 men) were recruited. Plasma was obtained from StemExpress (Folsom, CA, USA). Gaucher Disease (GD) diagnosis was confirmed through GCase activity and GBA1 sequencing.

10. Treatment Status : 3 were treatment-naïve 22 were on enzyme replacement therapy (ERT) 13 were on substrate reduction therapy (SRT) 1 switched from SRT to ERT Genotypes: 17 patients had homozygous p.N370S 12 had p.L444P 1 had L444P/L444P

11. The GD cohort , aged 18 to 68 years (mean 42 ± 15 years), was categorized by bone mineral density (BMD) into: Normal BMD (NB; n = 11) Osteopenia (OSN; n = 14) Osteoporosis (OSR; n = 14) Healthy controls had a mean age of 48 ± 11 years .

12. Blood Sample Collection

13. Differentiation of M2 Macrophages from PBMCs

14. Differentiation of Macrophages from THP-1

15. Cell Treatment

16. RNA Isolation and Quantitative Real-Time PCR (q-RT-PCR)

17. Measurement of TRAP5 Isoforms and Macrophage Activation Biomarkers in Plasma

18. Analysis of Chitotriosidase Enzymatic Activity in Dried Blood Spot Samples

19. Statistical Analysis Software: GraphPad Prism Two Groups: Student’s t-tests or F-tests Multiple Groups: One-way ANOVA followed by Kruskal–Wallis tests Significance Level: p < 0.05 Correlation: Pearson or Spearman methods

20. RESULT Assessment of TRAP5a and TRAP5b Plasma Levels in GD Cohorts TRAP5b: Elevated in GD patients, positively correlated with osteopenia, osteoporosis & biomarkers (CCL18, Lyso-Gb-1, Chito)

21. TRAP5a: Elevated in 15 of 40 GD patients; mean level was 13.1 ± 1.9 vs. 8.9 ± 0.8 in healthy controls, with a difference of 4.5 ± 2.3 (Figure 1A). Correlation: No significant correlation between TRAP5a and osteopenia/osteoporosis, unlike TRAP5b (Figure 1B). NB- no bone complication OSN-osteopenia OSR-Osteoporosis

22. Naïve GD patients were excluded from analysis due to small sample size (Figure 1C). In Substrate reduction therapy (SRT) Group: Significant increase in TRAP5a levels in the “Normal BMD” cohort compared to controls (Figure 1C).

23. TRAP5b Levels: In new GD patients, TRAP5b levels were remeasured and confirmed to be elevated, correlating with osteopenia osteoporosis (Figure 1D,E). Therapy Impact: Neither ERT nor SRT affected TRAP5b levels (Figure 1E).

24. TRAP5a and TRAP5b Correlation : No linear correlation between TRAP5a and TRAP5b was observed (Figure 1G), indicating that TRAP isoforms are regulated by distinct factors, highlighting their unique roles and regulatory mechanisms in GD pathology.

25. Long-Term Monitoring of TRAP5a and TRAP5b in GD patients TRAP5a Levels: Most GD patients with normal BMD or osteopenia did not show any significant changes in TRAP5a levels over 24 months, except for two patients with normal BMD and two patients with osteopenia (Figure 2 A,B)

26. TRAP5b Levels: Normal BMD: Remained stable , with only 2 patients showing slight elevations (Figure 2D,E).Among GD patients with osteopenia, the TRAP5b level remained unchanged in 5 patients, increased in 1 patient, and significantly decreased in another patient (Figure 2E). Plasma level of TRAP5b within 24 months of monitoring. GD patients with normal BMD (D), with osteopenia (E)

27. Most GD patients with osteoporosis had elevated TRAP5b levels, except for 2 patients who showed normal levels on visit one but elevated levels 24 months later (visit 5). Additionally, 1 patient showed a normalization of the TRAP5b level & the BMD test showed a stabilization of the T-score over 24 months (Figure 2F)

28. Correlation Analysis between TRAP5a, TRAP5b & Other Inflammatory Biomarkers in GD Chito (CHIT-1): Highly expressed in activated macrophages and Kupffer cells. CCL18: Produced by dendritic cells, monocytes, and macrophages. TRAP5b: Positively correlated with both Chito and CCL18 in the GD cohort .

29. TRAP5a and Chito : Exhibited heterogeneity of variance, suggesting different regulatory mechanisms of their expression in machrophages under chronic inflammation. CCL18: associated with TRAP5b but not with TRAP5a A strong correlation was noted b/w TRAP5b and Lyso-Gb-1 .

30. Scatterplot of TRAP5a and Chito: Spearman correlation analysis showed significant correlation (p < 0.05). (B, C) Scatterplots of TRAP5a with Lyso-Gb1: No significant correlation observed. TRAP5a with CCL18 : No significant correlation observed. (D–F) Correlation matrix and hierarchical clustering - Significant differences are marked in red (p < 0.05).

31. The Role of TRAP5b, TRAP5a, and the Bone Mineral Density Abnormalities in GD General Population : Bone Aging: Decreases BMD, leading to primary osteoporosis BMD Loss: Women ≥ 50: Start losing BMD, with rapid decline between 65–69 years. Men ≥ 60: Begin losing BMD, with rapid decline between 74–79 years

32. In GD Patients: Early Onset Osteoporosis: Significant BMD decrease occured early, even in patients undergoing ERT or SRT. GD patients with osteoporosis (OSN and OSR) were generally younger than those with osteoporosis in the general population No correlation b/w age and BMD was noted (Figures 3D–F and 4A,B).

33. ( A , B ) Scatterplot analysis of age and Z- or T-score. ( C , D ) Scatterplot analysis of age and TRAP5b & TRAP5a showed no correlation. ( E , F ) TRAP5b and Z-score & TRAP5b and T-score showed a medium correlation . ( G , H ) TRAP5a versus Z-score (G) and T-score showed no correlation.

34. ACP5 mRNA Expression in Cultured GD Macrophages The expression of TRAP (gene abbreviation ACP5) mRNA in macrophages from healthy control and GD patients was examined. MCP-1(monocyte chemoattractant protein-1) and Chit-1 mRNA levels were also evaluated. M2 macrophages were differentiated from PBMCs of healthy controls and GD patients (Table 1). M2 macrophages were obtained from 7 GD Patients.

35.   Controls P1 P2 * P15 P17 P23 * P35 * P37 * GBA   N370S/ N370S L444P/ L444P N370S/ N370S N370S/ N370S N370S/ N370S N370S/ N370S N370S/ Y412X Bone pathology NB NB NB OSN OSN OSN OSR OSR TRAP5b 1.47 ± 0.09 2.0 ± 0.3 2.7 ± 0.1 1.7 ± 0.8 4.9 ± 2.6 2.4 ± 0.2 9.2 ± 4 7.7 ± 2 TRAP5a 5.1 ± 1.1 7.3 ± 1.3 6.5 ± 1.6 5.7 ± 1.9 62.8 ± 16 5.0 ± 1.6 7.7 ± 1.8 10.1 ± 1.1 CCL2/MCP1 27.18 ± 4.9 74 ± 6.2 45.7 ± 6.4 141.5 19 ± 7.2 189 ± 28 27 ± 17 68 ± 16 GM-CSF 1.9 ± 0.3 2.0 ± 0.3 1.7 0.2 1.4 76.5 ± 5 1.8 10.3 TNF-alpha 12.2 ± 2.9 33.8 ± 6.4 28.1 25 ± 3.7 32 ± 5.7 31 ± 5.7 23 ± 17 18 ± 3.9 CCL18 71 ± 20 60.9 180 37.3 407.0 97.5 331 562.9 Lyso-Gb1 normal 2.7 15.9 1.0 49.0 5.1 39 85.6 Chito 95 ± 26 52 273 42.0 1194.0 164.0 97.8 546.0 Table 1. C/F of GD pts, including bone diseases, plasma levels of TRAP5b and TRAP5a, inflammatory markers (monocyte chemoattractant protein 1 (MCP1), granulocyte macrophage colony-stimulating factor (GM-CFS), tumor necrosis factor-alpha (TNF-alpha) & GD biomarkers (CCL18, Lyso-Gb-1, Chito). NB—normal BMD, OSN—osteopenia, OSR—osteoporosis. Healthy control (n = 5). * Plasma was used for the t/t of macrophages in vitro.

36. ACP5 Levels: Significantly decreased in GD macrophages after 12 days of differentiation, except for one cell line from a GD patient with osteoporosis (P7) (Figure 5A). CCL2/MCP-1 mRNA : significantly reduced in GD macrophages (Figure 5B). GCase Deficiency: Likely suppresses ACP5 and CCL2/MCP-1 expression since macrophages were cultured under the same conditions for both controls and GDs.

37. Cytokine Expression: Decreased for CCL5/RANTES and CXCR4 in GD macrophages. Elevated serum levels of CCL5/RANTES are observed in GD patients with osteonecrosis. Chit-1 mRNA: Significantly elevated in GD macrophages compared to controls (Figure 5C). ACP5 Expression: GD plasma increased ACP5 mRNA expression in both control and GD macrophages (Figure 5B, C).

38. CCL2/MCP-1 and Chit1 : Expression levels of CCL2/MCP-1 and Chit1 remained unchanged with plasma treatment (Figure 5D, E). Conclusion: The increase in ACP5 expression due to plasma t/t suggests that external factors in the blood influence ACP5 regulation.

39. Lyso-Gb-1 increased ACP5 expression in a concentration- and time-dependent manner (Figure 5F). Role of Lyso –Gb-1 levels in regulation of ACP 5 TRANSCRIPTION in macrophages from GD Patients

40. Increased ACP5 mRNA expression in a dose-dependent manner, similar to results from GD patient-derived macrophages (Figure 5G). Lyso-Gb-1 also activated TRAP5 expression, consistent with previous reports . Regulation ACP5 Expression and Lyso-Gb-1

41. Discussion TRAP in GD is traditionally used as a clinical biomarker for Gaucher Disease, but its role in GD pathology, especially regarding TRAP5a and TRAP5b, is not fully understood. Historically, studies have measured total TRAP5 levels using antibody-based assays. Development of antibodies distinguishing TRAP5 isoforms reveals: TRAP5a : An inflammatory biomarker. TRAP5b : A bone biomarker secreted by osteoclasts

42. The impact of Gb-1 and Lyso-Gb-1 on ACP5 gene expression and the differential roles of TRAP5 isoforms in GD are areas of ongoing research. TRAP5a is h ighly expressed in alveolar macrophages, inflammatory macrophages, and biomaterial-induced multinucleated giant cell and less expressed in activated macrophages & dendritic cells. TRAP5a levels correlate with rheumatoid factors and disease severity due to macrophage activation and inflammation.

43. Upregulation of TRAP5a indicates pro-inflammatory activities in macrophages and a higher proportion of glycolipid-enriched Gaucher cells. ACP5 mRNA expression in GD macrophages may be stimulated by Lyso-Gb-1 and other factors in GD plasma, like elevated RANKL. Positive correlation with Chit-1, suggested involvement in pro-inflammatory activities. No correlation with CCL18, indicated distinct roles in macrophage activation.

44. Increased TRAP5a in GD does not correlate with TRAP5b or decreased BMD, implying no direct link to osteoclast activity or accelerated bone resorption. TRAP5a is a monomer without enzymatic activity, highly expressed in alveolar macrophages, and linked to nitric oxide (NO) production for bacterial infection protection. It enhances migration in lung tissue; highly active in COPD and asthma patients, reflecting disease activity in chronic inflammatory conditions, including RA .

45. Transcription Factors like MITF (Microphthalmia Transcription Factor)NFATc1 (Nuclear Factor of Activated T-cells c1)RANKL (Receptor Activator of NF- κB Ligand) bind to the proximal TRAP promoter to increase TRAP activity. RANKL indirectly promotes osteoclasts to break down the tissue in bones and, after bone dissolution, release TRAP5b into the extracellular space . Thus, RANKL and components of the RANK pathway may be utilized as markers to track the progression of bone disease in GD patients

46. TRAP5b as a marker is highly sensitive and specific for detecting osteopenia and osteoporosis. It is recently shown to be a predictive marker for osteoporosis in GD. Increased TRAP5b indicates higher bone resorption due to more osteoclasts. TRAP5b can be used as an indicator of bone loss, complementing other GD biomarkers like Gb-1 and Lyso-Gb-1 for disease monitoring.

47. Conclusions TRAP5 isoforms could be targeted for adjunct therapies to address BMD abnormalities in GD. TRAP5b: Positively correlates with GD-specific biomarkers (Lyso-Gb-1, CCL18, Chito) & lower BMD Z-score, suggesting glucosylsphingosine accumulation's role in osteoporosis development (underlying mechanisms involved in bone pathology in GD). Relationship of TRAP5a and Chito indicates a shared origin of secretion, likely from activated macrophages or Gaucher cells which may effectively assess macrophage activation status & immune-driven inflammation in GD.

48. CYTOLOGY Utility of guided FNAC and cell block preparation from liver and gall bladder masses: Learning experience from a tertiary care center Bohara , Sangita; Shukla, Prakriti; Shah, Saman 1 ; Chaturvedi, Rashmi; Singh, Kushal 2

49. Published on : 22 JAN 2024 Journal of Cancer Research and Therapeutics. Conducted in : Hind Institute of Medical Sciences, Barabanki , Uttar Pradesh, India

50. Why I chose this article…? Guided FNAC in conjunction with the cell block technique is extremely helpful in the evaluation of mass lesions of the liver and gall bladder for cytological diagnosis.

51. INTRODUCTION Guided FNAC allows access to most liver and gall bladder lesions. It improves diagnostic accuracy for lesions near complex structures and those that are deep, small, or non-palpable.

52. Recent advancements have increased FNAC's diagnostic sensitivity (67–100%) and specificity (93–100%) for deep-seated lesions. Combining FNAC with cell blocks allows for multiple sections and the use of ancillary techniques such as IHC, special stains and molecular testing.

53. REVISION OF LIVER CYTOLOGY

54. Hepatocellular carcinoma

55. METASTATIC COLONIC ADENOCARCINOMA

56. REVISION OF GALL BLADDER CYTOLOGY

57. MATERIALS AND METHODS Study period - July 2020 to June 2022 Relevant clinical details, radiology reports & tumor marker levels (if available) were recorded for all patients before the procedure. CT and USG-guided FNACs were performed after obtaining informed consent. A brief discussion with the radiologist preceded the FNAC.

58. Needles Used: 22-gauge needle with a 10 ml syringe for superficial masses. Spinal needle for deep masses. Technique: Needle inserted into the lesion, monitored on the screen. Material adequacy was checked in the needle hub. Needle was withdrawn, noting the lesion's consistency and the type of aspirate.

59. 5 to 6 slides were prepared, including air-dried and wet-fixed smears (wet-fixed in isopropyl alcohol). 2 slides were stained with Hematoxylin and Eosin (H&E). 1 slide was stained with Papanicolaou and 2 air-dried slides were stained with MGG. Wet-fixed slide was dipped in hematoxylin staining solution for 10 seconds.

60. Slide was rinsed in tap water for rapid onsite assessment. It was examined under a microscope for blue-stained nuclei to confirm cytological material adequacy. Tentative diagnosis was noted based on clinico -radiological and intra-procedural findings. Differential diagnosis was provided in case of discrepancies.

61. Remaining material was c ollected and preserved in 10% neutral buffered formalin for cell block preparation. It was centrifuged at 1500 rpm for 5 minutes; supernatant was discarded. 4 drops each of pooled plasma and thrombin was added to 1 ml sediment and mixed gently. A soft ball was formed and placed into a labeled cassette. It was processed as routine biopsies and stained with H and E.

62. Cases were reported based on cellularity , cell composition , cytological atypia , and clinico -radiological diagnosis . Cell types were classified into: Atypical hepatocellular A typical-glandular/ squamoid /round/spindle A typical-unclassified A typia of undetermined significance.

63. C ellular pattern, size, shape, nuclear features and cytoplasmic details were assessed. A dditional findings like endothelial wrapping, blood vessels, hemorrhage , necrosis, mucin and parasites were noted. Histological grading (for malignancy) was based on nuclear atypia: Grade 1 (mild) Grade 2 (moderate) Grade 3 (severe).

64. A step-wise reporting questionnaire to reduce reporting time and enhance data reproducibility was used.

65. Statistical Analysis Statistical analysis was conducted using Microsoft Excel 2008. Results were expressed as numbers and percentages.

66. RESULT

67. Cytosmear showing atypical glandular clusters with enlarged round to columnar shaped nuclei with moderate to scant cytoplasm (H E; 40×) Cell block of the same case of metastatic adenocarcinoma showing atypical glandular nests (H E; 40×)

68. Diagnostic cytomorphological features included: Trabecular pattern. Polygonal cells with eosinophilic cytoplasm. Large vesicular nuclei with prominent nucleoli. Transgressing vessels. Intranuclear inclusions. Absence of bile duct epithelium. Among 7 hepatocellular carcinoma (HCC) cases: 5 cases (71.4%) were well differentiated. 2 cases (28.6%) were poorly differentiated.

69. (a) C ytosmear showing paucicellularity with scattered atypical hepatocytes of round to polygonal shape with moderate to scant cytoplasm and nuclear atypia (H E, 40×) (b) Cell block of the same case of poorly differentiated hepatocellular carcinoma showed better cellularity and ill-defined trabecular architecture (H E; 40×)

70. Final categorization of cases in liver and gall bladder masses

71. All 8 gall bladder masses were categorized as atypical glandular: 5 cases (62.5%) were moderately differentiated. 3 cases (37.5%) were poorly differentiated. Cytomorphology of GB malignancy showed sheets and acini of atypical cells with moderate-to-severe nuclear pleomorphism. 1 MD adenocarcinoma had a mucin-rich background with intracellular mucin pushing nuclei to the periphery.

72. Cell blocks prepared in 12 malignant cases: 9 from the liver 3 from the gall bladder 5 cases (41.67%) had diagnosis possible only on cell blocks due to better cellular yield. 2 cases (16.67%) had scant cellularity in cell blocks, making them non-contributory. 5 cases (41.67%) had cell blocks that were complementary and helpful in lesion characterization.

73. Clinico -radiological diagnosis was concordant with cytological diagnosis in 53 cases (74.6%). A follow-up biopsy was available in only 7 cases (8.75%) and was done when cytology was inconclusive. Of these, 6 cases (85.72) were consistent with the cytology findings.

74. The reporting questionnaire was helpful chiefly in terms of time-efficient reporting in 34 cases (42.5%) increasing the ease and confidence in 69 cases (86.25%) advantage of easy reproducibility of data in all cases (100%).

75. Discussion Early diagnosis of liver and gall bladder carcinomas is crucial for therapy and prognosis . Image-guided FNAC combined with cell blocking enhances diagnostic accuracy for deep-seated lesions.

76. In this study 80 cases: 50 males, 30 females Male-to-female ratio of 1.6:1 Mean age: 55 years Most cases - 5th and 6th decade. Similar to Talukder SI et al . and Nazir RT et al.

77. In this study Among the liver cases, the majority were adenocarcinomas (38 cases; 51.3%). Within these, 7 cases (8.8%) were graded as PD & 2 cases (3%) were graded as WD. Additionally, 2 cases of small cell tumors was observed. These findings are consistent with those of Das DK et al ., who studied 61 metastatic lesions and found adenocarcinomas to be the most common (70.49%), followed by small cell anaplastic carcinomas (9.8%), and undifferentiated carcinoma and soft tissue sarcomas (1.63% each).

78. In this study HCC was the 2nd most common diagnosis (10 cases; 13.5%), with half being WD and the other half PD. This contrasts with Gatphoh ED et al ., who reported a higher prevalence of HCC compared to adenocarcinomas among liver masses.

79. In this study cell blocks were prepared for 12 cases (15%), with 9 from liver lesions and 3 from gall bladder lesions. An overall improvement in diagnostic accuracy was observed when conventional cytological smears were supplemented with cell block sections. Previous research supports that cell blocks enhance the effectiveness of cytodiagnosis , particularly for malignant and suspicious cases. In this study 5 cases were diagnosed exclusively from cell block sections.

80. ROSE enhances diagnostic accuracy by ensuring adequate cellularity for analysis and aiding in sample collection for various tests. However, it requires skilled cytopathologists and high-quality staining methods . Structured formats and synoptic reporting have improved diagnostic accuracy in cytology, particularly for bile cytology and HCC. This indigenous reporting method streamlined the process, but further studies are needed to evaluate its effectiveness.

81. LIMITATIONS Cell blocks were prepared for a limited number of cases. Structured questionnaire-based reporting format could not be compared to past studies due to its indigenous nature.

82. CONCLUSION Guided FNAC in conjunction with the cell block technique is extremely helpful in cytological diagnosis of liver and gall bladder mass lesions. ROSE is an important pre-requisite in CT and USG-guided FNAC and can be done using hematoxylin staining to achieve adequate cellularity for reporting.

83. HISTOPATHOLOGY ABCD1 as a Novel Diagnostic Marker for Solid Pseudopapillary Neoplasm of the Pancreas Ying- ao Liu, BS, Yuanhao Liu, BS, Jiajuan Tu, PhD, Yihong Shi, BS, Junyi Pang, BS, Qi Huang, BS, Xun Wang, BS, Zhixiang Lin, PhD, Yupei Zhao, MM, Wenze Wang, MD, Junya Peng, PhD, and Wenming Wu, MD

84. The American Journal of Surgical Pathology:  5 MAY 2024 - Volume 48 Issue 5 Conducted in: Department of General Surgery and Pathology Peking Union Medical College Hospital, Beijing, China.

85. Why I chose this article…? Diagnosing solid pseudopapillary neoplasm (SPN) of the pancreas can be tricky because it may be mistaken for pancreatic neuroendocrine tumors (NETs) with current markers. ABCD1 shows promise as a highly effective and easy-to-use marker for distinguishing SPN from NETs through IHC staining.

86. RECAP Solid pseudopapillary neoplasm

87. INTRODUCTION Solid pseudopapillary neoplasm (SPN) is a rare, low-grade malignant pancreatic tumor representing 1% to 3% of pancreatic tumors . Diagnosis mainly relies on distinct histomorphologic features and immunohistochemical staining. SPN can be confused with pancreatic neuroendocrine tumors (NET), pancreatoblastoma (PB) and acinar cell carcinoma (ACC) based on morphological criteria alone.

88. In over 90% of SPN cases, a point mutation in exon 3 of the CTNNB1 gene leads to increased nuclear β-catenin accumulation. IHC staining typically shows nuclear positivity for β-catenin . However, similar β-catenin patterns can be seen in pancreatic neuroendocrine tumors (NETs), pancreatoblastoma (PB) and acinar cell carcinoma (ACC), complicating diagnosis.

89. Variability in β-catenin expression , with some regions showing strong nuclear staining and others not, can result in false negatives, particularly in biopsy samples. Thus, a more specific and sensitive diagnostic marker is needed to enhance accuracy in diagnosing SPN.

90. The use of β-catenin as a diagnostic marker for SPN is based on the presence of CTNNB1 mutations in over 90% of SPN cases. Gene expression datasets for SPNs are now readily available. To identify additional diagnostic markers, they analyzed mRNA expression profiles from SPNs, adjacent normal tissues & NETs. The study aimed to find upregulated pathways and potential diagnostic markers through IHC staining in a large patient cohort, including SPNs and other tumors with similar features.

91. MATERIALS AND METHODS The patient cohort for this study included a diverse range of pancreatic tumors collected from Peking Union Medical College Hospital between 2011 and 2023. Clinical information of all patients – TABLE 1

92. TABLE 1　 SPN NF-NET F-NEN Acinar cell carcinoma Pancreato-blastoma PDAC IPMN PanIN MCA SCA Total (n) 111 98 10 9 3 54 5 12 19 20 Sex                     Female [n (%)] 89（80.2） 54（55.1） 8（80） 4（44.4） 1（33.3） 20（37.0） 1（20.0） 6（50.0） 19（100.0） 17（85.0） Male [n (%)] 22（19.8） 44（44.9） 2（20） 5（55.6） 2（66.7） 34（63.0） 4（80.0） 6（50.0） 0（0.0） 3（15.0） Median age 31 53 53.5 58 32 63.5 63 63.5 35 50.5 [years(range)] 12-62 18-77 23-58 38-66 29-41 42-87 57-83 48-74 24-67 29-70 Maximum tumor volume(cm) 18x14x7 11x8x11 6x3x2.5 9.5x7.5x5 9x7.5x7.6 5x4.5x4 4.5x4x4 7x6.5x1.8 8.7x4.5x3.5 6.5x6.5x6.5

93. For the differential gene expression analysis, the researchers utilized the limma package to identify differentially expressed genes (DEGs) between two groups. Limma (linear model for microarray data) , a linear model-based method, enhanced analysis stability by incorporating shrinkage estimation and borrowing information across genes. The analysis adhered to the default parameter settings specified in the limma documentation. Differential Gene Expression Analysis

94. Analysis Workflow: Normalization : The raw read count data were normalized using a library size factor and log transformation. Statistical Analysis : The ImFit , eBayes , and top table functions within the limma package were used to compute P-values for each gene . Filtering : DEGs were filtered based on a false discovery rate (FDR) cutoff of 0.05. This approach enabled the researchers to effectively identify and validate significant gene expression differences between the groups.

95. Gene Set Enrichment Analysis The researchers ranked all genes based on log2 fold change (log2FC) values, which were derived from gene expression levels between different phenotypes: SPNs VS normal, NETs VS normal & SPNs VS NETs. The ranked gene lists were then imported into the R package clusterProfiler to perform gene set enrichment analysis (GSEA).

96. This analysis specifically targeted the Gene Ontology (GO) biological process gene sets , focusing on GOBP_PEROXISOME_ORGANIZATION from the Molecular Signatures Database ( MsigDB ). The results of the analysis were visualized using the R package Enrichplot .

97. Staining Antibodies and Reagents Anti-catalase rabbit monoclonal antibody , Cell Signaling Technology, 12980S, using concentration of 1:200 for immunofluorescence staining. BOND Ready-To-Use Primary Antibody Beta- Catenin (17C2), Leica, PA0083, using original reagents directly for IHC staining. Anti-ABCD1 rabbit monoclonal antibody , Abcam, ab197013, using concentration of 1:100 for IHC staining.

98. Goat anti-mouse IgG H&L (Alexa Fluor 488), Abcam, ab150113, using concentration of 1:500 for immunofluorescence staining. Goat anti-rabbit IgG H&L (HRP), Abcam, ab6721, using concentration of 1:200 for IHC staining. OptiView Amplification Kit , Roche, 860-099, using original reagents directly for IHC staining.

99. Immunohistochemistry Staining IHC assays were conducted using a fully automated immunohistochemistry stainer (Roche Ventana and Leica Bond III), with corresponding reagents, following the manufacturer’s instructions. Tissue samples were fixed in 10% neutral formalin, paraffin-embedded, cut into 4-μm thick consecutive sections & dewaxed as per standard procedures. Primary antibodies were appropriately diluted with a TBS diluent & dispensed into user-fillable containers.

100. Immunofluorescence Staining Fresh tissue samples were fixed in 4% paraformaldehyde, embedded in Optimal Cutting Temperature & sectioned into 4-μm thick frozen slices. The sections were incubated with 10% fetal bovine serum and 0.2% Triton X-100 for 30 minutes to block nonspecific binding and permeabilize cells.

101. Primary antibodies were applied for 2 hours, followed by 1 hour incubation with fluorescently labeled secondary antibodies at room temperature. 4’,6-Diamidino-2-phenylindole ( DAPI) was used for nuclear staining. Sections were washed 3 times with PBS, kept moist, and protected from light during secondary antibody incubation.

102. Western Blotting Frozen SPN and normal pancreas tissues were homogenized in RIPA buffer with 1% proteinase inhibitor. The lysates were incubated on ice for 1 hour, centrifuged at 14,000 rpm for 10 minutes & the supernatant was collected. Protein concentrations were measured using a BCA Protein Assay Kit. Protein samples were diluted with 5x loading buffer, separated by 10% SDS-PAGE (Sodium dodecyl-sulfate polyacrylamide gel electrophoresis) & transferred to nitrocellulose membranes.

103. Membranes were blocked with 5% skim milk for 1 hour, incubated with primary antibodies overnight at 4°C and then washed with Tris-Buffered Saline with Tween. Secondary antibodies were applied for 1 hour at room temperature. Immunoblotting images were acquired with a Tanon visualizer and β-actin was used as a loading control.

104. Histoscore Analysis Two pathologists independently evaluated all staining results using Histoscore (Hs) & they were blinded to the clinical data. The Histoscore was a semiquantitative assessment comprising 2 components: the intensity (I) of staining, which ranged from 0 (no staining) to 3 (strong), and the percentage (P) of cytoplasmic staining. Hs = (1 × [% weak staining]) + (2 × [% moderate staining]) + (3 × [% strong staining])

105. Quantification of Immunohistochemical Staining of ABCD1 Expression IHC sections were scanned with a NanoZoomer S360 . Five random regions from each image were analyzed using FIJI software to assess staining intensity. Images were segmented into hematoxylin and DAB components, converted to 8-bit format & analyzed for staining intensity and cell area. The ratio of these values was used to determine average intensity.

106. Statistical Analysis Statistical analysis - GraphPad Prism (Version 8.0). Continuous variables - 2-tailed Student's t test. Categorical variables - Fisher's exact test. Significance was set at P < 0.05. ROC analysis - MedCalc software.

107. Figure 1A outlines the study's experimental design, analyzing gene expression data from 13 SPNs, 6 NETs & 5 normal pancreases. Peroxisomes Are Enriched in SPNs

108. Figure 1B Gene Set Enrichment Analysis (GSEA) plots for SPN and normal pancreas The green line shows enrichment, and the colored band indicates gene correlation with peroxisomal organization (red: positive, blue: negative). GSEA showed higher expression of peroxisome-related genes in SPNs compared to normal tissues.

109. Figure C Immunofluorescence images of Catalase (peroxisome marker) in normal & SPN tissues. Catalase, an enzyme in peroxisomes that converts hydrogen peroxide into water and oxygen , was used to quantify peroxisomes. Figure D Immunofluorescence staining revealed a higher number of Catalase-positive dots in SPN tissues compared to normal

110. Figure E Transmission electron microscopy images showing peroxisome morphology in normal and SPN cells Figure F Elevated peroxisome levels was noted in SPN cells.

111. ABCD1 Exhibits Elevated Expression in SPN Analysis of differentially expressed genes (DEGs) revealed 1895 upregulated genes in SPNs compared to normal tissues (P≤0.05, Log2FC≥1). Among peroxisome genes , ABCD1 showed the highest expression in SPNs . Immunoblotting confirmed ABCD1 upregulation in SPN tissues supporting transcriptome data. IHC of ABCD1 in 72 primary SPNs, 16 metastatic SPNs, and 72 normal pancreas samples revealed strong or moderate positive staining in SPN tissues , while normal tissues were negative or weakly positive .

112. Figure A and B Volcano plots show differentially expressed genes (DEGs) b/w SPN tumor tissues & normal pancreas SPN and NETs Red dots represent peroxisome organization genes.

113. Figure C Immunoblotting analysis of ABCD1 protein expression in SPN & normal pancreas.

114. Figure D Representative H&E and IHC staining for ABCD1 in SPN tumor and adjacent normal tissues.

115. Figure E ABCD1 IHC images with varying histoscores (− to +++) in SPN and normal pancreas.

116. Figure F Histoscore distribution for ABCD1 in 72 normal pancreas, 72 primary SPNs, and 16 metastatic SPNs.

117. Figure G Fisher exact test results for histoscores, comparing different groups: P(−) for “−” vs. others; P(−/+) for “−” and “+” vs. others; P(−/+/++) for “+++” vs. others.

118. ABCD1 Specifically Identifies SPN From Morphologically Similar Pancreatic Neoplasms by IHC Uniformly homogeneous round to oval cells complicated the differentiation of SPN from NET, ACC & PB based on morphology alone. The study involved 111 SPN, 108 NET (98 nonfunctional, 10 functional), 9 ACC and 3 PB samples. The staining results were analyzed using the Hs scoring method.

119. Figure 3A: Representative IHC images with ABCD1 scores for NET, acinar cell carcinoma & pancreatoblastoma (“−”, “+”, “++”).

120. Over 95% of SPN samples showed strong positive staining (+++) , whereas similar staining was not observed in other pancreatic tumors, which were mostly negative or weakly positive (Figs. 3B, C). ABCD1 expression was notably higher in SPNs than in NET, ACC, and PB samples, suggesting its potential as a distinguishing marker for SPN.

121. Figure 3B: Percentage of ABCD1 histoscores in 111 SPN, 108 NET, 9 acinar cell carcinoma, and 3 pancreatoblastoma tissues. Figure 3C: Fisher exact test results for histoscores

122. Also Distinguishes SPN From Other Pancreatic Tumors by IHC To investigate if ABCD1 could distinguish SPN from other pancreatic tumors, IHC staining was conducted on 54 cases of pancreatic ductal adenocarcinoma (PDAC) 5 intraductal papillary mucinous neoplasms (IPMN) 12 pancreatic ductal intraepithelial neoplasias ( PanIN ) 19 pancreatic mucinous cystadenomas (MCA) 20 pancreatic serous cystadenomas (SCA) The Hs scoring method was used for analysis.

123. Results showed that 95.5% of SPN samples exhibited strong positive staining for ABCD1 (+++). In contrast, nearly all other pancreatic tumors displayed either negative (−) or weak positive (+) staining, except for PDAC. Of the 54 PDAC cases, only 3 showed moderate positive expression (++), while the rest were either negative (−) or weakly positive (+)

124. Performs Well as a Positive Marker for SPN Diagnosis To evaluate the diagnostic potential of ABCD1 as a marker for SPN, ROC curve analysis was performed to determine sensitivity and specificity.

125. Figure 5A shows the ROC curve for ABCD1 histoscore distinguishing SPN from NET Figure 5B displays the ROC curve for ABCD1 histoscore in differentiating SPN from all pancreatic tumors listed (NET, ACC, PB, PDAC, SCA, MCA, IPMN, and PanIN ).

126. These findings underscore the potential of ABCD1 as a valuable diagnostic marker for distinguishing SPN.

127. Discussion The study compared SPNs, NETs and normal pancreatic tissues, identifying significant upregulation of the peroxisome organization pathway in SPNs. Validation with catalase immunofluorescence and transmission electron microscopy, along with IHC staining of various tumor types, showed high ABCD1 expression in SPNs, confirming its effectiveness as a diagnostic marker.

128. While nuclear β-catenin is commonly used for SPN diagnosis, other markers like CD10, cyclin D1 and vimentin have varied sensitivity & specificity and some SPN cases may be negative for these markers. β-catenin alone also has limitations due to variable staining intensity and overlap with other tumors, underscoring the need for new, specific markers. The study found increased ABCD1 expression in both primary and metastatic SPN sites compared to normal pancreas. In contrast, NET, ACC, PB and most other pancreatic tumors showed negative or weakly positive ABCD1 staining.

129. High ABCD1 staining intensity was strongly indicative of SPN, particularly in cases with partially nuclear-negative or unclear β-catenin expression. IHC staining of 16 metastatic SPN samples consistently revealed strong positive results, suggesting ABCD1's utility in identifying SPN metastasis. Multicenter validation is needed to confirm its clinical feasibility.

130. To assess ABCD1 as a supplementary marker for SPN alongside β-catenin, the study examined ABCD1 expression in tumor cells with negative nuclear β-catenin. Although none of the 111 SPN cases were completely negative for nuclear β-catenin, some had areas with negative or obscure staining where ABCD1 was consistently positive. This suggests that ABCD1 could complement β-catenin, especially in biopsies where β-catenin staining may be variable .

131. Additionally, negative ABCD1 staining in some β-catenin-positive NET samples highlights its potential to distinguish SPN from similar pancreatic tumors and reduce false positives. Future research should explore the combined use of ABCD1 and β-catenin in SPN diagnosis.

132. In the cohort, 95.5% of SPN cases showed strong positive ABCD1 staining (+++), with none exhibiting negative staining. Although some similar tumors also expressed ABCD1, they lacked the strong positive pattern seen in SPN. This distinct staining profile makes ABCD1 suitable for future automated quantification.

133. CONCLUSION The study underscores ABCD1's specific enrichment in SPN compared to other pancreatic tumors, demonstrating its potential as a precise and reliable diagnostic marker for SPN.