Digital image analysis of pan-cytokeratin stained tumor slides for evaluation of tumor budding in pT1/pT2 colorectal cancer: Results of a feasibility study

Advantages of automated immunostain analyses for complex membranous immunostains: An exemplar investigating loss of E-cadherin expression in colorectal cancer

As pathology moves towards digitisation, biomarker profiling through automated image analysis provides potentially objective and time-efficient means of assessment. This study set out to determine how a complex membranous immunostain, E-cadherin, assessed using an automated digital platform fares in comparison to manual evaluation in terms of clinical correlations and prognostication. Tissue microarrays containing 1000 colorectal cancer samples, stained with clinical E-cadherin antibodies were assessed through both manual scoring and automated image analysis. Both manual and automated scores were correlated to clinicopathological and survival data. E-cadherin data generated through digital image analysis was superior to manual evaluation when investigating for clinicopathological correlations in colorectal cancer. Loss of membranous E-cadherin, assessed on automated platforms, correlated with: right sided tumours (p = <0.001), higher T-stage (p = <0.001), higher grade (p = <0.001), N2 nodal stage (p = <0.001), intramural lymphovascular invasion (p = 0.006), perineural invasion (p = 0.028), infiltrative tumour edge (p = 0.001) high tumour budding score (p = 0.038), distant metastasis (p = 0.035), and poorer 5-year (p= 0.042) survival status. Manual assessment was only correlated with higher grade tumours, though other correlations become apparent only when assessed for morphological expression pattern (circumferential, basolateral, parallel) irrespective of intensity. Digital assessment of E-cadherin is effective for prognostication of colorectal cancer and may potentially offer benefits of improved objectivity, accuracy, and economy of time. Incorporating tools to assess patterns of staining may further improve such digital assessment in the future.

Identification of neutrophils and eosinophils in upper airway mucosa with immunofluorescence multiplex image cytometry

Characterization of inflammation in chronic rhinosinusitis with (CRSwNP) and without nasal polyps (CRSsNP) is an ongoing research process. To overcome limitations of current cytologic techniques, we investigated whether immunofluorescence multiplex image cytometry could quantify intact neutrophils, eosinophils, and other immune cells in solid upper airway mucosa. We used a four-channel immunofluorescence-microscopy technique for the simultaneous detection of the leukocyte marker CD45, the neutrophil marker myeloperoxidase, two eosinophil markers, i.e., major basic protein and eosinophil peroxidase, and DAPI (4',6-diamidin-2-phenylindole), in formalin-fixed paraffin-embedded upper airway tissue samples of patients with CRSwNP and CRSsNP, as well as of patients free of CRS with inferior turbinate hypertrophy (controls). Image acquisition and analysis were performed with TissueFAXS and StrataQuest (TissueGnostics, Vienna, Austria), respectively. Positive and negative immunostaining were differentiated with a specific fluorescence signal/background signal ratio. Isotype controls were used as negative controls. In six controls, nine patients with CRSsNP, and 11 patients with CRSwNP, the median area scanned and median cell count per patient were 14.2 mm2 and 34,356, respectively. In CRSwNP, the number of eosinophils was three times higher (23%) than that of neutrophils (7%). Three times more immune cells were encountered in CRSwNP (33%) compared to CRSsNP (11%). In controls, inflammation was balanced between the epithelial layer and lamina propria, in contrast to CRS (three times more pronounced inflammation in the lamina propria). The quantification of intact neutrophils, eosinophils, and other immune cells in solid tissue with undisrupted architecture seems feasible with immunofluorescence multiplex image cytometry.

Lectin-Based Immunophenotyping and Whole Proteomic Profiling of CT-26 Colon Carcinoma Murine Model

A murine colorectal carcinoma (CRC) model was established. CT26 colon carcinoma cells were injected into BALB/c mice's spleen to study the primary tumor and the mechanisms of cell spread of colon cancer to the liver. The CRC was verified by the immunohistochemistry of Pan Cytokeratin and Vimentin expression. Immunophenotyping of leukocytes isolated from CRC-bearing BALB/c mice or healthy controls, such as CD19+ B cells, CD11+ myeloid cells, and CD3+ T cells, was carried out using fluorochrome-labeled lectins. The binding of six lectins to white blood cells, such as galectin-1 (Gal1), siglec-1 (Sig1), Sambucus nigra lectin (SNA), Aleuria aurantia lectin (AAL), Phytolacca americana lectin (PWM), and galectin-3 (Gal3), was assayed. Flow cytometric analysis of the splenocytes revealed the increased binding of SNA, and AAL to CD3 + T cells and CD11b myeloid cells; and increased siglec-1 and AAL binding to CD19 B cells of the tumor-bearing mice. The whole proteomic analysis of the established CRC-bearing liver and spleen versus healthy tissues identified differentially expressed proteins, characteristic of the primary or secondary CRC tissues. KEGG Gene Ontology bioinformatic analysis delineated the established murine CRC characteristic protein interaction networks, biological pathways, and cellular processes involved in CRC. Galectin-1 and S100A4 were identified as upregulated proteins in the primary and secondary CT26 tumor tissues, and these were previously reported to contribute to the poor prognosis of CRC patients. Modelling the development of liver colonization of CRC by the injection of CT26 cells into the spleen may facilitate the understanding of carcinogenesis in human CRC and contribute to the development of novel therapeutic strategies.

Investigating In Situ Expression of c-MYC and Candidate Ubiquitin-Specific Proteases in DLBCL and Assessment for Peptidyl Disruptor Molecule against c-MYC-USP37 Complex

Diffuse Large B-Cell Lymphoma (DLBCL) is the most common form of non-Hodgkin's lymphoma (NHL). Elevated expression of c-MYC in DLBCL is associated with poor prognosis of the disease. In different cancers, c-MYC has been found regulated by different ubiquitin-specific proteases (USPs), but to date, the role of USPs in c-MYC regulation has not been investigated in DLBCL. In this study, in situ co expression of c-MYC and three candidates USPs, USP28, USP36 and USP37, have been investigated in both the ABC and GCB subtypes of DLBCL. This shows that USP37 expression is positively correlated with the c-MYC expression in the ABC subtype of DLBCL. Structurally, both c-MYC and USP37 has shown large proportion of intrinsically disordered regions, minimizing their chances for full structure crystallization. Peptide array and docking simulations has shown that N-terminal region of c-MYC interacts directly with residues within and in proximity of catalytically active C19 domain of the USP37. Given the structural properties of the interaction sites in the c-MYC-USP37 complex, a peptidyl inhibitor has been designed. Molecular docking has shown that the peptide fits well in the targeted site of c-MYC, masking most of its residues involved in the binding with USP37. The findings could further be exploited to develop therapeutic interventions against the ABC subtype of DLBCL.

Automated Cell Phenotyping for Imaging Mass Cytometry

Imaging mass cytometry (IMC) is a new advancement in tissue imaging that is quickly gaining wider usage since its recent launch. It improves upon current tissue imaging methods by allowing for a significantly higher number of proteins to be imaged at once on a single tissue slide. For most analyses of IMC data, determining the phenotype of each cell is a crucial step. Current methods of phenotyping require sufficient biological knowledge regarding the protein expression profile of the various cell types. Here, we develop a deep convolutional autoencoder-classifier to automate the cell phenotyping process into four basic cell types. Biopsy tissue from bladder cancer patients is used to evaluate the efficacy of the classification. The model is evaluated and validated through feature importance, confirming that the significant features are biologically relevant. Our results demonstrate the potential of deep learning to automate the task of cell phenotyping for high-dimensional IMC data.

Development of a semi-automated method for tumour budding assessment in colorectal cancer and comparison with manual methods

AIMS

Tumour budding (TB) is an established prognostic feature in multiple cancers but is not routinely assessed in pathology practice. Efforts to standardise and automate assessment have shifted from haematoxylin and eosin (H&E)-stained images towards cytokeratin immunohistochemistry. The aim of this study was to compare manual H&E and cytokeratin assessment methods with a semi-automated approach built within QuPath open-source software.

METHODS AND RESULTS

TB was assessed in cores from the advancing tumour edge in a cohort of stage II/III colon cancers (n = 186). The total numbers of buds detected with each method were as follows: manual H&E, n = 503; manual cytokeratin, n = 2290; and semi-automated, n = 5138. More than four times the number of buds were identified manually with cytokeratin assessment than with H&E assessment. One thousand seven hundred and thirty-four individual buds were identified with both manual and semi-automated assessments applied to cytokeratin images, representing 75.7% of the buds identified manually (n = 2290) and 33.7% of the buds detected with the semi-automated method (n = 5138). Higher semi-automated TB scores were due to any discrete area of cytokeratin immunopositivity within an accepted area range being identified as a bud, regardless of shape or crispness of definition, and to the inclusion of tumour cell clusters within glandular lumina ('luminal pseudobuds'). Although absolute numbers differed, semi-automated and manual bud counts were strongly correlated across cores (ρ = 0.81, P < 0.0001). All methods of TB assessment demonstrated poorer survival associated with higher TB scores.

CONCLUSIONS

We present a new QuPath-based approach to TB assessment, which compares favourably with established methods and offers a freely available, rapid and transparent tool that is also applicable to whole slide images.

Development and initial validation of a deep learning algorithm to quantify histological features in colorectal carcinoma including tumour budding/poorly differentiated clusters

AIMS

To develop and validate a deep learning algorithm to quantify a broad spectrum of histological features in colorectal carcinoma.

METHODS AND RESULTS

A deep learning algorithm was trained on haematoxylin and eosin-stained slides from tissue microarrays of colorectal carcinomas (N = 230) to segment colorectal carcinoma digitised images into 13 regions and one object. The segmentation algorithm demonstrated moderate to almost perfect agreement with interpretations by gastrointestinal pathologists, and was applied to an independent test cohort of digitised whole slides of colorectal carcinoma (N = 136). The algorithm correctly classified mucinous and high-grade tumours, and identified significant differences between mismatch repair-proficient and mismatch repair-deficient (MMRD) tumours with regard to mucin, inflammatory stroma, and tumour-infiltrating lymphocytes (TILs). A cutoff of >44.4 TILs per mm² carcinoma gave a sensitivity of 88% and a specificity of 73% in classifying MMRD carcinomas. Algorithm measures of tumour budding (TB) and poorly differentiated clusters (PDCs) outperformed TB grade derived from routine sign-out, and compared favourably with manual counts of TB/PDCs with regard to lymphatic, venous and perineural invasion. Comparable associations were seen between algorithm measures of TB/PDCs and manual counts of TB/PDCs for lymph node metastasis (all P < 0.001); however, stronger correlations were seen between the proportion of positive lymph nodes and algorithm measures of TB/PDCs. Stronger associations were also seen between distant metastasis and algorithm measures of TB/PDCs (P = 0.004) than between distant metastasis and TB (P = 0.04) and TB/PDC counts (P = 0.06).

CONCLUSIONS

Our results highlight the potential of deep learning to identify and quantify a broad spectrum of histological features in colorectal carcinoma.

Characterization of epithelial cells, connective tissue cells and immune cells in human upper airway mucosa by immunofluorescence multichannel image cytometry: a pilot study

Epithelial, connective tissue and immune cells contribute in various ways to the pathophysiology of chronic rhinosinusitis (CRS). However, data of their distribution in upper airway mucosa are sparse. We aimed to provide quantitative, purely informative data on the distribution of these cell lineages and their coexpression patterns, which might help identifying, e.g., cells in the epithelium undergoing through epithelial-mesenchymal transition (EMT). For this purpose, we used immunofluorescence multichannel image cytometry (IMIC). We examined fixed paraffin-embedded tissue samples (FFPE) of six patients with chronic rhinosinusitis (CRS) and of three patients without CRS (controls). The direct-conjugated antibodies pancytokeratin, vimentin and CD45/CD18 were used for coexpression analysis in epithelial layer and lamina propria. Image acquisition and analysis were performed with TissueFAXS and StrataQuest, respectively. To distinguish positive from negative expression, a ratio between cell-specific immunostaining intensity and background was developed. Isotype controls were used as negative controls. Per patient, a 4.5-mm2 tissue area was scanned and a median of 14,875 cells was recognized. The most common cell types were cytokeratin-single-positive (26%), vimentin-single-positive (13%) and CD45/CD18-single-positive with CD45/CD18-vimentin-double-positive cells (29%). In the patients with CRS, CD45/CD18-single-positive cells were 3-6 times higher compared to the control patients. In the epithelial layer, cytokeratin-vimentin-double-positive EMT cells were observed 3-5 times higher in the patients with CRS than in the control patients. This study provided quantitative data for the distribution of crucial cell types in CRS. Future studies may focus on the distribution and coexpression patterns of different immune cells in CRS or even cancer tissue.

Taking tumour budding to the next frontier - a post International Tumour Budding Consensus Conference (ITBCC) 2016 review

Tumour budding in colorectal cancer, defined as single tumour cells or small clusters containing four or fewer tumour cells, is a robust and independent biomarker of aggressive tumour biology. On the basis of published data in the literature, the evidence is certainly in favour of reporting tumour budding in routine practice. One important aspect of implementing tumour budding has been to establish a standardised and evidence-based scoring method, as was recommended by the International Tumour Budding Consensus Conference (ITBCC) in 2016. Further developments have aimed at establishing methods for automated tumour budding assessment. A digital approach to scoring tumour buds has great potential to assist in performing an objective budding count but, like the manual consensus method, must be validated and standardised. The aim of the present review is to present general considerations behind the ITBCC scoring method, and a broad overview of the current situation and challenges regarding automated tumour budding detection methods.

Interobserver Variability in the Assessment of Tumor Budding in pT 3/4 Colon Cancer: Improvement by Supporting Immunohistochemistry?

The prognostic significance of tumor budding in colon cancer is unequivocally documented, and the recommendations of the International Tumor Budding Consensus Conference (ITBCC) are currently the accepted basis for its assessment. Up to now, it is unknown whether the general use of a supporting cytokeratin immunohistochemistry can improve the interobserver variability and prognostic significance. Six investigators with different levels of experience reassessed 229 cases of colon carcinoma (pT3/4, N+/−, M0) with a supporting cytokeratin immunohistochemistry. The results were compared to previous assessments, which have been performed only on H & E. Bd3 was significantly associated with the occurrence of distant metastases according to the assessments of three out of six investigators (p < 0.05). Only one single investigator reached significant results concerning the cancer specific survival (p = 0.01). The pairwise kappa values range between a poor and moderate level of agreement (range 0.17–0.45; median 0.21). In conclusion, the results show no superiority of the use of an additional cytokeratin immunohistochemistry compared to the conventional analysis on sole H & E slides. Therefore, the general supporting use of a cytokeratin immunohistochemical staining seems to be inadvisable in colon cancer in consideration of necessary resources and costs.

Tumour budding in solid cancers

Tumour budding is an emerging prognostic biomarker in colorectal cancer (CRC) and other solid cancers. Tumour buds are usually defined as isolated single cancer cells or clusters of up to four cancer cells located at the invasive tumour front. The prognostic value of tumour budding is now supported by a large body of evidence, whereas the utility of this phenotype as a predictive biomarker remains under investigation. The application of tumour budding indices in clinical practice requires a standardized scoring system that can be tailored to specific tumour types and clinical scenarios. In the context of CRC, tumour budding can be assessed according to the method agreed at the International Tumour Budding Consensus Conference (ITBCC) in 2016. Using the ITBCC scoring system, tumour budding is an independent predictor of lymph node metastasis in patients with pT1 CRC and of unfavourable survival in patients with stage II colon cancer. Regardless of the clinical scenario or tumour type, the assertion that 'the more tumour buds, the worse the clinical outcome' applies. In this Review, we provide an overview of tumour budding in solid cancers, highlighting the molecular and biological aspects of this phenomenon, including its associations with epithelial-mesenchymal transition and features of the tumour microenvironment. We also describe the available evidence demonstrating the value of tumour budding as a biomarker across various solid cancers.

Prognostic significance of microscopic size in peripherally located scar-associated clinical stage I lung carcinomas

OBJECTIVES

Staging of non-small cell lung carcinoma associated with scar is not discussed in detail in the current American Joint Committee on Cancer staging manual. The recommendation is to include the scar area in the tumor size measurement unless the tumor represents a small focus at the edge of the scar. The aim of this study is to investigate if subtraction of the size of the central scar from the total gross size of surgically resected peripheral clinical stage I non-small cell lung carcinoma improves patient stratification into more accurate prognostic groups.

MATERIALS AND METHODS

Hematoxylin and eosin sections of 148 non-small cell lung carcinomas (98 adenocarcinomas and 50 squamous cell carcinomas) were reviewed, including 44 adenocarcinomas and 9 squamous cell carcinomas with scar and 54 adenocarcinomas and 41 squamous cell carcinomas without scar. The microscopic size of the invasive tumor component was determined after the average percentage of scar tissue was subtracted from the grossly measured tumor diameter. Manual results were compared to digital image analysis.

RESULTS

Adenocarcinoma with scar were associated with better overall (80.5 % vs. 63.2 %, p = 0.026) and cancer specific survival (95.2 % vs. 73.3 %, p = 0.0053) when compared to adenocarcinoma without scar. Better cancer specific survival was observed in acinar and papillary predominant adenocarcinoma (95.8 % with scar vs. 67.8 % without scar, p = 0.01); while similar trend although not statistically significant was observed in adenocarcinomas with solid or micropapillary component. Using microscopic size, pathologic T stage was down-staged in 21 adenocarcinomas. Squamous cell carcinoma with or without scar did not show a difference in survival. Manual and quantitative image analysis showed strong correlation (r = 0.9769, p < 0.0001).

CONCLUSION

Our study suggests that microscopic size of the invasive component in acinar and papillary predominant adenocarcinoma with scar might be a better predictor of survival than the total gross size.

Tumor Budding Detection System in Whole Slide Pathology Images

Tumor budding is defined as the presence of single tumor cells or small tumor clusters (less than five cells) that 'bud' from the invasive front of the main tumor. Tumor budding (TB) has recently emerged as an important adverse prognostic factor for many different cancer types. In colorectal carcinoma (CRC), tumor budding has been independently associated with lymph node metastasis and poor outcome. Pathologic assessment of tumor budding by light microscopy requires close evaluation of tumor invasive front on intermediate to high power magnification, entailing locating the 'hotspot' of tumor budding, counting all TB in one high power field, and generating a tumor budding score. By automating these time-consuming tasks, computer-assisted image analysis tools can be helpful for daily pathology practice, since tumor budding reporting is now recommended on select cases. In this paper, we report our work on the development of a tumor budding detection system in CRC from whole-slide Cytokeratin AE1/3 images, based on de novo computer algorithm that automates morphometric analysis of tumor budding.

Hallmarks of cancer: Tumor budding as a sign of invasion and metastasis in head and neck cancer

Invasion and metastasis are hallmarks of cancer. The concept of tumor budding at tumor-host interface has been documented in many carcinomas. A growing body of evidence indicates that tumor budding is a sign of invasion and early step for metastasis of many epithelial cancers including head and neck squamous cell carcinoma (HNSCC). In addition, recent research has underlined the importance of tumor budding as a promising prognosticator in HNSCC. This review summarizes the findings regarding tumor budding in HNSCC and focuses on the role of tumor budding in invasion and metastasis. Also, we highlight the prognostic significance of tumor budding in HNSCC and its potential for improving clinical decision making in terms of recommending optimal individualized treatment for this patient population.

Immunohistochemical evaluation of tumor budding for stratifying T1 colorectal cancer: optimal cut-off value and a novel computer-assisted semiautomatic method

High-grade tumor budding is an adverse prognostic factor for submucosal invasive (T1) colorectal cancer used to predict the risk for lymph node metastasis in endoscopically resected specimens. Cytokeratin immunohistochemistry is a potential option for evaluating tumor budding. The optimal cut-off value between low- and high-grade budding has not yet been determined, however, and the high inter-observer variability in selecting budding foci remains problematic. We explored the optimal cut-off value for predicting lymph node metastasis using cytokeratin immunohistochemistry, and developed a novel computer-assisted semiautomatic quantification method to reduce inter-observer variability. A retrospective single-institution study of 463 T1 colorectal cancer cases was conducted. Cases were split into derivation and validation datasets. Tumor budding foci were counted manually and semiautomatically using Image J software on cytokeratin immunohistochemistry-stained specimens. We determined the cut-off values and compared inter-observer variability among pathologists between the two methods. Univariate and multivariate analyses of the derivation dataset were performed to select the risk factors for lymph node metastasis. Predictive simulation for the validation dataset was conducted. The optimal cut-off values for the manual and semiautomatic methods were ≥10 and ≥12, respectively. For both methods, multivariate analyses revealed that venous invasion, lymphatic invasion, and high-grade tumor budding were independent risk factors for lymph node metastasis. The semiautomatic method provided significantly better inter-observer agreement. The predictive and observed lymph node metastasis frequencies were highly correlated in the validation dataset.