Digital image analysis of multiplex fluorescence IHC in colorectal cancer recognizes the prognostic value of CDX2 and its negative correlation with SOX2

The prognostic effect of tumor-associated macrophages in stage I-III colorectal cancer depends on T cell infiltration

BACKGROUND

Tumor-associated macrophages (TAMs) are associated with unfavorable patient prognosis in many cancer types. However, TAMs are a heterogeneous cell population and subsets have been shown to activate tumor-infiltrating T cells and confer a good patient prognosis. Data on the prognostic value of TAMs in colorectal cancer are conflicting. We investigated the prognostic effect of TAMs in relation to tumor-infiltrating T cells in colorectal cancers.

METHODS

The TAM markers CD68 and CD163 were analyzed by multiplex fluorescence immunohistochemistry and digital image analysis on tissue microarrays of 1720 primary colorectal cancers. TAM density in the tumor stroma was scored in relation to T cell density (stromal CD3+ and epithelial CD8+ cells) and analyzed in Cox proportional hazards models of 5-year relapse-free survival. Multivariable survival models included clinicopathological factors, MSI status and BRAFV600E mutation status.

RESULTS

High TAM density was associated with a favorable 5-year relapse-free survival in a multivariable model of patients with stage I-III tumors (p = 0.004, hazard ratio 0.94, 95% confidence interval 0.90-0.98). However, the prognostic effect was dependent on tumoral T-cell density. High TAM density was associated with a good prognosis in patients who also had high T-cell levels in their tumors, while high TAM density was associated with poorer prognosis in patients with low T-cell levels (pinteraction = 0.0006). This prognostic heterogeneity was found for microsatellite stable tumors separately.

CONCLUSIONS

This study supported a phenotypic heterogeneity of TAMs in colorectal cancer, and showed that combined tumor immunophenotyping of multiple immune cell types improved the prediction of patient prognosis.

Glutaredoxin 2 Protein (Grx2) as an Independent Prognostic Factor Associated with the Survival of Colon Adenocarcinoma Patients

Glutaredoxin 2 (Grx2; Glrx2) is a glutathione-dependent oxidoreductase located in mitochondria, which is central to the regulation of glutathione homeostasis and mitochondrial redox, and plays a crucial role in highly metabolic tissues. In response to mitochondrial redox signals and oxidative stress, Grx2 can catalyze the oxidation and S-glutathionylation of membrane-bound thiol proteins in mitochondria. Therefore, it can have a significant impact on cancer development. To investigate this further, we performed an immunohistochemical analysis of Grx2 protein expression in colon adenocarcinoma samples collected from patients with primary colon adenocarcinoma (stage I and II) and patients with metastasis to regional lymph nodes (stage III). The results of our study revealed a significant relationship between the immunohistochemical expression of Grx2 and tumor histological grade, depth of invasion, regional lymph node involvement, angioinvasion, staging, and PCNA immunohistochemical expression. It was found that 87% of patients with stage I had high levels of Grx2 expression. In contrast, only 33% of patients with stage II and 1% of patients with stage III had high levels of Grx2 expression. Moreover, the multivariate analysis revealed that the immunohistochemical expression of Grx2 protein apart from the grade of tumor differentiation was an independent prognostic factors for the survival of patients with colon adenocarcinoma. Studies analyzing Grx2 levels in patients' blood confirmed that the highest levels of serum Grx2 protein was also found in stage I patients, which was reflected in the survival curves. A higher level of Grx2 in the serum has been associated with a more favorable outcome. These results were supported by in vitro analysis conducted on colorectal cancer cell lines that corresponded to stages I, II, and III of colorectal cancer, using qRT-PCR and Western Blot.

Quantitative image analysis of intracellular protein translocation in 3-dimensional tissues for pharmacodynamic studies of immunogenic cell death

A recent development in cancer chemotherapy is to use cytotoxics to induce tumor-specific immune response through immunogenic cell death (ICD). In ICD, calreticulin is translocated from endoplasmic reticulum to cell membrane (ecto-CRT) which serves as the 'eat-me-signal' to antigen-presenting cells. Ecto-CRT measurements, e.g., by ecto-CRT immunostaining plus flow cytometry, can be used to study the pharmacodynamics of ICD in single cells, whereas ICD studies in intact 3-dimensional tissues such as human tumors require different approaches. The present study described a method that used (a) immunostaining with fluorescent antibodies followed by confocal microscopy to obtain the spatial locations of two molecules-of-interest (CRT and a marker protein WGA), and (b) machine-learning (trainable WEKA segmentation) and additional image processing tools to locate the target molecules, remove the interfering signals in the nucleus, cytosol and extracellular space, enable the distinction of the inner and outer edges of the cell membrane and thereby identify the cells with ecto-CRT. This method, when applied to 3-dimensional human bladder cancer cell spheroids, yielded drug-induced ecto-CRT measurements that were qualitatively comparable to the flow cytometry results obtained with single cells disaggregated from spheroids. This new method was applied to study drug-induced ICD in short-term cultures of surgical specimens of human patient bladder tumors.

Inhibiting NR5A2 targets stemness in pancreatic cancer by disrupting SOX2/MYC signaling and restoring chemosensitivity

BACKGROUND

Pancreatic ductal adenocarcinoma (PDAC) is a profoundly aggressive and fatal cancer. One of the key factors defining its aggressiveness and resilience against chemotherapy is the existence of cancer stem cells (CSCs). The important task of discovering upstream regulators of stemness that are amenable for targeting in PDAC is essential for the advancement of more potent therapeutic approaches. In this study, we sought to elucidate the function of the nuclear receptor subfamily 5, group A, member 2 (NR5A2) in the context of pancreatic CSCs.

METHODS

We modeled human PDAC using primary PDAC cells and CSC-enriched sphere cultures. NR5A2 was genetically silenced or inhibited with Cpd3. Assays included RNA-seq, sphere/colony formation, cell viability/toxicity, real-time PCR, western blot, immunofluorescence, ChIP, CUT&Tag, XF Analysis, lactate production, and in vivo tumorigenicity assays. PDAC models from 18 patients were treated with Cpd3-loaded nanocarriers.

RESULTS

Our findings demonstrate that NR5A2 plays a dual role in PDAC. In differentiated cancer cells, NR5A2 promotes cell proliferation by inhibiting CDKN1A. On the other hand, in the CSC population, NR5A2 enhances stemness by upregulating SOX2 through direct binding to its promotor/enhancer region. Additionally, NR5A2 suppresses MYC, leading to the activation of the mitochondrial biogenesis factor PPARGC1A and a shift in metabolism towards oxidative phosphorylation, which is a crucial feature of stemness in PDAC. Importantly, our study shows that the specific NR5A2 inhibitor, Cpd3, sensitizes a significant fraction of PDAC models derived from 18 patients to standard chemotherapy. This treatment approach results in durable remissions and long-term survival. Furthermore, we demonstrate that the expression levels of NR5A2/SOX2 can predict the response to treatment.

CONCLUSIONS

The findings of our study highlight the cell context-dependent effects of NR5A2 in PDAC. We have identified a novel pharmacological strategy to modulate SOX2 and MYC levels, which disrupts stemness and prevents relapse in this deadly disease. These insights provide valuable information for the development of targeted therapies for PDAC, offering new hope for improved patient outcomes. A Schematic illustration of the role of NR5A2 in cancer stem cells versus differentiated cancer cells, along with the action of the NR5A2 inhibitor Cpd3. B Overall survival of tumor-bearing mice following allocated treatment. A total of 18 PDX models were treated using a 2 x 1 x 1 approach (two animals per model per treatment); n=36 per group (illustration created with biorender.com ).

Accounting for intensity variation in image analysis of large-scale multiplexed clinical trial datasets

Multiplex immunofluorescence (mIF) imaging can provide comprehensive quantitative and spatial information for multiple immune markers for tumour immunoprofiling. However, application at scale to clinical trial samples sourced from multiple institutions is challenging due to pre-analytical heterogeneity. This study reports an analytical approach to the largest multi-parameter immunoprofiling study of clinical trial samples to date. We analysed 12,592 tissue microarray (TMA) spots from 3,545 colorectal cancers sourced from more than 240 institutions in two clinical trials (QUASAR 2 and SCOT) stained for CD4, CD8, CD20, CD68, FoxP3, pan-cytokeratin, and DAPI by mIF. TMA slides were multi-spectrally imaged and analysed by cell-based and pixel-based marker analysis. We developed an adaptive thresholding method to account for inter- and intra-slide intensity variation in TMA analysis. Applying this method effectively ameliorated inter- and intra-slide intensity variation improving the image analysis results compared with methods using a single global threshold. Correlation of CD8 data derived by our mIF analysis approach with single-plex chromogenic immunohistochemistry CD8 data derived from subsequent sections indicates the validity of our method (Spearman's rank correlation coefficients ρ between 0.63 and 0.66, p ≪ 0.01) as compared with the current gold standard analysis approach. Evaluation of correlation between cell-based and pixel-based analysis results confirms equivalency (ρ > 0.8, p ≪ 0.01, except for CD20 in the epithelial region) of both analytical approaches. These data suggest that our adaptive thresholding approach can enable analysis of mIF-stained clinical trial TMA datasets by digital pathology at scale for precision immunoprofiling.

Molecular analyses of ADAMTS-1, -4, -5, and IL-17 a cytokine relationship in patients with ulcerative colitis

BACKGROUND

Ulcerative colitis (UC) is a chronic inflammatory bowel disease that develops due to the impaired immune response in genetically susceptible individuals, and its etiopathogenesis is not fully elucidated. IL-17 A is a cytokine that is produced by a type of immune cell called Th17 cells and is involved in the immune response and inflammation. On the other hand, ADAMTS-1, -4, and - 5 are enzymes that are involved in the breakdown of extracellular matrix proteins, including proteoglycans, which are important components of the intestinal wall. This study aimed to evaluate the relationship between interleukin 17 (IL-17 A) cytokine, which plays a role in the pathogenesis of ulcerative colitis, and the inflammation-controlled a disintegrin and metalloproteinase with thrombospondin motifs (ADAMTS)-1, -4, and - 5 protein members.

METHODS

Bowel tissue samples and blood serum from 51 patients with UC and 51 healthy controls were included in this study. mRNA expression levels of the ADAMTS-1, -4, -5, and IL-17 A were analyzed by RT-qPCR, and immunohistochemical analyses were performed to evaluate ADAMTS-1, -4, -5, and IL-17 A proteins in tissue samples. In addition, ELISA analysis determined serum levels of the ADAMTS-1, -4, -5, and IL-17 A.

RESULTS

RT-qPCR results reveal that the expression of ADAMTS-1, -4, -5, and IL-17 A genes in the UC tissue samples were significantly high according to the control tissue samples. Also, ADAMTS-1, -4, -5, and IL-17 A proteins revealed enhanced expression pattern UC groups according to the control. Also, ADAMTS-1, -4, -5, and IL-17 A protein showed cytoplasmic localization patterns in both control and UC groups. The serum levels of ADAMTS-1,-5, and IL-17 A were significantly higher in UC samples than in the control group.

CONCLUSIONS

We observed a positive correlation between the ADAMTS-1, -5 and IL17A cytokine expression in UC samples. These results provide a new understanding of controlling crucial ADAMTS family protein members by IL-17 A cytokines with UC.

Whole-Slide Imaging, Mutual Information Registration for Multiplex Immunohistochemistry and Immunofluorescence

Multiplex immunohistochemistry/immunofluorescence (mIHC/mIF) is a developing technology that facilitates the evaluation of multiple, simultaneous protein expressions at single-cell resolution while preserving tissue architecture. These approaches have shown great potential for biomarker discovery, yet many challenges remain. Importantly, streamlined cross-registration of multiplex immunofluorescence images with additional imaging modalities and immunohistochemistry (IHC) can help increase the plex and/or improve the quality of the data generated by potentiating downstream processes such as cell segmentation. To address this problem, a fully automated process was designed to perform a hierarchical, parallelizable, and deformable registration of multiplexed digital whole-slide images (WSIs). We generalized the calculation of mutual information as a registration criterion to an arbitrary number of dimensions, making it well suited for multiplexed imaging. We also used the self-information of a given IF channel as a criterion to select the optimal channels to use for registration. Additionally, as precise labeling of cellular membranes in situ is essential for robust cell segmentation, a pan-membrane immunohistochemical staining method was developed for incorporation into mIF panels or for use as an IHC followed by cross-registration. In this study, we demonstrate this process by registering whole-slide 6-plex/7-color mIF images with whole-slide brightfield mIHC images, including a CD3 and a pan-membrane stain. Our algorithm, WSI, mutual information registration (WSIMIR), performed highly accurate registration allowing the retrospective generation of an 8-plex/9-color, WSI, and outperformed 2 alternative automated methods for cross-registration by Jaccard index and Dice similarity coefficient (WSIMIR vs automated WARPY, P < .01 and P < .01, respectively, vs HALO + transformix, P = .083 and P = .049, respectively). Furthermore, the addition of a pan-membrane IHC stain cross-registered to an mIF panel facilitated improved automated cell segmentation across mIF WSIs, as measured by significantly increased correct detections, Jaccard index (0.78 vs 0.65), and Dice similarity coefficient (0.88 vs 0.79).

Current Methods for Identifying Plasma Membrane Proteins as Cancer Biomarkers

Plasma membrane proteins are a special class of biomolecules present on the cellular membrane. They provide the transport of ions, small molecules, and water in response to internal and external signals, define a cell's immunological identity, and facilitate intra- and intercellular communication. Since they are vital to almost all cellular functions, their mutants, or aberrant expression is linked to many diseases, including cancer, where they are a part of cancer cell-specific molecular signatures and phenotypes. In addition, their surface-exposed domains make them exciting biomarkers for targeting by imaging agents and drugs. This review looks at the challenges in identifying cancer-related cell membrane proteins and the current methodologies that solve most of the challenges. We classified the methodologies as biased, i.e., search cells for the presence of already known membrane proteins. Second, we discuss the unbiased methods that can identify proteins without prior knowledge of what they are. Finally, we discuss the potential impact of membrane proteins on the early detection and treatment of cancer.

Expanding the Multiplexing Capabilities of Raman Imaging to Reveal Highly Specific Molecular Expression and Enable Spatial Profiling

Profiling the heterogeneous landscape of cell types and biomolecules is rapidly being adopted to address current imperative research questions. Precision medicine seeks advancements in molecular spatial profiling techniques with highly multiplexed imaging capabilities and subcellular resolution, which remains an extremely complex task. Surface-enhanced Raman spectroscopy (SERS) imaging offers promise through the utilization of nanoparticle-based contrast agents that exhibit narrow spectral features and molecular specificity. The current renaissance of gold nanoparticle technology makes Raman scattering intensities competitive with traditional fluorescence methods while offering the added benefit of unsurpassed multiplexing capabilities. Here, we present an expanded library of individually distinct SERS nanoparticles to arm researchers and clinicians. Our nanoparticles consist of a ∼60 nm gold core, a Raman reporter molecule, and a final inert silica coating. Using density functional theory, we have selected Raman reporters that meet the key criterion of high spectral uniqueness to facilitate unmixing of up to 26 components in a single imaging pixel in vitro and in vivo. We also demonstrated the utility of our SERS nanoparticles for targeting cultured cells and profiling cancerous human tissue sections for highly multiplexed optical imaging. This study showcases the far-reaching capabilities of SERS-based Raman imaging in molecular profiling to improve personalized medicine and overcome the major challenges of functional and structural diversity in proteomic imaging.

Combination of CDX2 H-score quantitative analysis with CD3 AI-guided analysis identifies patients with a good prognosis only in stage III colon cancer

AIM

Stratification of colon cancer (CC) of patients with stage II and III for risk of relapse is still needed especially to drive adjuvant therapy administration. Our study evaluates the prognostic performance of two known biomarkers, CDX2 and CD3, standalone or their combined information in stage II and III CC.

PATIENTS AND METHODS

CDX2 and CD3 expression was evaluated in Prodige-13 study gathering 443 stage II and 398 stage III primary CC on whole slide colectomy. We developed for this study an H-score to quantify CDX2 expression and used our artificial intelligence (AI)-guided tissue analysis ColoClass to detect CD3 in tumour core and invasive margin. Association between biomarkers and relapse-free survival was investigated.

RESULTS

Univariate analysis showed that the combined variable CD3-TC and CD3-IM was associated with prognosis in both stage II and stage III. CDX2, on the contrary, was associated with prognosis only in stage III. We subsequently associated CDX2 and combined immune parameters only in stage III. This multivariate analysis allowed us to distinguish a proportion of stage III CC harbouring a high CDX2 expression and a high immune infiltration with a particularly good prognosis compared to their counterpart.

CONCLUSION

This study validated the prognostic role of CDX2 and CD3 evaluated with immunohistochemistry procedures in stage III but not in stage II. This association would be conceivable in a routine pathology laboratory and could help oncologist to consider chemotherapy de-escalation for a part of stage III patients.

CDX2-induced intestinal metaplasia in human gastric organoids derived from induced pluripotent stem cells

Intestinal metaplasia is related to gastric carcinogenesis. Previous studies have suggested the important role of CDX2 in intestinal metaplasia, and several reports have shown that the overexpression of CDX2 in mouse gastric mucosa caused intestinal metaplasia. However, no study has examined the induction of intestinal metaplasia using human gastric mucosa. In the present study, to produce an intestinal metaplasia model in human gastric mucosa in vitro, we differentiated human-induced pluripotent stem cells (hiPSC) to gastric organoids, followed by the overexpression of CDX2 using a tet-on system. The overexpression of CDX2 induced, although not completely, intestinal phenotypes and the enhanced expression of many, but not all, intestinal genes and previously reported intestinal metaplasia-related genes in the gastric organoids. This model can help clarify the mechanisms underlying intestinal metaplasia and carcinogenesis in human gastric mucosa and develop therapies to restitute precursor conditions of gastric cancer to normal mucosa.

Automated quantitative high-throughput multiplex immunofluorescence pipeline to evaluate OXPHOS defects in formalin-fixed human prostate tissue

Advances in multiplex immunofluorescence (mIF) and digital image analysis has enabled simultaneous assessment of protein defects in electron transport chain components. However, current manual methodology is time consuming and labour intensive. Therefore, we developed an automated high-throughput mIF workflow for quantitative single-cell level assessment of formalin fixed paraffin embedded tissue (FFPE), leveraging tyramide signal amplification on a Ventana Ultra platform coupled with automated multispectral imaging on a Vectra 3 platform. Utilising this protocol, we assessed the mitochondrial oxidative phosphorylation (OXPHOS) protein alterations in a cohort of benign and malignant prostate samples. Mitochondrial OXPHOS plays a critical role in cell metabolism, and OXPHOS perturbation is implicated in carcinogenesis. Marked inter-patient, intra-patient and spatial cellular heterogeneity in OXPHOS protein abundance was observed. We noted frequent Complex IV loss in benign prostate tissue and Complex I loss in age matched prostate cancer tissues. Malignant regions within prostate cancer samples more frequently contained cells with low Complex I & IV and high mitochondrial mass in comparison to benign-adjacent regions. This methodology can now be applied more widely to study the frequency and distribution of OXPHOS alterations in formalin-fixed tissues, and their impact on long-term clinical outcomes.

Deep learning tools and modeling to estimate the temporal expression of cell cycle proteins from 2D still images

Automatic characterization of fluorescent labeling in intact mammalian tissues remains a challenge due to the lack of quantifying techniques capable of segregating densely packed nuclei and intricate tissue patterns. Here, we describe a powerful deep learning-based approach that couples remarkably precise nuclear segmentation with quantitation of fluorescent labeling intensity within segmented nuclei, and then apply it to the analysis of cell cycle dependent protein concentration in mouse tissues using 2D fluorescent still images. First, several existing deep learning-based methods were evaluated to accurately segment nuclei using different imaging modalities with a small training dataset. Next, we developed a deep learning-based approach to identify and measure fluorescent labels within segmented nuclei, and created an ImageJ plugin to allow for efficient manual correction of nuclear segmentation and label identification. Lastly, using fluorescence intensity as a readout for protein concentration, a three-step global estimation method was applied to the characterization of the cell cycle dependent expression of E2F proteins in the developing mouse intestine.

E-cadherin is a robust prognostic biomarker in colorectal cancer and low expression is associated with sensitivity to inhibitors of topoisomerase, aurora, and HSP90 in preclinical models

Cell–cell and cell–matrix adhesion proteins that have been implicated in colorectal epithelial integrity and epithelial‐to‐mesenchymal transition could be robust prognostic and potential predictive biomarkers for standard and novel therapies. We analyzed in situ protein expression of E‐cadherin (ECAD), integrin β4 (ITGB4), zonula occludens 1 (ZO‐1), and cytokeratins in a single‐hospital series of Norwegian patients with colorectal cancer (CRC) stages I–IV (n = 922) using multiplex fluorescence‐based immunohistochemistry (mfIHC) on tissue microarrays. Pharmacoproteomic associations were explored in 35 CRC cell lines annotated with drug sensitivity data on > 400 approved and investigational drugs. ECAD, ITGB4, and ZO‐1 were positively associated with survival, while cytokeratins were negatively associated with survival. Only ECAD showed independent prognostic value in multivariable Cox models. Clinical and molecular associations for ECAD were technically validated on a different mfIHC platform, and the prognostic value was validated in another Norwegian series (n = 798). In preclinical models, low and high ECAD expression differentially associated with sensitivity to topoisomerase, aurora, and HSP90 inhibitors, and EGFR inhibitors. E‐cadherin protein expression is a robust prognostic biomarker with potential clinical utility in CRC.

Pixelwise H-score: A novel digital image analysis-based metric to quantify membrane biomarker expression from immunohistochemistry images

Immunohistochemistry (IHC) assays play a central role in evaluating biomarker expression in tissue sections for diagnostic and research applications. Manual scoring of IHC images, which is the current standard of practice, is known to have several shortcomings in terms of reproducibility and scalability to large scale studies. Here, by using a digital image analysis-based approach, we introduce a new metric called the pixelwise H-score (pix H-score) that quantifies biomarker expression from whole-slide scanned IHC images. The pix H-score is an unsupervised algorithm that only requires the specification of intensity thresholds for the biomarker and the nuclear-counterstain channels. We present the detailed implementation of the pix H-score in two different whole-slide image analysis software packages Visiopharm and HALO. We consider three biomarkers P-cadherin, PD-L1, and 5T4, and show how the pix H-score exhibits tight concordance to multiple orthogonal measurements of biomarker abundance such as the biomarker mRNA transcript and the pathologist H-score. We also compare the pix H-score to existing automated image analysis algorithms and demonstrate that the pix H-score provides either comparable or significantly better performance over these methodologies. We also present results of an empirical resampling approach to assess the performance of the pix H-score in estimating biomarker abundance from select regions within the tumor tissue relative to the whole tumor resection. We anticipate that the new metric will be broadly applicable to quantify biomarker expression from a wide variety of IHC images. Moreover, these results underscore the benefit of digital image analysis-based approaches which offer an objective, reproducible, and highly scalable strategy to quantitatively analyze IHC images.

Immune microenvironment composition in high-grade serous ovarian cancers based on BRCA mutational status

PURPOSE

An in-depth analysis of the tumor microenvironment of ovarian cancer is needed. The purpose of this study was to elucidate the architecture of the immune microenvironment of high-grade serous ovarian cancers (HGSCs) with or without BRCA1 and BRCA2 mutations.

METHODS

A cohort of highly annotated HGSC patients with known germline BRCA1 and BRCA2 status was selected, and pretreatment tumor tissue specimens were analyzed with a multiplexed staining technique aimed at detecting lymphocytes, macrophages, and fibroblasts in the whole tumor area and in specific regions including epithelium, stroma, and perivascular areas.

RESULTS

BRCA1- or BRCA2-mutated tumors showed a more immunogenic microenvironment, characterized by a higher abundance of CD8⁺ and PD-L1⁺ cells, than did tumors with wild-type BRCA1 and BRCA2. High numbers of PD-L1⁺ and PD-L1⁺CD8⁺ cells were prognostic for event-free survival (hazard ratio [HR]: 0.41, 95% CI 0.21-0.79, p = 0.008 and HR 0.49, 95% CI 0.26-0.91, p = 0.025, respectively), as were high numbers of epithelial PD-L1⁺ and FAP⁺PD-L1⁺ cells (HR 0.52, 95% CI 0.28-0.96, p = 0.037 and HR 0.27, 95% CI 0.08-0.87, p = 0.029) and CD8⁺ cells (HR 0.51, 95% CI 0.28-0.93, p = 0.027).

CONCLUSIONS

This study reveals substantial differences between the immune microenvironment composition of germline BRCA-mutated and BRCA wild-type HGSC.

Current and Prospective Methods for Assessing Anti-Tumor Immunity in Colorectal Cancer

Colorectal cancer (CRC) remains one of the deadliest malignancies worldwide despite recent progress in treatment strategies. Though immune checkpoint inhibition has proven effective for a number of other tumors, it offers benefits in only a small group of CRC patients with high microsatellite instability. In general, heterogenous cell groups in the tumor microenvironment are considered as the major barrier for unveiling the causes of low immune response. Therefore, deconvolution of cellular components in highly heterogeneous microenvironments is crucial for understanding the immune contexture of cancer. In this review, we assimilate current knowledge and recent studies examining anti-tumor immunity in CRC. We also discuss the utilization of novel immune contexture assessment methods that have not been used in CRC research to date.

PD-L1 Multiplex and Quantitative Image Analysis for Molecular Diagnostics

Multiplex immunofluorescence (mIF) and digital image analysis (DIA) have transformed the ability to analyse multiple biomarkers. We aimed to validate a clinical workflow for quantifying PD-L1 in non-small cell lung cancer (NSCLC). NSCLC samples were stained with a validated mIF panel. Immunohistochemistry (IHC) was conducted and mIF slides were scanned on an Akoya Vectra Polaris. Scans underwent DIA using QuPath. Single channel immunofluorescence was concordant with single-plex IHC. DIA facilitated quantification of cell types expressing single or multiple phenotypic markers. Considerations for analysis included classifier accuracy, macrophage infiltration, spurious staining, threshold sensitivity by DIA, sensitivity of cell identification in the mIF. Alternative sequential detection of biomarkers by DIA potentially impacted final score. Strong concordance was observed between 3,3'-Diaminobenzidine (DAB) IHC slides and mIF slides (R2 = 0.7323). Comparatively, DIA on DAB IHC was seen to overestimate the PD-L1 score more frequently than on mIF slides. Overall, concordance between DIA on DAB IHC slides and mIF slides was 95%. DIA of mIF slides is rapid, highly comparable to DIA on DAB IHC slides, and enables comprehensive extraction of phenotypic data and specific microenvironmental detail intrinsic to the sample. Exploration of the clinical relevance of mIF in the context of immunotherapy treated cases is warranted.

A narrative review of digital pathology and artificial intelligence: focusing on lung cancer

The emergence of whole slide imaging technology allows for pathology diagnosis on a computer screen. The applications of digital pathology are expanding, from supporting remote institutes suffering from a shortage of pathologists to routine use in daily diagnosis including that of lung cancer. Through practice and research large archival databases of digital pathology images have been developed that will facilitate the development of artificial intelligence (AI) methods for image analysis. Currently, several AI applications have been reported in the field of lung cancer; these include the segmentation of carcinoma foci, detection of lymph node metastasis, counting of tumor cells, and prediction of gene mutations. Although the integration of AI algorithms into clinical practice remains a significant challenge, we have implemented tumor cell count for genetic analysis, a helpful application for routine use. Our experience suggests that pathologists often overestimate the contents of tumor cells, and the use of AI-based analysis increases the accuracy and makes the tasks less tedious. However, there are several difficulties encountered in the practical use of AI in clinical diagnosis. These include the lack of sufficient annotated data for the development and validation of AI systems, the explainability of black box AI models, such as those based on deep learning that offer the most promising performance, and the difficulty in defining the ground truth data for training and validation owing to inherent ambiguity in most applications. All of these together present significant challenges in the development and clinical translation of AI methods in the practice of pathology. Additional research on these problems will help in resolving the barriers to the clinical use of AI. Helping pathologists in developing knowledge of the working and limitations of AI will benefit the use of AI in both diagnostics and research.

A panel of intestinal differentiation markers (CDX2, GPA33, and LI-cadherin) identifies gastric cancer patients with favourable prognosis

BACKGROUND

Gastric cancer is the fifth most common cancer and the third cause of global cancer mortality. CDX2 is an intestinal differentiation marker with prognostic value in gastric cancer and transcriptionally regulates the expression of glycoprotein A33 (GPA33) and liver intestine cadherin (LI-cadherin).

METHODS

This study evaluated the clinical significance of the combined expression of CDX2 and its targets GPA33 and LI-cadherin in gastric cancer by fluorescence-based multiplex immunohistochemistry together with digital image analysis and chromogenic immunohistochemistry in 329 gastric cancer samples arranged in tissue microarrays. Additionally, publicly available RNA-seq expression data from 354 gastric cancer samples from the TCGA database were used to validate the immunohistochemistry results.

RESULTS

Expression of the three markers (CDX2, GPA33, and LI-cadherin) was strongly correlated, defining an intestinal differentiation panel. Low or negative protein expression of the intestinal differentiation panel identified patients with particularly poor overall survival, irrespective of the methodology used, and was validated in the independent series at the RNA-seq level.

CONCLUSIONS

Expression of the intestinal differentiation panel (CDX2, GPA33, and LI-cadherin) defines a set of biomarkers with a strong biological rationale and favourable impact for prognostication of gastric cancer patients.