Comprehensive Immunohistochemistry: Digital, Analytical and Integrated

Digital Pathology for Better Clinical Practice

(1) Background: Digital pathology (DP) is transforming the landscape of clinical practice, offering a revolutionary approach to traditional pathology analysis and diagnosis. (2) Methods: This innovative technology involves the digitization of traditional glass slides which enables pathologists to access, analyze, and share high-resolution whole-slide images (WSI) of tissue specimens in a digital format. By integrating cutting-edge imaging technology with advanced software, DP promises to enhance clinical practice in numerous ways. DP not only improves quality assurance and standardization but also allows remote collaboration among experts for a more accurate diagnosis. Artificial intelligence (AI) in pathology significantly improves cancer diagnosis, classification, and prognosis by automating various tasks. It also enhances the spatial analysis of tumor microenvironment (TME) and enables the discovery of new biomarkers, advancing their translation for therapeutic applications. (3) Results: The AI-driven immune assays, Immunoscore (IS) and Immunoscore-Immune Checkpoint (IS-IC), have emerged as powerful tools for improving cancer diagnosis, prognosis, and treatment selection by assessing the tumor immune contexture in cancer patients. Digital IS quantitative assessment performed on hematoxylin-eosin (H&E) and CD3+/CD8+ stained slides from colon cancer patients has proven to be more reproducible, concordant, and reliable than expert pathologists' evaluation of immune response. Outperforming traditional staging systems, IS demonstrated robust potential to enhance treatment efficiency in clinical practice, ultimately advancing cancer patient care. Certainly, addressing the challenges DP has encountered is essential to ensure its successful integration into clinical guidelines and its implementation into clinical use. (4) Conclusion: The ongoing progress in DP holds the potential to revolutionize pathology practices, emphasizing the need to incorporate powerful AI technologies, including IS, into clinical settings to enhance personalized cancer therapy.

Photobiomodulation Therapy Improves Repair of Bone Defects Filled by Inorganic Bone Matrix and Fibrin Heterologous Biopolymer

Biomaterials are used extensively in graft procedures to correct bone defects, interacting with the body without causing adverse reactions. The aim of this pre-clinical study was to analyze the effects of photobiomodulation therapy (PBM) with the use of a low-level laser in the repair process of bone defects filled with inorganic matrix (IM) associated with heterologous fibrin biopolymer (FB). A circular osteotomy of 4 mm in the left tibia was performed in 30 Wistar male adult rats who were randomly divided into three groups: G1 = IM + PBM, G2 = IM + FB and G3 = IM + FB + PBM. PBM was applied at the time of the experimental surgery and three times a week, on alternate days, until euthanasia, with 830 nm wavelength, in two points of the operated site. Five animals from each group were euthanized 14 and 42 days after surgery. In the histomorphometric analysis, the percentage of neoformed bone tissue in G3 (28.4% ± 2.3%) was higher in relation to G1 (24.1% ± 2.91%) and G2 (22.2% ± 3.11%) at 14 days and at 42 days, the percentage in G3 (35.1% ± 2.55%) was also higher in relation to G1 (30.1% ± 2.9%) and G2 (31.8% ± 3.12%). In the analysis of the birefringence of collagen fibers, G3 showed a predominance of birefringence between greenish-yellow in the neoformed bone tissue after 42 days, differing from the other groups with a greater presence of red-orange fibers. Immunohistochemically, in all experimental groups, it was possible to observe immunostaining for osteocalcin (OCN) near the bone surface of the margins of the surgical defect and tartrate-resistant acid phosphatase (TRAP) bordering the newly formed bone tissue. Therefore, laser photobiomodulation therapy contributed to improving the bone repair process in tibial defects filled with bovine biomaterial associated with fibrin biopolymer derived from snake venom.

Digital pathology and artificial intelligence in translational medicine and clinical practice

Traditional pathology approaches have played an integral role in the delivery of diagnosis, semi-quantitative or qualitative assessment of protein expression, and classification of disease. Technological advances and the increased focus on precision medicine have recently paved the way for the development of digital pathology-based approaches for quantitative pathologic assessments, namely whole slide imaging and artificial intelligence (AI)-based solutions, allowing us to explore and extract information beyond human visual perception. Within the field of immuno-oncology, the application of such methodologies in drug development and translational research have created invaluable opportunities for deciphering complex pathophysiology and the discovery of novel biomarkers and drug targets. With an increasing number of treatment options available for any given disease, practitioners face the growing challenge of selecting the most appropriate treatment for each patient. The ever-increasing utilization of AI-based approaches substantially expands our understanding of the tumor microenvironment, with digital approaches to patient stratification and selection for diagnostic assays supporting the identification of the optimal treatment regimen based on patient profiles. This review provides an overview of the opportunities and limitations around implementing AI-based methods in biomarker discovery and patient selection and discusses how advances in digital pathology and AI should be considered in the current landscape of translational medicine, touching on challenges this technology may face if adopted in clinical settings. The traditional role of pathologists in delivering accurate diagnoses or assessing biomarkers for companion diagnostics may be enhanced in precision, reproducibility, and scale by AI-powered analysis tools.

Recent advances in nanomaterials-based electrochemical immunosensors and aptasensors for HER2 assessment in breast cancer

Human epidermal growth factor receptor 2 (HER2) is one of the key molecular targets in breast cancer pathogenesis. Overexpression and/or amplification of HER2 in approximately 15-20% of breast cancer patients is associated with high mortality and poor prognosis. Accumulating evidence shows that accurate and sensitive detection of HER2 improves the survival outcomes for HER2-positive breast cancer patients from targeted therapies. The current methods of clinical determination of HER2 expression levels are based on slide-based assays that rely on invasively collected primary tumours. Alternatively, ELISA-based detection of the shredded HER2 extracellular domain (HER2-ECD) of has been suggested as a surrogate method for monitoring disease progress and treatment response in breast cancer patients. In the past decade, biosensors have emerged as an alternative modality for the detection of circulating HER2-ECD in human serum samples. In particular, electrochemical biosensors based on nanomaterials and antibodies and aptamers have been increasingly developed as promising tools for rapid, sensitive, and cost-effective detection of HER2-ECD. These biosensors harness the high affinity and specificity of antibodies and aptamers, and unique conductive properties, biocompatibility, large surface area, and chemical stability of nanomaterials for selective and sensitive assessment of the HER2. This review provides an overview of the recent advances in the application of nanomaterials-based immunosensors and aptasensors for detection of circulating HER2-ECD. In particular, various electrochemical techniques, detection approaches, and nanomaterials are discussed. Further, analytical figures of merit of various HER2 immunosensors and aptasensors are compared. Finally, possible challenges and potential opportunities for biosensor-based detection of HER2-ECD are discussed.

Hormonal Receptor Immunochemistry Heterogeneity and 18F-FDG Metabolic Heterogeneity: Preliminary Results of Their Relationship and Prognostic Value in Luminal Non-Metastatic Breast Cancers

Introduction

We aimed to investigate whether ¹⁸F-FDG PET metabolic heterogeneity reflects the heterogeneity of estrogen receptor (ER) and progesterone receptor (PR) expressions within luminal non-metastatic breast tumors and if it could help in identifying patients with worst event-free survival (EFS).

Materials and methods

On 38 PET high-resolution breast bed positions, a single physician drew volumes of interest encompassing the breast tumors to extract SUV_max, histogram parameters and textural features. High-resolution immunochemistry (IHC) scans were analyzed to extract Haralick parameters and descriptors of the distribution shape. Correlation between IHC and PET parameters were explored using Spearman tests. Variables of interest to predict the EFS status at 8 years (EFS-8y) were sought by means of a random forest classification. EFS-8y analyses were then performed using univariable Kaplan-Meier analyses and Cox regression analysis. When appropriate, Mann-Whitney tests and Spearman correlations were used to explore the relationship between clinical data and tumoral PET heterogeneity variables.

Results

For ER expression, correlations were mainly observed with ¹⁸F-FDG histogram parameters, whereas for PR expression correlations were mainly observed with gray-level co-occurrence matrix (GLCM) parameters. The strongest correlations were observed between skewness__ER and uniformity__HISTO (ρ = −0.386, p = 0.017) and correlation__PR and entropy__GLCM (ρ = 0.540, p = 0.001), respectively. The median follow-up was 6.5 years and the 8y-EFS was 71.0%. Random forest classification found age, clinical stage, SUV_max, skewness__ER, kurtosis__ER, entropy__HISTO, and uniformity__HISTO to be variables of importance to predict the 8y-EFS. Univariable Kaplan-Meier survival analyses showed that skewness__ER was a predictor of 8y-EFS (66.7 ± 27.2 versus 19.1 ± 15.2, p = 0.018 with a cut-off value set to 0.163) whereas other IHC and PET parameters were not. On multivariable analysis including age, clinical stage and skewness__ER, none of the parameters were independent predictors. Indeed, skewness__ER was significantly higher in youngest patients (ρ = −0.351, p = 0.031) and in clinical stage III tumors (p = 0.023).

Conclusion

A heterogeneous distribution of ER within the tumor in IHC appeared as an EFS-8y prognosticator in luminal non-metastatic breast cancers. Interestingly, it appeared to be correlated with PET histogram parameters which could therefore become potential non-invasive prognosticator tools, provided these results are confirmed by further larger and prospective studies.

Immuno-Interface Score to Predict Outcome in Colorectal Cancer Independent of Microsatellite Instability Status

Simple Summary

For pathologists, how to precisely diagnose cancer from microscopy slides of tumor tissue samples so that each patient may receive the optimal treatment for his specific type of disease is a major task. Recent research based on digital pathology image analysis enables new approaches to assess tumor-host interaction at a microscopic level. The current study applies a novel spatial analysis method which computes Immunogradient indicators to estimate the migration of immune cells towards the tumor across the tumor/stroma interface. These indicators, computed for two types of immune cells (CD8 and CD20), proved to be independent prognostic factors in this study of 87 patients with colorectal cancer. The indicators were combined with infiltrative tumor growth pattern, assessed by a pathologist, into a new immuno-interface score which enabled prediction of the patient survival independent of other clinical, pathology and molecular characteristics of the tumor. The study demonstrates the value of computational pathology to advance the precision of clinical decision-making.

Abstract

Tumor-associated immune cells have been shown to predict patient outcome in colorectal (CRC) and other cancers. Spatial digital image analysis-based cell quantification increases the informative power delivered by tumor microenvironment features and leads to new prognostic scoring systems. In this study we evaluated the intratumoral density of immunohistochemically stained CD8, CD20 and CD68 cells in 87 cases of CRC (48 were microsatellite stable, MSS, and 39 had microsatellite instability, MSI) in both the intratumoral tumor tissue and within the tumor-stroma interface zone (IZ) which was extracted by a previously developed unbiased hexagonal grid analytics method. Indicators of immune-cell gradients across the extracted IZ were computed and explored along with absolute cell densities, clinicopathological and molecular data, including gene mutation (BRAF, KRAS, PIK3CA) and MSI status. Multiple regression modeling identified (p < 0.0001) three independent prognostic factors: CD8+ and CD20+ Immunogradient indicators, that reflect cell migration towards the tumor, were associated with improved patient survival, while the infiltrative tumor growth pattern was linked to worse patient outcome. These features were combined into CD8-CD20 Immunogradient and immuno-interface scores which outperformed both tumor-node-metastasis (TNM) staging and molecular characteristics, and importantly, revealed high prognostic value both in MSS and MSI CRCs.

Quantitative Characterization of CD8+ T Cell Clustering and Spatial Heterogeneity in Solid Tumors

Quantitative characterization of the tumor microenvironment, including its immuno-architecture, is important for developing quantitative diagnostic and predictive biomarkers, matching patients to the most appropriate treatments for precision medicine, and for providing quantitative data for building systems biology computational models able to predict tumor dynamics in the context of immune checkpoint blockade therapies. The intra- and inter-tumoral spatial heterogeneities are potentially key to the understanding of the dose-response relationships, but they also bring challenges to properly parameterizing and validating such models. In this study, we developed a workflow to detect CD8+ T cells from whole slide imaging data, and quantify the spatial heterogeneity using multiple metrics by applying spatial point pattern analysis and morphometric analysis. The results indicate a higher intra-tumoral heterogeneity compared with the heterogeneity across patients. By comparing the baseline metrics with PD-1 blockade treatment outcome, our results indicate that the number of high-density T cell clusters of both circular and elongated shapes are higher in patients who responded to the treatment. This methodology can be applied to quantitatively characterize the tumor microenvironment, including immuno-architecture, and its heterogeneity for different cancer types.

Macrophage density in early surveillance biopsies predicts future renal transplant function

Inflammation impairs renal allograft survival but is difficult to quantify by eye at low densities. Here we measured leukocyte abundance in early surveillance biopsies by digital image analysis to test for a role of chemokine receptor genotypes and analyze the predictive value of leukocyte subsets to allograft function. In six-week surveillance biopsies, T-cell (CD3), B-cell (CD20), macrophage (CD68), and dendritic cell (CD209) densities were assessed in whole slide scans. Renal cortical CD3, CD20, and CD68 were significantly higher in histologic rejection. The CCR2 V64I genotype was associated with lower CD3 and CD209 densities. Above-median CD68 density was significantly associated with lower combined patient and graft survival with a hazard ratio of 3.5 (95% confidence interval 1.1-11.0). Both CD20 and CD68 densities inversely correlated with estimated glomerular filtration rate (eGFR) four years after transplantation. Additionally, CD68 correlated with eGFR loss. Among histological measurements including a complete Banff classification, only CD68 density was a significant predictor of an eGFR under 30ml/min after four years (odds ratio 7.4, 1.8-31.0) and part of the best eGFR prediction set in a multivariable linear regression analysis of multiple clinical and pathologic parameters. In a second independent cohort, the original CD68 median maintained its discriminative power for survival and eGFR. Thus, digital high-resolution assessment of CD68⁺ leukocyte infiltration significantly improves prognostic value of early renal transplant biopsies.

Pathology-Driven Comprehensive Proteomic Profiling of the Prostate Cancer Tumor Microenvironment

Prostate cancer is the second most common cancer in men worldwide. Gleason grading is an important predictor of prostate cancer outcomes and is influential in determining patient treatment options. Clinical decisions based on a Gleason score of 7 are difficult as the prognosis for individuals diagnosed with Gleason 4+3 cancer is much worse than for those diagnosed with Gleason 3+4 cancer. Laser capture microdissection (LCM) is a highly precise method to isolate specific cell populations or discrete microregions from tissues. This report undertook a detailed molecular characterization of the tumor microenvironment in prostate cancer to define the proteome in the epithelial and stromal regions from tumor foci of Gleason grades 3 and 4. Tissue regions of interest were isolated from several Gleason 3+3 and Gleason 4+4 tumors using telepathology to leverage specialized pathology expertise to support LCM. Over 2,000 proteins were identified following liquid chromatography tandem mass spectrometry (LC-MS/MS) analysis of all regions of interest. Statistical analysis revealed significant differences in protein expression (>100 proteins) between Gleason 3 and Gleason 4 regions-in both stromal and epithelial compartments. A subset of these proteins has had prior strong association with prostate cancer, thereby providing evidence for the authenticity of the approach. Finally, validation of these proteins by immunohistochemistry has been obtained using an independent cohort of prostate cancer tumor specimens.Implications: This unbiased strategy provides a strong foundation for the development of biomarker protein panels with significant diagnostic and prognostic potential. Mol Cancer Res; 15(3); 281-93. ©2017 AACR.