Quantitative multiplexed imaging technologies for single-cell analysis to assess predictive markers for immunotherapy in thoracic immuno-oncology: promises and challenges

In situ characterization of the tumor microenvironment

The development of new therapies for cancer is underpinned by an increasing need to comprehensively characterize the tumor microenvironment (TME). While traditional approaches have relied on bulk or single-cell approaches, these are limited in their ability to provide cellular context. Deconvolution of the complex TME is fundamental to understanding tumor dynamics and treatment resistance. Spatially resolved characterization of the TME is likely to provide greater insights into the cellular architecture, tumor-immune cell interactions, receptor-ligand interactions, and cell niches. In turn, these aid in dictating the optimal way in which to target each patient's individual cancer. In this review, we discuss a number of cutting-edge in situ spatial profiling methods giving us new insights into tumor biology.

Immunohistochemical double nuclear staining for cell-specific automated quantification of the proliferation index - A promising diagnostic aid for melanocytic lesions

AIMS

Pathologists often use immunohistochemical staining of the proliferation marker Ki67 in their diagnostic assessment of melanocytic lesions. However, the interpretation of Ki67 can be challenging. We propose a new workflow to improve the diagnostic utility of the Ki67-index. In this workflow, Ki67 is combined with the melanocytic tumour-cell marker SOX10 in a Ki67/SOX10 double nuclear stain. The Ki67-index is then quantified automatically using digital image analysis (DIA). The aim of this study was to optimise and test three different multiplexing methods for Ki67/SOX10 double nuclear staining.

METHODS

Multiplex immunofluorescence (mIF), multiplex immunohistochemistry (mIHC), and multiplexed immunohistochemical consecutive staining on single slide (MICSSS) were optimised for Ki67/SOX10 double nuclear staining. DIA applications were designed for automated quantification of the Ki67-index. The methods were tested on a pilot case-control cohort of benign and malignant melanocytic lesions (n = 23).

RESULTS

Using the Ki67/SOX10 double nuclear stain, malignant melanocytic lesions could be completely distinguished from benign lesions by the Ki67-index. The Ki67-index cut-offs were 1.8% (mIF) and 1.5% (mIHC and MICSSS). The AUC of the automatically quantified Ki67-index based on double nuclear staining was 1.0 (95% CI: 1.0;1.0), whereas the AUC of conventional Ki67 single-stains was 0.87 (95% CI: 0.71;1.00).

CONCLUSIONS

The novel Ki67/SOX10 double nuclear stain highly improved the diagnostic precision of Ki67 interpretation. Both mIHC and mIF were useful methods for Ki67/SOX10 double nuclear staining, whereas the MICSSS method had challenges in the current setting. The Ki67/SOX10 double nuclear stain shows potential as a valuable diagnostic aid for melanocytic lesions.

Prognostic and predictive biomarkers in non-small cell lung carcinoma

Lung cancer is the most common cause of cancer-related deaths globally, with the highest mortality rates among both men and women. Most lung cancers are diagnosed at late stages, necessitating systemic therapy. Modern clinical management of lung cancer relies heavily upon application of biomarkers, which guide the selection of systemic treatment. Here, we provide an overview of currently approved and emerging biomarkers of non-small cell lung cancer (NSCLC), including EGFR, ALK, ROS1, RET, NTRK1-3, KRAS, BRAF, MET, ERBB2/HER2, NRG1, PD-L1, TROP2, and CEACAM5. For practical purposes, we divide these biomarkers into genomic and protein markers, based on the tested substrate. We review the biology and epidemiology of the genomic and proteomic biomarkers, discuss optimal diagnostic assays for their detection, and highlight their contribution to the contemporary clinical management of NSCLC.

Current challenges and practical aspects of molecular pathology for non-small cell lung cancers

The continuing evolution of treatment options in thoracic oncology requires the pathologist to regularly update diagnostic algorithms for management of tumor samples. It is essential to decide on the best way to use tissue biopsies, cytological samples, as well as liquid biopsies to identify the different mandatory predictive biomarkers of lung cancers in a short turnaround time. However, biological resources and laboratory member workforce are limited and may be not sufficient for the increased complexity of molecular pathological analyses and for complementary translational research development. In this context, the surgical pathologist is the only one who makes the decisions whether or not to send specimens to immunohistochemical and molecular pathology platforms. Moreover, the pathologist can rapidly contact the oncologist to obtain a new tissue biopsy and/or a liquid biopsy if he/she considers that the biological material is not sufficient in quantity or quality for assessment of predictive biomarkers. Inadequate control of algorithms and sampling workflow may lead to false negative, inconclusive, and incomplete findings, resulting in inappropriate choice of therapeutic strategy and potentially poor outcome for patients. International guidelines for lung cancer treatment are based on the results of the expression of different proteins and on genomic alterations. These guidelines have been established taking into consideration the best practices to be set up in clinical and molecular pathology laboratories. This review addresses the current predictive biomarkers and algorithms for use in thoracic oncology molecular pathology as well as the central role of the pathologist, notably in the molecular tumor board and her/his participation in the treatment decision-making. The perspectives in this setting will be discussed.

High Throughput and Noninvasive Exosomal PD-L1 Detection for Accurate Immunotherapy Response Prediction via Tim4-Functionalized Magnetic Core-Shell Metal-Organic Frameworks

Exosomal PD-L1 has been increasingly considered a noninvasive and accurate predictive marker for immunotherapy treatment response. However, the clinical monitoring of exosomal PD-L1 expression is still limited by its complex biological environment as well as the lack of a robust isolation strategy. Here, a Tim4-functionalized magnetic core-shell metal-organic framework (denoted as Fe3O4@SiO2-ILI-01@Tim4) was facilely constructed via layer-by-layer assembly. Owing to the strongly hydrophilic organic ligand of 1,3-bis(4-carboxybutyl)imidazolium bromide (ILI), magnetic Fe3O4@SiO2-ILI-01@Tim4 was endowed with the merits of low nonspecific adsorption and quick, easy, and convenient isolation of exosomes. The capture efficiency of Fe3O4@SiO2-ILI-01@Tim4 reached as high as 90.3 ± 0.5% and the recovery rate for exosomes was up to 93.0 ± 6.1%. The purity of the isolated exosomes was 7.5 times higher than that via the ultracentrifugation (UC) method. By further combination with immunofluorescence assay, high throughput and noninvasive exosomal PD-L1 detection for accurate immunotherapy response prediction was achieved. The prognosis accuracy of the developed Fe3O4@SiO2-ILI-01@Tim4-based strategy reached 85.7%, whereas the prognosis accuracy of the clinical gold standard, the PD-L1 combined positive score (CPS) test, was only 57.1%. Most interestingly, the developed method is especially suitable for those patients receiving false negative results in the CPS test. The proposed Fe3O4@SiO2-ILI-01@Tim4 is a highly efficient and robust technique showing great potential in high throughput and noninvasive exosomal PD-L1 detection for accurately predicting immunotherapy efficacy.