Laser capture microdissection in lung cancer: a narrative review

Introduction

Lung cancer is the most common cause of cancer mortality worldwide and its treatment is particularly challenging since most patients are diagnosed in advanced tumor stages without surgical resection option, with either metastatic disease or unresectable tumor (1).

The advent of increasingly sophisticated molecular characterization techniques, aimed at identifying predictive and prognostic biomarkers, and the progressive development of new targeted drugs have now widely paved the way to the new precision treatment era of non-small-cell lung cancer (NSCLC) (2).

A correct mutational analysis is dependent on the quantity and quality of nucleic acids retrieved from the pathological samples. Frequently, the only material available for molecular testing is a cytological specimen (effusion fluids, liquid-based preparations, conventional fine needle aspirations or cell blocks). In cytopathological samples, which account for around 40% of NSCLC biopsied cases, tumor cells are scattered and mixed with normal elements making selecting for tumor enrichment difficult (3). On the other hand, small lung biopsies often contain only few available tumor cells, as they are usually consisted for the most part of non-neoplastic cells such as fibroblasts and endothelial cells of the tumor stroma, adjacent normal tissue, inflammatory infiltrate, histiocytes, mesothelium and other cells among the more than 42 identifiable lung cell types (4). In addition, nucleic acids and proteins extracted from formalin-fixed paraffin-embedded (FFPE) specimens are often highly fragmented and/or chemically modified (5).

Since the 90s, the role played by laser capture microdissection (LCM) is precisely isolating tumor cells from the surrounding elements in order to increase the genomic and proteomic diagnostic test performances, especially enhancing sensitivity, and reliably overcoming the cellular heterogeneity, starting either from smear cytology samples, cell block specimens and fresh, frozen or FFPE tissues (Figure 1) (5-9).

Figure 1 Sample sources and application fields of laser capture microdissection. FFPE, formalin-fixed paraffin-embedded.

Furthermore, it was found that in non-FFPE samples it is still feasible to maintain a good nucleic acid integrity for LCM up to 3 days if the tissue is kept at −80 ℃ (10). The procedure involves a laser excision (a laser cuts around the boundary of a selected area and successively a laser pulse forces the cells into a collecting device) and a high-resolution microscope usually coupled with a video system (Figures 2,3).

Figure 2 Example of laser capture microdissection workstation (11).

Figure 3 Laser capture microdissection performed on a NSCLC Papanicolaou-stained ThinPrep slide (PAP, ×40, yellow numbers stand for the count of manual dashes employed for the ROI selection, figure courtesy of the authors). (A) Manual ROI selection, the yellow line delineates a tumour cells aggregate. (B) Corresponding area after dissection (12). NSCLC, non-small-cell lung cancer; ROI, regions of interest.

Due to the increasing necessity for lung cancer (and not only) molecular characterization in routine practice, there is as well an urgent need for an efficient total automatization of this procedure, in fact currently LCM has a daily-routine little use owed mostly to high costs and long cells selection and collection times, with rather a more extensive employment in the multi-omics research fields (13-15).

In this narrative review, we will provide a brief report about the feasible various applications of LCM in routine clinical practice lung cancer scenarios and we will take stock of the situation about the attempts to combine it with some newer diagnostic techniques. Finally, we summarize in Table S1 the main recent LCM progress in lung setting.

We present the following article in accordance with the Narrative Review reporting checklist (available at https://jxym.amegroups.com/article/view/10.21037/jxym-21-55/rc).

Methods

We performed a literature search (date of search: December 21, 2021) in PubMed (Medline) for studies published from January 1, 1995 to December 20, 2021 using a predefined search strategy combining the following search terms: “lung cancer” and “laser capture microdissection” requiring the term “laser” to appear in either the title or the abstract of the papers.

Articles satisfying the following inclusion criteria were included in our review (regardless of the study design): (I) study was written in English language; (II) the full article could be obtained.

Articles satisfying the following exclusion criteria were excluded in our review: (I) study was written in non-English language; (II) the full article was not available; (III) study was not related to lung cancer; (IV) study was not published in a peer-reviewed journal.

The literature review and the data extraction were conducted independently by two reviewers (D.S. and F.P.). A secondary search of the literature was manually conducted from the references of our primary search included papers by the application of the same inclusion and exclusion criteria. Doubts or disagreements regarding the inclusion or exclusion of manuscripts were resolved through a discussion between the reviewers until a consensus was reached (search strategy summary at Table 1 and detailed Medline search strategy at Table S2).

Discussion

Undissected samples with traditional tissue-block homogenization contain a tangled mixture of tumor and non-neoplastic cells. This heterogeneity and the usual low tumor content are the two major problems in the investigation of lung cancer molecular signatures in cell blocks because they determine the inability to perform an efficient neoplastic cells selection for molecular characterization, unlike what occurs with macrodissection carried out on histological sections from surgical resections. Hence cell blocks are typically used in their entirety by whole slide scrapes for DNA/RNA extraction, thus strongly diluting the tumor DNA/RNA content, obscuring signals from the malignant compartment and decreasing the sensitivity of the molecular assays by raising the limits of detection for genomic variants (16-18). To remedy this issue, it is possible to use a LCM system to precisely dissect the morphologically malignant cells and so enhance the desired cell population before subsequent nucleic acid or protein isolation. Manual microdissection (microscope plus sterile scalpel) is feasible with lower costs and greater temporal efficiency and throughput for tissue separation, although precision may not be as good as for LCM (19). Furthermore, both manual and laser techniques are subjected to a time-consuming and tedious user-dependent cell-by-cell selection of regions of interest (ROI) under direct microscopic visualization accomplished by a pathologist or cytotechnologist (20). In addition, laser-associated heat as well as the presence of nucleases or proteases tissue-specific (e.g., lung, pancreatic, spleen) may accelerate DNA, RNA and protein degradation processes, thereby a safety margin laser application and different protocols depending on the tissue type are employed (10,21).

A rapid and eventually user-independent ROI selection is achievable with the immunoguided LCM, based on cancer specific immunostaining (e.g., anti-cytokeratin-7 primary antibody for lung adenocarcinoma), even by the use of handheld and computer-aided laser devices (21-24).

Immunoguided LCM, compatible with both immuno-cyto/histochemistry or immunofluorescence targeting approaches, may be either human operator-based, computer-assisted via stain recognition algorithms or expression-based (16). In particular, this last user-independent method relies on a semiautomated identification and dissecting software in need of minimal supervision due to its ability to properly judge antibody staining. In immunoguided LCM the stained slices may also be previously covered with an ethylene-vinyl-acetate (EVA) membrane and then a laser irradiation can be performed on the whole slide: the heat derived from the localized energy absorption by the dark DAB (diaminobenzidine) stained tumor cells leads to the corresponding melting of the EVA membrane at the sites of most intensive staining. Subsequently, when the complete EVA membrane is removed, the attached tumor cells are isolated from the non-neoplastic elements, with an efficiency strongly related to immunostaining intensity and laser energy (19,23). An attempt to further optimize this process is the use of Vektor Black as chromogen, which provides to positive cells a dark black staining able of increasing the absorbed energy of the infrared laser irradiation better than slides stained with DAB (19). The immunoguided LCM has been even combined with a digital whole-slide scanning and image analysis performed before and after microdissection as a quality control protocol (19). However, it should be considered that the immunoguided LMC brings with it time, cost and technical issues related to the immunostaining steps, as well as their potential deleterious effects on the nucleic acid quality (25).

Conversely, a method that does not necessarily require immunostaining is the spatially invariant vector quantization, a pattern-matching algorithm for identification of specific cell types based on an iterative testing and real-time evaluation of match quality (16,24). With this kind of platform, the pathologist just has to identify the cell type or the morphologic pattern of interest and then the machine learning algorithm performs a whole-slide research to find similar features, including cell size, shape, nucleus and nucleolus (Figure 4).

Figure 4 Example of semi-automated LCM procedure (H&E, ×5). (A) ROI are manually selected. Here, yellow lines delineate tumour areas, the blue ones normal stroma. (B) The algorithm then segments the whole-section to identify cell boundaries and automatically classify them based on the previous ROI detection. (C,D) This generates a heat map of the tumour probability score assigned by the classifier to each cell. The user can choose different confidence contours with constant tumour probability for the subsequent dissection (26). LCM, laser capture microdissection; ROI, regions of interest.

Selbach et al. in 2021 conceived a hyperspectral infrared microscopy LCM procedure, feasible on label-free or even on hematoxylin and eosin (H&E) stained tissue sections (13). This method is based on the Fourier transform infrared (FTIR) imaging technique, which assigns a vibrational spectrum to each tissue component at high spatial resolution (27). This fully automated approach relies on a trained random forest classifier able to correctly recognize each infrared pixel spectrum, distinguishing different types of tissue (e.g., normal, tumoral, inflamed) as well as various subtypes of thoracic tumors, and thus proceed to a 95% success rate ROI dissection (28).

A combined protocol of LCM and mass spectrometry (LCM-MS) on FFPE specimens of lung tissue, applicable even on the matrix-assisted laser desorption/ionization (MALDI) technique, has been recently proposed (29). The LCM-MS has the advantage of investigating the content of cells within their morphological context, even at single cell resolution (30). Nevertheless, it must be stated that larger sampling is needed in case of LCM for protein identification, since their analysis usually needs a larger amount of template compared to the few nanograms required by either PCR assays and next generation sequencing (NGS) technology, with which a wide range of molecular analysis (genomic, epigenomic and transcriptomic) can be performed with low quality/quantity material (31,32). In order to reduce sample loss and therefore improve sensitivity of LCM-based proteomics, different preparation protocols have been developed in the years (21,33-36).

Interestingly, LCM may also have a role in lung adenocarcinoma programmed death ligand-1 (PDL-1) expression assessment through Reverse Phase Protein Microarrays (RPPA), in fact this combination allows a continuous quantitative scaled detection with performances comparable to the immunohistochemistry (IHC), but potentially less dependent upon subjective operator evaluations or IHC clones employed, with an improved insight on immune cells classes and their spatial relationship with the tumour cells (37).

Finally, the opportunity to have LCM tools under the direct Pathology Department Laboratory Information System (LIS) control may allow the pathologist to speed up the procedure by immediately selecting the ROI, in the strength of his expertise and from what he has already investigated in the course of the diagnostic phase, thus being able to promptly provide a correct estimate of either the quantitative and qualitative adequacy of the specimen sent to the downstream molecular analyzes. Additionally, the progressive digitalisation of all the pathologist’s activities will lead to the development of a single fluent comprehensive diagnostic and molecular workflow, while the insertion of a proper LCM section in the pathology report would allow the full respect of the traceability criteria (synoptic report).

In conclusion, the LCM daily-routine application in clinical practice, after an initial great enthusiasm, is currently heavily constrained by numerous and well-known limitations (Table 2), nevertheless the development of tailor-made digital pathology tools and machine learning algorithms may lead to an efficacy and reliable, as well as rapid and sensitive, automatization of the LCM workflow and therefore result in a potential large-scale use, while the LCM combination with advanced high resolution techniques (e.g., MALDI) may open up new scenarios in the research setting (38-40).

Conclusions

LCM is a reliable procedure for the investigation of protein and gene expression in specific areas of interest, especially crucial nowadays in the characterization of lung cancer molecular signatures. In the future, its reliability and ease of use will make LCM an essential step in the application of the numerous available downstream analyses.

Acknowledgments

Funding: None.

Footnote

Provenance and Peer Review: This article was commissioned by the Guest Editors (Umberto Malapelle and Giancarlo Troncone) for the series “Predictive Molecular Pathology in Lung Cancer” published in Journal of Xiangya Medicine. The article has undergone external peer review.

Reporting Checklist: The authors have completed the Narrative Review reporting checklist. Available at https://jxym.amegroups.com/article/view/10.21037/jxym-21-55/rc

Peer Review File: Available at https://jxym.amegroups.com/article/view/10.21037/jxym-21-55/prf

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jxym.amegroups.com/article/view/10.21037/jxym-21-55/coif). The series “Predictive Molecular Pathology in Lung Cancer” was commissioned by the editorial office without any funding or sponsorship. The authors have no other conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

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