Prachi Bogetto leads the Translational Research business at Leica Microsystems, and discusses the importance of imaging innovations when it comes to the fight against cancer.
Drug discovery is increasingly informed by scientific insight derived using artificial intelligence (AI). In microscopy, in particular, computational solutions powered by AI and machine learning deliver a deeper understanding of biology and unprecedented knowledge of how patients will respond to clinical interventions, providing the blueprint for the development of breakthrough therapies and precision medicine.
Nowhere is this trend more pronounced than in the world of cancer research and immunotherapy. The field has advanced exponentially in recent years and scientists’ understanding of the mechanisms that lead to abnormal cell growth is deeper than ever. Microscopy and immunohistochemistry (IHC) have become a core diagnostic tool for visualising the distribution and density of cellular components in relation to each other within a tumour tissue sample.
This anatomic pathology information supports clinical decision-making regarding treatment options, which are increasingly personalised in cancer care. To optimise patient care with existing treatment options and rationally refine new drug discovery, scientists need further understanding of the cellular functions and interactions occurring in the tumour microenvironment (TME). This will elucidate disease mechanisms and identify potential biochemical pathways for therapy1,2.
The heterogeneity of cancer tissue has long presented both prohibitive challenges and tantalising opportunities to better understand and even predict tumour development, treatment response, and clinical outcomes. The main hurdle to visualising the tumour microenvironment is not the imaging devices—we have had the technology to image at the needed resolution for almost 10 years3. The challenge has been analysing and using the data that the imaging device acquires. With the advent of systems and computational biology, a whole new avenue of investigation has opened for understanding complex biological and pathological systems. We can finally progress in unravelling the mysteries of cancer and how to successfully fight it.
The rise of multiplex immunofluorescence imaging
While routinely used to visualise tumour tissue pathology for diagnostic purposes, conventional fluorescent IHC—where fluorescent antibody probes are used to tag proteins—is typically limited to visualising at most seven biomarkers per tissue section3,4. Moreover, quantitation often relies on semi-quantitative techniques, which are limited by non-linear biomarker staining intensities, subjective inter-observer variabilities and technical inter-laboratory variabilities.
To address these limitations, protocols were developed about a decade ago that enabled scientists to perform multiplex immunofluorescence (MxIF) microscopy, which can visualise more than 60 protein biomarkers in a single tissue section, with resolutions that discerned single cells clearly3.
However, these largely manual methods were complex, difficult to standardise, time-consuming, and costly. To relieve these constraints, multiple technologies have been developed to provide high-throughput multiplex staining and standardised quantitative analysis for highly reproducible, efficient and cost-effective tissue imaging4,5. Recent advances in image acquisition and analysis software using AI/ML algorithms, combined with automation and simplified workflows for multiplex imaging systems, have enabled researchers to better understand tumour cell characteristics and interactions and how they may relate to clinical outcomes and unmet needs3,5-9.
Revealing ‘homogeneous patterns’ in highly heterogeneous tumour tissue
A systematic study used MxiF to investigate the spatial distribution of tumour cells and their interactions with host immune cells within the very heterogeneous TME of metastatic melanoma7. The researchers used the Leica Microsystems Cell DIVE multiplex imaging platform to provide a simplified MxIF workflow, which allowed the researchers to use 21 biomarkers—in iterated staining procedures—to characterise individual cells, all of which remained preserved in their spatial micro-anatomic locations within the tumour sections5,7.
The platform also incorporated automated imaging and the application of advanced image processing and acquisition software and proprietary ML algorithms for image analysis. These processing technologies objectively corrected illumination, removed autofluorescence, registered images, segmented cells, quantified biomarkers, and classified cell types, resulting in data that was standardised between sections, biopsy samples, and experiments5,7,11-13.
The Cell DIVE combination of single-cell protein-expression and spatial anatomic data enabled further statistical algorithmic analysis to elucidate the quantitative and spatial distribution and inter-relationships between the tumour and immune cells7. The MxIF study revealed that the heterogenous tumour tissues contained some regions of heavy infiltration by T cells, as well as some regions with minimal T cell infiltration. Moreover, the composition and spatial distribution of cells were drastically different between these regions7. This is an important finding because heavy T cell infiltration is associated with the human immune system attacking the tumour, while minimal infiltration may be indicative of the tumour successfully evading the host immune system10.
The study also looked at the expression of human leukocyte antigen (HLA) by tumour cells, which is known to attract T cells to attack the tumour and is switch off in tumours that evade immune attack10,14. They found that while this was still true, the expression of HLA alone was not enough for T cell infiltration to occur7. Rather, other immune cells, like B cells, also seem to have an important role to play in determining the extent of T cell infiltration7.
Identifying potential anti-cancer pathways
A later study also investigated the heterogeneity of tumour tissue, this time using 43 hallmark cancer biomarkers with the Cell DIVE multiplex imaging solution, in combination genomic and magnetic resonance imaging (MRI) analyses, to unravel the differences in heterogeneity between wild-type diffuse glioma tumours and those containing the isocitrate dehydrogenase (IDH) mutation8.
Statistical algorithmic spatial and clustering analysis of the Cell DIVE single-cell data revealed that the expression of the hallmark cancer proteins was generally lower in the IDH mutant tumour cells than the wild-type ones8. Combined with the exome and RNA sequencing and MRI data, a coherent picture emerged of enhanced angiogenesis in the wild-type tumours compared with the mutant tumours8. The gene expression data also showed that the IDH mutant tumours tended to have significantly lower expression of hallmark cancer genes, such as those for replicative immortality, evading growth suppressors, and inducing angiogenesis8. These results align with the finding that patients with gliomas harbouring the IDT mutation survive overall for longer than those who have gliomas without the mutation18,15
Hyperplex imaging for translational research
These are just two recent studies leveraging recent advances in MxIF technologies to examine and understand the complexity of tumours and cancer biology – neither of which examined the maximum number of biomarkers possible, which with Cell DIVE is more than 60 (the actual upper limit has not yet been reached)5,7,8. Other researchers are applying MxIF to better understand treatment response in clinical studies, which may not only shed light on therapeutic mechanisms but also eventually inform personalised treatment decisions in the clinic9.
To realise the full potential of MxIF technologies in translational research and even clinical practice for patients with cancer, there now needs to be a step change in innovation and investment in computational modelling, predictive analytics, and ML/AI – all focused on taking MxIF data and using it to understand the cell changes that lead to cancer development and progression. If we double down on these technologies now, it may not be long before they enable us to predict these changes, and then act to prevent them.
About the author
Prachi Bogetto leads the Translational Research business at Leica Microsystems. She has degrees in biochemistry and molecular biology and pursues ideas from inception to scientific business.
Bogetto and her team work closely with researchers who are dedicated to examining and understanding cells, the basis of all life, and working toward the next steps in treatments and progressing human health. The consistent objective of the team is to take scientific ideas and tools from the bench into large-scale labs, making them accessible to more people for the benefit of patients.
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- GenomeWeb. GE Healthcare’s Clarient, GSK Creating Clinical Lab Network to Improve Cancer Dx. Sep 30, 2014. https://www.genomeweb.com/diagnostics/ge-healthcares-clarient-gsk-creating-clinical-lab-network-improve-cancer-dx (accessed Sep 29, 2021).
- Gerdes MJ, Sevinksky CJ, Sood A, et al. Highly multiplexed single-cell analysis of formalin-fixed, paraffin-embedded cancer tissue. Proc Natl Acad Sci USA. 2013; 110(29): 11982–11987. doi: 10.1073/pnas.1300136110.
- Tan WCC, Nerurkar SN, Cai HY, et al. Overview of multiplex immunohistochemistry/immunofluorescence techniques in the era of cancer immunotherapy. Cancer Commun (Lond). 2020; 40(4): 135–153. doi: 10.1002/cac2.12023.
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- Lindner AU, Salvucci M, Stachtea X, et al. Abstract LB-088: Exploratory multiplex tissue image analysis of the impact of heterogeneity in the microenvironment of primary colorectal cancer on apoptosis markers in patients. Cancer Res. 2019; 79 (13 Supplement): LB-088. doi: 10.1158/1538-7445.AM2019-LB-088.
- Yan Y, Leontovich AA, Gerdes MJ, et al. Understanding heterogeneous tumour microenvironment in metastatic melanoma. PLoS One. 2019; 14(6): e0216485. doi: 10.1371/journal.pone.0216485.
- Berens ME, Sood A, Barnholtz-Sloan JS, et al. Multiscale, multimodal analysis of tumour heterogeneity in IDH1 mutant vs wild-type diffuse gliomas. PLoS One. 2019; 14(12): e0219724. doi: 10.1371/journal.pone.0219724.
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- Oldford SA, Robb JD, Codner D, et al. Tumour cell expression of HLA-DM associates with a Th1 profile and predicts improved survival in breast carcinoma patients. Int Immunol. 2006; 18: 1591–1602. doi 10.1093/intimm/dxl092.
- Carrillo JA, Lai A, Nghiemphu PL, et al. Relationship between tumour enhancement, edema, IDH1 mutational status, MGMT promoter methylation, and survival in glioblastoma. AJNR Am J Neuroradiol. 2012; 33(7): 1349–1355. doi: 10.3174/ajnr.A2950.