The connection between the biomechanics of cancer cells and their functions (rather than morphology) is emerging as a new method to "measure" and classify cancer activity.

The dynamic and heterogeneous nature of cancer, with its numerous manifestations varying in types and degrees from patient to patient, defies a singular definition. Its heterogeneity is evident in the «differences between cancers of the same type in different patients, among cancer cells within the same tumour, and between a primary and a secondary tumour» [source: National Cancer Institute].

Addressing this heterogeneity, a team of researchers from Massachusetts General Hospital described it as «the fuel for tumour resistance. Its driving force», highlighting that a thorough clinical analysis of this heterogeneity is crucial for developing more effective therapies. Particularly troublesome, they note, is intratumoral heterogeneity, which is being studied to develop methodologies to counteract its consequences and accelerate the creation of personalised pharmaceutical treatments [source: “Tumour heterogeneity and resistance to cancer therapies” – Nature Reviews Clinical Oncology].

Studying cellular biomechanics allows for the identification of highly deformable cells as an indicator of cancerous conditions. These cells, due to their particularly elastic behaviour, can absorb micro and nanoparticles.
Recent research from the Hebrew University of Jerusalem, focusing on the phagocytosis process triggered by cancer cells and utilising machine learning to measure and classify it, introduces a new “system” aimed at enhancing understanding of the cancer environment and predicting the level of malignancy and potential resistance to certain therapies.
In a future scenario, the confirmation of the laboratory results in clinical tests would signify a significant advancement towards personalised treatments for patients with cancer.

Insights from the biomechanics of cancer cells

In understanding the onset and progression of cancer, the study of cellular biomechanics is crucial, as it aims to balance the myriad functions of cells. The mechanical properties of cells are categorised into three groups, focusing on attributes such as viscoelasticity, the ability to generate mechanical tension, and the capacity to «alter their morphological and functional characteristics in response to mechanical stimuli».

Each of these properties can be quantified, and their alterations are linked to significant biological activities relevant to cancer, including those that determine its malignancy. For instance, viscoelasticity – the ability of a single cell to exhibit both elastic and viscous behaviour in response to internal or external forces, thereby resisting deformation – is an important indicator in the context of cancer. Notably, within the same tissue, the viscoelasticity level of cancer cells is higher than that of healthy cells. «Higher cellular viscoelasticity tends to be associated with increased malignancy levels in many cancer cell lines». Furthermore, the alteration of morphological and functional characteristics in response to mechanical stimuli (also known as “mechanosensing“) is more pronounced in cancer cells. Their environment tends to favour phenotypic changes that are ideal for the formation and growth of metastases [source: “Biomechanics of cancer cells” – Bioengineering Innovative Solutions for Cancer, 2020].

Correlation between cancer cells deformation and particle uptake

A study published on 29 May 2024 in Science Advances, titled “Particle uptake in cancer cells can predict malignancy and drug resistance using machine learning” by the Faculties of Pharmacy, Engineering, and Computer Science at the Hebrew University of Jerusalem, highlights, in addition to their biological functions, that highly deformable cells – indicative of a tumoural condition – «are more likely to phagocytise submicrometric and micrometric particles».

More specifically, «during phagocytosis, which typically involves particles sized between >200 nm and 10 μm, cancer cells undergo substantial deformations, remodelling their cytoskeleton. This involves an initial adhesion of the particles to the cell membrane, followed by a multi-phase and multi-factor process leading to the engulfment of these particles».

The analysis and measurement of this process, the research team explains, can provide a wealth of information about the membrane fluidity of the cells in question, the level of particle adhesion, and cytoskeletal remodelling.

These are valuable data, as they allow the detection of the malignancy level reached by the tumour and its potential resistance to certain chemotherapeutic agents. Hence, the Hebrew University’s focus on the described phagocytosis aims to lay the foundations for a new method for studying biomechanics at the level of individual cancer cells and cancer cell groups.

Algorithmic analysis of micro and nanoparticle absorption

The objective of the working group was to define “absorption models” for micro and nanoparticles to provide a deeper understanding of cellular deformability and, consequently, the tumour environment. 

During the testing phase, fluorescently labelled polystyrene particles, ranging in size from 0.04 to 3.36 μm, were used. These particles were analysed using flow cytometry, «a technique that allows for the identification, counting, and isolation of cell subgroups based on certain physical characteristics and signals generated by fluorescent markers».

To classify images of tumour cells (which are highly deformable and phagocytic) based on models of particle absorption of varying sizes, with which they were allowed to interact, a machine learning system was developed. This system was trained using video data of ten thousand tumour cells of various morphologies and mechanical properties, with and without micro and nanoparticles around them.

The AI system was then tested in the analysis and classification of three pairs of tumour cells – differing in malignancy and drug resistance levels. These pairs comprised human lung cancer cells with varying resistance to the chemotherapeutic agent known as “cisplatin“; human prostate cancer cells with different metastatic potentials; and human melanoma cells with different levels of invasiveness.

For each variable – namely, cell membrane fluidity, particle adhesion level, and cytoskeletal remodelling – eleven characteristics were analysed.

Beyond cell morphology-based methods for cancer cells

«Machine learning algorithms were able to classify the three pairs of tumour cells with accuracy rates exceeding 95%», reported researchers from the Hebrew University.

As hypothesised, the experimental results revealed that within groups of tumour cells with similar morphological properties (thus homogeneous in this respect), there were cells with distinct mechanical properties (here heterogeneity comes into play), which cannot be differentiated based on their morphological parameters but rather on data related to their micro and nanoparticle absorption mechanisms. This highlights the importance of analysing the phagocytosis process to accurately identify cancer progression dynamics and its resistance to certain chemotherapy treatments.

«We have demonstrated that morphological parameters alone are not always sufficient to distinguish the functional characteristics of tumour cells», conclude the authors, emphasising that the functional characteristics-based approach allows for the classification of early phenotypic alterations in tumour cell populations that have not yet undergone morphological changes and, therefore, cannot be easily identified using morphology-based analysis methods alone.

Glimpses of Futures

It will take years before the methodology developed by the team at the University of Jerusalem can be tested on samples and in clinical contexts. However, for the time being, the results of the initial test have shown a clear association between cellular absorption patterns and the functions of cancer cells.

The mechanism of this connection has been analysed and classified through machine learning, and in the future, the refined “system” could become a clinically strategic predictive tool regarding the drug resistance of cancer cells and their malignancy levels.

Let us now attempt to foresee potential future scenarios by analysing the impacts of the method’s evolution on multiple fronts using the STEPS framework.

S – SOCIAL: Should the results achieved by the researchers at the Hebrew University be clinically validated in the future, it would represent a significant step forward in the direction of “tailored treatments” for cancer patients. This would range from even more timely and precise cancer diagnoses – based not on the study of the structure of cancer cells (which in the very early stages of the disease are still morphologically intact) but on their functions – to predicting their malignancy, progression dynamics, and potential resistance to specific chemotherapeutic drugs, all parameters that vary greatly from person to person. The benefits for patients would include shorter waiting times for an accurate diagnosis and more effective treatments (customised rather than standardised).

T – TECNOLOGICAL: The gathering and analysis of new types of information about cancer cells, different from those derived mainly from diagnostic imaging and tumour markers through blood tests, could open up new perspectives in the future. This could lead to the development of new equipment for clinical testing and innovative blood tests for cancer patients. Furthermore, since the method involves examining cancer cells (obtained from biopsies) which show significant variability from person to person, the authors suggest that future clinical validation should include a statistical tool. This tool would provide statistical data on the different biomechanical characteristics of cancer cells pertaining to specific categories of patients, classified by age, medical history, gender, and ethnicity.

E – ECONOMIC: Should the described framework evolve into a predictive tool for gauging the progression level of cancer and be systematically adopted in clinical settings in the coming years, the global reliance on imaging diagnostics (which only captures the morphology of cancer cells and not their biomechanics) is likely to decrease. This includes X-rays, CT scans, ultrasounds, and predominantly MRIs, which are typically prescribed to diagnose the presence of tumours in organs and tissues and monitor their progression. This shift would lead to significant savings for the healthcare system in every country, many of which are already burdened by the cost of unnecessary medical procedures. This concern was notably highlighted by Italy’s Minister of Health, Orazio Schillaci, who stated that at least 20% of all examination and consultation requests are deemed inappropriate.

P – POLITICAL: As previously mentioned, the approach outlined—focused on the functional characteristics of cancer cells rather than their morphological ones—enables the identification of cancer from its early stages, when phenotypic changes have not yet altered cellular structure. This allows for even earlier and quicker diagnosis of the disease, aligning with the recent parliamentary document “Policies for Combating Cancer in Italy” and the European Council Recommendation on strengthening prevention through early cancer detection. In a potential future scenario where the method developed by the Hebrew University team is systematically applied, the Italian Government must ensure no region is left behind in terms of early diagnosis of oncological diseases. This concern is particularly relevant given the ongoing legislative process regarding the bill on differentiated regional autonomy, which could exacerbate inequities in healthcare access across different areas of the country.

S – SUSTAINABILITY: The potential future reduction in radiological examinations—such as X-rays, CT scans, ultrasounds, and MRIs—due to the adoption of a predictive system for cancer progression based on cellular function analysis rather than imaging structures would not only have economic benefits but also enhance environmental sustainability. Radiology is one of the major consumers of electricity in the global healthcare system, with substantial energy consumption documented in the literature. For instance, an American study published in April 2023, titled “Radiology Environmental Impact: What Is Known and How Can We Improve?” in Academic Radiology, references a report by the non-profit Canadian Coalition for Green Health. This report reveals the average annual energy consumption for MRI devices (111,000 kWh/year), CT scans (41,000 kWh/year), X-rays (9,500 kWh/year), and ultrasounds (760 kWh/year) in Canada. These figures become alarming when multiplied across all countries worldwide.

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