Rapid Diagnosis of Metastatic Risk

Novel Method Can Diagnose Cancer and Determine Metastatic Risk Within Two Hours

Novel Method Can Diagnose Cancer and Determine Metastatic Risk Within Two Hours

Novel Method Can Diagnose Cancer and Determine Metastatic Risk Within Two Hours

Professor Daphne Weihs’ mechanobiology-based technology can predict the metastatic potential of a tumor. The method entails seeding tumor-sampled cells on a specialized gel that mimics tissue-stiffness and quantifying the number of cells that push into the gel surface and other measures.

Metastasis, the spread of cancer cells from the primary tumor to new areas of the body and the generation of secondary tumors, is responsible for 90% of cancer-related deaths. A research team led by Professor Daphne Weihs of the Technion Faculty of Biomedical Engineering has developed and tested a mechanobiology-based technology to predict whether a tumor is cancerous and, if so, its clinical metastatic potential. The study was published in the scientific journal Annals of Biomedical Engineering.

Prof. Weihs’ technology can rapidly determine metastatic risk by evaluating the invasiveness of cells sampled from a tumor by seeding them on a synthetic gel that mimics tissue stiffness and wherein invasive cells will rapidly and forcefully push into the gel’s surface. Quantification of the number of indenting cells and other measures provides the likelihood of metastasis formation. 

Currently, Prof. Weihs and her team successfully tested the technology on pancreatic tumor cell samples collected from volunteers at the Rambam Health Care campus and on established breast and pancreatic cell lines. The results were validated against the current standard clinical protocols and agreed with the clinical diagnoses, prognoses, and patient outcomes. The results also matched established cell lines of the same cancer types. The mechanical invasiveness assay, which may be applied to many, and potentially all, solid tumor types, successfully differentiates between benign (not cancerous), non-metastatic, and metastatic tumor samples, and concurrently gages their metastatic likelihood.

Cancer morbidity is caused by uncontrolled tumor progression, which results in tumor growth and, potentially, metastatic spread. Early prediction of increased metastatic risk can significantly affect disease management and improve treatment outcomes. Current methods used to estimate the likelihood of metastasis and tumor recurrence are time-consuming, qualitative, and require extensive examinations that take days or even weeks – valuable time that cancer patients may not have. Rapidly progressing tumors require swift and aggressive treatment. Moreover, current methods are not infallible. For example, 30% of cancer-negative lymph node assessments in breast cancer patients fail to predict eventual metastases development.      

In previous research, Prof. Weihs established that invasive, cancerous cells will push into synthetic, polyacrylamide gels to cell-scale depths within 1-2 hours, while normal or non-invasive cells do not significantly indent the gel. Prof. Weihs explained: “The gels are a customized platform that mimics the physiological stiffness of soft tissue. The invasiveness of cells sampled from tissues is rapidly and quantitatively evaluated using our innovative mechanical invasiveness assay, which we are currently developing into a clinically applicable technology.” 

The great advantage of this technology is that it rapidly provides physicians with quantitative measures to determine patient-specific treatment protocols within hours after an initial biopsy. Consequently, informed decisions can be made, such as, if an aggressive chemotherapy course is needed (for highly invasive tumor cells) or an aggressive surgical approach is warranted (for less invasive tumor cells, where the tumor will likely remain localized). Such a swift assessment, at first diagnosis, can improve disease management and patient survival by increasing the precision of the chosen treatment plan and reducing psychological and physical stress brought on by long wait times and potentially overly aggressive treatments.   

The research was partially funded by the Elias Fund for Medical Research, the Polak Fund for Applied Research, the Ber-Lehmsdorf Foundation and the Gerald O. Mann Charitable Foundation. 

For the article in Annals of Biomedical Engineering click here