Home Cellular health Personalized medicine: the platform allows the compa

Personalized medicine: the platform allows the compa

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Researchers at the Technion’s Rappaport School of Medicine have developed an innovative algorithm that detects an unbroken common denominator in multidimensional data collected from tumors from different patients. The study, published in Cellular systems, was led by Prof. Shai Shen-Orr, Dr Yishai Ofran and Dr Ayelet Alpert, and conducted in collaboration between researchers from Technion, Rambam Health Care Campus, Shaare Zedek Medical Center and University of Texas .

In recent years, cancer research has undergone a series of important revolutions, including the introduction of high-resolution single-cell characterization capabilities, or, more specifically, the simultaneous high-throughput profiling of cancer samples at the using single-cell RNA sequencing and proteomic analysis. This has led to the generation of large amounts of multidimensional data on a large number of cells, allowing the characterization of both healthy and malignant tissue. This large amount of data revealed the great variability between tumors from different patients, where the cellular characterization derived from the patient’s genetic makeup is unique to each patient.

Despite the substantial advantage that derives from such a precise characterization of the specific patient, this development prevents the comparison of different patients: in the absence of a common denominator, the comparison, which is essential to identify prognostic markers (e.g. mortality or severity of the disease), becomes impossible.

The tuMap algorithm developed by Technion researchers provides a solution to this complex challenge by means of a “variance-based comparison”. The innovative algorithm offers the possibility of placing many different tumors on a uniform scale which provides a benchmark for comparison. In this way, tumors from different patients can be compared meaningfully, as well as tumors from the same patient during the course of the disease (for example, at diagnosis and after treatment). The resolution provided by the algorithm can be exploited for clinical applications such as the prediction of various clinical indices with very high precision, surpassing traditional tools. Although the researchers tested the algorithm on leukemic tumors, they believe it will be relevant for other types of cancer as well.

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The research was sponsored by the Israel Science Foundation, the Rappaport Family Institute for Research in the Medical Sciences, and the National Institutes of Health (NIH).


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