Oct 17, 2021
JERUSALEM WEATHER

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Cancers can affect nearly every organ in the body, and even when the location is the same, malignant tumors in one patient may be very different from that in another person and need personalized medicine to treat it. 

Now, researchers from the Rappaport Faculty of Medicine in Haifa’s Technion-Israel Institute of Technology have developed an innovative algorithm – a process or set of rules to be followed in calculations or other problem-solving operations by a computer) – that detects an uninterrupted common denominator in multidimensional data gathered from tumors of different patients. 

The study, which was published in Cell Systems under the title “Alignment of single-cell trajectories by tuMap enables high-resolution quantitative comparison of cancer samples” that was led by Prof. Shai Shen-Orr, Dr. Yishai Ofran and Dr. Ayelet Alpert and conducted in collaboration among researchers at the Technion, Haifa’s Rambam Medical Center, Jerusalem’s Shaare Zedek Medical Center and the University of Texas.

In recent years, cancer research has undergone a series of significant revolutions, including the introduction of single-cell high-resolution characterization capabilities, or, more specifically, simultaneous high-throughput profiling of cancer samples using single-cell RNA sequencing and proteomics analysis. 

This has led to the production of hug quantities of multidimensional data on a huge number of cells, allowing for the characterization of both the healthy tissue and malignant tissues. This large amount of data has revealed the great variability between tumors of different patients, where cellular characterization that is derived from the patient’s genetic background is unique to each patient.

Despite the substantial advantage that is derived from such an accurate characterization of the specific patient, this development hinders comparison of different patients; without a common denominator, the comparison – which is essential for identifying prognostic markers such as causing death or the severity of illness – becomes impossible.

The tuMap algorithm developed by the Technion researchers provides a solution to this complex challenge by means of a “variance-based comparison.” The innovative algorithm delivers the possibility to place numerous different tumors on a uniform scale that provides a benchmark for comparison. 

Tumors of different patients can then be compared meaningfully as well as can tumors in the same patient over the disease course, such as at diagnosis and after treatment. The resolution provided by the algorithm can be leveraged for clinical applications such as prediction of various clinical indices with a very high accuracy, outperforming traditional tools. Although the researchers tested the algorithm on leukemia tumors, they believe that it will be relevant for other cancer types as well.