Platform enables comparative research on cancerous tumors

Researchers at the Technion-Israel Institute of Technology’s Rappaport Faculty of Medicine have developed an innovative algorithm to detect uninterrupted common denominators in multidimensional data collected from tumors in a variety of patients.Studies published in Cell systemWas led by Professor Shai Shen-Orr, Dr. Yishai Ofran, and Dr. Ayelet Alpert, and was jointly conducted by researchers at Technion, Rambam Health Care Campus, 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, and more specifically, simultaneous high-throughput profiling of cancer samples using single-cell RNA sequencing and proteomics analysis. .. This has generated a huge amount of multidimensional data about a huge number of cells, enabling characterization of both healthy and malignant tissues. This large amount of data reveals large variability between tumors in different patients, where cell characteristics derived from the patient’s genetic background are unique to each patient.

Despite the substantial benefits gained from such accurate characterization of a particular patient, this development hinders comparisons of different patients: in the absence of a common denominator, comparisons are prognostic markers (eg, mortality). Or essential to identify the severity of the disease), it becomes impossible.

Developed by Technion researchers, the tuMap algorithm provides a solution to this complex challenge through “distributed-based comparisons.” Innovative algorithms offer the possibility of placing a large number of different tumors on a uniform scale that provides a benchmark for comparison. In this way, tumors of different patients, and tumors of the same patient, can be meaningfully compared throughout the course of the disease (eg, at diagnosis and after treatment). The resolution provided by the algorithm can be leveraged for clinical applications such as predicting various clinical indicators with much higher accuracy than traditional tools.Researchers have tested algorithm They believe that leukemia tumors are also associated with other types of cancer.