Quantification of tumor heterogeneity: from data acquisition to metric generation

• Published in: Trends in biotechnology (Regular ed.) 2022-06, Vol.40 (6), p.647-676
• Main Authors: Kashyap, Aditya, Rapsomaniki, Maria Anna, Barros, Vesna, Fomitcheva-Khartchenko, Anna, Martinelli, Adriano Luca, Rodriguez, Antonio Foncubierta, Gabrani, Maria, Rosen-Zvi, Michal, Kaigala, Govind
• Format: Article
• Language: English
• Subjects: Biopsy; Cancer; computational metrics; Computer applications; Data acquisition; Heterogeneity; Medical imaging; personalized medicine; Precision medicine; Proteins; Radiology; Subpopulations; tumor heterogeneity; Tumors
• ISSN: 0167-7799; 1879-3096
• DOI: 10.1016/j.tibtech.2021.11.006

Tumors are unique and complex ecosystems, in which heterogeneous cell subpopulations with variable molecular profiles, aggressiveness, and proliferation potential coexist and interact. Understanding how heterogeneity influences tumor progression has important clinical implications for improving diagnosis, prognosis, and treatment response prediction. Several recent innovations in data acquisition methods and computational metrics have enabled the quantification of spatiotemporal heterogeneity across different scales of tumor organization. Here, we summarize the most promising efforts from a common experimental and computational perspective, discussing their advantages, shortcomings, and challenges. With personalized medicine entering a new era of unprecedented opportunities, our vision is that of future workflows integrating across modalities, scales, and dimensions to capture intricate aspects of the tumor ecosystem and to open new avenues for improved patient care. Understanding tumor complexity and applying that knowledge to advance patient care is a cornerstone of cancer research. However, tumor heterogeneity obfuscates this vision.Contemporary research contains a diverse set of technologies and computational tools for detecting, characterizing, and quantifying tumor heterogeneity at the molecular, architectural, organ, patient, or population level.Evaluation of the clinical relevance of heterogeneity metrics and creating actionable intelligence for the clinics, requires a common lexicon across disciplines, and a unified look toward multimodal data acquisition and computational analysis, the constraints they present, and how they partake in whole workflows.