Intratumor Heterogeneity Analysis: From Whole Body to Single Cell

• Somaye Zareian

  Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran 13164, Iran

• Soroush Sardari

  Drug Design and Bioinformatics Unit, Medical Biotechnology Department, Biotechnology Research Center, Pasteur Institute of Iran, Tehran 13164, Iran

Cancer heterogeneity, including both intertumoral and intratumoral heterogeneity (ITH), represents a major challenge in cancer diagnosis, prognosis, and therapeutic response. ITH arises through genetic, epigenetic, and microenvironmental alterations that drive phenotypic diversity and contribute to metastasis, therapy resistance, and disease recurrence. Conventional bulk tumor analyses often fail to detect rare but clinically significant subclonal populations, highlighting the need for higher-resolution analytical approaches.

This review summarizes current methodologies for ITH analysis, including single-cell omics, spatial transcriptomics, molecular imaging, mass cytometry, and integrative radiomics. Spatial techniques preserve tissue architecture and cellular localization, whereas single-cell approaches provide detailed characterization of genomic, transcriptomic, and proteomic variability among individual tumor cells. In addition, multimodal strategies integrating imaging, molecular profiling, and computational analysis offer improved insight into the dynamic interactions between tumor subpopulations and their microenvironment.

We further discuss major technical and analytical limitations associated with current ITH methodologies, including amplification bias, dissociation-induced transcriptional artifacts, loss of spatial information, reproducibility challenges, and difficulties in integrating multi-modal datasets. Emerging artificial intelligence and machine learning approaches may help address some of these limitations through automated image analysis, multimodal data integration, and predictive modeling, although issues related to interpretability, standardization, and external validation remain significant barriers to clinical translation.

Overall, comprehensive characterization of ITH through multi-region and multi-omics approaches may improve precision oncology by enabling more accurate identification of aggressive, treatment-resistant, and metastatic tumor subpopulations. Continued advances in spatially resolved and single-cell technologies, together with robust computational frameworks, are expected to enhance the understanding of tumor evolution and support the development of more adaptive therapeutic strategies.

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