AI Article Synopsis

  • Heterogeneity in cancer refers to the diverse characteristics of cancer cells, including differences in their structure, genetic expression, metabolism, and ability to spread.
  • This variation poses challenges for effective treatment, as it often leads to resistance, more severe metastasis, and recurrence of tumors.
  • Advancements in single-cell and spatial genomic technologies are crucial for understanding tumor dynamics, which can inform the development of personalized therapies and improve cancer treatment outcomes through immunotherapy.

Article Abstract

Heterogeneity describes the differences among cancer cells within and between tumors. It refers to cancer cells describing variations in morphology, transcriptional profiles, metabolism, and metastatic potential. More recently, the field has included the characterization of the tumor immune microenvironment and the depiction of the dynamics underlying the cellular interactions promoting the tumor ecosystem evolution. Heterogeneity has been found in most tumors representing one of the most challenging behaviors in cancer ecosystems. As one of the critical factors impairing the long-term efficacy of solid tumor therapy, heterogeneity leads to tumor resistance, more aggressive metastasizing, and recurrence. We review the role of the main models and the emerging single-cell and spatial genomic technologies in our understanding of tumor heterogeneity, its contribution to lethal cancer outcomes, and the physiological challenges to consider in designing cancer therapies. We highlight how tumor cells dynamically evolve because of the interactions within the tumor immune microenvironment and how to leverage this to unleash immune recognition through immunotherapy. A multidisciplinary approach grounded in novel bioinformatic and computational tools will allow reaching the integrated, multilayered knowledge of tumor heterogeneity required to implement personalized, more efficient therapies urgently required for cancer patients.

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http://www.ncbi.nlm.nih.gov/pmc/articles/PMC10175698PMC
http://dx.doi.org/10.3389/fonc.2023.1164535DOI Listing

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