Unraveling the Complexity of Intratumor Heterogeneity
How advances in our understanding of ITH may lay a path for clinical applications
George Francis Lee | | 2 min read | News
Intratumor heterogeneity (ITH) is strongly associated with cancer progression, making it a hot area of research. In some cases, ITH is being used as a diagnostic marker – and certain HER2+ cancers already benefit from ITH assessments in pathology reports, which allow practitioners to predict effectiveness of targeted therapies. Although ITH exploration has been somewhat held back by the cost of clinical samples and specialist equipment, the field is moving forward – and a recent review in Nature Cell Biology (1) considers the progress made so far, hoping to “illuminate future directions.” One experimental model being used to investigate ITH combines molecular barcoding and multi-omic profiling at single-cell resolution, and the authors note that its utility has been demonstrated through the development of ClonMapper (2) – software that maps single-cell transcriptomics to clonality, thus allowing specific clones of interest to be studied. Another research avenue relies on CRISPR-based technologies; for example, by combining single-cell RNA sequencing and Cas9-enabled indels, researchers are able to reconstruct evolutionary descent and molecular drivers of metastasis.
The tumor microenvironment and cell-to-cell interactions are also fertile ground for investigation. Here, a number of different approaches are in play, say the authors, including in vitro and in vivo reporter systems, a hypoxia fate-mapping system, and the creation of an in vivo pH ratiometric bioluminescent sensor. All of these technologies can be incorporated into experimental models to gain greater insight into the factors that shape ITH.
Elsewhere, researchers have created a novel computational method – nicknamed HUNTRESS – that is capable of deducing mutational ITH from single-cell sequencing data. When tested on simulations and real-life tumor data, HUNTRESS is able to calculate a tumor’s progression with high probability – at a faster rate and with more accuracy than other methods. When it comes to the real life data, HUNTRESS’ inferred tumor progressions were consistent with “the best known evolution scenarios for the associated tumors.”
Based on recent research, artificial intelligence (AI) looks set to tackle a number of challenges currently plaguing ITH’s full transition into the laboratory – helping pathologists deal with rivers of data and a significant need for “number crunching.” But AI is only as powerful as the data with which it is fed, highlighting the importance of increasingly accurate experimental models.
- Z Li et al., “Untangling the web of intratumor heterogeneity,” Nat Cell Biol, 24, 1192 (2022). PMID: 35941364.
- C Gutierrez et al., “Multifunctional barcoding with ClonMapper enables high-resolution study of clonal dynamics during tumor evolution and treatment,” Nat Cancer, 2, 758 (2021). PMID: 34939038.
- C Kızılkale et al., “Fast intratumor heterogeneity inference from single-cell sequencing data,” Nat Comput Sci, 2, 577 (2022).