Sayak Banerjee, Amity University Kolkata
Intra-tumor Heterogeneity ( ITH)
Aggregation of a distinct set of somatic mutations leads to the generation of tumors. The set of mutations assembled by the prime tumor is known as clonal and the mutations which take place in pre-existing tumors comprise cancer cell sub-populations is known as subclonal. Consequently, the cancer cells are depicted as an intrinsic genetic diversity, and this overall phenomenon is termed Intra-tumor Heterogeneity ( ITH).
ITH being highly responsible for tumor relapse and failure in treatments has been a topic of huge interest for the cancer research community. The assessment of ITH has been usually done using deconvolution techniques on bulk DNA sequencing data which are based on models of machine learning. It mainly involves clustering the mutations into subclones based on their occurrence and exploiting these clusters to decipher the tumor phylogenetic structure. It has been observed in many studies that using multiple samples taken from different areas of the same tumor enhances the efficacy to decipher the subclonal structure of tumors and evaluate ITH.
Methods for evaluation of ITH
In their paper, the scientists have mentioned the use of the single-cell DNA sequencing (scDNA-seq) method for achieving such efficacy so that the subclonal somatic mutations are not misinterpreted as clonal. This technique enabled them to analyze ITH with extraordinary resolution. The single-cell whole-genome sequencing is convenient for the detection of chromosomal aberrations which can be exploited to reconstruct the subclonal cell population structure. Methods for single-cell Copy Number Aberrations ( scCNA) analysis are limited although the scCNA profiles from different samples of the same tumor could be exploited to analyze the spatial distribution of subclones inside a tumor mass.
Working of PhyliCS
Hence, they have presented a modular, flexible, and user-friendly package of python libraries called PhyliCS. PhyliCS is a valuable instrument that allows us to discover the extent of spatial heterogeneity in the multiple samples from different regions of the tumor mass. It not only aids in evaluating various tumors based on their heterogeneity but also determines the most differing spatial samples of a given tumor. Moreover, the researchers stated that it might assist in exploring multiple tumors without selecting numerous sequenced cells or specific regional samples. In addition to that, PhyliCS serves as easy access to many clustering techniques for both single and multiple samples to users. This makes it easier to compare results and modify each analysis to each experiment.
The scientists ran it on 300 simulated datasets for confirming the SHscore (Spatial Heterogeneity score) on some selected ideal situations where it compares sets of cells with known relationships. This was followed by a more extensive simulation experiment where they displayed the correlation between the anticipated SHscore and the evolutionary distance between the sample cells in the analysis. Finally, they demonstrated the results of the analysis on three publicly accessible scDNA datasets. One of them consisted of a primary lung tumor and its derived metastasis, another one with multiple spatial samplings from a breast tumor, and the last one with a cell line and two clonal expansions of two single cells. All of them were implemented using the SHscore to illustrate how their CN profiles vary when the researchers considered a fine-grained single-cell level in a bigger context of multiple sampling.
They concluded that in the future, scDNA sequencing must gain recognition and more datasets would be available on a public platform. By then they promised to increase their score on large-scale datasets. Furthermore, they also mentioned that it would be fascinating to exploit different single-cell measurements like scRNA and ATACseq, and this would widen the horizon. The option to build a library will decrease the difficulties of future endeavors in this field.
Reference:
- Montemurro, M., Grassi, E., Pizzino, C.G. et al. PhyliCS: a Python library to explore scCNA data and quantify spatial tumor heterogeneity. BMC Bioinformatics 22, 360 (2021). https://doi.org/10.1186/s12859-021-04277-3
- The thumbnail image has been extracted from Servier Medical Art.
About author:
Sayak Banerjee is a 3rd-year Biotechnology Engineering Student with a great interest in Immunology and Molecular genetics. He is a creative scientific writer in Bioxone with an inclination towards gaining knowledge regarding various sections of Biotechnology and emphasizing himself in various wet lab skills.
Publications:
- https://bioxone.in/news/worldnews/car-t-cells-scientists-discover-on-off-switches-for-cell-immunotherapy/
- https://bioxone.in/news/worldnews/neutrophil-derived-nanovesicles-a-novel-drug-delivery-system/
- https://bioxone.in/news/worldnews/pig-to-human-heart-transplantation-a-solution-to-the-rarity-of-donor-organs/
Culture-free rapid bacterial infection diagnosis without laboratory
Richa Prakash, Central University of Punjab Currently, we rely on the enrichment method for the detection and identification of any bacterial infection. This may include enrichment bacterial culture (increasing the number of the organism of interest to a level it can be detected) or PCR (Polymerase Chain Reaction) to amplify the nucleic acids to increase […]