Unique genetic signatures are found in different types of cancer

A study led by Mabel Vidal of Bioinformatics at the University of Concepción, in collaboration with researchers from the MELISA Institute and other academic institutions, identified unique genetic signatures
in subpopulations of invasive T cells of different types of cancer after analyzing data from the public repository CD4-T, CD8-T cells and Treg.
The study was published in the International Journal of
Molecular Science.

The immune system is made up of different cells that work together to protect the body from infection or tumor cell growth
.
T lymphocytes play a major role in
identifying pathogenic antigens or tumor transformation.
These can be divided into three main subgroups: cytotoxic CD8+ T cells, which destroy causative agents, and helper CD4+ T cells, which participate in the differentiation of different effector lineages, and tregs, which regulate or modulate immune responses
.

While these subsets of T cells are known to permeate different types of cancer, it is unclear whether they exhibit similar mRNA gene expression profiles — i.
e.
, transcriptomes — compared to resident T cells from healthy tissue
.
Therefore, this study analyzed the single-cell transcriptome of 5 invasive CD4-T, CD8-T, and Tregs tumors from different types of cancer in an attempt to identify specific pathways
for each subset in a malignant environment.

First, the Gene Expression Omnibus searched the transcriptome data for the most common cancers (colorectal, breast, lung, head and neck, and melanoma), classifying different cell types, and filtering only the mRNA data
corresponding to T cells.
The researchers then identified biological pathways and functions
that are common in different types of cancer and are not expressed under control conditions.

For Dr.
Mabel Vidal, the use of artificial intelligence (AI) is an essential tool to analyze large amounts of data, identify and eliminate errors: "Classifying different types of cells using an unsupervised approach is a huge challenge, because even if you know the identity of each T cell beforehand, as a scientist, you have to generate a classification algorithm capable of automating this task of large amounts of data," says the scientist
.
She added that data standardization is also a related important challenge
when accessing public data, where information comes from different experiments, instruments, and research goals.

The second part of the study consisted of experimentally validating the classification algorithm, performing proteomics in the laboratory of the MELISA Institute, and using transcriptomic data
previously obtained from the laboratory of Dr.
Estefania Nova-Lamperti of the University of Concepción.

Regarding the validation phase, Nova-Lamperti explains: "We already have samples from patients with different types of cancer, and we are partially aware of the common signaling pathways
between them.
In this study, we found an increase
in cytokine signaling that mediates the TH2 response.
”

Mauricio Hernandez, chief laboratory officer at the MELISA Institute, explains: "To validate the results obtained from the bioinformatics analysis of transcriptomes of different T cell populations, we used mass spectrometry to determine the protein profile or proteome
of CD4 T cells.
Overall, from the pathways that have been identified, we get a very good correlation
.
”

Dr.
Vidal observes that the research was conducted in a machine learning environment, so the next step is to model in a deep learning environment and generate new models for more accurate single-cell analysis: "The idea is to integrate more data, identify common patterns, maybe more specific
.
" So, the idea is to continue along
the lines of artificial intelligence.
”

Finally, Professor Elard Koch, Senior Researcher and Chair of the MELISA Institute, said they were delighted with the collaboration on this research led by Dr Vidal: "Collaboration and support with our proteomics capabilities is inspiring for our young scientists and a major objective
of our institution.
"