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Cell: A rare immune cell that can predict whether cancer patients will relapse
Cell: A rare immune cell that can predict if cancer patients will relapse. A new single-cell analysis tool from the Columbia University Anti-Fatigue Medical Center links immune cells to kidney cancer recurrence.
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Compared with other cancers, the immune characteristics of kidney cancer are more prominent: Compared with most other solid tumors, kidney cancer has more immune cell infiltration, and kidney cancer is currently one of the most sensitive malignant tumors to cancer immunotherapy. .
However, despite treatment, many patients with clear cell kidney cancer (the most common kidney cancer) eventually relapse and develop incurable metastatic disease.
A new study shows that there is a rare, previously unknown type of immune cell in kidney tumors that can predict which patients may relapse after surgery. These cells may even be driving tumor aggressiveness.
Corresponding author of the article, Associate Professor of Medicine Charles Drake said: “Our findings indicate that the presence of these cells can be used to identify patients with a high risk of recurrence after surgery, and these patients may be candidates for more aggressive treatment.
Analyze cells with new tools
Although kidney tumors are highly infiltrated by immune cells, the cell subtype and its relationship with the outcome after surgery are still unknown.
The first article, Aleksandar Obradovic, said, “It’s like looking down on Manhattan. A large number of people flood into the city every morning. To understand how these diverse commuters interact with Manhattan residents, we need better details: who they are; What kind of people are they? Where are they going? What are they doing?”
In order to reveal the subtle details of immune cells infiltrating kidney cancer, the researchers combined two latest technologies in cancer research.
The first is called single-cell RNA sequencing, which captures a snapshot of gene activity in a single tumor cell. This high-throughput technology allows researchers to obtain such snapshots from thousands of cells in a tumor in one experiment to understand the characteristics and behavior of various cell types.
This powerful technique can identify new types of cells, but there is a drawback. Because single-cell sequencing works by detecting a small number of mRNA molecules in each cell, it often fails to detect the mRNA of low-expressed genes, including key signaling genes and drug targets (such as immunotherapy checkpoints).
Obradovic said: “In many experiments, single-cell RNA sequencing will lose up to 90% of gene activity. This phenomenon is called gene dropout (gene dropout).”
Predictive algorithm solves genetic deletions
The researchers solved the problem of gene deletion by developing a predictive algorithm that can infer which genes are active by looking at the expression of other related genes. Obradovic said: “Even if a lot of data is missing due to loss, we still have enough clues to infer the activity of upstream regulatory genes. It’s like playing’Wheel of Fortune’: Even if most of the letters are lost, I can usually guess. What’s on the chessboard.”
This algorithm is called meta-VIPER, and it is based on the VIPER algorithm developed by the Califano laboratory. Researchers estimate that by adding metaVIPER, they can accurately detect the activity of 70% to 80% of all regulatory genes in each cell, thereby eliminating the effects of gene deletion.
Use newly identified macrophages to track the patient’s condition
The researchers used this combined method to analyze more than 200,000 tumor cells and normal cells in normal tissues taken from 11 patients with clear cell renal cancer who underwent surgery at the Columbia Urology Department.
The results revealed a unique subset of immune cells that are associated with the eventual relapse of the disease after initial treatment. VIPER analysis also revealed the most advanced genes (or master regulators) that control the activity of these cells. By collaborating with researchers at Vanderbilt University, they obtained a second set of patient data and verified this marker, where this feature strongly predicted the recurrence of more than 150 patients in the second batch.
In addition, it was found that these cells directly interact with tumor cells through receptor-ligand gene pairs. Obradovic said: “These data raise an interesting possibility that these cells are not only markers of more dangerous diseases, but may actually lead to the recurrence and progression of the disease. Targeting these cells can improve clinical outcomes. .”
Therefore, VIPER-based technologies (such as the Oncotreat test) can be used to identify drugs that target these rare but critical subgroups, thereby preventing presence-related adverse results.
Can be applied to other cancers and diseases
Researchers say that single-cell sequencing combined with the VIPER algorithm may also decompose other types of cancer.
“Our research shows that the combined use of the two technologies is very effective for characterizing cells in tumors and surrounding tissues, and has a wide range of applicability, even beyond the scope of cancer research.”
(source:internet, reference only)