Researchers from the University of Helsinki developed a new method to study differences between the surviving cancer cells after chemotherapy and sensitive cells before treatment. To enable this cellular time travel, they developed ReSisTrace, a methodology that takes advantage of the similarity of sister cells to trace back pre-existing treatment resistance in cancer.

Their new study is published in Nature Communications in an article titled, “Tracing back primed resistance in cancer via sister cells.”

“Exploring nongenetic evolution of cell states during cancer treatments has become attainable by recent advances in lineage-tracing methods,” the researchers wrote. “However, transcriptional changes that drive cells into resistant fates may be subtle, necessitating high resolution analysis. Here, we present ReSisTrace that uses shared transcriptomic features of sister cells to predict the states priming treatment resistance.”

“In ReSisTrace, we label cancer cells uniquely with genetic barcodes and allow them to divide once, so that we get two identical sister cells that share the same barcode. We then analyze single-cell gene expression from half of the cells before the treatment, while treating the other half with chemotherapy, or other anticancer treatment. From the surviving cells we can identify the barcodes of resistant cells. Using their sister cells analyzed before the treatment, we can discover how the cells that will survive through treatment differ from the pre-sensitive cells, thus revealing the pre-existing resistant states,” said Jun Dai, a PhD student in Anna Vähärautio’s group, who developed the methodology to trace sister cells.

“We found that genes associated with proteostasis and mRNA surveillance are important to explain pre-existing treatment resistance. Interestingly, we found that DNA repair deficiency that is very common in ovarian cancer, sensitized these cells to not only chemotherapy and PARP inhibitors but also to NK killing,” said Shuyu Zheng, a PhD student from Jing Tang’s group, who spearheaded the computational analysis.

Tang’s laboratory then leveraged the revealed gene expression changes to predict small molecules that could shift the cells from a resistant state to a sensitive state. “We developed a computational method to correlate the resistant states with the gene expression changes induced by a drug. Ideally, if a drug can reverse the resistant cells’ gene expression profiles, then it can be considered as a potential hit to overcome the resistance,” said Tang, associate professor and a team leader in the systems oncology research program at the University of Helsinki.

Researchers observed that most of the predicted small molecules indeed changed the gene expression patterns of cancer cells toward sensitive states. Most importantly, after adding these drugs, cancer cells were significantly more sensitive to carboplatin, PARP inhibitor, or NK killing.

“Our novel experimental-computational approach really leverages the power of single-cell omics and pharmacological data integration,” said Tang.

“The method we developed reveals the features of cells that will—in the future—become resistant to anticancer treatments by coupling cell state and fate in sister cell resolution. It is widely applicable to identify and target pre-existing resistant cell states across cancer types, as well as against different treatment modalities, including immunotherapies. Our approach paves the way for development of sequential cancer therapies that can block resistance before it even emerges,” concluded Anna Vähärautio, PhD, a K. Albin Johansson Cancer Research Fellow, Foundation for the Finnish Cancer Institute and a team leader in the systems oncology research program at the University of Helsinki.

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