Sister Cells Reveal Cancer’s Fate

Cancer is notoriously exhausting to deal with. Partially, it is because the cells making up a tumor are heterogeneous, expressing totally different genes and molecules that decide their response to therapy. Even when a therapy kills most most cancers cells, one survivor is sufficient for the most cancers to persist.

As scientists struggled to search out these treatment-resistant cells, they turned to an surprising software: sister cells. Whereas human sisters could share garments or toys, sister cells share their gene expression profiles, which might trace at whether or not the cells are therapy resistant.

In a examine printed in Nature Communications, researchers on the College of Helsinki introduced a brand new technique known as ReSisTrace that makes use of sister cells to determine the molecular states driving therapy resistance in most cancers cell traces.1 Guided by these resistance signatures, the researchers devised a technique to foretell medication that will sensitize the cells to therapy. 

“We will have knowledge [on] each drug sensitivity and transcriptomics on the single-cell degree,” stated Jing Tang, a bioinformatician on the College of Helsinki and coauthor of the examine. “That is distinctive and novel, and never accessible through the use of different strategies.”

Tang and Anna Vähärautio, a most cancers biologist on the College of Helsinki and coauthor of the examine, needed to develop a technique that mixed lineage tracing—the method of monitoring cell destiny and offspring—with the flexibility to profile gene expression in particular person cells. Nonetheless, measuring gene expression in a cell usually destroys it, so scientists can’t hint its lineage on the identical time. Enter sister cells: a technique to obtain each targets in parallel.

Vähärautio’s staff devised a technique to insert distinctive DNA barcodes into an ovarian most cancers cell line utilizing lentiviral transduction. Then, they allowed the cells to bear a single division to every produce two sister cells, which they discovered had related gene expression profiles. The researchers break up the pool of cells in half: in a single half, they measured gene expression by single cell RNA-sequencing (scRNA-seq) to assemble an image of every cell’s state, and within the different half, they examined whether or not the cells responded to sure widespread most cancers remedies.

Utilizing the treatment-resistant cells’ barcodes, the researchers matched them with their sister cells within the pre-treatment pool and analyzed their gene expression profiles. This comparability helped them determine genes that may have prompted the cell to evade being killed. 

At first, the researchers tried to give attention to particular person genes, however they quickly realized this strategy may not be sufficient. “We do not know if [the genes] are actually driving the resistance or if they’re secondary results,” Vähärautio stated. This impressed the staff to look the entire transcriptome for broader gene expression signatures of therapy sensitivity or resistance. Vähärautio and Tang suspected that these signatures might even assist predict further medication that would sensitize the cells to a subsequent therapy.

Utilizing printed gene expression data collected from cell traces handled with a wide range of compounds, Tang’s staff recognized potential medication that would push treatment-resistant cells’ gene expression towards that of treatment-responsive cells.2 By doing so, the added drug might prime the cells to reply to most cancers therapy. Utilizing computational fashions, the researchers predicted that administering pevonedistat—a drug that inhibits an enzyme concerned in protein degradation—earlier than carboplatin chemotherapy would make the most cancers cell line that they have been finding out simpler to kill. They examined their predictions and located that pevonedistat pretreatment, and plenty of different predicted compounds, labored synergistically with widespread most cancers therapies to kill the most cancers cells. 

These findings got here as a nice shock to Vähärautio, and so they satisfied Tang that this could possibly be a brand new strategy for creating more practical most cancers remedies to beat drug resistance. 

Amy Brock, a bioengineer on the College of Texas at Austin who was not concerned on this examine, famous that the authors outlined gene expression signatures by evaluating all resistant cells to all delicate cells, however that there could be much more patterns hidden in particular person resistant cells. “It might be attention-grabbing to additional look at whether or not sister cells grow to be resistant through widespread or distinct mechanisms,” Brock stated.

Brock hopes that, with a slew of comparable strategies to track cell lineages and single-cell gene expression, researchers will now give attention to making use of these instruments to higher perceive how cells evade particular remedies.3,4 Vähärautio and Tang at the moment are making use of their technique to extra pattern sorts, together with most cancers organoids and acute myeloid leukemia cell traces. However Vähärautio thinks this technique might even be helpful for finding out how cells’ states affect their fates in different contexts, equivalent to growth or responses to chemical substances. With the computational fashions for drug prediction, ReSisTrace might even determine methods to vary these fates.

“I believe the tactic is de facto extensively relevant and can be utilized to check many alternative cell state and destiny connections,” Vähärautio stated.


  1. Dai J, et al. Tracing back primed resistance in cancer via sister cells. Nat Commun. 2024;15(1):1158.
  2. Subramanian A, et al. A next generation connectivity map: L1000 platform and the first 1,000,000 profiles. Cell. 2017;171(6):1437-1452.
  3. Oren Y, et al. Cycling cancer persister cells arise from lineages with distinct programs. Nature. 2021;596(7873):576-582.
  4. Gutierrez C, et al. Multifunctional barcoding with ClonMapper enables high-resolution study of clonal dynamics during tumor evolution and treatment. Nat Most cancers. 2021;2(7):758-772.

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