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Single-Cell Method Could Eliminate Need for Mouse Models to Study Cancer Treatment Resistance

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NEW YORK – To study how cancer cells develop resistance to treatment, researchers are largely limited to using patient-derived cell lines or mouse models. But such methods, while informative, don't always give the whole picture: For example, they can't show how the mutated cells that are able to evade treatment interact with the tumor microenvironment. 

To study these cells and their evolution in their native context, researchers need a way to study them in patient samples. A new single-cell transcriptomics method developed by researchers at Weill Cornell Medicine, the New York Genome Center, and elsewhere may offer investigators this opportunity.

In a study recently published in Nature, the researchers described their development of Genotyping of Transcriptomes (GoT), a method to integrate genotyping with high-throughput droplet-based single-cell RNA sequencing.

The challenge they were addressing was how to connect genotypes to phenotypes in single cells, according to the paper's senior author Dan Landau, a cancer researcher at Weill Cornell Medicine and a core member of the New York Genome Center. Although there have been many studies looking at oncogenic mutations in single cells, annotating somatic mutations at high throughput has been a problem. 

"Somatic mutations are present in the single cell transcriptome because coding mutations are often transcribed," Landau said. "We did some analysis in the paper showing that the large majority of driver mutations are transcribed, often either at the equivalent rate or at a superior rate compared to what we see in the exome."

Further, defining the transcriptomic identity of malignant cells is challenging when the cancer clones lack any cell surface markers that could distinguish them from one another. But while droplet-based sequencing — such as the 10x Genomics platform, which the team modified to create the GoT method — enables researchers to profile the transcriptomes of thousands of cells, current methods provide sequence information for only short fragments at the transcript end, limiting the ability of these techniques to jointly genotype somatic mutations. 

"Single-cell methods [like the 10x Genomics platform] have used digital sequencing, which means that they only preserve a small tag at the end of the transcript to quantify the number of transcripts found in each cell," Landau said. "We reasoned that we would need to extend this to loci of interest. This is essentially the crux of the method."

The researchers modified the 10x Genomics platform to amplify the targeted transcript and locus of interest, then investigated amplicon reads for mutational status and linked the genotype to single-cell gene-expression profiles using shared cell barcodes. They used GoT to profile 38,290 CD34+ cells from patients with CALR-mutated myeloproliferative neoplasms to study how somatic mutations can corrupt the process of human hematopoiesis. 

They mixed mouse cells harboring a mutant human CALR transgene with human cells containing a wild-type human CALR transgene and applied GoT in order to test its ability to co-map single-cell genotypes and transcriptomes in a mixed-species context. They found that a significant majority of the cells with transcripts aligned to the mouse genome showed mutant CALR whereas cells with transcripts aligned to the human genome showed wild-type CALR — in total, 96.7 percent of the cells matched their expected species.

"CALR is expressed at the level of something like 200 to 300 [transcripts per million] in bone marrow cells, and we genotyped 90 percent of the cell," Landau said. "I think what we're offering here is a method that includes the ability to genotype across a wide range of scenarios in terms of high efficiency across the expression range. Obviously the more the gene is expressed, the easier it is to capture it in the transcriptome [and] if the mutation is very close to the transcript end, it is easier to capture. If it's further away, you'd need the methods we've developed."

The researchers have also created an analytical platform called Iron Throne that is capable of connecting the single cell transcriptome to the genotype information, while filtering out multiple sources of background noise such as PCR recombination and PCR errors, to provide high-accuracy precision genotyping, Landau noted. They've made both tools available on GitHub.

The potential for being able to perform controlled gene expression analysis experiments is significant, according to Landau. Using this method, a researcher can compare wild-type and mutant cells within the same individual, with the wild-type cells serving as the ultimate controls. 

"Everything is the same — the microenvironmental conditions, the technical confounders, the patient's breakfast. The only thing that's different is the genotyping," Landau said. "Now, we can read it out with this very high-precision tool." 

In the context of myeloproliferative neoplasms, which the team studied for the Nature paper, the ability to distinguish cells from one another with high fidelity is especially important. "These patients have both normal and mutated bone marrow development coexisting within their marrow, and in this case the cells are phenotypically indistinguishable with current methods. If you look under the microscope, you'll see that there are too many cells, but you can't distinguish between the wild-type and the mutant — the flow markers are the same," Landau said. "Therefore, there was a significant challenge. We also show that in the RNA-seq data alone, again these two processes are intermingled."

Another context to which the researchers applied GoT was clonal growths in normal tissues. "It's been reported by multiple groups that each one of us essentially harbors hundreds of distinct clonal growths in multiple tissues," Landau said. "And this is in normal-appearing tissue — the tissue is morphologically normal, and the cells look the same. And yet somehow, these clones are growing, and they contain somatic mutations."

Studies have described mutations affecting NOTCH1 in the esophagus, NOTCH1 and p53 in the skin, and mutations in various genes affecting the blood, he added. In this context, studying the transcriptome is not likely to provide answers, so there's a need for a method that can annotate cells that look alike and yet may be differentially mutated. 

"We need to be able to start reading out what allows these cells to grow better than their wild-type counterparts," Landau said.

Importantly, this method could change the way that treatment resistance is studied. The way researchers currently probe questions on resistance is by using mouse models or cells lines, but to understand how resistance forms in a native context, "if we want to know what's the relation to the immune microenvironment, we need a method that can actually capture this information in patients," Landau said.

By isolating cells that are both wild-type and mutated for a particular subclonal mutation that confers resistance, and studying them within patient samples, researchers can begin to look at the differences between them in a well-controlled way, without the problem of patient-specific confounders. 

"Let's say that you have a genotype that you hypothesize is related to therapeutic resistance. Now you're faced with the question of finding the mechanism. Why are these cells more resistant or less resistant?" Landau said. "The cells are not distinguishable by any other means. They don't have different cell surface markers. They're morphologically the same, so you can't sort them." 

Therefore, he added, "you need a method that can annotate individual cells by their genotype and, at the same time, capture at high throughput their single-cell transcriptomes. That allows you to develop a hypothesis directly in patients. You take these cells and you ask what's different about them when they are exposed to therapy in the patient's blood or in the patient's tissue."

At this point, he noted, the GoT method is better suited for basic research, though there wouldn't be a limit to the types of cancers it could be used to study. The method could even be used as a drug discovery tool, to help pharmaceutical companies determine treatment efficacy.

"I think that this could be something that would empower a combination therapy design because you can — and we are collecting this sort of data — have a sort of clinical trial where combination therapies are introduced gradually," Landau added. "And again, you have this unique ability to compare wild-type and mutated subclones."

The team is continuing to develop GoT in multiple ways, and is looking at the possibility of adding layers of information to the analysis, such as DNA methylation.

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