The diversity of different forms of cancer, and the varying levels of patient responses to different therapies means that treatment strategies are often thwarted. The next era of anticancer therapeutics may center on drug combinations that are tailored specifically to a patient’s own tumor cells.

An international team of researchers headed by the group of Christoph Merten, PhD, former group leader at EMBL in Heidelberg, has now devised a way to test the efficacy of hundreds of anticancer drug combinations simultaneously, rapidly, and accurately. The technology, known as Combi-seq, utilizes a microfluidic platform that allows scientists to carry out highly multiplexed screens of hundreds of barcoded drug combinations in an emulsion of picoliter-sized droplets.

The researchers hope that Combi-seq will increase the power of drug efficacy testing, and pave the way to more personalized cancer treatments. Merton, who is presently at the Swiss Federal Institute of Technology Lausanne (EPFL), and colleagues reported on their platform in Nature Communications, in a paper titled, “Combi-seq for multiplexed transcriptome-based profiling of drug combinations using deterministic barcoding in single-cell droplets.” In their paper the team stated, “We demonstrated that the presented Combi-Seq approach can be applied to determine the impact of drugs on cell viability and cellular signaling, thus providing a high-throughput approach to discover synergistic drug pairs and decipher their mode of action.”

The study was carried out in collaboration with the group of Julio Saez-Rodriguez, PhD, professor at the medical faculty of Heidelberg University, a group leader at EMBL – University Hospital Heidelberg Molecular Medicine Partnership Unit (MMPU), and former group leader at EMBL-EBI, who supervised the computational analysis, led by Bence Szalai, PhD.

Each year, around 10 million lives around the world are cut short by cancer, but while the last few decades have witnessed the number of approved anticancer drugs grow significantly, anticancer therapies often exhibit only short-term effects, the authors wrote. “Our increased molecular understanding of the molecular basis of cancer has led to the development of targeted therapies. These therapies have so far provided limited efficacy and only in a small subset of patients, despite major efforts to characterize patients genomically to find response biomarkers.”

Since every patient’s tumor cells are different and continuously accumulate mutations, it is difficult to predict how they will react to a specific drug. The chance of tumor cells developing resistance to a particular drug is also high. For these reasons, combinations of two or more drugs are often considered more promising than single-drug chemotherapy, the team pointed out. “Tumors typically develop drug resistance causing relapses that might be tackled with drug combinations … Cancer patient stratification for personalizing treatments with chemotherapeutics and targeted drugs have shown to increase the success of cancer therapies.”

However, testing such combinations also presents challenges. “When testing the effects of drugs, we are limited by the amount of tissue we can obtain from patient biopsies, which is generally low,” Merten said. “This means that with conventional technology, it’s impossible to screen hundreds of drug combinations for their effect on patient tumor cells.”

Due to the limited amount of patient tissue, only a few drug combinations can be tested in the traditional approach. The results of testing are binary in nature, i.e., whether the tumor cells live or die upon treatment, so we can only answer the question: Is the patient resistant or responsive to this drug combination? [Aleksandra Krolik/EMBL]
Due to this reliance on large tissue volumes, present technologies can only test a handful of drug conditions at a time. They also usually provide purely yes-or-no answers, for example, whether the cells live or die after treatment. “While many approaches can be used to perform drug screenings, they are often low in throughput, cost and time extensive, and/or require a large amount of cells, which together strongly restricts the number of potential drugs that can be screened per tumor biopsy,” the investigators stated. “This limitation gets more pronounced when considering drug combinations due to the sheer number of potential combinations, which increases exponentially with the number of tested drugs.”

On the contrary, Combi-seq allows researchers to test hundreds of drug combinations simultaneously and provides statistical insight into the effect of various drug combinations on tumor cell gene expression. Scientists can now answer the question: Which drug combination works best for this patient? [Aleksandra Krolik/EMBL]
The team’s new Combi-seq technology overcomes these hurdles through the use of microfluidics—the precise control and manipulation of fluids with very miniaturized devices. Because of the low volume of liquids required, researchers can carry out large-scale experiments with very small sample volumes. “Due to the miniaturization over several orders of magnitude as compared to conventional plate-based screens, the number of drugs or drug combinations can be massively upscaled while working with low input cell numbers,” the team continued.

In 2018, the groups of Merten and Saez-Rodriguez employed microfluidics to test 56 anticancer drug combinations in cancer cells from patients. But as the investigators further explained, “While our previous approach provided the first proof of concept in directly screening patient material, the still relatively large volumes of 500 nL limited the number of drug pairs tested.”

The new Combi-seq technique, established by former EMBL PhD student Lukas Mathur, takes this process even further, allowing researchers to test 420 drug combinations with only a tiny amount of biological material. “Based on assay miniaturization in a droplet format, only about 250 cells were needed per tested condition, hence opening a way for personalized screens directly on patient material and drastically increasing the scale at which combinatorial screens can be performed on patient-derived cell lines or organoids and spheroids,” the team stated.

Combi-seq works by precise microfluidic manipulation of cells in solution. The researchers used this to isolate cells in droplets, each of which was only around a tenth of a millimeter in diameter. In addition to a cancer cell, each droplet contained a specific drug combination and a DNA sequence “barcode,” used as a label for the applied treatment condition. After 12 hours of treatment, the researchers could pool the cells, collect their genetic material for sequencing (identified by the barcodes), and analyze the results. “By introducing a deterministic combinatorial barcoding approach, where sets of two barcodes encode drug pairs, we managed to screen all conditions in a highly multiplexed fashion, without the need to keep any spatial order (e.g., wells, plug-sequence),” they noted.

The teams suggest that as well as massively scaling up how many treatment conditions can be tested simultaneously, Combi-seq also allows scientists to gather accurate transcriptomics data from the drug-exposed cells. It does so by incorporating next-generation sequencing into the workflow. Instead of just indicating whether a cell lives or dies after drug treatment, the new method gives us a wealth of information about the cell’s response, which might help scientists develop treatment strategies. “Since the DNA barcodes were designed for whole transcriptome analysis of cells after drug perturbation, we were additionally able to perform massively parallelized gene expression-based profiling of drug combinations,” the scientists noted.

“Using such transcriptomics data, we can make statements about how the signaling pathways in the cell react to the drug or about which genes are up- or downregulated in response. This is so much more powerful than anything we have had previously,” Merten said. “Generating these datasets for different patients for a tumor type and applying advanced computational methods on them can improve our understanding of why drugs often do not work and ultimately improve patient care,” added Saez-Rodriguez.

The method is both powerful and versatile. “Our primary interest is in anticancer therapeutics,” Merten noted. “However, this technique can potentially be extended to any conditions where one needs to analyze the response of primary human cells to drugs in detail.