The landscape of precision medicine has changed dramatically over the years. The completion of the Human Genome Project in 2003 has greatly accelerated our understanding of individual genomes, leading to the idea of precision medicine: medical care tailored to the individual.
Since then, science has progressed greatly. Today, we are able to look beyond identifying mutations and instead gain a very comprehensive view of the molecular biology taking place in tumor cells. We now understand that each person has a unique genetic and molecular setup, and each cell in the human body is different. Based on this knowledge, single-cell analysis tools are being developed to analyze the genetic, transcriptomic, proteomic, and epigenetic features of each cell.
As a relatively new suite of technologies, single-cell analysis can greatly improve precision medicine, a feat that is especially important in cancer biology. By providing deeper insights into each individual’s disease, single-cell analysis can address many of the current challenges in precision medicine.
One area in which single-cell analysis is starting to make a difference is personalized cancer treatment. Tumors often contain very heterogeneous cell populations, which makes selecting treatments extremely challenging. Conventional diagnostic techniques often rely on the bulk analysis of cells. By taking the average of all cells in a sample, it is easy to miss a cell subpopulation that can become resistant to treatment and cause a relapse.
Using single-cell proteogenomics to fight multiple myeloma
Now one company has taken it upon itself to improve precision medicine using single-cell analysis: Proteona. Among its first targets are blood cancers, such as multiple myeloma. As one of the most aggressive blood cancers, the treatment of multiple myeloma comes with a number of challenges.
“Myeloma is a very difficult disease to treat because it constantly relapses,” explains Chng Wee Joo, Senior Consultant and Head of Department of Haematology-Oncology at the National University Cancer Institute Singapore. “The tumor cells are highly heterogeneous, and the immune microenvironment also plays an important role, creating additional complexity. Typically, patients are treated with multiple drugs at the same time, which triggers side effects and is extremely costly. There is definitely a need for better precision medicine here.”
While the traditional bulk analysis of a tumor biopsy only provides information on the average of all cells present in a sample, single-cell analysis allows clinicians to analyze different tumor cell subpopulations, which might be resistant to a specific treatment and could cause a recurrence of the disease. In the case of multiple myeloma, single-cell proteogenomics can also be used to analyze the malignant plasma cells’ gene and protein expression and can be used to decide what treatments are plausible for the individual patient.
Getting single-cell sequencing into the clinic by adding protein information
By adding protein expression analysis to single-cell sequencing, clinicians can gain valuable information on protein and gene expression as well as a better resolution between different cell types compared to analyzing gene expression alone. This is important for understanding patient cell heterogeneity.
For example, single-cell proteogenomics can provide a detailed analysis of the immune cells involved in a disease, can help clinicians understand possible resistance, and can be used for patient stratification in precision medicine trials.
“There is a huge value in combining total mRNA profiling and proteomics in single-cell sequencing,” says Andreas Schmidt, CEO of Proteona. “While single-cell proteogenomics can be applied in various fields, what we find most interesting is the ability to gain a good snapshot of the immune cells as well as of tumor cells in the same sample. And that is valid for solid tumors as well as for liquid tumors. The information we are dealing with is already directly relevant for immuno-oncology, immunotherapy, and cell therapy. But despite the huge clinical value that we can already see on the horizon, there are no commercial solutions that can move single-cell sequencing into the clinic. And that is where we see the sweet spot.”
Proteona has developed a suite of technologies called the Enhanced Single-Cell Analysis with Protein Expression (ESCAPETM) platform. ESCAPE uses DNA-barcoded antibodies to obtain protein and gene expression information from individual cells. Available as an in-house service or a kit, the ESCAPE platform has been used extensively for peripheral blood mononuclear cells (PBMC) profiling, whole tumor analysis, and cell therapy characterization.
Overcoming the complexity of single-cell data
One of the key challenges of single-cell analysis is the enormous amount of data generated. Even the sequencing of a single tumor results in hundreds of millions of data points that have to be analyzed and interpreted.
Combining datasets from different sources is also challenging. Cross-experimental comparison is often impeded by batch-to-batch differences. Moreover, single-cell analysis often results in the detection of previously unknown cell populations. Manual intervention is often needed for cell clustering and cell annotation, which demands specialist expertise, takes time and is susceptible to human error and bias.
“Proteona definitely evolved from being a wet lab company with a bit of IT to a company that is equal parts bio and tech,” says Schmidt. “With single-cell proteogenomics you end up with thousands of mRNAs and potentially hundreds of proteins. If you were to gate them all by yourself and annotate that data, it would take a long time and it would create a lot of bias. We try to avoid this by using machine learning and automatic algorithms. One key point of our work is that we started with the wet lab and have a very deep understanding of the techniques and biology. At the end of the day, the biology is what informs us, not the algorithms.”
In a partnership with AI Singapore, Proteona is further developing its computational workflows to support the knowledge-driven analysis of single-cell proteogenomics data to create unbiased data sets. The collaboration aims to further the development of artificial intelligence (AI) tools for single-cell multi-omics data analysis.
Scouting for cool ideas that fight cancer using single-cell proteogenomics
In Autumn 2019, Proteona issued an oncology challenge, co-sponsored by NovogeneAIT, in which it called for proposals from scientists and clinicians working on major clinical problems in oncology. Participants were asked to send in an abstract describing how they would use the ESCAPE platform with the chance to win $50.000 worth of single-cell analysis services from Proteona and NovogeneAIT.
The grant was awarded to Cesar Rodriguez Valdes from the Wake Forest School of Medicine in Winston-Salem, US. The proposal aims to tackle the issue of treatment selection for multiple myeloma.
By applying single-cell proteogenomics in their patient-derived 3D organoid models, the team will compare cell populations in response to multiple drugs, identifying the difference in protein and gene expression patterns. These studies will help to elucidate the mechanism of chemo-sensitivity, and potentially help to choose the suitable chemotherapy combination for each patient.
“We are excited about combining Proteona’s single-cell proteogenomic analysis with our patient-derived organoid screening platform,” says Rodriguez Valdes. “Our ambition is to develop a predictive, validated test that will facilitate clinical decision-making and improve the outcome of multiple myeloma treatment. This grant will help us to move closer to that goal.”
“One major challenge in multiple myeloma management is how to select the most suitable treatment strategy for each patient, given the wide range of available therapies,” adds Hartmut Goldschmidt, Head of Hemato-oncology in Heidelberg. “Currently, the decision of which drug to use and when depends largely on the clinician’s experience. There is an urgent need for tools that help clinicians make evidence-based choices. That is where single-cell multi-omics can be extremely valuable.”
Other awardees of Proteona’s challenge include the runner-up, Sanjay de Mel (National University Cancer Institute, Singapore), and the finalists Nicholas Gascoigne (National University of Singapore, Singapore), Steve Bilodeau (Université Laval, Canada), and Aaron Tan (National Cancer Centre Singapore, Singapore).
The future of single-cell proteogenomics
With the growing complexity of drug development and the advancement of cell therapies and gene editing techniques, the need for single-cell proteogenomics in research and the clinic is increasing. A key application of single-cell proteogenomics will be the quality control of cell therapies.
Furthermore, although there is already genomic monitoring in place for many clinical trials, oncology trials would greatly benefit from single-cell proteogenomics monitoring.
“Besides blood cancers, we see multiple potential areas where single-cell multi-omics will make a clinical impact,” Schmidt says. “By providing in-depth data on cell heterogeneity and combining that with powerful analysis tools, we are in the best position to help clinicians to decipher complex cases, from cell therapy to solid tumors. We are only starting to harness the power of single-cell analysis.”
Images via Shutterstock.com and Proteona