A small drop of blood for an ocean of information

August 11, 2017, CORDIS

Patient response to treatment—especially personalised medicine—can be very difficult to predict. To overcome this issue, the CHEMOS project has been advancing a new method for screening thousands of single-cell drug responses from small blood samples.

The new method, called pharmacoscopy, combines automated microscopy and single-cell image analysis to provide over 20 million cells worth of data. Thanks to the I-FIVE project, which ran from 2010 to 2015, it had successfully been used to screen for novel anti-viral or immune modulating drugs. The project team had also demonstrated that the approach could help haema-oncologists to make therapeutic decisions in a concrete clinical setting using primary myelofibrosis and lymphoma as test diseases.

With CHEMOS (Chemical Haematology: breaking resistance of haematological malignancies through personalised trials), Prof. Dr. Giulio Superti-Furga and his team aimed to bring their screening method closer to market: the project looks to obtain clinical data through retrospective trials and use results to attract potential investors.

Prof. Superti-Furga agreed to discuss the project results ahead of its completion in September.

How would you explain the fact that personalised medicine for blood cancer has so far failed to deliver on its promises?

For the most part, personalised medicine for both blood and solid cancers relies on functional screening technology that focuses on the average characteristics of response to drugs. This generalisation does not discriminate against target effects—whereas we believe that such discrimination is very important in predicting patient response.

Besides, prior functional assays have measured early- or late-stage cytotoxicity using readouts such as global ATP levels, which have not provided robust enough responses to be used routinely within a clinical setting. On top of that, these assays require a lot of material to get above detection limit thresholds, and assays such as automated flow cytometry pose the additional problem that they require a hands-on component. Of course, these functional assays have paved the way for our research. But work on these approaches focused on patient stratification, ex vivo response profiling, drug discovery and mechanism of action elucidation: they have yet to become clinical routine.

Another issue lies in the fact that genetics—which really has shown the path towards personalised treatment of solid tumours—may prove more difficult to apply to haematological malignancies due to the diversity of clonal evolution during cancer progression and treatment. We find that our work combines very well with genetics, be it focused genetics and or more global genetic approaches, and should lead to mechanistic insights and new targetable pathways.

How does your new screening method provide a solution to these problems?

We believe that discrimination of drugs' effect on healthy versus cancer cells—an idea that is lost upon averaging a read-out in prior assays—is key to predicting response. Therefore, in our programme, we use high-content microscopy to determine—at single-cell resolution—the effects of drugs on each individual cell. In most cases, these effects imply cell death, as determined by nuclear disintegration of each cell measured in microscopy images.

The cancer cell phenotypes can be separated from the healthy cell phenotypes using fluorescent antibodies against diagnostic markers, just like a pathologist would do it. By performing this assay at single-cell resolution and on a large-scale, automated manner, each cell becomes an assay. This enables us to gather a differential cell response and to track precisely the drugs that kill cancer cells while leaving all other healthy cell material viable.

We can do this over thousands of cells per drug, and hundreds of thousands of drugs per patient sample. This all results in very robust measurements with dramatic statistical power, gathered with little human intervention, as the setup can be fully automated and the need for material is minimal. The images are also unique in that they provide a treasure-trove of data for us to dig into.

How do you proceed exactly?

Each well, part of a 384-well screening plate, is coated with a drug. Patient cells are put into each plate, and we create a monolayer of the cells which we view under an automated confocal microscope. This results in about 2 000 images per 384-well plate, and a total of 20 million cells worth of data. These images are then placed into an image analysis pipeline that extracts features of interest.

What would you say makes the project outcomes so innovative?

We found a solution where a 384-well plate doesn't imply 384 tests, but a single one that contains the data from approximately 20 million . This is really the basis, we think, of a 'big data' era for medicine, and we may just be scratching the surface of what data is contained in these images, and what part of that data can be translated. This is a major finding. From a more conceptual point of view, we found that some 10 % of commonly used therapeutics bear the property for modulating the immune system.

Which diseases could be targeted by this method? How so?

We have focused on haematological malignancies because of the ease at which samples can be extracted from patients during routine visits (much of the sample we get is left over from routine pathology). We have also started to look at other types of diseases, such as autoinflammatory diseases, starting with rheumatoid arthritis, albeit for other types of personalised medicine programmes.

What has been the feedback from potential investors so far?

Feedback has been very positive from both business and strategic investors, as well as government-backed programmes here in Austria.

How do you plan to get CHEMOS results to the market?

We have founded a company, Allcyte, here in Vienna that will focus on bringing this technology to market.

Explore further: Methodology to identify drug candidates for multidrug resistant cancers

More information: Project page: cordis.europa.eu/project/rcn/205773

Related Stories

Methodology to identify drug candidates for multidrug resistant cancers

July 7, 2017
NUS chemists have developed a simple and robust bio-screening method to identify anticancer drug candidates to overcome multidrug resistance (MDR).

Screening drugs to kill cancer cells in their safe spaces

October 31, 2016
Existing chemotherapy approaches treat cancer by targeting cells that are actively multiplying and have a high metabolic rate. However, cancer stem cells can escape this targeting, leading to chemotherapy-resistant cancer ...

First-in-class drug ONC201 shows potential for some blood cancers

February 16, 2016
ONC201, an anti-cancer drug that triggers cell death in various tumor types, may have clinical potential for some blood cancers including mantel cell lymphoma (MCL) and acute myeloid leukemia (AML), according to a recent ...

Research suggests new tool for cancer treatment based on cell type

August 11, 2016
A new tumor model has been shown to predict how certain types of cancer cells react differently to a commonly used chemotherapy drug, a potential tool for "precision medicine," in which drug treatment is tailored to individual ...

A 'big data' approach to developing cancer drugs

July 7, 2016
Scientists are starting to accumulate huge datasets on which genes mutate during cancer, allowing for a more systematic approach to "precision medicine." In a study publishing July 7 in Cell, researchers compared genetic ...

Recommended for you

Researchers create a drug to extend the lives of men with prostate cancer

March 16, 2018
Fifteen years ago, Michael Jung was already an eminent scientist when his wife asked him a question that would change his career, and extend the lives of many men with a particularly lethal form of prostate cancer.

Machine-learning algorithm used to identify specific types of brain tumors

March 15, 2018
An international team of researchers has used methylation fingerprinting data as input to a machine-learning algorithm to identify different types of brain tumors. In their paper published in the journal Nature, the team ...

Higher doses of radiation don't improve survival in prostate cancer

March 15, 2018
A new study shows that higher doses of radiation do not improve survival for many patients with prostate cancer, compared with the standard radiation treatment. The analysis, which included 104 radiation therapy oncology ...

Joint supplement speeds melanoma cell growth

March 15, 2018
Chondroitin sulfate, a dietary supplement taken to strengthen joints, can speed the growth of a type of melanoma, according to experiments conducted in cell culture and mouse models.

Improved capture of cancer cells in blood could help track disease

March 15, 2018
Tumor cells circulating throughout the body in blood vessels have long been feared as harbingers of metastasizing cancer - even though most free-floating cancer cells will not go on to establish a new tumor.

Area surrounding a tumor impacts how breast cancer cells grow

March 14, 2018
Cancer is typically thought of as a tumor that needs to be removed or an area that needs to be treated with radiation or chemotherapy. As a physicist and cancer researcher, Joe Gray, Ph.D., thinks differently.


Please sign in to add a comment. Registration is free, and takes less than a minute. Read more

Click here to reset your password.
Sign in to get notified via email when new comments are made.