Gerd Binnig Ph.D. Founder and Chief Technology Officer Definiens

Correlation of Genomic Information, Tissue Imaging Data, and Clinical Data Facilitates the Discovery of Precision Therapies

Genomics have been heralded as ushering in the era of personalized medicine and enabling the development of more targeted and precise therapies. As the industry continues to work toward a stronger approach to personalized medicine—in other words, really being able to stratify patients and match treatments to the right patients at the right time—the use of tissue data in conjunction with genetic data is becoming ever more important. This is particularly true in oncology, where tissue remains the primary factor in cancer diagnosis, tumor heterogeneity, and drug behavior.

The realization of true personalized medicine will ultimately come from the combination and correlation of genomic information, tissue imaging data, and clinical outcomes. In particular, a big data approach to biomarker discovery and clinical diagnostic assay development in oncology will help enable the discovery and development of precise diagnostics for more effective therapies. 

Tissue Information for Personalized Medicine

It’s well known that cancer needs to be treated as a complex disease versus a simple one; the industry appreciates the complicated nature of cancer and is working to adapt drug development processes accordingly. If cancer is a complex disease, it requires a powerful and precise solution, and this has, ultimately, led us to the need for robust companion diagnostics.

Companion diagnostics are the key to developing and eventually prescribing personalized therapies for patients—it’s not about being more expensive, but about conducting more clever tests to understand which patients are candidates for a certain treatment or combination of treatments. This is especially true with combination therapies, since diagnostics can help determine which combination of drugs is the most effective for a patient. Identifying biomarkers and developing companion diagnostics represents an incredible challenge for the industry, however.

Thus, the biotechnology industry is in a kind of transition mode. It’s clear that cancer is a genetic disease and there is always a mutation involved, which means the genetic expressions and genomic data must be studied as part of developing a therapy to the cancer. This genetic-focused mindset is changing, however, since the industry has found that there are limitations with genomics; the genes alone can’t tell researchers exactly what happened to a patient, how a tumor evolved or how a drug altered a patient. Only the tissue can shed light on this information.

The immune system also plays an important role. Industry opinion is that if the immune system functioned properly, a patient likely wouldn’t get cancer. For a patient with cancer, there is a battle being waged between the immune system and the cancer—the immune cells kill the cancer cells and the cancer cells kill the immune cells. The genes, while an important part of cancer research, cannot explain how this battle is taking place. The tissue, however, provides a direct look at the battlefield; by looking at the tissue, researchers can see where the immune cells are, identify which immune cells and which kind of molecules are invading the tumor, and also how the tumor behaves in response. Researchers can examine the individual molecules the tumor expresses to defend itself, and also use tools to measure the various cells, molecules, and other objects in the tissue and view them in relation to one another.

All of this tissue data, combined with genomics or gene expression data, helps give a more clear picture of the cancer battlefield in a way that no one discipline can provide on its own.

Leveraging Tissue Data and Analysis

By extracting and analyzing all the relevant data from tissue samples, and correlating that to genomic and other data in order to get a clear picture of what is happening inside a patient, several areas of diagnostic and drug discovery and development are impacted:

Evaluating combination therapies

With combination therapies, the diagnosis—in terms of which drugs will be effective in combination—becomes very complicated. Looking at tissue data in conjunction with the other patient data available enables researchers to combine many different molecules to understand which one tells the right story—in other words, which combination of those molecules demonstrate patterns that predict drug response, and thus which combination is the right one for a target group of patients.

Gaining insight into biological processes driving disease

In the past, the industry usually took a “bottom up” approach where a molecule or protein was considered first, and then researchers thought upward in terms of how that biological molecule could help a patient. This paradigm is changing and pathological data is now being used to drive biological research. By looking at tissue in a structured, statistical, and analytical way in addition to the molecules and pathways, new discoveries can be made, which ultimately triggers more purposeful research.

Identifying novel tissue diagnostics with prognostic or predictive value

Historically, researchers searching for biomarkers would stain certain proteins in the tissue, such as with immunohistochemistry (IHC), which they would then investigate with the naked eye. Much of this investigation is being automated now, however. Machines can identify more objects and more precise measurements in tissue than the human eye, and this approach is being used to identify biomarkers and develop diagnostics that could not previously be found.

The use of an integrated data approach to drug discovery and development has been slow to get off the ground, but the need and possibilities for a big data approach is growing. This is changing, however. Technologies are emerging that can collect, correlate, and structure a significant volume and multiple kinds of data—including genetic, tissue, clinical outcomes and other kinds of patient data—in a meaningful way, giving researchers the ability to see the bigger picture and make discoveries that couldn’t previously be found.

Dr. Gerd Binnig ([email protected]) is Founder and Chief Technology Officer of Definiens. He is also a member of the Executive Board. Dr. Binnig was awarded the Nobel Prize in Physics for his work in scanning tunneling microscopy. 

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