Precision medicine has been a buzzword in the medical field for over a decade. But what does it mean for cancer care, and how does it affect new therapies for patients? Originally, precision cancer medicine focused on targeting specific mutated genes. We think we understand genetic mutation of a tumor will help us develop targeted drugs to solve the problem, one gene at a time.
What we found instead is that when you build up an inventory of all the parts that fail in cancer, the number of mutations that can give rise to cancer is greater than the number of antigens. death in the universe. Each mutation can even define a different response to treatment, which varies from person to person, and so targeting genetic mutations is becoming an increasingly difficult task. surprised. Besides the challenge created by such a multitude of possibilities, we also discovered that genetics do not tell the full story of a person’s cancer because cancer cells with the same mutations also have the same mutations. may have different drug sensitivities.
Humans have about 20,000 genes that work together in different ways from cell to cell and from individual to individual. The vast amount of data we’ve been able to gather about cancer has helped us build computational models that, instead of trying to explain everything one by one, explain how all these genes work. working together in a system.
Moving on DNA: The Role of RNA in Cancer
Approaching cancer as an ex-physicist, I wanted to open the “box” of cancer, look inside and understand exactly how it works; not just one gene at a time, but based on all gene products working together. With that said, my passion is to create the “assembly manual” of the cancer cell – a map of the complex network of molecular interactions that define its behavior and response to treatment. This way, like looking at the manual of a complex piece of machinery, when something breaks, we know exactly where to find the root cause and how to fix it.
The basis for this manual begins at the interface between two important molecules in cells, DNA and RNA.
DNA, which I call the “book of the possible,” contains information about all the possible things that a cell can become or do. In contrast, RNA represents the “book of what is” because it provides only faithful copies of the genes that a particular cell needs at a given time. RNA is directly translated into proteins, molecules that actually “do the job” in the cell by performing important functions. For example, liver cells and brain cells in the same individual will have the same DNA but different patterns of RNA and protein expression that allow them to perform different functions.
A great paradox in cancer is that not only can the same DNA mutations produce very different RNA landscapes, but equally important, the opposite is also true. Different DNA mutations can create a virtually indistinguishable RNA landscape with identical responses to certain drugs. And the latter may hold the key to successful cancer treatment.
Right! The problem is as complicated as it sounds.
Take, for example, the BRAF gene. Although this is the most frequently mutated gene in melanoma, we now know that mutations in this gene can cause a number of other cancers, such as a small subset of bowel cancer. conclude. The drug effectively targets BRAF mutant melanoma, although in the short term, has almost no effect on colon cancer. Same gene mutation, but different drug response.
Because RNA tells us specifically what is happening in cancer cells at a particular time, we have developed algorithms that accurately predict cancer cell control proteins based solely on RNA measurement. While this is much more complicated than looking for a DNA mutation, it also holds promise for being much more efficient at eliminating cancer because the active state of a cancer cell’s protein provides informative data. to predict whether a drug will kill it.
As cancer progresses, so does our approach to solving the problem
In my lab, we assemble computational networks of molecular interactions between proteins and genes, and then analyze them to identify and target several “master regulator” proteins. are basically the “pillars” that define cancer cell behavior and represent its most critical vulnerabilities.
We have show that these master regulatory proteins work together to power cancer cells, like a building erected on a small number of bearing pillars. You target one or more of these pillars, and the entire building will collapse. We have developed Methodology to determine exactly which proteins in each cell are the backbone of the cancer cell’s state and which drugs can best target their activities.
The exact cancer drug so far hasn’t quite lived up to its promise. Patients with late-stage metastatic breast cancer had no specific options more than 10 years ago. In the next wave of precision oncology, cancer treatment will depend on better predictability with our approaches, one step ahead of the possibility of mutation or adaptive adaptation. fear of cancer and its environment, including adaptation to therapies.
Beat cancer at its game to mutate and stop treatment
The only way to stay one step ahead is to surrender to the complexities of cancer and understand that every cancer cell is different, even the same cancer in the same patient. Our algorithms identify groups or types of cancer cells that will respond to certain treatments to know which cells are killed and which are released when we treat one type specific drugs. That way, we can determine the combinations and sequences of drugs that will kill off specific populations of cells in an individual patient’s tumor, eliminating the guesswork of identifying drugs or outcomes. drug combinations are truly personalized for the patient. This leaves us with previously treating cancers that used to have genetic mutations but instead a complex network of damaged genes and proteins that determine their drug response.
Cancer is complex and cannot be oversimplified to a certain extent in our study, just as we cannot simplify cancer for our patients. We need to embrace its complexity, combining its complexity and sophistication with equally complex and sophisticated approaches.
Source: Columbia University
