Developing new therapies requires a deep understanding of cancer hallmarks. This (Cancer Research Data is Changing the Treatment Landscape) requires massive amounts of molecular and phenotypic data. However, omics-based approaches can quickly generate data than human analysis capacity. This gap must be addressed with future investments from the research community.
Immunotherapy
Immunotherapy treatments use your body’s natural strength to fight cancer. These approaches bolster your immune system by blocking proteins on the surface of immune cells that put the brakes on the immune response to cancerous cells. Cancer cells mutate into abnormal ones that look like healthy cells and camouflage themselves from our immune cells. That’s how they can grow and spread unchecked throughout the body. New immunotherapies can help the immune system see cancer cells, allowing it to destroy them. They can also help prevent tumors from growing or spreading to other body parts. Predicting which people will benefit from these therapies is a major area of cancer research data. Your doctor can discuss which immunotherapy treatment options are right for you.
Personalized Medicine
In the past, medical treatment programs have mostly been one-size-fits-all. But personalized medicine, which tailors medical care to individual patients based on their biological makeup, is changing that. Genetic testing can help doctors identify inherited gene changes that might raise the risk of certain diseases. They can then recommend healthier lifestyle habits, earlier screening tests, and medicines that might reduce that risk.
However, developing predictive models based on real-world clinical data is challenging. The genomic and proteomic techniques used in personalized medicine are complex, and the generation of medical data requires professionally trained scientists. In addition, many studies of genomic variation have focused on populations of European descent, leading to the underrepresentation of racials and ethnic in the data. This can lead to algorithms that only partially account for the impact of these variants on disease causation and response to drug therapy.
Targeted Therapy
Scientists can now scour large public databases for information on patients from continents, races, cultures, and genders. And in some cases, doctors can even use genetic or DNA analyses of tumors. Data science tools like these are helping researchers discover the genetic mechanisms of cancer, which are key to developing targeted therapy. These are drugs that target specific mutations in a patient’s tumor.
Combination Therapy
Combination therapy is used to treat tuberculosis, HIV, and many cancers. The theoretical rationale for combination treatment stems from the assumption that multiple drugs work better together than any single drug alone (pharmacologic synergy). Yet despite a high percentage of clinical trials testing combination therapies, few preclinical studies describe the mechanism by which such combinations confer benefit. Determining how much benefit results from synergistic interaction in clinical trial data is also difficult because patient responses to individual drugs vary widely.
The ability to predict a patient’s response to drugs would help guide therapeutic strategies, such as sequential versus concurrent administration of therapies. This can improve the success rates and duration of response to current combination therapy approaches. But, it will require more precise measurements of drug-drug interactions and pharmacodynamic biomarkers to measure how well drugs interact with a patient’s disease.
Targeted Drugs
Cancer researchers are decoding the genetic instruction book to find drug treatments that attack cancer cells and stop them from growing and spreading. These drugs are called targeted therapy and can work alone or combined with surgery, traditional chemotherapy, and radiation. These drugs disrupt the biochemical pathways that support cancer growth and spread without harming normal cells.
They target a specific feature of the cancer cell—such as proteins, receptors, or cytokines—that send signals that tell cancer cells to grow and reproduce. Examples of targeted therapies include angiogenesis inhibitors like bevacizumab, which blocks the formation of new blood vessels that feed tumors; HER2-targeted therapy drugs such as trastuzumab and lapatinib, which treat breast cancer; and apoptosis inducers such as bortezomib (Velcade) to kill lymphoma and multiple myeloma cells.
How has research helped in cancer treatment?
Research has built our understanding of how cancer starts, grows, and spreads in the body. This knowledge has improved treatments and ways to prevent it.
