THE FUTURE OF CANCER TREATMENT

By Caroline Kellog Eve Tanios

An effective treatment for cancer has eluded scientists and doctors for centuries. For as long as we have studied cancer, it remains the second-leading cause of death globally and a disease that half of men and a third of women will develop in their lifetimes. But why is it so difficult to find an effective treatment? Because there isn’t one. As each tumor has its own set of mutations and variations, they respond differently to chemotherapy, radiation, and other known cancer treatments. While a single universal cancer treatment remains elusive, scientists are developing methods to personalize the treatment of cancer instead of searching for a cure-all.

The central philosophy behind the personalization of cancer treatment is predicting the effectiveness of treatments through models. However, modeling human cancer is extremely difficult. The wide variety of potential mutations combined with the randomness of cancer development makes it impossible to predict exactly how a tumor will develop. So, why not observe it in real time? The most accurate model involves taking a sample of a human tumor and implanting it into immunocompromised mice. Following implantation, several therapeutic strategies are tested on the mice to determine which one most effectively limits tumor growth and should be administered to the patient.

While it seems impossible to grow human tissue within a mouse, these tumors have been shown to have similar molecular profiles to those grown in humans. There are many benefits of growing a tumor in real-time, including the ability to study the tumor at various stages of development or understand the changes that lead to metastasis. However, there are several drawbacks that cause these models to be inefficient. Unsurprisingly, one of the most significant limitations is the fact that oftentimes implanted tumor cells do not grow in specific mice. Testing immunotherapies is also impossible when working with immunocompromised mice, and the human tumor microenvironment can’t be perfectly replicated in mice. The financial burden of creating and maintaining multiple mouse generations also cannot be understated; it can cost up to $25,000 to propagate tumors in mice, all of which is not covered by insurance. In response to these difficulties, scientists have developed an alternative for modeling human cancers: organoid models.

Grown in specialized media

conditions and a 3D matrix, organoid models aim to address the issues associated with 2D cell cultures by more accurately replicating the tumor environment. Already, organoid models have proven successful, with one group finding a 90% success in replicating 22 cases of colon cancer. However, the same central limitation remains: the inability to replicate cancer development in humans with reliable accuracy. Organoid models fail to model specific kinds of cancer such as prostate cancer. Further, for certain tissue types, the specialized media used in the creation of organoid models still needs to be developed.

Stepping away from in vitro and in vivo models of human cancer or anything physical at all, AI models represent another potential avenue for the personalization of cancer treatment. One popular AI model is simulating cancer physiology to screen drugs and predict drug combinations that can reduce the size of tumors. Using genomic information from patient tumors, a model is created that can predict the response of the tumor to different therapeutic strategies. This specific model has shown success in treating four cases of multiple myeloma (MM), a kind of cancer notorious for developing drug-resistance and having unpredictable responses to treatment. As with every predictive cancer model thus far, however, AI models still struggle to accurately predict how a tumor will develop and therefore have not been used on a large scale.

The future of cancer treatment is highly personalized. However, regardless of the model, it is difficult to predict the development of tumors with 100% accuracy. While the aforementioned methods are far more advanced than the simple 2D cell cultures used just 50 years prior, they still remain unreliable. It’s important to emphasize the gravity of cancer and the reason why it’s so important that we find a cure for it. Billions of dollars have been donated to cancer research over several centuries, and for good reason: it is one of the most grave human diseases that we have yet to find a truly effective cure for.

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