What Advancements are Shaping the Future of Oncology?

By Jenna Philpott, BScReviewed by Lily Ramsey, LLM

Background 
Artificial Intelligence in oncology
Genomic medicine
Next-generation cancer organoids
Nanoparticles
New chemotherapy-delivery system
References 
Further reading

As we progress into the future, oncology is witnessing remarkable breakthroughs driven by cutting-edge technologies and innovative approaches. Five key advancements are at the forefront: Artificial Intelligence (A.I.), Genomic Medicine, Next-Generation Cancer Organoids, Nanoparticles, and Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC).

Image Credit: Gorodenkoff/Shutterstock.com

Background

Cancer is a non-communicable disease that has significant prevalence globally. Every day, revolutionary advancements are made by scientists and researchers across the globe, reshaping the landscape of oncology. These advancements provide hope to both patients and medical professionals alike.

Chemotherapies, radiotherapies, and surgery have characterized the battle against this disease for decades. Cancer research has made significant advancements since the rise of personalized treatments and targeted therapies.

We are currently in a transformative era of cancer research, with remarkable innovations paving the way for breakthrough treatments.

Artificial Intelligence in oncology

Artificial intelligence (A.I.) and machine learning (ML) are computer systems designed and trained to aid oncology doctors and health professionals in treating patients with cancer.

These systems are extremely valuable, as they can make the diagnosis and treatment process faster and more accurate.

ML has been used to view medical images, like mammograms for breast cancer or scans for brain tumors. Evidence has shown that it can be very good at finding and understanding these images, better than experienced doctors in some cases.

The main advantage of using ML is that it speeds up the time it takes to find and analyze cancer in these images. The results from the ML system are consistent and reliable, so the level of experience the doctor has using it is irrelevant.

One big challenge faced in ML systems is that they require a lot of data to learn from, which may not be available everywhere in the world. Cancers such as breast and colon cancer are more common, meaning there is a high volume of data, meaning it is a good place to study and improve the use of A.I. on a global scale, improving patient outcomes.

Genomic medicine

Genomic medicine involves studying and analyzing a patient’s genetic information, specifically their DNA, better to understand the genetic basis of diseases such as cancer.

Next-Generation Sequencing (NGS) was discovered about ten years ago, making reading all the genetic information in a person’s DNA Whole Genome Sequencing (WGS) much easier and cheaper. This advancement made WGS more widely available for research and to provide help to cancer patients.

The 100,000 Genome Project was set up in the U.K., using WGS to observe the DNA of more than 15,000 cancer patients. They compared the normal genetic information (germline) from the patient to the genetic makeup of their tumor.

The project provided extensive information to the patients and their families, allowing them to understand the genetic basis of their cancer and how it can be treated.

The success of the 100,000 Genome Project made it a valuable resource used in cancer research worldwide. Researchers can use this information to improve patient outcomes.

The National Health Service (NHS) in England set up the NHS Genomic Medicine Service following the project, offering genetic testing for patients with rare diseases and cancer, making it more accessible for future patients to benefit from the latest oncogenic advancements.

Next-generation cancer organoids

Next-Generation Cancer Organoids are advanced 3D models of cancer cells that closely replicate the characteristics and behavior of tumors found in the human body. The organoids are created from the patient’s cancer cells and cultured in the laboratory.

These models are powerful because they can maintain the important features of the original tumor, such as its genetics, proteins and appearance, while allowing scientists to manipulate the genes and environment in ways that were not previously possible.

Some challenges are encountered in creating these tumor models, as the methods used in the laboratory can vary, leading to inconsistency and unreliable results. Researchers are working to make these models more reliable and useful for patient care by standardizing the techniques used to create them.

By standardizing the methods, researchers can gain better insights into how different tumors behave and respond to treatments, leading to tailored therapies and improving patient outcomes in the future.

Nanoparticles

Nanoparticles are tiny particles designed to deliver drugs or therapeutic agents specifically to cancer cells.

The use of nanoparticles in cancer treatment is a part of nanomedicine. This field explores how nanotechnology, including oncology, can improve disease diagnosis, treatment, and monitoring.

Due to their tiny size, they are more stable and safer for the body. They can also stay in the cancer area longer, allowing the drugs time to work. They can be designed to target cancerous cells, reducing the side effects and making the treatment more effective.

Nanoparticle-based drug delivery has demonstrated potential in overcoming drug resistance observed in cancer treatment. By targeting specific mechanisms responsible for drug resistance, nanoparticles can help reverse multidrug resistance in cancer cells. As we uncover more tumor drug resistance mechanisms, nanoparticles are further engineered to address these challenges.

New chemotherapy-delivery system (pressurized intraperitoneal aerosol chemotherapy)

Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC) is an exciting and hopeful method of chemotherapy to treat specific advanced abdominal cancers.

In PIPAC, chemotherapy drugs are directly delivered into the abdominal cavity as an aerosol, targeting and concentrating the treatment on tumors in this area. This approach shows great promise in improving the effectiveness of cancer treatment in the abdomen.

PIPAC is still considered a relatively new and evolving technique. Clinical trials and research are ongoing to determine its long-term effectiveness and safety compared to traditional treatment approaches.

PIPAC is unsuitable for all cancer patients and is typically recommended for those with peritoneal metastases who have exhausted other treatment options.

With personalized treatments and targeted therapies, cancer research has changed significantly. These ground-breaking advancements have brought us to a time when new and promising treatments are now possible.

References

• LeSavage, B.L., Suhar, R.A., Broguiere, N., Lutolf, M.P. and Heilshorn, S.C. (2021). Next-generation cancer organoids. Nature Materials. doi: https://doi.org/10.1038/s41563-021-01057-5.
• Nadiradze, G., Horvath, P., Sautkin, Y., Archid, R., Weinreich, F.-J., Königsrainer, A. and Reymond, M.A. (2019). Overcoming Drug Resistance by Taking Advantage of Physical Principles: Pressurized Intraperitoneal Aerosol Chemotherapy (PIPAC). Cancers, 12(1), p.34. doi: https://doi.org/10.3390/cancers12010034.
• Seed, L.M. (2021). Horizon Scanning in Cancer Genomics: How Advances in Genomic Medicine Will Change Cancer Care Over the Next Decade. Current Genetic Medicine Reports, 9(3), pp.37–46. doi: https://doi.org/10.1007/s40142-021-00200-7.
• Vobugari, N., Raja, V., Sethi, U., Gandhi, K., Raja, K. and Surani, S.R. (2022). Advancements in Oncology with Artificial Intelligence—A Review Article. Cancers, 14(5), p.1349. doi: https://doi.org/10.3390/cancers14051349.
• Yao, Y., Zhou, Y., Liu, L., Xu, Y., Chen, Q., Wang, Y., Wu, S., Deng, Y., Zhang, J. and Shao, A. (2020). Nanoparticle-Based Drug Delivery in Cancer Therapy and Its Role in Overcoming Drug Resistance. Frontiers in Molecular Biosciences, [online] 7(193). doi.org/10.3389/fmolb.2020.00193

Further Reading

• The current state of clinical diagnostics 
• Machine Learning for Targeted Disease Treatment
• How is AI Used in the Treatment of Bladder Cancer?​​​​​​​