Advances in Cancer Biology

Cancer is defined as the uncontrollable division and proliferation of abnormal cells, leading to their dissemination and invasion of distant sites around the body. These cells form malignant tumors which spread and metastasize, destroying tissues and disrupting organ function.

According to the World Health Organization (WHO), cancer is a leading cause of death worldwide. Almost one in six deaths were attributable to cancer in 2020, and the number of new cancer cases – the inci­dence rate – has been rising. Furthermore, recent estimates predict that as many as one in two people could develop a form of cancer in their lifetime.

Nevertheless, scientists and researchers continue to develop sophisticated and innovative strategies to manage the growing disease burden caused by cancer. Research into cancer biology entails the study of intricate interactions between the genes, proteins and biological pathways that drive the growth and development of the disease. In essence, cancer biology is the study of what makes cancer cells different from normal cells in order to provide clues for how we might be able to treat it.

With advancements in our knowledge of cancer, ever more effective treatments and technologies are being developed which is helping to increase survival rates. In this regard, this listicle will explore some of the recent advances in cancer biology, covering developments in immunotherapy, machine learning/ artificial intelligence (AI) and cancer vaccines.

The immune system attacks cells that are “non-self”, such as infected or cancerous cells. However, the immune system can struggle to keep pace with the proliferation of cancer cells and can have difficulty preventing tumor growth, as cancer cells employ several methods to evade detection and destruction by the immune system. In these cases, immunotherapies are designed to stimulate and augment the immune system’s ability to target cancer cells.

In 2018, the Nobel prize in physiology or medicine was presented to Prof. James Allison and Prof. Tasuku Honjo for their contributions to immunotherapy research. Specifically, for their discoveries of programmed cell death protein 1 (PD-1) and its ligand PD-L1, as well as the development of neutralizing antibodies for these proteins that increase the anti-cancer capabilities of the immune system.

Examples of immunotherapies include monoclonal antibodies, cytokines and immune checkpoint inhibitors. The first immune checkpoint inhibitor was the cytotoxic T-lymphocyte-associated protein 4 (CTLA-4) inhibitor ipilimumab, approved by the FDA in 2011 for use in patients with advanced melanoma.

However, many obstacles for the use of immunotherapies remain such as difficulties predicting which patients will respond to therapy, the development of resistance as well as high treatment costs. To this end, researchers continue to advance our knowledge of immunotherapies to overcome these complications.

A recent advancement in this field came from researchers that demonstrated the overwhelming success of a new “immunoablative” immunotherapy in a small Phase 2 clinical trial. This treatment was designed to replace the need for surgery or chemoradiation for cancer treatment. Fourteen patients with DNA mismatch repair-deficient (MMRd), locally advanced rectal cancers were treated with dostarlimab (an anti-PD-1 antibody) every three weeks over a six-month period. Dostarlimab is already established as a treatment for metastatic MMRd colorectal cancer – therefore, the team hypothesized that dostarlimab for locally advanced disease may alter the requirements for additional therapy. All trial participants demonstrated a complete clinical response, whereby multiple imaging techniques revealed no signs of tumors after 6–25 months of follow-up. This supports the neoadjuvant use of similar therapies, i.e., treatment to first shrink the tumor prior to other major treatments such as surgery or radiation.

Other advances include the development of a small synthetic molecule as an alternative to antibody-based immunotherapies. The use of monoclonal antibody therapies is limited due to their high cost in addition to their large size, which prevents them from reaching the less accessible areas within solid tumors. In this case, researchers used computer-aided drug design and bioinformatics approaches to develop small synthetic molecules that would inhibit PD-L1 as an alternative to therapeutic antibodies. Experimental data confirmed the final candidate molecule was effective at inhibiting PD-L1 in mice that expressed humanized T cells. The molecule requires further evaluation in cancer models, but if successful, could potentially be cheaper to produce than antibody therapies and could be suitable to take as an oral pill.

AI describes technologies designed to mimic human intelligence, simulated by machines and computer systems. Machine learning is an application of AI, designed to adapt algorithms and predictions based on experience. The use of AI tools in research and medicine can aid the identification of patterns across enormous datasets, helping to make decisions or predictions. For cancer, this could potentially assist with screening, diagnosis and establishing treatment plans. A recent study identified that, as of 2021, a total of 71 AI-based medical devices have been approved by the FDA in oncology-related fields. Most of these focus on radiology and pathology disciplines, with breast, lung and prostate cancers receiving the most benefit.

To tackle the issue of low response rates to immune checkpoint inhibitors, researchers have used network-based machine learning to identify biomarkers that could indicate favorable immunotherapy responses. Their machine-learning algorithm analyzed the clinical outcomes of over 700 melanoma, gastric and bladder cancer patients alongside the transcriptomic analysis of their tumor tissue. The newly discovered biomarkers were able to successfully predict which patients would respond to immune checkpoint inhibitors – a breakthrough that could help the identification of patients ahead of treatment.

Additionally, another research team have developed an AI they call “ikarus” which they demonstrated to be successful in differentiating between cancerous and healthy cells from single-cell sequencing data. In the study, ikarus was initially trained using data from colorectal and lung cancer cells, but the researchers later demonstrated it was also able to distinguish cancerous from healthy cells in liver and brain cancers.

Advances in AI and cancer biology have also been made in the field of radiology. Cedars-Sinai researchers developed an AI that was able to identify individuals that would go on to develop pancreatic cancer from their CT scans taken years before they were ever diagnosed. Pancreatic cancer has some of the worst survival rates of any cancer type, with just 2–9% of patients surviving 5 years after their diagnosis. Diagnosis of pancreatic cancer is frequently made late due to its vague symptoms, which allows the cancer to advance into severe disease. Therefore, improving rates of early diagnosis may help to increase survival. With further study of its prediction capabilities, the AI could eventually be used to indicate the possibility of future pancreatic cancer for patients undergoing CT scans for other concerns.

The first vaccines developed to treat bacterial diseases were developed some 200 years ago. In modern research settings, cancer biologists have been using this knowledge to develop vaccines against cancer, though efforts to design and produce effective vaccines have proved challenging. Tumor antigens targeted by vaccines may not produce strong immune responses, or indeed tumor cells may mutate to evade destruction.

Nevertheless, the COVID-19 pandemic has brought with it a new interest in mRNA vaccine technology that may help efforts to develop effective cancer vaccines which aim to stimulate or enhance anti-tumor immune responses. Most mRNA cancer vaccines are currently in Phase 2/3 trials and none to date have been approved by the FDA. Recently, a preclinical trial in mice demonstrated the efficacy of modified lipid nanoparticle “bubbles”. Other lipid nanoparticles, like those used in some COVID vaccines, can favor delivery of the vaccine to the liver, potentially resulting in inflammation. These newly developed lipid nanoparticles favored delivery of the vaccine into the lymphatic system, where immune cells are “trained”, to provoke a stronger immune response. The vaccine, which contained mRNA molecules encoding tyrosi­nase-related protein-2 (TRP-2), a tumor-associated protein expressed in melanoma, was tested in mouse models of the disease. The findings showed that increased delivery into the lymph by the modified lipid nanoparticles led to a stronger immune response. This significantly inhibited tumor growth, leading to a 40% complete response rate in combination with anti-PD-1 therapy.

Another research team used a different method of development to produce an “off-the-shelf” vaccine. Cancer vaccines often require tailoring to an individual patient’s neoantigens produced by their tumor. This treatment targeted MICA and MICB, stress proteins expressed in many cancer types in response to DNA damage, which flag damaged cells to be eliminated by the immune system. Cancer cells can evade this mechanism and cleave MICA/B from their surface to escape detection. The vaccine, which produces antibodies that bind to MICA/B, prevents the cleavage and loss of these proteins. Together, this strengthens the immune response from both T and NK cells against tumor cells, delivering a powerful “double-punch” that reduced the rate of metastasis in mouse models of melanoma and triple-negative breast cancer.

Cancer biology is a diverse discipline which includes many other sub-fields such as genetics, cell biology, pharmacology and medicinal chemistry. Stepwise advances in these fields made over the past several decades have improved our knowledge of the biological processes underpinning cancer development. Together, these advances could enable the development of new and innovative technologies and therapies with the aim of turning today’s discoveries into tomorrow’s treatments.