by Mona Jhaveri - September 15, 2021

In the United States, 1 in 2 men and 1 in 3 women will be diagnosed with some type of cancer in their lifetime.

The development of new treatments to combat this disease has been difficult because it is a complex problem. There is no one type of cancer. Instead, there are over 100 different types of cancer, each with its own set of symptoms and treatment methods.

The challenges of cancer treatment include:

  • Designing more effective and more predictable trials.
  • Improving access to resources for researchers.
  • Using genomics more effectively.

Let’s take a closer look at these challenges and how they can be overcome.

Inefficient Cancer Clinical Trials

Traditional clinical trials involve multiple stages, most commonly four:

  • Phase one – The drug, vaccine, or treatment is used on a small group of people (typically 20-100) to test its safety, formulation method, best dose, and side effects.
  • Phase two – The effectiveness of the drug is tested in large groups (50-300). Safety tests from phase one are continued, as well as assessing how well the new drug works.
  • Phase three – The value of the new drug in clinical practice is studied in even larger groups (300-3000 or more). Its effectiveness is compared to the ‘gold standard’ (the treatment typically used) for that specific disease.
  • Phase four – The drug is tested for long-term effectiveness and safety in a large population. This phase is also called post-marketing surveillance because the drug has already been released to the public.

The primary disadvantage of this clinical trial model is that it can take more than a decade for one stage to be completed before going onto the next stage. Traditional trials are slow, extremely expensive, and not always efficient. It can take more than ten years to develop one efficacy trial for just one disease.

The solution would be to look into a different approach. Several countries and institutions are exploring options for multi-arm, multi-stage clinical trials, such as the STAMPEDE trial for prostate cancer in the United Kingdom. With a more efficient clinical trial model, researchers will be able to complete one trial in only a few years instead of many.

Unpredictable Efficacy

The ability to accurately identify patients who would benefit from a particular therapy is critical to improving outcomes with any therapeutic intervention. However, this task can be particularly challenging for cancer immunotherapy because there are no validated predictive markers available.

This challenge stems from several factors: the complexity of tumor-immune interactions, the heterogeneity of tumors, the dynamic nature of the immune microenvironment within individual tumors, and the fact that most cancers arise as a result of multiple genetic alterations in cells that have been exposed to environmental insults over time.

These challenges make it difficult for researchers to predict which therapies will work best against specific types of malignancies or even whether they will work at all. The unpredictability makes clinical trials more expensive than necessary and limits their success rate.

Tumor Heterogeneity

Cancer cells evolve due to their interactions with both intrinsic factors within themselves and extrinsic factors from the environment.

The heterogeneity among individual tumors occurs due to differences between subclones arising during clonal evolution, including variations in mutation rates, patterns of chromosomal rearrangements, copy number alterations, epigenetic modifications, transcriptional profiles, microRNA expression levels, protein abundance, metabolic activity, cell cycle status, and response to therapy. These changes may occur early on in tumor development or later in disease progression. As such, each patient’s tumor has its own unique set of characteristics that determine how well it responds to treatment.

In addition, many different combinations of these features exist across various tissues and organs. For example, some solid tumors exhibit high mutational loads while others show low somatic point mutations. Some breast carcinomas contain few driver gene mutations but instead rely heavily upon genomic instability caused by defects in DNA repair pathways. Other lung adenocarcinoma cases harbor numerous driver genes yet lack evidence of significant intratumoral inflammation.

To better understand why specific treatments fail to elicit an effective anti-tumor immune response, scientists must first characterize the molecular landscape of individual tumors. Identifying biomarkers associated with responsiveness could then help guide future research efforts to develop new therapeutics.

Poor Access to Resources

Even though clinical trials are expensive and cancer research can always benefit from more funding, resources are not limited to finances.

Researchers are in dire need of better data, more tumor samples, and more computational power. The National Cancer Institute is currently working to improve cancer detection in patients through a nationwide program called The Cancer Genome Atlas that aims to provide a complete understanding of the genomic sequences of all major cancers.

Open access to gene expression and radiomics data is vital for collaboration between different institutions. Cancer research highly depends on joint work for the successful development of new treatments. The data is also needed to develop models that will help doctors detect cancer earlier in their patients, find the most effective treatment options based on genetic information, and monitor how these different approaches affect individual responses.

Finally, new approaches to preserving tissue, such as cryo-preservation, provide an essential tool for researchers to study cancer. The ability to store tissue at very low temperatures has enabled scientists to explore how a variety of gene mutations affect the stability and quality of DNA, which can yield insights into what causes cancers as well as potential new treatments for them.

Low-Quality Data

Some of the new questions popping up in cancer research include:

  • Why are some transcriptional programs robust to mutations?
  • Does identifying more cancer risk mutations have any therapeutic relevance?
  • How do different states of cancer (subtypes) affect cancer cell dynamics?
  • Why and how do certain cancers retain lineage identity?
  • Does the cell of origin matter?

To answer these, vast amounts of cancer genomic data are needed. The problem is that many of the datasets have been low-quality, and it was difficult to extract reliable data from them.

However, thanks to the rapid rise of genome sequencing, there are now many more high-quality datasets available. The potential for developing new cancer treatments based on genomics is enormous.

For example, genomics and system biology consist of computational techniques – such as bioinformatics and molecular modeling. Their main goal is to understand cancer progression from a biological perspective.

The first step in this process is to explore the available genomic data sets (e.g., samples) with an experimental approach called target discovery or sequencing-based approaches like ChIP-Seq, Hi-C (chromatin interaction analysis), RNAi, and knockout screens. The genome is scanned for mutations that may influence cancer progression or other biological processes relevant to treatment development.

Even though these methods are complex, they allowed researchers to realize that cancer is not a disease of genes but of pathways. The hope is that drugs can be developed to target these specific pathways by understanding the pathways involved in cancer development. The challenge now lies in which targets are best for drug development.

Conclusion

With the rise of immunotherapy in the last decade, the fight for cancer treatment has been reinvigorated. The study of cancer is a fight against the disease and for research funding and resources to develop new treatment options that are no longer bound by one single mechanism.

To continue trudging forward, our researchers advocate for more efficient cancer trials, better access to resources (including high-quality datasets), and integrating genomics into cancer treatment. The future will rely on these efforts to yield a new, more hopeful age in cancer care.Contribute to these efforts by donating to our active campaigns and doing your part in the war on cancer.

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