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Tumor Heterogeneity: Navigating the Next Frontier in Cancer Research

Red cancer cells adhered to a grey surface.
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Over the last decade, scientific discoveries have led to huge strides in understanding how cancer develops, which has ushered in a new era of precision medicine. Thanks to these advances in treatment, overall cancer survival rates are improving – but huge challenges remain. Some types of cancer are still extremely challenging to successfully treat. Once the disease has spread, it is very hard to cure.

In recent years, there has been an increasing awareness that cancer is difficult to eradicate because it is so complex and genetically diverse – and continually adapts and evolves. Patients can initially respond well to treatment, only for their disease to return, more resistant and aggressive.

“We used to think of tumors as a collection of similar cells growing out of control,” says Dr. Simone Zaccaria, group leader of the computational cancer genomics lab at University College London Cancer Institute. “But in the last decade or so, we’ve come to realize that a tumor is a much more heterogeneous ecosystem composed of different cell subpopulations – or subclones.”

As a tumor grows, it accumulates new DNA mutations, which can give rise to clusters of genetically distinct cells. Each of these subclones may exhibit varying behaviors – such as how quickly they grow, how well they respond to treatment and their ability to spread. This diversity between cancer cells within the same tumor – known as intra-tumor heterogeneity – offers an explanation for why some patients relapse.

“Under selective therapeutic pressure, drug-resistant cells may evolve – either as a result of the expansion of pre-existing resistant clones or plastic adaption,” explains Dr. Marco Bezzi, group leader of the tumor functional heterogeneity team at The Institute of Cancer Research, London.

Recent advances in computational and experimental technologies are empowering researchers to study individual tumor cells and track their evolution in unprecedented detail. This is opening new opportunities to understand why some treatments fail – offering potential avenues to devise novel strategies to prevent or overcome resistance to treatment.

Precision medicine

In recent decades, cancer medicine has evolved from the traditional one-size-fits-all approach to an era of precision medicine, where therapies are targeted to specific characteristics driving the growth and spread of an individual’s cancer. Researchers around the world are busy characterizing the molecular variations between tumors – known as inter-tumor heterogeneity – to enable the delivery of precision medicine to more patients.


But intra-tumor heterogeneity also has significant implications for precision medicine.

Traditionally, the gold standard for molecular diagnostics has involved sequencing a small sample of cells from a single biopsy collected from one region of a tumor. However, if the tumor is highly heterogeneous, this tiny fraction of cells is unlikely to capture a fully comprehensive picture of the disease. As a result, a potentially effective therapy could be overlooked if a certain molecular variation isn’t detected – or conversely, an unsuitable drug may be selected based on identifying a characteristic that isn’t that widespread within the tumor. In addition, the molecular profile of a person’s cancer is likely to change over time – and so treatments may need to be adjusted accordingly.

Developing ways to accurately determine tumor heterogeneity – and to track its evolution – is a major goal for precision medicine.

Large-scale DNA sequencing

Historically, the ability to analyze the DNA of different subclones within tumors has been restricted. But this is now changing thanks to recent advances in sequencing technologies, computational methodologies and access to high-quality patient samples collected in large-scale clinical studies.

High-throughput DNA sequencing of bulk tumor samples is one of the most widely used techniques for investigating genetic heterogeneity and deciphering the evolutionary relationships among cancer cells. However, interpreting these datasets can prove challenging as each sample comprises a blend of thousands to millions of different cells from various subclones.

“My lab focuses on the design and development of computational methods to separate this mixed signal into the individual components arising from each of these subclones,” explains Zaccaria. “We also design algorithms to investigate spatial heterogeneity from multiple bulk tumor samples and reconstruct tumor phylogenetic trees to describe the history of tumor evolution.”

The emergence of single-cell technologies presents exciting new opportunities to study tumor evolution with unparalleled resolution. One of Zaccaria’s goals is to create computational methods that can analyze these datasets, unveiling the evolutionary histories and migration patterns of metastatic cancer cells.

“If we can find out which subclones have the ability to metastasize, we will gain insights into the mechanisms that enable these cells to disseminate,” he explains. “We hope this knowledge can then be used to develop new therapies – or therapeutic strategies – aimed at preventing the spread of cancer cells to other parts of the body.”

Zaccaria’s research relies on high-quality sequencing data sourced from patient tumor samples – either collected from multiple regions of a primary tumor, matched pairs of primary and metastatic tumors or longitudinal samples. He is part of a large-scale multidisciplinary consortium called TRACERx –TRAcking Cancer Evolution through therapy (Rx) – which aims to decipher evolutionary trajectories in certain cancer types. He also examines data from samples collected through the PEACE – Posthumous Evaluation of Advanced Cancer Environment – study, which enables the collection of multiple metastatic tumors posthumously collected from patients.

A complex ecosystem

While genomic alterations in cancer cells drive some of the functional differences among subclones, this is only part of the story. Tumor cells don’t exist in isolation but instead live within a complex ecosystem of immune cells, stromal cells, the extracellular matrix, blood vessels and many other factors. The tumor microenvironment can significantly influence the behavior of individual cancer cells, even those that are genetically identical.


“The diversity in the cancer ecosystem adds another level of complexity to tumor heterogeneity,” explains Bezzi. “For example, different tumors can have completely different immunological profiles – and this can play an important role in how they respond to treatments like immunotherapies.”

Bezzi’s research is focused on deconstructing tumor heterogeneity, evolution and drug resistance in prostate cancer. “We can see differences in the genetic profiles of patient tumors even at the early stages of the disease – and over time, they acquire more and more genetic diversity,” he says.

His laboratory work was previously limited to studying prostate cancer cell lines grown in culture, as well as genetically engineered mouse models. However, he acknowledges these systems did not accurately reflect the diversity of genetics and the complexity of the tumor microenvironment found in human cancers. “While it’s possible to discover important biological mechanisms using these models, they may not apply to all tumors – limiting the opportunities to translate discoveries into wider patient benefits,” he says.

In response to this challenge, Bezzi is leveraging the latest advances in cell culture technologies. He is aiming to create a biobank of lab-based mini-tumors – or 3D organoids – representing the diverse genetic profiles found in prostate tumors. These mini-tumors exhibit many of the features found in tumors in vivo, but are not possible to recreate using standard 2D cell culture.

“If we can manage to do this at a reasonable scale, we are likely to find commonalities among the different genetic profiles,” says Bezzi. “Using this strategy, we hope any new mechanisms we discover that are potentially targetable will have a better chance of wider clinical relevance to more patients.”

Bezzi’s team is using these organoids to carry out a variety of ex vivo experiments – as well as transplanting them into immunocompetent mice to study how they grow and develop in the body and their interactions with the tumor microenvironment. Additionally, they have developed a barcoding system that enables them to follow individual cells over time – for example, to understand how different cells within a tumor are related to each other, or to track how they move within the body.

Living with cancer: A new treatment paradigm

Cancer cells develop, adapt and evolve within a complex interconnected ecosystem. Unraveling the complexities of tumor heterogeneity and evolution holds the potential to revolutionize treatment, leading to better long-term outcomes for patients.

“Some cancers remain extremely challenging to treat, such as those that have spread to other parts of the body or show high rates of relapse,” says Bezzi. “In these cases, we need to start thinking about cancer as a chronic disease that we can control rather than eradicate.”

Implementing this paradigm shift will require understanding and treating the tumor as an ecosystem, leveraging the inherent competition between subclones.

“Cancer will continuously evolve whatever you throw at it, so we need to reach a point where we can predict what will happen next,” says Zaccaria. “If we can stay one step ahead of the disease, we can then select the right treatment that will keep it in check – and we may have patients who live with cancer rather than die from it.”

About the interviewees:

Simone Zaccaria is group leader of the Computational Cancer Genomics (CCG) laboratory at the UCL Cancer Institute. His research focuses on the design and development of algorithms and mathematical models to analyze tumor sequencing data for understanding different cancer evolutionary processes.

Marco Bezzi leads the Tumor Functional Heterogeneity team at The Institute of Cancer Research, London. He uses a variety of approaches to experimentally model the cancer ecosystem and to investigate how tumor heterogeneity can be controlled and exploited in light of evolution.