Academic research can feel quite impersonal. Generally, professors prefer the intellectual purity of the academy to the chaotic details of real life. But then life intervenes.

Starting in 2005, a series of personal tragic events set me on a path that would radically alter my destination in academia. That year, my wife died from a rare sarcoma on her abdominal wall that was diagnosed too late and so led to uncontrollable metastasis.

Before this, I was siloed in my own research lane of scientific computation, far removed from any connection to cancer research. Her death changed everything. I had never felt so helpless after trying all I could to prevent the inevitable. Determined to never feel that way again, I refocused my research on the disease that took her at the age of 47.

Armed with my computational skills, I crossed lanes into the complex world of cancer research.

The statistics are frightening — one in three of us will get cancer in our lifetimes and one in five of us will die from it. Every family will likely lose someone to this terrible disease. Cancer is alarmingly common, but death from it doesn’t have to be.

We owe much of its fatalities to our inability to effectively and accurately identify its presence early enough to do something about it. The typical diagnostic processes rely on visible detection of physical changes, and when tumour cells grow to the point of being observed, it is often too late to stop the spread.

Compounding this issue is that the general approach taken in cancer diagnosis, prognosis and therapies are mostly informed by experiences of common types of the disease.

A lot is known about the warning signs and treatment responses of the common cancers, so it makes sense this is where the healthcare focus lies. But this also creates blind spots.

If our diagnostic net is designed to only catch the big cancers, far too many slip through the gaps. Cancerous tumours micrometres in size are hard to detect. They go unnoticed as they begin in our own cells, which develop into tumour cells that often present none of the familiar immune responses, like fever or aches. Often, we don’t know a threat exists until we’re in pain.

Because of cancer’s insidious nature, combating it requires specialist methods that allow us to detect microscopic differences in our bodies before they have grown large enough to hurt.

Spotting cancer early is key to fighting it effectively

A few years after my wife’s passing and just weeks before my eldest son’s departure to graduate school in the United States, tragedy struck again. Our celebratory mood was cut short when my son complained of a persistent abdominal pain that resulted in his hospitalisation. The experience felt all too familiar.

As a worried father, I wasn’t satisfied with the traditional means of diagnosis, so I had tissue samples sent to a globally recognised research centre in Boston — my first real contact with the molecular diagnostics of cancer. Fortunately, genetic analysis showed that the cells taken from the surgically removed growth were benign and had not yet taken on all the characteristics of rogue tumour cells.

This would not be the last time my immediate family would confront the threat of cancer. At 48 I was diagnosed with stage 3 lymphoma. Thankfully, advanced molecular therapeutic methods helped so much so that I am free of symptoms more than eight years on.

Few were looking at carbohydrates

My expertise lies in software development and computational modelling, where I focus on carbohydrate chemistry and biology of carbohydrates — a field known as Glycobiology.

Although DNA/RNA and proteins have entered the language of everyday conversation, the same is not true for complex carbohydrates or glycans. Even scientists have typically avoided the study of these molecules, as it can get extremely complicated.

I began to wonder what we might be missing by skirting this field of study: could it hold the key to more nuanced cancer diagnosis? Professional curiosity met personal impetus.

The usual approach in cancer care is through a visual analysis of tumour tissues of the organ under attack. However, tissues are made from a biochemical network of biological highways, molecular structures and machines called enzymes. Studying the connections of this vastly complex network can reveal internal abnormalities attacking the host system early on.

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This intelligence gathering is critical to not only identifying the rogue cells as soon as they appear (diagnostics) but understanding how to disable the machines that construct their networks (therapeutics).

When our normal cells go rogue and become tumour cells, the carbohydrates that encase them go through identifiable changes. The genome of tumour tissues harbours the map of these changes, which we have discovered as biomarkers, or biological indicators of normal or divergent cell function.

The challenge is to link each cancer with an easy-to-detect biomarker and find special biomarkers for less common variations. If we can do this, we can catch cancers early, and identify the ones that have fallen through the big net.

But spotting these biomarkers is difficult. Imagine looking at a map of a big city’s internet cables, roads, train lines, tunnels, and having to pinpoint the subtle changes in these networks to predict disaster.

That’s where computer modelling can help. Through analysing the patterns of huge amounts of genetic data, our sophisticated computers and algorithms could hone in on these markers. Computational methods make big data analyses possible at lightning speed. We can use them to detect the genetic patterns that uniquely identify cancer types.

Cross-disciplinary science is the future

After more than 10 years of setting up labs at the University of Cape Town, connecting with colleagues and conducting clinical trials, we’re starting to make progress at the Scientific Computing Research Unit. We are not a cancer institute, although our interdisciplinary labs are chasing the same goals as many dedicated global cancer research centres: an accurate, low-cost, and non-invasive diagnostic test for cancer.

We are also competing with the most well-resourced research centres across the globe on a budgetary ratio of a dollar to their millions. And yet, we punch above our weight by using a unique approach of learning from diagnostic genomic patterns to design therapeutics.

Technology is reshaping medicine, making laboratory science a central pillar of patient care. Computational scientists have the potential to analyse problems and provide understanding through models at an incomprehensible pace by training machine learning algorithms on complex data.

Combine this with the power of multi-disciplinary science — integrating the perspectives of biologists, chemists, physical and computational scientists, and clinical scientists — means a multitude of insights and intuitions that are far greater than the sum of their parts.

Computational modelling has a central role to play in the future of medicinal research — especially in its role to reach across academic disciplines and form multi-skilled teams to combat this complex disease.

While my loss was personal, I know I share this pain with almost everyone reading these words. For my son, early molecular diagnosis brought relief. For me, it was a new course of molecular therapy that has given me more time.

And I am happy to use this time to join my clinical and scientific collaborators working to detect cancer early using our unique strategy of analysing biomarker patterns for clues, and to be building a new class of cancer drugs.

Together, we are stepping out of our singular paths into a new multidisciplinary highway that promises accelerated solutions to one of the most complex healthcare challenges facing humanity.

Optimism has now replaced my helpless feelings. DM