The threat of cancer continues to grow as the disease claims around ten million lives worldwide each year, with the World Health Organization predicting that the global cancer burden will increase 60% by 2040.

While the world is still seeking a cure for cancer, technology’s ability to accelerate the pace of innovation while reducing the cost is changing the dynamic of the struggle in myriad ways.

The scalability and relative affordability of cloud technology is enabling healthcare professionals (HCPs) and patients to tackle the disease more effectively at every stage, from discovery to remission. Artificial intelligence (AI) and machine learning (ML) are generating deeper insights from genetic, clinical and image data, accelerating the pace of data analysis; researchers around the world can now collaborate in real time and share key findings, and remote monitoring and telemedicine have enabled patients to take more of the fight into their own hands.

Cancer is a disease caused by the uncontrolled division of abnormal cells, and in the vast majority of cases this results from some form of genetic alteration. This makes most cancer a disease of the DNA. The challenges of treating it stem from the fact that, as genes mutate, they change, and as tumours grow, they continue changing. Each type of cancer might have a mutational signature, but the genes in each tumour and the cells within each tumour are different.

Mapping this genetic diversity requires the generation and analysis of an immense amount of data. Equipping physicians with the insights they need involves collecting that data in a way that protects patient privacy, storing it securely – and transforming it through visualisation and analysis to guide both patient treatment and new drug development.

‘The complexity of the challenge that cancer presents to medicine is that each case, like the genes in each tumour, is unique’

The Cancer Genome Atlas has collected data from nearly 20,000 tumours and comparative normal tissue samples from 11,328 patients across 33 cancer types. The visualisations produced through this data illustrate how different cancers develop and spread, including patterns in the cells where they originate, the role of different virus variants in triggering mutations and the signalling pathways within the body that can be leveraged for effective treatments.

Scaling this data-led approach to decoding cancer depends on sequencing the DNA of each potential patient cost-effectively. That’s the goal of California-based Ultima Genomics. It’s developing a genetic sequencer on AWS that can make individual DNA sequencing, which cost $1,000 a decade ago, available to patients for as little as $100.

Genome sequencing of the type pioneered by Ultima Genomics can dramatically accelerate the detection of cancer through genetic analysis. Munich Leukemia Lab, using AWS Cloud to accelerate its genomics data processing, has cut the compute time required from 20 hours to three and improved both diagnostic speed and accuracy.

Many individuals are being diagnosed late, due to a lack of symptoms or only when having routine doctor checkups. Identifying subtle visual indicators of the disease, even when it’s not the focus of the medical appointment, is one of the most important ways in which AI is improving cancer survival rates.

One of the most dramatic ways in which AI can accelerate diagnosis involves training foundational models (FMs), within careful guidelines, to develop their own approach to analysing images.

Although cancer can affect anyone, the stage at which cancer is diagnosed can vary significantly across different communities, genders, body types, ages and even ethnic backgrounds. AI can help to address these disparities by moving diagnosis beyond a one-size-fits-all approach, as made evident in recent developments that have been able to specifically help identify masses and swelling within the breast tissue of Asian women. The relatively higher density of this breast tissue had previously made the signs of cancer more challenging to spot.

Although research and diagnosis are key to fighting cancer, an individual’s prognosis ultimately depends on having the right treatments available. According to the industry group PhRMA, it takes an average of ten to 15 years and $2.6bn to bring a new drug to market, and even with these staggering levels of investment, only 12% of new drug molecules gain approval from the United States Food and Drug Administration (FDA).

When HCPs are able to bring DNA-level insights about a patient’s cancer together with options for more personalised treatment, it can translate into better patient outcomes on a national scale.

Genomics England sequences the DNA of cancer patients and their tumours, in order to inform treatment. This includes highlighting the genes helping to spread cancer, the treatments most likely to affect them and the likely side effects for each patient. A study supported by Genomics England data, and published in the journal Nature Medicine this year, found that treatment for nine out of ten brain tumours and bowel and lung cancers could be guided by genetic insights.

‘AI and machine learning are generating deeper insights from genetic, clinical and image data, accelerating the pace of data analysis’

Personalised treatments for cancer patients can involve targeted therapy drugs – a form of chemotherapy that zeroes in on the changes that make cancer cells different and avoids some of the health impacts that come from blunter approaches killing healthy cells.

The campaign against cancer doesn’t just involve treating the disease more quickly and effectively. As the cancer burden spreads, improving access to treatment for all will be just as important, as will ensuring that future treatments are developed with all cancer sufferers in mind.

In remote regions of China, the limited availability of expert ultrasound operators is one of the most significant barriers to diagnosis. Shangyiyun has developed a breast cancer screening AI assistant that is able to detect and label lesions automatically, and upload ultrasound video and images to the cloud for additional, expert analysis. This is helping to scale the reach of breast cancer screening and quickly identify cases in need of scrutiny.

Hurone AI uses predictive AI and LLMs to help bridge gaps in cancer care in sub-Saharan Africa. It has built a two-way messaging system on AWS, which enables overstretched oncologists to monitor and support cancer patients at scale, even when local infrastructure is lacking. It’s exploring leveraging this same model to identify potential candidates for inclusion in clinical trials, which can play a vital role in developing treatment for all.

The complexity of the challenge that cancer presents to medicine is that each case, like the genes in each tumour, is unique. By making the campaign against cancer more individualised and more inclusive, the cloud is helping to turn this challenge into a strength. At the same time as enabling more personalised, targeted and effective treatment, it’s also delivering important psychological benefits. Helping each patient to feel understood and supported on their own terms doesn’t just enable more targeted treatment. It contributes significantly to improved patient experiences and patient outcomes.