Taking the campaign against cancer into the cloud

(MENAFN- Hill & Knowlton) Humanity has been recording its struggle against cancer since the disease was first described in ancient Egypt some 5000 years ago. In the many generations since, the heroic efforts of researchers, healthcare providers and caregivers have helped to drive huge advances in understanding, diagnosis, treatment and care. However, the threat of cancer continues to grow. The disease claims some 10 million lives worldwide each year, and the World Health Organization predicts we will see more than 35 million new cancer cases by 2050, a 77% increase from the estimated 20 million cases in 2022.

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 in particular is enabling healthcare providers and patients to tackle the disease more effectively from discovery to remission. Artificial Intelligence and Machine Learning (AI/ML) are generating deeper insights from genetic, clinical and image data, accelerating the pace of data analysis; and by enabling secure access to data at the point of need, researchers around the world can now collaborate in real time and share key findings; remote monitoring and telemedicine have enabled patients to take more of the fight into their own hands.

“Technology is not a silver bullet to end cancer but it is an enabler of the real heroes,” says Dr. Rowland Illing, Chief Medical Officer and Director of Global Healthcare and Nonprofits at AWS. “I’ve experienced first-hand how technology can make research more efficient and effective, diagnosis faster and more accurate, speed drug discovery and development, and increase accessibility through remote care and more inclusive clinical trials.”

Research: Mapping the cancer landscape through genome sequencing

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 tumors grow, they continue changing. Each type of cancer might have a mutational signature, but the genes in each tumor and the cells within each tumor, 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 visualization and analysis to guide both patient treatment and new drug development.

The Cancer Genome Atlas has collected data from nearly 20,000 tumors and comparative normal tissue samples from 11,328 patients across 33 cancer types. The visualizations 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. The Atlas is part of the AWS Registry of Open Data allowing researchers from around the world to expand their knowledge base.

Scaling this data-led approach to decoding cancer depends on sequencing the DNA of each potential patient cost-effectively. This is the goal of California-based Ultima Genomics. Ultima has developed a high-throughput, cost- effective next generation sequencer on AWS that can sequence an entire human genome, which cost $1,000 a decade ago, for as little as $100.

Speeding up and democratizing diagnosis

Genome sequencing of the type pioneered by Ultima Genomics can dramatically accelerate the detection of cancer through cost-effective genetic analysis. Munich Leukemia Lab, which uses Amazon Elastic Compute Cloud (Amazon EC2) to accelerate its genomics data processing, has cut the compute time required from 20 hours to three and improved both diagnostic speed and accuracy.

However, most patients won’t discover their cancer diagnosis through an in-depth genetic analysis. Many are simply visiting their doctor for a regular check-up, or perhaps a seemingly unrelated ailment. 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 Artificial Intelligence (AI) is improving cancer survival rates.

“Cancer diagnosis still mostly works by pathologists looking down microscopes at tissue on glass slides,” says Michael Rivers, VP Digital Pathology at Roche Tissue Diagnostics, which has collaborated with Ibex and AWS to bring AI diagnostics tools to pathology labs through its Navify platform. “Digitizing those slides gives us the potential to apply AI-based image analysis to help make a diagnosis and inform the treatment plan for a patient.”

As Rivers explains, 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 analyzing images. “Explainable AI is a really important component in the solutions we’re developing,” he says. “We provide heat maps and overlays to indicate to the pathologist how the algorithm is interpreting an image, but the pathologist has ultimate control over the final diagnosis.”

While breast cancer is a concern worldwide, diagnosing it in certain populations can present unique challenges. For Asian women, for example, the relatively higher density of breast tissue has historically made early detection more difficult.

FathomX, a Singapore-based AI company originating from the National University of Singapore, has developed the FxMammo algorithm that addresses this issue. Using AI technology, FxMammo is designed to improve accuracy in identifying masses and abnormalities in dense breast tissue, offering more reliable and cost-effective early detection and reducing false positives by up to 75.5%.


Changing the numbers game in cancer drug discovery

Though 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 10-15 years and $2.6 billion 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).

Inclusive, personalized cancer treatment at scale

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

Genomics England sequences the DNA of cancer patients and their tumors, 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 tumors and bowel and lung cancers could be guided by genetic insights. Personalized 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.

Genomics England is building on this work using Anthropic’s Claude models on Amazon Bedrock to help researchers identify associations between genetic variants and medical conditions, including cancer.

Empowering cancer patients and carers, everywhere

The campaign against cancer doesn’t just involve treating the disease faster and more 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, titled Dr. J, to help fill this gap. Dr. J 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. With AWS’s compute, storage, Dr. J can provide stable, efficient and accurate screening services for institutions and users. AWS also provided a solid data security foundation for the global deployment of Dr. J.

Hurone AI uses predictive AI and Large Language Models (LLMs) built on AWS 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.

“If you look at all of the cancer drugs that were approved by the Federal Drug Administration (FDA) in the last 20 years, less than 5% of the trial participants for those drugs were of African or Hispanic descent,” explains Kingsley Ndoh, Founder and CEO of Hurone AI, which uses predictive AI and LLMs built on AWS to help bridge the gaps in cancer care. “There’s currently only one oncologist to every 3,000 cancer patients in sub-Saharan Africa, and only one to every 1,000 patients in Latin America. These patients inevitably face side effects from drugs and it’s difficult for them to access the constant care and support that they need.”

Expanding the reach of cancer care geographically is one crucial way of expanding access and supporting more patients. Extending the availability of in-home care is another. This enables patients to fight cancer on their own terms and their own territory, increasing their sense of wellbeing, dignity and agency.

The complexity of the challenge that cancer presents to medicine is that each case, like the genes in each tumor, is unique. By making the campaign against cancer more individualized and more inclusive, the cloud is helping to turn this challenge into a strength. At the same time as enabling more personalized, 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 both patient experiences and patient outcomes.

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