Home Business & Economics Study Uses Real-world Oncology Data to Understand Lung Cancer Treatment

Study Uses Real-world Oncology Data to Understand Lung Cancer Treatment

French-American startup Owkin and announced CancerLinQ, a real-world oncology data platform developed by the American Society of Clinical Oncology (ASCO) that collects and aggregates longitudinal electronic health record data from oncology practices throughout the United States have agreed to work together in a new research collaboration to use artificial intelligence to analyze real-world oncology data.

The purpose of the collaboration is to better understand why some cases of metastatic non-small cell lung cancer (NSCLC) are resistant to first-line immunotherapy treatment.

Worldwide, lung cancer is the second most commonly diagnosed cancer and the leading cause of cancer deaths. NSCLC is the most common type of lung cancer in the United States, accounting for 82% of all lung cancer diagnoses.[1]

In 2022, an estimated 236,740 adults (117,910 men and 118,830 women) in the United States will be diagnosed with lung cancer. Worldwide, an estimated 2,206,771 people were diagnosed with lung cancer in 2020. These statistics include both small cell lung cancer and NSCLC.[1]

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CancerLinQ® Discovery
Using data from CancerLinQ® Discovery, one of the largest and most-diverse real-world-oncology databases that include de-identified data from more than 6 million patients with cancer, as well as de-identified data from a European research site, Owkin will deploy its proprietary federated learning algorithms to identify possible predictive factors that could inform the understanding of why some patients with NSCLC respond poorly to immunotherapy treatment.

The study will also compare patient characteristics, treatment factors, and clinical outcomes of patients with advanced NSCLC.

Sean Khozin, MD, MPH, Chief Executive Officer of CancerLinQ.

Decentralized machine learning
Federated learning, a decentralized machine learning approach that trains machine learning models with multiple data sources, maintains privacy and ownership while allowing participants to benefit from a larger amount of data than their own. Instead of gathering data on a single server, the data remains locked on servers as the algorithms and only the predictive models travel between the servers.

“Unlocking the full potential of real-world data to advance cancer care and research requires new modes of data sharing at scale and applying next-generation analytical methods such as AI to complex multimodal datasets, both of which are key features of this exciting research collaboration,” said Sean Khozin, MD, MPH, Chief Executive Officer of CancerLinQ.

“Federated learning allows a safe and secure method of sharing data and collaborating across siloes and continents as one global cancer research community focused on advancing discoveries that can improve the lives of patients with cancer,” Khozin added.

“The next generation of medical breakthroughs will be unlocked by the application of artificial intelligence to vast amounts of rich patient data,” said Thomas Clozel MD, Co-founder and CEO of Owkin.

“We are excited to use our federated learning software to safely and securely analyze  CancerLinQ’s diverse patient data. We are excited to work together to make discoveries that can contribute to improving treatment for millions of lung cancer patients across the world.”

Owkin was co-founded in 2016 by Thomas Clozel MD, a former assistant professor in clinical onco-hematology, and Gilles Wainrib, a pioneer in the field of machine learning in biology.

Reference
[1] Lung Cancer – Non-Small Cell: Statistics. ASCO | Cancer.Net. Online. Last accesses on June 7, 2022.

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