Lung cancer is the leading cause of cancer death, with an annual incidence of approximately 2 million cases worldwide.
Although lung cancer is traditionally associated with smoking, lung cancer in never-smokers (LCINS) is increasingly recognized as a distinct subgroup. LCINS exhibits distinct clinical and molecular traits, occurring more frequently in women, individuals of Asian descent, and younger patients who do not smoke, and is characterized by a different mutational landscape and lower tumor mutational burden. In the U.S., more than 50% of Asian American women with lung cancer have never smoked, and never-smoking rates among Chinese and Indian American women with lung cancer approach 80%.
Human researchers need data as much as AI models, but the system does not incentivize anyone to fund big datasets for tumor subtypes, even lethal ones like non-smoking lung cancer and TNBC.
Academics are motivated to diversify grants across multiple initiatives while clinicians are encouraged to focus on immediate impact.
This is a system bug, and people from outside must bridge the gap.
We seek to create multi-site, field-defining datasets for TNBC and lung cancer in non-smokers (LCINS), and freely publish these datasets for researchers to study worldwide.
One of these datasets alone could power tens to hundreds of studies and hopefully set a new precedent for tackling tumor subtypes. Funding them is a powerful way to push cancer research forward.
The more data society gives researchers, the faster they can accelerate toward novel targeted therapies and screening programs.
To eliminate donor risk, donations go directly to Stanford. Our involvement is optional, and we receive no financial benefit from this effort.
In this world, there are perhaps 50 people who can understand this system bug, embrace contrarian perspectives, and streamline the risk/reward calculation due to extremely high net worth.
These founders and investors are trained in outside innovation. If you know any of these individuals, please share this page with them.
We do not financially benefit from this effort nor do we provide products or services related to the creation of these cancer datasets.
In fact, we lose money on this initiative by diverting lab resources. Our objective is merely to raise awareness of glaring deficiencies in cancer datasets and help address them however possible.
Moreover, our involvement is optional. While we would like to advise -- for free -- on how to create the dataset and minimize overhead, many external groups can also provide guidance on which datasets would advance LCINS and TNBC research the most.
