The most lethal cancer is lung cancer, accounting for roughly 20% of all cancer deaths and the highest mortality rates among both men and women.
Mysteriously, a new subtype is emerging among non-smoking individuals and especially young Asian-American women. Of Asian-American women with lung cancer, over 50% have never smoked, and this figure approaches 90% for Chinese- and Indian-American women.
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 field-changing datasets for TNBC and non-smoking lung cancer, 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. HotpotBio involvement is optional and would only entail advising on dataset guidelines and ensuring proceeds pay for data rather than overhead. No one from HotpotBio financially benefits.
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.
Clarence Hu
