I found out this week that my dad has metastatic cancer, with tumors up and down his spine. It's probably prostate cancer, will find out for sure on Monday.
The National Cancer Institute (cancer.gov) and the American Cancer Society (cancer.org) have _great_ websites. They don't presuppose you know anything about cancer (I didn't), but yet they get you into advanced topics pretty quickly. It will be interesting to see whether effective immunotherapy emerges for prostate cancer. There are lots of trials right now, but it's true--most of them seem like variations on a theme. Pembrolizumab has emerged as effective if PD-1 is expressed (https://www.icr.ac.uk/news-archive/asco-2018-major-trial-is-...). PARP inhibitors (e.g., olaparib) also seem promising for a subset with other mutations (http://www.cancernetwork.com/prostate-cancer/synthetic-letha...). What's striking is the binary nature of immunotherapy. When the genes are expressed, they work; when not, they don't.
The large-scale "platform" testing that Allison is testing seems like a great application of data-driven science.
> What's striking is the binary nature of immunotherapy. When the genes are expressed, they work; when not, they don't.
Antibodies and the hypervariable regions of the T cell apparatus are the van der Waals equivalent of a socket wrench: it either fits or it doesn't. That's what makes them so attractive for drug development. We can design the code, insert it into a bacterium or phage and then make as many socket wrenches as we want using little more than the principles and equipment used for brewimg beer.
If you are a tech / software engineering person, and have any sort of inclination towards biology or medicine, there are some amazing opportunities in biotech for smart and energetic people.
Most of the big pharma companies today -- Roche, Merck, Pfizer, Etc -- have been around for over 100 years. They are still by far the dominant groups in pharma. It's like if GE and IBM were still the dominant tech companies, because they bought Google / FB / Apple / Amazon before they got big, then ruined them
Their R&D models have not evolved for decades, and they have basically squeezed all possible juice out of those processes. Their return on R&D investment is approaching 0, and they are buying startups to replenish their dying pipelines
Pharma companies spend over $200B on R&D per year, and in 10-15 years I bet a lot of that R&D will be replaced by startups that are founded today
VCs (who start most biotech companies in house) have developed some pretty effective methods for improving R&D productivity, but the scale is orders of magnitude lower than big pharma. The main constraint is talent
Beyond the high level economic opportunity, there are tons of interesting engineering problems to work on -- although you really need to do the work to understand basic biology. There are tens : hundreds of thousands of smart young biomedical engineers working in academia, but not as many working on solving engineering problems related to translational research (although many are) -- ie developing medicines and bringing them to market. These are brilliant people with biology and engineering chops, learn from them and ask what software / data bottlenecks they are facing
I am a non-scientist but have been working in early stage biotech my whole career. If you want to learn about biology, I'd suggest you 1) read a lot of papers on fields you are interested in, even if you only understand 5% of it, 2) look up every term you don't know, and 3) talk to as many scientists as you can about the papers you read. I find this gives you better yield of useful info per unit time than reading textbooks, of course this will vary based on your learning style. Sort of akin to learning to code by reading a textbook vs just diving into a project -- personal preference to an extent
As a forcing function, try investing in early stage biotech stocks (though invest like you would in bitcoin -- only invest what you can afford to lose and expect a wild ride). Putting your money on the line will incentivize you to learn fast. When I talk to scientists about the non-scientists they know who understand biology well, many work in biotech finance
I'd also really encourage you to find a friend who works in a wet lab, and just follow their work real-time on a particular experiment. Ask them to describe the design in detail and rationale for each choice. Then follow up with them every week to see how the experiment is going, what they did, what went wrong. This is the closest you can get to troubleshooting biology without access to a wet lab, and it is a huge part of what scientists do
The National Cancer Institute (cancer.gov) and the American Cancer Society (cancer.org) have _great_ websites. They don't presuppose you know anything about cancer (I didn't), but yet they get you into advanced topics pretty quickly. It will be interesting to see whether effective immunotherapy emerges for prostate cancer. There are lots of trials right now, but it's true--most of them seem like variations on a theme. Pembrolizumab has emerged as effective if PD-1 is expressed (https://www.icr.ac.uk/news-archive/asco-2018-major-trial-is-...). PARP inhibitors (e.g., olaparib) also seem promising for a subset with other mutations (http://www.cancernetwork.com/prostate-cancer/synthetic-letha...). What's striking is the binary nature of immunotherapy. When the genes are expressed, they work; when not, they don't.
The large-scale "platform" testing that Allison is testing seems like a great application of data-driven science.