Integrating Genomic Data Into Oncology

Episode sponsored by Tidelift: tidelift.com

It’s been a while since we talked about biostatistics and bioinformatics on this podcast, so I thought it could be interesting to talk to Jacki Buros — and that was a very good idea!

She’ll walk us through examples of Bayesian models she uses to, for instance, work on biomarker discovery for cancer immunotherapies. She’ll also introduce you to survival models — their usefulness, their powers and their challenges.

Interestingly, all of this will highlight a handful of skills that Jacki would try to instill in her students if she had to teach Bayesian methods.

The Head of Data and Analytics at Generable, a state-of-the-art Bayesian platform for oncology clinical trials, Jacki has been working in biostatistics and bioinformatics for over 15 years. She started in cardiology research at the TIMI Study Group at Harvard Medical School before working in Alzheimer’s Disease genetics at Boston University and in biomarker discovery for cancer immunotherapies at the Hammer Lab. Most recently she was the Lead Biostatistician at the Institute for Next Generation Health Care at Mount Sinai.

An open-source enthusiast, Jacki is also a contributor to Stan and rstanarm, and the author of the survivalstan package, a library of Stan models for survival analysis.

Last but not least, Jacki is an avid sailor and skier!

Our theme music is « Good Bayesian », by Baba Brinkman (feat MC Lars and Mega Ran). Check out his awesome work at https://bababrinkman.com/ !

Thank you to my Patrons for making this episode possible!

Yusuke Saito, Avi Bryant, Ero Carrera, Brian Huey, Giuliano Cruz, Tim Gasser, James Wade, Tradd Salvo, Adam Bartonicek, William Benton, Alan O'Donnell, Mark Ormsby, Demetri Pananos, James Ahloy, Jon Berezowski, Robin Taylor, Thomas Wiecki, Chad Scherrer, Nathaniel Neitzke, Zwelithini Tunyiswa, Elea McDonnell Feit, Bertrand Wilden, James Thompson, Stephen Oates, Gian Luca Di Tanna, Jack Wells, Matthew Maldonado, Ian Costley, Ally Salim, Larry Gill, Joshua Duncan, Ian Moran, Paul Oreto, Colin Caprani, George Ho, Colin Carroll, Nathaniel Burbank, Michael Osthege, Rémi Louf, Clive Edelsten, Henri Wallen, Jonathan Sedar, Hugo Botha, Vinh Nguyen, Raul Maldonado, Marcin Elantkowski, Tim Radtke, Adam C. Smith, Will Kurt and Andrew Moskowitz.

Visit https://www.patreon.com/learnbayesstats to unlock exclusive Bayesian swag ;)

Links from the show:

• Nominate "Learn Bayes Stats" as "Best Podcast of 2021" and "Best Tech Podcast" by entering its Apple feed in this form!
• Jacki on Twitter: https://twitter.com/jackiburos
• Jacki on GitHub: https://github.com/jburos
• Jacki on Orcid: https://orcid.org/0000-0001-9588-4889
• survivalstan -- Survival Models in Stan: https://github.com/hammerlab/survivalstan
• rstanarm -- R model-fitting functions using Stan: http://mc-stan.org/rstanarm/
• Generable -- Bayesian platform for oncology clinical trials: https://www.generable.com/
• StanCon 2020 ArviZ presentation : https://github.com/arviz-devs/arviz_misc/tree/master/stancon_2020
• Thinking in Bets -- Making Smarter Decisions When You Don't Have All the Facts : https://www.goodreads.com/book/show/35957157-thinking-in-bets
• Scott Kelly and his space travels (in French): https://www.franceculture.fr/emissions/la-methode-scientifique/la-methode-scientifique-mardi-30-janvier-2018
• Bayesian Workflow paper: https://arxiv.org/pdf/2011.01808v1.pdf
• Bayesian Survival Analysis: https://www.springer.com/gp/book/9780387952772
• Bayesian Survival Analysis Using the rstanarm R Package: https://arxiv.org/pdf/2002.09633.pdf
• Survival Analysis, A Self-Learning Text: https://www.springer.com/gp/book/9781441966452
• Survival and Event History Analysis, A Process Point of View: https://www.springer.com/gp/book/9780387202877
• Prognostic Significance of Tumor-Infiltrating B Cells and Plasma Cells in Human Cancer: https://clincancerres.aacrjournals.org/content/24/24/6125