Sprint for the Cure: p1RCC Hackathon
Introduction
Following the first hackathon and its significant momentum for NF2 research and our NF2 patient community partner, Silicon Valley Artificial Intelligence (SVAI) hosted its second annual hackathon: p1RCC Cancer Genomics Research. We welcomed brilliant scientists, engineers, and students from all over North America, and Canada — and were blown away by the talent and results from the weekend.
What does it take to bring over 150 participants together to solve for a rare disease with no known treatment? Let’s dive right in.
Inspiration Behind the Hack
After our successful first hackathon event, NF2, a key hackathon contributor, Bill Paseman, reached out to us: he was diagnosed with stage IIIB papillary renal-cell carcinoma type 1 (p1RCC).
Bill’s diagnosed disease falls under the carcinoma family: Papillary renal-cell carcinoma (pRCC). pRCC accounts for between 15 to 20% of all kidney cancers. It occurs in the cells lining the small tubules in the kidney that filter waste from the blood and make urine. Little is known about the genetic basis of sporadic pRCC, and no effective forms of therapy for advanced disease exist.
Realizing Bill was racing against time, SVAI jumped into deep conversations with Bill and after eight months, SVAI announces its second computational cancer genomics event in SVAI’s Collaborative Research Series, p1RCC, in partnership with RareKidneyCancer.org, Salesforce, Google, NIH, NCBI and more.
The Event Itself
SVAI hosted over 150 researchers, engineers and enthusiasts at Salesforce in San Francisco for an intense weekend of exploration in computational biomedicine. Interdisciplinary teams of computer scientists and biologists collaborated to further understand the disease, for development of potential interventions and to advance the standard of care for p1RCC. In addition to sequencing Bill’s genomic data for this event, SVAI used genomic datasets for p1RCC through the NIH’s Cancer Genome Atlas.
Major Gold-striking: “Nexflix for genes, genomic deep learning, …”
Let’s dive into the deep end of this pool — the scientific advancements that came out of this event. All teams did brilliant work, which can be found here, but a few teams stole the show with some amazing discoveries:
DeeperDrugs —Implementation of rigorous variant filtering and target pruning, including a CRISPR/Cas9 repair design, that pipelines into drug discovery with deep learning, which includes training a DeepChem graph convolutional model, searching for optimal hyperparameters, and applying downstream experimental verification.
AIzheng — Modeled TCGA-RCC tumors as a “time series” across stage (Normal, I, II, III, IV) for each subtype of p1RCC: KIRC, KIRP, KICH. This team evaluated ability of a neural network to discriminate across subtype and stage, constructed stage-specific co-expression networks, and finally identified shared gene interaction communities across each tumor stage.
RecausalNucleotideNetworks — Evaluated the analytical methods being used on BGI-SEQ and to explore the possibility to improve tools for this data type by training and applying genomic deep learning models, including training a BGI-SEQ model for Clairvoyante.
DamTheRiver — Identified of neo-antigens present within patent P1RCC sequence data by machine learning major histocompatibility complex affinity tool. This team implemented a very clean, sophisticated pipeline that ultimately identified 25 peptides with high MHC binding affinity.
ExpressForce — “Nexflix For Genes”: Provided candidate biomarkers for p1RCC via a collaborative filtering using probability matrix factorization after obtaining data from COSMIC, a well-known online cancer catalogue. As an overview, this team created a Sample ID vs Gene matrix table, implemented Naive Bayes algorithm, created entity embeddings of categorical variables, and applied dimensional reduction to find candidate biomarkers for p1RCC.
Overall, these highlighted teams contributed greatly into out understanding of p1RCC, relevant genomics variants and biomarkers of p1RCC, and the accuracy of genomic tools involved in generating and analyzing p1RCC-related data. These teams did outstanding work, and so did all the other teams! I highly recommend checking out the unique discoveries and approaches made by all the teams here.
Gold is good, Diamond is Better
The hackathon was an outstanding success and the teams were all too brilliant, but as movers and shakers in this world, a few teams discovered a drive to dive deeper into the mysteries of p1RCC:
DeeperDrugs, HelloKidney2, and KidneyBean.
In coordination with Bill Paseman and RareKidneyCancer.org, SVAI currently strives to provide more structure, support and visibility to the projects started at our research events. More news to come! Check out SVAI’s research page for the latest updates.
Are You In?
SVAI is a 501(c)(3) non-profit dedicated to building a strong collaborative community committed to accelerating AI for computational biomedicine.
Here’s how you can get involved:
- Join our community. ❤ Participate in our ongoing and future activities!
- Donate: Your support helps us sustain and grow our activities.
- Something else in mind? (*cough* COLLAB?) You’re welcome to contact me at lily@sv.ai
Thanks for reading! Let us know your thoughts.
Cheers,
Lily
Special shoutout to the main event organizers and collaborators: Bill Paseman, Clayton Mellina, Ben Busby, Sean Davis, Pete Kane, Nina Sardesh, Ryan Leung, Hunter Dunbar, Annabelle Tang, and Ripley Jene.
