Research to the People and rarekidneycancer.org are bringing 3 projects to this hackathon.

The papillary renal cell carcinoma type 1 (p1RCC) sample was provided by a rarekidneycancer.org  patient and processed by UCSF's Max Meng/Tasha Lea and Yale's Kaya Bilguvar.  It includes:

Exome Data:
WES BLOOD NORMAL 150X
WES KIDNEY TUMOR 300X

RNA-seq data:
RNA-seq KIDNEY TUMOR - 300M reads
RNA-seq KIDNEY NORMAL – 150M reads

More data is described here.

Teams will have the opportunity to present their work at the beginning and end of the event.  Some have already looked at the Suggested Readings and are working on identifying driver mutations, clinical trials and therapeutics associated with the sample.  In particular, Clemson's Alex Feltus lab has registered two teams for the event.

Clemson’s Reed Bender/Ben Shealy Team description: “We have isolated a list of statistically significant, up and down regulated genes which track the tumor’s unique progression from normal to cancerous. This work is the next stage of the project Reed Bender presented at the NIH “Cancer Moonshot Symposium on Patient Control of Genomic Data for Research and Health”.  There, Reed described how he combined the sample’s RNAseq vector with hundreds of correlated TCGA samples. At TRI-con the team will use a tool called TSPG to compare the sample’s single vector of KIRP expression data with the larger collections of normal tissue (GTEx) and cancer tissue (TCGA) data. TSPG allows for statistical analysis of the vector, even though the sample size is n=1.  TSPG does this by leveraging the publicly available data that associated with the tumor. The step after this will take this list of statistically important genes that are significantly up/down regulated and search for drugs that will alter the transcriptome state of the cells to match the findings of our perturbation generator.  TSPG (transcriptome state perturbation generator) was developed by Ben Shealy along with a former member of Alex Feltus’ lab.”

Clemson's Benafsh Husain Team description: "This study focuses on extracting novel gene-gene relationships based on differential RNA-seq expression levels between GTEx and TCGA kidney samples. We develop two algorithms, one using blob detection and the other using a deep learning architecture on a compressed data representation of the original gene expression matrix to construct a differentially expressed gene correlation network (GCN). We hypothesize that this GCN captures genetic relationships that are specific to kidney cancer.  We are currently in the process of comparing the extracted networks of kidney cancer relationships between the aforementioned algorithms to previously published network extraction tools as well as performing validation to detect biological relevance within the detected GCNs."

Note that teams are not limited to these areas of research.  Other potential projects include:

Adding a "molecular pathways" column to a machine readable version of Table 1 (cancer substypes and therapeutics) in Recommendations for the Management of Rare Kidney Cancers.  This column could use terms from geneontology.org or an equivalent source, and could include rows discovered by the driver mutations team.

Determine the suitability of p1RCC to CAR-T therapy. E.g. Identify neoantigen candidates that could be exploited by CAR-T or RNA based therapeutics or calculate other biomarker signatures that have been investigated and applied in oncology such as tumor mutational burden (TMB) and microsatellite instability (MSI)

For more information and to join a team please visit https://www.triconference.com/hackathon. To join a Research to the People team please indicate your choice on the confirmation qualifier form once you register.