Introduction

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We are excited to announce the second computational cancer genomics event in SVAI's Collaborative Research Series. This event will focus on papillary renal-cell carcinoma type 1 (p1RCC), in partnership with RareKidneyCancer.org, Salesforce, Google, NIH, and NCBI.

We will invite 150 researchers, engineers and enthusiasts to join us at Salesforce in San Francisco for an intense weekend of exploration in computational biomedicine. Interdisciplinary teams will work to further understand, develop potential interventions and advance the standard of care for p1RCC. In addition to sequencing a patient for this event, we will use genomic datasets for p1RCC through the NIH's Cancer Genome Atlas. 

BACKGROUND

Papillary renal-cell carcinoma, 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 papillary renal-cell carcinoma, and no effective forms of therapy for advanced disease exist.

PURPOSE

• Advance papillary renal-cell carcinoma research.

• Contribute to real, ongoing patient case.

• Create interdisciplinary opportunities for computer scientists and biologists.

• Learn and develop skills in AI/ML, computational biology and cancer genomics.

• Build an open community for collaborative biomedicine discovery.

DATASETS

[1] SVAI facilitated sequencing for one p1RCC patient (paid for by UCSF Health): RNA and DNA Whole Genome Sequencing for Tumor and Blood samples, sequenced at 90x using a BGISEQ-500. The data will be available as .bam and .vcf files.

Detailed write up on the dataset

Register your team to get access to the dataset (For accepted in-person and remote participants)

[2] NIH Cancer Genome Atlas (TCGA) for data for Papillary Renal Cell Carcinoma which includes: RNA-Seq gene expression, identifiable germ-line mutations and some clinical information:

Cases (291)

Genes (12,063)

Mutations (25,723)

PROBLEM TRACKS

• Exploration of disease pathology and current development efforts/ongoing clinical trials

• Identification of genomic characteristics and driver events of p1RCC.

• Ranking somatic mutations of p1RCC.

• Targeting methods for getting constructs into the RCC cells.

• Identifying gene locations where CRISPR or zinc finger can be applied for treatment.

• Identifying off-label therapeutics through mutational homogeneity.

• Orthogonal assessment of mutated alleles by tumour RNA.

• In Silico HLA Typing Using Standard RNA-Seq Sequence Reads.

• Predicting likelihood of mutated peptides binding autologous HLA-A or HLA-B proteins.

• Create a novel drug intervention with DeepChem.

• Create a pRCC clonal evolution model using sequencing info from individual pRCC cases. Here's an similar model for chRCC.

• Analyze the gene expression signature and come up with a treatment regimen. See here.

 

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