Skip to Main Content

COVID-19 is an emerging, rapidly evolving situation.

What people with cancer should know:

Get the latest public health information from CDC:

Get the latest research information from NIH:

Principal Investigator
Sachet Shukla
Dana-Farber Cancer Institute
Position Title
Computational Lead Scientist
About this CDAS Project
PLCO (Learn more about this study)
Project ID
Initial CDAS Request Approval
Oct 22, 2020
Investigating multi-modal signatures of clinical outcome to immunotherapy in renal cell carcinoma
Recent studies have highlighted the power of integrated analysis of high-throughput genomic and transcriptomic profiling data in identifying signatures of response and resistance to cancer therapies. Furthermore, metabolomic profiling can also be a useful tool in understanding the correlates of clinical outcome. We have previously analyzed data from a checkpoint blockade cohort of >700 patients in clear cell renal cell carcinoma which has yielded multiple genomic, transcriptomic, immunoflourescence and metabolomic correlates of response (Li, Nat Comm 2019; Braun, Nat Med 2020). In the next phase of the project we intend to perform an integrated analysis across data types, particularly through comparative analysis of metabolomic, proteomic and other omic profiles of healthy individuals. We specifically plan to use metabolomics data from PLCO. We plan to use metabolomic data from healthy (non-cancer) individuals from PLCO and compare it to the data from the renal cancer samples in our cohort to elucidate signatures of cancer and changes associated with treatment. We also plan to compare baseline case samples from PLCO with immediately prior-to-treatment samples from our cohort in order to investigate metabolic phenotypes that are observable at baseline.

The main objectives of this project are to:
(i) identify baseline metabolomic markers distinguishing renal cancer patients from healthy donors.
(ii) uncover novel pathways governing clinical outcome to immunotherapy in renal cell carcinoma by simultaneous interrogation across different data types, including metabolomics data from PLCO.
(iii) identify multi-modal signatures of response and resistance to immunotherapy in kidney cancer.
(iv) elucidate metabolic phenotypes that are common as well as those that are divergent between baseline and at-diagnosis time points.


Dr. Toni Choueiri, Dana-Farber Cancer Institute
Dr. Marios Giannakis, Dana-Farber Cancer Institute