# Relating Drugs to Genotypes to Transform Precision Cancer Therapeutics with Tuba-seq - a Novel, Highly Scalable and Quantitative Preclinical Experimental Oncology Platform

> **NIH NIH R44** · D2G ONCOLOGY, INC. · 2020 · $1,134,487

## Abstract

PROJECT SUMMARY
D2G Oncology, Inc. proposes to develop a novel preclinical experimental platform that will effectively relate
cancer drugs to genotypes (“D2G”) to predict pharmacogenomic interactions. D2G Oncology's innovative
approach dramatically improves on established autochthonous mouse models of human cancer. These proven
models allow controlled genomic alterations to initiate tumors in vivo in an appropriate immune-competent
microenvironment and faithfully recapitulate progression of human cancer. D2G's innovative methods for the
first time enable these animal models to become truly scalable and rigorously quantitative, and hence prac-
tical to support drug discovery. D2G's approach can efficiently interrogate a large matrix of tumor genotypes to
predict differential patient responses to therapies. Pharmaceutical companies are eager to obtain this infor-
mation. D2G will significantly advance the state of the art in precision cancer therapy by helping pharma to
rationally select candidate compounds to advance and better match them to patients. D2G's oncology platform
will increase the success rate of clinical trials and lead to more effective personalized cancer treatments.
The key innovation is a novel tumor barcoding and sequencing (Tuba-seq) pipeline. Every clonal tumor is
uniquely barcoded, so the identity and number of cancer cells in each tumor can be readily quantified from
bulk tumor-bearing tissues. Combined with lentiviral-mediated CRISPR/Cas9 somatic genome editing, tumor
barcoding allows many predefined tumor genotypes to be generated all at once in individual animals and
tracked separately. Tuba-seq enables many tens of experiments (which would each ordinarily require separate
cohorts of mice) to be multiplexed into a single mouse. Compared with conventional genetically engineered
mouse models, this approach enormously enhances scalability, introduces rigorous quantification, and reduces
sources of variation.
The overall goal of the proposed Direct Phase 2 SBIR project is to transform the Tuba-seq pipeline into a robust
platform that can be marketed as a commercial service to pharmaceutical companies. Specific aims are to (1)
expand the panel of tumor suppressor genes that the platform interrogates and carefully calibrate their effect
sizes and (2) rigorously validate the ability of the platform to resolve small but clinically meaningful differences
in tumor suppressor gene-drug effect sizes with high statistical confidence, relying only on small cohorts of ani-
mals.
D2G will create the first practical and scalable preclinical experimental modeling approach that can assess how
candidate drugs interact with diverse, precisely-engineered cancer genotypes to predict differential patient
responses to therapy.

## Key facts

- **NIH application ID:** 10007689
- **Project number:** 1R44CA250672-01
- **Recipient organization:** D2G ONCOLOGY, INC.
- **Principal Investigator:** Ian Paul Winters
- **Activity code:** R44 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2020
- **Award amount:** $1,134,487
- **Award type:** 1
- **Project period:** 2020-09-08 → 2022-08-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10007689

## Citation

> US National Institutes of Health, RePORTER application 10007689, Relating Drugs to Genotypes to Transform Precision Cancer Therapeutics with Tuba-seq - a Novel, Highly Scalable and Quantitative Preclinical Experimental Oncology Platform (1R44CA250672-01). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10007689. Licensed CC0.

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