# Clinical trial data analysis to design novel treatment regimens in oncology

> **NIH NIH F30** · HARVARD MEDICAL SCHOOL · 2021 · $37,751

## Abstract

Project summary
Despite the recent development of new treatment modalities in oncology, the overall approval rate for cancer
therapeutics is low: only 3% of drugs tested in a phase 1 setting are ultimately found superior to the standard
of care in a phase 3 setting. Methods to more accurately understand drug activity in small patient populations,
such as those in phase 1 clinical trials, could better estimate drug efficacy early in the drug development
pipeline, help improve the success rate of clinical trials in oncology, and is one of the NCI’s 2020 “provocative
questions.” Such methods will become critical as the number of novel drug monotherapies continues to grow
and it becomes increasingly impractical to test all promising drug combinations. My proposal aims to develop
statistical methods to make more precise estimates of combination drug efficacy from small amounts of clinical
data, and to develop experimental methods to select combination therapies likely to be effective in human
trials. Aim 1 will develop methods to make more precise estimates of drug efficacy from traditional early-phase
(phase 1 and phase 2) clinical trials. Through the systematic analysis of 152 clinical trials for breast, colorectal,
lung, and prostate cancer, I found that a single parametric form describes survival distributions across cancer
types and therapies. I will test if application of this parametric form increases the precision of estimates for
phase 3 drug efficacy from early-phase trials. I will make this dataset and methods publicly available to
catalyze future progress in the analysis of clinical trials. Aim 2 will apply new statistical methodology, including
that described in Aim 1, to analyze early-phase monotherapy data and to estimate the efficacy of drug
combinations. We used this approach to analyze data from small numbers of patient-derived xenografts. We
estimated the benefit expected for a novel combination under a mathematical "sum of benefits" model, in which
monotherapies exert independent effects on tumor shrinkage, to identify a promising drug combination for T-
cell lymphomas. I will use a similar approach to analyze early-phase human clinical trial monotherapy data in
the setting of advanced solid tumors and model the expected survival benefit of drug combinations. Aim 3 will
develop an experimental paradigm for selecting combination therapies likely to be successful in human clinical
trials, and apply it to triple-negative breast cancer. Previous analysis of clinical trial data by our group
demonstrates that in the setting of advanced solid malignancies, most successful drug combinations are made
of effective single agents with nonoverlapping mechanisms of drug resistance, a principle described as
independent action. Aim 3 will test combinations of drugs selected based on independent action across a
heterogeneous panel of 18 triple-negative breast cancer cell lines, as well as those identified in Aim 2, to
assess whether this design par...

## Key facts

- **NIH application ID:** 10230716
- **Project number:** 1F30CA260780-01
- **Recipient organization:** HARVARD MEDICAL SCHOOL
- **Principal Investigator:** Deborah Plana
- **Activity code:** F30 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $37,751
- **Award type:** 1
- **Project period:** 2021-06-01 → 2024-05-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10230716

## Citation

> US National Institutes of Health, RePORTER application 10230716, Clinical trial data analysis to design novel treatment regimens in oncology (1F30CA260780-01). Retrieved via AI Analytics 2026-05-23 from https://api.ai-analytics.org/grant/nih/10230716. Licensed CC0.

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