# Forecasting tumor evolution: can the past reveal the future?

> **NIH NIH DP1** · STANFORD UNIVERSITY · 2021 · $1,099,000

## Abstract

Summary
Clonal evolution is the driving force behind many current public health issues such as cancer and infectious
disease. However, limited efforts have been invested in treating and preventing these conditions from an
evolutionary perspective. Critically, the ability to forecast tumor evolution depends on the relative contribution
of deterministic and stochastic processes. Although direct observations of human tumor evolution are
impractical, patterns of somatic alterations amongst cells within a tumor faithfully report on their past
proliferative history. Unexpectedly, we recently found that after transformation, some tumors grow in the
absence of stringent selection, compatible with effectively neutral evolution. This led to our description of a
novel Big Bang model of tumor growth where the tumor grows as a single terminal expansion populated by
numerous heterogeneous—and effectively equally fit subclones. This new model contrasts with the de facto
sequential clonal expansion model, and suggests that tumor-initiating events are both necessary and sufficient
to propagate subsequent growth. Moreover, these findings raise the tantalizing possibility that the earliest
events during tumor growth shape its subsequent evolutionary trajectory. Here we rigorously test the novel
hypothesis that early tumor evolution is deterministic and seek to define its contingencies. We thus perform
oncogene-engineering and cellular barcoding of wild-type human organoids to characterize clonal dynamics
and the functional determinants of increased fitness during in vitro tumor evolution. This innovative lineage
tracing strategy enables the direct measurement of evolutionary parameters in human cells, while rendering a
comprehensive genotype to phenotype map during tumor progression. In parallel, we will infer the timing of
metastatic dissemination and evaluate whether the metastatic phenotype is specified early through
computational and mathematical modeling of patient genomic data. This systems biology approach will
evaluate the predictability of tumor evolution towards the development of models to forecast disease
progression and guide earlier detection, thereby reducing cancer related mortality.
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## Key facts

- **NIH application ID:** 10224138
- **Project number:** 5DP1CA238296-04
- **Recipient organization:** STANFORD UNIVERSITY
- **Principal Investigator:** Christina N Curtis
- **Activity code:** DP1 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2021
- **Award amount:** $1,099,000
- **Award type:** 5
- **Project period:** 2018-09-30 → 2023-07-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10224138

## Citation

> US National Institutes of Health, RePORTER application 10224138, Forecasting tumor evolution: can the past reveal the future? (5DP1CA238296-04). Retrieved via AI Analytics 2026-05-26 from https://api.ai-analytics.org/grant/nih/10224138. Licensed CC0.

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