# Recording the natural history of cancer progression using a Crainbow model of HER2+ cancer

> **NIH NIH R03** · ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI · 2022 · $99,796

## Abstract

PROJECT SUMMARY/ABSTRACT
Background: The step-wise accumulation of somatic mutations and progressive changes to cell behavior
provides a rationale for the cancer screening era. However, this fails to explain de novo diagnosis of lethal
metastatic disease in the apparent absence of screen detectable precancerous lesions. Accumulating evidence
suggests that invasive cancers may initiate without a prolonged and detectable precancerous phase. In addition,
proliferative lesions do not deterministically progress, suggesting that many early cancers are overtreated.
Although a dramatic increase in early-detection has been observed, a similar dramatic decrease in lethal
metastatic disease has not been observed. Some have even argued that many screen-detected early lesions
spontaneously resolve or regress. In contrast, the most lethal cancers escape detection and metastasize prior
to detection.
Hypothesis: We hypothesize that cancer progression, like normal tissue homeostasis, is stochastic and subject
to defined spatial and microenvironmental constraints.
Specific Aims: Aim 1. To define the homeostatic principles underlying the progression of breast cancer in a
mouse model of HER2+ breast cancer. Aim 2. To dissect the role of HER2 isoforms on the metastatic cascade
of breast tumors.
Study design/Methods: Here we will use a proven cancer rainbow modeling system for visualizing and
analyzing tumor growth rates in vivo. State-of-the-art multiphoton live imaging will be used to image and quantify
tumor progression potential in space and time within the mammary gland along with microenvironmental features
(ECM, macrophages, blood vessels). Longitudinal imaging over eight weeks will capture the rates of tumor
progression and the origin of invasive breast cancers in an in vivo and immune intact mouse model.
Relevance: More than six decades ago, surgical oncologists seeking to explain why earlier surgical intervention
had not improved breast cancer survival, postulated that adaptations between the tumor and host set the
trajectory of an invasive cancer many years before diagnosis. Proponents argued that the formative early years
of tumor-TME adaptation had already destined some tumors to become invasive and lethal. In addition, many
breast cancer lesions may actually regress suggesting that cancer progression may be stochastic. Novel imaging
modalities and sophisticated tumor lineage training strategies are needed to solve this challenging problem. Our
experiments utilizing mouse models and high-resolution longitudinal imaging during the course of tumor
progression will determine whether cancer progression is deterministic or not, adding an important value towards
understanding breast cancer progression.

## Key facts

- **NIH application ID:** 10437462
- **Project number:** 1R03CA270679-01
- **Recipient organization:** ICAHN SCHOOL OF MEDICINE AT MOUNT SINAI
- **Principal Investigator:** Jose Javier Bravo-Cordero
- **Activity code:** R03 (R01, R21, SBIR, etc.)
- **Funding institute:** NIH
- **Fiscal year:** 2022
- **Award amount:** $99,796
- **Award type:** 1
- **Project period:** 2022-05-27 → 2024-03-31

## Primary source

NIH RePORTER: https://reporter.nih.gov/project-details/10437462

## Citation

> US National Institutes of Health, RePORTER application 10437462, Recording the natural history of cancer progression using a Crainbow model of HER2+ cancer (1R03CA270679-01). Retrieved via AI Analytics 2026-05-24 from https://api.ai-analytics.org/grant/nih/10437462. Licensed CC0.

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