ctDNA Trials in Breast Cancer: How TREAT ctDNA, DARE and Apollo Are Redefining Early Intervention
Adam Blum
Feb 11, 2026
Acting Before the Scan: The New Era of ctDNA-Guided Breast Cancer Trials:
In breast cancer, recurrence is often detected when it becomes visible on imaging. But what if we could intervene months earlier — when relapse is detectable only at the molecular level?
That’s the promise of ctDNA (circulating tumor DNA). Tiny fragments of tumor DNA circulating in the blood can signal molecular relapse before metastases appear on scans.
Several groundbreaking breast cancer trials are testing this strategy right now. The opportunity is enormous. But so is the complexity.
What Is a ctDNA-Guided Trial?
These trials don’t wait for radiographic recurrence.
Instead, they:
Monitor patients after primary therapy
Detect molecular residual disease (MRD) via ctDNA
Trigger treatment escalation when ctDNA becomes positive
This is not traditional adjuvant therapy.
This is molecularly triggered intervention.
Let’s look at three major examples.
1️⃣ TREAT ctDNA
Population: ER+/HER2− early breast cancer
Trigger: ctDNA positivity after completion of primary therapy
Intervention: Elacestrant vs standard endocrine therapy
This Phase III trial asks a critical question:
If we switch endocrine therapy at the moment of molecular relapse, can we delay or prevent metastatic disease?
No radiographic recurrence is required. The “event” is ctDNA positivity.
If successful, this trial could redefine how we manage late relapse in ER+/HER2− disease.
2️⃣ DARE
Population: Stage II–III ER+/HER2− high-risk patients
Strategy: ctDNA surveillance to guide “second-line adjuvant therapy”
DARE focuses on patients who appear clinically stable but are biologically high risk.
If ctDNA becomes detectable, therapy is escalated.
The trial is built on a simple but powerful idea:
Biology may reveal recurrence before imaging does.
3️⃣ Apollo
Population: Triple-negative breast cancer (TNBC)
Setting: After neoadjuvant chemotherapy and surgery
Strategy: Randomize ctDNA-positive patients to intensified “boost” therapy
TNBC carries a high relapse risk, particularly after residual disease.
Apollo tests whether intervening at molecular relapse can improve outcomes in this aggressive subtype.
Why ctDNA Trials Are Hard to Match
These trials are among the most eligibility-sensitive in oncology.
Common barriers include:
⏱ Timing Windows
Must be within X months of surgery
Must have completed chemotherapy within Y weeks
Must have recent negative imaging
🧬 Assay Requirements
Tumor-informed vs tumor-naïve testing
Specific detection thresholds
Approved laboratory platforms only
💊 Prior Therapy Restrictions
Prior CDK4/6 inhibitor exposure
SERD exposure
Prior immunotherapy (especially in TNBC)
📊 Disease Definitions
“No radiographic evidence of disease”
Specific staging requirements
Residual disease criteria after neoadjuvant therapy
A patient may look like a match on the surface —
but fail on a single timing detail or prior-drug rule.
How CancerBot Helps Patients Find the Truly Matching ctDNA Trial
CancerBot doesn’t rely on keyword search.
It converts trial eligibility criteria into structured data and compares them directly to the patient’s profile.
Here’s how that matters:
1️⃣ Precision Extraction of Eligibility Rules
CancerBot identifies:
ctDNA assay definitions
Timing cutoffs relative to surgery or chemo
Required imaging status
Prior therapy exclusions
Biomarker requirements (ER, HER2, PD-L1, etc.)
2️⃣ Real Eligibility Matching
Instead of “you might qualify,” CancerBot can show:
Hard exclusions
Soft gray areas for investigator discussion
Missing data needed to confirm eligibility
3️⃣ Faster Site Engagement
For ctDNA trials, speed matters.
When ctDNA turns positive, enrollment windows can be narrow.
CancerBot generates:
Structured eligibility summaries
Investigator-ready checklists
Clear explanations of fit
That means fewer false leads and faster action.
Precision Trials Require Precision Matching
ctDNA-guided therapy represents one of the most important shifts in breast cancer research:
From treating visible disease
to treating molecular relapse
But the trials are complex.
And complexity demands structured matching — not guesswork.
CancerBot was built for this.
