For several years, there's been steady news about using drugs or combinations in ultra-low Her2 categories.
See a new concensus article by Rakha et al., 'International Expert Consensus Recommendations for HER2 Reporting in Breast Cancer: Focus on HER2-LOW and ULTRALOW Categories."
It's open-access at Modern Pathology.
https://www.modernpathology.org/action/showPdf?pii=S0893-3952%2825%2900223-6
The provenance is:
On behalf of the UK National Coordinating Committee of Breast Pathology, the Association of Breast Pathology, the European Working Group for Breast Screening Pathology and the International Society of Breast Pathology.
The abstract states in part,
- Updated clinical management guidelines now, therefore, incorporate the spectrum of HER2 results into treatment selection algorithms in the metastatic setting.
- To align histopathologic practice with these developments, the College of American Pathologists has issued a new biomarker-reporting template that recommends explicit distinction between IHC 0/absent membrane staining and IHC 0+/with membrane staining.
- Key concerns among pathologists include assay variability, scoring reproducibility, and quality assurance standards for accurately detecting such low levels of HER2 expression.
- This manuscript provides expert consensus, evidence-based practical recommendations for identifying and reporting tumors with HER2-low and HER2-ultralow expression.
- We emphasize standardized testing protocols, validated assays, robust internal and external controls, and focused training for pathologists. A universal structured pathology report is proposed to highlight the accurate distinction between IHC 0 (null), IHC 0+ (ultralow), and HER2-low expressions.
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AI CORNER
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Below, find first an AI summary-in-detail of the whole paper.
After that, I ask AI to go further into 3 topics. (1) Will AI imaging help? (2) What about other cancers (eg lung). (3) What about molecular studies - could they be cleaner than endless fuss about ultra-low.
Below is a graduate-level synthesis of what is genuinely new, non-obvious, or strategically disruptive in the Rakha et al. international consensus, followed by a clear articulation of the major unresolved problems that remain. I assume readers already know the title, abstract, and DESTINY trial headlines.
What Is New or Surprising
1. HER2 is no longer binary—even at IHC 0
The most consequential shift is the formal fracture of “HER2-negative” into HER2-null (0) versus HER2-ultralow (0+), where any faint membranous staining in ≤10% of cells is now clinically meaningful. This is not just semantic. In DESTINY-Breast06, 64% of tumors locally called IHC 0 were reclassified centrally as ultralow or low, meaning a majority of patients historically excluded from HER2-directed therapy were miscategorized .
This reframes HER2 testing from a gatekeeper for amplification to a sensitivity assay for drug delivery.
2. Therapeutic logic has inverted
The paper makes explicit something often left implicit:
T-DXd efficacy is largely independent of HER2 oncogenic signaling.
Response correlates with presence of surface protein, not amplification.
Bystander killing and membrane-permeable payload explain activity even in HER2-ultralow and nominally “HER2-null” disease.
DAISY trial responses (~30%) in HER2-nonexpressing tumors underscore that HER2 may function as a delivery address, not a driver.
This is a profound conceptual break from the trastuzumab era and quietly undermines decades of HER2 biology-centric thinking .
3. Regulators moved faster than professional guidelines
ASCO/CAP explicitly declined to endorse HER2-low as a reporting category in 2023—yet FDA and EMA approvals already require identifying HER2-low and ultralow patients.
The result is a rare regulatory asymmetry:
Drug labels require granularity
Guidelines historically discouraged it
CAP’s 2025 synoptic template (0 / 0+ / 1+ / 2+ / 3+) is effectively a retroactive repair to align pathology infrastructure with regulatory reality .
4. Locked companion diagnostics are now mandatory
A subtle but critical operational change:
For T-DXd eligibility, only the FDA-locked Ventana 4B5 protocol is validated.
User-adjustable parameters (e.g., antigen retrieval) are no longer allowed.
“More sensitive” assays are explicitly discouraged absent outcome data.
Analytical superiority ≠ clinical validity.
This is a notable tightening of the diagnostic ecosystem and signals a broader FDA posture toward drug-anchored diagnostics .
5. HER2 heterogeneity is now actionable
The consensus legitimizes practices that would previously have felt excessive:
Testing multiple specimens (primary, metastasis, residual disease)
Accepting any HER2-low result across the disease course as sufficient
Recognizing temporal drift in HER2 expression as common, not exceptional
This formally acknowledges that sampling error is now a treatment-limiting variable, not a nuisance artifact .
Key Unmet Problems and Open Risks
1. Reproducibility at the low end is poor—and known
Interobserver agreement collapses precisely where decisions now matter most (0 vs 0+ vs 1+). Even central labs reclassify a third to half of cases. This is not a training defect alone; it reflects biological signal hovering at assay detection limits.
The consensus concedes misclassification is unavoidable.
2. No validated quantitative alternative exists
Despite enthusiasm for:
Quantitative immunofluorescence
RNA expression
HERmark-type assays
None are validated against clinical outcomes for T-DXd. Pathology is therefore trapped in a paradox:
We know IHC is crude—but it is the only assay regulators accept.
3. AI cannot fix missing ground truth
AI mirrors human performance:
Excellent at 3+
Weak at 1+ and 0+
Without a stable reference standard, AI risks automating uncertainty, not resolving it. The paper is refreshingly blunt that AI is adjunctive at best, not transformative—yet implementation costs remain high.
4. Pharma-driven taxonomy creep
The authors openly acknowledge discomfort with drug-defined biomarker categories. HER2-low and ultralow are not biologically discrete entities, yet pathology workflows are being reshaped around them.
This sets a precedent: biomarkers may increasingly be re-partitioned retroactively as new ADCs arrive.
5. Clinical benefit thresholds remain undefined
Even if detection is perfect:
Response rates in ultralow disease are modest (~20%)
Toxicity (ILD risk with T-DXd) is nontrivial
Predictive enrichment beyond “any HER2” is absent
We still lack a principled answer to:
How little HER2 is enough—and for whom?
Bottom Line for Expert Readers
This consensus does not merely update HER2 reporting—it redefines what HER2 testing is for. Pathology is being asked to detect signal at the edge of physics to support a therapy whose mechanism no longer depends on the biology the test was designed to measure.
The infrastructure is straining, the categories are unstable, and the clinical upside—while real—is bluntly distributed. Yet the direction is irreversible.
HER2 has quietly become the prototype for a post-driver, post-binary biomarker era—and pathology is being rebuilt in real time to keep up.
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OK. Discuss (and there may not be much in the paper itself) about the role of (1) computer imaging, (2) other disorders (eg Her2 in lung cancer) and (3) the role of molecular Her2 studies, will they replace all this fuss about "ultra low" with molecular answers.
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Below is a forward-looking, expert discussion that deliberately goes beyond what is fully settled in the Rakha consensus, while staying anchored to what the paper does say—and just as importantly, what it implicitly admits it cannot yet solve.
1. Computer Imaging & AI: Helpful, but Fundamentally Constrained
The paper is unusually candid about AI’s limits. While digital pathology and AI-assisted HER2 scoring are actively explored, the consensus is clear that AI currently mirrors—not surpasses—human variability, especially at the low end of expression .
What AI can realistically do (near term)
Reduce random noise: quantify percent staining, enforce consistency.
Standardize workflows across labs once locked protocols are used.
Assist triage: flag borderline cases for secondary human review.
What AI cannot yet do
Create a ground truth where biology itself is continuous and sparse.
Reliably discriminate 0 vs 0+ vs 1+, where interobserver and inter-algorithm agreement is weakest.
Correct for pre-analytic degradation, fixation artifacts, or true tumor heterogeneity.
The meta-analytic data cited in the paper are striking: AI sensitivity is excellent for HER2 3+, acceptable for 2+, and drops sharply for 1+, precisely where therapeutic decisions now hinge .
Bottom line: AI can discipline IHC interpretation, but it cannot rescue a biologically faint signal that sits at (or below) the assay’s detection floor. AI will be incremental, not revolutionary, unless paired with a fundamentally different measurement modality.
2. HER2 Beyond Breast Cancer: A Canary in the Coal Mine
Although the Rakha paper is breast-centric, its implications extend far wider—and more uncomfortably—to HER2 across solid tumors.
Lung cancer (and others) complicate the story
In lung, gastric, colorectal, and other cancers:
HER2 alterations are often mutational (e.g., exon 20 insertions) rather than amplificational.
Protein expression may be discordant with genomic status.
ADC responsiveness may depend more on payload delivery and bystander effects than classical receptor biology.
This creates an uncomfortable truth:
The breast cancer HER2 IHC paradigm is already misaligned with HER2 biology elsewhere.
What breast cancer is experiencing now—arguing over faint protein detection to justify ADC use—is likely a preview of similar debates in lung and GI cancers as ADCs expand indications.
The key signal from breast cancer is this:
Once ADCs decouple efficacy from oncogenic dependence, any detectable protein becomes actionable—and pathology becomes a delivery eligibility assay, not a biology assay.
3. Molecular HER2 Studies: Will They Replace the “Ultralow” Fuss?
Short answer: not yet—and not cleanly
The Rakha consensus explicitly acknowledges emerging molecular approaches—HER2 mRNA quantification, quantitative IF, HERmark, and other platforms—but draws a firm line: none are validated against T-DXd outcomes .
Why molecular assays seem like the solution
They offer continuous variables rather than coarse ordinal bins.
They promise better sensitivity and reproducibility.
They align more naturally with ADC pharmacology.
Why they haven’t replaced IHC
Regulatory anchoring
T-DXd approvals are tied to specific IHC assays, not abstract HER2 biology.Clinical trial inertia
DESTINY trials defined eligibility by IHC. Molecular cutpoints lack outcome-calibrated thresholds.Biological mismatch
ADC efficacy depends on surface protein density, internalization, and spatial distribution—not just RNA abundance or total protein mass.Practical pathology economics
IHC remains cheap, fast, and globally deployable. Molecular assays are not.
The deeper irony
Even if molecular assays become exquisitely sensitive, they may worsen the problem by revealing:
HER2 expression in nearly all tumors at some level
A blurred boundary between “null” and “expressing”
At that point, the field faces a new question:
If everyone has some HER2, who shouldn’t get the ADC?
That is a clinical utility problem, not a technical one.
Synthesis: Where This Is Actually Heading
Taken together, the paper and the broader landscape suggest the following trajectory:
Short term (now–3 years)
Locked IHC + heavy QA
AI as an assistive consistency tool
Continued angst over 0 vs 0+ vs 1+
Medium term
Hybrid models: IHC + quantitative overlays
Selective molecular testing in equivocal cases
Tumor-agnostic ADC eligibility debates
Long term (if ADCs dominate)
HER2 redefined as a drug delivery biomarker
Pathology reporting shifts from categories → probabilities
Molecular assays may replace IHC, but only after trials redefine eligibility biologically, not historically
The Quiet Takeaway
The “ultralow” debate is not a failure of pathology—it is a symptom of a therapeutic revolution outrunning its biomarkers.
Until trials are designed around delivery biology rather than legacy receptor scoring, no amount of AI or molecular precision will fully resolve the discomfort. For now, the field is forced to extract ever more clinical meaning from a test never designed to work at this scale.
That tension—more than any staining nuance—is the real message of the paper.
