78 samples from 2002 Nature study used for recalibration
—
307 samples from the TransBig study used for validation of classification threshold
—
Indications for Use
MammaPrint® is a qualitative in vitro diagnostic test service, performed in a single laboratory, using the gene expression profile of fresh breast cancer tissue samples to assess a patients' risk for distant metastasis. The test is performed for breast cancer patients who are less than 61 years old, with Stage I or Stage II disease, with tumor size ≤ 5.0 cm and lymph node negative. The MammaPrint® result is indicated for use by physicians as a prognostic marker only, along with other clinicopathological factors.
Device Story
MammaPrint is a laboratory-developed service assessing 70-gene expression profiles in fresh breast cancer tissue. Input: RNA extracted from tumor tissue preserved in RNAretain or snap-frozen. Process: RNA isolation, DNase treatment, linear amplification, labeling, and hybridization to custom Agilent oligonucleotide microarrays. Output: Gene expression fingerprint compared to a reference template (mean of 44 tumors with known good clinical outcome). Result: Risk classification (Low/High Risk, including borderline categories). Used in Agendia’s central laboratory; results provided to physicians to assist in prognosis alongside clinicopathological factors. Benefits: Provides prognostic information for recurrence risk in early-stage breast cancer patients.
Clinical Evidence
Clinical evidence includes four studies: Nature (2002, n=78), NEJM (2002, n=151), MammaPrint Paper (2006, reproducibility), and Transbig Paper (2006, n=302). Studies validated the 70-gene signature's ability to predict metastasis-free survival. At 10 years, low-risk profile patients showed 87-88% metastasis-free survival, while high-risk profile patients showed 44-71%. Analytical accuracy is 98.5% with 97.7% classification accuracy.
Technological Characteristics
Microarray-based gene expression analysis. Uses fresh tissue samples preserved in RNAlater. Process involves RNA isolation, DNA'se treatment, linear amplification, labeling, cRNA purification, and microarray hybridization. Classification is based on a 70-gene expression signature compared to a reference standard of 44 tumors.
Indications for Use
Indicated for breast cancer patients <61 years old, Stage I or II, tumor size ≤5.0 cm, and lymph node negative. Not for diagnosis, therapy response prediction, or therapy selection.
Regulatory Classification
Identification
A gene expression profiling test system for breast cancer prognosis is a device that measures the ribonucleic acid (RNA) expression level of multiple genes and combines this information to yield a signature (pattern or classifier or index) to aid in prognosis of previously diagnosed breast cancer.
Special Controls
The device is classified as Class II under regulation 21 CFR 866.6040 with special controls. The special control guidance document "Class II Special Controls Guidance Document: Gene expression profiling test system for breast cancer prognosis" is available at http://www.fda.gov/cdrh/oivd/guidance/1627.html.
*Classification.* Class II (special controls). The special control is FDA's guidance document entitled “Class II Special Controls Guidance Document: Gene Expression Profiling Test System for Breast Cancer Prognosis.” See § 866.1(e) for the availability of this guidance document.
Predicate Devices
MammaPrint® (k062694)
Submission Summary (Full Text)
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# 510(k) SUBSTANTIAL EQUIVALENCE DETERMINATION DECISION SUMMARY
A. 510(k) Number:
k070675
B. Purpose for Submission:
Modifications of the cleared device by changing the specimen type from fresh frozen tissue to fresh tissue stored in a specific RNA preservative solution and XPrint software v1.33 to v1.40.
C. Measurand:
70 gene expression profile
D. Type of Test:
Expression microarray
Test service performed in a single laboratory in Agendia’s Amsterdam facility.
E. Applicant:
Agendia BV
F. Proprietary and Established Names:
MammaPrint®
G. Regulatory Information:
1. Regulation section:
21 CFR 866.6040 Gene expression profiling test system for breast cancer prognosis
2. Classification:
Class II
3. Product code:
NYI, Classifier, prognostic, recurrence risk assessment, RNA gene expression, breast cancer
4. Panel:
Immunology (82)
H. Intended Use:
1. Intended use(s):
MammaPrint® is a qualitative in vitro diagnostic test service, performed in a single laboratory, using the gene expression profile of fresh breast cancer tissue samples to assess a patients' risk for distant metastasis.
The test is performed for breast cancer patients who are less than 61 years old, with Stage I or Stage II disease, with tumor size ≤ 5.0 cm and lymph node negative. The MammaPrint® result is indicated for use by physicians as a prognostic marker only, along with other clinicopathological factors.
2. Indication(s) for use:
Same as intended use
3. Special conditions for use statement(s):
For prescription use only
MammaPrint® is not intended for diagnosis, or to predict or detect response to therapy, or to help select the optimal therapy for patients.
4. Special instrument requirements:
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Agilent 2100 Bioanalyzer: Serial number DE54700497 en DE24802382
Agilent DNA microarray scanner: Serial number us22502555
Note: The scanner and bioanalyzer are components of this assay and are cleared only for this assay and not for any other application. In addition, clearance is only limited to the bioanalyzer and scanner with the serial numbers as specified above.
I. Device Description:
The MammaPrint® test is performed and provided as a service by Agendia Laboratory. The test is a microarray based gene expression analysis of RNA extracted from breast tumor tissue. The test is a custom-designed array chip manufactured by Agilent Technologies using the Agilent oligonucleotide microarray platform which assesses the mRNA expression of the 70 genes in triplicate. The MammaPrint® microarray features eight 1900-feature subarrays per glass slide which can each be individually hybridized. Per subarray 232 reporter genes are printed in triplicate, including the 70 genes which make up the MammaPrint® prognostic profile. Each subarray additionally includes 915 normalization genes and 289 spots for hybridization and printing quality control.
The analysis is based on several processes: isolation of RNA from fresh tumor tissue sections, DNase treatment of isolated RNA, linear amplification and labeling of DNase treated RNA, cRNA purification, hybridization of the cRNA to the MammaPrint® microarray, scanning the MammaPrint® microarray and data acquisition (feature extraction), calculation and determination of the risk of recurrence in breast cancer patients.
The MammaPrint® analysis is designed to determine the gene activity of specific genes in a tissue sample compared to a reference standard. The result is an expression profile, or fingerprint, of the sample. The correlation of the sample expression profile to a template (the mean expression profile of 44 tumors with a known good clinical outcome) is calculated and the molecular profile of the sample is determined (Low Risk, High Risk, Low Risk Borderline, High Risk Borderline).
J. Substantial Equivalence Information:
1. Predicate device name(s):
Agendia BV’s MammaPrint®
2. Predicate 510(k) number(s):
k062694
3. Comparison with predicate:
The device is the same as the predicate, except for the sample collection method. The previous recommended sample collection method was to freeze the sample immediately after collection and then ship fresh frozen to Agendia. The current method uses RNAretain which is a non-toxic tissue storage reagent to stabilize and preserve cellular RNA and therefore, eliminates the need to freeze the samples immediately after harvesting. Samples are transported in RNAretain to Agendia BV for MammaPrint testing. Upon arrival at Agendia, samples are snap-frozen and stored at -70°C.
K. Standard/Guidance Document Referenced (if applicable):
None
L. Test Principle:
The MammaPrint® service is a microarray based gene expression analysis of a tumor. Refer
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to k062694 for detailed description.
## M. Performance Characteristics (if/when applicable):
### 1. Analytical performance:
#### a. Precision/Reproducibility:
i. Reproducibility of multiple isolations starting from tissue sample collected in RNAretain:
In order to determine the reproducibility of the MammaPrint® device process from tissue processing to the end result, five previously analyzed tumor samples (one borderline, two high risk and two low risk) were isolated in duplicate. Over multiple days, the ten isolations from five tumors were processed according to standard MammaPrint® protocols.
| Sample | Original index | Result | Index from first isolation | Index from second isolation |
| --- | --- | --- | --- | --- |
| S1 | 0.376 | High risk (borderline) | 0.254 | 0.374 |
| S2 | 0.608 | Low risk | 0.564 | 0.553 |
| S3 | 0.659 | Low risk | 0.639 | 0.680 |
| S4 | -0.105 | High risk | 0.068 | 0.067 |
| S5 | -0.305 | High risk | -0.171 | -0.337 |
No statistically significant difference in MammaPrint® risk group assignment or MammaPrint® index between the two separate RNA isolations was observed.
#### b. Linearity/assay reportable range:
Not applicable.
#### c. Traceability, Stability, Expected values (controls, calibrators, or methods):
Same as previous submission.
## Stability of MammaPrint outcomes in RNAretain
To determine if shipment in RNAretain affects stability of MammaPrint outcomes, an experiment was performed to determine MammaPrint variance in a tumor of which RNAretain and frozen tissue was available.
A tumor was selected from which both immediately snap-frozen and RNAretain preserved sections were available. Both sections of the tumor had a similar tumor cell percentage and similar RNA quality. Both samples were labeled 5 times and hybridized on MammaPrint microarrays according to standard protocols.
MammaPrint Indices were compared to determine if samples shipped in RNAretain have a greater stability than tumor sections which are immediately stored at $-70^{\circ}\mathrm{C}$ after excision.
Results showed that the incorporation of both Cy5 and Cy3 were well above the Agendia quality control minimum threshold of 3.0. Incorporation was observed to be significantly higher for samples shipped in RNAretain. (Unpaired T-tests, $p = 0.018$ and $p = 0.001$ respectively). The variance in MammaPrint indices was smaller for samples that were stored in RNAretain compared to the samples that were frozen immediately (Stdev 0.022 vs. 0.042). An unpaired T-test of the MammaPrint index
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revealed no significant difference in the actual MammaPrint indices for the RNAretain and frozen samples (p=0.24). Based on these experiments, the stability in MammaPrint index is greater in samples stored in RNAretain than in samples that were immediately frozen. The difference in MammaPrint index between RNAretain and frozen tissue (Δ 0.027) was within the previously determined acceptable limit of index variation.
d. Detection limit:
Same as previous submission.
e. Analytical specificity:
Same as previous submission.
f. Assay cut-off:
Using Feature Extraction 8.5 and XPrint v1.40 software, the classification threshold is set at 0.415 (See 2c).
2. Comparison studies:
a. Method comparison with predicate device:
The samples for this study were collected in 2003 as a pilot study for the Dutch Raster clinical trial sponsored by the Dutch Health Insurance Council where tissue would be shipped in RNAretain from 20 hospitals. One set consisted of 33 breast tumor samples of which one part of the sample was immediately snap-frozen in liquid nitrogen and stored at -70°C, another part was stored in RNAretain for 3 to 5 days at room temperature and subsequently removed from the preservation solution, snap frozen and stored at -70°C. Another set comprised of 18 tumors of which two parts were available for research that were immediately snap frozen and stored at -70°C. RNA isolation and DNase treatment were performed in this same period. HE stained section were re-examined by a pathologist to confirm invasive ductal carcinoma and sufficient tumor cell content. All samples were hybridized on MammaPrint microarrays, and passed all sample, labeling and hybridization QCs. Analysis was performed using Feature Extraction version 8.5 and XPrint version 1.40.
Results of MammaPrint indices of paired RNAretain and frozen samples are shown in Figure 1A. The median difference between the RNAretain preserved and the snap-frozen is 0.070. The Pearson correlation (0.94) and regression analysis indicate a high similarity (R² = 0.90).
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Figure 1: Comparison of MammaPrint Indices between two samplings of the same tumor. A RNAretain-frozen; B frozen-frozen.

This finding is similar to the results of a series of tumors of which two frozen samples were available and were collected in the same time period (Figure 1B). The median difference in MammaPrint Index was 0.105. A comparison of the differences in both series (RNAretain-frozen vs. frozen-frozen) showed no significant difference ( $t$ -test, $p = 0.57$ ) indicating no variation is introduced by RNAretain.

Figure 2: A Passing and Bablok regression analysis on the RNAretain vs. frozen samples
b. Matrix comparison: As described above.
c. Comparison of XPrint software v1.40 and v1.33 Due to the update of the Feature Extraction (FE) software from version 7.5 to 8.5, the MammaPrint analysis software XPrint was updated from v1.33 to v1.40. To validate
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this new combination of FE v.8.5/XPrint v1.40 software, 504 samples from the intended use population were analyzed with both versions of the FE and XPrint software. Regression analysis showed that the samples analyzed with FE v8.5 had on average a slightly higher correlation to the XPrint v1.40 than previously observed with FE v7.5 and XPrint v1.33. The regression equation is shown below: XPrint v1.40 Index = 1.0675 (XPrint v.1.33 Index) - 0.0116.
The classification threshold for the FE v8.5/XPrint v1.40 combination was set at 0.415 instead of 0.40 after recalibration of the MammaPrint index using the 78 samples from the 2002 Nature study. The new classification threshold was then validated using 307 samples from the TransBig study and results were analyzed in relation to time to distant metastasis (see 4x4 and 2x2 tables below).
| Poor (<5 year) | | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | FE7.5/XPrint 1.33 | | | | | |
| | | High Risk | High Risk Borderline | Low Risk Borderline | Low Risk | Total |
| FE8.5/XPrint 1.40 | High Risk | 50 | 0 | 0 | 0 | 50 |
| | High Risk Borderline | 0 | 0 | 0 | 0 | 0 |
| | Low Risk Borderline | 0 | 0 | 3 | 0 | 3 |
| | Low Risk | 0 | 0 | 1 | 3 | 4 |
| | Total | 50 | 0 | 4 | 3 | 57 |
| Good (>5 year) | | | | | | |
| --- | --- | --- | --- | --- | --- | --- |
| | FE7.5/XPrint 1.33 | | | | | |
| | | High Risk | High Risk Borderline | Low Risk Borderline | Low Risk | Total |
| FE8.5/XPrint 1.40 | High Risk | 128 | 3 | 0 | 0 | 131 |
| | High Risk Borderline | 0 | 6 | 0 | 0 | 6 |
| | Low Risk Borderline | 0 | 0 | 10 | 0 | 10 |
| | Low Risk | 0 | 1 | 4 | 98 | 103 |
| | Total | 128 | 10 | 14 | 98 | 250 |
| Poor (<5 year) | | | | |
| --- | --- | --- | --- | --- |
| | FE7.5/XPrint 1.33 | | | |
| | | High Risk | Low Risk | Total |
| FE8.5/XPrint 1.40 | High Risk | 50 | 0 | 50 |
| | Low Risk | 0 | 7 | 7 |
| | Total | 50 | 7 | 57 |
| Good (>5 year) | | | | |
| --- | --- | --- | --- | --- |
| | FE7.5/XPrint 1.33 | | | |
| | | High Risk | Low Risk | Total |
| FE8.5/XPrint 1.40 | High Risk | 137 | 0 | 137 |
| | Low Risk | 1 | 112 | 113 |
| | Total | 138 | 112 | 250 |
3. Clinical studies:
Same as previous submission.
a. Clinical Sensitivity:
{6}
Same as previous submission.
b. Clinical specificity:
Same as previous submission.
c. Other clinical supportive data (when a. and b. are not applicable):
Same as previous submission.
4. Clinical cut-off:
Same as Assay cut-off.
5. Expected values/Reference range:
Same as previous submission.
N. Proposed Labeling:
The labeling is sufficient and it satisfies the requirements of 21 CFR Part 809.10.
O. Conclusion:
The submitted information in this premarket notification is complete and supports a substantial equivalence decision.
7
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Two short videos show you everything — or skip straight to the written tutorial if you'd rather read. You can reopen this any time from the Tutorial button in the top bar.
Part 1 — Search, results, and everyday workflows 16 min
Part 2 — Embeddings: the galaxy map 3 min
1. Search: exact and fuzzy
Type a phrase like "coronary artery calcification" into the search box. You get two kinds of results. Exact results match the literal phrase — prefix searches work ("coronary artery calcificati") but suffix searches do not. Fuzzy results match on the meaning and intent of your phrase rather than the exact words, and are sorted by relevance score. Hover over the Exact or Fuzzy badge on any row to see exactly why it matched.
Use the checkboxes above the results to narrow: SaMD keeps only software-only devices, AI / ML keeps only devices with AI.
Exact vs. fuzzy search: what's the difference?
Exact matches on the literal phrase (prefix search works, suffix does not). Fuzzy matches on the meaning and intent of the phrase rather than the exact words. Hover over the badge on any row to see why it matched.
You search "coronary artery calcification" and want only software devices with AI. What two filters do you apply?
Narrow by SaMD (software-only devices), then narrow by AI/ML (devices with AI).
2. The results table
Scroll right in the results table. The intended use is extracted for you — no need to open the PDF. The device story gives a high-level snapshot of what the device does and how it's used. The AI Performance sub-table shows each output name, acceptance criteria, observed values, and development/test dataset descriptions — the same format Innolitics uses for regulatory strategy outputs, and the fastest high-level fingerprint of an AI device. It is AI-generated but has been very reliable in practice.
Where do you find a device's intended use without opening the PDF?
Scroll right in the search results table. The intended use column is extracted for you; no need to dig into the 510(k) summary PDF.
What does the AI Performance sub-table show, and why is it useful?
Output name, acceptance criteria, observed values, development dataset description, and test dataset description. It's the same format we use for regulatory strategy output and Fast 510(k) input, and the fastest high-level fingerprint of an AI device. AI-generated but reliable in practice.
3. Judging fuzzy relevance
Fuzzy results trail off in relevance as you scroll. Use three signals to decide how far down to go: the fuzzy badge explanations, the intended use column, and whether your target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, you're past the relevant zone. A top hit with a low score (~0.4) and a stretched explanation is a hint the closest predicates are far away — the project may be headed for De Novo. Note the fuzzy search is a pattern match: it doesn't handle negation ("not") well, and hardware devices can appear — filter by SaMD/AI ML to cut them.
How do you judge how far down fuzzy search results to go?
Use the relevancy signals: the fuzzy badge explanations, the intended use column, and whether the target output (e.g., Cobb angle) still appears in the AI Performance sub-table. Once it stops appearing, results are trailing off in relevancy.
4. Device detail page: chat and citations
Click a device name to open its detail page: device facts on the left, a chat window on the right. Ask something like "Describe the training data". The answer carries little citation bubbles — click one to jump to the highlighted passage in the source PDF, so you can verify every AI answer against the document. There's also a Download PDF button for sharing.
How do you verify an AI chat answer on the device detail page?
Click the citation bubbles to jump to the relevant highlight in the source document.
Reading rule for every project: how many summaries do you read in full?
At least the three most relevant 510(k) or De Novo summaries, in full. After that, use targeted chat questions to confirm your memory quickly. The tool supports this professional habit — it doesn't replace it.
5. Side-by-side comparison
Select multiple rows in the results table (aim for under ~10), then open the PDF Viewer tab. Ask one question — it goes to all selected devices in parallel, each with citations. This is the fastest way to compare and contrast devices: training data, PCCP scope, how they handled adding new scanners, and so on.
What does the side-by-side PDF viewer mode do?
Select multiple devices, open the PDF viewer tab, and ask one question (e.g., "Describe the training data"). It queries all selected devices simultaneously with citations, so you can compare and contrast quickly.
6. Collections
With rows selected, go to the Collections tab and create a labeled collection (e.g., "Cobb Angle Project"). Reload that selection any time — before a client call, pull up the collection and ask questions across all of its devices at once.
How do you save a set of selected devices for later use?
Select the rows, go to the Collections tab, and create a labeled collection (e.g., "Cobb Angle Project"). You can reload the selection anytime and carry it into the PDF viewer and other tabs that support selections.
7. Product codes and the regulations tree
Click a product code in the results to jump to it in the regulations tree — identification text, sibling product codes, and devices you can open in a PDF viewer on the right. Click a regulation number to see its identification, special controls, and related product codes. You can also search by product code or regulation number at the top of the tree. Always read the special controls if any exist for your device — it broadens your search and sharpens pre-kickoff research.
What can you do from the regulations tree view?
Browse product codes and regulation numbers, read the identification text and special controls, browse sibling product codes, open device PDFs on the right, and search by product code or regulation number at the top of the tree.
8. Chart view
Click Show Chart and segment by regulation number (or product code) to see which regulations dominate your result set. Clicking a regulation takes you into the regulations tree. Great for spotting that most matches are, say, hardware laparoscopic devices — a cue to go back and filter.
How do you see which regulations dominate a search result set?
Click "Show Chart" and segment by Regulation Number. Clicking a regulation takes you to the regulations tree.
9. The predicate graph
Open the Predicates tab for a family-tree view of predicate relationships. Click a node to trace its parents and children; selections from search carry over pre-selected. Commonly predicated devices are worth reading — a lot of people predicated them for a reason. The visual lineage is also handy on client calls, e.g. to show how a predicate family evolved and justify why your predicate still holds.
In the predicate graph, why are commonly predicated devices worth reading?
A lot of people predicated them for a reason. Clicking a node traces parents and children, and selections from search carry over pre-selected.
10. Embeddings: the galaxy map
The Embeddings tab plots every matching document in a 2-D "galaxy map" where semantically similar devices cluster together. Hover or click clusters to explore, and let AI label the clusters for you. Embeddings beat product codes for grouping: two devices can carry different product codes (LLZ vs. QIH) yet do the same thing — the embedding captures the meaning of the intended use and device story. This is also exactly how retrieval-augmented generation (RAG) works under the hood, and it makes a great visual on client calls.
Try it yourself
Head to the search page and work through a few of these AI/ML fuzzy searches to build intuition: perivascular fat on CT · aortic valve calcification opportunistic screening on noncontrast CT · breast cancer prediction on digital pathology slides · autism detection · gestational age prediction · a hearing aid that can also detect a pulse · foundation model based analysis of ECG · large language models · penetration test. Watch how the relevance scores, intended use, and AI Performance tables tell you when results stop being meaningful.