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Our technology

“The message is in the blood.”

Multi-gene mRNA expression analysis by RT-PCR, combined with machine-learning algorithms, distilled into a 0–100 score and an easily interpretable binary readout on clinically validated cutoffs.

From data to decision

Hundreds of measurements. One clear answer.

Our approach combines multi-gene mRNA expression analysis via RT-PCR with advanced machine-learning algorithms. Complex data becomes a score from 0–100, then a binary readout — positive/negative, detected/undetected, or high/low — based on clinically validated cutoffs.

MULTI-GENE mRNA (RT-PCR) ML ALGORITHM 0–100 POS / NEG
Live 3D · hover a base pair · click to move transcription
Why whole blood mRNA?

The Central Dogma breaks down in cancer.

As NGS and digital pathology advanced, focus shifted to DNA and proteomics, leaving mRNA gene expression underappreciated. The classic model — DNA to RNA to protein — may not hold true in the presence of mutated DNA, making DNA mutation profiling insufficient for predicting cancer behavior and outcomes.

We have dedicated over a decade to mRNA expression as a vital biomarker in oncology and other diseases. Tumor tissue has long been the gold standard, but inter- and intra-tumor heterogeneity can mislead, and poor-quality nucleic acids make many samples unusable. Liquid biopsies are a less invasive alternative — and we are exploring saliva as a complementary medium.

Wren’s proprietary collection tube stabilizes mRNA from whole blood, capturing a comprehensive spectrum of liquid biopsy information without the degradation risks of traditional methods.

Role of machine learning

Patterns invisible to the human eye.

Whole exome, transcriptome and genome sequencing generate datasets that exceed human interpretive capacity. Machine learning finds the intricate patterns that matter.

Earlier detection

ML enhances early disease detection and improves diagnostic accuracy.

Smarter IVDs

Analyzing genomic sequences and expression profiles, ML identifies overlooked patterns — building better predictive models for detection and treatment monitoring.

Personalized plans

Facilitates personalized treatment plans built on each patient’s tumor biology.

Our gene signature building process

From unmet need to clinical decision.

Our R&D team — experts in biology, pathophysiology, transcriptomics and AI/ML — follows a structured path to clinically validated in vitro diagnostics.

STEP 01Unmet need
For clinical or pharma
STEP 02Hypothesis
Signaling pathways
STEP 03Test hypothesis
Public and controlled-access databases
STEP 04Proof of concept
Paired tissue–blood whole transcriptome
STEP 05Identify signature
Best representative for tumor, TME, tumor–host
STEP 06Analytical validation
Fit-for-purpose
STEP 07Clinical cutoff
Machine-learning algorithm
STEP 08Clinical validation
Clinical studies to address each clinical utility

IUO / IDE for CDx

Investigational-use assays supporting companion diagnostic programs.

For clinical decisions

Validated tests that inform patient care.

LDT for early-phase trials

Laboratory-developed tests to select and stratify patients.

Gene expression profiles

Visualizing what changes — and where.

We analyze RNA sequencing data to explore differential gene expression across patient populations — for example, between partial responders and non-responders to specific prostate cancer therapies.

UMAP of cell types by gene expression
UMAP — visual distribution of cells by gene-expression profile, identifying associations with specific cell types.
Targetgram windrose of top differentially expressed genes
Targetgram — average expression of top differentially expressed genes across prostate tissues.

Volcano plots

Highlight differential gene expression quantitatively, showcasing patterns between patient groups.

Circos plots

Show the chromosomal distribution of differentially expressed genes — up- and down-regulated across patients vs controls.

Pathway analysis

From genes to biology.

We map differentially expressed genes to biological processes, cellular components and molecular functions — revealing affected pathways such as hematopoietic lineage and cancer mechanisms.

Pathway enrichment in blood, cancer vs healthy
Blood, cancer vs healthy: pathway analysis, biological processes, cellular components and molecular functions.
Enrichment of biological processes
Enrichment of biological processes among differentially expressed genes.
Our pipeline

Where every test stands today.

We are developing diagnostics for colorectal, pancreatic, lung, breast, melanoma and myeloma, plus non-cancer diseases such as endometriosis.

TestDevelopmentRUOExploratory clinical studyFull analytical validationClinical validationCLIA/CAP permissionNYS approvalFDA
NETest — Blood
PROSTest — Blood
COLOcheck — Blood
Multiple myeloma — Blood
Pancreatic — Blood
Melanoma — Blood
Breast — Blood
Lung — Blood
Endometriosis — Blood
Solid tumors — Saliva*

*Breast, CRC, lung, melanoma, pancreas and prostate. Stage reached as shown on Wren’s pipeline chart (April 2026).

Medical devices

DeviceDevelopmentValidation for limited useFDA EUA for limited useValidation for general useFDA clearance for general use
Blood collection tube for RNA and DNA
Saliva collection tube for RNA and DNA
Get started

Build your next biomarker with us

From custom gene panels to companion diagnostics — talk to our team.