
Predict Who Will Respond to B-Cell Depletion Before You Enroll Them
AI-powered B-cell receptor repertoire analysis that identifies patients with active B-cell-driven autoimmunity. Reduce non-responder rates. Enrich your trial population. Accelerate your program.
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The Challenge
Current Patient Selection Fails in Autoimmune Trials
B-cell-directed therapies such as anti-CD20 antibodies and CD19 CAR-T cells deliver meaningful benefit in autoimmune disease, yet a substantial minority of patients fail to meet standard clinical response criteria. In real-world and refractory lupus cohorts, overall response rates to rituximab typically range around 40–70% — roughly 30–60% non-responders, depending on population and endpoint (Rheumatology, 2018; Reumatología Clínica, 2016). Both pivotal randomized rituximab trials in lupus — EXPLORER in extrarenal SLE and LUNAR in proliferative lupus nephritis — failed to achieve their prespecified primary endpoints, despite robust B-cell depletion and serologic changes (Nature Reviews Rheumatology, 2012).
Even next-generation CD19 CAR-T therapies, which can induce profound drug-free remissions, show incomplete response: in Kyverna's Phase 2 KYSA-8 trial in stiff-person syndrome, 81% of 26 treated patients achieved at least a 20% improvement in a timed 25-foot walk test at week 16 — implying that about 19% did not reach this clinically meaningful threshold (Kyverna Therapeutics, 2025; FirstWord Pharma, 2025). Emerging case series also describe relapse after initially successful CAR-T in autoimmune indications, underscoring that durable disease control is not universal (Nature Medicine, 2026).
Despite this, patient selection for B-cell-targeted therapies still depends on broad serological markers (ANA, anti-dsDNA) and global activity indices such as SLEDAI and BILAG in SLE or DAS28 in rheumatoid arthritis — tools that confirm diagnosis and quantify overall activity but do not determine whether disease is presently driven by pathogenic B-cell clones (Emerging B-Cell Therapies in SLE, 2021).
Mechanistic studies show that anti-CD20 agents spare long-lived plasma cells lacking CD20, and that persistent disease may be sustained by autoreactive T cells, established fibrosis, or innate immune pathways such as type I interferon signaling. Enrolling patients whose disease is not primarily B-cell-driven dilutes observed effect sizes and increases required sample sizes, contributing to costly late-stage trial failures — with Phase 3 autoimmune programs often demanding investments in the tens to low hundreds of millions of dollars (Novel and Future Therapeutic Options in Systemic Autoimmune Diseases, 2024).
The development landscape is crowded: contemporary reviews describe well over one hundred therapeutic candidates in clinical development for lupus and related systemic autoimmune diseases worldwide, intensifying pressure to match mechanism to the right patients at the right time.
The Platform
Deep BCR Repertoire Profiling Reveals Who Will Respond
Five dimensions of B-cell receptor repertoire biology combine into a single, quantitative Autoimmune B-Cell Activity Score — an actionable metric for patient stratification.
Clonal Expansion Burden
Identifies dominant, expanded B-cell clones indicating active antigen-driven immune responses. Measured by Gini index, top-clone frequency, and Shannon entropy.
Class-Switch Signature
Determines whether expanded clones are IgG/IgA class-switched (mature, potentially pathogenic) versus IgM/IgD (naïve, less likely pathogenic).
Somatic Hypermutation Load
Quantifies affinity maturation in dominant clones. High SHM indicates prolonged antigen exposure and increased likelihood of autoreactivity.
Autoreactive V-Gene Usage
Detects enrichment for IGHV gene segments associated with autoimmunity — such as IGHV4-34 in SLE and specific IGHV/IGHJ combinations linked to autoreactive antibodies.
Autoantibody Matching
Compares expanded BCR sequences against known autoantibody databases (IEDB, CoV-AbDab, OAS) using transformer-based embedding similarity search.
Composite Activity Score
All five dimensions combine into a single Autoimmune B-Cell Activity Score — quantitative, reproducible, and ready for regulatory submission.
Illustrative Patient BCR Clonal Landscape
Synthetic dataWorkflow
From Blood Sample to Stratification Report
A streamlined end-to-end pipeline. Compatible with fresh or banked specimens, bulk or single-cell. Target turnaround: 2–3 weeks.
Sample Collection
Standard peripheral blood draw at screening or baseline. Compatible with fresh PBMC isolation or banked specimens. gDNA-based assay available for retrospective analysis.
BCR Sequencing
High-throughput BCR heavy chain sequencing — bulk BCR-seq or paired single-cell VDJ (10x Genomics). Clono handles library preparation, sequencing, and QC.
AI-Powered Analysis
Proprietary pipeline: automated QC, transformer-based clonal clustering, repertoire-wide feature extraction, embedding-based autoantibody search, composite score generation.
Stratification Report
Quantitative Autoimmune B-Cell Activity Score, clonal landscape visualization, autoreactive signature flags, and risk classification (High / Medium / Low).
Trial Integration
Define score cutoffs with your biostatistics team, receive analysis-ready datasets for regulatory submissions, and support adaptive trial designs.
Where Clono Integrates in Your Clinical Trial
Applications
Four Ways to Use BCR Intelligence in Your Trial
Prospective Enrichment
Phase 2/3 sponsors seeking to reduce sample size and increase effect size
Use Clono's Autoimmune B-Cell Activity Score at screening to enroll only patients with high B-cell-driven autoimmunity — those most likely to respond to B-cell depletion therapy.
- Reduced required sample size
- Increased treatment effect size
- Accelerated time to clinical readout
- Stronger efficacy signal for regulatory submission
- Crude biomarkers (ANA, anti-dsDNA, disease activity scores)
- Confirms diagnosis, not active B-cell mechanism
- 30–60% non-responder rates in anti-CD20 trials
- Larger sample sizes required to power studies
- Tens to hundreds of millions per failed Phase 3
- BCR repertoire-informed patient stratification
- Quantifies active B-cell-driven autoimmunity at enrollment
- Enriched responder population with higher effect sizes
- Reduced sample size and accelerated timelines
- Molecular-level pharmacodynamic evidence for regulators
Evidence
Built on Published Clinical Evidence
BCR repertoire analysis as a treatment-response biomarker is supported by peer-reviewed studies in Nature, JCI Insight, Arthritis Research & Therapy, and Rheumatology.
In 24 RA patients treated with rituximab, incomplete disruption of dominant baseline BCR clones at 4 weeks predicted clinical non-response at 3 months.
Pollastro S, et al.
In 28 RA patients, BCR repertoire dynamics during reconstitution (Gini index, Shannon entropy, SHM load) correlated with clinical outcomes.
Pollastro S, et al.
BCR repertoire signatures are disease-specific across 6 immune-mediated diseases (n=209), with distinct clonal expansion, isotype, and V-gene patterns.
Bashford-Rogers R, et al.
Higher cumulative frequency of pre-treatment dominant BCR clones predicted IVIG response in treatment-naïve inflammatory myopathy patients.
Anang DC, et al.
Erlangen CAR-T group confirmed via 10x scRNA-seq + BCR-seq in SLE patients that a naïve reconstitution signature equals immune reset.
Wilhelm A, et al.
Single-cell BCR-seq in MuSK-MG showed pre-treatment autoreactive clones persisted through rituximab and dominated at relapse.
Stathopoulos P & O'Connor KC
Institutional Partners
Academic-Grade Platform, Institutional Backing
Fraunhofer Heinrich Hertz Institute
Part of the Fraunhofer-Gesellschaft — Europe's largest applied research organization with 76 institutes and a €3.4B annual budget. HHI is Germany's leading applied AI research institute, specializing in deep learning and transformer architectures.
Berlin, Germany

Stanford University
Co-founded by postdocs in immunology and digital health from Stanford University, bringing deep expertise in B-cell biology, repertoire analysis, and translational biomarker development from bench to clinical trial integration.
Stanford, California
Get in Touch
Discuss How BCR Intelligence Can Strengthen Your Program
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