rchestrated.bio

Custom analysis and pipeline development.

We identify biomarkers and build analysis pipelines on your data.

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Selected publications

Annals of Oncology · 2025 Efficacy of cabozantinib and nivolumab in cluster 1/2 metastatic clear cell renal cell carcinoma: Results from OPTIC RCC, a phase II trial of a novel RNAseq-based biomarker J Clinical Oncology · 2023 Biomarker-driven prospective clinical trial in renal cell carcinoma: Developing machine learning models to allocate patients to treatment arms using RNA sequencing Science Advances · 2022 A comprehensive long-read isoform analysis platform and sequencing resource for breast cancer Mobile DNA · 2025 Alternative splicing of transposable elements in human breast cancer

Full list on Google Scholar

Analyses and pipelines we run

  • Bulk, single-cell, and spatial transcriptomics
  • Pathway and gene set enrichment
  • Genetic association analysis (GWAS)
  • Isoform and alternative splicing
  • Nextflow pipeline development for variant calling
  • Clinical variant processing and report generation
  • Data preparation for AI and agentic pipelines
  • Reproducible pipelines and cloud deployment

Modalities we have worked on

  • Small-molecule inhibitors
  • Antibody-drug conjugates
  • Biologics
  • Real-world data biomarkers

Track record

  • Rare cardiac disease response signature for a biotech client. Reanalyzed public mouse RNA-seq across three cohorts. Collagen and matrix-remodeling programs rise in the mutant by six months, and we flagged secreted genes from them as candidate blood markers.
  • Kidney cancer Phase II trial (OPTIC RCC). Before founding Orchestrated, our CTO built the sample-processing and patient-data pipeline for this multi-center trial, which used an RNA-seq biomarker to assign patients to treatment arms.
Gene programs in a rare cardiac disease model, mutant versus wild type Five gene programs across three cohorts: 3 months, 6 months, and 6 months with exercise. At 3 months no gene changes significantly. At 6 months 9 of 9 collagen and extracellular-matrix genes and 4 of 6 matrix-remodeling genes are significantly higher in the mutant, and both programs stay higher with exercise. Disc and cytoskeleton genes trend lower: at 6 months 2 of 5 are significantly lower and 1 higher. With exercise, 1 collagen gene is significantly higher and 2 disc genes are significantly lower. Inflammation: 1 of 3 higher at 6 months. Metabolism: 1 of 4 lower at 6 months and again with exercise. Gene names are withheld. 3 mo 6 mo 6 mo + exercise Collagen / ECM (9) ↑9 ↑1 Matrix remodeling (6) ↑4 Inflammation (3) ↑1 Disc / cytoskeleton (5) ↑1 ↓2 ↓2 Metabolism (4) ↓1 ↓1 Median log2 FC −1.5 0 +1.5
Mutant versus wild type, from public RNA-seq we reanalyzed. Color is the median log2 fold change; arrows count significant genes. Gene names withheld.

Single-cell target selection

Map of 515 single cells from 11 breast cancer patients, colored by cell type A two-dimensional embedding of 515 cells. The 83 B cells, 54 T cells and 38 myeloid cells group together in one region. The 317 tumor cells form several separate groups. The 23 stromal cells are scattered between them. Cell types Tumor 317 B cell 83 T cell 54 Myeloid 38 Stromal 23 Share of cells carrying six clinical antibody-drug conjugate targets, one row per patient Share of cells carrying each target above 10 TPM, by patient. BC01 (20 cells): HER2 30%, TROP2 55%, MUC1 40%, FRα 0%, CEA 0%, B7-H3 30%, CD45 5%. BC02 (53 cells): HER2 26%, TROP2 45%, MUC1 89%, FRα 0%, CEA 2%, B7-H3 15%, CD45 2%. BC03 (25 cells): HER2 100%, TROP2 68%, MUC1 80%, FRα 0%, CEA 8%, B7-H3 16%, CD45 40%. BC04 (47 cells): HER2 96%, TROP2 32%, MUC1 47%, FRα 49%, CEA 87%, B7-H3 21%, CD45 11%. BC05 (75 cells): HER2 100%, TROP2 47%, MUC1 100%, FRα 8%, CEA 1%, B7-H3 21%, CD45 80%. BC06 (8 cells): HER2 100%, TROP2 88%, MUC1 50%, FRα 62%, CEA 12%, B7-H3 0%, CD45 0%. BC07 (52 cells): HER2 42%, TROP2 90%, MUC1 79%, FRα 36%, CEA 12%, B7-H3 15%, CD45 40%. BC08 (15 cells): HER2 47%, TROP2 73%, MUC1 67%, FRα 33%, CEA 0%, B7-H3 7%, CD45 0%. BC10 (11 cells): HER2 27%, TROP2 82%, MUC1 64%, FRα 54%, CEA 0%, B7-H3 9%, CD45 0%. BC11 (11 cells): HER2 0%, TROP2 91%, MUC1 100%, FRα 9%, CEA 0%, B7-H3 18%, CD45 27%. Immune (175 cells): HER2 4%, TROP2 3%, MUC1 5%, FRα 1%, CEA 3%, B7-H3 4%, CD45 90%. Stromal (23 cells): HER2 13%, TROP2 17%, MUC1 13%, FRα 13%, CEA 4%, B7-H3 22%, CD45 4%. CD45 is a control, not a target. Values are RNA, not protein, and cell labels are the ones published with the data. Targets by patient HER2 TROP2 MUC1 FRα CEA B7-H3 CD45 control BC01 20 BC02 53 BC03 25 BC04 47 BC05 75 BC06 8 BC07 52 BC08 15 BC10 11 BC11 11 Immune 175 Stromal 23 Share of cells 0% 25% 50% 100%
Public breast tumor data (GSE75688), reanalyzed. Circle size is the share of that row’s cells above 10 TPM.

Variants that change the dose

Myopathy risk on 80 mg simvastatin by SLCO1B1 rs4149056 genotype Odds of myopathy relative to patients with no C allele. One C copy: 4.5 times, 95% confidence interval 2.6 to 7.7. Two copies: 16.9 times, 95% confidence interval 4.7 to 61.1. From the SEARCH genome-wide study of 85 cases and 90 controls, New England Journal of Medicine 2008. Myopathy risk One C copy 4.5× Two copies 16.9× 1× 4× 16× 64× odds vs no C copy Share of people carrying at least one copy of SLCO1B1 rs4149056, by population Computed from 1000 Genomes phase 3 allele frequencies under Hardy-Weinberg equilibrium. European (EUR): allele frequency 0.161, 30 percent carry at least one copy. American (AMR): allele frequency 0.134, 25 percent carry at least one copy. East Asian (EAS): allele frequency 0.123, 23 percent carry at least one copy. South Asian (SAS): allele frequency 0.043, 8 percent carry at least one copy. African (AFR): allele frequency 0.014, 3 percent carry at least one copy. Carriers European 30% American 25% East Asian 23% South Asian 8% African 3% carry at least one copy
SLCO1B1 rs4149056 and simvastatin myopathy, from the SEARCH genome-wide study (2008). Carrier shares computed from 1000 Genomes. This variant now sets the dose in CPIC prescribing guidance.