Explore genomics and imaging data from Sid Sijbrandij's osteosarcoma tumor samples — openly shared to advance cancer research.
Please contact us with any questions or comments.
Sid was diagnosed with TFE3-amplified osteosarcoma in November 2022. The primary tumor was resected from the spine in December 2022 (T0). Disease recurred at the Th4 vertebra in June 2024 (T1, UCLA), at Th4–Th5 in January 2025 (T2, UCLA), and was excised in April 2025 (T3, MSKCC). The interactive Timeline tracks the clinical journey, including treatments, residual disease testing (MRD), flow cytometry measurements, and lab results.
| Date | Timepoint | Location | Assays |
|---|---|---|---|
| 2022-12-16 | T0 | UCSF (spine resection) | WES ×4, WGS, WTS |
| 2024-06-06 | T1 | UCLA (Th4 biopsy) | WGS, WTS, scRNA-seq |
| 2025-01-28 | T2 | UCLA (Th4–Th5 biopsy) | WGS, WTS, scRNA-seq |
| 2025-04-17 | T3 | MSKCC (Th4–Th5 excision) | scRNA-seq |
Interactive visualization of treatment history, MRD measurements, procedures, and imaging from November 2022 to present.
Tumor and blood specimens have been profiled with whole-genome sequencing (WGS), whole-exome sequencing (WES), bulk RNA-seq, and single-cell RNA-seq across multiple timepoints and sequencing providers. These pages show complementary views of the molecular data: raw alignments, gene expression, pathway enrichment, copy-number changes, and neoantigen targets of personalized vaccines.
Interactive explorer for bulk RNA-seq gene expression across multiple assays (BostonGene, Tempus, Personalis) with GTEx and PCAWG comparisons, copy number context, and single-cell overlays.
Single-cell RNA-seq from:
GSEA of the single-cell RNA-seq data from tumor. Limma and DESeq2 results from two levels of cell clusters from tumor single-cell RNA-seq from T1, T2, and T3.
Interactive IGV genome browser for viewing aligned sequencing data (BAM/BigWig) across WGS, long-read, single-cell, and RNA-seq experiments.
Interactive genome browser showing ASCAT copy number analysis from whole genome sequencing at two timepoints (T1: June 2024, T2: January 2025).
UpSet plot of neoantigen mutations across five personalized cancer vaccines (mRNA, JLF V1–V3, CeGaT) with variant allele frequency trends and ELISPOT results.
Longitudinal whole-body PET/CT scans (FDG and Ga68-PSMA) track disease across timepoints, while tissue from the T0 resection was profiled with complementary microscopy platforms: H&E histology, immunohistochemistry (B7-H3, EphA2), Orion 18-plex immune imaging (HMS), and Xenium spatial transcriptomics — characterizing the tumor microenvironment at the protein and transcript levels.
Full-body PET/CT scans with LION tumor segmentation, plus 12 high-resolution microscopy images of tumor tissue specimens.
Reference material for this dataset — osteosarcoma cell lines (HOS, U-2 OS, CAL-72) for comparative analyses, and a catalog of all raw and processed files (~25 TB) in the project bucket.
Information about osteosarcoma cell lines including HOS, U-2 OS, and CAL-72 with genomic profiles and curated citations.
25TB of data: bulk WGS, WES, RNA-seq, scRNA-seq, long-read sequencing (ONT), spatial transcriptomics, imaging (H&E, IHC), and HLA typing.
Selected articles, podcasts, and talks about Sid’s osteosarcoma journey and his data-driven approach to treatment.
Long-form interview on applying first-principles thinking to cancer treatment decisions and navigating special access regulatory pathways.
Sid and geneticist Jacob Stern on using AI and advanced diagnostics to design personalized osteosarcoma treatments.
Essay on regulatory barriers to early-stage clinical trials, citing Sid’s experience navigating obstacles to access experimental therapies.
Sid’s own announcement of his startup-style approach to recurrent osteosarcoma and his decision to share medical data publicly.
The original profile of Sid’s data-driven approach — maximal diagnostics, organoid testing, and parallel personalized treatments.
Long-form interview covering Sid’s bone cancer diagnosis and treatment journey, plus his post-GitLab work including Kilo Code.
