Biology subjects

Erber, R.

Publications and source records attributed to Erber, R..

3 recordsLinked to original sources

bioRxiv2026

Multi-modal benchmarking of the Ultima UG100 and Illumina NovaSeq sequencing platforms using clinically relevant FFPE tissues

Emerging high-throughput sequencing technologies promise lower costs and higher scalability, yet their performance on archival clinical samples remains poorly characterized. Here, we benchmarked Ultima Genomics UG100 against Illumina Novaseq platforms across single-nuclei RNA-seq (snRNA-seq), whole-transcriptome (WTS), whole-exome (WES), and whole-genome sequencing (WGS) using FFPE tissues from oncologic and immune-mediated diseases. Across matched samples, we systematically assessed data quality, coverage profiles, error spectra, variant concordance and transcriptomic reproducibility. UG100 produced highly comparable results to Illumina, capturing key oncogenic and immune-related transcripts, accurately resolving cellular composition in snRNA-seq, and maintaining sensitivity for lowly expressed genes, despite characteristic insertion-biased indels and modest differences in multi-mapping reads. Discrepancies were subtle, largely limited to pseudogene and non-coding transcripts, and did not affect pathway-level conclusions. Ultima UG100 platform prioritized high precision and reduced low-frequency artifacts, offering a cleaner but more conservative variant-calling profile compared to the more sensitive, yet noisier, Illumina/DRAGEN workflow. This multimodal, clinically oriented assessment provides the first comprehensive evaluation of UG100, demonstrating its translational utility in population-scale genomics, and highlighting the potential for emerging sequencing technologies to lower the cost of biomedical research and clinical diagnostics.

genomics↗

bioRxiv2024

Multimodal histopathologic models stratify hormone receptor-positive early breast cancer

For patients with hormone receptor-positive, early breast cancer without HER2 amplification, multigene expression assays including Oncotype DX (R) recurrence score (RS) have been clinically validated to identify patients who stand to derive added benefit from adjuvant cytotoxic chemotherapy. However, cost and turnaround time have limited its global adoption despite recommendation by practice guidelines. We investigated if routinely available hematoxylin and eosin (H&E)-stained pathology slides could act as a surrogate triaging data substrate by predicting RS using machine learning methods. We trained and validated a multimodal transformer model, Orpheus, using 6,203 patients across three independent cohorts, taking both H&E images and their corresponding synoptic text reports as input. We showed accurate inference of recurrence score from whole-slide images (r = 0.63 (95% C.I. 0.58 - 0.68); n = 1,029), the raw text of their corresponding reports (r = 0.58 (95% C.I. 0.51 - 0.64); n = 972), and their combination (r = 0.68 (95% C.I. 0.64 - 0.73); n = 964) as measured by Pearsons correlation. To predict high-risk disease (RS>25), our model achieved an area under the receiver operating characteristic curve (AUROC) of 0.89 (95% C.I. 0.83 - 0.94), and area under the precision recall curve (AUPRC) of 0.64 (95% C.I. 0.60 - 0.82), compared to 0.49 (95% C.I. 0.36 - 0.64) for an existing nomogram based on clinical and pathologic features. Moreover, our model generalizes well to external international cohorts, effectively identifying recurrence risk (r = 0.61, p < 10-4, n = 452; r = 0.60, p < 10-4, n = 575) and high-risk status (AUROC = 0.80, p < 10-4, AUPRC = 0.68, p < 10-4, n = 452; AUROC = 0.83, p < 10-4, AUPRC = 0.73, p < 10-4, n = 575) from whole-slide images. Probing the biologic underpinnings of the model decisions uncovered tumor cell size heterogeneity, immune cell infiltration, a proliferative transcription program, and stromal fraction as correlates of higher-risk predictions. We conclude that at an operating point of 94.4% precision and 33.3% recall, this model could help increase global adoption and shorten lag between resection and adjuvant therapy.

pathology↗

bioRxiv2019

Panel Sequencing in Uterine Leiomyomas identifies Missense Mutations associated with immunohistochemical Fumarate Hydratase (FH)-Loss

ABSTRACTUterine leiomyomas (ULs) constitute a considerable health burden in the general female population. The fumarate hydratase (FH) deficient subtype is found in up to 1.6% and can occur in hereditary leiomyomatosis and renal cell carcinoma (HLRCC) syndrome.We sequenced 13 FH deficient ULs from a previous immunohistochemical screen using a targeted panel and identified biallelic FH variants in all. In eight, we found a FH point mutation (two truncating, six missense) with evidence for loss of the second allele. Variant allele-frequencies in all cases with a point mutation pointed to somatic variants. Spatial clustering of the identified missense variants in the lyase domain indicated altered fumarase oligomerization with subsequent degradation as explanation for the observed FH deficiency. Biallelic FH deletions in five tumors confirm the importance of copy number loss as mutational mechanism.By curating all pathogenic FH variants and calculating their population frequency, we estimate a carrier frequency of up to 1/2,563. Comparing with the prevalence of FH deficient ULs, we conclude that most are sporadic and estimate 2.7 - 13.9% of females with an FH deficient UL to carry a germline FH variant.Further prospective tumor/normal sequencing studies are needed to develop a reliable screening strategy for HLRCC in women with ULs.Competing Interest StatementThe authors have declared no competing interest.View Full Text

pathology↗