Biology subjects

Noller, K.

Publications and source records attributed to Noller, K..

4 recordsLinked to original sources

bioRxiv2026

Clear cell renal cell carcinoma consensus transcriptomic programs reveal converging trajectories towards aggressive disease

Clear cell renal cell carcinoma (ccRCC) is characterized by a branching genomic trajectory in which early biallelic VHL inactivation splits into PBRM1- and BAP1-mutant lineages. However, driver mutations alone do not account for the molecular and phenotypic heterogeneity. To dissect this heterogeneity, we developed a non-negative matrix factorization (NMF)-based gene expression analytical framework for identifying recurrent transcriptional programs across factorization dimensionalities and across datasets. We applied it to three curated datasets (IMmotion151, n = 823; JAVELIN Renal 101, n = 726; TCGA, n = 614) to define 17 consensus transcriptomic programs (CTPs). Mapping CTPs onto single-cell RNAseq (scRNAseq) of human ccRCC tumors and patient-derived tumorgraft models linked these programs to their cellular sources, distinguishing RCC-intrinsic, tumor-cell-extrinsic, and mixed programs. RCC-intrinsic CTPs associated with canonical drivers, including VHL (R1), PBRM1 (R2), BAP1 (R4), PTEN/TSC1 (R3), TFE3/TFEB fusions (R5), NF2 (R6), and CDKN2A/TP53 (MP-Prolif). Additional CTPs captured tumor microenvironment (TME) composition (TME-Tcell, TME-Myelo, TME-Endo, TME-Stroma) and biological processes active across multiple cellular compartments, including proliferation, Y-chromosome-linked expression in male tumors, ciliary biology, and translation. Trajectory inference methods revealed PBRM1-like and BAP1-like branches that converged on a shared aggressive late transcriptomic stage (TS) associated with higher nuclear grade, additional driver alterations, myeloid/stromal infiltration, and poor clinical outcomes. Spatial transcriptomics (and multiregional sequencing) of paired conventional ccRCC and sarcomatoid regions linked TS advancement with morphological progression and clonal evolution. After adjusting for stage, grade, and BAP1/PBRM1 status, TS remained independently prognostic. Furthermore, our data suggest that immune checkpoint inhibitor combinations are particularly beneficial for MP-Prolif and not R1 utilizing specimens. In summary, we present an atlas of recurring transcriptomic programs in RCC and an ontological framework bridging genotype, tumor-cell-intrinsic gene expression, and microenvironment remodeling, with implications for risk stratification and treatment selection in ccRCC.

Cancer Biology↗

bioRxiv2026

Reconstructing clone-resolved transcriptional programs from bulk tumor sequencing

Tumor clones acquire distinct transcriptional programs as they evolve, but bulk RNA-seq averages over clonal mixtures and obscures the lineage-specific biology that DNA-sequencing reveals. We present PICTographPlus, the first method to infer clone-resolved transcriptional programs by integrating bulk DNA-derived clonal phylogenies and proportions with bulk RNA-seq alone, without single-cell data. Benchmarked against experimentally measured ground truth, scDNA/scRNA co-profiled cells from a wellDR-seq cancer dataset, across 320 pseudo-bulk replicates spanning four tumor purities and four sample counts and evaluated under seven regularization models, PICTographPlus recovers clone-level expression at mean Pearson r [≥] 0.92 and localizes pathway gains and losses to correct evolutionary branches (median F1 0.31-0.40, well above a no-deconvolution baseline). Applied to multi-region NSCLC, pancreatic precursor lesions, and rapid-autopsy PDAC, it localizes metabolic reprogramming, precursor-to-invasive transitions, and organ-adapted metastatic states to specific clonal branches. PICTographPlus turns standard bulk assays into clone-resolved transcriptional maps, enabling retrospective analyses where single-cell profiling is impractical.

genomics↗

bioRxiv2025

Integrative analysis of genomic and transcriptomic data informs precancer progression in the pancreas

Pancreatic ductal adenocarcinoma (PDAC) arises from heterogeneous precursor lesions, including intraductal papillary mucinous neoplasms (IPMNs), but the features distinguishing indolent from progressive lesions remain unclear. We performed an integrative analysis of transcriptomic, genomic, and microenvironmental profiles of IPMNs to define multi-omic phenotypes. Using transfer learning, we projected IPMN-derived transcriptional programs onto spatial transcriptomic datasets from IPMNs and pancreatic intraepithelial neoplasias (PanINs). We identified two major phenotypes: one associated with cancer-associated fibroblasts and epithelial-to-mesenchymal transition, shared across IPMN, PanIN, and PDAC; and a second, glycolysis-enriched phenotype with a unique somatic mutation profile specific to IPMN. Spatial mapping further revealed grade-specific enrichment of transcriptional programs and distinct interactions with stromal and immune subtypes, underscoring the role of the precancer microenvironment in progression. These findings establish multi-omic phenotypes that unify genetic, transcriptional, and microenvironmental heterogeneity, providing a framework for distinguishing progressive from indolent precancers and a web-based public atlas for future exploration of these data and transcriptional phenotypes.

bioinformatics↗

bioRxiv2024

Cell cycle expression heterogeneity predicts degree of differentiation

Methods that predict fate potential or degree of differentiation from transcriptomic data have identified rare progenitor populations and uncovered developmental regulatory mechanisms. However, some state-of-the-art methods are too computationally burdensome for emerging large-scale data and all methods make inaccurate predictions in certain biological systems. We developed a method in R (stemFinder) that predicts single cell differentiation time based on heterogeneity in cell cycle gene expression. Our method is computationally tractable and is as good as or superior to competitors. As part of our benchmarking, we implemented four different performance metrics to assist potential users in selecting the tool that is most apt for their application. Finally, we explore the relationship between differentiation time and cell fate potential by analyzing a lineage tracing dataset with clonally labelled hematopoietic cells, revealing that metrics of differentiation time are correlated with the number of downstream lineages.

bioinformatics↗