Biology subjects

Wolsky, R. J.

Publications and source records attributed to Wolsky, R. J..

4 recordsLinked to original sources

bioRxiv2026

Cross-assay RNA modeling reveals cancer biomarkers

The clinical heterogeneity of cancer poses a major challenge for precision medicine. Limited cohort sizes across evolving assay platforms impede reliable biomarker discovery. Here, we systematically evaluate how to integrate data from four transcriptomics platforms: bulk and single-cell (sc) RNA sequencing (RNA-seq), NanoString, and microarray for predictive modeling in cancer. We use high-grade serous carcinoma (HGSC) of tube-ovarian origin as a model system, as it is highly heterogeneous in both biology and assay data. We find that using fold-change of gene expression in patients with matched pre- and post-neoadjuvant chemotherapy samples reduces inter-patient and inter-assay variability but is insufficient to overcome platform-specific biases. Microarray and scRNA-seq data exhibit systematic biases, while RNA-seq and NanoString show the most promise for combination into a single training cohort. To mitigate inter-assay limitations, we generate a new data set of HGSC tumor samples profiled with both RNA-seq and NanoString, and use it to identify the limits of detection and optimal harmonization strategies. Our approaches enable integration of cohorts for separate and combined RNA-seq and NanoString predictive models of disease recurrence (test-set AUROCs > 0.8), validated in external microarray cohorts. We leverage single-cell and bulk RNA-seq network-based analyses to provide mechanistic context for genes in the predictive models. Our models indicate that GBP4 expression is a key predictor of recurrence and marks immune remodeling towards cytotoxicity. We provide an interactive web portal to facilitate exploration of data and results. These findings guide cross-assay harmonization of transcriptomic data and enable improved predictive modeling in heterogeneous cancers. Statement of SignificanceWe present a framework for integrating RNA-seq, NanoString, microarray, and single-cell transcriptomic data for predictive modeling, enabling robust biomarker discovery in heterogeneous cancers and identifying GBP4 as a marker of immune remodeling.

bioinformatics↗

bioRxiv2026

On the predictability of progression-free survival in ovarian cancer from NanoString gene expression data

In the treatment of high grade serous ovarian cancer (HGSC), patients initially diagnosed with unresectable tumors are first treated with neoadjuvant chemotherapy (NACT) to reduce tumor burden prior to surgery. Analysis of matched pre- and post-NACT samples from the same patients enables the investigation of chemotherapy impacts and the biomarkers of progression. Although the tumor immune microenvironment (TIME) has increasingly been recognized as critical in shaping the development and progression of HGSC, we lack a comprehensive understanding of how chemotherapy remodels the TIME. Previous studies have found evidence for a general inflammatory response post-NACT, despite inconsistencies regarding which differentially expressed genes and pathways are implicated. We combine matched NanoString gene expression data from multiple sources to create a large dataset of matched pre- and post- NACT samples (N=83, with 29 novel to this study) and investigate reproducibility. Further, we use machine learning methods to investigate whether patient progression-free survival (PFS) can be predicted from the observed impact of chemotherapy on the TIME as represented by the comprehensive set of NanoString features. We find overall low predictability of PFS from all NanoString features, suggesting that previous results may have been limited by small sample size effects and that larger datasets are needed to identify more generalizable and translatable findings. We identify a set of differential expression features that are the most important for predicting patient outcomes that can be validated in future computational and biological studies. Author summaryA subset of patients with high grade serous ovarian cancer are treated with chemotherapy before surgery to reduce tumor burden. We investigate a large dataset of samples taken before and after chemotherapy. These matched samples enable an investigation of how the environment around tumors, for example immune cell infiltration, reacts to chemotherapy, providing insights into biomarkers for treatment response and treatments that could complement chemotherapy. This larger dataset only partially replicates results from previous studies, while also providing new insights. Machine learning models designed to predict the time to patient recurrence from available biomarkers indicate that they are not strongly predictive of patient outcomes, in contrast to past studies. These results suggest that larger datasets are needed. We identify a set of genes that change with chemotherapy and are indicative of and potentially useful for predicting time to disease recurrence and can be further investigated.

cancer biology↗

bioRxiv2024

The spatial structure of the tumor immune microenvironment can explain and predict patient response in high-grade serous carcinoma

Despite ovarian cancer being the deadliest gynecological malignancy, there has been little change to therapeutic options and mortality rates over the last three decades. Recent studies indicate that the composition of the tumor immune microenvironment (TIME) influences patient outcomes but are limited by a lack of spatial understanding. We performed multiplexed ion beam imaging (MIBI) on 83 human high-grade serous carcinoma tumors -- one of the largest protein-based, spatially-intact, single-cell resolution tumor datasets assembled -- and used statistical and machine learning approaches to connect features of the TIME spatial organization to patient outcomes. Along with traditional clinical/immunohistochemical attributes and indicators of TIME composition, we found that several features of TIME spatial organization had significant univariate correlations and/or high relative importance in high-dimensional predictive models. The top performing predictive model for patient progression-free survival (PFS) used a combination of TIME composition and spatial features. Results demonstrate the importance of spatial structure in understanding how the TIME contributes to treatment outcomes. Furthermore, the present study provides a generalizable roadmap for spatial analyses of the TIME in ovarian cancer research.

cancer biology↗

bioRxiv2023

Combination CDC-like kinase inhibition (CLK)/Dual-specificity tyrosine-regulated kinase (DYRK) and taxane therapy in CTNNB1-mutated endometrial cancer

SM08502 (cirtuvivint) is a novel pan CDC-like kinase (CLK) and Dual specificity tyrosine kinase (DYRK) inhibitor that targets mRNA splicing and is optimized for Wnt pathway inhibition. Previous evaluation of single agent CLK/DYRK inhibition (SM04690) demonstrated inhibition of tumor progression and {beta}-catenin/TCF transcriptional activity in CTNNB1-mutant endometrial cancer (EC). In-vitro analysis of SM08502 similarly decreases Wnt transcriptional activity and cellular proliferation while increasing cellular apoptosis. SM08502 is an active single-agent therapy with IC50s in the nanomolar range for all EC cell lines evaluated. Combination of SM08502 with paclitaxel has synergistic effect in vitro, as demonstrated by Combination Index <1, and inhibits tumor progression in four endometrial cancer models (HEC265, Ishikawa, Ishikawa-S33Y, and SNGM). In our in vivo mouse models, Ishikawa demonstrated significantly lower tumor volumes of combination vs SM08502 alone (Repeated Measures one-way ANOVA, p = 0.04), but not vs paclitaxel alone. HEC265, SNGM, and Ishikawa-S33Y tumors all had significantly lower tumor volumes with combination SM08502 and paclitaxel compared to single-agent paclitaxel (Repeated Measures one-way ANOVA, p = 0.01, 0.004, and 0.0008, respectively) or single-agent SM08502 (Repeated Measures one-way ANOVA, p = 0.002, 0.005, and 0.01, respectively) alone. Mechanistically, treatment with SM08502 increases alternative splicing (AS) events compared to treatment with paclitaxel. AS regulation is an important post-transcriptional mechanism associated with the oncogenic process in many cancers, including EC. Results from these studies have led to a Phase I evaluation of this combination in recurrent EC.

cancer biology↗