Biology subjects

Eschrich, S. A.

Publications and source records attributed to Eschrich, S. A..

7 recordsLinked to original sources

bioRxiv2026

Genomic-Adjusted Radiation Dose from Bulk RNA Sequencing for Personalized Radiotherapy

Radiotherapy is delivered to more than half of all patients with cancer yet is prescribed using uniform physical doses despite well-established interpatient variability in biological response. The genomic-adjusted radiation dose (GARD), derived from the radiosensitivity index (RSI), integrates tumor transcriptomics with radiation dose to estimate patient-specific treatment effect, and has been clinically validated as a predictor of radiotherapy benefit across diverse disease sites, including breast, lung, head and neck, glioma, sarcoma, rectal, and endometrial cancers. However, further clinical validation and deployment has been limited by reliance on microarray-based expression. Here we develop an RNA sequencing-based formulation of RSI (RSI-seq) and show that it preserves the functional properties of the original model across measurement platforms. RSI-seq maintains concordance with microarray RSI, including preservation of patient rank ordering (pooled Spearman{rho} = 0.86), and, when integrated into GARD, reproduces predicted changes in biological effect under clinically relevant dose perturbations (R2 [≥] 0.78 for {Delta}GARD in both directions). This preservation of interventional prediction is robust to expression noise and invariant to normalization strategy, enabling consistent application across RNA-seq pipelines. Application across the TCGA pan-cancer transcriptomic atlas demonstrates scalability across tumor types, with cohort medians agreeing closely with previously published microarray RSI medians (Spearman{rho} = 0.68, Pearson r = 0.85 across 20 matched cohorts). By bringing a clinically validated radiogenomic dose model into the RNA-sequencing era, RSI-seq makes biologically personalized radiotherapy directly accessible, retrospectively in existing RNA-seq cohorts and prospectively in modern clinical sequencing workflows, across the full range of tumor types treated with radiation.

genomics↗

bioRxiv2026

Synthetic Data Generation and Nonparametric Techniques for Assessing Multivariate Similarity to Address Small-Sample Size Challenges

Data modeling in biomedical research often operates in the small-sample regime, where the number of observations is small relative to the data dimensionality; the detrimental effects of limited sample sizes are well documented in cancer studies. Synthetic data offers a potential solution to data shortfalls provided that the data generated is an adequate facsimile of the underlying distribution; the adequacy of such synthetic data remains an open-ended problem. In this work, we evaluate a synthetic generator proposed previously. The generator applies a series of transformations to the observed data to accommodate the small-sample size resulting in an uncoupled representation, where uncorrelated marginal distributions are modeled with optimized univariate kernel density estimation. In this report, (1) we develop a nonparametric method for assessing multivariate similarity based on the Cramer-Wold theorem and random projection testing, (2) investigate when the absence of bivariate correlation approximates independence in a non-normal setting, and (3) evaluate artifacts induced by data compression. The presentation is primarily methodological; low-dimensional data were used so each stage of the generation process could be analyzed explicitly. A formal testing framework was developed by comparing random projection level outcomes with a two-sample test, modeling these outcomes as Bernoulli trials, aggregating replicate outcomes within each projection direction, and pooling outcomes across many directions, yielding a scalable standardized normal test-statistic. The key innovation was decoupling the two-sample test significance level from that governing finalized normal inference. We showed the same projection framework also evaluates the full multivariate covariance structure. The generator produced high-fidelity multivariate synthetic data when the bivariate correlation approximates independence in the non-normal setting; in highly compressed data, residual modes were best modeled as normally distributed regardless of their intrinsic distributional form. Ongoing work includes applying these methods to higher-dimensional, diverse data.

bioinformatics↗

bioRxiv2025

CLONEID: A Framework for Monitoring and Steering Subclonal Dynamics

Molecular assays can resolve clonal structure, but they are expensive and typically sparse in time, whereas phenotypic observations such as imaging can be collected frequently but often are not preserved in the context needed for later interpretation. We present CLONEID, an event-based framework for organizing clone-resolved phenotypic, molecular, and specimen-context records so that genotype-to-phenotype interpretation can be maintained across time. CLONEID links time-stamped Events, assay-specific Perspectives, and reconciled Identities through structured ingestion, provenance-aware retrieval, and reproducible export, complementing upstream clone-calling methods. In a long-term gastric cancer density-selection experiment, CLONEID linked repeated culture events, growth measurements, and late karyotypic profiling within a shared record, supporting longitudinal interpretation of phenotypic adaptation together with underlying chromosomal state.

bioinformatics↗

bioRxiv2024

Evaluating the Radiation Sensitivity Index and 12-chemokine gene expression signature for clinical use in a CLIA laboratory

BackgroundThe radiation sensitivity index (RSI) and 12-chemokine gene expression signature (12CK GES) are two gene expression signatures (GES) that were previously developed to predict tumor radiation sensitivity or identify the presence of tertiary lymphoid structures in tumors, respectively. To advance the use of these GES into clinical trial evaluation, their assays must be assessed within the context of the Clinical Laboratory Improvement Amendments (CLIA) process. MethodsUsing HG-U133Plus 2.0 arrays, we first established CLIA laboratory proficiency. Then the accuracy (limit of detection and macrodissection impact), precision (variability by time and operator), sample type (surgery vs. biopsy), and concordance with reference laboratory were evaluated. ResultsRSI and 12CK GES were reproducible (RSI: 0.01 mean difference, 12CK GES 0.17 mean difference) and precise with respect to time and operator. Taken together, the reproducibility analysis of the scores indicated a median RSI difference of 0.06 (6.47% of range) across samples and a median 12CK GES difference of 0.92 (12.29% of range). Experiments indicated that the lower limit of input RNA is 5 ng. Reproducibility with a second CLIA laboratory demonstrated reliability with the median RSI score difference of 0.065 (6% of full range) and 12CK GES difference of 0.93 (12 % of observed range). ConclusionsOverall, under CLIA, RSI and 12CK GES were demonstrated by the Moffitt Cancer Center Advanced Diagnostic Laboratory to be reproducible GES for clinical usage.

genomics↗

bioRxiv2024

spatialGE: A user-friendly web application to democratize spatial transcriptomics analysis

Spatial transcriptomics (ST) is a powerful tool for understanding tissue biology and disease mechanisms. However, its potential is often underutilized due to the advanced data analysis and programming skills required. To address this, we present spatialGE, a web application that simplifies the analysis of ST data. The application spatialGE provides a user-friendly interface that guides users without programming expertise through various analysis pipelines, including quality control, normalization, domain detection, phenotyping, and multiple spatial analyses. It also enables comparative analysis among samples and supports various ST technologies. We demonstrate the utility of spatialGE through its application in studying the tumor microenvironment of melanoma brain metastasis and Merkel cell carcinoma. Our results highlight the ability of spatialGE to identify spatial gene expression patterns and enrichments, providing valuable insights into the tumor microenvironment and its utility in democratizing ST data analysis for the wider scientific community.

bioinformatics↗

bioRxiv2021

Techniques to Produce and Evaluate Realistic Multivariate Synthetic Data

BackgroundData modeling in biomedical-healthcare research requires a sufficient sample size for exploration and reproducibility purposes. A small sample size can inhibit model performance evaluations (i.e., the small sample problem). ObjectiveA synthetic data generation technique addressing the small sample size problem is evaluated. We show: (1) from the space of arbitrarily distributed samples, a subgroup (class) has a latent multivariate normal characteristic; (2) synthetic populations (SPs) of unlimited size can be generated from this class with univariate kernel density estimation (uKDE) followed by standard normal random variable generation techniques; and (3) samples drawn from these SPs are statistically like their respective samples. MethodsThree samples (n = 667), selected pseudo-randomly, were investigated each with 10 input variables (i.e., X). uKDE (optimized with differential evolution) was used to augment the sample size in X (i.e., the input variables). The enhanced sample size was used to construct maps that produced univariate normally distributed variables in Y (mapped input variables). Principal component analysis in Y produced uncorrelated variables in T, where the univariate probability density functions (pdfs) were approximated as normal with specific variances; a given SP in T was generated with normally distributed independent random variables with these specified variances. Reversing each step produced the respective SPs in Y and X. Synthetic samples of the same size were drawn from these SPs for comparisons with their respective samples. Multiple tests were deployed: to assess univariate and multivariate normality; to compare univariate and multivariate pdfs; and to compare covariance matrices. ResultsOne sample was approximately multivariate normal in X and all samples were approximately multivariate normal in Y, permitting the generation of unlimited sized SPs. Uni/multivariate pdf and covariance comparisons (in X, Y and T) showed similarity between samples and synthetic samples. ConclusionsThe work shows that a class of multivariate samples has a latent normal characteristic; for such samples, our technique is a simplifying mechanism that offers an approximate solution to the small sample problem by generating similar synthetic data. Further studies are required to understand this latent normal class, as two samples exhibited this characteristic in the study.

bioinformatics↗

bioRxiv2020

Hypoxia-related radiomics predict immunotherapy response: A multi-cohort study of NSCLC

Checkpoint blockade immunotherapy provides improved long-term survival in a subset of advanced stage non-small cell lung cancer (NSCLC) patients. However, highly predictive biomarkers of immunotherapy response are unmet clinical need. In this study, we utilized pre-treatment clinical factors and quantitative image-based biomarkers (radiomics) to identify a parsimonious model that predicts survival outcomes among NSCLC patients treated with immunotherapy. The NSCLC patients treated with single or double agent immunotherapy were included in three different cohorts: Training (N = 180), test (N = 90) and validation (N = 62) cohorts. The models were created based on overall survival (OS) and were additionally assessed for progression-free survival (PFS). Including most predictive radiomic features and clinical covariates, Classification and Regression Tree analysis was applied to stratify patients into survival risk-groups in the training cohort. The risk groups were later generated in the test and validation cohorts. Four independent NSCLC cohorts (total N = 446) were utilized for further validation of the radiomic signature. The biological underpinnings of the most informative radiomics were assessed using gene expression data from a radiogenomics dataset and validated by immunohistochemistry data (IHC). A parsimonious clinical-radiomics model was found to be significantly associated with OS and PFS after stratifying patients into groups of low-, moderate-, high-, and very-high risk of death and progression. This trained model was further tested and validated in two independent cohorts. When the extreme phenotypes were compared, the very-high risk group was found to be associated with extremely poor OS in both the test (hazard ratio [HR] = 5.35, 95% confidence interval [CI]: 2.14 - 13.36; 1-year OS = 11.1%) and validation (HR = 13.81, 95% CI: 2.58 - 73.93; 1-year OS = %47.6) cohorts when compared to the low risk group (HRs = 1.00; 1-year OS = 85.0% & 80.2%). Similar findings were observed for PFS. The final radiomic feature (GLCM inverse difference) was associated with OS in four independent NSCLC cohorts and was found to be positively associated with the hypoxia-related carbonic anhydrase, CAIX, by gene expression profiling and immunohistochemistry. We validated a novel clinical-radiomics model that is associated with OS and PFS among NSCLC patients treated with immunotherapy and identified a highly vulnerable subset of patients that are unlikely to respond to immunotherapy. The most informative radiomic feature was associated with CAIX, a marker of tumor hypoxia, tumor acidosis, and treatment resistance.

bioinformatics↗