Biology subjects

Baker, E. A.

Publications and source records attributed to Baker, E. A..

5 recordsLinked to original sources

bioRxiv2026

Inference of cancer driver mutations from tumor microenvironmentcomposition: a pan-cancer study with cross-platform external validation

Cancer driver mutations shape the tumor microenvironment (TME), yet whether TME composition alone can predict genotype has not been systematically evaluated across cancers with external validation. We trained machine learning models to predict driver mutation status from TME cell-type composition signatures derived from bulk transcriptomes. Tissue-specific TME signatures (22-28 programs per cancer) were scored from RNA-seq data in TCGA for glioblastoma (GBM, n=157 total; n=90 EGFR-amplification evaluable), breast cancer (BRCA, n=1,082 total; n=994 evaluable), lung adenocarcinoma (LUAD, n=510 total; n=502 evaluable), and colorectal cancer (CRC, n=592 total; n=524 evaluable), then externally validated on independent cohorts spanning different platforms: CPTAC (GBM, n=65), METABRIC (BRCA, n=1,859), GSE72094 (LUAD, n=442), and GSE39582 (CRC, n=585). Of 15 driver-cancer pairs tested, 14 achieved external AUC [≥]0.65, with top performance for ERBB2 amplification in BRCA (AUC=0.980), BRAF mutation in CRC (0.899), and TP53 mutation in BRCA (0.871). TME-predicted ERBB2 status stratified overall survival in METABRIC (Cox HR=1.73, p=7.95x10-8). Marginal KRAS performance in LUAD (AUC=0.615) reflected opposing TME profiles in KRAS+STK11 versus KRAS+TP53 co-mutant tumors. These results demonstrate that TME composition encodes sufficient information to infer driver mutations across cancers.

bioinformatics↗

bioRxiv2026

CD276 (B7-H3) as a Companion Diagnostic Biomarker for Glioblastoma: Multi-Platform Validation and Therapeutic Implications

Glioblastoma (GBM) remains the most lethal primary brain tumor, with median survival of 14-16 months despite aggressive multimodal therapy[1,2]. The failure of PD-1/PD-L1 checkpoint inhibitors in GBM (CheckMate-143)[3] has highlighted the need for alternative immunotherapeutic targets and companion diagnostics. CD276 (B7-H3) has emerged as a promising target, with multiple anti-B7-H3 therapies in clinical development including monoclonal antibodies, antibody-drug conjugates (ADCs), and CAR-T cells[4-6]. However, no validated companion diagnostic exists to stratify patients for these therapies. Here we present comprehensive validation of CD276 as a prognostic biomarker in GBM across multiple independent platforms. Using discovery analysis in TCGA (n=154) and independent validation in CPTAC proteomics (n=99)[7], we demonstrate that CD276-high expression is associated with significantly shorter survival ({Delta}=3.5-4.0 months, p=0.003-0.013). RNA expression correlates strongly with protein (r=0.75, p<0.0001), enabling flexible companion diagnostic development. Single-cell analysis of 338,564 cells from 110 patients[8] reveals CD276 is highest on tumor vasculature, supporting ADC targeting strategies that bypass the blood-brain barrier. Critically, we identify a novel therapeutic vulnerability: CD276-high tumors exhibit significantly reduced expression of ATP-binding cassette (ABC) drug efflux transporters ABCG2 (0.61-fold, p=0.0002) and ABCB1 (0.64-fold, p=0.005)[9]. Since these transporters actively efflux common ADC payloads including MMAE and DXd, their reduced expression suggests CD276-high tumors may be paradoxically more vulnerable to cytotoxic payloads despite their aggressive phenotype. This inverse relationship between target expression and drug efflux capacity provides mechanistic rationale for prioritizing CD276-high patients for ADC therapy. CD276 significantly outperforms PD-L1 across all metrics, consistent with PD-L1s clinical failure in GBM.

cancer biology↗

bioRxiv2025

Microenvironment-Inferred Genotyping: An Exclusionary Classifier for EGFR Amplification When DNA Testing Fails

EGFR amplification occurs in approximately 40-50% of glioblastoma (GBM) cases and is critical for treatment selection [1]. However, GBM tissue samples frequently yield insufficient material for comprehensive molecular testing due to extensive necrosis and tissue quality limitations [2]. This affects thousands of patients annually in the United States [4]. We developed a microenvironment-inferred genotyping approach, enabling molecular classification by measuring the "oligodendrocyte desert" effect when direct genetic testing is impossible. Using single-cell RNA-seq data from 102 GBM patients (1.47M cells) [19], we identified oligodendrocyte exclusion patterns associated with EGFR amplification. We developed a 13-feature machine learning classifier and computationally validated it across an independent external cohort (CPTAC, n=96) and cross-pipeline technical validation using TCGA data processed through three distinct bioinformatic pipelines (n=148 patients) [20,21,22,23]. EGFR-amplified tumors created detectable oligodendrocyte deserts (60-70% depletion, p<0.001). Our exclusionary classifier achieved AUC 0.845 in discovery cohorts and 0.756 average across validation analyses (n=244 unique patients). With a positive predictive value of 94%, this tool identifies high-confidence candidates for EGFR-targeted therapies who would otherwise be excluded from treatment. To our knowledge, this is the first algorithm enabling microenvironment-inferred genotyping from routine RNA-seq data, providing rescue diagnosis for EGFR classification when DNA testing fails.

cancer biology↗

bioRxiv2025

Acute Effects of Intra-Articular Liposomal IDO-1 following Anterior Cruciate Ligament Injury

Joint injuries, such as rupture of the anterior cruciate ligament (ACL), is associated with the development of post-traumatic osteoarthritis (PTOA). It is known that ACL rupture can lead to disruption of metabolic pathways, including the conversion of the essential amino acid tryptophan to kynurenine, which is associated with a sustained inflammatory response. An in vivo study was undertaken to determine the acute effects of intra-articular administration of liposomes loaded with the tryptophan-catabolizing enzyme indoleamine 2,3-dioxygenase-1 (IDO-1) following ACL rupture. Using an established rat model of non-surgical ACL injury, male and female Lewis rats underwent a single intra-articular injection of empty liposomes, or liposomes loaded with IDO-1 and were subsequently randomized to 1- or 2-week endpoints. IDO-1 treatment after ACL injury was associated with a significant reduction in synovial fluid concentration of tryptophan at both 1-and 2-week endpoints. In addition to a reduction in tryptophan, IDO-1 treatment led to significantly lower synovial fluid concentrations of IL-1b and TNF-a. Intra-articular administration of IDO-1-loaded liposomes also increased the ratio of regulatory T lymphocytes (Tregs) to IL-17-secreting helper T lymphocytes (Th17 cells). Similarly, IDO-1 treatment increased the number of CTLA4+ cells relative to IL-17A+ cells that infiltrated joint tissues at a 2-week endpoint. Contrast-enhanced micro-computed tomography (CE-uCT) was used to quantify treatment-based effects on articular cartilage thickness and surface roughness at at 2-week endpoint. In addition to sex-based differences, IDO-1-loaded liposome treatment was associated with increased cartilage thickness, with no significant effects on surface roughness. Histologic characterization is needed to determine whether this increased cartilage thickness represents a chondroprotective effect, or a degenerative effect of IDO-1-treatment.

physiology↗

bioRxiv2022

What does heritability of Alzheimer's disease represent?

INTRODUCTIONBoth Alzheimers disease (AD) and ageing have a strong genetic component. In each case, many associated variants have been discovered, but how much missing heritability remains to be discovered is debated. Variability in the estimation of SNP-based heritability could explain the differences in reported heritability. METHODSWe compute heritability in five large independent cohorts (N=7,396, 1,566, 803, 12,528 and 3,963) to determine whether a consensus for the AD heritability estimate can be reached. These cohorts vary by sample size, age of cases and controls and phenotype definition. We compute heritability a) for all SNPs, b) excluding APOE region, c) excluding both APOE and genome-wide association study hit regions, and d) SNPs overlapping a microglia gene-set. RESULTSSNP-based heritability of Alzheimers disease is between 38 and 66% when age and genetic disease architecture are correctly accounted for. The heritability estimates decrease by 12% [SD=8%] on average when the APOE region is excluded and an additional 1% [SD=3%] when genome-wide significant regions were removed. A microglia gene-set explains 69-84% of our estimates of SNP-based heritability using only 3% of total SNPs in all cohorts. CONCLUSIONThe heritability of neurodegenerative disorders cannot be represented as a single number, because it is dependent on the ages of cases and controls. Genome-wide association studies pick up a large proportion of total AD heritability when age and genetic architecture are correctly accounted for. Around 13% of SNP-based heritability can be explained by known genetic loci and the remaining heritability likely resides around microglial related genes. Author SummaryEstimates of heritability in Alzheimers disease, the proportion of phenotypic variance explained by genetics, are very varied across different studies, therefore, the amount of missing heritability not yet captured by current genome-wide association studies is debated. We investigate this in five independent cohorts, provide estimates based on these cohorts and detail necessary suggestions to accurately calculate heritability in age-related disorders. We also confirm the importance of microglia relevant genetic markers in Alzheimers disease. This manuscript provides suggestions for other researchers computing heritability in late-onset disorders and the microglia gene-set used in this study will be published alongside this manuscript and made available to other researchers. The correct assessment of disease heritability will aid in better understanding the amount of missing heritability in Alzheimers disease.

genetics↗