Biology subjects

Ricker, C. A.

Publications and source records attributed to Ricker, C. A..

5 recordsLinked to original sources

bioRxiv2026

Clustered structural variant hotspots enable oncogenic addiction and plasticity in osteosarcoma

The genomic landscape of osteosarcoma, the most common bone cancer worldwide, is among the most structurally complex of all human malignancies. The identification of recurrent and functionally consequential patterns has thus remained a challenge. Across 236 whole-genome sequencing osteosarcoma samples, we uncovered five genomic hotspots of clustered structural variation collectively altered in 58% of tumors. Four were associated with amplification of oncogenes ( MYC , CCND3 , CCNE1 , CDK4) , while the fifth mapped largely upstream of TP53 . Hotspot events showed coordinated patterns of co-occurrence and mutual exclusivity with each other and with tumor suppressor alterations, suggesting genomic context-specific selection. We found localized transcriptional dysregulation at hotspot event loci, and single cells harboring these events converged on a neural crest-like program, linking these structural alterations to a less differentiated cell state. These events were also detectable non-invasively through liquid biopsies and displayed ongoing structural evolution throughout disease progression, a finding with potential clinical utility. Our results provide novel insight into how complex rearrangements shape oncogenesis in osteosarcoma, with broader relevance to other cancers characterized by complex genomes.

cancer biology↗

bioRxiv2026

Genomic, Clinical, and Spatial Predictors of Durable Response to BRAF/MEK Inhibition in BRAF-Mutant Melanoma

BRAF-targeted therapy (BRAFi/MEKi) and immune checkpoint blockade (anti-PD-1/anti-CTLA-4) have transformed the treatment of BRAF-mutant metastatic melanoma. While most patients who respond to targeted therapy eventually progress, a subset derives durable benefit, and biomarkers to identify this subset would inform optimal treatment selection. In this study, we analyzed pre-treatment tumor samples from a clinically annotated cohort of 155 patients with BRAF-mutant metastatic melanoma treated with first-line BRAFi/MEKi and followed for up to five years. We stratified patients into durable responders (PFS [≥] 24 months) and rapid progressors (PFS < 6 months with progression) and found that a global metric of tumor genomic heterogeneity, rather than individual gene alterations, distinguished these groups. Combining genomic heterogeneity with baseline tumor burden (e.g., lactate dehydrogenase (LDH) or radiographic lesion dimensions), we developed a parsimonious model that predicted durable responders with high precision and specificity. Notably, the analogous population of patients treated instead with immunotherapy were not durable responders, suggesting that the selected predictors of durable responders are targeted therapy specific. Spatial profiling of a subset of pre-treatment biopsies (n = 47) demonstrated that high intratumoral, but not peritumoral, CD8+ T-cell infiltration correlated with prolonged survival on BRAF-targeted therapy and served as an independent predictive factor when considered with genomic heterogeneity and features of clinical tumor burden. Together, these findings highlight the distinct baseline intrinsic and extrinsic features underlying durable response to BRAF-targeted therapy and support their potential implication in guiding treatment selection for patients with BRAF-mutant metastatic melanoma. One-Sentence SummaryIntegrated clinical, tumor genomic, and immune microenvironmental features predict durable responses to BRAF-targeted therapy.

cancer biology↗

bioRxiv2025

Immune Spatial Organization Predicts Metastasis Risk in Aggressive Localized Prostate Cancer

Risk stratification in localized prostate cancer (PCa) remains imprecise. Computational pathology has emerged as an attractive option for improving risk stratification, but current approaches either lack interpretability or focus solely on tumor morphology. Here, we identify, validate, and provide interpretability for an immune microenvironment-derived computational pathology biomarker for high-grade PCa. Using digitized hematoxylin and eosin-stained (H&E) slides from two independent cohorts of patients treated with radical prostatectomy (n=490), we found that spatial clustering of immune clusters, but not immune cell abundance, was independently associated with reduced risk of distant metastasis for high-grade disease but not low-grade. Joint analysis of H&E images and RNA sequencing (n=326) revealed that in high-grade disease, high-cluster samples were enriched in CD8+ T cells, activated memory CD4+ T cells, and Tregs, as well as clonal T cell populations, overall suggestive of an underlying T cell-mediated antitumor response. No such enrichment was found in low-grade tumors. These findings establish immune spatial architecture as a novel, interpretable computational pathology biomarker and provide insight into the immune landscape of high-grade PCa.

bioinformatics↗

bioRxiv2022

Genomic heterogeneity and ploidy identify patients with intrinsic resistance to PD-1 blockade in metastatic melanoma

While the introduction of immune checkpoint blockade (ICB) has dramatically improved clinical outcomes for patients with advanced melanoma, a significant proportion of patients develop resistance to therapy, and mechanisms of resistance are poorly elucidated in most cases. Further, while combination ICB has higher response rates and improved progression free survival compared to single agent therapy in the front line setting, there is significantly increased toxicity with combination ICB, and biomarkers to identify patients who would disproportionately benefit from combination therapy vs aPD-1 ICB are poorly characterized. To understand resistance mechanisms to single vs combination ICB therapy, we analyze whole-exome-sequencing (WES) of pre-treatment tumor and matched normals of 4 cohorts (n=140) of previously ICB-naive aPD-1 ICB treated patients. We find that high intratumoral genomic heterogeneity and low ploidy identify patients with intrinsic resistance to aPD-1 ICB. Comparing to a melanoma cohort from a pre-targeted therapy and ICB time period ("untreated" cohort), we find that genomic heterogeneity specifically predicts response and survival in the ICB treated cohorts, but not in the untreated cohort, while ploidy is also prognostic of overall survival in the "untreated" (by targeted therapy or ICB) group. To establish clinically actionable predictions, we optimize a simple decision tree using genomic ploidy and heterogeneity to identify with high confidence (90% PPV) a subset of patients with intrinsic resistance to and significantly worse survival on aPD1 ICB treatment. We then validate this model in independent cohorts, and further show that a significant proportion of patients predicted to have intrinsic resistance to single agent aPD-1 ICB respond to combination ICB, which suggests that nominated patients may benefit disproportionately from combination ICB. We further show that the features and predictions of the model are independent of known clinical features and previously nominated molecular biomarkers. These findings highlight the clinical and biological importance of genomic heterogeneity and ploidy, and sets a concrete framework towards clinical actionability, broadly advancing precision medicine in oncology.

cancer biology↗

bioRxiv2019

MEK1/2 in Rhabdomyosarcoma

Alveolar and embryonal rhabdomyosarcoma (RMS) are soft-tissue cancers that affect children, adolescents, and young adults. Sometimes referred to as muscle cancer, RMS is a cancer of muscle and non-muscle origin that phenocopies incompletely differentiated myoblasts or activated satellite (muscle stem) cells. Interestingly, embryonal RMS (ERMS) has been observed to undergo terminal myogenic differentiation in response to stress induced by chemotherapy and radiation therapy4, 9, 24. Given the propensity of rhabdomyosarcoma to differentiation, in this report we explore the use of differentiation therapy combining MEK inhibitor (MEKi) cobimetinib and chemotherapy as a strategy to halt RMS growth. We evaluated a representative panel of RMS cell lines with cobimetinib and chemotherapy in two dosing schedules that mimic clinical use followed by cell growth evaluation and high content analysis (differentiation) assays. We uncovered that cobimetinib does not have significant additive or synergistic effects on cell differentiation or cell growth with chemotherapy in RMS and can have unanticipated antagonistic effects; specifically, pre-exposure of cobimetinib to cells can decrease the effectiveness of chemotherapy-mediated cell growth inhibition in vitro. Although differentiation-therapy is still a potential viable strategy in RMS, our data do not support MEKi/chemotherapy co-treatment in this context.

cancer biology↗