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Profil bibliographique

Katelyn Herm

Informations fournies par OpenAlex. Research Africa ne déduit ni nationalité, ni poste, ni coordonnées personnelles.

3Publications signalées
1Citations signalées
1Affiliations récentes

Les institutions déclarées

Les domaines associés

Cancer Genomics and DiagnosticsProstate Cancer Treatment and ResearchGenetic factors in colorectal cancerRNA Research and SplicingPARP inhibition in cancer therapy

Les publications récentes

Accès ouvert 2026 conference-abstract OpenAlex

Abstract A008: Reconstructing prostate evolution with Stochastically Emergent Tumors (SETs) reveals in vivo therapeutic vulnerabilities

Ruhollah Moussavi-Baygi, Matthew J. Ryan, Woogwang Sim, Samuel B. Hoelscher et autres

Abstract Rising incidence of early-onset prostate and other solid tumors underscores the need for experimental systems that model how normal tissues traverse premalignant states, acquire mutations, and become therapy-responsive malignancies under authentic immune and stromal pressures. A major barrier has been the …

us, ch (code pays fourni par la source)

0 citations Cancer Research
2025 conference-abstract OpenAlex

Abstract IA006: Reconstructing evolution with Stochastically Emergent Tumors (SETs) reveals in vivo vulnerabilities

Ruhollah Moussavi-Baygi, Matthew J. Ryan, Woogwang Sim, Samuel B. Hoelscher et autres

Abstract Rising incidence of early-onset cancers demands experimental systems that model how normal tissues traverse premalignant states to form clinically relevant, therapy-responsive tumors, and how host immunity and ancestry-linked genetics shape that trajectory. A major obstacle has been the lack of tractable …

us (code pays fourni par la source)

0 citations Cancer Research
Accès ouvert 2025 preprint OpenAlex

Stochastically Emergent Tumors offer in vivo whole genome interrogation of cancer evolution from non-malignant precursors

Ruhollah Moussavi-Baygi, Matthew J. Ryan, Woogwang Sim, Samuel B. Hoelscher et autres

Interrogating the stochastic events underlying tumor evolution from non-malignant precursors is crucial for understanding therapy resistance. Current methods are complicated by chromosomal instability, obscuring driver identification and yielding non-representative genetics. Inspired by patient tumors that evolve without chromosomal instability, we developed Stochastically …

us, ch (code pays fourni par la source)

1 citation bioRxiv (Cold Spring Harbor Laboratory)

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