Alana L. Welm

This study identifies RIPK1 activation as a key determinant of response to SMAC mimetics in TNBC, showing that resistance is linked to impaired RIPK1 autophosphorylation and HOIP overexpression.

DS5 is an open-source framework that standardizes HTS data storage, analysis, and drug prioritization, validated against public benchmarks and applied in clinical settings.

This study shows that loss of short-form Ron enhances B and T cell cooperation, which prevents breast cancer bone metastasis.

The paper identifies 9S-HODE, a lipid secreted by lean adipocytes, as a ferroptosis inducer that suppresses breast cancer while sparing normal cells, with therapeutic potential in obese mice.

This paper demonstrates that multiparametric QPI can assess drug sensitivity in patient-derived breast cancer models, including cryopreserved samples, revealing cellular heterogeneity and resistance mechanisms.

This paper introduces ScreenDL, a deep learning model that predicts effective cancer therapies using tumor omic and drug screening data, outperforming existing methods in preclinical trials.

This paper develops chemotherapy-specific predictors for triple-negative breast cancer using patient-derived xenografts and multi-omic data, achieving high AUROC scores for response prediction.

The paper presents a pan-cancer PDX histology image repository with genomic and pathological annotations for deep learning analysis of tumor regions and xenograft-transplant lymphoproliferative disorder prediction.