Biology subjects

Strauch, J.

Publications and source records attributed to Strauch, J..

4 recordsLinked to original sources

bioRxiv2026

Tissue-aware elastic net decomposition reveals shared and lineage-specific drug response biomarkers

MotivationComputational models that predict cancer drug response from genomic features are central to biomarker discovery, yet a recent audit found data leakage in 72% of 32 published methods, and complex models offer little interpretability while only modestly exceeding simple baselines under honest evaluation. Tissue lineage is a largely untapped source of legitimate inductive bias, but existing tissue-aware methods neither separate pan-cancer from lineage-specific signal nor report leakage-free performance. ResultsWe introduce the Data Shared Elastic Net (DSEN), a tissue-aware regression that decomposes each drugs model into a shared coefficient block common to all lineages and tissue-specific deviation blocks. Under leakage-free cross-validation across 265 drugs, 1,462 cell lines and 31 tissue lineages, DSEN improved mean squared error over a standard elastic net for 92.5% of drugs (mean 4.95%) while selecting 58% fewer stable shared features. Shared coefficients generalized to held-out tissues (59% tissue-level win rate) and recurrently recovered transferable pathway modules (p53, MAPK), whereas tissue blocks captured lineage markers such as the skin MITF /S100B program. The closest tissue-aware comparator, TG-LASSO, performed worse than the tissue-agnostic baseline (-13.8% mean MSE). Ablation shows tissue-aware modeling helps most when features are scarce, with no single modality dominating. Availability and implementationhttps://github.com/AsiaeeLab/tissue-aware-drug-response. Contactamir.asiaeetaheri@vumc.org

bioinformatics↗

bioRxiv2026

Widespread data leakage inflates performance estimates in cancer drug response prediction

Drug response prediction models guide biomarker discovery, yet their reported accuracy depends on rigorous cross-validation (CV). We show that supervised feature screening applied to all samples before CV, a widespread practice, introduces data leakage that systematically underestimates prediction error. Across 265 drugs and 1,462 cancer cell lines, leakage-free CV raises mean squared error by 16.6% on average, with near-zero feature overlap between leaked and corrected pipelines (mean Jaccard 0.18; 36.2% of drugs share no features). Despite selecting five times more features, the leaked pipeline recovers known drug targets at nearly the same rate as the corrected pipeline, indicating that the inflated feature sets capture statistical artifacts rather than biological signal. A code-level audit of 32 published methods (2017-2024) confirms leakage in 23 (72%), spanning five distinct modes and cited over 3,000 times. The magnitude of inflation from this single leakage mode is comparable to improvement margins typically claimed over elastic net baselines, raising the possibility that some reported advances reflect evaluation artifacts. We provide a leakage taxonomy, audit guide and reference implementation for leakage-free evaluation.

bioinformatics↗

bioRxiv2024

Improving Drug Sensitivity Prediction and Inference by Multitask Learning

The development of models to predict sensitivity to anticancer drugs is an area of significant interest, given the diverse responses to treatment among patients and the considerable expense and time involved in anticancer drug development. Leveraging "omic" data and anticancer response information from the Cancer Cell Line Encyclopedia, we propose a novel approach utilizing multitask learning to enhance prediction accuracy and inference. We extended a multitask learning framework called the Data Shared Lasso to develop the Data Shared Elastic Net. This enabled the construction of tissue-specific models with information sharing while maintaining the attractive properties of Elastic Net regression. By employing this approach, we observed improvements in prediction accuracy compared to single-task Elastic Net models, particularly for cell lines displaying high sensitivity to treatment. Furthermore, the Data Shared Elastic Net facilitated the identification of predictors for anticancer drug sensitivity within specific tissue types, shedding light on cellular pathways targeted by these drugs across tissues. We also investigated the impact of data leakage on modeling outcomes from previous studies, which led to underestimating prediction error and erroneous inferences

bioinformatics↗

bioRxiv2024

Orbitofrontal cortex to dorsal striatum circuit is critical for incubation of oxycodone craving after forced abstinence

Relapse is a major challenge in treating opioid addiction, including oxycodone. During abstinence, oxycodone seeking progressively increases, and we previously demonstrated a causal role of the orbitofrontal cortex (OFC) in this incubation of oxycodone craving after forced abstinence. Here, we explored critical downstream targets of OFC by focusing on dorsal striatum (DS). We first examined dorsal striatal Fos (a neuronal activity marker) expression associated with oxycodone seeking after abstinence. Using a dopamine D1 receptor (D1R) antagonist, we also tested the causal role of DS in incubated oxycodone seeking. Next, we combined fluorescence-conjugated cholera toxin subunit B (CTb-555, a retrograde tracer) with Fos to assess whether the activation of OFC[->]DS projections was associated with incubated oxycodone seeking. We then used a series of pharmacological procedures to examine the causal role of the interaction between glutamatergic projections from OFC and D1R signaling in DS in incubation of oxycodone craving. We found that dorsal striatal Fos expression in DS exhibited a time-dependent increase in parallel with incubation of oxycodone craving, and DS inactivation decreased incubated oxycodone seeking. Moreover, OFC[->]DS projections were activated during incubated oxycodone seeking, and anatomical disconnection of OFC[->]DS projections, but not unilateral inactivation of OFC or DS, decreased incubated oxycodone seeking. Lastly, contralateral disconnection of OFC[->]DS projections had no effect on oxycodone seeking on abstinence day 1. Together, these results demonstrated a causal role of OFC[->]DS projections in incubation of oxycodone craving.

neuroscience↗