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1. Identifying gene expression patterns associated with drug-specific survival in cancer patients

   https://doi.org/10.1038/s41598-021-84211-y  

   Neary, Bridget; Zhou, Jie; Qiu, Peng (December 2021, Scientific Reports)

   null (Ed.)

   Abstract The ability to predict the efficacy of cancer treatments is a longstanding goal of precision medicine that requires improved understanding of molecular interactions with drugs and the discovery of biomarkers of drug response. Identifying genes whose expression influences drug sensitivity can help address both of these needs, elucidating the molecular pathways involved in drug efficacy and providing potential ways to predict new patients’ response to available therapies. In this study, we integrated cancer type, drug treatment, and survival data with RNA-seq gene expression data from The Cancer Genome Atlas to identify genes and gene sets whose expression levels in patient tumor biopsies are associated with drug-specific patient survival using a log-rank test comparing survival of patients with low vs. high expression for each gene. This analysis was successful in identifying thousands of such gene–drug relationships across 20 drugs in 14 cancers, several of which have been previously implicated in the respective drug’s efficacy. We then clustered significant genes based on their expression patterns across patients and defined gene sets that are more robust predictors of patient outcome, many of which were significantly enriched for target genes of one or more transcription factors, indicating several upstream regulatory mechanisms that may be involved in drug efficacy. We identified a large number of genes and gene sets that were potentially useful as transcript-level biomarkers for predicting drug-specific patient survival outcome. Our gene sets were robust predictors of drug-specific survival and our results included both novel and previously reported findings, suggesting that the drug-specific survival marker genes reported herein warrant further investigation for insights into drug mechanisms and for validation as biomarkers to aid cancer therapy decisions. 

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2. Estimation and optimization of composite outcomes

   Lucket, Daniel (May 2021, Journal of Machine Learning Research)

   There is tremendous interest in precision medicine as a means to improve patient out- comes by tailoring treatment to individual characteristics. An individualized treatment rule formalizes precision medicine as a map from patient information to a recommended treatment. A treatment rule is defined to be optimal if it maximizes the mean of a scalar outcome in a population of interest, e.g., symptom reduction. However, clinical and intervention scientists often seek to balance multiple and possibly competing outcomes, e.g., symptom reduction and the risk of an adverse event. One approach to precision medicine in this setting is to elicit a composite outcome which balances all competing outcomes; unfortunately, eliciting a composite outcome directly from patients is difficult without a high-quality instrument, and an expert-derived composite outcome may not account for heterogeneity in patient preferences. We propose a new paradigm for the study of precision medicine using observational data that relies solely on the assumption that clinicians are approximately (i.e., imperfectly) making decisions to maximize individual patient utility. Estimated composite outcomes are subsequently used to construct an estimator of an individualized treatment rule which maximizes the mean of patient-specific composite out- comes. The estimated composite outcomes and estimated optimal individualized treatment rule provide new insights into patient preference heterogeneity, clinician behavior, and the value of precision medicine in a given domain. We derive inference procedures for the pro- posed estimators under mild conditions and demonstrate their finite sample performance through a suite of simulation experiments and an illustrative application to data from a study of bipolar depression. 

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3. Discovering and interpreting transcriptomic drivers of imaging traits using neural networks

   https://doi.org/10.1093/bioinformatics/btaa126  

   Smedley, Nova F; El-Saden, Suzie; Hsu, William; Valencia, Alfonso (February 2020, Bioinformatics)

   Abstract Motivation Cancer heterogeneity is observed at multiple biological levels. To improve our understanding of these differences and their relevance in medicine, approaches to link organ- and tissue-level information from diagnostic images and cellular-level information from genomics are needed. However, these ‘radiogenomic’ studies often use linear or shallow models, depend on feature selection, or consider one gene at a time to map images to genes. Moreover, no study has systematically attempted to understand the molecular basis of imaging traits based on the interpretation of what the neural network has learned. These studies are thus limited in their ability to understand the transcriptomic drivers of imaging traits, which could provide additional context for determining clinical outcomes. Results We present a neural network-based approach that takes high-dimensional gene expression data as input and performs non-linear mapping to an imaging trait. To interpret the models, we propose gene masking and gene saliency to extract learned relationships from radiogenomic neural networks. In glioblastoma patients, our models outperformed comparable classifiers (>0.10 AUC) and our interpretation methods were validated using a similar model to identify known relationships between genes and molecular subtypes. We found that tumor imaging traits had specific transcription patterns, e.g. edema and genes related to cellular invasion, and 10 radiogenomic traits were significantly predictive of survival. We demonstrate that neural networks can model transcriptomic heterogeneity to reflect differences in imaging and can be used to derive radiogenomic traits with clinical value. Availability and implementation https://github.com/novasmedley/deepRadiogenomics. Contact whsu@mednet.ucla.edu Supplementary information Supplementary data are available at Bioinformatics online. 

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4. Development and application of a novel tumor habitat analysis technique based on dynamical modeling

   https://doi.org/10.1002/mp.18032  

   Stevens, Jack B; Je, Jihyeon; Riley, Breylon A; Mowery, Yvonne M; Brizel, David M; Liu, Jian‐Guo; Wang, Chunhao; Lafata, Kyle J (September 2025, Medical Physics)

   Abstract BackgroundOropharyngeal cancer (OPC) exhibits varying responses to chemoradiation therapy, making treatment outcome prediction challenging. Traditional imaging‐based methods often fail to capture the spatial heterogeneity within tumors, which influences treatment resistance and disease progression. Advances in modeling techniques allow for more nuanced analysis of this heterogeneity, identifying distinct tumor regions, or habitats, that drive patient outcomes. PurposeTo interrogate the association between treatment‐induced changes in spatial heterogeneity and chemoradiation resistance of oropharyngeal cancer (OPC) based on a novel tumor habitat analysis. MethodsA mathematical model was used to estimate tumor time dynamics of patients with OPC based on the applied analysis of partial differential equations. The position and momentum of each voxel was propagated according to Fokker‐Planck dynamics, that is, a common model in statistical mechanics. The boundary conditions of the Fokker‐Planck equation were solved based on pre‐ and intra‐treatment (i.e., after 2 weeks of therapy)¹⁸F‐FDG‐PET SUV images of patients (n = 56) undergoing definitive (chemo)radiation for OPC as part of a previously conducted prospective clinical trial. Tumor‐specific time dynamics, measured based on the solution of the Fokker‐Planck equation, were generated for each patient. Tumor habitats (i.e., non‐overlapping subregions of the primary tumor) were identified by measuring vector similarity in voxel‐level time dynamics through a fuzzy c‐means clustering algorithm. The robustness of our habitat construction method was quantified using a mean silhouette metric to measure intra‐habitat variability. Fifty‐four habitat‐specific radiomic texture features were extracted from pre‐treatment SUV images and normalized by habitat volume. Univariate Kaplan‐Meier analyses were implemented as a feature selection method, where statistically significant features (p < 0.05, log‐rank) were used to construct a multivariate Cox proportional‐hazards model. Parameters from the resulting Cox model were then used to construct a risk score for each patient, based on habitat‐specific radiomic expression. The patient cohort was stratified by median risk score value and association with recurrence‐free survival (RFS) was evaluated via log‐rank tests. ResultsDynamic tumor habitat analysis partitioned the gross disease of each patient into three spatial subregions. Voxels within each habitat suggested differential response rates in different compartments of the tumor. The minimum mean silhouette value was 0.57 and maximum mean silhouette value was 0.8, where values above 0.7 indicated strong intra‐habitat consistency and values between 0.5 and 0.7 indicated reasonable intra‐habitat consistency. Nine radiomic texture features (three GLRLM, two GLCOM, and three GLSZM) and SUVmax were found to be prognostically significant and were used to build the multivariate Cox model. The resulting risk score was associated with RFS (p = 0.032). By contrast, potential confounding factors (primary tumor volume and mean SUV) were not significantly associated with RFS (p = 0.286 andp = 0.231, respectively). ConclusionWe interrogated spatial heterogeneity of oropharyngeal tumors through the application of a novel algorithm to identify spatial habitats on SUV images. Our habitat construction technique was shown to be robust and habitat‐specific feature spaces revealed distinct underlying radiomic expression patterns. Radiomic features were extracted from dynamic habitats and used to build a risk score which demonstrated prognostic value. 

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5. A Functional Precision Medicine Pipeline Combines Comparative Transcriptomics and Tumor Organoid Modeling to Identify Bespoke Treatment Strategies for Glioblastoma

   https://doi.org/10.3390/cells10123400  

   Reed, Megan R.; Lyle, A. Geoffrey; De Loose, Annick; Maddukuri, Leena; Learned, Katrina; Beale, Holly C.; Kephart, Ellen T.; Cheney, Allison; van den Bout, Anouk; Lee, Madison P.; et al (December 2021, Cells)

   Li Fraumeni syndrome (LFS) is a hereditary cancer predisposition syndrome caused by germline mutations in TP53. TP53 is the most common mutated gene in human cancer, occurring in 30–50% of glioblastomas (GBM). Here, we highlight a precision medicine platform to identify potential targets for a GBM patient with LFS. We used a comparative transcriptomics approach to identify genes that are uniquely overexpressed in the LFS GBM patient relative to a cancer compendium of 12,747 tumor RNA sequencing data sets, including 200 GBMs. STAT1 and STAT2 were identified as being significantly overexpressed in the LFS patient, indicating ruxolitinib, a Janus kinase 1 and 2 inhibitors, as a potential therapy. The LFS patient had the highest level of STAT1 and STAT2 expression in an institutional high-grade glioma cohort of 45 patients, further supporting the cancer compendium results. To empirically validate the comparative transcriptomics pipeline, we used a combination of adherent and organoid cell culture techniques, including ex vivo patient-derived organoids (PDOs) from four patient-derived cell lines, including the LFS patient. STAT1 and STAT2 expression levels in the four patient-derived cells correlated with levels identified in the respective parent tumors. In both adherent and organoid cultures, cells from the LFS patient were among the most sensitive to ruxolitinib compared to patient-derived cells with lower STAT1 and STAT2 expression levels. A spheroid-based drug screening assay (3D-PREDICT) was performed and used to identify further therapeutic targets. Two targeted therapies were selected for the patient of interest and resulted in radiographic disease stability. This manuscript supports the use of comparative transcriptomics to identify personalized therapeutic targets in a functional precision medicine platform for malignant brain tumors. 

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