Pathway-Guided Optimization of Deep Generative Molecular Design Models for Cancer Therapy
Texas A&M University（德克萨斯农工大学）；Frederick National Laboratory for Cancer Research（弗雷德里克国家癌症研究所）；Leidos Biomedical Research（莱多斯生物医学研究所）；Brookhaven National Laboratory（布鲁克海文国家实验室）
Alif Bin Abdul Qayyum, Susan D. Mertins, Amanda K. Paulson, Nathan M. Urban, Byung-Jun Yoon
The data-driven drug design problem can be formulated as an optimization task of a potentially expensive black-box objective function over a huge high-dimensional and structured molecular space. The junction tree variational autoencoder (JTVAE) has been shown to be an efficient generative model that can be used for suggesting legitimate novel drug-like small molecules with improved properties. While the performance of the generative molecular design (GMD) scheme strongly depends on the initial training data, one can improve its sampling efficiency for suggesting better molecules with enhanced properties by optimizing the latent space. In this work, we propose how mechanistic models - such as pathway models described by differential equations - can be used for effective latent space optimization(LSO) of JTVAEs and other similar models for GMD. To demonstrate the potential of our proposed approach, we show how a pharmacodynamic model, assessing the therapeutic efficacy of a drug-like small molecule by predicting how it modulates a cancer pathway, can be incorporated for effective LSO of data-driven models for GMD.
Melanoma is the most lethal type of skin cancer. Patients are vulnerable to mental health illnesses which can reduce the effectiveness of the cancer treatment and the patients adherence to drug plans. It is crucial to preserve the mental health of patients while they are receiving treatment. However, current art therapy approaches are not personal and unique to the patient. We aim to provide a well-trained image style transfer model that can quickly generate unique art from personal dermoscopic melanoma images as an additional tool for art therapy in disease management of melanoma. Visual art …
Large variability between cell lines brings a difficult optimization problem of drug selection for cancer therapy. Standard approaches use prediction of value for this purpose, corresponding e.g. to expected value of their distribution. This article shows superiority of working on, predicting the entire probability distributions - proposing basic tools for this purpose. We are mostly interested in the best drug in their batch to be tested - proper optimization of their selection for extreme statistics requires knowledge of the entire probability distributions, which for distributions of drug p…
Cardiotoxicity induced by the breast cancer treatments (i.e., chemotherapy, targeted therapy and radiation therapy) is a significant problem for breast cancer patients. The cardiotoxicity risk for breast cancer patients receiving different treatments remains unclear. We developed and evaluated risk predictive models for cardiotoxicity in breast cancer patients using EHR data. The AUC scores to predict the CHF, CAD, CM and MI are 0.846, 0.857, 0.858 and 0.804 respectively. After adjusting for baseline differences in cardiovascular health, patients who received chemotherapy or targeted therapy a…
University of Birmingham（伯明翰大学）；University of Melbourne（墨尔本大学）；RMIT University（皇家墨尔本理工大学）
Survival risk prediction using gene expression data is important in making treatment decisions in cancer. Standard neural network (NN) survival analysis models are black boxes with lack of interpretability. More interpretable visible neural network (VNN) architectures are designed using biological pathway knowledge. But they do not model how pathway structures can change for particular cancer types. We propose a novel Mutated Pathway VNN or MPVNN architecture, designed using prior signaling pathway knowledge and gene mutation data-based edge randomization simulating signal flow disruption. As …
University of Illinois at Chicago（伊利诺伊大学芝加哥分校）；University of Iowa（爱荷华大学）；MD Anderson Cancer Center at the University of Texas（德克萨斯大学MD安德森癌症中心）
Although cancer patients survive years after oncologic therapy, they are plagued with long-lasting or permanent residual symptoms, whose severity, rate of development, and resolution after treatment vary largely between survivors. The analysis and interpretation of symptoms is complicated by their partial co-occurrence, variability across populations and across time, and, in the case of cancers that use radiotherapy, by further symptom dependency on the tumor location and prescribed treatment. We describe THALIS, an environment for visual analysis and knowledge discovery from cancer therapy sy…
European Georges Pompidou Hospital (AP-HP)（欧洲乔治·蓬皮杜医院（AP-HP））；Univ. Paris-Sud, Université Paris Saclay（巴黎南大学，巴黎萨克雷大学）；Center of Data Science, Université Paris-Saclay（巴黎萨克雷大学数据科学中心）；Ecole Polytechnique（巴黎综合理工学院）；INRIA（法国国家信息与自动化研究所）；Télécom ParisTech（巴黎高等电信学校）；LAL, CNRS（法国国家科学研究中心线性加速器实验室）
Monoclonal antibodies constitute one of the most important strategies to treat patients suffering from cancers such as hematological malignancies and solid tumors. In order to guarantee the quality of those preparations prepared at hospital, quality control has to be developed. The aim of this study was to explore a noninvasive, nondestructive, and rapid analytical method to ensure the quality of the final preparation without causing any delay in the process. We analyzed four mAbs (Inlfiximab, Bevacizumab, Ramucirumab and Rituximab) diluted at therapeutic concentration in chloride sodium 0.9% …
Guiding Treatment Strategies: The Role of Adjuvant Anti-Her2 Neu Therapy and Skin/Nipple Involvement in Local Recurrence-Free Survival in Breast Cancer Patients
Mathematical Modeling of Cancer-Bacterial Therapy: Analysis and Numerical Simulation via Physics-Informed Neural Networks
TICLab, International University of Rabat（里海大学拉巴特分校TIC实验室）；Université de Lorraine, CNRS, CRAN（洛林大学，法国国家科学研究中心，CRAN研究所）；Université de Lorraine, CNRS, IECL（洛林大学，法国国家科学研究中心，IECL研究所）；Univ Rennes, INSA, CNRS, IRMAR-UMR 6625（里昂大学，法国国家科学研究中心，IRMAR-UMR 6625研究所）
In this paper, a methodology is proposed that enables to analyze the sensitivity of the outcome of a therapy to unavoidable high dispersion of the patient specific parameters on one hand and to the choice of the parameters that define the drug delivery feedback strategy on the other hand. More precisely, a method is given that enables to extract and rank the most influent parameters that determine the probability of success/failure of a given feedback therapy for a given set of initial conditions over a cloud of realizations of uncertainties. Moreover predictors of the expectations of the amou…
We propose the molecular omics network (MOOMIN) a multimodal graph neural network used by AstraZeneca oncologists to predict the synergy of drug combinations for cancer treatment. Our model learns drug representations at multiple scales based on a drug-protein interaction network and metadata. Structural properties of compounds and proteins are encoded to create vertex features for a message-passing scheme that operates on the bipartite interaction graph. Propagated messages form multi-resolution drug representations which we utilized to create drug pair descriptors. By conditioning the drug c…
University of Texas Southwestern Medical Center（德克萨斯大学西南医学中心）
With the advancement of treatment modalities in radiation therapy for cancer patients, outcomes have improved, but at the cost of increased treatment plan complexity and planning time. The accurate prediction of dose distributions would alleviate this issue by guiding clinical plan optimization to save time and maintain high quality plans. We have modified a convolutional deep network model, U-net (originally designed for segmentation purposes), for predicting dose from patient image contours of the planning target volume (PTV) and organs at risk (OAR). We show that, as an example, we are able…
Learning personalized cancer treatment with machine learning holds great promise to improve cancer patients' chance of survival. Despite recent advances in machine learning and precision oncology, this approach remains challenging as collecting data in preclinical/clinical studies for modeling multiple treatment efficacies is often an expensive, time-consuming process. Moreover, the randomization in treatment allocation proves to be suboptimal since some participants/samples are not receiving the most appropriate treatments during the trial. To address this challenge, we formulate drug screeni…
The analysis of cancer genomic data has long suffered "the curse of dimensionality". Sample sizes for most cancer genomic studies are a few hundreds at most while there are tens of thousands of genomic features studied. Various methods have been proposed to leverage prior biological knowledge, such as pathways, to more effectively analyze cancer genomic data. Most of the methods focus on testing marginal significance of the associations between pathways and clinical phenotypes. They can identify relevant pathways, but do not involve predictive modeling. In this article, we propose a Pathway-ba…
Predicting Targeted Therapy Resistance in Non-Small Cell Lung Cancer Using Multimodal Machine Learning
Geisel School of Medicine at Dartmouth（达特茅斯盖泽尔医学院）；Ochsner Health System（奥克斯纳医疗系统）；Leiden University Medical Center（莱顿大学医学中心）；Dartmouth-Hitchcock Medical Center（达特茅斯-希区柯克医疗中心）；Dartmouth College（达特茅斯学院）
Patient-Specific Deep Reinforcement Learning for Automatic Replanning in Head-and-Neck Cancer Proton Therapy
Department of Radiation and Cellular Oncology University of Chicago（辐射与细胞肿瘤学系，芝加哥大学）；Division of Environmental and Occupational Health Sciences University of Illinois at Chicago（环境与职业卫生科学系，伊利诺伊大学芝加哥分校）；Department of Radiation Oncology and Winship Cancer Institute Emory University（放射肿瘤学系和Winship癌症研究所，埃默里大学）
UT Southwestern Medical Center（德克萨斯大学西南医学中心）；University of North Texas（北德克萨斯大学）；UCLouvain（鲁汶大学）；Erasmus University Medical Center（伊拉斯姆斯大学医学中心）
This work aims to study the generalizability of a pre-developed deep learning (DL) dose prediction model for volumetric modulated arc therapy (VMAT) for prostate cancer and to adapt the model to three different internal treatment planning styles and one external institution planning style. We built the source model with planning data from 108 patients previously treated with VMAT for prostate cancer. For the transfer learning, we selected patient cases planned with three different styles from the same institution and one style from a different institution to adapt the source model to four targ…
AI-Enhanced Cognitive Behavioral Therapy: Deep Learning and Large Language Models for Extracting Cognitive Pathways from Social Media Texts
School of Software Engineering, Beijing University of Technology（北京工业大学软件学院）；School of Nursing, Wuhan University（武汉大学护理学院）；Sorbonne Université, Institut du Cerveau – Paris Brain Institute - ICM, CNRS, Inria, Inserm, AP-HP, Hôpital de la Pitié-Salpêtrière（索邦大学，巴黎脑研究所-ICM，法国国家科学研究中心，法国国家信息与自动化研究所，法国国家健康与医学研究院，巴黎公共医疗集团，皮蒂-萨尔皮特里埃医院）
Characterizing patient somatic mutations through next-generation sequencing technologies opens up possibilities for refining cancer subtypes. However, catalogues of mutations reveal that only a small fraction of the genes are altered frequently in patients. On the other hand different genomic alterations may perturb the same pathways. We propose a novel clustering procedure that quantifies the similarities of patients from their mutational profile on pathways via a novel graph kernel. We represent each KEGG pathway as an undirected graph. For each patient the vertex labels are assigned based o…
