Deep Cox Mixtures for Survival Regression

Survival analysis is a challenging variation of regression modeling because of the presence of censoring, where the outcome measurement is only partially known, due to, for example, loss to follow up. Such problems come up frequently in medical applications, making survival analysis a key endeavor in biostatistics and machine learning for healthcare, with Cox regression models being amongst the most commonly employed models. We describe a new approach for survival analysis regression models, based on learning mixtures of Cox regressions to model individual survival distributions. We propose an approximation to the Expectation Maximization algorithm for this model that does hard assignments to mixture groups to make optimization efficient. In each group assignment, we fit the hazard ratios within each group using deep neural networks, and the baseline hazard for each mixture component non-parametrically. We perform experiments on multiple real world datasets, and look at the mortality rates of patients across ethnicity and gender. We emphasize the importance of calibration in healthcare settings and demonstrate that our approach outperforms classical and modern survival analysis baselines, both in terms of discriminative performance and calibration, with large gains in performance on the minority demographics.Comment: Machine Learning for Healthcare Conference, 202

Synergy Of Bcl2 And Histone Deacetylase Inhibition Against Leukemic Cells From Cutaneous T-Cell Lymphoma Patients

The presence and degree of peripheral blood involvement in patients with cutaneous T-cell lymphoma (CTCL) portend a worse clinical outcome. Available systemic therapies for CTCL may variably decrease tumor burden and improve quality of life, but offer limited effects on survival; thus, novel approaches to the treatment of advanced stages of this non-Hodgkin lymphoma are clearly warranted. Mutational analyses of CTCL patient peripheral blood malignant cell samples suggested the anti-apoptotic mediator BCL2 as a potential therapeutic target. To test this, we developed a screening assay for evaluating the sensitivity of CTCL cells to targeted molecular agents, and compared a novel BCL2 inhibitor, venetoclax, alone and in combination with a histone deacetylase (HDAC) inhibitor, vorinostat or romidepsin. Peripheral blood CTCL malignant cells were isolated from 25 patients and exposed ex vivo to the three drugs alone and in combination, and comparisons were made to four CTCL cell lines (Hut78, Sez4, HH, MyLa). The majority of CTCL patient samples were sensitive to venetoclax, and BCL2 expression levels were negatively correlated (r=-0.52, P=.018) to IC50 values. Furthermore, this anti-BCL2 effect was markedly potentiated by concurrent HDAC inhibition with 93% of samples treated with venetoclax and vorinostat and 73% of samples treated with venetoclax and romidepsin showing synergistic effects. These data strongly suggest that concurrent BCL2 and HDAC inhibition may offer synergy in the treatment of patients with advanced CTCL. By using combination therapies and correlating response to gene expression in this way, we hope to achieve more effective and personalized treatments for CTCL

An Effective Meaningful Way to Evaluate Survival Models

One straightforward metric to evaluate a survival prediction model is based on the Mean Absolute Error (MAE) -- the average of the absolute difference between the time predicted by the model and the true event time, over all subjects. Unfortunately, this is challenging because, in practice, the test set includes (right) censored individuals, meaning we do not know when a censored individual actually experienced the event. In this paper, we explore various metrics to estimate MAE for survival datasets that include (many) censored individuals. Moreover, we introduce a novel and effective approach for generating realistic semi-synthetic survival datasets to facilitate the evaluation of metrics. Our findings, based on the analysis of the semi-synthetic datasets, reveal that our proposed metric (MAE using pseudo-observations) is able to rank models accurately based on their performance, and often closely matches the true MAE -- in particular, is better than several alternative methods.Comment: Accepted to ICML 202

Variation of Medicago sativa varieties tolerance to Phoma medicaginis infection.

Due to its very important agronomic value and nutritional quality, Medicago sativa L. is considered as the queen of fodder and the first cultivated forage crop in the world. In field conditions, M. sativa is exposed to several biotic and/or abiotic constraints that affect its quality. In this regard, research is still underway to improve M. sativa resistance to many biotic stresses and, in this context, we analyzed the responses of a core collection of 10 varieties of M. sativa to Phoma medicaginis infection. Results from ANOVA showed that most growth parameters exhibited significant differences between the studied varieties. Nevertheless, only the number of healthy leaves among infection parameters varied significantly between the varieties. The local variety Gabès2355 exhibited the highest biomass. Positive correlations were found between the measured parameters. PCA based on the traits showing significant differences among the studied lines showed that the Gabès variety formed a separate group. Cluster analysis revealed that the studied varieties are classified into three major groups. The first group is formed by Gabès2353, the second group is composed of the Californian and El Hamma varieties, and the third group is constituted of the seven remaining varieties. Gabès2355 was the most tolerant to the Pm8 strain of P. medicaginis while Magna601 variety was the most susceptible. These two varieties will be useful to analyze the physiological and genetic determinants for M. sativa tolerance to P. medicaginis infection

A case study of survival and presentation of gastroesophageal cancer in local neighbourhoods

This thesis presents a quantitative case study on incidence, survival and presentation of patients diagnosed with gastroesophageal cancer to evaluate whether where people live affects how they present and survive with a gastroesophageal cancer diagnosis. The focus research evolved from studies on gastroesophageal cancer’s ‘geographic affiliation’ and a desire to review whether patient and population attributes could be harnessed to reveal potential ‘hotspots’ to inform targeted health intervention strategies. As the most crucial stage for intervention was associated with patients detecting symptoms early enough for intervention, the focus of this case study was narrowed to survival and presentation.This research analysed data from 2785 patients who presented to a regional referral specialist cancer treatment centre between the years 2000 and 2013. Cohort analysis revealed common attributes and survival, and data were merged with demographic information in a geographic information system to present findings in mapped format.Descriptive analysis revealed an association between later stage presentation and reduced survival outcome. Emergency presentations tended to have worse outcomes. Survival deteriorated with advancing age. Gastroesophageal cancer diagnoses in the under 54 age group was more common in lower socioeconomic groups and survival outcomes were marginally lower than in those patients from the least deprived areas. Spatial analysis revealed variation in incidence, presentation and survival across the region. Though this case study revealed several new findings on gastroesophageal cancer presentation and survival, there remains no single solution to informing and encouraging earlier diagnosis interventions. Though presenting data at finer scales of resolution is more clinically relevant, it threatens patient confidentiality

Weakly unsupervised conditional generative adversarial network for image-based prognostic prediction for COVID-19 patients based on chest CT

Because of the rapid spread and wide range of the clinical manifestations of the coronavirus disease 2019 (COVID-19), fast and accurate estimation of the disease progression and mortality is vital for the management of the patients. Currently available image-based prognostic predictors for patients with COVID-19 are largely limited to semi-automated schemes with manually designed features and supervised learning, and the survival analysis is largely limited to logistic regression. We developed a weakly unsupervised conditional generative adversarial network, called pix2surv, which can be trained to estimate the time-to-event information for survival analysis directly from the chest computed tomography (CT) images of a patient. We show that the performance of pix2surv based on CT images significantly outperforms those of existing laboratory tests and image-based visual and quantitative predictors in estimating the disease progression and mortality of COVID-19 patients. Thus, pix2surv is a promising approach for performing image-based prognostic predictions

A Network-based Approach to Breast Cancer Systems Medicine.

Breast cancer is the most commonly diagnosed cancer and the second leading cause of cancer death in women. Although recent improvements in the prevention, early detection, and treatment of breast cancer have led to a significant decrease in the mortality rate, the identification of an optimal therapeutic strategy for each patient remains a difficult task because of the heterogeneous nature of the disease. Clinical heterogeneity of breast cancer is in part explained by the vast genetic and molecular heterogeneity of this disease, which is now emerging from large-scale screening studies using \u201c-omics\u201d technologies (e.g. microarray gene expression profiling, next-generation sequencing). This genetic and molecular heterogeneity likely contributes significantly to therapy response and clinical outcome. The recent advances in our understanding of the molecular nature of breast cancer due, in particular, to the explosion of high-throughput technologies, is driving a shift away from the \u201cone-dose-fits-all\u201d paradigm in healthcare, to the new \u201cPersonalized Cancer Care\u201d paradigm. The aim of \u201cPersonalized Cancer Care\u201d is to select the optimal course of clinical intervention for individual patients, maximizing the likelihood of effective treatment and reducing the probability of adverse drug reactions, according to the molecular features of the patient. In light to this medical scenario, the aim of this project is to identify novel molecular mechanisms that are altered in breast cancer through the development of a computational pipeline, in order to propose putative biomarkers and druggable target genes for the personalized management of patients. Through the application of a Systems Biology approach to reverse engineer Gene Regulatory Networks (GRNs) from gene expression data, we built GRNs around \u201chub\u201d genes transcriptionally correlating with clinical-pathological features associated with breast tumor expression profiles. The relevance of the GRNs as putative cancer-related mechanisms was reinforced by the occurrence of mutational events related to breast cancer in the \u201chub\u201d genes, as well as in the neighbor genes. Moreover, for some networks, we observed mutually exclusive mutational patterns in the neighbors genes, thus supporting their predicted role as oncogenic mechanisms. Strikingly, a substantial fraction of GRNs were overexpressed in Triple Negative Breast Cancer patients who acquired resistance to therapy, suggesting the involvement of these networks in mechanisms of chemoresistance. In conclusion, our approach allowed us to identify cancer molecular mechanisms frequently altered in breast cancer and in chemorefractory tumors, which may suggest novel cancer biomarkers and potential drug targets for the development of more effective therapeutic strategies in metastatic breast cancer patients