Survival rate | EPFL Graph Search

Survival rate is a part of survival analysis. It is the proportion of people in a study or treatment group still alive at a given period of time after diagnosis. It is a method of describing prognosis in certain disease conditions, and can be used for the assessment of standards of therapy. The survival period is usually reckoned from date of diagnosis or start of treatment. Survival rates are based on the population as a whole and cannot be applied directly to an individual. There are various types of survival rates (discussed below). They often serve as endpoints of clinical trials and should not be confused with mortality rates, a population metric. Overall survival Patients with a certain disease (for example, colorectal cancer) can die directly from that disease or from an unrelated cause (for example, a car accident). When the precise cause of death is not specified, this is called the overall survival rate or observed survival rate. Doctors often use mean overall survi

Inhibition of mitophagy drives macrophage activation and antibacterial defense during sepsis

Johan Auwerx, Antoine Jalil

Mitochondria have emerged as key actors of innate and adaptive immunity. Mitophagy has a pivotal role in cell homeostasis, but its contribution to macrophage functions and host defense remains to be delineated. Here, we showed that lipopolysaccharide (LPS) in combination with IFN-gamma inhibited PINK1-dependent mitophagy in macrophages through a STAT1-dependent activation of the inflammatory caspases 1 and 11. In addition, we demonstrated that the inhibition of mitophagy triggered classical macrophage activation in a mitochondrial ROS-dependent manner. In a murine model of polymicrobial infection (cecal ligature and puncture), adoptive transfer of Pink1-deficient bone marrow or pharmacological inhibition of mitophagy promoted macrophage activation, which favored bactericidal clearance and led to a better survival rate. Reciprocally, mitochondrial uncouplers that promote mitophagy reversed LPS/IFN-gamma-mediated activation of macrophages and led to immunoparalysis with impaired bacterial clearance and lowered survival. In critically ill patients, we showed that mitophagy was inhibited in blood monocytes of patients with sepsis as compared with nonseptic patients. Overall, this work demonstrates that the inhibition of mitophagy is a physiological mechanism that contributes to the activation of myeloid cells and improves the outcome of sepsis.

AMER SOC CLINICAL INVESTIGATION INC2020

A Tale of Two Tumors: Unraveling the Complexities of Epigenetic Dysregulation in Cancer

Maria Christine Donaldson

Epigenetics plays an important role in cancer development and progression. Cancer cells hijack the epigenome by modifying the histone protein units responsible for packaging DNA, or by modifying the DNA itself, resulting in changes to chromatin topology and transcriptional programming within the cell. In this thesis, I report our investigation to uncover the mechanisms of epigenetic regulation and how epigenetic control of transcriptional programming goes awry in cancer. We investigate these processes in two separate contexts. In our first study, we investigate the mechanisms by which EZH2 oncogenic mutations alter structure and function of topologically associating domains in non-Hodgkin lymphoma. The process of chromatin folding leads to a systematic arrangement of hierarchical structural and regulatory elements found within the nucleus. A topologically associating domain (TAD) is a regulatory unit of self interacting DNA, where the transcription of the genes within a TAD is usually dictated by the presence of active (H3K36me3) or inactive (H3K27me3) histone marks. In cancer, the somatic point mutation in EZH2Y646X leads to a genome wide increase in H3K27me3, associated with transcriptional repression. While this alteration leads to changes in the global epigenetic status of the cell, its impact on chromatin organization and TAD-related function remain unclear. By combining transcriptomics and epigenetics with analyses of chromatin structure, we demonstrate a functional interplay between TADs and the epigenetic and transcriptional program of the genes found within them. This balance is altered by EZH2Y646X, leading to the synergistic silencing of entire domains directly targeting cell differentiation and tumor suppressive programs. A closer look reveals that the silencing of tumor suppressive TADs are coupled with structural modifications and changes in promoter interactions within EZH2Y646X target TAD6.139. Impressively, the TADâs transcriptional and epigenetic programs are restored by pharmacological inhibition of EZH2Y646X. Our results indicate that EZH2Y646X alters the topology and function of chromatin domains to promote synergistic oncogenic programs. In our second study, we dissect the epigenetic, transcriptomic, and metabolic signaling dependencies using a novel model of central nervous system primitive neural ectodermal tumors (CNS-PNETs). Central nervous system (CNS) tumors are the leading cause of cancer-associated death in children. Primitive neural-ectodermal tumors (CNS-PNETs) are a particularly aggressive subtype of embryonal CNS-tumor, with a five-year overall survival rate in less than 50% of patients. Despite sharing a similar cell of origin of other CNS tumors, CNS-PNETs have a different anatomical location, unique genetic and epigenetic features, and significantly worse clinical outcome. In addition, a lack of in vivo models for studying CNS tumors challenges the opportunity to dissect the genetic variables that underlie the origin of these tumors. We developed a novel CNS-PNET mouse model, called CNS-NPCs, using neural progenitor cells derived from human-iPS cells. Through histological, DNA methylation, and RNA sequencing analyses, we find that the CNS-NPC model recapitulates the the morphologic, epigenetic, and transcriptomic features of primary CNS-PNETs. In addition, through in vivo metabolic analyses of CNS-NPCs and the patient derived cell line, PFSK-1, we identified dysregulation in the neurotransmitter...

EPFL2019

Prediction of Survival and Risk Assessment Using Joint Analysis of Microarray Gene Expression Data

Haleh Yasrebi

Gene expression profiles have been widely used in molecular classification, diagnosis and prediction, particularly in the area of oncology where accurate and early diagnosis is needed for appropriate treatment. Avoiding under-/over-treatment when it is not necessary can extend a patient's survival and prevent disease recurrence. These high-throughput assay technologies have generated terabytes of data exploited extensively to provide insights on cancer biology and the underlying mechanism of disease progression. The ultimate goal is to identify possibly tailored treatment and therapy for personalized medicine. Analysis of microarray data is constrained by the following characteristics: (i) noisy due to missing or erroneous values; (ii) high dimensional due to a large number of genes versus a few number of samples in which their expression levels are measured; (iii) costly due to expensive microarray experiments. Abundant microarray gene expression data should be processed by appropriate computational and statistical learning methodologies such as machine learning techniques. These methods are robust to noisy data and have a great capacity to analyze high dimensional data. Their computational power is nevertheless limited to sample size based on which these methods are built. These algorithms have been widely applied to microarray gene expression data to identify a set of genes known as a gene signature whose expressions are highly correlated to a target value or outcome such as disease status, tumor subtype, a patient's survival time, risk of mortality or cancer relapse. Prediction of survival time and a patient's risk which is unknown at diagnosis presents a more challenging task for machine learning methods than tumor subtype or disease classification, which is already established by oncologists. The properties of microarray data cited above, the limitation of the number of samples in cancer patients and dependency of the machine learning methods' performance on sample size justify joint analysis of microarray data to increase the number of samples. We applied joint analysis methods to breast and lung cancer data sets to improve survival prediction and risk assessment. In overall, no significant improvement or deterioration of the performance accuracy was obtained with joint analysis. However, increasing sample size helped to identify robust or stable gene signatures predictive of survival time and risk assessment. Our achievements and learned-lessons from joint analysis of microarray gene expression data can be used as a guideline for future research studies in classification and prediction.