Medicine and Health Sciences Commons^™

Machine Learning Analyses Of Highly-Multiplexed Immunofluorescence Identifies Distinct Tumor And Stromal Cell Populations In Primary Pancreatic Tumors, Krysten Vance, Alphan Alitinok, Seth Winfree, Heather Jensen Smith, Benjamin Swanson Md, Phd, Paul M. Grandgenett, Kelsey Klute, Daniel J Crichton, Michael A. Hollingsworth

Journal Articles: Eppley Institute

BACKGROUND: Pancreatic ductal adenocarcinoma (PDAC) is a formidable challenge for patients and clinicians.

OBJECTIVE: To analyze the distribution of 31 different markers in tumor and stromal portions of the tumor microenvironment (TME) and identify immune cell populations to better understand how neoplastic, non-malignant structural, and immune cells, diversify the TME and influence PDAC progression.

METHODS: Whole slide imaging (WSI) and cyclic multiplexed-immunofluorescence (MxIF) was used to collect 31 different markers over the course of nine distinctive imaging series of human PDAC samples. Image registration and machine learning algorithms were developed to largely automate an imaging analysis pipeline identifying distinct cell …

The Distinct Metabolic Phenotype Of Lung Squamous Cell Carcinoma Defines Selective Vulnerability To Glycolytic Inhibition, Justin Goodwin, Michael L. Neugent, Shin Yup Lee, Joshua H. Choe, Hyunsung Choi, Dana M. R. Jenkins, Robin J. Ruthenborg, Maddox W. Robinson, Ji Yun Jeong, Masaki Wake, Hajime Abe, Norihiko Takeda, Hiroko Endo, Masahiro Inoue, Zhenyu Xuan, Hyuntae Yoo, Min Chen, Jung-Mo Ahn, John D. Minna, Kristi L. Helke, Pankaj K. Singh, David B. Shackelford, Jung-Whan Kim

Journal Articles: Eppley Institute

Adenocarcinoma (ADC) and squamous cell carcinoma (SqCC) are the two predominant subtypes of non-small cell lung cancer (NSCLC) and are distinct in their histological, molecular and clinical presentation. However, metabolic signatures specific to individual NSCLC subtypes remain unknown. Here, we perform an integrative analysis of human NSCLC tumour samples, patient-derived xenografts, murine model of NSCLC, NSCLC cell lines and The Cancer Genome Atlas (TCGA) and reveal a markedly elevated expression of the GLUT1 glucose transporter in lung SqCC, which augments glucose uptake and glycolytic flux. We show that a critical reliance on glycolysis renders lung SqCC vulnerable to glycolytic inhibition, …

Invariant Characteristics Of Carcinogenesis., Simon Sherman, Nirosha Rathnayake, Tengiz Mdzinarishvili

Journal Articles: Eppley Institute

Carcinogenic modeling is aimed at mathematical descriptions of cancer development in aging. In this work, we assumed that a small fraction of individuals in the population is susceptible to cancer, while the rest of the population is resistant to cancer. For individuals susceptible to cancer we adopted methods of conditional survival analyses. We performed computational experiments using data on pancreatic, stomach, gallbladder, colon and rectum, liver, and esophagus cancers from the gastrointestinal system collected for men and women in the SEER registries during 1975-2009. In these experiments, we estimated the time period effects, the birth cohort effects, the age effects …

Heuristic Modeling Of Carcinogenesis For The Population With Dichotomous Susceptibility To Cancer: A Pancreatic Cancer Example., Tengiz Mdzinarishvili, Simon Sherman

Journal Articles: Eppley Institute

At present, carcinogenic models imply that all individuals in a population are susceptible to cancer. These models either ignore a fall of the cancer incidence rate at old ages, or use some poorly identifiable parameters for its accounting. In this work, a new heuristic model is proposed. The model assumes that, in a population, only a small fraction (pool) of individuals is susceptible to cancer and decomposes the problem of the carcinogenic modeling on two sequentially solvable problems: (i) determination of the age-specific hazard rate in individuals susceptible to cancer (individual hazard rate) from the observed hazard rate in the …

A Heuristic Solution Of The Identifiability Problem Of The Age-Period-Cohort Analysis Of Cancer Occurrence: Lung Cancer Example., Tengiz Mdzinarishvili, Simon Sherman

Journal Articles: Eppley Institute

BACKGROUND: The Age-Period-Cohort (APC) analysis is aimed at estimating the following effects on disease incidence: (i) the age of the subject at the time of disease diagnosis; (ii) the time period, when the disease occurred; and (iii) the date of birth of the subject. These effects can help in evaluating the biological events leading to the disease, in estimating the influence of distinct risk factors on disease occurrence, and in the development of new strategies for disease prevention and treatment.

METHODOLOGY/PRINCIPAL FINDINGS: We developed a novel approach for estimating the APC effects on disease incidence rates in the frame of …