Engineering topics
Publications and source records attributed to Hlatky, Lynn.
Provided herein are an isolated or enriched population of tumor initiating cells derived from normal cells, cells susceptible to neoplasia, or neoplastic cells. Methods of use of the cells for screening for anti-hyperproliferative agents, and use of the cells for animal models of hyperproliferative disorders including metastatic cancer, diagnostic methods, and therapeutic methods are provided.
As the number of cancer survivors grows, prediction of radiotherapy-induced second cancer risks becomes increasingly important. Because the latency period for solid tumors is long, the risks of recently introduced radiotherapy protocols are not yet directly measurable. In the accompanying article, we presented a new biologically based mathematical model, which, in principle, can estimate second cancer risks for any protocol. The novelty of the model is that it integrates, into a single formalism, mechanistic analyses of pre-malignant cell dynamics on two different time scales: short-term during radiotherapy and recovery; long-term during the entire life span. Here, we apply the model to nine solid cancer types (stomach, lung, colon, rectal, pancreatic, bladder, breast, central nervous system, and thyroid) using data on radiotherapy-induced second malignancies, on Japanese atomic bomb survivors, and on background US cancer incidence. Potentially, the model can be incorporated into radiotherapy treatment planning algorithms, adding second cancer risk as an optimization criterion.
Mathematical models of radiation carcinogenesis are important for understanding mechanisms and for interpreting or extrapolating risk. There are two classes of such models: (1) long-term formalisms that track premalignant cell numbers throughout an entire lifetime but treat initial radiation dose-response simplistically and (2) short-term formalisms that provide a detailed initial dose-response even for complicated radiation protocols, but address its modulation during the subsequent cancer latency period only indirectly. We argue that integrating short- and long-term models is needed. As an example of this novel approach, we integrate a stochastic short-term initiation/ inactivation/repopulation model with a deterministic two-stage long-term model. Within this new formalism, the following assumptions are implemented: radiation initiates, promotes, or kills pre-malignant cells; a pre-malignant cell generates a clone, which, if it survives, quickly reaches a size limitation; the clone subsequently grows more slowly and can eventually generate a malignant cell; the carcinogenic potential of pre-malignant cells decreases with age.
Ionizing radiation can damage cells by breaking both strands of DNA in multiple locations, essentially cutting chromosomes into pieces. The cell has enzymatic mechanisms to repair such breaks; however, these mechanisms are imperfect and, in an exchange process, may produce a large-scale rearrangement of the genome, called a chromosome aberration. Chromosome aberrations are important in killing cells, during carcinogenesis, in characterizing repair/misrepair pathways, in retrospective radiation biodosimetry, and in a number of other ways. DNA staining techniques such as mFISH ( multicolor fluorescent in situ hybridization) provide a means for analyzing aberration spectra by examining observed final patterns. Unfortunately, an mFISH observed final pattern often does not uniquely determine the underlying exchange process. Further, resolution limitations in the painting protocol sometimes lead to apparently incomplete final patterns. We here describe an algorithm for systematically finding exchange processes consistent with any observed final pattern. This algorithm uses aberration multigraphs, a mathematical formalism that links the various aspects of aberration formation. By applying a measure to the space of consistent multigraphs, we will show how to generate model-specific distributions of aberration processes from mFISH experimental data. The approach is implemented by software freely available over the internet. As a sample application, we apply these algorithms to an aberration data set, obtaining a distribution of exchange cycle sizes, which serves to measure aberration complexity. Estimating complexity, in turn, helps indicate how damaging the aberrations are and may facilitate identification of radiation type in retrospective biodosimetry.
Because many cancer patients are diagnosed earlier and live longer than in the past, second cancers induced by radiation therapy have become a clinically significant issue. An earlier biologically based model that was designed to estimate risks of high-dose radiation induced solid cancers included initiation of stem cells to a premalignant state, inactivation of stem cells at high radiation doses, and proliferation of stem cells during cellular repopulation after inactivation. This earlier model predicted the risks of solid tumors induced by radiation therapy but overestimated the corresponding leukemia risks. Methods: To extend the model to radiation-induced leukemias, we analyzed in addition to cellular initiation, inactivation, and proliferation a repopulation mechanism specific to the hematopoietic system: long-range migration through the blood stream of hematopoietic stem cells (HSCs) from distant locations. Parameters for the model were derived from HSC biologic data in the literature and from leukemia risks among atomic bomb survivors v^ ho were subjected to much lower radiation doses. Results: Proliferating HSCs that migrate from sites distant from the high-dose region include few preleukemic HSCs, thus decreasing the high-dose leukemia risk. The extended model for leukemia provides risk estimates that are consistent with epidemiologic data for leukemia risk associated with radiation therapy over a wide dose range. For example, when applied to an earlier case-control study of 110000 women undergoing radiotherapy for uterine cancer, the model predicted an excess relative risk (ERR) of 1.9 for leukemia among women who received a large inhomogeneous fractionated external beam dose to the bone marrow (mean = 14.9 Gy), consistent with the measured ERR (2.0, 95% confidence interval [CI] = 0.2 to 6.4; from 3.6 cases expected and 11 cases observed). As a corresponding example for brachytherapy, the predicted ERR of 0.80 among women who received an inhomogeneous low-dose-rate dose to the bone marrow (mean = 2.5 Gy) was consistent with the measured ERR (0.62, 95% Cl =-0.2 to 1.9). Conclusions: An extended, biologically based model for leukemia that includes HSC initiation, inactivation, proliferation, and, uniquely for leukemia, long-range HSC migration predicts, %Kith reasonable accuracy, risks for radiationinduced leukemia associated with exposure to therapeutic doses of radiation.
he position of chromosomes in the interphase nucleus is believed to be associated with a number of biological processes. Here, we present a web-based application that helps analyze the relative position of chromosomes during interphase in human cells, based on observed radiogenic chromosome aberrations. The inputs of the program are a table of yields of pairwise chromosome interchanges and a proposed chromosome geometric cluster. Each can either be uploaded or selected from provided datasets. The main outputs are P-values for the proposed chromosome clusters. SCHIP is designed to be used by a number of scientific communities interested in nuclear architecture, including cancer and cell biologists, radiation biologists and mathematical/computational biologists.