scholarly journals Stochastic Evolution of Pancreatic Cancer Metastases During Logistic Clonal Expansion

2019 ◽  
pp. 1-11 ◽  
Author(s):  
Kimiyo N. Yamamoto ◽  
Lin L. Liu ◽  
Akira Nakamura ◽  
Hiroshi Haeno ◽  
Franziska Michor
Keyword(s):  
Pancreatic Cancer ◽  
Cancer Progression ◽  
Growth Models ◽  
Treatment Strategies ◽  
Evolutionary Model ◽  
Growth Patterns ◽  
Logistic Growth ◽  
Metastatic Progression ◽  
Treatment Regimens ◽  
Analytical Approximations

Despite recent progress in diagnostic and multimodal treatment approaches, most cancer deaths are still caused by metastatic spread and the subsequent growth of tumor cells in sites distant from the primary organ. So far, few quantitative studies are available that allow for the estimation of metastatic parameters and the evaluation of alternative treatment strategies. Most computational studies have focused on situations in which the tumor cell population expands exponentially over time; however, tumors may eventually be subject to resource and space limitations so that their growth patterns deviate from exponential growth to adhere to density-dependent growth models. In this study, we developed a stochastic evolutionary model of cancer progression that considers alterations in metastasis-related genes and intercellular growth competition leading to density effects described by logistic growth. Using this stochastic model, we derived analytical approximations for the time between the initiation of tumorigenesis and diagnosis, the expected number of metastatic sites, the total number of metastatic cells, the size of the primary tumor, and survival. Furthermore, we investigated the effects of drug administration and surgical resection on these quantities and predicted outcomes for different treatment regimens. Parameter values used in the analysis were estimated from data obtained from a pancreatic cancer rapid autopsy program. Our theoretical approach allows for flexible modeling of metastatic progression dynamics.

Targeting Tumour Metastasis: The Emerging Role of Nanotechnology

2020 ◽  
Vol 27 (8) ◽  
pp. 1367-1381 ◽  
Author(s):  
Sarah Visentin ◽  
Mirela Sedić ◽  
Sandra Kraljević Pavelić ◽  
Krešimir Pavelić
Keyword(s):  
Cancer Progression ◽  
Metastatic Cancer ◽  
Treatment Strategies ◽  
Metastatic Progression ◽  
Therapeutic Concept ◽  
Molecular Alterations ◽  
Tumour Metastasis ◽  
Metastatic Cells ◽  
Metastatic Process ◽  
Underlying Mechanisms

The metastatic process has still not been completely elucidated, probably due to insufficient knowledge of the underlying mechanisms. Here, we provide an overview of the current findings that shed light on specific molecular alterations associated with metastasis and present novel concepts in the treatment of the metastatic process. In particular, we discuss novel pharmacological approaches in the clinical setting that target metastatic progression. New insights into the process of metastasis allow optimisation and design of new treatment strategies, especially in view of the fact that metastatic cells share common features with stem cells. Nano- and micro-technologies are herein elaborated in details as a promising therapeutic concept in targeted drug delivery for metastatic cancer. Progression in the field could provide a more efficient way to tackle metastasis and thus bring about advancements in the treatment and management of patients with advanced cancer.

Role of the immune system in pancreatic cancer progression and immune modulating treatment strategies

2014 ◽  
Vol 40 (4) ◽  
pp. 513-522 ◽  
Author(s):  
K. Sideras ◽  
H. Braat ◽  
J. Kwekkeboom ◽  
C.H. van Eijck ◽  
M.P. Peppelenbosch ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
Immune System ◽  
Cancer Progression ◽  
Treatment Strategies

scholarly journals Pancreatic Cancer: Molecular, Biochemical, Chemopreventive, and Therapeutic Aspects

2010 ◽  
Vol 10 ◽  
pp. 1967-1970 ◽  
Author(s):  
Juan Iovanna ◽  
José Luis Neira
Keyword(s):  
Pancreatic Cancer ◽  
Research Effort ◽  
Treatment Strategies ◽  
Diagnostic Tools ◽  
Signal Pathways ◽  
Major Pathway ◽  
Treatment Regimens ◽  
Oncogenic Pathways ◽  
Clinically Significant ◽  
Molecular Aspects

Pancreatic cancer (PC) is the fourth leading cause of cancer death, with a median survival of 6 months and a dismal 5-year survival rate of 3–5%, a figure which has remained relatively unchanged over the past 25 years. PC is one of the most difficult diseases to treat due to late initial diagnosis and to resistance to the usual treatments. The presence of occult or clinical metastases at the time of diagnosis, together with the lack of effective chemotherapies, contributes to the high mortality in patients with PC. Its lethal nature stems from its propensity to disseminate rapidly to the lymphatic system and distant organs. Yet, understanding and stopping metastasis may prove to be one of the great potential strategies of treating PC. There is a dire need for the design of new and targeted therapeutic strategies that can overcome the drug resistance and improve the clinical outcome for patients diagnosed with the illness. The knowledge of the molecular aspects of PC is very important, and it is likely to be helpful in the design of newer drugs and the molecular selection of existing agents for targeted therapy. The inhibition of signal pathways can be carried out not only by small molecules, able to bind to selected regions of the target protein, but also by using large molecules as antibodies. The pathway to successful new therapies has been inhibited because of the rapidity with which agents tend to move into randomized, controlled trials without the extensive early testing necessary to optimize treatment regimens. However, lessons have been learned and our collective research effort has generated a substantial platform of knowledge from which further work will spring. The bioavailability of compounds such as antisense oligonucleotides and siRNAs in humans remains a big hurdle, which will require further improvement of gene-delivery strategies. Finally, the long-term goal of the therapy individualization for patients is possible if factors that predict treatment response, such as biological markers, could be determined accurately. These approaches are likely to comprise a mixture of targeted agents in combination with conventional chemotherapy and radiotherapy. For a clinically significant effect to be achieved, treatment strategies should either be in the form of (1) a “horizontal” approach, in which several oncogenic pathways (as those described in this series of reviews) are inhibited; or (2) a “vertical” approach, whereby multiple levels of a major pathway are targeted. Combination therapies, together with improved diagnostic tools and predictive markers, are ultimately desired in order to improve the bleak outlook for patients diagnosed with PC.

Growth patterns of two widely distributed freshwater turtles and a comparison of common methods used to estimate age

2002 ◽  
Vol 50 (5) ◽  
pp. 477 ◽  
Author(s):  
Ricky-John Spencer
Keyword(s):  
Growth Models ◽  
Sympatric Species ◽  
Growth Patterns ◽  
Juvenile Stage ◽  
Age And Growth ◽  
Logistic Growth ◽  
Freshwater Turtles ◽  
Dietary Shift ◽  
Demographic Models ◽  
Murray River

Turtles are long lived and demographic models requiring estimates of age, growth, fecundity and survival are central for management. Most studies that estimate age and growth of freshwater turtles use annuli as an index of age without estimating its error and very few studies that use growth models include many juveniles, where growth is often large and variable. In this paper, I compare the reliability of growth annuli and common models in determining age and growth of two widely distributed turtles in Australia. Most turtles are carnivorous during the juvenile stage but many species shift to a lower-quality omnivorous diet prior to maturing. Patterns of growth are often characterised by this dietary shift and I compared the growth of a common omnivorous turtle (Emydura macquarii) and a vulnerable sympatric species that is an obligate carnivore (Chelodina expansa). Mark–recapture programs were established in three lagoons on the Murray River. In total, 1218 hatchling E. macquarii were released into two of the lagoons and growth annuli were found to be unreliable in estimating their age by Year 2. The von Bertalanffy and logistic growth models can reliably estimate age of both male and female E. macquarii and C. expansa respectively. Growth is extremely rapid during the juvenile stage of E. macquarii, but is highly variable in C. expansa, with rapid growth occurring only beyond three years of age. Hence growth models fail to predict age when juveniles are excluded from the analyses. Female E. macquarii delay maturity until 9–12 years of age because clutch size is positively related to body size and they can produce only one large clutch per year. Female C.�expansa mature later (at ~14 years) than female E. macquarii and both species are sexually dimorphic, as males mature earlier at smaller sizes than females. Common growth models describe the growth of two widely distributed freshwater turtles, but different patterns of growth and age at maturity relate to quality of diet and reproduction.

scholarly journals Growth characteristics of noiler chickens as determined by nonlinear algorithms

2021 ◽  
Vol 48 (5) ◽  
pp. 12-19
Author(s):  
J. Adamu ◽  
A. Y. Shuaibu ◽  
A. O. Raji
Keyword(s):  
Goodness Of Fit ◽  
Growth Models ◽  
Growth Patterns ◽  
Growth Characteristics ◽  
Coefficient Of Determination ◽  
Logistic Growth ◽  
Mathematical Function ◽  
Morphometric Traits ◽  
Male And Female ◽  
Males And Females

The assessment of growth characteristics of noilers chickens as determined by non- linear algorithms will provide the best mathematical function in the growth of male and female noilers chickens This study sought to determine the adequacy of two mathematical functions for modeling growth characteristics of male and female Noiler® chickens. Body weights and morphometric traits of 200 Noiler chickens were measured bimonthly for 20 weeks and the data obtained fitted to the Gompertz and Logistic growth models using the nonlinear regression. The results showed significant (P<0.05) difference between males and females only at 16, 18 and 20 weeks of age, with values of 2316.2 vs 2121.9 g, 2624.3 vs 2378.1 g, and 3002.7 vs 2718.7g, respectively. There were no discernable differences between males and females for most body measurements except body length which was longer in the latter than former from 14 weeks of age. The asymptomatic weight (A) of the models revealed that Gompertz model had higher values for both male and female Noilers than the Logistic for body weight and all morphometric traits. The reverse was observed with the scale parameter 2 (B) and relative growth rate (C) for all traits. The coefficient of determination (R ) values for both models (male and female) were generally high (>80%) indicating a good fit for the data. The other goodness of fit criteria; Akaike Information Criterion (AIC) and standard deviation (SD) were lower for the Gompertz compared to Logistic for both male and female. Thus, the study revealed that the Gompertz was the better model for explaining the growth patterns of both male and female Noiler chickens.     L'évaluation des caractéristiques de croissance des poulets de Noilers tels que déterminées par des algorithmes non linéaires fournira la meilleure fonction mathématique de la croissance des poulets de Noilers masculins et féminins. Cette étude a cherché à déterminer l'adéquation de deux fonctions mathématiques pour la modélisation des caractéristiques de croissance des poulets mâles et femelles Noiler. Les poids corporels et les traits morphométriques de 200 poulets nilaques ont été mesurés bimenshly pendant 20 semaines et les données obtenues dans les modèles de Gompertz et croissance logistiques utilisant la régression non linéaire. Les résultats ont montré une différence significative (p <0,05) entre les mâles et les femmes seulement à 16, 18 et 20 semaines, avec des valeurs de 2316,2 vs 2121,9 g, 2624.3 contre 2378,1 g et 3002,7 vs 2718.7g, respectivement. Il n'y avait pas de  différences discernables entre les hommes et les femmes pour la plupart des mesures du corps, à l'exception de la longueur du corps, ce qui était plus long que l'ancien de 14 semaines. Le poids asymptomatique (A) des modèles a révélé que le modèle de Gompertz avait des valeurs plus élevées pour les noilers mâles et femelles que la logistique pour le poids corporel et tous les traits morphométriques. L'inverse a été observé avec le paramètre d'échelle (B) et le taux de croissance relative (C) pour tous les traits. Le coefficient de valeurs de détermination (R ) pour les deux modèles (hommes et femmes) était généralement élevé (> 80%) indiquant un bon ajustement pour les données. L'autre bonté des critères d'ajustement; Le critère d'information Akaike (CIA) et l'écart type (ET) étaient plus bas pour le Gompertz par rapport à la logistique pour les hommes et les femmes. Ainsi, l'étude a révélé que le Gompertz était le meilleur modèle d'explication des schémas de croissance des poulets mâles et femelles Noilers. 

scholarly journals Long Non-Coding RNA HOTAIR in Breast Cancer Therapy

Cancers ◽  
2020 ◽  
Vol 12 (5) ◽  
pp. 1197 ◽  
Author(s):  
Monica Cantile ◽  
Maurizio Di Bonito ◽  
Margherita Cerrone ◽  
Francesca Collina ◽  
Michelino De Laurentiis ◽  
...  
Keyword(s):  
Breast Cancer ◽  
Drug Resistance ◽  
Cancer Progression ◽  
Experimental Studies ◽  
Treatment Strategies ◽  
Cancer Type ◽  
Metastatic Progression ◽  
Aberrant Expression ◽  
Non Coding Rna ◽  
Long Non Coding Rna

Breast cancer (BC) is the most common cancer type among women, and morbidity and mortality rates are still very high. Despite new innovative therapeutic approaches for all BC molecular subtypes, the discovery of new molecular biomarkers involved in tumor progression has been fundamental for the implementation of personalized treatment strategies and improvement of patient management. Many experimental studies indicate that long non-coding RNAs (lncRNAs) are strongly involved in BC initiation, metastatic progression, and drug resistance. In particular, aberrant expression of HOX transcript antisense intergenic RNA (HOTAIR) lncRNA plays an important role in BC contributing to its progression and represents a predictor of BC metastasis. For its proven prognostic value, HOTAIR could represent a potential therapeutic target in BC. In the present review, we summarize the role of HOTAIR in cancer progression and drug resistance, in particular in BC, and we illustrate the main approaches for silencing it.

scholarly journals Context Matters—Why We Need to Change From a One Size Fits all Approach to Made-to-Measure Therapies for Individual Patients With Pancreatic Cancer

2021 ◽  
Vol 9 ◽  
Author(s):  
Sushmitha Sankarasubramanian ◽  
Ulrike Pfohl ◽  
Christian R. A. Regenbrecht ◽  
Christoph Reinhard ◽  
Lena Wedeken
Keyword(s):  
Pancreatic Cancer ◽  
Cancer Progression ◽  
Genetic Alterations ◽  
Clinical Engineering ◽  
Ductal Adenocarcinoma ◽  
Driver Mutations ◽  
Treatment Regimens ◽  
Personalized Approach ◽  
Molecular Landscape ◽  
The Individual

Pancreatic cancer is one of the deadliest cancers and remains a major unsolved health problem. While pancreatic ductal adenocarcinoma (PDAC) is associated with driver mutations in only four major genes (KRAS, TP53, SMAD4, and CDKN2A), every tumor differs in its molecular landscape, histology, and prognosis. It is crucial to understand and consider these differences to be able to tailor treatment regimens specific to the vulnerabilities of the individual tumor to enhance patient outcome. This review focuses on the heterogeneity of pancreatic tumor cells and how in addition to genetic alterations, the subsequent dysregulation of multiple signaling cascades at various levels, epigenetic and metabolic factors contribute to the oncogenesis of PDAC and compensate for each other in driving cancer progression if one is tackled by a therapeutic approach. This implicates that besides the need for new combinatorial therapies for PDAC, a personalized approach for treating this highly complex cancer is required. A strategy that combines both a target-based and phenotypic approach to identify an effective treatment, like Reverse Clinical Engineering® using patient-derived organoids, is discussed as a promising way forward in the field of personalized medicine to tackle this deadly disease.

scholarly journals Investigation of the growth patterns of non-functioning pituitary macroadenomas using volumetric assessments on serial MRI investigations

2016 ◽  
Vol 20 (1) ◽  
Author(s):  
Jaco Pieterse ◽  
Coert S. De Vries ◽  
Susanna F. Otto
Keyword(s):  
Growth Curve ◽  
Exponential Growth ◽  
Tumour Growth ◽  
Growth Models ◽  
Mass Effect ◽  
Growth Patterns ◽  
Logistic Growth ◽  
Serial Mri ◽  
Pituitary Macroadenomas

Background: Benign non-functioning pituitary macroadenomas (NFMA) often cause mass effect on the optic chiasm necessitating transsphenoidal surgery to prevent blindness.However, surgery is complicated and there is a high tumour recurrence rate. Currently, very little is known about the natural (and residual post-surgical) growth patterns of these NFMA. Conflicting data describe decreased growth to exponential growth over various time periods.Due to lack of information on growth dynamics of these NFMA, suitable follow-up imaging protocols have not been described to date.Objective: To determine if NFMA grow or stay quiescent over a time period using serial MRI investigations and a stereo logical method to determine tumour volume. In addition, to evaluate if NFMA adhere to a certain growth pattern or grow at random.Method: Thirteen patients with NFMA had serial MRI investigations over a 73-month period at the Universitas Academic Hospital. Six of the selected patients had undergone previous surgery, while seven patients had received no medical or surgical intervention. By using astereological method, tumour volumes were calculated and plotted over time to demonstrate growth curves. The data were then fitted to tumour growth models already described in literature in order to obtain the best fit by calculating the r2 value.Results: Positive tumour growth was demonstrated in all cases. Tumour growth patterns of nine patients best fitted the exponential growth curve while the growth patterns of three patients best fitted the logistic growth curve. The remaining patient demonstrated a linear growth pattern.Conclusion: A specific growth model best described tumour growth observed in non-surgical and surgical cases. If follow-up imaging confirms positive growth, future growth can be predicted by extrapolation. This information can then be used to determine the relevant follow-up-imaging interval in each individual patient.

scholarly journals Identification of Novel Metabolism-Associated Subtypes for Pancreatic Cancer to Establish an Eighteen-Gene Risk Prediction Model

2021 ◽  
Vol 9 ◽  
Author(s):  
Yang Gao ◽  
Enchong Zhang ◽  
Xiang Fei ◽  
Lingming Kong ◽  
Peng Liu ◽  
...  
Keyword(s):  
Pancreatic Cancer ◽  
High Risk ◽  
Cancer Progression ◽  
Risk Score ◽  
Risk Model ◽  
Treatment Strategies ◽  
The Cancer Genome Atlas ◽  
Gene Desert ◽  
Metabolic Gene ◽  
Therapeutic Responses

Pancreatic cancer (PanC) is an intractable malignancy with a high mortality. Metabolic processes contribute to cancer progression and therapeutic responses, and histopathological subtypes are insufficient for determining prognosis and treatment strategies. In this study, PanC subtypes based on metabolism-related genes were identified and further utilized to construct a prognostic model. Using a cohort of 171 patients from The Cancer Genome Atlas (TCGA) database, transcriptome data, simple nucleotide variants (SNV), and clinical information were analyzed. We divided patients with PanC into metabolic gene-enriched and metabolic gene-desert subtypes. The metabolic gene-enriched subgroup is a high-risk subtype with worse outcomes and a higher frequency of SNVs, especially in KRAS. After further characterizing the subtypes, we constructed a risk score algorithm involving multiple genes (i.e., NEU2, GMPS, PRIM2, PNPT1, LDHA, INPP4B, DPYD, PYGL, CA12, DHRS9, SULT1E1, ENPP2, PDE1C, TPH1, CHST12, POLR3GL, DNMT3A, and PGS1). We verified the reproducibility and reliability of the risk score using three validation cohorts (i.e., independent datasets from TCGA, Gene Expression Omnibus, and Ensemble databases). Finally, drug prediction was completed using a ridge regression model, yielding nine candidate drugs for high-risk patients. These findings support the classification of PanC into two metabolic subtypes and further suggest that the metabolic gene-enriched subgroup is associated with worse outcomes. The newly established risk model for prognosis and therapeutic responses may improve outcomes in patients with PanC.

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