Analysis of multiple basal cell carcinomas (BCCs) arising in one individual highlights genetic tumor heterogeneity and identifies novel driver mutations

Basal cell carcinoma (BCC) is the most common human cancer, especially in individuals with light skin phototypes (i.e., Fitzpatrick I-II skin type). Many affected develop multiple BCCs during their lifetime. It is not uncommon to observe elderly patients with >5 BCCs. In this study, we explored whether for patients diagnosed with multiple BCCs, analyzing the genomic mutations in one tumor could be sufficient to derive meaningful molecular/genetic conclusions regarding the other BCC tumors. Following the Genome Analysis Toolkit (GATK) best practices we have completed the study of 6 BCCs that occurred in an 83-year-old Caucasian male due to sun exposure. We have analyzed exome sequencing data of each BCC tumor and matched normal skin samples. We identified that BCCs from the same patient shared some of the key driver mutations, but they also displayed significant intertumoral heterogeneity. This finding may in part explain the different clinical progression/evolution of BCCs observed in the same patient. This work also highlights the value of characterizing multiple BCCs in one individual to identify patient-specific genetic events with a potential link to other malignancies and implications for personalized medicine.     

Modeling age-specific incidence of colon cancer via niche competition

Cancer development is a multistep process often starting with a single cell in which a number of epigenetic and genetic alterations have accumulated thus transforming it into a tumor cell. The progeny of such a single benign tumor cell expands in the tissue and can at some point progress to malignant tumor cells until a detectable tumor is formed. The dynamics from the early phase of a single cell to a detectable tumor with billions of tumor cells are complex and still not fully resolved, not even for the well-known prototype of multistage carcinogenesis, the adenoma-adenocarcinoma sequence of colorectal cancer. Mathematical models of such carcinogenesis are frequently tested and calibrated based on reported age-specific incidence rates of cancer, but they usually require calibration of four or more parameters due to the wide range of processes these models aim to reflect. We present a cell-based model, which focuses on the competition between wild-type and tumor cells in colonic crypts, with which we are able reproduce epidemiological incidence rates of colon cancer. Additionally, the fraction of cancerous tumors with precancerous lesions predicted by the model agree with clinical estimates. The correspondence between model and reported data suggests that the fate of tumor development is majorly determined by the early phase of tumor growth and progression long before a tumor becomes detectable. Due to the focus on the early phase of tumor development, the model has only a single fit parameter, the time scale set by an effective replacement rate of stem cells in the crypt. We find this effective rate to be considerable smaller than the actual replacement rate, which implies that the time scale is limited by the processes succeeding clonal conversion of crypts.     

In silico approach to quantify nucleus self-deformation on micropillared substrates

Biomechanics and Modeling in Mechanobiology - Considering the major role of confined cell migration in biological processes and diseases, such as embryogenesis or metastatic cancer, it has become...     

MAP6-F Is a Temperature Sensor That Directly Binds to and Protects Microtubules from Cold-induced Depolymerization

Microtubules are dynamic structures that present the peculiar characteristic to be ice-cold labile in vitro. In vivo, microtubules are protected from ice-cold induced depolymerization by the widely expressed MAP6/STOP family of proteins. However, the mechanism by which MAP6 stabilizes microtubules at 4 °C has not been identified. Moreover, the microtubule cold sensitivity and therefore the needs for microtubule stabilization in the wide range of temperatures between 4 and 37 °C are unknown. This is of importance as body temperatures of animals can drop during hibernation or torpor covering a large range of temperatures. Here, we show that in the absence of MAP6, microtubules in cells below 20 °C rapidly depolymerize in a temperature-dependent manner whereas they are stabilized in the presence of MAP6. We further show that in cells, MAP6-F binding to and stabilization of microtubules is temperature- dependent and very dynamic, suggesting a direct effect of the temperature on the formation of microtubule/MAP6 complex. We also demonstrate using purified proteins that MAP6-F binds directly to microtubules through its Mc domain. This binding is temperature-dependent and coincides with progressive conformational changes of the Mc domain as revealed by circular dichroism. Thus, MAP6 might serve as a temperature sensor adapting its conformation according to the temperature to maintain the cellular microtubule network in organisms exposed to temperature decrease.     

Accuracy in the prediction of disease epidemics when ensembling simple but highly correlated models

Ensembling combines the predictions made by individual component base models with the goal of achieving a predictive accuracy that is better than that of any one of the constituent member models. Diversity among the base models in terms of predictions is a crucial criterion in ensembling. However, there are practical instances when the available base models produce highly correlated predictions, because they may have been developed within the same research group or may have been built from the same underlying algorithm. We investigated, via a case study on Fusarium head blight (FHB) on wheat in the U.S., whether ensembles of simple yet highly correlated models for predicting the risk of FHB epidemics, all generated from logistic regression, provided any benefit to predictive performance, despite relatively low levels of base model diversity. Three ensembling methods were explored: soft voting, weighted averaging of smaller subsets of the base models, and penalized regression as a stacking algorithm. Soft voting and weighted model averages were generally better at classification than the base models, though not universally so. The performances of stacked regressions were superior to those of the other two ensembling methods we analyzed in this study. Ensembling simple yet correlated models is computationally feasible and is therefore worth pursuing for models of epidemic risk.     

Xyloglucan-cellulose interaction depends on the sidechains and molecular weight of xyloglucan.

Recent papers have brought evidence against the hypothesis that the fucosyl branching of primary wall xyloglucans (Xg) are responsible for their higher capacity of binding to cellulose. Reinforcement of this questioning has been obtained in this work where we show that the binding capacity was improved when the molecular weight (MW) of the Xg polymers is decreased by enzymatic hydrolysis. Moreover, the enthalpy changes associated with the adsorption process between Xg and cellulose is similar for Xgs with similar MW (but differing in the fine structure such as the presence/absence of fucose). On the basis of these results, we suggest that the fine structure and MW of Xg determines the energy and amount of binding to cellulose, respectively. Thus, the occurrence of different fine structural domains of Xg (e.g. the presence of fucose and the distribution of galactoses) might have several different functions in the wall. Besides the structural function in primary wall, these results might have impact on the packing features of storage Xg in seed cotyledons, since the MW and absence of fucose could also be associated with the self-association capacity.