No abstract

The ability of tissues and cells to move and rearrange is central to a broad range of diverse biological processes such as tissue remodeling and rearrangement in embryogenesis, cell migration in wound healing, or cancer progression. These processes are linked to a solid-like to fluid-like transition, also known as unjamming transition, a not rigorously defined framework that describes switching between a stable, resting state and an active, moving state. Various mechanisms, that is, proliferation and motility, are critical drivers for the (un)jamming transition on the cellular scale. However, beyond the scope of these fundamental mechanisms of cells, a unifying understanding remains to be established. During embryogenesis, the proliferation rate of cells is high, and the number density is continuously increasing, which indicates number-density-driven jamming. In contrast, cells have to unjam in tissues that are already densely packed during tumor progression, pointing toward a shape-driven unjamming transition. Here, we review recent investigations of jamming transitions during embryogenesis and cancer progression and pursue the question of how they might be interlinked. We discuss the role of density and shape during the jamming transition and the different biological factors driving it.

The diagnosis of breast cancer—including determination of prognosis and prediction—has been traditionally based on clinical and pathological characteristics such as tumor size, nodal status, and tumor grade. The decision-making process has been expanded by the recent introduction of molecular signatures. These signatures, however, have not reached the highest levels of evidence thus far. Yet they have been brought to clinical practice based on statistical significance in prospective as well as retrospective studies. Intriguingly, it has also been reported that most random sets of genes are significantly associated with disease outcome. These facts raise two highly relevant questions: What information gain do these signatures procure? How can one find a signature that is substantially better than a random set of genes? Our study addresses these questions. To address the latter question, we present a hybrid signature that joins the traditional approach with the molecular one by combining the Nottingham Prognostic Index with gene expressions in a data-driven fashion. To address the issue of information gain, we perform careful statistical analysis and comparison of the hybrid signature, gene expression lists of two commercially available tests as well as signatures selected at random, and introduce the Signature Skill Score—a simple measure to assess improvement on random signatures. Despite being based on in silico data, our research is designed to be useful for the decision-making process of oncologists and strongly supports association of random signatures with outcome. Although our study shows that none of these signatures can be considered as the main candidate for providing prognostic information, it also demonstrates that both the hybrid signature and the gene expression list of the OncotypeDx signature identify patients who may not require adjuvant chemotherapy. More importantly, we show that combining signatures substantially improves the identification of patients who do not need adjuvant chemotherapy.

Clinical MRI is an important method for the delineation and characterization of gliomas for resection planning and therapy monitoring in neurosurgery. Here, we used a 3D curl- and phase-gradient based inversion algorithm in multifrequency magnetic resonance elastography (MRE) to generate maps of the mechanical tumor properties, wave speed and wave damping. Overall, our method improved the spatial resolution of MRE maps in glioma patients and allowed precise measurement of tumor mechanical properties. In the future, the method could help with characterization, surgical planning and treatment monitoring of neuro tumors.

Gene expression signatures refer to patterns of gene activities and are used to classify different types of cancer, determine prognosis, and guide treatment decisions. Advancements in high-throughput technology and machine learning have led to improvements to predict a patient's prognosis for different cancer phenotypes. However, computational methods for analyzing signatures have not been used to evaluate their prognostic power. Contention remains on the utility of gene expression signatures for prognosis. The prevalent approaches include random signatures, expert knowledge, and machine learning to construct an improved signature. We unify these approaches to evaluate their prognostic power. Re-evaluation of publicly available gene-expression data from 8 databases with 9 machine-learning models revealed previously unreported results. Gene-expression signatures are confirmed to be useful in predicting a patient's prognosis. Convergent evidence from ≈10,000 signatures implicates a maximum prognostic power. By calculating the concordance index, which measures how well patients with different prognoses can be discriminated, we show that a signature can correctly discriminate patients’ prognoses no more than 80% of the time. Additionally, we show that more than 50% of the potentially available information is still missing at this value. We surmise that an accurate prognosis must incorporate molecular, clinical, histological, and other complementary factors.