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Genetic and epigenetic variation, together with transcriptional plasticity, contribute to intratumour heterogeneity 1 . The interplay of these biological processes and their respective contributions to tumour evolution remain unknown. Here we show that intratumour genetic ancestry only infrequently affects gene expression traits and subclonal evolution in colorectal cancer (CRC). Using spatially resolved paired whole-genome and transcriptome sequencing, we find that the majority of intratumour variation in gene expression is not strongly heritable but rather 'plastic'. Somatic expression quantitative trait loci analysis identified a number of putative genetic controls of expression by cis-acting coding and non-coding mutations, the majority of which were clonal within a tumour, alongside frequent structural alterations. Consistently, computational inference on the spatial patterning of tumour phylogenies finds that a considerable proportion of CRCs did not show evidence of subclonal selection, with only a subset of putative genetic drivers associated with subclone expansions. Spatial intermixing of clones is common, with some tumours growing exponentially and others only at the periphery. Together, our data suggest that most genetic intratumour variation in CRC has no major phenotypic consequence and that transcriptional plasticity is, instead, widespread within a tumour., (© 2022. The Author(s).)

Oncogene amplification on extrachromosomal DNA (ecDNA) is a common event, driving aggressive tumor growth, drug resistance and shorter survival. Currently, the impact of nonchromosomal oncogene inheritance-random identity by descent-is poorly understood. Also unclear is the impact of ecDNA on somatic variation and selection. Here integrating theoretical models of random segregation, unbiased image analysis, CRISPR-based ecDNA tagging with live-cell imaging and CRISPR-C, we demonstrate that random ecDNA inheritance results in extensive intratumoral ecDNA copy number heterogeneity and rapid adaptation to metabolic stress and targeted treatment. Observed ecDNAs benefit host cell survival or growth and can change within a single cell cycle. ecDNA inheritance can predict, a priori, some of the aggressive features of ecDNA-containing cancers. These properties are facilitated by the ability of ecDNA to rapidly adapt genomes in a way that is not possible through chromosomal oncogene amplification. These results show how the nonchromosomal random inheritance pattern of ecDNA contributes to poor outcomes for patients with cancer., (© 2022. The Author(s).)

Diffuse midline gliomas (DMGs) are highly aggressive, incurable childhood brain tumors. They present a clinical challenge due to many factors, including heterogeneity and diffuse infiltration, complicating disease management. Recent studies have described the existence of subclonal populations that may co-operate to drive pro-tumorigenic processes such as cellular invasion. However, a precise quantification of subclonal interactions is lacking, a problem that extends to other cancers. In this study, we combine spatial computational modeling of cellular interactions during invasion with co-evolution experiments of clonally disassembled patient-derived DMG cells. We design a Bayesian inference framework to quantify spatial subclonal interactions between molecular and phenotypically distinct lineages with different patterns of invasion. We show how this approach could discriminate genuine interactions, where one clone enhanced the invasive phenotype of another, from those apparently only due to the complex dynamics of spatially restricted growth. This study provides a framework for the quantification of subclonal interactions in DMG., Competing Interests: Declaration of interests The authors declare no competing interests., (Copyright © 2022 The Author(s). Published by Elsevier Inc. All rights reserved.)

Additive manufacturing methods (AM) allow the production of complex-shaped lattice structures from a wide range of materials with enhanced mechanical properties, e.g., high strength to relative density ratio. These structures can be modified for various applications considering a transfer of a specific load or to absorb a precise amount of energy with the required deformation pattern. However, the structure design requires knowledge of the relationship between nonlinear material properties and lattice structure geometrical imperfections affected by manufacturing process parameters. A detailed analytical and numerical computational investigation must be done to better understand the behavior of lattice structures under mechanical loading. Different computational methods lead to different levels of result accuracy and reveal various deformational features. Therefore, this study focuses on a comparison of computational approaches using a quasi-static compression experiment of body-centered cubic (BCC) lattice structure manufactured of stainless steel 316L by selective laser melting technology. Models of geometry in numerical simulations are supplemented with geometrical imperfections that occur on the lattice structure's surface during the manufacturing process. They are related to the change of lattice struts cross-section size and actual shape. Results of the models supplemented with geometrical imperfections improved the accuracy of the calculations compared to the nominal geometry.

Cancers accumulate mutations that lead to neoantigens, novel peptides that elicit an immune response, and consequently undergo evolutionary selection. Here we establish how negative selection shapes the clonality of neoantigens in a growing cancer by constructing a mathematical model of neoantigen evolution. The model predicts that, without immune escape, tumor neoantigens are either clonal or at low frequency; hypermutated tumors can only establish after the evolution of immune escape. Moreover, the site frequency spectrum of somatic variants under negative selection appears more neutral as the strength of negative selection increases, which is consistent with classical neutral theory. These predictions are corroborated by the analysis of neoantigen frequencies and immune escape in exome and RNA sequencing data from 879 colon, stomach and endometrial cancers.

Most cancer genomic data are generated from bulk samples composed of mixtures of cancer subpopulations, as well as normal cells. Subclonal reconstruction methods based on machine learning aim to separate those subpopulations in a sample and infer their evolutionary history. However, current approaches are entirely data driven and agnostic to evolutionary theory. We demonstrate that systematic errors occur in the analysis if evolution is not accounted for, and this is exacerbated with multi-sampling of the same tumor. We present a novel approach for model-based tumor subclonal reconstruction, called MOBSTER, which combines machine learning with theoretical population genetics. Using public whole-genome sequencing data from 2,606 samples from different cohorts, new data and synthetic validation, we show that this method is more robust and accurate than current techniques in single-sample, multiregion and longitudinal data. This approach minimizes the confounding factors of nonevolutionary methods, thus leading to more accurate recovery of the evolutionary history of human cancers.

Drug resistance mediated by clonal evolution is arguably the biggest problem in cancer therapy today. However, evolving resistance to one drug may come at a cost of decreased fecundity or increased sensitivity to another drug. These evolutionary trade-offs can be exploited using 'evolutionary steering' to control the tumour population and delay resistance. However, recapitulating cancer evolutionary dynamics experimentally remains challenging. Here, we present an approach for evolutionary steering based on a combination of single-cell barcoding, large populations of 10 8 -10 9 cells grown without re-plating, longitudinal non-destructive monitoring of cancer clones, and mathematical modelling of tumour evolution. We demonstrate evolutionary steering in a lung cancer model, showing that it shifts the clonal composition of the tumour in our favour, leading to collateral sensitivity and proliferative costs. Genomic profiling revealed some of the mechanisms that drive evolved sensitivity. This approach allows modelling evolutionary steering strategies that can potentially control treatment resistance.

The distribution of fitness effects (DFE) defines how new mutations spread through an evolving population. The ratio of non-synonymous to synonymous mutations (dN/dS) has become a popular method to detect selection in somatic cells. However the link, in somatic evolution, between dN/dS values and fitness coefficients is missing. Here we present a quantitative model of somatic evolutionary dynamics that determines the selective coefficients of individual driver mutations from dN/dS estimates. We then measure the DFE for somatic mutant clones in ostensibly normal oesophagus and skin. We reveal a broad distribution of fitness effects, with the largest fitness increases found for TP53 and NOTCH1 mutants (proliferative bias 1-5%). This study provides the theoretical link between dN/dS values and selective coefficients in somatic evolution, and measures the DFE of mutations in human tissues., Competing Interests: MW, LZ, BW, CB, AS, TG No competing interests declared, (© 2020, Williams et al.)

Both normal tissue development and cancer growth are driven by a branching process of cell division and mutation accumulation that leads to intra-tissue genetic heterogeneity. However, quantifying somatic evolution in humans remains challenging. Here, we show that multi-sample genomic data from a single time point of normal and cancer tissues contains information on single-cell divisions. We present a new theoretical framework that, applied to whole-genome sequencing data of healthy tissue and cancer, allows inferring the mutation rate and the cell survival/death rate per division. On average, we found that cells accumulate 1.14 mutations per cell division in healthy haematopoiesis and 1.37 mutations per division in brain development. In both tissues, cell survival was maximal during early development. Analysis of 131 biopsies from 16 tumours showed 4 to 100 times increased mutation rates compared to healthy development and substantial inter-patient variation of cell survival/death rates.

Prefilled syringes are a popular choice for the delivery of biopharmaceuticals. However, glass syringes might not be the optimal primary packaging material for all biopharmaceuticals. There is evidence that the necessary lubricant silicone oil in glass syringes can interact with proteins and can be shed from the surface into the product solution. In recent years, silicone oil-free polymer syringes were developed. Despite several advantages, however, a major shortcoming of these polymer systems is their relatively high gas permeability, which might be a limitation for the storage of oxygen sensitive biopharmaceuticals. So far, no long-term protein stability studies regarding such polymer systems have been published. In this study, 2 therapeutic proteins were stored in glass syringes and in silicone oil-free polymer syringes. In addition, polymer syringes stored in nitrogen-filled aluminum pouches or covered with oxygen-tight labels were included. Similar chemical protein stability was achieved at 4°C for all syringes. However, in contrast to the polymer syringes, high particle counts were observed in the glass syringes. Polymer syringes stored in nitrogen-filled aluminum pouches presented a promising alternative for the storage of biopharmaceuticals as they do not expose patients to silicone oil and silicone oil-protein aggregates., (Copyright © 2019 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

Protein immunogenicity is intensively researched by academics, biopharmaceutical companies, and authorities as it can compromise the safety and efficacy of a biopharmaceutical drug. So far, the exact protein aggregate properties inducing immune responses are not known. Possible protein-related factors could be size, chemical modifications, or higher order structures. It is impossible to achieve an absolute absence of protein aggregates even for very stable formulations. The application of "bedside filtration," meaning filtration during the preparation or administration of the drug product immediately before injection, has the potential to increase the safety of every drug container and could prevent the undesired injection of particulate matter into the patient. In this study, the high efficiency of filtration for reducing the amount of protein particles was demonstrated with more than 19 stressed and nonstressed biopharmaceutical products which covered a broad concentration and molecular weight range. Furthermore, critical aspects regarding the usage of filters such as particle shedding from filters, protein loss as a result of protein adsorption, or the hold-up volume of the filters were assessed. Although differences between the filters were observed, no negative impact by the investigated filters could be found. A broader application of bedside filtration is therefore proposed., (Copyright © 2018 American Pharmacists Association®. Published by Elsevier Inc. All rights reserved.)

Purpose: The particle counts and the nature of particles of three different antivascular endothelial growth factor agents (VEGF) in different containers in a laboratory setting were compared., Methods: Original prefilled ranibizumab glass syringes, original vials with aflibercept, and repacked ready-to-use plastic syringes with bevacizumab from a compounding pharmacy and a compounding company (CC) were analyzed. Particle counts and size distributions were quantified by different particle characterization methods (nephelometry, light obscuration, Micro-Flow Imaging, nanotracking analysis, resonant mass measurement). Using high-performance size-exclusion chromatography (HP-SEC), levels of protein drug monomer and soluble aggregates were determined., Results: Nearly all samples showed similar product quality. Light obscuration and Micro-Flow Imaging showed a 4-fold to 9-fold higher total particle count in compounding company bevacizumab (other samples up to 42,000 particles/mL). Nanotracking analysis revealed highest values for compounding company bevacizumab (6,375 million particles/mL). All containers showed similar amounts of silicone oil microdroplets. Ranibizumab showed lowest particle count of all tested agents with only one monomer peak in HP-SEC. Repackaged bevacizumab from different suppliers showed varying product quality., Conclusion: All three tested agents are available in similar quality regarding particulate purity and silicone oil microdroplet count. Repackaging can have a major impact on the quality.

Subclonal architectures are prevalent across cancer types. However, the temporal evolutionary dynamics that produce tumor subclones remain unknown. Here we measure clone dynamics in human cancers by using computational modeling of subclonal selection and theoretical population genetics applied to high-throughput sequencing data. Our method determined the detectable subclonal architecture of tumor samples and simultaneously measured the selective advantage and time of appearance of each subclone. We demonstrate the accuracy of our approach and the extent to which evolutionary dynamics are recorded in the genome. Application of our method to high-depth sequencing data from breast, gastric, blood, colon and lung cancer samples, as well as metastatic deposits, showed that detectable subclones under selection, when present, consistently emerged early during tumor growth and had a large fitness advantage (>20%). Our quantitative framework provides new insight into the evolutionary trajectories of human cancers and facilitates predictive measurements in individual tumors from widely available sequencing data.

Protein drug products play an important role in the treatment of severe diseases. However, due to the instability of these complex molecules, protein aggregates can form which can compromise drug safety and efficacy including immunogenic reactions. In-line filtration during the administration of these drugs can serve as a final safeguarding step to protect patients from risks associated with proteinaceous particles. A unique analysis of more than 300 marketed protein drug products revealed that already around 16% of all these products are filtered during preparation or administration. Further, the research revealed that no standardized filtration practice exists. Broad variances regarding filter membrane or pore size are found and sometimes no specifications are mentioned at all. The benefits as well as possible negative impacts of filtration like filter shedding, extractables or drug adsorption are critically assessed. Several proposals to improve the current filtration practice and to expand the number of in-line filtered protein drug products are made. The suggestions include the demand for the specific usage of one filter membrane type, the establishment of a filtration routine for unfiltered protein drugs and a statistical analysis between filtered and non-filtered products with similar formulations to identify possible differences in the immunogenicity rate., (Copyright © 2015 Elsevier B.V. All rights reserved.)

Background: In addition to selection, the process of evolution is accompanied by stochastic effects, such as changing environmental conditions, genetic drift and mutations. Commonly it is believed that without genetic drift, advantageous mutations quickly fixate in a halpoid population due to strong selection and lead to a continuous increase of the average fitness. This conclusion is based on the assumption of constant fitness. However, for frequency dependent fitness, where the fitness of an individual depends on the interactions with other individuals in the population, this does not hold., Results: We propose a mathematical model that allows to understand the consequences of random frequency dependent mutations on the dynamics of an infinite large population. The frequencies of different types change according to the replicator equations and the fitness of a mutant is random and frequency dependent. To capture the interactions of different types, we employ a payoff matrix of variable size and thus are able to accommodate an arbitrary number of mutations. We assume that at most one mutant type arises at a time. The payoff entries to describe the mutant type are random variables obeying a probability distribution which is related to the fitness of the parent type., Conclusions: We show that a random mutant can decrease the average fitness under frequency dependent selection, based on analytical results for two types and simulations for n types. Interestingly, in the case of at most two types the probabilities to increase or decrease the average fitness are independent of the concrete probability density function. Instead, they only depend on the probability that the payoff entries of the mutant are larger than the payoff entries of the parent type.