Explore the vast range of titles available.

Minimally invasive approaches to detect residual disease after surgery are needed to identify patients with cancer who are at risk for metastatic relapse. Circulating tumour DNA (ctDNA) holds promise as a biomarker for molecular residual disease and relapse 1 . We evaluated outcomes in 581 patients who had undergone surgery and were evaluable for ctDNA from a randomized phase III trial of adjuvant atezolizumab versus observation in operable urothelial cancer. This trial did not reach its efficacy end point in the intention-to-treat population. Here we show that ctDNA testing at the start of therapy (cycle 1 day 1) identified 214 (37%) patients who were positive for ctDNA and who had poor prognosis (observation arm hazard ratio = 6.3 (95% confidence interval: 4.45–8.92); P  < 0.0001). Notably, patients who were positive for ctDNA had improved disease-free survival and overall survival in the atezolizumab arm versus the observation arm (disease-free survival hazard ratio = 0.58 (95% confidence interval: 0.43–0.79); P  = 0.0024, overall survival hazard ratio = 0.59 (95% confidence interval: 0.41–0.86)). No difference in disease-free survival or overall survival between treatment arms was noted for patients who were negative for ctDNA. The rate of ctDNA clearance at week 6 was higher in the atezolizumab arm (18%) than in the observation arm (4%) ( P  = 0.0204). Transcriptomic analysis of tumours from patients who were positive for ctDNA revealed higher expression levels of cell-cycle and keratin genes. For patients who were positive for ctDNA and who were treated with atezolizumab, non-relapse was associated with immune response signatures and basal–squamous gene features, whereas relapse was associated with angiogenesis and fibroblast TGFβ signatures. These data suggest that adjuvant atezolizumab may be associated with improved outcomes compared with observation in patients who are positive for ctDNA and who are at a high risk of relapse. These findings, if validated in other settings, would shift approaches to postoperative cancer care. The authors report on prospective exploratory analyses of circulating tumour DNA in an urothelial carcinoma immunotherapy clinical trial.

One of the great challenges in therapeutic oncology is determining who might achieve survival benefits from a particular therapy. Studies on longitudinal circulating tumor DNA (ctDNA) dynamics for the prediction of survival have generally been small or nonrandomized. We assessed ctDNA across 5 time points in 466 non-small-cell lung cancer (NSCLC) patients from the randomized phase 3 IMpower150 study comparing chemotherapy-immune checkpoint inhibitor (chemo-ICI) combinations and used machine learning to jointly model multiple ctDNA metrics to predict overall survival (OS). ctDNA assessments through cycle 3 day 1 of treatment enabled risk stratification of patients with stable disease (hazard ratio (HR) = 3.2 (2.0–5.3), P  < 0.001; median 7.1 versus 22.3 months for high- versus low-intermediate risk) and with partial response (HR = 3.3 (1.7–6.4), P  < 0.001; median 8.8 versus 28.6 months). The model also identified high-risk patients in an external validation cohort from the randomized phase 3 OAK study of ICI versus chemo in NSCLC (OS HR = 3.73 (1.83–7.60), P  = 0.00012). Simulations of clinical trial scenarios employing our ctDNA model suggested that early ctDNA testing outperforms early radiographic imaging for predicting trial outcomes. Overall, measuring ctDNA dynamics during treatment can improve patient risk stratification and may allow early differentiation between competing therapies during clinical trials. A machine learning model that uses longitudinal ctDNA metrics robustly predicts survival in two phase 3 trials of patients with metastatic NSCLC, which may improve therapy selection and risk stratification.

Immune checkpoint inhibitors targeting the PD-1/PD-L1 axis lead to durable clinical responses in subsets of cancer patients across multiple indications, including non-small cell lung cancer (NSCLC), urothelial carcinoma (UC) and renal cell carcinoma (RCC). Herein, we complement PD-L1 immunohistochemistry (IHC) and tumor mutation burden (TMB) with RNA-seq in 366 patients to identify unifying and indication-specific molecular profiles that can predict response to checkpoint blockade across these tumor types. Multiple machine learning approaches failed to identify a baseline transcriptional signature highly predictive of response across these indications. Signatures described previously for immune checkpoint inhibitors also failed to validate. At the pathway level, significant heterogeneity is observed between indications, in particular within the PD-L1 + tumors. mUC and NSCLC are molecularly aligned, with cell cycle and DNA damage repair genes associated with response in PD-L1- tumors. At the gene level, the CDK4/6 inhibitor CDKN2A is identified as a significant transcriptional correlate of response, highlighting the association of non-immune pathways to the outcome of checkpoint blockade. This cross-indication analysis reveals molecular heterogeneity between mUC, NSCLC and RCC tumors, suggesting that indication-specific molecular approaches should be prioritized to formulate treatment strategies. PD-L1 immune checkpoint inhibition has been used for several tumour types. Here, the authors use immunohistochemistry, tumour mutation burden and RNA-seq data from 366 patients with different indications to identify molecular signatures of response to atezolizumab and reveal pathway heterogeneity and the involvement of non-immune pathways.

Although comprehensive biomarker testing is recommended for all patients with advanced/metastatic non-small cell lung cancer (NSCLC) before initiation of first-line treatment, tissue availability can limit testing. Genomic testing in liquid biopsies can be utilized to overcome the inherent limitations of tissue sampling and identify the most appropriate biomarker-informed treatment option for patients. The Blood First Assay Screening Trial is a global, open-label, multicohort trial that evaluates the efficacy and safety of multiple therapies in patients with advanced/metastatic NSCLC and targetable alterations identified by liquid biopsy. We present data from Cohort D ( ROS1 -positive). Patients ≥18 years of age with stage IIIB/IV, ROS1 -positive NSCLC detected by liquid biopsies received entrectinib 600 mg daily. At data cutoff (November 2021), 55 patients were enrolled and 54 had measurable disease. Cohort D met its primary endpoint: the confirmed objective response rate (ORR) by investigator was 81.5%, which was consistent with the ORR from the integrated analysis of entrectinib (investigator-assessed ORR, 73.4%; data cutoff May 2019, ≥12 months of follow-up). The safety profile of entrectinib was consistent with previous reports. These results demonstrate consistency with those from the integrated analysis of entrectinib in patients with ROS1 -positive NSCLC identified by tissue-based testing, and support the clinical value of liquid biopsies to inform clinical decision-making. The integration of liquid biopsies into clinical practice provides patients with a less invasive diagnostic method than tissue-based testing and has faster turnaround times that may expedite the reaching of clinical decisions in the advanced/metastatic NSCLC setting. ClinicalTrials.gov registration: NCT03178552 . Results from this single-arm cohort of the BFAST trial showed that the clinical efficacy of entrectinib in patients with ROS1 -positive NSCLC, selected using liquid biopsies, is consistent with that seen in previous reports where patients were selected using tissue-based testing methods.

Giardia intestinalis, a human intestinal parasite and member of what is perhaps the earliest-diverging eukaryotic lineage, contains the most divergent eukaryotic actin identified to date and is the first eukaryote known to lack all canonical actin-binding proteins (ABPs). We sought to investigate the properties and functions of the actin cytoskeleton in Giardia to determine whether Giardia actin (giActin) has reduced or conserved roles in core cellular processes. In vitro polymerization of giActin produced filaments, indicating that this divergent actin is a true filament-forming actin. We generated an anti-giActin antibody to localize giActin throughout the cell cycle. GiActin localized to the cortex, nuclei, internal axonemes, and formed C-shaped filaments along the anterior of the cell and a flagella-bundling helix. These structures were regulated with the cell cycle and in encysting cells giActin was recruited to the Golgi-like cyst wall processing vesicles. Knockdown of giActin demonstrated that giActin functions in cell morphogenesis, membrane trafficking, and cytokinesis. Additionally, Giardia contains a single G protein, giRac, which affects the Giardia actin cytoskeleton independently of known target ABPs. These results imply that there exist ancestral and perhaps conserved roles for actin in core cellular processes that are independent of canonical ABPs. Of medical significance, the divergent giActin cytoskeleton is essential and commonly used actin-disrupting drugs do not depolymerize giActin structures. Therefore, the giActin cytoskeleton is a promising drug target for treating giardiasis, as we predict drugs that interfere with the Giardia actin cytoskeleton will not affect the mammalian host.

Data from circulating tumor DNA (ctDNA) testing were generated for over 1,900 samples across at least 3 time points in a phase 3 clinical trial and used to build a machine learning model to predict patient survival. The model accurately identified patients with a high risk of disease recurrence and could provide a basis for assigning therapies in phase 1/2 clinical trials.

Recessive deleterious mutations are common, causing many genetic disorders in humans and producing inbreeding depression in the majority of sexually reproducing diploids. The abundance of recessive deleterious mutations in natural populations suggests they are likely to be present on a chromosome when a new adaptive mutation occurs, yet the dynamics of recessive deleterious hitchhikers and their impact on adaptation remains poorly understood. Here we model how a recessive deleterious mutation impacts the fate of a genetically linked dominant beneficial mutation. The frequency trajectory of the adaptive mutation in this case is dramatically altered and results in what we have termed a “staggered sweep.” It is named for its three-phased trajectory: (i) Initially, the two linked mutations have a selective advantage while rare and will increase in frequency together, then (ii), at higher frequencies, the recessive hitchhiker is exposed to selection and can cause a balanced state via heterozygote advantage (the staggered phase), and (iii) finally, if recombination unlinks the two mutations, then the beneficial mutation can complete the sweep to fixation. Using both analytics and simulations, we show that strongly deleterious recessive mutations can substantially decrease the probability of fixation for nearby beneficial mutations, thus creating zones in the genome where adaptation is suppressed. These mutations can also significantly prolong the number of generations a beneficial mutation takes to sweep to fixation, and cause the genomic signature of selection to resemble that of soft or partial sweeps. We show that recessive deleterious variation could impact adaptation in humans andDrosophila.

Mutations provide the raw material of evolution, and thus fundamental to our ability to study genome evolution is the need to have precise measurements of mutational rates and patterns, as well as a quantitative understanding of the population dynamics of new adaptive and deleterious mutations. To this end, this dissertation explores the causes and consequences of new mutations. In the first chapter, along with coauthors Susanne Tilk, Jane Park, and Dmitri A. Petrov, I explore the rates and patterns of de novo mutations which were generated in laboratory strains of Drosophila melanogaster. These are mutation accumulation (MA) lines, which are the product of maintaining the flies in tiny populations for many generations, therefore rendering natural selection ineffective and thus allowing new mutations to accrue in the genome. In addition to generating a novel dataset of sequenced MA lines, I perform a meta-analysis of all published MA studies, which allows more precise estimates of mutational patterns across the genome. In the second chapter, along with coauthor Dmitri A. Petrov, I explore patterns of mutation using polymorphisms segregating at extremely low frequencies, which I identify by leveraging the availability of population genomic data from natural populations of Drosophila melanogaster. Extremely rare polymorphisms are difficult to detect with high confidence due to the problem of distinguishing them from sequencing error, however a dataset of true rare polymorphisms would allow the quantification of mutational patterns. This is due to the fact that rare polymorphisms share two important characteristics with truly de novo mutations - rare polymorphisms are on average younger, and, because the frequency dynamics of rare polymorphisms are dominated by stochastic forces, rare polymorphisms will have a spectrum of genetic variants that is relatively unbiased by selective forces (e.g. natural selection). In this second chapter I identify a high quality set of rare polymorphisms in populations of Drosophila melanogaster, and then use this dataset to measure mutational patterns, including the variation in mutational spectrum across different base pair contexts. In the third chapter, along with coauthors Jamie Blundell and Dmitri A. Petrov, I investigate the effect of recessive deleterious mutations on rates of adaptation in sexually reproducing diploids using a mathematical modeling approach. There has been much work modeling the dynamics of genetic hitchhiking, which is the study of how the fate of a mutation is altered when it is genetically linked on the chromosome to other mutations. Of particular interest is the question of how adaptive mutations are affected by linked deleterious neighbors, and to that end the vast majority of published work has focused on deleterious mutations with codominant effects. Codominance is when having one copy of a mutation has half the fitness effect as having two copies, however it is known that many deleterious mutations have recessive effects, where for example recessive lethal mutations have close to zero effect in just one copy. In this third chapter I model how new adaptive mutations are impacted when they land on a genetic background containing a recessive deleterious mutation, and show that their dynamics are drastically altered, resulting in a phenomenon we name a 'staggered sweep'. In the fourth chapter, along with coauthors Benjamin A. Wilson, Nandita R. Garud, Alison F. Feder, and Pleuni S. Pennings, I perform a literature review of the population genetics of rapid adaptation in human pathogens, in particular the evolution of drug resistance. Human pathogens can have very interesting population dynamics, for example large population sizes that are undergoing extreme selective pressures and evolving adaptive responses on observable time scales. In this chapter we review the use of sequence data and population genetic theory in studying the evolution of drug resistance in five organisms: HIV, influenza, tuberculosis, Staphylococcus aureus, and the malaria parasite Plasmodium falciparum.

Giardia intestinalis, a human intestinal parasite and member of what is perhaps the earliest-diverging eukaryotic lineage, contains the most divergent eukaryotic actin identified to date and is the first eukaryote known to lack all canonical actin-binding proteins (ABPs). We sought to investigate the properties and functions of the actin cytoskeleton in Giardia to determine whether Giardia actin (giActin) has reduced or conserved roles in core cellular processes. In vitro polymerization of giActin produced filaments, indicating that this divergent actin is a true filament-forming actin. We generated an anti-giActin antibody to localize giActin throughout the cell cycle. GiActin localized to the cortex, nuclei, internal axonemes, and formed C-shaped filaments along the anterior of the cell and a flagella-bundling helix. These structures were regulated with the cell cycle and in encysting cells giActin was recruited to the Golgi-like cyst wall processing vesicles. Knockdown of giActin demonstrated that giActin functions in cell morphogenesis, membrane trafficking, and cytokinesis. Additionally, Giardia contains a single G protein, giRac, which affects the Giardia actin cytoskeleton independently of known target ABPs. These results imply that there exist ancestral and perhaps conserved roles for actin in core cellular processes that are independent of canonical ABPs. Of medical significance, the divergent giActin cytoskeleton is essential and commonly used actin-disrupting drugs do not depolymerize giActin structures. Therefore, the giActin cytoskeleton is a promising drug target for treating giardiasis, as we predict drugs that interfere with the Giardia actin cytoskeleton will not affect the mammalian host.