Implementing the EffTox dose-finding design in the Matchpoint trial

Background: The Matchpoint trial aims to identify the optimal dose of ponatinib to give with conventional chemotherapy consisting of fludarabine, cytarabine and idarubicin to chronic myeloid leukaemia patients in blastic transformation phase. The dose should be both tolerable and efficacious. This paper describes our experience implementing EffTox in the Matchpoint trial. Methods: EffTox is a Bayesian adaptive dose-finding trial design that jointly scrutinises binary efficacy and toxicity outcomes. We describe a nomenclature for succinctly describing outcomes in phase I/II dose-finding trials. We use dose-transition pathways, where doses are calculated for each feasible set of outcomes in future cohorts. We introduce the phenomenon of dose ambivalence, where EffTox can recommend different doses after observing the same outcomes. We also describe our experiences with outcome ambiguity, where the categorical evaluation of some primary outcomes is temporarily delayed. Results: We arrived at an EffTox parameterisation that is simulated to perform well over a range of scenarios. In scenarios where dose ambivalence manifested, we were guided by the dose-transition pathways. This technique facilitates planning, and also helped us overcome short-term outcome ambiguity. Conclusions: EffTox is an efficient and powerful design, but not without its challenges. Joint phase I/II clinical trial designs will likely become increasingly important in coming years as we further investigate non-cytotoxic treatments and streamline the drug approval process. We hope this account of the problems we faced and the solutions we used will help others implement this dose-finding clinical trial design. Trial registration: Matchpoint was added to the European Clinical Trials Database (2012-005629-65) on 2013-12-30

Pathway Signature and Cellular Differentiation in Clear Cell Renal Cell Carcinoma

BACKGROUND: Clear cell renal cell carcinoma (ccRCC) is the most common kidney cancer. The purpose of this study is to define a biological pathway signature and a cellular differentiation program in ccRCC. METHODOLOGY: We performed gene expression profiling of early-stage ccRCC and patient-matched normal renal tissue using Affymetrix HG-U133a and HG-U133b GeneChips combined with a comprehensive bioinformatic analyses, including pathway analysis. The results were validated by real time PCR and IHC on two independent sample sets. Cellular differentiation experiments were performed on ccRCC cell lines and their matched normal renal epithelial cells, in differentiation media, to determine their mesenchymal differentiation potential. PRINCIPAL FINDINGS: We identified a unique pathway signature with three major biological alterations-loss of normal renal function, down-regulated metabolism, and immune activation-which revealed an adipogenic gene expression signature linked to the hallmark lipid-laden clear cell morphology of ccRCC. Culturing normal renal and ccRCC cells in differentiation media showed that only ccRCC cells were induced to undergo adipogenic and, surprisingly, osteogenic differentiation. A gene expression signature consistent with epithelial mesenchymal transition (EMT) was identified for ccRCC. We revealed significant down-regulation of four developmental transcription factors (GATA3, TFCP2L1, TFAP2B, DMRT2) that are important for normal renal development. CONCLUSIONS: ccRCC is characterized by a lack of epithelial differentiation, mesenchymal/adipogenic transdifferentiation, and pluripotent mesenchymal stem cell-like differentiation capacity in vitro. We suggest that down-regulation of developmental transcription factors may mediate the aberrant differentiation in ccRCC. We propose a model in which normal renal epithelial cells undergo dedifferentiation, EMT, and adipogenic transdifferentiation, resulting in ccRCC. Because ccRCC cells grown in adipogenic media regain the characteristic ccRCC phenotype, we have identified a new in vitro ccRCC cell model more resembling ccRCC tumor morphology

Genomic, Pathway Network, and Immunologic Features Distinguishing Squamous Carcinomas

This integrated, multiplatform PanCancer Atlas study co-mapped and identified distinguishing molecular features of squamous cell carcinomas (SCCs) from five sites associated with smokin

Pan-Cancer Analysis of lncRNA Regulation Supports Their Targeting of Cancer Genes in Each Tumor Context

Long noncoding RNAs (lncRNAs) are commonly dys-regulated in tumors, but only a handful are known toplay pathophysiological roles in cancer. We inferredlncRNAs that dysregulate cancer pathways, onco-genes, and tumor suppressors (cancer genes) bymodeling their effects on the activity of transcriptionfactors, RNA-binding proteins, and microRNAs in5,185 TCGA tumors and 1,019 ENCODE assays.Our predictions included hundreds of candidateonco- and tumor-suppressor lncRNAs (cancerlncRNAs) whose somatic alterations account for thedysregulation of dozens of cancer genes and path-ways in each of 14 tumor contexts. To demonstrateproof of concept, we showed that perturbations tar-geting OIP5-AS1 (an inferred tumor suppressor) andTUG1 and WT1-AS (inferred onco-lncRNAs) dysre-gulated cancer genes and altered proliferation ofbreast and gynecologic cancer cells. Our analysis in-dicates that, although most lncRNAs are dysregu-lated in a tumor-specific manner, some, includingOIP5-AS1, TUG1, NEAT1, MEG3, and TSIX, synergis-tically dysregulate cancer pathways in multiple tumorcontexts

Pan-cancer Alterations of the MYC Oncogene and Its Proximal Network across the Cancer Genome Atlas

Although theMYConcogene has been implicated incancer, a systematic assessment of alterations ofMYC, related transcription factors, and co-regulatoryproteins, forming the proximal MYC network (PMN),across human cancers is lacking. Using computa-tional approaches, we define genomic and proteo-mic features associated with MYC and the PMNacross the 33 cancers of The Cancer Genome Atlas.Pan-cancer, 28% of all samples had at least one ofthe MYC paralogs amplified. In contrast, the MYCantagonists MGA and MNT were the most frequentlymutated or deleted members, proposing a roleas tumor suppressors.MYCalterations were mutu-ally exclusive withPIK3CA,PTEN,APC,orBRAFalterations, suggesting that MYC is a distinct onco-genic driver. Expression analysis revealed MYC-associated pathways in tumor subtypes, such asimmune response and growth factor signaling; chro-matin, translation, and DNA replication/repair wereconserved pan-cancer. This analysis reveals insightsinto MYC biology and is a reference for biomarkersand therapeutics for cancers with alterations ofMYC or the PMN

Spatial Organization and Molecular Correlation of Tumor-Infiltrating Lymphocytes Using Deep Learning on Pathology Images

Beyond sample curation and basic pathologic characterization, the digitized H&E-stained images of TCGA samples remain underutilized. To highlight this resource, we present mappings of tumorinfiltrating lymphocytes (TILs) based on H&E images from 13 TCGA tumor types. These TIL maps are derived through computational staining using a convolutional neural network trained to classify patches of images. Affinity propagation revealed local spatial structure in TIL patterns and correlation with overall survival. TIL map structural patterns were grouped using standard histopathological parameters. These patterns are enriched in particular T cell subpopulations derived from molecular measures. TIL densities and spatial structure were differentially enriched among tumor types, immune subtypes, and tumor molecular subtypes, implying that spatial infiltrate state could reflect particular tumor cell aberration states. Obtaining spatial lymphocytic patterns linked to the rich genomic characterization of TCGA samples demonstrates one use for the TCGA image archives with insights into the tumor-immune microenvironment

Inhibition of TGF-β Signaling and Decreased Apoptosis in IUGR-Associated Lung Disease in Rats

Intrauterine growth restriction is associated with impaired lung function in adulthood. It is unknown whether such impairment of lung function is linked to the transforming growth factor (TGF)-β system in the lung. Therefore, we investigated the effects of IUGR on lung function, expression of extracellular matrix (ECM) components and TGF-β signaling in rats. IUGR was induced in rats by isocaloric protein restriction during gestation. Lung function was assessed with direct plethysmography at postnatal day (P) 70. Pulmonary activity of the TGF-β system was determined at P1 and P70. TGF-β signaling was blocked in vitro using adenovirus-delivered Smad7. At P70, respiratory airway compliance was significantly impaired after IUGR. These changes were accompanied by decreased expression of TGF-β1 at P1 and P70 and a consistently dampened phosphorylation of Smad2 and Smad3. Furthermore, the mRNA expression levels of inhibitors of TGF-β signaling (Smad7 and Smurf2) were reduced, and the expression of TGF-β-regulated ECM components (e.g. collagen I) was decreased in the lungs of IUGR animals at P1; whereas elastin and tenascin N expression was significantly upregulated. In vitro inhibition of TGF-β signaling in NIH/3T3, MLE 12 and endothelial cells by adenovirus-delivered Smad7 demonstrated a direct effect on the expression of ECM components. Taken together, these data demonstrate a significant impact of IUGR on lung development and function and suggest that attenuated TGF-β signaling may contribute to the pathological processes of IUGR-associated lung disease

COVID-19 trajectories among 57 million adults in England: a cohort study using electronic health records

BACKGROUND: Updatable estimates of COVID-19 onset, progression, and trajectories underpin pandemic mitigation efforts. To identify and characterise disease trajectories, we aimed to define and validate ten COVID-19 phenotypes from nationwide linked electronic health records (EHR) using an extensible framework. METHODS: In this cohort study, we used eight linked National Health Service (NHS) datasets for people in England alive on Jan 23, 2020. Data on COVID-19 testing, vaccination, primary and secondary care records, and death registrations were collected until Nov 30, 2021. We defined ten COVID-19 phenotypes reflecting clinically relevant stages of disease severity and encompassing five categories: positive SARS-CoV-2 test, primary care diagnosis, hospital admission, ventilation modality (four phenotypes), and death (three phenotypes). We constructed patient trajectories illustrating transition frequency and duration between phenotypes. Analyses were stratified by pandemic waves and vaccination status. FINDINGS: Among 57 032 174 individuals included in the cohort, 13 990 423 COVID-19 events were identified in 7 244 925 individuals, equating to an infection rate of 12·7% during the study period. Of 7 244 925 individuals, 460 737 (6·4%) were admitted to hospital and 158 020 (2·2%) died. Of 460 737 individuals who were admitted to hospital, 48 847 (10·6%) were admitted to the intensive care unit (ICU), 69 090 (15·0%) received non-invasive ventilation, and 25 928 (5·6%) received invasive ventilation. Among 384 135 patients who were admitted to hospital but did not require ventilation, mortality was higher in wave 1 (23 485 [30·4%] of 77 202 patients) than wave 2 (44 220 [23·1%] of 191 528 patients), but remained unchanged for patients admitted to the ICU. Mortality was highest among patients who received ventilatory support outside of the ICU in wave 1 (2569 [50·7%] of 5063 patients). 15 486 (9·8%) of 158 020 COVID-19-related deaths occurred within 28 days of the first COVID-19 event without a COVID-19 diagnoses on the death certificate. 10 884 (6·9%) of 158 020 deaths were identified exclusively from mortality data with no previous COVID-19 phenotype recorded. We observed longer patient trajectories in wave 2 than wave 1. INTERPRETATION: Our analyses illustrate the wide spectrum of disease trajectories as shown by differences in incidence, survival, and clinical pathways. We have provided a modular analytical framework that can be used to monitor the impact of the pandemic and generate evidence of clinical and policy relevance using multiple EHR sources. FUNDING: British Heart Foundation Data Science Centre, led by Health Data Research UK