Precision oncology seeks to leverage molecular information about cancer to improve patient outcomes. Tissue biopsy samples are widely used to characterize tumours but are limited by constraints on ...sampling frequency and their incomplete representation of the entire tumour bulk. Now, attention is turning to minimally invasive liquid biopsies, which enable analysis of tumour components (including circulating tumour cells and circulating tumour DNA) in bodily fluids such as blood. The potential of liquid biopsies is highlighted by studies that show they can track the evolutionary dynamics and heterogeneity of tumours and can detect very early emergence of therapy resistance, residual disease and recurrence. However, the analytical validity and clinical utility of liquid biopsies must be rigorously demonstrated before this potential can be realized.
Deregulation of transcription factors (TFs) is an important driver of tumorigenesis, but non-invasive assays for assessing transcription factor activity are lacking. Here we develop and validate a ...minimally invasive method for assessing TF activity based on cell-free DNA sequencing and nucleosome footprint analysis. We analyze whole genome sequencing data for >1,000 cell-free DNA samples from cancer patients and healthy controls using a bioinformatics pipeline developed by us that infers accessibility of TF binding sites from cell-free DNA fragmentation patterns. We observe patient-specific as well as tumor-specific patterns, including accurate prediction of tumor subtypes in prostate cancer, with important clinical implications for the management of patients. Furthermore, we show that cell-free DNA TF profiling is capable of detection of early-stage colorectal carcinomas. Our approach for mapping tumor-specific transcription factor binding in vivo based on blood samples makes a key part of the noncoding genome amenable to clinical analysis.
The development of diagnostic assays using highly targeted specific aptamers with existing detection platforms has been an endeavor with few opportunities until now. Many current commercially ...available diagnostic platforms make use of detection systems employing capture agents composed of modified antigen-specific antibodies coupled with a variety of detection modalities, including radioimmunoassays, fluorescence-based detection assays, electro/chemiluminescence assays, and immunoradiometric assays. In the studies presented here, a novel frequency-modulating technology from BioScale called Acoustic Membrane MicroParticle (AMMP) detection was used to demonstrate a sensitive and reproducible method of incorporating aptamers as capture and detection agents. The method provides a robust and rapid detection of thrombin in human serum while also eliminating the labor-intensive efforts of Western blot analysis and is not affected by the interfering substances found in serum that often affect optical-based detection systems. In addition, we have demonstrated, for the first time, the adaptation of the AMMP platform to exploit aptamers against a clinically relevant target. The AMMP platform is an ideal medium for using aptamers in commercial assay development for application in a clinical setting.
Myocardial infarction (MI), a leading cause of death around the world, displays a complex pattern of inheritance. When MI occurs early in life, genetic inheritance is a major component to risk. ...Previously, rare mutations in low-density lipoprotein (LDL) genes have been shown to contribute to MI risk in individual families, whereas common variants at more than 45 loci have been associated with MI risk in the population. Here we evaluate how rare mutations contribute to early-onset MI risk in the population. We sequenced the protein-coding regions of 9,793 genomes from patients with MI at an early age (≤50 years in males and ≤60 years in females) along with MI-free controls. We identified two genes in which rare coding-sequence mutations were more frequent in MI cases versus controls at exome-wide significance. At low-density lipoprotein receptor (LDLR), carriers of rare non-synonymous mutations were at 4.2-fold increased risk for MI; carriers of null alleles at LDLR were at even higher risk (13-fold difference). Approximately 2% of early MI cases harbour a rare, damaging mutation in LDLR; this estimate is similar to one made more than 40 years ago using an analysis of total cholesterol. Among controls, about 1 in 217 carried an LDLR coding-sequence mutation and had plasma LDL cholesterol > 190 mg dl(-1). At apolipoprotein A-V (APOA5), carriers of rare non-synonymous mutations were at 2.2-fold increased risk for MI. When compared with non-carriers, LDLR mutation carriers had higher plasma LDL cholesterol, whereas APOA5 mutation carriers had higher plasma triglycerides. Recent evidence has connected MI risk with coding-sequence mutations at two genes functionally related to APOA5, namely lipoprotein lipase and apolipoprotein C-III (refs 18, 19). Combined, these observations suggest that, as well as LDL cholesterol, disordered metabolism of triglyceride-rich lipoproteins contributes to MI risk.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, KISLJ, NUK, PILJ, PNG, SAZU, SBMB, SIK, UILJ, UKNU, UL, UM, UPUK
For large libraries of small molecules, exhaustive combinatorial chemical screens become infeasible to perform when considering a range of disease models, assay conditions, and dose ranges. Deep ...learning models have achieved state-of-the-art results in silico for the prediction of synergy scores. However, databases of drug combinations are biased toward synergistic agents and results do not generalize out of distribution. During 5 rounds of experimentation, we employ sequential model optimization with a deep learning model to select drug combinations increasingly enriched for synergism and active against a cancer cell line—evaluating only ∼5% of the total search space. Moreover, we find that learned drug embeddings (using structural information) begin to reflect biological mechanisms. In silico benchmarking suggests search queries are ∼5–10× enriched for highly synergistic drug combinations by using sequential rounds of evaluation when compared with random selection or ∼3× when using a pretrained model.
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•The RECOVER pipeline guides cell viability assays and selects drug combinations•Increasing enrichment for synergism is achieved through five rounds of experiments•In silico benchmarking suggests a ∼5–10× fold enrichment compared to random selection
Galvanized by the COVID-19 pandemic, we wanted to systematically identify efficacious drug combinations from the plethora of safe drugs that could hypothetically exhibit antiviral activity. The infeasibility of extensive combinatorial screens triggered the need for new methods that would require substantially less screening than an exhaustive evaluation. Outside of biology, there has been much interest in how areas of machine learning, including active learning and sequential model optimization, can be utilized to efficiently explore large spaces of possibilities through the intelligent acquisition and interpretation of data. Sequential model optimization has received much interest within biomedicine, with a focus on systems with well-described individual components, e.g., biomolecular design, chemical assays, etc. We wanted to apply a similar philosophy to quickly identify synergistic drug combinations to alter the phenotype of a cellular model system (cell viability as proof of concept), where the relationship between the chemical inputs and resulting phenotypic output is not well understood and is subject to experimental biases.
For large libraries of small molecules, exhaustive combinatorial screens become infeasible. Through five rounds of experimentation, Bertin et al. utilize a deep learning model to guide cell viability assays and select drug combinations, evaluating only ∼5% of the total search space while selecting drugs with increasing levels of synergism.
Trebananib is a first-in-class antiangiogenic peptibody (peptide-Fc fusion protein) that inhibits Angiopoietin 1 and 2. A pediatric phase 1 trial was performed to define trebananib dose-limiting ...toxicities (DLT), recommended phase 2 dose (RP2D), and pharmacokinetics (PK).
Trebananib was administered by weekly infusion. Three dose levels (10, 15, or 30 mg/kg/dose) were evaluated using a rolling-six design. Part 2 evaluated a cohort of subjects with primary central nervous system (CNS) tumors. Pharmacokinetic sampling and analysis of peripheral blood biomarkers was performed during the first 4 weeks. Response was evaluated after 8 weeks. Correlative studies included angiogenic protein expression and DCE-MRI.
Thirty-seven subjects were enrolled (31 evaluable for toxicity) with median age 12 years (range, 2 to 21). Two of 19 evaluable non-CNS subjects developed DLT at the 30 mg/kg dose level, including venous thrombosis and pleural effusion. In the CNS cohort, 3/12 subjects developed DLT, including decreased platelet count, transient ischemic attack, and cerebral edema with headache and hydrocephalus. Other grade 3 or 4 toxicities included lymphopenia (
= 4), anemia, thrombocytopenia, neutropenia, vomiting, and hypertension (
= 1 each). Response included stable disease in 7 subjects, no partial or complete responses. Two subjects continued study treatment with prolonged stable disease for 18 cycles (neuroblastoma) and 26 cycles (anaplastic astrocytoma). Pharmacokinetics appeared linear over 3 dose levels. Correlative studies demonstrated increased PlGF and sVCAM-1, but no change in endoglin or perfusion by DCE-MRI.
Trebananib was well tolerated in pediatric patients with recurrent or refractory solid or CNS tumors. RP2D is 30 mg/kg.
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We present a consensus report pertaining to the improved clarity of definitions and classification of glomerular lesions in lupus nephritis that derived from a meeting of 18 members of an ...international nephropathology working group in Leiden, Netherlands, in 2016. Here we report detailed recommendations on issues for which we can propose adjustments based on existing evidence and current consensus opinion (phase 1). New definitions are provided for mesangial hypercellularity and for cellular, fibrocellular, and fibrous crescents. The term “endocapillary proliferation” is eliminated and the definition of endocapillary hypercellularity considered in some detail. We also eliminate the class IV-S and IV-G subdivisions of class IV lupus nephritis. The active and chronic designations for class III/IV lesions are replaced by a proposal for activity and chronicity indices that should be applied to all classes. In the activity index, we include fibrinoid necrosis as a specific descriptor. We also make recommendations on issues for which there are limited data at present and that can best be addressed in future studies (phase 2). We propose to proceed to these investigations, with clinicopathologic studies and tests of interobserver reproducibility to evaluate the applications of the proposed definitions and to classify lupus nephritis lesions.
The extent of tumor heterogeneity is an emerging theme that researchers are only beginning to understand. How genetic and epigenetic heterogeneity affects tumor evolution and clinical progression is ...unknown. The precise nature of the environmental factors that influence this heterogeneity is also yet to be characterized. Nature Medicine, Nature Biotechnology and the Volkswagen Foundation organized a meeting focused on identifying the obstacles that need to be overcome to advance translational research in and tumor heterogeneity. Once these key questions were established, the attendees devised potential solutions. Their ideas are presented here.
Celotno besedilo
Dostopno za:
DOBA, IJS, IZUM, KILJ, NUK, PILJ, PNG, SAZU, SBMB, UILJ, UKNU, UL, UM, UPUK
To evaluate the relationship of telomere length to the prevalence and incidence of hand osteoarthritis in a longitudinal cohort.
We conducted a cross-sectional and longitudinal analysis of data from ...a subset of participants in the Osteoarthritis Initiative (OAI) recruited between February 2004 and May 2006. 274 individuals were eligible for the study based on availability of both baseline and 48-month hand radiographs and peripheral blood leucocyte telomere length data. Mean telomere length of peripheral blood leukocytes (PBL)s from the DNA samples was determined using a validated quantitative polymerase chain reaction (PCR)-based assay, and hand radiographs were analyzed and graded using the Kellgren–Lawrence scale.
In joint –level analyses, prevalent Interphalangeal Joint Osteoarthritis (IPJOA) was significantly associated with PBL telomere length in the baseline sample in unadjusted analyses (RR = 2.84; 95% CI:0.87–9.29) or in models adjusted for age, sex, and body mass index (aRR = 1.10; 95% CI: 0.96–1.27). The association in crude and adjusted analyses appeared slightly stronger with incident IPJOA, especially in the subset with normal hands at baseline (aRR = 1.62; 95% CI: 1.02–2.57). PBL telomere length was also associated with prevalent HOA at baseline (significant in unadjusted analysis: RR = 1.22; 95% CI 1.06–1.42), but not after adjusting for covariates: aRR = 1.12; 95% CI: 0.96–1.30). The magnitude of association was stronger for incident HOA, especially incident symptomatic HOA (aRR = 1.53; 95% CI: 1.09–2.15).
In summary, the results of this exploratory analysis are confirmatory of previous work showing a cross-sectional relationship between telomere length and HOA and add to the field by demonstrating an even stronger association with incident IPJOA, both radiographic and symptomatic.
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