Circulating Biomarkers and Resistance to Endocrine Therapy in Metastatic Breast Cancers: Correlative Results from AZD9496 Oral SERD Phase I Trial.

PURPOSE: Common resistance mechanisms to endocrine therapy (ET) in estrogen receptor (ER)-positive metastatic breast cancers include, among others, ER loss and acquired activating mutations in the ligand-binding domain of the ER gene (ESR1LBDm). ESR1 mutational mediated resistance may be overcome by selective ER degraders (SERD). During the first-in-human study of oral SERD AZD9496, early changes in circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) were explored as potential noninvasive tools, alongside paired tumor biopsies, to assess pharmacodynamics and early efficacy. EXPERIMENTAL DESIGN: CTC were enumerated/phenotyped for ER and Ki67 using CellSearch in serial blood draws. ctDNA was assessed for the most common ESR1LBDm by droplet digital PCR (BioRad). RESULTS: Before starting AZD9496, 11 of 43 (25%) patients had ≥5 CTC/7.5 mL whole blood (WB), none of whom underwent reduction to <5 CTC/7.5 mL WB on C1D15. Five of 11 patients had baseline CTC-ER+, two of whom had CTC-ER+ reduction. CTC-Ki67 status did not change appreciably. Patients with ≥5 CTC/7.5 mL WB before treatment had worse progression-free survival (PFS) than patients with <5 CTC (P = 0.0003). Fourteen of 45 (31%) patients had ESR1LBDm + ctDNA at baseline, five of whom had ≥2 unique mutations. Baseline ESR1LBDm status was not prognostic. Patients with persistently elevated CTC and/or ESR1LBDm + ctDNA at C1D15 had worse PFS than patients who did not (P = 0.0007). CONCLUSIONS: Elevated CTC at baseline was a strong prognostic factor in this cohort. Early on-treatment changes were observed in CTC-ER+ and ESR1LBDm + ctDNA, but not in overall CTC number. Integrating multiple biomarkers in prospective trials may improve outcome prediction and ET resistance mechanisms' identification over a single biomarker

RNA-Seq Differentiates Tumour and Host mRNA Expression Changes Induced by Treatment of Human Tumour Xenografts with the VEGFR Tyrosine Kinase Inhibitor Cediranib.

Pre-clinical models of tumour biology often rely on propagating human tumour cells in a mouse. In order to gain insight into the alignment of these models to human disease segments or investigate the effects of different therapeutics, approaches such as PCR or array based expression profiling are often employed despite suffering from biased transcript coverage, and a requirement for specialist experimental protocols to separate tumour and host signals. Here, we describe a computational strategy to profile transcript expression in both the tumour and host compartments of pre-clinical xenograft models from the same RNA sample using RNA-Seq. Key to this strategy is a species-specific mapping approach that removes the need for manipulation of the RNA population, customised sequencing protocols, or prior knowledge of the species component ratio. The method demonstrates comparable performance to species-specific RT-qPCR and a standard microarray platform, and allowed us to quantify gene expression changes in both the tumour and host tissue following treatment with cediranib, a potent vascular endothelial growth factor receptor tyrosine kinase inhibitor, including the reduction of multiple murine transcripts associated with endothelium or vessels, and an increase in genes associated with the inflammatory response in response to cediranib. In the human compartment, we observed a robust induction of hypoxia genes and a reduction in cell cycle associated transcripts. In conclusion, the study establishes that RNA-Seq can be applied to pre-clinical models to gain deeper understanding of model characteristics and compound mechanism of action, and to identify both tumour and host biomarkers

Serial monitoring of genomic alterations in circulating tumor cells of ER-positive/HER2-negative advanced breast cancer: feasibility of precision oncology biomarker detection.

Nearly all estrogen receptor (ER)-positive (POS) metastatic breast cancers become refractory to endocrine (ET) and other therapies, leading to lethal disease presumably due to evolving genomic alterations. Timely monitoring of the molecular events associated with response/progression by serial tissue biopsies is logistically difficult. Use of liquid biopsies, including circulating tumor cells (CTC) and circulating tumor DNA (ctDNA), might provide highly informative, yet easily obtainable, evidence for better precision oncology care. Although ctDNA profiling has been well investigated, the CTC precision oncology genomic landscape and the advantages it may offer over ctDNA in ER-POS breast cancer remain largely unexplored. Whole-blood (WB) specimens were collected at serial time points from patients with advanced ER-POS/HER2-negative (NEG) advanced breast cancer in a phase I trial of AZD9496, an oral selective ER degrader (SERD) ET. Individual CTC were isolated from WB using tandem CellSearch® /DEPArray™ technologies and genomically profiled by targeted single-cell DNA next-generation sequencing (scNGS). High-quality CTC (n = 123) from 12 patients profiled by scNGS showed 100% concordance with ctDNA detection of driver estrogen receptor α (ESR1) mutations. We developed a novel CTC-based framework for precision medicine actionability reporting (MI-CTCseq) that incorporates novel features, such as clonal predominance and zygosity of targetable alterations, both unambiguously identifiable in CTC compared to ctDNA. Thus, we nominated opportunities for targeted therapies in 73% of patients, directed at alterations in phosphatidylinositol-4,5-bisphosphate 3-kinase catalytic subunit alpha (PIK3CA), fibroblast growth factor receptor 2 (FGFR2), and KIT proto-oncogene, receptor tyrosine kinase (KIT). Intrapatient, inter-CTC genomic heterogeneity was observed, at times between time points, in subclonal alterations. Our analysis suggests that serial monitoring of the CTC genome is feasible and should enable real-time tracking of tumor evolution during progression, permitting more combination precision medicine interventions

Prognostic model to predict postoperative acute kidney injury in patients undergoing major gastrointestinal surgery based on a national prospective observational cohort study.

Background: Acute illness, existing co-morbidities and surgical stress response can all contribute to postoperative acute kidney injury (AKI) in patients undergoing major gastrointestinal surgery. The aim of this study was prospectively to develop a pragmatic prognostic model to stratify patients according to risk of developing AKI after major gastrointestinal surgery. Methods: This prospective multicentre cohort study included consecutive adults undergoing elective or emergency gastrointestinal resection, liver resection or stoma reversal in 2-week blocks over a continuous 3-month period. The primary outcome was the rate of AKI within 7 days of surgery. Bootstrap stability was used to select clinically plausible risk factors into the model. Internal model validation was carried out by bootstrap validation. Results: A total of 4544 patients were included across 173 centres in the UK and Ireland. The overall rate of AKI was 14·2 per cent (646 of 4544) and the 30-day mortality rate was 1·8 per cent (84 of 4544). Stage 1 AKI was significantly associated with 30-day mortality (unadjusted odds ratio 7·61, 95 per cent c.i. 4·49 to 12·90; P < 0·001), with increasing odds of death with each AKI stage. Six variables were selected for inclusion in the prognostic model: age, sex, ASA grade, preoperative estimated glomerular filtration rate, planned open surgery and preoperative use of either an angiotensin-converting enzyme inhibitor or an angiotensin receptor blocker. Internal validation demonstrated good model discrimination (c-statistic 0·65). Discussion: Following major gastrointestinal surgery, AKI occurred in one in seven patients. This preoperative prognostic model identified patients at high risk of postoperative AKI. Validation in an independent data set is required to ensure generalizability

Cerebellar Volume and Disease Staging in Parkinson's Disease: An ENIGMA-PD Study.

peer reviewed[en] BACKGROUND: Increasing evidence points to a pathophysiological role for the cerebellum in Parkinson's disease (PD). However, regional cerebellar changes associated with motor and non-motor functioning remain to be elucidated. OBJECTIVE: To quantify cross-sectional regional cerebellar lobule volumes using three dimensional T1-weighted anatomical brain magnetic resonance imaging from the global ENIGMA-PD working group. METHODS: Cerebellar parcellation was performed using a deep learning-based approach from 2487 people with PD and 1212 age and sex-matched controls across 22 sites. Linear mixed effects models compared total and regional cerebellar volume in people with PD at each Hoehn and Yahr (HY) disease stage, to an age- and sex- matched control group. Associations with motor symptom severity and Montreal Cognitive Assessment scores were investigated. RESULTS: Overall, people with PD had a regionally smaller posterior lobe (dmax  = -0.15). HY stage-specific analyses revealed a larger anterior lobule V bilaterally (dmax  = 0.28) in people with PD in HY stage 1 compared to controls. In contrast, smaller bilateral lobule VII volume in the posterior lobe was observed in HY stages 3, 4, and 5 (dmax  = -0.76), which was incrementally lower with higher disease stage. Within PD, cognitively impaired individuals had lower total cerebellar volume compared to cognitively normal individuals (d = -0.17). CONCLUSIONS: We provide evidence of a dissociation between anterior "motor" lobe and posterior "non-motor" lobe cerebellar regions in PD. Whereas less severe stages of the disease are associated with larger motor lobe regions, more severe stages of the disease are marked by smaller non-motor regions

Validated structural variant detection with prioritisation of known cancer related changes

<p>Reliable detection of structural variation (SV) is playing an increasingly important role in cancer diagnostics and treatments. While RNA-Seq fusion workflows exist detecting large structural changes from exome- and WGS data remains challenging. Current DNA-based SV callers predict a large percentage of false positive events, and the resulting event lists are not prioritised for validation. We introduce a workflow for detecting large SVs such as deletions, duplications, inversions, fusions and translocations from DNA re-sequencing techniques, annotating and prioritising for clinically actionable events.</p

Prioritisation of structural variant calls in cancer genomes

Sensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events from existing structural variant calls. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants

Distributed under Creative Commons CC-BY 4.0 Prioritisation of structural variant calls in cancer genomes

ABSTRACT Sensitivity of short read DNA-sequencing for gene fusion detection is improving, but is hampered by the significant amount of noise composed of uninteresting or false positive hits in the data. In this paper we describe a tiered prioritisation approach to extract high impact gene fusion events from existing structural variant calls. Using cell line and patient DNA sequence data we improve the annotation and interpretation of structural variant calls to best highlight likely cancer driving fusions. We also considerably improve on the automated visualisation of the high impact structural variants to highlight the effects of the variants on the resulting transcripts. The resulting framework greatly improves on readily detecting clinically actionable structural variants

Profiling of circulating free DNA using targeted and genome wide sequencing in patients with Small Cell Lung Cancer

Introduction: SCLC accounts for approximately 250,000 deaths worldwide each year. Acquisition of adequate tumor biopsy samples is challenging, and liquid biopsies present an alternative option for patient stratification and response monitoring. Methods: We applied whole genome next-generation sequencing to circulating free DNA (cfDNA) from 39 patients with limited-stage (LS) SCLC and 30 patients with extensive-stage SCLC to establish genome-wide copy number aberrations and also performed targeted mutation analysis of 110 SCLC associated genes. Quantitative metrics were calculated for copy number aberrations, including percent genome amplified (PGA [the percentage of genomic regions amplified]), Z-score (a measure of standard deviation), and Moran’s I (a measure of spatial autocorrelation). In addition CellSearch, an epitope-dependent enrichment platform, was used to enumerate circulating tumor cells (CTCs) from a parallel blood sample. Results: Genome-wide and targeted cfDNA sequencing data identified tumor-related changes in 94% of patients with LS SCLC and 100% of patients with extensive-stage SCLC. Parallel analysis of CTCs based on at least 1 CTC/7.5 mL of blood increased tumor detection frequencies to 95% for LS SCLC. Both CTC counts and cfDNA readouts correlated with disease stage and overall survival. Conclusions: We demonstrate that a simple cfDNA genome-wide copy number approach provides an effective means of monitoring patients through treatment and show that targeted cfDNA sequencing identifies potential therapeutic targets in more than 50% of patients. We are now incorporating this approach into additional studies and trials of targeted therapies