Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution.

The early detection of relapse following primary surgery for non-small-cell lung cancer and the characterization of emerging subclones, which seed metastatic sites, might offer new therapeutic approaches for limiting tumour recurrence. The ability to track the evolutionary dynamics of early-stage lung cancer non-invasively in circulating tumour DNA (ctDNA) has not yet been demonstrated. Here we use a tumour-specific phylogenetic approach to profile the ctDNA of the first 100 TRACERx (Tracking Non-Small-Cell Lung Cancer Evolution Through Therapy (Rx)) study participants, including one patient who was also recruited to the PEACE (Posthumous Evaluation of Advanced Cancer Environment) post-mortem study. We identify independent predictors of ctDNA release and analyse the tumour-volume detection limit. Through blinded profiling of postoperative plasma, we observe evidence of adjuvant chemotherapy resistance and identify patients who are very likely to experience recurrence of their lung cancer. Finally, we show that phylogenetic ctDNA profiling tracks the subclonal nature of lung cancer relapse and metastasis, providing a new approach for ctDNA-driven therapeutic studies

ICAR: endoscopic skull‐base surgery

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Risk of Bowel Obstruction in Patients Undergoing Neoadjuvant Chemotherapy for High-risk Colon Cancer

Objective: This study aimed to identify risk criteria available before the point of treatment initiation that can be used to stratify the risk of obstruction in patients undergoing neoadjuvant chemotherapy (NAC) for high-risk colon cancer. Background: Global implementation of NAC for colon cancer, informed by the FOxTROT trial, may increase the risk of bowel obstruction. Methods: A case-control study, nested within an international randomized controlled trial (FOxTROT; ClinicalTrials.gov: NCT00647530). Patients with high-risk operable colon cancer (radiologically staged T3-4 N0-2 M0) that were randomized to NAC and developed large bowel obstruction were identified. First, clinical outcomes were compared between patients receiving NAC in FOxTROT who did and did not develop obstruction. Second, obstructed patients (cases) were age-matched and sex-matched with patients who did not develop obstruction (controls) in a 1:3 ratio using random sampling. Bayesian conditional mixed-effects logistic regression modeling was used to explore clinical, radiologic, and pathologic features associated with obstruction. The absolute risk of obstruction based on the presence or absence of risk criteria was estimated for all patients receiving NAC. Results: Of 1053 patients randomized in FOxTROT, 699 received NAC, of whom 30 (4.3%) developed obstruction. Patients underwent care in European hospitals including 88 UK, 7 Danish, and 3 Swedish centers. There was more open surgery (65.4% vs 38.0%, P=0.01) and a higher pR1 rate in obstructed patients (12.0% vs 3.8%, P=0.004), but otherwise comparable postoperative outcomes. In the case-control–matched Bayesian model, 2 independent risk criteria were identified: (1) obstructing disease on endoscopy and/or being unable to pass through the tumor [adjusted odds ratio: 9.09, 95% credible interval: 2.34–39.66] and stricturing disease on radiology or endoscopy (odds ratio: 7.18, 95% CI: 1.84–32.34). Three risk groups were defined according to the presence or absence of these criteria: 63.4% (443/698) of patients were at very low risk (10%). Conclusions: Safe selection for NAC for colon cancer can be informed by using 2 features that are available before treatment initiation and identifying a small number of patients with a high risk of preoperative obstruction

Feature extraction and classification for EEG signals using wavelet transform and machine learning techniques

This paper describes a discrete wavelet transform-based feature extraction scheme for the classification of EEG signals. In this scheme, the discrete wavelet transform is applied on EEG signals and the relative wavelet energy is calculated in terms of detailed coefficients and the approximation coefficients of the last decomposition level. The extracted relative wavelet energy features are passed to classifiers for the classification purpose. The EEG dataset employed for the validation of the proposed method consisted of two classes: (1) the EEG signals recorded during the complex cognitive task—Raven’s advance progressive metric test and (2) the EEG signals recorded in rest condition—eyes open. The performance of four different classifiers was evaluated with four performance measures, i.e., accuracy, sensitivity, specificity and precision values. The accuracy was achieved above 98 % by the support vector machine, multi-layer perceptron and the K-nearest neighbor classifiers with approximation (A4) and detailed coefficients (D4), which represent the frequency range of 0.53–3.06 and 3.06–6.12 Hz, respectively. The findings of this study demonstrated that the proposed feature extraction approach has the potential to classify the EEG signals recorded during a complex cognitive task by achieving a high accuracy rate.</p