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BackgroundColon microbiome composition contributes to the pathogenesis of colorectal cancer (CRC) and prognosis. We analyzed 16S rRNA sequencing data from tumor samples of patients with metastatic CRC and determined the clinical implications.Materials and methodsWe enrolled 133 patients with metastatic CRC at St. Vincent Hospital in Korea. The V3-V4 regions of the 16S rRNA gene from the tumor DNA were amplified, sequenced on an Illumina MiSeq, and analyzed using the DADA2 package.ResultsAfter excluding samples that retained

Background/Aims The concentration of serum ferritin, a storage form of iron, may be associated with carcinogenesis in various cancers. There are only limited studies on the relationship between serum ferritin levels and colorectal cancer (CRC) risk, especially in the Asian population. This study aimed to analyze the association between CRC incidence and serum ferritin levels. Methods This was a national cohort study that used health checkup and insurance claims data of the Korean population. CRC incidence according to the serum ferritin level was analyzed during 2008–2018 in 17,116 participants. Results The hazard ratio (HR) of CRC incidence decreased as serum ferritin levels increased (Q1: HR, 1.000 [95% confidence interval [CI], reference]; Q2: HR, 0.811 [95% CI, 0.558 to 1.178]; Q3: HR, 0.654 [95% CI, 0.442 to 0.968]; Q4: HR, 0.443 [95% CI, 0.285 to 0.687]; p = 0.0026). In subgroup analysis, 40 to 64 years of age, sex, body mass index of < 25 kg/ m2, presence of metabolic syndrome, absence of diabetes mellitus, and absence of anemia had HRs of < 0.5 (95% CI) in the highest quartiles compared with that in the lowest quartiles. Conclusions This study shows an inverse association between serum ferritin and CRC risk. Serum ferritin measurement can aid in identifying young adults requiring active CRC screening.

Background: The clinical utility of plasma tumor mutational burden (pTMB) requires further validation. Herein, the pTMB and genetic alterations were investigated as predictive biomarkers for anti-PD-1 monotherapy outcome in metastatic non-small cell lung cancer (NSCLC). Methods: The GuardantOMNI panel (Guardant Health) was used to identify pTMB and genetic alterations. Data from 99 patients with metastatic NSCLC treated with pembrolizumab or nivolumab in first-, second-, or third-line settings between June 2016 and December 2020 were collected. Associations between pTMB and clinical benefit rate (CBR, stable disease ⩾6 months or partial response), progression-free survival (PFS), and overall survival (OS) were assessed. Results: Median pTMB in 84 patients was 10.8 mutations/megabase (mut/Mb). Histological analyses revealed that 61 and 36% of the patients had adenocarcinomas and squamous NSCLC, respectively. Most patients were treated with nivolumab (74%) and most anti-PD-1 agents were administered as second-line treatment (70%). The median follow-up duration was of 10.9 months (range, 0.2–40.7). Patients with high pTMB (⩾19 mut/Mb) had a higher CBR (69%) compared with low pTMB patients (33%; p = 0.01). ARID1A ( p = 0.007) and either ERBB2 or KIT mutations ( p = 0.012) were positive and negative determinants, respectively, for clinical benefit. Multivariate analysis further showed that high pTMB was an independent predictive biomarker for both PFS [hazard ratio (HR) = 0.44, 95% confidence interval (CI): 0.22–0.88, p = 0.02] and OS (HR = 0.37, 95% CI: 0.18–0.76, p = 0.007). Conclusion: High pTMB (⩾19 mut/Mb) is significantly associated with CBR in patients with NSCLC treated with anti-PD-1 agents.

Studies have been actively conducted to identify actionable mutations and incorporate them into clinical practice in pancreatic ductal adenocarcinoma (PDAC), which is known to have a poor prognosis with traditional cytotoxic chemotherapy. A BRAF point mutation in V600E is commonly reported in KRAS wild-type PDAC, and targeting BRAF_V600E is already being applied to various carcinomas, including PDAC. Accumulated evidence also shows that not only BRAF_V600E but also short in-frame deletions of BRAF have an oncogenic function. Here, we report that a patient with BRAF N486_P490 deletion initiated on dabrafenib or trametinib, a BRAF inhibitor, and a MEK inhibitor, respectively, after cytotoxic chemotherapy failure. The patient then presented with a partial response.

Abstract Chemotherapy‐induced nausea and vomiting (CINV) is an unbearable side effect. Identifying high emetic risk patients and providing more active antiemetics strategies are mandatory to improve the tolerability of chemotherapy. In this prospective cohort study, leptin, ghrelin, and substance P were measured at baseline, day 3, and day 14 during the first cycle of chemotherapy. Nausea and vomiting were measured each day for the first 4 days of the first cycle of chemotherapy. Eighty‐two patients were enrolled. Colorectal cancer (61%) and gastric cancer (35.4%) were common cancer types. All patients received moderate emetic risk chemotherapy. Forty‐five (54.9%) patients had nausea, and 15 (18.3%) patients experienced vomiting. In univariate analysis, a higher level of baseline substance P, which is a target of NK1‐RA (Neurokinin 1 receptor antagonist), was a significant predictive marker for chemotherapy‐induced nausea [odds ratio (OR): 2.6, 95% confidence interval (CI): 1.02–6.62, p = 0.046]. Regarding chemotherapy‐induced vomiting, patients with higher levels of substance P had a greater chance of vomiting [OR: 1.72, 95% CI: 0.49–5.99, p = 0.395] than those with lower levels of substance P. In patients receiving moderate emetic risk chemotherapy, active antiemetics, including NK1‐RA, could be considered for those with high levels of substance P.

Background Clinical impact of the Geriatric Nutritional Risk Index (GNRI) in patients with extensive‐stage disease small cell lung cancer (ED‐SCLC) have not previously been reported. Methods This study analyzed 352 patients enrolled in a previous randomized phase III trial comparing the efficacy of irinotecan plus cisplatin with that of etoposide plus cisplatin as the first‐line therapy for ED‐SCLC. GNRI values were calculated using serum albumin levels and actual and ideal bodyweights. Patients with a GNRI > 98, 92–98, and

The prediction of lymphovascular invasion (LVI) or pathological nodal involvement of tumor cells is critical for successful treatment in early stage non-small cell lung cancer (NSCLC). We developed and validated a Deep Cubical Nodule Transfer Learning Algorithm (DeepCUBIT) using transfer learning and 3D Convolutional Neural Network (CNN) to predict LVI or pathological nodal involvement on chest CT images. A total of 695 preoperative CT images of resected NSCLC with tumor size of less than or equal to 3 cm from 2008 to 2015 were used to train and validate the DeepCUBIT model using five-fold cross-validation method. We also used tumor size and consolidation to tumor ratio (C/T ratio) to build a support vector machine (SVM) classifier. Two-hundred and fifty-four out of 695 samples (36.5%) had LVI or nodal involvement. An integrated model (3D CNN + Tumor size + C/T ratio) showed sensitivity of 31.8%, specificity of 89.8%, accuracy of 76.4%, and AUC of 0.759 on external validation cohort. Three single SVM models, using 3D CNN (DeepCUBIT), tumor size or C/T ratio, showed AUCs of 0.717, 0.630 and 0.683, respectively on external validation cohort. DeepCUBIT showed the best single model compared to the models using only C/T ratio or tumor size. In addition, the DeepCUBIT model could significantly identify the prognosis of resected NSCLC patients even in stage I. DeepCUBIT using transfer learning and 3D CNN can accurately predict LVI or nodal involvement in cT1 size NSCLC on CT images. Thus, it can provide a more accurate selection of candidates who will benefit from limited surgery without increasing the risk of recurrence.

Background/Aims This study was conducted to identify risk factors that predict vulnerability to cancer therapy on the basis of the clinical, geriatric, and quality of life assessment before starting treatment in elderly patients. Methods Seventy-five patients aged 65 years and over with newly diagnosed stage IV solid cancer receiving chemotherapy were analyzed. Clinical and laboratory data were collected. The geriatric assessment was performed using the Korean versions of the Modified Barthel Index, Instrumental Activities of Daily Living, Mini-Mental State Examination, and Geriatric Depression Scale. The European Organisation for Research and Treatment of Cancer Quality-of-Life Core Questionnaire (EORTC-QLQ-C30) was also performed. Results Forty-one patients stopped cancer treatment during or after the end of first-line therapy and were classified as the treatment interruption group. By univariate analysis, treatment interruption was associated with metastases to ≥ 2 distant sites, lower albumin level, lower EORTC-QLQ-C30 physical and role functioning scores, and higher EORTC-QLQ-C30 fatigue and appetite loss symptom scores. By multivariate analysis, treatment interruption was significantly associated with low score for the EORTC-QLQ-C30 physical functioning scale (odds ratio [OR], 1.020; 95% confidence interval [CI], 1.002 to 1.039; p = 0.030), and ≥ 2 sites of distant metastases (OR, 2.965; 95% CI, 1.012 to 8.681; p = 0.047). Conclusions The EORTC-QLQ-C30 physical functioning score and metastases to ≥ 2 organs, which indicate a poor physical functional status and metastatic high tumor burden, were significantly associated with interruption of first-line treatment in elderly patients with cancer.

Physicians often encounter patients with unexplained muscle weakness and dysphagia. Lambert–Eaton myasthenic syndrome (LEMS) can cause unexplained weakness or dysphagia and is often accompanied by neoplastic conditions. A 64-year-old man who had several risk factors—14 kg weight loss over the last 4 years, 20 years of experience working as a coal miner, and being a 50 pack-year ex-smoker—complained of dysphagia, intermittent diplopia, mild weakness, and hypotonia. The initial computed tomography (CT) and follow-up positron emission tomography (PET) CT did not reveal any malignancy. After continuous follow-up for this LEMS, small-cell lung cancer (SCLC, cTxN1M0) was found on a serial follow-up chest CT 21 months after the LEMS diagnosis. The patient was treated with chemotherapy. LEMS is rare and is often accompanied by malignancy. This case highlights the importance of being concerned about LEMS diagnoses and of long-term follow-up for unexplained LEMS.

Objective: The rapidly increasing prevalence and application of machine learning (ML) across disciplines creates a pressing need to establish guidance for data curation professionals. However, we must first understand the characteristics of ML-related objects shared in generalist and specialist repositories and the extent to which repository metadata fields enable findability and reuse of ML objects.Methods: We used a combination of API queries and web scraping to retrieve metadata for ML objects in eight commonly used generalist and ML-specific data repositories. We assessed both metadata schema and characteristics of deposited ML objects, within the context of the widely adopted FAIR Principles. We also calculated summary statistics for properties of objects, including number of objects per year, dataset size, domains represented, and availability of related resources.Results: Generalist repositories excelled at providing provenance metadata, specifically unique identifiers, unambiguous citations, clear licenses, and related resources, while specialist repositories emphasized ML-specific descriptive metadata, such as number of attributes and instances and task type. In terms of object content, we noted a wide range of file formats, as well as licenses, all of which impact reusability.Conclusions: Generalist repositories will benefit from some of the practices adopted by specialists, and specialist repositories will benefit from adopting proven data curation practices of generalist repositories. A step forward for repositories will be to invest more into use of labels and persistent identifiers to improve workflow documentation, provenance, and related resource linking of ML objects, which will increase their findability, interoperability, and reusability.

Background and Objectives: Patients with stroke have a forward neck posture due to neurological damage and often have impaired pulmonary function. This study investigated the effect of diaphragmatic breathing with cervical mobilization to improve pulmonary function cervical alignments. Materials and Methods: This study used a one-group pre-test–post-test design including 20 patients with stroke. Two types of cervical joint mobilization techniques, consisting of left and right lateral glide mobilization and posterior–anterior mobilization, were utilized. During joint mobilization, the patients performed diaphragmatic breathing. The measurements were performed immediately after the intervention. Pulmonary function was evaluated using a spirometer to measure the forced expiratory volume in 1 s (FEV1), forced vital capacity (FVC), and peak expiratory flow (PEF). The craniovertebral angle (CVA) was measured using lateral photographs. Results: After diaphragm breathing with cervical joint mobilization, subjects had significantly increased FEV1, FVC, PEF and CVA. Conclusion: Diaphragm breathing with cervical joint mobilization are possible interventions to increase pulmonary function and improve the craniovertebral angle in patients with stroke. However, a complete conclusion can be reached only after a follow-up study has been conducted with a comparison of more subjects and controls.

Artificial neural network-based analysis has recently been used to predict clinical outcomes in patients with solid cancers, including lung cancer. However, the majority of algorithms were not originally developed to identify genes associated with patients’ prognoses. To address this issue, we developed a novel prognosis-related feature selection framework called Cascaded Wx (CWx). The CWx framework ranks features according to the survival of a given cohort by training neural networks with three different high- and low-risk groups in a cascaded fashion. We showed that this approach accurately identified features that best identify the patients’ prognoses, compared to other feature selection algorithms, including the Cox proportional hazards and Coxnet models, when applied to The Cancer Genome Atlas lung adenocarcinoma (LUAD) transcriptome data. The prognostic potential of the top 100 genes identified by CWx outperformed or was comparable to those identified by the other methods as assessed by the concordance index (c-index). In addition, the top 100 genes identified by CWx were found to be associated with the Wnt signaling pathway, providing biologically relevant evidence for the value of these genes in predicting the prognosis of patients with LUAD. Further analyses of other cancer types showed that the genes identified by CWx had the highest prognostic values according to the c-index. Collectively, the CWx framework will potentially be of great use to prognosis-related biomarker discoveries in a variety of diseases.

Background: In this study, we explored the potential of predicting dysglycemia in patients who need to continuously manage blood glucose levels using a non-invasive method via electrocardiography (ECG). Methods: The data were collected from patients with diabetes, and heart rate variability (HRV) features were extracted via ECG processing. A residual block-based one-dimensional convolution neural network model was used to predict dysglycemia. Results: The dysglycemia prediction results at each time point, including at the time of blood glucose measurement, 15 min prior to measurement, and 30 min prior to measurement, exhibited no significant differences compared with the blood glucose measurement values. This result confirmed that the proposed artificial intelligence model for dysglycemia prediction performed well at each time point. Additionally, to determine the optimal number of features required for predicting dysglycemia, 77 HRV features were individually eliminated in the order of decreasing importance with respect to the prediction accuracy; the optimal number of features for the model to predict dysglycemia was determined to be 12. The dysglycemia prediction results obtained 30 min prior to measurement, which exhibited the highest prediction range in this study, were as follows: accuracy = 90.5, sensitivity = 87.52, specificity = 92.74, and precision = 89.86. Conclusions: Furthermore, we determined that no significant differences exist in the blood glucose prediction results reported in previous studies, wherein various vital signs and blood glucose values were used as model inputs, and the results obtained in this study, wherein only ECG data were used to predict dysglycemia.