Citations

Abstract Resistance to chemotherapy remains a major obstacle to improving outcomes of cancer treatment. Recent data suggest intrinsic functions of immune checkpoint proteins, including PD-L1, that may be distinct from their immunoregulatory roles. We found that colorectal cancer (CRC) cells with mutant BRAF(V600E) or KRASalleles show increased PD-L1 transcripts and expression of cell surface PD-L1 protein compared to those with wild-type copies. Furthermore, ectopic expression of BRAFV600E or mutant KRAS induced PD-L1 whose upregulation was attenuated by inhibition of RAS-RAF-MEK-ERK signaling. Combined knockdown of MEK/ERK effectors c-JUN and YAP markedly reduced PD-L1. Knockout of PD-L1 in RKO cells resulted in a reduction in chemotherapy-induced DNA double strand breaks (pH2AX) and caspase-3 cleavage compared to parental cells. Results were confirmed in PD-L1 knockout MC38 murine CRC cells where re-expression of PD-L1, but not its deletion mutants, was shown to induced pH2AX and apoptosis. In cells with knockout of PD-L1, reductions in p-AKT and the BH3-only proteins BIM and BIK were observed that could be restored by re-expression of PD-L1. Treatment of cells with an anti-PD-L1 antibody reduced p-AKT, BIM and BIK, and also attenuated chemotherapy-induced apoptosis. Re-expression of BIM in PD-L1 knockout cells restored apoptosis. Murine tumor xenografts generated from cells with knockout of PD-L1 displayed resistance to oxaliplatin-induced tumor regression compared to tumors derived from parental cells. Using TCGA datasets, human CRCs with high vs low PD-L1 mRNA levels were significantly associated with better survival. In summary, we identified a non-immune function of tumor cell-intrinsic PD-L1 that upregulates BH3-only proteins BIM and BIK to promote apoptosis, thereby suggesting its potential as a predictive biomarker. Citation Format: Daofu Feng, Krishnendu Pal, Debabrata Mukhopadhyay, Xin Liu, Haidong Dong, Shengbing Huang, Frank A. Sinicrope. Tumor intrinsic PD-L1 is mediated by BRAFV600E and can regulate drug-induced apoptosis in human colon cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2019; 2019 Mar 29-Apr 3; Atlanta, GA. Philadelphia (PA): AACR; Cancer Res 2019;79(13 Suppl):Abstract nr 705.

Objectives To examine the ability of surgeons to predict the outcome of treatment for meniscal tears by arthroscopic partial meniscectomy (APM) and exercise therapy in middle-aged patients. Design and setting Electronic survey. Orthopaedic surgeon survey participants were presented 20 patient profiles. These profiles were derived from a randomised clinical trial comparing APM with exercise therapy in middle-aged patients with symptomatic non-obstructive meniscal tears. From each treatment group (APM and exercise therapy), we selected five patients with the best (responders) and five patients with the worst (non-responders) knee function after treatment. 1111 orthopaedic surgeons and residents in the Netherlands and Australia were invited to participate in the survey. Interventions For each of the 20 patient profiles, surgeons (unaware of treatment allocation) had to choose between APM and exercise therapy as preferred treatment and subsequently had to estimate the expected change in knee function for both treatments on a 5-point Likert Scale. Finally, surgeons were asked which patient characteristics affected their treatment choice. Main outcomes The primary outcome was the surgeons’ percentage correct predictions. We also compared this percentage between experienced knee surgeons and other orthopaedic surgeons, and between treatment responders and non-responders. Results We received 194 (17%) complete responses for all 20 patient profiles, resulting in 3880 predictions. Overall, 50.0% (95% CI 39.6% to 60.4%) of the predictions were correct, which equals the proportion expected by chance. Experienced knee surgeons were not better in predicting outcome than other orthopaedic surgeons (50.4% vs 49.5%, respectively; p=0.29). The percentage correct predictions was lower for patient profiles of non-responders (34%; 95% CI 21.3% to 46.6%) compared with responders (66.0%; 95% CI 57.0% to 75.0%; p=0.01). In general, bucket handle tears, knee locking and failed non-operative treatment directed the surgeons’ choice towards APM, while higher level of osteoarthritis, degenerative aetiology and the absence of locking complaints directed the surgeons’ choice towards exercise therapy. Conclusions Surgeons’ criteria for deciding that surgery was indicated did not pass statistical examination. This was true regardless of a surgeon’s experience. These results suggest that non-surgical management is appropriate as first-line therapy in middle-aged patients with symptomatic non-obstructive meniscal tears. Clinical trial registration ClinicalTrials.gov Identifier: NCT03462134.

OBJECTIVENonhome discharge and unplanned readmissions represent important cost drivers following spinal fusion. The authors sought to utilize different machine learning algorithms to predict discharge to rehabilitation and unplanned readmissions in patients receiving spinal fusion.METHODSThe authors queried the 2012–2013 American College of Surgeons National Surgical Quality Improvement Program (ACS-NSQIP) for patients undergoing cervical or lumbar spinal fusion. Outcomes assessed included discharge to nonhome facility and unplanned readmissions within 30 days after surgery. A total of 7 machine learning algorithms were evaluated. Predictive hierarchical clustering of procedure codes was used to increase model performance. Model performance was evaluated using overall accuracy and area under the receiver operating characteristic curve (AUC), as well as sensitivity, specificity, and positive and negative predictive values. These performance metrics were computed for both the imputed and unimputed (missing values dropped) datasets.RESULTSA total of 59,145 spinal fusion cases were analyzed. The incidence rates of discharge to nonhome facility and 30-day unplanned readmission were 12.6% and 4.5%, respectively. All classification algorithms showed excellent discrimination (AUC > 0.80, range 0.85–0.87) for predicting nonhome discharge. The generalized linear model showed comparable performance to other machine learning algorithms. By comparison, all models showed poorer predictive performance for unplanned readmission, with AUC ranging between 0.63 and 0.66. Better predictive performance was noted with models using imputed data.CONCLUSIONSIn an analysis of patients undergoing spinal fusion, multiple machine learning algorithms were found to reliably predict nonhome discharge with modest performance noted for unplanned readmissions. These results provide early evidence regarding the feasibility of modern machine learning classifiers in predicting these outcomes and serve as possible clinical decision support tools to facilitate shared decision making.

The 6-minute walk test is an easy to perform and practical test that has been used in the assessment of patients with a variety of cardiopulmonary diseases including pulmonary arterial hypertension. The correlation with maximal cardiac output renders the test an indirect measure of right ventricular function in patients with significant pulmonary arterial hypertension. The test has been employed in the majority of studies evaluating the efficacy of pulmonary arterial vasodilators. In addition, the distance walked has been demonstrated to correlate with prognosis. An understanding of the 6-minute walk test indications, logistics, limitations, and interpretation is important to the clinician utilizing this test to evaluate patients with pulmonary arterial hypertension.

Background: Patient-reported outcome (PRO) data may be used at 2 levels: to evaluate impacts of disease and treatment aggregated across individuals (group-level) and to screen/monitor individual patients to inform their management (individual-level). For PRO data to be useful at either level, we need to understand their clinical relevance. Purpose: To provide clarity on whether and how methods historically developed to interpret group-based PRO research results might be applied in clinical settings to enable PRO data from individual patients to inform their clinical management and decision-making. Methods: We first differentiate PRO-based decision-making required at group versus individual levels. We then summarize established group-based approaches to interpretation (anchor-based and distribution based), and more recent methods that draw on item calibrations and qualitative research methods. We then assess the applicability of these methods to individual patient data and individual-level decision-making. Findings: Group-based methods provide a range of thresholds that are useful in clinical care: some provide screening thresholds for patients who need additional clinical assessment and/or intervention, some provide thresholds for classifying an individual’s level of severity of symptoms or problems with function, and others provide thresholds for meaningful change when monitoring symptoms and functioning over time during or after interventions. Availability of established cut-points for screening and symptom severity, and normative/reference values, may play into choice of PRO measures for use in clinical care. Translatability of thresholds for meaningful change is more problematic because of the greater reliability needed at the individual-level versus group-level, but group-based methods may provide lower bound estimates. Caution is needed to set thresholds above bounds of measurement error to avoid “false-positive changes” triggering unwarranted alerts and action in clinic. Conclusions: While there are some challenges in applying available methods for interpreting group-based PRO results to individual patient data and clinical care—including myriad contextual factors that may influence an individual patient’s management and decision-making—they provide a useful starting point, and should be used pragmatically.