Citations

Abstract Rationale Urinary TIMP-2 (tissue inhibitor of metalloproteinases-2) and IGFBP7 (insulin-like growth factor–binding protein 7) can predict acute kidney injury (AKI) in patients with sepsis. Objectives To address critical questions about whether biomarkers can inform the response to treatment and whether they might be used to guide therapy, as most sepsis patients present with AKI. Methods We measured [TIMP-2] · [IGFBP7] before and after a 6-hour resuscitation in 688 patients with septic shock enrolled in the ProCESS (Protocol-based Care for Early Septic Shock) trial. Our primary endpoint was stage 3 AKI, renal replacement therapy, or death within 7 days. Measurements and Main Results: The endpoint was reached in 113 patients (16.4%). In patients with negative [TIMP-2] · [IGFBP7] at baseline, those who became positive (>0.3 U) after resuscitation had three-times higher risk compared with those who remained negative (21.8% vs. 8.5%; P = 0.01; odds ratio [OR], 3.0; 95% confidence interval [CI], 1.31–6.87). Conversely, compared with patients with a positive biomarker at baseline that were still positive at Hour 6, risk was reduced for patients who became negative (23.8% vs. 9.8%; P = 0.01; OR, 2.15; 95% CI, 1.17–3.95). A positive [TIMP-2] · [IGFBP7] after resuscitation was associated with worse outcomes in both patients with and without AKI at that time point. The clinical response to resuscitation, as judged by the Acute Physiology and Chronic Health Evaluation II score, was weakly predictive of the endpoint (area under the curve, 0.68; 95% CI, 0.62–0.73) and improved with addition of [TIMP-2] · [IGFBP7] (0.72; 95% CI, 0.66–0.77; P = 0.03). Different resuscitation protocols did not alter biomarker trajectories, nor did they alter outcomes in biomarker-positive or biomarker-negative patients. However, biomarker trajectories were associated with outcomes. Conclusions Changes in urinary [TIMP-2] · [IGFBP7] after initial fluid resuscitation identify patients with sepsis who have differing risk for progression of AKI. Clinical trial registered with www.clinicaltrials.gov (NCT 00510835).

Abstract Purpose: Gene expression–based molecular subtypes of high-grade serous tubo-ovarian cancer (HGSOC), demonstrated across multiple studies, may provide improved stratification for molecularly targeted trials. However, evaluation of clinical utility has been hindered by nonstandardized methods, which are not applicable in a clinical setting. We sought to generate a clinical grade minimal gene set assay for classification of individual tumor specimens into HGSOC subtypes and confirm previously reported subtype-associated features. Experimental Design: Adopting two independent approaches, we derived and internally validated algorithms for subtype prediction using published gene expression data from 1,650 tumors. We applied resulting models to NanoString data on 3,829 HGSOCs from the Ovarian Tumor Tissue Analysis consortium. We further developed, confirmed, and validated a reduced, minimal gene set predictor, with methods suitable for a single-patient setting. Results: Gene expression data were used to derive the predictor of high-grade serous ovarian carcinoma molecular subtype (PrOTYPE) assay. We established a de facto standard as a consensus of two parallel approaches. PrOTYPE subtypes are significantly associated with age, stage, residual disease, tumor-infiltrating lymphocytes, and outcome. The locked-down clinical grade PrOTYPE test includes a model with 55 genes that predicted gene expression subtype with >95% accuracy that was maintained in all analytic and biological validations. Conclusions: We validated the PrOTYPE assay following the Institute of Medicine guidelines for the development of omics-based tests. This fully defined and locked-down clinical grade assay will enable trial design with molecular subtype stratification and allow for objective assessment of the predictive value of HGSOC molecular subtypes in precision medicine applications. See related commentary by McMullen et al., p. 5271

AbstractBackgroundEndobronchial ultrasound‐guided transbronchial lung biopsy (EBUS‐TBLB) and fluorodeoxyglucose positron emission tomography (FDG‐PET) have been widely used in the diagnosis of peripheral pulmonary lesions (PPLs). This study was conducted to determine the diagnostic value of EBUS‐TBLB combined with FDG‐PET in the assessment of PPLs.MethodsThe clinical data of 76 patients with PPLs who received both FDG‐PET and EBUS‐TBLB from January 2016 to February 2018 were retrospectively evaluated. Further subgroup analysis was performed according to lesion diameter (≤20 mm or >20 mm). Related diagnostic indices were calculated and compared between groups.ResultsWhen combining EBUS‐TBLB with FDG‐PET, the diagnostic accuracy rate, sensitivity, specificity, Youden's index, positive predictive value, and negative predictive value for PPLs were 86.8%, 90.2%, 73.3%, 63.5%, 93.2%, and 64.7%, respectively. In addition, the diagnostic accuracy rate of the combined approach was significantly higher than the single EBUS‐TBLB and FDG‐PET (P < 0.01 and P < 0.05, respectively), and its Youden's index was also at a higher level. When stratified by lesion diameter, the combined approach showed a significantly higher diagnostic accuracy rate (P < 0.05) and a higher Youden's index for PPLs >20 mm than PPLs ≤20 mm. In addition, we found that positive bronchus sign and probe within the probe were two important factors conducing to enhancing the diagnostic accuracy rate for EBUS‐TBLB.ConclusionsAn integrated approach combining EBUS‐TBLB with FDG‐PET is particularly useful for diagnosing PPLs, and the improved diagnostic yields were especially evident for PPLs >20 mm.

AbstractBackground and ObjectivesNeoadjuvant endocrine therapy (NET) for ER+ breast cancer can downstage primary tumors. We evaluated NET efficacy in node‐positive patients.MethodsNode‐positive patients undergoing NET for ER+ breast cancer from 2012 to 2019 were reviewed. Primary endpoints included rates of axillary lymphadenectomy (ALND), pathologic complete response (pCR), and final nodal staging.ResultsThirty‐nine patients were included. Before NET, all were clinically node‐positive (cN1 = 36, 94%; cN2 = 2, 5%; cN3 = 1, 3%; Stage II = 23, 59%, Stage III = 16, 41%). After NET, nine (23%) had clinically persistent axillary disease necessitating ALND. The remaining 30 (77%) underwent sentinel lymph node biopsy (SLNB). Of these, 25 (83%) were SLNB+ on frozen section, undergoing immediate ALND. Five patients were negative on frozen section: one had a confirmed axillary pCR, and four had residual nodal disease on permanent pathology. One underwent delayed ALND, and for the remaining three patients, decision was made to forgo ALND. Final overall axillary staging was: N0 (pCR) = 1, 3%, pN1mic = 1, 3%, pN1 = 20, 51%, pN2 = 12, 30%, pN3 = 5, 13%; Stage II = 16, 41%, Stage III = 23, 59%.ConclusionsWhile NET is reported to downstage primary tumors, downstaging of the axilla was unsuccessful in the majority of patients.

BackgroundSubtyping chronic rhinosinusitis (CRS) by tissue eosinophilia has prognostic and therapeutic implications, and is difficult to predict using peripheral eosinophil counts or polyp status alone. The objective of this study was to test machine learning for prediction of eosinophilic CRS (eCRS).MethodsInput variables were defined as peripheral eosinophil count, urinary leukotriene E4 (uLTE4) level, and polyp status. The output was diagnosis of eCRS, defined as tissue eosinophil count >10 per high‐power field. Patients undergoing surgery for CRS were retrospectively reviewed for complete datasets. Univariate analysis was performed for each input as a predictor of eCRS. Logistic regression and artificial neural network (ANN) machine learning models were developed using random and surgeon‐specific training/test datasets.ResultsA total of 80 patients met inclusion criteria. In univariate analysis, area under the receiver operator characteristic curve (AUC) for peripheral eosinophil count and uLTE4 were 0.738 (95% confidence interval [CI], 0.616 to 0.840) and 0.728 (95% CI, 0.605 to 0.822), respectively. Presence of polyps was 94.1% sensitive, but 51.7% specific. Logistic regression models using random and surgeon specific datasets resulted in AUC of 0.882 (95% CI, 0.665 to 0.970) and 0.945 (95% CI, 0.755 to 0.995), respectively. ANN models resulted in AUC of 0.918 (95% CI, 0.756 to 0.975) and 0.956 (95% CI, 0.828 to 0.999) using random and surgeon‐specific datasets, respectively. Model comparison of logistic regression and ANN was not statistically different. All machine learning models had AUC greater than univariate analyses (all p < 0.003).ConclusionMachine learning of 3 clinical inputs has the potential to predict eCRS with high sensitivity and specificity in this patient population. Prospective investigation using larger and more diverse populations is warranted.

4090 Background: EGFR mAbs have become incorporated into clinical practice for the management of mCRC over the last decade. KRAS and NRAS mutations are used as predictive biomarkers and BRAF V600E mutations are associated with an adverse prognosis. The observed TE within biomarker subpopulations has varied between studies. Methods: IPD from randomized trials with head-to-head comparison between EGFR mAb versus no EGFR mAb (chemotherapy alone or BSC) in mCRC, across all lines of therapy (first, second and later), were pooled. Biomarker subpopulations are defined in the table. Overall survival (OS) and progression-free survival (PFS) were compared between groups by Cox model, stratified by studies and adjusted by age, gender, and performance status. TEs were estimated by adjusted hazard ratio (HRadj) and 95% confidence interval (CI). Within each biomarker subgroup, EGFR mAb efficacy was explored according to multiple exploratory factors, including line of therapy, type of backbone chemo, gender, sidedness and site of metastasis. Interaction tests were performed. P-values < 0.01 were considered statistically significant to account for multiple comparisons. Results: 5729 pts from 8 studies with data available for ≥ 1 biomarker were analysed. PFS benefits (median 9.2 mos in EGFR mAbs, 8.0 mos in no EGFR mAbs) were confirmed in triple-WT pts, but not for OS (refer to table). No OS/PFS benefits were observed for pts with any of the MT tumors. Exploratory analyses showed a potential detrimental TE of EGFR mAbs in KRAS MT mCRC with liver metastasis (OS: HRadj 1.22, p = .003, pinteraction .0056; PFS: HRadj 1.24, p = .0009, pinteraction .0008). These results were confirmed within the subgroup of pts with all 3 biomarkers available. Conclusions: This is the largest IPD analysis to explore the predictive value of RAS/BRAF biomarkers in mCRC. Our findings demonstrate that there is no evidence of efficacy of EGFR mAbs in KRAS, BRAF and/or NRAS MT mCRC. EGFR mAbs might have a detrimental effect in KRAS MT mCRC with liver metastases. [Table: see text]

Abstract Patients with primary brain tumors often experience seizures, which can be the presenting symptom or occur for the first time at any point along the illness trajectory. In addition to causing morbidity, seizures negatively affect independence and quality of life in other ways, for example, by leading to loss of driving privileges. Long-term therapy with antiepileptic drugs (AEDs) is the standard of care in brain tumor patients with seizures, but the role of prophylactic AEDs in seizure-naive patients remains controversial. In this article, experts in the field discuss the issues of AED efficacy and toxicity, and explain their differing recommendations for routine use of prophylactic AEDs.