Failure rates of nonoperative management of low-grade splenic injuries with active extravasation: an Eastern Association for the Surgery of Trauma multicenter study.

Objectives: There is little evidence guiding the management of grade I-II traumatic splenic injuries with contrast blush (CB). We aimed to analyze the failure rate of nonoperative management (NOM) of grade I-II splenic injuries with CB in hemodynamically stable patients. Methods: A multicenter, retrospective cohort study examining all grade I-II splenic injuries with CB was performed at 21 institutions from January 1, 2014, to October 31, 2019. Patients >18 years old with grade I or II splenic injury due to blunt trauma with CB on CT were included. The primary outcome was the failure of NOM requiring angioembolization/operation. We determined the failure rate of NOM for grade I versus grade II splenic injuries. We then performed bivariate comparisons of patients who failed NOM with those who did not. Results: A total of 145 patients were included. Median Injury Severity Score was 17. The combined rate of failure for grade I-II injuries was 20.0%. There was no statistical difference in failure of NOM between grade I and II injuries with CB (18.2% vs 21.1%, p>0.05). Patients who failed NOM had an increased median hospital length of stay (p=0.024) and increased need for blood transfusion (p=0.004) and massive transfusion (p=0.030). Five patients (3.4%) died and 96 (66.2%) were discharged home, with no differences between those who failed and those who did not fail NOM (both p>0.05). Conclusion: NOM of grade I-II splenic injuries with CB fails in 20% of patients. Level of evidence: IV.

Early Cancer Detection in Li-Fraumeni Syndrome with Cell-Free DNA.

People with Li-Fraumeni syndrome (LFS) harbor a germline pathogenic variant in the TP53 tumor suppressor gene, face a near 100% lifetime risk of cancer, and routinely undergo intensive surveillance protocols. Liquid biopsy has become an attractive tool for a range of clinical applications, including early cancer detection. Here, we provide a proof-of-principle for a multimodal liquid biopsy assay that integrates a targeted gene panel, shallow whole-genome, and cell-free methylated DNA immunoprecipitation sequencing for the early detection of cancer in a longitudinal cohort of 89 LFS patients. Multimodal analysis increased our detection rate in patients with an active cancer diagnosis over uni-modal analysis and was able to detect cancer-associated signal(s) in carriers prior to diagnosis with conventional screening (positive predictive value = 67.6%, negative predictive value = 96.5%). Although adoption of liquid biopsy into current surveillance will require further clinical validation, this study provides a framework for individuals with LFS. SIGNIFICANCE: By utilizing an integrated cell-free DNA approach, liquid biopsy shows earlier detection of cancer in patients with LFS compared with current clinical surveillance methods such as imaging. Liquid biopsy provides improved accessibility and sensitivity, complementing current clinical surveillance methods to provide better care for these patients. See related commentary by Latham et al., p. 23. This article is featured in Selected Articles from This Issue, p. 5.

Multiple Germline Events Contribute to Cancer Development in Patients with Li-Fraumeni Syndrome.

Li-Fraumeni syndrome (LFS) is an autosomal dominant cancer-predisposition disorder. Approximately 70% of individuals who fit the clinical definition of LFS harbor a pathogenic germline variant in the TP53 tumor suppressor gene. However, the remaining 30% of patients lack a TP53 variant and even among variant TP53 carriers, approximately 20% remain cancer-free. Understanding the variable cancer penetrance and phenotypic variability in LFS is critical to developing rational approaches to accurate, early tumor detection and risk-reduction strategies. We leveraged family-based whole-genome sequencing and DNA methylation to evaluate the germline genomes of a large, multi-institutional cohort of patients with LFS (n = 396) with variant (n = 374) or wildtype TP53 (n = 22). We identified alternative cancer-associated genetic aberrations in 8/14 wildtype TP53 carriers who developed cancer. Among variant TP53 carriers, 19/49 who developed cancer harbored a pathogenic variant in another cancer gene. Modifier variants in the WNT signaling pathway were associated with decreased cancer incidence. Furthermore, we leveraged the noncoding genome and methylome to identify inherited epimutations in genes including ASXL1, ETV6, and LEF1 that confer increased cancer risk. Using these epimutations, we built a machine learning model that can predict cancer risk in patients with LFS with an area under the receiver operator characteristic curve (AUROC) of 0.725 (0.633-0.810). Significance: Our study clarifies the genomic basis for the phenotypic variability in LFS and highlights the immense benefits of expanding genetic and epigenetic testing of patients with LFS beyond TP53. More broadly, it necessitates the dissociation of hereditary cancer syndromes as single gene disorders and emphasizes the importance of understanding these diseases in a holistic manner as opposed to through the lens of a single gene.

Screening and intervention for intimate partner violence at trauma centers and emergency departments: an evidence-based systematic review from the Eastern Association for the Surgery of Trauma.

Background: Intimate partner violence (IPV) is a serious public health issue with a substantial burden on society. Screening and intervention practices vary widely and there are no standard guidelines. Our objective was to review research on current practices for IPV prevention in emergency departments and trauma centers in the USA and provide evidenced-based recommendations. Methods: An evidence-based systematic review of the literature was conducted to address screening and intervention for IPV in adult trauma and emergency department patients. The Grading of Recommendations, Assessment, Development and Evaluations methodology was used to determine the quality of evidence. Studies were included if they addressed our prespecified population, intervention, control, and outcomes questions. Case reports, editorials, and abstracts were excluded from review. Results: Seven studies met inclusion criteria. All seven were centered around screening for IPV; none addressed interventions when abuse was identified. Screening instruments varied across studies. Although it is unclear if one tool is more accurate than others, significantly more victims were identified when screening protocols were implemented compared with non-standardized approaches to identifying IPV victims. Conclusion: Overall, there were very limited data addressing the topic of IPV screening and intervention in emergency medical settings, and the quality of the evidence was low. With likely low risk and a significant potential benefit, we conditionally recommend implementation of a screening protocol to identify victims of IPV in adults treated in the emergency department and trauma centers. Although the purpose of screening would ultimately be to provide resources for victims, no studies that assessed distinct interventions met our inclusion criteria. Therefore, we cannot make specific recommendations related to IPV interventions. PROSPERO registration number: CRD42020219517.

Differential Expression Enrichment Tool (DEET): an interactive atlas of human differential gene expression.

Differential gene expression analysis using RNA sequencing (RNA-seq) data is a standard approach for making biological discoveries. Ongoing large-scale efforts to process and normalize publicly available gene expression data enable rapid and systematic reanalysis. While several powerful tools systematically process RNA-seq data, enabling their reanalysis, few resources systematically recompute differentially expressed genes (DEGs) generated from individual studies. We developed a robust differential expression analysis pipeline to recompute 3162 human DEG lists from The Cancer Genome Atlas, Genotype-Tissue Expression Consortium, and 142 studies within the Sequence Read Archive. After measuring the accuracy of the recomputed DEG lists, we built the Differential Expression Enrichment Tool (DEET), which enables users to interact with the recomputed DEG lists. DEET, available through CRAN and RShiny, systematically queries which of the recomputed DEG lists share similar genes, pathways, and TF targets to their own gene lists. DEET identifies relevant studies based on shared results with the user's gene lists, aiding in hypothesis generation and data-driven literature review.

Diagnostic accuracy of imaging approaches for early tumor detection in children with Li-Fraumeni syndrome.

BACKGROUND: The Toronto protocol for cancer surveillance in children with Li-Fraumeni syndrome has been adopted worldwide. OBJECTIVE: To assess the diagnostic accuracy of the imaging used in this protocol. MATERIALS AND METHODS: We conducted a blinded retrospective review of imaging modalities in 31 pediatric patients. We compared imaging findings with the reference standards, which consisted of (1) histopathological diagnosis, (2) corresponding dedicated imaging or subsequent surveillance imaging or (3) clinical outcomes. We individually analyzed each modality's diagnostic performance for cancer detection and assessed it on a per-study basis for chest and abdominal regional whole-body MRI (n=115 each), brain MRI (n=101) and abdominal/pelvic US (n=292), and on a per-lesion basis for skeleton/soft tissues on whole-body MRI (n=140). RESULTS: Of 763 studies/lesions, approximately 80% had reference standards that identified 4 (0.7%) true-positive, 523 (85.3%) true-negative, 5 (0.8%) false-positive, 3 (0.5%) false-negative and 78 (12.7%) indeterminate results. There were 3 true-positives on whole-body MRI and 1 true-positive on brain MRI as well as 3 false-negatives on whole-body MRI. Sensitivities and specificities of tumor diagnosis using a worst-case scenario analysis were, respectively, 40.0% (95% confidence interval [CI]: 7.3%, 83.0%) and 38.2% (95% CI: 29.2%, 48.0%) for skeleton/soft tissues on whole-body MRI; sensitivity non-available and 97.8% (95% CI: 91.4%, 99.6%) for chest regional whole-body MRI; 100.0% (95% CI: 5.5%, 100.0%) and 96.8% (95% CI: 90.2%, 99.2%) for abdominal regional whole-body MRI; sensitivity non-available and 98.3% (95% CI: 95.3, 99.4) for abdominal/pelvic US; and 50.0% (95% CI: 2.7%, 97.3%) and 93.8% (95% CI: 85.6%, 97.7%) for brain MRI. CONCLUSION: Considerations for optimizing imaging protocol, defining criteria for abnormalities, developing a structured reporting system, and practicing consensus double-reading may enhance the diagnostic accuracy for tumor surveillance.

Large scale genotype- and phenotype-driven machine learning in Von Hippel-Lindau disease.

Von Hippel-Lindau (VHL) disease is a hereditary cancer syndrome where individuals are predisposed to tumor development in the brain, adrenal gland, kidney, and other organs. It is caused by pathogenic variants in the VHL tumor suppressor gene. Standardized disease information has been difficult to collect due to the rarity and diversity of VHL patients. Over 4100 unique articles published until October 2019 were screened for germline genotype-phenotype data. Patient data were translated into standardized descriptions using Human Genome Variation Society gene variant nomenclature and Human Phenotype Ontology terms and has been manually curated into an open-access knowledgebase called Clinical Interpretation of Variants in Cancer. In total, 634 unique VHL variants, 2882 patients, and 1991 families from 427 papers were captured. We identified relationship trends between phenotype and genotype data using classic statistical methods and spectral clustering unsupervised learning. Our analyses reveal earlier onset of pheochromocytoma/paraganglioma and retinal angiomas, phenotype co-occurrences and genotype-phenotype correlations including hotspots. It confirms existing VHL associations and can be used to identify new patterns and associations in VHL disease. Our database serves as an aggregate knowledge translation tool to facilitate sharing information about the pathogenicity of VHL variants.

Development and validation of an ensemble machine learning framework for detection of all-cause advanced hepatic fibrosis: a retrospective cohort study.

BACKGROUND: Cirrhosis is the result of advanced scarring (or fibrosis) of the liver, and is often diagnosed once decompensation with associated complications has occurred. Current non-invasive tests to detect advanced liver fibrosis have limited performance, with many indeterminate classifications. We aimed to identify patients with advanced liver fibrosis of all-causes using machine learning algorithms (MLAs). METHODS: In this retrospective study of routinely collected laboratory, clinical, and demographic data, we trained six MLAs (support vector machine, random forest classifier, gradient boosting classifier, logistic regression, artificial neural network, and an ensemble of all these algorithms) to detect advanced fibrosis using 1703 liver biopsies from patients seen at the Toronto Liver Clinic (TLC) between Jan 1, 2000, and Dec 20, 2014. Performance was validated using five datasets derived from patient data provided by the TLC (n=104 patients with a biopsy sample taken between March 24, 2014, and Dec 31, 2017) and McGill University Health Centre (MUHC; n=404). Patients with decompensated cirrhosis were excluded. Performance was benchmarked against aspartate aminotransferase-to-platelet ratio index (APRI), fibrosis-4 index (FIB-4), non-alcoholic fatty liver disease fibrosis score (NFS), transient elastography, and an independent panel of five hepatology experts (MB, GS, HK, KP, and RSK). MLA performance was evaluated using the area under the receiver operating characteristic curve (AUROC) and the percentage of determinate classifications. FINDINGS: The best MLA was an ensemble algorithm of support vector machine, random forest classifier, gradient boosting classifier, logistic regression, and neural network algorithms, which achieved 100% determinate classifications (95% CI 100·0-100·0), an AUROC score of 0·870 (95% CI 0·797-0·931) on the TLC validation set (fibrosis stages F0 and F1 vs F4), and an AUROC of 0·716 (95% CI 0·664-0·766) on the MUHC validation set (fibrosis stages F0, F1, and F2 vs F3 and F4). The ensemble MLA outperformed all routinely used biomarkers and achieved comparable performance to hepatologists as measured by AUROC and percentage of indeterminate classifications in both the TLC validation dataset (APRI AUROC score 0·719 [95% CI 0·611-0·820], 83·7% determinate [95% CI 76·0-90·4]; FIB-4 AUROC score 0·825 [95% CI 0·730-0·912], 72·1% determinate [95% CI 63·5-80·8]) and the MUHC validation dataset (APRI AUROC score 0·618 [95% CI 0·548-0·691], 75·5% determinate [95% CI 71·5-79·2]; FIB-4 AUROC score 0·717 (95% CI 0·652-0·776), 75·5% determinate [95% CI 0·713-0·797]), and achieving only slightly lower AUROC than transient elastography (0·773 [95% CI 0·699-0·834] vs 0·826 [95% CI 0·758-0·889]). INTERPRETATION: We have shown that an ensemble MLA outperforms non-imaging-based methods in detecting advanced fibrosis across different causes of liver disease. Our MLA was superior to APRI, FIB-4, and NFS with no indeterminate classifications, while achieving performance comparable to an independent panel of experts. MLAs using routinely collected data could identify patients at high-risk of advanced hepatic fibrosis and cirrhosis among patients with chronic liver disease, allowing intervention before onset of decompensation. FUNDING: Toronto General Hospital Foundation.

Management of rhabdomyolysis: A practice management guideline from the Eastern Association for the Surgery of Trauma.

BACKGROUND: The treatment of rhabdomyolysis remains controversial. Although there is no question that any associated compartment syndrome needs to be identified and released, debate persists regarding the benefit of further therapy including aggressive intravenous fluid resuscitation (IVFR), urine alkalization with bicarbonate, and the use of mannitol. The goal of this practice management guideline was to evaluate the effects of bicarbonate, mannitol, and aggressive intravenous fluids on patients with rhabdomyolysis. METHODS: A systematic review and meta-analysis comparing treatments in patients with rhabdomyolysis was performed. The Grading of Recommendations Assessment, Development and Evaluation (GRADE) methodology was applied to assess the quality of evidence and to create evidence-based recommendations regarding the use of bicarbonate, mannitol, and aggressive IVFR in patients with rhabdomyolysis. RESULTS: A total of 12 studies were identified for analysis. On quantitative analysis, IVFR decreased the incidence of acute renal failure (ARF) and need for dialysis in patients with rhabdomyolysis. Neither bicarbonate nor mannitol administration improved the incidence of acute renal failure and need for dialysis in patients with rhabdomyolysis. Quality of evidence was deemed to be very low, with the vast majority of the literature being retrospective studies. CONCLUSION: In patients with rhabdomyolysis, we conditionally recommend for aggressive IVFR to improve outcomes of ARF and lessen the need for dialysis. We conditionally recommend against treatment with bicarbonate or mannitol in patients with rhabdomyolysis.

The Toronto Postliver Transplantation Hepatocellular Carcinoma Recurrence Calculator: A Machine Learning Approach.

Liver transplantation (LT) listing criteria for hepatocellular carcinoma (HCC) remain controversial. To optimize the utility of limited donor organs, this study aims to leverage machine learning to develop an accurate posttransplantation HCC recurrence prediction calculator. Patients with HCC listed for LT from 2000 to 2016 were identified, with 739 patients who underwent LT used for modeling. Data included serial imaging, alpha-fetoprotein (AFP), locoregional therapies, treatment response, and posttransplantation outcomes. We compared the CoxNet (regularized Cox regression), survival random forest, survival support vector machine, and DeepSurv machine learning algorithms via the mean cross-validated concordance index. We validated the selected CoxNet model by comparing it with other currently available recurrence risk algorithms on a held-out test set (AFP, Model of Recurrence After Liver Transplant [MORAL], and Hazard Associated with liver Transplantation for Hepatocellular Carcinoma [HALT-HCC score]). The developed CoxNet-based recurrence prediction model showed a satisfying overall concordance score of 0.75 (95% confidence interval [CI], 0.64-0.84). In comparison, the recalibrated risk algorithms' concordance scores were as follows: AFP score 0.64 (outperformed by the CoxNet model, 1-sided 95% CI, >0.01; P = 0.04) and MORAL score 0.64 (outperformed by the CoxNet model 1-sided 95% CI, >0.02; P = 0.03). The recalibrated HALT-HCC score performed well with a concordance of 0.72 (95% CI, 0.63-0.81) and was not significantly outperformed (1-sided 95% CI, ≥0.05; P = 0.29). Developing a comprehensive posttransplantation HCC recurrence risk calculator using machine learning is feasible and can yield higher accuracy than other available risk scores. Further research is needed to confirm the utility of machine learning in this setting.

Assessing therapy response in patient-derived xenografts.

Quantifying response to drug treatment in mouse models of human cancer is important for treatment development and assignment, yet remains a challenging task. To be able to translate the results of the experiments more readily, a preferred measure to quantify this response should take into account more of the available experimental data, including both tumor size over time and the variation among replicates. We propose a theoretically grounded measure, KuLGaP, to compute the difference between the treatment and control arms. We test and compare KuLGaP to four widely used response measures using 329 patient-derived xenograft (PDX) models. Our results show that KuLGaP is more selective than currently existing measures, reduces the risk of false-positive calls, and improves translation of the laboratory results to clinical practice. We also show that outcomes of human treatment better align with the results of the KuLGaP measure than other response measures. KuLGaP has the potential to become a measure of choice for quantifying drug treatment in mouse models as it can be easily used via the kulgap.ca website.

Pathogenic Germline Variants in Cancer Susceptibility Genes in Children and Young Adults With Rhabdomyosarcoma.

Rhabdomyosarcoma (RMS) is the most common pediatric soft-tissue sarcoma and accounts for 3% of all pediatric cancer. In this study, we investigated germline sequence and structural variation in a broad set of genes in two large, independent RMS cohorts. MATERIALS AND METHODS: Genome sequencing of the discovery cohort (n = 273) and exome sequencing of the secondary cohort (n = 121) were conducted on germline DNA. Analyses were performed on 130 cancer susceptibility genes (CSG). Pathogenic or likely pathogenic (P/LP) variants were predicted using the American College of Medical Genetics and Genomics (ACMG) criteria. Structural variation and survival analyses were performed on the discovery cohort. RESULTS: We found that 6.6%-7.7% of patients with RMS harbored P/LP variants in dominant-acting CSG. An additional approximately 1% have structural variants (ATM, CDKN1C) in CSGs. CSG variants did not influence survival, although there was a significant correlation with an earlier age of tumor onset. There was a nonsignificant excess of P/LP variants in dominant inheritance genes in the patients with FOXO1 fusion-negative RMS patients versus the patients with FOXO1 fusion-positive RMS. We identified pathogenic germline variants in CSGs previously (TP53, NF1, DICER1, mismatch repair genes), rarely (BRCA2, CBL, CHEK2, SMARCA4), or never (FGFR4) reported in RMS. Numerous genes (TP53, BRCA2, mismatch repair) were on the ACMG Secondary Findings 2.0 list. CONCLUSION: In two cohorts of patients with RMS, we identified pathogenic germline variants for which gene-specific therapies and surveillance guidelines may be beneficial. In families with a proband with an RMS-risk P/LP variant, genetic counseling and cascade testing should be considered, especially for ACMG Secondary Findings genes and/or with gene-specific surveillance guidelines.

Predicting Malignancy in Pediatric Thyroid Nodules: Early Experience With Machine Learning for Clinical Decision Support.

OBJECTIVE: To develop a machine learning tool to integrate clinical data for the prediction of non-benign thyroid cytology and histology. CONTEXT: Papillary thyroid carcinoma is the most common endocrine malignancy. Since most nodules are benign, the challenge for the clinician is to identify those most likely to harbor malignancy while limiting exposure to surgical risks among those with benign nodules. METHODS: Random forests (augmented to select features based on our clinical measure of interest), in conjunction with interpretable rule sets, were used on demographic, ultrasound, and biopsy data of thyroid nodules from children younger than 18 years at a tertiary pediatric hospital. Accuracy, false-positive rate (FPR), false-negative rate (FNR), and area under the receiver operator curve (AUROC) are reported. RESULTS: Our models predict nonbenign cytology and malignant histology better than historical outcomes. Specifically, we expect a 68.04% improvement in the FPR, 11.90% increase in accuracy, and 24.85% increase in AUROC for biopsy predictions in 67 patients (28 with benign and 39 with nonbenign histology). We expect a 23.22% decrease in FPR, 32.19% increase in accuracy, and 3.84% decrease in AUROC for surgery prediction in 53 patients (42 with benign and 11 with nonbenign histology). This improvement comes at the expense of the FNR, for which we expect 10.27% with malignancy would be discouraged from performing biopsy, and 11.67% from surgery. Given the small number of patients, these improvements are estimates and are not tested on an independent test set. CONCLUSION: This work presents a first attempt at developing an interpretable machine learning based clinical tool to aid clinicians. Future work will involve sourcing more data and developing probabilistic estimates for predictions.

Machine learning approaches to drug response prediction: challenges and recent progress.

Cancer is a leading cause of death worldwide. Identifying the best treatment using computational models to personalize drug response prediction holds great promise to improve patient's chances of successful recovery. Unfortunately, the computational task of predicting drug response is very challenging, partially due to the limitations of the available data and partially due to algorithmic shortcomings. The recent advances in deep learning may open a new chapter in the search for computational drug response prediction models and ultimately result in more accurate tools for therapy response. This review provides an overview of the computational challenges and advances in drug response prediction, and focuses on comparing the machine learning techniques to be of utmost practical use for clinicians and machine learning non-experts. The incorporation of new data modalities such as single-cell profiling, along with techniques that rapidly find effective drug combinations will likely be instrumental in improving cancer care.

Use of ShotSpotter detection technology decreases prehospital time for patients sustaining gunshot wounds.

BACKGROUND: Shorter prehospital time in patients sustaining penetrating trauma has been shown to be associated with improved survival. Literature has also demonstrated that police transport (vs. Emergency Medical Services [EMS]) shortens transport times to a trauma center. The purpose of this study was to determine if ShotSpotter, which triangulates the location of gunshots and alerts police, expedited dispatch and transport of injured victims to the trauma center. METHODS: All shootings which occurred in Camden, NJ, from 2010 to 2018 were reviewed. Demographic, geographic, response time, transport time, and field intervention data were collected from medical and police records. We compared shootings where the ShotSpotter was activated versus shootings where ShotSpotter was not activated. Incidents, which did not occur in Camden or where complete data were not available, were excluded as were patients not transported by police or EMS. RESULTS: There were 627 shootings during the study period which met inclusion criteria with 190 (30%) activating the ShotSpotter system. Victims involved in shootings with ShotSpotter activation were more severely injured, more likely to be transported by police, less likely to undergo trauma bay resuscitative measures, and more likely to receive blood products. Mortality, when adjusted for distance, Trauma, and Injury Severity Score, Injury Severity Score, and shock index, was not significantly different between ShotSpotter and non-ShotSpotter incidents. ShotSpotter activation significantly reduced both the response time as well as transport time for both police and EMS (all p < 0.05). CONCLUSION: The activation of the ShotSpotter technology increased the likelihood of police transport of gunshot victims. Furthermore, the use of this technology resulted in shorter response times as well as transport times for both police and EMS. This technology may be beneficial in enhancing the care of victims of penetrating trauma. LEVEL OF EVIDENCE: Therapeutic/Care management, level III.

Integrative Pharmacogenomics Analysis of Patient-Derived Xenografts.

Identifying robust biomarkers of drug response constitutes a key challenge in precision medicine. Patient-derived tumor xenografts (PDX) have emerged as reliable preclinical models that more accurately recapitulate tumor response to chemo- and targeted therapies. However, the lack of computational tools makes it difficult to analyze high-throughput molecular and pharmacologic profiles of PDX. We have developed Xenograft Visualization & Analysis (Xeva), an open-source software package for in vivo pharmacogenomic datasets that allows for quantification of variability in gene expression and pathway activity across PDX passages. We found that only a few genes and pathways exhibited passage-specific alterations and were therefore not suitable for biomarker discovery. Using the largest PDX pharmacogenomic dataset to date, we identified 87 pathways that are significantly associated with response to 51 drugs (FDR < 0.05). We found novel biomarkers based on gene expressions, copy number aberrations, and mutations predictive of drug response (concordance index > 0.60; FDR < 0.05). Our study demonstrates that Xeva provides a flexible platform for integrative analysis of preclinical in vivo pharmacogenomics data to identify biomarkers predictive of drug response, representing a major step forward in precision oncology. SIGNIFICANCE: A computational platform for PDX data analysis reveals consistent gene and pathway activity across passages and confirms drug response prediction biomarkers in PDX.See related commentary by Meehan, p. 4324.

Infection after penetrating brain injury-An Eastern Association for the Surgery of Trauma multicenter study oral presentation at the 32nd annual meeting of the Eastern Association for the Surgery of Trauma, January 15-19, 2019, in Austin, Texas.

BACKGROUND: Fatality rates following penetrating traumatic brain injury (pTBI) are extremely high and survivors are often left with significant disability. Infection following pTBI is associated with worse morbidity. The modern rates of central nervous system infections (INF) in civilian survivors are unknown. This study sought to determine the rate of and risk factors for INF following pTBI and to determine the impact of antibiotic prophylaxis. METHODS: Seventeen institutions submitted adult patients with pTBI and survival of more than 72 hours from 2006 to 2016. Patients were stratified by the presence or absence of infection and the use or omission of prophylactic antibiotics. Study was powered at 85% to detect a difference in infection rate of 5%. Primary endpoint was the impact of prophylactic antibiotics on INF. Mantel-Haenszel χ and Wilcoxon's rank-sum tests were used to compare categorical and nonparametric variables. Significance greater than p = 0.2 was included in a logistic regression adjusted for center. RESULTS: Seven hundred sixty-three patients with pTBI were identified over 11 years. 7% (n = 51) of patients developed an INF. Sixty-six percent of INF patients received prophylactic antibiotics. Sixty-two percent of all patients received one dose or greater of prophylactic antibiotics and 50% of patients received extended antibiotics. Degree of dural penetration did not appear to impact the incidence of INF (p = 0.8) nor did trajectory through the oropharynx (p = 0.18). Controlling for other variables, there was no statistically significant difference in INF with the use of prophylactic antibiotics (p = 0.5). Infection was higher in patients with intracerebral pressure monitors (4% vs. 12%; p = <0.001) and in patients with surgical intervention (10% vs. 3%; p < 0.001). CONCLUSION: There is no reduction in INF with prophylactic antibiotics in pTBI. Surgical intervention and invasive intracerebral pressure monitoring appear to be risk factors for INF regardless of prophylactic use. LEVEL OF EVIDENCE: Therapeutic, level IV.

Determining Trajectory to Predict Injury: The Use of X-Ray During Resuscitation in Gunshot Wounds.

BACKGROUND: The practice of marking gunshot wounds and obtaining X-rays (XRs) has been performed to determine the trajectory of missiles to help identify internal injuries. We hypothesized that surgeons would have poor accuracy in predicting injuries and that X-rays do not alter the clinical decision. METHODS: We developed a 50-patient (89 injury sites) PowerPoint survey based on cases seen at our level 1 trauma center from 2012 to 2014. Images of a silhouetted BodyMan (BM) with wounds marked, XRs, and vital signs (VSs) were shown in series for 20 s each. Surgeons were asked to record which organs they thought could be injured and to document their clinical decision. Data were analyzed to determine the inter-rater reliability (agreement, intraclass correlation coefficient [ICC]) for each mode of clinical information (BM, XR, VS). Predicted versus actual injuries were compared using absolute agreements. RESULTS: Ten surgeons completed the survey. We found that no single piece of information was helpful in allowing the surgeon to accurately predict injuries. Pulmonary injury had the highest agreement among all injuries (ICC = 0.727). VSs had the highest ICC in determining the clinical plan for the patient (ICC = 0.342), whereas both BM and XR had low ICCs (0.162 and 0.183, respectively). CONCLUSIONS: We found that marking wounds and obtaining X-rays, other than a chest X-ray, did not result in accuracy in predicting injury nor alter the clinical decision. VSs were the only piece of information found significant in determining clinical management. We conclude that marking wounds for X-rays is an unnecessary step during the initial resuscitation of patients with gunshot wounds.

Dr.VAE: improving drug response prediction via modeling of drug perturbation effects.

MOTIVATION: Individualized drug response prediction is a fundamental part of personalized medicine for cancer. Great effort has been made to discover biomarkers or to develop machine learning methods for accurate drug response prediction in cancers. Incorporating prior knowledge of biological systems into these methods is a promising avenue to improve prediction performance. High-throughput cell line assays of drug-induced transcriptomic perturbation effects are a prior knowledge that has not been fully incorporated into a drug response prediction model yet. RESULTS: We introduce a unified probabilistic approach, Drug Response Variational Autoencoder (Dr.VAE), that simultaneously models both drug response in terms of viability and transcriptomic perturbations. Dr.VAE is a deep generative model based on variational autoencoders. Our experimental results showed Dr.VAE to do as well or outperform standard classification methods for 23 out of 26 tested Food and Drug Administration-approved drugs. In a series of ablation experiments we showed that the observed improvement of Dr.VAE can be credited to the incorporation of drug-induced perturbation effects with joint modeling of treatment sensitivity. AVAILABILITY AND IMPLEMENTATION: Processed data and software implementation using PyTorch (Paszke et al., 2017) are available at: https://github.com/rampasek/DrVAE. SUPPLEMENTARY INFORMATION: Supplementary data are available at Bioinformatics online.