key: cord-349396-a6zyioc1 authors: Tsurumi, Amy; Flaherty, Patrick J.; Que, Yok-Ai; Ryan, Colleen M.; Mendoza, April E.; Almpani, Marianna; Bandyopadhaya, Arunava; Ogura, Asako; Dhole, Yashoda V.; Goodfield, Laura F.; Tompkins, Ronald G.; Rahme, Laurence G. title: Multi-biomarker Prediction Models for Multiple Infection Episodes Following Blunt Trauma date: 2020-10-07 journal: iScience DOI: 10.1016/j.isci.2020.101659 sha: doc_id: 349396 cord_uid: a6zyioc1 Severe trauma predisposes patients to multiple independent infection episodes (MIIE), leading to augmented morbidity and mortality. We developed a method to identify increased MIIE risk before clinical signs appear, which is fundamentally different from existing approaches entailing infections’ detection after their establishment. Applying machine learning algorithms to genome-wide transcriptome data from 128 adult blunt trauma patients’ (42 MIIE cases and 85 non-cases) leukocytes collected ≤48 hours of injury and ≥3 days before any infection, we constructed a 15-transcript and a 26-transcript multi-biomarker panel model with the least absolute shrinkage and selection operator (LASSO) and Elastic Net, respectively, which accurately predicted MIIE (AUROC [95% CI]: 0.90 [0.84-0.96] and 0.92 [0.86-0.96]), and significantly outperformed clinical models. Gene Ontology and network analyses found various pathways to be relevant. External validation found our model to be generalizable. Our unique precision medicine approach can be applied to a wide range of patient populations and outcomes. Severe trauma predisposes patients to multiple independent infection episodes (MIIE), leading to augmented morbidity and mortality. We developed a method to identify increased MIIE risk before clinical signs appear, which is fundamentally different from existing approaches entailing infections' detection after their establishment. Applying machine learning algorithms to genomewide transcriptome data from 128 adult blunt trauma patients' (42 MIIE cases and 85 noncases) leukocytes collected ≤48 hours of injury and ≥3 days before any infection, we The high value of a tailored approaches in the care of patients is increasingly appreciated (Chaussabel, 2015; Parikh et al., 2016; Sweeney and Wong, 2016) . A method to expedite the timeline of threat detection to before infection happens could yield valuable time for early prophylactic and therapeutic interventions. Moreover, the ability to identify patients at high risk for repeated infections, or infection-related morbidity and mortality, might be considered an important measure to fairly allocate resources such as medication, personal protective equipment, or another high-value scarce intervention (Massachusetts, 2020; Medicine, 2013; Medicine, 2020) . Trauma is one of the leading causes of morbidity and mortality worldwide (Heron, 2018; Krug et al., 2000) . Severe trauma induces various immune-related responses acutely -it can trigger a state of immunosuppression (Islam et al., 2016; Ward, 2005) , prolonged inappropriate immune response (Heffernan et al., 2012; Huber-Lang et al., 2018) , leukocytosis (Paladino et al., 2010) , and the elevation of specific subpopulations of myeloid cells (Cuenca et al., 2011) . Among trauma patients, infections and infections-related complications contribute to substantial mortality and morbidity, and prolonged hospital stay, significantly adding to health care costs (Cole et al., 2014; Dutton et al., 2010; Glance et al., 2011; Hashmi et al., 2014) . Infections and infections-related outcomes vary across individuals, suggesting the importance of considering individual patients' underlying susceptibility and the degree of immunosuppression, or inappropriate immune response experienced. Methods to identify trauma patients with particularly increased risk of infection could be advantageous for ensuring timely and appropriate delivery of preventative measures (such as early immune-modulating nutrition, microbiome modulation, early mobilization, early removal of lines/tubes, taking all transmission-based precautions), improving surveillance and promoting antibiotic stewardship to limit the emergence of multi-drug resistance bacteria, reduce toxicity to patients and decrease health care costs. Previous studies have evaluated the use of injury severity scores, such as the Acute Physiology and Chronic Health Evaluation (APACHE) II J o u r n a l P r e -p r o o f (Knaus et al., 1985) Injury Severity Score (ISS) (Baker et al., 1974) and New Injury Severity Score (NISS) (Osler et al., 2010) as predictors of infection, in addition to their intended use to predict mortality (Cheadle et al., 1989; Jamulitrat et al., 2002) . Using genome-wide transcriptomic information from leukocytes provided at triage to assess the underlying susceptibility, well before the onset of infections, is expected to significantly improve the accuracy of identifying patients who are most at-risk of multiple independent infection episodes. A recent study described that employing a combination of predictors could be more effective than using a single predictor with strong statistical significance, further suggesting that multibiomarker panels could be highly effective (Lo et al., 2015) . Previous studies have utilized transcriptome data in the trauma setting to find transcripts that correlate with poor outcome (Desai et al., 2011) or sepsis (Sweeney et al., 2015) . The objective of this study is distinct, as we aim to develop a method to predict multiple infections prior to classic clinical signs of infection. And thus, our approach focuses on the prevention of infections, aiming to predict the outcome before its onset, using early blood samples. In a previous study among burn trauma patients, we developed a blood transcriptomic multi-biomarker panel for predicting multiple independent infection episodes (MIIE) outcome during the course of recovery (Yan et al., 2015) . We employed the least absolute shrinkage and selection operator (LASSO) machine learning algorithm to select probe sets that together (i.e., multi-transcriptome panel) resulted in highly accurate prediction. This model performed significantly better than those based on injury severity assessments at triage and demographic information (Yan et al., 2015) . Here, we employed a new approach of combining the use of two algorithms, LASSO and Elastic Net regression, to investigate MIIE outcome. LASSO and Elastic Net were used to reduce the complexity of regression models, in conjunction with crossvalidation to select the optimal parameter for reducing the number of predictors. These techniques are highly beneficial in cases such as transcriptome data where the number of potential predictors is extremely large, to overcome the problem of overfitting. LASSO J o u r n a l P r e -p r o o f regression reduces highly correlated predictors and selects a minimal panel of predictors, compared to Elastic Net that includes some correlated predictors. We investigated blunt trauma patients in the multi-center Inflammation and Host Response to Injury ("Glue Grant") cohort. This cohort enrolled a high number of patients, generated genome-wide transcriptome data, and collected data longitudinally, allowing us to effectively build a predictive model. Our approach employing unbiased analyses of genome-wide information in the identification of patients at increased risk for MIIE before clinical signs of infection may also be advantageous for providing new insights into the molecular pathways that characterize the patho-physiology underlying hypersusceptibility to infections. Baseline characteristics of the 128 blunt trauma patients included in the study ( Figure 1A , B) are presented in Table 1 while AIS (highest score in any body region) was comparable. As expected, orthopedic procedures were the most frequent surgical interventions that patients received overall (76.6%), followed by laparotomy (47.7%), vascular procedures (23.4%), thoracotomy (6.3%) and craniotomy (1.6%) ( Table 1) . Apart from the proportion of patients having undergone laparotomy, which was significantly higher among MIIE-cases J o u r n a l P r e -p r o o f compared to non-cases (41.2% vs. 60.5%, p=0.04), other procedures were similar between non-cases and MIIE-cases. There were five total patients who did not survive, and the cause of death was different for each (Supplementary Table S2 ). Mortality was similar between non-cases (3.5%) and MIIEcases (4.7%, p=1.00). Among survivors, MIIE-cases had significantly longer hospital stay than (Table 2) . Among all 128 patients, 36 (28.1%) experienced surgical site infections, compared to 80 (62.5%) who experienced other nosocomial infections (Table 2 ). Comparing specific subtypes of J o u r n a l P r e -p r o o f nosocomial infections, pneumonia (39.1% overall) was highest, followed by urinary tract infection (18.8%), blood infection (17.2%), pseudomembranous colitis (3.9%), catheter-related bloodstream infection (3.9%), empyema (2.3%), and other unspecified infections (5.5%). When comparing the incidence of various microorganisms among non-cases with one infection episode versus MIIE-cases, relatively higher proportion was found for MIIE-cases specifically for Gram positives of Staphylococcus aureus (18.2% vs. 39.5%), Enterococcus species (11.4% vs. 25.6%), Coagulase negative staphylococci (2.3% vs. 16.3%), and Streptococcus pneumoniae and viridans (2.3% vs. 4.7% for both). The incidence of the Gram positive, Clostridium species was the same for non-cases and MIIE-cases (both 7.0%) ( Table 2 ). For Gram negative bacteria, the incidences of the following microorganism were higher for MIIE-cases compared to non-cases: Enterobacter species (11.4% vs. 37.2%), Acinetobacter species (9.1% vs. 30.2%), Pseudomonas aeruginosa (11.4% vs. 16.3%), Haemophilus influenza (4.5% vs. 14.0%), Bacteroides species (0% vs. 9.3%), Klebsiella pneumoniae (2.3% vs. 7.0%), Neisseria (0% vs. 7.0%), Proteus (2.3% vs. 4.7%), Serratia marcescens (2.3% vs. 4.7%), and Gram negative, not otherwise specified (NOS) (2.3% vs. 11.6%). The incidence was higher among non-cases than MIIE-cases for Escherichia coli (11.4% vs. 4.7%), and Stenotrophomonas (4.6% vs. 0%). Fungi incidences were higher among MIIE-cases compared to non-cases: Candida species (9.1% vs. 11.6%) and unspecified fungi (0% vs. 2.3%). The median time to the first day of detection of different microorganisms ranged widely ( Figure 2 ). The Gram positives, Streptococcus pneumoniae, and Streptococcus viridans were found first (at median day 4 and 5, respectively), followed by various Gram negatives and the Gram positive, Clostridium species, between median day 7 and 9.5. Microorganisms that appeared relatively later (day 11 to 12) include Candida species, Enterobacter species, and J o u r n a l P r e -p r o o f Serratia marcescens, Stenotrophomonas, Enterococcus, Coagulase negative staphylococci and Staphylococcus aureus. We identified 137 probe sets showing at least 1.5-fold up-or down-regulated difference in expression levels between non-cases and MIIE-cases ( Figure 3A ), and performed Gene Ontology (GO) analyses to find enriched biological processes and KEGG pathway terms (Supplementary Table S3 ). As expect, terms relevant to various immune response pathways were among the top fold enrichment. They included interleukin-4 secretion, regulation of interferon-gamma secretion, cytolysis, regulation of natural killer cell-mediated cytotoxicity, viral genome replication, cellular defense response, adaptive immune response, humoral immune response, T cell co-stimulation, regulation of immune response, T cell receptor signaling pathway, response to tumor necrosis factor, response to virus, immune response and innate immune response. Other biological processes terms with high fold enrichment were signaling pathways with relevance to cell proliferation and differentiation, including regulation of p38 MAPK kinase, calcium-mediated signaling, regulation of fat cell differentiation, organ regeneration, MAP kinase activity, and phosphatidylinositol 3-kinase (PI3K) signaling. Similarly, KEGG pathway terms with high fold enrichment included those relevant to immune response, including natural killer cell-mediated cytotoxicity, malaria, hematopoietic cell lineage, and T cell receptor signaling pathway. Additionally, regulation of cancer, which may have overlapping signaling pathways with infections-related terms, and related to tissue regeneration, also appeared among the enriched terms. Although according to the FDR-adjusted p-values, statistical significance in enrichment was detected only for a small number of terms, the fold enrichment ranking consistently points to the relevance of immune response terms, as expected. To identify a multi-biomarker panel that collectively predicts the outcome of MIIE, we analyzed the 137 differentially-regulated probe sets by employing a machine learning pipeline that we previously developed and successfully used in our previous study among burn patients (Yan et al., 2015) . In the current study, we further added to our analyses pipeline by utilizing a combination of LASSO and Elastic Net regression methods. The LASSO regression that reduces redundancy in predictor selection would allow for a narrow selection of a minimal biomarker panel, which is expected to be more practical. Elastic Net regression that includes correlated predictors would allow for a more comprehensive discovery of additional probe sets that are potentially biologically relevant. With LASSO, 15 probe sets were selected, mostly relevant to immune functions and signaling cascades for cellular proliferation and differentiation We assessed the molecular network connection among the 26 probe sets that were selected by Elastic Net ( Figure 3B ). The major nodes with the most extensive edges that were central to the connections consisted of major signaling pathway components that are key regulators of inflammation, mitogentic response, and tissue regeneration. These notable nodes included tumor necrosis factor (TNF), transforming growth factor beta-1 (TGFβ-1), and various chemokine (C-X-C motif) ligand (CXCL) members and chemokine (C-C motif) ligand (CCL) members. TNF and TGFβ-1 are major cytokines that can act both synergistically or antagonistically to each other, depending on the cell context. Further substantiating the involvement of these factors, we found p38 mitogen-activated protein kinase (MAPK), which can act downstream of both TNF-α and TGFβ-1 pathways. The other major nodes, extracellular signal-regulated kinases 1/2 (ERK1/2), and mothers against decapentaplegic homolog 3 (SMAD3) are also key downstream components of the TGFβ pathway. A key downstream transcriptional regulator for the canonical Wnt pathway, β-catenin (CTNNB1), another major pathway known to cross-talk with TGFβ, was also found as a major node in this network. Table S8 ). For the various injury severity scores (APACHEII, ISS, NISS), the sensitivity, specificity, PPV, and NPV were generally lower compared to the multi-biomarker panel models (Supplementary Table S8 Our study shows that employing novel prognostic models based on early blood transcriptome profiling following severe trauma is an effective method for identifying patients who are particularly at high risk for MIIE and thus, hypersusceptible to infections. That the transcriptome information provides much better prediction than injury severity information, argues for the importance of considering each patient's underlying susceptibility and of elucidating relevant molecular mechanisms. The comprehensive dataset we used had genomewide transcriptome and clinical data collected longitudinally from a large number of patients, providing the opportunity to assess early susceptibility. Notably, our results suggest that by J o u r n a l P r e -p r o o f measuring the biomarkers among our panel at admission, patients at increased risk for MIIE could be identified before any clinical signs of infection appear. The biomarker panel models in our study had particularly high specificity and NPV measures, while also exerting good sensitivity and PPV. Moreover, when applied to an external validation cohort, it still performed decently. On the other hand, none of the injury severity scores used often at triage in the trauma setting were effective in predicting the MIIE. The objective of the study was to provide proof-of-concept results for developing a method to gain additional insights into patients' course of recovery from a simple blood draw at admission. Further prospective studies that entail blood draws at admission and measuring the biomarkers we describe here to compare with the severity scores and physiological measures taken normally, would provide additional confirmation of the notion that transcriptome data has the potential to improve outcome predictions in the clinical setting. This study focused on the outcome of MIIE and the potential prevention of infections, however it is conceivable that the biomarker discovery method described here can be applied to develop prediction methods for other outcomes. A personalized medicine approach and rapid identification of patients with high risk of specific outcomes based on a simple blood draw at admission, is expected to improve surveillance, facilitate decision-making and adequate resource allocation, and improve prevention and management before outcomes occur. Our findings could potentially facilitate clinical decision-making by effectively discriminating between those who are expected to develop multiple infections and those who are not. A proposed approach could be that patients who are found not to be hypersusceptible to MIIE could continue to receive the currently established standard of care, whereas those who are identified to be at high risk could benefit from increased surveillance and additional preventative measures to be taken early. Additional interventions for the high risk group may include increased surveillance for early mobilization and removal of lines/tubes, coating IV lines and urine catheters with antimicrobials and/or antibiotics, immunomodulatory nutrition therapies (Aghaeepour et al., 2017; Lorenz et al., 2015) , and microbiome alterations (Harris et al., 2017; Tosh and McDonald, 2012) . Such additional measures would incur unnecessary costs if implemented in all trauma patients, however, could be cost-effective when used in this targeted set of patients. Efforts aimed at increased prevention have the potential to contribute to alleviating the current antibiotic resistance crisis, toxicity of antibiotics, and the imposed burden on healthcare costs. It is also conceivable that accurate outcome prediction and risk stratification methodologies, such as one we describe here, could be valuable amid crisis situations that result in severe hospital overload with critically ill patients and scarcity of medical resources. The ability to identify patients at low risk for specific morbidity and mortality could aid in informed prioritization of resource allocation to patients with better potential for recovery and survival (Massachusetts, 2020; Medicine, 2013; Medicine, 2020) . Having applied both LASSO and Elastic Net regression methods allowed us to construct a highly predictive model from a minimal set of predictors, meanwhile also more comprehensively assess underlying biological mechanisms by allowing additional transcripts to be included. The LASSO approach selects a stringent set of predictors with less redundancy, which is advantageous in the clinical setting, where a device requiring less measurements is more practical and easier to implement. The Elastic Net approach that allows for correlated predictors to be selected found additional transcripts for a more comprehensive discovery of biological mechanisms. The probe sets selected consisted of transcripts with GO terms relevant to infections, as expected, and signaling pertinent to oncogenesis and cancer progression. In our study, HGF was the transcript showing the highest upregulation among MIIE patient blood and in both the 15 probe set and 26 probe set panels. HGF and Met expression levels have been suggested as a putative biomarker for monitoring infections, as it is wellestablished that the HGF-Met signaling pathway deregulation promotes the growth and invasion by various pathogens (Imamura and Matsumoto, 2017) . Another upregulated transcript, ADORA3, has also been implicated in the clinical setting, and agonists have been developed and shown to induce anti-inflammatory effects by altering the Wnt and NF-κB pathways. As such, the agonists are considered for purposes of treating cancers, and inflammatory diseases such as rheumatoid arthritis and psoriasis (Fishman et al., 2012) . NEAT1 is a non-coding RNA that is shown to colocalize with MALAT1, a long non-coding RNA often associated with metastatic cancer, at many genomic sites to transcriptionally regulate target genes (West et al., 2014) . CD96 is highly expressed in T and NK cells and well-established to be a regulator of immune responses during infection and cancer (Georgiev et al., 2018) . Elastic Net selected two probe sets corresponding to MME, providing further support for its importance in MIIE outcome. Studies on its molecular mechanisms and clinical use of inhibitors to its protein product, Neprilysin has been conducted widely, including in Alzheimer's, heart failures, hypertension, and renal diseases (Riddell and Vader, 2017) . Our study suggests that its potential role in immunity among patients warrants further investigation. KLRK1 and KLRF1, both killer cell lectin-like receptor subfamily members, were found by Elastic Net, providing evidence of their relevance in infections in the blunt trauma setting. These receptors are abundant on NK cells, and it is well-established that they play crucial roles in innate immunity (Barten et al., 2001) . These previous findings provide additional confidence in the relevance of our methodology. The pathway analysis found key signaling pathway components among the central nodes having extensive edges, including the major cytokines, TNF, TGFβ-1, CCLs, and CXCLs, as well as key signaling components, p38 MAPK, ERK1/2, SMAD3, and CTNNB1. These components represent the chief signaling pathways that regulate inflammation, mitogenic response, and tissue regeneration, which are also often dysregulated in cancer. Notably, our results suggest that the TNF, TGFβ-1, and Wnt signaling pathways, which are known to also cross-talk with one another through downstream cascades, may be important central pathways that explain the interconnection between the prognostic biomarkers identified. These results may suggest that these signaling pathways may represent new host J o u r n a l P r e -p r o o f immunomodulatory targets that warrant future mechanistic studies. Follow-up studies in model organisms and controlled studies would aid in establishing whether the genes identified in this study drive susceptibility, and in uncovering further mechanistic insights. It is noteworthy that when comparing the current biomarker panels in the blunt trauma setting with that from our previous study among burn patients (Yan et al., 2015) , we observed that none of the transcripts in the panels were shared. These differences may indicate that increased risk depends on the interaction of the type of trauma with each patient's underlying susceptibility to MIIE. Such observation may suggest the need for developing different multibiomarker panels catered to different types of trauma. Our study describes methods towards the development of precision medicine tools and offers the possibility of analyses also for outcomes other than multiple infections. The failure of drug trials targeting sepsis (Marshall, 2014; Mitka, 2011) highlights the importance of further studies elucidating the underlying molecular mechanisms and components of heterogeneity in susceptibility to infection and infection-related morbidity within a population. It is conceivable that measuring our biomarker panel to triage patients according to susceptibility to multiple infections will strategically guide prophylactic patient management and help reduce the incidence of infections to limit sepsis (Boomer et al., 2011; Chaussabel, 2015; Parikh et al., 2016) . Moreover, the analyses process we describe in this study can potentially also be applied to towards biomarker development for sepsis outcome. This study provides for the first time, prediction models for hypersusceptibility to infections, which is highly relevant for critically injured trauma patients, using a machine learning approach. A concern in general for prediction model building is that models may overfit to a specific dataset, making them less generalizable. However, using the multi-biomarker panel dataset was very small in sample size. Despite these differences, our multi-biomarker panels still conferred prediction, providing additional assurance in the validity of our results and evidence for the generalizability of our model. Additional large prospective studies would more rigorously test the validity and generalizability of the multi-biomarker panel identified in this study. Nevertheless, this study provides the first step towards the idea of developing novel approaches for predicting outcomes from blood transcriptome information at admissions. The value of early MIIE identification, prior to any clinical sign of infection, could be an indispensable tool in other types of trauma and to a wide range of clinical settings. Uncovering biomarkers of increased susceptibility to infections may open new avenues for novel therapeutic targets, as well as contribute to standardizing populations in clinical trials. Although predictive algorithms cannot eliminate medical uncertainty, our analysis method is expected to be widely applicable to other susceptible populations, such as those with diabetes or cardiac disease, the frail elderly population, those treated with immunosuppressive medication, as well as others. The described methodology of multi-biomarker panel development has the potential to be applied to outcomes and clinical contexts other than MIIE and trauma, providing additional value. This study entailed a secondary analysis, with external validation using a small dataset. Additional external datasets with larger sample sizes, and moreover, a large prospective study would provide additional concrete evidence for the validity and utility of our biomarker panel. J o u r n a l P r e -p r o o f Further information and requests for resources should be directed to and will be fulfilled by the Lead Contact, Laurence G. Rahme (rahme@molbio.mgh.harvard.edu). This study did not generate new unique reagents Dataset requests should be made to the Glue Grant Consortium, due to human study IRB restrictions. The code for the analyses is available from the lead contact upon request. 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