Sweeney, TE, et al. “Unsupervised analysis of transcriptomics in bacterial sepsis across multiple datasets reveals three robust clusters.” Critical Care Medicine. March 13, 2018.  
Three sepsis subtypes we termed as Inflammopathic, Adaptive, and Coagulopathic were developed and validated identified via advanced informatics methods. The Adaptive subtype is associated with a lower clinical severity and lower mortality rate, and the Coagulopathic subtype is associated with higher mortality and clinical coagulopathy. These findings may enable a precision medicine approach of matching novel immunomodulatory therapies with septic patients most likely to benefit.

Sweeney TE, et al. “A community approach to mortality prediction in sepsis via gene expression analysis.” Nature Communications. Feb 15, 2018.
The HostDx Sepsis severity gene set licensed from Stanford by Inflammatix, when combined with clinical severity scores (the current standard of care), demonstrated a substantial increase in prognostic power for 30-day mortality (i.e., an AUC increase of 9.8 percent, from 77 percent to 87 percent). This would translate to an ability to rule out approximately 20 percent more sepsis cases, compared to clinical severity scores alone. Such findings suggest this approach could help save substantial resources by avoiding unnecessary care.

Sweeney TE, et al. “Validation of the Sepsis MetaScore for diagnosis of neonatal sepsis.” J Ped Infect Dis Soc. Apr 13, 2017.
Neonates are at increased risk for developing sepsis, but this population often exhibits ambiguous clinical signs that complicate the diagnosis of infection. No biomarker has yet shown enough diagnostic accuracy to rule out sepsis at the time of clinical suspicion.

Sweeney TE, Khatri P. “Benchmarking sepsis gene expression diagnostics using public data.” Critical Care Medicine, Jan  2017.
In response to a need for better sepsis diagnostics, several new gene expression classifiers have been recently published, including the 11-gene “Sepsis MetaScore,” the “FAIM3-to-PLAC8” ratio, and the Septicyte Lab. We performed a systematic search for publicly available gene expression data in sepsis and tested each gene expression classifier in all included datasets. We also created a public repository of sepsis gene expression data to encourage their future reuse.

Sweeney TE, et al. “Mortality prediction in sepsis via gene expression analysis: a community approach.”  BioRx, Dec 19, 2016.
Improved risk stratification and prognosis in sepsis is a critical unmet need. Clinical severity scores and available assays such as blood lactate reflect global illness severity with suboptimal performance, and do not specifically reveal the underlying dysregulation of sepsis.

Sweeney TE, Wong HR, Khatri P. “Robust classification of bacterial and viral infections via integrated host gene expression diagnostics.” Science Translational Medicine, Jul 6 2016.
Sepsis, a severe inflammation caused by infection, is a common and deadly medical condition. Sepsis therapy combines supportive treatment with interventions directed at the underlying cause of the illness, especially antibiotics for bacterial infections.

Sweeney TE, Shidham A, Wong HR, Khatri P. “A Comprehensive time-course-based meta-analysis of sepsis and sterile inflammation reveals a robust discriminatory gene set.” Science Translational Medicine, May 13 2015.
Although several dozen studies of gene expression in sepsis have been published, distinguishing sepsis from a sterile systemic inflammatory response syndrome (SIRS) is still largely up to clinical suspicion. We hypothesized that a multicohort analysis of the publicly available sepsis gene expression data sets would yield a robust set of genes for distinguishing patients with sepsis from patients with sterile inflammation.

Cheng H, et al. “Integration of Next-Generation Sequencing, Viral Sequencing, and Host-Response Profiling for the Diagnosis of Acute Infections”  ID Week 2017, October 7, 2017.
Aspiring to improve the diagnosis of patients at risk for acute infections, 200 adult patients with systemic inflammatory response syndrome (SIRS) at the Stanford Emergency Department, were profiled with microbial NGS and host response diagnostics.  The authors concluded that he diagnosis of suspected infections may be enhanced by integrating host-response and microbial data alongside clinical judgment

Sweeney TE, Azad TD, Donoto M, et al, “The derivation and validation of three novel sepsis molecular subtypes to help guide precision therapies.”  Sepsis 2017, Sept 11-13, 2017.
We identified three sepsis subtypes (Inflammopathic, Adaptive, and Coagulopathic) associated with significant difference in mortality among sepsis patients. These sepsis subtypes may assist in the development of targeted therapies.

Sweeney TE, Khatri P, “Host-response biomarkers for diagnosing acute infections.” MHSRS, August 28-29, 2017.
Through rigorous statistical analysis and a custom informatics pipeline, we have derived a set of gene expression biomarkers that can diagnose sepsis in the setting of severe traumatic injury.

Sweeney TE, Mansur A, Hinz J, Khatri P, “Prospective validation of the Sepsis MetaScore for diagnosis of infections post traumatic injury.”  American Surgical Congress, Feb 7-9, 2017.
Severe traumatic injuries lead to immune dysregulation and a predisposal to hospital-acquired infections. While rapid diagnosis and treatment of infections improves outcomes, it is often difficult to clinically distinguish the signs and symptoms of infection from normal inflammation after major traumatic injury.

Sweeney TE, et al. “Generalizable biomarkers in critical care: towards precision medicine.” Critical Care Medicine, June 2017.
The sequencing of the human genome and the subsequent availability of inexpensive robust methods for “omics” profiling have lead to optimism of a new era of biomarkers that would allow for a “precision medical” approach to medical care.

Aditya, R, Sweeney TE, Khatri, “A Robust host-based gene expression diagnostic for malaria versus other infectious diseases.” 27th ECCMID, Vienna Austria, April 22-25, 2017.
The global incidence of malarial infection is approximately 200 million, with annual mortality of at least 438,000. While early diagnosis of malaria can lead to rapid treatment and cure, the similarity of symptoms to other infectious diseases and the long incubation time causes delayed and incorrect diagnoses.

Sweeney TE, et al. “Methods to increase reproducibility in differential gene expression via meta-analysis.” Nucleic Acid Research, Jan 9, 2017.
Findings from clinical and biological studies are often not reproducible when tested in independent cohorts. Due to the testing of a large number of hypotheses and relatively small sample sizes, results from whole-genome expression studies in particular are often not reproducible.

Lofgren S, et al. “Integrated, multicohort analysis of systemic scoliosis identifies robust transcriptional signature of disease severity.” Journal of Clinical Investigation, Dec. 22, 2016.
Systemic sclerosis (SSc) is a rare autoimmune disease with the highest case-fatality rate of all connective tissue diseases. Current efforts to determine patient response to a given treatment using the modified Rodnan skin score (mRSS) are complicated by interclinician variability, confounding, and the time required between sequential mRSS measurements to observe meaningful change. There is an unmet critical need for an objective metric of SSc disease severity.

Sweeney TE, et al. “Genome-wide expression for diagnosis of pulmonary tuberculosis: a multicohort analysis.” Lancet Respiratory Medicine, February 20, 2016.
Active pulmonary tuberculosis is difficult to diagnose and treatment response is difficult to effectively monitor. A WHO consensus statement has called for new non-sputum diagnostics. The aim of this study was to use an integrated multicohort analysis of samples from publically available datasets to derive a diagnostic gene set in the peripheral blood of patients with active tuberculosis.