Diagnosis of sepsis or SIRS using biomarker profiles

• US 7,645,573 B2
• Filed: 11/12/2003
• Issued: 01/12/2010
• Est. Priority Date: 11/12/2002
• Status: Active Grant

• Current Assignee: Becton, Dickinson & Co
• Original Assignee: Becton, Dickinson & Co
• Inventors: Tice, Gregory, Bachur, Nicholas Jr., Gentle, Thomas M. Jr., Rosenstein, Robert W., Goldenbaum, Paul E., Copertino, Donald, Ivey, Richard M., Towns, Michael L., Garrett, James, Shi, Song, Moore, Richard L.
• Primary Examiner(s): Kapushoc; Stephen
• Application Number:
  US10/704,666
• Publication Number:
  US 20040106142A1
• Time in Patent Office: 2,253 Days
• Field of Search: None
• US Class Current: 435/6.16
• CPC Class Codes: C12Q 1/6837 using probe arrays or probe chips C12Q1/6874 takes precedence
• CPC Class Codes: C12Q 1/686 Polymerase chain reaction [PCR]
• CPC Class Codes: C12Q 1/6883 for diseases caused by alterations of genetic material
• CPC Class Codes: C12Q 2600/158 Expression markers
• CPC Class Codes: G01N 2800/26 Infectious diseases, e.g. generalised sepsis
• CPC Class Codes: G01N 33/6893 related to diseases not provided for elsewhere

Abstract

The early prediction or diagnosis of sepsis advantageously allows for clinical intervention before the disease rapidly progresses beyond initial stages to the more severe stages, such as severe sepsis or septic shock, which are associated with high mortality. Early prediction or diagnosis is accomplished using a molecular diagnostics approach, involving comparing an individual'"'"'s profile of biomarker expression to profiles obtained from one or more control, or reference, populations, which may include a population who develops sepsis. Recognition of features in the individual'"'"'s biomarker profile that are characteristic of the onset of sepsis allows a clinician to diagnose the onset of sepsis from a bodily fluid isolated at the individual at a single point in time. The necessity of monitoring the patient over a period of time is, therefore, avoided, advantageously allowing clinical intervention before the onset of serious symptoms. Further, because the biomarker expression is assayed for its profile, identification of the particular biomarkers is unnecessary. The comparison of an individual'"'"'s biomarker profile to biomarker profiles of appropriate reference populations likewise can be used to diagnose SIRS in the individual.

Claims

1. A method of predicting an increased likelihood of developing sepsis in a human SIRS patient comprising:

determining abundances of Fc fragment of IgG, high affinity 1A receptor (FCGR1A) mRNA and extra-hepatic arginase (ARG2) mRNA in a first blood sample taken from the human SIRS patient; and

comparing the abundances of FCGR1A and ARG2 mRNAs in the first blood sample to abundances of FCGR1A and ARG2 mRNAs in blood samples taken 0-48 hours prior to sepsis in a SIRS-positive human patient population that progresses to sepsis,

wherein the abundances of FCGR1A and ARG2 mRNAs in the blood samples taken from the SIRS-positive human patient population that progresses to sepsis are statistically significantly greater than abundances of FCGR1A and ARG2 mRNAs in blood samples taken from a SIRS-positive human patient population that does not progress to sepsis, and

wherein an increased likelihood of developing sepsis is predicted in the SIRS patient when it is determined that the FCGR1A and ARG2 mRNA abundances in the first blood sample are statistically significantly similar to the abundances of FCGR1A and ARG2 mRNAs in the blood samples taken from the SIRS-positive human patient population that progresses to sepsis, and

wherein the SIRS-positive patient population that progresses to sepsis and the SIRS-positive human patient population that does not progress to sepsis each comprise at least 20 individuals.

2. The method of claim 1, wherein the comparison comprises applying a decision rule.

3. The method of claim 2, wherein applying the decision rule comprises using a data analysis algorithm.

4. The method of claim 3, wherein the data analysis algorithm comprises the use of a classification tree.

5. The method of claim 3, wherein the data analysis algorithm is nonparametric.

6. The method of claim 5, wherein the data analysis algorithm detects differences in a distribution of feature values.

7. The method of claim 6, wherein the nonparametric algorithm comprises using a Wilcoxon Signed Rank Test.

8. The method of claim 3, wherein the data analysis algorithm comprises using a multiple additive regression tree.

9. The method of claim 3, wherein the data analysis algorithm is a logistic regression.

10. The method of claim 3, wherein the data analysis algorithm comprises at least two input parameters.

11. The method of claim 10, wherein the data analysis algorithm comprises at least five input parameters.

12. The method of claim 11, wherein the data analysis algorithm comprises at least ten input parameters.

13. The method of claim 12, wherein the data analysis algorithm comprises at least twenty input parameters.

14. The method of claim 1, wherein the blood samples from the SIRS-positive patient population that progresses to sepsis are taken within about 48 hours prior to sepsis.

15. The method of claim 1, wherein the blood samples from the SIRS-positive patient population that progresses to sepsis are taken within about 24 hours prior to sepsis.

16. The method of claim 1, wherein the comparison predicts sepsis in the SIRS patient with an accuracy of at least 60%.

17. The method of claim 16, wherein the comparison predicts sepsis in the SIRS patient with an accuracy of at least 70%.

18. The method of claim 16, wherein the comparison predicts sepsis in the SIRS patient with a sensitivity of at least 60%.

19. The method of claim 1 further comprising comparing the abundances of FCGR1A and ARG2 mRNAs in the first blood sample to abundances of FCGR1A and ARG2 mRNAs in blood samples taken from a SIRS-positive patient population that does not progress to sepsis.

20. The method of claim 1 further comprising

determining abundances of one or more nucleic acids in the first blood sample, wherein the one or more nucleic acids are mRNAs selected from the group consisting of CD86 antigen (CD86) mRNA;

interleukin 18 receptor 1 (IL18R1) mRNA;

matrix metalloproteinase 9 (MMP9) mRNA;

CD4 antigen (CD4) mRNA;

interleukine 1, beta (IL1B) mRNA;

interleukin 8 (IL8) mRNA;

interleukin 4 (IL4) mRNA;

toll-like receptor 4 (TLR4) mRNA;

granulocyte-monocyte colony stimulating factor (CSF2) mRNA;

heat shock protein 70 (HSPA1A) mRNA;

lipopolysaccharide-binding protein (LBP) mRNA;

interleukin 1 receptor antagonist (IL1RN) mRNA;

phospholipase A2, group VII (platelet activating factor acetylhydrolase, plasma) (PLA2G7) mRNA;

integrin, alpha-M (ITGAM) mRNA;

tumor necrosis factor (ligand) superfamily, member 5 (TNFSF5) mRNA;

tumor necrosis factor (ligand) superfamily, member 13b (TNFSF13B) mRNA;

nitric oxide-synthase 3 (NOS3) mRNA;

interleukin 10 (IL10) mRNA;

chemokine (CC-motif) receptor 1 (CCR1) mRNA;

gamma interferon inducible protein 16 (IFI16) mRNA;

tumor necrosis factor (ligand) superfamily, member 6 (TNFSF6) mRNA;

tissue inhibitor of metalloproteinase 1 (TIMP1) mRNA;

interleukin 1 receptor, type 1 (IL1R1) mRNA;

transforming growth factor-beta1 (TGFB1) mRNA;

intercellular adhesion molecule 1 (ICAM1) mRNA;

CD8 antigen, alpha polypeptide (CD8A) mRNA;

toll-like receptor 2 (TLR2) mRNA;

chemokine (CC-motif) receptor 3 (CCR3) mRNA;

transforming growth factor-beta receptor 2 (TGFBR2) mRNA;

serine (or cysteine) protease inhibitor, clade B (ovalbumin), member 1 (SERPINE1) mRNA;

bactericidal/permeability-increasing protein (BPI) mRNA; and

surfactant, pulmonary-associated protein D (SFTPD) mRNA; and

comparing the abundances of the one or more nucleic acids in the first blood sample to abundances of the one or more nucleic acids in blood samples taken 0-48 hours prior to sepsis in a SIRS-positive human patient population that progresses to sepsis.

21. The method of claim 20, wherein the one or more nucleic acids comprise at least five mRNAs.

22. The method of claim 14, wherein the abundances of FCGR1A and ARG2 mRNAs in the first blood sample are determined by hybridizing FCGR1A and AGR2 mRNAs to an array comprising oligonucleotides or cDNAs.

23. The method of claim 1, wherein the abundances of FCGR1A and ARG2 mRNAs in the first blood sample are determined by reverse transcription of mRNAs in the first blood sample and amplification and detection of FCGR1A and ARG2 cDNAs.

24. The method of claim 1, wherein the compared abundances of FCGR1A and ARG2 mRNAs in the first blood sample and in the blood samples taken from the SIRS-positive patient population that progresses to sepsis are relative to abundances of an internal control nucleic acid.

25. The method of claim 24, wherein the internal control nucleic acid is 18S rRNA or β

-actin mRNA.

26. The method of claim 1, further comprising isolating the first blood sample from the human SIRS patient.

27. The method of claim 1, wherein said comparing step is performed on a computer, the method further comprising reporting a result of the comparing step to a clinician.