biomarkers for treatment, relapse and curebiomarkers for ... · biomarkers for treatment, relapse...

MycobacteriaMycobacteria Research LaboratoriesResearch LaboratoriesColorado State UniversityColorado State University

Biomarkers for Treatment, Relapse and CureBiomarkers for Treatment, Relapse and Cure

7th National Conference on Laboratory Aspects of Tuberculosis

John T. Belisle, Ph.D.

Mycobacteria Research Laboratories Department of Microbiology, Immunology, and Pathology

Colorado State University

Pathogenesis ofPathogenesis of TB and DiagnosticsTB and Diagnostics


Pathogenesis of Pathogenesis of TB and DiagnosticsTB and DiagnosticsInhalation of M. tuberculosis

Host-Pathogen interactions

Continued growth of bacteria and granuloma, liquefaction of the granuloma

Bacteria cease to grow,lesion calcifies

Dissemination of theinfection, active disease (5%)Reactivation (5%)

Immune suppression

No disease, dormant infection (90%)

Prognostic MarkerPrognostic Marker

Differential DiagnosticDifferential Diagnostic

Pathogenesis ofPathogenesis of TB and DiagnosticsTB and Diagnostics


Pathogenesis of Pathogenesis of TB and DiagnosticsTB and DiagnosticsInhalation of M. tuberculosis

Host-Pathogen interactions

Continued growth of bacteria and granuloma, liquefaction of the granuloma

Bacteria cease to grow,lesion calcifies

Dissemination of theinfection, active disease (5%)Reactivation (5%)

Immune suppression

No disease, dormant infection (90%) •• Adult Adult vsvs Pediatric TuberculosisPediatric Tuberculosis

•• Advanced Advanced vsvs Early Pulmonary Early Pulmonary Tuberculosis Tuberculosis

•• HIV CoHIV Co--Infection Infection

•• Marker of protection Marker of protection

•• NonNon--Pulmonary TuberculosisPulmonary Tuberculosis•• Drug Resistance/Disease Drug Resistance/Disease

Resolution Resolution


Diagnostic Targets (Assays)Diagnostic Targets (Assays)

End Product Measurement Induced Product Measurement

Diagnostic Targets (Assays)Diagnostic Targets (Assays)

MicroscopyMicroscopyBacteriologyBacteriologyMicroscopyMicroscopyBacteriologyBacteriology

Skin Test/ DTHSkin Test/ DTHIn Vitro In Vitro T Cell AssayT Cell AssaySkin Test/ DTHSkin Test/ DTHIn Vitro In Vitro T Cell AssayT Cell Assay

Clinical SymptomsClinical SymptomsAntibody DetectionAntibody DetectionA ti D t tiA ti D t ti

Clinical SymptomsClinical SymptomsAntibody DetectionAntibody DetectionA ti D t tiA ti D t tiAntigen DetectionAntigen DetectionMolecular DetectionMolecular DetectionHost Immune ResponseHost Immune Response

Antigen DetectionAntigen DetectionMolecular DetectionMolecular DetectionHost Immune ResponseHost Immune ResponseHost Immune ResponseHost Immune ResponseHost Immune ResponseHost Immune Response

Diagnostic Targets (Products)


Diagnostic Targets (Products)Immunological

Antibody Based Protein-Protein

-Lipid -Carbohydrate

T cell basedT cell based-Protein-Lipids

Product Based (Antigen detection)-Whole Cells -Proteins-Lipids-Carbohydrates-Nucleic Acid S ll M l l-Small Molecule

Major Challenges for TB Biomarker Discovery


Major Challenges for TB Biomarker Discovery

1. Target multiple disease states vs single disease states

2. Target Prognostic vs Diagnostic

3 A Pl tf T t3. Assay Platform Target

4. What are the characteristics of the biomarkers?(Target Product Profile)(Target Product Profile)

5. Unbiased vs Biased (hypothesis driven) approach.

6. Variability in patient population

7. Variability in the bacterium (strain variability and variable y ( yphenotypes)

Immediate Needs for TB Biomarker Discovery

Mycobacteria Research LaboratoriesMycobacteria Research LaboratoriesColorado State UniversityColorado State University

Immediate Needs for TB Biomarker Discovery

One of the greatest needs: One of the greatest needs: Acceleration of anti-tuberculosis drug development and tools that allow for more efficient and cost effective clinical t iltrails.

Solutions:Biomarkers or biosignatures that:•Differentiate treatment failure and treatment success• Are useful 2 to 4 weeks into the treatment phaseAre useful 2 to 4 weeks into the treatment phase• Differentiate between durable cure and relapse

Biomarkers of Drug Treatment Biomarkers of Drug Treatment


Host Changes

Macro- Metabolic C ll T &

gg

Gene Expression

Macromolecules

Metabolic Processes

Cell Type & Function Tissue

Architecture

Active Disease Remission or Cure Chemotherapy

Gene Expression Macro- Metabolic Cell

Cell Viabilityp ess o Macro

moleculesMetabolic Processes

Cell Architecture

Pathogen ChangesPathogen Changes

Analysis of Analysis of MetabolitesMetabolites


Analysis of Analysis of MetabolitesMetabolites

Clinical Sample (Serum or Urine)

Processing Wet Bench

Computational Analyses

Analysis by LC-MS Validation

Computational Analyses Computational

Disease/Treatment Specific M t b li P fil

The vast majority of metabolites are found at 800 Daltons and below. Therefore the The vast majority of metabolites are found at 800 Daltons and below. Therefore the

Metabolic Profile

analysis platform was chosen to focus on metabolites of this class.analysis platform was chosen to focus on metabolites of this class.

Metabolomics and Biomarker: Rationale for the approach


Metabolomics and Biomarker: Rationale for the approach

Disease states are associated with changes in the biochemistry of• Disease states are associated with changes in the biochemistry of a system resulting in abnormal metabolite profiles

• Metabolites rapidly change in abundance with alterations of a biological system

• Metabolites can be used to monitor host or pathogen changes

M t b lit t i ll i i i l i l ti i t• Metabolites typically require minimal manipulation prior to analyses

• Metabolic flux provides a biosignature

Metabolites are relatively unexplored as biomarkers for treatment of bacterial diseases

Selecting the Appropriate Biological Specimens for TB Selecting the Appropriate Biological Specimens for TB


g pp p g pg pp p g p

SputumPositive Negative C l d f di i S i-Currently used for diagnosis - Sputum processing

-Most disease pulmonary - Poor for non-pulmonary-Easy to equate biomarker diseaseabundance to CFU

Blood/Serum

abundance to CFU

Positive Negative No chemical processing Invasive

Urine

-No chemical processing - Invasive -Standard platforms available - Protein rich and complex-Multiple disease states

UrinePositive Negative -Easy to obtain -Complex-Standard platforms available -May only be useful in specificp y y p-Processing relatively simple disease states

Urine Samples Obtained and Analyses Performed


Urine Samples Obtained and Analyses Performed

Date Sample Set Number #s

Time Points LC/MSAnalysis

DataAnalysis

April 2008

Micro Pilot* Project

20 D0, 2W, 2M, 6M Yes all time points

Yes all time points

April NAA2m* 14 D0, 2W, 1M, Yes all for Yes for D0, 2009 Set 1 1.5M, 2M, 4M D0, 2W, and

1M2W, and 1M

February2010

Stellenbosch 40 D0, 2W, 1M, 1 5M 6M

Yes all time points

Yes for D0, 2W 1M2010 1.5M, 6M points 2W, 1M, 1.5M, 6M

October2010

NAA2mSet 2

36 D0, 2W,1M, 1.5M, 2M, 3M,

No No

*All the samples were collected from adults (18 to 50 years), males and females,

4M, 7M, 8M, 9M

p ( y ), ,

cavitary tuberculosis (Chest x-ray and high bacterial burden), HIV positive and

negative in Kampala, Uganda. All patients treated with INH, RIF, PZA, EMB

Condition Tree Analysis of Molecular FeaturesCondition Tree Analysis of Molecular Features


yyD0 normalized data values greater or less than those for W2 or W4 by a factor of 5 fold.

Starting Mass List: Masses present in at least one condition with minimum 75 0%Molecular F t

Increased Abundance

Starting Mass List: Masses present in at least one condition with minimum 75.0%.Features

Decreased Abundance

D0 R d W2 Y ll

Features present in at least 75.0% of samples for one time point

bu da ce

D0 = Red W2 = YellowW4 = Green

time point.

Principal Component Analysis (PCA) Demonstrates Urine Principal Component Analysis (PCA) Demonstrates Urine Metabolites can Differentiate Stages of TB Treatment Metabolites can Differentiate Stages of TB Treatment


Metabolites can Differentiate Stages of TB Treatment Metabolites can Differentiate Stages of TB Treatment

MP NAA2m

Red = Day 0 (Day of Diagnosis) Blue = 2 week post treatment start

Brown = 4 or 8 week post treatment start

Stringent Conditions: - Molecular feature in at least 70% of samples from one group- Molecular feature must have at least a five fold change

between groups

Molecular Features Decreased in Abundance Following Treatment


TreatmentObserved Accurate Mass Calculated Formula Observed Accurate Mass Calculated Formula

126.1162 C7H14N2 234.1221 C11H14N4O2

129.0434 C5H7NO3 239.0781 C11H13NO5

131.057 C5H9NO3 241.1539 C10H19N5O2

Over 55 Over 55

135.0687 C5H11O4 245.098 C10H11N7O/C9H15N3O5

136.0395 C5H4N4O 248.1002 C9H16N2O6

137.0401 C7H7NO2 257.1014 C10H15N3O5

142.127 C6H14N4 258.147 C13H14N4O2

143.1314 C7H16N2O 263.1127 C10H13N7O2

149 0116 C7H3NO3 266 0519 C6H10N4O8 Over 55 Over 55 metabolites metabolites identified thatidentified thatdecrease in decrease in

b d b d

149.0116 C7H3NO3 266.0519 C6H10N4O8

159.126 C8H17NO2 268.0942 C14H20OS2

161.0692 C6H11NO4 272.1014 C12H12N6O2

161.0825 C10H11NO 282.0267 C6H10N4O7S

165.0428 C8H7NO3 282.1576 C14H22N2O4

165.0795 C9H11NO2 283.1282 C11H17N5O4abundance or abundance or disappear with disappear with antianti--tuberculosis tuberculosis

167.0225 C8H7O4 286.1284 C11H18N4O5

167.033 C6H5N3O3 293.1048 C10H19N3O5S

168.0286 C5H4N4O3 294.0599 C11H10N4O6

169.0854 C7H11N3O2 295.1037 C14H17NO6

170.1421 C9H18N2O 301.1524 C14H23NO6

treatment treatment 173.9949 C7H11N3O2 308.091 C16H12N4O3

174.0639 C6H10N2O4 327.1069 C18H17NO5

175.0485 C6H9NO5/C7H5N5O 338.1342 C18H17NO5

181.0753 C9H11NO3 345.0475 C16H11NO8

182.1054 C9H14N2O2 350.1511 C14H26N2O6S

202 143 C8H18N4O2 370 165 C14H22N6O6202.143 C8H18N4O2 370.165 C14H22N6O6

203.1269 C8H17N3O3 384.122 C14H20N6O5S

214.0094 C8H6O7 405.1746 C21H23N7S

218.1343 C20H20N2OS2 421.2046 C14H31N9O2S2

221.0729 C8H15NO4S 422.2133 C23H34O5S

Identified Molecular Features Decreased in Abundance Following Treatment


Following Treatment

# Identified biomarkers candidates Formula Mass Nature of change

1 Hydroxyproline C5H9NO3 131.058243 Down by W2 then increase slightly

2 N‐Acetyl‐L‐aspartic acid C6H9NO5 175.048065 Down by W2 then increase slightly

3 Dimethyl‐L‐arginine C8H18N4O2 202.142975 Down gradually

4 N‐Acetylasparagine C6H10N2O4 174.064056 Down by W2 and then steady

5 1 M th lhi tidi C H N O 169 085129 D d ll5 1‐Methylhistidine C7H11N3O2 169.085129 Down gradually

6 L‐Phenylalanine C9H11NO2 165.078979 Down by W2 then increase slightly

7 2,2,5,5‐Tetramethyl‐3‐ C9H18N2O 170.141913 Down by W2 and then steadypyrrolidinecarboxamide

8 Pyroglutamic acid C5H7NO3 129.042587 Down by W2 and then steady

9 Acetylcysteine C5H9NO3S 163.030319 Down gradually

10 Trigonelline C7H7NO2 137.047684 Down by W2 then increase slightly

11 S‐Adenosylhomocysteine C14H20N6O5S 384.121582 Down gradually

12 L‐Tyrosine C9H11NO3 181.073898 Down by W2 and then steady

13 Alpha‐aminoadipic acid C6H11NO4 161.068802 Down by W2 and then steady

14 Quinolinic acid C7H5NO4 167.021851 Down gradually

15 Hypoxanthine C5H4N4O 136.038513 Down by W2 then increase slightly

Validation Validation Urine Urine Samples Obtained and Analyses Performed Samples Obtained and Analyses Performed


Urine Urine Samples Obtained and Analyses Performed Samples Obtained and Analyses Performed

Date Sample Set Number #s

Time Points LC/MSAnalysis

DataAnalysis

April 2008

TBRU PilotProject

20 D0, 2W, 2M, 6M Yes all time points

Yes all time points

April NAA2m 14 D0, 2W, Yes all for Yes for D0, 2009 Set 1 1M, 4M D0, 2W, and

1M2W, and 1M

February2010

Stellenbosch 40 D0, 2W, 1M, 6M Yes all time points

Yes for D0, 2W and 6M2010 points 2W, and 6M

October2010

NAA2mSet 2

22 D0, 2W,1M, 1.5M, 2M, 3M, 4M, 7M, 8M, 9M

No No

, , ,

MPP analyses conducted with same parameters as for the Uganda samples: MPP analyses conducted with same parameters as for the Uganda samples: Present in at least70% of one condition, changes by at least 5 fold, and has a p value < 0.05

PCA Analysis of Metabolomic Data from Stellenbosch PCA Analysis of Metabolomic Data from Stellenbosch Urine Samples Urine Samples


Urine Samples Urine Samples

Validation of Signature with Stellenbosch Sample Set: 21 of 58


Validation of Signature with Stellenbosch Sample Set: 21 of 58

Observed Accurate Mass Calculated Formula 129.0434 C5H7NO3135.0687 C5H11O4137.0401 C7H7NO2159.126 C8H17NO2167.0225 C8H7O4169 0854 C7H11N3O2169.0854 C7H11N3O2170.1421 C9H18N2O174.0639 C6H10N2O4175.0481 C6H9NO5181.0753 C9H11NO3202.143 C8H18N4O2221.0729 C8H15NO4S234.1221 C11H14N4O2257.1014 C10H15N3O5263 1127 C10H13N7O2263.1127 C10H13N7O2268.0942 C14H20OS2272.1014 C12H12N6O2282.0267 C6H10N4O7S293.1048 C10H19N3O5S93 0 8 C 0 9 3O5S301.1524 C14H23NO6338.1342 C18H17NO5

Validated Molecular Features (9 of 16) that Decreased in Abundance Following Treatment


Following Treatment

# Identified biomarkers candidates Formula Mass Nature of change

1 Hydroxyproline C5H9NO3 131.058243 Down by W2 then increase slightly

2 N‐Acetyl‐L‐aspartic acid C6H9NO5 175.048065 Down by W2 then increase slightly

3 Dimethyl‐L‐arginine C8H18N4O2 202.142975 Down gradually

4 N‐Acetylasparagine C6H10N2O4 174.064056 Down by W2 and then steady

h lhi idi C O 69 08 29 d ll5 1‐Methylhistidine C7H11N3O2 169.085129 Down gradually

6 L‐Phenylalanine C9H11NO2 165.078979 Down by W2 then increase slightly

7 2,2,5,5‐Tetramethyl‐3‐ C9H18N2O 170.141913 Down by W2 and then steadypyrrolidinecarboxamide

8 Pyroglutamic acid C5H7NO3 129.042587 Down by W2 and then steady

9 Acetylcysteine C5H9NO3S 163.030319 Down gradually

10 Trigonelline C7H7NO2 137.047684 Down by W2 then increase slightly

11 S‐Adenosylhomocysteine C14H20N6O5S 384.121582 Down gradually

12 L‐Tyrosine C9H11NO3 181.073898 Down by W2 and then steady

13 Alpha‐aminoadipic acid C6H11NO4 161.068802 Down by W2 and then steady

14 Quinolinic acid C7H5NO4 167.021851 Down gradually

15 Hypoxanthine C5H4N4O 136.038513 Down by W2 then increase slightly

Summary of Urine Analysis for BiomarkersSummary of Urine Analysis for Biomarkers


y yy y

16 metabolites with known or predicted structures were found with decreased abundance in patients after start of drug treatmentdecreased abundance in patients after start of drug treatment.

38 metabolites with predicted chemical formulas, but unknown structure were found with decreased abundance in patients after start of drug treatment.

Drug metabolites serve as markers to confirm treatment adherence.

Continued definition of structures of products will be facilitated by increased availability of metabolite databases, in particular one for M. tuberculosis.

Current data and identified compounds may add to understanding of disease state. Several of the metabolites are associated with inflammation

Can Urine Metabolites ServesCan Urine Metabolites Serves as Diagnostic Markers?as Diagnostic Markers?


Region of Sample

C ll tiInfection

SitPersistent C h M l i F

Night S t C E i ti Ill

Final Culture R lt

gg

Collection Site Cough Malaise Fever Sweats Co‐Existing Illness ResultSalvador pulmonary yes yes yes yes none MTB complexSalvador pulmonary yes yes yes yes none MTB complexSalvador pulmonary yes yes yes yes diabetes MTB complexBARCELONA pulmonary yes yes yes no none MTB complexBARCELONA pulmonary yes yes yes no none MTB complexBARCELONA pulmonary yes no yes no hta MTB complexBARCELONA pulmonary yes yes yes yes none MTB complexVietnam Pulmonary yes yes yes yes n/a MTBcomplexVietnam Pulmonary yes yes yes yes n/a MTBcomplexVietnam Pulmonary yes yes yes no n/a MTBcomplexVietnam Pulmonary yes yes yes yes n/a MTBcomplexVietnam Pulmonary yes yes yes no n/a NegativeVietnam Pulmonary yes yes yes yes n/a NegativeSalvador pulmonary yes no no no none NegativeSalvador pulmonary yes no no no none NegativeWinnipeg pulmonary yes yes no no copd NegativeWinnipeg pulmonary yes yes no no influenza NegativeWinnipeg pulmonary yes yes no yes copd NegativeBARCELONA pulmonary yes no no no none Negativep y y gBARCELONA pulmonary yes no no no copd NegativeBARCELONA pulmonary yes yes no no copd/chronic pancreatitis NegativeBARCELONA pulmonary yes yes no no copd Negative

Can Urine Metabolites ServesCan Urine Metabolites Serves as Diagnostic Markers?as Diagnostic Markers?


gg

Bl e = Confirmed TBBlue = Confirmed TBRed = TB Suspect

Acknowledgments


Case Western Reserve UniversityDr. W. Henry Boom Dr John L Johnson

Colorado State UniversityDr. Seba MahapatraDr Mary Ann DeGroote

c o edg e ts

Dr. John L. Johnson

University of Arkansas Medical CenterDr. Kathy Eisenach

Dr. Mary Ann DeGroote Dr. Mark Sartain Dr. Suresh BhamidiDr. Paulina ZuritaB i C

Public Health Research InstituteDr. Gilla Kaplan

Brian Cranmer

University of ColoradoThomas Campbell

Joint Clinical Research Center, UgandaDr. Moses Joloba Phineas GittaGrace M an e

p

Denver Health Bill Burman

Grace Muzanye

Stellenbosch University Gerhard Walzl

UC-San Francisco Payam Nahid

Funding TBRU : NO1-AI70022FDA : U18-FD004038

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