Urinary proteins/peptides as early biomarkers for CKD Justyna Siwy, Hanover, Germany

Chairs: Angel Argiles, Montpellier, FranceGoce Spasovski, Skopje, F.Y.R. of Macedonia

Mrs. Justyna SiwyMosaiques Diagnostics GmbH

Hanover, Germany

Slide 1

First thank you very much for the invitation. It's my pleasure to present to you our data ofurinary proteins and peptides as early biomarkers for CKD.

Slide 2

First, I will give you a short introduction to urinary proteome analysis using capillaryelectrophoresis mass spectrometry. Then I will show you our data of discovery and validationof urinary biomarkers for CKD and also present here our recently obtained results ofassociation of urinary peptides with GFR and urinary albumin excretion. At the end, a shortsummary of the results.

Slide 3

First the introduction.

Slide 4

Why urine? In the early 17th century the physician obtained urine -- as a first and directinsight for proteinuria. Urine is especially interesting for biomarker discovery for the followingreasons. It can be obtained easily, non-invasively and in large quantities. Urinary peptidesare stable enough to perform a proteomic analysis for example, they are stable for about 6hours at room temperature and for 3 days at 4°C or they can be stored over many years at-20 °C. Furthermore, urinary peptides display the 'status' of the kidney.

Slide 5

So, urine includes thousands of peptides and proteins and in contrast to genome, which isunique and relatively stable, proteins change over time in response to different situations likenutrition, drugs, or age. So, that's why the peptides can display the status of the organs likethe kidney.

Slide 6

We analyse urinary peptides using capillary electrophoresis coupled to mass spectrometry.First, during the preparation we remove large proteins from the samples. Then because of thehigh complexity of the samples we first separate the peptides using capillary electrophoresisand then, we analyse them immediately after that with mass spectrometry. Our massspectrometer here has a high resolution and scan speed and analyses fast, robust,inexpensive and reproducible. All data are stored in the database. This allows the biomarkerdiscovery and comparison of the samples from database and diagnosis of the patient's. Oneanalysis results in detection of over 1000 different peptides in about 60 minutes per onesample. The detected peptides are characterized by the CE migration time, mass and signalintensity.

Slide 7

All CE MS datasets are stored in our database. At the moment human urinary proteomedatabase includes more than 20,000 datasets of patients with different conditions anddiseases.

Slide 8

For example, we already have more than 2,200 CE MS datasets of patients with differentrenal disorders.

Slide 9

Together with all the individual CE MS profiles in the database also the relevant demographicand clinical data are stored and the sequencing information for the peptides. This allows usthe selection of patients and differential proteomics profiling for the purpose of biomarkerdiscovery. Here we compare the proteomics profiles of, for example, controls and cases usingstatistical analysis and because of the high numbers of features in the datasets we use, theanalysis must be corrected for multiple testing. The defined peptides we combine to oneclassifier. In general high dimensional classifier like a support vector machine is used. Thisclassifier can be then used for the sample of patient classification.

Slide 10

So, this technology knows how and the database was used for the discovery and validationof urinary biomarkers for CKD.

Slide 11

First the definition of CKD specific peptides. Mosaiques and collaborators analysed samples of250 patients with different CKD like minimal change disease, EAG nephropathy, diabeticnephropathy and compared the data to datasets of 379 healthy controls. This comparisonresulted in the definition of 273 different biomarkers. These biomarkers were combined in aclassifier and the classifier includes different collagen fragments, different plasma proteins likeserum albumin or alpha-1-antitrypsin and some kidney specific proteins like uromodulin or

various secreted proteins.

Slide 12

In the next step we validated the CKD 273 classifier. First in the cohort of patients withdifferent CKD and then, also in respect to diabetic nephropathy we validated this model in acohort of patients with diabetes with and without diabetic nephropathy. In both cases theCKD 273 classifier resulted in an area under the curve of 0.96.

Slide 13

In the next study we used this model, this pattern for the identification of patients at a riskfor diabetic nephropathy. Here we analysed samples of type 1 and type 2 diabetic patients ina mean period of over 9 years. Some of the patients were normoalbuminuric at baseline andalso normoalbuminuric after follow-up we call them non-progressors. Some patientsdeveloped macroalbuminuria during the study period and we called them progressors.

Slide 14

On the next slide we see the results. On the left side the results for CKD 273 pattern areshown. Here we see that the progressors scored positive 4 years before onset ofmacroalbuminuria and also from here we observe a really good separation between theprogressors and non-progressors. In the case of albumin excretion rate, we observed a goodseparation under 2 years before onset of macroalbuminuria.

Slide 15

Here the same data analysed with ROC statistics. We used the samples at least 5 yearsbefore onset of macroalbuminuria and analysed the ROC statistics and this resulted in an AUCfor the CKD273 classifier of 0.93, which was significantly higher than the AUC of albuminexcretion rate of 0.86. Then to obtain more information about predictive value of CKD273pattern we selected from this cohort only normoalbuminuric samples at sampling day andanalysed them with the ROCS statistics. Here this resulted in an AUC for the CKD classifier of0.92 and for albumin of 0.67.

Slide 16

So, that was what the definition and validation of the proteomics pattern for CKD is, but tohave more information about the pathophysiology of the disease we also investigatedassociation of urinary peptides with clinical parameters such as GFR and urinary albuminexcretion. Here are our recently obtained results.

Slide 17

So, we used our database and expanded initial studies to a really large case scale cross-sectional cohort of 990 patients. To have deeper insight into the pathophysiology and todefine peptides which were specific for early or late stage of disease we divided the cohortinto the early stage with patients with eGFR above 45 ml/min and in the late stages withpatients with eGFR lower than 45 ml/min.

Slide 18

First, we analysed the correlation of urine albumin with eGFR. This resulted in a correlationcoefficient of -0.34. We did the same for our CKD273 classifier and this resulted in a higherand better correlation coefficient of -0.44.

Slide 19

In the next step we correlated all our urinary peptides with eGFR and this resulted in thedefinition of 194 different peptides significantly associated with eGFR. 123 of them werenegatively correlated and 71 positively correlated. I have listed on the right side thesepeptide fragments that showed a better correlation than urinary albumin. Here the bestnegative correlation showed peptides like beta-2 microglobulins, Apolipoprotein, alpha-1 anti-trypsin o serum albumin. The best positive correlation with eGFR showed different fragmentsof collagens. The positive correlation of collagens was better than the negative correlation ofblood derived proteins.

Slide 20

So, next we divided a cohort in the late and early stages and here we divided 151 peptides,which were significantly correlated with eGFR at the early stage of disease. 134 of them wereunique for the late stage. Here 43% originated from blood derived proteins like albumin,Apolipoproteins, alpha-1-anti -trypsin and 29% of them originated from different collagens.Unique peptides for late stage of disease were for example, these are only some examples,clustering and annexing basement membrane specific heparan sulfate proteoglycan coreprotein. At early stage of disease we could find 45 specific peptides or peptides correlated toeGFR. 27 were uniquely associated with eGFR. Here we have a higher number of differentfragments of collagen alpha-1(I) and 45% of these uniquely associated peptides were bloodderived proteins and interestingly here 67% of them were fragments of alpha-1-anti-trypsin.

Unique peptides are for example VIP36 or Peptidase inhibitor 16.

Slide 21

The peptides defined above and also the availability of the sequencing information allows usto give some hypothesis on the development or progression of CKD. The first observation isthe reduction in percentage of collagen fragments when progressing from early to late stageof disease, which suggests that extracellular matrix remodelling is the main process at theearly stage of disease. Furthermore, peptides detected as associated to the disease at thelate stage of disease suggests modification of basement membrane, fibrosis and repairprocesses. Peptides associated with inflammation or renin activation was observed at bothstages.

Slide 22

So, now I will summarise the results.

Slide 23

So, we could define a proteomics pattern that enabled the detection and progression of CKD.The biomarker panel could be validated in independent multicentre blinded case/controlcohorts. The panel and also some of the urinary peptides showed better correlation witheGFR compared to albumin excretion rate. Further peptides could be defined which give usinsight into the pathophysiology of CKD at early and late stage. The number of collagenderived peptides at early and late stage suggest a turnover of extracellular matrix at theearly stage of disease and the late stage peptides could describe repair and fibrosisprocesses and the modification of the basement membrane. /td>

Slide 24

So, now I would like to thank all of the Mosaiques team and also the Glasgow team and alsoall our collaborators who were involved in these two projects. Thank you very much.

Questions

Chairman: Many thanks for your nice presentation. Now, this presentation is open fordiscussion or comments. Welcome please? Thank you, Donadio from Pisa, Italy.

Question: Thank you for your very interesting presentation. I have some questions and somecomments. You used the pattern method that means we do not know what we're looking at,we do not identify by using the system of different patterns you don't know what you'remeasuring precisely, do you? So you identify single proteins and single peptides is that right?You presented some data on CKD273 pattern, what does CKD273 pattern mean? May I askyou this for my information?

Mrs. Siwy: Yes the CKD273 pattern is a combination of 273 different peptides which weresignificant for CKD.

Question: So, are they all measured singularly or do you have a pattern? So, do you identifysingle proteins and fragments?

Mrs. Siwy: Yes.

Question: By using a tandem mass spectrometry.

Mrs. Siwy: We didn't identify them by tandem MS but we analysed the sample with coupledCE MS.

Question: Thanks for this clarification I did not understand. The second thing I would like toask you we have some data on uremic proteins, some small proteins like beta cystatin andparticularly – protein the concentration of which increases starting from I don't know beta-2-microglobulin increases in urine starting lower than 30 ml/min of GFR while other proteins likebeta -- protein begin to increase in the urine at a very early stage of CKD. That meansalready at a GFR lower than 90 ml/min you can see a specific increase of beta – protein in theurine which could be used as an early marker. This is the topic of your talk, isn't it? To look forearly markers of urine as a specific indicator of GFR impairment right?

Mrs. Siwy: I didn't really understand the question.

Question: The aim of your study is to identify specific kidney disease by means of urinaryproteomic pattern to identify the kind of disease, which kind of disease, I don't know diabeticnephropathy and others right? The other topic is to identify early or late GFR impairmentright?

Chairman: I would suggest that afterwards you can talk with each other because we're on astrict programme. Is that ok? Many thanks for your comments. Another one?

Question: I just want to know if you've published this work somewhere.

Mrs. Siwy: Our results have been published yet. The Association of Urinary peptides to eGFRthese are recently obtained results and not yet published.

Chairman: Ok thank you very much. I would like now to invite professor Mischack fromGermany who is one of the pioneers in this field and has really done a lot in order to give.