11 april 2024: zie ook dit artikel: https://kanker-actueel.nl/vloeibare-biopsie-gericht-op-exosomen-tumorblaasjes-registreerde-97-procent-van-de-patienten-met-alvleesklierkanker-stadium-1-en-2-in-combinatie-met-de-biomarker-ca-19-9-van-gezonde-mensen.html

16 mei 2022: Bron: Nature, Communications Medicine volume 2, Article number: 29 (2022) 

Een nieuwe techniek in het opsporen van alvleesklierkanker in een vroeg stadium (Stadium I en II) blijkt bijzonder succesvol. Een bloedtest gebaseerd op een eiwit dat biomarkers meet in zogeheten extracellulaire blaasjes (EV's) ontdekte 95 procent van beginnende alvleeskliertumoren. Bij eierstokkanker en blaaskanker waren de percentages minder, respectievelijk 74 procent en 43 procent, maar ook deze vormen van kanker worden vaak pas ontdekt als het al uitgezaaid is. 

Alvleesklierkanker is een van de dodelijkste kankers. Het wordt zelden gediagnosticeerd voordat het zich begint te verspreiden en heeft een overlevingspercentage van minder dan 5% over een periode van vijf jaar. Aan de University of California San Diego School of Medicine hebben wetenschappers nu een test ontwikkeld die 95% van de vroege alvleesklierkankers in een onderzoek kon identificeren.

Het onderzoek, gepubliceerd in Nature Communications Medicine, legt uit hoe biomarkers in extracellulaire blaasjes – deeltjes die de communicatie tussen cellen reguleren – werden gebruikt om alvleesklierkanker, eierstokkanker en blaaskanker op te sporen in de stadia I en II.

"Alvleesklierkanker is bijzonder moeilijk om vroeg te detecteren, in een stadium waarin chirurgische resectie - operatie de enige genezende behandeling mogelijk is.", zo zegt Dr. Andrew Lowy, clinical director for Cancer Surgery at UC San Diego School of Medicine. De mediane overall overleving op 5 jaar is voor alvleesklierkanker de laagste van alle vormen van kanker. 

In de afgelopen decennia hebben kankeronderzoekers tientallen aan kanker gerelateerde biomarkers ontdekt die een rol spelen bij de groei en overleving van kanker. Deze ontdekkingen hebben geleid tot de ontwikkeling van effectieve medicijnen tegen kanker. De onderzoekers van UCSD vermoedden dat deze moleculen kunnen worden gebruikt om kanker vroeg te identificeren. Helaas zijn deze moleculen heel lastig te vinden.

Multi-kankerdetectietests (MCDT's) omvatten screening op bloedgebaseerde eiwitten of nucleïnezuren die indicatief zijn voor kanker. Verschillende MCD-tests zijn veelbelovend gebleken voor het opsporen van kanker in een laat stadium. Het opsporen van kanker in een vroeg stadium is echter nog steeds een uitdaging.
Tijdens de vroege stadia van kwaadaardige tumoren zijn er zeer weinig kankergerelateerde biomarkers en veel niet-gerelateerde moleculen. Bijgevolg zijn Multi-kankerdetectietests (MCDT's) niet gevoelig genoeg om vroege tekenen van kanker te herkennen. Met andere woorden, er is teveel achtergrondgeluid.

In deze studie besloten de onderzoekers dat ze zich maar op één ding zouden concentreren: extracellulaire blaasjes – deeltjes die de communicatie tussen cellen reguleren.

Extracellulaire blaasjes (EV's) kunnen worden gebruikt om te screenen op vroege kankersignalen.

Gezonde cellen en kankercellen stoten EV's uit in de bloedbaan. Van kanker afgeleide EV's bevatten vaak veel kankergerelateerde eiwitbiomarkers, die problemen kunnen veroorzaken. Wanneer deze eiwitten aan andere kankercellen worden afgeleverd, kunnen ze de resistentie tegen chemotherapeutica verhogen, de metastase versterken, de afgifte van voedingsstoffen verhogen en het immuunsysteem verstoren, is aangetoond in eerdere studies. Maar zeggen de onderzoekers: de inhoud van extracellulaire blaasjes heeft diagnostisch potentieel.

Sommige van alvleesklierkanker afgeleide EV's dragen bijvoorbeeld een eiwit genaamd macrofaagremmende factor (MIF), dat het immuunsysteem onderdrukt. De hoeveelheid MIF in het blaasje kan echter dienen als een voorspellende marker voor een leveruitzaaiing. Dat wil zeggen, hoe meer MIF er is, hoe groter de kans dat de kanker zich naar de lever zal verspreiden.
Lowy en zijn team zuiverden EV's uit het bloed van patiënten met vroege alvleesklierkanker, eierstokkanker en blaaskanker, en daarnaast van gezonde controle patiënten.
Vervolgens analyseerden ze de eiwitsamenstelling van de monsters. Door de monsters van kanker- en controlepatiënten te vergelijken, ontwikkelden de wetenschappers een machinaal lerend algoritme om een ​​kleine set EV-eiwitten te identificeren die kunnen worden gebruikt om alvleesklierkanker, eierstokkanker en blaaskanker in een vroeg stadium te detecteren.

Hun algoritme detecteerde met succes 95,5% van stadium I alvleesklierkanker, 73,1% van stadium I eierstokkanker en 43,8% van stadium I blaaskanker, wat de potentiële waarde van deze technologie voor vroege opsporing van kanker illustreert.

In onderstaande grafiek de gevoeligheid van de test bij de drie vormen van kanker.

figure 5


a Sensitivity for detecting either stage I (N = 22) or stage II (N = 25) pancreatic cancer. b Sensitivity for detecting either stage I (N = 39) or stage II (N = 5) ovarian cancer. c Sensitivity for detecting either stage I (N = 27) or stage II (N = 21) bladder cancer. All sensitivities represent values at >99% specificity for the held-out test sets. Error bars in all panels represent the two-sided 95% Wilson confidence intervals.

Het studierapport is gratis in te zien. Klik op de titel van het abstract: 

Early-stage multi-cancer detection using an extracellular vesicle protein-based blood test

Communications Medicine volume 2, Article number: 29 (2022) Cite this article

Abstract

Background

Detecting cancer at early stages significantly increases patient survival rates. Because lethal solid tumors often produce few symptoms before progressing to advanced, metastatic disease, diagnosis frequently occurs when surgical resection is no longer curative. One promising approach to detect early-stage, curable cancers uses biomarkers present in circulating extracellular vesicles (EVs). To explore the feasibility of this approach, we developed an EV-based blood biomarker classifier from EV protein profiles to detect stages I and II pancreatic, ovarian, and bladder cancer.

Methods

Utilizing an alternating current electrokinetics (ACE) platform to purify EVs from plasma, we use multi-marker EV-protein measurements to develop a machine learning algorithm that can discriminate cancer cases from controls. The ACE isolation method requires small sample volumes, and the streamlined process permits integration into high-throughput workflows.

Results

In this case-control pilot study, comparison of 139 pathologically confirmed stage I and II cancer cases representing pancreatic, ovarian, or bladder patients against 184 control subjects yields an area under the curve (AUC) of 0.95 (95% CI: 0.92 to 0.97), with sensitivity of 71.2% (95% CI: 63.2 to 78.1) at 99.5% (97.0 to 99.9) specificity. Sensitivity is similar at both early stages [stage I: 70.5% (60.2 to 79.0) and stage II: 72.5% (59.1 to 82.9)]. Detection of stage I cancer reaches 95.5% in pancreatic, 74.4% in ovarian (73.1% in Stage IA) and 43.8% in bladder cancer.

Conclusions

This work demonstrates that an EV-based, multi-cancer test has potential clinical value for early cancer detection and warrants future expanded studies involving prospective cohorts with multi-year follow-up.

Plain Language Summary

Finding cancer early can make treatment easier and improve odds of survival. However, many tumors go unnoticed until they have grown large enough to cause symptoms. While scans can detect tumors earlier, routine full-body imaging is impractical for population screening. New cancer detection methods being explored are based on observations that tumors release tiny particles called extracellular vesicles (EVs) into the bloodstream, containing proteins from the tumor. Here, we used a method to purify EVs from patients’ blood followed by a method to detect tumor proteins in the EVs. Our method quickly and accurately detected early-stage pancreatic, ovarian, or bladder cancer. With further testing, this method may provide a useful screening tool for clinicians to detect cancers at an earlier stage.

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Acknowledgements

We are grateful to Proteogenex for their support in collecting clinical samples for this study, Drs. Alex Aleshin and Varia Kirchner for their input during the development of this study, and Drs. Eric Varma and Irwin Jacobs for their continued support of Biological Dynamics and advice on this study and manuscript preparation. Zen Bio Incorporated (Research Triangle, NC) is acknowledged for help on the differential ultracentrifugation isolation. Figures 1 and 2 were created with the assistance from Biorender.com.

Author information

Affiliations

  1. Biological Dynamics, Inc, San Diego, CA, USA

    Juan Pablo Hinestrosa, Jean M. Lewis, Gregor Schroeder, Orlando Perrera, David Searson, Kiarash Rastegar, Jake R. Hughes, Victor Ortiz, Iryna Clark, Heath I. Balcer, Larry Arakelyan, Robert Turner, Paul R. Billings & Rajaram Krishnan

  2. WIN Consortium for Personalized Cancer Therapy, Villejuif, France

    Razelle Kurzrock

  3. Medical College of Wisconsin, Milwaukee, WI, USA

    Razelle Kurzrock

  4. University of Nebraska, Omaha, NE, USA

    Razelle Kurzrock

  5. The Translational Genomics Research Institute (TGen), Phoenix, AZ, USA

    Nicholas J. Schork

  6. City of Hope National Medical Center, Duarte, CA, USA

    Nicholas J. Schork

  7. University of California San Diego, Departments of Family Medicine and Public Health, San Diego, CA, USA

    Nicholas J. Schork

  8. University of Texas MD Anderson Cancer Center, Department of Urology, Houston, TX, USA

    Ashish M. Kamat

  9. University of California San Diego, Moores Cancer Center, San Diego, CA, USA

    Andrew M. Lowy, Ramez N. Eskander & Scott M. Lippman

  10. San Diego Cancer Research Institute, San Diego, CA, USA

    Mark J. Adler

Contributions

R. Kurzrock, R. Krishnan, J.P.H., J.M.L., R.T., P.B., M.A., I.C., H.I.B., and S.M.L. designed the study, wrote, and revised the manuscript. J.P.H. and L.A. supervised the experimental work. J.P.H., V.O., O.P., and H.I.B. collected and organized samples for the cohort. J.M.L., D.S., O.P., K.R., J.R.H., L.A., J.P.H., and S.M.L. performed or supervised experimental protocols. J.P.H., G.S., and N.J.S. analyzed the experimental data and developed the classification model. R. Kurzrock, A.M.K., R.E., A.M.L., and S.M.L. reviewed clinical data. All authors approved the final manuscript.

Corresponding authors

Correspondence to Nicholas J. Schork, Scott M. Lippman or Rajaram Krishnan.

Ethics declarations

Competing interests

R. Krishnan, J.P.H., R.T., I.C., H.I.B., V.O., J.M.L., O.P., L.A., J.R.H., G.S., and D.S. are employees of Biological Dynamics. R. Krishnan is a co-founder and board member of Biological Dynamics. R. Krishnan is an inventor on patents held by the University of California San Diego and Biological Dynamics that covers aspects of the Verita™ platform used in this manuscript. The terms of these arrangements are being managed by the University of California–San Diego in accordance with its conflict-of-interest policies. R. Kurzrock receives research funding from Boehringer Ingelheim, Debiopharm, Foundation Medicine, Genentech, Grifols, Guardant, Incyte, Konica Minolta, Medimmune, Merck Serono, Omniseq, Pfizer, Sequenom, Takeda, and TopAlliance; as well as consultant and/or speaker fees and/or advisory board for Actuate Therapeutics, Bicara Therapeutics, Inc., Biological Dynamics, Neomed, Pfizer, Roche, TD2/Volastra, Turning Point Therapeutics, X-Biotech; has an equity interest in CureMatch Inc. and ID by DNA; serves on the Board of CureMatch and CureMetrix, and is a co-founder of CureMatch. R.E. receives research funding to his institution from Clovis Oncology, AVITA, Merck and AstraZenca, as well as consultant and/or speaker fees and/or advisory board from AstraZeneca, GSK/Tesaro, Seagen, Myriad, Merck, Eisai as well as the GOG Foundation. A.K. consultant/advisory board member for Abbott Molecular, Arquer, ArTara, Asieris, Astra Zeneca, BioClin Therapeutics, Biological Dynamics, BMS, Cepheid, Cold Genesys, Eisai, Engene, Inc., Ferring, FerGene, Imagin, Janssen, MDxHealth, Medac, Merck, Pfizer, Photocure, ProTara, Roviant, Seattle Genetics, Sessen Bio, Theralase, TMC Innovation, US Biotest. AM Kamat has received grant/research support from Adolor, BMS, FKD Industries, Heat Biologics, Merck, Photocure, SWOG/NIH, SPORE, AIBCCR. A.M.K. has patents for CyPRIT (Cytokine Predictors of Response to Intravesical Therapy) jointly with UT MD Anderson Cancer Center is a paid consultant of Biological Dynamics. S.M.L. is a co-founder of io9. N.J.S., S.M.L., P.B., and M.A. are members of the Biological Dynamics scientific advisory board. S.M.L. received principal investigator support from the UC San Diego Moores Cancer Center, Specialized Cancer Center Support Grant NIH/NCI P30CA023100, and SU2C-AACR-DT-25-17 Pancreatic Cancer Interception Dream Team award. A.M.L. and R.E. declare no competing interests. P.B. holds equity in CytoBay, Synergenz, and LungLifeAI, all cancer diagnostic or risk assessment enterprises.

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alvleesklierkanker, bloedtest op eiwit, extracellulaire blaasjes, diagnose in vroeg stadium, eierstokkanker, blaaskanker


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