13 oktober 2022: met dank aan Freddie die mij de vertaling van dit persbericht stuurde. Maar ik heb er nog meer informatie aan toegevoegd en vertaald.
Op ESMO 2022 werden de resultaten van de Pathfinderstudie gepresenteerd. Specificiteit voor detectie van kankersignalen was 99,5% [95% betrouwbaarheidsinterval (BI): 99,0% tot 99,8%]. Een studie waarin via de Galleri test via in bloed circulerend DNA ver van te voren beginnende kanker wordt opgespoord. Vaak nog voordat er zich symptomen of klachten voordoen. Daarmee werden twee keer zoveel vormen van kanker opgespoord dan wordt gevonden via klassieke screeningsmethoden en bevolkingsonderzoeken. Simpelweg omdat bij bv alvleesklierkanker en eierstokkanker pas laat klachten ontstaan en mensen dan pas naar een arts of oncoloog gaan. En dan zijn deze vormen van kanker vaak al uitgezaaid. Maar dat geldt eigenlijk voor de meeste vormen van kanker. En hoe eerder een vorm van kanker wordt ontdekt hoe groter de kans dat er een genezende behandeling mogelijk is.
Dit waren de belangrijkste punten die uit de Pathfinderstudie naar voren kwamen:
- Het toevoegen van Multi-Cancer Early Detection (MCED)-screening aan de standaard van zorgscreening heeft het aantal gedetecteerde kankers meer dan verdubbeld
- Specificiteit voor detectie van kankersignalen was 99,5% [95% betrouwbaarheidsinterval (BI): 99,0% tot 99,8%].
- 71% van de deelnemers met MCED-gedetecteerde kankers had kankertypes waarvoor geen routinematige screeningtests beschikbaar waren
- Ongeveer de helft van de MCED-gedetecteerde nieuwe kankers waren stadium I of II
- MCED-voorspelde oorsprong van kankersignalen had een nauwkeurigheid van 97,1% en maakte gerichte diagnostische evaluaties mogelijk
- MCED-screening werd geïmplementeerd bij volwassenen met een verhoogd risico op kanker zonder studiegerelateerde ernstige bijwerkingen
- Deelnemers rapporteerden hoge tevredenheid en lage negatieve psychologische impact met MCED-screening
De Pathfinderstudie studie maakt gebruik van de Galleri test, een test die ontworpen is om via één enkele bloedtest meerdere soorten kanker op te sporen. De bloedtest zoekt naar DNA-fragmenten van mogelijke tumoren in het bloed. Als er chemische veranderingen in deze stukjes DNA optreden, kan de bloedtest ze ontdekken en zo eventuele kanker opsporen, ook al heeft de man of vrouw nog geen symptomen ontwikkeld.
6.600 mensen ouder dan 50 jaar namen deel aan de studie. Zij kregen de bloedtest aangeboden, maar daarnaast ook de veelgebruikte screeningsmethoden als mammografie onderdeel van het bevolkkingsonderzoek voor borstkanker bv. of een colonoscopie onderdeel van het bevolkingsonderzoek naar darmkanker.
Uit de test kwam naar voren dat deze manier van screenen het mogelijk maakte 36 verschillende vormen van kanker bij 35 verschillende mensen te ontdekken. Veel diagnoses werden al gesteld nog voor er symptomen van de kanker werden waargenomen. "Als men de MCED-test toevoegt aan de standaardscreening, wordt het aantal ontdekte kankers meer dan verdubbeld in vergelijking met de standaardscreening alleen", aldus dr. Jeffrey Venstrom, hoofdonderzoeker van de Pathfinderstudie in een verklaring van GRAIL .
Dit was de studieopzet grafisch weergegeven:
Figure 1 Study design.
En zo waren de resultaten zoals in deze grafiek weergegeven.
Hier het abstract van de studie zoals die op ESMO 2022 is gepresenteerd. Klik op de titel voor het volledige studieverslag:
Figure 3MCED test performance for cancer signal detection (A) overall sensitivity and specificity, (B) sensitivity by cancer class, and (C) sensitivity by stage in 12 pre-specified cancers.
Uit de test kwam naar voren dat deze manier van screenen het mogelijk maakte 36 verschillende vormen van kanker bij 35 verschillende mensen te ontdekken. Veel diagnoses werden al gesteld nog voor er symptomen van de kanker werden waargenomen. "Als men de MCED-test toevoegt aan de standaardscreening, wordt het aantal ontdekte kankers meer dan verdubbeld in vergelijking met de standaardscreening alleen", aldus dr. Jeffrey Venstrom, hoofdonderzoeker van de Pathfinderstudie in een verklaring van GRAIL .
Dit was de studieopzet grafisch weergegeven:
Figure 1 Study design.
En zo waren de resultaten zoals in deze grafiek weergegeven.
Hier het abstract van de studie zoals die op ESMO 2022 is gepresenteerd. Klik op de titel voor het volledige studieverslag:
Clinical validation of a targeted methylation-based multi-cancer early detection test using an independent validation set
Highlights
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In a validation study, an MCED test identified a diversity of cancer signals with high specificity.
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The MCED test predicted the origin of the cancer signal with high accuracy across multiple cancer types.
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Results support the use of this MCED test on a population scale as a complement to existing single-cancer screening tests.
Background
A multi-cancer early detection (MCED) test used to complement existing screening could increase the number of cancers detected through population screening, potentially improving clinical outcomes. The Circulating Cell-free Genome Atlas study (CCGA; NCT02889978) was a prospective, case-controlled, observational study and demonstrated that a blood-based MCED test utilizing cell-free DNA (cfDNA) sequencing in combination with machine learning could detect cancer signals across multiple cancer types and predict cancer signal origin (CSO) with high accuracy. The objective of this third and final CCGA substudy was to validate an MCED test version further refined for use as a screening tool.
Patients and methods
This pre-specified substudy included 4077 participants in an independent validation set (cancer: n = 2823; non-cancer: n = 1254, non-cancer status confirmed at year-one follow-up). Specificity, sensitivity, and CSO prediction accuracy were measured.
Results
Specificity for cancer signal detection was 99.5% [95% confidence interval (CI): 99.0% to 99.8%]. Overall sensitivity for cancer signal detection was 51.5% (49.6% to 53.3%); sensitivity increased with stage [stage I: 16.8% (14.5% to 19.5%), stage II: 40.4% (36.8% to 44.1%), stage III: 77.0% (73.4% to 80.3%), stage IV: 90.1% (87.5% to 92.2%)]. Stage I-III sensitivity was 67.6% (64.4% to 70.6%) in 12 pre-specified cancers that account for approximately two-thirds of annual USA cancer deaths and was 40.7% (38.7% to 42.9%) in all cancers. Cancer signals were detected across >50 cancer types. Overall accuracy of CSO prediction in true positives was 88.7% (87.0% to 90.2%).
Conclusion
In this pre-specified, large-scale, clinical validation substudy, the MCED test demonstrated high specificity and accuracy of CSO prediction and detected cancer signals across a wide diversity of cancers. These results support the feasibility of this blood-based MCED test as a complement to existing single-cancer screening tests.
Clinical trial number
Figure 3MCED test performance for cancer signal detection (A) overall sensitivity and specificity, (B) sensitivity by cancer class, and (C) sensitivity by stage in 12 pre-specified cancers.
Conclusion
Taken together, these results demonstrate that this targeted methylation-based MCED test has high specificity that is generalizable across study populations, detects cancer signals across a broad range of cancer types with diverse biologic features (including those that currently lack screening tests), and provides accurate CSO prediction that may inform patient management. These results support that this blood-based MCED test may complement existing single-cancer screening tests and result in reduced cancer mortality.
Acknowledgements
The authors thank the following GRAIL, Inc. colleagues for critical input and help with manuscript development: Eric Fung, Earl Hubbell, and Ruhi Ubale. The following GRAIL, Inc. colleagues are acknowledged for significant contributions to the assay design: Farnaz Absalan, Leila Bazargan, Jen Berman, Jeremy Carter, Chenlu Hou, Byoungsok Jung, and Kristan Steffen. The following GRAIL, Inc. colleagues are acknowledged for biostatistical support: Gregory Alexander, Lane Eubank, Tony Wu, Lori (Quan) Zhang, and Nan Zhang. Authors also thank the members of the GRAIL, Inc. Data Sciences group: Hamed Amini, Siddhartha Bagaria, John Beausang, Joerg Bredno, Jackie Brooks, Robert Calef, Daniel Civello, Soleil Damangir, Konstantin Davydov, Zhao Dong, Alexander Fields, Peter Freese, Sam Gross, Earl Hubbell, Payam Khodabakhshi, Ting-Chin Liu, Ting Ma, Cyrus Maher, Collin Melton, Patriss Mordi, Josh Newman, Costanza Rojo, Neda Ronaghi, Onur Sakarya, Jan Schellenberger, Eric Scott, Avinash Shanmugam, Nima Shojajee, Pranav Singh, Archana Shenoy, Oliver Venn, Sharon Wootton, and Chris Yakym. The following GRAIL, Inc. colleagues are acknowledged for clinical development: Jason Carlson, Xiaoji Chen, Beth Chang, Kalyani Chilukuri, Clarice Grant-Coles, Jordan Frisbie, Neelima Gandepally, Nathan Gliner, David Jones, Smit Shah, Robert Shortt, Jessica Quan, Jennifer Woo, and Dave Nguyen.
Editorial and writing support was funded by GRAIL, Inc. and provided by Jennifer Hepker and Merrilee R. Johnstone from Prescott Medical Communications Group (Chicago, IL).
Funding
This work was supported by GRAIL, Inc., and GRAIL, Inc. was involved in the study’s design, conduct, data collection, analysis and interpretation, and reporting (no grant number).
Disclosure
EAK is a consultant for GRAIL, Inc.; DC is a consultant for Pfizer and Merck; AJ and KNK are full-time employees of GRAIL, Inc. and have stock in Illumina and GRAIL, Inc.; JG and NH are full-time employees of GRAIL Inc. and own stock in GRAIL, Inc. MVS has stock in McKesson Corporation, is a clinical adviser for GRAIL, Inc. and a director of Next Oncology and Nemucore Medical Innovations; CS has stock in GRAIL, Inc., Epic Biosciences and Apogen Biotech, grants from Pfizer and AstraZeneca, received honoraria or consultant fees from Roche Ventana, Celgene, Pfizer, Novartis, Genentech, and BMS, and is a co-founder of Achilles Therapeutics; MCL participated as an advisory board member for GRAIL, Inc. All other authors have declared no conflicts of interest.
Supplementary data
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Article Info
Publication History
Published online: June 24, 2021
Identification
Copyright
© 2021 The Authors. Published by Elsevier Ltd on behalf of European Society for Medical Oncology.
User License
Creative Commons Attribution – NonCommercial – NoDerivs (CC BY-NC-ND 4.0) |
ScienceDirect
Access this article on ScienceDirectFigures
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Figure 1Study design.
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Figure 2Participant disposition.
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Figure 3MCED test performance for cancer signal detection (A) overall sensitivity and specificity, (B) sensitivity by cancer class, and (C) sensitivity by stage in 12 pre-specified cancers.
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Figure 3MCED test performance for cancer signal detection (A) overall sensitivity and specificity, (B) sensitivity by cancer class, and (C) sensitivity by stage in 12 pre-specified cancers. -
Figure 4Accuracy of CSO prediction (confusion matrix).
Tables
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