A blood-based miRNA signature with prognostic value for overall survival in advanced stage non-small cell lung cancer treated with immunotherapy
Abstract Immunotherapies have recently gained traction as highly effective therapies in a subset of late-stage cancers. Unfortunately, only a minority of patients experience the remarkable benefits of immunotherapies, whilst others fail to respond or even come to harm through immune related adverse events. For immunotherapies within the PD-1/PD-L1 inhibitor class, patient stratification is currently performed using tumor (tissue-based) PD-L1 expression. However, PD-L1 is an accurate predictor of response in only ∼30% of cases. There is pressing need for more accurate biomarkers for immunotherapy response prediction.We sought to identify peripheral blood biomarkers, predictive of response to immunotherapies against lung cancer, based on whole blood microRNA profiling. Using three well characterized cohorts consisting of a total of 334 stage IV NSCLC patients, we have defined a 5 microRNA risk score (miRisk) that is predictive of overall survival following immunotherapy in training and independent validation (HR 2.40, 95% CI 1.37-4.19; P < 0.01) cohorts. We have traced the signature to a myeloid origin and performed miRNA target prediction to make a direct mechanistic link to the PD-L1 signalling pathway and PD-L1 itself. The miRisk score offers a potential blood-based companion diagnostic for immunotherapy that outperforms tissue-based PD-L1 staining..
Medienart: |
Preprint |
---|
Erscheinungsjahr: |
2022 |
---|---|
Erschienen: |
2022 |
Enthalten in: |
bioRxiv.org - (2022) vom: 25. Mai Zur Gesamtaufnahme - year:2022 |
---|
Sprache: |
Englisch |
---|
Beteiligte Personen: |
Rajakumar, Timothy [VerfasserIn] |
---|
Links: |
---|
doi: |
10.1101/2021.10.31.21265722 |
---|
funding: |
|
---|---|
Förderinstitution / Projekttitel: |
|
PPN (Katalog-ID): |
XBI032922450 |
---|
LEADER | 01000caa a22002652 4500 | ||
---|---|---|---|
001 | XBI032922450 | ||
003 | DE-627 | ||
005 | 20230429094400.0 | ||
007 | cr uuu---uuuuu | ||
008 | 211102s2022 xx |||||o 00| ||eng c | ||
024 | 7 | |a 10.1101/2021.10.31.21265722 |2 doi | |
035 | |a (DE-627)XBI032922450 | ||
035 | |a (biorXiv)10.1101/2021.10.31.21265722 | ||
040 | |a DE-627 |b ger |c DE-627 |e rakwb | ||
041 | |a eng | ||
082 | 0 | |a 570 |q DE-84 | |
100 | 1 | |a Rajakumar, Timothy |e verfasserin |4 aut | |
245 | 1 | 0 | |a A blood-based miRNA signature with prognostic value for overall survival in advanced stage non-small cell lung cancer treated with immunotherapy |
264 | 1 | |c 2022 | |
336 | |a Text |b txt |2 rdacontent | ||
337 | |a Computermedien |b c |2 rdamedia | ||
338 | |a Online-Ressource |b cr |2 rdacarrier | ||
520 | |a Abstract Immunotherapies have recently gained traction as highly effective therapies in a subset of late-stage cancers. Unfortunately, only a minority of patients experience the remarkable benefits of immunotherapies, whilst others fail to respond or even come to harm through immune related adverse events. For immunotherapies within the PD-1/PD-L1 inhibitor class, patient stratification is currently performed using tumor (tissue-based) PD-L1 expression. However, PD-L1 is an accurate predictor of response in only ∼30% of cases. There is pressing need for more accurate biomarkers for immunotherapy response prediction.We sought to identify peripheral blood biomarkers, predictive of response to immunotherapies against lung cancer, based on whole blood microRNA profiling. Using three well characterized cohorts consisting of a total of 334 stage IV NSCLC patients, we have defined a 5 microRNA risk score (miRisk) that is predictive of overall survival following immunotherapy in training and independent validation (HR 2.40, 95% CI 1.37-4.19; P < 0.01) cohorts. We have traced the signature to a myeloid origin and performed miRNA target prediction to make a direct mechanistic link to the PD-L1 signalling pathway and PD-L1 itself. The miRisk score offers a potential blood-based companion diagnostic for immunotherapy that outperforms tissue-based PD-L1 staining. | ||
700 | 1 | |a Horos, Rastislav |e verfasserin |4 aut | |
700 | 1 | |a Jehn, Julia |e verfasserin |4 aut | |
700 | 1 | |a Schenz, Judith |e verfasserin |4 aut | |
700 | 1 | |a Muley, Thomas |e verfasserin |4 aut | |
700 | 1 | |a Pelea, Oana |e verfasserin |4 aut | |
700 | 1 | |a Hofmann, Sarah |e verfasserin |4 aut | |
700 | 1 | |a Kittner, Paul |e verfasserin |4 aut | |
700 | 1 | |a Kahraman, Mustafa |e verfasserin |4 aut | |
700 | 1 | |a Heuvelman, Marco |e verfasserin |4 aut | |
700 | 1 | |a Sikosek, Tobias |e verfasserin |4 aut | |
700 | 1 | |a Feufel, Jennifer |e verfasserin |4 aut | |
700 | 1 | |a Skottke, Jasmin |e verfasserin |4 aut | |
700 | 1 | |a Nötzel, Dennis |e verfasserin |4 aut | |
700 | 1 | |a Hinkfoth, Franziska |e verfasserin |4 aut | |
700 | 1 | |a Tikk, Kaja |e verfasserin |4 aut | |
700 | 1 | |a Daniel-Moreno, Alberto |e verfasserin |4 aut | |
700 | 1 | |a Ceiler, Jessika |e verfasserin |4 aut | |
700 | 1 | |a Mercaldo, Nathaniel |e verfasserin |4 aut | |
700 | 1 | |a Uhle, Florian |e verfasserin |4 aut | |
700 | 1 | |a Uhle, Sandra |e verfasserin |4 aut | |
700 | 1 | |a Weigand, Markus A |e verfasserin |4 aut | |
700 | 1 | |a Elshiaty, Mariam |e verfasserin |4 aut | |
700 | 1 | |a Lusky, Fabienne |e verfasserin |4 aut | |
700 | 1 | |a Schindler, Hannah |e verfasserin |4 aut | |
700 | 1 | |a Ferry, Quentin |e verfasserin |4 aut | |
700 | 1 | |a Sauka-Spengler, Tatjana |e verfasserin |4 aut | |
700 | 1 | |a Wu, Qianxin |e verfasserin |4 aut | |
700 | 1 | |a Rabe, Klaus F |e verfasserin |4 aut | |
700 | 1 | |a Reck, Martin |e verfasserin |4 aut | |
700 | 1 | |a Thomas, Michael |e verfasserin |4 aut | |
700 | 1 | |a Christopoulos, Petros |e verfasserin |4 aut | |
700 | 1 | |a Steinkraus, Bruno R |e verfasserin |4 aut | |
773 | 0 | 8 | |i Enthalten in |t bioRxiv.org |g (2022) vom: 25. Mai |
773 | 1 | 8 | |g year:2022 |g day:25 |g month:05 |
856 | 4 | 0 | |u https://doi.org/10.1038/s41698-022-00262-y |z lizenzpflichtig |3 Volltext |
856 | 4 | 0 | |u http://dx.doi.org/10.1101/2021.10.31.21265722 |z kostenfrei |3 Volltext |
912 | |a GBV_XBI | ||
912 | |a SSG-OLC-PHA | ||
951 | |a AR | ||
952 | |j 2022 |b 25 |c 05 | ||
953 | |2 045F |a 570 |