Abstract
Introduction
Patients with advanced, non-oncogene-driven NSCLC with high programmed death-ligand 1 (PD-L1) expression are eligible for treatment with immunotherapy. There is, however, an urgent medical need for biomarkers identifying cases that require additional combination with chemotherapy. We previously uncovered a myeloid-based 5-microRNA (5-miRNA) signature that identified responders to immunotherapy in PD-L1 unstratified patients; however, its potential utility in treatment guidance for patients with PD-L1 high tumors remained unclear.
Methods
We trained (n = 68) and validated (n = 56) a 5-miRNA multivariable Cox proportional hazards model predictive of overall survival on small RNA sequencing data of whole blood samples prospectively collected before the commencement of immunotherapy for stage IV NSCLC with PD-L1 tumor proportion score greater than or equal to 50%, treated with PD-1 inhibitor monotherapy (immunotherapy alone [IO]). Specificity was demonstrated in a control cohort treated with immunochemotherapy (ICT) (n = 31).
Results
The revised 5-miRNA risk score (miRisk) stratified IO-treated patients and identified a high-risk group with significantly shorter overall survival (hazard ratio = 5.24, 95% confidence interval: 2.17–12.66, p < 0.001). There was a significant interaction between the miRisk score and type of treatment (IO or ICT, p = 0.036), indicating that the miRisk score may serve as a predictive biomarker for immunotherapy response. Furthermore, the miRisk score could identify a group of high-risk patients who may benefit from treatment with ICT as opposed to IO (hazard ratio = 0.35, 95% confidence interval: 0.15–0.82, p = 0.018).
Conclusions
The miRisk score can distinguish a group of patients with PD-L1 high, stage IV NSCLC likely to benefit from adding chemotherapy to immunotherapy and may support treatment decisions as a blood-based complementary diagnostic.
Keywords: NSCLC, Immunotherapy, PD-L1, miRNAs, Biomarker
Introduction
Immunotherapy is revolutionizing the standard of care for numerous cancers; however, an important limitation is the lack of reliable efficacy biomarkers. In advanced-stage NSCLC expressing high levels of programmed death-ligand 1 (PD-L1) (PD-L1 tumor proportion score [TPS] ≥ 50%), both immunotherapy alone (IO) or in combination with chemotherapy (ICT) are recommended as treatment options in major international guidelines.1,2 Nevertheless, in clinical reality, these two options may not be equal, and there are patients unresponsive to IO monotherapy, who need additional chemotherapy. So far, no results from prospective trials can support decision-making on this clinically important issue and there is an unmet need for therapy selection biomarkers.
A positive response to immunotherapy is dependent both on local interactions between cancer and immune cells in the tumor microenvironment3 and on systemic immune processes that can be assessed in the periphery.4 Defining the latter has the potential to provide noninvasive therapy guidance, but no blood-based biomarkers have yet entered routine clinical use.
We previously reported the development of a circulating myeloid cell-derived 5 microRNA (miRNA) signature—5-miRNA risk score (miRisk)—to predict overall survival (OS) of patients with advanced NSCLC treated with IO in PD-L1 TPS unstratified patients.5 Here, we systematically evaluated the ability of a revised miRisk score to identify PD-L1 high patients who are likely to benefit from chemotherapy in addition to immunotherapy.
Materials and Methods
This study included a total of 155 prospectively recruited patients with stage IV NSCLC and PD-L1 TPS greater than or equal to 50% whose blood samples were collected before immunotherapy treatment (2017–2020). Patients with actionable mutations in EGFR, ALK, and ROS1 were identified by combined DNA and RNA next-generation sequencing6 and excluded from this study. All patients provided written informed consent. Samples were obtained from the Lungenbiobank Heidelberg and Biobank Nord within the German Center for Lung Research (DZL) according to the pertinent regulations after approval of the ethics committees at Heidelberg University (S-296/2016, S-089/2019) and LungenClinic Grosshansdorf (AZ 12-238, AZ 19-286). Sample processing and generation of small RNA expression profiles are described in detail in our previous work.5 All anonymized small RNA sequencing data have been deposited at the European Nucleotide Archive under accession number PRJEB50502. Survival analyses were performed in Python (3.8.8), using the packages scikit-survival (version 0.15.1)7 and Lifelines (version 0.26.0).8 miRisk low and high groups were defined based on the median risk score within the training cohort (low risk ≤ −0.0725 < high risk). Relevant clinical confounders to include in multivariable models were selected in consultation with a panel of thoracic oncologists (P.C., M.T., M.R.). Visualization was performed in GraphPad Prism (version 9.3.1, GraphPad Software).
Results
This study included 155 patients with stage IV NSCLC, divided into training (n = 68) and validation (n = 56) cohorts treated with IO, and split according to the time-point of sample collection, and a control cohort treated with ICT (n = 31) (Table 1). These three cohorts had similar clinicopathologic characteristics. Fitting a multivariable Cox proportional hazards model to the training cohort using the five previously identified miRNAs as input features led to a revised linear predictor risk score: miRisk = (ln(miR-2115-3p RPM + 1) × 2.190467) + (ln (miR-218-5p RPM + 1) × 0.303095) + (ln (miR-224-5p RPM + 1) × 0.598415) + (ln (miR-4676-3p RPM + 1) × 1.101122) + (ln (miR-6503-5p RPM + 1) × 0.958823).
Table 1.
Cohort Overview
| Training | Validation | Control | |
|---|---|---|---|
| Treatment |
Immunotherapy |
Immunotherapy |
Immunochemotherapy |
| Characteristics | (n = 68) | (n = 56) | (n = 31) |
| Site | |||
| Heidelberg | 68 | 41 | 31 |
| Grosshansdorf | — | 15 | — |
| Sex, n (%) | |||
| Male | 40 (58.8) | 38 (67.9) | 20 (64.5) |
| Female | 28 (41.2) | 18 (32.1) | 11 (35.5) |
| Age at enrollment, y | |||
| Mean ± SD | 68.0 ± 10.0 | 68.2 ± 8.8 | 62.5 ± 10.6 |
| Median (range) | 67.7 (38.9–86.7) | 69.1 (51.2–87.0) | 64.7 (37.6–78.6) |
| Histologic subtype, n (%) | |||
| Adenocarcinoma | 43 (63.2) | 34 (60.7) | 25 (80.6) |
| Squamous cell carcinoma | 18 (26.5) | 18 (32.1) | 3 (9.7) |
| Other | 7 (10.3) | 4 (7.1) | 3 (9.7) |
| ECOG performance status, n (%) | |||
| 0 | 23 (33.8) | 22 (39.) | 10 (32.3) |
| 1 | 42 (61.8) | 28 (50.0%) | 20 (64.5) |
| 2 | 3 (4.4) | 3 (5.4) | 1 (3.2) |
| NA | — | 3 (5.4) | — |
| Smoking status, n (%) | |||
| Never | 6 (8.8) | 1 (1.8) | 3 (9.7) |
| Former | 36 (52.9) | 37 (66.1) | 14 (45.2) |
| Current | 26 (38.2) | 18 (32.1) | 14 (45.2) |
| Therapy, n (%) | |||
| Nivolumab | 6 (8.8) | 4 (7.1) | — |
| Pembrolizumab | 62 (91.2) | 52 (92.9) | — |
| Platinum doublet + pembrolizumab | — | — | 31 (100) |
| Therapy line, n (%) | |||
| 1 | 46 (67.6) | 35 (62.5) | 27 (87.1) |
| 2 | 21 (30.9) | 15 (26.8) | 4 (12.9) |
| 3 | 1 (1.5) | 3 (5.4) | — |
| >3 | — | 3 (5.4) | — |
| PD-L1 TPS, % | |||
| Mean ± SD | 81.0 ± 12.8 | 79.9 ± 14.0 | 74.8 ± 15.0 |
| Median (range) | 80 (50–100) | 85 (50–100) | 70 (50–100) |
ECOG, Eastern Cooperative Oncology Group; NA, not applicable; PD-L1, programmed death-ligand 1; TPS, tumor proportion score.
We observed significantly longer OS in the miRisk-low patients in the training cohort (hazard ratio [HR] = 3.65, 95% confidence interval [CI]: 1.84–7.24, p < 0.001) (Supplementary Fig. 1). This finding was confirmed in the validation cohort (HR = 5.24, 95% CI: 2.17–12.66, p < 0.001) (Fig. 1A). In contrast, the miRisk score was not associated with OS in patients in the ICT control cohort (HR = 1.40, 95% CI: 0.21–9.28, p = 0.753) (Fig. 1B), consistent with previous reports that IO-specific biomarkers do not predict response to ICT.9,10
Figure 1.
OS of patients with NSCLC stratified by miRisk. (A, B) Comparison of OS between miRisk low and high groups in IO validation (n = 56) and the ICT control cohorts (n = 31). Significant differences in OS are observed in the validation but not the control cohort. (C, D) Comparison of OS between IO and ICT in miRisk-stratified cohorts. HR and 95% CIs were calculated using a univariable Cox regression analysis; p values were calculated using the log-rank test. All statistical analyses were two-sided. CI, confidence interval; HR, hazard ratio; ICT, immunochemotherapy; IO, immunotherapy alone; miRisk, 5-microRNA risk score; OS, overall survival.
To explore the utility of the miRisk score as a complementary diagnostic for IO versus ICT treatment decisions, we merged the validation and control cohorts to explore the interaction between the biomarker and type of treatment in terms of relationship with OS. In the miRisk-low patients, there was no difference in OS between those treated with IO or ICT (HR = 1.22, 95% CI: 0.12–12.25, p = 0.849) (Fig. 1C). In contrast, in miRisk-high patients, we observed significantly improved OS in patients receiving ICT compared with IO monotherapy (HR = 0.35, 95% CI: 0.15–0.82, p = 0.018) (Fig. 1D). The interaction between treatment and miRisk score in a multivariable Cox proportional hazards model was significant (HR = 1.62, 95% CI: 1.03–2.55, p = 0.036), suggesting the miRisk score is both prognostic and predictive for the efficacy of IO11 (Supplementary Table 1).
Finally, we used multivariable Cox proportional hazards models to investigate the performance of the miRisk score when controlling for other relevant clinicopathological covariates. This revealed that the miRisk score had a stronger association with OS in IO-treated patients (HR = 3.82, 95% CI: 1.29–11.30, p = 0.015) than PD-L1 TPS, histologic subtype, Eastern Cooperative Oncology Group performance status, and therapy line (Table 2).
Table 2.
Univariable and Multivariable Cox Regression Analysis of miRisk and Clinical Covariates
| Overall Survival |
Univariable Analysis | Multivariable Analysis | ||||
|---|---|---|---|---|---|---|
| Covariate | HR | 95% CI | p Value | HR | 95% CI | p Value |
| IO training cohort | ||||||
| ECOG performance status | 1.39 | 0.76–2.52 | 0.281 | 0.87 | 0.46–0.51 | 0.682 |
| Histology (nonadeno vs. adeno) | 1.09 | 0.55–2.17 | 0.806 | 1.40 | 0.69–1.04 | 0.354 |
| Therapy line | 0.75 | 0.38–1.49 | 0.409 | 0.79 | 0.38–0.49 | 0.532 |
| PD-L1 TPS | 0.99 | 0.97–1.01 | 0.433 | 0.96 | 0.93–0.01 | 0.006 |
| miRisk (high vs. low) | 3.84 | 1.86–7.95 | <0.001 | 7.41 | 2.95–2.93 | <0.001 |
| IO validation cohort | ||||||
| ECOG performance status | 3.53 | 1.55–8.05 | 0.003 | 3.32 | 1.24–2.18 | 0.017 |
| Histologic subtype (nonadeno vs. adeno) | 1.89 | 0.79–4.50 | 0.151 | 1.79 | 0.69–1.54 | 0.235 |
| Therapy line | 1.57 | 1.05–2.34 | 0.029 | 1.23 | 0.81–0.63 | 0.335 |
| PD-L1 TPS | 1.00 | 0.97–1.03 | 0.753 | 1.00 | 0.97–0.04 | 0.848 |
| miRisk (high vs. low) | 5.37 | 1.96–14.74 | 0.001 | 3.82 | 1.29–2.42 | 0.015 |
| ICT control cohort | ||||||
| ECOG performance status | 3.70 | 0.85–16.06 | 0.081 | 6.15 | 1.00–37.99 | 0.050 |
| Histology (nonadeno vs. adeno) | 1.10 | 0.12–9.88 | 0.935 | 1.68 | 0.16–17.36 | 0.665 |
| Therapy line | 0.80 | 0.09–6.75 | 0.838 | 4.00 | 0.18–90.51 | 0.384 |
| PD-L1 TPS | 1.00 | 0.94–1.05 | 0.906 | 1.00 | 0.94–1.07 | 0.925 |
| miRisk (high vs. low) | 1.41 | 0.17–11.91 | 0.754 | 1.17 | 0.13–10.82 | 0.889 |
Note: ECOG performance status, therapy line, and PD-L1 TPS were modeled as continuous variables. Histologic subtype and miRisk were modeled as categorical variables.
Adeno, adenocarcinoma; CI, confidence interval; ECOG, Eastern Cooperative Oncology Group; HR, hazard ratio; ICT, immunochemotherapy; IO, immunotherapy alone; miRisk, 5-microRNA risk score; PD-L1, programmed death-ligand 1; TPS, tumor proportion score.
Discussion
Currently, both IO and ICT are recommended as treatment options for patients with stage IV NSCLC and PD-L1 TPS greater than or equal to 50%. Nevertheless, the response rate to IO monotherapy is only 40%,12 and many apparent nonresponders may benefit from ICT, as the chemotherapy component can sensitize the tumor to concurrent immunotherapy.13 This additional therapeutic burden comes at the cost of increased frequency and severity of toxicity, with grade 3 to 4 adverse events noted in approximately 70% of patients.14,15 Because there is currently no reliable biomarker to predict response to therapy, the decision to treat with IO versus ICT is largely based on clinical judgment, considering factors such as general health status, number of metastatic sites, and disease aggressiveness. Still, the optimal therapy for a given patient often remains unclear.
There is great interest in the discovery of blood-based biomarkers that are predictive of immunotherapy response due to their ease of noninvasive collection and their potential to capture signal both from the peripheral immune system and material shed from the tumor itself.16 Efforts to measure soluble PD-L1 status17 and plasma tumor mutational burden18 were found to be promising; however, these are yet to be approved for clinical use. Immune cell RNA profiling and next-generation sequencing-based methods could enable improved prediction with additional integration of signal from multiple sources, such as peripheral effector cell counts.4,19 Encouraging results have been reported from the measurement of circulating miRNAs, known to be master regulators of gene expression, and implicated in multiple processes in immune regulation and cancer.20
The miRisk score exploits these opportunities as a multivariable model that measures the expression of predominantly myeloid-derived miRNAs with predicted interactions with the PD-(L)1 signaling pathway.5 This blood-based biomarker is found to have robust generalizable performance in a validation cohort for survival prediction after IO treatment (HR = 5.24, 95% CI: 2.17–12.66, p < 0.001) and utility as a complementary diagnostic for the decision to treat miRisk-high patients with ICT (HR = 0.35, 95% CI: 0.15–0.82, p = 0.018).
There are limitations to the current study. There was no randomization of treatment between the IO- and ICT-treated cohorts. Therefore, despite controlling for known confounders with multivariable analyses, it is impossible to rule out the influence of potential hidden confounders. We further acknowledge a lack of ethnic diversity in study participants. We aim to address these issues in an upcoming prospective clinical trial.
In summary, the therapeutic landscape in advanced NSCLC is rapidly developing, in large part due to the successes of immunotherapies. The only currently used biomarker, PD-L1 TPS, has several limitations, including poor predictive performance, the necessity for an invasive tissue biopsy, and the subsequent exposure to sampling bias because of tumor heterogeneity and different assay platforms.21 As a result, there is an unmet need for more accurate and noninvasive diagnostics to guide treatment decisions. The miRisk score represents an immune-focused biomarker that is specifically predictive of response to immunotherapy and could serve as the foundation for a complementary diagnostic to guide therapeutic decisions and thereby allow physicians to more accurately choose treating patients between with IO alone versus with ICT.
CRediT Authorship Contribution Statement
Timothy Rajakumar: Conceptualization, Investigation, Formal analysis, Writing—original draft, Writing—review and editing.
Rastislav Horos, Paul Kittner, Mustafa Kahraman, Tobias Sikosek: Investigation, Formal analysis, Writing—review and editing.
Franziska Hinkfoth, Kaja Tikk: Data curation, Writing—review and editing.
Nathaniel D. Mercaldo: Formal analysis, Writing—review and editing.
Albrecht Stenzinger, Klaus F. Rabe, Martin Reck, Michael Thomas: Data curation, Investigation, Formal analysis, Writing—review and editing.
Petros Christopoulos: Conceptualization, Data curation, Investigation, Formal analysis, Writing—original draft, Writing—review and editing.
Bruno R. Steinkraus: Conceptualization, Investigation, Formal analysis, Writing—original draft, Writing—review and editing.
Acknowledgments
The authors thank Stephanie Axenfeld, Urszula Jagos-Ihde, Claudia Genest, Marlen Szewczyk, Milena Schmidt, and all other contributing medical personnel at the LungenClinic in Grosshansdorf for their outstanding work with patient enrollment and data collection. The authors also thank Elena Gleim and the entire laboratory team at Hummingbird Diagnostics GmbH for their excellent technical assistance during the study conduct. The authors thank Bruce Johnson for feedback on the manuscript.
Footnotes
Disclosure: Prof. Christopoulos declares receiving research funding from AstraZeneca, Novartis, Roche, and Takeda and advisory board, lecture, or educational fees from AstraZeneca, Boehringer Ingelheim, Chugai, Kite, Novartis, Pfizer, Roche, and Takeda. Prof. Reck reports receiving honoraria for lectures and consultancy from AstraZeneca, Amgen, Bristol-Myers Squibb, Boehringer Ingelheim, Eli Lilly, Merck, Merck Sharp & Dohme, Mirati, Novartis, Sanofi, Pfizer, and Roche. Prof. Rabe reports receiving payments or honoraria from Boehringer Ingelheim, AstraZeneca, Novartis, Roche, Chiesi Pharmaceuticals, Regeneron, Sanofi, and Berlin Chemie outside of the submitted work. Prof. Thomas discloses receiving honoraria from AstraZeneca, Bristol-Myers Squibb, Boehringer Ingelheim, Celgene, Chugai, Eli Lilly, Merck Sharp & Dohme, Novartis, Pfizer, Roche, Takeda, Sanofi, Beigene, and GlaxoSmithKline and research funding from AstraZeneca, Bristol-Myers Squibb, Roche, and Takeda. Prof. Stenzinger reports having advisory board, or speaker’s bureau engagements with Aignostics, Amgen, AstraZeneca, Bayer, Bristol-Myers Squibb, Eli Lilly, Illumina, Incyte, Janssen, Merck Sharp & Dohme, Novartis, Pfizer, Roche, Seattle Genetics, Takeda, and Thermo Fisher and receiving grants from Bayer, Bristol-Myers Squibb, Chugai, and Incyte. Dr. Rajakumar, Dr. Horos, Mr. Kittner, Dr. Kahraman, Dr. Sikosek, Ms. Hinkfoth, Dr. Tikk and Dr. Steinkraus are employees of Hummingbird Diagnostics and hold company stock options. Dr. Rajakumar, Dr. Horos, Dr. Sikosek, and Dr. Steinkraus are inventors of patent applications related to response prediction for immunotherapy submitted by Hummingbird Diagnostics. Prof. Christopoulos and Prof. Reck serve on the clinical advisory board of Hummingbird Diagnostics. Dr. Mercaldo reports receiving consulting fees from Hummingbird Diagnostics.
Cite this article as: Rajakumar T, Horos R, Kittner P, et al. Brief report: a blood-based microRNA complementary diagnostic predicts immunotherapy efficacy in advanced-stage NSCLC with high programmed death-ligand 1 expression. JTO Clin Res Rep. 2022;3:100369.
Note: To access the supplementary material accompanying this article, visit the online version of the JTO Clinical and Research Reports at www.jtocrr.org and at https://doi.org/10.1016/j.jtocrr.2022.100369.
Supplementary Data
References
- 1.Planchard D., Popat S., Kerr K., et al. Metastatic non-small cell lung cancer: ESMO Clinical Practice Guidelines for diagnosis, treatment and follow-up. Ann Oncol. 2018;29(suppl 4):iv192–iv237. doi: 10.1093/annonc/mdy275. [DOI] [PubMed] [Google Scholar]
- 2.Hanna N.H., Robinson A.G., Temin S., et al. Therapy for stage IV non-small-cell lung cancer with driver alterations: ASCO and OH (CCO) joint guideline update. J Clin Oncol. 2021;39:1040–1091. doi: 10.1200/JCO.20.03570. [DOI] [PubMed] [Google Scholar]
- 3.Tumeh P.C., Harview C.L., Yearley J.H., et al. PD-1 blockade induces responses by inhibiting adaptive immune resistance. Nature. 2014;515:568–571. doi: 10.1038/nature13954. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 4.Spitzer M.H., Carmi Y., Reticker-Flynn N.E., et al. Systemic immunity is required for effective cancer immunotherapy. Cell. 2017;168:487–502.e15. doi: 10.1016/j.cell.2016.12.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 5.Rajakumar T., Horos R., Jehn J., et al. A blood-based miRNA signature with prognostic value for overall survival in advanced stage non-small cell lung cancer treated with immunotherapy. Npj Precis Oncol. 2022;6:19. doi: 10.1038/s41698-022-00262-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 6.Volckmar A., Leichsenring J., Kirchner M., et al. Combined targeted DNA and RNA sequencing of advanced NSCLC in routine molecular diagnostics: analysis of the first 3,000 Heidelberg cases. Int J Cancer. 2019;145:649–661. doi: 10.1002/ijc.32133. [DOI] [PubMed] [Google Scholar]
- 7.Pölsterl S. scikit-survival: a library for time-to-event analysis built on top of scikit-learn. J Mach Learn Res. 2020;21:1–6. [Google Scholar]
- 8.Davidson-Pilon C. Lifelines, survival analysis in Python (v0.27.0). Zenodo. https://zenodo.org/record/6359609 Accessed July 4, 2022.
- 9.Mountzios G., Samantas E., Senghas K., et al. Association of the advanced lung cancer inflammation index (ALI) with immune checkpoint inhibitor efficacy in patients with advanced non-small-cell lung cancer. ESMO Open. 2021;6 doi: 10.1016/j.esmoop.2021.100254. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 10.Paz-Ares L., Langer C.J., Novello S., et al. LBA80Pembrolizumab (pembro) plus platinum-based chemotherapy (chemo) for metastatic NSCLC: tissue TMB (tTMB) and outcomes in KEYNOTE-021. Ann Oncol. 2019;30(suppl 5):v917–v918. [Google Scholar]
- 11.Ballman K.V. Biomarker: predictive or prognostic? J Clin Oncol. 2015;33:3968–3971. doi: 10.1200/JCO.2015.63.3651. [DOI] [PubMed] [Google Scholar]
- 12.Man J., Millican J., Mulvey A., Gebski V., Hui R. Response rate and survival at key timepoints with PD-1 blockade vs chemotherapy in PD-L1 subgroups: meta-analysis of metastatic NSCLC trials. JNCI Cancer Spectr. 2021;5:pkab012. doi: 10.1093/jncics/pkab012. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 13.Pfirschke C., Engblom C., Rickelt S., et al. Immunogenic chemotherapy sensitizes tumors to checkpoint blockade therapy. Immunity. 2016;44:343–354. doi: 10.1016/j.immuni.2015.11.024. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 14.Gandhi L., Rodríguez-Abreu D., Gadgeel S., et al. Pembrolizumab plus chemotherapy in metastatic non–small-cell lung cancer. N Engl J Med. 2018;378:2078–2092. doi: 10.1056/NEJMoa1801005. [DOI] [PubMed] [Google Scholar]
- 15.Paz-Ares L., Luft A., Vicente D., et al. Pembrolizumab plus chemotherapy for squamous non–small-cell lung cancer. N Engl J Med. 2018;379:2040–2051. doi: 10.1056/NEJMoa1810865. [DOI] [PubMed] [Google Scholar]
- 16.Angeles A.K., Janke F., Bauer S., Christopoulos P., Riediger A.L., Sültmann H. Liquid biopsies beyond mutation calling: genomic and epigenomic features of cell-free DNA in cancer. Cancers. 2021;13:5615. doi: 10.3390/cancers13225615. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 17.Oh S.Y., Kim S., Keam B., Kim T.M., Kim D.W., Heo D.S. Soluble PD-L1 is a predictive and prognostic biomarker in advanced cancer patients who receive immune checkpoint blockade treatment. Sci Rep. 2021;11 doi: 10.1038/s41598-021-99311-y. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 18.Gandara D.R., Paul S.M., Kowanetz M., et al. Blood-based tumor mutational burden as a predictor of clinical benefit in non-small-cell lung cancer patients treated with atezolizumab. Nat Med. 2018;24:1441–1448. doi: 10.1038/s41591-018-0134-3. [DOI] [PubMed] [Google Scholar]
- 19.Christopoulos P., Schneider M., Bozorgmehr F., et al. Large cell neuroendocrine lung carcinoma induces peripheral T-cell repertoire alterations with predictive and prognostic significance. Lung Cancer. 2018;119:48–55. doi: 10.1016/j.lungcan.2018.03.002. [DOI] [PubMed] [Google Scholar]
- 20.Shukuya T., Ghai V., Amann J.M., et al. Circulating miRNAs and extracellular vesicle containing miRNAs as response biomarkers of anti PD-1/PD-L1 therapy in non-small-cell lung cancer. J Thorac Oncol. 2020;15:1773–1781. doi: 10.1016/j.jtho.2020.05.022. [DOI] [PMC free article] [PubMed] [Google Scholar]
- 21.Doroshow D.B., Bhalla S., Beasley M.B., et al. PD-L1 as a biomarker of response to immune-checkpoint inhibitors. Nat Rev Clin Oncol. 2021;18:345–362. doi: 10.1038/s41571-021-00473-5. [DOI] [PubMed] [Google Scholar]
Associated Data
This section collects any data citations, data availability statements, or supplementary materials included in this article.