Role and room for biomarkers in non-small cell lung cancer
Review Article

Role and room for biomarkers in non-small cell lung cancer

Glen J. Weiss

Beth Israel Deaconess Medical Center/Harvard Medical School, Boston, Massachusetts, USA

Correspondence to: Glen J. Weiss, MD, MBA. 330 Brookline Ave. ST8M 18A, Boston, MA 02215, USA. Email: drglenweiss@outlook.com.

Abstract: While molecular profiling of tumor tissue from patients with advanced cancer has been ordered by physicians to assist with treatment decision making, it is important to note that the cancer theranostics literature is rampant with many improperly validated biomarkers for prognostic and/or predictive features. This review outlines commonly assessed biomarkers in non-small cell lung cancer (NSCLC) associated with a clinical benefit. Overall, there remains room for improvements in this field and the undertaking to have validated and reproducible biomarkers associated with a clinical benefit is not a trivial undertaking.

Keywords: Advanced non-small cell lung cancer (advanced NSCLC); next-generation sequencing (NGS); somatic mutation; biomarkers; theranostics

Received: 09 April 2019; Accepted: 29 May 2019; Published: 06 June 2019.

doi: 10.21037/pcm.2019.05.05

For several years, molecular profiling of tumor tissue from patients with advanced cancer has been ordered by physicians to assist with treatment decision making. It is important to note that the cancer theranostics literature is rampant with many improperly validated biomarkers for prognostic and/or predictive features (1,2). The most commonly used non-small cell lung cancer (NSCLC) biomarkers for which there are validated studies conducted by more than one group using clinically-annotated samples from prospective clinical trials are listed in Table 1. These are mainly somatic mutations or gene rearrangements of known oncogene drivers in cancer.

Table 1

Commonly assessed biomarkers in NSCLC associated with a clinical benefit

Biomarker Feature associated with clinical benefit Linkage to approved drug class in NSCLC
EGFR Gene mutation EGFR inhibitor
ALK Gene rearrangement ALK inhibitor
ROS1 Gene rearrangement ROS1 inhibitor
BRAF Gene mutation BRAF + MEK inhibitor
NTRK Gene rearrangement NTRK inhibitor
PD-L1 Protein expression PD-1 or PD-L1 inhibitor
Tumor mutation burden Numerical cutoff PD-1 or PD-L1 inhibitor +/− CTLA-4 inhibitor

NSCLC, non-small cell lung cancer.

The association of biomarkers with clinically meaningful endpoints, defined as conferring an overall survival (OS) benefit, are even more limited. Endpoints that patients care about are OS or quality of life improvements, in contrast to those that they have been told are important, such as progression-free survival (PFS) or overall response rate (ORR) (3). While PFS and ORR are criteria considered by regulatory authorities for drug approval, they often do not correlate with an OS advantage (4,5). Furthermore, the reported substantial time saved for bringing new drugs to market utilizing surrogate endpoints compared with OS appears to be exaggerated (6). This is particularly salient when the likelihood of regulatory withdrawal of an approved anticancer drug for lack of efficacy is rare.

Use of next-generation sequencing (NGS) platforms, with their ability to measure hundreds of biomarkers, are increasingly touted as cost-effective relative to assessing a limited panel of more clinically meaningful biomarkers (7-10). However, the cost to patients and/or payor often exceeds thousands of dollars per NGS test, not including the cost of tissue/blood acquisition and the associated facility/provider fees that can be extraordinarily high. Determination of utility, particularly with the dearth of randomized clinical trials testing the superiority of NGS relative to routine testing has been challenging (8,11).

The Food & Drug Administration (FDA) has either approved or authorized two different molecular profiling platforms for tumor tissue testing in 2017 (12). A summary of commercially available NGS platforms both for tumor tissue and blood are listed in Table 2. Several papers have evaluated the concordance between NGS platforms. Few of these have focused on DNA level changes and contemporaneously collected matched sample pairs of tumor and/or blood (13,14). Longer time intervals between paired sample collection from the same patient (months or years) increases the probability of altered clonal changes and a different mutational profile (15,16).

Table 2

Sample of commercially-available NGS platforms

Platform Name Manufacturer Type of analyte Amount of material Number of genes Typical turnaround in calendar days
FoundationOne® Foundation Medicine (Cambridge, MA) Tumor tissue 11 slides, 25 mm2 Entire coding sequence of 315 genes, introns of 28 genes 8–14 days
MSK-IMPACT Memorial Sloan Kettering (New York, NY) Tumor tissue and matching whole blood 5–20 unstained 10-μm thick sections, blood volume not specified-minimum 50 ng DNA input 468 genes 20 days
Caris Intelligence Caris (Phoenix, AZ) Tumor tissue 25 unstained 4-ìm thick sections, 25 mm2 592 genes 14 days
PCDxTM Paradigm (Phoenix, AZ) Tumor tissue 1–2 4 μm slides, 3 mm2 234 genes 3–5 days
PlasmaSELECTTM-R64 Personal Genome Diagnostics (Baltimore, MD) Plasma 6–10 mL 64 genes 14–21 days
Circulogene Genomics Profiling Circulogene (Birmingham, AL) Plasma 0.5 mL 53 genes 5–7 days
FoundationOne® Liquid Foundation Medicine (Cambridge, MA) Whole blood 17 mL 70 genes <14 days

NGS, nest-generation sequencing.

There are many other theranostic biomarkers under evaluation that are considered exploratory at this point in time. These include non-coding RNA, exosomes, and microbiota. Other types of analytes that have been examined beyond tumor and blood include urine, bronchial lavage, stool, and saliva. Limitations of biomarker discovery and validation include the use convenient samples that are readily available to investigators at the time of research and were collected without the previous intent for biomarker discovery, use of invalid surrogate endpoints, insufficient sample size, low quality samples, and/or the inability to reproduce results in independent cohorts (2).

Two commonly cited predictive biomarkers for benefit with immunotherapy in NSCLC are PD-L1 immunohistochemistry (IHC) staining and tumor mutation burden (TMB). The KEYNOTE-024 (KN-024) trial of checkpoint inhibitor pembrolizumab vs. chemotherapy in first-line advanced NSCLC with PD-L1 IHC ≥50% met its primary endpoints for both overall survival (OS) and PFS (17). While there are several different IHC stains and criteria, one of the most prevalently used is the commercially available 22C3 pharmDx assay as a result of the KN-024 and subsequent supporting prospective trials involving its use in NSCLC. TMB has been proposed as a predictive biomarker based on data suggesting that the prevalence of somatic mutations leads to increased novel peptide epitopes that may result in enhanced tumor immunogenicity. Discordances between assays for mutation calling, reproducibility of threshold cut-offs, and turnaround time for results are factors that weigh in on TMB’s broad applicability as a predictive biomarker (18). To date, TMB has not been validated to predict OS benefit in NSCLC.

While there are many NSCLC theranostics reported as promising in the literature, an overwhelming majority do not pan out after rigorous validation. There remains room and it is a nontrivial endeavor for improvements in this field, either with linkage of existing drugs and previously identified biomarker targets or future yet uncharacterized biomarkers and undeveloped anti-cancer agents.

Acknowledgments

Funding: None.

Footnote

Provenance and Peer Review: This article was commissioned by the Guest Editors Alfredo Addeo and Giuseppe Banna for the series “Non-Small Cell Lung Cancer (NSCLC)” published in Precision Cancer Medicine. The article has undergone external peer review.

Conflicts of Interest: The author has completed the ICMJE uniform disclosure form (available at http://dx.doi.org/10.21037/pcm.2019.05.05). The series “Non-Small Cell Lung Cancer (NSCLC)” was commissioned by the editorial office without any funding or sponsorship. GJW reported receiving grants (to his institution) from GlaxoSmithKline, Pfizer, Incyte, Asana, Macrogenics, and Agenus; receiving consulting fees from and holding ownership interest (service provider units) in Circulogene; receiving consulting fees from Paradigm Diagnostics, Angiex, GLG Council, Guidepoint Global, Imaging Endpoints II, and IBEX Medical Analytics; receiving honoraria from Igynta, Pfizer, and IDEA Pharma; receiving travel and accommodation expenses from Cambridge Healthtech Institute, Tesaro, and GlaxoSmithKline; and holding a patent (PCT/ US2011/020612) related to small cell lung cancer. The author has no other conflicts of interest to declare.

Ethical Statement: The author is accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

  1. Poste G. Bring on the biomarkers. Nature 2011;469:156-7. [Crossref] [PubMed]
  2. Goossens N, Nakagawa S, Sun X, et al. Cancer biomarker discovery and validation. Transl Cancer Res 2015;4:256-69. [PubMed]
  3. Hwang TJ, Gyawali B. Association between progression-free survival and patients' quality of life in cancer clinical trials. Int J Cancer 2019;144:1746-51. [Crossref] [PubMed]
  4. Gyawali B, Hey SP, Kesselheim AS. A Comparison of Response Patterns for Progression-Free Survival and Overall Survival Following Treatment for Cancer With PD-1 Inhibitors: A Meta-analysis of Correlation and Differences in Effect Sizes. JAMA Netw Open 2018;1:e180416. [Crossref] [PubMed]
  5. Haslam A, Hey SP, Gill J, et al. A systematic review of trial-level meta-analyses measuring the strength of association between surrogate end-points and overall survival in oncology. Eur J Cancer 2019;106:196-211. [Crossref] [PubMed]
  6. Chen EY, Joshi SK, Tran A, et al. Estimation of Study Time Reduction Using Surrogate End Points Rather Than Overall Survival in Oncology Clinical Trials. JAMA Intern Med 2019; [Epub ahead of print]. [Crossref] [PubMed]
  7. Tan O, Shrestha R, Cunich M, et al. Application of next-generation sequencing to improve cancer management: A review of the clinical effectiveness and cost-effectiveness. Clin Genet 2018;93:533-44. [Crossref] [PubMed]
  8. Regier DA, Weymann D, Buchanan J, et al. Valuation of Health and Nonhealth Outcomes from Next-Generation Sequencing: Approaches, Challenges, and Solutions. Value Health 2018;21:1043-7. [Crossref] [PubMed]
  9. Doble B, John T, Thomas D, et al. Cost-effectiveness of precision medicine in the fourth-line treatment of metastatic lung adenocarcinoma: An early decision analytic model of multiplex targeted sequencing. Lung Cancer 2017;107:22-35. [Crossref] [PubMed]
  10. Marino P, Touzani R, Perrier L, et al. Cost of cancer diagnosis using next-generation sequencing targeted gene panels in routine practice: a nationwide French study. Eur J Hum Genet 2018;26:314-23. [Crossref] [PubMed]
  11. Prasad V. Why the US Centers for Medicare and Medicaid Services (CMS) should have required a randomized trial of Foundation Medicine (F1CDx) before paying for it. Ann Oncol 2018;29:298-300. [Crossref] [PubMed]
  12. Goldberg KB, Blumenthal GM, Pazdur R. The First Year of the Food and Drug Administration Oncology Center of Excellence: Landmark Approvals in a Dynamic Regulatory Environment. Cancer J 2018;24:131-5. [Crossref] [PubMed]
  13. Heeke S, Hofman V, Long-Mira E, et al. Use of the Ion PGM and the GeneReader NGS Systems in Daily Routine Practice for Advanced Lung Adenocarcinoma Patients: A Practical Point of View Reporting a Comparative Study and Assessment of 90 Patients. Cancers (Basel) 2018; [Crossref] [PubMed]
  14. Chen K, Zhang J, Guan T, et al. Comparison of plasma to tissue DNA mutations in surgical patients with non-small cell lung cancer. J Thorac Cardiovasc Surg 2017;154:1123-1131.e2. [Crossref] [PubMed]
  15. Toor OM, Ahmed Z, Bahaj W, et al. Correlation of Somatic Genomic Alterations Between Tissue Genomics and ctDNA Employing Next-Generation Sequencing: Analysis of Lung and Gastrointestinal Cancers. Mol Cancer Ther 2018;17:1123-32. [Crossref] [PubMed]
  16. Chae YK, Davis AA, Carneiro BA, et al. Concordance between genomic alterations assessed by next-generation sequencing in tumor tissue or circulating cell-free DNA. Oncotarget 2016;7:65364-73. [Crossref] [PubMed]
  17. Reck M, Rodríguez-Abreu D, Robinson AG, et al. Pembrolizumab versus Chemotherapy for PD-L1-Positive Non-Small-Cell Lung Cancer. N Engl J Med 2016;375:1823-33. [Crossref] [PubMed]
  18. Addeo A, Weiss GJ. Measuring tumor mutation burden in cell-free DNA: advantages and limits. Transl Lung Cancer Res 2019; [Epub ahead of print].
doi: 10.21037/pcm.2019.05.05
Cite this article as: Weiss GJ. Role and room for biomarkers in non-small cell lung cancer. Precis Cancer Med 2019;2:18.

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