Next-generation technologies in predictive molecular pathology of lung cancers

Introduction

Nowadays overall incidence of lung cancers in the world is 22.4 cases per 100,000, with a mortality of 18.0 per 100,000, the highest worldwide (Figure 1) (1-3).

Figure 1 Incidence and mortality rate of lung cancer worldwide (https://gco.iarc.fr/today) (1-3). ASR, age-standardised rate; GLOBOCAN, Global Cancer Observatory; IARC, International Agency for Research on Cancer.

The principle of targeted therapy consists of the identification of well-defined molecules that can be used as targets for drug treatment, or as molecular markers that play a key role in tumor progression and/or survival. The number of target therapies in non-squamous non-small cell lung cancer (NSCLC) has notably increased since 2013, after that the U.S. Food and Drug Administration (FDA) approved targeted therapy for EGFR/ALK mutated lung adenocarcinomas (4).

A number of predictive biomarkers have to be tested in advanced NSCLC, like EGFR, ALK, ROS1, BRAF, RET, NTRK, HER2, and MET (5,6). In the half of 2021, the first targeted therapy (sotorasib) for patients with NSCLC and KRAS p.Gly12Cys (p.G12C) mutation has been approved by the FDA (7). According to the 2020 European Society for Medical Oncology (ESMO) guidelines, all patients with advanced adenocarcinoma of the lung should be tested for the above-reported markers (8,9) (Table 1). Even if single-gene biomarker testing is still diffused in clinical practice, it is clear that multi-gene approach analyses would be more cost-effective (10,11).

The development of these advanced techniques led to the concept of “personalized oncology” (12), i.e., each tumor in each person is unique in terms of cause, the form of progression, and response to treatment.

The transition from an old-fashion sequential approach to a multi-gene strategy, due to an increasing number of clinically relevant markers, needs that the technologies used for analysis improved together with the clinical medical needs. The aim of this review is to focus on the next-generation techniques currently available for the characterization of lung tumors in clinical practice for predictive purposes. We considered as “next-generation techniques” those methods that overcome the technical limits of the standard methods usually used in molecular characterization of solid tumors and that have been considered the “gold standard” for several years, such as Sanger sequencing, fluorescence in situ hybridization (FISH), immunohistochemistry (IHC), real-time PCR, pyrosequencing. We then focused our attention on next-generation sequencing (NGS), digital PCR (dPCR)/droplet digital PCR (ddPCR), NanoString, Nanopore, and nonoverlapping integrated read sequencing system (NOIR-SS).

Starting material

Tissue-based specimens

To date, several clinically relevant markers have to be analyzed in advanced-stage NSCLCs (13). This evidence leads to the need of performing several molecular analyses in NSCLC starting from the same specimen, often represented by a low-amount tissue sample. Formalin-fixed and paraffin-embedded (FFPE) specimens are usually the gold-standard material for performing molecular characterization of solid tumors, but this type of starting material is not so commonly available for NSCLCs. In fact, in the majority of routine cases, molecular laboratories dispose of biopsy specimens or cytological material for performing molecular analysis in NSCLC (14-19) (Table 2).

The low amount of material available from fine-needle aspiration biopsy (FNAB) specimens may be successfully overcome by implementing the simultaneous analysis of multiple biomarkers using the same analytical technique, as NGS. To date, the College of American Pathologists (CAP)/International Association for the Study of Lung Cancer (IASLC)/Association for Molecular Pathology (AMP) guidelines recommend the use of cytological specimens for molecular analysis in advanced-stage NSCLC samples (20).

Liquid biopsy

Liquid biopsy refers to a minimally invasive method of analysis of molecular neoplastic biomarkers [e.g., circulating tumor cells (CTC), circulating tumor DNA (ctDNA), cell-free DNA (cfDNA)] performed starting from any type of patient’s body fluid, such as peripheral blood (plasma), bile, urine, saliva, cerebrospinal fluid, and pleural effusion (21,22). In cases in which the lung tumor is inaccessible or the patient’s performance status does not allow invasive tissue biopsy, liquid biopsy can provide valuable material for molecular diagnosis (23-26).

The applications of liquid biopsy analysis are several (Table 2): (I) diagnosis of lung lesions in patients where it is not possible to obtain pathological material or if the available material is inadequate for molecular analyses; (II) monitoring the treatment response; (III) detection of minimal residual disease. Moreover, the liquid biopsy procedure can be serially repeated to monitor the development of resistance (27), the development of co-mutations (28), and allows to detect intra- and inter-tumoral heterogeneity that cannot be assessed by analyzing a biopsy specimen at a single site (29). Several scientific studies have demonstrated the efficacy of liquid biopsy in monitoring and characterizing lung tumors (23,30-37), and nowadays liquid biopsy analysis in NSCLC should be routinely integrated into molecular tests currently available in molecular pathology laboratories. If compared to tissue-based specimens, liquid biopsy for NGS has the advantages that can be performed also in those patients where it is not possible to obtain a tissue biopsy, and it can be used for early detection, or disease recurrence using minimally invasive techniques. The analysis performed on liquid biopsy may be an early predictor of response to treatment, allowing to identify also possible acquired drug resistance (32,38). However, it should be taken into consideration that NGS results obtained starting from liquid biopsy specimens require a very careful validation, because of low diagnostic sensitivity in those patients with low tumor burden, the lack of a correlation between molecular results and morphological data (i.e., the type of the lung tumor), and the impossibility to test markers using IHC (e.g., PD-L1 expression) (32,38).

Analytical techniques

NGS

NGS, also called massive parallel sequencing (MSP), is a multigene mutational assay that can simultaneously screen multiple markers starting from a small amount of DNA/RNA. The low amount of nucleic acid input makes NGS a suitable technique for molecular analysis in lung biopsies and cytological samples. Adequacy of samples for NGS analysis is assessed according to tumor cellularity and enrichment in neoplastic cells. Both factors may, however, vary depending on the analytical sensitivity of the platform used (39). NGS allows starting not only from FFPE specimens (both biopsy and surgical specimens), but also from cell block preparations, cytological smears, liquid-based cytology (LBC), and fluids of fine needle aspirate procedure (19,40-43). The minimum amount of nucleic acid varies according to the platform and panel used, but usually ranges from 10 to 50 ng for DNA or RNA input for amplicon-based methods, and up to 200 ng for capture-based panels. As a general rule, before clinical applications, laboratories performing NGS must validate the adequacy of the entire pipeline analysis, from acid nucleic extraction to the interpretation of the output results (41,44,45).

ESMO guidelines highlight the key role of NGS in the molecular characterization of patients with NSCLC in clinical practice (5), stating that “If available, multiplex platforms (NGS) for molecular testing are preferable” (5). In fact, NGS allows the simultaneous detection of EGFR and BRAF, RET, NTRK, ALK, HER2, and ROS1 rearrangements, and MET exon skipping, other than other potentially useful biomarkers (as KRAS mutations). NGS is not only a multi-gene multi-patients technique but allows to perform these multiple tests at a very high depth of coverage (Table 3). This high analytical sensitivity is crucial mainly in those samples with a low enrichment in neoplastic cells. Using NGS panels it is then possible to investigate a set of genes in a single test, able to identify alterations even in the scarce biopsy tissue often available in everyday practice (46).

NGS technology allows the analysis of DNA alterations, copy number aberrations (CNA), and gene translocations in the same run. NGS panels usually used for lung-adenocarcinomas should then allow detecting at least the markers that are nowadays available for targeted therapy, such as EGFR/KRAS/BRAF/MET exon14 skipping mutations, ALK/ROS1/RET/NTRK rearrangements in lung adenocarcinoma (5).

Profiling all markers in the same NGS “run” allows for defining the more appropriate treatment for the NSCLC patients. In fact, using a single analysis it is possible to determine if a patient should be treated using EGFR tyrosine kinase inhibitors (EGFR-TKIs) (gefitinib, erlotinib, afatinib, osimertinib, and dacomitinib) in tumors with EGFR mutations, using ALK-/ROS- or RET-inhibitors (in patients with NSCLC harboring ALK/ROS1/RET rearrangements), BRAF inhibitors (dabrafenib, vemurafenib) if BRAF activating mutations are detected, or using NTRK inhibitors (in patients whit tumors harboring NTRK1-2-3 rearrangements).

The multi-gene NGS approach avoids performing several tests for different markers. In fact, an alternative to the NGS approach would force to perform real-time PCR for EGFR and BRAF point mutations and real-time PCR and/or in situ techniques [i.e., IHC—and/or in situ hybridization (ISH) techniques] for ALK/ROS1/NTRK/RET rearrangements.

Several multi-gene panels have become commercially available in recent years. These panels may be designed for investigating specific markers for specific tumors or include a very high number of targets (“comprehensive” panels). Moreover, the NGS approach allows the set-up of custom/laboratory-developed multi-gene panels for the selection of targets, according to the needs of the medical community, serviced by the molecular laboratory (43).

Hybridization capture and amplicon sequencing are the two most common types of NGS panels used in clinical practice. In the hybridization capture panels, target sequences are captured using Biotinylated probes, while in the amplicon-based panels a first PCR amplifies the desired target sequences with specific primers (47). The great advantages of hybridization capture panels are the scalability and the detecting of gene rearrangements without requiring prior knowledge of fusion partners. On the other hand, hybridization capture protocols are usually more laborious and may be inadequate in case of a low quantity of acid nucleic availability. Amplicon-based panels are characterized by a simpler and faster workflow and allow to obtain evaluable results also starting from a low amount of nucleic acid (up to 10 ng per reaction). The amplicon-based panels are very efficient in DNA mutation detection but may suffer from PCR bias and are not optimal for the identification of gene rearrangements because these panels allow detecting only previously known rearrangements that have been included in the primers pool.

The potentially very high analytical sensitivity of MPS allows the investigation of the actionable mutations in NSCLC patients also starting from liquid biopsy/cfDNA (48-51).

Another interesting point about the NGS approach for lung tumor analysis is that multi-gene panels allow analyzing also markers that nowadays are not in clinical practice but that would allow driving patients onto therapeutic trials (5). The analysis of hundreds of targets simultaneously has been defined as comprehensive genomic profiling (CGP) (4,52). If on one hand using CGP multiple actionable targets can be identified with small amounts of samples (thereby improving the success of the tests), on the other hand, the clinician had to manage a lot of possible alterations also for genes considered as “non-actionable targets”. However, it has been hypothesized that the routine use of CGP for NSCLC would: (I) improve the likelihood of obtaining evaluable samples for analyses, decreasing the need to obtain new specimens; (II) CGP tests are probably more sensitive for identifying actionable targets in routine tumor samples than traditional sequential testing techniques (4).

Since 2011 more than 200 scientific papers have been published about the use of NGS in lung tumors, with an ever-increasing number in the last years (Figure 2). Turnaround time (TAT) is a crucial aspect in the management of molecular tests of NSLC. Guidelines by CAP/IASLC/AMP, as well as local guidelines (20), recommend that molecular testing TAT should not exceed 10 working days. However, in real-world clinical practice, some delays in NGS TAT may be observed. Molecular laboratories must be equipped with different platforms, that prove themselves to be useful in case of NGS would fail due to pre-analytical tissues or for orthogonal confirmation. In fact, single-gene testing approaches may be adopted as orthogonal techniques useful to confirm challenging cases, while broader NGS testing for patients with advanced NSCLC should be the best strategy (53,54). The cost of a multi-gene panel for NSCLC is strongly dependent on the choice of the panel. However, a recent study about the feasibility of NGS in NSCLC revealed a median cost for reagents of about €500 per sample (55). Moreover, comparing the total cost per patient (i.e., reagents, consumables, personnel time, equipment investment and maintenance, and overheads) the authors have observed that the median cost for the NGS strategy was lower (about €1,400) if compared to that of standard strategies (about €3,000) in the current NSCLC scenario (55). These differences are going to increase if we consider the increasing number of useful markers in NSCLC. To date, ESMO guidelines recommend that an NSCLC specimen is profiled using NGS. Moreover, the guidelines suggest that those medical centers that perform development programs and clinical trials use multigene sequencing in the context of molecular screening programs, even if it should be considered that using NGS it is not possible to evaluate the protein expression and localization (e.g., it is not possible to evaluate PD-L1 expression) (5). In the future, the role of NGS multi-gene testing will be always more crucial for the characterization and clinical management of advanced NSCLC. In fact, the number of emerging biomarkers that need to be tested in the clinical practice of NSLC is expected to highly increase within the next 3 years, including for example microsatellite instability (MSI), FGFR, BRCA, and homologous recombination deficiency (HRD) genes (Table 1).

Figure 2 Number of scientific articles published on Next-generation sequencing in lung tumors (source: PubMed; Query: “(Next generation sequencing[Title]) AND (Lung[Title]) AND (cancer[Title] OR carcinoma[Title])”).

dPCR/ddPCR

dPCR is a very highly sensitive method for the analysis of alteration both in solid tissue specimens (biopsy or cytological smears) and in ctDNA from patients with NSCLC (56,57). The dPCR maybe then also used in liquid biopsy specimens for therapy monitoring of NSCLC patients (57). dPCR has been successfully used for ctDNA detection of EGFR mutations in advanced lung adenocarcinomas (48,49), allowing to detect mutations in cfDNA with a fractional abundance of at least 0.1% (58-82) (Table 3). dPCR may be also used for quantitative measurements of T790M mutant copy number in plasma cfDNA to predict treatment response and survival outcomes in NCSLC patients (48,83,84).

dPCR also allows the use of multiplex assays for simultaneous detection of multiple EGFR tyrosine-kinase inhibitor-sensitizing mutations (85-90). Interestingly, using dPCR it has been demonstrated that the detection rates of EGFR mutations were higher in bronchial washing fluid than in the plasma of patients with lung adenocarcinoma (91). Even if nowadays ddPCR has been mainly used for EGFR detection in cfDNA, this technique may also be used for the detection of alterations in other clinical biomarkers, such as ALK alterations, KRAS mutations, and Myc amplification (88,92-96). Intriguingly has been used for quantifying PD-L1 levels in NSCLC biopsy specimens (97). As reported above, liquid biopsy refers to any type of patient’s body fluid. dPCR has been used for evaluating EGFR mutations in sputum samples of patients with advanced EGFR-mutated NSCLC (98).

NanoString

NanoString nCounter technology is a dual-probe system that allows performing direct profiling of target nucleic acid molecules in a single reaction, without the need for retro-transcription and amplification, and with a very high degree of multiplexing (99-101). Using NanoString nCounter it is possible to analyze up to 800 target genes per reaction. The quantification of target molecules is performed without reverse transcription or amplification steps. In this way, it is possible to obtain faithful and reliable data not influenced by any type of bias, inevitably introduced by enzymatic reactions. This analysis can be then performed starting from degraded clinical samples, as tissues FFPE specimens, allowing the numerous samples preserved in the archives of pathological anatomies to be used retrospectively. In fact, the direct counting of messenger RNA (mRNA) molecules leads NanoString to be less sensitive to the preanalytic treatment of samples (102). The advantages of this technology are (Table 3): (I) the low amount of input RNA (less than 200 ng) needed for the evaluation of expression of a huge number of genes; (II) the evaluation of gene rearrangement does not require prior knowledge of fusion alterations.

The NanoString technology represents then a potentially useful genomic platform for the detection of gene fusions in clinical practice with high sensitivity, reproducibility, and technical robustness (99).

In NSCLC NanoString technology has been successfully used to characterize druggable rearrangements as those in ALK, ROS1, and RET genes, with high accuracy and sensitivity (103-108).

NanoString analysis performed on a cohort of 214 lung squamous cell carcinomas detected no ALK, ROS1, or RET gene rearrangements, confirming that these rearrangements are very rare in lung squamous cancer (109).

NanoString has been successfully applied in NSCLCs for the detection of rearrangement not only in FFPE but also in cytological specimens (102). The efficacy of NanoString also on cytological specimens put this platform even more as one of the molecular tools applicable in the routine practice of lung cancer.

Other techniques

Nanopore

Nanopore is one of the so-called third-generation sequencing techniques allowing a single molecule of nucleic acid (DNA or RNA) to be sequenced without the need for PCR pre-amplification. Nanopore works recording a sequence-dependent electrical signal when DNA molecules pass through a pore. To date, Nanopore technology is used in clinical practice for rapid identification of viral pathogens, environmental and food safety monitoring, plant genome sequencing, and monitoring of antibiotic resistance (110). Nanopore technology is nowadays optimized for long-read sequencing, and then not ideal for analysis from ex vivo neoplastic samples, but in the next future could be also implemented in the characterization of tumors in the pathology laboratories. In a recent study performed on cfDNA from 6 lung cancer patients and 5 healthy subjects, Martignano and colleagues demonstrated the technical feasibility of Nanopore sequencing for copy number variation (CNV) analysis of short plasmatic cfDNA (111).

NOIR-SS

NOIR-SS is a sequencing method that uses molecular barcodes. This method allows a high-fidelity target sequencing system of individual molecules in plasma cfDNA (112). NOIR-SS enables a more accurate quantification and measurement of allele fractions than conventional barcode sequencing by removing erroneous barcode tags during data analysis and then reducing the number of false positives. NOIR-SS technology has been successfully adopted for targeting EGFR p.L858R ctDNA mutation in plasma of patients with lung adenocarcinoma (113).

Conclusions

The advent of next-generation techniques (e.g., NGS) into clinical practice of lung tumors has allowed to introduce molecularly driven treatment choices. Moreover, an integrated morphological and molecular characterization of the lung tumors is now performed for the diagnosis. It should be then considered that it has become increasingly crucial to have sufficient material for histological, immunohistochemical, and molecular characterization. For this reason, it is mandatory to dispose of highly sensitive and specific techniques that allow the molecular characterization of the lung tumors in a robust way starting also from a low amount of tissue specimens. In our experience, to date, the multi-gene approach using an NGS technique is preferable to sequential testing. The proper application of the best suitable technique for specific clinical requests and for the availability of biological material provides a powerful tool to laboratory and physicians for having the best management of patients affected by lung tumors.

Acknowledgments

Funding: None.

Footnote

Provenance and Peer Review: This article was commissioned by the Guest Editors (Umberto Malapelle and Giancarlo Troncone) for the series “Predictive Molecular Pathology in Lung Cancer” published in Journal of Xiangya Medicine. The article has undergone external peer review.

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://jxym.amegroups.com/article/view/10.21037/jxym-22-2/coif). The series “Predictive Molecular Pathology in Lung Cancer” was commissioned by the editorial office without any funding or sponsorship. DdB received personal fees (as a speaker) from Boehringer Ingelheim, and Eli Lilly, unrelated to the current work. FG received personal fees for presentations from Astrazeneca and honoraria for Advisory Board from Eli-Lilly. A Ardizzoni received personal fees as member of Advisor Board or conference speaker from: BMS, MSD, ROCHE, AZ, Eli-Lilly, Takeda, Pfizer, Bayer. A Ardizzoni also received institutional research from BMS, Roche, AZ, Ipsen. The authors have no other conflicts of interest to declare.

Ethical Statement:The authors are 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. Ferlay J, Colombet M, Soerjomataram I, et al. Cancer statistics for the year 2020: An overview. Int J Cancer 2021; Epub ahead of print. [Crossref] [PubMed]
2. Sung H, Ferlay J, Siegel RL, et al. Global Cancer Statistics 2020: GLOBOCAN Estimates of Incidence and Mortality Worldwide for 36 Cancers in 185 Countries. CA Cancer J Clin 2021;71:209-49. [Crossref] [PubMed]
3. Ferlay J, Ervik M, Lam F, et al. Global Cancer Observatory: Cancer Today. Lyon, France: International Agency for Research on Cancer. 2020. Available online: https://gco.iarc.fr/today. Accessed 15/01/2022 2022.
4. Torres GF, Bonilla CE, Buitrago G, et al. How clinically useful is comprehensive genomic profiling for patients with non-small cell lung cancer? A systematic review. Crit Rev Oncol Hematol 2021;166:103459. [Crossref] [PubMed]
5. Mosele F, Remon J, Mateo J, et al. Recommendations for the use of next-generation sequencing (NGS) for patients with metastatic cancers: a report from the ESMO Precision Medicine Working Group. Ann Oncol 2020;31:1491-505. [Crossref] [PubMed]
6. Kerr KM, Bibeau F, Thunnissen E, et al. The evolving landscape of biomarker testing for non-small cell lung cancer in Europe. Lung Cancer 2021;154:161-75. [Crossref] [PubMed]
7. FDA grants accelerated approval to sotorasib for KRAS G12C mutated NSCLC. 2021. Available online: https://www.fda.gov/drugs/resources-information-approved-drugs/fda-grants-accelerated-approval-sotorasib-kras-g12c-mutated-nsclc. Accessed 14/01/2022.
8. Clinical Practice Living Guidelines—Metastatic Non-Small-Cell Lung Cancer. Available online: https://www.esmo.org/guidelines/lung-and-chest-tumours/clinical-practice-living-guidelines-metastatic-non-small-cell-lung-cancer
9. 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:iv192-237. [Crossref]
10. Dall'Olio FG, Conci N, Rossi G, et al. Comparison of Sequential Testing and Next Generation Sequencing in advanced Lung Adenocarcinoma patients - A single centre experience. Lung Cancer 2020;149:5-9. [Crossref] [PubMed]
11. Pruneri G, De Braud F, Sapino A, et al. Next-Generation Sequencing in Clinical Practice: Is It a Cost-Saving Alternative to a Single-Gene Testing Approach? Pharmacoecon Open 2021;5:285-98. [Crossref] [PubMed]
12. Ross JS. Cancer biomarkers, companion diagnostics and personalized oncology. Biomark Med 2011;5:277-9. [Crossref] [PubMed]
13. Malapelle U, Muscarella LA, Pisapia P, et al. Targeting emerging molecular alterations in the treatment of non-small cell lung cancer: current challenges and the way forward. Expert Opin Investig Drugs 2020;29:363-72. [Crossref] [PubMed]
14. Barbareschi M, Barberis M, Buttitta F, et al. Predictive markers in lung cancer: a few hints for the practicing pathologist. Pathologica 2018;110:29-38. [PubMed]
15. Aisner DL, Sams SB. The role of cytology specimens in molecular testing of solid tumors: techniques, limitations, and opportunities. Diagn Cytopathol 2012;40:511-24. [Crossref] [PubMed]
16. Roy-Chowdhuri S, Mehrotra M, Bolivar AM, et al. Salvaging the supernatant: next generation cytopathology for solid tumor mutation profiling. Mod Pathol 2018;31:1036-45. [Crossref] [PubMed]
17. Bellevicine C, Malapelle U, Vigliar E, et al. How to prepare cytological samples for molecular testing. J Clin Pathol 2017;70:819-26. [Crossref] [PubMed]
18. Dietel M, Bubendorf L, Dingemans AM, et al. Diagnostic procedures for non-small-cell lung cancer (NSCLC): recommendations of the European Expert Group. Thorax 2016;71:177-84. [Crossref] [PubMed]
19. Pisapia P, Pepe F, Iaccarino A, et al. Next Generation Sequencing in Cytopathology: Focus on Non-Small Cell Lung Cancer. Front Med (Lausanne) 2021;8:633923. [Crossref] [PubMed]
20. Lindeman NI, Cagle PT, Aisner DL, et al. Updated Molecular Testing Guideline for the Selection of Lung Cancer Patients for Treatment With Targeted Tyrosine Kinase Inhibitors: Guideline From the College of American Pathologists, the International Association for the Study of Lung Cancer, and the Association for Molecular Pathology. Arch Pathol Lab Med 2018;142:321-46. [Crossref] [PubMed]
21. Ignatiadis M, Sledge GW, Jeffrey SS. Liquid biopsy enters the clinic - implementation issues and future challenges. Nat Rev Clin Oncol 2021;18:297-312. [Crossref] [PubMed]
22. Siravegna G, Marsoni S, Siena S, et al. Integrating liquid biopsies into the management of cancer. Nat Rev Clin Oncol 2017;14:531-48. [Crossref] [PubMed]
23. Rolfo C, Mack P, Scagliotti GV, et al. Liquid Biopsy for Advanced NSCLC: A Consensus Statement From the International Association for the Study of Lung Cancer. J Thorac Oncol 2021;16:1647-62. [Crossref] [PubMed]
24. Rolfo C, Mack PC, Scagliotti GV, et al. Liquid Biopsy for Advanced Non-Small Cell Lung Cancer (NSCLC): A Statement Paper from the IASLC. J Thorac Oncol 2018;13:1248-68. [Crossref] [PubMed]
25. Ferreira D, Miranda J, Martins-Lopes P, et al. Future Perspectives in Detecting EGFR and ALK Gene Alterations in Liquid Biopsies of Patients with NSCLC. Int J Mol Sci 2021;22:3815. [Crossref] [PubMed]
26. Malapelle U, Tiseo M, Vivancos A, et al. Liquid Biopsy for Biomarker Testing in Non-Small Cell Lung Cancer: A European Perspective. J Mol Pathol 2021;2:255-73. [Crossref]
27. Esposito Abate R, Frezzetti D, Maiello MR, et al. Next Generation Sequencing-Based Profiling of Cell Free DNA in Patients with Advanced Non-Small Cell Lung Cancer: Advantages and Pitfalls. Cancers (Basel) 2020;12:3804. [Crossref] [PubMed]
28. Mograbi B, Heeke S, Hofman P. The Importance of STK11/LKB1 Assessment in Non-Small Cell Lung Carcinomas. Diagnostics (Basel) 2021;11:196. [Crossref] [PubMed]
29. Bettegowda C, Sausen M, Leary RJ, et al. Detection of circulating tumor DNA in early- and late-stage human malignancies. Sci Transl Med 2014;6:224ra24. [Crossref] [PubMed]
30. Russo A, Incorvaia L, Del Re M, et al. The molecular profiling of solid tumors by liquid biopsy: a position paper of the AIOM-SIAPEC-IAP-SIBioC-SIC-SIF Italian Scientific Societies. ESMO Open 2021;6:100164. [Crossref] [PubMed]
31. Malapelle U, Buono M, Pisapia P, et al. Circulating tumor DNA in cancer: Predictive molecular pathology meets mathematics. Crit Rev Oncol Hematol 2021;163:103394. [Crossref] [PubMed]
32. Malapelle U, Pisapia P, Addeo A, et al. Liquid biopsy from research to clinical practice: focus on non-small cell lung cancer. Expert Rev Mol Diagn 2021;21:1165-78. [Crossref] [PubMed]
33. Zulato E, Tosello V, Nardo G, et al. Implementation of Next Generation Sequencing-Based Liquid Biopsy for Clinical Molecular Diagnostics in Non-Small Cell Lung Cancer (NSCLC) Patients. Diagnostics (Basel) 2021;11:1468. [Crossref] [PubMed]
34. Verzè M, Minari R, Gnetti L, et al. Monitoring cfDNA in Plasma and in Other Liquid Biopsies of Advanced EGFR Mutated NSCLC Patients: A Pilot Study and a Review of the Literature. Cancers (Basel) 2021;13:5403. [Crossref] [PubMed]
35. Brueckl NF, Wirtz RM, Reich FPM, et al. Predictive value of mRNA expression and dynamic changes from immune related biomarkers in liquid biopsies before and after start of pembrolizumab in stage IV non-small cell lung cancer (NSCLC). Transl Lung Cancer Res 2021;10:4106-19. [Crossref] [PubMed]
36. Sardarabadi P, Kojabad AA, Jafari D, et al. Liquid Biopsy-Based Biosensors for MRD Detection and Treatment Monitoring in Non-Small Cell Lung Cancer (NSCLC). Biosensors (Basel) 2021;11:394. [Crossref] [PubMed]
37. Gragnano G, Nacchio M, Sgariglia R, et al. Performance evaluation of a fully closed real-time PCR platform for the detection of KRAS p.G12C mutations in liquid biopsy of patients with non-small cell lung cancer. J Clin Pathol 2022;75:350-3. [Crossref] [PubMed]
38. Angerilli V, Galuppini F, Pagni F, et al. The Role of the Pathologist in the Next-Generation Era of Tumor Molecular Characterization. Diagnostics (Basel) 2021;11:339. [Crossref] [PubMed]
39. Jennings LJ, Arcila ME, Corless C, et al. Guidelines for Validation of Next-Generation Sequencing-Based Oncology Panels: A Joint Consensus Recommendation of the Association for Molecular Pathology and College of American Pathologists. J Mol Diagn 2017;19:341-65. [Crossref] [PubMed]
40. de Biase D, Visani M, Malapelle U, et al. Next-generation sequencing of lung cancer EGFR exons 18-21 allows effective molecular diagnosis of small routine samples (cytology and biopsy). PLoS One 2013;8:e83607. [Crossref] [PubMed]
41. Malapelle U, Mayo-de-Las-Casas C, Molina-Vila MA, et al. Consistency and reproducibility of next-generation sequencing and other multigene mutational assays: A worldwide ring trial study on quantitative cytological molecular reference specimens. Cancer Cytopathol 2017;125:615-26. [Crossref] [PubMed]
42. Roy-Chowdhuri S, Pisapia P, Salto-Tellez M, et al. Invited review-next-generation sequencing: a modern tool in cytopathology. Virchows Arch 2019;475:3-11. [Crossref] [PubMed]
43. de Biase D, Acquaviva G, Visani M, et al. Molecular Diagnostic of Solid Tumor Using a Next Generation Sequencing Custom-Designed Multi-Gene Panel. Diagnostics (Basel) 2020;10:250. [Crossref] [PubMed]
44. Malapelle U, Pepe F, Pisapia P, et al. Reference standards for gene fusion molecular assays on cytological samples: an international validation study. J Clin Pathol 2021; Epub ahead of print. [Crossref] [PubMed]
45. Pisapia P, Malapelle U, Roma G, et al. Consistency and reproducibility of next-generation sequencing in cytopathology: A second worldwide ring trial study on improved cytological molecular reference specimens. Cancer Cytopathol 2019;127:285-96. [Crossref] [PubMed]
46. Morganti S, Tarantino P, Ferraro E, et al. Role of Next-Generation Sequencing Technologies in Personalized Medicine. In: Pravettoni G, Triberti S. editors. P5 eHealth: An Agenda for the Health Technologies of the Future. 1st edition. Springer; 2019:125-54.
47. Bruno R, Fontanini G. Next Generation Sequencing for Gene Fusion Analysis in Lung Cancer: A Literature Review. Diagnostics (Basel) 2020;10:521. [Crossref] [PubMed]
48. Lettig L, Sahnane N, Pepe F, et al. EGFR T790M detection rate in lung adenocarcinomas at baseline using droplet digital PCR and validation by ultra-deep next generation sequencing. Transl Lung Cancer Res 2019;8:584-92. [Crossref] [PubMed]
49. Iwama E, Sakai K, Azuma K, et al. Monitoring of somatic mutations in circulating cell-free DNA by digital PCR and next-generation sequencing during afatinib treatment in patients with lung adenocarcinoma positive for EGFR activating mutations. Ann Oncol 2017;28:136-41. [Crossref] [PubMed]
50. Ding PN, Becker T, Bray V, et al. Plasma next generation sequencing and droplet digital PCR-based detection of epidermal growth factor receptor (EGFR) mutations in patients with advanced lung cancer treated with subsequent-line osimertinib. Thorac Cancer 2019;10:1879-84. [Crossref] [PubMed]
51. Tran LS, Pham HT, Tran VU, et al. Ultra-deep massively parallel sequencing with unique molecular identifier tagging achieves comparable performance to droplet digital PCR for detection and quantification of circulating tumor DNA from lung cancer patients. PLoS One 2019;14:e0226193. [Crossref] [PubMed]
52. Ross JS, Cronin M. Whole cancer genome sequencing by next-generation methods. Am J Clin Pathol 2011;136:527-39. [Crossref] [PubMed]
53. De Maglio G, Pasello G, Dono M, et al. The storm of NGS in NSCLC diagnostic-therapeutic pathway: How to sun the real clinical practice. Crit Rev Oncol Hematol 2022;169:103561. [Crossref] [PubMed]
54. Pennell NA, Mutebi A, Zhou ZY, et al. Economic Impact of Next-Generation Sequencing Versus Single-Gene Testing to Detect Genomic Alterations in Metastatic Non-Small-Cell Lung Cancer Using a Decision Analytic Model. JCO Precis Oncol 2019;3:1-9. [Crossref] [PubMed]
55. Pisapia P, Pepe F, Baggi A, et al. Next generation diagnostic algorithm in non-small cell lung cancer predictive molecular pathology: The KWAY Italian multicenter cost evaluation study. Crit Rev Oncol Hematol 2022;169:103525. [Crossref] [PubMed]
56. Oxnard GR, Paweletz CP, Kuang Y, et al. Noninvasive detection of response and resistance in EGFR-mutant lung cancer using quantitative next-generation genotyping of cell-free plasma DNA. Clin Cancer Res 2014;20:1698-705. [Crossref] [PubMed]
57. Batra U, Nathany S, Sharma M, et al. EGFR detection by liquid biopsy: ripe for clinical usage. Future Oncol 2022;18:85-92. [Crossref] [PubMed]
58. Wang Z, Chen R, Wang S, et al. Quantification and dynamic monitoring of EGFR T790M in plasma cell-free DNA by digital PCR for prognosis of EGFR-TKI treatment in advanced NSCLC. PLoS One 2014;9:e110780. [Crossref] [PubMed]
59. Ishii H, Azuma K, Sakai K, et al. Digital PCR analysis of plasma cell-free DNA for non-invasive detection of drug resistance mechanisms in EGFR mutant NSCLC: Correlation with paired tumor samples. Oncotarget 2015;6:30850-8. [Crossref] [PubMed]
60. Zhu YJ, Zhang HB, Liu YH, et al. Association of mutant EGFR L858R and exon 19 concentration in circulating cell-free DNA using droplet digital PCR with response to EGFR-TKIs in NSCLC. Oncol Lett 2017;14:2573-9. [Crossref] [PubMed]
61. Feng WN, Gu WQ, Zhao N, et al. Comparison of the SuperARMS and Droplet Digital PCR for Detecting EGFR Mutation in ctDNA From NSCLC Patients. Transl Oncol 2018;11:542-5. [Crossref] [PubMed]
62. Chan DLH, Toh GLX, Goh LL. Clinical implementation of plasma EGFR T790M testing using droplet digital PCR in TKI-resistant NSCLC patients. Exp Mol Pathol 2020;116:104515. [Crossref] [PubMed]
63. Siggillino A, Ulivi P, Pasini L, et al. Detection of EGFR Mutations in Plasma Cell-Free Tumor DNA of TKI-Treated Advanced-NSCLC Patients by Three Methodologies: Scorpion-ARMS, PNAClamp, and Digital PCR. Diagnostics (Basel) 2020;10:1062. [Crossref] [PubMed]
64. Malapelle U, de Luca C, Vigliar E, et al. EGFR mutation detection on routine cytological smears of non-small cell lung cancer by digital PCR: a validation study. J Clin Pathol 2016;69:454-7. [Crossref] [PubMed]
65. Huang R, Xu X, Li D, et al. Digital PCR-Based Detection of EGFR Mutations in Paired Plasma and CSF Samples of Lung Adenocarcinoma Patients with Central Nervous System Metastases. Target Oncol 2019;14:343-50. [Crossref] [PubMed]
66. Yung TK, Chan KC, Mok TS, et al. Single-molecule detection of epidermal growth factor receptor mutations in plasma by microfluidics digital PCR in non-small cell lung cancer patients. Clin Cancer Res 2009;15:2076-84. [Crossref] [PubMed]
67. Gao F, Pfeifer E, Farah H, et al. Microdroplet digital PCR: detection and quantitation of biomarkers in archived tissue and serial plasma samples in patients with lung cancer. J Thorac Oncol 2015;10:212-7. [Crossref] [PubMed]
68. Li X, Liu Y, Shi W, et al. Droplet digital PCR improved the EGFR mutation diagnosis with pleural fluid samples in non-small-cell lung cancer patients. Clin Chim Acta 2017;471:177-84. [Crossref] [PubMed]
69. Gu J, Zang W, Liu B, et al. Evaluation of digital PCR for detecting low-level EGFR mutations in advanced lung adenocarcinoma patients: a cross-platform comparison study. Oncotarget 2017;8:67810-20. [Crossref] [PubMed]
70. Garcia J, Dusserre E, Cheynet V, et al. Evaluation of pre-analytical conditions and comparison of the performance of several digital PCR assays for the detection of major EGFR mutations in circulating DNA from non-small cell lung cancers: the CIRCAN_0 study. Oncotarget 2017;8:87980-96. [Crossref] [PubMed]
71. Masago K, Fujita S, Hata A, et al. Validation of the digital PCR system in tyrosine kinase inhibitor-resistant EGFR mutant non-small-cell lung cancer. Pathol Int 2018;68:167-73. [Crossref] [PubMed]
72. Kuang Y, O'Connell A, Sacher AG, et al. Monitoring of Response and Resistance in Plasma of EGFR-Mutant Lung Cancer Using Droplet Digital PCR. Methods Mol Biol 2018;1768:193-207. [Crossref] [PubMed]
73. Yang K, Li J, Zhao J, et al. Developing Ultrasensitive Library-Aliquot-Based Droplet Digital PCR for Detecting T790M in Plasma-Circulating Tumor DNA of Non-small-Cell-Lung-Cancer Patients. Anal Chem 2018;90:11203-9. [Crossref] [PubMed]
74. Suryavanshi M, Mehta A, Panigrahi MK, et al. The detection of primary and secondary EGFR mutations using droplet digital PCR in patients with nonsmall cell lung cancer. Lung India 2018;35:384-9. [Crossref] [PubMed]
75. Buder A, Setinek U, Hochmair MJ, et al. EGFR Mutations in Cell-free Plasma DNA from Patients with Advanced Lung Adenocarcinoma: Improved Detection by Droplet Digital PCR. Target Oncol 2019;14:197-203. [Crossref] [PubMed]
76. Wang X, Li X, Guo H, et al. Highly Sensitive Droplet Digital PCR Method for Detection of de novo EGFR T790M Mutation in Patients with Non-Small Cell Lung Cancer. Onco Targets Ther 2020;13:10621-30. [PubMed]
77. Esteva-Socias M, Enver-Sumaya M, Gómez-Bellvert C, et al. Detection of the EGFR G719S Mutation in Non-small Cell Lung Cancer Using Droplet Digital PCR. Front Med (Lausanne) 2020;7:594900. [Crossref] [PubMed]
78. Carvalho TM, Dourado RM, Nakatani SM, et al. Digital PCR detection of EGFR somatic mutations in non-small-cell lung cancer formalin fixed paraffin embedded samples. Mol Cell Probes 2021;58:101745. [Crossref] [PubMed]
79. Xu J, Wu W, Wu C, et al. A large-scale, multicentered trial evaluating the sensitivity and specificity of digital PCR versus ARMS-PCR for detecting ctDNA-based EGFR p.T790M in non-small-cell lung cancer patients. Transl Lung Cancer Res 2021;10:3888-901. [Crossref] [PubMed]
80. Ntzifa A, Kotsakis A, Georgoulias V, et al. Detection of EGFR Mutations in Plasma cfDNA and Paired CTCs of NSCLC Patients before and after Osimertinib Therapy Using Crystal Digital PCR. Cancers (Basel) 2021;13:2736. [Crossref] [PubMed]
81. Guo QM, Wang L, Yu WJ, et al. Detection of Plasma EGFR Mutations in NSCLC Patients with a Validated ddPCR Lung cfDNA Assay. J Cancer 2019;10:4341-9. [Crossref] [PubMed]
82. Suryavanshi M, Jaipuria J, Panigrahi MK, et al. CSF cell-free DNA EGFR testing using DdPCR holds promise over conventional modalities for diagnosing leptomeningeal involvement in patients with non-small cell lung cancer. Lung Cancer 2020;148:33-9. [Crossref] [PubMed]
83. Isobe K, Hata Y, Tochigi N, et al. Usefulness of nanofluidic digital PCR arrays to quantify T790M mutation in EGFR-mutant lung adenocarcinoma. Cancer Genomics Proteomics 2015;12:31-7. [PubMed]
84. Li JY, Ho JC, Wong KH. T790M mutant copy number quantified via ddPCR predicts outcome after osimertinib treatment in lung cancer. Oncotarget 2018;9:27929-39. [Crossref] [PubMed]
85. Song X, Gong J, Zhang X, et al. Plasma-based early screening and monitoring of EGFR mutations in NSCLC patients by a 3-color digital PCR assay. Br J Cancer 2020;123:1437-44. [Crossref] [PubMed]
86. Watanabe M, Kawaguchi T, Isa SI, et al. Multiplex Ultrasensitive Genotyping of Patients with Non-Small Cell Lung Cancer for Epidermal Growth Factor Receptor (EGFR) Mutations by Means of Picodroplet Digital PCR. EBioMedicine 2017;21:86-93. [Crossref] [PubMed]
87. Madic J, Jovelet C, Lopez J, et al. EGFR C797S, EGFR T790M and EGFR sensitizing mutations in non-small cell lung cancer revealed by six-color crystal digital PCR. Oncotarget 2018;9:37393-406. [Crossref] [PubMed]
88. de Kock R, Deiman B, Kraaijvanger R, et al. Optimized (Pre) Analytical Conditions and Workflow for Droplet Digital PCR Analysis of Cell-Free DNA from Patients with Suspected Lung Carcinoma. J Mol Diagn 2019;21:895-902. [Crossref] [PubMed]
89. Madic J, Jovelet C, Dehri I, et al. 6-Color Crystal Digital PCRTM for the High-Plex Detection of EGFR Mutations in Non-Small Cell Lung Cancer. Methods Mol Biol 2021;2279:127-44. [Crossref] [PubMed]
90. de Kock R, van den Borne B, Youssef-El Soud M, et al. Therapy Monitoring of EGFR-Positive Non-Small-Cell Lung Cancer Patients Using ddPCR Multiplex Assays. J Mol Diagn 2021;23:495-505. [Crossref] [PubMed]
91. Lee SH, Kim EY, Kim T, et al. Compared to plasma, bronchial washing fluid shows higher diagnostic yields for detecting EGFR-TKI sensitizing mutations by ddPCR in lung cancer. Respir Res 2020;21:142. [Crossref] [PubMed]
92. Lund HL, Hughesman CB, Fakhfakh K, et al. Initial Diagnosis of ALK-Positive Non-Small-Cell Lung Cancer Based on Analysis of ALK Status Utilizing Droplet Digital PCR. Anal Chem 2016;88:4879-85. [Crossref] [PubMed]
93. Liu Y, Wu S, Shi X, et al. Clinical evaluation of the effectiveness of fusion-induced asymmetric transcription assay-based reverse transcription droplet digital PCR for ALK detection in formalin-fixed paraffin-embedded samples from lung cancer. Thorac Cancer 2020;11:2252-61. [Crossref] [PubMed]
94. Brik A, Weber DG, Casjens S, et al. Digital PCR for the Analysis of MYC Copy Number Variation in Lung Cancer. Dis Markers 2020;2020:4176376. [Crossref] [PubMed]
95. Michaelidou K, Koutoulaki C, Mavridis K, et al. Detection of KRAS G12/G13 Mutations in Cell Free-DNA by Droplet Digital PCR, Offers Prognostic Information for Patients with Advanced Non-Small Cell Lung Cancer. Cells 2020;9:2514. [Crossref] [PubMed]
96. Peñaloza Coronas C, Montilla Fonseca SM, Sánchez ML. Response to clinical evaluation of the effectiveness of fusion-induced asymmetric transcription assay-based reverse transcription droplet digital PCR for ALK detection in formalin-fixed paraffin-embedded samples from lung cancer. Thorac Cancer 2022;13:146. [Crossref] [PubMed]
97. Vannitamby A, Hendry S, Irving L, et al. Novel multiplex droplet digital PCR assay for scoring PD-L1 in non-small cell lung cancer biopsy specimens. Lung Cancer 2019;134:233-7. [Crossref] [PubMed]
98. Hackner K, Buder A, Hochmair MJ, et al. Detection of EGFR Activating and Resistance Mutations by Droplet Digital PCR in Sputum of EGFR-Mutated NSCLC Patients. Clin Med Insights Oncol 2021;15:1179554921993072. [Crossref] [PubMed]
99. Veldman-Jones MH, Brant R, Rooney C, et al. Evaluating Robustness and Sensitivity of the NanoString Technologies nCounter Platform to Enable Multiplexed Gene Expression Analysis of Clinical Samples. Cancer Res 2015;75:2587-93. [Crossref] [PubMed]
100. Tsang HF, Xue VW, Koh SP, et al. NanoString, a novel digital color-coded barcode technology: current and future applications in molecular diagnostics. Expert Rev Mol Diagn 2017;17:95-103. [Crossref] [PubMed]
101. Geiss GK, Bumgarner RE, Birditt B, et al. Direct multiplexed measurement of gene expression with color-coded probe pairs. Nat Biotechnol 2008;26:317-25. [Crossref] [PubMed]
102. Alì G, Bruno R, Savino M, et al. Analysis of Fusion Genes by NanoString System: A Role in Lung Cytology? Arch Pathol Lab Med 2018;142:480-9. [Crossref] [PubMed]
103. Lindquist KE, Karlsson A, Levéen P, et al. Clinical framework for next generation sequencing based analysis of treatment predictive mutations and multiplexed gene fusion detection in non-small cell lung cancer. Oncotarget 2017;8:34796-810. [Crossref] [PubMed]
104. Reguart N, Teixidó C, Giménez-Capitán A, et al. Identification of ALK, ROS1, and RET Fusions by a Multiplexed mRNA-Based Assay in Formalin-Fixed, Paraffin-Embedded Samples from Advanced Non-Small-Cell Lung Cancer Patients. Clin Chem 2017;63:751-60. [Crossref] [PubMed]
105. Lira ME, Kim TM, Huang D, et al. Multiplexed gene expression and fusion transcript analysis to detect ALK fusions in lung cancer. J Mol Diagn 2013;15:51-61. [Crossref] [PubMed]
106. Suehara Y, Arcila M, Wang L, et al. Identification of KIF5B-RET and GOPC-ROS1 fusions in lung adenocarcinomas through a comprehensive mRNA-based screen for tyrosine kinase fusions. Clin Cancer Res 2012;18:6599-608. [Crossref] [PubMed]
107. Rogers TM, Arnau GM, Ryland GL, et al. Multiplexed transcriptome analysis to detect ALK, ROS1 and RET rearrangements in lung cancer. Sci Rep 2017;7:42259. [Crossref] [PubMed]
108. Evangelista AF, Zanon MF, Carloni AC, et al. Detection of ALK fusion transcripts in FFPE lung cancer samples by NanoString technology. BMC Pulm Med 2017;17:86. [Crossref] [PubMed]
109. Zhao W, Choi YL, Song JY, et al. ALK, ROS1 and RET rearrangements in lung squamous cell carcinoma are very rare. Lung Cancer 2016;94:22-7. [Crossref] [PubMed]
110. Schmidt J, Blessing F, Fimpler L, et al. Nanopore Sequencing in a Clinical Routine Laboratory: Challenges and Opportunities. Clin Lab 2020; [Crossref] [PubMed]
111. Martignano F, Munagala U, Crucitta S, et al. Nanopore sequencing from liquid biopsy: analysis of copy number variations from cell-free DNA of lung cancer patients. Mol Cancer 2021;20:32. [Crossref] [PubMed]
112. Kukita Y, Matoba R, Uchida J, et al. High-fidelity target sequencing of individual molecules identified using barcode sequences: de novo detection and absolute quantitation of mutations in plasma cell-free DNA from cancer patients. DNA Res 2015;22:269-77. [Crossref] [PubMed]
113. Akahori D, Inoue Y, Inui N, et al. Comparative assessment of NOIR-SS and ddPCR for ctDNA detection of EGFR L858R mutations in advanced L858R-positive lung adenocarcinomas. Sci Rep 2021;11:14999. [Crossref] [PubMed]