Oncogenic variants guiding treatment in thoracic malignancies Meng, Pei
DOI:
10.33612/diss.160074057
IMPORTANT NOTE: You are advised to consult the publisher's version (publisher's PDF) if you wish to cite from it. Please check the document version below.
Document Version
Publisher's PDF, also known as Version of record
Publication date: 2021
Link to publication in University of Groningen/UMCG research database
Citation for published version (APA):
Meng, P. (2021). Oncogenic variants guiding treatment in thoracic malignancies. University of Groningen. https://doi.org/10.33612/diss.160074057
Copyright
Other than for strictly personal use, it is not permitted to download or to forward/distribute the text or part of it without the consent of the author(s) and/or copyright holder(s), unless the work is under an open content license (like Creative Commons).
Take-down policy
If you believe that this document breaches copyright please contact us providing details, and we will remove access to the work immediately and investigate your claim.
Downloaded from the University of Groningen/UMCG research database (Pure): http://www.rug.nl/research/portal. For technical reasons the number of authors shown on this cover page is limited to 10 maximum.
Chapter 8
Summary
Personalized medicine has dramatically changed the clinical management, improved treatment outcomes and reduced adverse events in cancer patients. Application of genomics techniques in routine diagnostics facilitates personalized medicine. The field of molecular diagnostics has evolved from selecting optimal treatment strategy to monitoring treatment response and identification of resistance mechanisms. The aim of this PhD thesis was to improve diagnostic methods, explore resistance mechanisms and prognostic value of molecular biomarkers in non-small cell lung cancer (NSCLC) and/or esophageal squamous cell carcinoma (ESCC).
Treatment of advanced NSCLC patients largely depends on the presence of specific genomic aberrations. Presence of oncogenic driver events predicts response to specific kinase inhibitors. Moreover, subsequent treatment lines often depend on presence of treatment-induced aberrations that cause resistance. Genomic aberrations that determine initial and secondary treatment lines include single nucleotide variants (SNV), small insertion-deletions (InDel), exon skipping events, gene fusions and amplifications. Thus far, multiple tests and sample preparations are needed to test all relevant aberrations in a clinical setting. In chapter 2, we designed and investigated the efficiency of our customized all-in-one transcriptome-based next generation sequencing (NGS) assay to detect genomic variations in non-small cell lung cancer (NSCLC). We first tested cell lines in early versions of our assay and followed-up with frozen biopsies, pleural effusions, and formalin-fixed paraffin-embedded (FFPE) samples from NSCLC patients with previously identified mutations in EGFR, KRAS, ALK, PIK3CA, BRAF, AKT1, MET, NRAS, or ROS1 at the DNA level or gene fusions affecting ALK, ROS1, RET, and NTRK1 at the protein or RNA level. By using a threshold of ≥50 K unique reads and a DV200 RNA quality of ≥30, all expected variants, i.e. 19 SNVs and InDels, three MET exon 14 skipping, and 13 fusion genes were detected, giving a test accuracy of 100%. The results show that this all-in-one assay has a good performance when RNA quality is sufficient and sufficient unique reads are generated. Of course, the test accuracy declines when the quality of samples decreases, and in such cases careful interpretation of the data is essential. Moreover, the design of this all-in-one assay is flexible allowing addition of new druggable, or resistance associated variants, making it applicable in a clinical diagnostic setting. Blood based liquid biopsies provide a minimally invasive approach to test the molecular profile of tumors. Well-studied elements of liquid biopsies are circulating tumor (ct)DNA, circulating tumor cells (CTCs), exosomes/microvesicles and platelets. Platelets can grab RNA or exosomes derived from tumor cells and as such will have an RNA content that is partly derived from tumor cells reflects the transcriptome profiles of tumor cells. This implies that therapy-guiding aberrations can be detected in platelet RNA. In chapter 3, we first applied our customized transcriptome-based NGS assay to test presence of tumor-derived aberrations in platelet RNA samples isolated from patients with active disease and known driver mutations. Despite successful application of our assay and generation of sufficient unique reads, we did not detect any of the known variants. As a next step, we applied the more sensitive droplet digital PCR (ddPCR) assay for T790M, L858R and E19del in EGFR, codon 12 and 13 of KRAS and the ALK-EML4/KIF5B fusion transcript. This revealed the expected variants in only three out of 22 expected variants in platelet RNA samples. The three detected variants were all KRAS hotspot mutations with very low fractional abundances, ranging from 0.07% to 0.55%. This indicated that platelets contain high levels of wild type KRAS transcripts. Transcript levels of wild type EGFR turned out to be extremely low in platelets and we were unable to detect any mutant EGFR transcripts. Lastly, we detected none of the expected eight ALK fusion transcripts in platelet RNAs. To further establish the presence of blood-based biomarkers, we
8
analyzed 11 matched cell free (cf)DNA samples isolated from the same blood tubes. In these samples we were able to detect circulating tumor (ct)DNA in eight out of 11 plasma samples, with fractional abundances varying between 3% and 37%. Thus, the level of tumor derived RNA transcript levels in platelets from NSCLC patients is too low to allow reliable detection of clinically relevant alterations in EGFR, KRAS and ALK fusion gene transcripts.
A small proportion of patients that carry a specific druggable variant does not respond to the variant-targeting tyrosine kinase inhibitor (TKI) treatment, while other patients relapse after an initial good response. This can be explained by pre-existing resistance variants or by evolution of the tumor cells during treatment. Combined dabrafenib (BRAF inhibitor) and trametinib (MEK inhibitor) therapy is routinely used in advanced NSCLC patients harboring a BRAF p.(V600E) mutation. In chapter 4, we studied resistance mechanisms and survival of 34 advanced NSCLC patients sequenced by NGS and treated with combined dabrafenib and trametinib. Three of the patients received a systematic treatment before the combined dabrafenib and trametinib therapy. We aimed to investigate whether presence of a concurrent oncogenic variant co-existing with the BRAF p.(V600E) in the tissue biopsies obtained before start of BRAF and MEK inhibition therapy correlated with progression-free survival (PFS). Details regarding the presence of oncogenic variants were retrieved from clinical diagnostics records. These data were complemented by manual inspection of whole exome sequencing data using the integrated genome viewer (IGV). We observed concurrent mutations in eight out of the 36 patients. Concurrent mutations involved genes in the PI3K pathway (PIK3CA and AKT1) in four patients, MAPK pathway (BRAF non-V600E/ G466V and KIT) in one patient, IDH1 in two patients, and GNAS in one patient. Presence of concurrent mutations, either overall or of those with mutations in PI3K or MAPK pathways, did not correlate with PFS.
The 1st and/or 2nd generation EGFR tyrosine kinase inhibitor (TKI) treatment are given based on the presence of EGFR activating mutations, such as the commonly observed deletions in exon 19 and the L858R. Originally, osimertinib was approved only for patients with acquired EGFR p.(T790M) resistance mutations after 1st or 2nd generation TKIs. More recently, osimertinib has also been approved for first-line treatment of patients with activating EGFR-mutations. In chapter 5, we reported clinical characteristics of two EGFR mutation-positive patients who developed a BRAF p.(V600E) to second-line osimertinib treatment. Guidelines for treatment options for patients with acquired BRAF p.(V600E) resistance mutations have not been established yet. In our report we gave an overview of the treatment regimens and survival of currently reported cases. Treatment of the two patients with combined dabrafenib and trametinib concurrently with osimertinib gave a partial tumor response and provided a PFS of 14 months in one patient. The other patient responded clinically within two weeks, although brain metastases progressed based on magnetic resonance imaging (MRI) after six weeks. Similar favorable results on survival were observed for the cases reported in the literature.
The clinical relevance of EGFR copy number gain in patients with EGFR-mutated advanced NSCLC on first-line TKI treatment has not been fully elucidated. In chapter 6, we investigated two approaches (internal reference and normal comparison) to identify EGFR copy number gain using existing amplicon-based NGS data originally generated for molecular diagnostics. The percentage of patients with EGFR copy number gain was higher in EGFR mutated patients as compared to EGFR wild type patients by both approaches, which is concordant with previous studies that have determined copy number gains by fluorescent in situ hybridization (FISH). EGFR copy number gain in pre-TKI samples was associated with a worse overall survival. Copy number gains as determined by the internal reference approach turned out to be a better predictor of overall survival compared to the normal comparison (HR 3.58, log-rank P=0.0004 and HR 2.76, 0.013 respectively).
The hazard ratios for PFS were not different for both approaches. Patients who developed an EGFR p.(T790M) upon first-line TKI, without EGFR copy number gain had the longest median overall survival. We concluded that the presence of high EGFR copy numbers as determined by amplicon-based NGS data predicted a worse survival in EGFR mutated patients treated with EGFR-TKI, also in those who developed a T790M mutation.
Clinical value of ctDNA in dynamic monitoring of minimal residual disease and treatment response has been demonstrated in NSCLC patients [1,2]. In contrast, studies focusing on the clinical value of
ctDNA in ESCC patients are rare. In chapter 7, we studied the potential of ctDNA as a disease biomarker in ESCC. We performed targeted NGS on DNA isolated from tumor tissue and matched pre- and post-surgery cell free (cf)DNA plasma obtained from 17 early stage ESCC patients. White blood cell samples (WBCs) were used to discriminate between personal variants and somatic variants. We identified somatic variants in pre-surgery cfDNA in eight out of 12 patients. In post-surgery samples the variants were detected with a lower variant allele frequency or were undetectable in all patients. Thus, our explorative study showed that cfDNA could potentially be used to monitor disease load, even in low disease-stage ESCC patients.
8
Discussion and future perspectives1. NGS utilization in molecular diagnosis
1.1 Companion diagnostics
There are a number of FDA-approved broad molecular profiling NGS-based tests including MSK-IMPACT (2017), FoundationOne CDx (2017), Omics Core (2019), FoundationOne Liquid CDx (2020), Guardant360® CDx (2020), PGDx elio tissue complete (2020) (https://www.fda.gov/medical-devices/vitro-diagnostics/nucleic-acid-based-tests). Oncomine Dx Target Test is another FDA-approved NGS tests that is focused on genes relevant for selection of lung cancer patients eligible for targeted therapies, such as crizotinib, gefitinib, pralsetinib, or combined dabrafenib and trametinib.
In the United States, the companion diagnostic should be already/concomitantly approved by the FDA for a drug that requires an in vitro diagnostic test to identify the target mutations. In the EU, vitro diagnostic test guidelines are not as stringent as in the United States. There is no European Medicines Agency (EMA) approved NGS test in Europe. Approximately 80% of the diagnostic tests currently on the market are self-assessed for quality control by the manufacturer. EMA-approved NGS panels will definitely come either from public health institutes or from industries with standardized workflows and quality control guidelines similar to the United states.
The Chinese FDA has approved a number of NGS-based assays focusing on 3-10 genes in lung cancer, including the human 10 gene mutation combined detection kit (AmoyDx), the NovoFocus NSCLC CDx assayand others. The human 10 gene mutation combined detection kit is designed for the detection of drug sensitivity or resistance relevant gene mutations in NSCLC and colorectal cancer. The NovoFocus NSCLC CDx assay covers target mutations of gefitinib, osimertinib and crizotinib in 6 genes. Larger NGS panels will be available along with the emergence of new approved drugs.
1.2 Copy number detection by NGS to routine diagnosis
Thus far, the detection of amplifications are largely determined using FISH or chromogenic in situ hybridization (CISH) [3]. However, FISH analyses typically rely on inspection of 50–100 nuclei [4].
This could induce a bias when non-neoplastic cells are selected (resulting in underestimation) and when nuclei with multiple signals are selected (overestimation of amplifications). The advantage of NGS-based approaches is that the result will be based on the average copy number as present in >106 cells as DNA isolation is typically done on larger tumor samples. A disadvantage of DNA-based approaches is that the admixture of normal cells might dilute the signal of the amplifications, especially in case amplifications are only present in a subset of the tumor cells.
A current limitation of all approaches is that the optimal threshold to predict treatment responses has not been established and differs among treatments, gene loci and tumor types [5]. Moreover, establishment of optimal amplification criteria such as for FGFR1 amplification to predict sensitivity towards FGFR1 inhibitor therapy, are ongoing in clinical trials [6]. Genomic regions
subjected to focal amplifications frequently affect known cancer driver gene loci. This suggests that it is better to focus on focal amplifications as opposed to polysomy and amplifications or duplications of larger chromosomal regions. Our study for calling EGFR amplifications/copy number gains using targeted NGS data is a stepping-stone to explore the clinical value of EGFR copy number changes. To proceed with NGS-based amplification analysis in routine diagnostics,
well-organized robust clinical validation studies are needed. As EGFR amplification has been reported as a resistance mechanism to osimertinib [7], it would be interesting to investigate the correlation of survival and EGFR amplification or copy number gain determined by the NGS in patients treated with osimertinib.
1.3 Limitations of NGS based companion diagnostics
Limitations of NGS tests can be distinguished based on technical and tissue dependent issues. Technical issues include differences in sequencing techniques [8], pipelines used to call variants [9]
and blood collection tubes/sampling methods to obtain cfDNA from plasma or other liquid biopsies. Other potential limitations are related to intra-tumor heterogeneity [10,11], and
inconsistent findings in paired tumor tissue and blood samples. To overcome the potential problem of tumor heterogeneity, multi-region sampling and subsequent sequencing could be implemented, but this comes at a marked increase in costs [10,11]. Guidelines for NGS-based
testing in precision medicine as provided by the FDA “Design, Development, and Analytical Validation” is a big step forward towards reliable integration of NGS-based tests into clinical practice with high accuracy and will ascertain consistency between different labs [12].
Availability of limited tumor tissue or low tumor cell content is a common problem in routine diagnostics, particularly for cases that require analysis of both DNA and RNA with large gene NGS-panels [13-15]. The recommended minimal tumor percentage required for NGS-based testing varies
but is generally set around a lower limit of 20% [16]. One study evaluated 1,402 NSCLC tissue
samples for suitability for clinical testing by NGS. The majority of them were small tissue samples coming from fine needle aspirations (FNA) (10% of all samples) and core needle biopsies (CNB) (70% of all samples) [14]. The success rates of using Oncomine Dx Target Test was 69.2% for the FNA
samples, 75.4% for the CNB samples. The majority of the currently used NGS panels need both DNA for detection of mutations and RNA samples for detection of fusion genes. The All-In-One Transcriptome-Based Assay we developed (see chapter 2) is based on RNA samples for both fusion gene and mutation calling [17]. This combined approach allows a reliable analysis of all targetable
aberrations even when limited material is available. To further validate performance of our customized All-In-One Transcriptome-Based Assay in a clinical setting, we tested a lager cohort of clinical samples without knowledge of the diagnostic test results. The results of this efficiency study will become available in the near future.
A problem of using formalin-fixed paraffin-embedded (FFPE) tissues is the higher background frequencies of specific variants as compared with frozen tissues [18,19]. The formalin fixation step
leads to the introduction of C:G>T:A variants which can be falsely interpreted as crucial clinical mutations such as the EGFR p.(T790M) and NRAS p.(G12D) [20,21]. The C:G>T:A variants are
generated during the amplification reaction when deaminated cytosine residues present as uracil, leading to incorporation of thymidine. In theory, pre-treatment of FFPE DNA using uracil-DNA glycosylase (UDG) could reduce the formalin-induced C:G>T:A background variants [21,22], but the
efficiency still needs to be evaluated. As FFPE samples are usually the primary tissue source for detection of activating mutations in clinical practice, low frequent C>T and G>A mutations should be interpreted with care.
2. Resistance to inhibition of EGFR and BRAF
2.1 Resistance to EGFR inhibitors
A secondary EGFR p.(T790M) has been reported as resistance mechanism in approximately 60% of patients to 1st generation TKIs [23,24], and in more than 73% of patients to 2nd generation TKI
8
afatinib [25]. Osimertinib (3rd generation) has shown good efficacy as a second-line therapy for
advanced EGFR-mutated NSCLC with 1st/2nd generation EGFR-TKI-induced T790M mutations. In contrast to the commonly observed T790M mutation in response to first-and second-line EGFR-TKI, resistance mechanisms to osimertinib are much more heterogeneous. The reported mechanisms can be summarized as EGFR-dependent and EGFR-independent mechanisms [7]. The BRAF
p.(V600E) mutation has been identified as an Osimertinib-induced resistance mechanism in a limited number of patients [26]. Combined osimertinib, dabrafenib and trametinib therapy has
been demonstrated to be an effective approach to counteract the osimertinib resistance in a limited number of patients. However, the currently applied treatment schedules varied between different studies, making a direct comparison challenging. Moreover, toxicity is an issue when combining those treatments. A multicenter study or clinical trial with standardized combined therapy regimens should be performed to evaluate the response rate and adverse events in a larger patient cohort. Dabrafenib and trametinib have limited brain distribution due to the blood-brain barrier [27,28]. During treatment of dabrafenib/trametinib plus osimertinib, one of the patients included in chapter 5 suffered from progressive brain metastases 6 weeks after start of treatment. This can be caused by limited brain distribution of dabrafenib and trametinib. This shows that patients with brain metastases might have limited benefit from dual EGFR and BRAF therapy. Whether or not such patients should be included in a combined EGFR/BRAF trial should be discussed further.
2.2 Resistance to combined BRAF and MEK inhibitors
Primary or intrinsic resistance mechanisms to BRAF inhibition are clinically characterized by the absence of a tumor response despite presence of a BRAF p.(V600) [29]. In colorectal cancers,
intrinsic resistance to BRAF inhibition were caused by feedback mechanisms leading to activation of EGFR through upregulation of Tyr 1068 p-EGFR and Ser 473 phosphorylated-AKT (p-AKT) [30].
Hence, blockade of EGFR using antibody drug cetuximab or the small-molecule inhibitors gefitinib or erlotinib showed strong synergy with BRAF p.(V600E) inhibition. In contrast to colon cancer, tumor cells of melanoma express low levels of EGFR and are not subject to upregulated EGFR via feedback mechanisms. Intrinsic resistance mechanisms in melanoma include intracellular activation of the MAPK and PI3K pathway via increased RTK-ligand levels [31], loss of PTEN [32],
RAC1 activating mutation [33], dysregulation of cell cycle proteins [34], changes in the tumor
micro-environment leading to enhanced secretion of Hepatocyte Growth Factor [35], activation of IGF-1R
signaling [36], expression of COT (encoded by MAP3K8 gene) [37], deletions of CDKN2A,
amplifications of CCND1 a [38], et.al. The intrinsic resistance mechanisms are mainly validated in
vitro using cancer cell lines. Clinical validation to test their relevance in patient cohorts are needed. In lung cancer, studies focusing on dissecting the intrinsic resistance mechanisms to BRAF and MEK inhibition are rare. We have collected pleural effusion derived NSCLC cells from a patient with a BRAF p.(V600E) mutation and a co-existing AKT1 p.(E17K) mutation. This patient did not respond to the treatment indicating that AKT1 p.(E17K) could be responsible for the intrinsic resistance against BRAFi/MEKi. The AKT1 p.(E17K) mutation has been demonstrated to stimulate downstream signals causing tumor cell survival. Further studies focusing on treatment of ex-vivo cultures of this patient might help to validate relevance of this specific mutation. As an alternative approach, genomic or transcriptomic analysis of patients that do and patients that do not respond to combined inhibitors might pinpoint other potential intrinsic resistant mechanisms in NSCLC. WES studies of the BRAF p.(V600E) NSCLC patient cohort as described in chapter 4 are currently ongoing. Collection of viable primary tumor cells of pleural effusion or tissue samples of NSCLC patients with a broad spectrum of different targetable aberrations might be instrumental to study potential intrinsic resistance mechanisms in the future.
In melanomas, similar to the intrinsic resistance, the acquired resistance mechanisms can be summarized as aberrations leading to activation of the MAPK and PI3K pathway [39]. The resistance mechanisms of BRAF inhibitor monotherapy included BRAF copy number gains, aberrant BRAF splicing (exon skipping), BRAF fusion genes such as AGAP3-BRAF [40,41], mutations
in NRAS or MEK1/2 inducing reactivation of the MAPK pathway [42-45], and activation of PI3K/AKT
signaling pathway [46]. Interestingly, melanoma patients that developed a BRAF fusion after vemurfenib therapy continue to show a response to MEK inhibitors in combination with a PI3K inhibitor or CDK4/6 inhibitor both in vitro and in vivo [41]. Acquired resistance to combined
RAF/MEK Inhibition was reported less commonly in melanoma. The resistance mechanisms included alterations in the MAPK pathway including mutations in MEK1/2, NRAS, KRAS, alternative BRAF splicing or BRAF amplifications [47,48].
In lung cancer, the reported acquired resistance mechanisms are limited to a few case reports describing mutations in KRAS, NRAS, MEK1 [49-51]. None of these studies investigated BRAF copy
number gain, alternative splicing or BRAF fusion genes as a resistance mechanism. More basic and clinical studies are needed to elucidate the complete spectrum of acquired resistance mechanisms. The subsequent treatment strategies after resistance need more validation using both in vitro and in vivo approaches. For example, we collected an ascites sample at the time of resistance to dabrafenib and trametinib from a NSCLC patient with BRAF p.(V600E) mutation. Presence of oncogenic mutations that may cause resistance were tested by a targeted NGS panel in the diagnostic setting but turned out to be negative. Whole exome sequencing of both pre- and post-treatment samples of this patient is ongoing and might reveal the underlying resistance causing aberration. Based on the findings, potential follow-up treatment strategies based on the sequencing result will be tested on the ascites-derived tumor cells from this patient. Combined treatment with vemurafenib/cobimetinib and encorafenib/binimetinib are alternative approaches to achieve combined BRAF/MEK inhibition. As an option, these other BRAF/MEK inhibitor combinations can be tested on the ascites-derived cells to compare the sensitivity.
3. Clinical applications of blood-based liquid biopsies in NSCLC
For the identification of actionable genomic alterations in advanced NSCLC patients, NGS-based ctDNA assays covering multiple actionable genomic alterations has demonstrated overall concordance rates ranging from 60-80% with matched tissue [52-56]. The efficiency of a ddPCR
assay was reported to be equivalent to test EGFR mutations in cfDNA as detected in matched tissue [57,58]. Currently, the analysis of cfDNA has been adopted as a standard test in routine
diagnostics. Testing of ctDNA has been recommended in lung cancer patients for whom there is no or insufficient tissue for molecular testing [59]. If feasible, patients who are negative for the
mutations in plasma tested by FoundationOne Liquid CDx should be reflexed to routine tissue biopsy as recommended by FDA. However, the use of ctDNA for early-stage NSCLC is challenging as there might be insufficient amounts of ctDNA in the cfDNA samples. The signature can be unveiled by more sensitive NGS tests that make use of integrated digital error suppression in combination with unique molecular identifiers to accurately quantify ctDNA variants. However, a main issue is that sensitivity of an assay is limited by the genetic noise of the technique or the material [60].
For dynamic monitoring of minimal residual disease and treatment response, the value of ctDNA analysis has also been demonstrated. Presence of ctDNA in blood was associated with relapse of disease after surgery [1,2]. Changes in ctDNA levels were shown to be correlated with radiologic
responses [61]. Identification of resistance associated variants, such as EGFR p.(T790M) and ALK
8
between liquid and tissue biopsies is still a major challenge for clinical implementation of ctDNA. With the ongoing progress of NGS technology and the development of standardized approaches to obtain cfDNA, the use of ctDNA will become more feasible and is likely to lead to improved management of NSCLC patients to guide treatment selection and for disease surveillance.
Presence of circulating tumor cells (CTCs) at diagnosis indicated a poor prognosis in both early and advanced NSCLC [64-66]. However, CTC assessment did not enter into the routine clinical
management of NSCLC patients, due to technical challenges and because it is a mainly prognostic and not a predictive factor. One of the main limitations was differences in methodology and thresholds being employed for CTC assessment in NSCLC in different studies. Large prospective trials are needed for clinical approval of CTC analysis in NSCLC where not only the enumeration of CTCs dominate but also single cell DNA/RNA sequencing approaches will be implemented. This will reveal new information on the value of CTCs in a clinical setting and possibly lead to establishment of new predictive markers.
Tumor-derived exosomes/microvesicles were reported to support tumor growth by creating a microenvironment that promotes tumor cell vitality and formation of metastasis [67]. However,
the exact mode of action of exosomes/microvesicles in creating a favorable microenvironment remains unknown. The clinical utility of exosomes/microvesicles as a tumor biomarker is still in an early stage. The unsolved questions include standardization of procedures to obtain high purity exosomes/microvesicles, as well as sensitivity and specificity of exosomes/microvesicles based assays. These questions need to be addressed to allow implementation as clinical biomarkers. Using a targeted RNA-based NGS on RNA isolated from platelets, both early- and late-stage NSCLC could be identified with an accuracy of 81% and 88%, respectively [68]. This study indicated the
potential of using blood platelets in a diagnostic setting. However, platelets might not be the best choice for diagnostic testing of somatic variants, as the success rate of detecting EGFR and KRAS mutations and ALK fusion transcripts in our study was very low. We showed low abundance of both mutant and wild type EGFR transcripts and high abundance of wild type KRAS transcripts, making it hard to identify mutant transcripts reflecting the genomic make up of tumor cells. Moreover, in contrast to a previous study, we were unable to detect EML4-ALK fusion transcripts in platelets of NSCLC patients with a proven EML4-ALK fusion transcript in the tissue sample. With the development of more sensitive techniques, it might become possible to detect KRAS mutations and ALK fusion transcripts in platelet-derived RNA samples, but this will most likely not be helpful for the detection of mutant EGFR transcripts in platelet RNA samples from NSCLC patients.
References
1. Abbosh, C.; Birkbak, N.J.; Wilson, G.A.; Jamal-Hanjani, M.; Constantin, T.; Salari, R.; Le Quesne, J.; Moore, D.A.; Veeriah, S.; Rosenthal, R., et al. Phylogenetic ctDNA analysis depicts early-stage lung cancer evolution. Nature
2017, 545, 446-451, doi:10.1038/nature22364.
2. Chaudhuri, A.A.; Chabon, J.J.; Lovejoy, A.F.; Newman, A.M.; Stehr, H.; Azad, T.D.; Khodadoust, M.S.; Esfahani, M.S.; Liu, C.L.; Zhou, L., et al. Early Detection of Molecular Residual Disease in Localized Lung Cancer by Circulating Tumor DNA Profiling. Cancer discovery 2017, 7, 1394-1403, doi:10.1158/2159-8290.cd-17-0716. 3. Kato, N.; Itoh, H.; Serizawa, A.; Hatanaka, Y.; Umemura, S.; Osamura, R.Y. Evaluation of HER2 gene
amplification in invasive breast cancer using a dual-color chromogenic in situ hybridization (dual CISH).
Pathology international 2010, 60, 510-515, doi:10.1111/j.1440-1827.2010.02553.x.
4. Shan, L.; Wang, Z.; Guo, L.; Sun, H.; Qiu, T.; Ling, Y.; Li, W.; Li, L.; Liu, X.; Zheng, B., et al. Concurrence of EGFR amplification and sensitizing mutations indicate a better survival benefit from EGFR-TKI therapy in lung adenocarcinoma patients. Lung cancer (Amsterdam, Netherlands) 2015, 89, 337-342,
doi:10.1016/j.lungcan.2015.06.008.
5. Eijkelenboom, A.; Tops, B.B.J.; van den Berg, A.; van den Brule, A.J.C.; Dinjens, W.N.M.; Dubbink, H.J.; Ter Elst, A.; Geurts-Giele, W.R.R.; Groenen, P.; Groenendijk, F.H., et al. Recommendations for the clinical interpretation and reporting of copy number gains using gene panel NGS analysis in routine diagnostics.
Virchows Archiv : an international journal of pathology 2019, 474, 673-680, doi:10.1007/s00428-019-02555-3.
6. Porta, R.; Borea, R.; Coelho, A.; Khan, S.; Araújo, A.; Reclusa, P.; Franchina, T.; Van Der Steen, N.; Van Dam, P.; Ferri, J., et al. FGFR a promising druggable target in cancer: Molecular biology and new drugs. Critical reviews
in oncology/hematology 2017, 113, 256-267, doi:10.1016/j.critrevonc.2017.02.018.
7. Leonetti, A.; Sharma, S.; Minari, R.; Perego, P.; Giovannetti, E.; Tiseo, M. Resistance mechanisms to osimertinib in EGFR-mutated non-small cell lung cancer. British journal of cancer 2019, 121, 725-737, doi:10.1038/s41416-019-0573-8.
8. Wang, Q.; Kotoula, V.; Hsu, P.C.; Papadopoulou, K.; Ho, J.W.K.; Fountzilas, G.; Giannoulatou, E. Comparison of somatic variant detection algorithms using Ion Torrent targeted deep sequencing data. BMC medical
genomics 2019, 12, 181, doi:10.1186/s12920-019-0636-y.
9. Bian, X.; Zhu, B.; Wang, M.; Hu, Y.; Chen, Q.; Nguyen, C.; Hicks, B.; Meerzaman, D. Comparing the performance of selected variant callers using synthetic data and genome segmentation. BMC bioinformatics
2018, 19, 429, doi:10.1186/s12859-018-2440-7.
10. Gerlinger, M.; Rowan, A.J.; Horswell, S.; Math, M.; Larkin, J.; Endesfelder, D.; Gronroos, E.; Martinez, P.; Matthews, N.; Stewart, A., et al. Intratumor heterogeneity and branched evolution revealed by multiregion sequencing. The New England journal of medicine 2012, 366, 883-892, doi:10.1056/NEJMoa1113205.
11. Salcedo, A.; Tarabichi, M.; Espiritu, S.M.G.; Deshwar, A.G.; David, M.; Wilson, N.M.; Dentro, S.; Wintersinger, J.A.; Liu, L.Y.; Ko, M., et al. A community effort to create standards for evaluating tumor subclonal reconstruction. Nature biotechnology 2020, 38, 97-107, doi:10.1038/s41587-019-0364-z.
12. Luh, F.; Yen, Y. FDA guidance for next generation sequencing-based testing: balancing regulation and innovation in precision medicine. NPJ genomic medicine 2018, 3, 28, doi:10.1038/s41525-018-0067-2. 13. Garrido, P.; Conde, E.; de Castro, J.; Gómez-Román, J.J.; Felip, E.; Pijuan, L.; Isla, D.; Sanz, J.; Paz-Ares, L.;
López-Ríos, F. Updated guidelines for predictive biomarker testing in advanced non-small-cell lung cancer: a National Consensus of the Spanish Society of Pathology and the Spanish Society of Medical Oncology. Clinical
& translational oncology : official publication of the Federation of Spanish Oncology Societies and of the National Cancer Institute of Mexico 2020, 22, 989-1003, doi:10.1007/s12094-019-02218-4.
14. Yu, T.M.; Morrison, C.; Gold, E.J.; Tradonsky, A.; Layton, A.J. Multiple Biomarker Testing Tissue Consumption and Completion Rates With Single-gene Tests and Investigational Use of Oncomine Dx Target Test for Advanced Non-Small-cell Lung Cancer: A Single-center Analysis. Clinical lung cancer 2019, 20, 20-29.e28, doi:10.1016/j.cllc.2018.08.010.
15. Sholl, L. Molecular diagnostics of lung cancer in the clinic. Translational lung cancer research 2017, 6, 560-569, doi:10.21037/tlcr.2017.08.03.
16. Jennings, L.J.; Arcila, M.E.; Corless, C.; Kamel-Reid, S.; Lubin, I.M.; Pfeifer, J.; Temple-Smolkin, R.L.; Voelkerding, K.V.; Nikiforova, M.N. 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. The Journal of molecular diagnostics : JMD 2017, 19, 341-365, doi:10.1016/j.jmoldx.2017.01.011.
17. Wei, J.; Rybczynska, A.A.; Meng, P.; Terpstra, M.; Saber, A.; Sietzema, J.; Timens, W.; Schuuring, E.; Hiltermann, T.J.N.; Groen, H.J.M., et al. An All-In-One Transcriptome-Based Assay to Identify Therapy-Guiding
8
Genomic Aberrations in Nonsmall Cell Lung Cancer Patients. Cancers (Basel) 2020, 12, doi:10.3390/cancers12102843.
18. Williams, C.; Pontén, F.; Moberg, C.; Söderkvist, P.; Uhlén, M.; Pontén, J.; Sitbon, G.; Lundeberg, J. A high frequency of sequence alterations is due to formalin fixation of archival specimens. The American journal of
pathology 1999, 155, 1467-1471, doi:10.1016/s0002-9440(10)65461-2.
19. Quach, N.; Goodman, M.F.; Shibata, D. In vitro mutation artifacts after formalin fixation and error prone translesion synthesis during PCR. BMC clinical pathology 2004, 4, 1, doi:10.1186/1472-6890-4-1.
20. Chen, G.; Mosier, S.; Gocke, C.D.; Lin, M.T.; Eshleman, J.R. Cytosine deamination is a major cause of baseline noise in next-generation sequencing. Molecular diagnosis & therapy 2014, 18, 587-593, doi:10.1007/s40291-014-0115-2.
21. Do, H.; Dobrovic, A. Sequence artifacts in DNA from formalin-fixed tissues: causes and strategies for minimization. Clinical chemistry 2015, 61, 64-71, doi:10.1373/clinchem.2014.223040.
22. Do, H.; Wong, S.Q.; Li, J.; Dobrovic, A. Reducing sequence artifacts in amplicon-based massively parallel sequencing of formalin-fixed paraffin-embedded DNA by enzymatic depletion of uracil-containing templates.
Clinical chemistry 2013, 59, 1376-1383, doi:10.1373/clinchem.2012.202390.
23. Morgillo, F.; Della Corte, C.M.; Fasano, M.; Ciardiello, F. Mechanisms of resistance to EGFR-targeted drugs: lung cancer. ESMO open 2016, 1, e000060, doi:10.1136/esmoopen-2016-000060.
24. Tetsu, O.; Hangauer, M.J.; Phuchareon, J.; Eisele, D.W.; McCormick, F. Drug Resistance to EGFR Inhibitors in Lung Cancer. Chemotherapy 2016, 61, 223-235, doi:10.1159/000443368.
25. Hochmair, M.J.; Buder, A.; Schwab, S.; Burghuber, O.C.; Prosch, H.; Hilbe, W.; Cseh, A.; Fritz, R.; Filipits, M. Liquid-Biopsy-Based Identification of EGFR T790M Mutation-Mediated Resistance to Afatinib Treatment in Patients with Advanced EGFR Mutation-Positive NSCLC, and Subsequent Response to Osimertinib. Targeted
oncology 2019, 14, 75-83, doi:10.1007/s11523-018-0612-z.
26. Meng, P.; Koopman, B.; Kok, K.; Ter Elst, A.; Schuuring, E.; van Kempen, L.C.; Timens, W.; Hiltermann, T.J.N.; Groen, H.J.M.; van den Berg, A., et al. Combined osimertinib, dabrafenib and trametinib treatment for advanced non-small-cell lung cancer patients with an osimertinib-induced BRAF V600E mutation. Lung cancer
(Amsterdam, Netherlands) 2020, 146, 358-361, doi:10.1016/j.lungcan.2020.05.036.
27. Vaidhyanathan, S.; Mittapalli, R.K.; Ma, D.J.; Schroeder, M.A.; Sarkaria, J.N.; Elmquist, W.F. Abstract 3714: Trametinib (Mekinist TM) has limited brain distribution in the mouse model. Cancer research 2014, 74, 3714-3714.
28. Vaidhyanathan, S.; Mittapalli, R.K.; Sarkaria, J.N.; Elmquist, W.F. Factors influencing the CNS distribution of a novel MEK-1/2 inhibitor: implications for combination therapy for melanoma brain metastases. Drug
metabolism and disposition: the biological fate of chemicals 2014, 42, 1292-1300,
doi:10.1124/dmd.114.058339.
29. Amaral, T.; Sinnberg, T.; Meier, F.; Krepler, C.; Levesque, M.; Niessner, H.; Garbe, C. The mitogen-activated protein kinase pathway in melanoma part I - Activation and primary resistance mechanisms to BRAF inhibition. European journal of cancer (Oxford, England : 1990) 2017, 73, 85-92, doi:10.1016/j.ejca.2016.12.010.
30. Prahallad, A.; Sun, C.; Huang, S.; Di Nicolantonio, F.; Salazar, R.; Zecchin, D.; Beijersbergen, R.L.; Bardelli, A.; Bernards, R. Unresponsiveness of colon cancer to BRAF(V600E) inhibition through feedback activation of EGFR. Nature 2012, 483, 100-103, doi:10.1038/nature10868.
31. Wilson, T.R.; Fridlyand, J.; Yan, Y.; Penuel, E.; Burton, L.; Chan, E.; Peng, J.; Lin, E.; Wang, Y.; Sosman, J., et al. Widespread potential for growth-factor-driven resistance to anticancer kinase inhibitors. Nature 2012, 487, 505-509, doi:10.1038/nature11249.
32. Paraiso, K.H.; Xiang, Y.; Rebecca, V.W.; Abel, E.V.; Chen, Y.A.; Munko, A.C.; Wood, E.; Fedorenko, I.V.; Sondak, V.K.; Anderson, A.R., et al. PTEN loss confers BRAF inhibitor resistance to melanoma cells through the suppression of BIM expression. Cancer research 2011, 71, 2750-2760, doi:10.1158/0008-5472.can-10-2954. 33. Krauthammer, M.; Kong, Y.; Ha, B.H.; Evans, P.; Bacchiocchi, A.; McCusker, J.P.; Cheng, E.; Davis, M.J.; Goh, G.;
Choi, M., et al. Exome sequencing identifies recurrent somatic RAC1 mutations in melanoma. Nature genetics
2012, 44, 1006-1014, doi:10.1038/ng.2359.
34. Smalley, K.S.; Lioni, M.; Dalla Palma, M.; Xiao, M.; Desai, B.; Egyhazi, S.; Hansson, J.; Wu, H.; King, A.J.; Van Belle, P., et al. Increased cyclin D1 expression can mediate BRAF inhibitor resistance in BRAF V600E-mutated melanomas. Molecular cancer therapeutics 2008, 7, 2876-2883, doi:10.1158/1535-7163.mct-08-0431. 35. Straussman, R.; Morikawa, T.; Shee, K.; Barzily-Rokni, M.; Qian, Z.R.; Du, J.; Davis, A.; Mongare, M.M.; Gould,
J.; Frederick, D.T., et al. Tumour micro-environment elicits innate resistance to RAF inhibitors through HGF secretion. Nature 2012, 487, 500-504, doi:10.1038/nature11183.
36. Karasic, T.B.; Hei, T.K.; Ivanov, V.N. Disruption of IGF-1R signaling increases TRAIL-induced apoptosis: a new potential therapy for the treatment of melanoma. Experimental cell research 2010, 316, 1994-2007, doi:10.1016/j.yexcr.2010.04.014.
37. Johannessen, C.M.; Boehm, J.S.; Kim, S.Y.; Thomas, S.R.; Wardwell, L.; Johnson, L.A.; Emery, C.M.; Stransky, N.; Cogdill, A.P.; Barretina, J., et al. COT drives resistance to RAF inhibition through MAP kinase pathway reactivation. Nature 2010, 468, 968-972, doi:10.1038/nature09627.
38. Nathanson, K.L.; Martin, A.M.; Wubbenhorst, B.; Greshock, J.; Letrero, R.; D'Andrea, K.; O'Day, S.; Infante, J.R.; Falchook, G.S.; Arkenau, H.T., et al. Tumor genetic analyses of patients with metastatic melanoma treated with the BRAF inhibitor dabrafenib (GSK2118436). Clinical cancer research : an official journal of the American
Association for Cancer Research 2013, 19, 4868-4878, doi:10.1158/1078-0432.ccr-13-0827.
39. Sanchez, J.N.; Wang, T.; Cohen, M.S. BRAF and MEK Inhibitors: Use and Resistance in BRAF-Mutated Cancers.
Drugs 2018, 78, 549-566, doi:10.1007/s40265-018-0884-8.
40. Kulkarni, A.; Al-Hraishawi, H.; Simhadri, S.; Hirshfield, K.M.; Chen, S.; Pine, S.; Jeyamohan, C.; Sokol, L.; Ali, S.; Teo, M.L., et al. BRAF Fusion as a Novel Mechanism of Acquired Resistance to Vemurafenib in BRAF(V600E) Mutant Melanoma. Clinical cancer research : an official journal of the American Association for Cancer
Research 2017, 23, 5631-5638, doi:10.1158/1078-0432.ccr-16-0758.
41. Kim, H.S.; Jung, M.; Kang, H.N.; Kim, H.; Park, C.W.; Kim, S.M.; Shin, S.J.; Kim, S.H.; Kim, S.G.; Kim, E.K., et al. Oncogenic BRAF fusions in mucosal melanomas activate the MAPK pathway and are sensitive to MEK/PI3K inhibition or MEK/CDK4/6 inhibition. Oncogene 2017, 36, 3334-3345, doi:10.1038/onc.2016.486.
42. Rizos, H.; Menzies, A.M.; Pupo, G.M.; Carlino, M.S.; Fung, C.; Hyman, J.; Haydu, L.E.; Mijatov, B.; Becker, T.M.; Boyd, S.C., et al. BRAF inhibitor resistance mechanisms in metastatic melanoma: spectrum and clinical impact.
Clinical cancer research : an official journal of the American Association for Cancer Research 2014, 20,
1965-1977, doi:10.1158/1078-0432.ccr-13-3122.
43. Shi, H.; Hugo, W.; Kong, X.; Hong, A.; Koya, R.C.; Moriceau, G.; Chodon, T.; Guo, R.; Johnson, D.B.; Dahlman, K.B., et al. Acquired resistance and clonal evolution in melanoma during BRAF inhibitor therapy. Cancer
discovery 2014, 4, 80-93, doi:10.1158/2159-8290.cd-13-0642.
44. Van Allen, E.M.; Wagle, N.; Sucker, A.; Treacy, D.J.; Johannessen, C.M.; Goetz, E.M.; Place, C.S.; Taylor-Weiner, A.; Whittaker, S.; Kryukov, G.V., et al. The genetic landscape of clinical resistance to RAF inhibition in metastatic melanoma. Cancer discovery 2014, 4, 94-109, doi:10.1158/2159-8290.cd-13-0617.
45. Nazarian, R.; Shi, H.; Wang, Q.; Kong, X.; Koya, R.C.; Lee, H.; Chen, Z.; Lee, M.K.; Attar, N.; Sazegar, H., et al. Melanomas acquire resistance to B-RAF(V600E) inhibition by RTK or N-RAS upregulation. Nature 2010, 468, 973-977, doi:10.1038/nature09626.
46. Perna, D.; Karreth, F.A.; Rust, A.G.; Perez-Mancera, P.A.; Rashid, M.; Iorio, F.; Alifrangis, C.; Arends, M.J.; Bosenberg, M.W.; Bollag, G., et al. BRAF inhibitor resistance mediated by the AKT pathway in an oncogenic BRAF mouse melanoma model. Proceedings of the National Academy of Sciences of the United States of
America 2015, 112, E536-545, doi:10.1073/pnas.1418163112.
47. Long, G.V.; Fung, C.; Menzies, A.M.; Pupo, G.M.; Carlino, M.S.; Hyman, J.; Shahheydari, H.; Tembe, V.; Thompson, J.F.; Saw, R.P., et al. Increased MAPK reactivation in early resistance to dabrafenib/trametinib combination therapy of BRAF-mutant metastatic melanoma. Nature communications 2014, 5, 5694, doi:10.1038/ncomms6694.
48. Wagle, N.; Van Allen, E.M.; Treacy, D.J.; Frederick, D.T.; Cooper, Z.A.; Taylor-Weiner, A.; Rosenberg, M.; Goetz, E.M.; Sullivan, R.J.; Farlow, D.N., et al. MAP kinase pathway alterations in BRAF-mutant melanoma patients with acquired resistance to combined RAF/MEK inhibition. Cancer discovery 2014, 4, 61-68, doi:10.1158/2159-8290.cd-13-0631.
49. Niemantsverdriet, M.; Schuuring, E.; Elst, A.T.; van der Wekken, A.J.; van Kempen, L.C.; van den Berg, A.; Groen, H.J.M. KRAS Mutation as a Resistance Mechanism to BRAF/MEK Inhibition in NSCLC. J Thorac Oncol
2018, 13, e249-e251, doi:10.1016/j.jtho.2018.07.103.
50. Facchinetti, F.; Lacroix, L.; Mezquita, L.; Scoazec, J.Y.; Loriot, Y.; Tselikas, L.; Gazzah, A.; Rouleau, E.; Adam, J.; Michiels, S., et al. Molecular mechanisms of resistance to BRAF and MEK inhibitors in BRAF(V600E) non-small cell lung cancer. European journal of cancer (Oxford, England : 1990) 2020, 132, 211-223, doi:10.1016/j.ejca.2020.03.025.
51. Ortiz-Cuaran, S.; Mezquita, L.; Swalduz, A.; Aldea, M.; Mazieres, J.; Leonce, C.; Jovelet, C.; Pradines, A.; Avrillon, V.; Chumbi Flores, W.R., et al. Circulating Tumor DNA Genomics Reveal Potential Mechanisms of Resistance to BRAF-Targeted Therapies in Patients with BRAF-Mutant Metastatic Non-Small Cell Lung Cancer.
Clinical cancer research : an official journal of the American Association for Cancer Research 2020,
10.1158/1078-0432.ccr-20-1037, doi:10.1158/1078-0432.ccr-20-1037.
52. Xu, S.; Lou, F.; Wu, Y.; Sun, D.Q.; Zhang, J.B.; Chen, W.; Ye, H.; Liu, J.H.; Wei, S.; Zhao, M.Y., et al. Circulating tumor DNA identified by targeted sequencing in advanced-stage non-small cell lung cancer patients. Cancer
letters 2016, 370, 324-331, doi:10.1016/j.canlet.2015.11.005.
53. Couraud, S.; Vaca-Paniagua, F.; Villar, S.; Oliver, J.; Schuster, T.; Blanché, H.; Girard, N.; Trédaniel, J.; Guilleminault, L.; Gervais, R., et al. Noninvasive diagnosis of actionable mutations by deep sequencing of circulating free DNA in lung cancer from never-smokers: a proof-of-concept study from BioCAST/IFCT-1002.
8
Clinical cancer research : an official journal of the American Association for Cancer Research 2014, 20,
4613-4624, doi:10.1158/1078-0432.ccr-13-3063.
54. Paweletz, C.P.; Sacher, A.G.; Raymond, C.K.; Alden, R.S.; O'Connell, A.; Mach, S.L.; Kuang, Y.; Gandhi, L.; Kirschmeier, P.; English, J.M., et al. Bias-Corrected Targeted Next-Generation Sequencing for Rapid, Multiplexed Detection of Actionable Alterations in Cell-Free DNA from Advanced Lung Cancer Patients.
Clinical cancer research : an official journal of the American Association for Cancer Research 2016, 22,
915-922, doi:10.1158/1078-0432.ccr-15-1627-t.
55. Thompson, J.C.; Yee, S.S.; Troxel, A.B.; Savitch, S.L.; Fan, R.; Balli, D.; Lieberman, D.B.; Morrissette, J.D.; Evans, T.L.; Bauml, J., et al. Detection of Therapeutically Targetable Driver and Resistance Mutations in Lung Cancer Patients by Next-Generation Sequencing of Cell-Free Circulating Tumor DNA. Clinical cancer research : an
official journal of the American Association for Cancer Research 2016, 22, 5772-5782,
doi:10.1158/1078-0432.ccr-16-1231.
56. Yao, Y.; Liu, J.; Li, L.; Yuan, Y.; Nan, K.; Wu, X.; Zhang, Z.; Wu, Y.; Li, X.; Zhu, J., et al. Detection of circulating tumor DNA in patients with advanced non-small cell lung cancer. Oncotarget 2017, 8, 2130-2140, doi:10.18632/oncotarget.12883.
57. Feng, W.N.; Gu, W.Q.; Zhao, N.; Pan, Y.M.; Luo, W.; Zhang, H.; Liang, J.M.; Yang, J.; Deng, Y.M. Comparison of the SuperARMS and Droplet Digital PCR for Detecting EGFR Mutation in ctDNA From NSCLC Patients.
Translational oncology 2018, 11, 542-545, doi:10.1016/j.tranon.2018.02.007.
58. Ding, P.N.; Becker, T.; Bray, V.; Chua, W.; Ma, Y.; Xu, B.; Lynch, D.; de Souza, P.; Roberts, T. 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. Thoracic cancer
2019, 10, 1879-1884, doi:10.1111/1759-7714.13154.
59. Lindeman, N.I.; Cagle, P.T.; Aisner, D.L.; Arcila, M.E.; Beasley, M.B.; Bernicker, E.H.; Colasacco, C.; Dacic, S.; Hirsch, F.R.; Kerr, K., 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.
Archives of pathology & laboratory medicine 2018, 142, 321-346, doi:10.5858/arpa.2017-0388-CP.
60. Malapelle, U.; Pisapia, P.; Rocco, D.; Smeraglio, R.; di Spirito, M.; Bellevicine, C.; Troncone, G. Next generation sequencing techniques in liquid biopsy: focus on non-small cell lung cancer patients. Translational lung
cancer research 2016, 5, 505-510, doi:10.21037/tlcr.2016.10.08.
61. Imamura, F.; Uchida, J.; Kukita, Y.; Kumagai, T.; Nishino, K.; Inoue, T.; Kimura, M.; Oba, S.; Kato, K. Monitoring of treatment responses and clonal evolution of tumor cells by circulating tumor DNA of heterogeneous mutant EGFR genes in lung cancer. Lung cancer (Amsterdam, Netherlands) 2016, 94, 68-73, doi:10.1016/j.lungcan.2016.01.023.
62. Uchida, J.; Imamura, F.; Kukita, Y.; Oba, S.; Kumagai, T.; Nishino, K.; Inoue, T.; Kimura, M.; Kato, K. Dynamics of circulating tumor DNA represented by the activating and resistant mutations in epidermal growth factor receptor tyrosine kinase inhibitor treatment. Cancer science 2016, 107, 353-358, doi:10.1111/cas.12860. 63. Ihuegbu, N.; Banks, K.C.; Fairclough, S.R.; Zill, O.A.; Chudova, D.; Lanman, R.B.; Blakely, C.M. Non-invasive
detection of crizotinib resistance in ALK-rearranged lung adenocarcinoma directs treatment with next-generation ALK inhibitors. American Society of Clinical Oncology: 2016.
64. Tamminga, M.; de Wit, S.; Hiltermann, T.J.N.; Timens, W.; Schuuring, E.; Terstappen, L.; Groen, H.J.M. Circulating tumor cells in advanced non-small cell lung cancer patients are associated with worse tumor response to checkpoint inhibitors. Journal for immunotherapy of cancer 2019, 7, 173, doi:10.1186/s40425-019-0649-2.
65. Bayarri-Lara, C.; Ortega, F.G.; Cueto Ladrón de Guevara, A.; Puche, J.L.; Ruiz Zafra, J.; de Miguel-Pérez, D.; Ramos, A.S.; Giraldo-Ospina, C.F.; Navajas Gómez, J.A.; Delgado-Rodriguez, M., et al. Circulating Tumor Cells Identify Early Recurrence in Patients with Non-Small Cell Lung Cancer Undergoing Radical Resection. PLoS
One 2016, 11, e0148659, doi:10.1371/journal.pone.0148659.
66. Reddy, R.M.; Murlidhar, V.; Zhao, L.; Grabauskiene, S.; Zhang, Z.; Ramnath, N.; Lin, J.; Chang, A.C.; Carrott, P.; Lynch, W., et al. Pulmonary venous blood sampling significantly increases the yield of circulating tumor cells in early-stage lung cancer. The Journal of thoracic and cardiovascular surgery 2016, 151, 852-858, doi:10.1016/j.jtcvs.2015.09.126.
67. Azmi, A.S.; Bao, B.; Sarkar, F.H. Exosomes in cancer development, metastasis, and drug resistance: a comprehensive review. Cancer metastasis reviews 2013, 32, 623-642, doi:10.1007/s10555-013-9441-9. 68. Best, M.G.; Sol, N.; In 't Veld, S.; Vancura, A.; Muller, M.; Niemeijer, A.N.; Fejes, A.V.; Tjon Kon Fat, L.A.; Huis
In 't Veld, A.E.; Leurs, C., et al. Swarm Intelligence-Enhanced Detection of Non-Small-Cell Lung Cancer Using Tumor-Educated Platelets. Cancer cell 2017, 32, 238-252.e239, doi:10.1016/j.ccell.2017.07.004.
