Optimized NGS-based de novo MET amplification detection for improved lung cancer patient management

doi:10.21203/rs.3.rs-5026064/v1

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Optimized NGS-based de novo MET amplification detection for improved lung cancer patient management

https://doi.org/10.21203/rs.3.rs-5026064/v1

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Background: MET amplification (MET^amp) is a noteworthy genomic alteration that can occur in patients with non-small cell lung cancer (NSCLC). It has been demonstrated to occur as a primary oncogenic driver that may exist prior to any treatment and is referred to as de novo MET^amp. Despite the recognized significance of this genetic alteration, routine large-scale screening for the early detection of de novo MET^amp is currently lacking in clinical practice and the clinical impact of de novo MET^amp in NSCLC remains poorly investigated.

Methods: In this study, we developed a NGS-based screening method for detecting and stratifying MET^amp optimized in silico, validated in a patient cohort (n = 72) and applied to 1,932 NSCLC patients. Clinical outcomes (OS and PFS) were assessed in de novo MET^amp cases (n = 46).

Results: The optimized NGS-based method achieved high confidence (F-score > 0.99) during in silico optimization. In vivo validation demonstrated high sensitivity (0.93) and specificity (0.97) compared to fluorescent in situ hybridization. de novo MET^amp was found in 2.4% of cases stratified into distinct amplification groups based on the amplification copy number ratio (CNR): Low- (1.5 < CNR ≤ 2.2), Medium- (2.2 < CNR ≤ 4), and High-amplification (CNR > 4). Significant differences in patient outcome (p < 0.001) were observed between the Low- (median OS: 35.9 months), Medium- (median OS: 14.3 months) and High-amplification (median OS: 3.3 months) groups. PFS under chemotherapy was notably reduced in the Medium/High-amplification groups compared to the Low-amplification group (p = 0.001).

Conclusions: Screening for MET^amp detection followed by stratification based on MET^amp levels may be considered in all NSCLC patients at diagnosis. This approach could potentially enhance treatment management effectiveness by facilitating inclusion in clinical trials.

Non-small cell lung cancer (NSCLC) accounts for approximately 85% of lung cancer cases and includes various histological subtypes, such as adenocarcinoma, squamous cell carcinoma and large cell carcinoma (1). Recent advancements in molecular biology have led to the development of targeted therapies, including epidermal growth factor receptor tyrosine kinase inhibitors (EGFR-TKIs), which are effective in NSCLC patients with sensitive EGFR alterations (2, 3). However, the clinical effectiveness of EGFR-TKIs is significantly reduced due to the inevitable emergence of resistance within 12–18 months (4).

Amplification of the mesenchymal epithelial transition gene (MET^amp) has been identified as a common mechanism of acquired resistance to EGFR-TKIs, accounting for up to 20% of resistance cases to third generation EGFR-TKI (4–6). However, MET^amp has also been demonstrated to occur as a primary oncogenic driver that may exist prior to any treatment and is referred to as de novo MET^amp (7). MET^amp occurs in 2–5% of NSCLC, including both acquired and de novo instances (8, 9). Variations in reported prevalence are due to the lack of precise and standardized scoring systems, the absence of established consensus on the definition of MET positivity and the diversity in screening methods employed (10–14).

The gene copy number (GCN), which quantifies the extent of amplification, is a continuous variable. Defining a specific threshold to categorize samples as either amplified or not has significant implications. It influences not only the prevalence rate and occurrence frequency, but also the evaluation of therapeutic inhibitor effectiveness. Recently, Camidge et al. confirmed the clinical benefits of patient stratification based on MET^amp levels in Low-, Medium-, and High-amplification groups (15, 16). This stratification correlated with the response to crizotinib, an effective MET inhibitor, wherein high levels of amplification were notably associated with enhanced treatment outcome. This ultimately underscores the importance of establishing standardized and systematic early screening methods for the detection of MET^amp in NSCLC patients, thereby facilitating the determination of precise management strategies.

Immunohistochemistry (IHC) is a common screening method used to identify MET overexpression. However, the correlation between overexpression as determined by MET IHC and MET^amp is weak, and results in a significant number of false positive results (13, 17). Furthermore, IHC lacks the ability to stratify samples based on the amplification level. The gold standard for MET^amp detection, fluorescence in situ hybridization (FISH), offers high sensitivity and specificity and does not depend on the proportion of tumor cells in the sample. However, it has several limitations that impede its routine use as a screening method. These limitations include subjectivity, susceptibility to tissue sectioning artifacts, the requirement for a new FFPE slide for each analysis and the lack of consensus on scoring systems and cutoffs. Moreover, MET FISH signals frequently form clusters due to overlapping nuclei or saturation in samples with high levels of MET^amp, making it challenging to accurately quantify the signal. To address these issues, RNA-based techniques have been proposed (18). Digital droplet PCR (ddPCR) has also been employed for MET^amp detection and was shown to be a highly sensitive method (12, 19). However, similar to FISH, ddPCR is a non-exploratory approach that permits the analysis of only a single target region at a time, thereby limiting its implementation as a screening method.

Next-generation sequencing (NGS) is progressively becoming standard practice in molecular diagnostics, offering a methodology to evaluate MET^amp within the scope of comprehensive genomic profiling (14). The definition of NGS-based MET^amp positivity varies in the literature, making the assessment of this status a challenge and preventing the inclusion of patients in clinical trials. Proper standardization and validation of NGS-based MET^amp are of utmost importance, particularly in the stratification of enrolled patients based on de novo MET^amp detection (20).

In this study, we developed a NGS-based MET^amp detection method using a multi-step approach. First, we optimized the NGS amplification detection algorithm using in silico simulations. We then validated this algorithm by comparing the results obtained with NGS to those obtained with other techniques, notably FISH, and determined the optimal thresholds allowing effective detection of MET^amp samples and their stratification into distinct groups based on amplification level. Finally, we applied the refined algorithm to a retrospective cohort of 1,932 patients, and stratified patients into three groups based on their level of amplification: Low, Medium, and High. Importantly, we explored the patients’ overall survival (OS) and progression-free survival (PFS) following conventional therapies (chemotherapy and immunotherapy) within these groups.

NGS-based MET^amp detection algorithm

A recently published tool called ifCNV was employed for MET^amp detection from NGS data. ifCNV is a machine learning-based method that utilizes isolation forest to detect copy number variations based on read-depth distribution obtained from NGS data (21). Briefly, it generates read matrices from sequencing files and subsequently identifies potentially amplified regions. These regions are further validated using a specific scoring method. Two principal parameters, that can be adjusted in ifCNV, were extensively tested on synthetic datasets to identify the optimal values yielding the best performance for MET^amp detection: (i) the contamination parameter of the isolation forest algorithm, which helps control the outlier detection rate; and (ii) the threshold for copy number ratio (CNR, i.e. GCN/2) that determines whether a particular gene is considered amplified or not. Briefly, the contamination parameter represents the expected proportion of outliers in the data set. The CNR is calculated by dividing the normalized reads of the sample of interest by the mean of the normalized reads of all non-amplified samples in the run. Gene amplifications are differentiated from polysomies by the inclusion of three additional genes located on chromosome 7 (Rac Family Small GTPase 1, RAC1, EGFR, and B-Raf Proto-Oncogene, BRAF) in the two panels used for this study. The algorithm can identify a polysomy when the CNRs of these four genes (including MET) increase within the same order of magnitude (Supplementary Figure S1). Synthetic datasets simulating the characteristics of real-life datasets (20 to 40 samples, 500 amplicons, and similar reads distribution) were created following a method described elsewhere (21). To simulate amplifications, the number of reads covering MET was multiplied by a factor ranging from 2 to 5 (representing a GCN from 4 to 10).

Binary classification indicators

Sensitivity (Se), specificity (Sp) and F-score were computed for each tested threshold and contamination value. These were calculated based on the number of true positives (TP), true negatives (TN), false positives (FP) and false negatives (FN) as follows:

$$\:Se=\frac{TP}{TP+FN}$$

$$\:Sp=\frac{TN}{TN+FP}$$

$$\:\text{F-score}=\frac{2TP}{2TP+FP+FN}$$

Patients and sample collection

A total of 1,932 formalin-fixed and paraffin-embedded (FFPE) specimens from lung cancer samples, submitted from January 2019 to December 2023 to the University Hospital of Montpellier and Toulouse Oncopôle (France) for variant detection analysis by NGS, were retrospectively screened using ifCNV and the optimal thresholds determined. The percentage of tumor cells in the series ranged from 20–100%. Tissue punches using a 1 mm needle or macro-dissection of 10 µm thick sections were performed on FFPE tumor blocks to increase the percentage of tumor cells in the sample. This study was approved by the Institutional Review Board of the University Hospital of Montpellier under the approval number IRB-MTP_2023_01_202201240 and was conducted in accordance with the Declaration of Helsinki. Samples with a NGS-based MET CNR of 1.5 or higher and with a de novo MET^amp (i.e. MET^amp occurred before any treatment) were included in the study (n = 46). Patients with chr7 polysomies were not included. Information regarding clinicopathological data and response to treatment was retrieved from the medical records of these patients.

Immunohistochemistry

Slides were de-paraffinized in xylene, hydrated in alcohol and baked in a microwave. Endogenous peroxidase was blocked. Immunohistochemical (IHC) analysis using cMET antibody (clone SP44, Roche Diagnostics) was performed on the Ventana Benchmark (Roche) automated staining system, according to the manufacturer’s recommendations. A polymer linked to a peroxidase system with DAB was used to amplify and visualize the signal. Immunoscoring was performed by experienced pathologists who evaluated both staining intensity (negative, weak, moderate, or strong) and the percentage of these intensities, as previously described (22). This immunoscore allowed to delineate 4 subgroups: 3+ (≥ 50% of tumor cells stained exhibiting strong staining intensity); 2+ (≥ 50% of tumor cells with moderate or higher staining intensity but < 50% strong intensity); 1+ (≥ 50% of tumor cells with weak or higher staining intensity but < 50% with moderate or higher intensity); or 0 (no staining or < 50% of tumor cells with any intensity). MET positivity was defined as a score of 3+.

Fluorescence in situ hybridization

Sections of the formalin-fixed and paraffin-embedded tissues were sliced 3mm thick and deposited on silanized slides. After an incubation of 1 hour at 56°C, deparaffinization, pretreatment and hybridization were performed on the Dako Omnis automated system according to the manufacturer’s recommendations. The MET IQFISH Probe with CEp7 (#G111803-2, Dako Omnis) was used to detect amplifications of MET. Slides were then scanned with a slide scanner (Panoramic 250 FlashII, 3DHisTech, Sysmex, Villepinte, France) available in our laboratory. The number of fluorescence signals in at least 100 cells was then counted using the Slideviewer software (3DHisTech). MET was considered amplified if the MET-to-CEP7 ratio was higher than 1.8, if clusters were observed in more than 10% of the nucleus, or if the average MET gene copy number per cell was higher than 6. MET^amp samples were categorized as follows: those with a MET-to-CEP7 ratio below 2.2 as the Low amplification group; those with a ratio greater than 2.2 but less than 4.0 as the Medium amplification group, and those with a ratio greater than 4.0 as the High amplification group (15).

DNA extraction and quantification

Genomic DNA was extracted using the Maxwell® RSC DNA FFPE Kit (Promega, Madison, WI, USA) according to the manufacturer’s recommendations. Extracted DNA was then quantified using the Qubit dsDNA High Sensitivity Assay Kit in combination with a Qubit fluorimeter (Thermo Fisher Scientific, Waltham, MA).

NGS experiments

Libraries were prepared following the manufacturers’ procedures using either the Advanta Solid Tumor NGS Library Prep Assay with the automated Juno™ system on integrated fluidic circuits (LP 8.8.6 IFC) (Fluidigm, San Francisco, CA, USA) or the TruSeq Custom Amplicon kit (Illumina, San Diego, CA, USA) with a home-made panel. These panels are developed to allow the detection of somatic alterations on hotspots in 33 and 35 oncology-relevant genes, respectively (Supplementary Table S1). 20 ng of DNA were used for library preparations, as previously described (21). After preparation, libraries were then quantified, normalized and paired-end sequenced on a NextSeq instrument (2×150 cycles, Illumina).

Droplet digital PCR (ddPCR)

Forward and reverse primers for MET and RPP30 genes and gene-specific fluorescent hydrolysis probes were obtained from Bio-Rad (Hercules, CA, USA; reference: dHsaCP2500321 and dHsaCP2500350, respectively). Briefly, 10 ng of gDNA was used per ddPCR reaction. Samples were emulsified in an automated droplet generator (Bio-Rad) and amplified using the ddPCR Supermix (Bio-Rad) under the following cycling conditions: 95°C for 10 min; 40 cycles of 94°C for 30 s and 55°C for 1 min; and 98°C for 10 min. After amplification, the fluorescence signal of individual droplets was analyzed with a QX200 Droplet Reader and the QuantaSoft V.1.7.4 software. The MET copy number was determined by calculating the ratio of target MET positive (FAM-labeled) droplets over the housekeeping RPP30 positive (HEX-labeled) droplets multiplied by the number of RPP30 copies present in the human genome. As with NGS, a CNR greater than 1.5 was considered indicative of amplification.

Statistical analyses

The relationship between MET^amp level and the clinical parameters was analyzed using the χ² test. OS and PFS were estimated using the Kaplan–Meier method and the significance of differences between survival rates was ascertained with the log-rank test using SPSS Software (SPSS Inc.). Candidate prognostic factors with a 0.1 significance level in univariate analysis were entered in a multivariate Cox model, and a backward selection procedure was used to determine independent prognostic markers.

Identification of optimized MET^amp detection parameters using in silico analysis

We first determined the most appropriate parameters for NGS-based MET^amp detection using ifCNV. We conducted 2,000 simulations for each CNR threshold value (ranging from 0.5 to 2.4, with an increment of 0.1) and contamination value (ranging from 0.017 to 0.031 with an increment of 0.01) (Fig. 1a). This process yielded a total of 750,000 distinct simulations using a training set of over 2,000,000 samples. We calculated the binary classification indicators for each CNR threshold and contamination value, aiming to maximize the F-score. The optimal values were identified as a CNR threshold of 1.5 and a contamination value of 0.028 (Fig. 2a).

Using these optimized parameters, we evaluated the ability of the NGS-based MET^amp detection method to accurately stratify samples. We conducted an additional set of 7,000 simulations, increasing the number of reads on the MET amplicons in the synthetic datasets by factors ranging from 2 to 5. Subsequently, we calculated the corresponding CNR using ifCNV for each simulation. Interestingly, Pearson correlation analysis revealed a strong correlation (exceeding 0.99) between expected and observed ratios (Fig. 2b). In addition, despite a slight under-evaluation in the observed CNR, the stratification of the samples into Low-, Medium-, and High-amplification groups exhibited 100% accuracy (Fig. 2b).

In vivo validation of optimized MET^amp detection parameters

Using these established parameters, we next evaluated the NGS-based MET^amp detection and stratification method on a validation set of 72 NSCLC samples with a tumor cell percentage of 20% or higher (Fig. 1b). We assessed the MET^amp status using IHC, ddPCR, and NGS, and compared the results obtained with those of the current gold standard approach FISH. IHC exhibited a perfect Se but a low Sp of 0.19, revealing a considerable number of FPs. The ddPCR analysis on the other hand, when possible (25 samples), gave a Se of 0.83 and Sp of 0.77. Our NGS-based MET^amp detection method had a Se of 0.91 and Sp of 0.98, with only one FP and 2 FN when compared to the FISH results (Table 1 and Supplementary Table S2).

Next, we evaluated the ability of the NGS-based MET^amp detection method to accurately stratify patients into groups based on amplification levels. We utilized FISH as the reference standard and evaluated the performance of the NGS-based method for MET^amp stratification relative to this reference. Among the 20 samples identified as amplified by both FISH and NGS, the NGS-based stratification achieved global Se and Sp of 0.93 and 0.97, respectively (Table 1). NGS misclassified only one sample as belonging to the Medium-amplification group whereas FISH classed the sample in the High-amplification group (Supplementary Table S2). Of note, ddPCR-based classification, performed on 11 samples, gave a Se and Sp of 0.78 and 1, respectively (Table 1).

Table 1

Binary classification indicators of *MET*^amp detection and stratification. *MET*^amp status and level stratification of the samples was determined using FISH as follows: *MET-to-CEP7* ratios between 1.8 and 2.2 are classified as Low-amplification, those between 2.2 and 4 as Medium-amplification, and those over 4 as High-amplification. IHC, immunohistochemistry; ddPCR, digital droplet PCR; NGS, next-generation sequencing; TP, true positive; TN, true negative; FP, false positive; FN, false negative; Se, sensitivity; Sp, specificity; NA, not applicable.
			IHC	ddPCR	NGS
MET^amp detection		TP	18	10	20
		TN	3	10	49
		FP	13	3	1
		FN	0	2	2
		Se	1.00	0.83	0.91
		Sp	0.19	0.77	0.98
MET^amp level stratification	Low (n = 5)	TP	NA	1	5
		TN	NA	9	15
		FP	NA	0	0
		FN	NA	1	0
		Se	NA	0.50	1.00
		Sp	NA	1.00	1.00
	Medium (n = 10)	TP	NA	5	10
		TN	NA	5	9
		FP	NA	0	1
		FN	NA	1	0
		Se	NA	0.83	1.00
		Sp	NA	1.00	0.90
	High (n = 5)	TP	NA	3	4
		TN	NA	8	15
		FP	NA	0	0
		FN	NA	0	1
		Se	NA	1.00	0.80
		Sp	NA	1.00	1.00
	Global (n = 20)	Se	NA	0.78	0.93
	Global (n = 20)	Sp	NA	1.00	0.97

Evaluation of the NGS-based MET^amp detection method as a relevant screening assay

We next applied the NGS-based MET^amp detection method to an independent retrospective cohort encompassing 1,932 NSCLC patients (Fig. 1c). Among these patients, 62 (3.2%) tested positive for MET^amp, with 16 having undergone EGFR-TKI treatment prior to the analysis. The remaining 46 patients had not received any treatment prior to the NGS analysis and were therefore classified as de novo cases. Stratification of these de novo cases based on MET^amp levels into three distinct groups resulted in: 22 (47.8%) in the Low- (1.5 ≤ CNR < 2.2); 17 (37.0%) in the Medium- (2.2 ≤ CNR < 4); and 7 (15.2%) in the High- (CNR ≥ 4) amplification groups, respectively (Table 2). The three amplification groups showed no significant differences with regards to clinicopathological data except for the PD-L1 TPS were the High-amplification group was enriched in negative (< 1%) patients (Table 2). Although not statistically significant (p = 0.07), it is noteworthy that the High-amplification group displayed advanced stage disease, exclusively comprising stage IV patients, while the other groups also included stage III patients (45% and 29% for the Low- and Medium-amplification groups, respectively) and one stage II patient in the Low-amplification group.

We next determined the prevalence of co-occurring molecular alterations in the cohort of 46 de novo MET^amp patients (Fig. 3). Within the subgroup of patients with TP53 in their sequencing panel (n = 33), 29 (87.9%) exhibited a partially or non-functional TP53 mutation, in accordance with the International Agency for Research on Cancer guidelines (23). Five (10.9%) of the de novo MET^amp patients also harbored a concurrent targetable alteration defined as ESCAT Tier I by the European Society for Medical Oncology (ESMO) (24) (Fig. 3 and Supplementary Table S3). These included 2 patients with an EGFR-activating alteration, 2 with a KRAS^G12C variant and 1 with a variant on MET causing the skipping of exon 14. Of note, we detected all these alterations in tumor samples from patients stratified into the Low-amplification group. A further 17 samples (37.0%) showed other concurrent alterations classified as ESCAT Tier II/III/IV/V/X: 5 (10.9%) had a variant on STK11; 4 (8.7%) on CDKN2A; 3 (6.5%) on BRAF; 2 (4.35%) on MET; 2 (4.35%) on FGFR2; 1 (2.2%) on KRAS; 1 (2.2%) on KIT; 1 (2.2%) on ALK; and 1 (2.2%) on PIK3CA (Fig. 3 and Supplementary Table S3). Interestingly, as previously reported, 6 out of 7 patients in the High-amplification group exhibited only TP53 alterations, suggesting that MET^amp is the primary oncogenic driver in these cases (25).

Table 2

Clinicopathological characteristics of the *de novo MET*^amp patients of the retrospective cohort. ^ap-value (χ² test) was considered significant when p < 0.05. NS, not significant. TPS, tumor proportion score.
		de novo MET^amp groups
	Total	Low	Medium	High	p^a
n	46	22	17	7
Age, y		63.8 (47–83)	57.9 (30–75)	58.9 (39–67)
Sex
Male	32	14 (64%)	11 (65%)	7 (100%)	NS (0.16)
Female	14	8 (36%)	6 (35%)	0 (0%)
Smoking status
Ever	35	14 (64%)	15 (88%)	6 (86%)	NS (0.48)
Never	1	1 (5%)	0 (0%)	0 (0%)
Unknown	10	7 (32%)	2 (12%)	1 (14%)
Stage
II-III	15	10 (45%)	5 (29%)	0 (0%)	NS (0.07)
IV	31	12 (55%)	12 (71%)	7 (100%)
Brain metastasis
Yes	11	6 (27%)	4 (24%)	1 (14%)	NS (0.75)
No	33	15 (68%)	12 (71%)	6 (86%)
Unknown	2	1 (5%)	1 (6%)	0 (0%)
Histological type
Adenocarcinoma	39	20 (91%)	14 (82%)	5 (71%)	NS (0.43)
Others	7	2 (9%)	3 (18%)	2 (29%)
Treatment
Chemotherapy	29	15 (68%)	11 (65%)	3 (43%)	NS (0.26)
Immunotherapy	27	15 (68%)	11 (65%)	1 (14%)
EGFR-TKI	2	2 (9%)	0 (0%)	0 (0%)
Crizotinib	1	0 (0%)	1 (6%)	0 (0%)
No treatment	5	2 (9%)	1 (6%)	2 (29%)
Unknown	2	0 (0%)	1 (6%)	1 (14%)
EGFR mutation status
Negative	44	20 (91%)	17 (100%)	7 (100%)	NS (0.32)
Positive	2	2 (9%)	0 (0%)	0 (0%)
TPS PD-L1
> 1%	32	17 (77%)	12 (71%)	3 (43%)	0.04
< 1%	9	2 (9%)	3 (18%)	4 (57%)
Unknown	5	3 (14%)	2 (12%)	0 (0%)

Clinical implications of NGS-based MET^amp detection and stratification

We then explored the correlation between de novo MET^amp level and patient outcomes to evaluate its potential prognostic significance in terms of OS. Groups showed significant differences (log-rank test, p < 0.001) (Fig. 4a), i.e. patients from the Low-amplification group displaying a more favorable outcome (median OS: 35.9 months) than those from the Medium- (median OS: 14.3 months) or from the High-amplification groups (median OS: 3.3 months). No prognostic value was observed for the other clinical parameter evaluated, even if PD-L1 TPS and the stage tended to be significant (p = 0.08 and 0.06, respectively, Table 3). Prognostic factors with a p < 0.1 in univariate analysis were subsequently entered in a multivariate Cox model, and only the prognostic significance of MET^amp level persisted in the model.

Due to the limited number of patients in the Medium- and High-amplification groups, we merged them to evaluate patients’ response to treatment. Interestingly with regards to PFS, we observed a reduced response to chemotherapy in patients from the Medium/High-amplification groups compared to those in the Low-amplification group (log-rank test, p = 0.001) (Fig. 4b and Table 3). Of note, we found no statistically significant difference in response to immunotherapy between the two groups (log-rank test, p = 0.745) (Fig. 4c), in accordance with previously published data (26).

Table 3

Univariate and multivariate analysis of the prognostic value of METamp. ^aHR, Hazard ratio. ^b95% CI, 95% confidence interval. ^cp was considered significant when p < 0.05. NS, not significant. ND, not done. TPS, tumor proportion score.
				Univariate (Log-rank test)			Multivariate Cox proportional hazard analysis
			Number of samples	HR^a	95% CI^b	P^c	HR	95% CI	P
Overall Survival
	Brain metastasis (No; Yes)		44	0.757	0.283–2.025	NS (0.58)			ND
	PD-L1 TPS (< 1; > 1)		41	0.481	0.205–1.127	NS (0.08)			NS
	Stage (II + III; IV)		46	2.317	0.931–5.762	NS (0.06)			NS
	MET^amp level (Low; Medium; High)		46			< 0.001	3.819	2.179–6.794	< 0.001
		Low; Medium		2.542	1.019–6.339
		Medium; High		5.946	1.835–19.267
		Low; High		12.229	3.019–49.537
PFS under chemotherapy
	Brain metastasis (No; Yes)		28	0.945	0.310–2.885	NS (0.92)			ND
	TPS PD-L1 (< 1; > 1)		26	0.463	0.166–1.291	NS (0.13)			ND
	Stage (II + III; IV)		29	1.356	0.564–3.260	NS (0.49)			ND
	MET^amp level (Low; Medium + High)		29	6.326	1.934–20.693	0.001	6.326	1.934–20.693	0.001
PFS under immunotherapy
	Brain metastasis (No; Yes)		27	0.269	0.061–1.175	NS (0.06)			ND
	TPS PD-L1 (< 1; > 1)		25	0.702	0.246–2.008	NS (0.51)			ND
	Stage (II + III; IV)		29	0.932	0.378–2.298	NS (0.88)			ND
	MET^amp level (Low; Medium + High)		29	0.864	0.356–2.097	NS (0.74)			ND

Multigene panel testing, as proposed by NGS, has been widely implemented in response to the increase in clinically actionable alterations in genes involved in NSCLC, such as EGFR, ALK, ROS1, RET, and BRAF, as well as emerging targets such as MET alterations or KRAS^G12C. Integrating the detection of new targets in an « all-in-one » solution minimally disrupts laboratory workflows and leads to both cost-effective and efficient testing processes. To date, FISH remains the gold standard approach for MET^amp detection in clinical practice (25–27). However, this approach has some limitations that hinder its use as a screening method. FISH is a low-throughput non-automatable technique that requires specialized microscopy equipment, which must be operated by proficient pathologists to avoid inter-operator variability that could affect patient stratification. Moreover, it is a non-exploratory approach that allows the analysis of only one target region at a time.

To address the need for an « all-in-one » approach to MET^amp detection, we developed a novel NGS-based method to detect MET^amp and stratify patients according to their amplification level. This method achieved a concordance of 94% with a Se of 0.91 and Sp of 0.98 for MET^amp detection and a Se of 0.93 and a Sp of 0.97 for MET^amp level stratification. These results represent an improved performance compared to a recent report by Solomon et al., that demonstrated a concordance of detection of 91% after refinement of their algorithm (28). In contrast, Peng et al. reported a concordance rate between FISH and NGS of only 62.5% (25/40) (29). The findings from the TATTON study also revealed a limited agreement between NGS and FISH for MET^amp. Indeed only 26% (12/47) of the patients in the FISH-positive cohort were identified as having MET^amp by NGS (30). Recently, Heydt et al. stated that only samples with high MET^amp levels (GCN ≥ 6 by FISH) are detectable using NGS, whereas cases with intermediate or low-level amplification remain undetectable (31). In our study, Low and Medium MET^amp samples were effectively detected and classified with a Se and a Sp of 1 for Low MET^amp and a Se of 1 and a Sp of 0.90 for Medium MET^amp. All these findings demonstrate that the use of commercial or non-tailored bioinformatics approaches is not optimal for accurately detecting MET^amp (29, 32). The implementation of bioinformatics solutions in clinical practice to detect MET^amp should involve the use of training and validation sets to carefully refine detection parameters and achieve a high-performing NGS-based method for detecting and stratifying patients with MET^amp.

We also investigated the potential correlation between clinicopathological data, such as sex, patient age, smoking history and concurrent MET^amp detected by NGS. We were unable to establish any correlation between the amplification levels and any of these criteria. Of note, almost all patients with a MET^amp for which the information was available were smokers (35 out of 36, 97.2%). Upon examination of concurrent molecular alterations however, we observed that MET^amp samples displayed concomitant molecular changes that mainly affected TP53, CDKN2A, STK11, and KRAS. Notably, two patients, P03 and P12, from the Low-amplification group, exhibited activating EGFR alterations (EGFR^del19 in both cases) leading to the administration of third-generation EGFR-TKIs. Although statistical analysis could not be conducted, it is noteworthy that their PFS under osimertinib fell several months below the median PFS reported in the literature (12.5 and 14.4 months vs 17.2 months) (2). As proposed by Lai et al. (33), the shorter PFS observed in these two patients may represent a potential early resistance to treatment via mechanisms similar to those proposed by Li et al. in 2019 for first-generation EGFR-TKIs (34).

Moreover, we observed that patients within the High- and Medium-amplification groups exhibited no concomitant targetable alterations (ESCAT Tier I). Due to the rarity of this genetic alteration, our study included a relatively small number of patients. Nevertheless, our results suggest a poor OS and PFS under chemotherapy for patients within the High- and Medium-amplification groups. Given the importance of offering therapeutic alternatives to de novo MET^amp patients, further investigations to validate in a larger cohort the results from this pilot study are warranted. These observations underscore the significance of early detection of de novo MET^amp and accurate patient stratification based on amplification levels. Particularly, distinguishing Low-amplified patients from Medium- and High-amplified ones is crucial, as therapeutic alternatives are limited for the latter group. It is noteworthy that first-line chemotherapy, the standard of care for NSCLC patients with advanced stages lacking targetable alteration, may not yield favorable responses in these patients. Such diagnostic improvements gain particular significance when considering the relevance of data from recent clinical trials and studies exploring the effectiveness of MET-TKIs in patients with MET^amp (4, 6, 35–38). One of the primary findings of these clinical trials is that the efficacy of the investigated molecules depends upon the level of amplification as determined by FISH. However, the thresholds used for MET^amp detection and level stratification varied among studies. For example, when comparing the amplification levels in the PROFILE 1001 trial on crizotinib with those in the GEOMETRY-mono 1 trial on capmatinib, the per group differences were: for the Low group 1.8 ≤ MET-to-CEP7 ratio ≤ 2.2 versus 4 < GCN ≤ 5; for the Medium group 2.2 < MET-to-CEP7 ratio < 4 versus 6 ≤ GCN ≤ 9; and for the High group MET-to-CEP7 ratio ≥ 4 versus GCN > 10 (6, 15). The absence of universally accepted standards could pose challenges for future adoption in oncology laboratories and the determination of definitive cut-offs allowing clinically relevant stratification remains crucial.

In summary, we combined an in silico training set with an in vivo validation set to permit the determination of optimal parameters allowing NGS-based MET^amp detection and patient stratification comparable to FISH. This approach allowed the easy implementation of an efficient MET^amp screening that was applied in a retrospective cohort of more than 1,900 NSCLC patients and demonstrated clinical relevance through OS and PFS assessments along stratified de novo MET^amp patients. Our approach also enables the distinction of MET^amp from chr7 polysomies, which is particularly relevant in a clinical context. Furthermore, the NGS bioinformatics approach developed here represents a powerful objective tool offering the potential for standardization and high-throughput applications. As NGS is already largely employed in the management of patients with lung cancer, NGS-based MET^amp detection only requires the customization of the bioinformatics tool to be directly implemented within a comprehensive genomic profiling. We anticipate NGS-based MET^amp detection permitting a more time-efficient procedure requiring less tissue specimen and minimizing the need for supplementary methods, such as IHC or FISH. Further investigations and multi-disciplinary discussions among specialists are now required to establish universally accepted methods and associated cut-off values for NGS-based MET^amp detection and amplification level stratification.

Ethics approval and consent to participate

This study was approved by the Institutional Review Board of the University Hospital of Montpellier under the approval number IRB-MTP_2023_01_202201240 and was conducted in accordance with the Declaration of Helsinki.

Consent for publication

Not applicable

Availability of data and materials

The datasets used and/or analyzed during the current study are available from the corresponding author on reasonable request.

Competing interests

The authors declare that they have no competing interests.

Funding

No specific funding was used for this study.

Authors' contributions

SCA: Conceptualization, Data curation, Formal Analysis, Investigation, Visualization, Writing original draft and review & editing. JAV: Conceptualization, Formal Analysis, Investigation, Writing original draft and review & editing. SE: Data curation, Resources, Writing – review & editing. QT, BR, FE and PB: Resources. IS: Data curation, Investigation, Writing – review & editing. JM: Resources, Writing – review & editing. JS: Conceptualization, Investigation, Supervision, Validation, Project administration, Writing original draft, and review & editing.

Acknowledgements

Not applicable

Conflict of interest statement

The authors declare no conflict of interest.

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No competing interests reported.

FigureSupp1.png
Supplementary Figure S1. NGS-based chromosome 7 polysomy detection. a, Chromosome 7 showing the positions of the probes included in the panel. b, Barplots of the normalized number of reads on the genes of the panel for a patient with chromosome 7 polysomy. c, FISH image of this patient’s tumor cells. Red fluorophores indicate the staining of the MET gene, and green fluorophores indicate the staining of the chromosome 7 centromere. Chr, chromosome.
SuppTable1.xlsx
Supplementary Table S1. NGS panel used.
SuppTable2.xlsx
Supplementary Table S2. Validation dataset used to assess the performance of the NGS-based MET^amp detection method. Samples were stratified according to the MET-to-CEP7 ratio obtained by FISH as follows: samples with a MET-to-CEP7 ratio between 1.8 and 2.2 are labelled as Low-amplification; those between 2.2 and 4 as Medium-amplification; and those over 4 as High-amplification. FISH, fluorescence in situ hybridization; NGS, next-generation sequencing; CNR, copy number ratio; ddPCR, droplet digital PCR; IHC, immunohistochemistry; ND: not done.
SuppTable3.xlsx
Supplementary Table S3: Concomitant molecular alterations detected in the de novo MET^amp samples. WT, wild-type.

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Optimized NGS-based de novo MET amplification detection for improved lung cancer patient management

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Background

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Binary classification indicators

Patients and sample collection

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DNA extraction and quantification

NGS experiments

Droplet digital PCR (ddPCR)

Statistical analyses

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