Exploring genomic analysis and methylome profiling in longitudinal series of p.G12C KRAS mutated NSCLC patients treated with Sotorasib

Exploring genomic analysis and methylome profiling in longitudinal series of p.G12C KRAS mutated NSCLC patients treated with Sotorasib | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Article Exploring genomic analysis and methylome profiling in longitudinal series of p.G12C KRAS mutated NSCLC patients treated with Sotorasib Francesco Pepe, Francesco Passiglia, Claudia Scimone, Gianluca Russo, and 12 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7860201/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract Aims : in the genomic era, the advent of next generation sequencing (NGS) technologies has rapidly transformed the clinical paradigm of NSCLC patients who could benefit from a wide series of clinically approved biomarker drive therapies. Among them, KRAS p.G12C hotspot mutation became part of the mandatory testing gene panel by electing NSCLC patient’s candidate to Sotorasib. Epigenomic signatures, including hypermethylation of CpGs islands, may be relevant in tailorizing therapeutic algorithms in oncogene addicted NSCLC patients. Here we aimed to dynamically track KRAS p.G12C genomic variations by integrating methylation profile in a longitudinal series of n=91 liquid biopsy samples from n=22 p.G12C positive NSCLC patients treated with Sotorasib. A combined NGS panel (Avida Duo Methyl Reagent Kit, Avida Biomed) simultaneously evaluating n=105 cancer-related genes and calculating methylation index (MI) score among 3400 differentially methylated regions (DMRs) was adopted, correlating molecular data with clinical outcomes. Overall, exon 2 p.G12C KRAS mutation was detected in 40.9%, 15.8% % and 70.6% baseline, T1 and TP samples, respectively. MI was successfully measured in all instances. Of note, exon 2 p.G12C KRAS mutation and MI score highlighted an overlapping trend moving forward T1 point (r = 0.68, p = 0.06) and TP (r = 0.87, p = 0.000103). Methylation signature may be combined with genomic analysis to personalize therapeutic strategies for KRAS p.G12C mutated NSCLC patients. Multiomic analysis of tumor-informative molecular targets (genomic assessment, methylation status) lay the basis for dynamic fingerprints of NSCLC patients preventing early relapses and augmenting clinical benefits of targeted therapies. Health sciences/Biomarkers Biological sciences/Cancer Biological sciences/Computational biology and bioinformatics Biological sciences/Genetics Health sciences/Oncology Genomic analysis methylation profile liquid biopsy lung cancer target treatment Figures Figure 1 Figure 2 Figure 3 Figure 4 Figure 5 Figure 6 INTRODUCTION Lung cancer (LC) still represents one of the most leading causes of death worldwide across solid malignancies. (1) As regards, conventional diagnostic procedures are affected by lack of sensitivity and specificity to early detect LC patients drastically impacting on the rate of advanced stage (IIIB-IV) diagnosed disease. (2) In the last decade, precision medicine has revolutionized the clinical paradigm of advanced non-small cell lung cancer (NSCLC) patients. (3) Particularly, guidelines from CAP/AMP/IASCL societies established a panel of mandatory testing genes able to identify oncogene-addicted patients sensitive to targeted therapies. (4) This panel includes DNA-based [( EGFR (Epidermal Grow Factor Receptor), BRAF (v-Raf murine sarcoma viral oncogene homolog B), HER-2 (Human Epidermal growth factor Receptor 2)] and RNA-based [( ALK (anaplastic lymphoma kinase), ROS1 (protooncogene 1 receptor tyrosine kinase), RET (Proto-Oncogene Tyrosine-Protein Kinase Receptor Ret), NTRK (Neurotrophic Tyrosine Receptor Kinase) and MET (MET Proto-Oncogene, Receptor Tyrosine Kinase)] biomarkers crucial for the therapeutic management of NSCLC patients. (5, 6) Recently, exon 2 p.G12C KRAS hotspot mutation emerged as a novel therapeutic target identifying NSCLC patients eligible to selective covalent inhibitors, like Sotorasib and Adagrasib. (7) In addition, NRG1 (Neuregulin 1) has been accelerating to stratify NSCLC patients to novel mABs. (8) Not surprisingly, both innate and acquired resistance mechanisms significantly reduce the clinical benefit of KRAS p.G12C covalent inhibitors in NSCLC cases. (9) Novel molecular hallmarks should be identified to optimize the clinical stratification of KRAS p.G12C mutant advanced NSCLC patients. (10, 11) It has been demonstrated that specific patterns of epigenetic alterations including DNA methylation, histone modification, and the aberrant expression of non-coding RNA, are recurrent in NSCLC playing a pivotal role in tumor progression and potentially impacting on clinical outcomes. (12, 13, 14) Considering the low abundance of circulating tumor DNA (ctDNA) in NSCLC patients, multi-omics analysis integrating tumor addicted genomic alterations and methylation signatures may act as potential tool for guiding clinical decision-making procedures in advanced NSCLC patients. (15, 16) Here, we sought to evaluate technical and clinical performance of a combined genomic plus methylome NGS analysis on a longitudinal series of liquid biopsy specimens from KRAS p.G12C mutant advanced NSCLC patients receiving a targeted treatment with Sotorasib in the real-world scenario. RESULTS Patients’ Characteristics Between November 2020 and December 2022, a total of n = 22 KRAS p.G12C mutant advanced NSCLC patients receiving Sotorasib in the real-world were enrolled. Baseline clinical characteristics are summarized in Table 1 . The median age was 70.5 years (range, 46–81), and 52.2% of patients were male. Most patients had an ECOG performance status (PS) of 0 (63.6%), while all of them had a history of tobacco exposure and were diagnosed with lung adenocarcinoma. The tumour PD-L1 expression was ≥ 50% in 22.7% of cases, 1–49% in 45.5%, and < 1% in 31.8%. All patients were metastatic at enrolment, with brain metastases being the most frequent site (36.4%). Moreover, 63.6% of patients had previously received anti–PD-L1 therapy. All patients included in the study were evaluable for tumor response assessment: 7 (31.8%) experienced a PR, 11 (50%) had stable disease (SD), and 4 (18.2%) had progressive disease (TP) as their best response to Sotorasib. The median follow-up calculated with the reverse Kaplan-Meier method was 32 months (range 2–35) for the overall cohort at the time of data cut-off. (Table 1 ) Table 1 Methylation Index according to patients’ characteristics. Abbreviations : ECOG-PS ( Eastern Cooperative Oncology Group - Performance Status ); PD-L1 ( Programmed Death-Ligand 1 ). Patients’ Characteristics Methylation Index Median [Min, Max] P-value Age < 75y (N = 16) ≥ 75y (N = 6) 0.636 [0.0800, 14.8] 0.564 [0.130, 3.30] 0.641 ECOG-PS 0 (N = 14) 1–2 (N = 8) 0.653 [0.0800, 14.8] 0.474 [0.130, 12.3] 0.441 Smoking history < 30p/y (N = 8) ≥ 30p/y (N = 11) Missing (N = 3) 0.666 [0.08, 14.8] 0.589 [0.130, 2.13] 0.492 Brain metastases Yes (N = 8) No (N = 14) 0.548 [0.0800, 12.3] 0.653 [0.130, 14.8] 0.815 PD-L1 TPS PD-L1 Negative (N = 17) PD-L1 Positive (N = 5) 0.555 [0.08, 12.3] 3.30 [0.366, 14.8] 0.164 Previous Immunotherapy Yes (N = 14) No (N = 8) 0.747 [0.0800, 14.8] 0.539 [0.13, 12.3] 0.441 Best Response Responders (N = 14) Non-Responders (N = 8) 0.932 [0.409, 14.5] 0.540 [0.0800, 14.8] 0.123 cfDNA abundance in liquid biopsy samples Overall, cfDNA measurement was successfully carried out in all instances. Of note, cfDNA score was clearly inspected in 78 out of 91 cases (85.7%) achieving a median value of 73.0% ranging from 32.0% to 93.0%. In particular, baseline and TP samples highlighted a median cfDNA score of 72.0% (from 53.0% to 83.0%) and 77.0% (from 57.0% to 91.0%). In addition, a median of 0.2 ng/µl (from 0.1 to 2.4 ng/µl) was inspected. In detail, baseline and TP samples revealed a median of 0.2 ng/µl (from 0.1 ng/µl to 0.8ng/µl) and 0.3 ng/µl (from 0.1ng/µl to 1.3 ng/µl), respectively. ( Supplementary Table 1 ) Genomic and methylome data Genomic analysis and methylation index were measured in all instances. In particular, an average of 12460161.9 (ranging from 5139660.0 to 45165008.0) total number of raw reads, of 3052515.0 (ranging from 458720.0 to 11360524.0) number of mapped reads; of 2949844.9 (ranging from 424865.0 to 11077138.0) high quality reads; of 434.7 (ranging from 63.0 to 1617.0) median depth in targeted region were identified among genomic data. Moreover, an average of 15596177.5 (ranging from 2247880.0 to 47794630.0) total number of raw reads, of 6932619.7 (ranging from 107636.0 to 15259654.0) number of mapped reads, of 6330388.8 (ranging from 81958.0 to 13361940.0) high quality reads, a median of 10486874,43 (ranging from 135213.0 to 95510093.0) total number of analyzed cytosines, of 11.8 (ranging from 5.3 to 27.5) methylated C in CpG context, of 4.2 (ranging from 0.5 to 18.0) methylated C in CHG context, of 3.7 (ranging from 0.5 to 13.5) methylated C in CHH was identified among methylation data. ( Supplementary Table 2A-B ) Considering a technical cut-off of 0.2%, exon 2 p.G12C KRAS mutation was successfully identified in 9 out of 22 (40.9%) and 12 out of 17 (70.6%) baseline and TP samples, respectively, showing a median variant allele fraction (VAF) of 9.4% (from 0.7% to 44.2%) and 19.2% (from 0.2% to 65.4%), respectively. (Table 2 ) In addition, dPCR system successfully analyzed all samples highlighting a median VAF of 18.6% (from 0.2% to 76.7%) in 27 out of 91 (29.7%) p.G12C positive samples. ( Supplementary Table 3 ) A median VAF of 11.6% (ranging from 0.4% to 56.5%) and 25.9% (ranging from 0.2% to 76.7%) was measured in basal and TP samples, respectively. A statistically significant correlation was yielded comparing NGS and dPCR analysis (≥ 2 positive partitions) of KRAS p.G12C hotspot mutation. (r = 099, p-value of 6.91 × 10⁻⁷⁷). (Supplementary Fig. 1) Table 2 Schematizing report of Methylation index and KRAS p.G12C VAF across longitudinal plasma samples. ID Sample Collection point Methylation Index KRAS p.G12C (VAF%) ID Sample Collection point Methylation Index KRAS p.G12C (VAF%) ID01 T 0 1.7 ND ID12 T 0 0.5 ND T 1 0.4 ND T 1 0.3 ND T r 0.3 0.6 T 2 0.1 ND ID02 T 0 12.3 44.2 T 3 0.1 ND T 1 4.6 43.1 T 4 0.5 ND T r 7.4 65.4 T 5 0.1 ND ID03 T 0 14.5 ND T 6 0.3 ND T 1 1.5 ND ID13 T 0 0.1 ND T 2 2.4 ND T 1 0.3 ND T r 5.6 ND T 2 0.9 ND ID04 T 0 0.2 ND T 3 0.5 0.2 T 1 1.3 ND T 4 0.5 ND ID05 T 0 0.5 ND T 5 1.2 ND T 1 0.2 ND T r 2.0 1.0 T 2 0.3 ND ID14 T 0 0.1 ND T 3 0.6 ND T 1 0.3 ND T 4 0.7 ND T r 0.3 ND T 5 0.7 ND ID15 T 0 0.6 1.1 T 6 0.6 0.6 T 1 0.2 ND T 7 0.4 1.1 T 2 0.7 ND ID06 T 0 0.4 ND T r 0.3 ND T 1 0.1 ND ID16 T 0 0.9 ND T 2 0.4 ND T 1 0.4 ND T r 0.1 ND T 2 3.0 0.9 ID07 T 0 0.3 ND T 3 12.9 22.7 T 1 0.2 ND T r 55.8 64.8 T 2 0.2 1.1 ID17 T 0 0.4 ND T r 0.1 4.3 T r 0.3 ND ID08 T 0 0.1 ND ID18 T 0 0.6 3.2 T 1 0.1 ND T 1 0.2 ND T 2 0.1 ND T r 0.5 14.8 T 3 0.1 ND ID19 T 0 0.7 6.4 T 4 0.3 ND T r 1.8 23.1 T 5 0.4 ND ID20 T 0 0.4 NA T r 0.7 0.2 T 1 0.7 2.1 ID09 T 0 2.1 2.5 T r 11.0 37.3 T 1 0.2 ND ID21 T 0 3.3 5.5 T 2 0.2 ND T 1 0.5 ND T 3 0.3 ND T 2 0.4 0.8 T 4 0.1 ND T 3 0.3 1.0 T 5 0.4 1.4 T 4 0.7 2.4 ID10 T 0 1.8 7.3 T r 2.5 7.6 T r 0.9 5.9 ID22 T 0 0.8 0.7 ID11 T 0 14.8 14.0 T 1 0.8 ND T 1 5.4 3.0 T 2 0.4 ND T r 7.1 5.5 A median of 2.2 (ranging from 0.1 to 55.8) MI was calculated among all liquid biopsy samples. A median of 2.6 (ranging from 0.1 to 14.8) and 5.7 (ranging from 0.1 to 55.8) MI was identified at baseline and TP timepoints, respectively. SeqOne Genomics (Montpellier, France) successfully calculated MI in all instances overlapping with data from previous MI analysis. ( Supplementary Table 4) Beyond p.G12C hotspot mutation, no clinically actionable alterations were found in driver genes both at baseline and TP collection points. Filtering algorithm was designed as follows: synonymous, intronic and not assessed (NA) molecular alterations below 0.5% of VAF were discarded. Early dynamic variations and patients’ outcomes Overall, genomic data from both baseline sample and first longitudinal timepoint (T1) were available in 19 out 22 (86.3%) NSCLC patients. Among the seven patients with a detectable KRAS p.G12C mutation at baseline, genomic analysis accurately revealed a decreasing mutation rate by comparing baseline and T1in five cases (83.3%) (Δ = 2.6%, 0.7–5.5%); all of them achieved a complete ctDNA clearance of KRAS p.G12C mutation. (Table 2 , Fig. 1 A) whereas a decreasing trend was identified in ID#02,11 (median Δ = 12.1%) (p-value = 0.07). Among the other n = 12 patients having undetectable p.G12C KRAS mutation at baseline, positive signal at T1 was observed in a single instance. (Table 2 ) Overall, methylome data both from baseline and T1 samples were available in all NSCLC patients. MI score was significantly lower (median 0.9, from 0.1 to 5.4; p-value = 0.04) in T1 samples compared with basal specimens (median 2.9, from 0.1 to 14.8). (Table 2 , Fig. 1 A) Interestingly, MI scored a decreasing trend (4.1 vs 1.1 from Δ = 2.9, from 0.1 to 13.0) between baseline and T1 in 13 out of 19 (68.4%) NSCLC patients. Conversely, 4 out of 19 (21.0%) NSCLC patients showed an increasing MI (0.2 vs 0.6; Δ = 0.5, from 0.2 to 1.1) in T1 samples, while two cases highlighted a stable MI (ID#08, ID#22) comparing baseline and T1 timepoint. (Table 2 ) Matching p.G12C KRAS mutation and MI score, 1 out of 8 (12.5%) NSCLC patients simultaneously increased whereas a decreasing trend of p.G12C and MI was identified in 75.0% of cases (6 out of 8). Moreover, p.G12C KRAS mutation concomitantly diminished to a stable MI score (p.G12C VAF 0.7 to 0.0%, MI = 0.8) in a single case (12.5%) comparing baseline and T1. (Fig. 2 ) Liquid biopsy data at sotorasib resistance A total of 17 out of 22 (77.3%) patients with liquid biopsy samples available within 90 days of radiologic progression disease (PD) were selected. Of note, 12 out of 17 (70.6%) patients highlighted positive signal (VAF ≥ 0.2%) of KRAS p.G12C at TP. Not surprisingly, a median p.G12C VAF of 19.2% (from 0.2 to 65.4%) was augmented compared TP with paired basal samples. (median 11.7%, range 1.1–44.2%) (p-value = 0.06). (Table 2 , Fig. 1 B) Particularly, in 10 out of 17 (58.8%) cases, a median VAF of p.G12C hot spot mutations increased (median VAF 5.9%, 0.0 to 44.2% at baseline vs 21.9, 0.2 to 65.4% at TP) (Δ = 16.0%, from 0.2% to 64.8%). (Table 2 , Fig. 1 B) Conversely, three patients (17.6%) highlighted a decreasing rate of p.G12C VAF (median VAF 7.5%, 1.1 to 14.0% at baseline vs 3.8%, 0.0 to 5.9% at TP) (Δ = 3.6%, from 1.1% to 8.5%). (Table 2 ) MI was higher in TP (median 5.7, from 0.1 to 55.8) compared with baseline samples (median 3.1, from 0.1 to 14.8 p value = 0.46). (Fig. 1 B) In addition, an increasing trend between baseline and TP (0.4 vs 12.0; Δ = 11.5, from 0.2 to 54.9) was identified in 6 out of 17 (35.3%) NSCLC patients. ( Table 2 ) Interestingly, 5 out of 13 (38.5%) NSCLC patients displayed a simultaneously increasing rate of KRAS p.G12C mutation and MI score whereas in 23.1% of cases (3 out of 13) both p.G12C KRAS mutation and MI simultaneously decreased. In addition, a divergent trend, where p.G12C KRAS increased, matching with progressive decrease of MI at TP, was identified in 5 out of 13 NSCLC patients (38.5%). ( Fig. 3 ) Methylation index and patients’ outcomes Among the n = 22 NSCLC patients included in the study, any potential differences of baseline median MI across the main patients’ subgroups were explored and the results were summarized in Table 1 . A trend toward an increased median MI was found in patients with PD-L1 positive vs negative tumors (3.30 vs 0.55, p:0.164) but statistically significant variation was not assessed. Considering n = 19 NSCLC patients with T1 collecting points, a not significant trend toward both an increased median MI (0.43 vs 0.27, p: 0.773) and a decreased MI fold-change (0.267 vs 0.504, p:0.08) was found in responders (PR) vs non responders (SD + PD). ( Fig. 4 ) Setting the median MI as reference cut-off, an increased ORR was observed in patients with high vs low MI detected both at baseline (45.5% vs 18.2%, p:0.36) and at T1 (50.0% vs 22.2%, p: 0.44) timepoints, even if variations did not reach statistical significance ( Supplementary Fig. 2 ). Similar median PFS and OS emerged in NSCLC patients with high vs low MI both at baseline (mPFS: 6.08 vs 6.21 months, p: 0.57; mOS: 13 vs 9.6 months, p:0.96) ( Supplementary Fig. 3A ) and at T1 (mPFS: 7.06 vs 7.10 months, p: 0.58; mOS: 13.3 vs 14.1 months, p:0.98) timepoints. ( Supplementary Fig. 3B ) Setting the median MI fold change between baseline and T1 as reference cut-off, no differences in terms of ORR (40% vs 33%, p:1) and mPFS (7.43 vs 7.10, p: 0.81) ( Supplementary Fig. 4A-B ) were observed, while a not significant trend toward increased mOS (15.3 vs 9.63, p: 0.77) ( Supplementary Fig. 4C ) were identified in patients with low vs high MI fold-change values. DISCUSSION In the era of personalized treatments, an accurate stratification of NSCLC patients is pivotal. Given the rapidly transforming scenario of clinically approved biomarkers, including genomic alterations and aberrant RNA rearrangements, NSCLC patients may benefit from several drugs selectively targeting specific oncoproteins. Despite these advances, a consistent fraction of NSCLC patients with driver molecular alterations experienced a limited clinical benefit from current targeted therapies. In this scenario, epigenomic markers (chromatin remodeling, methylation status, histone modifications) may impact on the clinical selection of NSCLC patients guiding clinical decision-making procedures. Here, we sought to evaluate how methylation index, calculated by NGS panel integrating genomic and methylome analysis, may optimize clinical stratification of p.G12C KRAS positive NSCLC patients undergoing Sotorasib treatment in the real-world. A series of n = 22 NSCLC patients were investigated by NGS combined panel (Avida Duo Methyl Reagent Kit, Avida Biomed) automatically scoring MI by proprietary bioinformatic pipeline able to assess methylation patterns of 3400 cancer related CpGs after bisulfite conversion. Comparing bisulfite-based methods with other strategies for DNA methylation profiling, lower technical performance in terms of reference range (> 28 million of CpGs vs < 23 million of CpGs covered) and technical resolution (comprehensive CpGs analysis vs target CpGs patterns) were observed in affinity enrichment and restriction enzymes-based methods. (20) Dynamic evaluation of ctDNA was inspected at T1 and TP simultaneously investigating p.G12C KRAS driver mutation and MI score. Before molecular analysis, cfDNA abundance was calculated as 170/700 bp ratio by microfluidic system showing that no statistically significant variations were observed between baseline, TI and TP samples (72.0, 72.0, 77.0 cfDNA%, respectively, p-value = 0.21). Interestingly, 6 out of 7 (85.7%) patients showing traces of p.G12C alteration at baseline highlighted decreasing p.G12C level at T1 achieving a complete clearance of ctDNA in 83.3% of cases. (Table 2 ) Switching from early detection to disease progression, p.G12C KRAS mutation was found in 70.6% of cases demonstrating a significant variation of the median VAF comparing baseline and TP points (11.7% vs 19.2%) (p value = 0.07). (Fig. 1 A) In 10 out of 13 (76.9%) NSCLC patients, p.G12C KRAS increased at TP (5.9% vs 21.9% VAF). In line with previous reported data, persisting ctDNA traces in longitudinal timepoints were associated with worse clinical outcomes. Moreover, abundance of ctDNA depending on higher p.G12C VAF at TP can also impact on the clinical response and relapsing timeline. In a previous metanalysis, Zaman et al. highlighted that ctDNA-negative patients at baseline had a longer PFS (pooled hazard ratio [pHR] = 1.35; 95%CI: 0.83–1.87; p < 0.001; I 2 = 96%) compared with baseline ctDNA positive. In addition, clearance of ctDNA levels after target treatment was significantly associated with increasing PFS (pHR = 2.71; 95%CI: 1.85–3.65; I 2 = 89.4%) in comparison with persistence ctDNA levels at recollection points. (21) Similarly, Passiglia et al. also confirmed that early ctDNA KRAS p.G12C mutation correlated with longer OS (16.8 vs. 6.4 months; p < .001) in advanced NSCLC patients undergoing sotorasib in the real world, while increasing of ctDNA mutation VAF anticipated radiological PD, suggesting a potential role for ctDNA driven escalation and de-escalation strategies in KRAS mutated patients. (22) The clinical evidence confirms that the clinical response of p.G12C mutant NSCLC patients treated with target therapy is modulated by the clearance of driver alteration. Paweletz et al . highlighted that p.G12C clearance within cycle 2 had a higher objective response rate (ORR) compared with persistent p.G12C series (60.6% vs 33.3%). (23) Conversely, the lack of common driver mechanisms between basal and longitudinal samples (baseline vs recollection; baseline vs TP) suggested that additional non genetic resistance mechanisms should be explored. (24, 25). On this basis, methylation pattern may significantly impact on the clinical outcomes of NSCLC patients driven by KRAS mutations. (26, 27) We successfully calculated MI in each sample by adopting both proprietary bioinformatic pipeline and SeqOne analysis software (Montpellier, France). ( Supplementary Table 4 ) The heterogeneous landscape of analytical strategies measuring methylation profile significantly affects the clinical application of methylome signature. (28, 29) No statistically significant variations (p value = 0.9) between proprietary bioinformatic pipeline and SeqOne analysis software (Montpellier, France) calculating MI technically validates methylome analysis paving the way for the clinical applications of these tools. In particular, 68.4% of NSCLC patients highlighted a consistent decreasing rate of MI (4.1 vs 1.1 from Δ = 2.9, from 0.1 to 13.0) at T1, in line with KRAS p.G12C mutation clearance (57.1%) (p-value = 0.04). (Fig. 2 , 5 ) Of note, MI was significantly higher in 35.3% (6 out of 17) of NSCLC patients at TP compared with baseline samples (12.0 vs 0.4; Δ = 11.5, from 0.2 to 54.9) matching with KRAS p.G12C mutation increase. ( Fig. 3 , 6 ) Remarkably, MI highlighted a similar trend of KRAS p.G12C VAF among baseline, TI and TP points (median VAF 0.0, 0.0, 2.1% - median MI 0.6, 0.3, 0.9, respectively, Person correlation basal and T1 = 0.68; Person correlation basal and TP = 0.87) ( Fig. 5 , 6 ) Even if limited by small sample size, this data suggest that dynamic modification of both methylation patterns and genomic assessment may track tumor under KRASG12C inhibitors. This proof of concept is clearly evident by looking at two patients of the clinical series, with five samples collecting points availability, demonstrating significantly correlated longitudinal variations of genomic and/or methylome data. ID#16 showed a significant peak both in KRAS p.G12C VAF and MI score at TP compared with other longitudinal timepoints (from 0.0 to 64.8%; 0.9 to 55.8, respectively). (Table 2 ) Interestingly, ID#21 was affected by simultaneous variations of p.G12C and MI score dynamically modified among the different collection timepoints. (Supplementary Fig. 5) Despite the powerful insights, several limitations should be considered. Firstly, sample set was retrospectively retrieved without any statistical tool calculating sample size. Secondly, MI was calculated on 3400 differentially methylated regions (DMRs) partially evaluating methylome signature in comparison with whole genome approaches. Finally, further investigations are required to validate clinical role of methylation profile in the clinical management of KRAS mutated NSCLC patients. In conclusion, methylation signature integrating genomic analysis may represent an informative tool successfully optimizing personalized therapeutic strategies for KRAS p.G12C mutant NSCLC patients. METHODS Participants Patients (≥ 18 years of age) with ECOG PS < 3 and p.G12C KRAS mutation on tissue sample, receiving a diagnosis of stage IIIB-IIIC/IV NSCLC (according to the eighth version of the American Joint Committee on Cancer/International Association for the Study of Lung Cancer tumor-node‐metastasis [TNM] staging system) on histological or cytological samples were enrolled. All patients relapsed from at least one line of previous therapy before receiving Sotorasib (960 mg orally once daily) until progression or unacceptable toxicity; participated to the PROMOLE translational study at the Department of Oncology of the University of Turin (Italy) and signed and dated the Informed Consent & privacy Form (ICF). Clinical, pathologic, and molecular data as well as treatment efficacy/tolerability outcomes were retrieved from the electronic medical repository archived at the University of Turin. The radiologic examination was performed as follows: computed tomography scans were approached at baseline, at week 12, and every 12 weeks of therapy until disease progression and clinical responses were defined in accordance with RECIST version 1.1. Written informed consent was acquired from all patients and documented according to “The Italian Data Protection Authority” ( http://www.garanteprivacy.it/web/guest/home/docweb/-/docwebdisplay/export/2485392 ). All information regarding human material was managed using anonymous numerical codes and all samples were handled in compliance with the Helsinki Declaration ( http://www.wma.net/en/30publications/10policies/b3/ ). The PROMOLE protocol was previously approved by the Independent Ethic Committee of S. Luigi Hospital, University of Turin (ethics approval number 73/2018 of 2024.01.30). Liquid biopsy collection and management Peripheral blood samples (ranging from two to eight collecting points) were withdrawn in accordance with clinical indication: 1) baseline (day 1, cycle 1 of sotorasib administration); 2) cycle 3 (56–66 days later); 3) each radiological evaluation (every 3 months) during the treatment. A longitudinal series of n = 91 liquid biopsy samples collected from n = 22 KRAS p.G12C mutant advanced NSCLC patients were retrieved from internal archive of University of Turin-San Luigi Hospital and shipped to the Cytopathology and Predictive Molecular Pathology Unit at University of Naples Federico II for both genomic and methylation analysis. Overall, two aliquots (containing two ml of plasma) were available for each clinically relevant time point. Plasma was separated centrifuging entire blood at 2300 revolutions per minute for 10 minutes, in accordance with standardized handling procedures and stored at -80C° until the shipment. Circulating-free DNA (cfDNA) was automatically purified from 2 ml of plasma samples adopting QIAsymphony instrument (Qiagen) equipped with the QIAsymphony DSPVirus/ Pathogen Midi Kit, following previously validated internal protocol. (17) ( Supplementary file 1 ) Finally, cfDNA was resuspended in 60 µl of DNAse and RNAse-free water (Thermo Fisher Scientifics, Waltham, MA, USA) and stored in dedicated tubes at -80C° until molecular analysis. cfDNA evaluation in liquid biopsy samples Before molecular analysis, cfDNA percentage was calculated. Briefly, 2 µl of extracted nucleic acids were automatically dispensed into Cell-free DNA ScreenTape (Agilent) equipped on TapeStation 4200 (Agilent) microfluidic platform, following manufacturer procedures. Proprietary software measured cfDNA abundance in biological samples comparing ratio between 170 bp and high molecular weight (HMW) DNA > 700 bp peaks. Molecular analysis and methylation index (MI) calculation A combined genomic and methylome NGS panel (Avida Duo Methyl Reagent Kit, Avida Biomed) was implemented to evaluate genomic alterations and measure methylation index (MI) in longitudinal series of liquid biopsy samples. This panel simultaneously analyzes clinically informative molecular alterations in n = 105 cancer related genes and 3400 differentially methylated regions (DMRs) across different tumor types distinguishing between tumor patients and healthy subjects. (18) The Avida Duo Methyl Reagent panel is built on a proprietary design of three-dimensional, biotinylated anchor probes scaffolding DNA regions and stabilizing interaction with target sites. Trimmed Unique molecular identifier (UMI) counts removing single strand duplicates and low-quality reads may increase technical sensitivity and specificity of Avida Duo Methyl Reagent Kit up to 94.0% and > 98.0%, respectively, starting from 5.0–10.0 ng of input. Of note, 1-100 ng of input at 170 ± bp was required to perform molecular analysis and calculate MI. Briefly, two consecutive target captures yielded targeted sequencing (TS) and targeted methylation sequencing (TMS). A sequential approach generating TMS libraries (based on bisulfite conversion) from unhybridized TS libraries was used to successfully carry out template (including n = 24 matched TS and TMS libraries) in accordance with manufacturer instructions. Libraries were diluted at 2–4 nM and pooled together unbalancing TS and TMS libraries 2.5:1 in accordance with manufacturer procedures. Finally, TS and TMS pooled libraries were sequenced on NextSeq 550 Dx platform (Illumina, San Diego, USA) following manufacturer instructions. FASTQ files were manually uploaded on Alissa Report software (v 2.0.0) (Agilent Technologies) where genomic and methylation data were automatically carried out adopting proprietary bioinformatic pipelines. Briefly, genomic and methylome data were filtered by inspecting required (total number of raw reads > 200.000, total number of raw bases > 15.000.000, average read length forward and reverse > 40) and recommended (mean insert size > 120, fraction of inaccessible targeted bases 0.6) technical parameters. As regards TMS analysis pipeline, CpGs calculated from CpG (5'C-phosphate-G-3') and CHX methylation data derived from the cytosine (C) to thymine (T) and guanine (G) to adenine (A) conversion rate were automatically inspected by analysis software. MI was measured using a proprietary bioinformatic algorithm able to identify tumor methylated CpGs (mCpGs) in biological samples counting CHH on target methylation profile (< 17.5). Moreover, methylation profile was also analyzed adopting early access SomaMethyl bioinformatic pipeline from SeqOne Genomics (Montpellier, France) able to automatically calculate MI by proprietary pipeline. Briefly, a training set of n = 14 previously tested NSCLC patients (n = 7 positive and n = 7 negative for MI scoring) was uploaded to set up threshold (≥ 1) for MI scoring. Moreover, p.G12C KRAS mutation was also longitudinally evaluated on Digital LightCycler® System (Roche Diagnostics) in accordance with manufacturer procedures. A total of 5.0 µl of cfDNA was manually combined with parameter specific reagents (PSR) (containing premixed, dried p.G12C KRAS primers and probes) and Digital LightCycler® master mix (Roche Diagnostics), then loaded into the Digital LightCycler® universal plate achieving 28.000 partitions in each well. (Roche Diagnostics). Moreover, cfDNA fragments were automatically partitioned adopting Digital LightCycler® Partitioning Engine platform (Roche Diagnostics) following technical instructions. (19) Up to n = 12 plates can be simultaneously processed by Digital LightCycler® System (Roche Diagnostics). Moreover, technical parameters including total valid partitions, positive partitions and number of copies/µl for mutant (FAM) and wild type (HEX), were automatically calculated by proprietary software (Digital LightCycler® System Development Software) correlating p.G12C positive signal with copies/µl of mutant fragments. In addition, molecular p.G12C status “positive or negative” was automatically called by proprietary software. Statistical analysis All statistical analyses on technical parameters and molecular records were conducted using Prism GraphPad software, version 10.0 for Windows (GraphPad Software, San Diego, CA, USA; www.graphpad.com ) performing either Student’s t (for two variables) or one-way ANOVA (for multiple variables) test followed by Tukey’s post hoc test setting * p < 0.05 as a cut-off for statistical significance. Pearson correlation coefficients were calculated to assess the linear relationship between variables by using GraphPad Prism. Briefly, evaluating r value, the corresponding p-values were measured, with ***p < 0.001 cut-off for statistical significance. Moreover, clinical, pathologic, and molecular characteristics of participants treated with Sotorasib were summarized either by descriptive statistics or as categorical tables. Descriptive analysis was performed, including means, standard deviations, medians, quartiles, and absolute/relative frequencies (with their respective two-sided 95% confidence interval [CI] limits, where relevant), according to the specific variables. Comparisons of continuous variables between groups were performed using the Wilcoxon rank-sum or Kruskal-Wallis tests, as appropriate. Categorical variables were compared using Fisher’s exact or chi-squared tests. Overall response rate (ORR) and progression-free survival (PFS) were evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. PFS was defined as the time from initiation of therapy to documented disease progression or death, whichever occurred first. Patients without progression were censored at the date of the last imaging assessment demonstrating no progression. Overall survival (OS) ranged between the immune checkpoint inhibitor (ICI) initiation to death from any cause. Survival outcomes were estimated using the Kaplan–Meier method and compared with the log-rank test, by using a p value < .05 as threshold for statistical significance. All analyses were conducted using R version 4.4.3. Declarations Funding : This study has partly been supported by the following grants: 1. POR Campania FESR 2014–2020 Progetto “Sviluppo di Approcci Terapeutici Innovativi per patologie Neoplastiche resistenti ai trattamenti—SATIN” 2. The Italian Health Ministry’s Research Program (ID: NET-2016–02363853). 3. The National Center for Gene Therapy and Drugs based on RNA Technology MUR-CN3 CUP E63C22000940007 to DS. 4. Italian Ministry of Health (Piano Operativo Salute Traiettoria 3, T3-AN-09, “Genomed”. No funding or sponsorship was received for the publication of this article. Acknowledgements : PH and CL thanks for the support of the ANR Grant IHU 2023 -0007 Author information: These authors contributed equally: Francesco Pepe, Francesco Passiglia, Claudia Scimone. These authors contributed equally as last authors: Silvia Novello, Giancarlo Troncone, Umberto Malapelle. Authors and Affiliations: Department of Public Health, Federico II University of Naples, Via S. Pansini, 5, 80131 Naples, Italy. Francesco Pepe, Claudia Scimone, Gianluca Russo, Domenico Cozzolino, Caterina De Luca, Giancarlo Troncone, Umberto Malapelle. Department of Oncology, University of Turin, S. Luigi Gonzaga Hospital, Orbassano (TO), Italy Francesco Passiglia, Angela Listì, Edoardo Garbo, Luisella Righi, Silvia Novello. Department of Biology, Complesso Universitario Monte Sant'Angelo, University of Naples Federico II, Via Cintia 4, 80126 Naples, Italy Giuseppina Roscigno, Viola Calabrò. DepIHU RespirERA Laboratory of Clinical and Experimental Pathology, FHU OncoAge, Biobank BB 0033-00025, University Côte d’Azur Nice France Caroline Lacoux, Paul Hofman. Competing interests : Francesco Pepe has received personal fees (as consultant and/or speaker bureau) from Menarini, Roche, Thermofisher, Jansen unrelated to the current work; Francesco Passiglia received speakers’ and consultants’ fee from AstraZeneca, Johnson&Johnson, Novartis, Roche, MSD, Amgen, Beone, Gilead, Pharmamar, Thermo Fisher Scientific unrelated to the current work. Luisella Righi received speakers’ and consultants’ fee from AstraZeneca, Novartis, Roche, Amgen, BeiGene, Novartis, EliLilly un related to the current work. Paul Hofman received fee and honoraria from AstraZeneca Roche Amgen Biocartis Thermo Fisher Scientific BMS MSD abbvie pierre Fabre Pfizer Novartis Daiichi Sankyo Ed Lilly Biodena Merck unrelated to the current work. Silvia Novello reports personal fees (as speaker bureau or advisor) from Eli Lilly, MSD, Roche, Takeda, Pfizer, Astra Zeneca, Amgen, Thermo Fisher, Novartis, Sanofi, Johnson&Johnson outside the current work. Giancarlo Troncone reports personal fees (as speaker bureau or advisor) from Roche, MSD, Pfizer and Bayer, unrelated to the current work; Umberto Malapelle has received personal fees (as consultant and/or speaker bureau) from Boehringer Ingelheim, Roche, MSD, Amgen, Thermo Fisher Scientific, Eli Lilly, Diaceutics, GSK, Merck and AstraZeneca, Janssen, Diatech, Novartis and Hedera for work performed Ethics approval statement : All information regarding human material was managed using anonymous numerical codes, and all samples were handled in compliance with the Helsinki Declaration. The PROMOLE protocol was previously approved by the Independent Ethic Committee of S. Luigi Hospital, University of Turin (ethics approval number 73/2018 of 2024.01.30). Contributorship Statement : Conceptualisation: FPE, FPA, CS, UM, and SN. Methodology: all authors. Software: all authors. Validation: all authors. Formal analysis: all authors. Investigation: all authors. Resources: all authors. 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Methylation Analyses in Liquid Biopsy of Lung Cancer Patients: A Novel and Intriguing Approach Against Resistance to Target Therapies and Immunotherapies. Cancers (Basel). 2025;17:3021. Additional Declarations Competing interest reported. Francesco Pepe has received personal fees (as consultant and/or speaker bureau) from Menarini, Roche, Thermofisher, Jansen unrelated to the current work; Francesco Passiglia received speakers’ and consultants’ fee from AstraZeneca, Johnson&Johnson, Novartis, Roche, MSD, Amgen, Beone, Gilead, Pharmamar, Thermo Fisher Scientific unrelated to the current work. Luisella Righi received speakers’ and consultants’ fee from AstraZeneca, Novartis, Roche, Amgen, BeiGene, Novartis, EliLilly un related to the current work. Paul Hofman received fee and honoraria from AstraZeneca Roche Amgen Biocartis Thermo Fisher Scientific BMS MSD abbvie pierre Fabre Pfizer Novartis Daiichi Sankyo Ed Lilly Biodena Merck unrelated to the current work. Silvia Novello reports personal fees (as speaker bureau or advisor) from Eli Lilly, MSD, Roche, Takeda, Pfizer, Astra Zeneca, Amgen, Thermo Fisher, Novartis, Sanofi, Johnson&Johnson outside the current work. Giancarlo Troncone reports personal fees (as speaker bureau or advisor) from Roche, MSD, Pfizer and Bayer, unrelated to the current work; Umberto Malapelle has received personal fees (as consultant and/or speaker bureau) from Boehringer Ingelheim, Roche, MSD, Amgen, Thermo Fisher Scientific, Eli Lilly, Diaceutics, GSK, Merck and AstraZeneca, Janssen, Diatech, Novartis and Hedera for work performed Supplementary Files SupplementaryFigure1.png Supplementary Figure 1: The heatmap highlights the Pearson correlation coefficients between NGS and dPCR detecting p.G12C KRAS mutation. The r value indicates a high correlation between the two analytical methods (r = 0.99). The color gradient reflects the magnitude and direction of the correlation, ranging from blue (negative) to orange (positive). The correlation is statistically significant (p-value of 6.91 × 10⁻⁷⁷). SupplementaryFigure2.png Supplementary Figure 2: Objective Response Rate to sotorasib therapy according to the median Methylation Index (MI) across different timepoints SupplementaryFigure3Ae3B.png Supplementary Figure 3A, B: Median progression free survival (mPFS) and median overall survival (mOS) of KRASp.G12C mutated advanced NSCLC patients undergoing sotorasib therapy according to the median Methylation Index (MI) at baseline (A) and at the first collection timepoints (B) SupplementaryFigure4A4Be4C.png Supplementary Figure 4A-C: Objective Response Rate (ORR) (A), Median progression free survival (mPFS) (B) and median overall survival (mOS) (C) of KRASp.G12C mutated advanced NSCLC patients undergoing sotorasib therapy according to the median fold change of Methylation Index (MI) between baseline and at the first collection timepoints. SupplementaryFigure5.png Supplementary Figure 5: Longitudinal variations of p.G12C KRAS mutation and MI score across collection points of patient ID#21. Supplementarytable1.docx Supplementarytable2A.docx Supplementarytable2B.docx Supplementarytable3.docx Supplementarytable4.docx Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7860201","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":529568896,"identity":"a43b2c26-b156-4e6c-8970-0750bead66da","order_by":0,"name":"Francesco Pepe","email":"","orcid":"","institution":"Federico II University of Naples","correspondingAuthor":false,"prefix":"","firstName":"Francesco","middleName":"","lastName":"Pepe","suffix":""},{"id":529568897,"identity":"c460914c-579e-46c5-a5be-7b0107d1690c","order_by":1,"name":"Francesco Passiglia","email":"","orcid":"","institution":"University of Turin, S. 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1","display":"","copyAsset":false,"role":"figure","size":223047,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003e(A) \u003c/strong\u003eBoxplot of \u003cem\u003eKRAS\u003c/em\u003e p.G12C variant allelic fraction (VAF%) and \u003cstrong\u003e(B)\u003c/strong\u003e Methylation Index score at three timepoints: T0 (baseline), T1 (post-treatment), and TP (progression). Boxplots were created by using RStudio® (v.2025.05.1) showing the median, interquartile range (IQR) and individual outliers for each group.\u003c/p\u003e","description":"","filename":"Figure1Ae1B.png","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/f6d1c17df6903b92bc8593b5.png"},{"id":93574781,"identity":"c2d45b91-ea2d-45d5-a2d5-9c369d56ef8d","added_by":"auto","created_at":"2025-10-15 09:18:11","extension":"png","order_by":2,"title":"Figure 2","display":"","copyAsset":false,"role":"figure","size":57871,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap with the variation of Methylation Index (MI) and \u003cem\u003eKRAS\u003c/em\u003e p.G12C at T0 (baseline) and T1 (post-treatment). Each row represents a single patient; color intensity indicates the magnitude of calculating VAF and MI variations. Patients are clustered as follows: Group A (MI increase; p.G12C VAF increase); Group B (MI decrease; p.G12C VAF decrease); Group C (MI stable; p.G12C VAF decrease); in the graph T0-TP Group A (MI increase; p.G12C VAF increase); Group B (MI decrease; p.G12C VAF decrease); Group C (MI decrease; p.G12C VAF increase).\u003c/p\u003e","description":"","filename":"Figure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/2d94978786025fd0bcc7360d.png"},{"id":93574786,"identity":"73d8fb71-083c-4a87-922a-4463afe7d81d","added_by":"auto","created_at":"2025-10-15 09:18:11","extension":"png","order_by":3,"title":"Figure 3","display":"","copyAsset":false,"role":"figure","size":250916,"visible":true,"origin":"","legend":"\u003cp\u003eHeatmap with the variation of Methylation Index (MI) and \u003cem\u003eKRAS\u003c/em\u003e p.G12C at and TP (progression). Each row represents a single patient; color intensity indicates the magnitude of calculating VAF and MI variations. Patients are clustered as follows: Group A (MI increase; p.G12C VAF increase); Group B (MI decrease; p.G12C VAF decrease); Group C (MI decrease; p.G12C VAF increase).\u003c/p\u003e","description":"","filename":"Figure3.png","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/e3a92a0caf3147a08ef4f620.png"},{"id":93575934,"identity":"2baae788-4bbe-457d-a030-dab03998fefd","added_by":"auto","created_at":"2025-10-15 09:26:11","extension":"png","order_by":4,"title":"Figure 4","display":"","copyAsset":false,"role":"figure","size":321367,"visible":true,"origin":"","legend":"\u003cp\u003eCorrealation between Methylation Index (MI) and radiological response to sotorasib across different timepoints\u003c/p\u003e","description":"","filename":"Figure4.png","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/1a3f593665c006c8f8d10d9c.png"},{"id":93576470,"identity":"7ab9d55a-c7d7-42c3-a500-c155667869f1","added_by":"auto","created_at":"2025-10-15 09:34:11","extension":"png","order_by":5,"title":"Figure 5","display":"","copyAsset":false,"role":"figure","size":271704,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCorrelation matrix between changes in Methylation index (Delta MI) and KRAS p.G12C VAF among different times points (T0/T1). \u003c/em\u003eThe heatmap displays Pearson correlation coefficients between Delta MI and Delta p.G12C hot spot mutation. The color gradient represents the magnitude and direction of the correlation, ranging from blue (negative correlation) to orange (positive correlation). A moderate positive correlation (r = 0.68, \u003cem\u003ep\u003c/em\u003e= 0.06) was observed between Delta MI and Delta p.G12C alteration, suggesting a positive association between T0 and T1, although not statistically significant association setting threshold at \u003cem\u003ep\u003c/em\u003e\u0026lt; 0.05 was achieved.\u003c/p\u003e","description":"","filename":"Figure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/d9d4dbf5fe7df4082a27d941.png"},{"id":93574794,"identity":"b6fd1bd3-7adb-4ad3-8d87-cf406aabcf01","added_by":"auto","created_at":"2025-10-15 09:18:11","extension":"png","order_by":6,"title":"Figure 6","display":"","copyAsset":false,"role":"figure","size":267118,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cem\u003eCorrelation matrix between changes in Methylation index (Delta MI) and KRAS p.G12C VAF among different times points (T0/TP).\u003c/em\u003e\u003cem\u003e\u003cstrong\u003e \u003c/strong\u003e\u003c/em\u003eThe heatmap displays Pearson correlation coefficients between Delta MI and Delta p.G12C hot spot mutation. The color gradient represents the magnitude and direction of the correlation, ranging from blue (negative correlation) to orange (positive correlation). A strong positive correlation was observed between Delta MI and Delta p.G12C alteration (r = 0.87, p = 0.000103), indicating a statistically significant association between Methylation changes and p.G12C abundance from T0 to TP.\u003c/p\u003e","description":"","filename":"Figure6.png","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/62e351b5ee1cc3297e84296b.png"},{"id":100368470,"identity":"7fe75cbe-3027-497a-9640-e413961270f3","added_by":"auto","created_at":"2026-01-16 07:57:59","extension":"pdf","order_by":0,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":3133065,"visible":true,"origin":"","legend":"","description":"","filename":"manuscript.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/7d366f56-9f19-4063-ab07-e0ddb9e09577.pdf"},{"id":93574783,"identity":"08049bca-1cac-485c-af85-eb84efa63c45","added_by":"auto","created_at":"2025-10-15 09:18:11","extension":"png","order_by":1,"title":"","display":"","copyAsset":false,"role":"supplement","size":180779,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 1\u003c/strong\u003e:\u003cstrong\u003e \u003c/strong\u003eThe heatmap highlights the Pearson correlation coefficients between NGS and dPCR detecting p.G12C \u003cem\u003eKRAS\u003c/em\u003emutation. The r value indicates a high correlation between the two analytical methods (r = 0.99). The color gradient reflects the magnitude and direction of the correlation, ranging from blue (negative) to orange (positive). The correlation is statistically significant (p-value of 6.91 × 10⁻⁷⁷).\u003c/p\u003e","description":"","filename":"SupplementaryFigure1.png","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/684c18d96e44fe0eebf00791.png"},{"id":93574788,"identity":"95b9e1a6-ef3f-4228-8f2b-73792f4db310","added_by":"auto","created_at":"2025-10-15 09:18:11","extension":"png","order_by":2,"title":"","display":"","copyAsset":false,"role":"supplement","size":431331,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 2: \u003c/strong\u003eObjective Response Rate to sotorasib therapy according to the median Methylation Index (MI) across different timepoints\u003c/p\u003e","description":"","filename":"SupplementaryFigure2.png","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/21b01529212e02d12a86f7d9.png"},{"id":93574789,"identity":"c5b4310a-28fa-49c7-aa62-237f64100f3d","added_by":"auto","created_at":"2025-10-15 09:18:11","extension":"png","order_by":3,"title":"","display":"","copyAsset":false,"role":"supplement","size":376513,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 3A, B: \u003c/strong\u003eMedian progression free survival (mPFS) and median overall survival (mOS) of KRASp.G12C mutated advanced NSCLC patients undergoing sotorasib therapy according to the median Methylation Index (MI) at baseline (A) and at the first collection timepoints (B)\u003c/p\u003e","description":"","filename":"SupplementaryFigure3Ae3B.png","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/fc67d635970ed6a4e971521a.png"},{"id":93574793,"identity":"449e1d52-31d0-4273-8577-6c6c4dee2e0c","added_by":"auto","created_at":"2025-10-15 09:18:11","extension":"png","order_by":4,"title":"","display":"","copyAsset":false,"role":"supplement","size":456255,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 4A-C: \u003c/strong\u003eObjective Response Rate (ORR) (A), Median progression free survival (mPFS) (B) and median overall survival (mOS) (C) of KRASp.G12C mutated advanced NSCLC patients undergoing sotorasib therapy according to the median fold change of Methylation Index (MI) between baseline and at the first collection timepoints.\u003c/p\u003e","description":"","filename":"SupplementaryFigure4A4Be4C.png","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/f17e20857222e17d1595798c.png"},{"id":93577735,"identity":"82dd2c07-296a-4972-8c1e-48d36ba3e63e","added_by":"auto","created_at":"2025-10-15 09:42:11","extension":"png","order_by":5,"title":"","display":"","copyAsset":false,"role":"supplement","size":228266,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cstrong\u003eSupplementary Figure 5: \u003c/strong\u003eLongitudinal variations of\u003cstrong\u003e \u003c/strong\u003ep.G12C \u003cem\u003eKRAS\u003c/em\u003e mutation and MI score across collection points of patient ID#21.\u003c/p\u003e","description":"","filename":"SupplementaryFigure5.png","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/08f6ba941c9c68c3554020ff.png"},{"id":93574799,"identity":"1257314d-2448-4927-b4ed-c5d300ca9554","added_by":"auto","created_at":"2025-10-15 09:18:11","extension":"docx","order_by":6,"title":"","display":"","copyAsset":false,"role":"supplement","size":34529,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Supplementarytable1.docx","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/d05b11064efca0eade7729fb.docx"},{"id":93575941,"identity":"889200e5-91ff-4b75-9713-74692e8ed55c","added_by":"auto","created_at":"2025-10-15 09:26:11","extension":"docx","order_by":7,"title":"","display":"","copyAsset":false,"role":"supplement","size":29885,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Supplementarytable2A.docx","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/1bca3d4fde4449d8607e70bf.docx"},{"id":93575939,"identity":"a0670aa6-f5e0-46eb-a810-9150e7e350c0","added_by":"auto","created_at":"2025-10-15 09:26:11","extension":"docx","order_by":8,"title":"","display":"","copyAsset":false,"role":"supplement","size":43631,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Supplementarytable2B.docx","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/1977c666c90bd96cce0a4e0f.docx"},{"id":93574803,"identity":"46887e78-b9e1-4d73-80f5-d23a9b372ec7","added_by":"auto","created_at":"2025-10-15 09:18:11","extension":"docx","order_by":9,"title":"","display":"","copyAsset":false,"role":"supplement","size":52151,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Supplementarytable3.docx","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/ea86a72f79ef0500306b1e24.docx"},{"id":93575940,"identity":"5c3bed8e-f2a6-4cb9-83cf-5b0c1b92bb43","added_by":"auto","created_at":"2025-10-15 09:26:11","extension":"docx","order_by":10,"title":"","display":"","copyAsset":false,"role":"supplement","size":30568,"visible":true,"origin":"","legend":"\u003cp\u003e\u003cbr\u003e\u003c/p\u003e","description":"","filename":"Supplementarytable4.docx","url":"https://assets-eu.researchsquare.com/files/rs-7860201/v1/9a06e54b478500ecc90ea958.docx"}],"financialInterests":"Competing interest reported. Francesco Pepe has received personal fees (as consultant and/or speaker bureau) from Menarini, Roche, Thermofisher, Jansen unrelated to the current work; Francesco Passiglia received speakers’ and consultants’ fee from AstraZeneca, Johnson\u0026Johnson, Novartis, Roche, MSD, Amgen, Beone, Gilead, Pharmamar, Thermo Fisher Scientific unrelated to the current work. Luisella Righi received speakers’ and consultants’ fee from AstraZeneca, Novartis, Roche, Amgen, BeiGene, Novartis, EliLilly un related to the current work. Paul Hofman received fee and honoraria from AstraZeneca Roche Amgen Biocartis Thermo Fisher Scientific BMS MSD abbvie pierre Fabre Pfizer Novartis Daiichi Sankyo Ed Lilly Biodena Merck unrelated to the current work. Silvia Novello reports personal fees (as speaker bureau or advisor) from Eli Lilly, MSD, Roche, Takeda, Pfizer, Astra Zeneca, Amgen, Thermo Fisher, Novartis, Sanofi, Johnson\u0026Johnson outside the current work. Giancarlo Troncone reports personal fees (as speaker bureau or advisor) from Roche, MSD, Pfizer and Bayer, unrelated to the current work; Umberto Malapelle has received personal fees (as consultant and/or speaker bureau) from Boehringer Ingelheim, Roche, MSD, Amgen, Thermo Fisher Scientific, Eli Lilly, Diaceutics, GSK, Merck and AstraZeneca, Janssen, Diatech, Novartis and Hedera for work performed","formattedTitle":"Exploring genomic analysis and methylome profiling in longitudinal series of p.G12C KRAS mutated NSCLC patients treated with Sotorasib","fulltext":[{"header":"INTRODUCTION","content":"\u003cp\u003eLung cancer (LC) still represents one of the most leading causes of death worldwide across solid malignancies. (1) As regards, conventional diagnostic procedures are affected by lack of sensitivity and specificity to early detect LC patients drastically impacting on the rate of advanced stage (IIIB-IV) diagnosed disease. (2) In the last decade, precision medicine has revolutionized the clinical paradigm of advanced non-small cell lung cancer (NSCLC) patients. (3) Particularly, guidelines from CAP/AMP/IASCL societies established a panel of mandatory testing genes able to identify oncogene-addicted patients sensitive to targeted therapies. (4) This panel includes DNA-based [(\u003cem\u003eEGFR\u003c/em\u003e (Epidermal Grow Factor Receptor), \u003cem\u003eBRAF\u003c/em\u003e (v-Raf murine sarcoma viral oncogene homolog B), HER-2 (Human Epidermal growth factor Receptor 2)] and RNA-based [(\u003cem\u003eALK\u003c/em\u003e (anaplastic lymphoma kinase), \u003cem\u003eROS1\u003c/em\u003e (protooncogene 1 receptor tyrosine kinase), \u003cem\u003eRET\u003c/em\u003e (Proto-Oncogene Tyrosine-Protein Kinase Receptor Ret), \u003cem\u003eNTRK\u003c/em\u003e (Neurotrophic Tyrosine Receptor Kinase) and \u003cem\u003eMET\u003c/em\u003e (MET Proto-Oncogene, Receptor Tyrosine Kinase)] biomarkers crucial for the therapeutic management of NSCLC patients. (5, 6) Recently, exon 2 p.G12C \u003cem\u003eKRAS\u003c/em\u003e hotspot mutation emerged as a novel therapeutic target identifying NSCLC patients eligible to selective covalent inhibitors, like Sotorasib and Adagrasib. (7) In addition, \u003cem\u003eNRG1\u003c/em\u003e (Neuregulin 1) has been accelerating to stratify NSCLC patients to novel mABs. (8) Not surprisingly, both innate and acquired resistance mechanisms significantly reduce the clinical benefit of \u003cem\u003eKRAS\u003c/em\u003e p.G12C covalent inhibitors in NSCLC cases. (9) Novel molecular hallmarks should be identified to optimize the clinical stratification of \u003cem\u003eKRAS\u003c/em\u003e p.G12C mutant advanced NSCLC patients. (10, 11) It has been demonstrated that specific patterns of epigenetic alterations including DNA methylation, histone modification, and the aberrant expression of non-coding RNA, are recurrent in NSCLC playing a pivotal role in tumor progression and potentially impacting on clinical outcomes. (12, 13, 14) Considering the low abundance of circulating tumor DNA (ctDNA) in NSCLC patients, multi-omics analysis integrating tumor addicted genomic alterations and methylation signatures may act as potential tool for guiding clinical decision-making procedures in advanced NSCLC patients. (15, 16) Here, we sought to evaluate technical and clinical performance of a combined genomic plus methylome NGS analysis on a longitudinal series of liquid biopsy specimens from \u003cem\u003eKRAS\u003c/em\u003e p.G12C mutant advanced NSCLC patients receiving a targeted treatment with Sotorasib in the real-world scenario.\u003c/p\u003e"},{"header":"RESULTS","content":"\u003cdiv id=\"Sec3\" class=\"Section2\"\u003e\u003ch2\u003ePatients\u0026rsquo; Characteristics\u003c/h2\u003e\u003cp\u003eBetween November 2020 and December 2022, a total of n\u0026thinsp;=\u0026thinsp;22 \u003cem\u003eKRAS\u003c/em\u003e p.G12C mutant advanced NSCLC patients receiving Sotorasib in the real-world were enrolled. Baseline clinical characteristics are summarized in Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. The median age was 70.5 years (range, 46\u0026ndash;81), and 52.2% of patients were male. Most patients had an ECOG performance status (PS) of 0 (63.6%), while all of them had a history of tobacco exposure and were diagnosed with lung adenocarcinoma. The tumour PD-L1 expression was \u0026ge;\u0026thinsp;50% in 22.7% of cases, 1\u0026ndash;49% in 45.5%, and \u0026lt;\u0026thinsp;1% in 31.8%. All patients were metastatic at enrolment, with brain metastases being the most frequent site (36.4%). Moreover, 63.6% of patients had previously received anti\u0026ndash;PD-L1 therapy. All patients included in the study were evaluable for tumor response assessment: 7 (31.8%) experienced a PR, 11 (50%) had stable disease (SD), and 4 (18.2%) had progressive disease (TP) as their best response to Sotorasib. The median follow-up calculated with the reverse Kaplan-Meier method was 32 months (range 2\u0026ndash;35) for the overall cohort at the time of data cut-off. (Table\u0026nbsp;\u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab1\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 1\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eMethylation Index according to patients\u0026rsquo; characteristics. \u003cem\u003eAbbreviations\u003c/em\u003e: ECOG-PS (\u003cem\u003eEastern Cooperative Oncology Group - Performance Status\u003c/em\u003e); PD-L1 (\u003cem\u003eProgrammed Death-Ligand 1\u003c/em\u003e).\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"3\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePatients\u0026rsquo; Characteristics\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eMethylation Index\u003c/p\u003e\u003cp\u003eMedian [Min, Max]\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eP-value\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eAge\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;75y (N\u0026thinsp;=\u0026thinsp;16)\u003c/p\u003e\u003cp\u003e\u0026ge;\u0026thinsp;75y (N\u0026thinsp;=\u0026thinsp;6)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.636 [0.0800, 14.8]\u003c/p\u003e\u003cp\u003e0.564 [0.130, 3.30]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.641\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eECOG-PS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e0 (N\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e\u003cp\u003e1\u0026ndash;2 (N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.653 [0.0800, 14.8]\u003c/p\u003e\u003cp\u003e0.474 [0.130, 12.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eSmoking history\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u0026lt;\u0026thinsp;30p/y (N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\u003cp\u003e\u0026ge;\u0026thinsp;30p/y (N\u0026thinsp;=\u0026thinsp;11)\u003c/p\u003e\u003cp\u003eMissing (N\u0026thinsp;=\u0026thinsp;3)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.666 [0.08, 14.8]\u003c/p\u003e\u003cp\u003e0.589 [0.130, 2.13]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.492\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBrain metastases\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes (N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\u003cp\u003eNo (N\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.548 [0.0800, 12.3]\u003c/p\u003e\u003cp\u003e0.653 [0.130, 14.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.815\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePD-L1 TPS\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003ePD-L1 Negative (N\u0026thinsp;=\u0026thinsp;17)\u003c/p\u003e\u003cp\u003ePD-L1 Positive (N\u0026thinsp;=\u0026thinsp;5)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.555 [0.08, 12.3]\u003c/p\u003e\u003cp\u003e3.30 [0.366, 14.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.164\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003ePrevious Immunotherapy\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eYes (N\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e\u003cp\u003eNo (N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.747 [0.0800, 14.8]\u003c/p\u003e\u003cp\u003e0.539 [0.13, 12.3]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.441\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003e\u003cb\u003eBest Response\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c3\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\"\u003e\u003cp\u003eResponders (N\u0026thinsp;=\u0026thinsp;14)\u003c/p\u003e\u003cp\u003eNon-Responders (N\u0026thinsp;=\u0026thinsp;8)\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003e0.932 [0.409, 14.5]\u003c/p\u003e\u003cp\u003e0.540 [0.0800, 14.8]\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.123\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003c/div\u003e\n\u003ch3\u003ecfDNA abundance in liquid biopsy samples\u003c/h3\u003e\n\u003cp\u003eOverall, cfDNA measurement was successfully carried out in all instances. Of note, cfDNA score was clearly inspected in 78 out of 91 cases (85.7%) achieving a median value of 73.0% ranging from 32.0% to 93.0%. In particular, baseline and TP samples highlighted a median cfDNA score of 72.0% (from 53.0% to 83.0%) and 77.0% (from 57.0% to 91.0%). In addition, a median of 0.2 ng/\u0026micro;l (from 0.1 to 2.4 ng/\u0026micro;l) was inspected. In detail, baseline and TP samples revealed a median of 0.2 ng/\u0026micro;l (from 0.1 ng/\u0026micro;l to 0.8ng/\u0026micro;l) and 0.3 ng/\u0026micro;l (from 0.1ng/\u0026micro;l to 1.3 ng/\u0026micro;l), respectively. (\u003cb\u003eSupplementary Table\u0026nbsp;1\u003c/b\u003e)\u003c/p\u003e\n\u003ch3\u003eGenomic and methylome data\u003c/h3\u003e\n\u003cp\u003eGenomic analysis and methylation index were measured in all instances. In particular, an average of 12460161.9 (ranging from 5139660.0 to 45165008.0) total number of raw reads, of 3052515.0 (ranging from 458720.0 to 11360524.0) number of mapped reads; of 2949844.9 (ranging from 424865.0 to 11077138.0) high quality reads; of 434.7 (ranging from 63.0 to 1617.0) median depth in targeted region were identified among genomic data. Moreover, an average of 15596177.5 (ranging from 2247880.0 to 47794630.0) total number of raw reads, of 6932619.7 (ranging from 107636.0 to 15259654.0) number of mapped reads, of 6330388.8 (ranging from 81958.0 to 13361940.0) high quality reads, a median of 10486874,43 (ranging from 135213.0 to 95510093.0) total number of analyzed cytosines, of 11.8 (ranging from 5.3 to 27.5) methylated C in CpG context, of 4.2 (ranging from 0.5 to 18.0) methylated C in CHG context, of 3.7 (ranging from 0.5 to 13.5) methylated C in CHH was identified among methylation data. (\u003cb\u003eSupplementary Table\u0026nbsp;2A-B\u003c/b\u003e) Considering a technical cut-off of 0.2%, exon 2 p.G12C \u003cem\u003eKRAS\u003c/em\u003e mutation was successfully identified in 9 out of 22 (40.9%) and 12 out of 17 (70.6%) baseline and TP samples, respectively, showing a median variant allele fraction (VAF) of 9.4% (from 0.7% to 44.2%) and 19.2% (from 0.2% to 65.4%), respectively. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) In addition, dPCR system successfully analyzed all samples highlighting a median VAF of 18.6% (from 0.2% to 76.7%) in 27 out of 91 (29.7%) p.G12C positive samples. (\u003cb\u003eSupplementary Table\u0026nbsp;3\u003c/b\u003e) A median VAF of 11.6% (ranging from 0.4% to 56.5%) and 25.9% (ranging from 0.2% to 76.7%) was measured in basal and TP samples, respectively. A statistically significant correlation was yielded comparing NGS and dPCR analysis (\u0026ge;\u0026thinsp;2 positive partitions) of \u003cem\u003eKRAS\u003c/em\u003e p.G12C hotspot mutation. (r\u0026thinsp;=\u0026thinsp;099, p-value of 6.91 \u0026times; 10⁻⁷⁷). \u003cb\u003e(Supplementary Fig.\u0026nbsp;1)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003cdiv class=\"gridtable\"\u003e\u003ctable float=\"Yes\" id=\"Tab2\" border=\"1\"\u003e\u003ccaption language=\"En\"\u003e\u003cdiv class=\"CaptionNumber\"\u003eTable 2\u003c/div\u003e\u003cdiv class=\"CaptionContent\"\u003e\u003cp\u003eSchematizing report of Methylation index and \u003cem\u003eKRAS\u003c/em\u003e p.G12C VAF across longitudinal plasma samples.\u003c/p\u003e\u003c/div\u003e\u003c/caption\u003e\u003ccolgroup cols=\"9\"\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c1\" colnum=\"1\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c2\" colnum=\"2\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c3\" colnum=\"3\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c4\" colnum=\"4\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c5\" colnum=\"5\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c6\" colnum=\"6\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c7\" colnum=\"7\"\u003e\u003c/div\u003e\u003cdiv align=\"char\" char=\".\" class=\"colspec\" colname=\"c8\" colnum=\"8\"\u003e\u003c/div\u003e\u003cdiv align=\"left\" class=\"colspec\" colname=\"c9\" colnum=\"9\"\u003e\u003c/div\u003e\u003cthead\u003e\u003ctr\u003e\u003cth align=\"left\" colname=\"c1\"\u003e\u003cp\u003eID Sample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c2\"\u003e\u003cp\u003eCollection point\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c3\"\u003e\u003cp\u003eMethylation Index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c4\"\u003e\u003cp\u003e\u003cem\u003eKRAS\u003c/em\u003e p.G12C (VAF%)\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/th\u003e\u003cth align=\"left\" colname=\"c6\"\u003e\u003cp\u003eID Sample\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c7\"\u003e\u003cp\u003eCollection point\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c8\"\u003e\u003cp\u003eMethylation Index\u003c/p\u003e\u003c/th\u003e\u003cth align=\"left\" colname=\"c9\"\u003e\u003cp\u003e\u003cem\u003eKRAS\u003c/em\u003e p.G12C (VAF%)\u003c/p\u003e\u003c/th\u003e\u003c/tr\u003e\u003c/thead\u003e\u003ctbody\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eID01\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003eID12\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eID02\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e12.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e44.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e4.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e43.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e65.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eID03\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003eID13\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eID04\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"7\" rowspan=\"8\"\u003e\u003cp\u003e\u003cb\u003eID05\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eID14\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eID15\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e6\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e7\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eID06\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"4\" rowspan=\"5\"\u003e\u003cp\u003e\u003cb\u003eID16\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"3\" rowspan=\"4\"\u003e\u003cp\u003e\u003cb\u003eID07\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e12.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e22.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e55.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e64.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eID17\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e4.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"6\" rowspan=\"7\"\u003e\u003cp\u003e\u003cb\u003eID08\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eID18\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.6\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e3.2\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e14.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eID19\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e6.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e23.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eID20\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eNA\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eID09\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e2.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e11.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e37.3\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"5\" rowspan=\"6\"\u003e\u003cp\u003e\u003cb\u003eID21\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e3.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.2\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e3\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e1.0\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e5\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e1.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e4\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e2.4\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"1\" rowspan=\"2\"\u003e\u003cp\u003e\u003cb\u003eID10\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e1.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e7.3\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e2.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e7.6\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e0.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.9\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eID22\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003e0.7\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c1\" morerows=\"2\" rowspan=\"3\"\u003e\u003cp\u003e\u003cb\u003eID11\u003c/b\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e0\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e14.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e14.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.8\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003e1\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e5.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e3.0\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u003cp\u003eT\u003csub\u003e2\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c8\"\u003e\u003cp\u003e0.4\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u003cp\u003eND\u003c/p\u003e\u003c/td\u003e\u003c/tr\u003e\u003ctr\u003e\u003ctd align=\"left\" colname=\"c2\"\u003e\u003cp\u003eT\u003csub\u003er\u003c/sub\u003e\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"char\" char=\".\" colname=\"c3\"\u003e\u003cp\u003e7.1\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c4\"\u003e\u003cp\u003e5.5\u003c/p\u003e\u003c/td\u003e\u003ctd align=\"left\" colname=\"c5\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c6\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c7\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c8\"\u003e\u0026nbsp;\u003c/td\u003e\u003ctd align=\"left\" colname=\"c9\"\u003e\u0026nbsp;\u003c/td\u003e\u003c/tr\u003e\u003c/tbody\u003e\u003c/colgroup\u003e\u003c/table\u003e\u003c/div\u003e\u003c/p\u003e\u003cp\u003eA median of 2.2 (ranging from 0.1 to 55.8) MI was calculated among all liquid biopsy samples. A median of 2.6 (ranging from 0.1 to 14.8) and 5.7 (ranging from 0.1 to 55.8) MI was identified at baseline and TP timepoints, respectively. SeqOne Genomics (Montpellier, France) successfully calculated MI in all instances overlapping with data from previous MI analysis. (\u003cb\u003eSupplementary Table\u0026nbsp;4)\u003c/b\u003e Beyond p.G12C hotspot mutation, no clinically actionable alterations were found in driver genes both at baseline and TP collection points. Filtering algorithm was designed as follows: synonymous, intronic and not assessed (NA) molecular alterations below 0.5% of VAF were discarded.\u003c/p\u003e\n\u003ch3\u003eEarly dynamic variations and patients’ outcomes\u003c/h3\u003e\n\u003cp\u003eOverall, genomic data from both baseline sample and first longitudinal timepoint (T1) were available in 19 out 22 (86.3%) NSCLC patients. Among the seven patients with a detectable \u003cem\u003eKRAS\u003c/em\u003e p.G12C mutation at baseline, genomic analysis accurately revealed a decreasing mutation rate by comparing baseline and T1in five cases (83.3%) (Δ\u0026thinsp;=\u0026thinsp;2.6%, 0.7\u0026ndash;5.5%); all of them achieved a complete ctDNA clearance of \u003cem\u003eKRAS\u003c/em\u003e p.G12C mutation. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) whereas a decreasing trend was identified in ID#02,11 (median Δ\u0026thinsp;=\u0026thinsp;12.1%) (p-value\u0026thinsp;=\u0026thinsp;0.07). Among the other n\u0026thinsp;=\u0026thinsp;12 patients having undetectable p.G12C \u003cem\u003eKRAS\u003c/em\u003e mutation at baseline, positive signal at T1 was observed in a single instance. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) Overall, methylome data both from baseline and T1 samples were available in all NSCLC patients. MI score was significantly lower (median 0.9, from 0.1 to 5.4; p-value\u0026thinsp;=\u0026thinsp;0.04) in T1 samples compared with basal specimens (median 2.9, from 0.1 to 14.8). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003eInterestingly, MI scored a decreasing trend (4.1 vs 1.1 from Δ\u0026thinsp;=\u0026thinsp;2.9, from 0.1 to 13.0) between baseline and T1 in 13 out of 19 (68.4%) NSCLC patients. Conversely, 4 out of 19 (21.0%) NSCLC patients showed an increasing MI (0.2 vs 0.6; Δ\u0026thinsp;=\u0026thinsp;0.5, from 0.2 to 1.1) in T1 samples, while two cases highlighted a stable MI (ID#08, ID#22) comparing baseline and T1 timepoint. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003cp\u003eMatching p.G12C \u003cem\u003eKRAS\u003c/em\u003e mutation and MI score, 1 out of 8 (12.5%) NSCLC patients simultaneously increased whereas a decreasing trend of p.G12C and MI was identified in 75.0% of cases (6 out of 8). Moreover, p.G12C \u003cem\u003eKRAS\u003c/em\u003e mutation concomitantly diminished to a stable MI score (p.G12C VAF 0.7 to 0.0%, MI\u0026thinsp;=\u0026thinsp;0.8) in a single case (12.5%) comparing baseline and T1. (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e)\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\n\u003ch3\u003eLiquid biopsy data at sotorasib resistance\u003c/h3\u003e\n\u003cp\u003eA total of 17 out of 22 (77.3%) patients with liquid biopsy samples available within 90 days of radiologic progression disease (PD) were selected. Of note, 12 out of 17 (70.6%) patients highlighted positive signal (VAF\u0026thinsp;\u0026ge;\u0026thinsp;0.2%) of \u003cem\u003eKRAS\u003c/em\u003e p.G12C at TP. Not surprisingly, a median p.G12C VAF of 19.2% (from 0.2 to 65.4%) was augmented compared TP with paired basal samples. (median 11.7%, range 1.1\u0026ndash;44.2%) (p-value\u0026thinsp;=\u0026thinsp;0.06). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) Particularly, in 10 out of 17 (58.8%) cases, a median VAF of p.G12C hot spot mutations increased (median VAF 5.9%, 0.0 to 44.2% at baseline vs 21.9, 0.2 to 65.4% at TP) (Δ\u0026thinsp;=\u0026thinsp;16.0%, from 0.2% to 64.8%). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) Conversely, three patients (17.6%) highlighted a decreasing rate of p.G12C VAF (median VAF 7.5%, 1.1 to 14.0% at baseline vs 3.8%, 0.0 to 5.9% at TP) (Δ\u0026thinsp;=\u0026thinsp;3.6%, from 1.1% to 8.5%). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e MI was higher in TP (median 5.7, from 0.1 to 55.8) compared with baseline samples (median 3.1, from 0.1 to 14.8 p value\u0026thinsp;=\u0026thinsp;0.46). (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eB) In addition, an increasing trend between baseline and TP (0.4 vs 12.0; Δ\u0026thinsp;=\u0026thinsp;11.5, from 0.2 to 54.9) was identified in 6 out of 17 (35.3%) NSCLC patients. \u003cb\u003e(\u003c/b\u003eTable\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e Interestingly, 5 out of 13 (38.5%) NSCLC patients displayed a simultaneously increasing rate of \u003cem\u003eKRAS\u003c/em\u003e p.G12C mutation and MI score whereas in 23.1% of cases (3 out of 13) both p.G12C \u003cem\u003eKRAS\u003c/em\u003e mutation and MI simultaneously decreased. In addition, a divergent trend, where p.G12C \u003cem\u003eKRAS\u003c/em\u003e increased, matching with progressive decrease of MI at TP, was identified in 5 out of 13 NSCLC patients (38.5%). \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cdiv id=\"Sec8\" class=\"Section2\"\u003e\u003ch2\u003eMethylation index and patients\u0026rsquo; outcomes\u003c/h2\u003e\u003cp\u003eAmong the n\u0026thinsp;=\u0026thinsp;22 NSCLC patients included in the study, any potential differences of baseline median MI across the main patients\u0026rsquo; subgroups were explored and the results were summarized in Table \u003cspan refid=\"Tab1\" class=\"InternalRef\"\u003e1\u003c/span\u003e. A trend toward an increased median MI was found in patients with PD-L1 positive vs negative tumors (3.30 vs 0.55, p:0.164) but statistically significant variation was not assessed. Considering n\u0026thinsp;=\u0026thinsp;19 NSCLC patients with T1 collecting points, a not significant trend toward both an increased median MI (0.43 vs 0.27, p: 0.773) and a decreased MI fold-change (0.267 vs 0.504, p:0.08) was found in responders (PR) vs non responders (SD\u0026thinsp;+\u0026thinsp;PD). \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig4\" class=\"InternalRef\"\u003e4\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e Setting the median MI as reference cut-off, an increased ORR was observed in patients with high vs low MI detected both at baseline (45.5% vs 18.2%, p:0.36) and at T1 (50.0% vs 22.2%, p: 0.44) timepoints, even if variations did not reach statistical significance (\u003cb\u003eSupplementary Fig.\u0026nbsp;2\u003c/b\u003e). Similar median PFS and OS emerged in NSCLC patients with high vs low MI both at baseline (mPFS: 6.08 vs 6.21 months, p: 0.57; mOS: 13 vs 9.6 months, p:0.96) (\u003cb\u003eSupplementary Fig.\u0026nbsp;3A\u003c/b\u003e) and at T1 (mPFS: 7.06 vs 7.10 months, p: 0.58; mOS: 13.3 vs 14.1 months, p:0.98) timepoints. (\u003cb\u003eSupplementary Fig.\u0026nbsp;3B\u003c/b\u003e) Setting the median MI fold change between baseline and T1 as reference cut-off, no differences in terms of ORR (40% vs 33%, p:1) and mPFS (7.43 vs 7.10, p: 0.81) (\u003cb\u003eSupplementary Fig.\u0026nbsp;4A-B\u003c/b\u003e) were observed, while a not significant trend toward increased mOS (15.3 vs 9.63, p: 0.77) (\u003cb\u003eSupplementary Fig.\u0026nbsp;4C\u003c/b\u003e) were identified in patients with low vs high MI fold-change values.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003c/div\u003e"},{"header":"DISCUSSION","content":"\u003cp\u003eIn the era of personalized treatments, an accurate stratification of NSCLC patients is pivotal. Given the rapidly transforming scenario of clinically approved biomarkers, including genomic alterations and aberrant RNA rearrangements, NSCLC patients may benefit from several drugs selectively targeting specific oncoproteins. Despite these advances, a consistent fraction of NSCLC patients with driver molecular alterations experienced a limited clinical benefit from current targeted therapies. In this scenario, epigenomic markers (chromatin remodeling, methylation status, histone modifications) may impact on the clinical selection of NSCLC patients guiding clinical decision-making procedures. Here, we sought to evaluate how methylation index, calculated by NGS panel integrating genomic and methylome analysis, may optimize clinical stratification of p.G12C \u003cem\u003eKRAS\u003c/em\u003e positive NSCLC patients undergoing Sotorasib treatment in the real-world. A series of n\u0026thinsp;=\u0026thinsp;22 NSCLC patients were investigated by NGS combined panel (Avida Duo Methyl Reagent Kit, Avida Biomed) automatically scoring MI by proprietary bioinformatic pipeline able to assess methylation patterns of 3400 cancer related CpGs after bisulfite conversion. Comparing bisulfite-based methods with other strategies for DNA methylation profiling, lower technical performance in terms of reference range (\u0026gt;\u0026thinsp;28\u0026nbsp;million of CpGs vs\u0026thinsp;\u0026lt;\u0026thinsp;23\u0026nbsp;million of CpGs covered) and technical resolution (comprehensive CpGs analysis vs target CpGs patterns) were observed in affinity enrichment and restriction enzymes-based methods. (20) Dynamic evaluation of ctDNA was inspected at T1 and TP simultaneously investigating p.G12C \u003cem\u003eKRAS\u003c/em\u003e driver mutation and MI score. Before molecular analysis, cfDNA abundance was calculated as 170/700 bp ratio by microfluidic system showing that no statistically significant variations were observed between baseline, TI and TP samples (72.0, 72.0, 77.0 cfDNA%, respectively, p-value\u0026thinsp;=\u0026thinsp;0.21).\u003c/p\u003e\u003cp\u003eInterestingly, 6 out of 7 (85.7%) patients showing traces of p.G12C alteration at baseline highlighted decreasing p.G12C level at T1 achieving a complete clearance of ctDNA in 83.3% of cases. (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) Switching from early detection to disease progression, p.G12C \u003cem\u003eKRAS\u003c/em\u003e mutation was found in 70.6% of cases demonstrating a significant variation of the median VAF comparing baseline and TP points (11.7% vs 19.2%) (p value\u0026thinsp;=\u0026thinsp;0.07). (Fig.\u0026nbsp;\u003cspan refid=\"Fig1\" class=\"InternalRef\"\u003e1\u003c/span\u003eA) In 10 out of 13 (76.9%) NSCLC patients, p.G12C \u003cem\u003eKRAS\u003c/em\u003e increased at TP (5.9% vs 21.9% VAF). In line with previous reported data, persisting ctDNA traces in longitudinal timepoints were associated with worse clinical outcomes. Moreover, abundance of ctDNA depending on higher p.G12C VAF at TP can also impact on the clinical response and relapsing timeline. In a previous metanalysis, Zaman \u003cem\u003eet al.\u003c/em\u003e highlighted that ctDNA-negative patients at baseline had a longer PFS (pooled hazard ratio [pHR]\u0026thinsp;=\u0026thinsp;1.35; 95%CI: 0.83\u0026ndash;1.87; \u003cem\u003ep\u003c/em\u003e\u0026thinsp;\u0026lt;\u0026thinsp;0.001; I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;96%) compared with baseline ctDNA positive. In addition, clearance of ctDNA levels after target treatment was significantly associated with increasing PFS (pHR\u0026thinsp;=\u0026thinsp;2.71; 95%CI: 1.85\u0026ndash;3.65; I\u003csup\u003e2\u003c/sup\u003e\u0026thinsp;=\u0026thinsp;89.4%) in comparison with persistence ctDNA levels at recollection points. (21) Similarly, Passiglia \u003cem\u003eet al.\u003c/em\u003e also confirmed that early ctDNA \u003cem\u003eKRAS\u003c/em\u003ep.G12C mutation correlated with longer OS (16.8 vs. 6.4 months; p\u0026thinsp;\u0026lt;\u0026thinsp;.001) in advanced NSCLC patients undergoing sotorasib in the real world, while increasing of ctDNA mutation VAF anticipated radiological PD, suggesting a potential role for ctDNA driven escalation and de-escalation strategies in \u003cem\u003eKRAS\u003c/em\u003e mutated patients. (22)\u003c/p\u003e\u003cp\u003eThe clinical evidence confirms that the clinical response of p.G12C mutant NSCLC patients treated with target therapy is modulated by the clearance of driver alteration. Paweletz \u003cem\u003eet al\u003c/em\u003e. highlighted that p.G12C clearance within cycle 2 had a higher objective response rate (ORR) compared with persistent p.G12C series (60.6% vs 33.3%). (23) Conversely, the lack of common driver mechanisms between basal and longitudinal samples (baseline vs recollection; baseline vs TP) suggested that additional non genetic resistance mechanisms should be explored. (24, 25). On this basis, methylation pattern may significantly impact on the clinical outcomes of NSCLC patients driven by \u003cem\u003eKRAS\u003c/em\u003e mutations. (26, 27) We successfully calculated MI in each sample by adopting both proprietary bioinformatic pipeline and SeqOne analysis software (Montpellier, France). (\u003cb\u003eSupplementary Table\u0026nbsp;4\u003c/b\u003e) The heterogeneous landscape of analytical strategies measuring methylation profile significantly affects the clinical application of methylome signature. (28, 29) No statistically significant variations (p value\u0026thinsp;=\u0026thinsp;0.9) between proprietary bioinformatic pipeline and SeqOne analysis software (Montpellier, France) calculating MI technically validates methylome analysis paving the way for the clinical applications of these tools. In particular, 68.4% of NSCLC patients highlighted a consistent decreasing rate of MI (4.1 vs 1.1 from Δ\u0026thinsp;=\u0026thinsp;2.9, from 0.1 to 13.0) at T1, in line with \u003cem\u003eKRAS\u003c/em\u003e p.G12C mutation clearance (57.1%) (p-value\u0026thinsp;=\u0026thinsp;0.04). (Fig.\u0026nbsp;\u003cspan refid=\"Fig2\" class=\"InternalRef\"\u003e2\u003c/span\u003e, \u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e) Of note, MI was significantly higher in 35.3% (6 out of 17) of NSCLC patients at TP compared with baseline samples (12.0 vs 0.4; Δ\u0026thinsp;=\u0026thinsp;11.5, from 0.2 to 54.9) matching with \u003cem\u003eKRAS\u003c/em\u003e p.G12C mutation increase. \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig3\" class=\"InternalRef\"\u003e3\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e Remarkably, MI highlighted a similar trend of \u003cem\u003eKRAS\u003c/em\u003e p.G12C VAF among baseline, TI and TP points (median VAF 0.0, 0.0, 2.1% - median MI 0.6, 0.3, 0.9, respectively, Person correlation basal and T1\u0026thinsp;=\u0026thinsp;0.68; Person correlation basal and TP\u0026thinsp;=\u0026thinsp;0.87) \u003cb\u003e(\u003c/b\u003eFig.\u0026nbsp;\u003cspan refid=\"Fig5\" class=\"InternalRef\"\u003e5\u003c/span\u003e, \u003cspan refid=\"Fig6\" class=\"InternalRef\"\u003e6\u003c/span\u003e\u003cb\u003e)\u003c/b\u003e Even if limited by small sample size, this data suggest that dynamic modification of both methylation patterns and genomic assessment may track tumor under KRASG12C inhibitors. This proof of concept is clearly evident by looking at two patients of the clinical series, with five samples collecting points availability, demonstrating significantly correlated longitudinal variations of genomic and/or methylome data. ID#16 showed a significant peak both in \u003cem\u003eKRAS\u003c/em\u003e p.G12C VAF and MI score at TP compared with other longitudinal timepoints (from 0.0 to 64.8%; 0.9 to 55.8, respectively). (Table\u0026nbsp;\u003cspan refid=\"Tab2\" class=\"InternalRef\"\u003e2\u003c/span\u003e) Interestingly, ID#21 was affected by simultaneous variations of p.G12C and MI score dynamically modified among the different collection timepoints. \u003cb\u003e(Supplementary Fig.\u0026nbsp;5)\u003c/b\u003e Despite the powerful insights, several limitations should be considered. Firstly, sample set was retrospectively retrieved without any statistical tool calculating sample size. Secondly, MI was calculated on 3400 differentially methylated regions (DMRs) partially evaluating methylome signature in comparison with whole genome approaches. Finally, further investigations are required to validate clinical role of methylation profile in the clinical management of \u003cem\u003eKRAS\u003c/em\u003e mutated NSCLC patients. In conclusion, methylation signature integrating genomic analysis may represent an informative tool successfully optimizing personalized therapeutic strategies for \u003cem\u003eKRAS\u003c/em\u003e p.G12C mutant NSCLC patients.\u003c/p\u003e\u003cp\u003e\u003c/p\u003e\u003cp\u003e\u003c/p\u003e"},{"header":"METHODS","content":"\u003cdiv id=\"Sec11\" class=\"Section2\"\u003e\n \u003ch2\u003eParticipants\u003c/h2\u003e\n \u003cp\u003ePatients (\u0026ge;\u0026thinsp;18 years of age) with ECOG PS\u0026thinsp;\u0026lt;\u0026thinsp;3 and p.G12C \u003cem\u003eKRAS\u003c/em\u003e mutation on tissue sample, receiving a diagnosis of stage IIIB-IIIC/IV NSCLC (according to the eighth version of the American Joint Committee on Cancer/International Association for the Study of Lung Cancer tumor-node‐metastasis [TNM] staging system) on histological or cytological samples were enrolled. All patients relapsed from at least one line of previous therapy before receiving Sotorasib (960 mg orally once daily) until progression or unacceptable toxicity; participated to the PROMOLE translational study at the Department of Oncology of the University of Turin (Italy) and signed and dated the Informed Consent \u0026amp; privacy Form (ICF). Clinical, pathologic, and molecular data as well as treatment efficacy/tolerability outcomes were retrieved from the electronic medical repository archived at the University of Turin. The radiologic examination was performed as follows: computed tomography scans were approached at baseline, at week 12, and every 12 weeks of therapy until disease progression and clinical responses were defined in accordance with RECIST version 1.1. Written informed consent was acquired from all patients and documented according to \u0026ldquo;The Italian Data Protection Authority\u0026rdquo; (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.garanteprivacy.it/web/guest/home/docweb/-/docwebdisplay/export/2485392\u003c/span\u003e\u003c/span\u003e). All information regarding human material was managed using anonymous numerical codes and all samples were handled in compliance with the Helsinki Declaration (\u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ehttp://www.wma.net/en/30publications/10policies/b3/\u003c/span\u003e\u003c/span\u003e). The PROMOLE protocol was previously approved by the Independent Ethic Committee of S. Luigi Hospital, University of Turin (ethics approval number 73/2018 of 2024.01.30).\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec12\" class=\"Section2\"\u003e\n \u003ch2\u003eLiquid biopsy collection and management\u003c/h2\u003e\n \u003cp\u003ePeripheral blood samples (ranging from two to eight collecting points) were withdrawn in accordance with clinical indication: 1) baseline (day 1, cycle 1 of sotorasib administration); 2) cycle 3 (56\u0026ndash;66 days later); 3) each radiological evaluation (every 3 months) during the treatment. A longitudinal series of n\u0026thinsp;=\u0026thinsp;91 liquid biopsy samples collected from n\u0026thinsp;=\u0026thinsp;22 \u003cem\u003eKRAS\u003c/em\u003e p.G12C mutant advanced NSCLC patients were retrieved from internal archive of University of Turin-San Luigi Hospital and shipped to the Cytopathology and Predictive Molecular Pathology Unit at University of Naples Federico II for both genomic and methylation analysis. Overall, two aliquots (containing two ml of plasma) were available for each clinically relevant time point. Plasma was separated centrifuging entire blood at 2300 revolutions per minute for 10 minutes, in accordance with standardized handling procedures and stored at -80C\u0026deg; until the shipment. Circulating-free DNA (cfDNA) was automatically purified from 2 ml of plasma samples adopting QIAsymphony instrument (Qiagen) equipped with the QIAsymphony DSPVirus/ Pathogen Midi Kit, following previously validated internal protocol. (17) (\u003cstrong\u003eSupplementary file 1\u003c/strong\u003e) Finally, cfDNA was resuspended in 60 \u0026micro;l of DNAse and RNAse-free water (Thermo Fisher Scientifics, Waltham, MA, USA) and stored in dedicated tubes at -80C\u0026deg; until molecular analysis.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec13\" class=\"Section2\"\u003e\n \u003ch2\u003ecfDNA evaluation in liquid biopsy samples\u003c/h2\u003e\n \u003cp\u003eBefore molecular analysis, cfDNA percentage was calculated. Briefly, 2 \u0026micro;l of extracted nucleic acids were automatically dispensed into Cell-free DNA ScreenTape (Agilent) equipped on TapeStation 4200 (Agilent) microfluidic platform, following manufacturer procedures. Proprietary software measured cfDNA abundance in biological samples comparing ratio between 170 bp and high molecular weight (HMW) DNA\u0026thinsp;\u0026gt;\u0026thinsp;700 bp peaks.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec14\" class=\"Section2\"\u003e\n \u003ch2\u003eMolecular analysis and methylation index (MI) calculation\u003c/h2\u003e\n \u003cp\u003eA combined genomic and methylome NGS panel (Avida Duo Methyl Reagent Kit, Avida Biomed) was implemented to evaluate genomic alterations and measure methylation index (MI) in longitudinal series of liquid biopsy samples. This panel simultaneously analyzes clinically informative molecular alterations in n\u0026thinsp;=\u0026thinsp;105 cancer related genes and 3400 differentially methylated regions (DMRs) across different tumor types distinguishing between tumor patients and healthy subjects. (18) The Avida Duo Methyl Reagent panel is built on a proprietary design of three-dimensional, biotinylated anchor probes scaffolding DNA regions and stabilizing interaction with target sites. Trimmed Unique molecular identifier (UMI) counts removing single strand duplicates and low-quality reads may increase technical sensitivity and specificity of Avida Duo Methyl Reagent Kit up to 94.0% and \u0026gt;\u0026thinsp;98.0%, respectively, starting from 5.0\u0026ndash;10.0 ng of input.\u003c/p\u003e\n \u003cp\u003eOf note, 1-100 ng of input at 170\u0026thinsp;\u0026plusmn;\u0026thinsp;bp was required to perform molecular analysis and calculate MI. Briefly, two consecutive target captures yielded targeted sequencing (TS) and targeted methylation sequencing (TMS). A sequential approach generating TMS libraries (based on bisulfite conversion) from unhybridized TS libraries was used to successfully carry out template (including n\u0026thinsp;=\u0026thinsp;24 matched TS and TMS libraries) in accordance with manufacturer instructions. Libraries were diluted at 2\u0026ndash;4 nM and pooled together unbalancing TS and TMS libraries 2.5:1 in accordance with manufacturer procedures. Finally, TS and TMS pooled libraries were sequenced on NextSeq 550 Dx platform (Illumina, San Diego, USA) following manufacturer instructions. FASTQ files were manually uploaded on Alissa Report software (v 2.0.0) (Agilent Technologies) where genomic and methylation data were automatically carried out adopting proprietary bioinformatic pipelines. Briefly, genomic and methylome data were filtered by inspecting required (total number of raw reads\u0026thinsp;\u0026gt;\u0026thinsp;200.000, total number of raw bases\u0026thinsp;\u0026gt;\u0026thinsp;15.000.000, average read length forward and reverse\u0026thinsp;\u0026gt;\u0026thinsp;40) and recommended (mean insert size\u0026thinsp;\u0026gt;\u0026thinsp;120, fraction of inaccessible targeted bases\u0026thinsp;\u0026lt;\u0026thinsp;0.02, fraction of targeted bases at 100x coverage\u0026thinsp;\u0026gt;\u0026thinsp;0.6) technical parameters. As regards TMS analysis pipeline, CpGs calculated from CpG (5\u0026apos;C-phosphate-G-3\u0026apos;) and CHX methylation data derived from the cytosine (C) to thymine (T) and guanine (G) to adenine (A) conversion rate were automatically inspected by analysis software. MI was measured using a proprietary bioinformatic algorithm able to identify tumor methylated CpGs (mCpGs) in biological samples counting CHH on target methylation profile (\u0026lt;\u0026thinsp;17.5). Moreover, methylation profile was also analyzed adopting early access SomaMethyl bioinformatic pipeline from SeqOne Genomics (Montpellier, France) able to automatically calculate MI by proprietary pipeline. Briefly, a training set of n\u0026thinsp;=\u0026thinsp;14 previously tested NSCLC patients (n\u0026thinsp;=\u0026thinsp;7 positive and n\u0026thinsp;=\u0026thinsp;7 negative for MI scoring) was uploaded to set up threshold (\u0026ge;\u0026thinsp;1) for MI scoring.\u003c/p\u003e\n \u003cp\u003eMoreover, p.G12C \u003cem\u003eKRAS\u003c/em\u003e mutation was also longitudinally evaluated on Digital LightCycler\u0026reg; System (Roche Diagnostics) in accordance with manufacturer procedures. A total of 5.0 \u0026micro;l of cfDNA was manually combined with parameter specific reagents (PSR) (containing premixed, dried p.G12C \u003cem\u003eKRAS\u003c/em\u003e primers and probes) and Digital LightCycler\u0026reg; master mix (Roche Diagnostics), then loaded into the Digital LightCycler\u0026reg; universal plate achieving 28.000 partitions in each well. (Roche Diagnostics). Moreover, cfDNA fragments were automatically partitioned adopting Digital LightCycler\u0026reg; Partitioning Engine platform (Roche Diagnostics) following technical instructions. (19) Up to n\u0026thinsp;=\u0026thinsp;12 plates can be simultaneously processed by Digital LightCycler\u0026reg; System (Roche Diagnostics). Moreover, technical parameters including total valid partitions, positive partitions and number of copies/\u0026micro;l for mutant (FAM) and wild type (HEX), were automatically calculated by proprietary software (Digital LightCycler\u0026reg; System Development Software) correlating p.G12C positive signal with copies/\u0026micro;l of mutant fragments. In addition, molecular p.G12C status \u0026ldquo;positive or negative\u0026rdquo; was automatically called by proprietary software.\u003c/p\u003e\n\u003c/div\u003e\n\u003cdiv id=\"Sec15\" class=\"Section2\"\u003e\n \u003ch2\u003eStatistical analysis\u003c/h2\u003e\n \u003cp\u003eAll statistical analyses on technical parameters and molecular records were conducted using Prism GraphPad software, version 10.0 for Windows (GraphPad Software, San Diego, CA, USA; \u003cspan class=\"ExternalRef\"\u003e\u003cspan class=\"RefSource\"\u003ewww.graphpad.com\u003c/span\u003e\u003c/span\u003e) performing either Student\u0026rsquo;s t (for two variables) or one-way ANOVA (for multiple variables) test followed by Tukey\u0026rsquo;s post hoc test setting * p\u0026thinsp;\u0026lt;\u0026thinsp;0.05 as a cut-off for statistical significance. Pearson correlation coefficients were calculated to assess the linear relationship between variables by using GraphPad Prism. Briefly, evaluating r value, the corresponding p-values were measured, with ***p\u0026thinsp;\u0026lt;\u0026thinsp;0.001 cut-off for statistical significance.\u003c/p\u003e\n \u003cp\u003eMoreover, clinical, pathologic, and molecular characteristics of participants treated with Sotorasib were summarized either by descriptive statistics or as categorical tables. Descriptive analysis was performed, including means, standard deviations, medians, quartiles, and absolute/relative frequencies (with their respective two-sided 95% confidence interval [CI] limits, where relevant), according to the specific variables. Comparisons of continuous variables between groups were performed using the Wilcoxon rank-sum or Kruskal-Wallis tests, as appropriate. Categorical variables were compared using Fisher\u0026rsquo;s exact or chi-squared tests. Overall response rate (ORR) and progression-free survival (PFS) were evaluated according to the Response Evaluation Criteria in Solid Tumors (RECIST) version 1.1. PFS was defined as the time from initiation of therapy to documented disease progression or death, whichever occurred first. Patients without progression were censored at the date of the last imaging assessment demonstrating no progression. Overall survival (OS) ranged between the immune checkpoint inhibitor (ICI) initiation to death from any cause. Survival outcomes were estimated using the Kaplan\u0026ndash;Meier method and compared with the log-rank test, by using a p value\u0026thinsp;\u0026lt;\u0026thinsp;.05 as threshold for statistical significance. All analyses were conducted using R version 4.4.3.\u003c/p\u003e\n\u003c/div\u003e"},{"header":"Declarations","content":"\u003cp\u003e\u003cstrong\u003eFunding\u003c/strong\u003e: This study has partly been supported by the following grants: 1. POR Campania FESR 2014\u0026ndash;2020 Progetto \u0026ldquo;Sviluppo di Approcci Terapeutici Innovativi per patologie Neoplastiche resistenti ai trattamenti\u0026mdash;SATIN\u0026rdquo; 2. The Italian Health Ministry\u0026rsquo;s Research Program (ID: NET-2016\u0026ndash;02363853). 3. The National Center for Gene Therapy and Drugs based on RNA Technology MUR-CN3 CUP E63C22000940007 to DS. 4. Italian Ministry of Health (Piano Operativo Salute Traiettoria 3, T3-AN-09, \u0026ldquo;Genomed\u0026rdquo;. No funding or sponsorship was received for the publication of this article.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAcknowledgements\u003c/strong\u003e: PH and CL thanks for the support of the ANR Grant IHU 2023 -0007\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eAuthor information:\u0026nbsp;\u003c/strong\u003eThese authors contributed equally: Francesco Pepe, Francesco Passiglia, Claudia Scimone. These authors contributed equally as last authors: Silvia Novello, Giancarlo Troncone, Umberto Malapelle.\u003c/p\u003e\n\u003cp\u003eAuthors and Affiliations:\u0026nbsp;\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Public Health, Federico II University of Naples, Via S. Pansini, 5, 80131 Naples, Italy.\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrancesco Pepe, Claudia Scimone, Gianluca Russo, Domenico Cozzolino, Caterina De Luca, Giancarlo Troncone, Umberto Malapelle.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Oncology, University of Turin, S. Luigi Gonzaga Hospital, Orbassano (TO), Italy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eFrancesco Passiglia, Angela List\u0026igrave;, Edoardo Garbo, Luisella Righi, Silvia Novello.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepartment of Biology, Complesso Universitario Monte Sant\u0026apos;Angelo, University of Naples Federico II, Via Cintia 4, 80126 Naples, Italy\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eGiuseppina Roscigno, Viola Calabr\u0026ograve;.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eDepIHU RespirERA Laboratory of Clinical and Experimental Pathology, FHU OncoAge, Biobank BB 0033-00025, University C\u0026ocirc;te d\u0026rsquo;Azur Nice France\u003c/strong\u003e\u003c/p\u003e\n\u003cp\u003eCaroline Lacoux, Paul Hofman.\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eCompeting interests\u003c/strong\u003e: Francesco Pepe has received personal fees (as consultant and/or speaker bureau) from Menarini, Roche, Thermofisher, Jansen unrelated to the current work; Francesco Passiglia \u0026nbsp;received speakers\u0026rsquo; and consultants\u0026rsquo; fee from AstraZeneca, Johnson\u0026amp;Johnson, Novartis, Roche, MSD, Amgen, Beone, Gilead, Pharmamar, Thermo Fisher Scientific unrelated to the current work. Luisella Righi received speakers\u0026rsquo; and consultants\u0026rsquo; fee from AstraZeneca, Novartis, Roche, Amgen, BeiGene, Novartis, EliLilly un related to the current work. Paul Hofman received fee and honoraria from AstraZeneca Roche Amgen Biocartis Thermo Fisher Scientific BMS MSD abbvie pierre Fabre Pfizer Novartis Daiichi Sankyo Ed Lilly Biodena Merck unrelated to the current work. Silvia Novello reports personal fees (as speaker bureau or advisor) from Eli Lilly, MSD, Roche, Takeda, Pfizer, Astra Zeneca, Amgen, Thermo Fisher, Novartis, Sanofi, \u0026nbsp;Johnson\u0026amp;Johnson outside the current work. Giancarlo Troncone reports personal fees (as speaker bureau or advisor) from Roche, MSD, Pfizer and Bayer, unrelated to the current work; \u0026nbsp;Umberto Malapelle has received personal fees (as consultant and/or speaker bureau) from Boehringer Ingelheim, Roche, MSD, Amgen, Thermo Fisher Scientific, Eli Lilly, Diaceutics, GSK, Merck and AstraZeneca, Janssen, Diatech, Novartis and Hedera for work performed\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eEthics approval statement\u003c/strong\u003e: All information regarding human material was managed using anonymous numerical codes, and all samples were handled in compliance with the Helsinki Declaration. The PROMOLE protocol was previously approved by the Independent Ethic Committee of S. Luigi Hospital, University of Turin (ethics approval number 73/2018 of 2024.01.30).\u003c/p\u003e\n\u003cp\u003e\u003cstrong\u003eContributorship Statement\u003c/strong\u003e: Conceptualisation: FPE, FPA, CS, UM, and SN. Methodology: all authors. Software: all authors. Validation: all authors. Formal analysis: all authors. Investigation: all authors. Resources: all authors. Data curation: all authors. Writing\u0026mdash;original draft preparation: FP, FPA, CS. Writing\u0026mdash;review and editing: all authors. Visualisation: all authors. Supervision: GT, SN and UM. Project administration: SN and UM. Funding acquisition: SN and UM.\u003c/p\u003e"},{"header":"References","content":"\u003col\u003e\n\u003cli\u003eLeiter A, Veluswamy RR, Wisnivesky JP. The global burden of lung cancer: current status and future trends. Nat Rev Clin Oncol. 2023;20:624–639.\u003c/li\u003e\n\u003cli\u003eDaly ME, Singh N, Ismaila N; Management of Stage III NSCLC Guideline Expert Panel. Management of Stage III Non-Small Cell Lung Cancer: ASCO Guideline Rapid Recommendation Update. 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Cancers (Basel). 2025;17:3021.\u003c/li\u003e\n\u003c/ol\u003e"}],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":false,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":false,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Genomic analysis, methylation profile, liquid biopsy, lung cancer, target treatment","lastPublishedDoi":"10.21203/rs.3.rs-7860201/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7860201/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003e\u003cem\u003e\u003cstrong\u003eAims\u003c/strong\u003e\u003c/em\u003e: in the genomic era, the advent of next generation sequencing (NGS) technologies has rapidly transformed the clinical paradigm of NSCLC patients who could benefit from a wide series of clinically approved biomarker drive therapies. Among them, \u003cem\u003eKRAS\u003c/em\u003ep.G12C hotspot mutation became part of the mandatory testing gene panel by electing NSCLC patient’s candidate to Sotorasib. Epigenomic signatures, including hypermethylation of CpGs islands, may be relevant in tailorizing therapeutic algorithms in oncogene addicted NSCLC patients. Here we aimed to dynamically track \u003cem\u003eKRAS\u003c/em\u003ep.G12C genomic variations by integrating methylation profile in a longitudinal series of n=91 liquid biopsy samples from n=22 p.G12C positive NSCLC patients treated with Sotorasib. A combined NGS panel (Avida Duo Methyl Reagent Kit, Avida Biomed) simultaneously evaluating n=105 cancer-related genes and calculating methylation index (MI) score among 3400 differentially methylated regions (DMRs) was adopted, correlating molecular data with clinical outcomes. Overall, exon 2 p.G12C \u003cem\u003eKRAS\u003c/em\u003e mutation was detected in 40.9%, 15.8% % and 70.6% baseline, T1 and TP samples, respectively. MI was successfully measured in all instances. Of note, exon 2 p.G12C \u003cem\u003eKRAS\u003c/em\u003e mutation and MI score highlighted an overlapping trend moving forward T1 point (r = 0.68, \u003cem\u003ep\u003c/em\u003e = 0.06) and TP (r = 0.87, p = 0.000103). Methylation signature may be combined with genomic analysis to personalize therapeutic strategies for KRAS p.G12C mutated NSCLC patients. Multiomic analysis of tumor-informative molecular targets (genomic assessment, methylation status) lay the basis for dynamic fingerprints of NSCLC patients preventing early relapses and augmenting clinical benefits of targeted therapies.\u003c/p\u003e","manuscriptTitle":"Exploring genomic analysis and methylome profiling in longitudinal series of p.G12C KRAS mutated NSCLC patients treated with Sotorasib","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-10-15 09:18:06","doi":"10.21203/rs.3.rs-7860201/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"a9dfc112-118d-4918-84e1-e5f4baaba293","owner":[],"postedDate":"October 15th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":56288628,"name":"Health sciences/Biomarkers"},{"id":56288629,"name":"Biological sciences/Cancer"},{"id":56288630,"name":"Biological sciences/Computational biology and bioinformatics"},{"id":56288631,"name":"Biological sciences/Genetics"},{"id":56288632,"name":"Health sciences/Oncology"}],"tags":[],"updatedAt":"2026-01-13T14:10:17+00:00","versionOfRecord":[],"versionCreatedAt":"2025-10-15 09:18:06","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7860201","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7860201","identity":"rs-7860201","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}