Genomic signatures define three subtypes of EGFR-mutant stage II–III non-small-cell lung cancer with

The ADJUVANT study reported the comparative superiority of adjuvant gefitinib over chemotherapy in disease-free survival of resected EGFR-mutant stage II–IIIA non-small cell lung cancer (NSCLC). However, not all patients experienced favorable clinical outcomes with tyrosine kinase inhibitors (TKI), raising the necessity for further biomarker assessment. In this work, by comprehensive genomic profiling of 171 tumor tissues from the ADJUVANT trial, five predictive biomarkers are identified (TP53 exon4/5 mutations, RB1 alterations, and copy number gains of NKX2-1, CDK4, and MYC). Then we integrate them into the Multiple-gene INdex to Evaluate the Relative benefit of Various Adjuvant therapies (MINERVA) score, which categorizes patients into three subgroups with relative disease-free survival and overall survival benefits from either adjuvant gefitinib or chemotherapy (Highly TKI-Preferable, TKI-Preferable, and Chemotherapy-Preferable groups). This study demonstrates that predictive genomic signatures could potentially stratify resected EGFR-mutant NSCLC patients and provide precise guidance towards future personalized adjuvant therapy.

Cisplatin-based adjuvant chemotherapy currently constitutes the standard-of-care after curative surgery for stage IIA-IIIB resected non-small cell lung cancer (NSCLC)1,2. However, the 5-year survival rate still remains unsatisfactory, with alarming levels of grade 3 toxicity observed in more than 60% of the patients3. Hence, alternative adjuvant regimens with epidermal growth factor receptor (EGFR) tyrosine kinase inhibitors (TKI) have been studied through several prospective trials4,5. The randomized phase III ADJUVANT study has actually presented significant prolonged disease-free survival (DFS) in EGFR-mutant NSCLC, after adjuvant gefitinib, as compared to the DFS after chemotherapy with vinorelbine and cisplatin (VP)6. Two phase 2 trials, SELECT and EVAN, have shown improved 2-year DFS with erlotinib. Early revelations of the ADAURA trial also presented remarkable improvements of DFS with the third generation EGFR-TKI, osimertinib7,8,9. However, approximately 19% to 40% of TKI-treated patients still relapse after these trials6,8, suggesting the inadequacy of EGFR-sensitizing mutants alone as a biomarker for adjuvant treatment selection.

The mixed clinical responses of NSCLC with targeted therapy can be attributed to molecular heterogeneity caused by different clonal populations, aggregated in a particular tumor, undergoing stage-specific evolution. Resulting selective pressure then further induces subclonal mutations, and promotes tumor expansion10,11,12,13. The most prevalent co-mutations, such as alterations in the TP53, RB1 and NKX2-1, usually cooperate to promote a local growth advantage, and support clonal expansion throughout tumor development10,11,14. In advanced EGFR mutant NSCLC, tumors with concurrent TP53 or RB1mutations then further disrupted genome stability and exerted higher risks for histological transformation and TKI resistance15. In addition to gene level alterations, co-mutations on the exon levels can also affect patient outcomes16.

As early-stage NSCLC also shows a high degree of intratumor heterogeneity with divergent evolutionary lineages17, the established norm of estimating only a single driver oncogene through randomized trials for adjuvant targeted therapies fails to address the underlying complications of intratumor molecular heterogeneity. In this regard, the development of next-generation sequencing (NGS) technology has accelerated the analysis and integration of huge bulks of genomic signatures, thereby increased the focus on developing multi-gene predictive models for therapeutic decisions18,19. Currently, in most single-armed cohort studies, biomarkers were analyzed for their prognostic effects by comparing survival differences between mutant and wildtype patients. However, the more challenging question is whether these biomarkers result in distinct outcomes under different treatments to ultimately guide therapeutic decisions. Therefore, it is important to distinguish predictive markers from the prognostic ones at first. The frequently used term “predict the prognosis” in many biomarker studies may confuse readers of the accurate definition for these two types of biomarkers. Specifically, a predictive biomarker differentiates treatment-specific survival benefits in biomarker-positive or biomarker-negative patients20 and further improves patients’ treatment outcomes, while a prognostic biomarker discriminates good or poor survival of patients regardless of treatment. For example, aberrations in the tumor suppressor TP53 gene are known to correlate with worse prognosis comparing to TP53wildtype cancers16,21.

Moreover, to reduce the complications in choosing the appropriate statistical tests, a standard and reliable analytical method has been endorsed by Rothwell22 and applied in numerous studies23,24. As suggested, testing subgroup-treatment effect interaction is a prerequisite in reporting the predictive significance other than subjective observations of the survival curves. Subsequently, a linear discriminant using summation of all predictive values over the set of selected biomarkers is usually adopted for composite score development25.

In this study, we conduct a thorough explorative analysis of cancer-related genes through NGS of tumor tissues from the EGFR-mutant patients of the ADJUVANT trial, in an attempt to address important co-mutations and identify key predictive biomarkers for adjuvant treatment. We also integrate them into a robust predictive score that can categorize patients into subgroups with distinct survival benefits under either adjuvant gefitinib, or chemotherapy for precision care.

Identification of predictive biomarkers from differential DFS

Total 171 patients from the ADJUVANT trial with available baseline surgical specimens have been enrolled for genomic profiling (Fig. 1). The basic characteristics of the patients included in this exploratory cohort have been summarized in Supplementary Table 1. Comprehensive genomic profiling of 422 cancer-related genes revealed comparable frequencies of the highest mutated genes between the two treatment groups (Supplementary Fig. 1). EGFR 19del (49% vs. 45%), L858R (47% vs. 53%), and copy number gain (CN gain, 17% vs. 26%) were equally distributed in the adjuvant gefitinib and VP groups. Other co-mutations, including TP53 (70% vs 64%), MCL1 (30% vs 16%), RB1 (25% vs 15%), NKX2-1 (20% in both), CDKN2A (16% vs 19%), PIK3CA (14% vs 17%), MDM2 (14% vs 9%), and CTNNB1 (7% vs 18%) also presented similar frequencies between the two cohorts. Of note, total 76/171 (44%) patients carried TP53 DNA binding domain missense mutations (exons 4–8). However, co-drivers frequently found in advanced diseases, e.g. BRAF mutations, amplifications of ERBB2, or MET13,14,26, were not as prevalent in our early-stage cohort.

Formalin-fixed paraffin-embedded (FFPE) samples of patients treated with adjuvant gefitinib or intravenous vinorelbine plus cisplatin (VP) in the ADJUVANT trial were collected for NGS-sequencing. Genomic alterations were analyzed for being predictive or prognostic biomarkers for adjuvant treatment. Predictive markers were selected to develop the Multiple-gene INdex to Evaluate the Relative benefit of Various Adjuvant therapies (MINERVA) score and validated through ten-fold cross validation (CV) or leave-one-out CV (LOOCV) procedures and an independent cohort.

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We adopted the popular approach of testing DFS-based gene-by-treatment interaction effects to identify predictive genetic biomarkers for guiding treatment selection22,27. Under this test, predictive biomarkers would show different treatment effect for biomarker-positive patients compared to the biomarker-negative population (Supplementary Fig. 2)20. We evaluated the predictive power of each mutated gene, and identified the following five predictive markers with significant treatment interactions (Table 1 and Methods): RB1alterations [interaction hazard ratio (iHR) 4.07, 95% confidence interval (CI) 1.56–10.58, P = 0.004], NKX2-1 CN gain [iHR 0.26 (95% CI 0.10–0.68), P = 0.006], CDK4 CN gain [iHR 0.14 (95% CI 0.03–0.77), P = 0.024], TP53 exon4/5 missense mutations [iHR 0.33 (95% CI 0.12–0.93), P = 0.035], and MYC CN gain (iHR 0.10 (95% CI 0.01–0.98), P = 0.048). Here, negative iHR indicated relative better survival with adjuvant TKI while positive iHR indicated relative benefit with adjuvant chemotherapy. Importantly, the treatment interactions remained significant for these five predictors even after adjusting for clinical parameters (Supplementary Table 2). The negative adjuvant TKI predictor, RB1 alterations, combined RB1 mutations and RB1 CN loss, since they were functionally similar and both presented marginal significance of treatment interaction due to small sample size of each category (Supplementary Table 3). Besides, as missense mutations on different TP53 exons might show distinct prognostic or predictive effects16,28, these exons were analyzed separately. Like RB1 alterations, both TP53 exon 4 and 5 missense mutations (but not exons 6–8) showed marginal significance for treatment interactions and were therefore combined as a single predictive factor. Further, for prognostic analysis, we found that TP53 exon4/5 missense mutations [multivariate HR 2.69 (95% CI 1.60–4.52), P < 0.001] and TP53 nonsense mutations [multivariate HR 1.69 (95% CI 1.08–2.65, P = 0.022)] were both significantly correlated with worse outcomes irrespective of treatment arms, in concordance with TP53 as a factor for negative prognosis (Supplementary Figs. 2 and 3a, b). Other genetic aberrations that were significantly associated with prognosis were summarized in Supplementary Figure 3 and Supplementary Table 4.

Integrated MINERVA score via genomic signature

Each of the five biomarkers individually can predict the treatment outcomes for patient subgroups harboring each specific genetic alteration, although, a multigene signature integrating all mutational events at patient level is essential for estimating a patient’s overall response to the molecular heterogeneity of early-stage NSCLC. We, therefore, constructed a MINERVA score to quantitatively assess individual tumors and their corresponding treatment responses by summing z scores from individual treatment-by-interaction test of the five selected genes. Many studies have previously reviewed the theoretical justification of creating such a composite variable and applied the method to combine multiple gene features25,29,30,31. The resultant MINERVA scores of all the 171 tumors ranged from −7.09 to 2.88 with lower score representing better response to adjuvant TKI. Of note, this composite score alone also significantly interacted with treatment (P = 4.29 × 10^−6), indicating its role as a stronger predictor of adjuvant treatment than any individual markers. To further stratify patient benefits, the genomic makeup behind each score and optimum separation of survival were considered. First, 81 tumors (47.4%) that did not carry any alterations in the predictive genes (score = 0) were grouped together. We also included 6 patients with both NKX2-1 and RB1 alterations in this group, who were scored 0.16. Under the gene-by-treatment test, NKX2-1 (z-score, −2.72) and RB1 (z-score, 2.88) were the strongest predictors for adjuvant therapies but in opposite directions (Table 1). By slightly relaxing the cutoff to include these six patients, we tolerated potential noise of the interaction statistics introduced by the current cohort size. Further, to stratify patients for particular treatment benefits, we evaluated cutoffs of MINERVA score at ±1, ±0.5, and 0 and chose to categorize the patients into three subgroups at −0.5 and 0.5 as they resulted in the best survival differences (Supplementary Fig. 4b and Methods). In the pre-categorized population, gefitinib significantly prolonged the median DFS, and increased the 2-year DFS rate, similar to the intention-to-treat (ITT) and modified ITT populations6 (Fig. 2a). Remarkably, after categorization by MINERVA, the three subgroups demonstrated distinct treatment responses and underlying molecular profiles (Fig. 2b, c). The Highly TKI-Preferable group [HTP, N = 60, 35% (score ≤ −0.5)] expressed significant superiority with adjuvant gefitinib [HR 0.21 (95% CI 0.10–0.44)], and was enriched with copy number gain of NKX2-1, CDK4, and MYC, and TP53 exon 4/5 missense mutations. The TKI-Preferable group [TP, N = 87, 51% (score −0.5 to 0.5)] showed improved DFS among the pre-categorized and ITT populations [HR 0.61 (95% CI 0.35–1.07)]. Besides, this subgroup was characterized by the absence of most predictive biomarkers, except for sporadic co-existence of NKX2-1 and RB1 alterations, with contrasting effects due to opposing iHRs (Table 1). Moreover, a small subset of patients, the Chemo-Preferable Group [CP, N = 24, 14% (score ≥ 0.5)], despite having EGFR-positive tumors, showed greater response and enhanced DFS [HR 3.06 (95% CI 0.99–9.53)] under VP treatment, and harbored RB1 alterations (Fig. 2c).

a Kaplan–Meier curves estimate DFS of the pre-categorized cohort which received adjuvant gefitinib or VP treatment (N = 171). Two-sided P value was calculated using the log-rank test. b Forest plot showing the treatment-by-interaction hazard ratio (iHR) of DFS with the cox regression model in subgroups (HTP, highly TKI-preferable group; TP, TKI-preferable group; CP, chemo-preferable group) as classified by MINERVA score. Error bars indicate 95% confidence intervals of the iHRs. cClinical characteristics and genetic alteration spectrums of five predictive biomarkers in three MINERVA subgroups. d–f Kaplan–Meier curves of DFS for patients treated by adjuvant gefitinib or VP in three MINERVA subgroups. Black dotted lines indicate median DFS. Blue doted lines indicate 2-year survival rates (24 months). Two-sided P values were derived from the log-rank test. Exact statistical significance of DFS difference in the HTP group was 2.47 × 10−5.

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In the TP group, the Kaplan–Meier estimate depicted similar curvatures as those observed in the pre-stratified and ITT populations6 (Fig. 2a, e), indicating that adjuvant gefitinib achieved a superior DFS. Importantly, the survival curves of the post-categorized HTP and CP populations did not converge at any point (Fig. 2d, f). In HTP, the Kaplan–Meier curves separated widely as early as six months, with a slow descent of the adjuvant gefitinib arm (median DFS, 34.5 months; P < 0.001). Conversely, a drastic drop of the VP arm towards a median DFS of 9.1 months was observed with all recurrence by 36 months. Therefore, the relative benefit of gefitinib was represented by a 6.4-fold increase in the 2-year DFS rate [70.3% (95% CI, 55.8–88.7) vs 11.0% (3.1–38.7)] and a 25.4-month longer median DFS (Fig. 2d). In the CP group, Kaplan–Meier curves diverged at 18 months with an immediate sharp decline of the gefitinib arm towards a median of 19.3 months. Meanwhile, 70% of the VP arm continued to benefit after 24 months (median DFS, 34.2 months, P = 0.041). The superiority of adjuvant VP was reflected by a 1.7-fold increase in the 2-year DFS rate [69.2% (48.2–99.5)], including a 14.9-month longer median DFS, compared to the 41.6% 2-year DFS rate for gefitinib (95% CI 19.9–86.8) (Fig. 2f).

Stratification of overall survival benefit by MINERVA score

Overall survival (OS) is generally considered as the standard endpoint for clinical trials. Although adjuvant gefitinib has shown significantly improved DFS relative to adjuvant VP, the DFS benefits in the ITT population did not translate into a significant difference in OS of the ADJUVANT trial32, probably due to the combined influences of downstream treatment crossovers and the genetic heterogeneity among the patient population. Hence, we further used MINERVA in an attempt to achieve stratification of OS.

As expected, OS of the 171 pre-categorized patients involved in this study showed no difference between the two treatment groups (median, 76.9 months in the gefitinib group vs 67.1 months in the VP group; HR 0.87 (95% CI 0.57–1.35), P = 0.54) (Fig. 3a and Supplementary Fig. 5). Promisingly, MINERVA successfully demonstrated the stratification of OS benefit as well. In HTP, gefitinib treatment led to significantly longer OS [median, not reached in the gefitinib group vs 48.7 months in the VP group; HR 0.43 (95% CI 0.21–0.88), P= 0.018] with a clear and early separation of the Kaplan–Meier curves (Fig. 3b, c). Conversely, adjuvant VP treatment substantially improved OS in the CP group after 18 months [median, 36.4 months in the gefitinib group vs not reached in the VP group; HR 2.47 (95% CI 0.76–8.02), P = 0.12] (Fig. 3b, e). OS in TP mirrored that of the pre- categorized cohort, suggesting no differences between the treatments (Fig. 3a, d). Likewise, the 2-, 3- and 5-year survival rates of the categorized subgroups demonstrated similar trends, with the survival differences between the two treatments in both HTP and CP groups widened over time (Supplementary Fig. 6). The 5-year OS rates of gefitinib-treated HTP patients and VP-treated CP patients were 67.3% (95% CI 52.4–86.4) and 61.5% (95% CI 40.0–94.6), respectively, both of which were significantly higher than those attained in the pre-categorized cohort [gefitinib, 55.7% (95% CI 46.2–67.0); VP, 51.5% (95% CI 41.2–64.3)].

a Kaplan–Meier estimates of OS for the pre-categorized cohort included in this study (N = 171). Two-sided P value was calculated using log-rank test. b Forest plot showing hazard ratio (HR) of OS in MINERVA subgroups. Error bars indicate 95% confidence intervals. c–e Kaplan-Meier curves estimate OS in each subgroup by treatments. Dotted lines in black indicate median OS. Dotted lines in blue indicate 5-year survival rate (60 months). Two-sided P values were derived from the log-rank test.

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Validation of MINERVA score

We employed both ten-fold cross validation as well as LOOCV methods (as internal validation procedures) to evaluate the robustness of our MINERVA score. For each fold of cross validation, a subset of markers was selected based on interaction P < 0.05, which were then used to build mock MINERVA scores for internal validation. A relatively superior survival with adjuvant gefitinib treatment was observed in both HTP and TP subgroups, with an average of 3.5- and 1.9-fold increase in the 2-year DFS rate, respectively (Fig. 4a). The median DFS in these two subsets also increased by an average of 20 and 15 months, respectively (Fig. 4b), while the 2-year gefitinib-to-VP DFS ratio was less than one, and the median DFS difference negative for all repeats in the CP group, suggesting greater survival benefit by adjuvant VP in this population. Among the 100 mock MINERVA score generated, 75% demonstrated significant treatment interaction with P values < 0.05, while 86% demonstrated interaction P values < 0.1 (Fig. 4c). We further validated the functionality of the original MINERVA score by LOOCV method. Adjuvant VP treatment in the HTP group was associated with markedly reduced DFS and OS (Fig. 4d, g). Meanwhile, adjuvant gefitinib treatment in the CP group was evidently inferior, similar to previously estimated results in Figs. 2 and 3.

a, b Ten-fold cross validation was repeated 100 times to assess relative benefit among three MINERVA risk groups by (a) mean ratio of 2-year disease-free survival (DFS) probability comparing gefitinib to VP from 100 repeats, and (b) mean difference in median DFS between gefitinib and VP treated patients from 100 repeats. Error bars indicate standard error of 100 repeats in each subgroup. c Curve showing the cumulative percentage of mock MINERVA models from 100 repeated 10-fold cross validation and corresponding p-values derived from the MINERVA-by-treatment interaction tests. Red dotted lines indicate percentage of repeats with interaction P < 0.05 or <0.1 (two-sided, wald test). d–f Kaplan-Meier estimates of DFS in three mock MINERVA subgroups derived by leave-one-out cross validation. P values were derived from the two-sided log-rank test. g–i Kaplan-Meier estimates of OS in three mock MINERVA subgroups. P values were derived from the two-sided log-rank test. Source data used to generate this figure are provided as a Source Data file.

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We further validated MINERVA in an independent cohort with similar clinical context (EGFR-mutant, Stage IIIA-N2 NSCLC) (see Methods). We performed the same NGS profiling of twenty-nine patients from the EMERGING-CTONG1103 trial recruited in our center (Guangdong Provincial People’s Hospital) and scored them according to MINERVA. Importantly, treatment interaction test indicated that the MINERVA score alone was a strong predictive biomarker to guide treatment selection in both exploratory and validation cohorts (ADJUVANT cohort, P = 4.29 × 10^−6; EMERGING cohort, P = 0.00032) (Supplementary Fig. 7b). Patients were then classified into HTP, TP and CP groups using score cutoffs at 0.5 and −0.5. We observed similar stratification of patient outcomes as the described above (Supplementary Fig. 7c–e). Specifically, TKI-treated patients showed significantly better progression-free survival (PFS) than the chemotherapy-treated in the HTP group (P = 0.039; erlotinib, median PFS 23.8 months, chemotherapy, median PFS 4.5 months) (Supplementary Fig. 7c). Also, comparable PFS benefit was seen with erlotinib in the TP group, in the validation cohort and the entire EMERGING population (Supplementary Fig. 7a, d)33. In the CP group, we observed shorter PFS with TKI (median PFS 11.8 months vs 17.7 months TKI-treated in the whole validation cohort) and potential sensitivity to chemotherapy despite the limited sample size (Supplementary Fig. 7e).

To date, several prospective clinical trials, including our ADJUVANT trial, have presented the superiority of adjuvant TKI in early-stage EGFR-mutant NSCLC. The ADJUVANT trial had reached a median OS of 75.5 months by the database lock date, which is one of the best survival outcomes ever recorded for this patient population34. However, gefitinib’s DFS superiority started declining after 36 months, and did not ultimately translate into OS benefit, raising concerns over achieving clinical cure by adjuvant TKI35. The heterogeneity in time-to-recurrence and OS observed within the ADJUVANT cohorts suggested high inter-tumor molecular heterogeneity in early-stage EGFR-mutant NSCLC11, necessitating additional predictive biomarkers to redefine personalized adjuvant therapy.

In this biomarker exploration of adjuvant TKI in resected NSCLC, we selected five genes that could independently predict the relative benefit between adjuvant TKI and chemotherapy. The multigene MINERVA score then integrated these biomarkers and effectively compensated for individuals’ molecular heterogeneity. Notably, the three risk groups separated using this score counteracted the controversial impermanence of DFS benefit with exciting stratification of OS benefits as well. For each risk groups, we also found characteristic enrichment of biomarkers, possibly explaining their differential responses to adjuvant treatments.

In the CP group, RB1-altered/EGFR-mutant patients showed better survival with adjuvant VP rather than gefitinib. In consistent with our observation, a number of studies have reported particularly poor EGFR-TKI response in RB1 altered patients. For example, Kim et al. reported a significantly shorter median PFS of only 1.9 months in RB1-mutant patients in contrast to 11.7 months in RB1 wildtype patients36. In advanced NSCLC, TKI-resistant RB1-inactivated/EGFR-mutated adenocarcinoma clones have been found to transdifferentiate into small-cell lung cancer (SCLC) and become more responsive to chemotherapy37,38. One of the potential mechanisms of SCLC transformation might be the disruption in expression of cell-state-determining factors due to RB1 inactivation15,39. The resulting lineage plasticity then converts the therapy-dependent cancer cells to those that express neuroendocrine lineage markers, making them refractory to targeted treatments40,41. RB1 often demonstrates mutual exclusivity with cell cycle pathway genes, and reflects chemosensitivity in rapidly progressing tumors42, which is in line with our CP population. In hope to eradicate this subclone, researchers have developed an upfront trial in which patients with advanced stage were assigned to receive TKI and small-cell directed chemotherapy (platinum plus etoposide) alternately (NCT03567642). Further research is required to explore whether TKI insensitivity of RB1-inactive/EGFR-mutant patients in the adjuvant setting also arise from early histological transformation events.

Despite the small VP-favoring population, patients in the HTP subgroup presented significant benefits from adjuvant gefitinib therapy. Genomic analysis showed the enrichment of other four biomarkers, among which copy number gain of NKX2-1 received nearly equal weightage as RB1, but in an opposite predictive direction that favors the choice of gefitinib. NKX2-1 copy number gain is a widely prevalent oncogene found in up to 30% of EGFR-mutant patients10. NKX2-1 amplification has been more frequently observed in TKI-treated patients with extended progression-free survival (≥24 months) and was reported to predict favorable TKI response14,43. These findings were consistent with its enrichment in the HTP population with relative gefitinib benefit. Previous studies mainly reported favorable prognosis with NKX2-1 expression in mixed onco-driver, tumor stages and pathological backgrounds44,45, while our study demonstrated the predictive value of NKX2-1copy number gain to favor adjuvant TKI treatment in a more defined population.

TP53 as a tumor suppressor occurred in more than 50% of NSCLC, with mutations of complicated functional properties16. Unlike most tumor suppressor genes, missense mutations in the critical DNA binding domain (exons 4 to 8) are the most common variants in TP53, which are associated with the lower disease control rate and poorer survival outcomes with TKI treatment in contrast to TP53 wildtypes in EGFR-mutant NSCLC16,46. Apart from poor prognosis associated with loss-of-function TP53 mutations, studies have also revealed varied prognostic effects of missense mutations on different TP53 exons, suggesting possible functional divergence28,47,48,49,50. Interestingly, these missense variants could also be predictive for worse adjuvant chemotherapy outcomes compared to observation in resected NSCLC51. Along these lines, in this study, the predictive power of TP53 variants was also assessed by exons. In the multigene model, the positive predictivity of TP53 exons 4/5 missense mutations suggested that patients harboring these TP53mutations would relatively benefit more from gefitinib than VP. We did not consider exons 6–8 because they lacked predictive significance for adjuvant therapies under the treatment-interaction test. In concert with the consensus on TP53’s negative prognostic effect, we did observe significantly lesser outcomes in TP53-positive patients despite the treatment they received. MYC amplification was found in approximately 10% NSCLC10. Here, we demonstrated that it was predictive for better outcome with adjuvant TKI than chemotherapy. In consistent with our finding, a previous report also showed that MYCamplification was associated with better response to gefitinib52. On the other hand, amplification and overexpression of MYC was often found to correlate with chemoresistance in lung cancers53. CDK4 also frequently overexpressed in lung cancers54, although it was not found to particularly affect gefitinib outcome according to a thorough literature search, as demonstrated by an indifferent efficacy (p = 0.813) in patients with or without CDK4 gain55. CDK4 was a key member of the cyclin-dependent kinase family that phosphorylates RB1 and facilities the cell cycle activities. It has been reported in many other cancers that CDK4 amplification could drive resistance to chemotherapy, including osteosarcoma and breast cancer56,57. Its role in predicting better adjuvant TKI response than chemotherapy might be worthy of future investigation.

The development of a multi-gene clinical predictor requires a well-designed prospective validation with appropriate assumptions and sample size structured to address the underlying molecular heterogeneity. However, this is challenging as there were no equivalent public or clinical datasets of adjuvant TKI-treated patients with regular follow-up of survival outcome available at the time of this study, and any prospective validation trials might span over another decade to reach maturity. In this regard, we tested the validity of our score in another prospective clinical trial conducted by the Chinese Thoracic Oncology Group (CTONG), the EMERGING-CTONG1103 trial, a multicenter phase II neoadjuvant study that enrolled patients with EGFR-mutant stage IIIA-N2 NSCLC33. As neoadjuvant treatment mainly contributed to the overall response rate, patients’ survival benefits were achieved primarily from adjuvant treatment. We considered the EMERGING study to be the most appropriate cohort by now to partly resemble the ADJUVANT dataset. Both cohorts examined EGFR-mutant patients in the early-stage context, indicating similar baseline genomic makeup that might influence treatment response11,58. Here, we saw similar separation of TKI or chemotherapy benefit and predictive value of the score as a composite variable. Results from this cohort verified the potential generalization of MINERVA-guided treatment strategies in early-stage EGFR-mutant patients. Despite inspiring results from this independent validation cohort, we acknowledged the fundamental differences in trial designs. We were cautious when interpreting the results as we cannot completely isolate the influence from neoadjuvant treatment in EMRGING-CTONG1103. Besides, only a small cohort was collected for validation and further validation in larger populations in the adjuvant setting is needed. Due to these concerns, we are providing results of this independent validation in the supplementary data. Moreover, development of this score was based on a relatively small training cohort, which may introduce biased biomarker selection, or an overfitted model. Therefore, it is important to exploit stringent statistical procedures to minimize cherry-picking during post hoc analyses. Cross-validating all the steps of model construction allowed us to evaluate whether the current algorithm could be uniformly applied to the entire cohort. Besides, only baseline specimen was examined in our study, while dynamic minimal residual disease (MRD) detection might provide additional information for the application of precise adjuvant TKI. However, consensus opinion on MRD’s definition and detection technologies needed to be settled first.

Other limitations include insufficient tissue availability for retrospective genomic analysis of all the enrolled participants. However, both clinical characteristics and survival outcomes of the pre-categorized patients in this study were matched with those of the ITT population from the ADJUVANT trial.

In summary, this exploratory retrospective analysis of the ADJUVANT trial has unraveled the genetic constructs of EGFR co-mutations in stage II and III resected NSCLC. Further, the interplay between identified predictive markers and clinical outcomes has been carefully examined, and incorporated into a multi-gene predictive score to aid the adjuvant paradigm. Our evidential MINERVA score presents a fresh perspective for future studies to examine its clinical validity, thereby guiding the development of more personalized adjuvant therapies, and their transition from bench to bedside.