Molecular correlation of response to pyrotinib in advanced NSCLC with HER2 mutation: biomarker analysis from two phase II trials

DNA was isolated from formalin-fixed paraffin-embedded (FFPE) tumor specimens with the TIANamp genomic DNA kit (TIANGEN, China) according to the manufacturers’ instructions. Genome DNA is extracted by TGuide S32 magnetic blood genomic DNA kit (TIANGEN, China) from peripheral blood lymphocyte (PBL), and circulating cell-free DNA (cfDNA) is extracted by MagMAX cell-free DNA isolation (ThermoFisher, USA) from the plasma sample. Fragmented DNA libraries were constructed with a KAPA HTP library preparation kit (Illumina Platform) (KAPA Biosystems, Massachusetts, USA) according to the manufacturer’s instructions. All libraries were quantified using AccuGreen high sensitivity dsDNA quantitation kit (Biotium, USA), with library size assessed on agilent bioanalyzer 2100 (Agilent, USA). DNA libraries from baseline tissue samples were captured with Panel 1, which was a designed panel spanning 769 cancer-related genes (Genecast, Wuxi, China), while DNA libraries from plasma samples were captured with panel 2, which covered exon regions of 95 genes (Genecast, Wuxi, China) related to drug resistance. The captured library was sequenced on Illumina Novaseq 6000 with paired end 150 bp mode.

After filtering low quality reads by Trimmomatic(v0.36) [16], clean reads were aligned to the human reference genome (hg19, NCBI Build 37.5) with the Burrows-Wheeler aligner (version 0.7.17) [17]. Then Picard toolkit (version 2.23.0) [18] was applied for making duplicates and genome analysis tool kit (version 3.7) [19] was used for realignment. VarDict (version 1.5.1) [20] was used to call single nucleotide variant (SNV) mutations while compound heterozygous mutations were merged by FreeBayes (version 1.2.0) [21]. Sentieon software (genomics-201911) was also used to improve the detection rate of mutations in plasma samples, and the mutations were annotated through ANNOVAR [22]. Typical QC-filtering such as variant quality and strand bias was applied to the raw variant list. Additionally, variants in low complex repeat and segmental duplication regions that matched to the lowly mappable regions were defined by ENCODE [23], and variants in an internally developed and validated list of recurrent sequence-specific errors (SSEs) were removed.

After filtering germline or hematopoietic origin mutations by comparing with paired normal sample, somatic mutations met the following criterions were used for the following analysis: (i) The variant allele frequency (VAF) threshold of mutations was 5% in tumor and 1% plasma; (ii) All low-frequency mutations in samples from the same patient in different time points were retained. For plasma samples, we used a pre-defined blacklist to remove false positive variants introduced for special processing of UMI data. These quality cut-offs were predetermined during the analytical validation of the internal NGS panel to optimize the test performance and measured according to sensitivity, specificity, repeatability and reproducibility.

This prespecified biomarker analysis was performed on longitudinal samples collected from two phase II clinical trials (NCT02535507: n = 15, NCT02834936: n = 60) which evaluated the efficacy and safety of pyrotinib in HER2-mutant advanced lung adenocarcinoma after platinum-based chemotherapy (Fig. 1a). Baseline tumor tissue samples and serial blood samples were collected and underwent DNA sequencing. 50 patients with baseline plasma samples that were successfully sequenced were finally enrolled, including 21 pretreated tissue samples and 112 serial blood samples (pretreated: n = 50, 40 days post-treatment: n = 37, 80 days post-treatment: n = 25). Baseline characteristics were presented in Table 1. The median PFS and ORR were 7.0 months and 28%, respectively (Fig. 1b, c). The consistency of variants detection between ctDNA and corresponding tissue samples among 21 patients with paired pretreated samples was compared (Fig. 1d), with a Spearman r value of 0.63 (p < 0.0001, Fig. 1e). A positive correlation was observed between mutation count in baseline ctDNA and tumor CT volume (Spearman r = 0.43, p = 0.02, Additional file 1: Fig. S1).

We listed the SNV and indel landscape of baseline ctDNA in Additional file 1: Fig. S2. TP53, NF1, PIK3CA, RET and MTOR were the top five mutant genes by frequency and were detected in 16 (32%), 10 (20%), 6 (12%), 6 (12%), 5 (10%) patients, respectively. In addition, circulating HER2 variants were detected in 45 of 50 (90%) pretreated plasma samples. The remaining 5 patients with undetectable HER2 variants were categorized as nonshedding tumor, with a trend of higher ORR (60.0% vs 24.4%, p = 0.126) and longer PFS (median: 10.2 vs. 6.8 months, p = 0.131), though not reaching statistical difference due to small sample size of nonshedding tumor. After comparing the clinicopathological characteristics of these two groups, we found that nonshedding tumor had a lower tumor burden (22.4 vs 53.0, p = 0.027), although the other variables were not statistically different (Additional file 2 Table S1).

As mentioned above, TP53 was the most frequent concurrent mutation. Therefore, we further demonstrated the impact of concurrent TP53 mutation on pyrotinib efficacy. As result, patients with wild type TP53 were significantly associated with superior disease control rate (DCR, 97.1% vs. 68.8%, p = 0.010, Fig. 2a), PFS (median: 8.4 vs. 2.8 months, p = 0.001, HR = 0.35 95% CI 0.18–0.67, p = 0.002, Fig. 2b) and OS (median: 26.7 vs. 10.4 months, p < 0.001, HR = 0.16, 95% CI 0.07–0.35, p < 0.001, Fig. 2c) than those with TP53 co-mutation, though the improvement of ORR (32.4% vs. 18.8%, p = 0.501) was marginal. Demographic analysis revealed that TP53 co-mutations were more common in younger patients, with no other significant differences identified (Table 1).

To characterize the discrepancy of genomic features between patients with or without TP53 co-mutation, we performed baseline ctDNA mutation landscape in patients stratified according to TP53 co-mutation status (Fig. 2d). Of note, patients without TP53 mutations had lower mutation load than those with TP53 mutations (median [P25–P75]: 3.78 [0.51–7.17] vs. 19.63 [7.32–32.51], p = 0.003, Fig. 2e). Furthermore, the mutation load of HER2 gene was also lower in patients without TP53 mutations (median [P25–P75]: 0.35 [0.15–0.91] vs. 2.48 [0.54–8.04], p = 0.001, Fig. 2f). To assess the predictive capability of TP53 mutation status on treatment efficacy, a multivariable COX regression analysis that included mutation load and various clinical factors was utilized (Fig. 3). The results showed that TP53 co-mutation was an independent risk factor for both PFS (p = 0.003) and OS (p < 0.001), while mutation load was not significantly correlated with treatment outcomes. These findings underscore the importance of considering TP53 mutation status as a prognostic marker when evaluating treatment response.

Previous researches have demonstrated that dynamic change of ctDNA was associated with treatment response and survival in solid tumors treated with targeted therapy [24]. In this study, we further evaluated ctDNA clearance as a predictor of pyrotinib response. A total of 34 patients had matched baseline and first evaluation plasma samples with evaluable response data. The individual VAF changes of HER2 mutations were presented in Fig. 4a. Patients who presented partial response (PR) or stable disease (SD) ≥ 12 weeks (defined as responder) exhibited a greater HER2-mutant ctDNA decrease compared with those who presented SD < 12 weeks or progressive disease (PD) (defined as non-responder), not reaching statistical significance (p = 0.056, Fig. 4b). Complete clearance (defined by conversion from ctDNA positive at baseline to ctDNA negative) of HER2-mutant ctDNA was observed at the first radiological evaluation in 17.6% (6/34) patients. These patients with HER2-mutant ctDNA clearance exhibited significantly longer PFS (median: 9.8 vs. 5.6 months, p = 0.032, HR = 0.33, 95% CI 0.12–0.97, p = 0.044, Fig. 4c) than those with HER2-mutant ctDNA remained but with similar OS (median: 18.1 vs. 14.6 months, p = 0.906, HR = 1.06, 95% CI 0.42–2.64, p = 0.906, Fig. 4d). Furthermore, we listed treatment duration and response to pyrotinib of patients with 3 types of ctDNA status (nonshedding ctDNA [n = 5], cleared HER2-mutant ctDNA [n = 6], remained HER2-mutant ctDNA [n = 28]) in Fig. 4e.

We presented here three cases in which ctDNA can be used to monitor patients' response to pyrotinib treatment in different conditions. Patient #12012 had decreased ctDNA VAF for both HER2-mutant gene and total mutant genes at both 40 and 80 days after pyrotinib treatment, and both increased at the time of PD, reflecting changes in ctDNA consistent with the efficacy evaluated by imaging (Fig. 4f). Patient #12001 had a rise in total mutant genes at the time of resistance despite no detectable HER2 mutation, and on imaging this patient presented with stable target lesion but was evaluated as progression due to new liver metastases (Fig. 4g). Patient #05002 had significantly elevated HER2-mutant ctDNA at first evaluation and also showed on imaging an enlarged target lesion and new interstitial hepatogastric lymph node metastases with a PFS of 2.9 months (Fig. 4h).