Patient-derived cell-based pharmacogenomic assessment to unveil underlying resistance mechanisms and novel therapeutics for advanced lung cancer
Journal of Experimental & Clinical Cancer Research volume 42, Article number: 37 (2023)
A pharmacogenomic platform using patient-derived cells (PDCs) was established to identify the underlying resistance mechanisms and tailored treatment for patients with advanced or refractory lung cancer.
Drug sensitivity screening and multi-omics datasets were acquired from lung cancer PDCs (n = 102). Integrative analysis was performed to explore drug candidates according to genetic variants, gene expression, and clinical profiles.
PDCs had genomic characteristics resembled with those of solid lung cancer tissues. PDC molecular subtyping classified patients into four groups: (1) inflammatory, (2) epithelial-to-mesenchymal transition (EMT)-like, (3) stemness, and (4) epithelial growth factor receptor (EGFR)-dominant. EGFR mutations of the EMT-like subtype were associated with a reduced response to EGFR-tyrosine kinase inhibitor therapy. Moreover, although RB1/TP53 mutations were significantly enriched in small-cell lung cancer (SCLC) PDCs, they were also present in non-SCLC PDCs. In contrast to its effect in the cell lines, alpelisib (a PI3K-AKT inhibitor) significantly inhibited both RB1/TP53 expression and SCLC cell growth in our PDC model. Furthermore, cell cycle inhibitors could effectively target SCLC cells. Finally, the upregulation of transforming growth factor-β expression and the YAP/TAZ pathway was observed in osimertinib-resistant PDCs, predisposing them to the EMT-like subtype. Our platform selected XAV939 (a WNT-TNKS-β-catenin inhibitor) for the treatment of osimertinib-resistant PDCs. Using an in vitro model, we further demonstrated that acquisition of osimertinib resistance enhances invasive characteristics and EMT, upregulates the YAP/TAZ-AXL axis, and increases the sensitivity of cancer cells to XAV939.
Our PDC models recapitulated the molecular characteristics of lung cancer, and pharmacogenomics analysis provided plausible therapeutic candidates.
Lung cancer is the leading cause of cancer-related mortality worldwide . The development of targeted therapies, such as epidermal growth factor receptor (EGFR)-tyrosine kinase inhibitors (TKIs), have helped to extend the survival time of patients; however, the improvement in progression-free survival eventually fails in cases of advanced lung cancer owing to resistance development . Thus, treatment tailored to overcome resistance is need to improve prognosis. To develop novel therapeutics, several cellular, organoid, and mouse models are available for use in pharmacological platforms [3,4,5]. Although organoid and mouse models recapitulate the heterogeneous molecular characteristics of patient biopsies, their establishment is labor-intensive and they have relatively low tumor-formation rates . Models based on patient-derived immortalized cells provide good reproducibility to the cohort, but have less heterogeneity, are more mesenchymal in nature, and have distinct chemical and genetic dependencies . Fortunately, models derived from short-term–cultured patient-derived cells (PDCs) retain the genomic characteristics of solid tumor biopsy better than immortalized cell lines [3, 7, 8]. Thus, the use of PDC models can help to enlarge the scope of drug screening to include more chemicals and multiple doses. To date, PDC pharmacogenomic platforms have been successfully used in treating glioblastoma and gynecologic and gastric cancers [3, 7, 8]. A PDC platform was also established to screen drugs for non-small cell lung cancer (NSCLC) . However, this platform required cell culture for 2–6 months and demonstrated a 50% success rate. These numbers represent an obstacle for its application in the medical field to fill the need of immediate patient-tailored drug prediction. Therefore, establishing a PDC platform for refractory lung cancers would satisfy the clinically unmet needs of therapeutic and drug-resistance research.
Advances in large-scale lung cancer genomics approaches have helped to tackle the challenges posed by tumor heterogeneity, therapeutic evolution, and histology [10,11,12]. EGFR-TKI therapies possess good EGFR-mutation selectivity, but resistance occasionally evolves via activation of non-targetable bypass pathways such as neuronal differentiation or KRAS amplification [11, 13, 14]. Treatment of small-cell lung cancer (SCLC) is associated with an additional challenge of the lack of further effective therapies in the face of rapid resistance acquisition to platinum-based chemotherapy . Moreover, information available on driver genes and various (TKI-resistant) variants of refractory lung cancers have not been effectively translated into clinical targeted therapies.
To bridge this gap, in this study, we developed PDC models using mainly the pleural effusions of refractory lung cancer patients. Using our platform, we explored drug candidates and target regulatory mechanisms according to genomic features and lung cancer molecular types. We further assessed the pharmacogenomic characteristics of the cells by screening their drug responses and performing next-generation sequencing. To establish integrative analysis for drug and genomic characteristics, statistical and machine-learning methods were employed to investigate the sensitivity to each drug according to molecular subtype, cancer types, therapeutic groups, and variants. This PDC platform and associated analysis can highlight novel drug candidates and target pathways to improve the personalized treatment of advanced lung cancer and facilitate further research in this regard.
Lung cancer sample acquisition and PDC establishment
Cancer samples were collected from patients with advanced or refractory lung cancer diagnosed and treated at the National Cancer Center in Korea between December 2016 and February 2020. The histological types were determined according to the 2015 World Health Organization classification of lung tumors. This study was approved by the National Cancer Center Institutional Review Board (approval number NCC2019-0082). All patients provided written informed consent. PDC establishment and drug screening details are described in Additional file 1 (Supplementary methods).
Drug sensitivity screening using PDCs and cell lines
Stabilized PDCs were seeded in 384-well plates (1000 cells/20 μl/well) in quadruplicate for each treatment. A total of 16 or 48 compounds were used for screening each PDC sample (Additional file 2: Table S1, S2). After overnight incubation, the cells were treated with one drug at a 5-fold serial dilution for a total of 6 doses (50 μM ~ 16 nM). Cell viability was measured after 72 hrs of treatment using the CellTiter-Glo Luminescent Cell Viability Assay kit (Promega, Madison, WI, USA) and an Infinite 200 Pro system (TECAN, Mannedorf, Switzerland). Each screening plate contained a dimethyl sulfoxide (DMSO)-only vehicle to calculate relative cell viability and normalize the data. Dose response curve (DRC) fitting and area under the curve (AUC) values were assessed using GraphPad Prism 5.3 (GraphPad Software Inc., San Diego, CA, USA). A screening compound library was newly prepared every month and tested for the preservation of chemical activities using NSCLC cancer cell lines (A549, PC9 and H1299). All library compounds were purchased from Selleckchem (Houston, TX, USA).
Targeted next-generation sequencing (NGS) dataset and molecular subtype identification
Mutation and copy number variant (CNV) calling were performed using NGS and targeted sequencing. Gene expression profiles and fusion genes were identified from RNA-sequencing data.
To identify somatic mutations from target-seq data, we started with preprocessing with quality check, read trimming using Trimmomatic 0.39, and alignment using BWA 0.7.17 to hg19 [16, 17]. Alignment bam files were recalibrated and realigned for target regions using Picard 1.119 and GATK 22.214.171.124 . Next, we called somatic mutations using Mutect2, referring to the panel-of-normal from the 1000 Genomes PON and gnomAD VCF files . The somatic mutation results were annotated using Oncotator . Additionally, we eliminated germline variants registered in the 1000 Genomes Project. The CNV profile was identified using GATK 126.96.36.199, and CNV peak calling was performed by GISTIC2 . Additionally, we selected CNV genes as those with high amplification (log2 CNV > 2), high amplification rate (> 5%), and a correlation with mRNA expression (P value < 0.01). The tumor mutation burden (TMB) of the mutation count per mega base pair (perMbp) values was calculated by Maftools . We classified TMB values into low (TMB < 0.1), middle (0.1 ≤ TMB < 0.2), and high (0.2 ≤ TMB) groups.
To additionally classify the functionality of the genes with the most recurrent mutations (TP53 and EGFR), we categorized previously reported hotspot and non-hotspot mutations. TP53 mutations were classified into hotspot and non-hotspot mutations . EGFR variants were divided into four types (TARGET: exon 19 deletion, L858R), T790M acquisition (T790Maq), not otherwise specified mutations (NOS), and NOS acquisition with target (NOSaq) . The co-occurrence of mutated genes was tested by Fisher’s exact test for genes with recurrence > 3 and MutSig P value < 0.05 . The double mutation pairs were referred for drug sensitivity test as well as single mutation cases.
RNA-seq analysis also proceeded with similar preprocessing with quality check, read trimming using Trimmomatic, and alignment using STAR v2.7.0a to hg19 referring to gene model ENSEMBL release 75 [16, 25]. The gene expression profile was extracted from RPKM quantified from bam files using RSEM v1.2.31 . Fusion genes were identified from RNA-Seq and merged with the results from three callers, Defuse, PRADA v1.2 and STAR-Fusion v1.7.0 [25, 27, 28]. Fusions were annotated using Pegasus, and we extracted drug-targetable candidates involved in kinase or oncogenic signaling from published databases from TCGA and COSMIC [29,30,31,32].
To classify the RNA molecular subtype of the samples, we performed nonnegative matrix factorization (NMF) clustering using the RPKM gene expression profile. We assessed optimal cluster size from 2 to 5. Finally, cluster size for molecular subtype was chosen to computationally present a clear consensus plot (n = 4). To extract the transcriptomic characteristics of RNA subtypes, pathway activities for each sample were estimated using gene set variation analysis (GSVA) according to HALLMARK gene set collections . Next, the difference in pathway activities according to RNA subtypes was tested by limma . Additionally, the up-regulation of stemness-associated gene signatures were assessed using GSVA . The gene signatures were collected from microarray, and ChIP-seq of two human embryonic stem cell gene sets, target genes of transcription factors (NANOG, OCT4, SOX2, and MYC), and Polycomb targets (Suz12, Eed, H2K27, and PRC2) to be under-expression to embryonic stem cells . The difference of collected signature scores for subtype was tested by Wilcoxon rank-sum test.
Comparison of PDCs with other lung cancer datasets
We evaluated the characteristics of PDCs and survival using external lung cancer cohorts. First, to evaluate the genomic concordance of PDCs with solid tumor biopsies, we assessed the similarity of mutations and expression profiles between our PDCs and The Cancer Genome Atlas (TCGA) lung adenocarcinoma (LUAD) datasets . Before comparison, the transcriptome batch effect was eliminated using ComBat . The expression profiles were verified from a principal component analysis plot. The frequencies of the most recurrent mutated genes in PDCs were also compared with those in TCGA dataset.
Next, prognostic significance according to distinct molecular subtypes determined based on gene expression profiles was evaluated using meta-transcriptome datasets: five National Center for Biotechnology Information Gene Expression Omnibus lung cancer datasets and TCGA-LUAD datasets (n = 1587 patients; see Additional file 3, Fig. S1). The batch effect among multiple datasets was also eliminated using ComBat. We respectively calculated the four subtype signature scores from the meta-transcriptome using GSVA to obtain differentially expressed gene (DEG) sets (n = 300). The subtype DEGs were acquired using the limma test from our PDCs. Next, the log-rank test and Cox proportional hazard analysis were performed to compare overall survival according to high (> 25%) and low (≤ 25%) scores for each subtype.
Drug sensitivity test according to genomic variants and groups
To evaluate the drug sensitivity of our PDCs according to genomic characteristics, we performed tests for multiple conditions, including lung cancer histologic type, EGFR-TKI therapy group, mutations, CNVs, fusions, RNA subtypes, and co-occurring mutation pairs. TP53 and EGFR mutations were additionally categorized (details are described in the Results). We performed the Wilcoxon rank-sum test using area under the dose response curve (AUC) values between the two groups. P-values were adjusted using the Benjamini–Hochberg method.
To compare alpelisib response of RB1/TP53 cells between PDCs and cell lines, we additionally collected drug screening dataset of CCLE lung cancer cell lines (n = 70) to include SCLC (n = 7) . Alpelisib sensitivity test was also performed by Wilcoxon rank-sum test for mutated cases, and SCLC lung cancer type.
Evaluation of cell cycle inhibitors effective for SCLC and the associated gene FOXM1
We investigated the transcriptome characteristics of SCLC from differentially expressed gene (DEG) analysis to compare SCLC with NSCLC using limma . Next, gene set enrichment analysis (GSEA) was performed using the upregulated DEGs for each group by referring to WikiPathways [37, 38] To evaluate drugs and genes for SCLC, we additionally investigated drug sensitivity data for SCLC versus NSCLC from cell lines. Our drug response screening result for AZD7762 was acquired from 13 NSCLC cell lines and 5 SCLC cell lines, like our PDCs. To assess the similarity of drug signature genes between cell line and PDC, we additionally calculated Pearson correlation coefficient .
The siRNAs targeting FOXM1 (Hs_FOXM1_6 and Hs_FOXM1_7) and the control siRNA were purchased from QIAGEN (Foster City, CA, USA). H69 and H209 cells were transiently transfected with siRNAs using a NEPA21 electroporator (NEPA GENE, Chiba, Japan). Suspension cells (1 × 106) with 100 pmole siRNA in 100 μl OPTI MEM media per cuvette were subjected to electroporation with program No. 5 following the manufacturer’s instructions.
Drug response-associated gene signature extracted using machine learning
To extract gene sets related to the response to each drug, we used the expression profiles to filter out genes according to mean expression level ≤ 1 and standard deviation ≤2. AUC values were transformed to a log2 scale, and gene expression values were converted to z scores of log2-scaled RPKM values. First, we selected the top 500 correlated genes with the AUC values for each drug. Next, we performed elastic net regularization to extract the gene feature importance for each drug response [3, 39]. The glmnet R package was applied to optimize parameters from nine values of α ∈ [0.1,0.9] and 50 values of λ ∈ [0.01,100] to minimize the root mean squared error using 10-fold cross-validation . Bootstrapping was performed 500 times using the R boot package to extract drug gene signatures from features [8, 41]. Finally, gene feature importance was extracted for each drug, and genes were ranked to define drug signature gene sets. Next, we performed GSVA to investigate pathways enriched for each drug gene signature using Reactome with significant enrichment was assessed at P < 0.1 [34, 42].
Molecular characteristics and drug identification according to EGFR-TKI therapeutics
To identify the molecular features related to EGFR-TKI therapeutics, we categorized therapeutic groups from 27 PDC samples. We categorized these patients into four groups (Fig. 5A): (1) BASELINE, PDCs acquired from EGFR-mutated patients that did not receive any treatment; (2) POST1, PDCs without EGFR T790M mutation, acquired after disease progression to the first-line use of first- or second-generation EGFR-TKIs; (3) POST2, PDCs with EGFR T790M, acquired from patients after first- or second-generation EGFR-TKI treatment; and (4) POST3, T790M-positive PDCs, acquired after disease progression to second-line use of the third-generation TKI osimertinib. To identify gene regulation according to these four treatment groups, we identified upregulated DEGs (P < 0.01) for each group and associated pathways using limma and GSEA (P < 0.1 )[38, 43]. Additionally, we collected known EGFR-TKI resistance pathway gene sets: MAPK, PI3K-AKT, JAK-STAT, Wnt β-catenin, plasminogen activation, neuroendocrine activation, YAP/TAZ, MET, HER2, RAS, ERK, KRAS, and TAM (TYRO3-AXL-MERTK) family genes [13, 14, 43,44,45,46]. Resistance pathway scores were calculated using GSVA for each PDC. The score difference of the four therapeutics groups was evaluated using the Wilcoxon rank-sum test .
When exploring sensitivity to drugs for each EGFR-TKI group, the Wilcoxon test was also performed to determine the AUC value difference. We additionally demonstrate our “sensitive-drug candidates” assessed from 27 PDCs extended to PDC pools (n = 70). To improve the statistical reliability of the current dataset, we also collected additional drug screening results of extended EGFR-mutated PDCs (n = 70) acquired from patients previously treated with EGFR-TKIs based only on the clinical treatment profile without the corresponding NGS profile.
The generation of osimertinib-resistant cell lines and experiments are described in Additional file 1 (Supplementary methods).
Establishment and molecular characteristics of lung cancer PDCs
We established a PDC collection of 102 samples (National Cancer Center; see Additional file 2, Table S1) from patients with advanced lung cancer enrolled in this study (Fig. 1A). PDCs were primarily collected from pleural effusions (92.2%), and secondarily from pericardial effusions (4.9%), ascites (2.0%), and tissues (1.0%; Additional file 2, Table S1). The pathologic type was adenocarcinoma (ADC; 84.3%), SCLC (5.9%), and miscellaneous types (squamous cell carcinoma, 4.9%; sarcomatoid carcinoma, 3.9%; and not otherwise specified, 1%). The drug panel used for response screening included 48 anti-cancer compounds of seven classes targeting angiogenesis (n = 3), the cell cycle (n = 8), DNA damage (n = 6), MAPK (n = 3), PI3K/AKT/mTOR (n = 3), protein tyrosine kinase (n = 7), and others (n = 18; see Additional file 2, Table S2). Among the 48 drugs, 16 were screened in all PDCs and 32 were screened in 38 PDCs.
To investigate the genomic characteristics, we identified somatic mutations and CNVs using target-seq (n = 98) and classified four molecular subtypes (C1–C4) from gene expression profiles (n = 102; Additional file 2, Table S1). Next, we checked whether the PDCs effectively recapitulate the mutations and expression profiles of the solid tumors. Owing to the dominance of LUAD in our cohort (> 80%), we compared PDC gene expression profiles with TCGA-LUAD dataset (n = 230). As expected, the PDC gene expression profile resembled with tumor samples and was separated from normal adjacent tissues (Fig. 1B) . Constitutive somatic gene mutations were similar in PDCs and TCGA samples. The recurrence of TP53, RB1, and BRAF mutations was highly preserved in both PDC and TCGA samples. The EGFR mutation frequency was higher in PDCs, whereas the recurrence of KRAS, KEAP1, and STK11 mutations was lower than that of TCGA samples not shown in Fig. 1B. Thus, somatic mutations in TP53 (47%), EGFR (29%), and RB1 (8%) were frequently observed in PDC models (Fig. 1C). Moreover, MET (10%), CDK4 (6%), and MDM2 (6%) variants, as well as EML4-ALK (4%) and CD74-ROS (2%) fusion genes, were detected.
Before in-depth analysis, the most frequent TP53 and EGFR mutations were additionally categorized according to selectivity or functionality [22, 47]. TP53 mutations were divided into hotspot (known as gain-of-function; 5.9%; R175, G245, R248, R249, R273, R282) and non-hotspot (unknown or loss-of-function; 41.2%) mutations. EGFR mutations were categorized into (1) TKI-targetable single variant (TARGET: L858R and exon 19 deletion = 19.6%), (2) T790M acquisition (T790Maq: TARGET and T790M = 2.9%), (3) non-other-specified (NOS) single variant except TARGET (4.9%), and (4) NOS acquisition (NOSaq; TARGET and NOS = 2%; Table 1) [22, 47].
Molecular subtype classification and targeted drug candidate identification
PDC samples could be classified into four molecular subtypes using gene expression profile by NMF clustering. To extrapolate the clinical characteristics for each subtype, we interrogated patient clinical profile encompassing histologic type, survival, smoking, and EGFR-TKI therapy record (Fig. 2 and Table 1). To uncover regulatory program for each molecular subtype, variant enrichment, and pathway regulation scores were assessed from multi-omics profile. In brief, subtype C1 was associated with a good outcome shown in Fig. 2A, and was activated in inflammatory and IL6-JAK-STAT3 signaling pathways; subtype C2 was associated with a modest outcome, dominance of TP53/EGFR wild-type, upregulation of epithelial-to-mesenchymal transition (EMT), and enrichment of osimertinib-resistant group (POST3) PDCs; subtype C3 was associated with the worst outcome, long-term smoking males, SCLC, TP53 hotspot mutation, MYC activation, and fasten G2M checkpoint; by contrast, subtype C4 exhibited the best survival, frequent TP53 non-hotspot mutation, the dominance of EGFR-TKI TARGET mutation, and NOTCH signaling activation (Fig. 2A, B). We also demonstrated the survival significance corresponding to C1–C4 subtype gene sets using additional transcriptome datasets (n = 1587; see Fig. S1A-B in Additional file 3 and Table S3 in Additional file 2). Upregulation of the C3 gene set concurrently exhibited the worst outcomes (P < 0.001 and hazard ratio [HR] = 2.6). When additionally demonstrating from known up-regulated genes of embryonic stem cell, we could observe the activation of human embryonic stem cell genes, and target genes’ upregulation of transcription factor MYC and SOX2 (Fig. S2 in Additional file 3). The activation of C1 and C2 subtype genes significantly showed better prognosis (P < 0.05 and HR < 0.79). Our subtype classification sustained a global prognostic signature for lung cancer. Finally, we could summarize the subtypes based on the following regulatory pathways: C1, inflammatory; C2, EMT-like; C3, stemness; and C4, EGFR-dominant.
Sensitive drug candidates exhibited remarkable concordance with the previously identified regulatory pathways for each molecular subtype (P < 0.05 and |log2 fold change (FC)| < 0.2; FC was assessed to compare average drug AUC values between corresponding subtype and another group; Fig. 2C). The C1 inflammatory subtype was sensitive to only ruxolitinib (JAK1/2) that targets the JAT/STAT pathway (Fig. 2B-D). The C2 EMT-like subtype showed MAPK class drug resistance and sensitivity to dasatinib (angiogenesis and SRC inhibitor), cabozantinib (VEGFA inhibitor), miscellaneous class repotrectinib (ROS inhibitor), and XAV939 (WNT-TNKS-β-catenin inhibitor). The C3 stemness subtype was sensitive to the top five-ranked cell cycle inhibitors. The C4 EGFR-dominant subtype exhibited the strongest resistance to most drug classes except MAPK inhibitors (selumetinib and trametinib) and EGFR-TKIs (gefitinib and afatinib). Notably, targets of predicted drugs belonged to pathways that were found to be activated in each subtype (Fig. 2B).
Predicting drugs for variants and dissecting the molecular subtype of EGFR-mutated PDCs
To interrogate drug candidates for variants, we tested the difference in drug responses of single and co-occurrent mutation cases (P < 0.005 and |log2 FC| < 0.2; Fig. 3A). Co-mutated cases were also explored using the Fisher’s exact test (P < 0.25; see Additional file 3, Fig. S3). Unexpectedly, EGFR-TARGET mutated PDCs showed a relatively modest response to three EGFR-TKIs (afatinib P = 0.17, gefitinib P = 0.19, osimertinib P = 0.08). To uncover EGFR-mutated cells’ molecular features interfering with the EGFR-TKI response, we dissected TKI TARGET mutations according to our four molecular subtypes (Fig. 3B). The C4 EGFR-dominant subtype was the most sensitive to all EGFR-TKIs, and the C1 inflammatory subtype also showed an especially good response to afatinib and osimertinib. Mutated cases (n = 2) of the C3 stemness subtype was insufficient for statistical test. Finally, mutated cases (n = 6) of the C2 EMT-like subtype did not respond to any EGFR-TKIs. Additionally, both T790Maq and NOSaq (EGFR R776G and I744M with L858R) PDCs were TKI-sensitive. In the EGFR NOS type mutations, G719A was observed, and it comprised 11.5% among the NOS mutations in previous NSCLC study, and patient-derived xenografts demonstrated that the mutation was resistant to osimertinib . The remaining NOS mutations excluded exon 18–21 and had a low possibility of finding a structure-based therapeutic target . Therefore, we concluded that the remaining NOS group mutations were not targetable by EGFR-TKIs. Especially, EGFR TARGET mutated PDCs classified to EMT-like subtype exhibited low response to EGFR-TKIs. Thus, our molecular subtype of EGFR mutations revealed that these PDC models can be used to verify the heterogeneous tumor environment affecting drug responses.
When interrogating drug candidates for mutations, BRAF variants were sensitive to trametinib (Fig. 2A). The TP53 non-hotspot exhibited resistance to olaparib (PARP inhibitor), repotrectinib, and dasatinib. RB1 variants responded well to alpelisib. Although TP53 mutation was not associated with any drug response (P < 0.28), RB1/TP53 co-mutated samples exhibited certain sensitivity that was stronger than RB1 (Fig. 3A). RB1/TP53 mutations were mostly enriched in SCLC (n = 4; P < 0.001) but were also observed in NSCLC (n = 3; Additional file 3, Fig. S3). We additionally investigated alpelisib sensitivity comparing PDCs with cell lines (n = 70; SCLC n = 7). Notably, although alpelisib inhibited SCLC (Fig. 3C), RB1/TP53 variants (P < 0.001) were more strongly inhibited than SCLC (P = 0.02). However, in contrast to RB1/TP53 (P < 0.01), we failed to identify SCLC sensitivity to alpelisib (P = 0.47; Fig. 3C) using PDCs. Meanwhile, another PIK3-AKT target drug capivasertib (AKT1) was sensitive in RB1/EGFR-NOS and EGFR-NOS cases. When dissecting details, two of the five (RB1/EGFR-NOS) PDCs was classified to SCLC type additionally harboring TP53 mutations (Fig. S3B in Additional file 3) . Therefore, we could infer that RB1/EGFR-NOS PDC cases accompanied the dependency to SCLC type. Therefore, PI3K-AKT class drugs detected from SCLC or RB1/TP53 cells also affect EGFR-NOS mutations. Collectively, mutation-based drug detection result emphasize that the genomic characteristics could predict both the cancer drug response and histological lung cancer type SCLC (Additional file 2, Table S1). Furthermore, our PDC model predicted alpelisib as an appropriate treatment for SCLCs, which was not identified in cell lines (Fig. 3C).
To delineate the drug susceptibility of alpelisib from transcriptome, we extracted the transcriptome signature for each drug using machine-learning approaches. The response to alpelisib was modulated by a transcriptome signature enriched “MITOTIC G1 PHASE AND G1 S TRANSITION” pathway gene set (Fig. 3D, and Additional file 2, Table S4). Upregulation of MYC, E2F1, and CCNB1 expression led to resistance to alpelisib. Previously, we uncovered that stem-like C3 subtype exhibited both MYC up-regulation, and SCLC enrichment (Additional file 3, Fig. S2). MYC facilitates SCLC tumorigenesis . Moreover, our SCLC PDCs predicted the response to alpelisib better than cell lines (Fig. 3C). Therefore, transcriptome gene signature extracted by machine-learning determined alpelisib response for SCLC like RB1/TP53.
FOXM1 over-expression in SCLC type and sensitivity to cell cycle inhibitors
For further in-depth exploration of the candidate drugs for treating different lung cancer types, we investigated the drug responses of ADC, SCLC, and miscellaneous cancers. SCLC was sensitive to drugs targeting cell cycle pathways (adavosertib, barasertib, berzosertib, and AZD7762) and DNA damage (vorinostat; P < 0.1 and |log2FC| < 0.3; Fig. 4A). The SCLC sensitivity to alpelisib was relatively lower than that to cell cycle drugs according to the FC value. The machine-learning signatures of three drugs (adavosertib, AZD7762, and berzosertib) consistently contained cell cycle pathways (Fig. 4B, and Additional file 2, Table S4). The transcriptome profile revealed the activation of cell cycle genes in SCLC. Moreover, the DNA damage response and RAS signal were downregulated in NSCLC (Additional file 3, Fig. S4). Collectively, these comprehensive pharmacogenomics result suggest the role of cell cycle inhibition in treating SCLC types.
PDCs and cell lines showing FOXM1 upregulation exhibited AZD7762 sensitivity (Fig. 4C, D; PDC R = –0.77; cell line R = –0.51). FOXM1 was clearly over-expressed in SCLC compared to that in LUAD (Fig. 4E) [10, 50,51,52]. To evaluate whether FOXM1 is a plausible target to AZD7762 for SCLC, we transfected H69 cells with small interfering RNAs (siRNAs) against FOXM1 (#6 and #7, siFOXM1) and control siRNA and measured the FOXM1 protein level using western blotting. The FOXM1 expression level was decreased in siFOXM1 (Fig. 4F). siFOXM1 cells also exhibited decreased cell proliferation compared to siControl-transfected cells. However, G2-M phase arrest was detected in siFOXM1 H69 cells (Fig. 4G). siFOXM1 became more resistant to AZD7762 (AUC FC > 1.1) than siControl-transfected cells (Fig. 4H). Another cell lines H209 also presented the same result (see Additional file 3, Fig. S5). Therefore, we concluded that FOXM1 plays essential role to SCLC participating in the G2-M phase, and can be targeted by cell cycle inhibitors.
Pharmacogenomic analysis according to EGFR-TKI treatment status
Among our PDCs, we identified 27 EGFR-TKI-treated cases with corresponding NGS results (independently obtained in the clinic using tissue samples). As described in methods, we these patients into four groups (Fig. 5A). Primarily, POST3 samples (86%) were enriched in the C2 EMT-like subtype (Table 1). EGFR mutation-calling failure was observed in the clinical tumor biopsy NGS record of some patients from which BASELINE (n = 2) and POST2 (n = 1) PDCs were derived. The mutation-calling failure likely originated from differences in the platform between the clinical biopsy and laboratory PDC testing. Moreover, TP53 non-hotspot mutations were present in all of the POST2 PDCs and were markedly absent in POST3 PDCs. EGFR T790Maq was identified in only POST2 PDCs (Fig. 5B red asterisk); interestingly, BRAF mutations highly co-occurred with EGFR T790Maq mutations (Fig. 5B and Additional file 3, Fig. S3). Thus, our PDC models significantly reflected EGFR mutation, acquisition, and extinction status according to EGFR-TKI therapies.
The four therapeutic groups exhibited distinct regulatory programs. To identify the resistance pathway, we applied two different methods. First, activated pathways were inferred from upregulated DEGs for each group (see Additional file 2, Table S5). The activation of ATM signaling and the DNA damage response according to CDK5 upregulation were enriched in the BASELINE PDCs. POST1 PDCs predisposed to RAS, IL-6, WNT, and PI3K-AKT-mTOR signals were enriched with upregulated genes (BRAP, KRAS, and NF1). POST2 PDCs were enriched in NOTCH and STAT3 signaling, including DLL4, EP300, and STAT3 over-expression. Finally, POST3 PDCs exhibited activated TGF-β signaling regulated by PDK1, SMAD2, TGFBR3, and ZEB1. The over-expression of TGF-β signaling and ZEB1 demonstrated that osimertinib-resistant cancer exhibited EMT pathway activation (Fig. 5C).
We next assessed the activities of 12 knowledge-based therapeutic resistance pathways for each sample according to the four EGFR-TKI therapeutic groups (Additional file 2, Table S6). The SERPINE1 signature was only activated in the BASELINE PDCs (Additional file 3, Fig. S6). The WNT, ERBB2, and MET pathway scores were increased in POST2 PDCs but were decreased in POST3 PDCs. The YAP/TAZ and PI3K-AKT pathways were elevated in POST3 PDCs. MET and ERBB2 gene expression also showed positive correlations with activation in the POST2 PDCs (R = 0.62), whereas the POST3 PDCs exhibited inactivation in both pathways (Fig. 5D). The EMT-like POST3 PDCs exhibited the over-expression of both YAP/TAZ and AXL (R = 0.86; Fig. 5D).
Among the four EGFR-TKI groups, we explored the sensitivity of POST3 PDCs to etoposide and XAV939 (Fig. 5E). To predict drugs for 27 PDCs, we prepared an additional drug screening dataset by including the extended PDC set without available sequencing data (n = 70; Additional file 3, Fig. S7A). To make up for the lack of genomic profiles, we collected EGFR-TKI therapy and clinical NGS test medical records. We could assign these samples to the four groups (BASELINE, n = 12; POST1, n = 23; POST2, n = 17; POST3, n = 18). The extended PDC dataset showed remarkable concordance in patient categorization obtained using NGS results and medical records. Finally, POST3 PDCs (PDC P < 0.2, extended PDC P < 0.083; Fig. 5E) were sensitive to etoposide and XAV939. We found that POST3 PDCs exhibited higher sensitivity to XAV939 than etoposide. The machine-learning XAV939 gene signature revealed that the response to XAV939 also involved EMT-like molecular features (i.e., collagen formation and extracellular matrix organization; Additional file 2, Table S4).
To evaluate the previous finding about XAV939 selectivity and pathways, we created two cell lines—EGFR-T790Maq (H1975) cells using POST2 PDCs and XAV939-resistant (H1975_OR3, H1975_OR4) cells using POST3 PDCs—with osimertinib exposure. Both cell lines acquired osimertinib resistance (FC > 1.7; Fig. 5F). TCGA pan-cancer and reverse transcription-quantitative polymerase chain reaction analyses (RT-PCR) revealed that osimertinib-resistant cells concurrently exhibited over-expression of YAP/TAZ target genes . Ten target genes, including YAP1 and AXL, were considerably upregulated in the osimertinib-resistant cell lines compared to that in parent H1975 cells (Fig. 5G). In particular, AXL was elevated with the highest FC (5.1–9.0), and YAP1 and AXL proteins showed consistent over-expression in osimertinib-resistant cell lines (Fig. 5H).
Next, we identified osimertinib resistance-related molecular subtypes and cellular alterations in POST3 PDCs. Since POST3 PDCs were classified as an EMT-like subtype (Fig. 5C), we assessed the EMT features of osimertinib-resistant PDCs. Two osimertinib-resistant PDCs showed decreased E-cadherin and EpCAM (epithelial markers) and increased N-cadherin and Vimentin (mesenchymal markers; Fig. 5H) expression levels. Subsequently, using flow cytometry, we established that cell surface EpCAM expression was considerably decreased in these cell lines (Fig. 5I). We further conducted a migration assay to verify these EMT-associated molecular changes (Fig. 5J). As expected, the two osimertinib-resistant PDCs had greater migration ability than H1975 cells. Collectively, these findings demonstrated that the two successfully generated osimertinib-resistant cell lines were representative of POST3 PDCs. Next, we checked if XAV939 had similar inhibitory effects on POST3 PDCs and H1975, H1975_OR3, and H1975_OR4 cells. XAV939 was not cytotoxic to H1975 cells (AUC = 105.5); however, H1975_OR3 (93.2) and OR4 (87.8) cells were clearly more sensitive to XAV939 (Fig. 5K). Overall, we validated that osimertinib resistance facilitates YAP/TAZ and AXL activation, and an EMT-like phenotype. Thus, XAV939 may be used to treat osimertinib-resistant tumors.
Our PDC platform was established with a lower labor burden and offers advantages of cost effectiveness and fast data acquisition compared with other platforms developed using PDXs, organoids, and cell lines. These advantages could increase the case number and, consequently, guaranteed the statistical significance and tumor heterogeneity of the test data. Our PDC platform currently includes 327 lung cancer cases and exhibits 77.7% success rates for an average 16.6 days duration time. Moreover, the PDC collection from 2020 achieved even better performance with an 83.9% success rate and 14.5 days duration time. Our platform performance exceeded that of a previous lung cancer screening . These characteristics indicate that our platform is effective to operate in the medical field for patient-tailored precision medicine. Moreover, in EGFR-TKI therapeutic groups, we observed approximately 11% EGFR-mutation calling loss compared with the medical record and patient NGS profile. This is likely due to multiple factors contributing to differences among platforms, such as the biopsy type, tumor clonality, purity, and sequencing depth . Meanwhile, our results were demonstrated using lung cancer cell lines. In further study, we have a plan to evaluate experimental demonstration from immortalized cells generated from PDCs. When adjusting the platform pipeline in the next collection, we expect to improve the accuracy of pharmacogenomic analysis.
Among molecular subtypes, stem-like type C3 exhibited the most significant up-regulation in SOX2 than other transcription factors. In previous reports, stemness-related transcription factors play a role to depend on cancer tissue type. SOX2 was required in early-stage ADC and SCLC, whereas OCT3, KLF4, and NANOG participate other cancer types . Despite low frequency of SCLC, these were also enriched in this subtype. RB1/TP53 co-mutation was SCLC driver gene, and its depletion facilitate aberrant cell-cycle . Additionally, MYC up-regulated in C3 type drives dynamic evolution of SCLC . Therefore, our molecular subtype successfully classified these comprehensive regulatory features.
Through detailed EGFR-TKI response analysis, we showed that the PDC molecular subtype uncovers the clinical characteristics and resistance factors of lung cancer patients. Our subtype classification suggested therapeutic candidates according to regulatory program. In additional classification of EGFR TARGET mutations, we could uncover that molecular subtype implicated in EGFR-TKI responses of PDCs harboring EGFR mutations. In particular, the C2 subtype designated as EMT-like exhibited FGFR2 over-expression. The FGF7-FGFR2 over-expressing lung cancer-associated fibrosis (CAF) type robustly protects EGFR-mutated cancer cells to maintain osimertinib resistance . CAF and EGFR-mutated co-cultured cells exhibit osimertinib resistance. Here, using our PDC models, we could directly assess EMT interference via EGFR-TKI resistance from spontaneous patient tumor clonal status. Moreover, our data showed that both the EMT-like subtype and osimertinib-resistant patients are sensitive to XAV939. Machine-learning gene signatures also revealed that regulatory programs that induce EMT-like characteristics were sensitive to XAV939.
The genomic variant RB1/TP53 showed more sensitivity to alpelisib than SCLC, whereas SCLC exhibited sensitivity to cell cycle inhibitors. Interestingly, NSCLC to SCLC transformation co-occurred with EGFR-TKI resistance, and RB1/TP53 loss-of-function mutation occurred earlier than expected in the cancer cell cycle . Upregulation of PIK3CA mutation and PI3K/AKT pathway genes also occurred earlier during the transformation . A patient-derived EGFR-mutant xenograft model verified that early PI3K/AKT pathway inhibition delays tumor growth in SCLC or NSCLC undergoing transformation. Importantly, our PDC model concurrently selected alpelisib, a PI3K/AKT inhibitor, for treating early-stage SCLC. Consequently, our results emphasize that progressive and de novo SCLC phenotypes are more sensitive to cell cycle inhibitors . We suggest that strategic combinatorial therapy with anti-cancer drugs belonging to two different classes can block the progression of early-stage SCLC.
EGFR-mutated lung cancer patients responding well to TKIs eventually develop resistance. Particularly, osimertinib resistance develops via heterogeneous and complex mechanisms, making establishment of an effective therapeutic strategy difficult. Our therapeutic follow-up delineated resistance pathways activated by EGFR-TKI treatment . ERBB2 and MET were activated in POST1 PDCs. BRAF mutations significantly co-occurred with EGFR-T790M. Furthermore, YAP/TAZ was activated in POST4 PDCs. Our results suggest XAV939 for POST3 PDCs. Interestingly, BASELINE PDCs, similar to POST3 PDCs, responded to XAV939 (Additional file 3, Fig. S7B). Thus, first-line combinatorial therapy with EGFR-TKI and XAV939 seems plausible. Gefitinib and XAV939 acted synergistically in the combinatorial therapy of the EGFR-mutated H1975 cell line (combination index (CI) = 0.388; synergism CI < 0.9). We expect effective translation of these results into treatments for patients with osimertinib-resistant lung cancer.
Precision oncology in lung cancer is mainly based on gene-targeted chemotherapy; however, evasive mutations in target genes confound the prognosis. The PDCs developed in this study offer an advantage in tailoring patient-specific drugs and understanding the comprehensive molecular features of cancer. Importantly, our molecular subtypes reflected the PDC heterogeneity and recapitulated the drug response-mediated interference of EMT. However, as a limitation, we observed mutation-calling failure in a few EGFR-TKI treatment cases (11%), which was attributed multiple factors such as biopsy differences, tumor clonality, purity, and sequencing depth . In the future, we expect to improve the accuracy of pharmacogenomic analysis by adjusting the platform pipeline.
Our well-established pharmacogenomic platform effectively predicted drugs and response mechanisms for refractory lung cancer. Following this pilot study, we expect consistent use of the established PDC bank in unveiling comprehensive drug–target associations of clinical relevance.
Availability of data and materials
Next-generation sequencing files of refractory lung cancer PDC samples have been deposited in the National Center for Biotechnology Information Gene Expression Omnibus (GSE165611) database for RNA-seq and in the Sequence Read Archive (PRJNA694788) for target-seq.
Differentially expressed gene
Epidermal growth factor receptor
Patient-derived tumor cells
Small-cell lung cancer
Small interfering RNA
The Cancer Genome Atlas
Tyrosine kinase inhibitor
Miller KD, Nogueira L, Mariotto AB, Rowland JH, Yabroff KR, Alfano CM, et al. Cancer treatment and survivorship statistics, 2019. CA Cancer J Clin. 2019;69:363–85.
Ramalingam SS, Vansteenkiste J, Planchard D, Cho BC, Gray JE, Ohe Y, et al. Overall survival with Osimertinib in untreated, EGFR-mutated advanced NSCLC. N Engl J Med. 2020;382:41–50.
Lee J-K, Liu Z, Sa JK, Shin S, Wang J, Bordyuh M, et al. Pharmacogenomic landscape of patient-derived tumor cells informs precision oncology therapy. Nat Genet. 2018;50:1399–411.
Kim M, Mun H, Sung CO, Cho EJ, Jeon H-J, Chun S-M, et al. Patient-derived lung cancer organoids as in vitro cancer models for therapeutic screening. Nat Commun. 2019;10:3991.
Ghandi M, Huang FW, Jané-Valbuena J, Kryukov GV, Lo CC, McDonald ER, et al. Next-generation characterization of the Cancer cell line encyclopedia. Nature. 2019;569:503–8.
Warren A, Chen Y, Jones A, Shibue T, Hahn WC, Boehm JS, et al. Global computational alignment of tumor and cell line transcriptional profiles. Nat Commun. 2021;12:22.
Sa JK, Hwang JR, Cho Y-J, Ryu J-Y, Choi J-J, Jeong SY, et al. Pharmacogenomic analysis of patient-derived tumor cells in gynecologic cancers. Genome Biol. 2019;20:1–13.
Sa JK, Hong JY, Lee I-K, Kim J, Sim M-H, Kim HJ, et al. Comprehensive pharmacogenomic characterization of gastric cancer. Genome Med. 2020;12:1–12.
Crystal AS, Shaw AT, Sequist LV, Friboulet L, Niederst MJ, Lockerman EL, et al. Patient-derived models of acquired resistance can identify effective drug combinations for cancer. Science. 2014;346:1480–6.
Collisson EA, Campbell JD, Brooks AN, Berger AH, Lee W, Chmielecki J, et al. Comprehensive molecular profiling of lung adenocarcinoma: the cancer genome atlas research network. Nature. 2014;511:543–50.
Roper N, Brown A-L, Wei JS, Pack S, Trindade C, Kim C, et al. Clonal evolution and heterogeneity of Osimertinib acquired resistance mechanisms in EGFR mutant lung Cancer. Cell Rep Med. 2020;1:100007.
Gazdar AF, Bunn PA, Minna JD. Small-cell lung cancer: what we know, what we need to know and the path forward. Nat Rev Cancer. 2017;17:725–37.
J R, TG B. Understanding and targeting resistance mechanisms in NSCLC. Nat Rev Cancer. 2017;17:637–58.
Maynard A, McCoach CE, Rotow JK, Harris L, Haderk F, Kerr DL, et al. Therapy-induced evolution of human lung Cancer revealed by single-cell RNA sequencing. Cell. 2020;182:1232–1251.e22.
Rudin CM, Brambilla E, Faivre-Finn C, Sage J. Small-cell lung cancer. Nat Rev Dis Primers. 2021;7:1–20.
Bolger AM, Lohse M, Usadel B. Trimmomatic: a flexible trimmer for Illumina sequence data. Bioinformatics. 2014;30:2114–20.
Li H, Durbin R. Fast and accurate long-read alignment with burrows-wheeler transform. Bioinformatics. 2010;26:589–95.
McKenna A, Hanna M, Banks E, Sivachenko A, Cibulskis K, Kernytsky A, et al. The genome analysis toolkit: a MapReduce framework for analyzing next-generation DNA sequencing data. Genome Res. 2010;20:1297–303.
Ramos AH, Lichtenstein L, Gupta M, Lawrence MS, Pugh TJ, Saksena G, et al. Oncotator: Cancer variant annotation tool. Hum Mutat. 2015;36:E2423–9.
Mermel CH, Schumacher SE, Hill B, Meyerson ML, Beroukhim R, Getz G. GISTIC2.0 facilitates sensitive and confident localization of the targets of focal somatic copy-number alteration in human cancers. Genome Biol. 2011;12:690–705.e9.
Mayakonda A, Lin DC, Assenov Y, Plass C, Koeffler HP. Maftools: efficient and comprehensive analysis of somatic variants in cancer. Genome Res. 2018;28:1747–56.
Herrero AB, Rojas EA, Misiewicz-Krzeminska I, Krzeminski P, Gutiérrez NC. Molecular mechanisms of p53 deregulation in cancer: an overview in multiple myeloma. Int J Mol Sci. 2016;17:2003.
Chong CR, Jänne PA. The quest to overcome resistance to EGFR-targeted therapies in cancer. Nat Med. 2013;19:1389–400.
Lawrence MS, Stojanov P, Polak P, Kryukov GV, Cibulskis K, Sivachenko A, et al. Mutational heterogeneity in cancer and the search for new cancer-associated genes. Nature. 2013;499:214–8.
Dobin A, Davis CA, Schlesinger F, Drenkow J, Zaleski C, Jha S, et al. STAR: Ultrafast universal RNA-seq aligner. Bioinformatics. 2013;29:15–21.
Li B, Dewey CN. RSEM: accurate transcript quantification from RNA-Seq data with or without a reference genome. BMC Bioinformatics. 2011;12:323.
McPherson A, Hormozdiari F, Zayed A, Giuliany R, Ha G, Sun MGF, et al. Defuse: an algorithm for gene fusion discovery in tumor rna-seq data. PLoS Comput Biol. 2011;7:e1001138.
Torres-García W, Zheng S, Sivachenko A, Vegesna R, Wang Q, Yao R, et al. PRADA: pipeline for RNA sequencing data analysis. Bioinformatics. 2014;30:2224–6.
Abate F, Zairis S, Ficarra E, Acquaviva A, Wiggins CH, Frattini V, et al. Pegasus: a comprehensive annotation and prediction tool for detection of driver gene fusions in cancer. BMC Syst Biol. 2014;8:97.
Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC, et al. Oncogenic signaling pathways in the Cancer genome atlas. Cell. 2018;173:321–337.e10.
Stransky N, Cerami E, Schalm S, Kim JL, Lengauer C. The landscape of kinase fusions in cancer. Nat Commun. 2014;5:4846.
Tate JG, Bamford S, Jubb HC, Sondka Z, Beare DM, Bindal N, et al. COSMIC: the catalogue of somatic mutations in Cancer. Nucleic Acids Res. 2019;47:D941–7.
Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, et al. Limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015;43:e47.
Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-Seq data. BMC Bioinformatics. 2013;14:7.
Ben-Porath I, Thomson MW, Carey VJ, Ge R, Bell GW, Regev A, et al. An embryonic stem cell-like gene expression signature in poorly differentiated aggressive human tumors. Nat Genet. 2008;40:499–507.
Johnson WE, Li C, Rabinovic A. Adjusting batch effects in microarray expression data using empirical Bayes methods. Biostatistics. 2007;8:118–27.
Wang J, Vasaikar S, Shi Z, Greer M, Zhang B. WebGestalt 2017: a more comprehensive, powerful, flexible and interactive gene set enrichment analysis toolkit. Nucleic Acids Res. 2017;45:W130–7.
Martens M, Ammar A, Riutta A, Waagmeester A, Slenter DN, Hanspers K, et al. WikiPathways: connecting communities. Nucleic Acids Res. 2021;49:D613–21.
Barretina J, Caponigro G, Stransky N, Venkatesan K, Margolin AA, Kim S, et al. The Cancer cell line encyclopedia enables predictive modelling of anticancer drug sensitivity. Nature. 2012;483:603–7.
Friedman J, Hastie T, Tibshirani R. Regularization paths for generalized linear models via coordinate descent. J Stat Softw. 2010;33:1–22.
Buckland ST, Davison AC, Hinkley DV. Bootstrap methods and their application. Biometrics. 1998;54:795.
Jassal B, Matthews L, Viteri G, Gong C, Lorente P, Fabregat A, et al. The reactome pathway knowledgebase. Nucleic Acids Res. 2020;48:D498–503.
Slenter DN, Kutmon M, Hanspers K, Riutta A, Windsor J, Nunes N, et al. WikiPathways: a multifaceted pathway database bridging metabolomics to other omics research. Nucleic Acids Res. 2018;46:D661–7.
Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The molecular signatures database Hallmark gene set collection. Cell Syst. 2015;1:417–25.
Kanehisa M, Sato Y, Kawashima M, Furumichi M, Tanabe M. KEGG as a reference resource for gene and protein annotation. Nucleic Acids Res. 2016;44:D457–62.
Wang Y, Xu X, Maglic D, Dill MT, Mojumdar K, Ng PK-S, et al. Comprehensive molecular characterization of the hippo signaling pathway in Cancer. Cell Rep. 2018;25:1304–1317.e5.
Bronte G, Rizzo S, La Paglia L, Adamo V, Siragusa S, Ficorella C, et al. Driver mutations and differential sensitivity to targeted therapies: a new approach to the treatment of lung adenocarcinoma. Cancer Treat Rev. 2010;36(SUPPL. 3):S21–9.
Robichaux JP, Le X, Vijayan RSK, Hicks JK, Heeke S, Elamin YY, et al. Structure-based classification predicts drug response in EGFR-mutant NSCLC. Nature. 2021;597:732–7.
Mollaoglu G, Guthrie MR, Böhm S, Brägelmann J, Can I, Ballieu PM, et al. MYC drives progression of small cell lung Cancer to a variant neuroendocrine subtype with vulnerability to Aurora kinase inhibition. Cancer Cell. 2017;31:270–85.
George J, Lim JS, Jang SJ, Cun Y, Ozretia L, Kong G, et al. Comprehensive genomic profiles of small cell lung cancer. Nature. 2015;524:47–53.
Sato T, Kaneda A, Tsuji S, Isagawa T, Yamamoto S, Fujita T, et al. PRC2 overexpression and PRC2-target gene repression relating to poorer prognosis in small cell lung cancer. Sci Rep. 2013;3:1911.
Jiang L, Huang J, Higgs BW, Hu Z, Xiao Z, Yao X, et al. Genomic landscape survey identifies SRSF1 as a key Oncodriver in small cell lung Cancer. PLoS Genet. 2016;12:e1005895.
Koboldt DC. Best practices for variant calling in clinical sequencing. Genome Med. 2020;12:91.
Zhao W, Li Y, Zhang X. Stemness-related markers in Cancer. Cancer Transl Med. 2017;3:87–95.
Ireland AS, Micinski AM, Kastner DW, Guo B, Wait SJ, Spainhower KB, et al. MYC drives temporal evolution of small cell lung Cancer subtypes by reprogramming neuroendocrine fate. Cancer Cell. 2020;38:60–78.e12.
Hu H, Piotrowska Z, Hare PJ, Chen H, Mulvey HE, Mayfield A, et al. Three subtypes of lung cancer fibroblasts define distinct therapeutic paradigms. Cancer Cell. 2021;39:1531–1547.e10.
Park CK, Oh IJ, Kim YC. Is transformed small cell lung cancer (SCLC) different from de novo SCLC? Transl Cancer Res. 2019;8:346–9.
Quintanal-Villalonga A, Taniguchi H, Zhan YA, Hasan MM, Chavan SS, Meng F, et al. Multiomic analysis of lung tumors defines pathways activated in neuroendocrine transformation. Cancer Discov. 2021;11:3028–47.
This work was partially supported by KISTI (Korea Institute of Science and Technology Information) for computational resources and the Creative Media Service in National Cancer Center Korea for illustration. We would like to acknowledge the patients with advanced or refractory lung cancer and the clinical help obtained for this project.
This work was supported by the National Cancer Center Research Grant (NCC-2210550 to JYH, CP, and SK); Ministry of Health & Welfare of Korea (HI21C0416 to JYH, CP, and SK); and National Research Foundation of Korea (NRF-2022R1A2C1005708 to CP). This work was also partially supported by Korea Institute of Science and Technology Information (KISTI) for computational resources and the Creative Media Service in National Cancer Center Korea for illustrations.
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This study was approved by the Institutional Review of Board of the National Cancer Center (Goyang, Korea; protocol number: NCC2019-0082).
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Clinical information and molecular subtypes of 102 lung cancer patients used to obtain PDCs. Table S2. The 48 drugs employed in the screening panel. We refer to the chemical or generic name of the drugs, and the target and class for each drug were classified. Table S3. Differentially expressed gene sets for the four molecular subtypes. Table S4. GSEA results (P < 0.1) using drug-associated gene signatures extracted with a machine-learning approach. Table S5. GSEA results (P < 0.1) acquired from analysis of the upregulated DEGs from four EGFR-TKI treatment groups. Table S6. EGFR-TKI resistance pathway and related gene sets.
Molecular subtype evaluation using six lung cancer cohorts (n = 1587). (A) Overall survival plots for each molecular subtype according to the subtype gene signature score. High and low groups were selected from the upper and lower quartiles. P-values and hazard ratios (HRs) were calculated using the log-rank test and Cox model. (B) HR forest plots for each subtype across six lung cancer cohorts. Fig. S2. Additional assessment of stemness scores for each molecular subtype. (A) A heatmap of average stemness scores according to molecular subtype. Stemness scores were assessed using GSVA from gene signatures: embryonic stem (ES) cell up-regulated genes (ES exp1, and ES exp2), and five transcription factors’ target genes as well as four PRC2 complex target signatures as control sets. (B) Boxplots of stemness scores according to subtypes. P-values were calculated by Wilcoxon rank-sum test to compare C3 and others. Fig. S3. Co-occurrent mutation case investigation. (A) Significant co-mutation pairs extracted using the Fisher’s exact test (recurrence > 5%). (B) The status of RB1/TP53 mutation and SCLC type. Fig. S4. Heatmap of DEGs between SCLC and NSCLC. The rows of the heatmap are the samples and the columns show the genes. Genes were extracted using limma (adjusted P < 0.005, |log2 FC| > 0.5) and the GSEA results (Table S3, Additional file 2) are summarized on the left. Fig. S5. Validation of the correlation of FOXM1 expression and AZD7762 sensitivity in H209 cells. (A) The protein levels of FOXM1 and β-actin in siRNA-transfected H209 cells (siFOXM1-#6, #7) analyzed using western blotting. Ctrl represents the control. H209 cells transfected with siRNAs were incubated for 48 h. (B) After incubation, the cells were analyzed using flow cytometry to evaluate the DNA content. Representative DNA content profiles from three independent experiments are shown. The graphs show the proportion of cells in each cell cycle phase. (C) Drug response curve of siRNA-transfected H209 cells treated with AZD7762 (x-axis). The area under the receiver operating characteristic curve (AUC) values of AZD7762 in siRNA-transfected cells are shown in the panel. All experiments were performed in quadruplicate. The values represent the mean ± SEM (Student’s t-test, *P < 0.05; ***P < 0.001). Fig. S6. Pathway score bar plots of six EGFR-TKI resistance signatures according to four EGFR-TKI therapeutic groups. The numbers on each bar plot indicate the P values obtained using the Wilcoxon rank-sum test. Fig. S7. Drug candidates extracted from EGFR-TKI group PDCs (n = 27) and extended EGFR PDCs (n = 70). (A) Volcano plots for EGFR-TKI groups of two datasets. The x-axis indicates the log2-fold change between drug responses and the y-axis shows the log-scale adjusted P-value. Red circles indicate sensitivity whereas blue circles indicate resistance. (B) Bar plots for etoposide and XAV939 AUC in both datasets. The x-axis indicates EGFR-TKI groups and the y-axis indicates AUC values. P values were obtained using the Wilcoxon rank-sum test.
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Yu, N., Hwang, M., Lee, Y. et al. Patient-derived cell-based pharmacogenomic assessment to unveil underlying resistance mechanisms and novel therapeutics for advanced lung cancer. J Exp Clin Cancer Res 42, 37 (2023). https://doi.org/10.1186/s13046-023-02606-3