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Variation in targetable genomic alterations in non-small cell lung cancer by genetic ancestry, sex, smoking history, and histology

Abstract

Background

Genomic alterations in 8 genes are now the targets of FDA-approved therapeutics in non-small cell lung cancer (NSCLC), but their distribution according to genetic ancestry, sex, histology, and smoking is not well established.

Methods

Using multi-institutional genetic testing data from GENIE, we characterize the distribution of targetable genomic alterations in 8 genes among 8675 patients with NSCLC (discovery cohort: DFCI, N = 3115; validation cohort: Duke, Memorial Sloan Kettering Cancer Center, Vanderbilt, N = 5560). For the discovery cohort, we impute genetic ancestry from tumor-only sequencing and identify differences in the frequency of targetable alterations across ancestral groups, smoking pack-years, and histologic subtypes.

Results

We identified variation in the prevalence of KRASG12C, sensitizing EGFR mutations, MET alterations, ALK, and ROS1 fusions according to the number of smoking pack-years. A novel method for computing continental (African, Asian, European) and Ashkenazi Jewish ancestries from panel sequencing enables quantitative analysis of the correlation between ancestry and mutation rates. This analysis identifies a correlation between Asian ancestry and EGFR mutations and an anti-correlation between Asian ancestry and KRASG12C mutation. It uncovers 2.7-fold enrichment for MET exon 14 skipping mutations and amplifications in patients of Ashkenazi Jewish ancestry. Among never/light smokers, targetable alterations in LUAD are significantly enriched in those with Asian (80%) versus African (49%) and European (55%) ancestry. Finally, we show that 5% of patients with squamous cell carcinoma (LUSC) and 17% of patients with large cell carcinoma (LCLC) harbor targetable alterations.

Conclusions

Among patients with NSCLC, there was significant variability in the prevalence of targetable genomic alterations according to genetic ancestry, histology, and smoking. Patients with LUSC and LCLC have 5% rates of targetable alterations supporting consideration for sequencing in those subtypes.

Background

Non-small cell lung cancer (NSCLC) is the most common cause of cancer-related death in the USA and around the world [1]. In the last two decades, recognition of an increasing number of specific mutations and corresponding molecular-targeted therapeutics has completely changed the clinical approach to the care of lung adenocarcinoma (LUAD) [2].

To date, there are eight genes that constitute well-defined targets of Food and Drug Administration (FDA)-approved drugs: EGFR, KRAS, ALK, MET, ROS1, BRAF, RET, and NTRK. While the distribution of genomic alterations in NSCLC is described in prior reports, most have focused on one alteration [3, 4], one histology [5], or a single race [6, 7]. Furthermore, reports describing lung squamous cell carcinoma (LUSC) have emphasized the landscape of squamous-specific alterations, with relatively little attention to targetable mutations in this histologic subset [8]. The prevalence of targetable alterations in less common histologic subtypes of non-small cell lung cancer (i.e., typical and atypical carcinoid tumors, large cell lung cancer) is also not well characterized.

Furthermore, although prior reports have described some differences in mutation profiles based on self-reported race [9], few have explored the correlation between genetically defined ancestry and somatic mutation frequency in detail. In admixed Latin American populations, native American ancestry has been shown to strongly influence the frequencies of KRAS, EGFR, and ALK mutations in NSCLC [10]. Both known and yet-to-be-discovered associations are of interest with respect to screening different populations for such mutations and may uncover mechanistic insight into mutation occurrence.

The American Association for Cancer Research (AACR) launched the Project Genomics Evidence Neoplasia Information Exchange (GENIE), an effort to bring together genomic data from multiple academic and biopharma institutions to enhance the sharing of clinical and genomic information. Here, we utilized the GENIE resource to examine the distribution of targetable genomic alterations in 8 genes in a cohort of 8675 patients with NSCLC and highlight differences across histology, sex, and smoking status. Recognizing the known limitations of self-reported race, we also developed a genetic ancestry inference method to determine ancestry components in each subject quantitatively and used this information to examine correlations between genomic alterations in these 8 genes and quantitative ancestry measurements.

Methods

Study design and patient cohorts

We identified patients with solid tumors from the publicly available AACR GENIE 10.1-registry, which is a multi-phase, multi-year, data-sharing project that captures genomic data for cancer patients at multiple institutions (https://www.aacr.org/wp-content/uploads/2021/07/GENIE_10.0-public_data_guide.pdf) [11]. All patients with NSCLC that met the following two criteria were included: (1) tumor sequencing data was available with bait-sets covering single nucleotide variants (SNVs), copy number alterations (CNAs), and fusion variants for 8 genes (EGFR, KRAS, ALK, MET, ROS1, BRAF, RET, and NTRK) and (2) gender and race were available. A total of 8675 patients met the inclusion criteria and were separated into a discovery cohort (Dana-Farber Cancer Center, DFCI: N = 3115) and a validation cohort (total, N = 5560; Duke Cancer Center, DCC: N = 5; Memorial Sloan Kettering Cancer Center, MSKCC: N = 5405; Vanderbilt-ingram cancer center, VICC: N = 150, Additional file 1: Fig. S1). For the DFCI cohort (n = 3115 patients), the number of smoking pack-years was determined by chart review, and genetic ancestry was inferred (see the “Estimation of genetic ancestry indices” section). For the validation cohort (n = 5560 patients), smoking history was not available, and raw sequencing data was also not available, so self-reported race was utilized.

To minimize the impact of referral bias for rare mutation subsets, and the use of repeat biopsies to assess mechanisms of resistance in patients with driver mutations, we restricted our analysis to the first tumor sample sequenced. For the DFCI cohort, a chart review was used to identify the first tumor sequencing analysis for each patient. For the validation cohort (DCC, MSKCC, VICC), we used tumor sequencing data obtained at the youngest age for each subject.

Patient consent

DFCI samples were selected and sequenced from patients who were consented under institutional review board (IRB)-approved protocol 11-104 and 17-000 from the Dana-Farber/Partners Cancer Care Office for the Protection of Research Subjects. Written informed consent was obtained from participants prior to inclusion in this study. Secondary analyses of previously collected data were performed with approval from the Dana-Farber IRB: DFCI IRB protocol 18-439 and 19-025; waiver of Health Insurance Portability and Accountability Act (HIPAA) authorization approved for both protocols.

Tumor specimens

Histologic subtypes included in both the discovery and validation cohorts were lung adenocarcinoma (LUAD), lung adenosquamous carcinoma (LUASC), large cell lung carcinoma (LCLC), lung squamous cell carcinoma (LUSC), and lung carcinoid. Histologic classification was based on the clinical diagnosis rendered in the pathology record for each case. All samples undergoing Oncopanel testing in the DFCI cohort had an official pathology review and formal diagnostic report. Diagnoses were made by board-certified surgical pathologists with subspecialty expertise in pulmonary pathology.

Genomic analysis

Details of the tissue collection, DNA extraction, and tumor-targeted sequencing were previously described in detail for each of the DFCI (Oncopanel/PROFILE), DCC (Foundation Medicine), MSKCC (MSK-IMPACT), and VICC (Foundation Medicine) cohorts [11,12,13]. Among the tumor sequencing panels at the four institutions, only bait-sets covering SNVs, CNAs, and fusion variants for all 8 genes were included (DFCI-ONCOPANEL 1-3, DUKE-F1-T5A, VICC-01-T5A, VICC-01-T7, MSK-IMPACT 410, and MSK-IMPACT 468). We focused our mutational analyses on the following alterations: L858R mutations, exon 19 deletions and exon 20 insertions in EGFR, KRASG12C mutations, exon 14 skipping mutations and amplifications in MET, V600E mutations in BRAF, and gene fusions in ALK, ROS1, RET, and NTRK.

Next-generation sequencing assays

Specifics about genomic profiling at each center are provided in the AACR Project GENIE Data Guide: https://www.aacr.org/wp-content/uploads/2020/07/20200706_GENIE_10.0-public_data_guide.pdf [11].

Estimation of genetic ancestry indices

For the DFCI discovery cohort, genetic ancestry was inferred using common polymorphisms called from off-target and on-target sequencing reads [14, 15]. Germline variant imputation was performed across all samples using the STITCH imputation software [16]. This method utilizes ultra-low coverage read data together with the 1000 Genomes reference panel to infer probabilistic germline calls for the autosomal chromosomes. Analysis was restricted to variants with imputation INFO > 0.4 and variant allele frequency (VAF) > 0.01. Ancestry components were inferred for each individual by linear projection using publicly available weights computed by the SNPWEIGHTS software [17], which had been trained on European, West African, and East Asian individuals in the 1000 Genomes project [18] as well as Ashkenazi Jewish Europeans from four large genome-wide association studies [19]. The projection was performed using the imputed dosages and the PLINK2 “--score” function. Ancestry components can have arbitrary rotation/scaling. For visualization purposes, a linear rescaling was applied to the two continental indices such that individuals self-reported as White had a mean score of 0% and individuals self-reported as Asian/Black had a mean score of 100% for the East Asian/African ancestry components, respectively. We note that this is a linear rescaling that does not impact the statistical significance of any association with the indices. As expected, this ancestry score was significantly correlated with self-reported race (Pearson correlation = 0.90; p < 2 × 10−16) for individuals reporting as either “White/Caucasian,” “Black/African American,” or “Asian” (Additional file 1: Fig. S2). In the DFCI cohort, a European group was defined as individuals with recalibrated African and Asian indices of less than 20% and 15%, respectively, computed by taking 4 standard deviations from the mean for the self-reported White individuals (recognizing that any binarization of quantitative ancestry will be heuristic). For individuals with self-reported race, this European group was >99% self-reported White, and the remaining non-European individuals were 20% self-reported White. Beyond the high correlations with self-reported features, the ancestry score is expected to capture additional variance in genetic ancestry due to admixture and is not susceptible to data missingness or misreporting. We note that we did not separately model Hispanic individuals, which derive ancestry from European, African, and Native American/East Asian populations and thus do not have a single quantitative ancestry score [20]. A Hispanic individual would thus typically have a high East Asian ancestry score (a proxy for Native American ancestry) [21] as well a non-zero African ancestry score, and their indices would be tested separately. However, the number of self-reported Hispanic individuals in this study was very low (1.6%) and thus was not expected to substantially influence the results.

The Ashkenazi Jewish ancestry inference was validated using a previously reported cohort from DFCI of 833 samples that were genotyped on a germline Illumina MEGA SNP array from blood and imputed to the 1000 Genomes reference panel [22]. Ashkenazi Jewish ancestry scores, determined as above from Oncopanel data, were found to be highly concordant with the MEGA SNP AJ genotyping analysis, with a Pearson correlation of 0.99 between the two scores (Additional file 1: Fig. S3). Similarly, high concordance was seen when transforming the SNP-based score into a binary classification for Ashkenazi individuals outside the main distribution of Europeans (87 individuals): the tumor-based score achieved an AUC of 1.0. We also confirmed that individuals self-reporting as religiously Jewish were highly enriched for having a high Ashkenazi Jewish score (Pearson correlation = 0.65, p < 10−100), though we note that these are not expected to be perfect surrogates. Both the SNP-based score and the tumor-based score achieved an AUC of 0.94 for classifying self-reported religious Jewish status. Hence, ancestry scores inferred from tumors sequenced by Oncopanel are nearly statistically identical to those inferred from germline SNPs and have comparable consistency with self-reported data.

In the DFCI NSCLC cohort (n = 3115 patients), patients did not report Ashkenazi Jewish ethnicity, but similar to the test cohort, the AJ ancestry score was significantly associated with self-reported Jewish religion (Pearson correlation = 0.71; p < 2 × 10−16), demonstrating face validity (though we stress that religion should not be used as a surrogate for ethnicity or genetic ancestry). The Ashkenazi Jewish group was defined as individuals with a rescaled Ashkenazi Jewish score of more than 0.75.

Statistical analysis

Logistic regression models were used to examine the association between ancestral indices and somatic alterations, with age at diagnosis, pack-years of smoking, tumor histology, and sex used as covariates. The models were used to calculate the odds ratios, 95% confidence intervals (CI), and p-values. We applied false discovery rate (FDR) correction by the Bonferroni method for the number of independent tests conducted (significant q-value cutoff of < 0.1).

When comparing the frequencies of targetable alterations across different smoking groups (never smokers, 1–15 pack-years, 16–30 pack-years, 31–45 pack-years, 46–59 pack-years, and 60+ pack-years), pairwise Fisher exact tests were performed to calculate odds ratios, 95% confidence intervals (CI), and p-values. A logistic regression model was used when pack-years of smoking was used as a continuous variable. Bonferroni correction of p-values was applied to account for comparisons among multiple groups. Unless otherwise indicated, only p-values that were significant after controlling for false discovery rate are reported (FDR < 0.1).

Results

Distribution of targetable genomic alterations across NSCLC histotypes

Among 3115 patients with NSCLC seen at the Dana-Farber Cancer Institute who had tumor sequencing performed (see the “Methods” section, Additional file 2: Table S1), the most common tumor histology was LUAD (2395/3115, 76.9%), followed by LUSC (337/3115, 10.8%) and carcinoid (74/3115, 2.4%). Most of the samples sequenced were derived from the primary cancer site (1863, 59.8%, Table 1 and Additional file 2: Table S1). Targetable genomic alterations in the 8 genes (EGFR, KRAS, ALK, MET, ROS1, BRAF, RET, and NTRK; see the “Methods” section) were detected in 44.0% of patients with LUAD (1051/3115, Additional file 2: Tables S2-S9), 33% (5/15) of patients with LUASC carcinoma, 17% (4/24) of patients with LCLC, 5.3% (18/337) of patients with LUSC, and 0% (0/74) of patients with lung carcinoid tumors (n = 50 typical and n = 24 atypical, Fig. 1A). LCLC (n = 24) harbored KRASG12C mutations at a frequency comparable to LUAD (13% vs. 15.1%, q-value = 1), but had a significantly lower frequency of EGFR L858R, exon 19 deletions, or exon 20 insertions (0% vs 17.9%, OR = 0 [95% CI = 0–0.76], p-value = 0.016). ALK/ROS1/NTRK/RET fusions and BRAFV600E mutations were not detected in any LCLC sample, although the number of samples was small. Targetable alterations in EGFR, KRAS, MET, ALK, and RET were identified in LUSC samples, although at much lower frequency compared to LUAD (all q-values < 0.05). ALK, ROS1, and NTRK gene fusions were found almost exclusively in patients with LUAD. The validation cohort of 5560 GENIE patients from several institutions showed a similar distribution of mutations for each histology (Additional file 2: Table S10, Fig. 1B), with the exception that mutation frequencies in rare pathologic subtypes (adenosquamous, large cell) were more variable, as expected.

Table 1 Baseline characteristics and frequency of eight targetable genomic alterations of 3115 NSCLC patients from the DFCI cohort according to tumor histology
Fig. 1
figure 1

Histology-specific differences in genomic alterations in NSCLC in the discovery (A) and validation (B) cohorts. Frequency of eight targetable genomic alterations in five main NSCLC histologic subtypes

Smoking status and targetable alterations in patients with lung adenocarcinoma and squamous cell carcinoma

To assess the effect of smoking on the frequency of each of the eight targetable alterations, we limited our analysis to patients with LUAD treated at DFCI with available quantitative smoking history data (Additional file 1: Fig. S4). To examine whether there is an incremental dose-effect of pack-years on the mutation rate, the cohort was split into six groups: never smokers, 1–15 pack-years, 16–30 pack-years, 31–45 pack-years, 46–60 pack-years, and 60+ pack-years, and the frequency of alterations was determined for each group (Additional file 2: Table S11). The prevalence of EGFR L858R, exon 19 deletions, and exon 20 insertions steadily declined with increasing smoking exposure (Fig. 2). ROS1 fusion mutations also declined dramatically with smoking exposure such that only one LUAD patient with 16 or more pack-years had a ROS1 fusion (1/1330, 0.07%, Fig. 2). In contrast, and as expected, KRASG12C mutation prevalence in our cohort showed an opposite pattern, with a fifteen-fold increase in the lightest smoking group, 1–15 pack-years (OR = 15 [7–35]; Bonferroni-corrected p-value < e−16, Fig. 2) compared to never smokers, in whom KRASG12C mutations were rare, seen in 8 of 600 (1.4%). In addition, the frequency of KRASG12C mutation was relatively stable over all smoking groups, from 1–15 pack-years to 60+ pack-years (Fig. 2). Note that we do not interpret these findings as indicating that smoking is protective against EGFR and other targetable mutations; rather smoking carcinogen-driven mechanisms of tumorigenesis steadily increase with increasing smoke exposure, causing an increase in KRAS mutations, and non-targetable mutations, and a relative decline in the frequency of other targetable driver mutations in the smoking-exposed population.

Fig. 2
figure 2

EGFR L858R/ex19del/ex20ins, ROS1 fusions, and KRASG12C in lung adenocarcinoma, grouped according to pack-years of smoking

ALK fusion frequency considerably decreased when comparing never smokers to 16+ pack-year smokers (Bonferroni-corrected p-values < 0.05, Additional file 1: Fig. S4). Consistent with previous studies, there was no significant association between MET and BRAF alteration prevalence and smoking exposure (q-values > 0.1, Additional file 1: Fig. S4, Additional file 2: Table S12). Associations with ALK fusions, KRASG12C mutations, and ROS1 fusions were maintained when analyzing smoking pack-years as a continuous variable (Additional file 2: Table S12). RET and NTRK fusions were too few to permit a pack-year association analysis.

For the subset of patients with LUSC, there was dramatic enrichment for “LUAD-specific” targetable alterations in the never-smoking group. Targetable alterations were seen in 6 of 32 (19%) never smokers with LUSC, in comparison to 12 of 305 (3.9%, p = 0.003) among smokers with LUSC. The 6 alterations seen in never smokers were 2 EGFR and 4 MET mutations. Seven of 12 (58%) alterations in smoker squamous cell carcinoma patients were KRASG12C (Additional file 2: Table S13).

Targetable alterations across continental genetic ancestries and self-reported race

To compare the somatic profiles of targetable alterations in individuals from different ancestral populations, we use a novel method to calculate ancestry indices for all DFCI subjects in the discovery cohort including African, Ashkenazi Jewish, and Asian markers [15] (see the “Methods” section). The ancestry score was significantly associated with self-reported ancestry (Additional file 1: Fig. S3) for individuals reporting as either “White/Caucasian,” “Black/African American,” or “Asian”. However, these ancestry indices provided quantitative information on admixture, which was present in many patients.

For each of the eight targetable alterations, we performed continental ancestry-specific multivariate logistic regression in the DFCI cohort, adjusting for age at diagnosis, tumor histology, number of smoking pack-years, and sex, using European ancestry individuals as the reference (see the “Methods” section). Consistent with previous reports [23], EGFR mutations were highly enriched in patients with higher Asian ancestry score (OR = 4.9, 95% CI = 3.1–7.9, p < 0.0001, q-value < 0.1; Fig. 3A, Additional file 2: Table S14). Conversely, KRASG12C mutations were much less frequent in patients with high Asian ancestry, again while controlling for age, histology, smoking, and sex (Fig. 3B, Additional file 2: Table S14). Limiting to EGFR-wild type NSCLC, patients of Asian ancestry still harbored significantly less KRASG12C mutations compared to other continental genetic ancestries (Additional file 2: Table S15). None of the remaining six targetable alterations was significantly enriched in any of the continental ancestries after accounting for sex differences and smoking history (Additional file 2: Table S14).

Fig. 3
figure 3

Logistic regression plots showing the association between continuous ancestry scores and the presence (1) or absence (0) of targetable alterations. A EGFR L858R/ex19del/ex20ins and Asian ancestry (AA) score. B KRASG12C and Asian Ancestry (AA) score. C METamp/ex14skipping and Ashkenazi Jewish ancestry (AJ) score. Each dot represents an individual patient

Among the validation cohort of 5560 samples, self-reported Asians were again enriched for EGFR mutations compared to Whites and Blacks (Asians: 267/538, 49.6% vs Blacks: 69/309, 22.3% vs Whites: 676/4591, 14.7%, p < 0.0001). In contrast, KRASG12C mutations were depleted in Asians (Asians: 18/538, 3.3% vs Blacks: 28/309, 9.1% vs Whites: 616/4591, 13.4%, p<0.0001, Additional file 2: Table S16).

Targetable alterations and tumor mutational burden in patients of Ashkenazi Jewish ancestry

Within the European cohort (n = 2837 patients, see the “Methods” section), we also examined the potential association between Ashkenazi Jewish (AJ) ancestry and mutation frequency. Patients with high AJ ancestry had a lower median TMB (6.1 mutations/MB, n = 215) compared to non-Ashkenazi Europeans (9.6 mutations/MB, n = 2622, p < 0.0001, Additional file 1: Fig. S5), an association which persisted after correcting for pack-years of smoking as a continuous variable (p < 0.0001). For validation, genetic ancestry inference was performed on tumor/normal paired samples from The Cancer Genome Atlas (TCGA) LUSC (n = 429) and LUAD (n = 441) cohorts. In the TCGA LUAD and LUSC cohorts, patients with high AJ ancestry had lower median TMB compared to non-AJ Europeans (Median TMB (LUAD and LUSC AJ) = 5.0 vs median TMB (LUAD and LUSC non-AJ) = 6.7, p > 0.05, Additional file 1: Fig. S6, Additional file 2: Table S17). This was not statistically significant, likely due to the small numbers of AJ samples (n = 24 total for LUAD and LUSC combined) but had the same trend.

Targetable alterations in three genes were found in at least 5% of patients with high AJ ancestry: EGFR, KRAS, and MET. Strikingly, patients with high AJ ancestry were significantly more likely to harbor MET exon 14 skipping mutations and amplifications (OR = 2.1; 95% CI = 1.0–4.3, p = 0.039; Fig. 3C) after accounting for age at diagnosis, tumor histology, number of pack-years, and sex. Of 215 patients with high AJ ancestry, 20 (9.3%) harbored MET genomic alterations compared to 97 of 2622 (3.7%) of Europeans with low AJ ancestry (p = 0.0004). Other targetable alterations were not significantly associated with AJ ancestry (Additional file 2: Table S18).

We pooled subjects with either never or light smoking history (1–15 pack-years) and adenocarcinoma histology and calculated the frequency of targetable alterations for each continental ancestral population. Asians harbored significantly more targetable alterations (82/102, 80%) compared to Africans (29/59, 49%) or Europeans (509/904, 56%, p < 0.001, Fig. 4), and these differences were mainly due to differences in EGFR mutation frequency (64%, 30%, 28%, respectively). There was no difference between Ashkenazi and non-Ashkenazi Europeans (51% vs. 49%, respectively).

Fig. 4
figure 4

Distribution of targetable alterations by inferred ancestral groups among light smokers (15 pack-year smoking history) with LUAD

Discussion

It is well known [10, 24, 25] that genetic ancestry influences the risk of cancer gene mutation, both through strong single gene (Mendelian) mechanisms and through collective weaker association effects. Genetic ancestry can be assessed quantitatively as done here and provide a more direct and accurate means of inferring population ancestry than questionnaire information [25, 26].

We computed ancestry indices for Asian, African, and Ashkenazi Jewish components for 3115 patients with NSCLC sequenced at DFCI, enabling us to assess the potential influence of these genetic backgrounds on mutation development. We confirmed enrichment of EGFR L858R mutations, exon 19 deletions, and exon 20 insertions, and depletion of KRASG12C mutations in patients with higher Asian ancestry. Associations between Asian ancestry and gene fusions (ALK, ROS1, RET, and NTRK) were found but were strongly confounded by smoking habits. Interestingly, among never and light smokers, 80% of those with high Asian ancestry harbored targetable alterations (Fig. 4), much higher than what was seen in our European ancestry and African ancestry patients. This suggests that, with the current armamentarium of targeted therapies in NSCLC, patients with higher Asian ancestry may benefit from a wider array of treatment options.

The AJ population is known to be enriched for several rare germline variants in genes that predispose to cancer development, including BRCA1 and BRCA2. Furthermore, recently, a rare variant in ATM (11-108326169-C-T (GRCh38), p.Leu2307Phe) present in AJ populations at an allele frequency (AF) of 0.029, and in all other populations at AF < 0.0003 (https://gnomad.broadinstitute.org/variant/11-108326169-C-T?dataset=gnomad_r3), was found to confer a marked relative risk (OR 3–8) of development of LUAD [27]. We observed that patients with a higher AJ ancestry score were more likely to harbor MET exon 14 skipping mutations and amplifications (Fig. 3C).

We also showed that the overall prevalence of targetable, traditionally LUAD, mutations in LUSC patients is about 5%, similar to the prior TCGA analysis in which such mutations were seen in 5 of 178 (3%) [8]. In addition, targetable mutations were greatly enriched in the never-smoking subset (OR = 5.2). Targetable LUAD mutations were also found in 17% of patients with large cell lung carcinoma (LCLC). These findings suggest that genetic testing for all patients with LUSC and LCLC is appropriate, similar to recent considerations [28]. None of the 143 carcinoid lung tumors (typical and atypical) that were sequenced had any LUAD targetable alterations, as expected. The statistical power of our observations reflects the value of large aggregate data resources such as GENIE.

Our analysis of the effect of smoking on targetable mutation prevalence confirmed some known associations. Moreover, it highlighted novel associations with clinical implications in several respects. Both MET and BRAF targetable alterations showed a similar mutation frequency independent of smoking exposure (Additional file 1: Fig. S3). This may also be true for RET and NTRK fusions, but the numbers were too few for statistical confidence. EGFR, ALK, and ROS1 alterations declined significantly with increasing smoking exposure. This was most dramatic for ROS1 fusions which were seen in 1/1330 (0.07%) patients with >16 pack-years exposure, in contrast to a 3.7% frequency in never smokers (Fig. 2). With the absence of ROS1 fusions detected in heavy smokers in our cohort, immunohistochemistry of ROS1 might be considered unnecessary in patients with extensive smoking history (>15 pack-years).

Our work has several limitations. First, the cohorts studied were from four (predominantly two) large tertiary academic cancer institutions whose patient populations are likely biased toward patients with unusual clinical features, including young age at onset, and rare targetable mutations. It is likely that this occurrence elevated the frequency of all of the rare cancer variants studied here. However, note that we considered only the first tumor sample sequenced to attempt to overcome this referral bias (see the “Methods” section). Second, targeted panel sequencing was used to identify mutations and copy number events in tumor samples only in the DFCI/DCC/VICC cohorts; this makes it possible that some of the genomic alterations captured in these cohorts are germline rather than somatic. However, germline alterations in the 8 targetable variants assessed are extremely rare [29]. Third, targeted panel DNA sequencing can miss structural variants; RNA-based methods are more sensitive for detection of fusion oncogene-activating events. As such, one might expect reduced detection of fusion variants in this series, meaning that the prevalence of such variants may be even higher than reported here.

Conclusions

In summary, we use a novel computational approach to infer ancestry quantitatively in patients with NSCLC. This method may be useful in other studies to elucidate the genetic contribution to cancer disparities. Using these ancestry indices, we demonstrate that the prevalence of targetable genomic alterations in NSCLC is variable across different populations after accounting for smoking and other factors. We provide further clarity on the impact of smoking pack-years on the frequency of targetable alterations and characterize the prevalence of these alterations in less common lung cancer histologies. Finally, we show that the Ashkenazi Jewish population appears to have an increased prevalence of MET mutations and amplifications in LUAD, which warrants further investigation to validate this observation, explore mechanism, and determine clinical relevance.

Availability of data and materials

All patient-level data is publicly available on cBioPortal.org (https://genie.cbioportal.org/study/summary?id=genie_public). All additional unidentifiable clinical data is available within the article and its supporting files. The individual-level sequencing and imputation data cannot be made publicly available because the research participant consent does not include authorization to share identifiable data. The full analysis workflow for ancestry imputation is available at https://github.com/gusevlab/panel-imp [15].

A containerized version of the imputation pipeline is available at https://hub.docker.com/r/stefangroha/stitch_gcs.

References

  1. Siegel RL, Miller KD, Jemal A. Cancer statistics, 2020. CA Cancer J Clin. 2020;70(1):7–30.

    Article  PubMed  Google Scholar 

  2. Arbour KC, Riely GJ. Systemic therapy for locally advanced and metastatic non-small cell lung cancer: a review. JAMA. 2019;322(8):764–74.

    CAS  Article  PubMed  Google Scholar 

  3. Bergethon K, Shaw AT, Ou SH, Katayama R, Lovly CM, McDonald NT, et al. ROS1 rearrangements define a unique molecular class of lung cancers. J Clin Oncol. 2012;30(8):863–70.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  4. Takeuchi K, Soda M, Togashi Y, Suzuki R, Sakata S, Hatano S, et al. RET, ROS1 and ALK fusions in lung cancer. Nat Med. 2012;18(3):378–81.

    CAS  Article  PubMed  Google Scholar 

  5. Chan AW, Chau SL, Tong JH, Chow C, Kwan JSH, Chung LY, et al. The landscape of actionable molecular alterations in immunomarker-defined large-cell carcinoma of the lung. J Thorac Oncol. 2019;14(7):1213–22.

    CAS  Article  PubMed  Google Scholar 

  6. Araujo LH, Lammers PE, Matthews-Smith V, Eisenberg R, Gonzalez A, Schwartz AG, et al. Somatic mutation spectrum of non-small-cell lung cancer in African Americans: a pooled analysis. J Thorac Oncol. 2015;10(10):1430–6.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  7. Chen J, Yang H, Teo ASM, Amer LB, Sherbaf FG, Tan CQ, et al. Genomic landscape of lung adenocarcinoma in East Asians. Nat Genet. 2020;52(2):177–86.

    CAS  Article  PubMed  Google Scholar 

  8. Cancer Genome Atlas Research N. Comprehensive genomic characterization of squamous cell lung cancers. Nature. 2012;489(7417):519–25.

    Article  Google Scholar 

  9. Campbell JD, Lathan C, Sholl L, Ducar M, Vega M, Sunkavalli A, et al. Comparison of prevalence and types of mutations in lung cancers among black and white populations. JAMA Oncol. 2017;3(6):801–9.

    Article  PubMed  PubMed Central  Google Scholar 

  10. Carrot-Zhang J, Soca-Chafre G, Patterson N, Thorner AR, Nag A, Watson J, et al. Genetic ancestry contributes to somatic mutations in lung cancers from admixed Latin American populations. Cancer Discov. 2021;11(3):591–8.

    CAS  Article  PubMed  Google Scholar 

  11. Consortium APG. AACR Project GENIE: Powering Precision Medicine through an International Consortium. Cancer Discov. 2017;7(8):818–31.

    Article  Google Scholar 

  12. Sholl LM, Do K, Shivdasani P, Cerami E, Dubuc AM, Kuo FC, et al. Institutional implementation of clinical tumor profiling on an unselected cancer population. JCI Insight. 2016;1(19):e87062.

    Article  PubMed  PubMed Central  Google Scholar 

  13. Zehir A, Benayed R, Shah RH, Syed A, Middha S, Kim HR, et al. Mutational landscape of metastatic cancer revealed from prospective clinical sequencing of 10,000 patients. Nat Med. 2017;23(6):703–13.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  14. Gusev A, Groha S, Taraszka K, Semenov YR, Zaitlen N. Constructing germline research cohorts from the discarded reads of clinical tumor sequences. Genome Med. 2021;13(1):179.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  15. Gusev A. Imputation workflow for targeted panels 2021 [Available from: https://github.com/gusevlab/panel-imp].  Accessed 26 May 2021.

  16. Davies RW, Flint J, Myers S, Mott R. Rapid genotype imputation from sequence without reference panels. Nat Genet. 2016;48(8):965–9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  17. Chen CY, Pollack S, Hunter DJ, Hirschhorn JN, Kraft P, Price AL. Improved ancestry inference using weights from external reference panels. Bioinformatics. 2013;29(11):1399–406.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  18. Genomes Project C, Auton A, Brooks LD, Durbin RM, Garrison EP, Kang HM, et al. A global reference for human genetic variation. Nature. 2015;526(7571):68–74.

    Article  Google Scholar 

  19. Price AL, Butler J, Patterson N, Capelli C, Pascali VL, Scarnicci F, et al. Discerning the ancestry of European Americans in genetic association studies. PLoS Genet. 2008;4(1):e236.

    Article  PubMed  PubMed Central  Google Scholar 

  20. Conomos MP, Laurie CA, Stilp AM, Gogarten SM, McHugh CP, Nelson SC, et al. Genetic diversity and association studies in US Hispanic/Latino populations: applications in the Hispanic community health study/study of Latinos. Am J Hum Genet. 2016;98(1):165–84.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  21. Banda Y, Kvale MN, Hoffmann TJ, Hesselson SE, Ranatunga D, Tang H, et al. Characterizing race/ethnicity and genetic ancestry for 100,000 subjects in the Genetic Epidemiology Research on Adult Health and Aging (GERA) cohort. Genetics. 2015;200(4):1285–95.

    Article  PubMed  PubMed Central  Google Scholar 

  22. Gusev A, Groha S, Taraszka K, Semenov YR, Zaitlen N. Constructing germline research cohorts from the discarded reads of clinical tumor sequences. medRxiv. 2021; 2021.04.09.21255197.

  23. Shi Y, Au JS, Thongprasert S, Srinivasan S, Tsai CM, Khoa MT, et al. A prospective, molecular epidemiology study of EGFR mutations in Asian patients with advanced non-small-cell lung cancer of adenocarcinoma histology (PIONEER). J Thorac Oncol. 2014;9(2):154–62.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  24. Oak N, Cherniack AD, Mashl RJ, Network TA, Hirsch FR, Ding L, et al. Ancestry-specific predisposing germline variants in cancer. Genome Med. 2020;12(1):51.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  25. Yuan J, Hu Z, Mahal BA, Zhao SD, Kensler KH, Pi J, et al. Integrated analysis of genetic ancestry and genomic alterations across cancers. Cancer Cell. 2018;34(4):549–60 e9.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  26. Royal CD, Novembre J, Fullerton SM, Goldstein DB, Long JC, Bamshad MJ, et al. Inferring genetic ancestry: opportunities, challenges, and implications. Am J Hum Genet. 2010;86(5):661–73.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  27. Ji X, Mukherjee S, Landi MT, Bosse Y, Joubert P, Zhu D, et al. Protein-altering germline mutations implicate novel genes related to lung cancer development. Nat Commun. 2020;11(1):2220.

    CAS  Article  PubMed  PubMed Central  Google Scholar 

  28. Sands JM, Nguyen T, Shivdasani P, Sacher AG, Cheng ML, Alden RS, et al. Next-generation sequencing informs diagnosis and identifies unexpected therapeutic targets in lung squamous cell carcinomas. Lung Cancer. 2020;140:35–41.

    Article  PubMed  Google Scholar 

  29. Lu S, Yu Y, Li Z, Yu R, Wu X, Bao H, et al. EGFR and ERBB2 germline mutations in Chinese lung cancer patients and their roles in genetic susceptibility to cancer. J Thorac Oncol. 2019;14(4):732–6.

    CAS  Article  PubMed  Google Scholar 

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Acknowledgements

We thank all individuals who participated in this study.

Funding

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Author information

Authors and Affiliations

Authors

Contributions

E.A., A.H.N., and D.J.K. conceived and designed this study. E.A and A.H.N. performed analyses. E.A., A.H.N., and D.J.K. interpreted the data. E.A., A.H.N., S.A.A., E.W.A., L.M.S., M.M.A., P.A.J., and D.J.K. provided guidance and scientific input. E.A., A.H.N., and D.J.K. drafted the manuscript. All authors read and approved the final manuscript.

Corresponding author

Correspondence to David J. Kwiatkowski.

Ethics declarations

Ethics approval and consent to participate

PROFILE samples were selected and sequenced from patients who were consented under institutional review board (IRB)-approved protocol 11-104 and 17-000 from the Dana-Farber/Partners Cancer Care Office for the Protection of Research Subjects. Written informed consent was obtained from participants or their guardian prior to inclusion in this study. Secondary analyses of previously collected data were performed with approval from the Dana-Farber IRB (DFCI IRB protocol 18-439 and 19-025; waiver of HIPAA authorization approved for both protocols). The research conformed to the principles of the Helsinki Declaration.

Consent for publication

Not applicable.

Competing interests

Pasi A. Jänne reports support from Hanmi Pharmaceutical Company, Ltd; grants from Boehringer-Ingelheim, Eli Lilly, Daiichi Sankyo, and Takeda Oncolog; personal fees from AstraZeneca, Boehringer-Ingelheim, Pfizer, Roche/Genentech, Chugai Pharmaceuticals, Ignyta, Loxo Oncology, Eli Lilly, SFJ Pharmaceuticals, Voronoi, Daiichi Sankyo, Biocartis, Novartis, Sanofi Oncology, Takeda Oncology, Mirati Therapeutics, Transcenta, Silicon Therapeutics, Syndax, Astellas, PUMA, and Revolution Medicines, outside the submitted work; and postmarketing royalties from a Dana-Farber Cancer Center–owned patent (EGFR Mutations) licensed to Lab Corp.

Lynette M. Sholl reports consulting fees from EMD Serono, Foghorn Therapeutics, and AstraZeneca and research grants from Genentech.

Mark M. Awad reports receiving grants and personal fees from Genentech, AstraZeneca, and Bristol-Myers Squibb; grants from Eli Lilly; and personal fees from Merck, Maverick, Blueprint Medicine, Syndax, Ariad, Nektar, and Gritstone, outside of the submitted work. David J. Kwiatkowski reports receiving grants from Genentech, Revolution Medicines, and AADI; and consulting fees from Guidepoint and BridgeBio Gene Therapy.

The remaining authors declare that they have no competing interests.

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Supplementary Information

Additional file 1: Fig. S1.

Consort diagram of the analysis workflow. Fig. S2. Correlation between genetically-inferred ancestry and self-reported race among NSCLC patients in the DFCI cohort. Fig. S3. Ashkenazi ancestry score benchmarking. Fig. S4. MET amplifications/exon 14 skipping mutations, BRAF V600E mutations, ALK/RET/NTRK fusions in lung adenocarcinoma, grouped according to pack-years of smoking in the DFCI cohort. Fig. S5. Tumor mutational burden in Ashkenazi vs. non-Ashkenazi Jewish Europeans with NSCLC in the DFCI cohort. Fig. S6. Tumor mutational burden in Ashkenazi vs. non-Ashkenazi Jewish Europeans with NSCLC in the TCGA.

Additional file 2: Table S1.

Master Table of clinicopathological characteristics of 3,115 patients with NSCLC sequenced at DFCI. Table S2. EGFRL858R/ex19del/ex20ins variants reported in patients with NSCLC sequenced at DFCI. Table S3. KRASG12C variants reported in patients with NSCLC sequenced at DFCI. Table S4. Exon 14 skipping mutations and/or amplifications in MET reported in patients with NSCLC sequenced at DFCI. Table S5. ALK fusions reported in patients with NSCLC sequenced at DFCI. Table S6. ROS1 fusions reported in patients with NSCLC sequenced at DFCI. Table S7. BRAFV600E variants reported in patients with NSCLC sequenced at DFCI. Table S8. RET fusions reported in patients with NSCLC sequenced at DFCI. Table S9. NTRK fusions reported in patients with NSCLC sequenced at DFCI. Table S10. Master Table of clinicopathological characteristics of 5,560 patients with NSCLC sequenced at one of three institutions (DCC, VICC, MSKCC). Table S11. Frequency of eight targetable alterations in DFCI LUAD patients by pack-year groups. Table S12. Comparison of targetable alteration frequencies in pack-year groups among patients with NSCLC at DFCI. Table S13. Distribution of targetable alterations by pack-year smoking history among patients with LUSC at DFCI. Table S14. Multivariable logistic regression model of eight targetable alterations, incorporating continental ancestry scores, age at diagnosis, smoking pack-year history, sex and histology DFCI cohort). Table S15. Comparison of KRASG12C mutation frequencies across continental ancestries (discovery cohort from DFCI) and self-reported racial groups (validation cohort from DCC, MSKCC, and VICC). Table S16. Multivariable logistic regression model of eight targetable alterations, incorporating sex and histology (validation cohort). Table S17. Tumor mutational burden and Ashkenazi Jewish ancestry assessment in TCGA LUAD and LUSC cohorts. Table S18. Multivariable logistic regression model of eight targetable alterations, incorporating Ashkenazi Jewish ancestry scores, age at diagnosis, smoking pack-year history, sex and histology.

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Adib, E., Nassar, A.H., Abou Alaiwi, S. et al. Variation in targetable genomic alterations in non-small cell lung cancer by genetic ancestry, sex, smoking history, and histology. Genome Med 14, 39 (2022). https://doi.org/10.1186/s13073-022-01041-x

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Keywords

  • Non-small cell lung cancer
  • Targeted therapies
  • Targetable genomic alterations
  • Genetic ancestry
  • Smoking