Impact of KRAS mutation subtypes on morphological heterogeneity and immune landscape in surgically treated lung adenocarcinoma

Introduction

Lung cancers are associated with exceptionally high mortality rates. Besides rare lung cancer subtypes, they can be divided into two main groups: small-cell lung cancer (SCLC) and non-small cell cancer (NSCLC) (1,2). NSCLC accounts for nearly 85% of all lung cancer cases and roughly 50% of NSCLCs are lung adenocarcinomas (LADCs). Histologically, LADCs can either present as invasive non-mucinous lesions or invasive mucinous tumors (3,4). Nevertheless, among these, additional morphological variability can also be seen since acinar, papillary, micropapillary, solid, and lepidic structural elements may be present within the tumor (2). Notably, high-grade morphological phenotypes such as solid, micropapillary, or mixed glandular forms are associated with poor prognosis (5,6).

The most common gain-of-function alteration in Caucasian LADC patients is the mutation of the Kirsten rat sarcoma virus (KRAS) oncogenic homologous gene belonging to the RAS gene family, detected in nearly 30% of LADCs in Western countries (7,8). KRAS mutation causes the RAS protein (a small GTPase molecule) to engage in constant activity, leading to continuous signaling activation in growth and anti-apoptotic processes. These mutations possess several subtypes whereas distinct amino acid changes can result in specific binding tendencies and kinase activity (9). Notably, there are a few differences within positive KRAS mutational status in various malignancies. While these mutations are typically already present in the initial steps of tumorigenesis in LADCs, colorectal adenocarcinomas acquire KRAS mutations throughout tumor development and progression (10). In both cases, existing KRAS mutations are linked to impaired survival outcomes (10-13). The majority of KRAS mutations in LADC constitute transition mutations (G12D) whereas patients with a positive smoking history predominantly demonstrate transversion mutations (G12C and G12V) (8). Varying KRAS mutational status has been associated with distinctive cellular signaling activity, reflecting differential response to therapeutic strategies and patient outcomes. While KRAS mutations were considered undruggable targets for decades, mutation-specific inhibitors have since then emerged. For example, sotorasib and adagrasib, targeting KRAS C12C, received accelerated Food and Drug Administration (FDA) approval in 2021 and 2022 for the treatment of KRAS-mutant LADC and colorectal carcinoma patients, respectively (14).

The intensively studied NOD-, LRR- and pyrin domain-containing protein 3 (NLRP3) inflammasome is a sensor protein complex that recognizes cytosolic cellular danger, especially in dendritic cells and NK cells (15-17). Two signals are required for NLRP3 activation. Initially, NF-κB is induced through Toll-like receptors, promoting the production of inactive NLRP3 and pro-IL-β1. Then, a wide range of pathogen-associated (PAMPs) and damage-associated molecular patterns (DAMPs) contribute to NLRP3 activation (17). Once activated, NLRP3 initiates inflammasome assembly containing the caspase-binding domain and pro-caspase-1 by binding to a speck-like protein associated with apoptosis (18). Notably, uncontrolled activation of the NLRP3 inflammasome is one of the main causes of several autoimmune diseases and metabolic disorders (19,20). In addition, IL-β1 cytokine-mediated inflammation may inhibit antitumor immunity, leading to tumor development, growth, and progression. Indeed, a previous study on melanoma cell lines demonstrated an association between NLRP3/IL-β1 production and immunosuppression (21). While a functioning inflammasome complex is essential for immune homeostasis, dysregulation and amplification of the inflammasome complex can eventually lead to tissue damage and tumor progression (22). Elevated NLRP3 expression is conducive to a permissive tumor microenvironment, thereby facilitating tumor progression. In this context, increased NLRP3 expression has been observed in many cancer entities (e.g., acute myeloid leukemia or glioblastoma multiforme) and oral NLRP3 inhibitors are currently being tested in clinical trials (NCT04971499 and NCT05552469). Therefore, it is of clinical relevance to investigate NLRP3 expression status. It is known that tumors have varying degrees of inflammatory infiltration. However, defining the cell composition in the peritumoral region, the dynamics of cell distribution, and the exact relationship to tumoral tissue remains difficult. Several studies have revealed that tumor adaptation influences the surrounding immune microenvironment. Specifically, cytokines produced by tumors deplete the immune cell pool and polarize myeloid dendritic cells (MDCs), thereby changing populations of peritumoral lymphoid cells (23).

Determining the mutational status and associated histological features with a particular focus on immune phenotype is of paramount importance in the era of personalized therapy. Therefore, in this study, we examined the histopathological associations between KRAS mutation subtypes, tumor heterogeneity and immune landscape in surgically treated LADC. We present this article in accordance with the STARD reporting checklist (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2024-1092/rc).

Methods

Patient selection

A total of 87 early-stage (IA to IIB) patients with histologically confirmed LADC who underwent surgical resection at the National Koranyi Institute of Pulmonology (Budapest, Hungary) between 2012 and 2017 were included in the current study. Selection criteria consisted of the presence of a KRAS mutation as determined by direct sequencing performed during previous routine genomic profiling. The study was conducted in accordance with the Declaration of Helsinki and its subsequent amendments. The study was approved by the national Ethics Committee of Hungary (Hungarian Scientific and Research Ethics Committee of the Medical Research Council, ETT-TUKEB 23636-2/2018, 23636/10/2018/EÜIG). Clinicopathological data concerning the age at the time of diagnosis, gender, comorbidities, and smoking history were retrospectively collected from medical records. Patient identifiers were removed and all data were made pseudonymous after clinical data collection. Due to the retrospective nature of the study, the requirement for written informed consent was waived. Survival data were provided by the Central Statistical Office of Hungary, if available.

Immunohistochemistry (IHC)

In order to analyze morphological heterogeneity, tumor tissue samples were obtained by surgical resection and three tumor tissue microarray (TMA) punctures per FFPE (formalin-fixed paraffin-embedded) block were performed. TMA tissue cores were retrieved from distinct representative regions within viable tumor area. TMA blocks were cut into 4 µm thick sections and stained with hematoxylin and eosin (H&E) as well as alcian blue to define histological patterns. The extent of tumor-infiltrating lymphocyte (TIL) and macrophage infiltration in the peritumoral region was determined by staining for CD3 and CD163 according to the scoring system of the International TILs Working Group 2014 (24). In addition, to obtain a comprehensive overview of inflammasome expression and overall immune landscape of the specimens, each slide was stained with NLRP3 and other immune markers. The following antibodies were used for IHC staining: CD3 (Leica, rabbit monoclonal antibody, clone number LN10, 1:200 dilution), CD163 (Invitrogen, rabbit monoclonal antibody, clone number MRQ-26 1:200 dilution), NLRP3 (Invitrogen, rabbit monoclonal antibody, clone number SC06-23, 1:200 dilution), and PD-L1 (Programmed Death-Ligand 1) (Quartett, rabbit monoclonal antibody, clone number QR001, 1:100 dilution). All stainings were performed according to the manufacturer’s protocols by the fully automated BenchMark ULTRA IHC/ISH system (Roche Diagnostics, Rotkreuz, Switzerland). Antibody binding was detected by using the ImmPACT DAB Substrate Kit from Vector Laboratories. Nuclei were counterstained with hematoxylin. All antibodies were validated by utilizing appropriate tissue controls. Expression of the given marker on a categorical, semi-quantitative scale was determined by two experienced pulmonary pathologists blinded to clinical data. In addition to analyzing the tumorous lesions, one—pathologically verified—non-tumorous lung tissue core was retrieved from each patient for control use. IHC and molecular analyses were performed on these non-tumorous cores as well.

Molecular analyses

All tumorous and non-tumorous TMA cores were re-analyzed for KRAS mutational status using the real-time PCR (RT-PCR)-based CASP (Competitive Allele-Specific PCR) TaqMan Mutation Detection Assays. A tumor cell-rich, 2 mm cylinder labelled by the pathologist was extracted from the FFPE tissue specimen with a TMA Master (3DHistec) tissue micro block maker. The samples were deparaffinated with Deparaffinization Solution (Zymo Research, Irvine, CA, USA). Additionally, DNA was isolated with the KingFisher Duo Prime (Thermo Scientific, Waltham, MA, USA) automatic nucleic acid purifier using the corresponding MagMAX FFPE DNA/RNA Ultra Kit (Applied Biosystems, Waltham, MA, USA). The following seven KRAS mutation subtypes were consecutively investigated by CASP TaqMan Mutant Allele Assay: G12D (Hs00000121_mu), G13D (Hs00000131_mu), G12V (Hs00000119_mu), G12R (Hs00000117_mu), G12A (Hs00000123_mu), G12S (Hs00000115_mu), and G12C (Hs00000113_mu). Notably, a KRAS gene reference assay (Hs00000174_rf) designed for the mutation-free region of the KRAS gene was added for each sample. RT-PCR reactions were performed in 96-well plates according to the manufacturer’s protocol. The difference in cycle threshold values (ΔCt) between the mutant allele assays and the gene reference assay was used for the quantification of the mutant allele frequency in the sample.

Statistical analysis

Associations between categorical variables were assessed with pairwise chi-square tests. As none of the results proved to be statistically significant even prior to adjusting for multiple testing, no correction was applied in this case. Expression levels, although originally defined on a categorical scale (e.g., none/slight, diffuse plasma staining/medium plasma staining with dominant spots/strong, diffuse plasma staining), could be converted to semi-quantitative numerical values (e.g., 0/1/2/3) due to their ordinal nature. This approach was used to re-assess relationships between different expression levels with Pearson correlation and between expression levels and categorical variables with pairwise t-tests adjusted for multiple testing with the Bonferroni correction. Additionally, when comparing different expression levels, a cruder categorization of none vs. any was also applied, and a Fisher’s exact test was used to determine whether different types of expressions appear independent. For specific analyses, tumor tissue cores were treated independently, even if they were obtained from the same patient. To compare the mutational status and expression levels between tumorous and normal tissues, each tumor sample was paired with the normal adjacent tissue sample from the given patient. This resulted in three sample pairs from a given patient, each containing the same normal sample. Survival curves for different patient groups were estimated using Kaplan-Meier plots. The differences between the groups were analyzed with the log-rank test. Overall survival (OS) was defined as the time in months from surgery to the last available follow-up or the date of death from any cause. All statistical analyses were performed in R version 4.4.1. A P value of less than 0.05 was considered statistically significant.

Results

Association of tumor morphology and KRAS mutational status

Pathological evaluation of the tumor cores showed that 71% of cases had a single dominant morphological component. As shown in Figure 1A, the most common histological growth pattern within morphologically homogenous tumors was the acinar pattern (47%), followed by solid (26%), lepidic (22%), and papillary architectures (5%). Interestingly, 23% of all tumors displayed two dominant morphological components. Meanwhile, three-component-LADCs occurred in 6% of cases. Next, KRAS mutational status was determined for all TMA specimens (Figure 1B). With regards to mutation subtypes, KRASG12C, KRASG12D and KRASG12V were the most frequently detected genetic alterations in our cohort, with 33%, 25% and 24% of all patients and 38%, 23% and 24% of all samples carrying the mutations, respectively. Of note, although the mutational landscape concerning the type of KRAS mutation was mostly homogenous across different TMA cores originating from the same tumor, in 19 cases, the dominant mutational subtype differed between the tumor punctures. In addition, out of all included samples, 14 LADCs completely lacked any KRAS mutation despite the initial diagnosis of KRAS-mutant LADC (established by direct sequencing), further highlighting the possible heterogeneous nature of the KRAS mutational landscape across tumor areas.

Figure 1 Morphological and mutational features of surgically resected LADC samples. (A) Histological growth pattern of included samples. Patients are represented as columns, while their three separate tumor tissue cores (i.e., TMAs #1, #2, and #3) are arranged as rows. Three distinct tumorous TMA cores were analyzed from each surgically resected LADC. (B) Heatmap of KRAS mutation status of different samples with regards to morphological features such as tumor content, growth pattern, mucus secretion, inflammation, and NLRP3, CD3, CD163 and PD-L1 expression. Patients are represented as columns, with the tumor samples belonging to each patient indicated as thin stripes within these. The color bar scale indicates the expression levels of the selected markers. Fourteen out of 87 patients had KRAS wild-type tumors across all three tumor tissue cores. Additionally, 12 more patients had at least one tumor tissue core identified as KRAS wild-type. This adds up to a total of 57 KRAS wild-type tumor tissue cores out of the total of 261 examined tumor tissue cores (three per patient). These KRAS wild-type samples are marked in grey in the “KRAS status row” of the corresponding heatmap (C) Association of dominant growth pattern and KRAS mutational status of surgically resected LADC tumor tissue cores. Tumor tissue cores were treated independently, even if they were obtained from the same patient. HPF, high power fields; KRAS, Kirsten rat sarcoma virus; LADC, lung adenocarcinoma; Mut, mutated; NLRP3, NOD-, LRR- and pyrin domain-containing protein 3; PD-L1, programmed death-ligand 1; TMA, tissue microarray; wt, wild-type.

Next, we examined whether the distribution of KRAS mutation subtypes differs across the various morphological patterns (Figure 1C). Interestingly, the KRASG12A mutation was not detected in LADC samples with a lepidic growth pattern, whereas the micropapillary LADC blocks lacked wild-type KRAS gene or multi-hit (simultaneously multiple types of) KRAS alterations. Nevertheless, despite these evident differences, KRAS mutational status was not statistically significantly associated with morphological growth patterns. Evaluation of mucin secretion revealed that 33.3% of tumors did not express mucin at all. Intracellular mucin secretion was detected in 11.5% of cases, whereas extracellular and mixed mucin secretion were seen in 43.7% and 11.5% of examined LADCs, respectively. Mucin secretion was not impacted by KRAS mutational status.

As shown in Figure S1, patients with KRAS wild-type tumors tended to have better survival outcomes than those with specific KRAS mutations, yet differences in median OS did not reach statistical significance (P=0.14). Tumors that exhibited heterogeneous KRAS mutational status across different tissue cores were linked to improved survival (vs. homogenous tumors; median OSs were 52 vs. 39 months, respectively; P=0.08), with borderline significant statistical outcomes. Given the relatively small cohort size, results related to OS should be interpreted with caution.

NLRP3 expression is associated with increased immune infiltration

The morphological growth patterns of examined LADCs did not influence the NLRP3 distribution when the surgically resected specimens were grouped according to the predefined NLRP3 expression subgroups (Figure 2A). Nevertheless, if NLRP3 expression was assessed on a semi-quantitative scale, significantly higher NLRP3 levels were revealed in solid LADCs than in acinar samples (P=0.001) (Figure 2B). Likewise, although PD-L1 groups did not correlate with tumor morphology (Figure 2C), PD-L1 expression was significantly higher in tumor tissue cores with solid morphology compared to those with acinar (means: 14.6% vs. 4.4%, t-test adjusted P value: 0.007) or lepidic growth pattern (means: 14.6% vs. 1.6%, t-test adjusted P value: 0.002) when assessing PD-L1 expression on a continuous scale (Figure 2D).

Figure 2 Association of NLRP3 and PD-L1 expression with tumor morphology in LADC tumor tissue cores. Distribution of growth patterns according to (A) NLRP3 and (C) PD-L1 subgroups. (B) NLRP3 and (D) PD-L1 expression on a semi-quantitative scale across different morphology groups. LADC, lung adenocarcinoma; NLRP3, NOD-, LRR- and pyrin domain-containing protein 3; PD-L1, programmed death-ligand 1.

Representative IHC images of NLRP3, CD3, CD163 and PD-L1 expression are shown in Figure 3. The abundance of CD3⁺ T lymphocytes (defined as CD3⁺ cells per high power field [HPF]) according to NLRP3 expression is shown in Figure 4A. Importantly, a statistically significant, weak positive linear correlation was found between CD3 expression and NLRP3 expression on a semi-quantitative scale (R=0.31, P<0.001, Figure 4B). Next, we categorized the samples into CD3/NLRP3 expressing or non-expressing groups, and found that NLRP3-expressing LADC samples most frequently also had detectable CD3 expression (P<0.001, Figure 4B). In contrast, tumors that lacked NLRP3 expression had a CD3-depleted phenotype. Concerning macrophage abundance, the number of CD163+ cells per HPF was considerably higher in samples with NLRP3 plasma staining (vs. those with no NLRP3 expression) (Figure 4C). Accordingly, we revealed a statistically significant positive linear correlation between CD163 and NLRP3 expression (Figure 4D). Moreover, we also found that LADC samples showing NLRP3 expression, in general, tended to have CD163 expression as well. PD-L1 expression was generally higher in tumor tissue cores with medium or strong NLRP3 staining; however, no statistically significant associations were found between PD-L1 expression and NLRP3, CD3, and CD163 levels (Figure S2).

Figure 3 IHC staining of formalin-fixed, paraffin-embedded LADC samples with markers of the immune microenvironment (NLRP3, CD3, CD163 and PD-L1). The representative images were captured with a 40× objective lens. The positive cells were visualized with 3-3'-diaminobenzidine (DAB), and the nuclei were labelled with hematoxylin. HPF, high power field; IHC, immunohistochemistry; LADC, lung adenocarcinoma; NLRP3, NOD-, LRR- and pyrin domain-containing protein 3; PD-L1, programmed death-ligand 1.

Figure 4 NLRP3 is associated with immune infiltration as defined by CD3 and CD163 expressions. (A) CD3 expression across different NLRP3 expressing groups in tumor tissue cores. (B) Correlation between NLRP3 and CD3 expression on semi-quantitative scales combined with a Fisher’s exact test. (C) CD163 expression across different NLRP3 expressing groups in tumor tissue cores. (D) Correlation between NLRP3 and CD163 expression on semi-quantitative scales combined with a Fisher’s exact test. HPF, high power field; NLRP3, NOD-, LRR- and pyrin domain-containing protein 3.

Relation between KRAS mutational subtype and NLRP3/PD-L1 expression

As shown in Figure 5A, the most frequently detected KRAS mutation subtype both in tissue cores with and without NLRP3 expression was KRASG12C. Although the second most common KRAS mutation subtype differed between NLRP3-expressing and NLRP3-non-expressing tumors (KRASG12V vs. KRASG12D, respectively), no statistically significant associations were detected between KRAS mutation subtype and NLRP3 expression. Likewise, examining expression levels on a semi-quantitative scale did not reveal a significant difference between different KRAS subtypes regarding NLRP3 expression either (Figure 5B). PD-L1 expression was not associated with KRAS mutation status either (Figure 5C,5D).

Figure 5 Comparative analysis of KRAS mutational landscape and NLRP3 and PD-L1 expression in surgically resected LADC tumor tissue cores. KRAS mutational subtypes with regards to (A) different NLRP3 subgroups and (B) NLRP3 expression on a semi-quantitative scale. NLRP3 expression data was not available in 2 KRAS wild-type tissue cores. Distribution of KRAS mutation subtypes according to (C) PD-L1 subgroups and (D) PD-L1 expression on a semi-quantitative scale. Colors indicate different mutational status. KRAS, Kirsten rat sarcoma virus; LADC, lung adenocarcinoma; NLRP3, NOD-, LRR- and pyrin domain-containing protein 3; PD-L1, programmed death-ligand 1.

KRAS mutation- and NLRP3 expression-based comparison of tumorous and adjacent non-tumorous tissue cores

Next, we compared the tumor tissue samples with non-tumorous control tissue from the same patient. It is important to note that each patient had only one non-tumorous tissue core analyzed compared to three paired tumorous sections. Although non-tumorous tissue specimens do not usually bear oncogenic driver mutations, 20 out of 83 non-tumorous lung tissues (24.1%) included in our study presented with KRAS mutations. In this context, almost half of the tumor tissue samples with KRASG12D mutation also had a corresponding KRASG12D mutation in their non-tumorous counterpart (Figure 6A). Interestingly, nine (3.61%) tumor samples had wild-type KRAS status, while their paired non-tumorous tissue cores displayed mutations in the KRAS gene. Regarding NLRP3 expression, only a few of the non-tumorous tissue samples showed absent or slight staining, underlining the lack of a relationship between the expression levels of non-tumorous and tumorous samples. Quantified expression of NLRP3 in control lung tissue did not affect expression levels measured in paired tumor samples (Figure 6B). Most of the control samples showed medium-level CD3 expression. Low CD3-expressing control tissues generally had low-to-medium-level CD3⁺ T cell abundance in their tumorous counterparts (Figure 6C). Likewise, in the case of high CD3-expressing tumor samples, the paired non-tumorous specimens had at least a medium level (3-10 cells per HPF) of CD3 expression. Concerning CD163+ macrophages, a statistically significant, although very weak positive linear correlation was found between the expression levels detected in the tumorous vs. non-tumorous lesions (R=0.16, P=0.01; Figure 6D).

Figure 6 KRAS mutation status and inflammatory characteristics of tumorous and adjacent non-tumorous tissue cores. (A) Association between the tumorous and non-tumorous tissue cores’ KRAS mutational status. Interestingly, nine tumor samples (bottom row) had wild-type KRAS status, while their paired non-tumorous tissue core displayed KRAS mutation. (B-D) Comparative analysis of tumorous and non-tumor tissue cores with regards to (B) NLRP3, (C) CD3 and (D) CD163 expression. All panels treat “sample pairs” as units, whether represented as points on scatter plots or numbers in cells. A “sample pair” is defined as the normal sample and the tumor sample from a specific patient. Most patients had one normal sample (Nor₁) and three tumor tissue cores (Tu₁, Tu₂, Tu₃), resulting in three “sample pairs” (i.e., Nor₁-Tu₁, Nor₁-Tu₂, Nor₁-Tu₃). Therefore, the numbers on the heatmaps do not sum up to either the total number of patients or the total number of individual tissue cores. HPF, high power field; KRAS, Kirsten rat sarcoma virus; NLRP3, NOD-, LRR- and pyrin domain-containing protein 3.

Discussion

Invasive LADCs have heterogeneous morphological profiles that usually display five distinct growth patterns, namely lepidic, acinar, papillary, micropapillary, and solid (2). While these growth patterns can manifest as a combination, a dominant monomorphic presentation is most common. In line with classification guidelines, LADCs are routinely categorized by the primary presenting morphology (5). Concerning grading, lepidic tumors are considered grade I lesions, whereas those with acinar/papillary or solid/micropapillary growth patterns are termed grade II or grade III tumors, respectively. In this context, both solid and micropapillary features are more common in higher-grade components of the tumor. Of note, micropapillary LADCs often demonstrate a high frequency of KRAS, EGFR, and BRAF mutations (25,26). Importantly, solid and micropapillary growth patterns as high-grade components are associated with poor prognosis for patients, partially because of the tendency to rapidly metastasize (e.g., to neighboring lymph nodes) (12,27). Several studies have reported poor patient outcomes in the presence of high-grade components, even if these patterns are not the primary morphological features of the tumor (12,27-29). Retrieving an accurate and timely pathological diagnosis is, therefore, essential for moving forward with appropriate treatment plans. In our patient cohort, one third of examined specimens showed heterogeneous growth patterns, with two samples constituting more than two components. Furthermore, almost half of our samples demonstrated acinar growth patterns, followed by solid and lepidic histologic morphologies. Solitary papillary tumor structures were rare (5%) in our analyzed sample cohort.

Whether the grade of differentiation is related to KRAS mutation subtypes is not yet defined. Although only approximately 30% of LADCs exhibit a detectable KRAS mutation, positive KRAS mutational status is associated with worse prognosis (13,30,31). Several different KRAS mutations have been identified in codons 12, 13, and 61. Amino acid substitution appears to result in varying binding affinities in signaling effector molecules. The transversion mutation of glycine to cysteine at codon 12 (KRASG12C) is a common mutation in smoking-associated LADCs (32,33). In line with this, KRASG12C was detected as the primary mutation in our study samples, followed by KRASG12D and KRASG12V. Mutations in both codons were observed in two cases and other mutations were the following: G12D, G12S, G12R, or G12A. Rare combinations such as G12R/G12D or G12S/G12A could not be analyzed in this study. Notably, while previous direct sequencing identified certain samples as KRAS-mutant LADCs, re-analyzing them using the CASP TaqMan Mutation Detection Assay revealed wild-type KRAS genes in some cases. This latter approach, however, focused on a predefined set of seven KRAS mutations. Therefore, patients with initially positive mutational status could eventually have a wild-type genotype in their investigated samples if their previously determined KRAS mutation did not coincide with any of the predefined sets of the assay. An additional explanation for the discrepancy between the results of direct sequencing and assay-based detection might be tumor heterogeneity, given that KRAS mutations can appear subclonally and, therefore, may not be present in all sections of the tumor tissue, as our current study outcomes also suggest. Lastly, tumor content might also play a pivotal role in the discrepancy between the two detection methods since low tumor content can hinder the reliability of mutation detection, even though the few tumor cells present in the analyzed TMA block may actually carry the mutation.

When we examined correlations between positive KRAS mutational status and morphological presentation, we could not find a statistically significant relationship. Nevertheless, samples of lepidic histology, constituting approximately fifth of all samples in this analysis, did not reveal KRASG12A mutations. Next, the micropapillary growth pattern did not present with wild-type KRAS status or multiple KRAS mutations. Results from previous LADC studies regarding associations between morphological growth patterns and genetic alterations (e.g., KRAS mutational status) have discovered relevant associations, while other investigations have not found significant correlations and are reflective of our results (34-36). This dichotomy underlines the need for further investigation of potential molecular links between mutations, patient prognosis, and dominant growth patterns.

To gain a more representative picture of the tumor landscape and possible within-patient heterogeneity, we analyzed three tumorous tissue cores complemented with one non-tumorous tissue sample. Surprisingly, 19 cases exhibited detectable differences in dominant mutation between tissue cores from the same patient. Furthermore, nine tumor tissue cores demonstrated a lack of positive KRAS mutation in tumorous samples, but showed positivity in healthy, non-tumorous tissue. These heterogeneous results regarding KRAS mutation are both diagnostically and clinically relevant. Specifically, divergences in mutational status of multiple samplings from tissue material of the same patient raise important questions concerning the reliability of diagnostic results. In the current study, KRAS mutation status was derived from analyses performed on surgically resected tissue which generally provides more material and ensuing increased diagnostic capability. In routine clinical settings, KRAS mutational status is more commonly assessed on biopsies derived through bronchoscopy or CT-guided sampling. However, based on our findings concerning KRAS-mutation subtype heterogeneity, these small biopsy specimens might not fully represent the mutational landscape of the whole tumor area, thus raising diagnostic concerns. Moreover, mutational heterogeneity might also have a direct impact on selecting the appropriate therapeutic regimens. LADC patients with specific mutations (e.g., KRASG12C) can benefit from targeted therapy. So, patients with a positive KRASG12C mutation are eligible for targeted treatment, whereas individuals with other KRAS mutations for which targeted therapy is not currently available are treated with chemotherapy (14). In this context, an inconclusive diagnosis of the mutational landscape leads to poor therapeutic efficacy and divergent response rates. Indeed, the non-representative nature of biopsy specimens from heterogeneous KRAS-mutant tumors might explain the divergent objective response rates seen in LADC patients treated with sotorasib, a covalent KRASG12C-inhibitor (37). Possible decisions concerning the correct procedure in cases with varying KRAS mutational status will need to be kept in mind during diagnostic interventions. Additionally, it’s worth highlighting that in some patients with inconsistent KRAS mutation status, the KRAS wild type samples had lower tumor content than the KRAS-mutant samples. This might be suggestive of potential technical artefacts induced by low tumor contents. However, it is important to emphasize that this trend does not apply to all patients with mixed mutational profiles, indicating that tumor heterogeneity also plays a critical role. Peritumoral inflammation is seen in varying degrees in all stages of tumor development. In fact, persistent inflammation plays a prominent role in tumor formation and invasion, with responses specifically instigated by the innate immune system (38,39). The numerous external and internal cytosolic stimuli affecting tumor cells lead to acute and chronic inflammation by activating the caspase-1 inflammasome through initiation of multiple signaling pathways. Notably, interleukin-β1 (IL-β1)-mediated inflammation may inhibit the antitumor immune response, thereby enabling tumor growth and progression (40). NLRP3 activation has been linked to IL-β1 in many tumor types. In vitro studies on lung tumor cell lines found elevated NLRP3 levels, suggesting a role in tumor behavior (19). Meanwhile Tengesdal et al. investigated NLRP3 expression in melanoma through in vitro and in vivo assays and discovered that NLRP3 expression led to inflammation and immunosuppression through IL-β1 activation and inflammasome production. Notably, elevated IL-β1 myeloid-derivative suppressor cells increased in the vicinity of the tumor and inhibited the growth of both NK cells and CD8⁺ T cell activity (21). In line with this, another study examining lung cancer tissue specimens and cell lines revealed greater NRLP3 gene expression in LADC and small cell lung cancer. High-grade growth patterns of surgically resected LADC tissue tended to bear increased NLRP3 levels (19). We also found elevated NRLP3 expression reflective of inflammasome activity in samples with a solid histological pattern compared to acinar samples when evaluated on a semi-quantitative scale. As was mentioned above, high-grade components such as solid morphology are known to be associated with worse prognosis. Furthermore, our analyses yielded a correlation between NLRP3 expression and increased immune infiltration. Particularly, the immune markers CD3 and CD163 were elevated in patients with increased NLRP3 activity, underlining greater immune system involvement. In the case of elevated immune marker expression, however, both tumorous and non-tumorous tissue showed a tendential increase in marker expression, thereby hampering direct targeting of immune modulating treatments directed towards the tumor. Lastly, increased immune involvement reflected in greater NLRP3 expression was not significantly associated with KRAS mutational status. With regards to PD-L1 expression, tumor samples with solid morphology expressed significantly higher levels of PD-L1 than either acinar- or lepidic-pattern LADCs. This is in line with the findings of others also concluding that PD-L1-positive tumors tend to show solid morphology, while LADCs with lepidic pattern rarely express PD-L1 (41).

As study results concerning inflammasome activity in different cancer entities have shown both inhibitory and tumor promoting qualities in tumor development and progression, the definitive role of NLRP3 inflammasomes has yet to be fully elucidated (42,43). Nevertheless, the clinical relevance of the association between greater NLRP3 expression and immune infiltration in our study needs to be considered. Importantly, NLRP3 inhibitors have found use in inflammatory diseases and might offer alleviation of inflammatory pathways propagating tumor progression in cancer patients (44). Additionally, several NLRP3 inhibitors are currently under clinical investigation as inflammasome targeting may help overcome resistance to immunotherapy (45). Our results concerning the growth pattern-specificity of NLRP3 could provide additional support for the refinement of patient selection criteria for future NLRP3-directed clinical trials.

A recent study conducted on tumor tissue cores originating from surgically resected LADCs used highly multiplexed imaging mass cytometry to assess the spatially resolved features of the tumor immune microenvironment (46). Importantly, in line with our current study outcomes, they also found that solid tumors exhibited the highest levels of immune cell infiltration compared to LADCs with other morphological architectures (46). Nevertheless, they discovered that CD8⁺ and CD4⁺ T cells had a stronger tendency to interact with cancer cells in low-grade LADCs than in high-grade solid tumors (46). This observation partly explains the worse survival outcomes seen in solid tumors, as the degree of spatial interaction of T cells and tumor cells is a known predictor of tumor recurrence (47).

Our study has some limitations that must be addressed in future settings. Although our findings have both diagnostic and therapeutic relevance by underscoring the potential challenges posed by tumor heterogeneity, they do not address the biological mechanistic links contributing to these outcomes. Furthermore, clinical data and follow-up data were not available in all included cases due to the study’s retrospective nature, and the patient cohort size is small for some results to reach statistical significance. Given these considerations, our hypothesis-generating outcomes presented in this study require further preclinical and clinical validation through larger studies with proper follow-up.

Conclusions

Although the KRAS mutational landscape is mostly homogenous in KRAS-mutant LADC samples, some cases may exhibit differences in KRAS mutation subtypes across tumor regions. Addressing this will be fundamental for establishing the molecular diagnosis and determining consecutive treatment plans. KRASG12A mutation was not detected in LADC samples with a lepidic growth pattern in our cohort and micropapillary LADCs lacked wild-type KRAS gene. Nevertheless, no statistically significant association could be established between the KRAS mutation subtype and the LADC growth pattern. Elevated NLRP3 expression is reflective of increased immune infiltration and is characteristic of solid LADCs, while PD-L1 expression is associated with solid morphology.

Acknowledgments

None.

Footnote

Reporting Checklist: The authors have completed the STARD reporting checklist. Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2024-1092/rc

Data Sharing Statement: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2024-1092/dss

Peer Review File: Available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2024-1092/prf

Funding: This work was supported by Austrian Science Fund (grant numbers FWF I3522, FWF I3977, and I4677 to B.D. and grant number FWF No. T 1062-B33 to K.S.); by the Hungarian National Research, Development, and Innovation Office (grant numbers 2020-1.1.6-JÖVŐ, TKP2021-EGA-33, FK-143751 and FK-147045 to B.D., Z.M. and J.F.); by the New National Excellence Program of the Ministry for Innovation and Technology of Hungary (grant numbers UNKP-20-3, UNKP-21-3 and UNKP-23-5 to Z.M.); by the Bolyai Research Scholarship of the Hungarian Academy of Sciences (to Z.M.); by the International Association for the Study of Lung Cancer (International Lung Cancer Foundation Young Investigator Grant in 2022 (to Z.M.); and by the Semmelweis University (grant number EFOP-3.6.3-VEKOP-16-2017-00009 to B.F.).

Conflicts of Interest: All authors have completed the ICMJE uniform disclosure form (available at https://tlcr.amegroups.com/article/view/10.21037/tlcr-2024-1092/coif). B.D. was supported by the Austrian Science Fund (FWF I3522, FWF I3977, and I4677). B.D., Z.M., and J.F. were supported by funding from the Hungarian National Research, Development, and Innovation Office (2020-1.1.6-JÖVŐ, TKP2021-EGA-33, FK-143751 and FK-147045). Z.M. was supported by the New National Excellence Program of the Ministry for Innovation and Technology of Hungary (UNKP-20-3, UNKP-21-3 and UNKP-23-5), and by the Bolyai Research Scholarship of the Hungarian Academy of Sciences. Z.M. is also the recipient of the International Association for the Study of Lung Cancer/International Lung Cancer Foundation Young Investigator Grant (2022). B.F. is a recipient of the Semmelweis 250+ Excellence PhD Scholarship (EFOP-3.6.3-VEKOP-16-2017-00009) of the Semmelweis University. K.S. received funding from the Austrian Science Fund (FWF No. T 1062-B33). The other authors have no conflicts of interest to declare.

Ethical Statement: The authors are accountable for all aspects of the work in ensuring that questions related to the accuracy or integrity of any part of the work are appropriately investigated and resolved. The study was conducted in accordance with Declaration of Helsinki and its subsequent amendments. The study was approved by the national Ethics Committee of Hungary (Hungarian Scientific and Research Ethics Committee of the Medical Research Council, ETT-TUKEB 23636-2/2018, 23636/10/2018/EÜIG). Due to the retrospective nature of the study, the requirement for written informed consent was waived.

Open Access Statement: This is an Open Access article distributed in accordance with the Creative Commons Attribution-NonCommercial-NoDerivs 4.0 International License (CC BY-NC-ND 4.0), which permits the non-commercial replication and distribution of the article with the strict proviso that no changes or edits are made and the original work is properly cited (including links to both the formal publication through the relevant DOI and the license). See: https://creativecommons.org/licenses/by-nc-nd/4.0/.

References

1. Bade BC, Dela Cruz CS. Lung Cancer 2020: Epidemiology, Etiology, and Prevention. Clin Chest Med 2020;41:1-24. [Crossref] [PubMed]
2. Nicholson AG, Tsao MS, Beasley MB, et al. The 2021 WHO Classification of Lung Tumors: Impact of Advances Since 2015. J Thorac Oncol 2022;17:362-87. [Crossref] [PubMed]
3. Schabath MB, Cote ML. Cancer Progress and Priorities: Lung Cancer. Cancer Epidemiol Biomarkers Prev 2019;28:1563-79. [Crossref] [PubMed]
4. Radeczky P, Moldvay J, Fillinger J, et al. Bone-Specific Metastasis Pattern of Advanced-Stage Lung Adenocarcinoma According to the Localization of the Primary Tumor. Pathol Oncol Res 2021;27:1609926. [Crossref] [PubMed]
5. Travis WD, Brambilla E, Noguchi M, et al. International association for the study of lung cancer/american thoracic society/european respiratory society international multidisciplinary classification of lung adenocarcinoma. J Thorac Oncol 2011;6:244-85. [Crossref] [PubMed]
6. Yoshizawa A, Motoi N, Riely GJ, et al. Impact of proposed IASLC/ATS/ERS classification of lung adenocarcinoma: prognostic subgroups and implications for further revision of staging based on analysis of 514 stage I cases. Mod Pathol 2011;24:653-64. [Crossref] [PubMed]
7. Ghimessy AK, Gellert A, Schlegl E, et al. KRAS Mutations Predict Response and Outcome in Advanced Lung Adenocarcinoma Patients Receiving First-Line Bevacizumab and Platinum-Based Chemotherapy. Cancers (Basel) 2019;11:1514. [Crossref] [PubMed]
8. Ghimessy A, Radeczky P, Laszlo V, et al. Current therapy of KRAS-mutant lung cancer. Cancer Metastasis Rev 2020;39:1159-77. [Crossref] [PubMed]
9. Ihle NT, Byers LA, Kim ES, et al. Effect of KRAS oncogene substitutions on protein behavior: implications for signaling and clinical outcome. J Natl Cancer Inst 2012;104:228-39. [Crossref] [PubMed]
10. Zhu G, Pei L, Xia H, et al. Role of oncogenic KRAS in the prognosis, diagnosis and treatment of colorectal cancer. Mol Cancer 2021;20:143. [Crossref] [PubMed]
11. Kerk SA, Papagiannakopoulos T, Shah YM, et al. Metabolic networks in mutant KRAS-driven tumours: tissue specificities and the microenvironment. Nat Rev Cancer 2021;21:510-25. [Crossref] [PubMed]
12. Yanagawa N, Shiono S, Abiko M, et al. The Clinical Impact of Solid and Micropapillary Patterns in Resected Lung Adenocarcinoma. J Thorac Oncol 2016;11:1976-83. [Crossref] [PubMed]
13. Yu HA, Sima CS, Shen R, et al. Prognostic impact of KRAS mutation subtypes in 677 patients with metastatic lung adenocarcinomas. J Thorac Oncol 2015;10:431-7. [Crossref] [PubMed]
14. Singhal A, Li BT, O'Reilly EM. Targeting KRAS in cancer. Nat Med 2024;30:969-83. [Crossref] [PubMed]
15. Kelley N, Jeltema D, Duan Y, et al. The NLRP3 Inflammasome: An Overview of Mechanisms of Activation and Regulation. Int J Mol Sci 2019;20:3328. [Crossref] [PubMed]
16. Huang Y, Xu W, Zhou R. NLRP3 inflammasome activation and cell death. Cell Mol Immunol 2021;18:2114-27. [Crossref] [PubMed]
17. Zhao C, Zhao W. NLRP3 Inflammasome-A Key Player in Antiviral Responses. Front Immunol 2020;11:211. [Crossref] [PubMed]
18. Zhang Z, Li X, Wang Y, et al. Involvement of inflammasomes in tumor microenvironment and tumor therapies. J Hematol Oncol 2023;16:24. [Crossref] [PubMed]
19. Kong H, Wang Y, Zeng X, et al. Differential expression of inflammasomes in lung cancer cell lines and tissues. Tumour Biol 2015;36:7501-13. [Crossref] [PubMed]
20. Yuan R, Zhao W, Wang QQ, et al. Cucurbitacin B inhibits non-small cell lung cancer in vivo and in vitro by triggering TLR4/NLRP3/GSDMD-dependent pyroptosis. Pharmacol Res 2021;170:105748. [Crossref] [PubMed]
21. Tengesdal IW, Menon DR, Osborne DG, et al. Targeting tumor-derived NLRP3 reduces melanoma progression by limiting MDSCs expansion. Proc Natl Acad Sci U S A 2021;118:e2000915118. [Crossref] [PubMed]
22. Tengesdal IW, Dinarello CA, Marchetti C. NLRP3 and cancer: Pathogenesis and therapeutic opportunities. Pharmacol Ther 2023;251:108545. [Crossref] [PubMed]
23. Binnewies M, Roberts EW, Kersten K, et al. Understanding the tumor immune microenvironment (TIME) for effective therapy. Nat Med 2018;24:541-50. [Crossref] [PubMed]
24. Salgado R, Denkert C, Demaria S, et al. The evaluation of tumor-infiltrating lymphocytes (TILs) in breast cancer: recommendations by an International TILs Working Group 2014. Ann Oncol 2015;26:259-71. [Crossref] [PubMed]
25. Cao Y, Zhu LZ, Jiang MJ, et al. Clinical impacts of a micropapillary pattern in lung adenocarcinoma: a review. Onco Targets Ther 2016;9:149-58. [Crossref] [PubMed]
26. De Oliveira Duarte Achcar R, Nikiforova MN, Yousem SA. Micropapillary lung adenocarcinoma: EGFR, K-ras, and BRAF mutational profile. Am J Clin Pathol 2009;131:694-700. [Crossref] [PubMed]
27. Wang W, Hu Z, Zhao J, et al. Both the presence of a micropapillary component and the micropapillary predominant subtype predict poor prognosis after lung adenocarcinoma resection: a meta-analysis. J Cardiothorac Surg 2020;15:154. [Crossref] [PubMed]
28. Ujiie H, Kadota K, Chaft JE, et al. Solid Predominant Histologic Subtype in Resected Stage I Lung Adenocarcinoma Is an Independent Predictor of Early, Extrathoracic, Multisite Recurrence and of Poor Postrecurrence Survival. J Clin Oncol 2015;33:2877-84. [Crossref] [PubMed]
29. Russell PA, Wainer Z, Wright GM, et al. Does lung adenocarcinoma subtype predict patient survival?: A clinicopathologic study based on the new International Association for the Study of Lung Cancer/American Thoracic Society/European Respiratory Society international multidisciplinary lung adenocarcinoma classification. J Thorac Oncol 2011;6:1496-504. [Crossref] [PubMed]
30. Chiosea SI, Sherer CK, Jelic T, et al. KRAS mutant allele-specific imbalance in lung adenocarcinoma. Mod Pathol 2011;24:1571-7. [Crossref] [PubMed]
31. Yang S, Yu X, Fan Y, et al. Clinicopathologic characteristics and survival outcome in patients with advanced lung adenocarcinoma and KRAS mutation. J Cancer 2018;9:2930-7. [Crossref] [PubMed]
32. Riely GJ, Kris MG, Rosenbaum D, et al. Frequency and distinctive spectrum of KRAS mutations in never smokers with lung adenocarcinoma. Clin Cancer Res 2008;14:5731-4. [Crossref] [PubMed]
33. Veluswamy R, Mack PC, Houldsworth J, et al. KRAS G12C-Mutant Non-Small Cell Lung Cancer: Biology, Developmental Therapeutics, and Molecular Testing. J Mol Diagn 2021;23:507-20. [Crossref] [PubMed]
34. Deng C, Zheng Q, Zhang Y, et al. Validation of the Novel International Association for the Study of Lung Cancer Grading System for Invasive Pulmonary Adenocarcinoma and Association With Common Driver Mutations. J Thorac Oncol 2021;16:1684-93. [Crossref] [PubMed]
35. Ahn B, Yoon S, Kim D, et al. Clinicopathologic and genomic features of high-grade pattern and their subclasses in lung adenocarcinoma. Lung Cancer 2022;170:176-84. [Crossref] [PubMed]
36. Fujikawa R, Muraoka Y, Kashima J, et al. Clinicopathologic and Genotypic Features of Lung Adenocarcinoma Characterized by the International Association for the Study of Lung Cancer Grading System. J Thorac Oncol 2022;17:700-7. [Crossref] [PubMed]
37. Skoulidis F, Li BT, Dy GK, et al. Sotorasib for Lung Cancers with KRAS p.G12C Mutation. N Engl J Med 2021;384:2371-81. [Crossref] [PubMed]
38. Diakos CI, Charles KA, McMillan DC, et al. Cancer-related inflammation and treatment effectiveness. Lancet Oncol 2014;15:e493-503. [Crossref] [PubMed]
39. Shalapour S, Karin M. Immunity, inflammation, and cancer: an eternal fight between good and evil. J Clin Invest 2015;125:3347-55. [Crossref] [PubMed]
40. Gouravani M, Khalili N, Razi S, et al. The NLRP3 inflammasome: a therapeutic target for inflammation-associated cancers. Expert Rev Clin Immunol 2020;16:175-87. [Crossref] [PubMed]
41. Miyazawa T, Marushima H, Saji H, et al. PD-L1 Expression in Non-Small-Cell Lung Cancer Including Various Adenocarcinoma Subtypes. Ann Thorac Cardiovasc Surg 2019;25:1-9. [Crossref] [PubMed]
42. Kantono M, Guo B. Inflammasomes and Cancer: The Dynamic Role of the Inflammasome in Tumor Development. Front Immunol 2017;8:1132. [Crossref] [PubMed]
43. Kolb R, Liu GH, Janowski AM, et al. Inflammasomes in cancer: a double-edged sword. Protein Cell 2014;5:12-20. [Crossref] [PubMed]
44. Ramalingam V. NLRP3 inhibitors: Unleashing their therapeutic potential against inflammatory diseases. Biochem Pharmacol 2023;218:115915. [Crossref] [PubMed]
45. Theivanthiran B, Haykal T, Cao L, et al. Overcoming Immunotherapy Resistance by Targeting the Tumor-Intrinsic NLRP3-HSP70 Signaling Axis. Cancers (Basel) 2021;13:4753. [Crossref] [PubMed]
46. Sorin M, Rezanejad M, Karimi E, et al. Single-cell spatial landscapes of the lung tumour immune microenvironment. Nature 2023;614:548-54. [Crossref] [PubMed]
47. Enfield KSS, Martin SD, Marshall EA, et al. Hyperspectral cell sociology reveals spatial tumor-immune cell interactions associated with lung cancer recurrence. J Immunother Cancer 2019;7:13. [Crossref] [PubMed]