Abstract
KRAS mutations lead to persistent activation of multiple downstream effectors that drive the cancer phenotype. Approximately 30%-50% of colorectal cancer (CRC) patients harbor KRAS mutations, which confer more aggressive tumor biology and shorter overall survival (OS), especially in microsatellite stable (MSS) metastatic CRC. Given that KRAS mutant protein has been proven difficult to target directly, identifying genes that function closely with KRAS and targeting these genes seems to be a promising therapeutic strategy for KRAS-mutated MSS CRC. Here, KRAS function-sensitive genes were identified by assessing the correlation between gene dependency scores from CRISPR knockout screens and KRAS mRNA expression in KRAS-mutated MSS CRC cell lines in the Cancer Cell Line Encyclopedia (CCLE) database. If the correlation coefficient was ≥ 0.6, the gene was considered a KRAS function-sensitive gene. Then KRAS function-sensitive genes related to prognosis were screened out in The Cancer Genome Atlas (TCGA) cohort, and the prognostic value was validated in the Gene Expression Omnibus (GEO) cohort. Single-sample gene set enrichment analysis (ssGSEA) was performed to investigate the potential mechanisms. PockDrug-Server was used to predict the druggability of candidate genes. The results showed that in 20 KRAS-mutated MSS CRC cell lines, 13 genes were identified as KRAS function-sensitive genes. Of these 13 genes, only BIK expression was significantly associated with progression-free survival (PFS) and OS, and the BIK-high patients had significantly poorer PFS (HR=3.18, P=0.020) and OS (HR=4.74, P=0.030) than the BIK-low patients. Multivariate Cox regression analysis revealed high BIK expression as an independent predictor for poorer prognosis in KRAS-mutated MSS CRC. The prognostic value of BIK was also successfully validated in a GEO cohort. The results of ssGSEA showed that the BIK-high group was more prone to strong metastasis activity than the BIK-low group. Pocket druggability prediction analysis presented that BIK had three druggable pockets, and their druggability scores were above 0.8. These findings suggested that BIK is a promising prognostic marker and therapeutic target in KRAS-mutated MSS CRC.
Keywords: Colorectal cancer, KRAS mutations, microsatellite stable, KRAS function-sensitive genes, BIK
Introduction
According to global cancer statistics in 2018, colorectal cancer (CRC) is the third most often-diagnosed cancer, with its mortality rate ranked second [1]. Microsatellite instability (MSI) and KRAS mutation status are among the few biomarkers recommended for routine clinical practice to guide treatment decisions in CRC [2]. According to MSI status, CRC falls into two subtypes: microsatellite stable (MSS) and MSI-high (MSI-H), representing approximately 95% and 5% of all advanced CRC cases, respectively [3]. MSI-H CRC has a better prognosis than MSS CRC [4,5]. Additionally, MSI-H CRC has higher immune cell infiltration than MSS CRC and shows an excellent response to immunotherapy, leading to the FDA approval of immunotherapy for metastatic MSI-H CRC [6-8].
KRAS is a proto-oncogene encoding a small GTP-binding protein that plays a role in many cellular processes by regulating multiple signaling cascades [9]. KRAS mutations are present in roughly 30%-50% of CRC patients, of which over 95% occur at the hotspots of codons G12, G13, or Q61 [10-12]. It has been well documented that KRAS mutations lead to more aggressive tumor biology and shorter overall survival (OS), particularly in MSS metastatic CRC (mCRC), and are a marker for acquired resistance to anti-EGFR therapy [13,14]. Even with the standard regimen of chemotherapy plus bevacizumab, the prognosis of KRAS-mutated MSS mCRC remains poor [15]. Thus, the therapeutic agents against KRAS-mutated MSS CRC have been under extensive exploration.
Targeted therapy and immunotherapy are two advanced strategies of cancer treatment. In terms of immunotherapy, recent clinical studies showed that in the KRAS-mutated MSS CRC patients, the addition of mono-immunotherapy to chemotherapy plus bevacizumab does not bring significant survival benefits; and the benefit of dual-immunotherapy plus chemotherapy is mediocre, which is similar to that of chemotherapy doublet plus target therapies [16,17]. Mutant KRAS is thought to be an undruggable target due to the lack of hydrophobic pockets for drugs to bind, although approaches for blocking KRAS activity are continually being developed. Current ongoing early-phase clinical trials of the KRAS G12C-specific inhibitors, AMG510 and MRTX849, demonstrated encouraging clinical benefit in advanced solid tumors harboring the KRAS p.G12C mutation, including CRC [18,19]. However, in G12 hotspot mutations accounting for around 68% of KRAS mutations in mCRC, G12D and G12V are the most frequently observed, with a frequency of about 45% and 31%, respectively, while G12C has a frequency of only 11% [20]. Therefore, identifying novel and effective therapeutic targets is still an urgent clinical need for treating KRAS-mutated CRC.
Identifying genes that function closely with KRAS and targeting these genes seems to be a promising therapeutic strategy for KRAS-mutated MSS CRC. Presently, potential therapeutic targets for KRAS-mutated tumors are mainly limited to KRAS activation-related genes and its downstream elements. For example, the inhibitors targeting SOS1 and SHP2 interfere with KRAS activation by shifting the equilibrium of KRAS to the GDP-bound state [21,22]. Clinical trials with several inhibitors of SHP2 and SOS1 (such as TNO155, RMC-4630, JAB-3068, JAB-3312, and BI 1701963) are currently ongoing. Additionally, targeting signaling elements downstream of KRAS, such as PI3K/mTOR, mTOR, AKT, and MEK, has shown limited or no improvement in patient survival in clinical trials [23-31]. Undoubtedly, more exhaustive identification of genes associated with KRAS function will provide more potential targets.
In this study, we first used KRAS-mutated MSS CRC cell lines to screen for KRAS function-sensitive genes and then identified the KRAS function-sensitive genes with the prognostic value in KRAS-mutated MSS CRC cases. We also investigated the potential mechanisms related to prognosis prediction and predicted the druggability of candidate genes.
Materials and methods
Data collection
The proteomic and phosphoproteomic data of KRAS and its downstream effectors in KRAS-mutated cancer cell lines were collected from a previous study, which provided a proteomic and phosphoproteomic landscape of 43 KRAS-mutated cancer cell lines across different tissue origins [32].
The KRAS mRNA expression and gene dependency scores in KRAS-mutated MSS CRC cell lines were collected from the Cancer Cell Line Encyclopedia (CCLE) https://portals.broadinstitute.org/ccle), a large-scale genomic dataset of human cancer cell lines. CCLE database included 82 CRC cell lines, of which 20 CRC cell lines had MSS phenotype and pathogenic or likely pathogenic KRAS mutations. The gene dependency scores were derived from CRISPR knockout screens and reflected the dependency size on a gene by calculating the effect size of knocking out or knocking down a gene [33].
The mRNA expression and clinical data of KRAS-mutated CRC patients with stage IV were downloaded from The Cancer Genome Atlas (TCGA) (https://portal.gdc.cancer.gov/) and Gene Expression Omnibus (GEO) (https://www.ncbi.nlm.nih.gov/geo/) database. The TCGA dataset contained 33 KRAS-mutated MSS CRC patients. The GEO dataset with the accessing number of GSE104645 consisted of 19 KRAS-mutated MSS CRC patients.
A flow chart of this study is shown in Supplementary Figure 1.
Screening for KRAS function-sensitive genes
KRAS function-sensitive genes are those whose functions are sensitive to KRAS expression. Here, we defined the KRAS function-sensitive gene as the gene whose effects on cell survival after knockout (i.e., gene dependency scores) positively correlate with KRAS mRNA expression. As KRAS expression increases, the dependency score of this gene increases, and cell survival is more sensitive to the loss of the protein encoded by this gene. In 20 KRAS-mutated MSS CRC cell lines, we investigated the correlation between the dependency scores of 15648 protein-coding genes and KRAS mRNA expression, and the genes with correlation coefficients of ≥ 0.6 were recognized as KRAS function-sensitive genes.
The prognostic value of KRAS function-sensitive genes
The prognostic value of the KRAS function-sensitive genes as continuous variables was initially evaluated in the TCGA cohort to identify the potential genes with prognostic and predictive utility. Then patients were stratified into two groups according to different cut-off values of the mRNA expression of potential prognostic genes identified. The prognostic differences between the two groups were compared via univariate Cox analysis and Kaplan-Meier survival analysis. Multivariate Cox regression analyses evaluated the independent predictive value of the potential prognostic genes regarding progression-free survival (PFS) and OS. The prognostic value was also investigated in the GEO dataset GSE104645, a separate external validation cohort.
Single-sample gene set enrichment analysis
The single-sample gene set enrichment analysis (ssGSEA) is an extension of GSEA and calculates the enrichment scores of every gene set for every sample. We considered the curated pathways with 2,289 gene sets from the Canonical pathways, BioCarta, KEGG, Reactome, and PID. Each ssGSEA enrichment score, reflecting the degree to which the genes in a particular gene set are coordinately up- or down-regulated within a sample, was calculated using the R package “GSVA” and compared between groups using the Wilcoxon rank-sum test. The gene signature was deemed significant if a P-value was less than 0.05 and the absolute difference between the mean enrichment scores was over 0.1 in the two groups. The genes in the discovered signature were further analyzed by a web-tool STRING (https://cn.string-db.org/) which estimated the protein-protein interaction networks.
Druggability analysis
PockDrug (http://pockdrug.rpbs.univ-paris-diderot.fr/), as a robust pocket druggability prediction server, was applied to estimate the druggability of candidate genes [34]. The PockDrug-Server provided every pocket with a druggability score between 0 to 1. The pocket with a score of ≥ 0.5 was considered druggable.
Statistical analysis
Differences between groups were evaluated using the Student’s t-test or Wilcoxon rank-sum test for continuous variables and the chi-square test or Fisher’s exact test for categorical variables. Survival was assessed by non-parametric Kaplan-Meier and semi-parametric Cox proportional hazards analysis. The multivariate Cox proportional hazards model was applied to adjust confounder variables. All P-values were reported for the two-tailed test. A P-value of less than 0.05 was considered statistically significant unless otherwise specified. All statistical tests were performed using available softwares, packages, and online tools listed in Supplementary Table 1.
Results
The correlation of KRAS mRNA expression with the total and phosphorylated protein levels of KRAS and its downstream effectors
KRAS mutation results in constitutive activation of the KRAS protein, which in turn activates a plethora of phosphorylation signaling pathways, such as the canonical RAF/MEK/ERK pathway, thereby contributing to cancer initiation, progression, and metastasis [35]. Here, we assessed the correlation of KRAS mRNA expression obtained from the CCLE database with the KRAS protein and its downstream effector BRAF phosphorylation protein level in 41 KRAS-mutated human cancer cell lines from a previous study [32]. The tissue origin and mutation sites of these 41 cell lines were shown in Figure 1A and 1B. Non-small cell lung cancer (NSCLC; 14 of 41), pancreatic adenocarcinoma (PAAD; 12 of 41), and CRC (10 of 41) cell lines were the most common. The most common KRAS mutation types were G12D, G12V, G12C, and G13D, accounting for almost 78.0% of all KRAS mutations. In these 41 KRAS-mutated human cancer cell lines, KRAS mRNA expression showed significant positive correlations with KRAS protein (r=0.66, P < 0.001) and phosphorylated BRAF protein level (r=0.33, P=0.012). Additionally, a near-significant correlation (r=0.60, P=0.073) between KRAS mRNA expression and phosphorylated BRAF protein level was observed in 10 KRAS-mutated CRC cell lines (Figure 1C). These findings suggested that in KRAS-mutated cancer cell lines, KRAS mRNA expression could reflect the activity of KRAS and its downstream effector proteins.
Figure 1.
Screening for KRAS function-sensitive genes. A. Tissue origin distribution of 41 KRAS-mutated human cancer cell lines. B. KRAS mutation type distribution of 41 KRAS-mutated human cancer cell lines. C. The correlation of KRAS mRNA expression with the KRAS protein and its downstream effector BRAF phosphorylation protein levels in KRAS-mutated human cancer cell lines. D. Lollipop plot visualizes the location of the entire mutation spots of KRAS in 20 KRAS-mutated MSS CRC cell lines. E. The distribution of the correlation coefficients between the dependency scores of 15648 genes and KRAS mRNA expression in 20 KRAS-mutated MSS CRC cell lines. MSS: Microsatellite Stable; CRC: Colorectal Cancer.
Screening for KRAS function-sensitive genes
Given the above results, we screened KRAS function-sensitive genes by exploring the correlation between the gene dependency scores derived from CRISPR knockout screens and KRAS mRNA expression in KRAS-mutated MSS CRC cell lines. There were 20 KRAS-mutated MSS CRC cell lines in the CCLE database. Lollipop plots visualized the entire mutation spots of KRAS, wherein G12V was the most common, followed by G12D, G12C, and G13D (Figure 1D). The percentage of mutation spots of these cell lines largely reflected the frequency of KRAS mutation types in human CRC. The distribution of the correlation coefficients between the dependency scores of 15648 genes and KRAS mRNA expression was a bell-shaped curve with a mean of 0.001 (Figure 1E). A total of 13 genes with correlation coefficients of ≥ 0.6 were recognized as KRAS function-sensitive genes.
The prognostic value of KRAS function-sensitive genes
We first determined the prognostic value of the identified KRAS function-sensitive genes as continuous variables in the TCGA cohort, including 33 KRAS-mutated MSS CRC patients. The baseline characteristics of the patients in the TCGA cohort are summarized in Table 1. Most KRAS mutations were G12V (11), G12D (9), G13D (5), and G12C (4), which accounted for 87.9% of all the mutations (Figure 2A). In the 13 KRAS function-sensitive genes, only BIK mRNA expression analyzed as a continuous variable was significantly associated with PFS (P=0.024) and OS (P=0.011) (Supplementary Table 2). Then discretizing BIK expression using quantiles from 0.1 to 0.9 as cut-off points, we found that the BIK-high group had significantly worse PFS from quantile 0.4 to 0.7 (P < 0.05) and OS from quantile 0.4 to 0.8 (P < 0.05) than the BIK-low group (Figure 2B and 2D). Kaplan-Meier survival curves also showed a significant difference in PFS and OS between the two groups using quantile 0.4 as the cut-off point, wherein the BIK-high group had significantly poorer PFS (P=0.020) and OS (P=0.030) than the BIK-low group (Figure 2C and 2E). These results suggest that interfering with BIK expression to less than 40% may be a rational goal when using BIK as a therapeutic target. Univariate Cox analysis was performed to assess the effects of all baseline characteristics on PFS and OS, and the significant factors (P < 0.1) were then submitted into multivariate Cox regression analysis (Table 2). Multivariate Cox regression analysis showed that BIK mRNA expression remained a significant independent predictor of PFS (HR=3.07, 95% CI 1.09-8.65, P=0.034) with a trend toward significance in the independent prediction of OS (HR=4.44, 95% CI 0.93-21.14, P=0.061) (Figure 3A and 3B).
Table 1.
Clinical baseline characteristics of the KRAS-mutated MSS CRC patients in the TCGA cohort
| Characteristics | Number of patients (N=33) | Patients % |
|---|---|---|
| Pathology subtype | ||
| COAD | 24 | 73% |
| READ | 9 | 27% |
| Age, median (range) | 67 (41-84) | |
| Sex | ||
| Female | 16 | 48% |
| Male | 17 | 52% |
| pT | ||
| T2 | 1 | 3% |
| T3 | 20 | 61% |
| T4 | 12 | 36% |
| pN | ||
| N0 | 3 | 9% |
| N1 | 13 | 39% |
| N2 | 17 | 52% |
| pM | ||
| M1 | 30 | 91% |
| Mx | 3 | 9% |
COAD: Colon Adenocarcinoma; READ: Rectum Adenocarcinoma; MSS: Microsatellite Stable; CRC: Colorectal Cancer; TCGA: The Cancer Genome Atlas.
Figure 2.
BIK expression is associated with the prognosis of KRAS-mutated MSS CRC patients in the TCGA cohort. (A) Lollipop plot visualizes the location of the entire mutation spots of KRAS in 33 KRAS-mutated MSS CRC patients. (B and D) Univariate Cox regression analyses show that the BIK-high group has significantly worse PFS (B; P < 0.05) from quantile 0.4 to 0.7 and OS (D; P < 0.05) from quantile 0.4 to 0.8 than the BIK-low group. (C and E) Kaplan-Meier survival curves show a significant difference in PFS (C; P=0.020) and OS (E; P=0.030) between the BIK-high/low groups using quantile 0.4 as the cut-off point. MSS: Microsatellite Stable; CRC: Colorectal Cancer; PFS: Progression-Free Survival; OS: Overall Survival; TCGA: The Cancer Genome Atlas.
Table 2.
Univariate Cox regression analyses of clinical pathological variables against PFS and OS in the TCGA cohort with 33 KRAS-mutated MSS CRC patients
| Characteristics | Number of patients | PFS | OS | ||||
|---|---|---|---|---|---|---|---|
|
|
|
||||||
| Number of events | HR, 95% CI | P-value | Number of events | HR, 95% CI | P-value | ||
| Pathology subtype | |||||||
| COAD | 24 | 17 | ref | 14 | ref | ||
| READ | 9 | 4 | 0.45 [0.15-1.35] | 0.155 | 1 | 0.14 [0.02-1.07] | 0.058 |
| Molecular subtype | |||||||
| CIN | 28 | 18 | ref | 13 | ref | ||
| GS | 2 | 1 | 0.72 [0.1-5.42] | 0.748 | 1 | 1.42 [0.18-11.05] | 0.738 |
| Sex | |||||||
| Female | 16 | 8 | ref | 6 | ref | ||
| Male | 17 | 13 | 0.87 [0.36-2.11] | 0.754 | 9 | 0.99 [0.35-2.8] | 0.985 |
| pN | |||||||
| N0 | 3 | 2 | ref | 2 | ref | ||
| N1 | 13 | 8 | 0.68 [0.14-3.24] | 0.627 | 5 | 0.34 [0.06-1.82] | 0.209 |
| N2 | 17 | 11 | 0.85 [0.19-3.86] | 0.829 | 8 | 0.52 [0.11-2.49] | 0.413 |
| pM | |||||||
| MX | 3 | 1 | ref | 0 | ref | ||
| M1 | 30 | 20 | 3.26 [0.44-24.39] | 0.250 | 15 | \ | |
| Age | 33 | 21 | 1.01 [0.97-1.05] | 0.647 | 15 | 1.09 [1.02-1.16] | 0.010 |
COAD: Colon Adenocarcinoma; READ: Rectum Adenocarcinoma; CIN: Chromosomal Instability; GS: Genomic Stable; MSS: Microsatellite Stable; CRC: Colorectal Cancer; TCGA: The Cancer Genome Atlas; PFS: Progression-Free Survival; OS: Overall Survival.
Figure 3.
The prognostic value of BIK in the TCGA and GEO cohorts. A and B. Multivariate Cox regression analysis shows that BIK mRNA expression is a significant independent predictor of PFS (P=0.034) with a trend toward significance in the independent prediction of OS (P=0.061). C. BIK mRNA expression as a continuous variable shows a significant association with PFS (P=0.049) and a near-significant association with OS (P=0.061). PFS: Progression-Free Survival; OS: Overall Survival; TCGA: The Cancer Genome Atlas; GEO: Gene Expression Omnibus.
Additionally, the prognostic value of BIK was also investigated in the GEO cohort comprising 19 KRAS-mutated MSS CRC patients. The baseline characteristics of this cohort are summarized in Table 3. In this cohort, BIK mRNA expression as a continuous variable showed a significant association with PFS (P=0.049) and a near-significant association with OS (P=0.103) (Figure 3C).
Table 3.
Clinical baseline characteristics of the KRAS-mutated MSS CRC patients in the GEO cohort
| Characteristics | No. patients | Patients % |
|---|---|---|
| Age, median (range) | 66 (42-83) | |
| Sex | ||
| Female | 5 | 26% |
| Male | 14 | 74% |
| Primary site | ||
| Ascending | 3 | 16% |
| Cecum | 2 | 11% |
| Rectum | 8 | 42% |
| Sigmoid | 6 | 32% |
| Number of metastasis | ||
| 1 | 8 | 42% |
| 2 | 9 | 47% |
| 3 | 2 | 11% |
| Molecular subtype | ||
| CMS2 | 3 | 16% |
| CMS3 | 5 | 26% |
| CMS4 | 11 | 58% |
| Chemotherapy | ||
| FOLFIRI | 1 | 5% |
| FOLFOX | 7 | 37% |
| FOLFOX + Bev | 8 | 42% |
| others | 1 | 5% |
| SOX + Bev | 2 | 11% |
| Anti-EGFR therapy | ||
| Cetuximab | 18 | 95% |
| Panitumumab | 1 | 5% |
MSS: Microsatellite Stable; CRC: Colorectal Cancer; GEO: Gene Expression Omnibus.
The mechanism of unfavorable prognosis directed by BIK expression
To investigate the molecular mechanisms underlying the prognostic role of BIK, we conducted the ssGSEA in the BIK-high/low groups in the TCGA cohort. As shown in Figure 4, the ssGSEA scores of 13 pathways showed a significant difference between the two groups (adjusted P value < 0.2), and all of them were higher in the BIK-low group (Figure 4A). It was worth noting that of these 13 pathways, five were integrin-related pathways, which controlled metastasis in various cancers [36-38]. We also analyzed the expression of genes within PID_INTEGRIN_CS_PATHWAY in the two groups. Most genes, including all key node genes, were enriched in the BIK-low group (Figure 4B).
Figure 4.
Single-sample gene set enrichment analysis in the TCGA cohort. A. The heatmap of single-sample gene set enrichment analysis of the enriched pathways in the BIK-high/low tumor samples of the TCGA cohort. B. The expression of genes within PID_INTEGRIN_CS_PATHWAY in the BIK-high/low groups. Blue and red circles separately represent down- and upregulated genes in the BIK-low group compared with the BIK-high group. TCGA: The Cancer Genome Atlas.
Druggability prediction of BIK protein
The druggability of a protein refers to its ability to bind to drug-like molecules with high affinity. Thus, assessing druggability is a necessary first step in discovering new drug targets. Here, we predicted the pocket druggability of BIK protein by the PockDrug-Server and found that BIK had five protein pockets. The parameters of these protein pockets are presented in Table 4. Of the five protein pockets, three were druggable pockets with a druggability probability of ≥ 0.5, wherein P2 (0.98, P=0.01) had the highest druggability probability, followed by P1 (0.95, P=0.02) and P0 (0.89, P=0.03). Figure 5 displayed the BIK protein structure and the potential small molecule binding pockets.
Table 4.
The parameters of the five protein pockets in BIK predicted by PockDrug-Server
| Diameter hull | Polar residues | Smallest size | Nlys atom | Ntrp atom | Aromatic residues | Volume hull | Otyr atom | Nb RES | Surface hull | Ooh atom | Hydrophobic kyte | Radius cylinder | Aliphatic residues | Nd1 atom | Hydrophobic residues | Druggability scores | |
|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|---|
| pocket2_atm | 11.6 | 0.5 | 7.2 | 0.0 | 0.0 | 0.4 | 256.3 | 0.0 | 8.0 | 224.3 | 0.0 | 0.5 | 5.6 | 0.1 | 0.0 | 0.8 | 0.98 |
| pocket1_atm | 12.6 | 0.6 | 6.6 | 0.0 | 0.0 | 0.2 | 337.5 | 0.0 | 9.0 | 265.4 | 0.0 | 0.4 | 6.0 | 0.2 | 0.0 | 0.7 | 0.95 |
| pocket0_atm | 16.5 | 0.5 | 7.6 | 0.0 | 0.0 | 0.2 | 560.6 | 0.0 | 11.0 | 394.6 | 0.0 | 0.2 | 8.5 | 0.4 | 0.0 | 0.6 | 0.89 |
| pocket4_atm | 13.1 | 0.6 | 7.0 | 0.0 | 0.0 | 0.1 | 348.9 | 0.0 | 8.0 | 282.2 | 0.0 | 0.8 | 6.4 | 0.3 | 0.0 | 0.6 | 0.31 |
| pocket3_atm | 16.1 | 0.7 | 7.7 | 0.0 | 0.0 | 0.1 | 453.5 | 0.0 | 10.0 | 330.3 | 0.0 | 2.0 | 7.9 | 0.1 | 0.0 | 0.5 | 0.02 |
Figure 5.
The protein pockets of BIK. Cartoon representation of the BIK protein structure and different protein pockets in BIK according to PockDrug-Server. The box with gray lines represents the enlarged images of the highest-scoring potential small molecule binding pocket (P2).
Discussion
KRAS-mutated MSS CRC accounts for about 40% of CRC and has a poor prognosis. Current targeted therapeutic drugs, however, are limited. In the present study, we screened KRAS function-sensitive genes in KRAS-mutated MSS CRC cell lines from the CCLE database and investigated their prognostic and predictive utility in the TCGA cohort. Among 13 identified KRAS function-sensitive genes, only BIK mRNA expression was significantly associated with PFS and OS, and high BIK expression was an independent predictor for poor prognosis in KRAS-mutated MSS CRC. Besides, the prognostic value of BIK was successfully validated in the GEO cohort. Strong metastasis activity might be a potential mechanism of poor prognosis in BIK-high patients. The pockDrug-Server analysis presented the druggability of three protein pockets of BIK. These results showed that BIK might be a promising prognostic marker and therapeutic target in KRAS-mutated MSS CRC.
KRAS mutant cancers have substantial molecular heterogeneity. Tina et al. [39] reported that each KRAS-mutated cell line has its unique combination of effector dependencies. Even with this heterogeneity, they identified two major subtypes of KRAS mutant cancer, either dependent on RSK or KRAS. Based on the correlation of node dependencies, RSK-type lines are closer to wild-type KRAS lines than their mutant counterparts in the KRAS subtype. Their findings warn against simply comparing KRAS mutant with KRAS wild-type lines to uncover the complex dependencies of KRAS mutant cells. Similarly, the simple comparison of KRAS mutant versus KRAS wild-type cell lines may not be conducive to identifying KRAS function-sensitive genes.
Additionally, we found that in KRAS-mutated cancer cell lines, KRAS mRNA expression could reflect the activity of KRAS and its downstream effector proteins. Accordingly, a quantitative assessment of the effects of KRAS expression on other molecules will be more helpful in discovering KRAS function-sensitive genes. A total of 13 KRAS function-sensitive genes were identified in KRAS-mutated MSS CRC cell lines. As KRAS expression increased, cell growth inhibition and/or death increased following the knockout of any gene in 13 KRAS function-sensitive genes. Given the enormous differences between the in vivo and in vitro environment, gene expression and function are likely to be dramatically affected. Thus we further investigated the role of KRAS function-sensitive genes in KRAS-mutated MSS CRC patients, and only BIK was identified as an independent exposure variable whose high expression was associated with a poorer prognosis.
BIK, the first member of the BH3-only proapoptotic proteins, is predominantly localized in the endoplasmic reticulum and induces apoptosis in various eukaryotic cells [40,41]. It has been reported that BIK acts as a proapoptotic tumor suppressor in several human tissues and plays a suppressive role in tumor progression and metastasis [42-45]. Conversely, BIK was reported to act as an unfavorable prognostic factor in breast cancer. In 2016, Pandya et al. [46] analyzed the clinical data of breast cancer patients and identified for the first time BIK as an independent prognostic biomarker for poor outcomes in breast cancer. Subsequently, they performed exhaustive cell experiments to probe the molecular mechanisms of BIK. They analyzed six independent cohorts from public databases to investigate the prognosis value of BIK in estrogen receptor-positive (ER+) breast cancer, the most frequently diagnosed breast cancer subtype. The results showed that BIK drives an aggressive breast cancer phenotype through sublethal apoptosis and predicts poor prognosis in ER-positive breast cancer [47]. As described above, the role of BIK in cancer development is complex, and it may act as a tumor suppressor gene or oncogene depending on the tumor microenvironment. One previous study reported that BIK inhibits cell proliferation, invasion, and migration in two MSS CRC cell lines in vitro, one KRAS wild-type and the other KRAS mutant; however, the role of BIK in tumor initiation and progression in CRC patients has yet to be identified [48]. In this study, we focused on KRAS-mutated MSS CRC and identified BIK as an oncogenic factor in KRAS-mutated MSS CRC for the first time by analyzing 20 KRAS-mutated MSS CRC cell lines and 52 KRAS-mutated MSS CRC patients. The results of ssGSEA revealed significant enrichment of integrin-related pathways in the BIK-low samples compared with the BIK-high samples. Integrin-related pathways have been reported to inhibit metastasis in a variety of cancers [36-38]. These findings suggested that the strong metastasis activity might be a contributing factor to a poor prognosis in BIK-high patients. To better understand the underlying molecular mechanisms of the prognostic role of BIK, more experimental studies are clearly required.
The druggability of protein pockets predicts their affinity to bind drug-like molecules and is considered a major criterion for identifying drug targets [49]. PockDrug-Server is an online bioinformatics tool for predicting druggability by calculating 66 physicochemical properties of the pockets, such as hydrophobicity, polarity, and aromaticity. The advantage over the recent druggability models for apo pockets is that PockDrug-Server presents consistent results using different pocket estimation methods and is able to distinguish druggable from less druggable pockets clearly [34]. An increasing number of studies adopted PockDrug-Server for druggability predictions [50-54]. Our results showed that BIK had three druggable pockets, and their druggability scores were above 0.8, suggesting that BIK was a promising druggable target for treating KRAS-mutated MSS CRC.
This study has some limitations. Firstly, this was a retrospective study based on available data from public datasets, and thus the present results should be viewed as exploratory rather than conclusive. Secondly, the relatively small sample size in the validation cohort restricted our analysis of the prognostic value of BIK, only treating BIK mRNA expression as a continuous variable. Third, the underlying mechanism of the prognostic role of BIK and its potential targeted drugs were not explored deeply. Future research based on in vitro and in vivo experiments and prospective clinical trials with larger sample sizes is warranted to assess the prognostic value of BIK, dig deep into its mechanisms, validate BIK as a drug target, and develop its potential targeted drugs.
In conclusion, we screened 13 KRAS function-sensitive genes by exploring the correlation between the gene dependency score and KRAS mRNA expression in KRAS-mutated MSS CRC cell lines and identified BIK, one KRAS function-sensitive gene, as an independent predictor of prognosis in KRAS-mutated MSS CRC patients. Pocket druggability prediction revealed that BIK was a promising druggable target. These findings will contribute to the research on new-targeted therapeutic drugs for KRAS-mutated MSS CRC.
Acknowledgements
This study was supported by National Natural Science Foundation of China (No. 82002477).
Disclosure of conflict of interest
None.
Supporting Information
References
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