Identification of a novel immune signature for optimizing prognosis and treatment prediction in colorectal cancer

Results

Construction of IRGs signature in CRC cohorts

A total of 638 CRC and 51 adjacent normal tissues were acquired from the TCGA database. To establish a predictive IRGs signature, we performed differential expression analysis of genes and IRGs between tumor and normal tissues. A total of 3741 DEGs were identified, including 2,502 upregulated genes and 1,239 downregulated genes (Supplementary Figure 1A, 1B). 2,483 IRGs were also collected from the ImmPort database (Supplementary Table 1). Fulfilling the screening criteria, 294 differentially expressed IRGs were obtained, containing 99 upregulated IRGs and 195 downregulated IRGs (Supplementary Figure 1C, 1D). In total, 606 CRC samples with complete gene expression profiles and intact follow-up information from the TCGA database were enrolled for establishing IRGs signature in the training cohort.

To determine the IRGs related to tumorigenesis and development in CRC, univariate Cox regression analysis was implemented on the differentially expressed IRGs in the training cohort (P < 0.01), and 11 PRIRGs in all were obtained (Supplementary Figure 1E). Moreover, lasso regression was conducted to lessen the number of PRIRGs, and eight PRIRGs were thus filtered out (Supplementary Figure 1F, 1G and Supplementary Table 2). Through multivariate Cox regression analysis, seven-IRGs based signature was ultimately established, as depicted in Supplementary Table 3. The formula for calculating risk score is: Risk score = 0.139 х Exp_FABP4 + 0.176 х Exp_AMH + 0.207 х Exp_GRP + 0.211 х Exp_INHBB - 0.691 х Exp_NRG1 + 0.274 х Exp_UCN + 0.366 х Exp_MC1R. Among these IRGs, NRG1 exhibited a negative coefficient, implying that it could be considered as a protective factor for CRCs; on the contrary, FABP4, AMH, GRP, INHBB, UCN, and MC1R possess positive coefficients, implying poor prognoses in CRCs with overexpression of these six genes.

According to the median value of the risk score (0.948), the 606 CRCs in the training cohort were divided into a high-risk group (n = 303) and a low-risk group (n = 303). The distribution of risk scores, survival status as well as the expression level of seven IRGs for the two subgroups in the training cohort were correspondingly displayed in Figure 1A. Kaplan-Meier survival analysis (Figure 1B) indicated dismal prognosis for patients in the high-risk group (P < 0.0001). To assess the predictive efficiency of the constructed seven IRGs signature, time-dependent ROC curves were plotted. As shown in Figure 1C, the AUCs for 1-, 3-, 5- year survival prediction was 0.692, 0.676, and 0.721, respectively.

Figure 1. Exploration of the predictive power and clinical characteristics of seven IRGs signature in the training cohort. (A) Distribution of risk score, survival status, and expression of seven IRGs of CRCs. (B) Kaplan-Meier survival curve of the high- and low- risk subgroups. (C) ROC curve analysis of IRGs. (D) Univariate Cox analysis of prognostic factors and OS of CRCs. (E) Violin plot illustrated the correlation between risk score and tumor site as well as tumor stage. (F) Alluvial diagram for the two subtypes versus different immune subtypes and CMS. (G) Violin plot illustrated the correlation between risk score and immune subtypes, and (H) CMS. AUC, area under the curve; OS, overall survival; CRC, colorectal cancer; IRGs, immune-related genes; ROC, receiver operating characteristic; CMS, consensus molecular subtypes.

To evaluate the performance of the seven IRGs signature, the GSE39582 dataset (n=562) was used for validation. On the basis of signature information acquired from the training cohort, CRCs in the validation cohort were also classified into high-risk group (n = 281) and low-risk group (n = 281). The distribution of risk scores, survival status, and the expression level of seven IRGs for different subclasses in the validation cohort are correspondingly displayed in Supplementary Figure 2A. Kaplan-Meier survival analysis in Supplementary Figure 2B also revealed poor prognosis in patients of high-risk score (P < 0.0001). Similarly, time-dependent ROC curves were plotted. As exhibited in Supplementary Figure 2C, the AUC for 1-, 3-, 5- year survival prediction was 0.615, 0.616, and 0.662, respectively.

Evaluation of the IRGs signature in CRC cohorts

A detailed summary of the training and the validation cohorts selected for univariate analysis in this study is presented in Supplementary Table 4. Univariate analysis of the TCGA dataset suggested that age (P = 0.003), history of colon polyps (P = 0.020), tumor stage (P < 0.001), mismatch repair system (MMR) status (P = 0.045), EGFR mutation (P = 0.003), and risk score (P < 0.001) were significantly associated with OS (Figure 1D). Meanwhile, a high-risk score was correlated with increased age, history of colon polyps, right half of CRC, dMMR as well as EGFR mutation (Supplementary Table 4). Analogous analyses in the validation dataset showed that tumor stage (P < 0.001), KRAS mutation (P = 0.048), and risk score (P < 0.001) were closely connected with patient survival (Supplementary Figure 2D). As shown in Figure 1E, the risk score was significantly higher in right-side colorectal cancer than the left-side, and the risk score was significantly elevated as colorectal cancer progressed to an advanced stage.

The differences in the distribution of molecular subtypes within the IRGs risk score model were also investigated. In the TCGA cohort, there was no significant difference between the risk score and the immune subtypes (Figure 1F, 1G). Similar results were manifested in the validation dataset (Supplementary Figure 2E, 2F), probably because six immune subtypes were generated by immunogenomics analyses encompassing multiple cancer types [39]. With regards to CMS, the CMS4 subtype had significantly higher IRGs risk score than the other three molecular subtypes, whereas the CMS2 subtype held the lowest risk score (Figure 1F, 1H). A significantly difference was demonstrated among the four CMSs (P = 1.5e−11). In the GEO validation dataset, CMS was likewise found distributed between high- and low- risk subgroups (Supplementary Figure 2E), and allied results (P < 2.2e−16) were displayed in violin plot (Supplementary Figure 2G). Remarkably, the international CRC Subtyping Consortium proposed that superior survival was demonstrated in CMS2 patients while CMS4 patients displayed worse OS [46], consistent with our finding that a larger proportion of long-term survivors were identified in low-risk CRCs than the high-risk subset. The GSEA of 50 hallmark gene sets indicated that up-regulated genes of the high-risk group were enriched in multiple carcinogenesis related pathways, such as epithelial-mesenchymal transition (EMT), angiogenesis, Hedgehog signaling, myogenesis, transforming growth factor-beta (TGFβ) signaling, as well as hypoxia pathway targeted HIF1A (Figure 2A and Supplementary Figure 3A). GSEA analyses revealed the enrichment of tumor proliferation-associated signatures, such as E2F targets, MYC targets, and G2M checkpoint in IRGs low-risk subgroup. Several evidences suggested that they might denote dual role of regulating anti-tumor immunity and tumor cell proliferation. It’s indicated that the E2F1/SP3/STAT6 axis induced by IL-4 promoted EMT in CRC cells [47]. Activation of IL-6/p-STAT3/c-MYC signaling was demonstrated to enhance colorectal tumor growth in a TLR4-dependent manner [48]. In addition, MYC/PVT1 signaling induced immune surveillance via CD8⁺ TILs and peripheral blood mononuclear cells in CRCs [49]. As for G2M checkpoint, in-vitro co-culture assays of T cells and HCT-116 colorectal cancer cells reflected that immune checkpoint TIGIT blockade suppressed G2M transit [50]. Overall, these tumor proliferation-related pathways might also exert tumor immunity associated effects on the TIME of CRC, and the underlying mechanism deserved future investigation. Metabolism-related processes consisting of oxidative phosphorylation and fatty acid metabolism, as well as immune-related signaling involved in IL6/JAK/STAT3, were observed in the low-risk group.

Figure 2. Evaluation of the role of IRGs-based risk score in the training cohort. (A) Results of GSEA of the high-risk group (red) compared with the low-risk group (blue). Color toward gray represents no statistical significance. (B) Heatmap and violin plots of the ESTIMATE score, immune score, stromal score, tumor purity between high- and low- risk subtypes. (C) Violin plot of the CYT score between high- and low- risk subtypes. (D) Violin plot of fibroblasts between two subtypes, and the association between risk score and the NES of fibroblasts. Statistical significance at the level of ns ≥ 0.05, * < 0.05, ** < 0.01 and *** < 0.001. GSEA, gene set enrichment analysis; CYT, cytolytic activity; NES, normalized enrichment score.

Besides, variation in the NES values of 10 common oncogenic pathways between the two subclasses were evaluated in the TCGA COAD and READ patients (Figure 3A). The Hippo-, Notch-, NRF2- and Wnt-related pathways exhibited significantly higher NES values in high-risk subtype than in low-risk subtype. The NES values of the PI3K, RAS, and TP53-related pathways were significantly higher in the immune risk score-low subtype than in the immune risk score-high subtype. Analogous effects in Hippo-, Notch- and Wnt-associated pathways were investigated in the GSE39582 validation cohort (Supplementary Figure 5C).

Figure 3. (A) Heatmap and violin plots of the NES of 10 oncogenic pathways between two subtypes in the TCGA cohort. (B) Kaplan-Meier survival curve of the high- and low- risk subgroups in GSE17538 dataset for identifying potential therapeutic agents. (C) Spearman’s correlation analysis and differential drug response analysis of 10 CTRP-derived compounds. (D) Spearman’s correlation analysis and differential drug response analysis of 10 PRISM-derived compounds. NES, normalized enrichment score.

The immune landscape of the microenvironment in CRC subclasses

To further evaluate the potential molecular mechanism, the connection between four types of score produced by the ESTIMATE algorithm and risk score was also examined. Among the training dataset, a higher risk score was unveiled with elevated ESTIMATE score and stromal score, nevertheless, with decreased tumor purity (Figure 2B). Analogous patterns were found in the validation dataset except for a significantly positive correlation between risk score and immune score (Supplementary Figure 3B). Besides, no statistical significance was shown in the CYT score between the two subclasses (Figure 2C and Supplementary Figure 3C). It has been documented that fibroblasts are critical in multiple immunologic responses and inflammatory responses to tumor tissue injury [51, 52]. In the training group, the risk score was markedly correlated with the NES of fibroblasts (Spearman’s r = 0.18, P-value < 0.001, Figure 2D). Likewise, we found increased NES in high-risk subgroup of the validation cohort (Supplementary Figure 3D).

Immunomodulators (IM) play a determinant role in clinical oncology and plenty of IM-related agonists and antagonists are being assessed [53]. To further figure out the underlying immune modules of the constructed IRGs model, the IM gene expression level between two subgroups in two CRC cohorts was compared. Among the IMs under investigation for cancer immunotherapy, certain of them were significantly related to the risk score (Figure 4A and Supplementary Figure 4A, 4B). In addition, we deeply investigated whether the risk score was associated with the expression level of T cell markers (CD4 and CD8A) and with six vital immune checkpoint genes (CD47, CTLA-4, LAG3, MAGE-A3, PD-1, and PD-L1). As shown in Figure 4B, the expression level of PD-1 was significantly higher in CRC of the high-risk subtype, while the risk score was negatively correlated with CD47 expression. Moreover, the differences in the expression level of CD47 and PD-1 between two subtypes of the TCGA dataset were statistically significant (Figure 4C). Even though the expression level of CD4 inclined to be elevated in high-risk subclass, no statistical difference was determined in the TCGA cohort. Statistical significance was verified in the validation cohort (Supplementary Figure 4C).

Figure 4. The immune landscape of two distinctive subclasses in the training cohort. (A) The differential expression level of immune checkpoint molecules between two subclasses with statistical significance. (B) Violin plots of the CD4, CD8A, CD47, CTLA4, LAG3, MAGE-A3, PD-1 and PD-L1 expression levels for two subtypes. (C) The association between risk score and CD47 as well as PD-1 expression levels. (D) Immune infiltration between high- and low- risk subtypes with statistical significance in the training cohort. (E) Correlation analysis between the expression of seven IRGs and the infiltration of immune cells. Statistical significance at the level of ns ≥ 0.05, * < 0.05, ** < 0.01 and *** < 0.001.

To investigate whether the immunophenotype may be shaped by immune cells, the relationship of immune infiltration with subtypes in both TCGA and GEO samples was examined in depth. We found that there was conspicuous heterogeneity in immune cell population among the established classifications, consistent with previous published TILs subpopulations in CRC [54]. As illustrated in Figure 4D and Supplementary Figure 5A, the infiltrated fractions of Tregs, activated NK cells, macrophage M0, and macrophage M2 was outstandingly augmented in the high-risk group. By contraries, markedly increased infiltration of CD4⁺ memory resting T cells, CD4⁺ memory activated T cells, activated dendritic cells (DCs), and neutrophils was observed in the low-risk group. Furthermore, we evaluate the correlation between the expression of seven IRGs and the infiltration of 24 types of immune cells by ssGSEA in CRC tissues. In the TCGA cohort (Figure 4E), there was a strong correlation of the FABP4 expression with the infiltration of NK cells (Spearman’s r = 0.34, P < 0.001), GRP with NK cells (Spearman’s r = 0.41, P < 0.001) or Th17 cells (Spearman’s r = -0.32, P < 0.001), INKBB with NK cells (Spearman’s r = 0.31, P < 0.001), as well as UCN with Tgd (Spearman’s r = -0.31, P < 0.001) (Supplementary Table 5). For the training dataset and validation dataset, strong connection was confirmed between the expression of FABP4 and the infiltration of DC (Spearman’s r = 0.38, P < 0.001), iDC (Spearman’s r = 0.44, P < 0.001), macrophages (Spearman’s r = 0.46, P < 0.001), and mast cells (Spearman’s r = 0.40, P < 0.001), the expression of GRP and infiltration of macrophages (Spearman’s r = 0.30, P < 0.001) included (Figure 4E and Supplementary Figure 5B and Supplementary Table 5).

Identification of potential therapeutic agents for CRCs with immune high-risk score

The CTRP and PRISM datasets shared 160 compounds, with 1770 compounds remained in total after removing duplication (Supplementary Table 6). Two approaches were utilized to screen candidate compounds with higher drug sensitivity in CRCs of high-risk score. By stratifying CRCs in GSE17538 dataset into high- and low- risk score subtypes based on seven IRGs (Figure 3B), the analyses were operated using CTRP and PRISM-derived drug response data, successively. First, differential drug response analysis between high- and low- risk groups was conducted to identify agents with differential estimated AUC values between subclasses (FDR < 0.05). Next, the Spearman correlation test between AUC value and risk score was adopted to identify drugs with negative correlation coefficient (Spearman’s r < −0.30 for CTRP or −0.40 for PRISM). Above analyses yielded 10 CTRP-derived agents (including AT7867, AZD4547, BRD-K37390332, Cytochalasin B, PLX-4720, SGX-523, PLX-4032, TG-101348, lovastatin, and BRD-K16147474) and 10 PRISM-derived agents (including AMG458, LE135, mevastatin, creatine, S-Crizotinib, Colforsin daproate, erythritol, CHIR-98014, epinephrine, and tandutinib). All these compounds presented lower estimated AUC values in the high-risk subgroup and a negative correlation with IRGs-based risk score (Figure 3C, 3D). Although the 20 candidate agents displayed a higher drug sensitivity in IRGs score-high patients, solely the analyses above could not draw to the conclusion that these compounds are promising treatment modality for the eradication of CRC. Therefore, an integrated literature retrieval was conducted in PubMed, DrugBank [55], and HERB [56] databases to search for the experimental and clinical evidence of candidate compounds for CRC (Supplementary Table 7). BRD-K16147474, SGX-523, BRD-K37390332, AMG458, LE135, creatine, colforsin daproate, erythritol, CHIR-98014, and epinephrine without supporting evidence for CRC were firstly excluded. Secondly, PLX-4032 [57, 58] and PLX-4720 [59] targeted B-raf^V600E, AT7867 targeted Akt [60], tandutinib targeted Akt/mTOR pathway [61], TG-101348 targeted the JAK2/STAT3/PIM1 pathway [62], lovastatin [63] and mevastatin [64] inhibiting 3-hydroxy-3-methylglutaryl coenzyme A (HMG-CoA) reductase weren’t considered as the potential compounds for risk score-high subclass. This is because these drugs functioned inconsistently with targets enriched in the immune score-high subclass through GSEA (Figure 2A and Supplementary Figure 3A). Collectively, AZD4547, Cytochalasin B and S-crizotinib, which held true in vitro and in silico evidence, were deemed the most promising therapeutic agents for CRCs with high IRG risk scores.

CRC subgroups have distinct responses to immunotherapy

Two different procedures were adopted in this study to identify subclass-specific candidate immunotherapies. Submap analysis was first used to find potential immunotherapeutic benefit of two subgroups through six immunotherapy datasets available with clinical response and gene expression information. As exhibited in Figure 5F, the high-risk subclass shared high similarity with anti-MAGE-A3 nonresponse group in Ulloa Montoya et al. (2013) dataset (P = 0.049) and anti-PD-1 nonresponse group in Hugo et al. (2016) dataset (P = 0.002), and the high-risk subgroup tended to be correlated with anti-PD-L1 nonresponse group in IMvigor210 cohort although no statistical significance was found (P = 0.08).

Figure 5. The immunotherapeutic benefit of the IRGs-based risk score in immunotherapeutic treatment cohorts. (A–D) Comparison of AUC values between IRGs-based signature and other eight previous published immune related signatures in four immunotherapeutic datasets. (E) Similarity comparison between IRGs-based signature and other seven previous signatures. (F) SubMap analysis utilizing six immunotherapy datasets. Kaplan-Meier survival curve of OS for patients with high- and low- risk score subtypes for (G) Van Allen et al. (2015) dataset, (H) Hugo et al. (2016) dataset, (I) IMvigor210 dataset, and (J) Snyder et al. (2017) dataset. Bar graph illustrated the treatment response to immunotherapy within high- and low- risk score subtypes in (K) Van Allen et al. (2015) dataset, (L) Hugo et al. (2016) dataset, (M) IMvigor210 dataset, and (N) Snyder et al. (2017) dataset. Violin plots illustrated the distribution of risk score for patients with different immunotherapy responses in (O) Van Allen et al. (2015) dataset, (P) Hugo et al. (2016) dataset, (Q) IMvigor210 dataset, and (R) Snyder et al. (2017) dataset.

Patients received immunotherapy in Van Allen et al. (2015) dataset, Hugo et al. (2016) dataset, IMvigor210 dataset, and Snyder et al. (2017) dataset were classified into high-risk subtype and low-risk subtype using the median IRGs-based risk score as the cutoff. Then, the AUC values for classifying the responder and non-responder cases of several previous signatures, including CD8 [65], CYT [17], T cell-inflamed GEP [66], IFNγ [66], IPRES [67], MHC [68], Chemokine [69], and PD-L1 [65] signatures as well as IRGs-based signature were calculated across all the four immunotherapeutic datasets with abundant gene expression profiles. Notably, IRGs signature outperformed the other eight signatures and the AUC values exceeded 0.7 in three out of four datasets (Figure 5A–5D). The results of performance comparison in four independent datasets suggested that the predictive power of IRGs signature ranked the highest. The association across these signatures indicated that five signatures, including IFNγ, CD8, MHC, IPRES, and PD-L1 signatures correlated closely with each other (Figure 5E). By contrast, IRGs signature displayed relatively weak correlation with other signatures, implying its complementary role rather than the alternative as an immunotherapeutic indicator. Patients in the low-risk subclass presented significant longer OS than those in the high-risk subclass of Van Allen et al. (2015) dataset (log-rank test P-value < 0.001, Figure 5G), IMvigor210 dataset (log-rank test P-value = 0.036, Figure 5I) and Snyder et al. (2017) dataset (log-rank test P-value = 0.027, Figure 5J), however, no statistical difference was observed in Hugo et al. (2016) dataset (log-rank test P-value = 0.096, Figure 5H). These findings demonstrated that the lower risk score was associated with better survival outcomes in tumor patients treated with immunotherapy. Collecting immunotherapeutic response data in four cohorts mentioned above, we determined the correlation between immunotherapeutic response and risk score. It’s shown that patients in the low-risk subtype had a dramatically higher response to immunotherapy than patients in the high-risk subtype among three datasets (P-value = 0.024 for Van Allen et al. anti-CTLA-4 cohort, P-value = 0.001 for IMvigor210 anti-PD-L1 cohort, and P-value = 0.029 for Snyder et al. anti-PD-L1 cohort; X² test, Figure 5K, 5M, 5N), apart from (P-value = 0.194 for Hugo et al. anti-PD-1 cohort, X² test, Figure 5L). According to Van Allen et al. (2015) anti-CTLA-4 cohort (Figure 5O), IMvigor210 anti-PD-L1 cohort (Figure 5Q), and Snyder et al. (2017) anti-PD-L1 cohort (Figure 5R), violin plots revealed that the risk score was significantly decreased in patients responsive to the immunotherapeutic invention, compared to non-responsive patients. Nonetheless, no statistical significance was observed in Hugo et al. (2016) anti-PD-1 cohort (Figure 5P).

Author Contributions

WY, and JC designed and edited this study; YL and ZJX searched the databases and collected data; YL, YYL, ZJX, DZ, XMC, XYW, and JL analyzed the data and wrote the manuscript. WY and JC critically revised the article for essential intellectual content and administrative support. All authors read and approved the final version of the manuscript. All authors reviewed and revised the manuscript.

Acknowledgments

We would like to thank our researchers for their hard work and the reviewers for their valuable advice. We thank Home for Researchers editorial team (https://www.home-for-researchers.com) for language editing service.

Conflicts of Interest

The authors declare no conflicts of interest related to this study.

Funding

This research was supported by the National Key R&D Program of China (2019YFA0110300, 2020YFA0509400); the National Natural Science Foundation of China (82071745); the Science and Technology Program of Guangzhou (202002030069); and the Guangdong project (2019QN01Y212).

References

• 1. Bray F, Ferlay J, Soerjomataram I, Siegel RL, Torre LA, Jemal A. Global cancer statistics 2018: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2018; 68:394–424. https://doi.org/10.3322/caac.21492 [PubMed]
• 2. Dekker E, Tanis PJ, Vleugels JL, Kasi PM, Wallace MB. Colorectal cancer. Lancet. 2019; 394:1467–80. https://doi.org/10.1016/S0140-6736(19)32319-0 [PubMed]
• 3. Inamura K. Colorectal Cancers: An Update on Their Molecular Pathology. Cancers (Basel). 2018; 10:26. https://doi.org/10.3390/cancers10010026 [PubMed]
• 4. Schmoll HJ, Van Cutsem E, Stein A, Valentini V, Glimelius B, Haustermans K, Nordlinger B, van de Velde CJ, Balmana J, Regula J, Nagtegaal ID, Beets-Tan RG, Arnold D, et al. ESMO Consensus Guidelines for management of patients with colon and rectal cancer. a personalized approach to clinical decision making. Ann Oncol. 2012; 23:2479–516. https://doi.org/10.1093/annonc/mds236 [PubMed]
• 5. Condamine T, Ramachandran I, Youn JI, Gabrilovich DI. Regulation of tumor metastasis by myeloid-derived suppressor cells. Annu Rev Med. 2015; 66:97–110. https://doi.org/10.1146/annurev-med-051013-052304 [PubMed]
• 6. Hinshaw DC, Shevde LA. The Tumor Microenvironment Innately Modulates Cancer Progression. Cancer Res. 2019; 79:4557–66. https://doi.org/10.1158/0008-5472.CAN-18-3962 [PubMed]
• 7. Mlecnik B, Tosolini M, Kirilovsky A, Berger A, Bindea G, Meatchi T, Bruneval P, Trajanoski Z, Fridman WH, Pagès F, Galon J. Histopathologic-based prognostic factors of colorectal cancers are associated with the state of the local immune reaction. J Clin Oncol. 2011; 29:610–8. https://doi.org/10.1200/JCO.2010.30.5425 [PubMed]
• 8. Pagès F, Mlecnik B, Marliot F, Bindea G, Ou FS, Bifulco C, Lugli A, Zlobec I, Rau TT, Berger MD, Nagtegaal ID, Vink-Börger E, Hartmann A, et al. International validation of the consensus Immunoscore for the classification of colon cancer: a prognostic and accuracy study. Lancet. 2018; 391:2128–39. https://doi.org/10.1016/S0140-6736(18)30789-X [PubMed]
• 9. Galon J, Costes A, Sanchez-Cabo F, Kirilovsky A, Mlecnik B, Lagorce-Pagès C, Tosolini M, Camus M, Berger A, Wind P, Zinzindohoué F, Bruneval P, Cugnenc PH, et al. Type, density, and location of immune cells within human colorectal tumors predict clinical outcome. Science. 2006; 313:1960–4. https://doi.org/10.1126/science.1129139 [PubMed]
• 10. Xiao Y, Freeman GJ. The microsatellite instable subset of colorectal cancer is a particularly good candidate for checkpoint blockade immunotherapy. Cancer Discov. 2015; 5:16–8. https://doi.org/10.1158/2159-8290.CD-14-1397 [PubMed]
• 11. Le DT, Durham JN, Smith KN, Wang H, Bartlett BR, Aulakh LK, Lu S, Kemberling H, Wilt C, Luber BS, Wong F, Azad NS, Rucki AA, et al. Mismatch repair deficiency predicts response of solid tumors to PD-1 blockade. Science. 2017; 357:409–13. https://doi.org/10.1126/science.aan6733 [PubMed]
• 12. Overman MJ, Lonardi S, Wong KY, Lenz HJ, Gelsomino F, Aglietta M, Morse MA, Van Cutsem E, McDermott R, Hill A, Sawyer MB, Hendlisz A, Neyns B, et al. Durable Clinical Benefit With Nivolumab Plus Ipilimumab in DNA Mismatch Repair-Deficient/Microsatellite Instability-High Metastatic Colorectal Cancer. J Clin Oncol. 2018; 36:773–9. https://doi.org/10.1200/JCO.2017.76.9901 [PubMed]
• 13. Overman MJ, McDermott R, Leach JL, Lonardi S, Lenz HJ, Morse MA, Desai J, Hill A, Axelson M, Moss RA, Goldberg MV, Cao ZA, Ledeine JM, et al. Nivolumab in patients with metastatic DNA mismatch repair-deficient or microsatellite instability-high colorectal cancer (CheckMate 142): an open-label, multicentre, phase 2 study. Lancet Oncol. 2017; 18:1182–91. https://doi.org/10.1016/S1470-2045(17)30422-9 [PubMed]
• 14. Hersom M, Jørgensen JT. Companion and Complementary Diagnostics-Focus on PD-L1 Expression Assays for PD-1/PD-L1 Checkpoint Inhibitors in Non-Small Cell Lung Cancer. Ther Drug Monit. 2018; 40:9–16. https://doi.org/10.1097/FTD.0000000000000460 [PubMed]
• 15. Garon EB, Rizvi NA, Hui R, Leighl N, Balmanoukian AS, Eder JP, Patnaik A, Aggarwal C, Gubens M, Horn L, Carcereny E, Ahn MJ, Felip E, et al, and KEYNOTE-001 Investigators. Pembrolizumab for the treatment of non-small-cell lung cancer. N Engl J Med. 2015; 372:2018–28. https://doi.org/10.1056/NEJMoa1501824 [PubMed]
• 16. Le DT, Uram JN, Wang H, Bartlett BR, Kemberling H, Eyring AD, Skora AD, Luber BS, Azad NS, Laheru D, Biedrzycki B, Donehower RC, Zaheer A, et al. PD-1 Blockade in Tumors with Mismatch-Repair Deficiency. N Engl J Med. 2015; 372:2509–20. https://doi.org/10.1056/NEJMoa1500596 [PubMed]
• 17. Rooney MS, Shukla SA, Wu CJ, Getz G, Hacohen N. Molecular and genetic properties of tumors associated with local immune cytolytic activity. Cell. 2015; 160:48–61. https://doi.org/10.1016/j.cell.2014.12.033 [PubMed]
• 18. Kreiter S, Vormehr M, van de Roemer N, Diken M, Löwer M, Diekmann J, Boegel S, Schrörs B, Vascotto F, Castle JC, Tadmor AD, Schoenberger SP, Huber C, et al. Mutant MHC class II epitopes drive therapeutic immune responses to cancer. Nature. 2015; 520:692–6. https://doi.org/10.1038/nature14426 [PubMed]
• 19. Mlecnik B, Bindea G, Angell HK, Maby P, Angelova M, Tougeron D, Church SE, Lafontaine L, Fischer M, Fredriksen T, Sasso M, Bilocq AM, Kirilovsky A, et al. Integrative Analyses of Colorectal Cancer Show Immunoscore Is a Stronger Predictor of Patient Survival Than Microsatellite Instability. Immunity. 2016; 44:698–711. https://doi.org/10.1016/j.immuni.2016.02.025 [PubMed]
• 20. Bruni D, Angell HK, Galon J. The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy. Nat Rev Cancer. 2020; 20:662–80. https://doi.org/10.1038/s41568-020-0285-7 [PubMed]
• 21. Jansen AM, van Wezel T, van den Akker BE, Ventayol Garcia M, Ruano D, Tops CM, Wagner A, Letteboer TG, Gómez-García EB, Devilee P, Wijnen JT, Hes FJ, Morreau H. Combined mismatch repair and POLE/POLD1 defects explain unresolved suspected Lynch syndrome cancers. Eur J Hum Genet. 2016; 24:1089–92. https://doi.org/10.1038/ejhg.2015.252 [PubMed]
• 22. Domingo E, Freeman-Mills L, Rayner E, Glaire M, Briggs S, Vermeulen L, Fessler E, Medema JP, Boot A, Morreau H, van Wezel T, Liefers GJ, Lothe RA, et al, and Epicolon consortium. Somatic POLE proofreading domain mutation, immune response, and prognosis in colorectal cancer: a retrospective, pooled biomarker study. Lancet Gastroenterol Hepatol. 2016; 1:207–16. https://doi.org/10.1016/S2468-1253(16)30014-0 [PubMed]
• 23. Ganesh K, Stadler ZK, Cercek A, Mendelsohn RB, Shia J, Segal NH, Diaz LA Jr. Immunotherapy in colorectal cancer: rationale, challenges and potential. Nat Rev Gastroenterol Hepatol. 2019; 16:361–75. https://doi.org/10.1038/s41575-019-0126-x [PubMed]
• 24. Li X, Wen D, Li X, Yao C, Chong W, Chen H. Identification of an Immune Signature Predicting Prognosis Risk and Lymphocyte Infiltration in Colon Cancer. Front Immunol. 2020; 11:1678. https://doi.org/10.3389/fimmu.2020.01678 [PubMed]
• 25. Lin K, Huang J, Luo H, Luo C, Zhu X, Bu F, Xiao H, Xiao L, Zhu Z. Development of a prognostic index and screening of potential biomarkers based on immunogenomic landscape analysis of colorectal cancer. Aging (Albany NY). 2020; 12:5832–57. https://doi.org/10.18632/aging.102979 [PubMed]
• 26. Bhattacharya S, Andorf S, Gomes L, Dunn P, Schaefer H, Pontius J, Berger P, Desborough V, Smith T, Campbell J, Thomson E, Monteiro R, Guimaraes P, et al. ImmPort: disseminating data to the public for the future of immunology. Immunol Res. 2014; 58:234–9. https://doi.org/10.1007/s12026-014-8516-1 [PubMed]
• 27. Ghandi M, Huang FW, Jané-Valbuena J, Kryukov GV, Lo CC, McDonald ER 3rd, Barretina J, Gelfand ET, Bielski CM, Li H, Hu K, Andreev-Drakhlin AY, Kim J, et al. Next-generation characterization of the Cancer Cell Line Encyclopedia. Nature. 2019; 569:503–8. https://doi.org/10.1038/s41586-019-1186-3 [PubMed]
• 28. Roh W, Chen PL, Reuben A, Spencer CN, Prieto PA, Miller JP, Gopalakrishnan V, Wang F, Cooper ZA, Reddy SM, Gumbs C, Little L, Chang Q, et al. Integrated molecular analysis of tumor biopsies on sequential CTLA-4 and PD-1 blockade reveals markers of response and resistance. Sci Transl Med. 2017; 9:eaah3560. https://doi.org/10.1126/scitranslmed.aah3560 [PubMed]
• 29. Van Allen EM, Miao D, Schilling B, Shukla SA, Blank C, Zimmer L, Sucker A, Hillen U, Foppen MH, Goldinger SM, Utikal J, Hassel JC, Weide B, et al. Genomic correlates of response to CTLA-4 blockade in metastatic melanoma. Science. 2015; 350:207–11. https://doi.org/10.1126/science.aad0095 [PubMed]
• 30. Ulloa-Montoya F, Louahed J, Dizier B, Gruselle O, Spiessens B, Lehmann FF, Suciu S, Kruit WH, Eggermont AM, Vansteenkiste J, Brichard VG. Predictive gene signature in MAGE-A3 antigen-specific cancer immunotherapy. J Clin Oncol. 2013; 31:2388–95. https://doi.org/10.1200/JCO.2012.44.3762 [PubMed]
• 31. Hugo W, Zaretsky JM, Sun L, Song C, Moreno BH, Hu-Lieskovan S, Berent-Maoz B, Pang J, Chmielowski B, Cherry G, Seja E, Lomeli S, Kong X, et al. Genomic and Transcriptomic Features of Response to Anti-PD-1 Therapy in Metastatic Melanoma. Cell. 2016; 165:35–44. https://doi.org/10.1016/j.cell.2016.02.065 [PubMed]
• 32. Mariathasan S, Turley SJ, Nickles D, Castiglioni A, Yuen K, Wang Y, Kadel EE II, Koeppen H, Astarita JL, Cubas R, Jhunjhunwala S, Banchereau R, Yang Y, et al. TGFβ attenuates tumour response to PD-L1 blockade by contributing to exclusion of T cells. Nature. 2018; 554:544–8. https://doi.org/10.1038/nature25501 [PubMed]
• 33. Snyder A, Nathanson T, Funt SA, Ahuja A, Buros Novik J, Hellmann MD, Chang E, Aksoy BA, Al-Ahmadie H, Yusko E, Vignali M, Benzeno S, Boyd M, et al. Contribution of systemic and somatic factors to clinical response and resistance to PD-L1 blockade in urothelial cancer: An exploratory multi-omic analysis. PLoS Med. 2017; 14:e1002309. https://doi.org/10.1371/journal.pmed.1002309 [PubMed]
• 34. Ritchie ME, Phipson B, Wu D, Hu Y, Law CW, Shi W, Smyth GK. limma powers differential expression analyses for RNA-sequencing and microarray studies. Nucleic Acids Res. 2015; 43:e47. https://doi.org/10.1093/nar/gkv007 [PubMed]
• 35. Yu G, Wang LG, Han Y, He QY. clusterProfiler: an R package for comparing biological themes among gene clusters. OMICS. 2012; 16:284–7. https://doi.org/10.1089/omi.2011.0118 [PubMed]
• 36. Liberzon A, Birger C, Thorvaldsdóttir H, Ghandi M, Mesirov JP, Tamayo P. The Molecular Signatures Database (MSigDB) hallmark gene set collection. Cell Syst. 2015; 1:417–25. https://doi.org/10.1016/j.cels.2015.12.004 [PubMed]
• 37. Hänzelmann S, Castelo R, Guinney J. GSVA: gene set variation analysis for microarray and RNA-seq data. BMC Bioinformatics. 2013; 14:7. https://doi.org/10.1186/1471-2105-14-7 [PubMed]
• 38. Eide PW, Bruun J, Lothe RA, Sveen A. CMScaller: an R package for consensus molecular subtyping of colorectal cancer pre-clinical models. Sci Rep. 2017; 7:16618. https://doi.org/10.1038/s41598-017-16747-x [PubMed]
• 39. Thorsson V, Gibbs DL, Brown SD, Wolf D, Bortone DS, Ou Yang TH, Porta-Pardo E, Gao GF, Plaisier CL, Eddy JA, Ziv E, Culhane AC, Paull EO, et al, and Cancer Genome Atlas Research Network. The Immune Landscape of Cancer. Immunity. 2018; 48:812–30.e14. https://doi.org/10.1016/j.immuni.2018.03.023 [PubMed]
• 40. Yoshihara K, Shahmoradgoli M, Martínez E, Vegesna R, Kim H, Torres-Garcia W, Treviño V, Shen H, Laird PW, Levine DA, Carter SL, Getz G, Stemke-Hale K, et al. Inferring tumour purity and stromal and immune cell admixture from expression data. Nat Commun. 2013; 4:2612. https://doi.org/10.1038/ncomms3612 [PubMed]
• 41. Xiao Y, Ma D, Zhao S, Suo C, Shi J, Xue MZ, Ruan M, Wang H, Zhao J, Li Q, Wang P, Shi L, Yang WT, et al, and AME Breast Cancer Collaborative Group. Multi-Omics Profiling Reveals Distinct Microenvironment Characterization and Suggests Immune Escape Mechanisms of Triple-Negative Breast Cancer. Clin Cancer Res. 2019; 25:5002–14. https://doi.org/10.1158/1078-0432.CCR-18-3524 [PubMed]
• 42. Sanchez-Vega F, Mina M, Armenia J, Chatila WK, Luna A, La KC, Dimitriadoy S, Liu DL, Kantheti HS, Saghafinia S, Chakravarty D, Daian F, Gao Q, et al, and Cancer Genome Atlas Research Network. Oncogenic Signaling Pathways in The Cancer Genome Atlas. Cell. 2018; 173:321–37.e10. https://doi.org/10.1016/j.cell.2018.03.035 [PubMed]
• 43. Barros L, Pretti MA, Chicaybam L, Abdo L, Boroni M, Bonamino MH. Immunological-based approaches for cancer therapy. Clinics (Sao Paulo). 2018; 73:e429s. https://doi.org/10.6061/clinics/2018/e429s [PubMed]
• 44. Bindea G, Mlecnik B, Tosolini M, Kirilovsky A, Waldner M, Obenauf AC, Angell H, Fredriksen T, Lafontaine L, Berger A, Bruneval P, Fridman WH, Becker C, et al. Spatiotemporal dynamics of intratumoral immune cells reveal the immune landscape in human cancer. Immunity. 2013; 39:782–95. https://doi.org/10.1016/j.immuni.2013.10.003 [PubMed]
• 45. Geeleher P, Cox N, Huang RS. pRRophetic: an R package for prediction of clinical chemotherapeutic response from tumor gene expression levels. PLoS One. 2014; 9:e107468. https://doi.org/10.1371/journal.pone.0107468 [PubMed]
• 46. Guinney J, Dienstmann R, Wang X, de Reyniès A, Schlicker A, Soneson C, Marisa L, Roepman P, Nyamundanda G, Angelino P, Bot BM, Morris JS, Simon IM, et al. The consensus molecular subtypes of colorectal cancer. Nat Med. 2015; 21:1350–6. https://doi.org/10.1038/nm.3967 [PubMed]
• 47. Chen J, Gong C, Mao H, Li Z, Fang Z, Chen Q, Lin M, Jiang X, Hu Y, Wang W, Zhang X, Chen X, Li H. E2F1/SP3/STAT6 axis is required for IL-4-induced epithelial-mesenchymal transition of colorectal cancer cells. Int J Oncol. 2018; 53:567–78. https://doi.org/10.3892/ijo.2018.4429 [PubMed]
• 48. Chen T, Li Q, Wu J, Wu Y, Peng W, Li H, Wang J, Tang X, Peng Y, Fu X. Fusobacterium nucleatum promotes M2 polarization of macrophages in the microenvironment of colorectal tumours via a TLR4-dependent mechanism. Cancer Immunol Immunother. 2018; 67:1635–46. https://doi.org/10.1007/s00262-018-2233-x [PubMed]
• 49. Kikuchi Y, Tokita S, Hirama T, Kochin V, Nakatsugawa M, Shinkawa T, Hirohashi Y, Tsukahara T, Hata F, Takemasa I, Sato N, Kanaseki T, Torigoe T. CD8⁺ T-cell Immune Surveillance against a Tumor Antigen Encoded by the Oncogenic Long Noncoding RNA PVT1. Cancer Immunol Res. 2021; 9:1342–53. https://doi.org/10.1158/2326-6066.CIR-20-0964 [PubMed]
• 50. Shao Q, Wang L, Yuan M, Jin X, Chen Z, Wu C. TIGIT Induces (CD3+) T Cell Dysfunction in Colorectal Cancer by Inhibiting Glucose Metabolism. Front Immunol. 2021; 12:688961. https://doi.org/10.3389/fimmu.2021.688961 [PubMed]
• 51. Spranger S. Mechanisms of tumor escape in the context of the T-cell-inflamed and the non-T-cell-inflamed tumor microenvironment. Int Immunol. 2016; 28:383–91. https://doi.org/10.1093/intimm/dxw014 [PubMed]
• 52. Hakelius M, Reyhani V, Rubin K, Gerdin B, Nowinski D. Normal Oral Keratinocytes and Head and Neck Squamous Carcinoma Cells Induce an Innate Response of Fibroblasts. Anticancer Res. 2016; 36:2131–7. [PubMed]
• 53. Tang J, Shalabi A, Hubbard-Lucey VM. Comprehensive analysis of the clinical immuno-oncology landscape. Ann Oncol. 2018; 29:84–91. https://doi.org/10.1093/annonc/mdx755 [PubMed]
• 54. Angelova M, Charoentong P, Hackl H, Fischer ML, Snajder R, Krogsdam AM, Waldner MJ, Bindea G, Mlecnik B, Galon J, Trajanoski Z. Characterization of the immunophenotypes and antigenomes of colorectal cancers reveals distinct tumor escape mechanisms and novel targets for immunotherapy. Genome Biol. 2015; 16:64. https://doi.org/10.1186/s13059-015-0620-6 [PubMed]
• 55. Wishart DS, Feunang YD, Guo AC, Lo EJ, Marcu A, Grant JR, Sajed T, Johnson D, Li C, Sayeeda Z, Assempour N, Iynkkaran I, Liu Y, et al. DrugBank 5.0: a major update to the DrugBank database for 2018. Nucleic Acids Res. 2018; 46:D1074–82. https://doi.org/10.1093/nar/gkx1037 [PubMed]
• 56. Fang S, Dong L, Liu L, Guo J, Zhao L, Zhang J, Bu D, Liu X, Huo P, Cao W, Dong Q, Wu J, Zeng X, et al. HERB: a high-throughput experiment- and reference-guided database of traditional Chinese medicine. Nucleic Acids Res. 2021; 49:D1197–206. https://doi.org/10.1093/nar/gkaa1063 [PubMed]
• 57. Herr R, Köhler M, Andrlová H, Weinberg F, Möller Y, Halbach S, Lutz L, Mastroianni J, Klose M, Bittermann N, Kowar S, Zeiser R, Olayioye MA, et al. B-Raf inhibitors induce epithelial differentiation in BRAF-mutant colorectal cancer cells. Cancer Res. 2015; 75:216–29. https://doi.org/10.1158/0008-5472.CAN-13-3686 [PubMed]
• 58. Whittaker SR, Cowley GS, Wagner S, Luo F, Root DE, Garraway LA. Combined Pan-RAF and MEK Inhibition Overcomes Multiple Resistance Mechanisms to Selective RAF Inhibitors. Mol Cancer Ther. 2015; 14:2700–11. https://doi.org/10.1158/1535-7163.MCT-15-0136-T [PubMed]
• 59. Herr R, Halbach S, Heizmann M, Busch H, Boerries M, Brummer T. BRAF inhibition upregulates a variety of receptor tyrosine kinases and their downstream effector Gab2 in colorectal cancer cell lines. Oncogene. 2018; 37:1576–93. https://doi.org/10.1038/s41388-017-0063-5 [PubMed]
• 60. Zhang S, Deng Z, Yao C, Huang P, Zhang Y, Cao S, Li X. AT7867 Inhibits Human Colorectal Cancer Cells via AKT-Dependent and AKT-Independent Mechanisms. PLoS One. 2017; 12:e0169585. https://doi.org/10.1371/journal.pone.0169585 [PubMed]
• 61. Ponnurangam S, Standing D, Rangarajan P, Subramaniam D. Tandutinib inhibits the Akt/mTOR signaling pathway to inhibit colon cancer growth. Mol Cancer Ther. 2013; 12:598–609. https://doi.org/10.1158/1535-7163.MCT-12-0907 [PubMed]
• 62. Liu Q, Li A, Wang L, He W, Zhao L, Wu C, Lu S, Ye X, Zhao H, Shen X, Xiao X, Liu Z. Stomatin-like Protein 2 Promotes Tumor Cell Survival by Activating the JAK2-STAT3-PIM1 Pathway, Suggesting a Novel Therapy in CRC. Mol Ther Oncolytics. 2020; 17:169–79. https://doi.org/10.1016/j.omto.2020.03.010 [PubMed]
• 63. Lee SJ, Lee I, Lee J, Park C, Kang WK. Statins, 3-hydroxy-3-methylglutaryl coenzyme A reductase inhibitors, potentiate the anti-angiogenic effects of bevacizumab by suppressing angiopoietin2, BiP, and Hsp90α in human colorectal cancer. Br J Cancer. 2014; 111:497–505. https://doi.org/10.1038/bjc.2014.283 [PubMed]
• 64. Wächtershäuser A, Akoglu B, Stein J. HMG-CoA reductase inhibitor mevastatin enhances the growth inhibitory effect of butyrate in the colorectal carcinoma cell line Caco-2. Carcinogenesis. 2001; 22:1061–7. https://doi.org/10.1093/carcin/22.7.1061 [PubMed]
• 65. Xiong D, Wang Y, You M. A gene expression signature of TREM2^hi macrophages and γδ T cells predicts immunotherapy response. Nat Commun. 2020; 11:5084. https://doi.org/10.1038/s41467-020-18546-x [PubMed]
• 66. Ayers M, Lunceford J, Nebozhyn M, Murphy E, Loboda A, Kaufman DR, Albright A, Cheng JD, Kang SP, Shankaran V, Piha-Paul SA, Yearley J, Seiwert TY, et al. IFN-γ-related mRNA profile predicts clinical response to PD-1 blockade. J Clin Invest. 2017; 127:2930–40. https://doi.org/10.1172/JCI91190 [PubMed]
• 67. Thompson JC, Hwang WT, Davis C, Deshpande C, Jeffries S, Rajpurohit Y, Krishna V, Smirnov D, Verona R, Lorenzi MV, Langer CJ, Albelda SM. Gene signatures of tumor inflammation and epithelial-to-mesenchymal transition (EMT) predict responses to immune checkpoint blockade in lung cancer with high accuracy. Lung Cancer. 2020; 139:1–8. https://doi.org/10.1016/j.lungcan.2019.10.012 [PubMed]
• 68. Lauss M, Donia M, Harbst K, Andersen R, Mitra S, Rosengren F, Salim M, Vallon-Christersson J, Törngren T, Kvist A, Ringnér M, Svane IM, Jönsson G. Mutational and putative neoantigen load predict clinical benefit of adoptive T cell therapy in melanoma. Nat Commun. 2017; 8:1738. https://doi.org/10.1038/s41467-017-01460-0 [PubMed]
• 69. Messina JL, Fenstermacher DA, Eschrich S, Qu X, Berglund AE, Lloyd MC, Schell MJ, Sondak VK, Weber JS, Mulé JJ. 12-Chemokine gene signature identifies lymph node-like structures in melanoma: potential for patient selection for immunotherapy? Sci Rep. 2012; 2:765. https://doi.org/10.1038/srep00765 [PubMed]
• 70. Ali HR, Chlon L, Pharoah PD, Markowetz F, Caldas C. Patterns of Immune Infiltration in Breast Cancer and Their Clinical Implications: A Gene-Expression-Based Retrospective Study. PLoS Med. 2016; 13:e1002194. https://doi.org/10.1371/journal.pmed.1002194 [PubMed]
• 71. Bense RD, Sotiriou C, Piccart-Gebhart MJ, Haanen JB, van Vugt MA, de Vries EG, Schröder CP, Fehrmann RS. Relevance of Tumor-Infiltrating Immune Cell Composition and Functionality for Disease Outcome in Breast Cancer. J Natl Cancer Inst. 2016; 109:djw192. https://doi.org/10.1093/jnci/djw192 [PubMed]
• 72. Toor SM, Murshed K, Al-Dhaheri M, Khawar M, Abu Nada M, Elkord E. Immune Checkpoints in Circulating and Tumor-Infiltrating CD4⁺ T Cell Subsets in Colorectal Cancer Patients. Front Immunol. 2019; 10:2936. https://doi.org/10.3389/fimmu.2019.02936 [PubMed]
• 73. Huang Y, Liao H, Zhang Y, Yuan R, Wang F, Gao Y, Wang P, Du Z. Prognostic value of tumor-infiltrating FoxP3+ T cells in gastrointestinal cancers: a meta analysis. PLoS One. 2014; 9:e94376. https://doi.org/10.1371/journal.pone.0094376 [PubMed]
• 74. Zhuo C, Xu Y, Ying M, Li Q, Huang L, Li D, Cai S, Li B. FOXP3+ Tregs: heterogeneous phenotypes and conflicting impacts on survival outcomes in patients with colorectal cancer. Immunol Res. 2015; 61:338–47. https://doi.org/10.1007/s12026-014-8616-y [PubMed]
• 75. Sideras K, Galjart B, Vasaturo A, Pedroza-Gonzalez A, Biermann K, Mancham S, Nigg AL, Hansen BE, Stoop HA, Zhou G, Verhoef C, Sleijfer S, Sprengers D, et al. Prognostic value of intra-tumoral CD8⁺ /FoxP3⁺ lymphocyte ratio in patients with resected colorectal cancer liver metastasis. J Surg Oncol. 2018; 118:68–76. https://doi.org/10.1002/jso.25091 [PubMed]
• 76. Kuwahara T, Hazama S, Suzuki N, Yoshida S, Tomochika S, Nakagami Y, Matsui H, Shindo Y, Kanekiyo S, Tokumitsu Y, Iida M, Tsunedomi R, Takeda S, et al. Intratumoural-infiltrating CD4 + and FOXP3 + T cells as strong positive predictive markers for the prognosis of resectable colorectal cancer. Br J Cancer. 2019; 121:659–65. https://doi.org/10.1038/s41416-019-0559-6 [PubMed]
• 77. Salama P, Phillips M, Grieu F, Morris M, Zeps N, Joseph D, Platell C, Iacopetta B. Tumor-infiltrating FOXP3+ T regulatory cells show strong prognostic significance in colorectal cancer. J Clin Oncol. 2009; 27:186–92. https://doi.org/10.1200/JCO.2008.18.7229 [PubMed]
• 78. Chaput N, Louafi S, Bardier A, Charlotte F, Vaillant JC, Ménégaux F, Rosenzwajg M, Lemoine F, Klatzmann D, Taieb J. Identification of CD8+CD25+Foxp3+ suppressive T cells in colorectal cancer tissue. Gut. 2009; 58:520–29. https://doi.org/10.1136/gut.2008.158824 [PubMed]
• 79. Blatner NR, Mulcahy MF, Dennis KL, Scholtens D, Bentrem DJ, Phillips JD, Ham S, Sandall BP, Khan MW, Mahvi DM, Halverson AL, Stryker SJ, Boller AM, et al. Expression of RORγt marks a pathogenic regulatory T cell subset in human colon cancer. Sci Transl Med. 2012; 4:164ra159. https://doi.org/10.1126/scitranslmed.3004566 [PubMed]
• 80. Reddy JP, Atkinson RL, Larson R, Burks JK, Smith D, Debeb BG, Ruffell B, Creighton CJ, Bambhroliya A, Reuben JM, Van Laere SJ, Krishnamurthy S, Symmans WF, et al. Mammary stem cell and macrophage markers are enriched in normal tissue adjacent to inflammatory breast cancer. Breast Cancer Res Treat. 2018; 171:283–93. https://doi.org/10.1007/s10549-018-4835-6 [PubMed]
• 81. Martinez FO. Regulators of macrophage activation. Eur J Immunol. 2011; 41:1531–4. https://doi.org/10.1002/eji.201141670 [PubMed]
• 82. Guo L, Wang C, Qiu X, Pu X, Chang P. Colorectal Cancer Immune Infiltrates: Significance in Patient Prognosis and Immunotherapeutic Efficacy. Front Immunol. 2020; 11:1052. https://doi.org/10.3389/fimmu.2020.01052 [PubMed]
• 83. Zhang R, Qi F, Zhao F, Li G, Shao S, Zhang X, Yuan L, Feng Y. Cancer-associated fibroblasts enhance tumor-associated macrophages enrichment and suppress NK cells function in colorectal cancer. Cell Death Dis. 2019; 10:273. https://doi.org/10.1038/s41419-019-1435-2 [PubMed]
• 84. Kuwahara T, Hazama S, Suzuki N, Yoshida S, Tomochika S, Nakagami Y, Matsui H, Shindo Y, Kanekiyo S, Tokumitsu Y, Iida M, Tsunedomi R, Takeda S, et al. Correction: Intratumoural-infiltrating CD4 + and FOXP3 + T cells as strong positive predictive markers for the prognosis of resectable colorectal cancer. Br J Cancer. 2019; 121:983–4. https://doi.org/10.1038/s41416-019-0605-4 [PubMed]
• 85. Tosolini M, Kirilovsky A, Mlecnik B, Fredriksen T, Mauger S, Bindea G, Berger A, Bruneval P, Fridman WH, Pagès F, Galon J. Clinical impact of different classes of infiltrating T cytotoxic and helper cells (Th1, th2, treg, th17) in patients with colorectal cancer. Cancer Res. 2011; 71:1263–71. https://doi.org/10.1158/0008-5472.CAN-10-2907 [PubMed]
• 86. Melssen M, Slingluff CL Jr. Vaccines targeting helper T cells for cancer immunotherapy. Curr Opin Immunol. 2017; 47:85–92. https://doi.org/10.1016/j.coi.2017.07.004 [PubMed]
• 87. Palucka K, Banchereau J. Cancer immunotherapy via dendritic cells. Nat Rev Cancer. 2012; 12:265–77. https://doi.org/10.1038/nrc3258 [PubMed]
• 88. Du XH, Liu HL, Li L, Xia SY, Ning N, Zou ZY, Teng D, Xiao CH, Li R, Xu YX. Clinical significance of immunotherapy with combined three kinds of cells for operable colorectal cancer. Tumour Biol. 2015; 36:5679–85. https://doi.org/10.1007/s13277-015-3242-4 [PubMed]
• 89. Hargadon KM. Strategies to Improve the Efficacy of Dendritic Cell-Based Immunotherapy for Melanoma. Front Immunol. 2017; 8:1594. https://doi.org/10.3389/fimmu.2017.01594 [PubMed]
• 90. Rao HL, Chen JW, Li M, Xiao YB, Fu J, Zeng YX, Cai MY, Xie D. Increased intratumoral neutrophil in colorectal carcinomas correlates closely with malignant phenotype and predicts patients’ adverse prognosis. PLoS One. 2012; 7:e30806. https://doi.org/10.1371/journal.pone.0030806 [PubMed]
• 91. Chua W, Charles KA, Baracos VE, Clarke SJ. Neutrophil/lymphocyte ratio predicts chemotherapy outcomes in patients with advanced colorectal cancer. Br J Cancer. 2011; 104:1288–95. https://doi.org/10.1038/bjc.2011.100 [PubMed]
• 92. Governa V, Trella E, Mele V, Tornillo L, Amicarella F, Cremonesi E, Muraro MG, Xu H, Droeser R, Däster SR, Bolli M, Rosso R, Oertli D, et al. The Interplay Between Neutrophils and CD8⁺ T Cells Improves Survival in Human Colorectal Cancer. Clin Cancer Res. 2017; 23:3847–58. https://doi.org/10.1158/1078-0432.CCR-16-2047 [PubMed]
• 93. Mei L, Xiong WC. Neuregulin 1 in neural development, synaptic plasticity and schizophrenia. Nat Rev Neurosci. 2008; 9:437–52. https://doi.org/10.1038/nrn2392 [PubMed]
• 94. Laskin J, Liu SV, Tolba K, Heining C, Schlenk RF, Cheema P, Cadranel J, Jones MR, Drilon A, Cseh A, Gyorffy S, Solca F, Duruisseaux M. NRG1 fusion-driven tumors: biology, detection, and the therapeutic role of afatinib and other ErbB-targeting agents. Ann Oncol. 2020; 31:1693–703. https://doi.org/10.1016/j.annonc.2020.08.2335 [PubMed]
• 95. Cadranel J, Liu SV, Duruisseaux M, Branden E, Goto Y, Weinberg BA, Heining C, Schlenk RF, Cheema P, Jones MR, Drilon A, Trombetta D, Muscarella LA, et al. Therapeutic Potential of Afatinib in NRG1 Fusion-Driven Solid Tumors: A Case Series. Oncologist. 2021; 26:7–16. https://doi.org/10.1634/theoncologist.2020-0379 [PubMed]
• 96. Guo Y, Duan Z, Jia Y, Ren C, Lv J, Guo P, Zhao W, Wang B, Zhang S, Li Y, Li Z. HER4 isoform CYT2 and its ligand NRG1III are expressed at high levels in human colorectal cancer. Oncol Lett. 2018; 15:6629–35. https://doi.org/10.3892/ol.2018.8124 [PubMed]
• 97. Fujii K, Kondo T, Yokoo H, Yamada T, Iwatsuki K, Hirohashi S. Proteomic study of human hepatocellular carcinoma using two-dimensional difference gel electrophoresis with saturation cysteine dye. Proteomics. 2005; 5:1411–22. https://doi.org/10.1002/pmic.200401004 [PubMed]
• 98. Nieman KM, Kenny HA, Penicka CV, Ladanyi A, Buell-Gutbrod R, Zillhardt MR, Romero IL, Carey MS, Mills GB, Hotamisligil GS, Yamada SD, Peter ME, Gwin K, Lengyel E. Adipocytes promote ovarian cancer metastasis and provide energy for rapid tumor growth. Nat Med. 2011; 17:1498–503. https://doi.org/10.1038/nm.2492 [PubMed]
• 99. Nieman KM, Romero IL, Van Houten B, Lengyel E. Adipose tissue and adipocytes support tumorigenesis and metastasis. Biochim Biophys Acta. 2013; 1831:1533–41. https://doi.org/10.1016/j.bbalip.2013.02.010 [PubMed]
• 100. Zhang Y, Zhao X, Deng L, Li X, Wang G, Li Y, Chen M. High expression of FABP4 and FABP6 in patients with colorectal cancer. World J Surg Oncol. 2019; 17:171. https://doi.org/10.1186/s12957-019-1714-5 [PubMed]
• 101. Martinez V, Wang L, Million M, Rivier J, Taché Y. Urocortins and the regulation of gastrointestinal motor function and visceral pain. Peptides. 2004; 25:1733–44. https://doi.org/10.1016/j.peptides.2004.05.025 [PubMed]
• 102. Chen H, Luo J, Guo J. Development and validation of a five-immune gene prognostic risk model in colon cancer. BMC Cancer. 2020; 20:395. https://doi.org/10.1186/s12885-020-06799-0 [PubMed]
• 103. Wang J, Yu S, Chen G, Kang M, Jin X, Huang Y, Lin L, Wu D, Wang L, Chen J. A novel prognostic signature of immune-related genes for patients with colorectal cancer. J Cell Mol Med. 2020; 24:8491–504. https://doi.org/10.1111/jcmm.15443 [PubMed]
• 104. Scherer D, Nagore E, Bermejo JL, Figl A, Botella-Estrada R, Thirumaran RK, Angelini S, Hemminki K, Schadendorf D, Kumar R. Melanocortin receptor 1 variants and melanoma risk: a study of 2 European populations. Int J Cancer. 2009; 125:1868–75. https://doi.org/10.1002/ijc.24548 [PubMed]
• 105. Chen S, Zhu B, Yin C, Liu W, Han C, Chen B, Liu T, Li X, Chen X, Li C, Hu L, Zhou J, Xu ZX, et al. Palmitoylation-dependent activation of MC1R prevents melanomagenesis. Nature. 2017; 549:399–403. https://doi.org/10.1038/nature23887 [PubMed]
• 106. MacLaughlin DT, Donahoe PK. Müllerian inhibiting substance/anti-Müllerian hormone: a potential therapeutic agent for human ovarian and other cancers. Future Oncol. 2010; 6:391–405. https://doi.org/10.2217/fon.09.172 [PubMed]
• 107. Clendenen TV, Ge W, Koenig KL, Afanasyeva Y, Agnoli C, Brinton LA, Darvishian F, Dorgan JF, Eliassen AH, Falk RT, Hallmans G, Hankinson SE, Hoffman-Bolton J, et al. Breast cancer risk prediction in women aged 35-50 years: impact of including sex hormone concentrations in the Gail model. Breast Cancer Res. 2019; 21:42. https://doi.org/10.1186/s13058-019-1126-z [PubMed]
• 108. Gong B, Kao Y, Zhang C, Sun F, Gong Z, Chen J. Identification of Hub Genes Related to Carcinogenesis and Prognosis in Colorectal Cancer Based on Integrated Bioinformatics. Mediators Inflamm. 2020; 2020:5934821. https://doi.org/10.1155/2020/5934821 [PubMed]
• 109. Pang B, Xu X, Lu Y, Jin H, Yang R, Jiang C, Shao D, Liu Y, Shi J. Prediction of new targets and mechanisms for quercetin in the treatment of pancreatic cancer, colon cancer, and rectal cancer. Food Funct. 2019; 10:5339–49. https://doi.org/10.1039/c9fo01168d [PubMed]
• 110. Namwanje M, Brown CW. Activins and Inhibins: Roles in Development, Physiology, and Disease. Cold Spring Harb Perspect Biol. 2016; 8:a021881. https://doi.org/10.1101/cshperspect.a021881 [PubMed]
• 111. Wijayarathna R, de Kretser DM. Activins in reproductive biology and beyond. Hum Reprod Update. 2016; 22:342–57. https://doi.org/10.1093/humupd/dmv058 [PubMed]
• 112. Kita A, Kasamatsu A, Nakashima D, Endo-Sakamoto Y, Ishida S, Shimizu T, Kimura Y, Miyamoto I, Yoshimura S, Shiiba M, Tanzawa H, Uzawa K. Activin B Regulates Adhesion, Invasiveness, and Migratory Activities in Oral Cancer: a Potential Biomarker for Metastasis. J Cancer. 2017; 8:2033–41. https://doi.org/10.7150/jca.18714 [PubMed]
• 113. Mylonas I. Inhibin-alpha, -betaA and -betaB subunits in uterine non-endometrioid carcinomas: prognostic significance and clinical implications. Eur J Cancer. 2010; 46:2485–93. https://doi.org/10.1016/j.ejca.2010.06.001 [PubMed]
• 114. Hofland J, van Weerden WM, Steenbergen J, Dits NF, Jenster G, de Jong FH. Activin A stimulates AKR1C3 expression and growth in human prostate cancer. Endocrinology. 2012; 153:5726–34. https://doi.org/10.1210/en.2011-2065 [PubMed]
• 115. Matsuo SE, Ebina KN, Kulcsar MA, Friguglietti CU, Kimura ET. Activin betaB expression in rat experimental goiter and human thyroid tumors. Thyroid. 2003; 13:239–47. https://doi.org/10.1089/105072503321582033 [PubMed]
• 116. Yuan J, Xie A, Cao Q, Li X, Chen J. INHBB Is a Novel Prognostic Biomarker Associated with Cancer-Promoting Pathways in Colorectal Cancer. Biomed Res Int. 2020; 2020:6909672. https://doi.org/10.1155/2020/6909672 [PubMed]
• 117. Pansky A, DE Weerth A, Fasler-Kan E, Boulay JL, Schulz M, Ketterer S, Selck C, Beglinger C, VON Schrenck T, Hildebrand P. Gastrin releasing peptide-preferring bombesin receptors mediate growth of human renal cell carcinoma. J Am Soc Nephrol. 2000; 11:1409–18. https://doi.org/10.1681/ASN.V1181409 [PubMed]
• 118. Thomas RP, Hellmich MR, Townsend CM Jr, Evers BM. Role of gastrointestinal hormones in the proliferation of normal and neoplastic tissues. Endocr Rev. 2003; 24:571–99. https://doi.org/10.1210/er.2002-0028 [PubMed]
• 119. Glover SC, Tretiakova MS, Carroll RE, Benya RV. Increased frequency of gastrin-releasing peptide receptor gene mutations during colon-adenocarcinoma progression. Mol Carcinog. 2003; 37:5–15. https://doi.org/10.1002/mc.10117 [PubMed]
• 120. Schally AV, Szepeshazi K, Nagy A, Comaru-Schally AM, Halmos G. New approaches to therapy of cancers of the stomach, colon and pancreas based on peptide analogs. Cell Mol Life Sci. 2004; 61:1042–68. https://doi.org/10.1007/s00018-004-3434-3 [PubMed]
• 121. Chave HS, Gough AC, Palmer K, Preston SR, Primrose JN. Bombesin family receptor and ligand gene expression in human colorectal cancer and normal mucosa. Br J Cancer. 2000; 82:124–30. https://doi.org/10.1054/bjoc.1998.0888 [PubMed]
• 122. Patel O, Dumesny C, Giraud AS, Baldwin GS, Shulkes A. Stimulation of proliferation and migration of a colorectal cancer cell line by amidated and glycine-extended gastrin-releasing peptide via the same receptor. Biochem Pharmacol. 2004; 68:2129–42. https://doi.org/10.1016/j.bcp.2004.08.009 [PubMed]
• 123. Matkowskyj KA, Keller K, Glover S, Kornberg L, Tran-Son-Tay R, Benya RV. Expression of GRP and its receptor in well-differentiated colon cancer cells correlates with the presence of focal adhesion kinase phosphorylated at tyrosines 397 and 407. J Histochem Cytochem. 2003; 51:1041–8. https://doi.org/10.1177/002215540305100807 [PubMed]
• 124. Rick FG, Schally AV, Block NL, Nadji M, Szepeshazi K, Zarandi M, Vidaurre I, Perez R, Halmos G, Szalontay L. Antagonists of growth hormone-releasing hormone (GHRH) reduce prostate size in experimental benign prostatic hyperplasia. Proc Natl Acad Sci USA. 2011; 108:3755–60. https://doi.org/10.1073/pnas.1018086108 [PubMed]
• 125. Schally AV, Comaru-Schally AM, Nagy A, Kovacs M, Szepeshazi K, Plonowski A, Varga JL, Halmos G. Hypothalamic hormones and cancer. Front Neuroendocrinol. 2001; 22:248–91. https://doi.org/10.1006/frne.2001.0217 [PubMed]
• 126. Rick FG, Buchholz S, Schally AV, Szalontay L, Krishan A, Datz C, Stadlmayr A, Aigner E, Perez R, Seitz S, Block NL, Hohla F. Combination of gastrin-releasing peptide antagonist with cytotoxic agents produces synergistic inhibition of growth of human experimental colon cancers. Cell Cycle. 2012; 11:2518–25. https://doi.org/10.4161/cc.20900 [PubMed]
• 127. Li C, Shen Z, Zhou Y, Yu W. Independent prognostic genes and mechanism investigation for colon cancer. Biol Res. 2018; 51:10. https://doi.org/10.1186/s40659-018-0158-7 [PubMed]
• 128. Bedke J, Hemmerlein B, Perske C, Gross A, Heuser M. Tumor-associated macrophages in clear cell renal cell carcinoma express both gastrin-releasing peptide and its receptor: a possible modulatory role of immune effectors cells. World J Urol. 2010; 28:335–41. https://doi.org/10.1007/s00345-009-0492-z [PubMed]
• 129. Ribatti D, Vacca A, Rusnati M, Presta M. The discovery of basic fibroblast growth factor/fibroblast growth factor-2 and its role in haematological malignancies. Cytokine Growth Factor Rev. 2007; 18:327–34. https://doi.org/10.1016/j.cytogfr.2007.04.011 [PubMed]
• 130. Sato T, Oshima T, Yoshihara K, Yamamoto N, Yamada R, Nagano Y, Fujii S, Kunisaki C, Shiozawa M, Akaike M, Rino Y, Tanaka K, Masuda M, Imada T. Overexpression of the fibroblast growth factor receptor-1 gene correlates with liver metastasis in colorectal cancer. Oncol Rep. 2009; 21:211–16. [PubMed]
• 131. Henriksson ML, Edin S, Dahlin AM, Oldenborg PA, Öberg Å, Van Guelpen B, Rutegård J, Stenling R, Palmqvist R. Colorectal cancer cells activate adjacent fibroblasts resulting in FGF1/FGFR3 signaling and increased invasion. Am J Pathol. 2011; 178:1387–94. https://doi.org/10.1016/j.ajpath.2010.12.008 [PubMed]
• 132. Gavine PR, Mooney L, Kilgour E, Thomas AP, Al-Kadhimi K, Beck S, Rooney C, Coleman T, Baker D, Mellor MJ, Brooks AN, Klinowska T. AZD4547: an orally bioavailable, potent, and selective inhibitor of the fibroblast growth factor receptor tyrosine kinase family. Cancer Res. 2012; 72:2045–56. https://doi.org/10.1158/0008-5472.CAN-11-3034 [PubMed]
• 133. Jang J, Kim HK, Bang H, Kim ST, Kim SY, Park SH, Lim HY, Kang WK, Lee J, Kim KM. Antitumor Effect of AZD4547 in a Fibroblast Growth Factor Receptor 2-Amplified Gastric Cancer Patient-Derived Cell Model. Transl Oncol. 2017; 10:469–75. https://doi.org/10.1016/j.tranon.2017.03.001 [PubMed]
• 134. Paik PK, Shen R, Berger MF, Ferry D, Soria JC, Mathewson A, Rooney C, Smith NR, Cullberg M, Kilgour E, Landers D, Frewer P, Brooks N, André F. A Phase Ib Open-Label Multicenter Study of AZD4547 in Patients with Advanced Squamous Cell Lung Cancers. Clin Cancer Res. 2017; 23:5366–73. https://doi.org/10.1158/1078-0432.CCR-17-0645 [PubMed]
• 135. Guan Z, Lan H, Sun D, Wang X, Jin K. A potential novel therapy for FGFR1-amplified pancreatic cancer with bone metastasis, screened by next-generation sequencing and a patient-derived xenograft model. Oncol Lett. 2019; 17:2303–7. https://doi.org/10.3892/ol.2018.9876 [PubMed]
• 136. Yao TJ, Zhu JH, Peng DF, Cui Z, Zhang C, Lu PH. AZD-4547 exerts potent cytostatic and cytotoxic activities against fibroblast growth factor receptor (FGFR)-expressing colorectal cancer cells. Tumour Biol. 2015; 36:5641–8. https://doi.org/10.1007/s13277-015-3237-1 [PubMed]
• 137. Scherlach K, Boettger D, Remme N, Hertweck C. The chemistry and biology of cytochalasans. Nat Prod Rep. 2010; 27:869–86. https://doi.org/10.1039/b903913a [PubMed]
• 138. Van Goietsenoven G, Mathieu V, Andolfi A, Cimmino A, Lefranc F, Kiss R, Evidente A. In vitro growth inhibitory effects of cytochalasins and derivatives in cancer cells. Planta Med. 2011; 77:711–7. https://doi.org/10.1055/s-0030-1250523 [PubMed]
• 139. MacLean-Fletcher S, Pollard TD. Mechanism of action of cytochalasin B on actin. Cell. 1980; 20:329–41. https://doi.org/10.1016/0092-8674(80)90619-4 [PubMed]
• 140. Foran E, McWilliam P, Kelleher D, Croke DT, Long A. The leukocyte protein L-plastin induces proliferation, invasion and loss of E-cadherin expression in colon cancer cells. Int J Cancer. 2006; 118:2098–104. https://doi.org/10.1002/ijc.21593 [PubMed]
• 141. Karatzas G, Karayiannakis AJ, Syrigos KN, Chatzigianni E, Papanikolaou S, Riza F, Papanikolaou D. E-cadherin expression correlates with tumor differentiation in colorectal cancer. Hepatogastroenterology. 1999; 46:232–5. [PubMed]
• 142. Chen X, Wang Y, Xia H, Wang Q, Jiang X, Lin Z, Ma Y, Yang Y, Hu M. Loss of E-cadherin promotes the growth, invasion and drug resistance of colorectal cancer cells and is associated with liver metastasis. Mol Biol Rep. 2012; 39:6707–14. https://doi.org/10.1007/s11033-012-1494-2 [PubMed]
• 143. Lamouille S, Xu J, Derynck R. Molecular mechanisms of epithelial-mesenchymal transition. Nat Rev Mol Cell Biol. 2014; 15:178–96. https://doi.org/10.1038/nrm3758 [PubMed]
• 144. Kalluri R, Weinberg RA. The basics of epithelial-mesenchymal transition. J Clin Invest. 2009; 119:1420–8. https://doi.org/10.1172/JCI39104 [PubMed]
• 145. Thiery JP, Acloque H, Huang RY, Nieto MA. Epithelial-mesenchymal transitions in development and disease. Cell. 2009; 139:871–90. https://doi.org/10.1016/j.cell.2009.11.007 [PubMed]
• 146. Gu J, Cui CF, Yang L, Wang L, Jiang XH. Emodin Inhibits Colon Cancer Cell Invasion and Migration by Suppressing Epithelial-Mesenchymal Transition via the Wnt/β-Catenin Pathway. Oncol Res. 2019; 27:193–202. https://doi.org/10.3727/096504018X15150662230295 [PubMed]
• 147. Marjaneh RM, Rahmani F, Hassanian SM, Rezaei N, Hashemzehi M, Bahrami A, Ariakia F, Fiuji H, Sahebkar A, Avan A, Khazaei M. Phytosomal curcumin inhibits tumor growth in colitis-associated colorectal cancer. J Cell Physiol. 2018; 233:6785–98. https://doi.org/10.1002/jcp.26538 [PubMed]
• 148. Zhang J, Jiang X, Jiang Y, Guo M, Zhang S, Li J, He J, Liu J, Wang J, Ouyang L. Recent advances in the development of dual VEGFR and c-Met small molecule inhibitors as anticancer drugs. Eur J Med Chem. 2016; 108:495–504. https://doi.org/10.1016/j.ejmech.2015.12.016 [PubMed]
• 149. Chang K, Karnad A, Zhao S, Freeman JW. Roles of c-Met and RON kinases in tumor progression and their potential as therapeutic targets. Oncotarget. 2015; 6:3507–18. https://doi.org/10.18632/oncotarget.3420 [PubMed]
• 150. Kwak EL, Bang YJ, Camidge DR, Shaw AT, Solomon B, Maki RG, Ou SH, Dezube BJ, Jänne PA, Costa DB, Varella-Garcia M, Kim WH, Lynch TJ, et al. Anaplastic lymphoma kinase inhibition in non-small-cell lung cancer. N Engl J Med. 2010; 363:1693–703. https://doi.org/10.1056/NEJMoa1006448 [PubMed]
• 151. Li C, Singh B, Graves-Deal R, Ma H, Starchenko A, Fry WH, Lu Y, Wang Y, Bogatcheva G, Khan MP, Milne GL, Zhao S, Ayers GD, et al. Three-dimensional culture system identifies a new mode of cetuximab resistance and disease-relevant genes in colorectal cancer. Proc Natl Acad Sci USA. 2017; 114:E2852–61. https://doi.org/10.1073/pnas.1618297114 [PubMed]
• 152. Niu Y, Pan D, Shi D, Bai Q, Liu H, Yao X. Influence of Chirality of Crizotinib on Its MTH1 Protein Inhibitory Activity: Insight from Molecular Dynamics Simulations and Binding Free Energy Calculations. PLoS One. 2015; 10:e0145219. https://doi.org/10.1371/journal.pone.0145219 [PubMed]
• 153. Huber KV, Salah E, Radic B, Gridling M, Elkins JM, Stukalov A, Jemth AS, Göktürk C, Sanjiv K, Strömberg K, Pham T, Berglund UW, Colinge J, et al. Stereospecific targeting of MTH1 by (S)-crizotinib as an anticancer strategy. Nature. 2014; 508:222–7. https://doi.org/10.1038/nature13194 [PubMed]
• 154. Rai P, Onder TT, Young JJ, McFaline JL, Pang B, Dedon PC, Weinberg RA. Continuous elimination of oxidized nucleotides is necessary to prevent rapid onset of cellular senescence. Proc Natl Acad Sci USA. 2009; 106:169–74. https://doi.org/10.1073/pnas.0809834106 [PubMed]
• 155. Rai P, Young JJ, Burton DG, Giribaldi MG, Onder TT, Weinberg RA. Enhanced elimination of oxidized guanine nucleotides inhibits oncogenic RAS-induced DNA damage and premature senescence. Oncogene. 2011; 30:1489–96. https://doi.org/10.1038/onc.2010.520 [PubMed]
• 156. Rai P. Human Mut T Homolog 1 (MTH1): a roadblock for the tumor-suppressive effects of oncogenic RAS-induced ROS. Small GTPases. 2012; 3:120–5. https://doi.org/10.4161/sgtp.19556 [PubMed]
• 157. Giribaldi MG, Munoz A, Halvorsen K, Patel A, Rai P. MTH1 expression is required for effective transformation by oncogenic HRAS. Oncotarget. 2015; 6:11519–29. https://doi.org/10.18632/oncotarget.3447 [PubMed]
• 158. Spranger S, Bao R, Gajewski TF. Melanoma-intrinsic β-catenin signalling prevents anti-tumour immunity. Nature. 2015; 523:231–5. https://doi.org/10.1038/nature14404 [PubMed]