Chemotherapy resistance in colorectal cancer (CRC) remains a major obstacle in clinical oncology. Analysis of clinical specimens from chemotherapy-resistant patients revealed elevated CXCL7 expression in tumor-associated macrophages (TAMs). Through integrated in vitro and in vivo studies, we demonstrated that chemotherapy induces tumor cell-macrophage crosstalk, leading to CXCL7 upregulation in TAMs. Using a co-culture system, we observed that CXCL7+ macrophages confer chemoresistance to CRC cells. Mechanistic investigations revealed that CXCL7 activates the CXCR2 receptor on tumor cells, triggering interferon signaling and promoting serine metabolism through STAT1-dependent transcriptional upregulation of phosphoglycerate dehydrogenase (PHGDH), the key enzyme in serine biosynthesis. This metabolic reprogramming enhances the paracrine secretion of S-adenosyl methionine (SAM), which drives chemotherapy resistance. Furthermore, CXCL7-mediated the paracrine secretion of SAM in tumor cells, which in turn promotes M2 macrophage polarization and sustains CXCL7 expression in TAMs. Our findings reveal that a CXCL7-SAM feedback loop between tumor cells and macrophages establishes a chemoresistant niche. This interaction represents a promising therapeutic target for overcoming chemoresistance in CRC.

While advancements in genome sequencing have identified millions of somatic mutations in cancer, their functional impact is poorly understood. We previously developed the expression-based variant impact phenotyping (eVIP) method to use gene expression data to characterize the function of gene variants. The eVIP method uses a decision tree-based algorithm to predict the functional impact of somatic variants by comparing gene expression signatures induced by introduction of wild-type (WT) versus mutant cDNAs in cell lines. The method distinguishes between variants that are gain-of-function, loss-of-function, change-of-function, or neutral. We present eVIP2, software that allows for pathway analysis (eVIP Pathways) and usage with RNA-seq data. To demonstrate the eVIP2 software and approach, we characterized two recurrent frameshift variants in RNF43, a negative regulator of Wnt signaling, frequently mutated in colorectal, gastric, and endometrial cancer. RNF43 WT, RNF43 R117fs, RNF43 G659fs, or GFP control cDNA were overexpressed in HEK293T cells. Analysis with eVIP2 predicted that the frameshift at position 117 was a loss-of-function mutation, as expected. The second frameshift at position 659 has been previously described as a passenger mutation that maintains the RNF43 WT function as a negative regulator of Wnt. Surprisingly, eVIP2 predicted G659fs to be a change-of-function mutation. Additional eVIP Pathways analysis of RNF43 G659fs predicted 10 pathways to be significantly altered, including TNF-α via NFκB signaling, KRAS signaling, and hypoxia, highlighting the benefit of a more comprehensive approach when determining the impact of gene variant function. To validate these predictions, we performed reporter assays and found that each pathway activated by expression of RNF43 G659fs, but not expression of RNF43 WT, was identified as impacted by eVIP2, supporting that RNF43 G659fs is a change-of-function mutation and its effect on the identified pathways. Pathway activation was further validated by Western blot analysis. Lastly, we show primary colon adenocarcinoma patient samples with R117fs and G659fs variants have transcriptional profiles similar to BRAF missense mutations with activated RAS/MAPK signaling, consistent with KRAS signaling pathways being GOF in both variants. The eVIP2 method is an important step towards overcoming the current challenge of variant interpretation in the implementation of precision medicine. eVIP2 is available at https://github.com/BrooksLabUCSC/eVIP2. Cancer is often caused by a DNA change, called a mutation, that creates an atypical protein variant. Many cancer-associated mutations remain uninvestigated because experimental validation of their effect on gene function is costly, time-consuming, and requires prior knowledge of a gene’s function. We present our computational tool, eVIP2, which advances our previous approach called expression-based variant impact phenotyping (eVIP) and can be used with RNA sequencing data. This method uses gene expression data to characterize a gene variant’s function, requiring no prior knowledge of the wild-type gene’s function. With eVIP2, we can predict if a mutation causes a gain, loss, or change in function, or if it is neutral. Here, we found two recurrent frameshift mutations in RNF43 that have different effects on gene function where one mutation causes multiple cancer pathways to be activated. This study highlights the usefulness of using the eVIP2 tool for profiling a mutation’s impact at the pathway level.

Breast cancer frequently metastasizes to the bone, but treatment options for bone metastatic breast cancer are limited. Amino acid metabolism is reprogrammed in the bone metastatic microenvironment, suggesting that it could represent a therapeutic vulnerability. In this study, we focused on the metabolism of hydroxyproline (Hyp), a key amino acid resulting from bone collagen degradation, which serves as a critical biomarker for bone metastases. Proline dehydrogenase 2 (PRODH2), the primary enzyme involved in Hyp metabolism, was significantly upregulated in clinical samples from breast cancer bone metastases. Notably, PRODH2-mediated Hyp metabolism drove osteoclast differentiation, enhancing collagen degradation and promoting breast cancer bone metastasis in vivo. Furthermore, PRODH2 facilitated tumor cell viability and osteoclast differentiation by upregulating the ferroptosis inhibitor SLC7A11 and the bone metastasis–related factor CXCL8 (IL8), respectively. Intriguingly, Hyp metabolism catalyzed by PRODH2 generated acetyl-CoA, which enhanced YY1 acetylation and thereby transcriptionally activated both SLC7A11 and IL8. Importantly, treatment with a PRODH2 inhibitor effectively disrupted the bone metastatic cascade. Together, these results reveal that collagen degradation from osteolysis produces Hyp that reinforces osteoclast differentiation and metastasis, creating a vicious cycle. Identification of the role of the PRODH2–SLC7A11–IL8 axis in promoting breast cancer bone metastasis suggests potential therapeutic strategies to improve patient outcomes. Significance: In the bone microenvironment, osteolysis increases PRODH2-mediated hydroxyproline metabolism that drives osteoclast differentiation and suppresses breast cancer cell ferroptosis, which can be reversed by targeting PRODH2 to suppress metastatic progression.

Colorectal cancer (CRC) is characterized by a complex tumor inflammatory microenvironment, while angiogenesis and immunosuppression frequently occur concomitantly. However, the exact mechanism that controls angiogenesis and immunosuppression in CRC microenvironment remains unclear. Herein, we found that expression levels of lipid raft protein STOML2 were increased in CRC and were associated with advanced disease stage and poor survival outcomes. Intriguingly, we revealed that STOML2 is essential for CRC tumor inflammatory microenvironment, which induces angiogenesis and facilitates tumor immune escape simultaneously both in vitro and in vivo. Moreover, tumors with STOML2 overexpression showed effective response to anti-angiogenesis treatment and immunotherapy in vivo. Mechanistically, STOML2 regulates CRC proliferation, angiogenesis, and immune escape through activated NF-κB signaling pathway via binding to TRADD protein, resulting in upregulation of CCND1, VEGF, and PD-L1. Furthermore, treatment with NF-κB inhibitor dramatically reversed the ability of proliferation and angiogenesis. Clinically, we also observed a strong positive correlation between STOML2 expression and Ki67, CD31, VEGFC and PD-1 of CD8⁺T cell expression. Taken together, our results provided novel insights into the role of STOML2 in CRC inflammatory microenvironment, which may present a therapeutic opportunity for CRC.

Purpose: Pancreatic ductal adenocarcinoma (PDAC) is one of the most malignant tumors. Endoplasmic reticulum stress (ERS) plays an essential role in PDAC progression. Here, we aim to identify the ERS-related genes in PDAC and build reliable risk models for diagnosis, prognosis and immunotherapy response of PDAC patients as well as investigate the potential mechanism. Methods: We obtained PDAC cohorts with transcriptional profiles and clinical data from the ArrayExpress, The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) databases. Univariate Cox regression, LASSO regression and multivariate Cox regression analyses were used to construct an ERS-related prognostic signature. The CIBERSORT and ssGSEA algorithms were applied to explore the correlation between the prognostic signature and immune cell infiltration and immune-related pathways. The GDSC database and TIDE algorithm were used to predict responses to chemotherapy and immunotherapy, identifying potential drugs for treating patients with PDAC. Results: We established and validated an ERS-related prognostic signature comprising eight genes (HMOX1, TGFB1, JSRP1, GAPDH, CAV1, CHRNE, CD74 and ERN2). Patients with higher risk scores displayed worse outcomes than those with lower risk scores. PDAC patients in low-risk groups might benefit from immunotherapy. Dasatinib and lapatinib might have potential therapeutic implications in high-risk PDAC patients. Conclusion: We established and validated an ERS-related prognostic signature comprising eight genes to predict the overall survival outcome of PDAC patients, which closely correlating with the response to immunotherapy and sensitivity to anti-tumor drugs, as well as could be beneficial for formulating clinical strategies and administering individualized treatments.

Background: Pancreatic ductal adenocarcinoma (PDAC) is a highly aggressive lethal malignancy. An effective prognosis prediction model is urgently needed for treatment optimization.Methods: The differentially expressed unfolded protein response (UPR)‒related genes between pancreatic tumor and normal tissue were analyzed using the TCGA-PDAC dataset, and these genes that overlapped with UPR‒related prognostic genes from the E-MTAB-6134 dataset were further analyzed. Univariate, LASSO and multivariate Cox regression analyses were applied to establish a prognostic gene signature, which was evaluated by Kaplan‒Meier curve and receiver operating characteristic (ROC) analyses. E‒MTAB‒6134 was set as the training dataset, while TCGA-PDAC, GSE21501 and ICGC-PACA-AU were used for external validation. Subsequently, a nomogram integrating risk scores and clinical parameters was established, and gene set enrichment analysis (GSEA), tumor immunity analysis and drug sensitivity analysis were conducted.Results: A UPR-related signature comprising twelve genes was constructed and divided PDAC patients into high- and low-risk groups based on the median risk score. The UPR-related signature accurately predicted the prognosis and acted as an independent prognostic factor of PDAC patients, and the AUCs of the UPR-related signature in predicting PDAC prognosis at 1, 2 and 3 years were all more than 0.7 in the training and validation datasets. The UPR-related signature showed excellent performance in outcome prediction even in different clinicopathological subgroups, including the female (p<0.0001), male (p<0.0001), grade 1/2 (p<0.0001), grade 3 (p=0.028), N0 (p=0.043), N1 (p<0.001), and R0 (p<0.0001) groups. Furthermore, multiple immune-related pathways were enriched in the low-risk group, and risk scores in the low-risk group were also associated with significantly higher levels of tumor-infiltrating lymphocytes (TILs). In addition, DepMap drug sensitivity analysis and our validation experiment showed that PDAC cell lines with high UPR-related risk scores or UPR activation are more sensitive to floxuridine, which is used as an antineoplastic agent.Conclusion: Herein, we identified a novel UPR-related prognostic signature that showed high value in predicting survival in patients with PDAC. Targeting these UPR-related genes might be an alternative for PDAC therapy. Further experimental studies are required to reveal how these genes mediate ER stress and PDAC progression.