An independent poor-prognosis subtype of breast cancer defined by a distinct tumor immune microenvironment

Breast cancer clinical outcomes and tumor immune microenvironment: cross-dialogue of multiple epigenetic modification profiles

The discovery of RNA methylation alterations associated with cancer holds promise for their utilization as potential biomarkers in cancer diagnosis, prognosis, and prediction. RNA methylation has been found to impact the immunological microenvironment of tumors, but the specific role of methylation-related genes (MRGs), particularly in breast cancer (BC), the most common cancer among women globally, within the tumor microenvironment remains unknown. In this study, we obtained data from TCGA and GEO databases to investigate the expression patterns of MRGs in both genomic and transcriptional domains in BC. By analyzing the data, we identified two distinct genetic groupings that were correlated with clinicopathological characteristics, prognosis, degree of TME cell infiltration, and other abnormalities in MRGs among patients. Subsequently, an MRG model was developed to predict overall survival (OS) and its accuracy was evaluated in BC patients. Additionally, a highly precise nomogram was created to enhance the practical usability of the MRG model. In low-risk groups, we observed lower TBM values and higher TIDE scores. We further explored how MRGs influence a patient's prognosis, clinically significant characteristics, response to therapy, and the TME. These risk signatures have the potential to improve treatment strategies for BC patients and could be applied in future clinical settings. Moreover, they may also be utilized to determine prognosis and biological features in these patients.

Benign breast tumors may arise on different immunological backgrounds

Benign breast tumors are a nonthreatening condition defined as abnormal cell growth within the breast without the ability to invade nearby tissue. However, benign lesions hold valuable biological information that can lead us toward better understanding of tumor biology. In this study, we have used two pathway analysis algorithms, Pathifier and gene set variation analysis (GSVA), to identify biological differences between normal breast tissue, benign tumors and malignant tumors in our clinical dataset. Our results revealed that one-third of all pathways that were significantly different between benign and malignant tumors were immune-related pathways, and 227 of them were validated by both methods and in the METABRIC dataset. Furthermore, five of these pathways (all including genes involved in cytokine and interferon signaling) were related to overall survival in cancer patients in both datasets. The cellular moieties that contribute to immune differences in malignant and benign tumors were analyzed using the deconvolution tool, CIBERSORT. The results showed that levels of some immune cells were specifically higher in benign than in malignant tumors, and this was especially the case for resting dendritic cells and follicular T-helper cells. Understanding the distinct immune profiles of benign and malignant breast tumors may aid in developing noninvasive diagnostic methods to differentiate between them in the future.

Applying image features of proximal paracancerous tissues in predicting prognosis of patients with hepatocellular carcinoma

BACKGROUND

Most of the methods using digital pathological image for predicting Hepatocellular carcinoma (HCC) prognosis have not considered paracancerous tissue microenvironment (PTME), which are potentially important for tumour initiation and metastasis. This study aimed to identify roles of image features of PTME in predicting prognosis and tumour recurrence of HCC patients.

METHODS

We collected whole slide images (WSIs) of 146 HCC patients from Sun Yat-sen Memorial Hospital (SYSM dataset). For each WSI, five types of regions of interests (ROIs) in PTME and tumours were manually annotated. These ROIs were used to construct a Lasso Cox survival model for predicting the prognosis of HCC patients. To make the model broadly useful, we established a deep learning method to automatically segment WSIs, and further used it to construct a prognosis prediction model. This model was tested by the samples of 225 HCC patients from the Cancer Genome Atlas Liver Hepatocellular Carcinoma (TCGA-LIHC).

RESULTS

In predicting prognosis of the HCC patients, using the image features of manually annotated ROIs in PTME achieved C-index 0.668 in the SYSM testing dataset, which is higher than the C-index 0.648 reached by the model only using image features of tumours. Integrating ROIs of PTME and tumours achieved C-index 0.693 in the SYSM testing dataset. The model using automatically segmented ROIs of PTME and tumours achieved C-index of 0.665 (95% CI: 0.556-0.774) in the TCGA-LIHC samples, which is better than the widely used methods, WSISA (0.567), DeepGraphSurv (0.593), and SeTranSurv (0.642). Finally, we found the Texture SumAverage Skew HV on immune cell infiltration and Texture related features on desmoplastic reaction are the most important features of PTME in predicting HCC prognosis. We additionally used the model in prediction HCC recurrence for patients from SYSM-training, SYSM-testing, and TCGA-LIHC datasets, indicating the important roles of PTME in the prediction.

CONCLUSIONS

Our results indicate image features of PTME is critical for improving the prognosis prediction of HCC. Moreover, the image features related with immune cell infiltration and desmoplastic reaction of PTME are the most important factors associated with prognosis of HCC.

Exploration of the relationship between tumor-infiltrating lymphocyte score and histological grade in breast cancer

BACKGROUND

The histological grade is an important factor in the prognosis of invasive breast cancer and is vital to accurately identify the histological grade and reclassify of Grade2 status in breast cancer patients.

METHODS

In this study, data were collected from 556 invasive breast cancer patients, and then randomly divided into training cohort (n = 335) and validation cohort (n = 221). All patients were divided into actual low risk group (Grade1) and high risk group (Grade2/3) based on traditional histological grade, and tumor-infiltrating lymphocyte score (TILs-score) obtained from multiphoton images, and the TILs assessment method proposed by International Immuno-Oncology Biomarker Working Group (TILs-WG) were also used to differentiate between high risk group and low risk group of histological grade in patients with invasive breast cancer. Furthermore, TILs-score was used to reclassify Grade2 (G2) into G2 /Low risk and G2/High risk. The coefficients for each TILs in the training cohort were retrieved using ridge regression and TILs-score was created based on the coefficients of the three kinds of TILs.

RESULTS

Statistical analysis shows that TILs-score is significantly correlated with histological grade, and is an independent predictor of histological grade (odds ratio [OR], 2.548; 95%CI, 1.648-3.941; P < 0.0001), but TILs-WG is not an independent predictive factor for grade (P > 0.05 in the univariate analysis). Moreover, the risk of G2/High risk group is higher than that of G2/Low risk group, and the survival rate of patients with G2/Low risk is similar to that of Grade1, while the survival rate of patients with G2/High risk is even worse than that of patients with G3.

CONCLUSION

Our results suggest that TILs-score can be used to predict the histological grade of breast cancer and potentially to guide the therapeutic management of breast cancer patients.

Diagnosis of benign and malignant nodules with a radiomics model integrating features from nodules and mammary regions on DCE-MRI

Objectives

To establish a radiomics model for distinguishing between the benign and malignant mammary gland nodules via combining the features from nodule and mammary regions on DCE-MRI.

Methods

In this retrospective study, a total of 103 cases with mammary gland nodules (malignant/benign = 80/23) underwent DCE-MRI, and was confirmed by biopsy pathology. Features were extracted from both nodule region and mammary region on DCE-MRI. Three SVM classifiers were built for diagnosis of benign and malignant nodules as follows: the model with the features only from nodule region (N model), with the features only from mammary region (M model) and the model combining the features from nodule region and mammary region (NM model). The performance of models was evaluated with the area under the curve of receiver operating characteristic (AUC).

Results

One radiomic features is selected from nodule region and 3 radiomic features is selected from mammary region. Compared with N or M model, NM model exhibited the best performance with an AUC of 0.756.

Conclusions

Compared with the model only using the features from nodule or mammary region, the radiomics-based model combining the features from nodule and mammary region outperformed in the diagnosis of benign and malignant nodules.

Measuring the composition of the tumor microenvironment with transcriptome analysis: past, present and future

Interactions between tumor cells and immune cells in the tumor microenvironment (TME) play a vital role the mechanisms of immune evasion, by which cancer cells escape immune elimination. Thus, the characterization and quantification of different components in the TME is a hot topic in molecular biology and drug discovery. Since the development of transcriptome sequencing in bulk tissue, single cells and spatial dimensions, there are increasing methods emerging to deconvolute and subtype the TME. This review discusses and compares such computational strategies and downstream subtyping analyses. Integrative analyses of the transcriptome with other data, such as epigenetics and T-cell receptor sequencing, are needed to obtain comprehensive knowledge of the dynamic TME.

Tretinoin improves the anti-cancer response to cyclophosphamide, in a model-selective manner

BACKGROUND

Chemotherapy is included in treatment regimens for many solid cancers, but when administered as a single agent it is rarely curative. The addition of immune checkpoint therapy to standard chemotherapy regimens has improved response rates and increased survival in some cancers. However, most patients do not respond to treatment and immune checkpoint therapy can cause severe side effects. Therefore, there is a need for alternative immunomodulatory drugs that enhance chemotherapy.

METHODS

We used gene expression data from cyclophosphamide (CY) responders and non-responders to identify existing clinically approved drugs that could phenocopy a chemosensitive tumor microenvironment (TME), and tested combination treatments in multiple murine cancer models.

RESULTS

The vitamin A derivative tretinoin was the top predicted upstream regulator of response to CY. Tretinoin pre-treatment induced an inflammatory, interferon-associated TME, with increased infiltration of CD8 + T cells, sensitizing the tumor to subsequent chemotherapy. However, while combination treatment significantly improved survival and cure rate in a CD4+ and CD8+ T cell dependent manner in AB1-HA murine mesothelioma, this effect was model-selective, and could not be replicated using other cell lines.

CONCLUSIONS

Despite the promising data in one model, the inability to validate the efficacy of combination treatment in multiple cancer models deprioritizes tretinoin/cyclophosphamide combination therapy for clinical translation.

Identification and validation of a new prognostic signature based on cancer-associated fibroblast-driven genes in breast cancer

BACKGROUND

Breast cancer (BC), a leading malignant disease, affects women all over the world. Cancer associated fibroblasts (CAFs) stimulate epithelial-mesenchymal transition, and induce chemoresistance and immunosuppression.

AIM

To establish a CAFs-associated prognostic signature to improve BC patient outcome estimation.

METHODS

We retrieved the transcript profile and clinical data of 1072 BC samples from The Cancer Genome Atlas (TCGA) databases, and 3661 BC samples from the The Gene Expression Omnibus. CAFs and immune cell infiltrations were quantified using CIBERSORT algorithm. CAF-associated gene identification was done by weighted gene co-expression network analysis. A CAF risk signature was established via univariate, least absolute shrinkage and selection operator regression, and multivariate Cox regression analyses. The receiver operating characteristic (ROC) and Kaplan-Meier curves were employed to evaluate the predictability of the model. Subsequently, a nomogram was developed with the risk score and patient clinical signature. Using Spearman's correlations analysis, the relationship between CAF risk score and gene set enrichment scores were examined. Patient samples were collected to validate gene expression by quantitative real-time polymerase chain reaction (qRT-PCR).

RESULTS

Employing an 8-gene (IL18, MYD88, GLIPR1, TNN, BHLHE41, DNAJB5, FKBP14, and XG) signature, we attempted to estimate BC patient prognosis. Based on our analysis, high-risk patients exhibited worse outcomes than low-risk patients. Multivariate analysis revealed the risk score as an independent indicator of BC patient prognosis. ROC analysis exhibited satisfactory nomogram predictability. The area under the curve showed 0.805 at 3 years, and 0.801 at 5 years in the TCGA cohort. We also demonstrated that a reduced CAF risk score was strongly associated with enhanced chemotherapeutic outcomes. CAF risk score was significantly correlated with most hallmark gene sets. Finally, the prognostic signature were further validated by qRT-PCR.

CONCLUSION

We introduced a newly-discovered CAFs-associated gene signature, which can be employed to estimate BC patient outcomes conveniently and accurately.

TNF-α induced NF-κB mediated LYRM7 expression modulates the tumor growth and metastatic ability in breast cancer

Tumor microenvironment (TME) of solid tumors including breast cancer is complex and contains a distinct cytokine pattern including TNF-α, which determines the progression and metastasis of breast tumors. The metastatic potential of triple negative breast cancer subtypes is high as compared to other subtypes of breast cancer. NF-κB is key transcription factor regulating inflammation and mitochondrial bioenergetics including oxidative phosphorylation (OXPHOS) genes which determine its oxidative capacity and generating reducing equivalents for synthesis of key metabolites for proliferating breast cancer cells. The differential metabolic adaptation and OXPHOS function of breast cancer subtypes in inflammatory conditions and its contribution to metastasis is not well understood. Here we demonstrated that different subunits of NF-κB are differentially expressed in subtypes of breast cancer patients. RELA, one of the major subunits in regulation of the NF-κB pathway is positively correlated with high level of TNF-α in breast cancer patients. TNF-α induced NF-κB regulates the expression of LYRM7, an assembly factor for mitochondrial complex III. Downregulation of LYRM7 in MDA-MB-231 cells decreases mitochondrial super complex assembly and enhances ROS levels, which increases the invasion and migration potential of these cells. Further, in vivo studies using Infliximab, a monoclonal antibody against TNF-α showed decreased expression of LYRM7 in tumor tissue. Large scale breast cancer databases and human patient samples revealed that LYRM7 levels decreased in triple negative breast cancer patients compared to other subtypes and is determinant of survival outcome in patients. Our results indicate that TNF-α induced NF-κB is a critical regulator of LYRM7, a major factor for modulating mitochondrial functions under inflammatory conditions, which determines growth and survival of breast cancer cells.

Development of a machine learning-based radiomics signature for estimating breast cancer TME phenotypes and predicting anti-PD-1/PD-L1 immunotherapy response

BACKGROUNDS

Since breast cancer patients respond diversely to immunotherapy, there is an urgent need to explore novel biomarkers to precisely predict clinical responses and enhance therapeutic efficacy. The purpose of our present research was to construct and independently validate a biomarker of tumor microenvironment (TME) phenotypes via a machine learning-based radiomics way. The interrelationship between the biomarker, TME phenotypes and recipients' clinical response was also revealed.

METHODS

In this retrospective multi-cohort investigation, five separate cohorts of breast cancer patients were recruited to measure breast cancer TME phenotypes via a radiomics signature, which was constructed and validated by integrating RNA-seq data with DCE-MRI images for predicting immunotherapy response. Initially, we constructed TME phenotypes using RNA-seq of 1089 breast cancer patients in the TCGA database. Then, parallel DCE-MRI images and RNA-seq of 94 breast cancer patients obtained from TCIA were applied to develop a radiomics-based TME phenotypes signature using random forest in machine learning. The repeatability of the radiomics signature was then validated in an internal validation set. Two additional independent external validation sets were analyzed to reassess this signature. The Immune phenotype cohort (n = 158) was divided based on CD8 cell infiltration into immune-inflamed and immune-desert phenotypes; these data were utilized to examine the relationship between the immune phenotypes and this signature. Finally, we utilized an Immunotherapy-treated cohort with 77 cases who received anti-PD-1/PD-L1 treatment to evaluate the predictive efficiency of this signature in terms of clinical outcomes.

RESULTS

The TME phenotypes of breast cancer were separated into two heterogeneous clusters: Cluster A, an "immune-inflamed" cluster, containing substantial innate and adaptive immune cell infiltration, and Cluster B, an "immune-desert" cluster, with modest TME cell infiltration. We constructed a radiomics signature for the TME phenotypes ([AUC] = 0.855; 95% CI 0.777-0.932; p < 0.05) and verified it in an internal validation set (0.844; 0.606-1; p < 0.05). In the known immune phenotypes cohort, the signature can identify either immune-inflamed or immune-desert tumor (0.814; 0.717-0.911; p < 0.05). In the Immunotherapy-treated cohort, patients with objective response had higher baseline radiomics scores than those with stable or progressing disease (p < 0.05); moreover, the radiomics signature achieved an AUC of 0.784 (0.643-0.926; p < 0.05) for predicting immunotherapy response.

CONCLUSIONS

Our imaging biomarker, a practicable radiomics signature, is beneficial for predicting the TME phenotypes and clinical response in anti-PD-1/PD-L1-treated breast cancer patients. It is particularly effective in identifying the "immune-desert" phenotype and may aid in its transformation into an "immune-inflamed" phenotype.

Prognostic value and tumour microenvironment characteristics of the Glasgow Microenvironment Score in primary triple-negative breast cancer

AIMS

The Glasgow Microenvironment Score (GMS) reflects the tumour microenvironment (TME) status by combining inflammatory cell infiltration and the tumour-stroma percentage. This study aimed to investigate the prognostic value and TME characteristics of the GMS for patients with triple-negative breast cancer (TNBC).

METHODS

A total of 123 patients with stage I-III TNBC were enrolled in this study. The association between GMS and clinicopathological characteristics was examined using the Pearson's χ2 test or Fisher's exact test. Kaplan-Meier plots were used to compare survival among the three GMS groups. Cox regression analyses were conducted to test the HR. Microenvironment Cell Populations-counter algorithm was used to estimate the TME components of each case.

RESULTS

We found that higher GMS score tended to exhibit the lower nuclear grade (p=0.016), more positive lymph nodes (p=0.014) and later tumour, node, metastases stage (p=0.012). GMS was an independent prognostic factor for disease-free survival in TNBC, and GMS 2 showed the worst prognosis (HR=6.42, p=0.028). GMS 0 was more infiltrated with cytotoxic lymphocytes, including CD8+ T cells (p=0.037) and natural killer cells (p=0.005), while GMS 2 was enriched in more endothelial cells (p=0.014) and fibroblasts (p=0.008).

CONCLUSION

Our study suggested that the GMS is a prognostic indicator for patients with TNBC. As an accessible and effective index, the GMS may be a promising tool to help clinicians assess prognostic risk and TME for patients with TNBC.

Identification of Proteome-Based Immune Subtypes of Early Hepatocellular Carcinoma and Analysis of Potential Metabolic Drivers

Hepatocellular carcinoma (HCC) is a leading cause of cancer-related mortality worldwide, ranking fourth in frequency. The relationship between metabolic reprogramming and immune infiltration has been identified as having a crucial impact on HCC progression. However, a deeper understanding of the interplay between the immune system and metabolism in the HCC microenvironment is required. In this study, we used a proteomic dataset to identify three immune subtypes (IM1-IM3) in HCC, each of which has distinctive clinical, immune, and metabolic characteristics. Among these subtypes, IM3 was found to have the poorest prognosis, with the highest levels of immune infiltration and T-cell exhaustion. Furthermore, IM3 showed elevated glycolysis and reduced bile acid metabolism, which was strongly correlated with CD8 T cell exhaustion and regulatory T cell accumulation. Our study presents the proteomic immune stratification of HCC, revealing the possible link between immune cells and reprogramming of HCC glycolysis and bile acid metabolism, which may be a viable therapeutic strategy to improve HCC immunotherapy.

Identification of the prognostic immune subtype in copy-number high endometrial cancer

OBJECTIVE

The TCGA molecular subtype of endometrial cancer (EC) is widely applied, among which the copy-number high (CNH) subtype has the poorest prognosis. However, the heterogeneity of this subtype remains elusive. In this study, we aimed to identify heterogeneous immune subtypes in CNH EC and explore their prognostic significance.

METHODS

We collected 60 CNH EC cases in the TCGA database and performed unsupervised cluster analysis based on the enrichment scores of immune-related gene signatures to identify immune subtypes. We described their immune characteristics and prognoses and conducted differential gene analysis and lasso regression to identify a prognostic biomarker, GZMM. For experimental validation, we performed immunohistochemical staining of GZMM in 39 p53-positive EC surgical samples.

RESULTS

We defined two immune subtypes, immune-hot (IH) and immune-cold (IC), which differed in immune cell infiltration, cytokine and chemokine expression and prognosis. The IH subtype has significantly stronger immune activation than the IC subtype, showing a significant infiltration of immune effector cells and high expression of relevant chemokines, with better prognosis. Moreover, the immunohistochemical staining of GZMM in a cohort of 39 p53-positive EC surgical samples confirmed GZMM as a unique prognostic biomarker, with high expression in both tumor cells and lymphocytes predicting a better prognosis.

CONCLUSION

Our study revealed heterogeneous immune subtypes in CNH EC and identified GZMM as a prognostic biomarker. The stratified classification strategy combining molecular and immune subtypes provides valuable insights for future clinical practice.

Patient-derived scaffolds representing breast cancer microenvironments influence chemotherapy responses in adapted cancer cells consistent with clinical features

BACKGROUND

The tumor microenvironment clearly influences cancer progressing properties but less is known about how individual cancer microenvironments potentially moderate cancer treatment effects. By cultivating and treating cancer cell lines in patient-derived scaffolds (PDS), the impact of specific characteristics of individual cancer microenvironments can be incorporated in human-like growth modelling and cancer drug treatment testing.

METHODS

PDSs from 78 biobanked primary breast cancer samples with known patient outcomes, were prepared and repopulated with donor breast cancer cell lines, followed by treatment with 5-fluorouracil or doxorubicin after cellular adaption to the various microenvironments. Cancer cell responses to the treatments were monitored by RNA-analyses, highlighting changes in gene sets representative for crucial tumor biological processes such as proliferation, cancer stem cell features, differentiation and epithelial-to-mesenchymal transition.

RESULTS

The chemotherapy treatments induced distinct gene expression patterns in adapted cancer cells with clusters of similar treatment responses depending on the patient-derived cancer microenvironment used as growth substrate. The doxorubicin treatment displayed a favorable gene signature among surviving cancer cells with low proliferation (MKI67) and pluripotency features (NANOG, POU5F1), in comparison to 5-fluorouracil showing low proliferation but increased pluripotency. Specific gene changes monitored post-treatment were also significantly correlated with clinical data, including histological grade (NANOG), lymph node metastasis (SLUG) and disease-free patient survival (CD44).

CONCLUSIONS

This laboratory-based treatment study using patient-derived scaffolds repopulated with cancer cell lines, clearly illustrates that the human cancer microenvironment influences chemotherapy responses. The differences in treatment responses defined by scaffold-cultures have potential prognostic and treatment predictive values.

Deep learning and transfer learning identify breast cancer survival subtypes from single-cell imaging data

BACKGROUND

Single-cell multiplex imaging data have provided new insights into disease subtypes and prognoses recently. However, quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at a population scale are currently missing.

METHODS

We quantified hundreds of single-cell resolution cell-cell interaction features through neighborhood calculation, in addition to cellular phenotypes. We applied these features to a neural-network-based Cox-nnet survival model to identify survival-associated features. We used non-negative matrix factorization (NMF) to identify patient survival subtypes. We identified atypical subpopulations of triple-negative breast cancer (TNBC) patients with moderate prognosis and Luminal A patients with poor prognosis and validated these subpopulations by label transferring using the UNION-COM method.

RESULTS

The neural-network-based Cox-nnet survival model using all cellular phenotype and cell-cell interaction features is highly predictive of patient survival in the test data (Concordance Index > 0.8). We identify seven survival subtypes using the top survival features, presenting distinct profiles of epithelial, immune, and fibroblast cells and their interactions. We reveal atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets.

CONCLUSIONS

This work provides an approach to bridge single-cell level information toward population-level survival prediction.

Targeting eIF4A triggers an interferon response to synergize with chemotherapy and suppress triple-negative breast cancer

Protein synthesis is frequently dysregulated in cancer and selective inhibition of mRNA translation represents an attractive cancer therapy. Here, we show that therapeutically targeting the RNA helicase eIF4A with zotatifin, the first-in-class eIF4A inhibitor, exerts pleiotropic effects on both tumor cells and the tumor immune microenvironment in a diverse cohort of syngeneic triple-negative breast cancer (TNBC) mouse models. Zotatifin not only suppresses tumor cell proliferation but also directly repolarizes macrophages toward an M1-like phenotype and inhibits neutrophil infiltration, which sensitizes tumors to immune checkpoint blockade. Mechanistic studies revealed that zotatifin reprograms the tumor translational landscape, inhibits the translation of Sox4 and Fgfr1, and induces an interferon (IFN) response uniformly across models. The induction of an IFN response is partially due to the inhibition of Sox4 translation by zotatifin. A similar induction of IFN-stimulated genes was observed in breast cancer patient biopsies following zotatifin treatment. Surprisingly, zotatifin significantly synergizes with carboplatin to trigger DNA damage and an even heightened IFN response, resulting in T cell-dependent tumor suppression. These studies identified a vulnerability of eIF4A in TNBC, potential pharmacodynamic biomarkers for zotatifin, and provide a rationale for new combination regimens consisting of zotatifin and chemotherapy or immunotherapy as treatments for TNBC.

Transforming Intratumor Bacteria into Immunopotentiators to Reverse Cold Tumors for Enhanced Immuno-chemodynamic Therapy of Triple-Negative Breast Cancer

Immunotherapy of triple-negative breast cancer (TNBC) has an unsatisfactory therapeutic outcome due to an immunologically "cold" microenvironment. Fusobacterium nucleatum (F. nucleatum) was found to be colonized in triple-negative breast tumors and was responsible for the immunosuppressive tumor microenvironment and tumor metastasis. Herein, we constructed a bacteria-derived outer membrane vesicle (OMV)-coated nanoplatform that precisely targeted tumor tissues for dual killing of F. nucleatum and cancer cells, thus transforming intratumor bacteria into immunopotentiators in immunotherapy of TNBC. The as-prepared nanoparticles efficiently induced immunogenic cell death through a Fenton-like reaction, resulting in enhanced immunogenicity. Meanwhile, intratumoral F. nucleatum was killed by metronidazole, resulting in the release of pathogen-associated molecular patterns (PAMPs). PAMPs cooperated with OMVs further facilitated the maturation of dendritic cells and subsequent T-cell infiltration. As a result, the "kill two birds with one stone" strategy warmed up the cold tumor environment, maximized the antitumor immune response, and achieved efficient therapy of TNBC as well as metastasis prevention. Overall, this strategy based on a microecology distinction in tumor and normal tissue as well as microbiome-induced reversal of cold tumors provides new insight into the precise and efficient immune therapy of TNBC.

Radiogenomic analysis of cellular tumor-stroma heterogeneity as a prognostic predictor in breast cancer

BACKGROUND

The tumor microenvironment and intercellular communication between solid tumors and the surrounding stroma play crucial roles in cancer initiation, progression, and prognosis. Radiomics provides clinically relevant information from radiological images; however, its biological implications in uncovering tumor pathophysiology driven by cellular heterogeneity between the tumor and stroma are largely unknown. We aimed to identify radiogenomic signatures of cellular tumor-stroma heterogeneity (TSH) to improve breast cancer management and prognosis analysis.

METHODS

This retrospective multicohort study included five datasets. Cell subpopulations were estimated using bulk gene expression data, and the relative difference in cell subpopulations between the tumor and stroma was used as a biomarker to categorize patients into good- and poor-survival groups. A radiogenomic signature-based model utilizing dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) was developed to target TSH, and its clinical significance in relation to survival outcomes was independently validated.

RESULTS

The final cohorts of 1330 women were included for cellular TSH biomarker identification (n = 112, mean age, 57.3 years ± 14.6) and validation (n = 886, mean age, 58.9 years ± 13.1), radiogenomic signature of TSH identification (n = 91, mean age, 55.5 years ± 11.4), and prognostic (n = 241) assessments. The cytotoxic lymphocyte biomarker differentiated patients into good- and poor-survival groups (p < 0.0001) and was independently validated (p = 0.014). The good survival group exhibited denser cell interconnections. The radiogenomic signature of TSH was identified and showed a positive association with overall survival (p = 0.038) and recurrence-free survival (p = 3 × 10-4).

CONCLUSION

Radiogenomic signatures provide insights into prognostic factors that reflect the imbalanced tumor-stroma environment, thereby presenting breast cancer-specific biological implications and prognostic significance.

Machine learning-based cluster analysis of immune cell subtypes and breast cancer survival

Host immunity involves various immune cells working in concert to achieve balanced immune response. Host immunity interacts with tumorigenic process impacting disease outcome. Clusters of different immune cells may reveal unique host immunity in relation to breast cancer progression. CIBERSORT algorithm was used to estimate relative abundances of 22 immune cell types in 3 datasets, METABRIC, TCGA, and our study. The cell type data in METABRIC were analyzed for cluster using unsupervised hierarchical clustering (UHC). The UHC results were employed to train machine learning models. Kaplan-Meier and Cox regression survival analyses were performed to assess cell clusters in association with relapse-free and overall survival. Differentially expressed genes by clusters were interrogated with IPA for molecular signatures. UHC analysis identified two distinct immune cell clusters, clusters A (83.2%) and B (16.8%). Memory B cells, plasma cells, CD8 positive T cells, resting memory CD4 T cells, activated NK cells, monocytes, M1 macrophages, and resting mast cells were more abundant in clusters A than B, whereas regulatory T cells and M0 and M2 macrophages were more in clusters B than A. Patients in cluster A had favorable survival. Similar survival associations were also observed in other independent studies. IPA analysis showed that pathogen-induced cytokine storm signaling pathway, phagosome formation, and T cell receptor signaling were related to the cell type clusters. Our finding suggests that different immune cell clusters may indicate distinct immune responses to tumor growth, suggesting their potential for disease management.

Cuproptosis-related LINC02454 as a biomarker for laryngeal squamous cell carcinoma based on a novel risk model and in vitro and in vivo analyses

PURPOSE

Laryngeal squamous cell carcinomas (LSCCs) are aggressive tumors with the second-highest morbidity rate in patients with head and neck squamous cell carcinoma. Cuproptosis is a type of programmed cell death that impacts tumor malignancy and progression. The purpose of this study was to investigate the relationship between cuproptosis-related long non-coding RNAs (crlncRNAs) and the tumor immune microenvironment and chemotherapeutic drug sensitivity in LSCC, and crlncRNA impact on LSCC malignancy.

MATERIALS AND METHODS

Clinical and RNA-sequencing data from patients with LSCC were retrieved from the Cancer Genome Atlas. Differentially expressed prognosis-related crlncRNAs were identified based on univariate Cox regression analysis, a crlncRNA signature for LSCC was developed and validated using LASSO Cox regression. Finally, the effect of LINC02454, the core signature crlncRNA, on LSCC malignancy progression was evaluated in vitro and in vivo.

RESULTS

We identified a four-crlncRNA signature (LINC02454, AC026310.1, AC090517.2, and AC000123.1), according to which we divided the patients into high- and low-risk groups. The crlncRNA signature risk score was an independent prognostic indicator for overall and progression-free survival, and displayed high predictive accuracy. Patients with a higher abundance of infiltrating dendritic cells, M0 macrophages, and neutrophils had worse prognoses and those in the high-risk group were highly sensitive to multiple chemotherapeutic drugs. Knockdown of LINC02454 caused tumor suppression, via cuproptosis induction.

CONCLUSIONS

A novel signature of four crlncRNAs was found to be highly accurate as a risk prediction model for patients with LSCC and to have potential for improving the diagnosis, prognosis, and treatment of LSCC.

Phytochemicals targeting epidermal growth factor receptor (EGFR) for the prevention and treatment of HNSCC: A review

Head and neck squamous cell carcinoma (HNSCC) develops from the mucosal epithelium of the oral cavity, pharynx, and larynx, and is the most common malignancy of the head and neck, the incidence of which continues to rise. The epidermal growth factor receptor is thought to play a key role in the pathogenesis of HNSCC. Inhibition of epidermal growth factor receptor has been identified as an effective target for the treatment of HNSCC. Many phytochemicals have emerged as potential new drugs for the treatment of HNSCC. A systematic search was conducted for research articles published in PubMed, and Medline on relevant aspects. This review provides an overview of the available literature and reports highlighting the in vitro effects of phytochemicals on epidermal growth factor in various HNSCC cell models and in vivo in animal models and emphasizes the importance of epidermal growth factor as a current therapeutic target for HNSCC. Based on our review, we conclude that phytochemicals targeting the epidermal growth factor receptor are potentially effective candidates for the development of new drugs for the treatment of HNSCC. It provides an idea for further development and application of herbal medicines for cancer treatment.

Targeting EIF4A triggers an interferon response to synergize with chemotherapy and suppress triple-negative breast cancer

Protein synthesis is frequently dysregulated in cancer and selective inhibition of mRNA translation represents an attractive cancer therapy. Here, we show that therapeutically targeting the RNA helicase eIF4A by Zotatifin, the first-in-class eIF4A inhibitor, exerts pleiotropic effects on both tumor cells and the tumor immune microenvironment in a diverse cohort of syngeneic triple-negative breast cancer (TNBC) mouse models. Zotatifin not only suppresses tumor cell proliferation but also directly repolarizes macrophages towards an M1-like phenotype and inhibits neutrophil infiltration, which sensitizes tumors to immune checkpoint blockade. Mechanistic studies revealed that Zotatifin reprograms the tumor translational landscape, inhibits the translation of Sox4 and Fgfr1, and induces an interferon response uniformly across models. The induction of an interferon response is partially due to the inhibition of Sox4 translation by Zotatifin. A similar induction of interferon-stimulated genes was observed in breast cancer patient biopsies following Zotatifin treatment. Surprisingly, Zotatifin significantly synergizes with carboplatin to trigger DNA damage and an even heightened interferon response resulting in T cell-dependent tumor suppression. These studies identified a vulnerability of eIF4A in TNBC, potential pharmacodynamic biomarkers for Zotatifin, and provide a rationale for new combination regimens comprising Zotatifin and chemotherapy or immunotherapy as treatments for TNBC.

Deep-learning and transfer learning identify new breast cancer survival subtypes from single-cell imaging data

Quantitative models that explicitly capture single-cell resolution cell-cell interaction features to predict patient survival at population scale are currently missing. Here, we computationally extracted hundreds of features describing single-cell based cell-cell interactions and cellular phenotypes from a large, published cohort of cyto-images of breast cancer patients. We applied these features to a neural-network based Cox-nnet survival model and obtained high accuracy in predicting patient survival in test data (Concordance Index > 0.8). We identified seven survival subtypes using the top survival features, which present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. We identified atypical subpopulations of TNBC patients with moderate prognosis (marked by GATA3 over-expression) and Luminal A patients with poor prognosis (marked by KRT6 and ACTA2 over-expression and CDH1 under-expression). These atypical subpopulations are validated in TCGA-BRCA and METABRIC datasets. This work provides important guidelines on bridging single-cell level information towards population-level survival prediction.

STATEMENT OF TRANSLATIONAL RELEVANCE

Our findings from a breast cancer population cohort demonstrate the clinical utility of using the single-cell level imaging mass cytometry (IMC) data as a new type of patient prognosis prediction marker. Not only did the prognosis prediction achieve high accuracy with a Concordance index score greater than 0.8, it also enabled the discovery of seven survival subtypes that are more distinguishable than the molecular subtypes. These new subtypes present distinct profiles of epithelial, immune, fibroblast cells, and their interactions. Most importantly, this study identified and validated atypical subpopulations of TNBC patients with moderate prognosis (GATA3 over-expression) and Luminal A patients with poor prognosis (KRT6 and ACTA2 over-expression and CDH1 under-expression), using multiple large breast cancer cohorts.

Identification of key immune cell-related genes involved in tumorigenesis and prognosis of cervical squamous cell carcinoma

The infiltration of immune cells can significantly affect the prognosis and immune therapy of patients with cervical squamous cell carcinoma (CSCC). This study aimed to explore key immune cell-related genes in the tumorigenesis and prognosis of CSCC. The module significantly related to immunity was screened by weighted gene co-expression network analysis (WGCNA) and ESTIMATE analysis, followed by correlation analysis with clinical traits. Key candidate genes were intersected with the protein-protein interaction (PPI) network genes for immune-related genes. The relationship between immune cell infiltration and key genes was analyzed. Tumor immune dysfunction and exclusion (TIDE) and immunophenoscore (IPS) predicted the response to immunotherapy in CSCC patients. Clinically, quantitative real-time polymerase chain reaction (qRT-PCR) and immunohistochemistry were manipulated for analyzing the changes in mRNA and protein expression of key genes in cancer. Western blot was conducted to assess the correlation between key genes and immune infiltration. The brown module was notably associated with the immune microenvironment of CSCC, from which three immune-related key genes (TYROBP, CCL5, and HLA-DRA) were obtained. High expression of these genes was significantly positively associated with the infiltration abundance of T cells, B cells, and other immune cells. High expression levels of three key genes were confirmed in para-cancer tissue and correlated with the abundance of immune cells. The high-expression group of key genes was more sensitive to immunotherapy. We provide a theoretical basis for searching for potential targets for effective treatment and diagnosis of CSCC and provide new ideas for developing novel immunotherapy strategies.

Impact of Nanomedicine in Women's Metastatic Breast Cancer

Metastatic breast cancer is responsible for 90% of mortalities among women suffering from various types of breast cancers. Traditional cancer treatments such as chemotherapy and radiation therapy can cause significant side effects and may not be effective in many cases. However, recent advances in nanomedicine have shown great promise in the treatment of metastatic breast cancer. For example, nanomedicine demonstrated robust capacity in detection of metastatic cancers at early stages (i.e., before the metastatic cells leave the initial tumor site), which gives clinicians a timely option to change their treatment process (for example, instead of endocrine therapy they may use chemotherapy). Here recent advances in nanomedicine technology in the identification and treatment of metastatic breast cancers are reviewed.

New hormone receptor-positive breast cancer mouse cell line mimicking the immune microenvironment of anti-PD-1 resistant mammary carcinoma

BACKGROUND

Progress in breast cancer (BC) research relies on the availability of suitable cell lines that can be implanted in immunocompetent laboratory mice. The best studied mouse strain, C57BL/6, is also the only one for which multiple genetic variants are available to facilitate the exploration of the cancer-immunity dialog. Driven by the fact that no hormone receptor-positive (HR⁺) C57BL/6-derived mammary carcinoma cell lines are available, we decided to establish such cell lines.

METHODS

BC was induced in female C57BL/6 mice using a synthetic progesterone analog (medroxyprogesterone acetate, MPA) combined with a DNA damaging agent (7,12-dimethylbenz[a]anthracene, DMBA). Cell lines were established from these tumors and selected for dual (estrogen+progesterone) receptor positivity, as well as transplantability into C57BL/6 immunocompetent females.

RESULTS

One cell line, which we called B6BC, fulfilled these criteria and allowed for the establishment of invasive estrogen receptor-positive (ER⁺) tumors with features of epithelial to mesenchymal transition that were abundantly infiltrated by myeloid immune populations but scarcely by T lymphocytes, as determined by single-nucleus RNA sequencing and high-dimensional leukocyte profiling. Such tumors failed to respond to programmed cell death-1 (PD-1) blockade, but reduced their growth on treatment with ER antagonists, as well as with anthracycline-based chemotherapy, which was not influenced by T-cell depletion. Moreover, B6BC-derived tumors reduced their growth on CD11b blockade, indicating tumor sustainment by myeloid cells. The immune environment and treatment responses recapitulated by B6BC-derived tumors diverged from those of ER⁺ TS/A cell-derived tumors in BALB/C mice, and of ER^- E0771 cell-derived and MPA/DMBA-induced tumors in C57BL/6 mice.

CONCLUSIONS

B6BC is the first transplantable HR⁺ BC cell line derived from C57BL/6 mice and B6BC-derived tumors recapitulate the complex tumor microenvironment of locally advanced HR⁺ BC naturally resistant to PD-1 immunotherapy.

Constructing metabolism-protein interaction relationship to identify glioma prognosis using deep learning

Glioma is heterogeneous disease that requires classification into subtypes with similar clinical phenotypes, prognosis or treatment responses. Metabolic-protein interaction (MPI) can provide meaningful insights into cancer heterogeneity. Moreover, the potential of lipids and lactate for identifying prognostic subtypes of glioma remains relatively unexplored. Therefore, we proposed a method to construct an MPI relationship matrix (MPIRM) based on a triple-layer network (Tri-MPN) combined with mRNA expression, and processed the MPIRM by deep learning to identify glioma prognostic subtypes. These Subtypes with significant differences in prognosis were detected in glioma (p-value < 2e-16, 95% CI). These subtypes had a strong correlation in immune infiltration, mutational signatures and pathway signatures. This study demonstrated the effectiveness of node interaction from MPI networks in understanding the heterogeneity of glioma prognosis.

A mitochondrial function-related LncRNA signature predicts prognosis and immune microenvironment for breast cancer

Mitochondrial function, as the core of the cell's energy metabolism, is firmly connected to cancer metabolism and growth. However, the involvement of long noncoding RNAs (lncRNAs) related to mitochondrial function in breast cancer (BRCA) has not been thoroughly investigated. As a result, the objective of this research was to dissect the prognostic implication of mitochondrial function-related lncRNAs and their link to the immunological microenvironment in BRCA. The Cancer Genome Atlas (TCGA) database was used to acquire clinicopathological and transcriptome information for BRCA samples. Mitochondrial function-related lncRNAs were recognized by coexpression analysis of 944 mitochondrial function-related mRNAs obtained from the MitoMiner 4.0 database. A novel prognostic signature was built in the training cohort using integrated analysis of mitochondrial function-related lncRNA and the corresponding clinical information through univariate analysis, lasso regression, and stepwise multivariate Cox regression analysis. The prognostic worth was judged in the training cohort and validated in the test cohort. In addition, functional enrichment and immune microenvironment analyses were performed to explore the risk score on the basis of the prognostic signature. An 8-mitochondrial function-related lncRNA signature was generated by integrated analysis. Individuals within the higher-risk category had a worse overall survival rate (OS) (training cohort: P < 0.001; validation cohort: P < 0.001; whole cohort: P < 0.001). The risk score was identified as an independent risk factor by multivariate Cox regression analysis (training cohort: HR 1.441, 95% CI 1.229-1.689, P < 0.001; validation cohort: HR 1.343, 95% CI 1.166-1.548, P < 0.001; whole cohort: HR 1.241, 95% CI 1.156-1.333, P < 0.001). Following that, the predictive accuracy of the model was confirmed by the ROC curves. In addition, nomograms were generated, and the calibration curves revealed that the model had excellent prediction accuracy for 3- and 5-year OS. Besides, the higher-risk BRCA individuals have relatively decreased amounts of infiltration of tumor-killing immune cells, lower levels of immune checkpoint molecules, and immune function. We constructed and verified a novel mitochondrial function-related lncRNA signature that might accurately predict the outcome of BRCA, play an essential role in immunotherapy, and might be exploited as a therapeutic target for precise BRCA therapy.

Identifying tumour microenvironment-related signature that correlates with prognosis and immunotherapy response in breast cancer

Tumor microenvironment (TME) plays important roles in prognosis and immune evasion. However, the relationship between TME-related genes and clinical prognosis, immune cell infiltration, and immunotherapy response in breast cancer (BRCA) remains unclear. This study described the TME pattern to construct a TME-related prognosis signature, including risk factors PXDNL, LINC02038 and protective factors SLC27A2, KLRB1, IGHV1-12 and IGKV1OR2-108, as an independent prognostic factor for BRCA. We found that the prognosis signature was negatively correlated with the survival time of BRCA patients, infiltration of immune cells and the expression of immune checkpoints, while positively correlated with tumor mutation burden and adverse treatment effects of immunotherapy. Upregulation of PXDNL and LINC02038 and downregulation of SLC27A2, KLRB1, IGHV1-12 and IGKV1OR2-108 in high-risk score group synergistically contribute to immunosuppressive microenvironment which characterized by immunosuppressive neutrophils, impaired cytotoxic T lymphocytes migration and natural killer cell cytotoxicity. In summary, we identified a TME-related prognostic signature in BRCA, which was connected with immune cell infiltration, immune checkpoints, immunotherapy response and could be developed for immunotherapy targets.

Identification of potential tumor antigens and immune subtypes for lung adenocarcinoma

In lung adenocarcinoma (LUAD), tumor antigens and immune phenotypes are important for cancer immunotherapy. This study aims to identify potential tumor antigens and immune subtypes for LUAD. In this study, the gene expression profiles and related clinical data of LUAD patients were collected from the TCGA and the GEO database. Then, we first identified four genes with copy number variation and mutation related to the survival of LUAD patients, in which FAM117A, INPP5J, and SLC25A42 were screened as potential tumor antigens. The expressions of these genes were significantly correlated with the infiltration of B cells CD4⁺ T cells and dendritic cells using TIMER and CIBERSORT algorithms. LUAD patients were divided into three immune clusters: C1(immune-desert), C2(immune-active), and C3(inflamed) using the Non-negative matrix factorization algorithm by using survival-related immune genes. The C2 cluster showed favorable overall survival compared to C1 and C3 clusters in both TCGA and two GEO LUAD cohorts. Different immune cell infiltration patterns, immune-associated molecular characteristics, and drug sensitivity were found among the three clusters. Moreover, different positions in the immune landscape map exhibited different prognostic characteristics using dimensionality reduction, providing further evidence of the immune clusters. The Weighted Gene Co-Expression Network Analysis was used to identify the co-expression modules of these immune genes. the three subtypes were significantly positively correlated with the turquoise module gene list, indicating a good prognosis with high scores. We hope that the identified tumor antigens and immune subtypes can be used for immunotherapy and prognosis in LUAD patients.

Neutrophil extracellular traps (NETs)-related lncRNAs signature for predicting prognosis and the immune microenvironment in breast cancer

Background: Neutrophil extracellular traps (NETs) are closely associated to tumorigenesis and development. However, the relationship between NETs-related long non-coding RNAs (lncRNAs) and the characteristics of breast tumor remains an enigma. This study aimed to explore the clinical prognostic value of NETs-related lncRNAs, their correlation with the tumor microenvironment (TME) and their predictive ability of drug sensitivity in patients with breast cancer (BC). Methods: The expression profiles of RNA-sequencing and relevant clinical data of BC patients were extracted from TCGA database. The co-expression network analysis, univariable, least absolute shrinkage and selection operator (LASSO) and multivariable Cox algorithms were employed to construct the NETs-related lncRNAs signature. A nomogram was established and validated to explore the clinical application. Furthermore, the immune microenvironment and drug sensitivity for BC with different prognostic risks were explored. Finally, the expression pattern of lncRNAs was validated using qRT-PCR in BC tissues and their adjacent non-cancerous tissues. Results: Based on NETs-related lncRNAs, a prognostic risk model consisted of 10 lncRNAs (SFTA1P, ACTA2-AS1, AC004816.2, AC000067.1, LINC01235, LINC01010, AL133467.1, AC092919.1, AL591468.1, and MIR200CHG) was established. The Kaplan-Meier analysis showed that the overall survival (OS) was significantly better in low-risk BC patients than in high-risk BC patients (P _{training cohort} < 0.001, P _{validation cohort} = 0.009). The nomogram also showed good predictive accuracy for OS of BC individuals in both training and validation cohorts. The function enrichment analysis revealed that high-risk group was mainly enriched in immune-related functions and pathways, and the tumor mutation burden in this group was markedly higher than that in the low-risk group (p = 0.022). Moreover, significant differences were observed in immune cells, immune functions and immune checkpoint genes among BC patients at different risks (p < 0.05). The response to chemotherapeutic agents and immunotherapy were also closely related with the expression of NETs-related lncRNAs (p < 0.001). The expression of lncRNAs from experimental validation were generally consistent with the bioinformatics analysis results. Conclusion: Our study provided a novel prognostic model for BC and yielded strong scientific rationale for individualized treatment strategies, elucidating immunotherapy in BC patients.

CXCL11 negatively regulated by MED19 favours antitumour immune infiltration in breast cancer

BACKGROUND

Through microarray results, we found that the C-X-C motif chemokine ligand 11 (CXCL11) was negatively regulated by mediator complex subunit 19 (MED19), a protumour factor. However, the biological role and potential mechanism of CXCL11 need to be explored in breast cancer (BRCA).

METHODS

The BRCA dataset was obtained from the Cancer Genome Atlas (TCGA) dataset. Our microarray data and the BRCA dataset of TCGA were analysed and visualized using the R software package. The mRNA and protein levels were measured by qRT-PCR and western blotting.

RESULTS

Inhibition of MED19 in MDA-MB-231 cells caused CXCL11 upregulation. The relative positive regulation of cytokine pathways was enriched after MED19 knockdown. High CXCL11 was determined to be positively correlated with immune response activation, increased antitumour immune cell infiltration, immune checkpoint molecule expression, and enhanced sensitivity to immunotherapy and chemotherapy. Collectively, CXCL11 promoted antitumour immunity and was regulated by MED19 in BRCA. Clarifying the prognostic value and underlying mechanism of CXCL11 in BRCA could provide a theoretical basis to find new diagnostic and therapeutic targets.

Triple-Negative PAM50 Non-Basal Breast Cancer Subtype Predicts Benefit from Extended Adjuvant Capecitabine

PURPOSE

Predictive biomarkers for capecitabine benefit in triple-negative breast cancer (TNBC) have been recently proposed using samples from phase III clinical trials, including non-basal phenotype and biomarkers related to angiogenesis, stroma, and capecitabine activation genes. We aimed to validate these findings on the larger phase III GEICAM/CIBOMA clinical trial.

EXPERIMENTAL DESIGN

Tumor tissues from patients with TNBC randomized to standard (neo)adjuvant chemotherapy followed by capecitabine versus observation were analyzed using a 164-gene NanoString custom nCounter codeset measuring mRNA expression. A prespecified statistical plan sought to verify the predictive capacity of PAM50 non-basal molecular subtype and tested the hypotheses that breast tumors with increased expression of (meta)genes for cytotoxic cells, mast cells, endothelial cells, PDL2, and 38 individual genes benefit from adjuvant capecitabine for distant recurrence-free survival (DRFS; primary endpoint) and overall survival.

RESULTS

Of the 876 women enrolled in the GEICAM/CIBOMA trial, 658 (75%) were evaluable for analysis (337 with capecitabine and 321 without). Of these cases, 553 (84%) were profiled as PAM50 basal-like whereas 105 (16%) were PAM50 non-basal. Non-basal subtype was the most significant predictor for capecitabine benefit [HRcapecitabine, 0.19; 95% confidence interval (CI), 0.07-0.54; P < 0.001] when compared with PAM50 basal-like (HRcapecitabine, 0.9; 95% CI, 0.63-1.28; P = 0.55; Pinteraction<0.001, adjusted P value = 0.01). Analysis of biological processes related to PAM50 non-basal subtype revealed its enrichment for mast cells, extracellular matrix, angiogenesis, and features of mesenchymal stem-like TNBC subtype.

CONCLUSIONS

In this prespecified correlative analysis of the GEICAM/CIBOMA trial, PAM50 non-basal status identified patients with early-stage TNBC most likely to benefit from capecitabine.

Predicting Microenvironment in CXCR4- and FAP-Positive Solid Tumors-A Pan-Cancer Machine Learning Workflow for Theranostic Target Structures

(1) Background: C-X-C Motif Chemokine Receptor 4 (CXCR4) and Fibroblast Activation Protein Alpha (FAP) are promising theranostic targets. However, it is unclear whether CXCR4 and FAP positivity mark distinct microenvironments, especially in solid tumors. (2) Methods: Using Random Forest (RF) analysis, we searched for entity-independent mRNA and microRNA signatures related to CXCR4 and FAP overexpression in our pan-cancer cohort from The Cancer Genome Atlas (TCGA) database-representing n = 9242 specimens from 29 tumor entities. CXCR4- and FAP-positive samples were assessed via StringDB cluster analysis, EnrichR, Metascape, and Gene Set Enrichment Analysis (GSEA). Findings were validated via correlation analyses in n = 1541 tumor samples. TIMER2.0 analyzed the association of CXCR4 / FAP expression and infiltration levels of immune-related cells. (3) Results: We identified entity-independent CXCR4 and FAP gene signatures representative for the majority of solid cancers. While CXCR4 positivity marked an immune-related microenvironment, FAP overexpression highlighted an angiogenesis-associated niche. TIMER2.0 analysis confirmed characteristic infiltration levels of CD8+ cells for CXCR4-positive tumors and endothelial cells for FAP-positive tumors. (4) Conclusions: CXCR4- and FAP-directed PET imaging could provide a non-invasive decision aid for entity-agnostic treatment of microenvironment in solid malignancies. Moreover, this machine learning workflow can easily be transferred towards other theranostic targets.

Immune microenvironment and clinical feature analyses based on a prognostic model in lymph node-positive breast cancer

Background

If lymph node metastasis occurs in breast cancer patients, the disease can progress rapidly. Based on the infiltrative immune cells of breast cancer patients with lymph node positivity, we constructed the LNPRS for selecting prognostic predictors.

Methods

The LNPRS was established and the predictive value of the LNPRS was verified by independent testing cohorts. A nomogram was also established to confirm the therapeutic guidance significance of the LNPRS. The correlation of the LNPRS with tumor mutation burden, immune microenvironment score, immune checkpoints, the proportion of tumor-infiltrating immune cells, and GSEA and GSVA enrichment pathways were also evaluated.

Results

In the training cohort, the overall survival of breast cancer patients who had high LNPRS was shorter than that of patients who had low LNPRS (7.98 years versus 20.42 years, P-value< 8.16E-11). The AUC values for 5-, 10-, and 15-years were 0.787, 0.739, and 0.800, respectively. The ability to predict prognosis for the LNPRS was also tested in 3 independent testing cohorts. Furthermore, the predictive value of the LNPRS for chemotherapy and immunotherapy was also proven. The GSEA and GSVA showed that the LNPRS was closely related to the activation of T and B lymphocytes and IFN-γ secretion. Moreover, breast cancer patients with low LNPRS had higher TME scores than those with high LNPRS.

Conclusion

We can conclude that the LNPRS is a robust prognostic biomarker in breast cancer patients with positive lymph nodes and may be helpful for patients to make a clinical decision.

The role of lactate metabolism-related LncRNAs in the prognosis, mutation, and tumor microenvironment of papillary thyroid cancer

BACKGROUND

Lactate, a byproduct of glucose metabolism, is primarily utilized for gluconeogenesis and numerous cellular and organismal life processes. Interestingly, many studies have demonstrated a correlation between lactate metabolism and tumor development. However, the relationship between long non-coding RNAs (lncRNAs) and lactate metabolism in papillary thyroid cancer (PTC) remains to be explored.

METHODS

Lactate metabolism-related lncRNAs (LRLs) were obtained by differential expression and correlation analyses, and the risk model was further constructed by least absolute shrinkage and selection operator analysis (Lasso) and Cox analysis. Clinical, immune, tumor mutation, and enrichment analyses were performed based on the risk model. The expression level of six LRLs was tested using RT-PCR.

RESULTS

This study found several lncRNAs linked to lactate metabolism in both The Cancer Genome Atlas (TCGA) and Genotype-Tissue Expression (GTEx) datasets. Using Cox regression analysis, 303 lactate LRLs were found to be substantially associated with prognosis. Lasso was done on the TCGA cohort. Six LRLs were identified as independent predictive indicators for the development of a PTC prognostic risk model. The cohort was separated into two groups based on the median risk score (0.39717 -0.39771). Subsequently, Kaplan-Meier survival analysis and multivariate Cox regression analysis revealed that the high-risk group had a lower survival probability and that the risk score was an independent predictive factor of prognosis. In addition, a nomogram that can easily predict the 1-, 3-, and 5-year survival rates of PTC patients was established. Furthermore, the association between PTC prognostic factors and tumor microenvironment (TME), immune escape, as well as tumor somatic mutation status was investigated in high- and low-risk groups. Lastly, gene expression analysis was used to confirm the differential expression levels of the six LRLs.

CONCLUSION

In conclusion, we have constructed a prognostic model that can predict the prognosis, mutation status, and TME of PTC patients. The model may have great clinical significance in the comprehensive evaluation of PTC patients.

CD4+ T cells drive an inflammatory, TNF-α/IFN-rich tumor microenvironment responsive to chemotherapy

While chemotherapy remains the first-line treatment for many cancers, it is still unclear what distinguishes responders from non-responders. Here, we characterize the chemotherapy-responsive tumor microenvironment in mice, using RNA sequencing on tumors before and after cyclophosphamide, and compare the gene expression profiles of responders with progressors. Responsive tumors have an inflammatory and highly immune infiltrated pre-treatment tumor microenvironment characterized by the enrichment of pathways associated with CD4⁺ T cells, interferons (IFNs), and tumor necrosis factor alpha (TNF-α). The same gene expression profile is associated with response to cyclophosphamide-based chemotherapy in patients with breast cancer. Finally, we demonstrate that tumors can be sensitized to cyclophosphamide and 5-FU chemotherapy by pre-treatment with recombinant TNF-α, IFNγ, and poly(I:C). Thus, a CD4⁺ T cell-inflamed pre-treatment tumor microenvironment is necessary for response to chemotherapy, and this state can be therapeutically attained by targeted immunotherapy.

Identification of Immune Infiltration and the Potential Biomarkers in Diabetic Peripheral Neuropathy through Bioinformatics and Machine Learning Methods

Diabetic peripheral neuropathy (DPN) is one of the most common chronic complications in diabetes. Previous studies have shown that chronic neuroinflammation was associated with DPN. However, further research is needed to investigate the exact immune molecular mechanism underlying the pathogenesis of DPN. Expression profiles were downloaded from the Gene Expression Omnibus (GEO) database. Differentially expressed genes (DEGs) were screened by R software. After functional enrichment analysis of DEGs, a protein-protein interaction (PPI) network analysis was performed. The CIBERSORT algorithm was used to evaluate the infiltration of immune cells in DPN. Next, the least absolute shrinkage and selection operator (LASSO) logistic regression and support vector machine-recursive feature elimination (SVM-RFE) algorithms were applied to identify potential DPN diagnostic markers. Finally, the results were further validated by qRT-PCR. A total of 1308 DEGs were screened in this study. Enrichment analysis identified that DEGs were significantly enriched in immune-related biological functions and pathways. Immune cell infiltration analysis found that M1 and M2 macrophages, monocytes, resting mast cells, resting CD4 memory T cells and follicular helper T cells were involved in the development of DPN. LTBP2 and GPNMB were identified as diagnostic markers of DPN. qRT-PCR results showed that 15 mRNAs, including LTBP2 and GPNMB, were differentially expressed, consistent with the microarray results. In conclusion, LTBP2 and GPNMB can be used as novel candidate molecular diagnostic markers for DPN. Furthermore, the infiltration of immune cells plays an important role in the progression of DPN.

Interdependence of sequential cytotoxic T lymphocyte and natural killer cell cytotoxicity against melanoma cells

Cytotoxic T lymphocytes (CTL) and natural killer (NK) cells recognize and eliminate cancer cells. However, immune evasion, downregulation of immune function by the tumour microenvironment and resistance of cancer cells are major problems. Although CTL and NK cells are both important to eliminate cancer, most studies address them individually. We quantified sequential primary human CTL and NK cell cytotoxicity against the melanoma cell line SK-Mel-5. At high effector-to-target ratios, NK cells or melan-A (MART-1)-specific CTL eliminated all SK-Mel-5 cells within 24 h, indicating that SK-Mel-5 cells are not resistant initially. However, at lower effector-to-target ratios, which resemble numbers of the immune contexture in human cancer, a substantial number of SK-Mel-5 cells survived. Pre-exposure to CTL induced resistance in surviving SK-Mel-5 cells to subsequent CTL or NK cell cytotoxicity, and pre-exposure to NK cells induced resistance in surviving SK-Mel-5 cells to NK cells. Higher human leucocyte antigen class I expression or interleukin-6 levels were correlated with resistance to NK cells, whereas reduction in MART-1 antigen expression was correlated with reduced CTL cytotoxicity. The CTL cytotoxicity was rescued beyond control levels by exogenous MART-1 antigen. In contrast to the other three combinations, CTL cytotoxicity against SK-Mel-5 cells was enhanced following NK cell pre-exposure. Our assay allows quantification of sequential CTL and NK cell cytotoxicity and might guide strategies for efficient CTL-NK cell anti-melanoma therapies. KEY POINTS: Cytotoxic T lymphocytes (CTL) and natural killer (NK) cells eliminate cancer cells. Both CTL and NK cells attack the same targets, but most studies address them individually. In a sequential cytotoxicity model, the interdependence of antigen-specific CTL and NK cell cytotoxicity against melanoma is quantified. High numbers of antigen-specific CTL and NK cells eliminate all melanoma cells. However, lower numbers induce resistance if secondary CTL or NK cell exposure follows initial CTL exposure or if secondary NK cell exposure follows initial NK cell exposure. On the contrary, if secondary CTL exposure follows initial NK cell exposure, cytotoxicity is enhanced. Alterations in human leucocyte antigen class I expression and interleukin-6 levels are correlated with resistance to NK cells, whereas a reduction in antigen expression is correlated with reduced CTL cytotoxicity; CTL cytotoxicity is rescued beyond control levels by exogenous antigen. This assay and the results on interdependencies will help us to understand and optimize immune therapies against cancer.

Characterisation of the immune microenvironment of primary breast cancer and brain metastasis reveals depleted T-cell response associated to ARG2 expression

BACKGROUND

Immune checkpoint inhibition is an established treatment in programmed death-ligand 1 (PD-L1)-positive metastatic triple-negative (TN) breast cancer (BC). However, the immune landscape of breast cancer brain metastasis (BCBM) remains poorly defined.

MATERIALS AND METHODS

The tumour-infiltrating lymphocytes (TILs) and the messenger RNA (mRNA) levels of 770 immune-related genes (NanoString™, nCounter™ Immuno-oncology IO360) were assessed in primary BCs and BCBMs. The prognostic role of ARG2 transcripts and protein expression in primary BCs and its association with outcome was determined.

RESULTS

There was a significant reduction of TILs in the BCBMs in comparison to primary BCs. 11.5% of BCs presented a high immune infiltrate (hot), 46.2% were altered (immunosuppressed/excluded) and 34.6% were cold (no/low immune infiltrate). 3.8% of BCBMs were hot, 23.1% altered and 73.1% cold. One hundred and twelve immune-related genes including PD-L1 and CTLA4 were decreased in BCBM compared to the primary BCs (false discovery rate <0.01, log2 fold-change >1.5). These genes are involved in matrix remodelling and metastasis, cytokine-chemokine signalling, lymphoid compartment, antigen presentation and immune cell adhesion and migration. Immuno-modulators such as PD-L1 (CD274), CTLA4, TIGIT and CD276 (B7H3) were decreased in BCBMs. However, PD-L1 and CTLA4 expression was significantly higher in TN BCBMs (P = 0.01), with CTLA4 expression also high in human epidermal growth factor receptor 2-positive (P < 0.01) compared to estrogen receptor-positive BCBMs. ARG2 was one of four genes up-regulated in BCBMs. High ARG2 mRNA expression in primary BCs was associated with worse distant metastasis-free survival (P = 0.038), while ARG2 protein expression was associated with worse breast-brain metastasis-free (P = 0.027) and overall survival (P = 0.019). High transcript levels of ARG2 correlated to low levels of cytotoxic and T cells in both BC and BCBM (P < 0.01).

CONCLUSION

This study highlights the immunological differences between primary BCs and BCBMs and the potential importance of ARG2 expression in T-cell depletion and clinical outcome.

Integrated analysis of bulk and single-cell RNA-seq reveals the role of MYC signaling in lung adenocarcinoma

MYC is one of the well-known oncogenes, and its important role in cancer still remains largely unknown. We obtained lung adenocarcinoma (LUAD) multi-omics data including genome, transcriptome, and single-cell sequencing data from multiple cohorts. We calculated the GSVA score of the MYC target v1 using the ssGSEA method, and obtained the genes highly correlated with this score by Spearman correlation analysis. Subsequent hierarchical clustering divided these genes into two gene sets highly associated with MYC signaling (S1 and S2). Unsupervised clustering based on these genes divided the LUAD samples into two distinct subgroups, namely, the MYC signaling inhibition group (C1) and activation group (C2). The MCP counter package in R was used to assess tumor immune cell infiltration abundance and ssGSEA was used to calculate gene set scores. The scRNA-seq was used to verify the association of MYC signaling to cell differentiation. We observed significant differences in prognosis, clinical characteristics, immune microenvironment, and genomic alterations between MYC signaling inhibition and MYC signaling activation groups. MYC-signaling is associated with genomic instability and can mediate the immunosuppressive microenvironment and promote cell proliferation, tumor stemness. Moreover, MYC-signaling activation is also subject to complex post-transcriptional regulation and is highly associated with cell differentiation. In conclusion, MYC signaling is closely related to the genomic instability, genetic alteration and regulation, the immune microenvironment landscape, cell differentiation, and disease survival in LUAD. The findings of this study provide a valuable reference to revealing the mechanism of cancer-promoting action of MYC in LUAD.

Immune landscape and risk prediction based on pyroptosis-related molecular subtypes in triple-negative breast cancer

The survival outcome of triple-negative breast cancer (TNBC) remains poor, with difficulties still existing in prognosis assessment and patient stratification. Pyroptosis, a newly discovered form of programmed cell death, is involved in cancer pathogenesis and progression. The role of pyroptosis in the tumor microenvironment (TME) of TNBC has not been fully elucidated. In this study, we disclosed global alterations in 58 pyroptosis-related genes at somatic mutation and transcriptional levels in TNBC samples collected from The Cancer Genome Atlas and Gene Expression Omnibus databases. Based on the expression patterns of genes related to pyroptosis, we identified two molecular subtypes that harbored different TME characteristics and survival outcomes. Then, based on differentially expressed genes between two subtypes, we established a 12-gene score with robust efficacy in predicting short- and long-term overall survival of TNBC. Patients at low risk exhibited a significantly better prognosis, more antitumor immune cell infiltration, and higher expression of immune checkpoints including PD-1, PD-L1, CTLA-4, and LAG3. The comprehensive analysis of the immune landscape in TNBC indicated that alterations in pyroptosis-related genes were closely related to the formation of the immune microenvironment and the intensity of the anticancer response. The 12-gene score provided new information on the risk stratification and immunotherapy strategy for highly heterogeneous patients with TNBC.

Thymic epithelial tumors: examining the GTF2I mutation and developing a novel prognostic signature with LncRNA pairs to predict tumor recurrence

Background

General transcription factor IIi (GTF2I) mutations are very common in thymic epithelial tumors (TETs) and are related to a more favorable prognosis in TET patients. However, limited research has been conducted on the role of GTF2I in the tumor immune microenvironment (TIME). Further, long non-coding RNAs (lncRNAs) have been associated with the survival of patients with TETs. Therefore, this study aimed to explore the relationship between GTF2I mutations and TIME and build a new potential signature for predicting tumor recurrence in the TETs. Research data was downloaded from The Cancer Genome Atlas database and the CIBERSORT algorithm was used to evaluate TIME differences between GTF2I mutant and wild-type TETs. Relevant differentially expressed lncRNAs based on differentially expressed immune-related genes were identified to establish lncRNA pairs. We constructed a signature using univariate and multivariate Cox regression analyses.

Results

GTF2I is the most commonly mutated gene in TETs, and is associated with an increased number of early-stage pathological types, as well as no history of myasthenia gravis or radiotherapy treatment. In the GTF2I wild-type group, immune score and immune cell infiltrations with M2 macrophages, activated mast cells, neutrophils, plasma, T helper follicular cells, and activated memory CD4 T cells were higher than the GTF2I mutant group. A risk model was built using five lncRNA pairs, and the 1-, 3-, and 5-year area under the curves were 0.782, 0.873, and 0.895, respectively. A higher risk score was related to more advanced histologic type.

Conclusion

We can define the GTF2I mutant-type TET as an immune stable type and the GTF2I wild-type as an immune stressed type. A signature based on lncRNA pairs was also constructed to effectively predict tumor recurrence.

Subtype and cell type specific expression of lncRNAs provide insight into breast cancer

Long non-coding RNAs (lncRNAs) are involved in breast cancer pathogenesis through chromatin remodeling, transcriptional and post-transcriptional gene regulation. We report robust associations between lncRNA expression and breast cancer clinicopathological features in two population-based cohorts: SCAN-B and TCGA. Using co-expression analysis of lncRNAs with protein coding genes, we discovered three distinct clusters of lncRNAs. In silico cell type deconvolution coupled with single-cell RNA-seq analyses revealed that these three clusters were driven by cell type specific expression of lncRNAs. In one cluster lncRNAs were expressed by cancer cells and were mostly associated with the estrogen signaling pathways. In the two other clusters, lncRNAs were expressed either by immune cells or fibroblasts of the tumor microenvironment. To further investigate the cis-regulatory regions driving lncRNA expression in breast cancer, we identified subtype-specific transcription factor (TF) occupancy at lncRNA promoters. We also integrated lncRNA expression with DNA methylation data to identify long-range regulatory regions for lncRNA which were validated using ChiA-Pet-Pol2 loops. lncRNAs play an important role in shaping the gene regulatory landscape in breast cancer. We provide a detailed subtype and cell type-specific expression of lncRNA, which improves the understanding of underlying transcriptional regulation in breast cancer.

RNA sequencing-based single sample predictors of molecular subtype and risk of recurrence for clinical assessment of early-stage breast cancer

Multigene assays for molecular subtypes and biomarkers can aid management of early invasive breast cancer. Using RNA-sequencing we aimed to develop single-sample predictor (SSP) models for clinical markers, subtypes, and risk of recurrence (ROR). A cohort of 7743 patients was divided into training and test set. We trained SSPs for subtypes and ROR assigned by nearest-centroid (NC) methods and SSPs for biomarkers from histopathology. Classifications were compared with Prosigna in two external cohorts (ABiM, n = 100 and OSLO2-EMIT0, n = 103). Prognostic value was assessed using distant recurrence-free interval. Agreement between SSP and NC for PAM50 (five subtypes) was high (85%, Kappa = 0.78) for Subtype (four subtypes) very high (90%, Kappa = 0.84) and for ROR risk category high (84%, Kappa = 0.75, weighted Kappa = 0.90). Prognostic value was assessed as equivalent and clinically relevant. Agreement with histopathology was very high or high for receptor status, while moderate for Ki67 status and poor for Nottingham histological grade. SSP and Prosigna concordance was high for subtype (OSLO-EMIT0 83%, Kappa = 0.73 and ABiM 80%, Kappa = 0.72) and moderate and high for ROR risk category (68 and 84%, Kappa = 0.50 and 0.70, weighted Kappa = 0.70 and 0.78). Pooled concordance for emulated treatment recommendation dichotomized for chemotherapy was high (85%, Kappa = 0.66). Retrospective evaluation suggested that SSP application could change chemotherapy recommendations for up to 17% of postmenopausal ER+/HER2-/N0 patients with balanced escalation and de-escalation. Results suggest that NC and SSP models are interchangeable on a group-level and nearly so on a patient level and that SSP models can be derived to closely match clinical tests.

Risk stratification of acute myeloid leukemia: Assessment using a novel prediction model based on ferroptosis-immune related genes

In acute myeloid leukemia (AML), the link between ferroptosis and the immune microenvironment has profound clinical significance. The objective of this study was to investigate the role of ferroptosis-immune related genes (FIRGs) in predicting the prognosis and therapeutic sensitivity in patients with AML. Using The Cancer Genome Atlas dataset, single sample gene set enrichment analysis was performed to calculate the ferroptosis score of AML samples. To search for FIRGs, differentially expressed genes between the high- and low-ferroptosis score groups were identified and then cross-screened with immune related genes. Univariate Cox and LASSO regression analyses were performed on the FIRGs to establish a prognostic risk score model with five signature FIRGs (BMP2, CCL3, EBI3, ELANE, and S100A6). The prognostic risk score model was then used to divide the patients into high- and low-risk groups. For external validation, two Gene Expression Omnibus cohorts were employed. Overall survival was poorer in the high-risk group than in the low-risk group. The novel risk score model was an independent prognostic factor for overall survival in patients with AML. Infiltrating immune cells were also linked to high-risk scores. Treatment targeting programmed cell death protein 1 may be more effective in high-risk patients. This FIRG-based prognostic risk model may aid in optimizing prognostic risk stratification and treatment of AML.

Fate decisions of breast cancer stem cells in cancer progression

Breast cancer has a marked recurrence and metastatic trait and is one of the most prevalent malignancies affecting women’s health worldwide. Tumor initiation and progression begin after the cell goes from a quiescent to an activated state and requires different mechanisms to act in concert to regulate t a specific set of spectral genes for expression. Cancer stem cells (CSCs) have been proven to initiate and drive tumorigenesis due to their capability of self-renew and differentiate. In addition, CSCs are believed to be capable of causing resistance to anti-tumor drugs, recurrence and metastasis. Therefore, exploring the origin, regulatory mechanisms and ultimate fate decision of CSCs in breast cancer outcomes has far-reaching clinical implications for the development of breast cancer stem cell (BCSC)-targeted therapeutic strategies. In this review, we will highlight the contribution of BCSCs to breast cancer and explore the internal and external factors that regulate the fate of BCSCs.

Comprehensive analysis of molecular features, prognostic values, and immune landscape association of m6A-regulated immune-related lncRNAs in smoking-associated lung squamous cell carcinoma

Lung squamous cell carcinoma (LUSC) is the second most common histopathological subtype of lung cancer, and smoking is the leading cause of this type of cancer. However, the critical factors that directly affect the survival rate and sensitivity to immunotherapy of smoking LUSC patients are still unknown. Previous studies have highlighted the role of N6-methyladenosine (m6A) RNA modification, the most common epigenetic modification in eukaryotic species, together with immune-related long non-coding RNAs (lncRNAs) in promoting the development and progression of tumors. Thus, elucidating m6A-modified immune lncRNAs in LUSC patients with smoking history is vital. In this study, we described the expression and mutation features of the 24 m6A-related regulators in the smoking-associated LUSC cohort from The Cancer Genome Atlas (TCGA) database. Then, two distinct subtypes based on the expression levels of the prognostic m6A-regulated immune lncRNAs were defined, and differentially expressed genes (DEGs) between the subtypes were identified. The distributions of clinical characteristics and the tumor microenvironment (TME) between clusters were analyzed. Finally, we established a lncRNA-associated risk model and exhaustively clarified the clinical features, prognosis, immune landscape, and drug sensitivity on the basis of this scoring system. Our findings give insight into potential mechanisms of LUSC tumorigenesis and development and provide new ideas in offering LUSC patients with individual and effective immunotherapies.

A Novel Risk Score Model of Lactate Metabolism for Predicting over Survival and Immune Signature in Lung Adenocarcinoma

Simple Summary

Since the discovery of the WarBurg effect, the veil of the tumorigenic role of lactic acid has been gradually revealed. Recently, it was proposed that lactic acid that is produced by tumor cells was secreted into the extracellular space to create immunosuppressive tumor microenvironment (TME) in a variety of ways. However, the intersection genes and the association with immunotherapy are unclear. At present, we identified six lactate-metabolism-associated genes, which were thought to enable tumor progression, that were related to LUAD immunotherapy and we constructed an LAR-score risk model.

Abstract

Background: The role of lactate acid in tumor progression was well proved. Recently, it was found that lactate acid accumulation induced an immunosuppressive microenvironment. However, these results were based on a single gene and it was unclear that lactate acid genes were associated with immunotherapy and able to predict overall survival. Methods: Genes and survival data were acquired from TCGA, GEO and GENECARDS. PCA and TSNE were used to distinguish sample types according to lactate metabolism-associated gene expression. A Wilcox-test examined the expression differences between normal and tumor samples. The distribution in chromatin and mutant levels were displayed by Circo and MAfTools. The lactate metabolism-associated gene were divided into categories by consistent clustering and visualized by Cytoscape. Immune cell infiltration was evaluated by CIBERSORT and LM22 matrix. Enrichment analysis was performed by GSVA. We used the ConsensusClusterPlus package for consistent cluster analysis. A prognostic model was constructed by Univariate Cox regression and Lasso regression analysis. Clinical specimens were detected their expression of genes in model by IHC. Results: Most lactate metabolism-associated gene were significantly differently expressed between normal and tumor samples. There was a strong correlation between the expression of lactate metabolism-associated gene and the abundance of immune cells. We divided them into two clusters (lactate.cluster A,B) with significantly different survival. The two clusters showed a difference in signal, immune cells, immune signatures, chemokines, and clinical features. We identified 162 differential genes from the two clusters, by which the samples were divided into three categories (gene.cluster A,B,C). They also showed a difference in OS and immune infiltration. Finally, a risk score model that was composed of six genes was constructed. There was significant difference in the survival between the high and low risk groups. ROC curves of 1, 3, 5, and 10 years verified the model had good predictive efficiency. Gene expression were correlated with ORR and PFS in patients who received anti-PD-1/L1. Conclusion: The lactate metabolism-associated genes in LUAD were significantly associated with OS and immune signatures. The risk scoring model that was constructed by us was able to well identify and predict OS and were related with anti-PD-1/L1 therapy outcome.

Cancer chemotherapy: insights into cellular and tumor microenvironmental mechanisms of action

Chemotherapy has historically been the mainstay of cancer treatment, but our understanding of what drives a successful therapeutic response remains limited. The diverse response of cancer patients to chemotherapy has been attributed principally to differences in the proliferation rate of the tumor cells, but there is actually very little experimental data supporting this hypothesis. Instead, other mechanisms at the cellular level and the composition of the tumor microenvironment appear to drive chemotherapy sensitivity. In particular, the immune system is a critical determinant of chemotherapy response with the depletion or knock-out of key immune cell populations or immunological mediators completely abrogating the benefits of chemotherapy in pre-clinical models. In this perspective, we review the literature regarding the known mechanisms of action of cytotoxic chemotherapy agents and the determinants of response to chemotherapy from the level of individual cells to the composition of the tumor microenvironment. We then summarize current work toward the development of dynamic biomarkers for response and propose a model for a chemotherapy sensitive tumor microenvironment.