Immune gene expression profiling reveals heterogeneity in luminal breast tumors

Zhu, Bin; Tse, Shelly L A; Wang, Difei; Koka, Hela; Zhang, Tongwu; Abubakar, Mustapha; Lee, Priscilla; Wang, Feng; Wu, Çherry; Tsang, Koon Ho; Chan, Wing-cheong; Law, Sze Hong; Li, Mengjie; Li, Wentao; Wu, S.Y.; Liu, Zhiguang; Huang, Bixia; Zhang, Han; Tang, Eric; Kan, Zhengyan; Lee, Soo Hyun; Park, Yeon Hee; Nam, Seok Jin; Wang, Mingyi; Sun, Xuezheng; Jones, Kristine; Hutchinson, Amy; Hicks, Belynda; Prokunina‐Olsson, Ludmila; Shi, Jianxin; García‐Closas, Montserrat; Chanock, Stephen J.; Yang, Xiaohong R.

doi:10.1101/515486

Cited by 9 publications

(9 citation statements)

References 44 publications

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“…After comparing the distributions of the three immunophenotypes and PAM50 subtypes, we revealed that the immune-activated class contained more luminal A-like patients, while the immune-suppressed subtype contained more luminal B-like patients. These results are consistent with Zhu et al's work [52], demonstrating that luminal A patients showed higher expression of immune checkpoint genes (PD-L1 and CTLA-4) and chemokine genes (CXCL9 and CXCL10). Recently, Thorsson et al [36] also generated the pan-cancer atlas of TCGA, which identified six pan-cancer immune cells.…”

Section: Discussionsupporting

confidence: 93%

Immune response drives outcomes in prostate cancer: implications for immunotherapy

Meng

Zhou

et al. 2020

Molecular Oncology

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The heterogeneity of the immune microenvironment leads to different responses in immune checkpoint blockade therapy. We aimed to propose a robust molecular classification system to investigate the relevance of the immune microenvironment subtype and prognosis of prostate cancer patients, as well as the therapeutic response to immune checkpoint blockade therapy. A total of 1,557 prostate cancer patients were enrolled, including 69 real‐world samples from our institute (titled the AHMU‐PC cohort). The non‐negative matrix factorization algorithm was employed to virtually microdissect patients. The immune enrichment was characterized by a high enrichment of T cell‐, B cell‐, NK cell‐, and macrophage‐associated signatures, by which patients were subclassified into nonimmune and immune classes. Subsequently, the immune class was dichotomized into immune‐activated and immune‐suppressed subtypes based on the stromal signature, represented by the activation of WNT/TGF‐β, TGF‐β1, and C‐ECM signatures. Approximately 14.9% to 24.3% of patients belonged to the immune‐activated subtype, which was associated with favorable recurrence‐free survival outcomes. In addition, patients in the immune‐activated subtype were predicted to benefit more from anti‐PD‐1/PD‐L1 therapy. In conclusion, our study identifies a novel immune molecular classifier that is closely related to clinical prognosis and provides novel insights into immunotherapeutic strategies for prostate cancer patients.

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Section: Discussionsupporting

confidence: 93%

Immune response drives outcomes in prostate cancer: implications for immunotherapy

Meng

Zhou

et al. 2020

Molecular Oncology

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“…Since our AIOs were created from gene expression data where each pixel represented a single gene, this difference of patterns within a class implied that different genes contributed to the patterns of the class and the class was heterogeneous. This was consistent with many studies that major subtypes of breast cancer were heterogeneous [22][23][24]. If we followed the patterns between classes or within class to identify their perspective genes, it could provide useful insights to understand the underpinning biology of these subtypes or subgroups.…”

Section: Conclusion and Discussionsupporting

confidence: 86%

Artificial Image Objects for Classification of Breast Cancer Biomarkers with Transcriptome Sequencing Data and Convolutional Neural Network Algorithms

Chen

CHEN²,

Zhao

et al. 2021

Preprint

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Background: Transcriptome sequencing has been broadly available in clinical studies. However, it remains a challenge to utilize these data effectively to due to the high dimension of the data and the high correlation of gene expression. Methods: We propose a novel method that transforms RNA sequencing data into artificial image objects (AIOs) and apply convolutional neural network (CNN) algorithm to classify these AIOs. The AIO technique considers each gene as a pixel in digital image, standardizes and rescales gene expression levels into a range suitable for image display. Using the GSE81538 (n = 405) and GSE96058 (n = 3,373) datasets, we create AIOs for the subjects and design CNN models to classify biomarker Ki67 and Nottingham histologic grade (NHG). Results: With 5-fold cross validation, we accomplish a classification accuracy and AUC of 0.797 ± 0.034 and 0.820 ± 0.064 for Ki67 status. For NHG, the weighted average of categorical accuracy is 0.726 ± 0.018, and the weighted average of AUC is 0.848 ± 0.019. With GSE81538 as training data and GSE96058 as testing data, the accuracy and AUC for Ki67 are 0.772 ± 0.014 and 0.820 ± 0.006, and that for NHG are 0.682 ± 0.013 and 0.808 ± 0.003 respectively. These results are comparable to or better than the results reported in the original study. For both Ki67 and NHG, the calls from our models have similar predictive power for survival as the calls from trained pathologists in survival analyses. Comparing the calls from our models and the pathologists, we find that the discordant subjects for Ki67 are a group of patients for whom estrogen receptor, progesterone receptor, PAM50 and NHG could not predict their survival rate, and their responses to chemotherapy and endocrine therapy are also different from the concordant subjects. Conclusions: RNA sequencing data can be transformed into AIOs and be used to classify the status of Ki67 and NHG by CNN algorithm. The AIO method can handle high dimension data with highly correlated variables with no requirement for variable selection, leading to a data-driven, consistent and automation-ready approach to model RNA sequencing data.

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“…With improved understanding of the association between the immune system and cancer biology, researchers have become increasingly aware of the importance of patient immunity in breast malignancies. Indeed, previous studies have detected the presence of significant immune-related genes in breast cancer patients (7).…”

Section: Introductionmentioning

confidence: 99%

The immune-related gene CD52 is a favorable biomarker for breast cancer prognosis

Ma¹,

Chen²,

Fang³

et al. 2021

Gland Surg

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Background: An increasing number of studies have demonstrated a role for the tumor microenvironment in tumorigenesis, disease progression, and therapeutic response. This present study aimed to screen the significant immune-related genes and their possible role in the prognosis of breast cancer (BRCA). Methods:The transcriptome data and clinical data of breast cancer were collected from The Cancer Genome Atlas (TCGA), and the immune scores and stromal scores were calculated by ESTIMATE algorithm. The differentially expressed genes were screened base on immune and stromal scores (high score vs. low score), than the intersected genes were used for subsequent functional enrichment analysis and protein-protein interaction (PPI) analysis. Furthermore, the key gene was identified by the intersection of the hub genes of PPI network and the prognostic genes of breast cancer. Finally, we explored the infiltration of immune cells of BRCA base on the CIBERSORT algorithm, and analysis the relationship between key gene and immune cells.Results: High levels of CD52 expression were detected in the early stages of breast cancer and were associated with favorable prognosis. Overexpression of CD52 led to higher infiltrations of M1 macrophages, monocytes, T follicular helper cells, and resting memory CD4 T cells. Downregulation of CD52 resulted in high infiltrations of M2 macrophages. Therefore, high expression of CD52 may negatively regulate the infiltration of M2 macrophages but accelerate the infiltration of anti-cancer immune cells, and thus, high expression of CD52 may have a protective effect in breast cancer patients.Conclusions: CD52 can increase the infiltration of anti-cancer immune cells but inhibit the infiltration of M2 macrophages, thereby improving the prognosis of breast cancer patients.

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Immune gene expression profiling reveals heterogeneity in luminal breast tumors

Cited by 9 publications

References 44 publications

Immune response drives outcomes in prostate cancer: implications for immunotherapy

Immune response drives outcomes in prostate cancer: implications for immunotherapy

Artificial Image Objects for Classification of Breast Cancer Biomarkers with Transcriptome Sequencing Data and Convolutional Neural Network Algorithms

The immune-related gene CD52 is a favorable biomarker for breast cancer prognosis

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