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Literature Review | Precision Treatment Strategies for Triple-Negative Breast Cancer Gain Two New Dimensions

Triple-negative breast cancer ( Triple-negative breast cancer , TNBC) With its highly aggressive nature and poor prognosis, it is the most formidable “beauty‑killing” subtype among all breast cancers. TNBC is characterized by the absence of estrogen receptors (ER), progesterone receptors (PR), and human epidermal growth factor receptor 2 (HER2). 2 ) Breast cancer characterized by negative expression , accounting for 15–20% of all breast cancers.

Advances in high-throughput molecular technologies have provided new insights into the profound heterogeneity of TNBC.

There are several existing classification systems, most of which rely on genomic and transcriptomic data; however, the distinguishing feature of each approach lies in the specific disease‑subtype definition method employed.

In 2011, Lehmann et al. integrated gene expression data from 21 datasets and performed k-means clustering, initially identifying six subtypes, which were later refined into four. According to Jezequel et al., fuzzy clustering of gene expression within an internal cohort classified TNBC into three subtypes. Using a more focused approach that selects specific gene expression signatures, TNBC has been further characterized as either three subtypes based on immune-related genes or three subtypes based on metabolism-related genes.

Interestingly, in 2019, Shao Zhimin’s team classified TNBC into four subtypes based on transcriptomic data: (1) luminal androgen receptor–positive (LAR), (2) immune‑inflammatory (IM), (3) basal‑like immune‑suppressed (BLIS), and (4) mesenchymal (MES), demonstrating substantial overlap with Lehmann’s classification. Subsequently, the study initiated a clinical trial (NCT03805399), in which two of the four subtypes—IM and MES—showed promising responses. However, treatment strategies for the remaining two subtypes—BLIS and LAR—failed to meet expectations, underscoring the need to develop new classification approaches.

February 1, 2022, Professor Zhi-min Shao and Professor Yi-zhou Jiang, Department of Breast Surgery, Fudan University Shanghai Cancer Center The leading team is in Cell Research (IF= 25.617 ) The journal published research titled “Comprehensive metabolomics expands precision medicine for triple-negative breast cancer.” We analyzed the polar metabolome and lipidome in 330 TNBC samples and 149 pairs of normal breast tissues, thereby constructing a large-scale metabolomic atlas of TNBC and classifying TNBC into three metabolic subtypes. 。

March 1, 2022, Professor Qin Jun of the National Center for Protein Sciences, Professor Shao Zhimin of the Department of Breast Surgery at Fudan University Shanghai Cancer Center, and Professor Zhu Weimin of the National Center for Protein Sciences The leading team is in Cell Reports (IF= 9.423 ) The journal published research titled “Proteome-centric cross-omics characterization and integrated network analyses of triple-negative breast cancer.” A comprehensive proteomic study was reported, analyzing 90 paired samples of triple-negative breast cancer (TNBC) in terms of quantitative protein levels, phosphorylation status, and DNA-binding capacity. The samples were classified into four subtypes (iP1–iP4), each exhibiting distinct molecular and pathway profiles as well as varying clinical outcomes. 。

 

In the first report, the researchers conducted a follow-up study on previous transcriptomic subtyping to identify metabolic reprogramming events occurring in specific TNBC subtypes, with a particular focus on novel therapeutic approaches for the LAR and BLIS subtypes.

To this end, the researchers A subset of TNBC patients was analyzed using liquid chromatography–mass spectrometry (LC-MS) to investigate over 2,500 polar metabolites and lipids. ， This makes it one of the studies with the largest number of analyzed samples and metabolites. Clustering performed using a similarity network fusion approach reveals that, TNBC can be classified into three subtypes based on its metabolic characteristics. , each subtype possesses a rich and distinct metabolite profile.

The C1 subtype is characterized by an increased diversity of lipid species, and thus relies more heavily on fatty acid metabolism to meet its energy demands. The C2 subtype exhibits a higher abundance of taxa involved in carbohydrate and oxidative pathways and is expected to depend more on glutamate metabolism, whereas the C3 subtype shows relatively minor overall differences compared with normal tissues.

Figure 1. Overview of polar metabolite and lipid profiling in TNBC

 

To assess the clinical significance of this new classification, the researchers examined its potential associations with previously defined transcriptomic subtypes and with recurrence status in patients with triple-negative breast cancer (TNBC).

There is a significant correlation between metabolic subtype C1 and transcriptomic subtype LAR, whereas the relationships between subtypes C2 and C3 and the remaining three transcriptomic subtypes remain unclear; tumors classified into these metabolic subtypes are evenly distributed across the other transcriptomic subtypes.

It has been confirmed that BLIS tumors overlapping with the C2 subtype exhibit poorer overall relapse-free survival. Using two machine learning methods—LASSO and SVM—it is possible to distinguish between BLIS-C2 and BLIS-C3 tumors based on the abundance of six metabolites.

A strong association has been established between metabolomic subtypes and transcriptomic subtypes, suggesting that novel therapeutic strategies may emerge in the LAR and BLIS subtypes, which could exhibit adverse effects in future clinical trials.

Identifying functional metabolites enriched in transcriptomic subtypes can highlight dependencies on clinically actionable metabolic pathways. Experiments conducted in patient-derived organoids and xenograft models demonstrate that targeting intermediate metabolites of the ceramide pathway… Sphingosine-1-phosphate (S1P) is a promising approach for treating LAR tumors. ， And N-Acetylaspartylglutamate (NAAG) ) It is an important pro-tumor metabolic product, It is also a potential therapeutic target for high-risk BLIS tumors. 。

Figure 2. NAAG is an important pro-tumorigenic metabolite in BLIS tumors.

 

In summary, this study elucidates the clinical significance of the metabolomics landscape in triple-negative breast cancer (TNBC), demonstrating that it not only refines the transcriptomic subtype classification system but also identifies novel therapeutic targets. The resulting metabolomics dataset serves as a valuable public resource to advance precision medicine for TNBC.

Figure 3. Integration of transcriptomic and metabolomic subtyping in TNBC for potential clinical applications.

 

In the second report, researchers collected tumor and paired non‑cancerous adjacent tissue (NAT) samples from 90 patients in the previously described FUSCCTNBC cohort.

Label-free proteomic analysis was performed on the samples: whole-proteome analysis was conducted on 89 tumors and 71 pairs of NATs, while phosphoproteome analysis was carried out on 88 tumors and 72 pairs of NATs. ， MS/MS analysis was performed on DNA-binding proteins (DBPs) collected from 78 tumor samples and 60 pairs of normal adjacent tissues using the concatenated transcription factor recognition element (catTFRE) sequence.

A two-component Gaussian mixture model was used to assess whether all samples (tumors and NATs) met the criteria for adequate identification or quantification in proteomic, phosphoproteomic, and DBP analyses; samples that failed to meet these criteria were excluded from further analysis. As a result, global proteomic analysis of 82 tumors and 66 paired NATs yielded 7,531 proteins, while phosphoproteomic analysis of 86 tumors and 70 paired NATs identified 4,194 proteins harboring 20,069 phosphorylation sites. Additionally, catTFRE analysis of 74 tumors and 57 paired NATs detected 2,967 DBPs.

The non‑proteomic datasets included in the study comprise 73 whole‑exome sequencing (WES) and 80 copy-number alteration (CNA) tumor samples from this cohort, as well as RNA‑sequencing datasets from 89 tumors and 59 paired normal tissues obtained in a previous FUSCC study.

Figure 4. Overview of multi-omics data in TNBC

 

Compared with NAT, tumor samples detected approximately twice as many proteins, phosphorylation sites, and DBPs as NAT, and uniquely identified proteins were observed in nearly all tumors. Specifically, 1,982 proteins, 1,889 phosphorylation sites, and 944 DBPs were upregulated in tumors, while 165 proteins, 136 phosphorylation sites, and 91 DBPs were downregulated.

Principal component analysis (PCA) of these three datasets reveals distinct proteomic profiles between tumors and normal adjacent tissues, with tumors exhibiting a higher degree of heterogeneity than normal adjacent tissues.

By employing iClusterPlus for integrated cluster analysis, a set of subtypes is generated that simultaneously encompasses both the sample proteome and the phosphoproteome. Tumor samples were classified into four subtypes (designated iP-1 to iP-4). These subtypes exhibit significant differences in prognosis, with each subtype characterized by distinct molecular and pathway profiles.

The iP‑1 subtype exhibits robust proliferative characteristics, as reflected by its signaling pathways and hallmark proteins. The poor prognosis associated with the iP‑2 subtype is closely linked to its distinctive lipid metabolism pathway, particularly fatty acid synthesis. The iP‑3 subtype is characterized by a marked enrichment of immune‑related processes. In the iP‑4 subtype, molecules implicated in epithelial–mesenchymal transition—potentially relevant to tumor metastasis—are prominently clustered.

Figure 5. Integrated Proteomic Subtypes

 

Using these proteomics datasets, along with genomic and transcriptomic data from the same cohort, we performed two types of network analyses to reveal regulatory networks between upstream regulators and downstream processes. Further findings revealed… NAE1 is a potential drug target for the iP‑1 subtype, while a series of key molecules involved in fatty acid metabolism, such as AKT1 and FASN, are potential targets for the iP‑2 subtype. 。 The protein, pathway, and network databases of TNBC provide a valuable molecular foundation for further clinical exploration and in-depth investigation into the molecular mechanisms of this disease.

Figure 6. Potential drug targets of iP-1 and iP-2

 

By leveraging multi-omics data types and integrated analytical approaches, researchers have constructed a valuable molecular atlas of triple-negative breast cancer (TNBC), encompassing subtype-specific proteins, signaling pathways, and molecular networks derived from network assembly and co-expression analyses. This discovery provides an invaluable molecular foundation for further clinical exploration and in-depth investigation into the disease’s molecular mechanisms.

 

Reference News:

https://doi.org/10.1016/j.celrep.2022.110460

https://doi.org/10.1038/s41422-022-00614-0

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