Construction of a novel multi-gene assay (42-gene classifier) for prediction of late recurrence in ER-positive breast cancer patients

Validation of late recurrence prediction by gene expression profiles and clinicopathological factors in estrogen receptor-positive breast cancer

BACKGROUND

The mechanism of late recurrence (LR) of estrogen receptor (ER)-positive breast cancer remains unclear, as previous studies have separately investigated "gene expression profiles" and "clinicopathological factors." Thus, this study aimed to evaluate the predictive capability of LR by combining the two independent factors of gene expression profiles (42-gene classifier: 42GC) and clinicopathological factors (Clinical Treatment Score post-5 years: CTS5) in multiple large cohorts.

METHODS

We analyzed microarray CEL file data downloaded from public databases of 28 global cohorts. A total of 2,454 patients with ER-positive breast cancer were analyzed for 42GC, and 1,263 of these, with complete clinicopathological data were analyzed for CTS5.

RESULTS

In the analysis of recurrent patients, the 42GC LR and CTS5 low-risk group tended to have LR. Notably, in the analysis of patients with and without recurrence, the highest LR rate beyond 5 years was observed in the CTS5 high-risk group. The combination of the 42GC and CTS5 high-risk groups showed the highest LR rate (16.9%), significantly exceeding that of the 42GC non-LR (NLR) and CTS5 low-risk combination (5.41%) (p = 0.038, odds ratio = 3.53). Furthermore, incorporating a third factor, 95GC, potentially reduced the number of patients prioritized for extended hormonal therapy for approximately one-quarter of patients.

CONCLUSIONS

Results confirmed that the two factors, gene expression profiles and clinicopathological factors, affect the time of recurrence. It also showed that the biological predisposition for LR (CTS5 low-risk) differed from the high LR rate (CTS5 high-risk). In clinical practice, patients with the 42GC LR and CTS5 high-risk combination should be prioritized for extended hormonal therapy. The addition of CTS5 and 95GC to 42GC allows for better risk classification of LR.

Development of an obesity-related multi-gene prognostic model incorporating clinical characteristics in luminal breast cancer

Despite adjuvant chemotherapy and endocrine therapy in luminal breast cancer (LBC), relapses are common. Addressing this, we aim to develop a prognostic model to refine adjuvant therapy strategies, particularly for patients at high recurrence risk. Notably, obesity profoundly affects the tumor microenvironment (TME) of LBC. However, it is unclear whether obesity-related biological features can effectively screen high-risk patients. Utilizing weighted gene coexpression network analysis (WGCNA) on RNA sequencing (RNAseq) data, we identified seven obese LBC genes (OLGs) closely associated with patient prognosis. Subsequently, we developed a luminal obesity-gene clinical prognostic index (LOG-CPI), combining a 7-gene signature, TNM staging, and age. Its predictive efficacy was confirmed across validation datasets and a clinical cohort (5-year accuracy = 0.828, 0.760, 0.751, and 0.792, respectively). LOG-CPI emerges as a promising predictor for clinical prognosis and treatment response, helping distinguish molecular and immunological features in LBC patients and guiding clinical practice by identifying varying prognoses.

Multi-gene assay 95- and 155-gene classifiers for prognosis prediction and chemotherapy omission in lymphnode positive luminal-type breast cancer

BACKGROUND

The prognosis of lymphnode positive breast cancer is worse than that of lymph node negative breast cancer but some cases may not require chemotherapy. We investigated the ability of the new multi-gene assays, 95GC and 155GC, to identify patients with lymphnode positive Luminal-type breast cancer whose chemotherapy can be omitted relatively safely.

PATIENTS AND METHODS

We extracted 1721 cases of lymphnode positive Luminal-type breast cancer from 22 public database Caucasoid cohorts and 3 Asian cohorts, and performed recurrence prognosis analysis with 95GC and 155GC.

RESULTS

Using 95GC, the cases were stratified as the high (n = 917) and low (n = 202) groups according to the prognosis of lymphnode positive Luminal-type endocrine only breast cancer. The 5 years DRFS in the low risk group was relatively good at 90%, and no additional effect of chemotherapy was observed, suggesting omission of chemotherapy. The recurrence prognosis was also significantly dichotomized into the high and low risks by 95GC in 21GC RS 0-25 cases. Here, we found a group with poor prognosis even in post-menopause RS 0-25 and requiring chemotherapy. Additionally, a group in which the prognosis was good in pre-menopause RS 0-25, and the omission of chemotherapy could be considered. Patients in the high-risk group at 155GC had poor prognosis after chemotherapy. 155GC also showed a group that chemotherapy alone was not sufficient.

CONCLUSION

In this study, we demonstrated the possibility of accurately selecting patient groups for which chemotherapy can be omitted from lymphnode positive Luminal-type breast cancer.

High SLC20A1 Expression Is Associated With Poor Prognosis for Radiotherapy of Estrogen Receptor-positive Breast Cancer

BACKGROUND/AIM

Radiotherapy is one of the main treatments for estrogen receptor-positive (ER+) breast cancer. However, in some ER+ breast cancer cases, radiotherapy is insufficient to inhibit progression and there is a lack of markers to predict radiotherapy insensitivity. Solute carrier family 20 member 1 (SLC20A1) is a sodium/inorganic phosphate symporter, which has been proposed to be a viable prognostic marker for luminal A and B types of ER+ breast cancer. The present study examined the possibility of SLC20A1 as a novel biomarker for the prediction of radiotherapy efficiency.

PATIENTS AND METHODS

The Molecular Taxonomy of Breast Cancer International Consortium dataset was downloaded from cBioportal and the prognosis of patients with high SLC20A1 expression (SLC20A1 ^high ) was compared with that of patients with low SLC20A1 expression, without or with radiotherapy and tumor stages I, II, and III, using the Kaplan-Meier method and multivariate Cox regression analyses of disease-specific and relapse-free survival.

RESULTS

Patients in the SLC20A1 ^high group with radiotherapy showed poor clinical outcomes in both luminal A and luminal B breast cancers. Furthermore, in luminal A breast cancer at tumor stage I, patients in the SLC20A1 ^high  group with radiotherapy also showed poor clinical outcomes. Therefore, these results suggest that radiotherapy is insufficient for patients in the SLC20A1 ^high group for both luminal A and B types, and especially for the luminal A type at tumor stage I.

CONCLUSION

SLC20A1 can be used as a prognostic marker for the prediction of the efficacy of radiotherapy for luminal A and luminal B breast cancers.

High expression of SLC20A1 is less effective for endocrine therapy and predicts late recurrence in ER-positive breast cancer

Estrogen receptor-positive (ER+) breast cancer intrinsically confers satisfactory clinical outcomes in response to endocrine therapy. However, a significant proportion of patients with ER+ breast cancer do not respond well to this treatment. Therefore, to evaluate the effects of endocrine therapy, there is a need for identification of novel markers that can be used at the time of diagnosis for predicting clinical outcomes, especially for early-stage and late recurrence. Solute carrier family 20 member 1 (SLC20A1) is a sodium/inorganic phosphate symporter that has been proposed to be a viable prognostic marker for the luminal A and luminal B types of ER+ breast cancer. In the present study, we examined the possible association of SLC20A1 expression with tumor staging, endocrine therapy and chemotherapy in the luminal A and luminal B subtypes of breast cancer. In addition, we analyzed the relationship between SLC20A1 expression and late recurrence in patients with luminal A and luminal B breast cancer following endocrine therapy. We showed that patients with higher levels of SLC20A1 expression (SLC20A1^high) exhibited poorer clinical outcomes in those with tumor stage I luminal A breast cancer. In addition, this SLC20A1^high subgroup of patients exhibited less responses to endocrine therapy, specifically in those with the luminal A and luminal B subtypes of breast cancer. However, patients with SLC20A1^high showed good clinical outcomes following chemotherapy. Patients tested to be in the SLC20A1^high group at the time of diagnosis also showed a higher incidence of recurrence compared with those with lower expression levels of SLC20A1, at >15 years for luminal A breast cancer and at 10–15 years for luminal B breast cancer. Therefore, we conclude that SLC20A1^high can be used as a prognostic biomarker for predicting the efficacy of endocrine therapy and late recurrence for ER+ breast cancer.

The Signal Transducer IL6ST (gp130) as a Predictive and Prognostic Biomarker in Breast Cancer

Novel biomarkers are needed to continue to improve breast cancer clinical management and outcome. IL6-like cytokines, whose pleiotropic functions include roles in many hallmarks of malignancy, rely on the signal transducer IL6ST (gp130) for all their signalling. To date, 10 separate independent studies based on the analysis of clinical breast cancer samples have identified IL6ST as a predictor. Consistent findings suggest that IL6ST is a positive prognostic factor and is associated with ER status. Interestingly, these studies include 4 multigene signatures (EndoPredict, EER4, IRSN-23 and 42GC) that incorporate IL6ST to predict risk of recurrence or outcome from endocrine or chemotherapy. Here we review the existing evidence on the promising predictive and prognostic value of IL6ST. We also discuss how this potential could be further translated into clinical practice beyond the EndoPredict tool, which is already available in the clinic. The most promising route to further exploit IL6ST's promising predicting power will likely be through additional hybrid multifactor signatures that allow for more robust stratification of ER+ breast tumours into discrete groups with distinct outcomes, thus enabling greater refinement of the treatment-selection process.

The multigene classifiers 95GC/42GC/155GC for precision medicine in ER-positive HER2-negative early breast cancer

In clinical decision‐making, to decide the indication for adjuvant chemotherapy for estrogen receptor‐positive (ER+), human epidermal growth factor receptor‐2‐negative (HER2−), and node‐negative (n0) breast cancer patients, the accurate estimation of recurrence risk is essential. Unfortunately, conventional prognostic factors, such as tumor size, histological grade and ER, progesterone receptor (PR), and HER2 status as well as Ki67 index, are not sufficiently accurate for such estimation. Therefore, several multigene assays (MGAs) based on the mRNA expression analysis of multiple genes in tumor tissue have been developed to better predict patient prognosis. These assays include Oncotype DX, MammaPrint, PAM50, GGI, EndoPredict, and BCI. We developed Curebest™ 95‐Gene Classifier Breast (95GC) classifier, which is unique in that mRNA expression data of all 20 000 human genes are secondarily obtainable, as the 95GC assay is performed using Affymetrix microarray. This can capture mRNA expression of not only 95 genes but also every gene at once, and such gene expression data can be utilized by the other MGAs that we have developed, such as the 155GC, which is used for the prognostic prediction of ER+/HER2− breast cancer patients treated with neoadjuvant chemotherapy. We also developed the 42GC for predicting late recurrence in ER+/HER2− breast cancer patients. In this mini‐review, our recent attempt at the development of various MGAs, which is expected to facilitate the implementation of precision medicine in ER+/HER2− breast cancer patients, is presented with a special emphasis on 95GC.

A prognostic 10-lncRNA expression signature for predicting the risk of tumour recurrence in breast cancer patients

Breast cancer is one of the most frequently diagnosed malignancies and a leading cause of cancer death among females. Multiple molecular alterations are observed in breast cancer. LncRNA transcripts were proved to play important roles in the biology of tumorigenesis. In this study, we aimed to identify lncRNA expression signature that can predict breast cancer patient survival. We developed a 10‐lncRNA signature‐based risk score which was used to separate patients into high‐risk and low‐risk groups. Patients in the low‐risk group had significantly better survival than those in the high‐risk group. Receiver operating characteristic analysis indicated that this signature exhibited excellent diagnostic efficiency for 1‐, 3‐ and 5‐year disease‐relapse events. Moreover, multivariate Cox regression analysis demonstrated that this 10‐lncRNA signature was an independent risk factor when adjusting for several clinical signatures such as age, tumour size and lymph node status. The prognostic value of risk scores was validated in the validation set. In addition, a nomogram was established and the calibration plots analysis indicated the good performance and clinical utility of the nomogram. In conclusion, our results demonstrated that this 10‐lncRNA signature effectively grouped patients at low and high risk of disease recurrence.