Construction of multi-gene classifier for prediction of response to and prognosis after neoadjuvant chemotherapy for estrogen receptor positive breast cancers

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.

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.

Gene Expression-Based Prediction of Neoadjuvant Chemotherapy Response in Early Breast Cancer: Results of the Prospective Multicenter EXPRESSION Trial

PURPOSE

Expression-based classifiers to predict pathologic complete response (pCR) after neoadjuvant chemotherapy (NACT) are not routinely used in the clinic. We aimed to build and validate a classifier for pCR after NACT.

PATIENTS AND METHODS

We performed a prospective multicenter study (EXPRESSION) including 114 patients treated with anthracycline/taxane-based NACT. Pretreatment core needle biopsies from 91 patients were used for gene expression analysis and classifier construction, followed by validation in five external cohorts (n = 619).

RESULTS

A 20-gene classifier established in the EXPRESSION cohort using a Youden index-based cut-off point predicted pCR in the validation cohorts with an accuracy, AUC, negative predictive value (NPV), positive predictive value, sensitivity, and specificity of 0.811, 0.768, 0.829, 0.587, 0.216, and 0.962, respectively. Alternatively, aiming for a high NPV by defining the cut-off point for classification based on the complete responder with the lowest predicted probability of pCR in the EXPRESSION cohort led to an NPV of 0.960 upon external validation. With this extreme-low cut-off point, a recommendation to not treat with anthracycline/taxane-based NACT would be possible for 121 of 619 unselected patients (19.5%) and 112 of 322 patients with luminal breast cancer (34.8%). The analysis of the molecular subtypes showed that the identification of patients who do not achieve a pCR by the 20-gene classifier was particularly relevant in luminal breast cancer.

CONCLUSIONS

The novel 20-gene classifier reliably identifies patients who do not achieve a pCR in about one third of luminal breast cancers in both the EXPRESSION and combined validation cohorts.

Development of prediction models for the sensitivity of oral squamous cell carcinomas to preoperative S-1 administration

S-1 is an anticancer agent that is comprised of tegafur, gimeracil, and oteracil potassium, and is widely used in various carcinomas including oral squamous cell carcinoma (OSCC). Although an established prediction tool is not available, we aimed to develop prediction models for the sensitivity of primary OSCC cases to the preoperative administration of S-1.

We performed DNA microarray analysis of 95 cases with OSCC. Using global gene expression data and the clinical data, we developed two different prediction models, namely, model 1 that comprised the complete response (CR) + the partial response (PR) versus stable disease (SD) + progressive disease (PD), and model 2 that comprised responders versus non-responders. Twelve and 18 genes were designated as feature genes (FGs) in models 1 and 2, respectively, and, of these, six genes were common to both models. The sensitivity was 96.3%, the specificity was 91.2%, and the accuracy was 92.6% for model 1, and the sensitivity was 95.6%, the specificity was 85.2%, and the accuracy was 92.6% for model 2. These models were validated using receiver operating characteristic analysis, and the areas under the curves were 0.967 and 0.949 in models 1 and 2, respectively. The data led to the development of models that can reliably predict the sensitivity of patients with OSCC to the preoperative administration of S-1. The mechanism that regulates S-1 sensitivity remains unclear; however, the prediction models developed provide hope that further functional investigations into the FGs will lead to a greater understanding of drug resistance.

Integrated pan-cancer gene expression and drug sensitivity analysis reveals SLFN11 mRNA as a solid tumor biomarker predictive of sensitivity to DNA-damaging chemotherapy

Background

Precision oncology seeks to integrate multiple layers of data from a patient’s cancer to effectively tailor therapy. Conventional chemotherapies are sometimes effective but accompanied by adverse events, warranting the identification of a biomarker of chemosensitivity.

Objective

Identify an mRNA biomarker that predicts chemosensitivity across solid tumor subtypes.

Methods

We performed a pan-solid tumor analysis integrating gene expression and drug sensitivity profiles from 3 cancer cell line datasets to identify transcripts correlated with sensitivity to a panel of chemotherapeutics. We then tested the ability of an mRNA biomarker to predictive clinical outcomes in cohorts of patients with breast, lung, or ovarian cancer.

Results

Expression levels of several mRNA transcripts were significantly correlated with sensitivity or resistance chemotherapeutics in cancer cell line datasets. The only mRNA transcript significantly correlated with sensitization to multiple classes of DNA-damaging chemotherapeutics in all 3 cell line datasets was encoded by Schlafen Family Member 11 (SLFN11). Analyses of multiple breast, lung, and ovarian cancer patient cohorts treated with chemotherapy confirmed SLFN11 mRNA expression as a predictive biomarker of longer overall survival and improved tumor response.

Conclusions

Tumor SLFN11 mRNA expression is a biomarker of sensitivity to an array of DNA-damaging chemotherapeutics across solid tumor subtypes.

Screening of biomarkers for prediction of response to and prognosis after chemotherapy for breast cancers

OBJECTIVE

To screen the biomarkers having the ability to predict prognosis after chemotherapy for breast cancers.

METHODS

Three microarray data of breast cancer patients undergoing chemotherapy were collected from Gene Expression Omnibus database. After preprocessing, data in GSE41112 were analyzed using significance analysis of microarrays to screen the differentially expressed genes (DEGs). The DEGs were further analyzed by Differentially Coexpressed Genes and Links to construct a function module, the prognosis efficacy of which was verified by the other two datasets (GSE22226 and GSE58644) using Kaplan-Meier plots. The involved genes in function module were subjected to a univariate Cox regression analysis to confirm whether the expression of each prognostic gene was associated with survival.

RESULTS

A total of 511 DEGs between breast cancer patients who received chemotherapy or not were obtained, consisting of 421 upregulated and 90 downregulated genes. Using the Differentially Coexpressed Genes and Links package, 1,244 differentially coexpressed genes (DCGs) were identified, among which 36 DCGs were regulated by the transcription factor complex NFY (NFYA, NFYB, NFYC). These 39 genes constructed a gene module to classify the samples in GSE22226 and GSE58644 into three subtypes and these subtypes exhibited significantly different survival rates. Furthermore, several genes of the 39 DCGs were shown to be significantly associated with good (such as CDC20) and poor (such as ARID4A) prognoses following chemotherapy.

CONCLUSION

Our present study provided a serial of biomarkers for predicting the prognosis of chemotherapy or targets for development of alternative treatment (ie, CDC20 and ARID4A) in breast cancer patients.

Identification of Novel Epigenetic Markers of Prostate Cancer by NotI-Microarray Analysis

A significant need for reliable and accurate cancer diagnostics and prognosis compels the search for novel biomarkers that would be able to discriminate between indolent and aggressive tumors at the early stages of disease. The aim of this work was identification of potential diagnostic biomarkers for characterization of different types of prostate tumors. NotI-microarrays with 180 clones associated with chromosome 3 genes/loci were applied to determine genetic and epigenetic alterations in 33 prostate tumors. For 88 clones, aberrations were detected in more than 10% of tumors. The major types of alterations were DNA methylation and/or deletions. Frequent methylation of the discovered loci was confirmed by bisulfite sequencing on selective sampling of genes: FGF12, GATA2, and LMCD1. Three genes (BHLHE40, BCL6, and ITGA9) were tested for expression level alterations using qPCR, and downregulation associated with hypermethylation was shown in the majority of tumors. Based on these data, we proposed the set of potential biomarkers for detection of prostate cancer and discrimination between prostate tumors with different malignancy and aggressiveness: BHLHE40, FOXP1, LOC285205, ITGA9, CTDSPL, FGF12, LOC440944/SETD5, VHL, CLCN2, OSBPL10/ZNF860, LMCD1, FAM19A4, CAND2, MAP4, KY, and LRRC58. Moreover, we probabilistically estimated putative functional relations between the genes within each set using the network enrichment analysis.