Clinical utility of gene-expression signatures in early stage breast cancer

70-Gene signature-guided adjuvant systemic treatment adjustments in early-stage ER+ breast cancer patients: 7-year follow-up of a prospective multicenter cohort study

BACKGROUND

A previous prospective multicenter study revealed the change of the oncologists' chemotherapy advice due to the 70-Gene signature (GS) test result in half of the estrogen receptor-positive (ER+) invasive early-stage breast cancer patients with disputable chemotherapy indication. This resulted in less patients receiving chemotherapy. This study aims to complement these results by the 7-year oncological outcomes according to the 70-GS test result and the oncologists' pre-test advice.

METHODS

Patients operated for early-stage ER+ breast cancer with disputable chemotherapy indication, had been prospectively included between 2013 and 2015. Oncologists were asked whether they intended to administer adjuvant chemotherapy before deployment of the 70-GS test. Information on adjuvant systemic treatment and oncological outcome was obtained through active follow-up by data managers of the Netherlands Cancer Registry. The primary endpoint of this study was distant metastasis-free survival (DMFS) according to the genomic risk. Exploratory analyses were done to evaluate DMFS in relation to the oncologists' pre-test advice.

RESULTS

After a median follow-up of 7 years, distant metastases were diagnosed in 23 of the 606 patients (3.8%) and 36 (5.9%) patients had died. The DMFS rate for the 357 70-GS genomic low-risk patients was 94.2% (95% CI 91.2-96.2) and 89.1% for the 249 genomic high-risk patients (95% CI 84.3-92.4). Of the low-risk patients 3% had received chemotherapy compared to 80% of the high-risk patients. For the subgroups based on the pre-test oncologists' advice (no chemotherapy/chemotherapy/unsure) there were no clinically relevant differences in DMFS (89.8, 93.2 and 92.0%, respectively), while comparable proportions of patients had received chemotherapy.

CONCLUSIONS

In patients with early-stage ER+ breast cancer with a disputable chemotherapy indication it is sensible to deploy the 70-GS to better select patients for adjuvant chemotherapy.

Application and challenge of HER2DX genomic assay in HER2+ breast cancer treatment

HER2-positive breast cancer is highly aggressive, with a significant risk of recurrence and metastasis, leading to a poor prognosis. While most early-stage HER2-positive breast cancer patients benefit from combining trastuzumab monoclonal antibody with chemotherapy, the therapeutic response to various drug combinations varies across the HER2+ patient population. Therefore, predicting the prognosis and treatment response of HER2+ breast cancer patients to specific regimens is crucial for selecting appropriate precision individualized therapies. HER2DX is the first genomic tool designed to guide the treatment of HER2+ breast cancer patients. The three scores provided by HER2DX inform the entire treatment process, including predicting survival outcomes, recurrence, metastasis, and treatment responses like Pathological Complete Response Rate (pCR). It offers recommendations on follow-up intervals, treatment plans, and the duration of drug therapy. This review examines the literature and analyzes studies applying HER2DX to guide the comprehensive treatment and predict prognosis in HER2+ breast cancer patients, aiming to promote the widespread use of HER2DX in individualized treatment.

PD-L2 Expression in Breast Cancer Promotes Tumor Development and Progression

Background

This work focused on investigating the role of programmed death ligand 2 (PD-L2) in the progression of breast cancer by utilizing breast cancer specimens and cells.

Materials and Methods

The serum levels of soluble PD-L2 (sPD-L2) in breast cancer patients and healthy individuals were analyzed by means of the enzyme-linked immunosorbent assay, and the PD-L2 levels within 416 resected breast cancer specimens were assessed through immunohistochemistry. Concurrently, in vitro cell experiments and in vivo animal experiments were carried out to analyze the relationship between PD-L2 and the invasion and migration of breast cancer.

Results

The concentration of sPD-L2 in breast cancer patients significantly increased compared to that in the control groups. Additionally, breast cancer patients with high concentrations of sPD-L2 had higher Ki67 values (≥30%) and tumor grades. PD-L2 was expressed in 79.09% of the cancer samples, which exhibited a positive correlation with the progesterone receptor (PR) and the human epidermal growth factor receptor 2 (HER2). Furthermore, we discovered that knockdown of PD-L2 inhibited the migratory and invasive abilities of both MCF-7 and MDA-MB231 cells.

Conclusion

Our findings demonstrated that knockdown of PD-L2 suppressed tumor growth, providing novel insights into important biological functions.

ESR1 and p53 interactome alteration defines mechanisms of tamoxifen response in luminal breast cancer

The canonical mechanism behind tamoxifen's therapeutic effect on estrogen receptor α/ESR1+ breast cancers is inhibition of ESR1-dependent estrogen signaling. Although ESR1+ tumors expressing wild-type p53 were reported to be more responsive to tamoxifen (Tam) therapy, p53 has not been factored into choice of this therapy and the mechanism underlying the role of p53 in Tam response remains unclear. In a window-of-opportunity trial on patients with newly diagnosed stage I-III ESR1+/HER2/wild-type p53 breast cancer who were randomized to arms with or without Tam prior to surgery, we reveal that the ESR1-p53 interaction in tumors was inhibited by Tam. This resulted in functional reactivation of p53 leading to transcriptional reprogramming that favors tumor-suppressive signaling, as well as downregulation of oncogenic pathways. These findings illustrating the convergence of ESR1 and p53 signaling during Tam therapy enrich mechanistic understanding of the impact of p53 on the response to Tam therapy.

Development and validation of a clinical breast cancer tool for accurate prediction of recurrence

Given high costs of Oncotype DX (ODX) testing, widely used in recurrence risk assessment for early-stage breast cancer, studies have predicted ODX using quantitative clinicopathologic variables. However, such models have incorporated only small cohorts. Using a cohort of patients from the National Cancer Database (NCDB, n = 53,346), we trained machine learning models to predict low-risk (0-25) or high-risk (26-100) ODX using quantitative estrogen receptor (ER)/progesterone receptor (PR)/Ki-67 status, quantitative ER/PR status alone, and no quantitative features. Models were externally validated on a diverse cohort of 970 patients (median follow-up 55 months) for accuracy in ODX prediction and recurrence. Comparing the area under the receiver operating characteristic curve (AUROC) in a held-out set from NCDB, models incorporating quantitative ER/PR (AUROC 0.78, 95% CI 0.77-0.80) and ER/PR/Ki-67 (AUROC 0.81, 95% CI 0.80-0.83) outperformed the non-quantitative model (AUROC 0.70, 95% CI 0.68-0.72). These results were preserved in the validation cohort, where the ER/PR/Ki-67 model (AUROC 0.87, 95% CI 0.81-0.93, p = 0.009) and the ER/PR model (AUROC 0.86, 95% CI 0.80-0.92, p = 0.031) significantly outperformed the non-quantitative model (AUROC 0.80, 95% CI 0.73-0.87). Using a high-sensitivity rule-out threshold, the non-quantitative, quantitative ER/PR and ER/PR/Ki-67 models identified 35%, 30% and 43% of patients as low-risk in the validation cohort. Of these low-risk patients, fewer than 3% had a recurrence at 5 years. These models may help identify patients who can forgo genomic testing and initiate endocrine therapy alone. An online calculator is provided for further study.

Systematic Characterization of p53-Regulated Long Noncoding RNAs across Human Cancers Reveals Remarkable Heterogeneity among Different Tumor Types

The p53 tumor suppressor protein, a sequence-specific DNA binding transcription factor, regulates the expression of a large number of genes, in response to various forms of cellular stress. Although the protein coding target genes of p53 have been well studied, less is known about its role in regulating long noncoding genes and their functional relevance to cancer. Here we report the genome-wide identification of a large set (>1,000) of long noncoding RNAs (lncRNA), which are putative p53 targets in a colon cancer cell line and in human patient datasets from five different common types of cancer. These lncRNAs have not been annotated by other studies of normal unstressed systems. In the colon cancer cell line, a high proportion of these lncRNAs are uniquely induced by different chemotherapeutic agents that activate p53, whereas others are induced by more than one agent tested. Further, subsets of these lncRNAs independently predict overall and disease-free survival of patients across the five different common cancer types. Interestingly, both genetic alterations and patient survival associated with different lncRNAs are unique to each cancer tested, indicating extraordinary tissue-specific variability in the p53 noncoding response. The newly identified noncoding p53 target genes have allowed us to construct a classifier for tumor diagnosis and prognosis.

IMPLICATIONS

Our results not only identify myriad p53-regulated long noncoding (lncRNA), they also reveal marked drug-induced, as well as tissue- and tumor-specific heterogeneity in these putative p53 targets and our findings have enabled the construction of robust classifiers for diagnosis and prognosis.

Deep learning-based risk stratification of preoperative breast biopsies using digital whole slide images

BACKGROUND

Nottingham histological grade (NHG) is a well established prognostic factor in breast cancer histopathology but has a high inter-assessor variability with many tumours being classified as intermediate grade, NHG2. Here, we evaluate if DeepGrade, a previously developed model for risk stratification of resected tumour specimens, could be applied to risk-stratify tumour biopsy specimens.

METHODS

A total of 11,955,755 tiles from 1169 whole slide images of preoperative biopsies from 896 patients diagnosed with breast cancer in Stockholm, Sweden, were included. DeepGrade, a deep convolutional neural network model, was applied for the prediction of low- and high-risk tumours. It was evaluated against clinically assigned grades NHG1 and NHG3 on the biopsy specimen but also against the grades assigned to the corresponding resection specimen using area under the operating curve (AUC). The prognostic value of the DeepGrade model in the biopsy setting was evaluated using time-to-event analysis.

RESULTS

Based on preoperative biopsy images, the DeepGrade model predicted resected tumour cases of clinical grades NHG1 and NHG3 with an AUC of 0.908 (95% CI: 0.88; 0.93). Furthermore, out of the 432 resected clinically-assigned NHG2 tumours, 281 (65%) were classified as DeepGrade-low and 151 (35%) as DeepGrade-high. Using a multivariable Cox proportional hazards model the hazard ratio between DeepGrade low- and high-risk groups was estimated as 2.01 (95% CI: 1.06; 3.79).

CONCLUSIONS

DeepGrade provided prediction of tumour grades NHG1 and NHG3 on the resection specimen using only the biopsy specimen. The results demonstrate that the DeepGrade model can provide decision support to identify high-risk tumours based on preoperative biopsies, thus improving early treatment decisions.

Migratory Tumor Cells Cooperate with Cancer Associated Fibroblasts in Hormone Receptor-Positive and HER2-Negative Breast Cancer

Hormone receptor-positive and HER2-negative breast cancer (HR+/HER2-BC) is the most common type with a favorable prognosis under endocrine therapy. However, it still demonstrates unpredictable progression and recurrences influenced by high tumoral diversity and microenvironmental status. To address these heterogeneous molecular characteristics of HR+/HER2-BC, we aimed to simultaneously characterize its transcriptomic landscape and genetic architecture at the same resolution. Using advanced single-cell RNA and DNA sequencing techniques together, we defined four distinct tumor subtypes. Notably, the migratory tumor subtype was closely linked to genomic alterations of EGFR, related to the tumor-promoting behavior of IL6-positive inflammatory tumor-associated fibroblast, and contributing to poor prognosis. Our study comprehensively utilizes integrated analysis to uncover the complex dynamics of this breast cancer subtype, highlighting the pivotal role of the migratory tumor subtype in influencing surrounding cells. This sheds light on potential therapeutic targets by offering enhanced insights for HR+/HER2-BC treatment.

The Impact of the Coexpression of MET and ESR Genes on Prognosticators and Clinical Outcomes of Breast Cancer: An Analysis for the METABRIC Dataset

Purpose

Breast cancer is a heterogeneous disease. Exploring new prognostic and therapeutic targets in patients with breast cancer is essential. This study investigated the expression of MET, ESR1, and ESR2 genes and their association with clinicopathologic characteristics and clinical outcomes in patients with breast cancer.

Methods

The METABRIC dataset for breast cancer was obtained from the cBioPortal public domain. Gene expression data for MET, ESR1, and ESR2, as well as the putative copy number alterations (CNAs) for MET were retrieved.

Results

The MET mRNA expression levels correlated inversely with the expression levels of ESR1 and positively with the expression levels of ESR2 (r = -0.379, p < 0.001 and r = 0.066, and p=0.004, respectively). The ESR1 mRNA expression was significantly different among MET CNAs groups (p < 0.001). Patients with high MET/ESR1 coexpression had favorable clinicopathologic tumor characteristics and prognosticators compared to low MET/ESR1 coexpression in terms of greater age at diagnosis, reduced Nottingham Prognostic Index, lower tumor grade, hormone receptor positivity, HER2-negative status, and luminal subtype (p < 0.001). In contrast, patients with high MET/ESR2 coexpression had unfavorable tumor features and advanced prognosticators compared to patients with low MET/ESR2 coexpression (p < 0.001). No significant difference in overall survival was observed based on the MET/ESR coexpression status. However, when data were stratified based on the treatment type (chemotherapy and hormonal therapy), survival was significantly different based on the coexpression status of MET/ESR.

Conclusions

Findings from our study add to the growing evidence on the potential crosstalk between MET and estrogen receptors in breast cancer. The expression of the MET/ESR genes could be a novel prognosticator and calls for future studies to evaluate the impact of combinational treatment approaches with MET inhibitors and endocrine drugs in breast cancer.

Real-world use of multigene signatures in early breast cancer: differences to clinical trials

PURPOSE

In Italy, Lombardy was the first region to reimburse multigene assays (MGAs) for patients otherwise candidates for chemotherapy. This is a real-world experience of MGAs usage in six referral cancer centers in Lombardy.

METHODS

Among MGAs, Oncotype DX (RS) was used in 97% of cases. Consecutive patients tested with Oncotype DX from July 2020 to July 2022 were selected. The distribution of clinicopathologic features by RS groups (low RS: 0-25, high RS: 26-100) was assessed using chi-square and compared with those of the TAILORx and RxPONDER trials.

RESULTS

Out of 1,098 patients identified, 73% had low RS. Grade and Ki67 were associated with RS (p < 0.001). In patients with both G3 and Ki67 > 30%, 39% had low RS, while in patients with both G1 and Ki67 < 20%, 7% had high RS. The proportion of low RS in node-positive patients was similar to that in RxPONDER (82% vs 83%), while node-negative patients with low RS were significantly less than in TAILORx (66% vs 86%, p < 0.001). The distribution of Grade was different from registration trials, with more G3 and fewer G1 (38% and 3%) than in TAILORx (18% and 27%) and RxPONDER (10% and 24%) (p < 0.001). Patients ≤ 50 years were overrepresented in this series (41%) than in TAILORx and RxPONDER (31% and 24%, respectively) (p < 0.001) and, among them, 42% were node positive.

CONCLUSIONS

In this real-world series, Oncotype DX was the test almost exclusively used. Despite reimbursement being linked to pre-test chemotherapy recommendation, almost 3/4 patients resulted in the low-RS group. The significant proportion of node-positive patients ≤ 50 years tested indicates that oncologists considered Oncotype DX informative also in this population.

Caveolin-1 gene expression provides additional prognostic information combined with PAM50 risk of recurrence (ROR) score in breast cancer

Combining information from the tumor microenvironment (TME) with PAM50 Risk of Recurrence (ROR) score could improve breast cancer prognostication. Caveolin-1 (CAV1) is a marker of an active TME. CAV1 is a membrane protein involved in cell signaling, extracellular matrix organization, and tumor-stroma interactions. We sought to investigate CAV1 gene expression in relation to PAM50 subtypes, ROR score, and their joint prognostic impact. CAV1 expression was compared between PAM50 subtypes and ROR categories in two cohorts (SCAN-B, n = 5326 and METABRIC, n = 1980). CAV1 expression was assessed in relation to clinical outcomes using Cox regression and adjusted for clinicopathological predictors. Effect modifications between CAV1 expression and ROR categories on clinical outcome were investigated using multiplicative and additive two-way interaction analyses. Differential gene expression and gene set enrichment analyses were applied to compare high and low expressing CAV1 tumors. All samples expressed CAV1 with the highest expression in the Normal-like subtype. Gene modules consistent with epithelial-mesenchymal transition (EMT), hypoxia, and stromal activation were associated with high CAV1 expression. CAV1 expression was inversely associated with ROR category. Interactions between CAV1 expression and ROR categories were observed in both cohorts. High expressing CAV1 tumors conferred worse prognosis only within the group classified as ROR high. ROR gave markedly different prognostic information depending on the underlying CAV1 expression. CAV1, a potential mediator between the malignant cells and TME, could be a useful biomarker that enhances and further refines PAM50 ROR risk stratification in patients with ROR high tumors and a potential therapeutic target.

Update on the Use of Molecular Subtyping in Breast Cancer

The standard of care for invasive cancers of the breast has been and continues to be to evaluate them for breast prognostic markers: estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 by immunohistochemistry. Over 2 decades ago, a study was the first to report on the molecular subtypes of breast cancer. Four main subtypes were reported. Since then there have been some changes in the molecular subtype classification, but overall many studies have shown that this subtyping has clinical prognostic and predictive value. More recently, molecular assays have been developed and studies have shown similar clinical prognostic and predictive value. We reviewed the literature for studies evaluating the clinical significance of all 3 of these methods of evaluation and the follow-up findings of that review are presented below.

The heterogeneity of tumour immune microenvironment revealing the CRABP2/CD69 signature discriminates distinct clinical outcomes in breast cancer

BACKGROUND

It has been acknowledged that the tumour immune microenvironment (TIME) plays a critical role in determining therapeutic responses and clinical outcomes in breast cancer (BrCa). Thus, the identification of the TIME features is essential for guiding therapy and prognostic assessment for BrCa.

METHODS

The heterogeneous cellular composition of the TIME in BrCa by single-cell RNA sequencing (scRNA-seq). Two subtype-special genes upregulated in the tumour-rich subtype and the immune-infiltrating subtype were extracted, respectively. The CRABP2/CD69 signature was established based on CRABP2 and CD69 expression, and its predictive values for the clinical outcome and the neoadjuvant chemotherapy (NAT) responses were validated in multiple cohorts. Moreover, the oncogenic role of CRABP2 was explored in BrCa cells.

RESULTS

Based on the heterogeneous cellular composition of the TIME in BrCa, the BrCa samples could be divided into the tumour-rich subtype and the immune-infiltrating subtype, which exhibited distinct prognosis and chemotherapeutic responses. Next, we extracted CRABP2 as the biomarker for the tumour-rich subtype and CD69 as the biomarker for the immune-infiltrating subtype. Based on the CRABP2/CD69 signature, BrCa samples were re-divided into three subtypes, and the CRABP2highCD69low subtype exhibited the worst prognosis and the lowest chemotherapeutic response, while the CRABP2lowCD69high subtype showed the opposite results. Furthermore, CARBP2 functioned as a novel oncogene in BrCa, which promoted tumour cell proliferation, migration, and invasion, and CRABP2 inhibition triggered the activation of cytotoxic T lymphocytes (CTLs).

CONCLUSION

The CRABP2/CD69 signature is significantly associated with the TIME features and could effectively predict the clinical outcome. Also, CRABP2 is determined to be a novel oncogene, which could be a therapeutic target in BrCa.

Identification of tumor-agnostic biomarkers for predicting prostate cancer progression and biochemical recurrence

The diverse clinical outcomes of prostate cancer have led to the development of gene signature assays predicting disease progression. Improved prostate cancer progression biomarkers are needed as current RNA biomarker tests have varying success for intermediate prostate cancer. Interest grows in universal gene signatures for invasive carcinoma progression. Early breast and prostate cancers share characteristics, including hormone dependence and BRCA1/2 mutations. Given the similarities in the pathobiology of breast and prostate cancer, we utilized the NanoString BC360 panel, comprising the validated PAM50 classifier and pathway-specific signatures associated with general tumor progression as well as breast cancer-specific classifiers. This retrospective cohort of primary prostate cancers (n=53) was stratified according to biochemical recurrence (BCR) status and the CAPRA-S to identify genes related to high-risk disease. Two public cohort (TCGA-PRAD and GSE54460) were used to validate the results. Expression profiling of our cohort uncovered associations between PIP and INHBA with BCR and high CAPRA-S score, as well as associations between VCAN, SFRP2, and THBS4 and BCR. Despite low levels of the ESR1 gene compared to AR, we found strong expression of the ER signaling signature, suggesting that BCR may be driven by ER-mediated pathways. Kaplan-Meier and univariate Cox proportional hazards regression analysis indicated the expression of ESR1, PGR, VCAN, and SFRP2 could predict the occurrence of relapse events. This is in keeping with the pathways represented by these genes which contribute to angiogenesis and the epithelial-mesenchymal transition. It is likely that VCAN works by activating the stroma and remodeling the tumor microenvironment. Additionally, SFRP2 overexpression has been associated with increased tumor size and reduced survival rates in breast cancer and among prostate cancer patients who experienced BCR. ESR1 influences disease progression by activating stroma, stimulating stem/progenitor prostate cancer, and inducing TGF-β. Estrogen signaling may therefore serve as a surrogate to AR signaling during progression and in hormone-refractory disease, particularly in prostate cancer patients with stromal-rich tumors. Collectively, the use of agnostic biomarkers developed for breast cancer stratification has facilitated a precise clinical classification of patients undergoing radical prostatectomy and highlighted the therapeutic potential of targeting estrogen signaling in prostate cancer.

Development of a Machine Learning-Based Prognostic Model for Hormone Receptor-Positive Breast Cancer Using Nine-Gene Expression Signature

Background

Determining the prognosis of hormone receptor positive (HR+) breast cancer (BC), which accounts for 80% of all BCs, is critical in improving survival outcomes. Stratifying individuals at high risk of BC-related mortality and improving prognosis has been the focus of research for over a decade. However, these tools are not universal as they are limited to clinical factors. We hypothesized that a new framework for predicting prognosis in HR+ BC patients can develop using artificial intelligence.

Methods

A total of 2,338 HR+ human epidermal growth factor receptor 2 negative (HER2-) BC cases were analyzed from Molecular Taxonomy of Breast Cancer International Consortium (METABRIC), The Cancer Genome Atlas (TCGA), and Gene Expression Omnibus (GEO) cohorts. Groups were then divided into high- and low-risk categories utilizing a recurrence prediction model (RPM). An RPM was created by extracting nine prognosis-related genes from over 18,000 genes using a logistic progression model.

Results

Risk classification by RPM was significantly stratified in both the discovery cohort and validation cohort. In the time-dependent area under the curve analysis, there was some variation depending on the cohort, but accuracy was found to decline significantly after about 10 years. Cell cycle related gene sets, MYC, and PI3K-AKT-mTOR signaling were enriched in high-risk tumors by the Gene Set Enrichment Analysis. High-risk tumors were associated with high levels of immune cells from the lymphoid and myeloid lineage and immune cytolytic activity, as well as low levels of stem cells and stromal cells. High-risk tumors were also associated with poor therapeutic effects of chemotherapy and endocrine therapy.

Conclusions

This model was able to stratify prognosis in multiple cohorts. This is because the model reflects major BC therapeutic target pathways and tumor immune microenvironment and, further is supported by the therapeutic effect of chemotherapy and endocrine therapy.

Genome-scale CRISPR-Cas9 screening stratifies pancreatic cancer with distinct outcomes and immunotherapeutic efficacy

Pancreatic cancer (PC) was featured by dramatic heterogeneity and dismal outcomes. An ideal classification strategy capable of achieving risk stratification and individualized treatment is urgently needed to significantly improve prognosis. In this study, using the 105 prognostic cancer essential genes identified by genome-scale CRISPR-Cas9 screening and univariate Cox analysis, we established and verified three heterogeneous subtypes via non-negative matrix factorization (NMF) and nearest template prediction (NTP) algorithms in the TCGA-PAAD cohort (176 samples) and four multi-center cohorts (233 samples), respectively. Among them, C1 with the worst prognosis was enriched in immune-related pathways, possessed superior infiltration abundance of immune cells and immune checkpoint molecules expression, and might be most sensitive to immunotherapy. C3, owing a moderate prognosis, might be featured by proliferative biological function, and despite its highest immunogenicity, the defects in antigen processing and presentation ability coupled with barren immune environment render it ineffective for immunotherapy, while it had potential sensitivity to paclitaxel and methotrexate. Besides, C2 harbored the best prognosis and was characterized by metabolism-related functions. These results could deepen our understanding of PC molecular heterogeneity and provide a trustworthy reference for prognostic stratification management and precision medicine in clinical practice.

Prognostic model of hepatocellular carcinoma based on cancer grade

BACKGROUND

Hepatocellular carcinoma (HCC) is the most common type of primary liver cancer. With highly invasive biological characteristics and a lack of obvious clinical manifestations, HCC usually has a poor prognosis and ranks fourth in cancer mortality. The aetiology and exact molecular mechanism of primary HCC are still unclear.

AIM

To select the characteristic genes that are significantly associated with the prognosis of HCC patients and construct a prognosis model of this malignancy.

METHODS

By comparing the gene expression levels of patients with different cancer grades of HCC, we screened out differentially expressed genes associated with tumour grade. By protein-protein interaction (PPI) network analysis, we obtained the top 2 PPI networks and hub genes from these differentially expressed genes. By using least absolute shrinkage and selection operator Cox regression, 13 prognostic genes were selected for feature extraction, and a prognostic risk model of HCC was established.

RESULTS

The model had significant prognostic ability in HCC. We also analysed the biological functions of these prognostic genes.

CONCLUSION

By comparing the gene profiles of patients with different stages of HCC, We have constructed a prognosis model consisting of 13 genes that have important prognostic value. This model has good application value and can be explained clinically.

An MRI-based Radiomics Approach to Improve Breast Cancer Histological Grading

RATIONALE AND OBJECTIVES

Nottingham histological grade (NHG) 2 breast cancer has an intermediate risk of recurrence, which is not informative for therapeutic decision-making. We sought to develop and independently validate an MRI-based radiomics signature (Rad-Grade) to improve prognostic re-stratification of NHG 2 tumors.

MATERIALS AND METHODS

Nine hundred-eight subjects with invasive breast cancer and preoperative MRI scans were retrospectively obtained. The NHG 1 and 3 tumors were randomly split into training and independent test cohort, with the NHG 2 as the prognostic validation set. From MRI image features, a radiomics-based signature predictive of the histological grade was built by use of the LASSO logistic regression algorithm. The model was developed for identifying NHG 1 and 3 radiological patterns, followed with re-stratification of NHG 2 tumors into Rad-Grade (RG)2-low (NHG 1-like) and RG2-high (NHG 3-like) subtypes using the learned patterns, and the prognostic value was assessed in terms of recurrence-free survival (RFS).

RESULTS

The Rad-Grade showed independent prognostic value for re-stratification of NHG 2 tumors, where RG2-high had an increased risk for recurrence (HR 2.20, 1.10-4.40, p = 0.026) compared with RG2-low after adjusting for established risk factors. RG2-low shared similar phenotypic characteristics and RFS outcomes with NHG 1, and RG2-high with NHG 3, revealing that the model captures radiomic features in NHG 2 that are associated with different aggressiveness. The prognostic value of Rad-Grade was further validated in the NHG2 ER+ (HR 2.53, 1.13-5.56, p = 0.023) and NHG 2 ER+LN- (HR 5.72, 1.24-26.44, p = 0.025) subgroups, and in specific treatment contexts.

CONCLUSION

The radiomics-based re-stratification of NHG 2 tumors offers a cost-effective promising alternative to gene expression profiling for tumor grading and thus may improve clinical decisions.

Ki67 and breast cancer mortality in women with invasive breast cancer

BACKGROUND

The percentage of cells staining positive for Ki67 is sometimes used for decision-making in patients with early invasive breast cancer (IBC). However, there is uncertainty regarding the most appropriate Ki67 cut points and the influence of interlaboratory measurement variability. We examined the relationship between breast cancer mortality and Ki67 both before and after accounting for interlaboratory variability and 8 patient and tumor characteristics.

METHODS

A multicenter cohort study of women with early IBC diagnosed during 2009-2016 in more than 20 NHS hospitals in England and followed until December 31, 2020.

RESULTS

Ki67 was strongly prognostic of breast cancer mortality in 8212 women with estrogen receptor (ER)-positive, human epidermal growth factor receptor 2 (HER2)-negative early IBC (Ptrend < .001). This relationship remained strong after adjustment for patient and tumor characteristics (Ptrend < .001). Standardization for interlaboratory variability did little to alter these results. For women with Ki67 scores of 0%-5%, 6%-10%, 11%-19%, and 20%-29% the corresponding 8-year adjusted cumulative breast cancer mortality risks were 3.3% (95% confidence interval [CI] = 2.8% to 4.0%), 3.7% (95% CI = 3.0% to 4.4%), 3.4% (95% CI = 2.8% to 4.1%), and 3.4% (95% CI = 2.8% to 4.1%), whereas for women with Ki67 scores of 30%-39% and 40%-100%, these risks were higher, at 5.1% (95% CI = 4.3% to 6.2%) and 7.7% (95% CI = 6.6% to 9.1) (Ptrend < .001). Similar results were obtained when the adjusted analysis was repeated with omission of pathological information about tumor size and nodal involvement, which would not be available preoperatively for patients being considered for neoadjuvant therapy.

CONCLUSION

Our findings confirm the prognostic value of Ki67 scores of 30% or more in women with ER-positive, HER2-negative early IBC, irrespective of interlaboratory variability. These results also suggest that Ki67 may be useful to aid decision-making in the neoadjuvant setting.

"Diagnostic and Prognostic Biomarkers of Luminal Breast Cancer: Where are We Now?"

Luminal breast cancers are hormone receptor (estrogen and/or progesterone) positive that are further divided into HER2-negative luminal A and HER2-positive luminal B subtypes. According to currently accepted convention, they represent the most common subtypes of breast cancer, accounting for approximately 70% of cases. Biomarkers play a critical role in the functional characterization, prognostication, and therapeutic prediction, rendering them indispensable for the clinical management of invasive breast cancer. Traditional biomarkers include clinicopathological parameters, which are increasingly extended by genetic and other molecular markers, enabling the comprehensive characterization of patients with luminal breast cancer. Liquid biopsies capturing and analyzing circulating tumor cells (CTCs) and circulating tumor DNA (ctDNA) are emerging technologies that envision personalized management through precision oncology. This article reviews key biomarkers in luminal breast cancer and ongoing developments.

The role of cancer-associated fibroblasts in breast cancer metastasis

Breast cancer deaths are primarily caused by metastasis. There are several treatment options that can be used to treat breast cancer. There are, however, a limited number of treatments that can either prevent or inhibit the spread of breast tumor metastases. Thus, novel therapeutic strategies are needed. Studies have increasingly focused on the importance of the tumor microenvironment (TME) in metastasis of breast cancer. As the most abundant cells in the TME, cancer-associated fibroblasts (CAFs) play important roles in cancer pathogenesis. They can remodel the structure of the extracellular matrix (ECM) and engage in crosstalk with cancer cells or other stroma cells by secreting growth factors, cytokines, and chemokines, as well as components of the ECM, which assist the tumor cells to invade through the TME and cause distant metastasis. Clinically, CAFs not only foster the initiation, growth, angiogenesis, invasion, and metastasis of breast cancer but also serve as biomarkers for diagnosis, therapy, and prediction of prognosis. In this review, we summarize the biological characteristics and subtypes of CAFs and their functions in breast cancer metastasis, focusing on their important roles in the diagnosis, prognosis, and treatment of breast cancer. Recent studies suggest that CAFs are vital partners of breast cancer cells that assist metastasis and may represent ideal targets for prevention and treatment of breast cancer metastasis.

Looking ahead in early-phase trial design to improve the drug development process: examples in oncology

BACKGROUND

Clinical trial design must consider the specific resource constraints and overall goals of the drug development process (DDP); for example, in designing a phase I trial to evaluate the safety of a drug and recommend a dose for a subsequent phase II trial. Here, we focus on design considerations that involve the sequence of clinical trials, from early phase I to late phase III, that constitute the DDP.

METHODS

We discuss how stylized simulation models of clinical trials in an oncology DDP can quantify important relationships between early-phase trial designs and their consequences for the remaining phases of development. Simulations for three illustrative settings are presented, using stylized models of the DDP that mimic trial designs and decisions, such as the potential discontinuation of the DDP.

RESULTS

We describe: (1) the relationship between a phase II single-arm trial sample size and the likelihood of a positive result in a subsequent phase III confirmatory trial; (2) the impact of a phase I dose-finding design on the likelihood that the DDP will produce evidence of a safe and effective therapy; and (3) the impact of a phase II enrichment trial design on the operating characteristics of a subsequent phase III confirmatory trial.

CONCLUSIONS

Stylized models of the DDP can support key decisions, such as the sample size, in the design of early-phase trials. Simulation models can be used to estimate performance metrics of the DDP under realistic scenarios; for example, the duration and the total number of patients enrolled. These estimates complement the evaluation of the operating characteristics of early-phase trial design, such as power or accuracy in selecting safe and effective dose levels.

Single-molecule RNA in situ detection in clinical FFPE tissue sections by vsmCISH

Although RNA plays a vital role in gene expression, it is less used as an in situ biomarker for clinical diagnostics than DNA and protein. This is mainly due to technical challenges caused by the low expression level and easy degradation of RNA molecules. To tackle this issue, methods that are sensitive and specific are needed. Here, we present an RNA single-molecule chromogenic in situ hybridization assay based on DNA probe proximity ligation and rolling circle amplification. When the DNA probes hybridize into close proximity to the RNA molecules, they form a V-shape structure and mediate the circularization of circle probes. Thus, our method was termed vsmCISH. We successfully applied our method to assess HER2 mRNA expression status in invasive breast cancer tissue and investigated the utility of albumin mRNA ISH for differentiating primary from metastatic liver cancer. The promising results on clinical samples indicate that our method has great potential for application in diagnosing diseases using RNA biomarkers.

Caveolin-1 genotypes as predictor for locoregional recurrence and contralateral disease in breast cancer

PURPOSE

Caveolin-1 (CAV1) has been implicated in breast cancer oncogenesis and metastasis and may be a potential prognosticator, especially for non-distant events. CAV1 functions as a master regulator of membrane transport and cell signaling. Several CAV1 SNPs have been linked to multiple cancers, but the prognostic impact of CAV1 SNPs in breast cancer remains unclear. Here, we investigated CAV1 polymorphisms in relation to clinical outcomes in breast cancer.

METHODS

A cohort of 1017 breast cancer patients (inclusion 2002-2012, Sweden) were genotyped using Oncoarray by Ilumina. Patients were followed for up to 15 years. Five out of six CAV1 SNPs (rs10256914, rs959173, rs3807989, rs3815412, and rs8713) passed quality control and were used for haplotype construction. CAV1 genotypes and haplotypes in relation to clinical outcomes were assessed with Cox regression and adjusted for potential confounders (age, tumor characteristics, and adjuvant treatments).

RESULTS

Only one SNP was associated with lymph node status, no other SNPs or haplotypes were associated with tumor characteristics. The CAV1 rs3815412 CC genotype (5.8% of patients) was associated with increased risk of contralateral breast cancer, adjusted hazard ratio (HR_adj) 4.26 (95% CI 1.86-9.73). Moreover, the TTACA haplotype (13% of patients) conferred an increased risk for locoregional recurrence HR_adj 2.24 (95% CI 1.24-4.04). No other genotypes or haplotypes were associated with clinical outcome.

CONCLUSION

CAV1 polymorphisms were associated with increased risk for locoregional recurrence and contralateral breast cancer. These findings may identify patients that could derive benefit from more tailored treatment to prevent non-distant events, if confirmed.

ZNF32 prevents the activation of cancer-associated fibroblasts through negative regulation of TGFB1 transcription in breast cancer

Breast cancer is the most frequently diagnosed malignancy and the leading cause of cancer-related deaths in women worldwide. Cancer-associated fibroblasts (CAFs) are one of the fundamental cellular components of the tumor microenvironment and play a critical role in the initiation, progression, and therapy resistance of breast cancer. However, the detailed molecular mechanisms of CAFs activation from normal fibroblasts (NFs) are still not well understood. In the present study, we reported that ZNF32 expression in breast cancer cells was negatively correlated with CAF-related markers (FSP1, α-SMA, and FAP) in stromal fibroblasts, and loss of ZNF32 promoted the activation of CAFs, as evidenced by the enhanced proliferation and contractility of CAFs. ZNF32 deficiency-mediated fibroblast activation promoted the growth and metastasis of breast cancer cells in vitro and in vivo. Mechanistically, we demonstrated that ZNF32 inhibited TGFB1 transcription by directly binding to the -1968/-1962 region of the TGFB1 promoter, leading to the prevention of fibroblast activation. Altogether, our findings reveal an important mechanism by which ZNF32 suppression increases the transcription of the TGFB1 gene in breast cancer cells, and subsequently, elevated levels of secretory TGF-β stimulate NFs transformation into CAFs, which in turn facilitates the malignant progression of breast cancer. Our data implicated ZNF32 as a potential therapeutic strategy against breast cancer.

CRISPR/Cas genome editing in triple negative breast cancer: Current situation and future directions

Triple negative breast cancer (TNBC) has been well-known to be closely associated with the abnormal expression of both oncogenes and tumor suppressors. Although several pathogenic mutations in TNBC have been identified, the current therapeutic strategy is usually aimed at symptom relief rather than correcting mutations in the DNA sequence. Of note, clustered regularly interspaced short palindromic repeats (CRISPR)/CRISPR-associated protein (Cas) has been gradually regarded as a breakthrough gene-editing tool with potential therapeutic applications in human cancers, including TNBC. Thus, in this review, we focus on summarizing the molecular subtypes of TNBC, as well as the CRISPR system and its potential applications in TNBC treatment. Moreover, we further discuss several emerging strategies for utilizing the CRISPR/Cas system to aid in the precise diagnosis of TNBC, as well as the limitations of the CRISPR/Cas system. Taken together, these findings would demonstrate that CRISPR/Cas system is not only an effective genome editing tool in TNBC, but a promising strategy for the future therapeutic purposes.

Circular RNAs: New layer of complexity evading breast cancer heterogeneity

Advances in high-throughput sequencing techniques and bioinformatic analysis have refuted the "junk" RNA hypothesis that was claimed against non-coding RNAs (ncRNAs). Circular RNAs (circRNAs); a class of single-stranded covalently closed loop RNA molecules have recently emerged as stable epigenetic regulators. Although the exact regulatory role of circRNAs is still to be clarified, it has been proven that circRNAs could exert their functions by interacting with other ncRNAs or proteins in their own physiologically authentic environment, regulating multiple cellular signaling pathways and other classes of ncRNAs. CircRNAs have also been reported to exhibit a tissue-specific expression and have been associated with the malignant transformation process of several hematological and solid malignancies. Along this line of reasoning, this review aims to highlight the importance of circRNAs in Breast Cancer (BC), which is ranked as the most prevalent malignancy among females. Notwithstanding the substantial efforts to develop a suitable anticancer therapeutic regimen against the heterogenous BC, inter- and intra-tumoral heterogeneity have resulted in an arduous challenge for drug development research, which in turn necessitates the investigation of other markers to be therapeutically targeted. Herein, the potential of circRNAs as possible diagnostic and prognostic biomarkers have been highlighted together with their possible application as novel therapeutic targets.

Molecular phenotyping of malignant canine mammary tumours: Detection of high-risk group and its relationship with clinicomolecular characteristics

Canine mammary gland tumours (CMTs) constitute the most common cancer in female dogs and comprise approximately 50% of all canine cancers. With the advent of high-throughput technologies such as microarray and next-generation sequencing, the molecular phenotyping (classification) of various cancers has been extensively developed. The present study used a canine RNA-sequencing dataset, namely GSE119810, to classify 113 malignant CMTs and 64 matched normal samples via an unsupervised hierarchical algorithm with a view to evaluating the association between the resulting subtypes (clusters) (n = 4) and clinical and molecular characteristics. Finally, a molecular classifier was developed, and it detected 1 high-risk molecular subtype in the training dataset (GSE119810) and 2 independent validation datasets (GSE20718 and GSE22516). Our results revealed four molecular subtypes (C2-C5) in malignant CMTs. Furthermore, the normal samples constituted a distinct group in the clustering analysis. Marked significant associations were observed between the molecular subtypes (especially C5) and clinical/molecular features, including positive lymphatic invasion, high tumour grades, histopathology diagnoses, short survival and high TP53 mutation rates (ps <.05). The high-risk subtype (C5) was further characterized through the development of a cell cycle-based gene signature, which comprised 37 proliferation-related genes according to the support vector machine algorithm. This signature identified the high-risk group in both training and validation datasets (ps <.001). In the validation analysis, our potential classifier robustly predicted patients with positive lymphatic invasion, metastases and short survival.

The prognostic value and response to immunotherapy of immunogenic cell death-associated genes in breast cancer

Breast cancer (BRCA) remains the most prevalent cancer worldwide and the tumor microenvironment (TME) has been discovered to exert a wide influence on the overall survival and therapeutic response. Numerous lines of evidence reported that the effects of immunotherapy of BRCA were manipulated by TME. Immunogenic cell death (ICD) is a form of regulated cell death (RCD) that is capable of fueling adaptive immune responses and aberrant expression of ICD-related genes (ICDRGs) can govern the TME system by emitting danger signals or damage-associated molecular patterns (DAMPs). In the current study, we obtained 34 key ICDRGs in BRCA. Subsequently, using the transcriptome data of BRCA from the TCGA database, we constructed a risk signature based on 6 vital ICDRGs, which had a good performance in predicting the overall survival of BRCA patients. We also examined the efficacy of our risk signature in the validation dataset (GSE20711) in the GEO database and it performed excellently. According to the risk model, patients with BRCA were divided into high-risk and low-risk groups. Also, the unique immune characteristics and TME between the two subgroups and 10 promising small molecule drugs targeting BRCA patients with different ICDRGs risk have been investigated. The low-risk group had good immunity indicated by T cell infiltration and high immune checkpoint expression. Moreover, the BRCA samples could be divided into three immune subtypes according to immune response severity (ISA, ISB, and ISC). ISA and ISB predominated in the low-risk group and patients in the low-risk group exhibited a more vigorous immune response. In conclusion, we developed an ICDRGs-based risk signature that can predict the prognosis of BRCA patients and offer a novel therapeutic strategy for immunotherapy, which would be of great significance in the BRCA clinical setting.

Development and Assessment of a Novel Core Biopsy-Based Prediction Model for Pathological Complete Response to Neoadjuvant Chemotherapy in Women with Breast Cancer

Purpose: Pathological complete response (pCR), the goal of NAC, is considered a surrogate for favorable outcomes in breast cancer (BC) patients administrated neoadjuvant chemotherapy (NAC). This study aimed to develop and assess a novel nomogram model for predicting the probability of pCR based on the core biopsy. Methods: This was a retrospective study involving 920 BC patients administered NAC between January 2012 and December 2018. The patients were divided into a primary cohort (769 patients from January 2012 to December 2017) and a validation cohort (151 patients from January 2017 to December 2018). After converting continuous variables to categorical variables, variables entering the model were sequentially identified via univariate analysis, a multicollinearity test, and binary logistic regression analysis, and then, a nomogram model was developed. The performance of the model was assessed concerning its discrimination, accuracy, and clinical utility. Results: The optimal predictive threshold for estrogen receptor (ER), Ki67, and p53 were 22.5%, 32.5%, and 37.5%, respectively (all p < 0.001). Five variables were selected to develop the model: clinical T staging (cT), clinical nodal (cN) status, ER status, Ki67 status, and p53 status (all p ≤ 0.001). The nomogram showed good discrimination with the area under the curve (AUC) of 0.804 and 0.774 for the primary and validation cohorts, respectively, and good calibration. Decision curve analysis (DCA) showed that the model had practical clinical value. Conclusions: This study constructed a novel nomogram model based on cT, cN, ER status, Ki67 status, and p53 status, which could be applied to personalize the prediction of pCR in BC patients treated with NAC.

ELOVL5 and IGFBP6 genes modulate sensitivity of breast cancer cells to ferroptosis

Introduction: Relapse of breast cancer is one of the key obstacles to successful treatment. Previously we have shown that low expression of ELOVL5 and IGFBP6 genes in breast cancer tissue corresponded to poor prognosis. ELOVL5 participates directly in the elongation of polyunsaturated fatty acids (PUFAs) that are considered to play an important role in cancer cell metabolism. Thus, in this work we studied the changes in lipid metabolism in breast cancer cells with reduced expression of either ELOVL5 or IGFBP6 gene. Methods: MDA-MB-231 cells with a stable knockdown of either ELOVL5 or IGFBP6 gene were used in this study. Transcriptomic and proteomic analysis as well as RT-PCR were utilized to assess gene expression. Content of individual fatty acids in the cells was measured with HPLC-MS. HPLC was used for analysis of the kinetics of PUFAs uptake. Cell viability was measured with MTS assay. Flow cytometry was used to measure activation of apoptosis. Fluorescent microscopy was utilized to assess accumulation of ROS and formation of lipid droplets. Glutathione peroxidase activity was measured with a colorimetric assay. Results: We found that the knockdown of IGFBP6 gene led to significant changes in the profile of fatty acids in the cells and in the expression of many genes associated with lipid metabolism. As some PUFAs are known to inhibit proliferation and cause death of cancer cells, we also tested the response of the cells to single PUFAs and to combinations of docosahexaenoic acid (DHA, a n-3 PUFA) with standard chemotherapeutic drugs. Our data suggest that external PUFAs cause cell death by activation of ferroptosis, an iron-dependent mechanism of cell death with excessive lipid peroxidation. Moreover, both knockdowns increased cells' sensitivity to ferroptosis, probably due to a significant decrease in the activity of the antioxidant enzyme GPX4. Addition of DHA to commonly used chemotherapeutic drugs enhanced their effect significantly, especially for the cells with low expression of IGFBP6 gene. Discussion: The results of this study suggest that addition of PUFAs to the treatment regimen for the patients with low expression of IGFBP6 and ELOVL5 genes can be potentially beneficial and is worth testing in a clinically relevant setting.

Identification and validation of an m6A-related gene signature to predict prognosis and evaluate immune features of breast cancer

Breast cancer is the most prevalent cancer, and it is accompanied by high heterogeneity. N6-methyladenosine (m6A) modification significantly contributes to breast cancer tumorigenesis and progression. However, how m6A-related genes affect the clinical outcomes and tumor immune microenvironment (TIME) of breast cancer is largely unknown. Our study developed an m6A-related gene signature on the basis of The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) databases. The m6A-related gene signature was constructed using univariate, least absolute shrinkage and selection operator (LASSO), and multivariate Cox regression analyses. Breast cancer patients were classified into low- and high-risk groups depending on the median risk score. The reliability and efficiency of the signature were validated using Kaplan-Meier analysis, receiver operating characteristic (ROC) curves, and principal component analysis (PCA). The risk score was validated as an independent indicator associated with overall survival, and a nomogram model was created to estimate the overall survival of patients with breast cancer. Functional annotation suggested that the risk score had a strong relationship with immune-related pathways. Different proportions of immune cell infiltration between the two groups were evaluated using various algorithms. The high-risk group had higher immune checkpoint expression levels. We discovered that one of the 6 prognostic genes, TMEM71, was downregulated in breast cancer tissues. In vitro experiments indicated that overexpression of TMEM71 suppressed breast cancer cell proliferation and migration. In conclusion, the m6A-related gene signature may be a sensitive biomarker for overall survival prediction and guide the individualized treatment for breast cancer patients.

Multigene signatures for early breast cancer in clinical practice: A report of the Lombardy genomic assays for breast cancer working group

The increasing understanding of breast cancer biology has provided the basis for the development of multigene signatures aimed to improve the capability of clinicians to assess patients' prognostication and risk stratification. Incorporating these tools in clinical practice has profoundly impacted on the decision-making process for the adjuvant therapy of patients with ER+/HER2- early breast cancer and the results from prospective adjuvant trials have strengthened the clinical utility of multigene signatures in this setting. In July 2019, Lombardy was the first Region in Italy to reimburse genomic testing for patients with ER+/HER2- early breast cancer. Three years later, a group of investigators from six referral Cancer Centers in Lombardy convened to debate the use of multigene signatures in clinical practice and share their own experience with the tests after reimbursement. Here, we reviewed relevant data on the role of multigene signatures in tailoring adjuvant chemotherapy for patients with ER+/HER2- early breast cancer and discussed about the optimal use of these assays in current clinical practice. As the treatment landscape of early breast cancer evolves and novel questions about the possible additional applications of multigene assays arise, we also provide our viewpoint on the potential implementation of the assays in the evolving scenario ER+/HER2- early breast cancer treatment.

Cost-effectiveness analysis of Oncotype DX from a Brazilian private medicine perspective: a GBECAM multicenter retrospective study

Background

Oncotype DX (ODX) is a validated assay for the prediction of risk of recurrence and benefit of chemotherapy (CT) in both node negative (N0) and 1-3 positive nodes (N1), hormone receptor positive (HR+), human epidermal growth factor receptor 2-negative (HER2-) early breast cancer (eBC). Due to limited access to genomic assays in Brazil, treatment decisions remain largely driven by traditional clinicopathologic risk factors. ODX has been reported to be cost-effective in different health system, but limited data are available considering the reality of middle-income countries such as Brazil. We aim to evaluate the cost-effectiveness of ODX across strata of clinical risk groups using data from a dataset of patients from Brazilian institutions.

Methods

Clinicopathologic and ODX information were analyzed for patients with T1-T3, N0-N1, HR+/HER2- eBC who had an ODX performed between 2005 and 2020. Projections of CT indication by clinicopathologic criteria were based on binary clinical risk categorization based on the Adjuvant! Algorithm. The ODX score was correlated with the indication of CT according to TAILORx and RxPONDER data. Two decision-tree models were developed. In the first model, low and high clinical risk patients were included while in the second, only high clinical risk patients were included. The cost for ODX and CT was based on the Brazilian private medicine perspective.

Results

In all, 645 patients were analyzed; 411 patients (63.7%) had low clinical risk and 234 patients (36.3%) had high clinical risk disease. The ODX indicated low (<11), intermediate (11-25), and high (>25) risk in 119 (18.4%), 415 (64.3%), and 111 (17.2%) patients, respectively. Among 645 patients analyzed in the first model, ODX was effective (5.6% reduction in CT indication) though with an incremental cost of United States Dollar (US$) 2288.87 per patient. Among 234 patients analyzed in the second model (high clinical risk only), ODX led to a 57.7% reduction in CT indication and reduced costs by US$ 4350.66 per patient.

Conclusions

Our study suggests that ODX is cost-saving for patients with high clinical risk HR+/HER2- eBC and cost-attractive for the overall population in the Brazilian private medicine perspective. Its incorporation into routine practice should be strongly considered by healthcare providers.

Identification of a Two-Gene Signature and Establishment of a Prognostic Nomogram Predicting Overall Survival in Diffuse-Type Gastric Cancer

BACKGROUND

It is widely acknowledged that the molecular biological characteristics of diffuse-type gastric cancer are different from intestinal-type gastric cancer. Notwithstanding that significant progress in high-throughput sequencing technology has been made, there is a paucity of effective prognostic biomarkers for diffuse gastric cancer for clinical practice.

METHODS

We downloaded four GEO datasets (GSE22377, GSE38749, GSE47007 and GSE62254) to establish and validate a prognostic two-gene signature for diffuse gastric cancer. The TGCA-STAD dataset was used for external validation. The optimal gene signature was established by using Cox regression analysis. Receiver operating characteristic (ROC) methodology was used to find the best prognostic model. Gene set enrichment analysis was used to analyze the possible signaling pathways of the two genes (MEF2C and TRIM15).

RESULTS

A total of four differently expressed genes (DEGs) (two upregulated and two downregulated) were identified. After a comprehensive analysis, two DEGs (MEF2C and TRIM15) were utilized to construct a prognostic model. A prognostic prediction model was constructed according to T stage, N stage, M stage and the expression of MEF2C and TRIM15. The area under the time-dependent receiver operator characteristic was used to evaluate the performance of the prognosis model in the GSE62254 dataset.

CONCLUSIONS

We demonstrated that MEF2C and TRIM15 might be key genes. We also established a prognostic nomogram based on the two-gene signature that yielded a good performance for predicting overall survival in diffuse-type gastric cancer.

Andrographolide suppresses the malignancy of triple-negative breast cancer by reducing THOC1-promoted cancer stem cell characteristics

Triple-negative breast cancers (TNBCs) are difficult to cure and currently lack of effective treatment strategies. Cancer stem cells (CSCs) are highly associated with the poor clinical outcome of TNBCs. Thoc1 is a core component of the THO complex (THOC) that regulates the elongation, processing and nuclear export of mRNA. The function of thoc1 in TNBC and whether Thoc1 serves as a drug target are poorly understood. In this study, we demonstrated that thoc1 expression is elevated in TNBC cell lines and human TNBC patient tissues. Knockdown of thoc1 decreased cancer stem cell populations, reduced mammosphere formation, impaired THOC function, and downregulated the expression of stemness-related proteins. Moreover, the thoc1-knockdown 4T1 cells showed less lung metastasis in an orthotopic breast cancer mouse model. Overexpression of Thoc1 promoted TNBC malignancy and the mRNA export of stemness-related genes. Furthermore, treatment of TNBC cells with the natural compound andrographolide reduced the expression of Thoc1 expression, impaired homeostasis of THOC, suppressed CSC properties, and delayed tumor growth in a 4T1-implanted orthotopic mouse model. Andrographolide also reduced the activity of NF-κB, an upstream transcriptional regulator of Thoc1. Notably, thoc1 overexpression attenuates andrographolide-suppressed cellular proliferation. Altogether, our results demonstrate that THOC1 promotes cancer stem cell characteristics of TNBC, and andrographolide is a potential natural compound for eliminating CSCs of TNBCs by downregulating the NF-κB-thoc1 axis.

Expression of c-MET in Estrogen Receptor Positive and HER2 Negative Resected Breast Cancer Correlated with a Poor Prognosis

Introduction: The mesenchymal-epithelial transition factor (c-MET) receptor is overexpressed in about 14−54% of invasive breast cancers, but its prognostic value in clinical practice is still unclear. Methods: In order to investigate the relationship between c-MET expression levels and prognosis, we retrospectively reviewed the clinical features and outcomes of 105 women with estrogen receptor positive HER2 negative (ER+/HER2-) resected breast cancer. We used the Kaplan Meier method to estimate Disease Free Survival (DFS) and Breast Cancer Specific Survival (BCSS) in the subgroups of patients with high (≥50%) and low (<50%) c-MET expression. Univariate and multivariate Cox proportional regression models were performed to assess the prognostic impact of clinicopathological parameters for DFS an BCSS. Results: High c-MET values significantly correlated with tumor size, high Ki67 and low (<20%) progesterone receptor expression. At a median follow up of 60 months, patients with high c-MET tumor had significantly worse (p = 0.00026) and BCSS (p = 0.0013). Univariate analysis showed a significant association between large tumor size, elevated Ki67, c-MET values and increased risk of recurrence or death. The multivariate COX regression model showed that tumor size and high c-MET expression were independent predictors of DFS (p = 0.019 and p = 0.022). Moreover, large tumor size was associated with significantly higher risk of cancer related death at multivariate analysis (p = 0.017), while a trend towards a poorer survival was registered in the high c-MET levels cohort (p = 0.084). Conclusions: In our series, high c-MET expression correlated with poor survival outcomes. Further studies are warranted to validate the clinical relevance and applicability of c-MET as a prognostic factor in ER+/HER2- early BC.

Molecular testing to deliver personalized chemotherapy recommendations: risking over and undertreatment

BACKGROUND

In the adjuvant setting of cancer treatment, de-escalation strategies have the goal of omitting or minimizing treatment in patients, without compromising outcomes. Historically, eligibility for adjuvant treatment solely relied on the patient's clinical and tumor's pathological characteristics. At the turn of the century, based on new biological understanding, molecular-based strategies were tested and sometimes implemented.

MAIN BODY

However, we illustrate how molecularly based de-escalation strategies may paradoxically lead to overtreatment. This may happen when the novel approach is tested in lieu of standard management and may not yield the same results when being implemented in addition to usual practice. In the DYNAMIC trial, adjuvant chemotherapy decision in stage II colon cancer was compared between a circulating tumor DNA (ctDNA)-based approach and the standard care. We show this may result in more patients receiving oxaliplatin-based chemotherapy and may expose a similar proportion of patients to chemotherapy if the novel strategy is implemented in addition to usual practice. The other potential risk is undertreatment. We provide an illustration of early breast cancer, where the decision of adjuvant chemotherapy based on the gene expression signature MammaPrint may lead to inferior outcomes as compared with the clinico-pathologic strategy. This may also happen when non-inferiority designs have large margins. Among solutions, it should be acknowledged that clinico-pathological features, like T4 in colon cancer, may not be abandoned and replaced by novel strategies in real-life practice. Therefore, novel strategies should be tested in addition to standard of care, and not in lieu of. Second, de-escalation trials should focus on the settings where the standard of care has a widespread agreement. This would avoid the risk of testing non-inferiority against an ineffective therapy, which guarantees successes without providing informative data.

CONCLUSION

Simply because a molecular test is rational does not mean it can improve patient outcomes. Here, we highlight how molecular test-based strategies may result in either overtreatment or undertreatment. In the rapidly evolving field of medicine, where technological advances may be transformative, our piece highlights scientific pitfalls to be aware of when considering running such trials or before implementing novel strategies in daily practice.

Extended adjuvant endocrine treatment for premenopausal women: A Delphi approach to guide clinical practice

The use of an aromatase inhibitor (AI) in combination with ovarian function suppression (OFS) has become the mainstay of adjuvant endocrine therapy in high-risk premenopausal patients with hormone receptor-positive breast cancer. Although five years of such therapy effectively reduces recurrence rates, a substantial risk of late recurrence remains in this setting. Multiple trials have shown that extending AI treatment beyond five years could offer further protection. However, as these studies comprised only postmenopausal patients, no direct evidence currently exists to inform about the potential benefits and/or side effects of extended AI + OFS therapies in premenopausal women. Given these grey areas, we conducted a Delphi survey to report on the opinion of experts in breast cancer treatment and summarize a consensus on the discussed topics. A total of 44 items were identified, all centred around two main themes: 1) defining reliable prognostic factors to pinpoint premenopausal patients eligible for endocrine therapy extension; 2) designing how such therapy should optimally be administered in terms of treatment combinations and duration based on patients' menopausal status. Each item was separately discussed and anonymously voted by 12 experts representing oncological institutes spread across Italy. The consensus threshold was reached in 36 out of 44 items (82%). Herein, we discuss the levels of agreement/disagreement achieved by each item in relation to the current body of literature. In the absence of randomized trials to guide the tailoring of extended AI treatment in premenopausal women, conclusions from our study provide a framework to assist routine clinical practice.

Integrated imaging and molecular analysis to decipher tumor microenvironment in the era of immunotherapy

Radiological imaging is an integral component of cancer care, including diagnosis, staging, and treatment response monitoring. It contains rich information about tumor phenotypes that are governed not only by cancer cellintrinsic biological processes but also by the tumor microenvironment, such as the composition and function of tumor-infiltrating immune cells. By analyzing the radiological scans using a quantitative radiomics approach, robust relations between specific imaging and molecular phenotypes can be established. Indeed, a number of studies have demonstrated the feasibility of radiogenomics for predicting intrinsic molecular subtypes and gene expression signatures in breast cancer based on MRI. In parallel, promising results have been shown for inferring the amount of tumor-infiltrating lymphocytes, a key factor for the efficacy of cancer immunotherapy, from standard-of-care radiological images. Compared with the biopsy-based approach, radiogenomics offers a unique avenue to profile the molecular makeup of the tumor and immune microenvironment as well as its evolution in a noninvasive and holistic manner through longitudinal imaging scans. Here, we provide a systematic review of the state of the art radiogenomics studies in the era of immunotherapy and discuss emerging paradigms and opportunities in AI and deep learning approaches. These technical advances are expected to transform the radiogenomics field, leading to the discovery of reliable imaging biomarkers. This will pave the way for their clinical translation to guide precision cancer therapy.

Identification of the prognostic and therapeutic values of cyclin E1 (CCNE1) gene expression in Lung Adenocarcinoma and Lung Squamous Cell Carcinoma: A database mining approach

Cyclin E1 (CCNE1) is a protein-coding gene that belongs to the Cyclin family of genes which controls the G1/S phase transition of the cell cycle. Previously, its abnormal expression pattern has been examined and found to be correlated with ovarian and breast cancer progression. Herein, we exploited a bioinformatics and database mining strategy to unveil the therapeutic and prognostic significance of CCNE1 gene expression in Lung Adenocarcinoma (LUAD) and Lung Squamous Cell Carcinoma (LUSC). CCNE1 gene was reported to be highly expressed in LUAD and LUSC tissues. Its promoter and coding sequences were reported to be aberrantly methylated in LUAD and LUSC tissues than in normal tissues. Moreover, around 12 somatic mutations (frequency: 0.7%) were recorded in the CCNE1 coding region from different studies involving LUAD and LUSC patients' whole genome sequences. The CCNE1 gene expression was also correlated with LUAD and LUSC patients' overall and disease-specific survival. Immune infiltration analysis revealed the association between CCNE1 gene expression and the abundance of numerous immune cells (i.e., T cells and B Cells) infiltration in LUAD and LUSC patients. Two previously known genes involved in oncogenic processes i.e., CDC45 and PDCD5 were identified as the most highly co-expressed genes of CCNE1 in LUAD and LUSC tissues. Altogether, the CCNE1 gene and its transcriptional and translational products may serve as a prognostic or therapeutic target in the diagnosis and treatment of LUAD and LUSC patients. The scientific findings of this study should assist in translating CCNE1 into clinical practice for lung cancer diagnosis and treatment.

Tailoring neoadjuvant treatment of HR-positive/HER2-negative breast cancers: Which role for gene expression assays?

Neoadjuvant chemotherapy (NACT) for breast cancer (BC) increases surgical and conservative surgery chances. However, a significant proportion of patients will not be eligible for conservative surgery following NACT because of large tumor size and/or low chemosensitivity, especially for hormone receptor (HR)-positive/ human epidermal growth factor receptor 2 (HER2)-negative tumors, for which pathological complete response rates are lower than for other BC subtypes. On the other hand, for luminal BC neoadjuvant endocrine therapy could represent a valid alternative. Several gene expression assays have been introduced into clinical practice in last decades, in order to define prognosis more accurately than clinico-pathological features alone and to predict the benefit of adjuvant treatments. A series of studies have demonstrated the feasibility of using core needle biopsy for gene expression risk testing, finding a high concordance rate in the risk result between biopsy sample and surgical samples. Based on these premises, recent efforts have focused on the utility of gene expression signatures to guide therapeutic decisions even in the neoadjuvant setting. Several prospective and retrospective studies have investigated the correlation between gene expression risk score from core needle biopsy before neoadjuvant therapy and the likelihood of 1) clinical and pathological response to neoadjuvant chemotherapy and endocrine therapy, 2) conservative surgery after neoadjuvant chemotherapy and endocrine therapy, and 3) survival following neoadjuvant chemotherapy and endocrine therapy. The purpose of this review is to provide an overview of the potential clinical utility of the main commercially available gene expression panels (Oncotype DX, MammaPrint, EndoPredict, Prosigna/PAM50 and Breast Cancer Index) in the neoadjuvant setting, in order to better inform decision making for luminal BC beyond the exclusive contribution of clinico-pathological features.

Predictive Biomarkers of Response to Neoadjuvant Chemotherapy in Breast Cancer: Current and Future Perspectives for Precision Medicine

Pathological complete response (pCR) after neoadjuvant chemotherapy in patients with early breast cancer is correlated with better survival. Meanwhile, an expanding arsenal of post-neoadjuvant treatment strategies have proven beneficial in the absence of pCR, leading to an increased use of neoadjuvant systemic therapy in patients with early breast cancer and the search for predictive biomarkers of response. The better prediction of response to neoadjuvant chemotherapy could enable the escalation or de-escalation of neoadjuvant treatment strategies, with the ultimate goal of improving the clinical management of early breast cancer. Clinico-pathological prognostic factors are currently used to estimate the potential benefit of neoadjuvant systemic treatment but are not accurate enough to allow for personalized response prediction. Other factors have recently been proposed but are not yet implementable in daily clinical practice or remain of limited utility due to the intertumoral heterogeneity of breast cancer. In this review, we describe the current knowledge about predictive factors for response to neoadjuvant chemotherapy in breast cancer patients and highlight the future perspectives that could lead to the better prediction of response, focusing on the current biomarkers used for clinical decision making and the different gene signatures that have recently been proposed for patient stratification and the prediction of response to therapies. We also discuss the intratumoral phenotypic heterogeneity in breast cancers as well as the emerging techniques and relevant pre-clinical models that could integrate this biological factor currently limiting the reliable prediction of response to neoadjuvant systemic therapy.

The current staging and classification systems of breast cancer and their pitfalls: Is it possible to integrate the complexity of this neoplasm into a unified staging system?

Breast cancer is one of the leading causes of cancer death in women worldwide due to its variable aggressiveness and high propensity to develop distant metastases. The staging can be performed clinically or pathologically, generating the stage stratification by the TNM (T - tumor size; N- lymph node metastasis; M - distant organ metastasis) system. However, cancers with virtually identical TNM characteristics can present highly contrasting behaviors due to the divergence of molecular profiles. This review focuses on the histopathological nuances and molecular understanding of breast cancer through the profiling of gene and protein expression, culminating in improvements promoted by the integration of this information into the traditional staging system. As a culminating point, it will highlight predictive statistical tools for genomic risks and decision algorithms as a possible solution to integrate the various systems because they have the potential to reduce the indications for such tests, serving as a funnel in association with staging and previous classification.

Mammography-based Radiomics in Breast Cancer: A Scoping Review of Current Knowledge and Future Needs

RATIONALE AND OBJECTIVES

Breast cancer is a highly complex heterogeneous disease. Current validated prognostic factors (e.g., histological grade, lymph node involvement, receptor status, and proliferation index), as well as multigene tests (e.g., Oncotype DX and PAM50) are helpful to describe breast cancer characteristics and predict the chance of recurrence risk and survival. Nevertheless, they are invasive and cannot capture a complete heterogeneity of the entire breast tumor resulting in up to 30% of patients being either over- or under-treated for breast cancer. Furthermore, multigene testings are time consuming and expensive. Radiomics is emerging as a reliable, accurate, non-invasive, and cost-effective approach of using quantitative image features to classify breast cancer characteristics and predict patient outcomes. Several recent radiomics reviews have been conducted in breast cancer, however, specific mammography-based radiomics studies have not been well discussed. This scoping review aims to assess and summarize the current evidence on the potential usefulness of mammography-based (i.e., digital mammography, digital breast tomosynthesis, and contrast-enhanced mammography) radiomics in predicting factors that describe breast cancer characteristics, recurrence, and survival.

MATERIALS AND METHODS

PubMed database and eligible text reference were searched using relevant keywords to identify studies published between 2015 and December 19, 2020. Studies collected were screened and assessed based on the inclusion and exclusion criteria.

RESULTS

Eighteen eligible studies were included and organized into three main sections: radiomics predicting breast cancer characteristics, radiomics predicting breast cancer recurrence and survival, and radiomics integrating with clinical data. Majority of publications reported retrospective studies while three studies examined prospective cohorts. Encouraging results were reported, suggesting the potential clinical value of mammography-based radiomics. Further efforts are required to standardize radiomics approaches and catalogue reproducible and relevant mammographic radiomic features. The role of integrating radiomics with other information is discussed.

CONCLUSION

The potential role of mammography-based radiomics appears promising but more efforts are required to further evaluate its reliability as a routine clinical tool.

Establishment and Analysis of an Individualized EMT-Related Gene Signature for the Prognosis of Breast Cancer in Female Patients

Background. The current high mortality rate of female breast cancer (BC) patients emphasizes the necessity of identifying powerful and reliable prognostic signatures in BC patients. Epithelial-mesenchymal transition (EMT) was reported to be associated with the development of BC. The purpose of this study was to identify prognostic biomarkers that predict overall survival (OS) in female BC patients by integrating data from TCGA database. Method. We first downloaded the dataset in TCGA and identified gene signatures by overlapping candidate genes. Differential analysis was performed to find differential EMT-related genes. Univariate regression analysis was then performed to identify candidate prognostic variables. We then developed a prognostic model by multivariate analysis to predict OS. Calibration curves, receiver operating characteristics (ROC) curves, $C$ -index, and decision curve analysis (DCA) were used to test the veracity of the prognostic model. Result. In this study, we identified and validated a prognostic model integrating age and six genes (CD44, P3H1, SDC1, COL4A1, TGFβ1, and SERPINE1). $C$ -index values for BC patients were 0.672 (95% CI 0.611–0.732) and 0.692 (95% CI 0.586–0.798) in the training cohort and test set, respectively. The calibration curve and the DCA curve show the good predictive performance of the model. Conclusion. This study offered a robust predictive model for OS prediction in female BC patients and may provide a more accurate treatment strategy and personalized therapy in the future.

How Dual-Energy Contrast-Enhanced Spectral Mammography Can Provide Useful Clinical Information About Prognostic Factors in Breast Cancer Patients: A Systematic Review of Literature

Introduction

In the past decade, a new technique derived from full-field digital mammography has been developed, named contrast-enhanced spectral mammography (CESM). The aim of this study was to define the association between CESM findings and usual prognostic factors, such as estrogen receptors, progesterone receptors, HER2, and Ki67, in order to offer an updated overview of the state of the art for the early differential diagnosis of breast cancer and following personalized treatments.

Materials and Methods

According to the PRISMA guidelines, two electronic databases (PubMed and Scopus) were investigated, using the following keywords: breast cancer AND (CESM OR contrast enhanced spectral mammography OR contrast enhanced dual energy mammography) AND (receptors OR prognostic factors OR HER2 OR progesterone OR estrogen OR Ki67). The search was concluded in August 2021. No restriction was applied to publication dates.

Results

We obtained 28 articles from the research in PubMed and 114 articles from Scopus. After the removal of six replicas that were counted only once, out of 136 articles, 37 articles were reviews. Eight articles alone have tackled the relation between CESM imaging and ER, PR, HER2, and Ki67. When comparing radiological characterization of the lesions obtained by either CESM or contrast-enhanced MRI, they have a similar association with the proliferation of tumoral cells, as expressed by Ki-67. In CESM-enhanced lesions, the expression was found to be 100% for ER and 77.4% for PR, while moderate or high HER2 positivity was found in lesions with non-mass enhancement and with mass closely associated with a non-mass enhancement component. Conversely, the non-enhancing breast cancer lesions were not associated with any prognostic factor, such as ER, PR, HER2, and Ki67, which may be associated with the probability of showing enhancement. Radiomics on CESM images has the potential for non-invasive characterization of potentially heterogeneous tumors with different hormone receptor status.

Conclusions

CESM enhancement is associated with the proliferation of tumoral cells, as well as to the expression of estrogen and progesterone receptors. As CESM is a relatively young imaging technique, a few related works were found; this may be due to the “off-label” modality. In the next few years, the role of CESM in breast cancer diagnostics will be more thoroughly investigated.