Spotlight on the utility of the Oncotype DX® breast cancer assay

The REMAR (Rhein-Main-Registry) real-world study: prospective evaluation of the 21-gene breast recurrence score® assay in addition to Ki-67 for adjuvant treatment decisions in early-stage breast cancer

PURPOSE

Ki-67 is recommended by international/national guidelines for risk stratification in early breast cancer (EBC), particularly for defining "intermediate risk," despite inter-laboratory/inter-observer variability and cutoff uncertainty. We investigated Ki-67 (> 10%- < 40%, determined locally) as a prognostic marker for intermediate/high risk in EBC, pN0-1 patients.

METHODS

This prospective, non-interventional, real-world study included females ≥ 18 years, with pN0/pN1mi/pN1, HR+ , HER2-negative EBC, and locally determined Ki-67 ranging 10%-40%. The primary outcome was changes in treatment recommendations after disclosing the Oncotype DX Breast Recurrence Score®(RS) assay result.

RESULTS

The analysis included 567 patients (median age, 57 [range, 29-83] years; 70%/1%/29%/ with pN0/pN1mi/pN1 disease; 81% and 19% with RS results 0-25 and 26-100, respectively). The correlations between local and central Ki-67, local Ki-67, and the RS, and central Ki-67 and the RS results were weak (r = 0.35, r = 0.3, and r = 0.46, respectively), and discrepancies were noted in both directions (e.g., local Ki-67 was lower or higher than central Ki-67). After disclosing the RS, treatment recommendations changed for 190 patients (34%). Changes were observed in pN0 and pN1mi/pN1 patients and in patients with centrally determined Ki-67 ≤ 10% and > 10%. Treatment changes were aligned with RS results (adding chemotherapy for patients with higher RS results, omitting it for lower RS results), and their net result was 8% reduction in adjuvant chemotherapy use (from 32% pre-RS results to 24% post-RS results).

CONCLUSION

The Oncotype DX® assay is a tool for individualizing treatments that adds to classic treatment decision factors. The RS result and Ki-67 are not interchangeable, and Ki-67, as well as nodal status, should not be used as gatekeepers for testing eligibility, to avoid under and overtreatment.

Aptamer as a targeted approach towards treatment of breast cancer

Aptamers, a novel type of targeted ligand used in drug delivery, have quickly gained popularity due to their high target specificity and affinity. Different aptamer-mediated drug delivery systems, such as aptamer-drug conjugate (ApDC), aptamer-siRNA, and aptamer-functionalised nanoparticle systems, are currently being developed for the successful treatment of cancer based on the excellent properties of aptamers. These systems can decrease potential toxicity and enhance therapeutic efficacy by targeting the drug moiety. In this review, we provide an overview of recent developments in aptamer-mediated delivery systems for cancer therapy, specifically for breast cancer, and talk about the potential applications and current issues of novel aptamer-based techniques. This study in aptamer technology for breast cancer therapy highlights key aptamers targeting well-established biomarkers such as HER2, oestrogen receptor, and progesterone receptor. Additionally, we explore the potential of aptamers in overcoming various challenges such as drug resistance and improving the delivery of therapeutic agents. This review aims to provide a deeper understanding of the present aptamer-based targeted delivery applications through in-depth analysis to increase efficacy and create new therapeutic approaches that may ultimately lead to better treatment outcomes for cancer patients.

Access and barriers to genomic classifiers for breast cancer and prostate cancer in India

The incidence of cancer in general, including breast and prostate cancer specifically, is increasing in India. Breast and prostate cancers have genomic classifiers developed to guide therapy decisions. However, these genomic classifiers are often inaccessible in India due to high cost. These classifiers may also be less suitable to the Indian population, as data primarily from patients in wealthy Western countries were used in developing these genomic classifiers. In addition to the limitations in using these existing genomic classifiers, developing and validating new genomic classifiers for breast and prostate cancer in India is challenging due to the heterogeneity in the Indian population. However, there are steps that can be taken to address the various barriers that currently exist for accurate, accessible genomic classifiers for cancer in India.

Development and validation of an individualized gene expression-based signature to predict overall survival of patients with high-grade serous ovarian carcinoma

BACKGROUND

High-grade serious ovarian carcinoma (HGSOC) is a subtype of ovarian cancer with a different prognosis attributable to genetic heterogeneity. The prognosis of patients with advanced HGSOC requires prediction by genetic markers. This study systematically analyzed gene expression profile data to establish a genetic marker for predicting HGSOC prognosis.

METHODS

The RNA-seq data set and information on clinical follow-up of HGSOC were retrieved from Gene Expression Omnibus (GEO) database, and the data were standardized by DESeq2 as a training set. On the other hand, HGSOC RNA sequence data and information on clinical follow-up were retrieved from The Cancer Genome Atlas (TCGA) as a test set. Additionally, ovarian cancer microarray data set was obtained from GEO as the external validation set. Prognostic genes were screened from the training set, and characteristic selection was performed using the least absolute shrinkage and selection operator (LASSO) with 80% re-sampling for 5000 times. Genes with a frequency of more than 2000 were selected as robust biomarkers. Finally, a gene-related prognostic model was validated in both the test and GEO validation sets.

RESULTS

A total of 148 genes were found to be significantly correlated with HGSOC prognosis. The expression profile of these genes could stratify HGSOC prognosis and they were enriched to multiple tumor-related regulatory pathways such as tyrosine metabolism and AMPK signaling pathway. AKR1B10 and ANGPT4 were obtained after 5000-time re-sampling by LASSO regression. AKR1B10 was associated with the metastasis and progression of several tumors. In this study, Cox regression analysis was performed to create a 2-gene signature as an independent prognostic factor for HGSOC, which has the ability to stratify risk samples in all three data sets (p < 0.05). The Gene Set Enrichment Analysis (GSEA) discovered abnormally active REGULATION_OF_AUTOPHAGY and OLFACTORY_TRANSDUCTION pathways in the high-risk group samples.

CONCLUSION

This study resulted in the creation of a 2-gene molecular prognostic classifier that distinguished clinical features and was a promising novel prognostic tool for assessing the prognosis of HGSOC. RiskScore was a novel prognostic model which might be effective in guiding accurate prognosis of HGSOC.

Integration of Postoperative Radiation Therapy with Cyclin-Dependent Kinase (CDK) Inhibitors

PURPOSE OF REVIEW

Sequential use of radiation therapy before cyclin-dependent kinase (CDK) inhibitors in women with early breast cancer seems reasonable and with a low toxicity rate. This study aimed to evaluate the possible interaction between RT and CDK inhibitors in the adjuvant setting for patients with positive hormone receptors and HER-2 negative, investigating toxicity and the treatment sequencing.

RECENT FINDINGS

CDK inhibitors have been studied in patients with localized breast cancer and can improve invasive disease-free survival outcomes. Regarding the time of RT, all trials used CDK inhibitors after the RT. Interruptions in the CDK inhibitors were performed in 27.1% in Pallas, 17.5% in Penelope-B, and 16.6% in Monarch-E trials due to adverse events. Data from the Natalee trial are still not reported. The main adverse event grade III was neutropenia, with good resolution of the symptoms over time. CDK inhibitors applied sequentially and after RT postoperative showed a low profile of acute toxicity and suitable oncological outcomes.

Unveiling the Power of Anticancer Drug Screening: A Clinical Case Study Comparing the Effectiveness of Hollow Fiber Assay Microtube Array Membrane (MTAM-HFA) in Breast Cancer Patients

Breast cancer is a severe public health problem, and early treatment with powerful anticancer drugs is critical for success. The researchers investigated the clinical results of a novel screening tool termed Microtube Array Membrane Hollow Fiber Assay (MTAM-HFA) in breast cancer patients in this clinical investigation. In all trial participants, the MTAM-HFA was utilized to identify active medicines for the treatment of breast cancer. The MTAM-HFA was shown to be extremely useful in predicting patient response to anticancer medication therapy in this study. Furthermore, the substantial association between the MTAM-HFA screening outcome and the clinical outcome of the respective patients emphasizes the promise of this unique screening technology in discovering effective anticancer medication combinations for the treatment of breast cancer. These findings indicate that the MTAM-HFA has clinical significance and might be a valuable tool in the development of tailored therapy for cancer care. This study provides helpful information for physicians and scientists working on breast cancer therapy research. The potential benefits of employing MTAM-HFA to find accurate therapies for breast cancer patients might lead to enhanced personalized medicine approaches to cancer care, resulting in better patient outcomes. Overall, the MTAM-HFA screening approach has the potential to revolutionize customized cancer therapy, providing hope to both patients and physicians.

Identification of novel prognostic indicators for oral squamous cell carcinoma based on proteomics and metabolomics

BACKGROUND

The low 5-year survival rate of oral squamous cell carcinoma (OSCC) suggests that new prognostic indicators need to be identified to aid the clinical management of patients.

METHODS

Saliva samples from OSCC patients and healthy controls were collected for proteomic and metabolomic sequencing. Gene expressed profiling was downloaded from TCGA and GEO databases. After the differential analysis, proteins with a significant impact on the prognosis of OSCC patients were screened. Correlation analysis was performed with metabolites and core proteins were identified. Cox regression analysis was utilized to stratify OSCC samples based on core proteins. The prognostic predictive ability of the core protein was then evaluated. Differences in infiltration of immune cells between the different strata were identified.

RESULTS

There were 678 differentially expressed proteins (DEPs), 94 intersected DEPs among them by intersecting with differentially expressed genes in TCGA and GSE30784 dataset. Seven core proteins were identified that significantly affected OSCC patient survival and strongly correlated with differential metabolites (R² > 0.8). The samples were divided into high- and low-risk groups according to median risk score. The risk score and core proteins were well prognostic factor in OSCC patients. Genes in high-risk group were enriched in Notch signaling pathway, epithelial mesenchymal transition (EMT), and angiogenesis. Core proteins were strongly associated with the immune status of OSCC patients.

CONCLUSIONS

The results established a 7-protein signatures with the hope of early detection and the capacity for risk assessment of OSCC patient prognosis. Further providing more potential targets for the treatment of OSCC.

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.

Classic and New Markers in Diagnostics and Classification of Breast Cancer

Simple Summary

With ever-increasing incidence, breast cancer is considered a most diagnosed type of cancer among women worldwide. Breast cancer arises through malignant transformation of ductal or lobular cells in female (or male) breast and the genetic, phenotypic and morphological heterogeneity has an effect on tumour’s behaviour, thereby instigating a need for individual personalized therapy. A traditional assessment of tumour’s characteristics involves a biopsy and histological analysis of a tumour tissue, and in recent years has been accompanied by analysis of molecular biomarkers to enhance the results. In this work we aimed to thoroughly investigate the latest data in this field of study and give a comprehensive review of novel molecular biomarkers of breast cancer and methodologies used to analyse them.

Abstract

Breast cancer remains the most frequently diagnosed form of female’s cancer, and in recent years it has become the most common cause of cancer death in women worldwide. Like many other tumours, breast cancer is a histologically and biologically heterogeneous disease. In recent years, considerable progress has been made in diagnosis, subtyping, and complex treatment of breast cancer with the aim of providing best suited tumour-specific personalized therapy. Traditional methods for breast cancer diagnosis include mammography, MRI, biopsy and histological analysis of tumour tissue in order to determine classical markers such as estrogen and progesterone receptors (ER, PR), cytokeratins (CK5/6, CK14, C19), proliferation index (Ki67) and human epidermal growth factor type 2 receptor (HER2). In recent years, these methods have been supplemented by modern molecular methodologies such as next-generation sequencing, microRNA, in situ hybridization, and RT-qPCR to identify novel molecular biomarkers. MicroRNAs (miR-10b, miR-125b, miR145, miR-21, miR-155, mir-30, let-7, miR-25-3p), altered DNA methylation and mutations of specific genes (p16, BRCA1, RASSF1A, APC, GSTP1), circular RNA (hsa_circ_0072309, hsa_circRNA_0001785), circulating DNA and tumour cells, altered levels of specific proteins (apolipoprotein C-I), lipids, gene polymorphisms or nanoparticle enhanced imaging, all these are promising diagnostic and prognostic tools to disclose any specific features from the multifaceted nature of breast cancer to prepare best suited individualized therapy.

Identification and verification of a 4-gene signature predicting the overall survival of cervical cancer

Cervical cancer (CC) is one of the most common gynecological malignancies, ranking fourth in both incidence and mortality in women worldwide. Early screening and treatment are of great significance in reducing the incidence and mortality of CC. Due to the complex molecular mechanisms of tumor progression, the predictive power of traditional clinical information is limited. In this study, an effective molecular model is established to assess prognosis of patients with CC and guide clinical treatment so as to improve their survival rate. Three high quality datasets (GSE138080, GSE52904, GSE67522) of expression profiling were obtained from gene expression omnibus (GEO) database. Another mRNA expression and clinicopathological data of CC were obtained from The Cancer Genome Atlas (TCGA) dataset. The bioinformatic analyses such as univariate analysis, multivariate Cox proportional-hazards model (Cox) analysis and lasso regression analysis were conducted to select survival-related differentially expressed genes (DEGs) and further establish a prognostic gene signature. Moreover, the performance of prognostic gene signature was evaluated based on Kaplan-Meier curve and receiver operating characteristic (ROC) curve. Gene set enrichment analysis (GSEA) and tumor immunity analysis were carried out to elucidate the molecular mechanisms and immune relevance. A 4-gene signature comprising procollagen-lysine, 2-oxoglutarate 5-dioxygenase 2 (PLOD2), spondin1 (SPON1), secreted phosphoprotein 1 (SPP1), ribonuclease H2 subunit A (RNASEH2A) was established to predict overall survival (OS) of CC. The ROC curve indicated good performance of the 4-gene signature in predicting OS of CC based on the TCGA dataset. The 4-gene signature classified the patients into high-risk and low-risk groups with distinct OS rates of CC. Univariate analysis and multivariate Cox regression analysis revealed that the 4-gene signature was an independent factor affecting the prognosis of patients with CC. Our study developed a 4-gene signature capable of predicting the OS of CC. The findings may be beneficial to individualized clinical treatment and timely follow-up for patients with CC.

MiRNA expression deregulation correlates with the Oncotype DX® DCIS score

Background

Current clinical criteria do not discriminate well between women who will or those who will not develop ipsilateral invasive breast cancer (IBC), or a DCIS recurrence after a ductal carcinoma in situ (DCIS) diagnosis. The 12-gene Oncotype DX® DCIS assay (RT qPCR gene-based scoring system) was established and shown to predict the risk of subsequent ipsilateral IBC or DCIS recurrence. Recent studies have shown that microRNA (miRNA) expression deregulation can contribute to the development of IBC, but very few have evaluated miRNA deregulation in DCIS lesions. In this study, we sought to determine whether specific miRNA expression changes may correlate with Oncotype DX® DCIS scores.

Methods

For this study, we used archived formalin-fixed, paraffin-embedded (FFPE) specimens from 41 women diagnosed with DCIS between 2012 and 2018. The DCIS lesions were stratified into low (n = 26), intermediate (n = 10), and high (n = 5) risk score groups using the Oncotype DX® DCIS assay. Total RNA was extracted from DCIS lesions by macro-dissection of unstained FFPE sections, and next-generation small-RNA sequencing was performed. We evaluated the correlation between miRNA expression data and Oncotype score, as well as patient age. RT-qPCR validations were performed to validate the topmost differentially expressed miRNAs identified between the different risk score groups.

Results

MiRNA sequencing of 32 FFPE DCIS specimens from the three different risk group scores identified a correlation between expression deregulation of 17 miRNAs and Oncotype scores. Our analyses also revealed a correlation between the expression deregulation of 9 miRNAs and the patient’s age. Based on these results, a total of 15 miRNAs were selected for RT-qPCR validation. Of these, miR-190b (p = 0.043), miR-135a (p = 0.05), miR-205 (p = 0.00056), miR-30c (p = 0.011), and miR-744 (p = 0.038) showed a decreased expression in the intermediate/high Oncotype group when compared to the low-risk score group. A composite risk score was established using these 5 miRNAs and indicated a significant association between miRNA expression deregulation and the Oncotype DX® DCIS Score (p < 0.0021), between high/intermediate and low risk groups.

Conclusions

Our analyses identified a subset of 5 miRNAs able to discriminate between Oncotype DX® DCIS score subgroups. Together, our data suggest that miRNA expression analysis may add value to the predictive and prognostic evaluation of DCIS lesions.

Supplementary Information

The online version contains supplementary material available at 10.1186/s13058-022-01558-4.

The Oncotype DX Recurrence Score's Impact on the Management of Oestrogen-Positive/Human Epidermal Growth Factor Receptor 2-Negative, Low-Burden Axillary Status Breast Cancer (REHAB Study): Results of a Single Centre

Background

The Oncotype DX Recurrence Score (ODX-RS) is increasingly utilized in oestrogen receptor (ER)-positive/human epidermal growth factor receptor 2 (HER2)-negative, low-burden axillary disease early operable breast cancer. It has been demonstrated to predict the benefits of adjuvant chemotherapy, hence supporting individualized decisions on adjuvant therapy.

Aim

To investigate the application of ODX-RS as an adjuvant treatment decision tool in breast cancer operated in our unit.

Methods

A total of 107 eligible patients who were operated on between 2017 and 2021 in Basildon University Hospital, UK were enrolled in this study. In this retrospective study, the clinical data, including patient’s age, tumour size, ER status, HER2 status, Ki67 proliferative index (Ki67-PI), nodal status, tumour grade, and ODX-RS, were collected. In the study design, the oncologist had the opportunity to assess the need for adjuvant chemotherapy for patients with ER-positive, HER2-negative, low-burden axillary lymph node disease, early breast cancer by using tumour characteristics and the PREDICT tool without knowing the ODX-RS results. The clinician's decision was matched against the breast multidisciplinary team's recommendations after ODX-RS utilisation, and the results were explored.

Results

The median ODX-RS of cohort tumours was 18 in the age group > 50 years, with ODX-RS ≥ 26 found in 18% of the group (n = 12). In the age group ≤ 50 years, 17% (n = 7) had ODX-RS between 21 and 25 and only 7% (n = 3) had ODX-RS ≥ 26. Without using ODX-RS, only 16% of the patients had been offered adjuvant chemotherapy in addition to the hormonal manipulation therapy; however, after using ODX-RS, up to 33% of the cohort was suitable for adjuvant chemotherapy in addition to the hormonal manipulation therapy. The changes in the recommendations after ODX-RS utilisation have been noticed in 29% of the cohort.

Conclusion

This study revealed that ODX-RS supported decision-making regarding postoperative adjuvant chemotherapy, especially when other tumour biomarkers, such as tumour size, grading, or Ki-67, indicated lower risk criteria. Patients with a high ODX-RS were offered chemotherapy where appropriate and its use led to a 15% rate of initial decision change in adjuvant treatment decisions; this involved either recommending chemotherapy or its omission.

An iron metabolism and immune related gene signature for the prediction of clinical outcome and molecular characteristics of triple-negative breast cancer

BACKGROUND

An imbalance of intracellular iron metabolism can lead to the occurrence of ferroptosis. Ferroptosis can be a factor in the remodeling of the immune microenvironment and can affect the efficacy of cancer immunotherapy. How to combine ferroptosis-promoting modalities with immunotherapy to suppress triple-negative breast cancer (TNBC) has become an issue of great interest in cancer therapy. However, potential biomarkers related to iron metabolism and immune regulation in TNBC remain poorly understand.

METHODS

We constructed an optimal prognostic TNBC-IMRGs (iron metabolism and immune-related genes) signature using least absolute shrinkage and selection operator (LASSO) cox regression. Survival analysis and ROC curves were analyzed to identify the predictive value in a training cohort and external validation cohorts. The correlations of gene signature with ferroptosis regulators and immune infiltration are also discussed. Finally, we combined the gene signature with the clinical model to construct a combined model, which was further evaluated using a calibration curve and decision curve analysis (DCA).

RESULTS

Compared with the high-risk group, TNBC patients with low-risk scores had a remarkably better prognosis in both the training set and external validation sets. Both the IMRGs signature and combined model had a high predictive capacity, 1/3/5- year AUC: 0.866, 0.869, 0.754, and 1/3/5-yaer AUC: 0.942, 0.934, 0.846, respectively. The calibration curve and DCA also indicate a good predictive performance of the combined model. Gene set enrichment analysis (GSEA) suggests that the high-risk group is mainly enriched in metabolic processes, while the low-risk group is mostly clustered in immune related pathways. Multiple algorithms and single sample GSEA further show that the low-risk score is associated with a high tumor immune infiltration level. Differences in expression of ferroptosis regulators are also observed among different risk groups.

CONCLUSIONS

The IMRGs signature based on a combination of iron metabolism and immune factors may contribute to evaluating prognosis, understanding molecular characteristics and selecting treatment options in TNBC.

Identification of Dysregulated Mechanisms and Candidate Gene Markers in Chronic Obstructive Pulmonary Disease

Purpose

This study aimed to identify candidate gene markers that may facilitate chronic obstructive pulmonary disease (COPD) diagnosis and treatment.

Methods

The GSE47460 and GSE151052 datasets were analyzed to identify differentially expressed mRNAs (DEmRs) between COPD patients and controls. DEmRs that were differentially expressed in the same direction in both datasets were analyzed for functional enrichment and for coexpression. Genes from the largest three modules were tested for their ability to diagnose COPD based on the area under the receiver operating characteristic curve (AUC). Genes with AUC > 0.7 in both datasets were used to perform regression based on the "least absolute shrinkage and selection operator" in order to identify feature genes. We also identified differentially expressed miRNAs (DEmiRs) between COPD patients and controls using the GSE38974 dataset, then constructed a regulatory network. We also examined associations between feature genes and immune cell infiltration in COPD, and we identified methylation markers of COPD using the GSE63704 dataset.

Results

A total of 1350 genes differentially regulated in the same direction in the GSE47460 and GSE151052 datasets were found. The genes were significantly enriched in immune-related biological functions. Of 186 modules identified using MEGENA, the largest were C1_ 6, C1_ 3, and C1_ 2. Of the 22 candidate genes screened based on AUC, 11 feature genes emerged from analysis of a subset of GSE47460 data, which we validated using another subset of GSE47460 data as well as the independent GSE151052 dataset. Feature genes correlated significantly with infiltration by immune cells. The feature genes GPC4 and RS1 were predicted to be regulated by miR-374a-3p. We identified 117 candidate methylation markers of COPD, including PRRG4.

Conclusion

The feature genes we identified may be potential diagnostic markers and therapeutic targets in COPD. These findings provide new leads for exploring disease mechanisms and targeted treatments.

The Utility of the Oncotype DX Test for Breast Cancer Patients in an Australian Multidisciplinary Setting

Introduction

The Oncotype DX test is a genomic assay that generates a Recurrence Score (RS) predicting the 10-year risk of recurrence and response to adjuvant chemotherapy in ER+/HER2− breast cancer patients. The aims were to determine breast cancer distant recurrence and correlate with adjuvant chemoendocrine prescribing patterns based on the Oncotype DX recurrence score.

Methods

We conducted a retrospective single-institution case series of 71 patients who had Oncotype DX assay testing after definitive surgery between 2012 and 2016. Both node-positive and node-negative patients were included. Patients were divided into Oncotype DX low risk (RS < 11) (n = 10, 14%), intermediate risk (RS 11–25) (n = 45, 63%), and high risk (RS > 25) (n = 16, 23%). Median follow-up was 6.1 years (range 4–8.9 years). Adjuvant treatment regimens and oncological outcomes were determined. Results. Mean age at diagnosis was 56 years (range, 33–77). Invasive ductal carcinoma (IDC) accounted for the majority (87%), with most tumors measuring between 10–20 mm (52%). 48% of the cohort were node positive. 15 of 16 high-risk patients (94%) received chemotherapy. 96% of intermediate-risk patients received endocrine therapy alone, one patient received chemoendocrine therapy (2%), and one declined systemic therapy (2%). In the low-risk group, 100% received endocrine therapy only. The high-risk group had the lowest mean ER% (P < 0.05), greatest mean mitotic rate (P < 0.05), and greatest proportion of Ki67% > 14. Five patients developed distant recurrence (7%): three from the intermediate-risk group (7%), one from the low-risk group (10%), and one from the high-risk group (6%).

Conclusion

This is the first Australian study reporting the experience with medium-term recurrence outcomes of using the Oncotype DX assay in breast cancer. Chemotherapy was rarely given for patients with low-to-intermediate RS and always offered in high RS. This pattern of prescribing was associated with low rates of distant recurrence. National funding models should be considered.

Clinical Utility of Oncuria™, a Multiplexed Liquid Biopsy for the Non-Invasive Detection of Bladder Cancer-A Pilot Study

Oncuria™ is a validated quantitative multiplex immunoassay capable of detecting bladder cancer from a voided urine sample. Herein, we sought to determine whether Oncuria™ affects physicians' use of non-invasive and invasive diagnostic tests for microhematuria, gross hematuria, and bladder cancer surveillance. We conducted a survey-based study to assess physician management of nine clinical scenarios involving real-world data from patients with gross hematuria, microhematuria, and bladder cancer on surveillance. We randomly sampled 15 practicing urologists and generated data including 135 patient-by-urologist interactions and 2160 decision points. Urologists recommended a selection of diagnostic tests and procedures before and after Oncuria™ results were provided. We assessed changes in provider use of non-invasive and invasive diagnostic tests after Oncuria™ results were provided. Over 90% of all urologists changed their diagnostic behavior in at least one patient case with the addition of Oncuria™ results. The total number of diagnostic procedures was reduced by 31% following the disclosure of a negative Oncuria™ test and 27% following the disclosure of a positive Oncuria™ test. This is pilot study has the potential to shed light on the analysis of our four large multicenter international studies deploying OncuriaTM. The Oncuria™ urine-based test, a molecular diagnostic capable of ruling out the presence of bladder cancer, reduces both unnecessary invasive and non-invasive diagnostics and has the potential to reduce costs and improve patient outcomes.

Identification of seven-gene marker to predict the survival of patients with lung adenocarcinoma using integrated multi-omics data analysis

Background

The mechanism of cancer occurrence and development could be understood with multi‐omics data analysis. Discovering genetic markers is highly necessary for predicting clinical outcome of lung adenocarcinoma (LUAD).

Methods

Clinical follow‐up information, copy number variation (CNV) data, single nucleotide polymorphism (SNP), and RNA‐Seq were acquired from The Cancer Genome Atlas (TCGA). To obtain robust biomarkers, prognostic‐related genes, genes with SNP variation, and copy number differential genes in the training set were selected and further subjected to feature selection using random forests. Finally, a gene‐based prediction model for LUAD was validated in validation datasets.

Results

The study filtered 2071 prognostic‐related genes and 230 genomic variants, 1878 copy deletions, and 438 significant mutations. 218 candidate genes were screened through integrating genomic variation genes and prognosis‐related genes. 7 characteristic genes (RHOV, CSMD3, FBN2, MAGEL2, SMIM4, BCKDHB, and GANC) were identified by random forest feature selection, and many genes were found to be tumor progression‐related. A 7‐gene signature constructed by Cox regression analysis was an independent prognostic factor for LUAD patients, and at the same time a risk factor in the test set, external validation set, and training set. Noticeably, the 5‐year AUC of survival in the validation set and training set was all ˃ 0.67. Similar results were obtained from multi‐omics validation datasets.

Conclusions

The study builds a novel 7‐gene signature as a prognostic marker for the survival prediction of patients with LUAD. The current findings provided a set of new prognostic and diagnostic biomarkers and therapeutic targets.

Exploring the Value of BRD9 as a Biomarker, Therapeutic Target and Co-Target in Prostate Cancer

Background and aims: Despite recent advances in advanced prostate cancer treatments, clinical biomarkers or treatments for men with such cancers are imperfect. Targeted therapies have shown promise, but there remain fewer actionable targets in prostate cancer than in other cancers. This work aims to characterise BRD9, currently understudied in prostate cancer, and investigate its co-expression with other genes to assess its potential as a biomarker and therapeutic target in human prostate cancer. Materials and methods: Omics data from a total of 2053 prostate cancer patients across 11 independent datasets were accessed via Cancertool and cBioPortal. mRNA M.expression and co-expression, mutations, amplifications, and deletions were assessed with respect to key clinical parameters including survival, Gleason grade, stage, progression, and treatment. Network and pathway analysis was carried out using Genemania, and heatmaps were constructed using Morpheus. Results: BRD9 is overexpressed in prostate cancer patients, especially those with metastatic disease. BRD9 expression did not differ in patients treated with second generation antiandrogens versus those who were not. BRD9 is co-expressed with many genes in the SWI/SNF and BET complexes, as well as those in common signalling pathways in prostate cancer. Summary and conclusions: BRD9 has potential as a diagnostic and prognostic biomarker in prostate cancer. BRD9 also shows promise as a therapeutic target, particularly in advanced prostate cancer, and as a co-target alongside other genes in the SWI/SNF and BET complexes, and those in common prostate cancer signalling pathways. These promising results highlight the need for wider experimental inhibition and co-targeted inhibition of BRD9 in vitro and in vivo, to build on the limited inhibition data available.

Impact of the 21-Gene Recurrence Score Assay on the Treatment of Estrogen Receptor-Positive, HER2-Negative, Breast Cancer Patients With 1-3 Positive Nodes: A Prospective Clinical Utility Study

PURPOSE

The use of the 21-gene Recurrence Score (RS) assay is emerging in node-positive estrogen receptor (ER)+ HER2-negative breast cancer (BC), particularly as initial data from the RxPONDER trial are now available. We investigated the impact of the RS result on adjuvant treatment decisions in such patients.

PATIENTS AND METHODS

This prospective, multi-center study enrolled patients with ER+, HER2-negative BC and 1 to 3 positive nodes (microscopic [N1mi] or macroscopic [N1]). Treating oncologists documented treatment recommendations/plan before and after knowing the RS result. Sample size was determined assuming an overall treatment change rate (from chemohormonal therapy [CHT] to hormone therapy [HT] and vice-versa) of ≥30%.

RESULTS

The study included 84 patients across 5 regional cancer centers, of whom 82 underwent 21-gene testing (77%, N1 disease; 63% grade 2 tumors). Of the RS-tested patients, 60%, 33%, and 7% had RS 0 to 17, 18 to 30, and 31 to 100, respectively. In 43 patients (52%), treatment changed post-RS: 40 patients (49%) from CHT to HT and 3 patients (4%) from HT to CHT. The net change was a 45% reduction in chemotherapy use. Treatment recommendation changes were consistent with the RS result. In RS 0 to 17 patients, the only documented change was from CHT to HT (27 patients). In RS 18-30 patients, change was noted in both directions (CHT-to-HT, 13 patients; HT-to-CHT, 3 patients). No treatment change was reported for the RS 31 to 100 patients, all of whom were recommended CHT pre-testing.

CONCLUSION

Our results support the clinical utility of the RS assay in ER+ HER2-negative BC with 1 to 3 positive nodes.

Ki-67 as a Prognostic Biomarker in Invasive Breast Cancer

Simple Summary

In breast cancer development, the expression of Ki-67 is strongly associated with cancer proliferation and is a known indicator of prognosis and outcome. Ki-67 expression levels are also useful to inform treatment decision making in some cases. As a result, routine measurement of Ki-67 is now widely performed during pathological tumour evaluation. However, the Ki-67 appraisal is not without its limitations and shortcomings—the aim of this study was to provide an overview of Ki-67 use in the clinical setting, the current challenges associated with its measurement, and the novel strategies that will hopefully enhance Ki-67 proliferation indices for prospective breast cancer patients.

Abstract

The advent of molecular medicine has transformed breast cancer management. Breast cancer is now recognised as a heterogenous disease with varied morphology, molecular features, tumour behaviour, and response to therapeutic strategies. These parameters are underpinned by a combination of genomic and immunohistochemical tumour factors, with estrogen receptor (ER) status, progesterone receptor (PgR) status, human epidermal growth factor receptor-2 (HER2) status, Ki-67 proliferation indices, and multigene panels all playing a contributive role in the substratification, prognostication and personalization of treatment modalities for each case. The expression of Ki-67 is strongly linked to tumour cell proliferation and growth and is routinely evaluated as a proliferation marker. This review will discuss the clinical utility, current pitfalls, and promising strategies to augment Ki-67 proliferation indices in future breast oncology.

Tissue Multiplex Analyte Detection in Anatomic Pathology - Pathways to Clinical Implementation

Background: Multiplex tissue analysis has revolutionized our understanding of the tumor microenvironment (TME) with implications for biomarker development and diagnostic testing. Multiplex labeling is used for specific clinical situations, but there remain barriers to expanded use in anatomic pathology practice.

Methods: We review immunohistochemistry (IHC) and related assays used to localize molecules in tissues, with reference to United States regulatory and practice landscapes. We review multiplex methods and strategies used in clinical diagnosis and in research, particularly in immuno-oncology. Within the framework of assay design and testing phases, we examine the suitability of multiplex immunofluorescence (mIF) for clinical diagnostic workflows, considering its advantages and challenges to implementation.

Results: Multiplex labeling is poised to radically transform pathologic diagnosis because it can answer questions about tissue-level biology and single-cell phenotypes that cannot be addressed with traditional IHC biomarker panels. Widespread implementation will require improved detection chemistry, illustrated by InSituPlex technology (Ultivue, Inc., Cambridge, MA) that allows coregistration of hematoxylin and eosin (H&E) and mIF images, greater standardization and interoperability of workflow and data pipelines to facilitate consistent interpretation by pathologists, and integration of multichannel images into digital pathology whole slide imaging (WSI) systems, including interpretation aided by artificial intelligence (AI). Adoption will also be facilitated by evidence that justifies incorporation into clinical practice, an ability to navigate regulatory pathways, and adequate health care budgets and reimbursement. We expand the brightfield WSI system “pixel pathway” concept to multiplex workflows, suggesting that adoption might be accelerated by data standardization centered on cell phenotypes defined by coexpression of multiple molecules.

Conclusion: Multiplex labeling has the potential to complement next generation sequencing in cancer diagnosis by allowing pathologists to visualize and understand every cell in a tissue biopsy slide. Until mIF reagents, digital pathology systems including fluorescence scanners, and data pipelines are standardized, we propose that diagnostic labs will play a crucial role in driving adoption of multiplex tissue diagnostics by using retrospective data from tissue collections as a foundation for laboratory-developed test (LDT) implementation and use in prospective trials as companion diagnostics (CDx).

Autophagy-Related Three-Gene Prognostic Signature for Predicting Survival in Esophageal Squamous Cell Carcinoma

Esophageal squamous cell carcinoma (ESCC) is one of the most aggressive malignant tumors in China, and its prognosis remains poor. Autophagy is an evolutionarily conserved catabolic process involved in the occurrence and development of ESCC. In this study, we described the expression profile of autophagy-related genes (ARGs) in ESCC and developed a prognostic prediction model for ESCC patients based on the expression pattern of ARGs. We used four ESCC cohorts, GSE53624 (119 samples) set as the discovery cohort, The Cancer Genome Atlas (TCGA) ESCC set (95 samples) as the validation cohort, 155 ESCC cohort, and Oncomine cohort were used to screen and verify differentially expressed ARGs. We identified 34 differentially expressed genes out of 222 ARGs. In the discovery cohort, we divided ESCC patients into three groups that showed significant differences in prognosis. Then, we analyzed the prognosis of 34 differentially expressed ARGs. Three genes [poly (ADP-ribose) polymerase 1 (PARP1), integrin alpha-6 (ITGA6), and Fas-associated death domain (FADD)] were ultimately obtained through random forest feature selection and were constructed as an ARG-related prognostic model. This model was further validated in TCGA ESCC set. Cox regression analysis confirmed that the three-gene signature was an independent prognostic factor for ESCC patients. This signature effectively stratified patients in both discovery and validation cohorts by overall survival (P = 5.162E-8 and P = 0.052, respectively). We also constructed a clinical nomogram with a concordance index of 0.713 to predict the survival possibility of ESCC patients by integrating clinical characteristics and the ARG signature. The calibration curves substantiated fine concordance between nomogram prediction and actual observation. In conclusion, we constructed a new ARG-related prognostic model, which shows the potential to improve the ability of individualized prognosis prediction in ESCC.

Real-world analysis of clinical and economic impact of 21-gene recurrence score (RS) testing in early-stage breast cancer (ESBC) in Ireland

PURPOSE

Results from TAILOR-X suggest that up to 70% of hormone receptor-positive (HR+) node-negative (N0) ESBC patients (pts) may avoid chemotherapy (CT) with RS ≤ 25. We assess clinical and economic impacts of RS testing on treatment using real-world data.

METHODS

From October 2011 to February 2019, a retrospective, cross-sectional observational study was conducted of HR+ N0 ESBC pts who had RS testing in Ireland. Pts were classified low risk (RS ≤ 25) and high risk (RS > 25). Clinical risk was calculated. Data were collected via electronic patient records. Cost data were supplied by the National Healthcare Pricing Regulatory Authority.

RESULTS

963 pts. Mean age is 56 years. Mean tumour size is 1.7 cm. 114 (11.8%), 635 (66%), 211 (22%), 3 (0.2%) pts had G1, G2, G3 and unknown G, respectively. 796 pts (82.8%) low RS, 159 (16.5%) high RS and 8 pts (0.7%) unknown RS. 263 pts (26%) were aged ≤ 50 at diagnosis; 117 (45%) had RS 0-15, 63 (24.5%) 16-20, 39 (15.3%) 21-25 and 40 (15.2%) RS 26-100. 4 pts (1.5%) had unknown RS. Post-RS testing, 602 pts (62.5%) had a change in CT decision; 593 changed to hormone therapy (HT) alone. In total, 262 pts received CT. Of pts receiving CT; 138 (53%) had RS > 25, 124 (47%) had RS ≤ 25. Of pts aged ≤ 50, 153 (58%) had high clinical risk, of whom 28 had RS 16-20. Assay use achieved a 62.5% change in treatment with 73% of pts avoiding CT. This resulted in savings of €4 million in treatment costs. Deducting assay costs, savings of €1.9 million were achieved.

CONCLUSION

Over the 8 years of the study, a 62.5% reduction in CT use was achieved with savings of over €1,900,000.

Integrating Adjuvant Radiation with Post-Neoadjuvant Therapies in Early Breast Cancer

PURPOSE OF REVIEW

Because of the strong prognostic value of pathologic complete response (pCR) in early breast cancer (EBC), patients who fail to achieve this outcome have increasingly been eligible to a new treatment modality, namely post-neoadjuvant systemic therapy (PNT). However, adjuvant radiation therapy (RT) retains a crucial role in EBC, and also needs to be timely administered to patients. To address how modern PNT optimally integrates with adjuvant RT is therefore the purpose of this review.

RECENT FINDINGS

How PNT administration optimally integrates with adjuvant RT has varied depending on the type of systemic therapy employed. The introduction of novel "targeted" agents has created new challenges, as for many of them limited information is available on the feasibility of concurrent systemic and RT administration or their optimal sequencing. PNT and RT are both of utmost importance to the management of EBC and need to be timely and safely administered to patients. The optimal strategy to integrate these modalities may vary according to the type of PNT agent and other factors.

Prognostic impact of the glypican family of heparan sulfate proteoglycans on the survival of breast cancer patients

Purpose

Dysregulated expression of proteoglycans influences the outcome and progression of numerous cancers. Several studies have investigated the role of individual glypicans in cancer, however, the impact of the whole glypican family of heparan sulfate proteoglycans on prognosis of a large patient cohort of breast cancer patients has not yet been investigated. In the present study, our aim was to investigate the prognostic power of the glypicans in breast cancer patients.

Methods

We used a public database including both gene expression data and survival information for 3951 breast cancer patients to determine the prognostic value of glypicans on relapse-free survival using Cox regression analysis. Moreover, we performed quantitative Real-Time PCR to determine glypican gene expression levels in seven representative breast cancer cell lines.

Results

We found that high GPC3 levels were associated with a better prognosis in overall breast cancer patients. When stratified by hormone receptor status, we found that in worse prognosis subtypes low GPC1 levels correlate with a longer relapse-free survival, and in more favorable subtypes low GPC6 was associated with longer survival.

Conclusion

Our study concludes that glypicans could act as subtype-specific biomarkers for the prognosis of breast cancer patients and sparks hope for future research on glypicans possibly eventually providing targets for the treatment of the disease.

Supplementary Information

The online version contains supplementary material available at 10.1007/s00432-021-03597-4.

Utility of a 31-gene expression profile for predicting outcomes in patients with primary cutaneous melanoma referred for sentinel node biopsy

BACKGROUND

A 31-gene genetic expression profile (31-GEP; Class 1 = low risk, Class 2 = high risk) developed to predict outcome in cutaneous melanoma (CM) has been validated by retrospective, industry-sponsored, or small series.

METHODS

Tumor features, sentinel node biopsy (SNB) results, and outcomes were extracted from a prospective database of 383 C M patients who underwent SNB and had a 31-GEP run on their primary tumor. Groups were compared by uni- and multi-variable analysis. Relapse-free and distant metastasis-free survival (RFS, DMFS) were estimated by Kaplan-Meier method.

RESULTS

Breslow thickness, T stage, and SNB positivity were significantly higher in Class 2 patients. Recurrence rates were higher for Class 2 vs Class 1 patients and highest in patients who were Class 2 and SNB positive. GEP class was predictive of RFS and DMFS and independently predicted relapse in AJCC "low risk" (stages IA-IIA) patients.

CONCLUSIONS

31-GEP adds prognostic information in CM patents undergoing SNB.

Genomic Assays in Node Positive Breast Cancer Patients: A Review

In recent years, developments in breast cancer have allowed yet another realization of individualized medicine in the field of oncology. One of these advances is genomic assays, which are considered elements of standard clinical practice in the management of breast cancer. These assays are widely used today not only to measure recurrence risk in breast cancer patients at an early stage but also to tailor treatment as well and minimize avoidable treatment side effects. At present, genomic tests are applied extensively in node negative disease. In this article, we review the use of these tests in node positive disease, explore their ramifications on neoadjuvant chemotherapy decisions, highlight sufficiently powered recent studies emphasizing their use and review the most recent guidelines.

Identification of 6 gene markers for survival prediction in osteosarcoma cases based on multi-omics analysis

Multiple-omics sequencing information with high-throughput has laid a solid foundation to identify genes associated with cancer prognostic process. Multiomics information study is capable of revealing the cancer occurring and developing system according to several aspects. Currently, the prognosis of osteosarcoma is still poor, so a genetic marker is needed for predicting the clinically related overall survival result. First, Office of Cancer Genomics (OCG Target) provided RNASeq, copy amount variations information, and clinically related follow-up data. Genes associated with prognostic process and genes exhibiting copy amount difference were screened in the training group, and the mentioned genes were integrated for feature selection with least absolute shrinkage and selection operator (Lasso). Eventually, effective biomarkers received the screening process. Lastly, this study built and demonstrated one gene-associated prognosis mode according to the set of the test and gene expression omnibus validation set; 512 prognosis-related genes (P < 0.01), 336 copies of amplified genes (P < 0.05), and 36 copies of deleted genes (P < 0.05) were obtained, and those genes of the mentioned genomic variants display close associations with tumor occurring and developing mechanisms. This study generated 10 genes for candidates through the integration of genomic variant genes as well as prognosis-related genes. Six typical genes (i.e. MYC, CHIC2, CCDC152, LYL1, GPR142, and MMP27) were obtained by Lasso feature selection and stepwise multivariate regression study, many of which are reported to show a relationship to tumor progressing process. The authors conducted Cox regression study for building 6-gene sign, i.e. one single prognosis-related element, in terms of cases carrying osteosarcoma. In addition, the samples were able to be risk stratified in the training group, test set, and externally validating set. The AUC of five-year survival according to the training group and validation set reached over 0.85, with superior predictive performance as opposed to the existing researches. Here, 6-gene sign was built to be new prognosis-related marking elements for assessing osteosarcoma cases' surviving state.

Factors associated with chemotherapy benefit in breast cancer patients with midrange Oncotype DX breast recurrence scores

Chemotherapy for patients with hormone receptor-positive, human epidermal growth factor receptor 2 (HER2)-negative, node-negative breast cancer, and with midrange Oncotype DX breast recurrence scores (RS) still needs to be further individualized. To improve the treatment decision making, we aimed to develop a method that provides a single score based on multiple factors associated with chemotherapy benefit. We analyzed Surveillance, Epidemiology, and End Results registry data from 31,731 patients with hormone receptor-positive, HER2-negative, node-negative breast cancer and midrange RS characterized by sociodemographic (age and marital status) and clinicopathologic (tumor size, histologic grade, progesterone receptor status, broad histological classification, lesion laterality, and lesion overlap) features and stratified by RS ranges. For the entire sample and for each characteristic, overall survival was compared between patients who underwent chemotherapy and those who did not (or status unknown) within each RS stratum. There was no any association between chemotherapy and survival for patients with RS = 11-15. However, for patients with RS = 16-25, a chemotherapy benefit was associated with tumor size, histologic grade, progesterone receptor status, histological type, and lesion laterality. In addition, overlapping lesion of breast and married at diagnosis might provide additional predictive information of chemotherapy benefit when RS = 21-25. A simple and effective algorithm was designed by combining these factors to output a novel and personalized chemotherapy benefit score to effectively identify patients with RS = 16-25 who would most likely benefit from chemotherapy, which might facilitate improved treatment by providing individualized recommendations.

Racial differences in genomic testing and receipt of endocrine therapy in early-stage breast cancer

PURPOSE

Genomic testing in early-stage hormone-positive breast cancer is the standard of care. However, decisions based on genomic testing results are predicated on the assumption that patients receive endocrine treatment. We sought to investigate racial differences in genomic testing and adjuvant treatment in breast cancer.

METHODS

A retrospective, population-based hospital registry study using the National Cancer Database. Participants included women with stages I-II, ER + breast cancer between 2010 and 2014. Sociodemographic factors were analyzed. Primary outcomes were the utilization of genomic testing and receipt of endocrine therapy. Logistic regression modeling was used to compute crude and adjusted odds of genomic testing and receipt of endocrine therapy.

RESULTS

Among a total sample size of 387,008 patients, 147,863 (38.2%) underwent genomic testing. Older age (≥ 70 years) was associated with a lower adjusted odd of genomic testing (OR 0.33; 95% CI 0.32-0.34, p =  < 0.0001). Black patients had lower odds of receiving genomic testing on multivariate analysis compared to Whites (OR 0.82; 95% CI 0.80-0.85, p =  < 0.0001). In patients who underwent a genomic test, compared to Whites, Blacks had a lower odds of receiving endocrine therapy (OR 0.86; 95% CI 0.80-0.93, p =  < 0.0001) even if they did not receive adjuvant chemotherapy (OR 0.90; 95% CI 0.82-0.98, p = 0.014).

CONCLUSIONS

In a national sample of breast cancer patients, Black women are less likely to get genomic testing and receive hormonal therapy, even when adjuvant chemotherapy is omitted. A priority in addressing breast cancer disparities is to ensure adherence to hormonal therapy among all women, including those who do not receive adjuvant chemotherapy.

Identification of a survival-related signature for sarcoma patients through integrated transcriptomic and proteomic profiling analyses

Sarcoma (SARC) represents a group of highly histological and molecular heterogeneous rare malignant tumors with poor prognosis. There are few proposed classifiers for predicting patient's outcome. The Cancer Proteome Atlas (TPCA) and The Cancer Genome Atlas (TCGA) databases provide multi-omics datasets that enable a comprehensive investigation for this disease. The proteomic expression profile of SARC patients along with the clinical information was downloaded. 55 proteins were found to be associated with overall survival (OS) of patients using univariate Cox regression analysis. We developed a prognostic risk signature that comprises seven proteins (AMPKALPHA, CHK1, S6, ARID1A, RBM15, ACETYLATUBULINLYS40, and MSH6) with robust predictive performance using multivariate Cox stepwise regression analysis. Additionally, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters. Patients in high-risk group also have worse progression free intervals (PFI) than that of patients in low-risk group, but not for disease free intervals (DFI). The signature was validated using transcriptomic profile of SARC patients from TCGA. Potential mechanisms between high- and low-risk groups were identified using differentially expressed genes (DEGs) analysis. These DEGs were primarily enriched in RAS and MPAK signaling pathways. The signature protein molecules are candidate biomarkers for SARC, and the analysis of computational biology in tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinctly immune landscapes of high- and low-risk patients. Together, we constructed a prognostic signature for predicting outcomes for SARC integrating proteomic and transcriptomic profiles, this might have value in guiding clinical practice.

Development of an Immune-Related Risk Signature for Predicting Prognosis in Lung Squamous Cell Carcinoma

Lung squamous cell carcinoma (LSCC) is the most common subtype of non-small cell lung cancer. Immunotherapy has become an effective treatment in recent years, while patients showed different responses to the current treatment. It is vital to identify the potential immunogenomic signatures to predict patient' prognosis. The expression profiles of LSCC patients with the clinical information were downloaded from TCGA database. Differentially expressed immune-related genes (IRGs) were extracted using edgeR algorithm, and functional enrichment analysis showed that these IRGs were primarily enriched in inflammatory- and immune-related processes. "Cytokine-cytokine receptor interaction" and "PI3K-AKT signaling pathway" were the most enriched KEGG pathways. 27 differentially expressed IRGs were significantly correlated with the overall survival (OS) of patients using univariate Cox regression analysis. A prognostic risk signature that comprises seven IRGs (GCCR, FGF8, CLEC4M, PTH, SLC10A2, NPPC, and FGF4) was developed with effective predictive performance by multivariable Cox stepwise regression analysis. Most importantly, the signature could be an independent prognostic predictor after adjusting for clinicopathological parameters, and also validated in two independent LSCC cohorts (GSE4573 and GSE17710). Potential molecular mechanisms and tumor immune landscape of these IRGs were investigated through computational biology. Analysis of tumor infiltrating lymphocytes and immune checkpoint molecules revealed distinct immune landscape in high- and low-risk group. The study was the first time to construct IRG-based immune signature in the recognition of disease progression and prognosis of LSCC patients.

Discordance in 21-gene recurrence scores between paired breast cancer samples is inversely associated with patient age

Background

The Oncotype DX 21-gene Recurrence Score is a genomic-based algorithm that guides adjuvant chemotherapy treatment decisions for women with early-stage, oestrogen receptor (ER)-positive breast cancer. However, there are age-related differences in chemotherapy benefit for women with intermediate Oncotype DX Recurrence Scores that are not well understood. Menstrual cycling in younger women is associated with hormonal fluctuations that might affect the expression of genomic predictive biomarkers and alter Recurrence Scores. Here, we use paired human breast cancer samples to demonstrate that the clinically employed Oncotype DX algorithm is critically affected by patient age.

Methods

RNA was extracted from 25 pairs of formalin-fixed paraffin-embedded, invasive ER-positive breast cancer samples that had been collected approximately 2 weeks apart. A 21-gene signature analogous to the Oncotype DX platform was assessed through quantitative real-time PCR, and experimental recurrence scores were calculated using the Oncotype DX algorithm.

Results

There was a significant inverse association between patient age and discordance in the recurrence score. For every 1-year decrease in age, discordance in recurrence scores between paired samples increased by 0.08 units (95% CI − 0.14, − 0.01; p = 0.017). Discordance in recurrence scores for women under the age of 50 was driven primarily by proliferation- and HER2-associated genes.

Conclusion

The Oncotype DX 21-gene Recurrence Score algorithm is critically affected by patient age. These findings emphasise the need for the consideration of patient age, particularly for women younger than 50, in the development and application of genomic-based algorithms for breast cancer care.

Bioinformatics profiling identifies seven immune-related risk signatures for hepatocellular carcinoma

Background

Density of tumor infiltrating lymphocytes (TIL) and expressions of certain immune-related genes have prognostic and predictive values in hepatocellular carcinoma (HCC); however, factors determining the immunophenotype of HCC patients are still unclear. In the current study, the transcript sequencing data of liver cancer were systematically analyzed to determine an immune gene marker for the prediction of clinical outcome of HCC.

Methods

RNASeq data and clinical follow-up information were downloaded from The Cancer Genome Atlas (TCGA), and the samples were assigned into high-stage and low-stage groups. Immune pathway-related genes were screened from the Molecular Signatures Database v4.0 (MsigDB) database. LASSO regression analysis was performed to identify robust immune-related biomarkers in predicting HCC clinical outcomes. Moreover, an immune gene-related prognostic model was established and validated by test sets and Gene Expression Omnibus (GEO) external validation sets.

Results

We obtained 319 immune genes from MsigDB, and the genes have different expression profiles in high-stage and low-stage of HCC. Univariate survival analysis found that 17 genes had a significant effect on HCC prognosis, among them, 13 (76.5%) genes were prognostically protective factors. Further lasso regression analysis identified seven potential prognostic markers (IL27, CD1D, NCOA6, CTSE, FCGRT, CFHR1, and APOA2) of robustness, most of which are related to tumor development. Cox regression analysis was further performed to establish a seven immune gene signature, which could stratify the risk of samples in training set, test set and external verification set (p < 0.01), and the AUC in both training set and test set was greater than 0.85, which also greater compared with previous studies.

Conclusion

This study constructed a 7-immunogenic marker as novel prognostic markers for predicting survival of HCC patients.

Development of a nine-lncRNA signature as a novel prognostic marker of estrogen receptor-negative breast cancer

Long non-coding RNAs (lncRNAs) have been demonstrated to be aberrantly expressed in several types of tumor, and dysregulated lncRNAs are suggested to play a prognostic role in breast cancer (BC). Estrogen receptor (ER) status is a prognostic factor in patients with ER-negative BC, which is associated with poor prognosis. Thus, the present study developed a prognostic lncRNA signature specifically for ER-negative BC, in order to predict the risk of post-surgery relapse and improve patient prognosis. A gene expression profile containing 1,631 lncRNAs was obtained by investigating and integrating publicly available cohorts of BC. Subsequently, a nine-lncRNA signature was developed and validated in two independent cohorts via the Cox regression model. Using the nine-lncRNA signature, patients in the discovery cohort were divided into high- and low-risk groups, with significantly different disease-free survival [DFS; hazard ratio (HR)=2.718, 95% confidence interval (CI)=2.115-3.494, P<0.0001]. Receiver operating characteristic curve analyses demonstrated that the area under the curve reached 0.908. Similar results were obtained in the two independent cohorts (HR=1.499, 95% CI=0.950-2.365, P=0.04; HR=1.262, 95% CI=1.056-1.510, P=0.01), respectively. Furthermore, the nine lncRNAs were demonstrated to play important roles in the cell invasion and metastasis of different types of tumor. The differentially expressed genes (DEGs) identified between the high- and low-risk groups were consistently high in the discovery and validation cohorts. Functional analysis indicated that these DEGs, as well as genes co-expressed with the nine lncRNAs, were involved in cancer-associated signaling pathways, all of which provide further evidence for the predictive ability of the nine-lncRNA signature. Overall, the present study developed a novel prognostic biomarker for ER-negative BC.

Six-gene signature for predicting survival in patients with head and neck squamous cell carcinoma

The prognosis of head and neck squamous cell carcinoma (HNSCC) patients remains poor. High-throughput sequencing data have laid a solid foundation for identifying genes related to cancer prognosis, but a gene marker is needed to predict clinical outcomes in HNSCC. In our study, we downloaded RNA Seq, single nucleotide polymorphism, copy number variation, and clinical follow-up data from TCGA. The samples were randomly divided into training and test. In the training set, we screened genes and used random forests for feature selection. Gene-related prognostic models were established and validated in a test set and GEO verification set. Six genes (PEX11A, NLRP2, SERPINE1, UPK, CTTN, D2HGDH) were ultimately obtained through random forest feature selection. Cox regression analysis confirmed the 6-gene signature is an independent prognostic factor in HNSCC patients. This signature effectively stratified samples in the training, test, and external verification sets (P < 0.01). The 5-year survival AUC in the training and verification sets was greater than 0.74. Thus, we have constructed a 6-gene signature as a new prognostic marker for predicting survival of HNSCC patients.

Is there an association between body mass index and 21-gene recurrence score?

PURPOSE

The 21-gene recurrence score (RS) is an established predictor of recurrence for early stage, hormone receptor positive breast cancer. The association between RS and other risk factors such as obesity has not been fully explored. We hypothesized that patients with obesity may present with primary breast cancers with higher recurrence scores.

METHODS

We identified 1546 patients who have body mass index (BMI) recorded around the time of RS assay. Obesity was classified as per CDC definitions of overweight (BMI 25-30 kg/m²) and obesity (BMI >30 kg/m²). RS was assessed as a continuous variable and according to pre- and post-TAILORx classifications. Kaplan Meier survival analysis was employed to assess the interaction between RS and BMI on overall survival (OS) and disease-free survival (DFS).

RESULTS

In univariate analyses, the median RS in patients with overweight was 15, which was significantly lower than the median RS (16) of patients with normal weight (p = 0.03). The overall recurrence rate of patients with obesity was 4.1%, which was significantly worse than the overall recurrence rate of patients with normal and overweight of 2.6% and 1.5%, respectively (p = 0.05). In multivariate analyses using the inverse probability weighted regression adjustment (IPWRA) method to adjust for imbalances between subgroups, patients with overweight or obesity had significantly lower RS than patients with normal weight, correlating to an average decrease in RS value of 2.37 and 1.71, respectively (both p < 0.01). A similar relationship was seen between BMI categories and RS as a categorical variable stratified according to pre- or post-TAILORx categories. This inverse effect was predominantly seen in post-menopausal patients. Despite the generally lower RS in patients with obesity, a high RS in these patients is associated with diminished DFS (p = 0.04).

CONCLUSION

Tumors in post-menopausal women with higher BMI generally have lower RS. DFS is significantly worse in women with obesity whose RS ≥ 30. The reasons for poor outcomes for postmenopausal patients with obesity despite lower presenting RS merits further study.

Diabetes and Metformin Association with Recurrence Score in a Large Oncotype Database of Breast Cancer Patients

<b><i>Background:</i></b> The Oncotype DX® (ODX) is a genomic assay that provides clinicians with a prediction of benefit of chemotherapy in node-negative, tamoxifen-treated breast cancer. However, the relationship between ODX recurrence score (RS) and diabetes, a common comorbidity in breast cancer patients, has been inadequately described in the literature. Specifically, the association of diabetes treatment with metformin and RS is inconclusive, with different studies reporting conflicting results. Because diabetes has been associated with higher RS, it has been suggested that management of diabetes with metformin in breast cancer patients may be associated with a lower RS. <b><i>Objectives:</i></b> We studied a large cohort of early-stage, hormone-positive breast cancer patients to determine if there is an association between RS and metformin treatment. <b><i>Methods:</i></b> In this study, we retrospectively examined the medical records of 514 early-stage, hormone-positive breast cancer patients who had oncotype testing performed between 2007 and 2017. Number (%) or median were used to describe the patients’ characteristics between groups and were compared by the Kruskal-Wallis test at a significance level of 5%. <b><i>Results:</i></b> Of this cohort, 67 (13%) had a diabetes diagnosis at the time of breast cancer diagnosis, including both diabetes mellitus and pre-diabetes. The median RS for non-diabetic patients was 16 and the median RS for diabetic patients was 15. This difference was not significant, nor was there a statistical difference in RS between diabetic patients taking metformin (median RS = 15) and diabetic patients not taking metformin (median RS = 15). These results held true even when controlling for BMI. <b><i>Conclusions:</i></b> We conclude that neither diabetes diagnosis nor metformin use is associated with a difference in oncotype RS in this population of diabetic patients.

Identification of an eight-gene signature for survival prediction for patients with hepatocellular carcinoma based on integrated bioinformatics analysis

Background

Hepatocellular carcinoma (HCC) remains one of the leading causes of cancer-related death worldwide. Despite recent advances in imaging techniques and therapeutic intervention for HCC, the low overall 5-year survival rate of HCC patients remains unsatisfactory. This study aims to find a gene signature to predict clinical outcomes in HCC.

Methods

Bioinformatics analysis including Cox’s regression analysis, Kaplan-Meier (KM) and receiver operating characteristic curve (ROC) analysis and the random survival forest algorithm were performed to mine the expression profiles of 553 hepatocellular carcinoma (HCC) patients from The Cancer Genome Atlas (TCGA) and Gene Expression Omnibus (GEO) public database.

Results

We selected a signature comprising eight protein-coding genes (DCAF13, FAM163A, GPR18, LRP10, PVRIG, S100A9, SGCB, and TNNI3K) in the training dataset (AUC = 0.77 at five years, n = 332). The signature stratified patients into high- and low-risk groups with significantly different survival in the training dataset (median 2.20 vs. 8.93 years, log-rank test P < 0.001) and in the test dataset (median 2.68 vs. 4.24 years, log-rank test P = 0.004, n = 221, GSE14520). Further multivariate Cox regression analysis showed that the signature was an independent prognostic factor for patients with HCC. Compared with TNM stage and another reported three-gene model, the signature displayed improved survival prediction power in entire dataset (AUC signature = 0.66 vs. AUC TNM = 0.64 vs. AUC gene model = 0.60, n = 553). Stratification analysis shows that it can be used as an auxiliary marker for many traditional staging models.

Conclusions

We constructed an eight-gene signature that can be a novel prognostic marker to predict the survival of HCC patients.

EarlyR signature predicts response to neoadjuvant chemotherapy in breast cancer

BACKGROUND

EarlyR gene signature uses ESPL1, SPAG5, MKI67, PLK1 and PGR to classify ER+ breast cancer (ER+ BC) into EarlyR-Low, EarlyR-Int, and EarlyR-High risk strata and is prognostic in patients treated with adjuvant chemotherapy. The ability of EarlyR to predict pathological complete response (pCR) and long-term survival following neoadjuvant chemotherapy (NACT) is evaluated herein.

MATERIALS

The ability of EarlyR gene signature to predict pCR was assessed in publicly available Affymetrix microarray datasets (Cohort A; n = 659; 74 pCR events) derived from NACT-treated ER+ BC patients. Distant relapse-free survival (DRFS) results were analyzed in patients treated with NACT and adjuvant hormone therapy (AHT) (n = 281) and compared with patients treated with AHT alone (n = 455) (Cohort B; n = 736; 142 events).

RESULTS

In cohort A, EarlyR was a significant predictor of pCR (p = 5.8 × 10-11) (EarlyR-Low, n = 400, pCR = 40, 5%; EarlyR-Int, n = 69, pCR = 7, 15% and EarlyR-High, n = 190, pCR = 47, 24%). In EarlyR-Low of Cohort B, the 5-year DRFS was not significantly (p = 0.55) different between NACT + AHT [0.81 (95%CI 0.73-0.90)] and AHT-only [0.85 (95%CI 0.81-0.90)]. In contrast, in EarlyR-High, the 5-year DRFS was higher (p = 0.019) in NACT + AHT [0.81 (95%CI 0.70-0.93)] as compared to AHT-only [0.60 (95%CI 0.51-0.71)].

CONCLUSIONS

High EarlyR is strongly associated with pCR in patients treated with neoadjuvant chemotherapy. EarlyR also predicts poor DRFS outcomes for patients in EarlyR-High not receiving NACT, and improved survival in NACT-treated EarlyR-High patients. EarlyR is not only a prognostic assay but also a predictive assay that identifies patients, who are also likely to respond to chemotherapy.

Breast specimen handling and reporting in the post-neoadjuvant setting: challenges and advances

Neoadjuvant systemic therapy is becoming more commonly used in patients with earlier stages of breast cancer. To assess tumour response to neoadjuvant chemotherapy, pathological evaluation is the gold standard. Depending on the treatment response, the pathological examination of these specimens can be quite challenging. However, a uniform approach to evaluate post-neoadjuvant-treated breast specimens has been lacking. Furthermore, there is no single universally accepted or endorsed classification system for assessing treatment response in this setting. Recent initiatives have attempted to create a standardised protocol for evaluation of post-neoadjuvant breast specimens. This review outlines the necessary information that should be collected prior to macroscopic examination of these specimens, the recommended and most pragmatic approach to tissue sampling for microscopic examination, describes the macroscopic and microscopic features of post-therapy breast specimens, summarises two commonly used systems for classifying treatment response and outlines the critical variables that should be included in the final pathology report.