The Era of Multigene Panels Comes? The Clinical Utility of Oncotype DX and MammaPrint

The Expression Profiles of lncRNAs Are Associated with Neoadjuvant Chemotherapy Resistance in Locally Advanced, Luminal B-Type Breast Cancer

lncRNAs are noncoding transcripts with tissue and cancer specificity. Particularly, in breast cancer, lncRNAs exhibit subtype-specific expression; they are particularly upregulated in luminal tumors. However, no gene signature-based laboratory tests have been developed for luminal breast cancer identification or the differential diagnosis of luminal tumors, since no luminal A- or B-specific genes have been identified. Particularly, luminal B patients are of clinical interest, since they have the most variable response to neoadjuvant treatment; thus, it is necessary to develop diagnostic and predictive biomarkers for these patients to optimize treatment decision-making and improve treatment quality. In this study, we analyzed the lncRNA expression profiles of breast cancer cell lines and patient tumor samples from RNA-Seq data to identify an lncRNA signature specific for luminal phenotypes. We identified an lncRNA signature consisting of LINC01016, GATA3-AS1, MAPT-IT1, and DSCAM-AS1 that exhibits luminal subtype-specific expression; among these lncRNAs, GATA3-AS1 is associated with the presence of residual disease (Wilcoxon test, p < 0.05), which is related to neoadjuvant chemotherapy resistance in luminal B breast cancer patients. Furthermore, analysis of GATA3-AS1 expression using RNA in situ hybridization (RNA ISH) demonstrated that this lncRNA is detectable in histological slides. Similar to estrogen receptors and Ki67, both commonly detected biomarkers, GATA3-AS1 proves to be a suitable predictive biomarker for clinical application in breast cancer laboratory tests.

Integrated single-cell and bulk RNA sequencing revealed an epigenetic signature predicts prognosis and tumor microenvironment colorectal cancer heterogeneity

BACKGROUND

Colorectal cancer (CRC) prognosis prediction is currently a major challenge. Epigenetic regulation has been widely reported for its role in cancer development.

AIM

To construct a robust prognostic signature, we used developed and validated across datasets.

METHODS

After constructing the signature, the prognostic value of the signature was evaluated in the TCGA cohort and six independent datasets (GSE17526, GSE17537, GSE33113, GSE37892, GSE39048 and GSE39582). The clinical, genomic and transcriptomic features related to the signature were identified. The correlations of the signature score with immune cell infiltration and cell-cell interactions were analyzed. The correlations between the signature score and the sensitivity to different drugs were also predicted.

RESULTS

In the TCGA cohort, patients in the low-risk group according to the signature score had longer survival than those in the high-risk group, and this finding was validated in the validation datasets. The signature was a prognostic factor independent of age and sex and was correlated with stage and PD-1/PD-L1 expression. Area under the receiving operating characteristic curve was 0.72. Genomic association analyses revealed that samples from high-risk patients exhibited chromosomal instability. Transcriptomic analyses revealed that the signature score was significantly associated with multiple cellular pathways. Bulk RNA-seq and single-cell sequencing data revealed that the signature reflected differences in infiltrating immune cell-tumor cell interactions, especially for macrophages. The signature also predicted the putative drug sensitivity of CRC samples.

CONCLUSION

The signature is a valuable biomarker for predicting CRC prognosis and reflects multiple features of CRC, especially macrophage infiltration in the microenvironment.

Evolving Management of Breast Cancer in the Era of Predictive Biomarkers and Precision Medicine

Breast cancer is the most common cancer among women in the world as well as in the United States. Molecular and histological differentiation have helped clinicians optimize treatments with various therapeutics, including hormonal therapy, chemotherapy, immunotherapy, and radiation therapy. Recently, immunotherapy has become the standard of care in locally advanced triple-negative breast cancer and an option across molecular subtypes for tumors with a high tumor mutation burden. Despite the advancements in personalized medicine directing the management of localized and advanced breast cancers, the emergence of resistance to these therapies is the leading cause of death among breast cancer patients. Therefore, there is a critical need to identify and validate predictive biomarkers to direct treatment selection, identify potential responders, and detect emerging resistance to standard therapies. Areas of active scientific and clinical research include novel personalized and predictive biomarkers incorporating tumor microenvironment, tumor immune profiling, molecular characterization, and histopathological differentiation to predict response and the potential emergence of resistance.

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.

MicroRNA as a promising molecular biomarker in the diagnosis of breast cancer

Introduction: Breast cancer represents the most prevalent malignancy among women. Recent advancements in translational research have focused on the identification of novel biomarkers capable of providing valuable insights into patient outcomes. Furthermore, comprehensive investigations aimed at discovering novel miRNAs, unraveling their biological functions, and deciphering their target genes have significantly contributed to our understanding of the roles miRNAs play in tumorigenesis. Consequently, these investigations have facilitated the way for the development of miRNA-based approaches for breast cancer prognosis, diagnosis, and treatment. However, conducting a more extensive array of studies, particularly among diverse ethnic groups, is imperative to expand the scope of research and validate the significance of miRNAs. This study aimed to assess the expression patterns of circulating miRNAs in plasma as a prospective biomarker for breast cancer patients within a population primarily consisting of individuals from Black, Indigenous, and People of Color (BIPOC) communities. Methods: We evaluated 49 patients with breast cancer compared to 44 healthy women. Results and discussion: All miRNAs analyzed in the plasma of patients with breast cancer were downregulated. ROC curve analysis of miR-21 (AUC = 0.798, 95% CI: 0.682-0.914, p <0.0001), miR-1 (AUC = 0.742, 95% CI: 0.576-0.909, p = 0.004), miR-16 (AUC = 0.721, 95% CI: 0.581-0.861, p = 0.002) and miR-195 (AUC = 0.672, 95% CI: 0.553-0.792, p = 0.004) showed better diagnostic accuracy in discrimination of breast cancer patients in comparison with healthy women. miR-210, miR-21 showed the highest specificities values (97.3%, 94.1%, respectively). Following, miR-10b and miR-195 showed the highest sensitivity values (89.3%, and 77.8%, respectively). The panel with a combination of four miRNAs (miR-195 + miR-210 + miR-21 + miR-16) had an AUC of 0.898 (0.765-0.970), a sensitivity of 71.4%, and a specificity of 100.0%. Collectively, our results highlight the miRNA combination in panels drastically improves the results and showed high accuracy for the diagnosis of breast cancer displaying good sensitivity and specificity.

Targeting Interleukin-13 Receptor α2 and EphA2 in Aggressive Breast Cancer Subtypes with Special References to Chimeric Antigen Receptor T-Cell Therapy

Breast cancer (BCA) remains the leading cause of cancer-related mortality among women worldwide. This review delves into the therapeutic challenges of BCA, emphasizing the roles of interleukin-13 receptor α2 (IL-13Rα2) and erythropoietin-producing hepatocellular receptor A2 (EphA2) in tumor progression and resistance. Highlighting their overexpression in BCA, particularly in aggressive subtypes, such as Her-2-enriched and triple-negative breast cancer (TNBC), we discuss the potential of these receptors as targets for chimeric antigen receptor T-cell (CAR-T) therapies. We examine the structural and functional roles of IL-13Rα2 and EphA2, their pathological significance in BCA, and the promising therapeutic avenues their targeting presents. With an in-depth analysis of current immunotherapeutic strategies, including the limitations of existing treatments and the potential of dual antigen-targeting CAR T-cell therapies, this review aims to summarize potential future novel, more effective therapeutic interventions for BCA. Through a thorough examination of preclinical and clinical studies, it underlines the urgent need for targeted therapies in combating the high mortality rates associated with Her-2-enriched and TNBC subtypes and discusses the potential role of IL-13Rα2 and EphA2 as promising candidates for the development of CAR T-cell therapies.

miRNA-Based Technologies in Cancer Therapy

The discovery of therapeutic miRNAs is one of the most exciting challenges for pharmaceutical companies. Since the first miRNA was discovered in 1993, our knowledge of miRNA biology has grown considerably. Many studies have demonstrated that miRNA expression is dysregulated in many diseases, making them appealing tools for novel therapeutic approaches. This review aims to discuss miRNA biogenesis and function, as well as highlight strategies for delivering miRNA agents, presenting viral, non-viral, and exosomic delivery as therapeutic approaches for different cancer types. We also consider the therapeutic role of microRNA-mediated drug repurposing in cancer therapy.

Clinical Utility of the 31-Gene Expression Profile Test on the Management of Cutaneous Melanoma by Nurse Practitioners and Physician Assistants

Objective

The 31-gene expression profile (31-GEP) can predict the risk of recurrence and metastasis in cutaneous melanoma (CM). We assessed the viewpoints and use of 31-GEP testing by physician assistants (PAs) and nurse practitioners (NPs) for patients with CM.

Methods

NPs and PAs (n = 369) completed an 18-question online survey about their viewpoints and use of the 31-GEP risk-stratification test.

Results

Most practitioners (n = 334, 90.5%) felt prognostic testing improved patient care and would recommend the 31-GEP to a colleague (n = 333, 90.2%) or a friend or family member (n = 289, 78.3%) who was diagnosed with CM. The 31-GEP test was used by 176 respondents in the preceding 12 months (53%). Among users of the 31-GEP test, 78% stated that the results would impact follow-up schedule and referral, 66% overall treatment decisions, 62% sentinel lymph node biopsy recommendations, and 50% surveillance imaging. In thin tumors (≤ 1 mm), 82% of 31-GEP users and 44% of nonusers stated that the 31-GEP results would impact their treatment plan decisions.

Conclusion

The 31-GEP test significantly impacts treatment plans in CM, particularly for thin and stage I melanomas. Importantly, even nonusers stated that 31-GEP test results would impact treatment plans as well as recommendations to a friend or family member.

Concordance of RT-qPCR with immunohistochemistry and its beneficial role in breast cancer subtyping

This study was to compare the concordance of transcription-quantitative polymerase chain reaction (RT-qPCR) with immunohistochemistry (IHC) in determining estrogen receptor (ER), progesterone receptor (PR), human epidermal growth factor receptor 2 (HER2) and tumor proliferation index (Ki67) status in breast cancer, and to assess the prognosis based on different subtypes. Totally 323 breast cancer patients were selected, including 216 in the training set and 107 in the validation set. Logistic regression models were constructed using 5-fold cross-validation with the mRNA expression of each biomarker as the predictor and the corresponding IHC expression level as the binary response variable. Receiver operating characteristic curve was used to determine the cutoff value. When the thresholds of ER, PR, HER2, and Ki67 were 0.764, 0.709, 0.161, and 0.554, there existed high concordance rates between IHC and RT-qPCR in ER (94.4%), PR (88.0%) and HER2 (89.4%) and a medium concordance rate in Ki67 (67.8%), which were further confirmed in the validation set (ER: 81.3%, PR: 78.3%, HER2: 80.4%, and Ki67: 69.1%). Based on the subtyping stratified by RT-qPCR, the 5-year recurrence-free interval rates of patients with luminal, HER2-enriched, and triple-negative subtypes were 88% (95% CI: 0.84-0.93), 82% (95% CI: 0.73-0.92) and 58% (95% CI: 0.42-0.80), respectively, which were similar to those assessed by IHC (88%, 78% and 47%). RT-qPCR may be a complementary method to IHC, which can not only provide additional useful information in clinic, but also show more advantages over IHC in determining certain subtypes of breast cancer.

Challenges and opportunities in building a health economic framework for personalized medicine in oncology

Personalized medicine has allowed for knowledge at an individual level for several diseases and this has led to improvements in prevention and treatment of various types of neoplasms. Despite the greater availability of tests, the costs of genomic testing and targeted therapies are still high for most patients, especially in low- and middle-income countries. Although value frameworks and health technology assessment are fundamental to allow decision-making by policymakers, there are several concerns in terms of personalized medicine pharmacoeconomics. A global effort may improve these tools in order to allow access to personalized medicine for an increasing number of patients with cancer.

Non-coding RNA's prevalence as biomarkers for prognostic, diagnostic, and clinical utility in breast cancer

Noncoding RNAs (ncRNAs), which make up a significant portion of the mammalian transcriptome and plays crucial regulatory roles in expression of genes and other biological processes, have recently been found. The most extensively researched of the sncRNAs, microRNAs (miRNAs), have been characterized in terms of their synthesis, roles, and significance in the tumor development. Its crucial function in the stem cell regulation, another class of sncRNAs known as aspirRNAs, has attracted attention in cancer research. The investigations have shown that long non-coding RNAs have a crucial role in controlling developmental stages, such as mammary gland development. Additionally, it has been discovered that lncRNA dysregulation precedes the development of several malignancies, including breast cancer. The functions of sncRNAs (including miRNAs and piRNAs) and lncRNAs in the onset and development of the breast cancer are described in this study. Additionally, future perspectives of various ncRNA-based diagnostic, prognostic, and therapeutic approaches also discussed.

The Clinical Utility of lncRNAs and Their Application as Molecular Biomarkers in Breast Cancer

Given their tumor-specific and stage-specific gene expression, long non-coding RNAs (lncRNAs) have demonstrated to be potential molecular biomarkers for diagnosis, prognosis, and treatment response. Particularly, the lncRNAs DSCAM-AS1 and GATA3-AS1 serve as examples of this because of their high subtype-specific expression profile in luminal B-like breast cancer. This makes them candidates to use as molecular biomarkers in clinical practice. However, lncRNA studies in breast cancer are limited in sample size and are restricted to the determination of their biological function, which represents an obstacle for its inclusion as molecular biomarkers of clinical utility. Nevertheless, due to their expression specificity among diseases, such as cancer, and their stability in body fluids, lncRNAs are promising molecular biomarkers that could improve the reliability, sensitivity, and specificity of molecular techniques used in clinical diagnosis. The development of lncRNA-based diagnostics and lncRNA-based therapeutics will be useful in routine medical practice to improve patient clinical management and quality of life.

A Novel Surrogate Nomogram Capable of Predicting OncotypeDX Recurrence Score©

Background: OncotypeDX Recurrence Score© (RS) is a commercially available 21-gene expression assay which estimates prognosis and guides chemoendocrine prescription in early-stage estrogen-receptor positive, human epidermal growth factor receptor-2-negative (ER+/HER2−) breast cancer. Limitations of RS testing include the cost and turnaround time of several weeks. Aim: Our aim is to develop a user-friendly surrogate nomogram capable of predicting RS. Methods: Multivariable linear regression analyses were performed to determine predictors of RS and RS > 25. Receiver operating characteristic analysis produced an area under the curve (AUC) for each model, with training and test sets were composed of 70.3% (n = 315) and 29.7% (n = 133). A dynamic, user-friendly nomogram was built to predict RS using R (version 4.0.3). Results: 448 consecutive patients who underwent RS testing were included (median age: 58 years). Using multivariable regression analyses, postmenopausal status (β-Coefficient: 0.25, 95% confidence intervals (CIs): 0.03−0.48, p = 0.028), grade 3 disease (β-Coefficient: 0.28, 95% CIs: 0.03−0.52, p = 0.026), and estrogen receptor (ER) score (β-Coefficient: −0.14, 95% CIs: −0.22−−0.06, p = 0.001) all independently predicted RS, with AUC of 0.719. Using multivariable regression analyses, grade 3 disease (odds ratio (OR): 5.67, 95% CIs: 1.32−40.00, p = 0.037), decreased ER score (OR: 1.33, 95% CIs: 1.02−1.66, p = 0.050) and decreased progesterone receptor score (OR: 1.16, 95% CIs: 1.06−1.25, p = 0.002) all independently predicted RS > 25, with AUC of 0.740 for the static and dynamic online nomogram model. Conclusions: This study designed and validated an online user-friendly nomogram from routinely available clinicopathological parameters capable of predicting outcomes of the 21-gene RS expression assay.

Prospect of exosomal circular RNAs in breast Cancer: presents and future

Implementing precision oncology for breast cancer (BC) is a critical method for improving patient outcomes, which relies on the use of reliable biomarkers to be effective and safe. exosomes represent a potential alternative for the diagnosis and therapy of BC, As a "liquid biopsy" and a novel source for biomarkers. Exosomes are nanoscale phospholipid bilayer vesicles released by most cells that contain a large payload of various RNA species that can alter recipient cell activity. Circular RNAs (circRNAs) were recently revealed as a looping subclass of competing endogenous noncoding RNAs (ceRNAs) capable of microRNA sponging to regulate gene expression. They provide critical regulatory functions in carcinogenesis, proliferation, invasion, metastasis, and treatment resistance, as well as cancer prognostic. However, there is still a major gap in our understanding of the role of circRNA in the advancement of BC. CircRNAs are abundant in exosomes, according to various studies, and exosomal circRNAs (exo-circRNAs) play a significant role in cancer biology. Exo-circRNAs can be picked up by nearby or distant cells, affecting many features of the target cells' pathophysiological states, thus boosting cell communication and tumor spread. In this review, we have briefly summarized the major properties and functions of exosomes. Then, we have focused on exo-circRNAs, discussing their potential roles in both driving and inhibiting BC, as well as for cancer diagnosis, prognosis, and monitoring.

Transcriptomic Analysis of Breast Cancer Patients Sensitive and Resistant to Chemotherapy: Looking for Overall Survival and Drug Resistance Biomarkers

Worldwide breast cancer ranks first in mortality and incidence rates in women over 20 years old. Rather than one disease, breast cancer is a heterogeneous group of diseases that express distinct molecular profiles. Neoadjuvant chemotherapy is an important therapeutic strategy for breast cancer patients independently of their molecular subtype, with the drawback of resistance development. In addition, chemotherapy has adverse effects that combined with resistance could contribute to lower overall survival. Although great efforts have been made to find diagnostic and prognostic biomarkers for breast cancer and for response to targeted and immune therapy for this pathology, little has been explored regarding biomarkers of response to anthracyclines and taxanes based neoadjuvant chemotherapy. This work aimed to evaluate the molecular profile of patients who received neoadjuvant chemotherapy to identify differentially expressed genes (DEGs) that could be used as biomarkers of chemotherapy response and overall survival. Breast cancer patients who were candidates for neoadjuvant chemotherapy were enrolled in this study. After treatment and according to their pathological response, they were assigned as sensitive or resistant. To evaluate DEGs, Gene Ontology, Kyoto Encyclopedia Gene and Genome (KEGG), and protein–protein interactions, RNA-seq information from all patients was obtained by next-generation sequencing. A total of 1985 DEGs were found, and KEGG analysis indicated a great number of DEGs in metabolic pathways, pathways in cancer, cytokine–cytokine receptor interactions, and neuroactive ligand-receptor interactions. A selection of 73 DEGs was used further for an analysis of overall survival using the METABRIC study and the ductal carcinoma dataset of The Cancer Genome Atlas (TCGA) database. Nine DEGs correlated with overall survival, of which the subexpression of C1QTNF3, CTF1, OLFML3, PLA2R1, PODN, KRT15, HLA-A, and the overexpression of TUBB and TCP1 were found in resistant patients and related to patients with lower overall survival.

Prognostic and immune infiltration signatures of proteasome 26S subunit, non-ATPase (PSMD) family genes in breast cancer patients

The complexity of breast cancer includes many interacting biological processes that make it difficult to find appropriate therapeutic treatments. Therefore, identifying potential diagnostic and prognostic biomarkers is urgently needed. Previous studies demonstrated that 26S proteasome delta subunit, non-ATPase (PSMD) family members significantly contribute to the degradation of damaged, misfolded, abnormal, and foreign proteins. However, transcriptional expressions of PSMD family genes in breast cancer still remain largely unexplored. Consequently, we used a holistic bioinformatics approach to explore PSMD genes involved in breast cancer patients by integrating several high-throughput databases, including The Cancer Genome Atlas (TCGA), cBioPortal, Oncomine, and Kaplan-Meier plotter. These data demonstrated that PSMD1, PSMD2, PSMD3, PSMD7, PSMD10, PSMD12, and PSMD14 were expressed at significantly higher levels in breast cancer tissue compared to normal tissues. Notably, the increased expressions of PSMD family genes were correlated with poor prognoses of breast cancer patients, which suggests their roles in tumorigenesis. Meanwhile, network and pathway analyses also indicated that PSMD family genes were positively correlated with ubiquinone metabolism, immune system, and cell-cycle regulatory pathways. Collectively, this study revealed that PSMD family members are potential prognostic biomarkers for breast cancer progression and possible promising clinical therapeutic targets.

Modeling factors critical for implementation of precision medicine at health systems-level: an IRT approach

BACKGROUND

Through recent advances in omics technologies, precision medicine (PM) promises to fundamentally change the way we approach health, disease and illness. Imperative applications of omics-based biomarkers are gradually moving from research to clinical settings, with huge long-term clinical and public health implications. Whereas much of research in PM is mainly focused on basic biomedical discoveries, currently there is little research on the clinical implementation of omics biomarkers, especially at health systems level.

AIM AND METHODS

This study investigated the application of multidimensional item response theory (IRT) models to validate a hypothesized PM implementation measurement model. This is a contribution to PM implementation at health systems level. Data obtained through an item-sort procedure involving 496 observations from 124 study participants formed the basis of a 22-item PMI measurement model.

CONCLUSION

Statistical significance of the bifactor model suggests PM implementation may have to be examined using factors that reflect a single common underlying implementation construct, as well as factors that reflect unique variances for the identified four content-specific factors.

Clinical utility of liquid biopsy in breast cancer: A systematic review

Advancements in genetic sequencing techniques along with the identification of specific mutations and structural changes in multiple cancer genes, make it possible to identify circulating tumor cells and cell free nucleic acids as blood-based biomarkers, serving as a liquid biopsy (LB) with great utility for the diagnosis, treatment and follow-up of patients with neoplasms. This systematic review focuses on the clinical utility of LB in patients with breast cancer (BC). Articles published between 1990 and 2021 were included. Databases searched: Trip Database, WoS, EMBASE, PubMed, SCOPUS, and Clinical Keys. Variables studied: Publication year, country, number of cases, primary study design, LB detection methods, genes found, overall survival, disease-free survival, stage, response to treatment, clinical utility, BC molecular type, systemic treatment and methodological quality of primary studies. Of 2619 articles, 74 were retained representing 12 658 patients, mainly cohort studies (66.2%), the majority were from China (15%) and Japan (12.2%). All primary studies described clinical stage and type of systemic treatment used. Most used biomarker detection method: DNA (52.7%) and type of analysis: quantification of total cfDNA (35.1%). PIK3CA mutation was most frequent (62.9%). Evidence suggests clinically useful applications of BC. Though heterogeneous, publications suggest that LB will constitute part of the standard diagnostic-therapeutic process of BC.

Liquid Biopsies for Molecular Biology-Based Radiotherapy

Molecular alterations drive cancer initiation and evolution during development and in response to therapy. Radiotherapy is one of the most commonly employed cancer treatment modalities, but radiobiologic approaches for personalizing therapy based on tumor biology and individual risks remain to be defined. In recent years, analysis of circulating nucleic acids has emerged as a non-invasive approach to leverage tumor molecular abnormalities as biomarkers of prognosis and treatment response. Here, we evaluate the roles of circulating tumor DNA and related analyses as powerful tools for precision radiotherapy. We highlight emerging work advancing liquid biopsies beyond biomarker studies into translational research investigating tumor clonal evolution and acquired resistance.

Ki-67 as a Prognostic Biomarker in Invasive Breast Cancer

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.

Prediction of Late Recurrence and Distant Metastasis in Early-stage Breast Cancer: Overview of Current and Emerging Biomarkers

Recently, a significant number of breast cancer (BC) patients have been diagnosed at an early stage. It is therefore critical to accurately predict the risk of recurrence and distant metastasis for better management of BC in this setting. Clinicopathologic patterns, particularly lymph node status, tumor size, and hormonal receptor status are routinely used to identify women at increased risk of recurrence. However, these factors have limitations regarding their predictive ability for late metastasis risk in patients with early BC. Emerging molecular signatures using gene expression-based approaches have improved the prognostic and predictive accuracy for this indication. However, the use of their based-scores for risk assessment has provided contradictory findings. Therefore, developing and using newly emerged alternative predictive and prognostic biomarkers for identifying patients at high- and low-risk is of great importance. The present review discusses some serum biomarkers and multigene profiling scores for predicting late recurrence and distant metastasis in early-stage BC based on recently published studies and clinical trials.

American Joint Committee on Cancer's Staging System for Breast Cancer, Eighth Edition: Summary for Clinicians

Breast cancer is commonly staged using the American Joint Committee on Cancer (AJCC) staging system. The 7^th edition of the AJCC Staging Manual, was a purely anatomic staging method, which uses primary tumor size (T), nodal involvement (N), and metastasis (M) based on clinical and pathological evaluations. Advancements in tumor biology and prognostic biological markers, such as estrogen receptor (ER)/progesterone receptor (PR), HER2/neu, and Ki-67, have allowed clinicians to understand why similarly staged patients had significantly different outcomes. The most recent update to the staging system integrates molecular markers with disease extent for more optimal estimation of prognosis. This change improves the prognosis of breast cancer patients and better informs physicians in the planning of treatments. This review summarizes the changes in the AJCC Staging Manual, 8^th edition and their impact on practicing radiologists in breast cancer management.

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.

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.

Automated immunohistochemical assessment ability to evaluate estrogen and progesterone receptor status compared with quantitative reverse transcription-polymerase chain reaction in breast carcinoma patients

BACKGROUND

This study aimed to investigate the capability of an automated immunohistochemical (IHC) evaluation of hormonal receptor status in breast cancer patients compared to a well-validated quantitative reverse transcription-polymerase chain reaction (RT-qPCR) method.

METHODS

This study included 93 invasive breast carcinoma cases that had both standard IHC assay and Oncotype Dx assay results. The same paraffin blocks on which Oncotype Dx assay had been performed were selected. Estrogen receptor (ER) and progesterone receptor (PR) receptor status were evaluated through IHC stains using SP1 monoclonal antibody for ER, and 1E2 monoclonal antibody for PR. All ER and PR immunostained slides were scanned, and invasive tumor areas were marked. Using the QuantCenter image analyzer provided by 3DHISTECH, IHC staining of hormone receptors was measured and converted to histochemical scores (H scores). Pearson correlation coefficients were calculated between Oncotype Dx hormone receptor scores and H scores, and between Oncotype Dx scores and Allred scores.

RESULTS

H scores measured by an automated imaging system showed high concordance with RT-qPCR scores. ER concordance was 98.9% (92/93), and PR concordance was 91.4% (85/93). The correlation magnitude between automated H scores and RT-qPCR scores was high and comparable to those of Allred scores (for ER, 0.51 vs. 0.37 [p=.121], for PR, 0.70 vs. 0.72 [p=.39]).

CONCLUSIONS

Automated H scores showed a high concordance with quantitative mRNA expression levels measured by RT-qPCR.

Identifying transcriptomic correlates of histology using deep learning

Linking phenotypes to specific gene expression profiles is an extremely important problem in biology, which has been approached mainly by correlation methods or, more fundamentally, by studying the effects of gene perturbations. However, genome-wide perturbations involve extensive experimental efforts, which may be prohibitive for certain organisms. On the other hand, the characterization of the various phenotypes frequently requires an expert's subjective interpretation, such as a histopathologist's description of tissue slide images in terms of complex visual features (e.g. 'acinar structures'). In this paper, we use Deep Learning to eliminate the inherent subjective nature of these visual histological features and link them to genomic data, thus establishing a more precisely quantifiable correlation between transcriptomes and phenotypes. Using a dataset of whole slide images with matching gene expression data from 39 normal tissue types, we first developed a Deep Learning tissue classifier with an accuracy of 94%. Then we searched for genes whose expression correlates with features inferred by the classifier and demonstrate that Deep Learning can automatically derive visual (phenotypical) features that are well correlated with the transcriptome and therefore biologically interpretable. As we are particularly concerned with interpretability and explainability of the inferred histological models, we also develop visualizations of the inferred features and compare them with gene expression patterns determined by immunohistochemistry. This can be viewed as a first step toward bridging the gap between the level of genes and the cellular organization of tissues.

Gene Expression Along with Genomic Copy Number Variation and Mutational Analysis Were Used to Develop a 9-Gene Signature for Estimating Prognosis of COAD

PURPOSE

This study aims to systematically analyze multi-omics data to explore new prognosis biomarkers in colon adenocarcinoma (COAD).

MATERIALS AND METHODS

Multi-omics data of COAD and clinical information were obtained from The Cancer Genome Atlas (TCGA). Univariate Cox analysis was used to select genes which significantly related to the overall survival. GISTIC 2.0 software was used to identify significant amplification or deletion. Mutsig 2.0 software was used to identify significant mutation genes. The 9-gene signature was screened by random forest algorithm and Cox regression analysis. GSE17538 dataset was used as an external dataset to verify the predictive ability of 9-gene signature. qPCR was used to detect the expression of 9 genes in clinical specimens.

RESULTS

A total of 71 candidate genes are obtained by integrating genomic variation, mutation and prognostic data. Then, 9-gene signature was established, which includes HOXD12, RNF25, CBLN3, DOCK3, DNAJB13, PYGO2, CTNNA1, PTPRK, and NAT1. The 9-gene signature is an independent prognostic risk factor for COAD patients. In addition, the signature shows good predicting performance and clinical practicality in training set, testing set and external verification set. The results of qPCR based on clinical samples showed that the expression of HOXD12, RNF25, CBLN3, DOCK3, DNAJB13, and PYGO2 was increased in colon cancer tissues and the expression of CTNNA1, PTPRK, NAT1 was decreased in colon cancer tissues.

CONCLUSION

In this study, 9-gene signature is constructed as a new prognostic marker to predict the survival of COAD patients.

Current advances in the diagnosis and personalized treatment of breast cancer: lessons from tumor biology

Breast cancer treatment has advanced enormously in the last decade. Most of this is due to advances reached in the knowledge regarding tumor biology, mainly in the field of diagnosis and treatment. This review brings information about how the genomics-based information contributed to advances in breast cancer diagnosis and prognosis perspective, as well as presents how tumor biology discoveries fostered the main therapeutic approaches available to treat such patients, based on a personalized point of view.

RDGN-based predictive model for the prognosis of breast cancer

Background

Breast cancer is the most diagnosed malignancy in females in the United States. The members of retinal determination gene network (RDGN) including DACH, EYA, as well as SIX families participate in the proliferation, apoptosis, and metastasis of multiple tumors including breast cancer. A comprehensive predictive model of RDGN might be helpful to herald the prognosis of breast cancer patients.

Methods

In this study, the Gene Expression Ominibus (GEO) and Gene Set Expression Analysis (GSEA) algorithm were used to investigate the effect of RDGN members on downstream signaling pathways. Besides, based on The Cancer Genome Atlas (TCGA) database, we explored the expression patterns of RDGN members in tumors, normal tissues, and different breast cancer subtypes. Moreover, we estimated the relationship between RDGN members and the outcomes of breast cancer patients. Lastly, we constructed a RDGN-based predictive model by Cox proportional hazard regression and verified the model in two separate GEO datasets.

Results

The results of GSEA showed that the expression of DACH1 was negatively correlated with cell cycle and DNA replication pathways. On the contrary, the levels of EYA2 and SIX1 were significantly positively correlated with DNA replication, mTOR, and Wnt pathways. Further investigation in TCGA database indicated that DACH1 expression was lower in breast cancers especially basal-like subtype. In the meanwhile, SIX1 was remarkably upregulated in breast cancers while EYA2 level was increased in Basal-like and Her-2 enriched subtypes. Survival analyses demonstrated that DACH1 was a favorable factor while EYA2 and SIX1 were risk factors for breast cancer patients. Given the results of Cox proportional hazard regression analysis, two members of RDGN were involved in the present predictive model and patients with high model index had poorer outcomes.

Conclusion

This study showed that aberrant RDGN expression was an unfavorable factor for breast cancer. This RDGN-based comprehensively framework was meaningful for predicting the prognosis of breast cancer patients.

The Utility of Oncotype DX for Adjuvant Chemotherapy Treatment Decisions in Estrogen Receptor-positive, Human Epidermal Growth Factor Receptor 2-negative, Node-negative Breast Cancer

Introduction Breast cancer is the most common cancer diagnosis in the UK. Recently, there has been a reduction in breast cancer-specific mortality and recurrence attributed, in part, to the delivery of adjuvant chemotherapy. The National Institute for Health and Care Excellence (NICE) recommends the use of genetic profiling with Oncotype DX (ODX) to guide decisions to offer adjuvant chemotherapy after surgery in intermediate-risk early breast cancer patients. This study aimed to evaluate the utility of ODX testing in routine clinical practice in a National Health Service (NHS) hospital.  Methods Consecutive early breast cancer patients, identified through the multidisciplinary team (MDT) records, treated in our institution over 12 months (October 2017-September 2018) were included. PREDICT and Nottingham prognostic index (NPI) scores (from online clinicopathological recurrence risk tools) were calculated for each patient, and ODX data obtained through Genomic Health, Inc. (Redwood City, California). Patients were divided into two groups, those that underwent ODX testing (ODX group) and those that did not (non-ODX group). Descriptive statistics were used to analyse patient and tumour characteristics. The Gaussian distribution of each data set was assessed using the Anderson-Darling test. For comparisons between patient groups, the non-parametric equivalent of the two-tailed t-test (Mann-Whitney) was used. Dichotomous variables (e.g. chemotherapy decisions) were compared using chi-squared tests. Results One-hundred thirty-three patients (mean age 62 years) treated for 152 early breast cancers, were included in the final analysis. Breast cancers in the ODX group were of greater median tumour size (24 vs 16 mm; P<0.0001) and higher median tumour grade (3 vs 2; P<0.0001). PREDICT scores (3 vs 1, P<0.0001) and NPI scores (3.40 vs 2.30, P<0.0001) for the ODX group were also significantly higher than the non-ODX group. A greater proportion of patients were offered chemotherapy in the ODX group (39.9% vs 6.9%, P<0.001). However, for the PREDICT-calculated intermediate-risk patients, ODX testing resulted in a lower proportion of patients being offered chemotherapy compared to the intermediate-risk patients who were not genetically profiled (54.5% vs 83.3%, P=0.3547), although this result was not statistically significant. Conclusions Patients selected for ODX testing were younger, with significantly higher-grade and larger-sized tumours compared to patients not selected for genetic profiling. ODX testing significantly impacted the delivery of chemotherapy, as the recurrence score generated through ODX testing guided the final decision.

Deconvolution of the Genomic and Epigenomic Interaction Landscape of Triple-Negative Breast Cancer

Triple-negative breast cancer (TNBC) is the most aggressive form of breast cancer. Emerging evidenced suggests that both genetics and epigenetic factors play a role in the pathogenesis of TNBC. However, oncogenic interactions and cooperation between genomic and epigenomic variation have not been characterized. The objective of this study was to deconvolute the genomic and epigenomic interaction landscape in TNBC using an integrative genomics approach, which integrates information on germline, somatic, epigenomic and gene expression variation. We hypothesized that TNBC originates from a complex interplay between genomic (both germline and somatic variation) and epigenomic variation. We further hypothesized that these complex arrays of interacting genomic and epigenomic factors affect entire molecular networks and signaling pathways which, in turn, drive TNBC. We addressed these hypotheses using germline variation from genome-wide association studies and somatic, epigenomic and gene expression variation from The Cancer Genome Atlas (TCGA). The investigation revealed signatures of functionally related genes containing germline, somatic and epigenetic variations. DNA methylation had an effect on gene expression. Network and pathway analysis revealed molecule networks and signaling pathways enriched for germline, somatic and epigenomic variation, among them: Role of BRCA1 in DNA Damage Response, Hereditary Breast Cancer Signaling, Molecular Mechanisms of Cancer, Estrogen-Dependent Breast Cancer, p53, MYC Mediated Apoptosis, and PTEN Signaling pathways. The investigation revealed that integrative genomics is a powerful approach for deconvoluting the genomic-epigenomic interaction landscape in TNBC. Further studies are needed to understand the biological mechanisms underlying oncogenic interactions between genomic and epigenomic factors in TNBC.

Glycosylation and its implications in breast cancer

Introduction: For decades, the role of glycans and glycoproteins in the progression of breast cancer and other cancers have been evaluated. Through extensive studies focused on elucidating the biological functions of glycosylation, researchers have been able to implicate alterations in these functions to tumor formation and metastasis. Areas covered: In this review, we summarize how changes in glycosylation are associated with tumorigenesis, with emphasis on breast cancers. An overview of the changes in N-linked and O-linked glycans associated with breast cancer tumors and biofluids are described. Recent advances in glycomics are emphasized in the context of continuing to decipher the glycosylation changes associated with breast cancer progression. Expert opinion: While changes in glycosylation have been studied in breast cancer for many years, the clinical relevance of these studies has been limited. This reflects the inherent biological and clinical heterogeneity of breast cancers. Glycomics analysis lags behind the advances in genomics and proteomics, but new approaches are emerging. A summary of known glycosylation changes associated with breast cancer is necessary to implement new findings in the context of clinical outcomes and therapeutic strategies. A better understanding of the dynamics of tumor and immune glycosylation is critical to improving emerging immunotherapeutic treatments.

Evaluating patterns of utilization of gene signature panels and impact on treatment patterns in patients with ductal carcinoma in situ of the breast

BACKGROUND

Broad patterns of use of the gene signature panel Oncotype DX DCIS and its large-scale impact on postoperative administration of radiation therapy in women with ductal carcinoma in situ of the breast remain unclear. This study sought to evaluate the patterns of use of this gene signature panel in women with ductal carcinoma in situ and the impact of these tools on postoperative radiation therapy administration.

METHODS

The National Cancer Database was queried for women with ductal carcinoma in situ treated with breast-conserving therapy who had information regarding whether a gene signature panel was performed between 2010 and 2015. Demographic characteristics, the characteristics of their ductal carcinoma in situ, and whether they received postoperative radiation therapy were compared among patients who did have a gene signature panel performed and those who did not. Patterns of radiation therapy administration were also evaluated based on the recurrence risk score by the gene signature panel.

RESULTS

Gene signature panel use increased over time, with a sharp increase in utilization occurring in 2015 (8.0% in 2015 vs 4.4% in 2014, P < .001). Patients with estrogen receptor-positive ductal carcinoma in situ were somewhat more likely to have a gene signature panel ordered (3.9% estrogen receptor positive vs 1.7% estrogen receptor negative, P < .001), as were patients with lower-grade ductal carcinoma in situ (4.5% grade I/II vs 3.1% grade III, P < .001). Gene signature panel utilization was associated with a decrease in the administration of postoperative radiation therapy (48.6% gene signature panel vs 83.4% no gene signature panel, P < .001). Among patients in whom a gene signature panel was performed, postoperative radiation therapy was administered in 81.9%, 72.0%, and 35.9% of patients with high-, intermediate-, and low-recurrence scores, respectively.

CONCLUSION

Gene signature panel use in patients with ductal carcinoma in situ has increased over time and is more commonly used in women with lower-risk, clinicopathologic features to determine the magnitude of benefit afforded by radiation therapy. Gene signature panel use is associated with decreased rates of postoperative radiation therapy administration, particularly among patients with scores suggesting a low rate of recurrence.

A comprehensive study of epigenetic alterations in hepatocellular carcinoma identifies potential therapeutic targets

BACKGROUND & AIMS

A causal link has recently been established between epigenetic alterations and hepatocarcinogenesis, indicating that epigenetic inhibition may have therapeutic potential. We aimed to identify and target epigenetic modifiers that show molecular alterations in hepatocellular carcinoma (HCC).

METHODS

We studied the molecular-clinical correlations of epigenetic modifiers including bromodomains, histone acetyltransferases, lysine methyltransferases and lysine demethylases in HCC using The Cancer Genome Atlas (TCGA) data of 365 patients with HCC. The therapeutic potential of epigenetic inhibitors was evaluated in vitro and in vivo. RNA sequencing analysis and its correlation with expression and clinical data in the TCGA dataset were used to identify expression programs normalized by Jumonji lysine demethylase (JmjC) inhibitors.

RESULTS

Genetic alterations, aberrant expression, and correlation between tumor expression and poor patient prognosis of epigenetic enzymes are common events in HCC. Epigenetic inhibitors that target bromodomain (JQ-1), lysine methyltransferases (BIX-1294 and LLY-507) and JmjC lysine demethylases (JIB-04, GSK-J4 and SD-70) reduce HCC aggressiveness. The pan-JmjC inhibitor JIB-04 had a potent antitumor effect in tumor bearing mice. HCC cells treated with JmjC inhibitors showed overlapping changes in expression programs related with inhibition of cell proliferation and induction of cell death. JmjC inhibition reverses an aggressive HCC gene expression program that is also altered in patients with HCC. Several genes downregulated by JmjC inhibitors are highly expressed in tumor vs. non-tumor parenchyma, and their high expression correlates with a poor prognosis. We identified and validated a 4-gene expression prognostic signature consisting of CENPA, KIF20A, PLK1, and NCAPG.

CONCLUSIONS

The epigenetic alterations identified in HCC can be used to predict prognosis and to define a subgroup of high-risk patients that would potentially benefit from JmjC inhibitor therapy.

LAY SUMMARY

In this study, we found that mutations and changes in expression of epigenetic modifiers are common events in human hepatocellular carcinoma, leading to an aggressive gene expression program and poor clinical prognosis. The transcriptional program can be reversed by pharmacological inhibition of Jumonji enzymes. This inhibition blocks hepatocellular carcinoma progression, providing a novel potential therapeutic strategy.

Molecules in cancer immunotherapy: benefits and side effects

This mini review describes some of the key interactions between cancer cells and the immune system. This includes the concept of tumour cell immunosurveillance, mechanisms of immune evasion by tumour cells and some of the novel immunology-based anticancer therapies that have recently been introduced. The latter are also set into the context of the enlarging spectrum of immunohistochemistry-based and molecular testing that can now be performed on formalin-fixed and paraffin-embedded tissues for predicting response to both well-established and newly developed agents. The emerging field of cancer immunotherapy requires and encourages close working between cellular and molecular pathology and clinical cancer treatment, while providing new hope for patients with cancers that may not have responded to conventional oncological treatments.

American Joint Committee on Cancer's Staging System for Breast Cancer, Eighth Edition: What the Radiologist Needs to Know

The TNM staging system for cancer was developed by Pierre Denoix in France in the 1940s and 1950s. The North American effort to standardize the TNM system for cancer staging was first organized in 1959 as the American Joint Committee for Cancer Staging and End-Results Reporting, which is now the American Joint Committee on Cancer (AJCC). The most recent edition of the AJCC Cancer Staging Manual, the eighth edition, was globally adopted on January 1, 2018. Previous editions of the manual have relied on anatomic methods of staging alone, which used population-based survival data to predict clinical outcomes. In the era of precision medicine, the major change in the eighth edition is the incorporation of prognostic biomarkers to more accurately predict clinical outcomes and treatment response on an individual basis, without relying solely on the anatomic extent of disease. Factors such as tumor grade, hormone receptor and oncogene expression, and multigene panel recurrence scores are now integrated with anatomic information to yield a final prognostic stage group, which will provide better stratification of patient prognosis. The purpose of this article is to review the major changes in the AJCC eighth edition for breast cancer staging, review anatomic TNM staging, familiarize the radiologist with prognostic biomarkers and prognostic staging, and identify key sites of disease that may alter clinical management. ^©RSNA, 2018.

Nomogram Integrating Genomics with Clinicopathologic Features Improves Prognosis Prediction for Colorectal Cancer

The current tumor staging system is insufficient for predicting the outcomes for patients with colorectal cancer because of its phenotypic and genomic heterogeneity. Integrating gene expression signatures with clinicopathologic factors may yield a predictive accuracy exceeding that of the currently available system. Twenty-seven signatures that used gene expression data to predict colorectal cancer prognosis were identified and re-analyzed using bioinformatic methods. Next, clinically annotated colorectal cancer samples (n = 1710) with the corresponding expression profiles, that predicted a patient's probability of cancer recurrence, were pooled to evaluate their prognostic values and establish a clinicopathologic-genomic nomogram. Only 2 of the 27 signatures evaluated showed a significant association with prognosis and provided a reasonable prediction accuracy in the pooled cohort (HR, 2.46; 95% CI, 1.183-5.132, P < 0.001; AUC, 60.83; HR, 2.33; 95% CI, 1.218-4.453, P < 0.001; AUC, 71.34). By integrating the above signatures with prognostic clinicopathologic features, a clinicopathologic-genomic nomogram was cautiously constructed. The nomogram successfully stratified colorectal cancer patients into three risk groups with remarkably different DFS rates and further stratified stage II and III patients into distinct risk subgroups. Importantly, among patients receiving chemotherapy, the nomogram determined that those in the intermediate- (HR, 0.98; 95% CI, 0.255-0.679, P < 0.001) and high-risk (HR, 0.67; 95% CI, 0.469-0.957, P = 0.028) groups had favorable responses.Implications: These findings offer evidence that genomic data provide independent and complementary prognostic information, and incorporation of this information refines the prognosis of colorectal cancer. Mol Cancer Res; 16(9); 1373-84. ©2018 AACR.

Multigene Signature Panels and Breast Cancer Therapy: Patterns of Use and Impact on Clinical Decision Making

BACKGROUND

A growing body of evidence supports the use of multigene signature panels (MSPs) in predicting recurrence risk in patients with invasive breast cancer. This study aimed to evaluate trends in MSP use over time and the effect of MSPs on administration of postoperative chemotherapy.

STUDY DESIGN

The National Cancer Database was queried for all women with invasive breast cancer who underwent resection between 2011 and 2014 and had information about performance of an MSP, hormone receptor status, and receipt of chemotherapy. Multigene signature panel use over time was evaluated, and patterns of use of Oncotype DX (ODX) and MammaPrint (MP) were compared.

RESULTS

In a total of 476,128 patients, an MSP was obtained in 153,782 (30.2%). Multigene signature panel use increased over time and was associated with a decreased rate of chemotherapy administration (24.6% MSP vs 37.2% no MSP; p < 0.001). Oncotype DX remained the most common MSP used throughout the study period. Oncotype DX was used more commonly in stage I disease than MP, and MP was used more commonly in stage II and III disease. MammaPrint was more commonly used in hormone receptor-negative patients, human epidermal growth factor receptor 2-positive patients, and patients with positive lymph nodes. Postoperative chemotherapy was administered to a higher proportion of patients assessed with MP than with ODX (41.3% vs 23.4%, respectively; p < 0.001).

CONCLUSIONS

Use of MSPs among patients with breast cancer has increased over time and is associated with a decreased use of adjuvant chemotherapy. Oncotype DX continues to be the most widely used MSP, although MP use has increased over time. Future studies are warranted to determine the optimal use of these MSPs in risk assessment and postoperative decision making.