Defining the Biology of Estrogen Receptor-Low-Positive Breast Cancer.

BACKGROUND: We sought to better define estrogen receptor-low-positive (ER-low+) breast cancer biology and determine the utility of the Oncotype DX Breast Recurrence Score® (RS) assay in this population. METHODS: Patients with information regarding percentage ER positivity and PAM50 subtype were identified in The Cancer Genome Atlas (TCGA) and subtype distribution was determined. Next, patients with ER-low+ (ER 1-10%), HER2- breast cancer undergoing upfront surgery with known RS result were identified in the National Cancer Database (NCDB) and our institutional Dana-Farber Brigham Cancer Center (DF/BCC) database; RS distribution was examined. Finally, patients with ER-low+, HER2- breast cancer treated at DF/BCC from 2011 to 2020 without prior RS results and in whom tissue was available to perform the assay were identified. RS results, treatment, recurrence and breast cancer-specific survival (BCSS) were determined. RESULTS: Of 1033 patients in TCGA, ER percentage and PAM50 subtype were available for 342 (33.1%) patients. Forty-six (13.5%) had ER-low+/HER2- tumors, among whom 82.6% were basal and 4.3% were luminal A. Among 3423 patients with ER-low+/HER2- disease in the NCDB, RS results were available for 689 (20.1%) patients; 67% had an RS ≥26. In our institutional database, only two patients with ER-low+/HER2- disease and an RS were identified, both with RS ≥26. Among 37 patients in our institutional cohort without prior RS, 35 (97.4%) had an RS ≥26, determined with testing. After a median follow-up of 40 months (range 3-106), three patients, all treated with chemotherapy, recurred. Three-year BCSS was 97.0% (95% confidence interval 96.9-97.1%). CONCLUSIONS: Most ER-low+/HER2- breast cancers are basal-like, with RS ≥26 suggesting these tumors are similar to triple-negative disease.

Studying Transcriptional Enhancers: The Founder Fallacy, Validation Creep, and Other Biases.

Transcriptional enhancers play a major role in regulating metazoan gene expression. Recent developments in genomics and next-generation sequencing have accelerated and revitalized the study of this important class of sequence elements. Increased interest and attention, however, has also led to troubling trends in the enhancer literature. In this Opinion, I describe some of these issues and show how they arise from shifting and nonuniform enhancer definitions, and genome-era biases. I discuss how they can lead to interpretative errors and an unduly narrow focus on certain aspects of enhancer biology to the potential exclusion of others.

Anomaly detection in genomic catalogues using unsupervised multi-view autoencoders.

BACKGROUND: Accurate identification of Transcriptional Regulator binding locations is essential for analysis of genomic regions, including Cis Regulatory Elements. The customary NGS approaches, predominantly ChIP-Seq, can be obscured by data anomalies and biases which are difficult to detect without supervision. RESULTS: Here, we develop a method to leverage the usual combinations between many experimental series to mark such atypical peaks. We use deep learning to perform a lossy compression of the genomic regions' representations with multiview convolutions. Using artificial data, we show that our method correctly identifies groups of correlating series and evaluates CRE according to group completeness. It is then applied to the ReMap database's large volume of curated ChIP-seq data. We show that peaks lacking known biological correlators are singled out and less confirmed in real data. We propose normalization approaches useful in interpreting black-box models. CONCLUSION: Our approach detects peaks that are less corroborated than average. It can be extended to other similar problems, and can be interpreted to identify correlation groups. It is implemented in an open-source tool called atyPeak.

Propensity score analysis of the prognostic value of genomic assays for breast cancer in diverse populations using the National Cancer Data Base.

BACKGROUND: Genomic assays such as Oncotype Dx (ODX) and MammaPrint are used for risk-adapted treatment decisions among patients with early breast cancer. However, to the authors' knowledge, concordance between genomic assays is modest. Using real-world data, the authors performed a comparative analysis of ODX and MammaPrint. METHODS: A cohort of women diagnosed with early-stage, hormone receptor-positive breast cancer who received ODX or MammaPrint was established using the National Cancer Data Base (NCDB) for 2010 through 2016. Using the propensity score matching method, 2 groups of patients with similar clinical and demographic characteristics were defined: one group received ODX and the other received MammaPrint. The authors examined the association between use of the ODX or MammaPrint assays and overall survival using Cox models. RESULTS: Of the 451,693 eligible patients, approximately 45.3% received ODX and 1.8% received MammaPrint testing. The use of ODX increased from 36.1% in 2010 to 49.9% in 2016, whereas use of MammaPrint increased from 0.5% in 2010 to 3.3% in 2016. The authors matched 5042 patients who received ODX with 5042 patients who received MammaPrint. The 5-year risks of death for the MammaPrint low-risk group and the ODX low-risk group were 3.4% and 4.7%, respectively. The prognostic value of MammaPrint was similar to that of ODX; the C-index was 0.614 (95% confidence interval, 0.572-0.657) for MammaPrint and 0.581 (95% confidence interval, 0.530-0.631) for ODX. There was a difference in the performance of the ODX assay observed across racial and/or ethnic groups (P < .001), with a slightly better performance noted among white compared with African American and Hispanic individuals. CONCLUSIONS: Both the ODX and MammaPrint tests are good at identifying low-risk individuals who could be spared chemotherapy. The suboptimal performance of ODX in ethnic minority individuals deserves further investigation.

Best practices for multidisciplinary integration of a DCIS genomic assay into clinical practice.

Most newly diagnosed ductal carcinoma in situ (DCIS) is treated with breast-conserving surgery (BCS) ± radiation therapy (RT). A key challenge is deciding whether or not to include RT with BCS. This decision is often determined by the degree of risk associated with disease recurrence. However, methods for risk assessment have not kept pace with diagnostic advances. The DCIS Score is an independent predictor and quantifier of individualized recurrence risk in patients with DCIS. Although the test is the only available genomic classifier for DCIS, the degree of adoption is varied, and it has not yet been fully accepted as standard practice. Recognizing the importance of individualizing recurrence assessment in patients with DCIS, the authors convened to review relevant clinical data, share best practices, and establish recommendations regarding how the assay should be incorporated into the decision-making process. Based on their clinical experiences, the authors concluded that effective integration of the DCIS Score should involve shared decision-making between surgeons and other specialties (radiation oncologists, pathologists, patient navigators, and physician assistants), with the patient's preference being a primary consideration. This manuscript aims to provide easy-to-use, clear-cut, and practical guidance to help physicians utilize the DCIS Score to improve risk assessment and inform treatment decisions for their patients with DCIS, including how to understand, run, interpret, and communicate the actionable results to patients.

Are online prediction tools a valid alternative to genomic profiling in the context of systemic treatment of ER-positive breast cancer?

BACKGROUND: Clinicians use clinical and pathological parameters, such as tumour size, grade and nodal status, to make decisions on adjuvant treatments for breast cancer. However, therapeutic decisions based on these features tend to vary due to their subjectivity. Computational and mathematical algorithms were developed using clinical outcome data from breast cancer registries, such as Adjuvant! Online and NHS PREDICT. More recently, assessments of molecular profiles have been applied in the development of better prognostic tools. METHODS: Based on the available literature on online registry-based tools and genomic assays, we evaluated whether these online tools could be valid and accurate alternatives to genomic and molecular profiling of the individual breast tumour in aiding therapeutic decisions, particularly in patients with early ER-positive breast cancer. RESULTS AND CONCLUSIONS: Early breast cancer is currently considered a systemic disease and a complex ecosystem with behaviour determined by the complex genetic and molecular signatures of the tumour cells, mammary stem cells, microenvironment and host immune system. We anticipate that molecular profiling will continue to evolve, expanding beyond the primary tumour to include the tumour microenvironment, cancer stem cells and host immune system. This should further refine therapeutic decisions and optimise clinical outcome. This article was specially invited by the editors and represents work by leading researchers.

Use of axillary ultrasound to guide breast cancer management in the genomic assay era.

INTRODUCTION: Chemotherapy is conventionally offered to non-stage IV breast cancer patients with metastatic nodes. However, the RxPONDER trial showed that chemotherapy can be omitted in selected patients with 1-3 metastatic nodes if the 21-gene assay recurrence score is ≤25. We aimed to investigate if axillary ultrasound can identify this group of patients with limited nodal burden so that they can undergo upfront surgery followed by gene assay testing, to potentially avoid chemotherapy. METHODS: T1-3, node positive, hormone receptor-positive and HER2-negative breast cancer patients ≥50 years old with axillary lymph node dissection (ALND) were reviewed from 2 centres. Patients with neoadjuvant chemotherapy and bilateral cancers were excluded. Number of ultrasound-detected abnormal axillary nodes, demographic and histological parameters were correlated with the number of metastatic nodes found on ALND. RESULTS: 138 patients were included, 59 (42.8%) and 79 (57.2%) patients had 1-3 and >3 metastatic nodes on ALND respectively. On logistic regression and ROC analysis, the number of ultrasound-detected abnormal nodes was significant (p < 0.001) for predicting limited nodal burden (ROC AUC = 0.7135). Probabilities of <4 metastatic nodes with ultrasound cut-offs of 5, 6 and 8 abnormal nodes were 0.057, 0.026 and 0.005 respectively, with 100% specificity. CONCLUSION: A cut-off of ≤5 ultrasound-detected abnormal nodes can distinguish between patients with limited versus high nodal burden, with high specificity. Hence, incorporating the number of abnormal ultrasound-detected nodes into clinical practice may prove useful in guiding between upfront surgery and gene assay testing or neoadjuvant chemotherapy in this group of patients.

Analytical validation and algorithm improvement of HepatoPredict kit to assess hepatocellular carcinoma prognosis before a liver transplantation.

Objectives: To verify the analytical performance of the HepatoPredict kit, a novel tool developed to stratify Hepatocellular Carcinoma (HCC) patients according to their risk of relapse after a Liver Transplantation (LT). Methods: The HepatoPredict tool combines clinical variables and a gene expression signature in an ensemble of machine-learning algorithms to forecast the benefit of a LT in HCC patients. To ensure the accuracy and reliability of this method, extensive analytical validation was conducted to verify its specificity and robustness. The experiments were designed following the guidelines for multi-target genomic assays such as ISO201395-2019, MIQE, CLSI-MM16, CLSI-MM17, and CLSI-EP17-A. The validation process included reproducibility between operators and between RNA extractions and RT-qPCR runs, and interference of input RNA levels or varying reagent levels. A recently retrained version of the HepatoPredict algorithms was also tested. Results: The validation process demonstrated that the HepatoPredict kit met the required standards for robustness (p > 0.05), analytical specificity (inclusivity of 95 %), and sensitivity (LoB, LoD, linear range, and amplification efficiency between 90 and 110 %). The operator, equipment, input RNA, and reagents used had no significant effect on the HepatoPredict results. Additionally, the testing of a recently retrained version of the HepatoPredict algorithm, showed that this new version further improved the accuracy of the kit and performed better than existing clinical criteria in accurately identifying HCC patients who are more likely to benefit LT. Conclusions: Even with the introduced variations in molecular and clinical variables, the HepatoPredict kit's prognostic information remains consistent. It can accurately identify HCC patients who are more likely to benefit from a LT. Its robust performance also confirms that it can be easily integrated into standard diagnostic laboratories.

Advances in Radiotherapy for Breast Cancer.

Breast cancer is the most prevalent cancer in women, and the second leading cause of cancer death in women in the United States. Radiation therapy is an important component in the multimodal management of breast cancer, including early stage and locally advanced breast cancers, as well as metastatic cases. Breast cancer radiation therapy has seen significant advancements over the past 20 years. This article discusses the latest advances in the radiotherapeutic management of breast cancer, especially focusing on the technological advances in radiation treatment planning and techniques that have exploited the understanding of radiation biology.

Application of a 21-Gene Recurrence Score in a Swiss Single-Center Breast Cancer Population: A Comparative Analysis of Treatment Administration before and after TAILORx.

In patients with hormone receptor positive, human epidermal receptor 2 negative (HR+/HER2-) negative breast cancer (BC), the TAILORx study showed the benefit of adding chemotherapy (CHT) to endocrine therapy (ET) in a subgroup of patients under 50 years with an intermediate Oncotype DX recurrence score (RS 11-25). The aim of the present study was to determine if the TAILORx findings, including the changes in the RS categories, impacted CHT use in the intermediate RS (11-25) group in daily practice, as well as to identify the main factors for CHT decisions. We conducted a retrospective study on 326 BC patients (59% node-negative), of which 165 had a BC diagnosis before TAILORx (Cohort A) and 161 after TAILORx publication (Cohort B). Changes in the RS categories led to shifts in patient population distribution, thereby leading to a 40% drop in the low RS (from 60% to 20%), which represented a doubling in the intermediate RS (from 30% to 60%) and an increase of 5% in the high RS (from 8-10% to 15%). The overall CHT recommendation and application did not differ significantly between cohort B when compared with A (19% vs. 22%, resp., p = 0.763). In the intermediate RS (11-25), CHT use decreased by 5%, while in the high-risk RS category (>25), there was an increase of 13%. The tumor board recommended CHT for 90% of the patients according to the new RS guidelines in cohort A and for 85% in cohort B. The decision for CHT recommendation was based on age (OR 0.93, 95% CI 0.08-0.97, p = 0.001), nodal stage (OR 4.77, 95% CI 2.03-11.22, p < 0.001), and RS categories (RS 11-25 vs. RS 0-10: OR 0.06 (95% CI 0.02-0.17), p < 0.001; RS > 26 vs. RS 11-25: OR 618.18 95% CI 91.64-4169.91, p < 0.001), but did not depend on the cohort. In conclusion, while the tumor board recommendation for CHT decreased in the intermediate RS category, there was an increase being reported in the high RS category, thus leading to overall minor changes in CHT application. As expected, among the younger women with intermediate RS and unfavorable histopathological factors, CHT use increased.

The Impact of Genomic Profiling on Adjuvant Therapy Recommendation in Postmenopausal Women with ER-Positive, T1-2 Breast Cancer: Can Genomic Profiling Eliminate the Need for Sentinel Lymph Node Biopsy?

INTRODUCTION: With the advent of genomic assays, sentinel lymph node biopsy (SLNB) may be less impactful in determining adjuvant breast cancer therapy. We evaluated the influence of SLNB on adjuvant therapy recommendation when the Oncotype DX recurrence score (RS) is known. METHODS: We reviewed postmenopausal women with ER-positive/HER2-negative, pT1-2 breast cancers with non-suspicious axillary ultrasound treated with SLNB at the time of cancer resection, from 2011 to 2015. For each case, the recommended adjuvant therapy based on the actual SLNB was compared with recommendations if SLNB had been omitted (presumed negative). RESULTS: Surgical nodal status was N0 in 184 patients (84.8%), Nmi-N1 in 29 patients (13.4%), and N2-3 in 4 patients (1.8%). SLNB resulted in a recommendation for axillary lymph node dissection in 4.1% (n = 9). Axillary surgery resulted in a change in radiation recommendation (nodal radiation considered or recommended) in 15.2% (n = 33). Of the 147 patients with known RS, 95 patients had RS > 18, 29 had RS 18-25, and 23 had RS < 25. When chemotherapy was only recommended for RS > 25, or N2-3 disease, SLNB changed the recommendation to have chemotherapy in one patient (0.7%), and the recommendation of which chemotherapy regimen (second- vs. third-generation) in an additional 5 patients. CONCLUSION: SLNB changed the recommendation for/against chemotherapy, or the chemotherapy regiment recommended, in 4.8% of postmenopausal women with early-stage, ER-positive/HER2-negative breast cancer, and sonographically negative axilla when using RS > 25 or N2-3 disease as an indication for chemotherapy. Preoperative genomic profiling can guide de-escalation of axillary surgery.

Using Breast Cancer Gene Expression Signatures in Clinical Practice: Unsolved Issues, Ongoing Trials and Future Perspectives.

The development of gene expression signatures since the early 2000's has offered standardized assays to evaluate the prognosis of early breast cancer. Five signatures are currently commercially available and recommended by several international guidelines to individualize adjuvant chemotherapy decisions in hormone receptors-positive/HER2-negative early breast cancer. However, many questions remain unanswered about their predictive ability, reproducibility and external validity in specific populations. They also represent a new hope to tailor (neo)adjuvant systemic treatment, adjuvant radiation therapy, hormone therapy duration and to identify a subset of patients who might benefit from CDK4/6 inhibitor adjuvant treatment. This review will highlight these particular issues, address the remaining questions and discuss the ongoing and future trials.

Cost-Effectiveness Analysis of Biological Signature DCISionRT Use for DCIS Treatment.

BACKGROUND: Currently it remains difficult to identify patients most likely to benefit from radiotherapy (RT) for ductal carcinoma-in-situ (DCIS), thus leading to wide variation in practice patterns. The genomic risk assessment tool DCISionRT (PreludeDX) has been validated to prognosticate recurrence risk and predict RT benefit. We aimed to study the cost-effectiveness analysis comparing DCIS treatments based on DCISionRT testing to traditional clinicopathologic risk factors. PATIENTS AND METHODS: A Markov state transition model was constructed to perform a cost-effectiveness analysis comparing breast-conserving surgery with or without RT using DCISionRT testing vs. traditional clinicopathologic risk factors. Clinical parameters were obtained from clinical trial data and cross-validation studies. Cost data were based on 2019 Medicare reimbursement. Incremental cost-effectiveness ratio (ICER) was calculated as incremental cost per quality-adjusted life-year (QALY) gained comparing DCIS treatments using DCISionRT testing to traditional clinicopathologic risk factors and evaluated with a willingness-to-pay threshold of US$100,000 per QALY gained. To account for uncertainty, 1-way and probabilistic sensitivity analyses were performed. RESULTS: Base case analysis showed that DCIS management using DCISionRT testing was a cost-effective strategy, resulting in an ICER of $74,331 per QALY gained compared to clinicopathology-based treatment. Model results were sensitive to a variation of the proportion of genomic-high, low-risk patients receiving RT in DCISionRT testing strategy, and changes in DCISionRT testing cost. CONCLUSION: DCISionRT testing could potentially be a cost-effective strategy compared to traditional decision making for DCIS treatments, optimizing RT benefit based on an accurate recurrence risk assessment.

Ultra Low-Coverage Whole-Genome Sequencing as an Alternative to Genotyping Arrays in Genome-Wide Association Studies.

An array-based genotyping approach has been the standard practice for genome-wide association studies (GWASs); however, as sequencing costs plummet over the past years, ultra low-coverage whole-genome sequencing (ulcWGS <0.5× coverage) has emerged as a promising alternative that provides superior genomic coverage with substantial reduction of genotyping cost. To evaluate the potential utility of ulcWGS, we performed a whole-genome sequencing (WGS) of 72 European individuals to a target coverage of 0.4× and compared its performance with the widely used Infinium Global Screening Multi-Disease Array (GSA-MD). We showed that the number of variants captured by ulcWGS is comparable with imputed GSA-MD platform, particularly for low-frequency (95.5%) and common variants (99.9%), with high imputation R2 accuracy (mean 0.93 for SNPs and 0.86 for indels). Using deep-coverage 30× WGS as the "truth" genotypes, we found that ulcWGS has higher overall nonreference genotype concordance compared with imputed GSA-MD for both SNPs (0.90 vs. 0.88) and indels (0.86 vs. 0.83). In addition, ulcWGS proved to be as sensitive as the genotyping-based method in sex imputation and ancestry prediction producing similar principal component (PC) scores. Our findings provide important evidence that the cost efficient ulcWGS of <0.5× generates high genotype accuracy, outperforming the standard genotyping arrays, making it an attractive alternative to the array-based method in next-generation GWAS design.

The 21-Gene Recurrence Score Assay and Prediction of Chemotherapy Benefit: A Propensity Score-Matched Analysis of the SEER Database.

BACKGROUND: To evaluate the performance of the 21-gene recurrence score (RS) assay in predicting chemotherapy benefit in the Surveillance, Epidemiology, and End Results population, we aimed to assess breast cancer-specific mortality (BCSM) by chemotherapy use within each of the RS categories. METHODS: Data on breast cancer (BC) cases diagnosed between 2004 and 2015 with available RS results were released. Our analysis included patients with hormone receptor-positive, node-negative early-stage BC (n = 89,402), and three RS groups were defined; RS < 11, low; RS 11-25, intermediate; RS > 25, high. A propensity score matched-analysis was performed to assess and compare BCSM. RESULTS: Chemotherapy was significantly associated with a reduced risk of BC death among patients in the high RS group (hazard ratio = 0.782; 95% CI, 0.618-0.990; p = 0.041). However, in the low and intermediate RS groups, there were no significant differences in BCSM between patients who received chemotherapy and those who did not. Among those with RS 11-25, chemotherapy benefit varied with tumor size (p = 0.001). CONCLUSIONS: Our findings provide real-world evidence that the 21-gene RS assay is predictive of chemotherapy benefit among patients in clinical practice. More refined risk estimates would be needed for patients with an intermediate RS.

Ki-67 index, progesterone receptor expression, histologic grade and tumor size in predicting breast cancer recurrence risk: A consecutive cohort study.

BACKGROUND: The 21-gene recurrence score (RS) assay has been recommended by major guidelines for treatment decision in hormone receptor (HR)-positive early breast cancer (EBC). However, the genomic assay is not accessible and affordable worldwide. Alternatively, an increasing number of studies have shown that traditional immunohistochemistry (IHC) can partially or even completely replace the role of the 21-gene genomic assay. Here, we developed and validated a predictive model (IHC3 model) combining the Ki-67 index, progesterone receptor (PR) expression, histologic grade, and tumor size to predict the recurrence risk of HR-positive EBC. METHODS: The data from 389 patients (development set) with HR-positive, human epidermal growth factor receptor 2-negative, lymph node non-metastasized invasive breast cancer were used to construct the IHC3 model based on the Surexam® 21-gene RS and the TAILORx clinical trial criteria. An additional 146 patients with the same characteristics constituted the validation set. The predictive accuracy of the IHC3 model was compared with that of Orucevic et al.'s nomogram. Invasive disease-free survival (IDFS) was analyzed in the IHC3 predictive low-recurrence risk (pLR) group and the predictive high-recurrence risk (pHR) group. The Pearson chi-square test, Fisher exact test, and log-rank test were used for analysis. RESULTS: The pLR and pHR group could be easily stratified using the decision tree model without network dependence. The accuracies of the IHC3 model were 86.1% in the development set and 87.7% in the validation set. The predictive accuracy of the IHC3 model and Orucevic et al.'s nomogram for the whole cohort was 86.5% and 86.9%, respectively. After a 52-month of median follow-up, a significant difference was found in IDFS between of the IHC3 pLR and the pHR groups (P = 0.001) but not in the IDFS between the low- and high-recurrence risk groups according to the Surexam® 21-gene RS and the TAILORx clinical trial criteria (P = 0.556) or 21-gene binary RS group (P = 0.511). CONCLUSIONS: The proposed IHC3 model could reliably predict low and high recurrence risks in most HR-positive EBC patients. This easy-to-use predictive model may be a reliable replacement for the 21-gene genomic assay in patients with EBC who have no access to or cannot afford the 21-gene genomic assay.

Neoadjuvant endocrine therapy: A potential strategy for ER-positive breast cancer.

A potential strategy for patients with estrogen receptor (ER)-positive breast cancer is necessary to replace neoadjuvant chemotherapy which has limited benefit. Neoadjuvant endocrine therapy (NAE) has been indicated to be a favorable alternate approach to downstage large or locally advanced breast cancer in ER-positive, human epidermal growth factor receptor 2 (HER2)-negative (ER+/HER2-) patients, especially postmenopausal women. Previous studies have demonstrated the efficacy of various endocrine agents in NAE. Aromatase inhibitors (AIs) have proven superiority over tamoxifen as a suitable choice to optimize treatment efficacy. Fulvestrant was recently reported as an effective agent, similar to AIs. Furthermore, the addition of targeted agents exerts synergistic antiproliferative effects with endocrine agents and rapidly improves response rates in both endocrine sensitive and resistant tumors. The neoadjuvant platform provides a unique opportunity to define the appropriate strategy and address the mechanisms of endocrine resistance. In addition, the predictive value of biomarkers and genomic assays in NAE is under investigation to evaluate individual effects and validate biomarker-based strategies. In this review, we discuss the most relevant evidence on the potential of NAE for ER+ breast cancer. The current understanding also offers new insights into the identification of the optimal settings and valuable predictive tools of NAE to guide clinical treatment decisions and achieve beneficial therapeutic effects.

Improving Therapeutic Ratios with the Oncotype DX® Ductal Carcinoma In Situ (DCIS) Score.

Ductal carcinoma in situ (DCIS) is a non-invasive breast cancer comprising nearly 25% of breast cancer diagnoses in the mammographic era. Current guidelines recommend breast-conserving surgery followed by adjuvant radiotherapy; however, controversy exists regarding the appropriateness of these recommendations. Some women with DCIS will never recur, which raises the concern of over-treatment. Conversely, a small number of women will develop invasive recurrences, raising concerns of under-treatment. Currently, several clinical and pathologic factors have been identified as prognostic markers for recurrence; however, these variables alone have been unable to identify low-risk and high-risk subgroups. The Oncotype DX® DCIS score is a multigene assay which allows for the addition of molecular information to traditional clinical and pathologic factors to help guide treatment decisions. Here, we present two case examples illustrating the use of the Oncotype DCIS score in clinical practice.

The Interplay of Genetics and Environmental Factors in the Development of Obesity.

Obesity is a major health issue in the developed nations, and it has been increasingly clear that both genetics and environment play an important role in determining if an individual will be obese or not. We reviewed the latest researches which were carried out to identify the obesity susceptible genes and to identify the metabolic pathways having a central role in energy balance. Obesity is a heritable disorder, and some of the many obesity susceptible genes are fat mass and obesity (FTO), leptin, and Melanocortin-4 receptor (MC4R). Glucose metabolism is the central pathway for fatty acid synthesis, de novo generating the major substrate acetyl-CoA. Further knowledge of these genes and their complex interaction with the environment will help devise individual, family and community-based preventive lifestyle interventions as well as nutritional and medical therapies.

Oncotype DX(®) colon cancer assay for prediction of recurrence risk in patients with stage II and III colon cancer: A review of the evidence.

Advances in molecular biology have enabled identification of tumor biomarkers that allow for individualized risk assessment for patients with cancer. Molecular predictors of clinical outcome can help inform discussion regarding the role of adjuvant chemotherapy in patients with resected colon cancer, such as those with stage II colon cancer in which the benefit of adjuvant therapy is controversial or those with stage III colon cancer who may have a lower risk of recurrence and less absolute benefit from oxaliplatin therapy. This article summarizes the data surrounding the development, validation, and clinical and economic utility of the Oncotype DX(®) colon cancer assay, a multigene expression assay validated to independently predict recurrence risk in patients with stage II and III colon cancer beyond traditional factors.