Mammaprint™: a comprehensive review

The biological and technical challenges facing utilizing circulating tumor DNA in non-metastatic breast cancer patients

Breast cancer is one of the most prevalent cancers and has emerged as a major global challenge. Circulating tumor DNA (ctDNA), a liquid biopsy method, overcomes the accessibility limitations of tissue-based testing and is widely used for monitoring minimal residual disease and molecular relapse, predicting prognosis, evaluating the response of neoadjuvant therapy, and optimizing treatment decisions in non-metastatic breast cancer. However, the application of ctDNA still faces many challenges. Here, we survey the clinical applications of ctDNA in non-metastatic breast cancer and discuss the significant biological and technical challenges of utilizing ctDNA. Importantly, we investigate potential avenues for addressing the challenges. In addition, emerging technologies, including fragmentomics detection, methylation sequencing, and long-read sequencing, have clinical potential and could be a future direction. Proper utilization of machine learning facilitates the identification of meaningful patterns from complex fragment and methylation profiles of ctDNA. There is still a lack of clinical trials focused on the subsets of ctDNA (e.g., circulating mitochondrial DNA), ctDNA-inferred drug-resistant clonal evolution, tumor heterogeneity, and ctDNA-guided clinical decision-making in non-metastatic breast cancer. Due to regional differences in the number of registered clinical trials, it is essential to enhance communication and foster global collaboration to advance the field.

Navigating precision: the crucial role of next-generation sequencing recurrence risk assessment in tailoring adjuvant therapy for hormone receptor-positive, human epidermal growth factor Receptor2-negative early breast cancer

Hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) breast cancer is the most common subtype, representing over two-thirds of new diagnoses. Adjuvant therapy, which encompasses various medications and treatment durations, is the standard approach for managing early stage HR+ HER2- breast cancer. Optimizing treatment is essential to minimize unnecessary side effects while addressing the biological variability inherent in HR+/HER2- breast cancers. Incorporating biological biomarkers into treatment decisions, alongside traditional clinical factors, is vital. Gene expression assays can identify patients unlikely to benefit from adjuvant chemotherapy, thereby refining treatment strategies and improving risk assessment. This paper reviews evidence for several genomic tests, including Oncotype DX, MammaPrint, Breast Cancer Index, RucurIndex, and EndoPredict, which assist in tailoring adjuvant therapy. Additionally, we explore the role of liquid biopsies in personalizing treatment, emphasizing the importance of considering late relapse risks and potential benefits of extended systemic therapy for HR+/HER2- breast cancer patients.

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.

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

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

ReProMSig: an integrative platform for development and application of reproducible multivariable models for cancer prognosis supporting guideline-based transparent reporting

Adequate reporting is essential for evaluating the performance and clinical utility of a prognostic prediction model. Previous studies indicated a prevalence of incomplete or suboptimal reporting in translational and clinical studies involving development of multivariable prediction models for prognosis, which limited the potential applications of these models. While reporting templates introduced by the established guidelines provide an invaluable framework for reporting prognostic studies uniformly, there is a widespread lack of qualified adherence, which may be due to miscellaneous challenges in manual reporting of extensive model details, especially in the era of precision medicine. Here, we present ReProMSig (Reproducible Prognosis Molecular Signature), a web-based integrative platform providing the analysis framework for development, validation and application of a multivariable prediction model for cancer prognosis, using clinicopathological features and/or molecular profiles. ReProMSig platform supports transparent reporting by presenting both methodology details and analysis results in a strictly structured reporting file, following the guideline checklist with minimal manual input needed. The generated reporting file can be published together with a developed prediction model, to allow thorough interrogation and external validation, as well as online application for prospective cases. We demonstrated the utilities of ReProMSig by development of prognostic molecular signatures for stage II and III colorectal cancer respectively, in comparison with a published signature reproduced by ReProMSig. Together, ReProMSig provides an integrated framework for development, evaluation and application of prognostic/predictive biomarkers for cancer in a more transparent and reproducible way, which would be a useful resource for health care professionals and biomedical researchers.

Incorporating clinicopathological and molecular risk prediction tools to improve outcomes in early HR+/HER2- breast cancer

Stratification of recurrence risk is a cornerstone of early breast cancer diagnosis that informs a patient's optimal treatment pathway. Several tools exist that combine clinicopathological and molecular information, including multigene assays, which can estimate risk of recurrence and quantify the potential benefit of different adjuvant treatment modalities. While the tools endorsed by treatment guidelines are supported by level I and II evidence and provide similar prognostic accuracy at the population level, they can yield discordant risk prediction at the individual patient level. This review examines the evidence for these tools in clinical practice and offers a perspective of potential future risk stratification strategies. Experience from clinical trials with cyclin D kinase 4/6 (CDK4/6) inhibitors in the setting of hormone receptor-positive (HR+)/human epidermal growth factor receptor 2-negative (HER2-) early breast cancer is provided as an illustrative example of risk stratification.

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

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

Clinical evaluation of molecular surrogate subtypes in patients with ipsilateral multifocal primary breast cancer

BACKGROUND

When ipsilateral multifocal primary breast cancer (IMBC) is detected, standard routine is to evaluate the largest tumor with immunohistochemistry (IHC). As all foci are not routinely characterized, many patients may not receive optimal adjuvant treatment. Here, we assess the clinical relevance of examining at least two foci present in patients with IMBC.

METHODS

Patients diagnosed and treated for IMBC at Sahlgrenska University Hospital (Gothenburg, Sweden) between 2012 and 2017 were screened. In total, 180 patients with ≥ 2 invasive foci (183 specimens) were assessed with IHC and included in this study. Expression of the estrogen (ER) and progesterone (PR) receptors, Ki67, HER2, and tumor grade were used to determine the molecular surrogate subtypes and discordance among the foci was recorded. An additional multidisciplinary team board was then held to re-assess whether treatment recommendations changed due to discordances in molecular surrogate subtype between the different foci.

RESULTS

Discordance in ER, PR, HER2, and Ki67 was found in 2.7%, 19.1%, 7.7%, and 16.9% of invasive foci, respectively. Discordance in the molecular surrogate subtypes was found in 48 of 180 (26.7%) patients, which resulted in therapy changes for 11 patients (6.1%). These patients received additional endocrine therapy (n = 2), chemotherapy (n = 3), and combined chemotherapy and trastuzumab (n = 6).

CONCLUSION

Taken together, when assessing at least two tumor foci with IHC, regardless of shared morphology or tumor grade between the different foci, 6.1% of patients with IMBC were recommended additional adjuvant treatment. A pathologic assessment using IHC of all foci is therefore recommended to assist in individualized treatment decision making.

Identification and validation of a novel senescence-related biomarker for thyroid cancer to predict the prognosis and immunotherapy

Introduction

Cellular senescence is a hallmark of tumors and has potential for cancer therapy. Cellular senescence of tumor cells plays a role in tumor progression, and patient prognosis is related to the tumor microenvironment (TME). This study aimed to explore the predictive value of senescence-related genes in thyroid cancer (THCA) and their relationship with the TME.

Methods

Senescence-related genes were identified from the Molecular Signatures Database and used to conduct consensus clustering across TCGA-THCA. Differentially expressed genes (DEGs) were identified between the clusters used to perform multivariate Cox regression and least absolute shrinkage and selection operator regression (LASSO) analyses to construct a senescence-related signature. TCGA dataset was randomly divided into training and test datasets to verify the prognostic ability of the signature. Subsequently, the immune cell infiltration pattern, immunotherapy response, and drug sensitivity of the two subtypes were analyzed. Finally, the expression of signature genes was detected across TCGA-THCA and GSE33630 datasets, and further validated by RT-qPCR.

Results

Three senescence clusters were identified based on the expression of 432 senescence-related genes. Then, 23 prognostic DEGs were identified in TCGA dataset. The signature, composed of six genes, showed a significant relationship with survival, immune cell infiltration, clinical characteristics, immune checkpoints, immunotherapy response, and drug sensitivity. Low-risk THCA shows a better prognosis and higher immunotherapy response than high-risk THCA. A nomogram with perfect stability constructed using signature and clinical characteristics can predict the survival of each patient. The validation part demonstrated that ADAMTSL4, DOCK6, FAM111B, and SEMA6B were expressed at higher levels in the tumor tissue, whereas lower expression of MRPS10 and PSMB7 was observed.

Discussion

In conclusion, the senescence-related signature is a promising biomarker for predicting the outcome of THCA and has the potential to guide immunotherapy.

Identifying Good Candidates for Active Surveillance of Ductal Carcinoma In Situ: Insights from a Large Neoadjuvant Endocrine Therapy Cohort

Ductal carcinoma in situ (DCIS) is a biologically heterogenous entity with uncertain risk for invasive ductal carcinoma (IDC) development. Standard treatment is surgical resection often followed by radiation. New approaches are needed to reduce overtreatment. This was an observational study that enrolled patients with DCIS who chose not to pursue surgical resection from 2002 to 2019 at a single academic medical center. All patients underwent breast MRI exams at 3- to 6-month intervals. Patients with hormone receptor-positive disease received endocrine therapy. Surgical resection was strongly recommended if clinical or radiographic evidence of disease progression developed. A recursive partitioning (R-PART) algorithm incorporating breast MRI features and endocrine responsiveness was used retrospectively to stratify risk of IDC. A total of 71 patients were enrolled, 2 with bilateral DCIS (73 lesions). A total of 34 (46.6%) were premenopausal, 68 (93.2%) were hormone-receptor positive, and 60 (82.1%) were intermediate- or high-grade lesions. Mean follow-up time was 8.5 years. Over half (52.1%) remained on active surveillance without evidence of IDC with mean duration of 7.4 years. Twenty patients developed IDC, of which 6 were HER2 positive. DCIS and subsequent IDC had highly concordant tumor biology. Risk of IDC was characterized by MRI features after 6 months of endocrine therapy exposure; low-, intermediate-, and high-risk groups were identified with respective IDC rates of 8.7%, 20.0%, and 68.2%. Thus, active surveillance consisting of neoadjuvant endocrine therapy and serial breast MRI may be an effective tool to risk-stratify patients with DCIS and optimally select medical or surgical management.

Significance

A retrospective analysis of 71 patients with DCIS who did not undergo upfront surgery demonstrated that breast MRI features after short-term exposure to endocrine therapy identify those at high (68.2%), intermediate (20.0%), and low risk (8.7%) of IDC. With 7.4 years mean follow-up, 52.1% of patients remain on active surveillance. A period of active surveillance offers the opportunity to risk-stratify DCIS lesions and guide decisions for operative management.

Network Pharmacology of Adaptogens in the Assessment of Their Pleiotropic Therapeutic Activity

The reductionist concept, based on the ligand–receptor interaction, is not a suitable model for adaptogens, and herbal preparations affect multiple physiological functions, revealing polyvalent pharmacological activities, and are traditionally used in many conditions. This review, for the first time, provides a rationale for the pleiotropic therapeutic efficacy of adaptogens based on evidence from recent gene expression studies in target cells and where the network pharmacology and systems biology approaches were applied. The specific molecular targets and adaptive stress response signaling mechanisms involved in nonspecific modes of action of adaptogens are identified.

Single cell cancer epigenetics

Bulk sequencing methodologies have allowed us to make great progress in cancer research. Unfortunately, these techniques lack the resolution to fully unravel the epigenetic mechanisms that govern tumor heterogeneity. Consequently, many novel single cell-sequencing methodologies have been developed over the past decade, allowing us to explore the epigenetic components that regulate different aspects of cancer heterogeneity, namely: clonal heterogeneity, tumor microenvironment (TME), spatial organization, intratumoral differentiation programs, metastasis, and resistance mechanisms. In this review, we explore the different sequencing techniques that enable researchers to study different aspects of epigenetics (DNA methylation, chromatin accessibility, histone modifications, DNA-protein interactions, and chromatin 3D architecture) at the single cell level, their potential applications in cancer, and their current technical limitations.

Definition of High-Risk Early Hormone-Positive HER2−Negative Breast Cancer: A Consensus Review

Breast cancer is one of the major causes of cancer-related morbidity and mortality in women worldwide. During the past three decades, several improvements in the adjuvant treatment of hormone receptor-positive/HER2−negative breast cancer have been achieved with the introduction of optimized adjuvant chemotherapy and endocrine treatment. However, estimating the risk of relapse of breast cancer on an individual basis is still challenging. The IRIDE (hIGh Risk DEfinition in breast cancer) working group was established with the aim of reviewing evidence from the literature to synthesize the current relevant features that predict hormone-positive/HER2−negative early breast cancer relapse. A panel of experts in breast cancer was involved in identifying clinical, pathological, morphological, and genetic factors. A RAND consensus method was used to define the relevance of each risk factor. Among the 21 features included, 12 were considered relevant risk factors for relapse. For each of these, we provided a consensus statement and relevant comments on the supporting scientific evidence. This work may guide clinicians in the practical management of hormone-positive/HER2−negative early breast cancers.

Hubness weighted svm ensemble for prediction of breast cancer subtypes

BACKGROUND

Breast cancer is a major disease causing panic among women worldwide. Since gene mutations are the root cause for cancer development, analyzing gene expressions can give more insights into various phenotype of cancer treatments. Breast Cancer subtype prediction from gene expression data can provide more information for cancer treatment decisions.

OBJECTIVE

Gene expressions are complex for analysis due to its high dimensional nature. Machine learning algorithms such as k-Nearest Neighbors, Support Vector Machine (SVM) and Random Forest are used with selection of features for prediction of breast cancer subtypes. Prediction accuracy of the existing methods are affected due to high dimensional nature of gene expressions. The objective of the work is to propose an efficient algorithm for the prediction of breast cancer subtypes from gene expression.

METHODS

For subtype prediction, a novel Hubness Weighted Support Vector machine algorithm (HWSVM) using bad hubness score as a weight measure to handle the outliers in the data has been proposed. Based on the various subtypes, features are projected into seven different feature sets and Ensemble based Hubness Aware Weighted Support Vector Machine (HWSVMEns) is implemented for breast cancer subtype prediction.

RESULTS

The proposed algorithms have been compared with the classical SVM and other traditional algorithms such as Random Forest, k-Nearest Neighbor algorithms and also with various gene selection methods.

CONCLUSIONS

Experimental results show that the proposed HWSVM outperforms other algorithms in terms of accuracy, precision, recall and F1 score due to the hubness weightage scheme and the ensemble approach. The experiments have shown an average accuracy of 92% across various gene expression datasets.

Disruptive innovations in the clinical laboratory: catching the wave of precision diagnostics

Disruptive innovation is an invention that disrupts an existing market and creates a new one by providing a different set of values, which ultimately overtakes the existing market. Typically, when disruptive innovations are introduced, their performance is initially less than existing standard technologies, but because of their ability to bring the cost down, and with gradual improvement, they end up replacing established service standards.Disruptive technologies have their fingerprints in health care. Pathology and laboratory medicine are fertile soils for disruptive innovations because they are heavily reliant on technology. Disruptive innovations have resulted in a revolution of our diagnostic ability and will take laboratory medicine to the next level of patient care. There are several examples of disruptive innovations in the clinical laboratory. Digitizing pathology practice is an example of disruptive technology, with many advantages and an extended scope of applications. Next-generation sequencing can be disruptive in two ways. The first is by replacing an array of laboratory tests, which each requires expensive and specialized instruments and expertise, with a single cost-effective technology. The second is by disrupting the current paradigm of the clinical laboratory as a diagnostic service by taking it into a new era of preventive or primary care pathology. Other disruptive innovations include the use of dry chemistry reagents in chemistry analyzers and also point of care testing. The use of artificial intelligence is another promising disruptive innovation that can transform the future of pathology and laboratory medicine. Another emerging disruptive concept is the integration of two fields of medicine to create an interrelated discipline such as "histogenomics and radiohistomics." Another recent disruptive innovation in laboratory medicine is the use of social media in clinical practice, education, and publication.There are multiple reasons to encourage disruptive innovations in the clinical laboratory, including the escalating cost of health care, the need for better accessibility of diagnostic care, and the increased demand on the laboratory in the era of precision diagnostics. There are, however, a number of challenges that need to be overcome such as the significant resistance to disruptive innovations by current technology providers and governmental regulatory bodies. The hesitance from health care providers and insurance companies must also be addressed.Adoption of disruptive innovations requires a multifaceted approach that involves orchestrated solutions to key aspects of the process, including creating successful business models, multidisciplinary collaborations, and innovative accreditation and regulatory oversight. It also must be coupled with successful commercialization plans and modernization of health care structure. Fostering a culture of disruptive innovation requires establishing unique collaborative models between academia and industry. It also requires uncovering new sources of unconventional funding that are open to high-risk high-reward projects. It should also be matched with innovative thinking, including new approaches for delivery of care and identifying novel cohorts of patients who can benefit from disruptive technology.

The Potential of OMICs Technologies for the Treatment of Immune-Mediated Inflammatory Diseases

Immune-mediated inflammatory diseases (IMIDs), such as inflammatory bowel diseases and inflammatory arthritis (e.g., rheumatoid arthritis, psoriatic arthritis), are marked by increasing worldwide incidence rates. Apart from irreversible damage of the affected tissue, the systemic nature of these diseases heightens the incidence of cardiovascular insults and colitis-associated neoplasia. Only 40–60% of patients respond to currently used standard-of-care immunotherapies. In addition to this limited long-term effectiveness, all current therapies have to be given on a lifelong basis as they are unable to specifically reprogram the inflammatory process and thus achieve a true cure of the disease. On the other hand, the development of various OMICs technologies is considered as “the great hope” for improving the treatment of IMIDs. This review sheds light on the progressive development and the numerous approaches from basic science that gradually lead to the transfer from “bench to bedside” and the implementation into general patient care procedures.

The cost impact of unselective vs selective MammaPrint testing in early-stage breast cancer in Southern Africa

Background

MammaPrint (MP) has been applied in South Africa (SA) for decision-making in early-stage hormone receptor-positive breast cancer since 2006. The cost-impact of MP in SA has not been assessed.

Aim

To assess different MP testing strategies for cost-minimization in early-stage breast carcinoma using a funder perspective.

Methods

Clinico-pathologic information was extracted from a prospectively collected database. Clinical risk stratification was done using Adjuvant Online! (AOL) and the Predict V2.1 algorithm (www.predict.nhs.uk). An unselected MP testing strategy was compared to a selective strategy, testing only clinically high risk (cHigh) patients. Excluding human epidermal growth factor receptor-2 positive tumours, the costs for chemotherapy treatment and MP using funding data were used to evaluate the financial impact of these strategies.

Results

In 583 patients with 601 tumours, 52% were clinically low risk (cLow) (AOL) while the average Predict 10-year survival with chemotherapy was 2.9%. MP correlated strongly with Predict and 318 (60%) patients were MP low risk. Unselective testing allowed omission of chemotherapy in 44 (8.4%) patients but escalated cost by 57.7%. Using a selective testing strategy, only 251 would be tested, de-escalating treatment in 138 (55%) and reducing cost by 19.5%. Considering a Predict value up to 3.2% as cHigh, cost would be up to 7.3% (p = 0.0467) lower with a selective testing strategy.

Conclusion

MP allowed reduction in the use of adjuvant chemotherapy. Unselective use of MP increases overall costs. A selective testing strategy through clinical risk stratification using AOL/Predict results in substantial cost saving.

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Current clinical application of MammaPrint in early breast cancer in South Africa results in the testing of a large percentage of clinically low risk patients.

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When expensive assays, is used in this (unselected) manner, it results in reduced use of chemotherapy, but the total direct cost is increased.

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A selective testing strategy could result in substantial cost savings.

The role of chemotherapy in patients with T1bN0M0 triple-negative breast cancer: a real-world competing risk analysis

The objective of the present study was to implement Kaplan-Meier analysis, competing risk analysis, and propensity score matching to evaluate whether the patients with T1bN0M0 triple-negative breast (TNBC) could benefit from adjuvant chemotherapy. A total of 1849 patients were identified in the Surveillance, Epidemiology, and End Results (SEER) database from 2010 to 2015. All eligible patients were divided into two cohorts, the chemotherapy (1155 patients) and the no-chemotherapy (694 patients) cohorts. Similar 5-year breast cancer-specific survival (BCSS) was observed in the chemotherapy and no-chemotherapy cohorts (96.1% vs. 96.0%, p=0.820). The results of the competing risk analysis showed a comparable 5-year breast cancer-specific death (BCSD) in both groups (chemotherapy 3.6% vs. no-chemotherapy 3.4%, p=0.778). Also, a higher 5-year other causes death (OCD) was observed in the no-chemotherapy cohort (0.7% vs. 5.4%, p<0.001). Multivariable competing risks regression models showed no association between chemotherapy and BCSS (HR, 1.21; 95%CI, 0.64-2.31; p=0.560). After 1:1 PSM, no significant difference was also observed for BCSD and OCD between two cohorts. The value of adjuvant chemotherapy in patients with T1bN0M0 TNBC is less than the present guidelines recommend, suggesting that de-escalated treatment could be a potentially beneficial strategy in appropriately selected patients.

A clinical validation study of MammaPrint in hormone receptor-positive breast cancer from the Austrian Breast and Colorectal Cancer Study Group 8 (ABCSG-8) biomarker cohort

Background

MammaPrint is a prognostic assay based on gene expression in tumors from patients with early breast cancer. MammaPrint has been extensively validated and Food and Drug Administration cleared in fresh and formalin-fixed and paraffin-embedded (FFPE) tissue. We aimed to assess its prognostic performance in the biomarker cohort of the Austrian Breast and Colorectal Cancer Study Group 8 (ABCSG-8) patient population, and to obtain a higher level of evidence with regard to its clinical validity after RNA extraction from FFPE biobank tissue.

Patients and methods

A prespecified retrospective analysis to test the prognostic performance of the MammaPrint test to predict distant recurrence-free survival at 5 and 10 years as primary end point was carried out. MammaPrint risk, clinicopathological factors (after central pathological review), and clinical risk (using a modified version of Adjuvant! Online) were evaluated by Cox regression analyses.

Results

From 1347 available samples, 607 (45%) failed quality control after RNA extraction. In total, 658 (49%) patients were included in survival analyses: MammaPrint low risk versus high risk is a significant prognostic factor for distant recurrence-free survival at 5 years (94.0% versus 91.6%) with a significant risk reduction of 6.5% at 10 years (log-rank P value = 0.017, low risk 91.3% versus high risk 84.8%). The multivariable models suggest that hazard ratio (HR) is primarily driven by tumor stage (5-year HR 3.89; confidence interval 1.97-7.71) and nodal status (5-year HR 1.73; confidence interval 0.91-3.21). After adjustment for clinical risk groups, MammaPrint HRs remain stable with values just below 2.0 after the first 3 years.

Conclusions

The MammaPrint test showed significant prognostic performance at 5 and 10 years of follow-up. In the particular cohort of ABCSG-8, the statistical independence from clinically assessed covariates remains unclear, and no conclusions concerning the clinical validity of the test can be drawn.

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MammaPrint is a prognostic assay based on gene expression in tumors from patients with early breast cancer.

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MammaPrint has been extensively and successfully validated- but mostly in fresh tissue.

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We aimed to assess its prognostic performance in FFPE tissue from the ABCSG 8 biomarker cohort.

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The MammaPrint test showed significant prognostic performance at 5 and 10 years of follow-up.

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The statistical independence from clinically assessed covariates is unclear- the clinical validity in ABCSG 8 uncertain.

Breast cancer, screening and diagnostic tools: All you need to know

Breast cancer is one of the most frequent malignancies among women worldwide. Methods for screening and diagnosis allow health care professionals to provide personalized treatments that improve the outcome and survival. Scientists and physicians are working side-by-side to develop evidence-based guidelines and equipment to detect cancer earlier. However, the lack of comprehensive interdisciplinary information and understanding between biomedical, medical, and technology professionals makes innovation of new screening and diagnosis tools difficult. This critical review gathers, for the first time, information concerning normal breast and cancer biology, established and emerging methods for screening and diagnosis, staging and grading, molecular and genetic biomarkers. Our purpose is to address key interdisciplinary information about these methods for physicians and scientists. Only the multidisciplinary interaction and communication between scientists, health care professionals, technical experts and patients will lead to the development of better detection tools and methods for an improved screening and early diagnosis.

Impact of Personalized Genetic Breast Cancer Risk Estimation With Polygenic Risk Scores on Preventive Endocrine Therapy Intention and Uptake

Endocrine therapy is underutilized to reduce breast cancer incidence among women at increased risk. Polygenic risk scores (PRSs) assessing 77 breast cancer genetic susceptibility loci personalizes risk estimates. We examined effect of personalized PRS breast cancer risk prediction on intention to take and endocrine therapy uptake among women at increased risk. Eligible participants had a 10-year breast cancer risk ≥5% by Tyrer-Cuzick model [International Breast Cancer Intervention Study (IBIS)] or ≥3.0 % 5-year Gail Model risk with no breast cancer history or hereditary breast cancer syndrome. Breast cancer risk was estimated, endocrine therapy options were discussed, and endocrine therapy intent was assessed at baseline. After genotyping, PRS-updated breast cancer risk estimates, endocrine therapy options, and intent to take endocrine therapy were reassessed; endocrine therapy uptake was assessed during follow-up. From March 2016 to October 2017, 151 patients were enrolled [median (range) age, 56.1 (36.0-76.4 years)]. Median 10-year and lifetime IBIS risks were 7.9% and 25.3%. Inclusion of PRS increased lifetime IBIS breast cancer risk estimates for 81 patients (53.6%) and reduced risk for 70 (46.4%). Of participants with increased breast cancer risk by PRS, 39 (41.9%) had greater intent to take endocrine therapy; of those with decreased breast cancer risk by PRS, 28 (46.7%) had less intent to take endocrine therapy (P < 0.001). On multivariable regression, increased breast cancer risk by PRS was associated with greater intent to take endocrine therapy (P < 0.001). Endocrine therapy uptake was greater among participants with increased breast cancer risk by PRS (53.4%) than with decreased risk (20.9%; P < 0.001). PRS testing influenced intent to take and endocrine therapy uptake. Assessing PRS effect on endocrine therapy adherence is needed.Prevention Relevance: Counseling women at increased breast cancer risk using polygenic risk score (PRS) risk estimates can significantly impact preventive endocrine therapy uptake. Further development of PRS testing to personalize breast cancer risk assessments and endocrine therapy counselling may serve to potentially reduce the incidence of breast cancer in the future.

Conserving the axilla in breast cancer

It is recognised that surgical conservatism is the most effective way of managing the axilla in breast cancer patients undergoing primary breast conserving surgery. The extended clinical scenarios in which a less aggressive approach can be safely adopted warrant consideration—including a group of patients who potentially could bypass surgical staging of the axilla altogether. The application of omission of further surgical management and axillary radiotherapy in the primary surgical and neoadjuvant chemotherapy settings are considered.

Identification of profiles of volatile organic compounds in exhaled breath by means of an electronic nose as a proposal for a screening method for breast cancer: a case-control study

The objective of the present study was to identify volatile prints from exhaled breath, termed breath-print, from breast cancer (BC) patients and healthy women by means of an electronic nose and to evaluate its potential use as a screening method. A cross-sectional study was performed on 443 exhaled breath samples from women, of whom 262 had been diagnosed with BC by biopsy and 181 were healthy women (control group). Breath-print analysis was performed utilizing the Cyranose 320 electronic nose. Group data were evaluated by principal component analysis (PCA), canonical discriminant analysis (CDA), and support vector machine (SVM), and the test's diagnostic power was evaluated by means of receiver operating characteristic (ROC) curves. The results obtained using the model generated from the CDA, which best describes the behavior of the assessed groups, indicated that the breath-print of BC patients was different from that of healthy women and that they presented with a variability of up to 98.8% and a correct classification of 98%. The sensitivity, specificity, negative predictive value, and positive predictive value reached 100% according to the ROC curve. The present study demonstrates the capability of the electronic nose to separate between healthy subjects and BC patients. This research could have a beneficial impact on clinical practice as we consider that this test could probably be used at the first point before the application of established gold tests (mammography, ultrasound, and biopsy) and substantially improve screening tests in the general population.

v-myb avian myeloblastosis viral oncogene homolog expression is a potential molecular diagnostic marker for B-cell acute lymphoblastic leukemia

BACKGROUND

B-cell acute lymphoblastic leukemia (B-ALL) is the most commonly diagnosed childhood malignancy worldwide and is especially common in Mexico. Additionally, the number of cases has increased in recent years. Thus, it is very important to develop molecular strategies to diagnose leukemia. The aim of this study was to investigate MYB expression and to determine its impact on the diagnosis of B-ALL.

METHODS

We analyzed the B-ALL gene expression profile by microarray data mining. Bioinformatics analysis was performed to identify the genes that are overexpressed in leukemia. We determined that MYB was highly expressed in leukemia. Then, we validated MYB expression in 70 patients with B-ALL and in 16 healthy controls (HCs) using qRT-PCR. The results were statistically analyzed using the Kolmogorov-Smirnov Z test, Mann-Whitney U test, receiver operating characteristic curves, and the Youden index.

RESULTS

The microarrays showed that MYB was overexpressed in B-ALL patients with a fold change of 57.8728 and a P value of 2.56^-195 . MYB expression showed great variability among the patients analyzed. However, compared to the HCs, the B-ALL patients had a P value < .0001, an area under the curve of 0.813, and a Youden index of 1.46, indicating the statistical significance.

CONCLUSION

MYB expression in B-ALL cells could be a potential molecular marker for childhood leukemia.

Unraveling the Genomic-Epigenomic Interaction Landscape in Triple Negative and Non-Triple Negative Breast Cancer

Background: The recent surge of next generation sequencing of breast cancer genomes has enabled development of comprehensive catalogues of somatic mutations and expanded the molecular classification of subtypes of breast cancer. However, somatic mutations and gene expression data have not been leveraged and integrated with epigenomic data to unravel the genomic-epigenomic interaction landscape of triple negative breast cancer (TNBC) and non-triple negative breast cancer (non-TNBC). Methods: We performed integrative data analysis combining somatic mutation, epigenomic and gene expression data from The Cancer Genome Atlas (TCGA) to unravel the possible oncogenic interactions between genomic and epigenomic variation in TNBC and non-TNBC. We hypothesized that within breast cancers, there are differences in somatic mutation, DNA methylation and gene expression signatures between TNBC and non-TNBC. We further hypothesized that genomic and epigenomic alterations affect gene regulatory networks and signaling pathways driving the two types of breast cancer. Results: The investigation revealed somatic mutated, epigenomic and gene expression signatures unique to TNBC and non-TNBC and signatures distinguishing the two types of breast cancer. In addition, the investigation revealed molecular networks and signaling pathways enriched for somatic mutations and epigenomic changes unique to each type of breast cancer. The most significant pathways for TNBC were: retinal biosynthesis, BAG2, LXR/RXR, EIF2 and P2Y purigenic receptor signaling pathways. The most significant pathways for non-TNBC were: UVB-induced MAPK, PCP, Apelin endothelial, Endoplasmatic reticulum stress and mechanisms of viral exit from host signaling Pathways. Conclusion: The investigation revealed integrated genomic, epigenomic and gene expression signatures and signing pathways unique to TNBC and non-TNBC, and a gene signature distinguishing the two types of breast cancer. The study demonstrates that integrative analysis of multi-omics data is a powerful approach for unravelling the genomic-epigenomic interaction landscape in TNBC and non-TNBC.

Sequential analysis of transcript expression patterns improves survival prediction in multiple cancers

BACKGROUND

Long-term survival in numerous cancers often correlates with specific whole transcriptome profiles or the expression patterns of smaller numbers of transcripts. In some instances, these are better predictors of survival than are standard classification methods such as clinical stage or hormone receptor status in breast cancer. Here, we have used the method of "t-distributed stochastic neighbor embedding" (t-SNE) to show that, collectively, the expression patterns of small numbers of functionally-related transcripts from fifteen cancer pathways correlate with long-term survival in the vast majority of tumor types from The Cancer Genome Atlas (TCGA). We then ask whether the sequential application of t-SNE using the transcripts from a second pathway improves predictive capability or whether t-SNE can be used to refine the initial predictive power of whole transcriptome profiling.

METHODS

RNAseq data from 10,227 tumors in TCGA were previously analyzed using t-SNE-based clustering of 362 transcripts comprising 15 distinct cancer-related pathways. After showing that certain clusters were associated with differential survival, each relevant cluster was re-analyzed by t-SNE with a second pathway's transcripts. Alternatively, groups with differential survival identified by whole transcriptome profiling were subject to a second, t-SNE-based analysis.

RESULTS

Sequential analyses employing either t-SNE➔t-SNE or whole transcriptome profiling➔t-SNE analyses were in many cases superior to either individual method at predicting long-term survival. We developed a dynamic and intuitive R Shiny web application to explore the t-SNE based transcriptome clustering and survival analysis across all TCGA cancers and all 15 cancer-related pathways in this analysis. This application provides a simple interface to select specific t-SNE clusters and analyze survival predictability using both individual or sequential approaches. The user can recreate the relationships described in this analysis and further explore many different cancer, pathway, and cluster combinations. Non-R users can access the application on the web at https://chpupsom19.shinyapps.io/Survival_Analysis_tsne_umap_TCGA. The application, R scripts performing survival analysis, and t-SNE clustering results of TCGA expression data can be accessed on GitHub enabling users to download and run the application locally with ease (https://github.com/RavulaPitt/Sequential-t-SNE/).

CONCLUSIONS

The long-term survival of patients correlated with expression patterns of 362 transcripts from 15 cancer-related pathways. In numerous cases, however, survival could be further improved when the cohorts were re-analyzed using iterative t-SNE clustering or when t-SNE clustering was applied to cohorts initially segregated by whole transcriptome-based hierarchical clustering.

Opportunities for improving cancer treatment using systems biology

Current cancer therapies target a limited set of tumor features, rather than considering the tumor as a whole. Systems biology aims to reveal therapeutic targets associated with a variety of facets in an individual's tumor, such as genetic heterogeneity and its evolution, cancer cell-autonomous phenotypes, and microenvironmental signaling. These disparate characteristics can be reconciled using mathematical modeling that incorporates concepts from ecology and evolution. This provides an opportunity to predict tumor growth and response to therapy, to tailor patient-specific approaches in real time or even prospectively. Importantly, as data regarding patient tumors is often available from only limited time points during treatment, systems-based approaches can address this limitation by interpolating longitudinal events within a principled framework. This review outlines areas in medicine that could benefit from systems biology approaches to deconvolve the complexity of cancer.

Cytogenetics and Cytogenomics Evaluation in Cancer

The availability of cytogenetics and cytogenomics technologies improved the detection and identification of tumor molecular signatures as well as the understanding of cancer initiation and progression. The use of large-scale and high-throughput cytogenomics technologies has led to a fast identification of several cancer candidate biomarkers associated with diagnosis, prognosis, and therapeutics. The advent of array comparative genomic hybridization and next-generation sequencing technologies has significantly improved the knowledge about cancer biology, underlining driver genes to guide targeted therapy development, drug-resistance prediction, and pharmacogenetics. However, few of these candidate biomarkers have made the transition to the clinic with a clear benefit for the patients. Technological progress helped to demonstrate that cellular heterogeneity plays a significant role in tumor progression and resistance/sensitivity to cancer therapies, representing the major challenge of precision cancer therapy. A paradigm shift has been introduced in cancer genomics with the recent advent of single-cell sequencing, since it presents a lot of applications with a clear benefit to oncological patients, namely, detection of intra-tumoral heterogeneity, mapping clonal evolution, monitoring the development of therapy resistance, and detection of rare tumor cell populations. It seems now evident that no single biomarker could provide the whole information necessary to early detect and predict the behavior and prognosis of tumors. The promise of precision medicine is based on the molecular profiling of tumors being vital the continuous progress of high-throughput technologies and the multidisciplinary efforts to catalogue chromosomal rearrangements and genomic alterations of human cancers and to do a good interpretation of the relation genotype-phenotype.

Application of Transcriptomics to Enhance Early Diagnostics of Mycobacterial Infections, with an Emphasis on Mycobacterium avium ssp. paratuberculosis

Mycobacteria cause a wide variety of disease in human and animals. Species that infect ruminants include M. bovis and M. avium ssp. paratuberculosis (MAP). MAP is the causative agent of Johne’s disease in ruminants, which is a chronic granulomatous enteric infection that leads to severe economic losses worldwide. Characteristic of MAP infection is the long, latent phase in which intermittent shedding can take place, while diagnostic tests are unable to reliably detect an infection in this stage. This leads to unnoticed dissemination within herds and the presence of many undetected, silent carriers, which makes the eradication of Johne’s disease difficult. To improve the control of MAP infection, research is aimed at improving early diagnosis. Transcriptomic approaches can be applied to characterize host-pathogen interactions during infection, and to develop novel biomarkers using transcriptional profiles. Studies have focused on the identification of specific RNAs that are expressed in different infection stages, which will assist in the development and clinical implementation of early diagnostic tests.

The Effect of the New Eighth Edition Breast Cancer Staging System on 100 Consecutive Patients

Background

In October 2016 the American Joint Committee on Cancer published the early eighth edition breast cancer prognostic staging system, incorporating biomarkers into previously accepted staging. The updated and current eighth edition became effective nationwide in January 2018 after a large update to its staging guidelines. This study's aim was to compare patients' anatomic seventh edition (anatomic), early eighth (pre-update, prognostic), and current eighth (post-update, prognostic) pathological stages and to assess the utility of recent inclusions to staging criteria. Additionally, we observed how the aforementioned stage changes aligned with breast cancer histologic subtypes.

Methods

An Institutional Review Board (IRB)-approved retrospective chart review was performed. Inclusion criteria included female patients between the ages of 35 to 95 years with a diagnosis of invasive ductal or lobular carcinoma of the breast (n = 100) at three Hackensack Meridian Health hospitals. The study evaluated any trends in patients' stage changes between the seventh edition, early eighth edition, and current eighth edition breast cancer staging guidelines. Breast cancer restaging was performed using a novel staging tool on Microsoft Excel.

Results

Only 26% of patients' stages changed when comparing the seventh edition stage vs. current eighth edition prognostic staging, most of which were downstaged. When comparing the seventh with early eighth edition prognostic staging, 38% of the patients' stages changed, with a majority of them being upstaged. Lastly, 95% of total stage changes were downstages between the early eighth and current eighth edition staging guidelines.

Conclusions

When comparing the seventh edition vs. current eighth edition staging, few patients (especially those with early stage cancer) underwent a stage change. However, there were significant changes in stage when comparing early eighth vs. current eighth stages. Considering these changes were mostly downstages and many patients reverted to their original seventh edition stage, the current eighth edition is based on a personalized, less radical staging approach, one that is more synonymous with original seventh edition staging.

Challenges in the Integration of Omics and Non-Omics Data

Omics data integration is already a reality. However, few omics-based algorithms show enough predictive ability to be implemented into clinics or public health domains. Clinical/epidemiological data tend to explain most of the variation of health-related traits, and its joint modeling with omics data is crucial to increase the algorithm’s predictive ability. Only a small number of published studies performed a “real” integration of omics and non-omics (OnO) data, mainly to predict cancer outcomes. Challenges in OnO data integration regard the nature and heterogeneity of non-omics data, the possibility of integrating large-scale non-omics data with high-throughput omics data, the relationship between OnO data (i.e., ascertainment bias), the presence of interactions, the fairness of the models, and the presence of subphenotypes. These challenges demand the development and application of new analysis strategies to integrate OnO data. In this contribution we discuss different attempts of OnO data integration in clinical and epidemiological studies. Most of the reviewed papers considered only one type of omics data set, mainly RNA expression data. All selected papers incorporated non-omics data in a low-dimensionality fashion. The integrative strategies used in the identified papers adopted three modeling methods: Independent, conditional, and joint modeling. This review presents, discusses, and proposes integrative analytical strategies towards OnO data integration.