Oncotype DX Breast Recurrence Score®: A Review of its Use in Early-Stage Breast Cancer

Molecular Changes in Breast Cancer Induced by Radiation Therapy

PURPOSE

Breast cancer treatments are based on prognostic clinicopathologic features that form the basis for therapeutic guidelines. Although the utilization of these guidelines has decreased breast cancer-associated mortality rates over the past three decades, they are not adequate for individualized therapy. Radiation therapy (RT) is the backbone of breast cancer treatment. Although a highly successful therapeutic modality clinically, from a biological perspective, preclinical studies have shown RT to have the potential to alter tumor cell phenotype, immunogenicity, and the surrounding microenvironment, potentially changing the behavior of cancer cells and resulting in a significant variation in RT response. This review presents the recent advances in revealing the complex molecular changes induced by RT in the treatment of breast cancer and highlights the complexities of translating this information into clinically relevant tools for improved prognostic insights and the revelation of novel approaches for optimizing RT.

METHODS AND MATERIALS

Current literature was reviewed with a focus on recent advances made in the elucidation of tumor-associated radiation-induced molecular changes across molecular, genetic, and proteomic bases. This review was structured with the aim of providing an up-to-date overview over the very broad and complex subject matter of radiation-induced molecular changes and radioresistance, familiarizing the reader with the broader issue at hand.

RESULTS

The subject of radiation-induced molecular changes in breast cancer has been broached from various physiological focal points including that of the immune system, immunogenicity and the abscopal effect, tumor hypoxia, breast cancer classification and subtyping, molecular heterogeneity, and molecular plasticity. It is becoming increasingly apparent that breast cancer clinical subtyping alone does not adequately account for variation in RT response or radioresistance. Multiple components of the tumor microenvironment and immune system, delivered RT dose and fractionation schedules, radiation-induced bystander effects, and intrinsic tumor physiology and heterogeneity all contribute to the resultant RT outcome.

CONCLUSIONS

Despite recent advances and improvements in anticancer therapies, tumor resistance remains a significant challenge. As new analytical techniques and technologies continue to provide crucial insight into the complex molecular mechanisms of breast cancer and its treatment responses, it is becoming more evident that personalized anticancer treatment regimens may be vital in overcoming radioresistance.

How much do we know about the metastatic process?

Cancer cells can leave their primary sites and travel through the circulation to distant sites, where they lodge as disseminated cancer cells (DCCs), even during the early and asymptomatic stages of tumor progression. In experimental models and clinical samples, DCCs can be detected in a non-proliferative state, defined as cellular dormancy. This state can persist for extended periods until DCCs reawaken, usually in response to niche-derived reactivation signals. Therefore, their clinical detection in sites like lymph nodes and bone marrow is linked to poor survival. Current cancer therapy designs are based on the biology of the primary tumor and do not target the biology of the dormant DCC population and thus fail to eradicate the initial or subsequent waves of metastasis. In this brief review, we discuss the current methods for detecting DCCs and highlight new strategies that aim to target DCCs that constitute minimal residual disease to reduce or prevent metastasis formation. Furthermore, we present current evidence on the relevance of DCCs derived from early stages of tumor progression in metastatic disease and describe the animal models available for their study. We also discuss our current understanding of the dissemination mechanisms utilized by genetically less- and more-advanced cancer cells, which include the functional analysis of intermediate or hybrid states of epithelial-mesenchymal transition (EMT). Finally, we raise some intriguing questions regarding the clinical impact of studying the crosstalk between evolutionary waves of DCCs and the initiation of metastatic disease.

Anatomy Versus Biology: What Guides Chemotherapy Decisions in Older Patients With Breast Cancer?

INTRODUCTION

With the increasing utilization of genomic assays, such as the Oncotype DX recurrence score (RS), the relevance of anatomic staging has been questioned for select older patients with breast cancer. We sought to evaluate differences in chemotherapy receipt and/or survival among older patients based on RS and sentinel lymph node biopsy (SLNB) receipt/result.

METHODS

Patients aged ≥ 65 diagnosed with pT1-2/cN0/M0 hormone-receptor-positive (HR+)/HER2-breast cancer (2010-2019) were selected from the National Cancer Database. Logistic regression was used to identify factors associated with chemotherapy receipt. Cox proportional hazards models were used to estimate the association of RS/SLNB group with overall survival. A cost-benefit study was also performed.

RESULTS

Of the 75,428 patients included, the majority had an intermediate RS (58.2% versus 27.9% low, 13.8% high) and were SLNB- (85.1% versus 11.6% SLNB+, 3.3% none). Chemotherapy was recommended for 13,442 patients (17.8%). After adjustment, chemotherapy receipt was more likely with higher RS and SLNB+. After adjustment, SLNB receipt/result was only associated with overall survival among those with an intermediate RS. However, returning to the OR for SLNB is not cost-effective.

CONCLUSIONS

SLNB receipt/result was associated with survival for those with an intermediate RS, but not a low or high RS, suggesting that an SLNB may indeed be unnecessary for select older patients with breast cancer.

Prediction of Disease-Free Survival in Breast Cancer using Deep Learning with Ultrasound and Mammography: A Multicenter Study

BACKGROUND

Breast cancer is a leading cause of cancer morbility and mortality in women. The possibility of overtreatment or inappropriate treatment exists, and methods for evaluating prognosis need to be improved.

MATERIALS AND METHODS

Patients (from January 2013 to December 2018) were recruited and divided into a training group and a testing group. All patients were followed for more than 3 years. Patients were divided into a disease-free group and a recurrence group based on follow up results at 3 years. Ultrasound (US) and mammography (MG) images were collected to establish deep learning models (DLMs) using ResNet50. Clinical data, MG, and US characteristics were collected to select independent prognostic factors using a cox proportional hazards model to establish a clinical model. DLM and independent prognostic factors were combined to establish a combined model.

RESULTS

In total, 1242 patients were included. Independent prognostic factors included age, neoadjuvant chemotherapy, HER2, orientation, blood flow, dubious calcification, and size. We established 5 models: the US DLM, MG DLM, US + MG DLM, clinical and combined model. The combined model using US images, MG images, and pathological, clinical, and radiographic characteristics had the highest predictive performance (AUC = 0.882 in the training group, AUC = 0.739 in the testing group).

CONCLUSION

DLMs based on the combination of US, MG, and clinical data have potential as predictive tools for breast cancer prognosis.

Software BreastAnalyser for the semi-automatic analysis of breast cancer immunohistochemical images

Breast cancer is the most diagnosed cancer worldwide and represents the fifth cause of cancer mortality globally. It is a highly heterogeneous disease, that comprises various molecular subtypes, often diagnosed by immunohistochemistry. This technique is widely employed in basic, translational and pathological anatomy research, where it can support the oncological diagnosis, therapeutic decisions and biomarker discovery. Nevertheless, its evaluation is often qualitative, raising the need for accurate quantitation methodologies. We present the software BreastAnalyser, a valuable and reliable tool to automatically measure the area of 3,3'-diaminobenzidine tetrahydrocholoride (DAB)-brown-stained proteins detected by immunohistochemistry. BreastAnalyser also automatically counts cell nuclei and classifies them according to their DAB-brown-staining level. This is performed using sophisticated segmentation algorithms that consider intrinsic image variability and save image normalization time. BreastAnalyser has a clean, friendly and intuitive interface that allows to supervise the quantitations performed by the user, to annotate images and to unify the experts' criteria. BreastAnalyser was validated in representative human breast cancer immunohistochemistry images detecting various antigens. According to the automatic processing, the DAB-brown area was almost perfectly recognized, being the average difference between true and computer DAB-brown percentage lower than 0.7 points for all sets. The detection of nuclei allowed proper cell density relativization of the brown signal for comparison purposes between the different patients. BreastAnalyser obtained a score of 85.5 using the system usability scale questionnaire, which means that the tool is perceived as excellent by the experts. In the biomedical context, the connexin43 (Cx43) protein was found to be significantly downregulated in human core needle invasive breast cancer samples when compared to normal breast, with a trend to decrease as the subtype malignancy increased. Higher Cx43 protein levels were significantly associated to lower cancer recurrence risk in Oncotype DX-tested luminal B HER2- breast cancer tissues. BreastAnalyser and the annotated images are publically available https://citius.usc.es/transferencia/software/breastanalyser for research purposes.

Nomogram Update to Predict the High Genomic Risk Breast Cancer by Different Races

PURPOSE

To develop a nomogram to predict the high-risk recurrence score (RS) and to customize the nomogram for different races in early-stage hormone receptor (HoR)-positive, human epidermal growth factor receptor-2 (HER2)-negative breast cancer.

METHODS

Patients diagnosed between 2010 and 2015 were included from the surveillance, epidemiology, and end results oncotype DX database. The nomogram was assessed with a receiver operating characteristic curve to measure the area under the curve (AUC) with a 95% confidence interval (95% CI). The nomogram was developed and internally validated for discrimination and calibration, and then validated in different races.

RESULTS

A total of 48,464 patients were included and randomly assigned to the training cohort (n = 36370, 75.0%) and validation cohort (n = 12,094, 25.0%). Patients in the training cohort were identified to develop the nomogram, including 32,683 (89.9%) White women, 3135 (8.6%) Black women, and 552 (1.5%) Chinese women. Five independent predictive factors for high-risk RS were included to develop the nomogram, including tumor grade, progesterone receptor status, histological subtype, race, and tumor stage. The AUC was 0.696 (95% CI, 0.682-0.710) in the training cohort and 0.700 (95% CI, 0.676-0.724) in the validation cohort. There was no significant difference between the training cohort and the validation cohort. When validating the nomogram classified by race, the AUC was 0.694 (95% CI, 0.682-0.706) for the White cohort, 0.708 (95% CI, 0.673-0.743) for the Black cohort, and 0.653 (95% CI, 0.565-0.741) for the Chinese cohort.

CONCLUSION

The developed nomogram for predicting high-risk RS is available for different races in patients with HoR+/HER2- breast cancer, which could be used as qualified surrogates before ordering the 21-gene RS testing.

Unveiling the HER2-low phenomenon: exploring immunohistochemistry and gene expression to characterise HR-positive HER2-negative early breast cancer

PURPOSE

HER2-low breast cancer (BC) is a novel entity with relevant therapeutic implications, especially in hormone receptor (HR) positive BC. This study examines whether HER2 mRNA through the 21-gene assay, Oncotype DX (ODX), can refine the diagnosis of HER2-low and HER2-zero, obtained by immunohistochemistry (IHC).

METHODS

Between Jan 2021 and Jan 2023, 229 consecutive HR-positive HER2-negative early BC (T1-3 N0-1) have been characterised by IHC and ODX. HER2 status by IHC was either zero (IHC-0) or low (IHC-1 + and IHC-2 + /ISH-negative) while HER2-zero was further divided into HER2-null (IHC-0) and HER2-ultralow (IHC-1-10%). HER2 gene expression by ODX was negative if lower 10.7.

RESULTS

The distribution of HER2 IHC was as follows: 53.3% HER2-0, 29.25% HER2-1 + , and 17.5% HER2-2 + . The clinicopathological characteristics were similar in the three groups, with higher PgR-negative rate in HER2-zero (13.9% vs 3% vs 5%). The distribution of RS was homogeneous in the three groups with the median HER2 gene expression of 9.20 [IQR: 8.70-9.60]. HER2 gene expression gradually increased as the IHC score, with substantial overlap. After adjusting for confounders, HER2-1 + and HER2 2 + had a significant positive correlation between HER2 gene expression and IHC [OR 1.42, 95% CI 1.21 to 1.68, p < 0.001; OR 1.96, 95% CI 1.61 to 2.37, p < 0.001] compared to the HER2-zero group. HER2 gene expression did not differ between HER2-null and HER2-ultralow subgroups.

CONCLUSION

Due to the substantial overlap, the HER2 gene expression is unable to properly distinguish HER2-low and HER2-zero IHC whose accurate identification is critical in the context of HER2-negative BC.

Digital profiling of cancer transcriptomes from histology images with grouped vision attention

Cancer is a heterogeneous disease that demands precise molecular profiling for better understanding and management. Recently, deep learning has demonstrated potentials for cost-efficient prediction of molecular alterations from histology images. While transformer-based deep learning architectures have enabled significant progress in non-medical domains, their application to histology images remains limited due to small dataset sizes coupled with the explosion of trainable parameters. Here, we develop SEQUOIA, a transformer model to predict cancer transcriptomes from whole-slide histology images. To enable the full potential of transformers, we first pre-train the model using data from 1,802 normal tissues. Then, we fine-tune and evaluate the model in 4,331 tumor samples across nine cancer types. The prediction performance is assessed at individual gene levels and pathway levels through Pearson correlation analysis and root mean square error. The generalization capacity is validated across two independent cohorts comprising 1,305 tumors. In predicting the expression levels of 25,749 genes, the highest performance is observed in cancers from breast, kidney and lung, where SEQUOIA accurately predicts the expression of 11,069, 10,086 and 8,759 genes, respectively. The accurately predicted genes are associated with the regulation of inflammatory response, cell cycles and metabolisms. While the model is trained at the tissue level, we showcase its potential in predicting spatial gene expression patterns using spatial transcriptomics datasets. Leveraging the prediction performance, we develop a digital gene expression signature that predicts the risk of recurrence in breast cancer. SEQUOIA deciphers clinically relevant gene expression patterns from histology images, opening avenues for improved cancer management and personalized therapies.

Genetic Testing Enhances the Precision Diagnosis and Treatment of Breast Cancer

The contemporary comprehension of breast cancer has progressed to the molecular level. As a heterogeneous malignancy, conventional pathological diagnosis and histological classification could no longer meet the needs of precisely managing breast cancer. Genetic testing based on gene expression profiles and gene mutations has emerged and substantially contributed to the precise diagnosis and treatment of breast cancer. Multigene assays (MGAs) are explored for early-stage breast cancer patients, aiding the selection of adjuvant therapy and predicting prognosis. For metastatic breast cancer patients, testing specific genes indicates potentially effective antitumor agents. In this review, genetic testing in early-stage and metastatic breast cancer is summarized, as well as the advantages and challenges of genetic testing in breast cancer.

Long-term outcomes and predictors of recurrence in node-negative early stage breast cancer patients

BACKGROUND

We evaluated the outcomes, and risk factors for recurrence in patients with early stage node-negative breast cancer in this study.

METHOD

Retrospective data analysis was done on patient treatment records from 1988 to 2018. The patient's demographic, clinical, pathological, and therapeutic characteristics were noted. To evaluate survival analysis and predictors of recurrence, we employed Kaplan-Meier analysis with the log-rank test.

RESULTS

A total of 357 patients in all were enrolled in the research. At the time of diagnosis, the median age was 50 (with a range of 18-81). A total of 85.5% of patients had undergone a lumpectomy, while 14.5% had a mastectomy. 78.7% of patients had sentinel lymph node biopsy, and 21.3% had axillary lymph node dissection. In addition, the patients received adjuvant radiotherapy (88.7%), adjuvant endocrine therapy (82.1%), and adjuvant chemotherapy (48.5%). Recurrence of the tumor occurred in 31 (8.7%) patients (local recurrence 45.2% and metastatic disease 54.8%). Ten- and twenty-year recurrence-free survival rates were 92% and 77%. 19 (5.3%) patients had also developed contralateral breast cancer. Ten-year survival rates were 91.6%, and 20-year survival rates were 76.6%, respectively. Aged over 65 years (p = 0.004), necrosis (p = 0.002), mitosis (p = 0.003), and nuclear pleomorphism (p = 0.049) were found as statistically significant factors for recurrence in univariate analysis. In the ROC analysis, the largest size of the tumor (over 1.45 cm, p = 0.07) remained outside the statistical significance limit in terms of recurrence.

CONCLUSIONS

Thirty-year outcomes in individuals with early stage, node-negative breast cancer were shown in this study. We found that the recurrence ratios between 10 and 20 years were more frequent than the first 10 years during the follow-up. Despite the small number of patients who experienced a recurrence, we demonstrated that, in univariate analysis, being older than 65 and having some pathological characteristics (nuclear pleomorphism, mitosis, and necrosis) were statistically significant factors for disease recurrence.

Clinicopathological Factors Associated with Oncotype DX Risk Group in Patients with ER+/HER2- Breast Cancer

Oncotype DX (ODX), a 21-gene assay, predicts the recurrence risk in early breast cancer; however, it has high costs and long testing times. We aimed to identify clinicopathological factors that can predict the ODX risk group and serve as alternatives to the ODX test. This retrospective study included 547 estrogen receptor-positive, human epidermal growth factor receptor 2-negative, and lymph node-negative breast cancer patients who underwent ODX testing. Based on the recurrence scores, three ODX risk categories (low: 0-15, intermediate: 16-25, and high: 26-100) were established in patients aged ≤50 years (n = 379), whereas two ODX risk categories (low: 0-25 and high: 26-100) were established in patients aged >50 years (n = 168). Factors selected for analysis included body mass index, menopausal status, type of surgery, and pathological and immunohistochemical features. The ODX risk groups showed significant association with histologic grade (p = 0.0002), progesterone receptor expression (p < 0.0001), Ki-67 (p < 0.0001), and p53 expression (p = 0.023) in patients aged ≤50 years. In patients aged >50 years, tumor size (p = 0.022), Ki-67 (p = 0.001), and p53 expression (p = 0.001) were significantly associated with the risk group. Certain clinicopathological factors can predict the ODX risk group and enable decision-making on adjuvant chemotherapy; these factors differ according to age.

Omics Technologies Improving Breast Cancer Research and Diagnostics

Breast cancer (BC) has yielded approximately 2.26 million new cases and has caused nearly 685,000 deaths worldwide in the last two years, making it the most common diagnosed cancer type in the world. BC is an intricate ecosystem formed by both the tumor microenvironment and malignant cells, and its heterogeneity impacts the response to treatment. Biomedical research has entered the era of massive omics data thanks to the high-throughput sequencing revolution, quick progress and widespread adoption. These technologies-liquid biopsy, transcriptomics, epigenomics, proteomics, metabolomics, pharmaco-omics and artificial intelligence imaging-could help researchers and clinicians to better understand the formation and evolution of BC. This review focuses on the findings of recent multi-omics-based research that has been applied to BC research, with an introduction to every omics technique and their applications for the different BC phenotypes, biomarkers, target therapies, diagnosis, treatment and prognosis, to provide a comprehensive overview of the possibilities of BC research.

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.

Xerna™ TME Panel is a machine learning-based transcriptomic biomarker designed to predict therapeutic response in multiple cancers

Introduction

Most predictive biomarkers approved for clinical use measure single analytes such as genetic alteration or protein overexpression. We developed and validated a novel biomarker with the aim of achieving broad clinical utility. The Xerna™ TME Panel is a pan-tumor, RNA expression-based classifier, designed to predict response to multiple tumor microenvironment (TME)-targeted therapies, including immunotherapies and anti-angiogenic agents.

Methods

The Panel algorithm is an artificial neural network (ANN) trained with an input signature of 124 genes that was optimized across various solid tumors. From the 298-patient training data, the model learned to discriminate four TME subtypes: Angiogenic (A), Immune Active (IA), Immune Desert (ID), and Immune Suppressed (IS). The final classifier was evaluated in four independent clinical cohorts to test whether TME subtype could predict response to anti-angiogenic agents and immunotherapies across gastric, ovarian, and melanoma datasets.

Results

The TME subtypes represent stromal phenotypes defined by angiogenesis and immune biological axes. The model yields clear boundaries between biomarker-positive and -negative and showed 1.6-to-7-fold enrichment of clinical benefit for multiple therapeutic hypotheses. The Panel performed better across all criteria compared to a null model for gastric and ovarian anti-angiogenic datasets. It also outperformed PD-L1 combined positive score (>1) in accuracy, specificity, and positive predictive value (PPV), and microsatellite-instability high (MSI-H) in sensitivity and negative predictive value (NPV) for the gastric immunotherapy cohort.

Discussion

The TME Panel's strong performance on diverse datasets suggests it may be amenable for use as a clinical diagnostic for varied cancer types and therapeutic modalities.

Whole slide image features predict pathologic complete response and poor clinical outcomes in triple-negative breast cancer

INTRODUCTION

Breast cancers are complex ecosystem like networks of malignant cells and their associated microenvironment. Applications for machine intelligence and the tumoral microenvironment are expanding frontiers in pathology. Previously, computational approaches have been developed to quantify and spatially analyze immune cells, proportionate stroma, and detect tumor budding. Little work has been done to analyze different types of tumor-associated stromata both quantitatively and computationally in relation to clinical endpoints.

METHODS

We aimed to quantify stromal features from whole slide images (WSI) including stromata (myxoid, collagenous, immune) and tumoral components and combined them with traditional clinical and pathologic parameters in 120 triple-negative breast cancer (TNBC) patients treated with neoadjuvant chemotherapy (NAC) to predict pathologic complete response (pCR) and poor clinical outcomes.

RESULTS

High collagenous stroma on WSI was best associated with lower rates of pCR, while combined high proportionated stroma (myxoid, collagenous, and immune) most optimally predicted worse clinical survival outcomes. When combining clinical, pathologic, and WSI features, Receiver Operator Characteristics (ROC) curves for LASSO features was up to 0.67 for pCR and 0.77 for poor outcomes.

CONCLUSION

The techniques demonstrated in the present study can be performed with appropriate quality assurance. Future trials are needed to demonstrate whether coupling applications for machine intelligence, inclusive of the tumor mesenchyme, can improve outcomes prediction for patients with breast cancer.

Applying a GAN-based classifier to improve transcriptome-based prognostication in breast cancer

Established prognostic tests based on limited numbers of transcripts can identify high-risk breast cancer patients, yet are approved only for individuals presenting with specific clinical features or disease characteristics. Deep learning algorithms could hold potential for stratifying patient cohorts based on full transcriptome data, yet the development of robust classifiers is hampered by the number of variables in omics datasets typically far exceeding the number of patients. To overcome this hurdle, we propose a classifier based on a data augmentation pipeline consisting of a Wasserstein generative adversarial network (GAN) with gradient penalty and an embedded auxiliary classifier to obtain a trained GAN discriminator (T-GAN-D). Applied to 1244 patients of the METABRIC breast cancer cohort, this classifier outperformed established breast cancer biomarkers in separating low- from high-risk patients (disease specific death, progression or relapse within 10 years from initial diagnosis). Importantly, the T-GAN-D also performed across independent, merged transcriptome datasets (METABRIC and TCGA-BRCA cohorts), and merging data improved overall patient stratification. In conclusion, the reiterative GAN-based training process allowed generating a robust classifier capable of stratifying low- vs high-risk patients based on full transcriptome data and across independent and heterogeneous breast cancer cohorts.

The Cancer Genomic Integration Model for Symptom Science (CGIMSS): A Biopsychosocial Framework

Current nursing research has characterized symptom clusters and trajectories in individuals with breast cancer. The existing literature describes the relationship between symptoms and biological variables and the potential moderating effects of individual and social factors. The genomic profiling of breast cancer has also been an area of much recent research. Emerging evidence indicates that incorporating cancer genomics into symptom science research can aid in the prognostication of symptoms and elucidate targets for symptom management interventions. The aim of this paper is to outline a model to integrate cancer genomics into symptom science research, illustrated using breast cancer and psychoneurological (PN) symptoms as an example. We present a review of the current literature surrounding breast cancer genomics (specifically cancer genomic instability) and the biological underpinnings of the PN symptom cluster. Advances in both of these areas indicate that inflammation may serve as the bridge between cancer genomics and the PN symptom cluster. We also outline how the integration of cancer genomics into symptom science research synergizes with current research of individual and social factors in relation to symptoms. This model aims to provide a framework to guide future biopsychosocial symptom science research that can elucidate new predictive methods and new targets for intervention.

MRI Radiomics and Machine Learning for the Prediction of Oncotype Dx Recurrence Score in Invasive Breast Cancer

AIM

To non-invasively predict Oncotype DX recurrence scores (ODXRS) in patients with ER+ HER2- invasive breast cancer (IBC) using dynamic contrast-enhanced (DCE) MRI-derived radiomics features extracted from primary tumor lesions and a ML algorithm.

MATERIALS AND METHODS

Pre-operative DCE-MRI of patients with IBC, no history of neoadjuvant therapy prior to MRI, and for which the ODXRS was available, were retrospectively selected from a public dataset. ODXRS was obtained on histological tumor samples and considered as positive if greater than 16 and 26 in patients aged under and over 50 years, respectively. Tumor lesions were manually annotated by three independent operators on DCE-MRI images through 3D ROIs positioning. Radiomic features were therefore extracted and selected using multistep feature selection process. A logistic regression ML classifier was then employed for the prediction of ODXRS.

RESULTS

248 patients were included, of which 87 with positive ODXRS. 166 (66%) patients were grouped in the training set, while 82 (33%) in the test set. A total of 1288 features was extracted. Of these, 1244 were excluded as 771, 82 and 391 were excluded as not stable (n = 771), not variant (n = 82), and highly intercorrelated (n = 391), respectively. After the use of recursive feature elimination with logistic regression estimator and polynomial transformation, 92 features were finally selected. In the training set, the logistic regression classifier obtained an overall mean accuracy of 60%. In the test set, the accuracy of the ML classifier was 63%, with a sensitivity of 80%, specificity of 43%, and AUC of 66%.

CONCLUSIONS

Radiomics and ML applied to pre-operative DCE-MRI in patients with IBC showed promises for the non-invasive prediction of ODXRS, aiding in selecting patients who will benefit from NAC.

Leveraging transcriptomics for precision diagnosis: Lessons learned from cancer and sepsis

Diagnostics require precision and predictive ability to be clinically useful. Integration of multi-omic with clinical data is crucial to our understanding of disease pathogenesis and diagnosis. However, interpretation of overwhelming amounts of information at the individual level requires sophisticated computational tools for extraction of clinically meaningful outputs. Moreover, evolution of technical and analytical methods often outpaces standardisation strategies. RNA is the most dynamic component of all -omics technologies carrying an abundance of regulatory information that is least harnessed for use in clinical diagnostics. Gene expression-based tests capture genetic and non-genetic heterogeneity and have been implemented in certain diseases. For example patients with early breast cancer are spared toxic unnecessary treatments with scores based on the expression of a set of genes (e.g., Oncotype DX). The ability of transcriptomics to portray the transcriptional status at a moment in time has also been used in diagnosis of dynamic diseases such as sepsis. Gene expression profiles identify endotypes in sepsis patients with prognostic value and a potential to discriminate between viral and bacterial infection. The application of transcriptomics for patient stratification in clinical environments and clinical trials thus holds promise. In this review, we discuss the current clinical application in the fields of cancer and infection. We use these paradigms to highlight the impediments in identifying useful diagnostic and prognostic biomarkers and propose approaches to overcome them and aid efforts towards clinical implementation.

Blood-Based mRNA Tests as Emerging Diagnostic Tools for Personalised Medicine in Breast Cancer

Molecular diagnostic tests help clinicians understand the underlying biological mechanisms of their patients' breast cancer (BC) and facilitate clinical management. Several tissue-based mRNA tests are used routinely in clinical practice, particularly for assessing the BC recurrence risk, which can guide treatment decisions. However, blood-based mRNA assays have only recently started to emerge. This review explores the commercially available blood mRNA diagnostic assays for BC. These tests enable differentiation of BC from non-BC subjects (Syantra DX, BCtect), detection of small tumours <10 mm (early BC detection) (Syantra DX), detection of different cancers (including BC) from a single blood sample (multi-cancer blood test Aristotle), detection of BC in premenopausal and postmenopausal women and those with high breast density (Syantra DX), and improvement of diagnostic outcomes of DNA testing (variant interpretation) (+RNAinsight). The review also evaluates ongoing transcriptomic research on exciting possibilities for future assays, including blood transcriptome analyses aimed at differentiating lymph node positive and negative BC, distinguishing BC and benign breast disease, detecting ductal carcinoma in situ, and improving early detection further (expression changes can be detected in blood up to eight years before diagnosing BC using conventional approaches, while future metastatic and non-metastatic BC can be distinguished two years before BC diagnosis).

Transforming Diagnosis and Therapeutics Using Cancer Genomics

In past quarter of the century, much has been understood about the genetic variation and abnormal genes that activate cancer in humans. All the cancers somehow possess alterations in the DNA sequence of cancer cell's genome. In present, we are heading toward the era where it is possible to obtain complete genome of the cancer cells for their better diagnosis, categorization and to explore treatment options.

Molecular Biomarkers in Cancer

Molecular cancer biomarkers are any measurable molecular indicator of risk of cancer, occurrence of cancer, or patient outcome. They may include germline or somatic genetic variants, epigenetic signatures, transcriptional changes, and proteomic signatures. These indicators are based on biomolecules, such as nucleic acids and proteins, that can be detected in samples obtained from tissues through tumor biopsy or, more easily and non-invasively, from blood (or serum or plasma), saliva, buccal swabs, stool, urine, etc. Detection technologies have advanced tremendously over the last decades, including techniques such as next-generation sequencing, nanotechnology, or methods to study circulating tumor DNA/RNA or exosomes. Clinical applications of biomarkers are extensive. They can be used as tools for cancer risk assessment, screening and early detection of cancer, accurate diagnosis, patient prognosis, prediction of response to therapy, and cancer surveillance and monitoring response. Therefore, they can help to optimize making decisions in clinical practice. Moreover, precision oncology is needed for newly developed targeted therapies, as they are functional only in patients with specific cancer genetic mutations, and biomarkers are the tools used for the identification of these subsets of patients. Improvement in the field of cancer biomarkers is, however, needed to overcome the scientific challenge of developing new biomarkers with greater sensitivity, specificity, and positive predictive value.

Drug Metabolism for the Identification of Clinical Biomarkers in Breast Cancer

Breast cancer is classified into four major molecular subtypes, and is considered a heterogenous disease. The risk profiles and treatment of breast cancer differ according to these subtypes. Early detection dramatically improves the prospects of successful treatment, resulting in a reduction in overall mortality rates. However, almost 30% of women primarily diagnosed with the early-stage disease will eventually develop metastasis or resistance to chemotherapies. Immunotherapies are among the most promising cancer treatment options; however, long-term clinical benefit has only been observed in a small subset of responding patients. The current strategies for diagnosis and treatment rely heavily on histopathological examination and molecular diagnosis, disregarding the tumor microenvironment and microbiome involving cancer cells. In this review, we aim to praise the use of pharmacogenomics and pharmacomicrobiomics as a strategy to identify potential biomarkers for guiding and monitoring therapy in real-time. The finding of these biomarkers can be performed by studying the metabolism of drugs, more specifically, immunometabolism, and its relationship with the microbiome, without neglecting the information provided by genetics. A larger understanding of cancer biology has the potential to improve patient care, enable clinical decisions, and deliver personalized medicine.

Atypical Ductal Hyperplasia-Ductal Carcinoma In Situ Spectrum: Diagnostic Considerations and Treatment Impact in the Era of Deescalation

As the first node in treatment algorithms for breast disease, pathologists have the potential to play a critical role in refining appropriate therapy for lesions in the atypical ducal hyperplasia-ductal carcinoma in situ (ADH-DCIS) spectrum by conservatively approaching diagnosis of lesions limited in size on core needle biopsy. Appropriate efforts to downgrade the diagnosis of lesions at the borderline of ADH and DCIS will certainly lead to more breast conservation and avoid the common morbidities of mastectomy, sentinel node biopsy, and radiation therapy. Whether results of clinical trials of active surveillance will successfully identify a subset of women who may successfully forgo even limited breast-conserving surgery is eagerly anticipated. Given the increasing concern that a significant number of women with DCIS are overtreated, identification of patients at very low risk for progression who may forgo surgery and radiation therapy safely is of significant interest.

Race-Specific Genetic Profiles of Homologous Recombination Deficiency in Multiple Cancers

Homologous recombination deficiency (HRD) has been used to predict both cancer prognosis and the response to DNA-damaging therapies in many cancer types. HRD has diverse manifestations in different cancers and even in different populations. Many screening strategies have been designed for detecting the sensitivity of a patient's HRD status to targeted therapies. However, these approaches suffer from low sensitivity, and are not specific to each cancer type and population group. Therefore, identifying race-specific and targetable HRD-related genes is of clinical importance. Here, we conducted analyses using genomic sequencing data that was generated by the Pan-Cancer Atlas. Collapsing non-synonymous variants with functional damage to HRD-related genes, we analyzed the association between these genes and race within cancer types using the optimal sequencing kernel association test (SKAT-O). We have identified race-specific mutational patterns of curated HRD-related genes across cancers. Overall, more significant mutation sites were found in ATM, BRCA2, POLE, and TOP2B in both the 'White' and 'Asian' populations, whereas PTEN, EGFG, and RIF1 mutations were observed in both the 'White' and 'African American/Black' populations. Furthermore, supported by pathogenic tendency databases and previous reports, in the 'African American/Black' population, several associations, including BLM with breast invasive carcinoma, ERCC5 with ovarian serous cystadenocarcinoma, as well as PTEN with stomach adenocarcinoma, were newly described here. Although several HRD-related genes are common across cancers, many of them were found to be specific to race. Further studies, using a larger cohort of diverse populations, are necessary to identify HRD-related genes that are specific to race, for guiding gene testing methods.

SABR in oligometastatic breast cancer: Current status and future directions

Oligometastatic breast cancer (OMBC) is a heterogeneous disease with intrinsic biological diversity. It is increasingly accepted in clinical practice that patients with OMBC could be treated with the expectation of long-term disease remission. Local ablative treatments, such as radiotherapy or surgery have a role in this setting. At present, patients that may benefit are characterised by low tumour burden, long disease-free interval and the capacity to completely ablate all sites of disease. In the future, biological or genomic classifiers may help predict which patients may benefit the most from local ablative treatments. This review provides an overview of the proposed classifications of oligometastatic disease and outlines the standard systemic treatment options of endocrine therapy, chemotherapy, and immunotherapy. The evidence for localized treatment with stereotactic ablative body radiotherapy (SABR) is presented. We discuss current active trials in oligometastatic cancer and discuss potential future directions for the use of SABR in the treatment of OMBC.

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The oligometastatic disease state is being increasingly recognised in breast cancer.

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Breast cancer is a heterogenous disease with varied subtypes and treatment paths.

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Stereotactic ablative body radiotherapy in addition to systemic therapy has merit.

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Evidence suggests benefit for cure, stable disease maintenance, and symptom control.

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Study of targeted strategies to treat oligometastatic breast cancer is encouraged.

Ultrasound-Based Radiomics Analysis for Predicting Disease-Free Survival of Invasive Breast Cancer

Background

Accurate prediction of recurrence is crucial for personalized treatment in breast cancer, and whether the radiomics features of ultrasound (US) could be used to predict recurrence of breast cancer is still uncertain. Here, we developed a radiomics signature based on preoperative US to predict disease-free survival (DFS) in patients with invasive breast cancer and assess its additional value to the clinicopathological predictors for individualized DFS prediction.

Methods

We identified 620 patients with invasive breast cancer and randomly divided them into the training (n = 372) and validation (n = 248) cohorts. A radiomics signature was constructed using least absolute shrinkage and selection operator (LASSO) Cox regression in the training cohort and validated in the validation cohort. Univariate and multivariate Cox proportional hazards model and Kaplan–Meier survival analysis were used to determine the association of the radiomics signature and clinicopathological variables with DFS. To evaluate the additional value of the radiomics signature for DFS prediction, a radiomics nomogram combining the radiomics signature and clinicopathological predictors was constructed and assessed in terms of discrimination, calibration, reclassification, and clinical usefulness.

Results

The radiomics signature was significantly associated with DFS, independent of the clinicopathological predictors. The radiomics nomogram performed better than the clinicopathological nomogram (C-index, 0.796 vs. 0.761) and provided better calibration and positive net reclassification improvement (0.147, P = 0.035) in the validation cohort. Decision curve analysis also demonstrated that the radiomics nomogram was clinically useful.

Conclusion

US radiomics signature is a potential imaging biomarker for risk stratification of DFS in invasive breast cancer, and US-based radiomics nomogram improved accuracy of DFS prediction.

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.