A Signature That May Be Predictive of Early Versus Late Recurrence After Radiation Treatment for Breast Cancer That May Inform the Biology of Early, Aggressive Recurrences

Machine Learning & Molecular Radiation Tumor Biomarkers

Developing radiation tumor biomarkers that can guide personalized radiotherapy clinical decision making is a critical goal in the effort towards precision cancer medicine. High-throughput molecular assays paired with modern computational techniques have the potential to identify individual tumor-specific signatures and create tools that can help understand heterogenous patient outcomes in response to radiotherapy, allowing clinicians to fully benefit from the technological advances in molecular profiling and computational biology including machine learning. However, the increasingly complex nature of the data generated from high-throughput and "omics" assays require careful selection of analytical strategies. Furthermore, the power of modern machine learning techniques to detect subtle data patterns comes with special considerations to ensure that the results are generalizable. Herein, we review the computational framework of tumor biomarker development and describe commonly used machine learning approaches and how they are applied for radiation biomarker development using molecular data, as well as challenges and emerging research trends.

Predicting tumour radiosensitivity to deliver precision radiotherapy

Owing to advances in radiotherapy, the physical properties of radiation can be optimized to enable individualized treatment; however, optimization is rarely based on biological properties and, therefore, treatments are generally planned with the assumption that all tumours respond similarly to radiation. Radiation affects multiple cellular pathways, including DNA damage, hypoxia, proliferation, stem cell phenotype and immune response. In this Review, we summarize the effect of these pathways on tumour responses to radiotherapy and the current state of research on genomic classifiers designed to exploit these variations to inform treatment decisions. We also discuss whether advances in genomics have generated evidence that could be practice changing and whether advances in genomics are now ready to be used to guide the delivery of radiotherapy alone or in combination.

Cisplatin-based concurrent chemoradiotherapy improved the survival of locoregionally advanced nasopharyngeal carcinoma after induction chemotherapy by reducing early treatment failure

PURPOSE

The aims of this study focusing on Locoregionally advanced nasopharyngeal carcinoma (LANPC) were mainly two-fold: on the one hand, to establish a cut-off value to differentiate early and late failure based on prognosis after recurrence or metastasis; and on the other hand, to investigate the duration of concurrent cisplatin benefit over follow-up time. The results of our study have the potential to guide clinical practice and follow-up.

METHODS

In total, 3123 patients with stage III-IVa NPC receiving Induction chemotherapy followed by concurrent cisplatin or not were analysed. The cut-off value of treatment failure was calculated using the minimum P-value approach. Random survival forest (RSF) model was to simulate the cumulative probabilities of treatment failure (locoregional recurrence and /or distant metastasis) over-time, as well as the monthly time-specific, event-occurring probabilities, for patients at different treatment groups.

RESULTS

Based on subsequent prognosis, early locoregional failure (ELRF) should be defined as recurrence within 14 months (P = 1.47 × 10 - 3), and early distant failure (EDF) should be defined as recurrence within 20 months (P = 1.95 × 10 - 4). A cumulative cisplatin dose (CCD) > 200 mg/m² independently reduced the risk of EDF (hazard ratio, 0.351; 95% confidence interval (CI), 0.169-0.732; P = 0.005). Better failure-free survival (FFS) and overall survival (OS) were observed in concurrent chemotherapy settings ([0 mg/m² vs. 1-200 mg/m² vs. >200 mg/m²]: FFS: 70.4% vs. 74.4% vs. 82.6%, all P < 0.03; OS: 79.5% vs. 83.8% vs. 90.8%, all P < 0.01). In the monthly analysis, treatment failure mainly occurred during the first 4 years, and the risk of distant failure in patients treated with concurrent chemotherapy never exceeded that of patients without concurrent chemotherapy.

CONCLUSION

Locoregional failure that developed within 14 months and/or distant failure within 20 months had poorer subsequent survival. Concurrent chemotherapy provides a significant FFS benefit, primarily by reducing EDF, translating into a long-term OS benefit.

Breast Cancer in Geriatric Patients: Current Landscape and Future Prospects

Breast cancer is the most common cancer diagnosed among women worldwide and more than half are diagnosed above the age of 60 years. Life expectancy is increasing and the number of breast cancer cases diagnosed among older women are expected to increase. Undertreatment, mostly due to unjustifiable fears of advanced-age and associated comorbidities, is commonly practiced in this group of patients who are under-represented in clinical trials and their management is not properly addressed in clinical practice guidelines. With modern surgery and anesthesia, breast surgeries are considered safe and is usually associated with very low complication rates, regardless of extent of surgery. However, oncoplastic surgery and management of the axilla can be tailored based on patients'- and disease-related factors. Most of chemotherapeutic agents, along with targeted therapy and anti-Human epidermal growth factor receptor-2 (HER2) drugs can be safely given for older patients, however, dose adjustment and close monitoring of potential adverse events might be needed. The recently introduced cyclin-D kinase (CDK) 4/6-inhibitors in combination with aromatase inhibitors (AI) or fulvestrant, which changed the landscape of breast cancer therapy, are both safe and effective in older patients and had substituted more aggressive and potentially toxic interventions. Despite its proven efficacy, adjusting or even omitting adjuvant radiation therapy, at least in low-risk older patients, is safe and frequently practiced. In this paper, we review existing data related to breast cancer management among older patients across the continuum; from resection of the primary tumor through adjuvant chemotherapy, radiation and endocrine therapy up to the management of recurrent and advanced-stage disease.

Harnessing the DNA Repair Pathway in Breast Cancer: Germline Mutations/Polymorphisms in Breast Radiation

Molecular profiling facilitates opportunities for personalization of breast cancer management. Increasing availability of germline and somatic sequencing provides insight into predictors of treatment efficacy and treatment tolerance of patients with breast cancer. The presence of pathologic mutations can guide patient selection for breast conserving surgery vs mastectomy. However, our understanding of the interplay between genetic variants and radiotherapy responses and side effects remains incomplete. Here we review the available data on germline mutations and polymorphisms in breast cancer. We also outline their association with treatment tolerance, locoregional outcomes, and ongoing efforts to transform these insights into more effective treatment strategies in combination with radiotherapy.

Identifying and Validating of an Autophagy-Related Gene Signature for the Prediction of Early Relapse in Breast Cancer

BACKGROUND

Compelling evidence has demonstrated the pivotal role of autophagy in the prognosis of breast cancer. Breast cancer (BC) patients with early relapse consistently exhibited worse survival.

METHODS

The autophagy-related genes were derived from the Human Autophagy Database (HADb) and high-sequencing data were obtained from The Cancer Genome Atlas (TCGA). Discrepantly expressed autophagy genes (DEAGs) between early relapse and long-term survival groups were performed using the Linear Models for Microarray data (LIMMA) method. Lasso Cox regression analysis was conducted for the selection of the 4-gene autophagy-related gene signature. GSE42568 and GSE21653 databases were enrolled in this study for the external validation of the signature. Then patients were divided into high and low-risk groups based on the specific score formula. GSEA was used to discover the related signaling pathway. The Kaplan-Meier curves and the receiver operating characteristic (ROC) curves were used to evaluate the discrimination and accuracy of the 4-gene signature.

RESULTS

A signature composed of four autophagy-related mRNA including APOL1, HSPA8, SIRT1, and TP73, was identified as significantly associated with the early relapse in BC patients. Time-dependent receiver-operating characteristic at 1 year suggested remarkable accuracy of the signature [area under the curve (AUC = 0.748)]. The risk score model based on the autophagy-related signature showed favorable predicting value in 1-, 2-, and 3-year relapse-free survival (RFS) in training and two validating cohorts. The GSEA displayed gene sets were remarkably enriched in carcinogenic activation pathways and autophagy-related pathways. The nomogram involving three variables (progesterone receptor status, T stage, and 4-gene signature) exhibited relatively good discrimination with a C-index of 0.766.

CONCLUSIONS

Our study establishes an autophagy-related 4-gene signature that can effectively stratify the high-risk and low-risk BC patients for early relapse. Combined with the clinicopathological variables, the signature could significantly help oncologists tailor more efficient treatment strategies for BC patients.

Genomically Guided Breast Radiation Therapy: A Review of the Current Data and Future Directions

Purpose

To highlight the current evidence and the limitations in data to support a personalized approach in breast oncology radiation therapy management and define steps needed for clinical implementation.

Methods and Materials

A critical review of the current literature on the use of genomics in breast radiation therapy was undertaken by a group of breast radiation oncologists to discuss current data, future directions, and challenges.

Results

A summary of the existing data, ongoing clinical trials, and future directions is provided. The authors note many groups have developed radiation-specific genomic assays, which demonstrate promise in prediction of local control and benefit from radiation therapy; however, prospective validation of their utility is needed. Limitations continue to exist in our understanding of tumor biology and how it can be integrated into clinical practice.

Conclusions

Given the relative ubiquity of breast radiation therapy, the variety of dose and fractionation approaches, and the current data to support a personalized approach, it is our belief that the delivery of breast radiation therapy is uniquely poised for a genomically personalized radiation therapy approach. Prospective clinical trials implementing genomic signatures are needed at this time to advance the field.

Post-mastectomy Radiation Therapy in Triple-Negative Breast Cancer Patients: Analysis of the BEATRICE Trial

PURPOSE

Post-mastectomy radiation therapy (PMRT) improves locoregional control and overall survival in patients with breast cancer. With the evolution of systemic therapy, the benefit of PMRT in patients with triple-negative disease requires further evaluation.

PATIENTS AND METHODS

BEATRICE is a phase III randomized clinical trial that examined the efficacy of bevacizumab in patients with triple-negative breast cancer (TNBC). The current study is a retrospective analysis of data on patients enrolled and treated with mastectomy and systemic therapy. The primary endpoint was determining the effect of PMRT on locoregional recurrence rates (LRR). Hazard ratios were estimated using Cox regression, and LRR curves were generated by the Kaplan-Meier method.

RESULTS

In total, 940 patients were included in our analysis, of whom 359 (38.2%) received PMRT while 581 (61.8%) did not. At median follow-up of 5 years, no significant difference in LRR was noted between the PMRT and no PMRT groups in node-negative patients (HR = 1.09). Patients with N1 disease had 5-year LRR-free survival of 96% for PMRT versus 91% for no PMRT (HR = 0.46). Most N2 patients received PMRT and had 5-year LRR-free survival of 76%.

CONCLUSION

PMRT benefit in TNBC patients treated with modern systemic therapy is lower than historical reports. Delivery of PMRT in patients with N1 disease enrolled in the BEATRICE trial was not shown to improve local control. As this might be due to patient selection for PMRT, future randomized controlled trials are required to assess the role of PMRT in this patient population.

A case-control study to identify molecular risk factors for local recurrence in young breast cancer patients

PURPOSE

To investigate risk factors for local recurrence (LR) after breast conserving therapy in young breast cancer patients randomized in the "Young Boost Trial".

MATERIAL & METHODS

In the "Young Boost Trial" 2421 patients ≤50 years old were randomized between a 16 Gy and 26 Gy boost after breast conserving surgery and whole breast radiation (50 Gy). We performed a case-control study comparing patients who developed a LR (cases) and for each of them three control patients free of recurrence (controls). Clinicopathological factors, copy number- and gene expression profiles of primary tumors were compared between cases and controls, and between primary tumors and local recurrences.

RESULTS

The cumulative 5-year LR rate was 1.07% (95% CI 0.72-1.59%) and 10-year LR rate 2.56% (1.81-3.62%). Analysis of a subset of primary tumors and local recurrences showed similar histopathological characteristics (n = 15), copy number (n = 13) and gene expression profiles (n = 14). Basal subtype was strongly associated with LR in univariable and multivariable analysis. Gains of CCND1 were identified more frequently among controls, while more frequent gains of FGFR1 and IGF1R were observed among cases. Upregulation of genes involved in the p53-pathway was observed in recurring tumors compared to non-recurring tumors. We could not identify a genomic classifier for LR.

CONCLUSIONS

All investigated local recurrences were true genomic recurrences. Although differences in copy number variation and gene expression pathways were observed in recurring tumors compared to non-recurring tumors, no genomic classifier for LR could be identified.