PAM-50 predicts local recurrence after breast cancer surgery in postmenopausal patients with ER+/HER2- disease: results from 1204 patients in the randomized ABCSG-8 trial

Clinical Biomarkers of Tumour Radiosensitivity and Predicting Benefit from Radiotherapy: A Systematic Review

Modern advanced radiotherapy techniques have improved the precision and accuracy of radiotherapy delivery, with resulting plans being highly personalised based on individual anatomy. Adaptation for individual tumour biology remains elusive. There is an unmet need for biomarkers of intrinsic radiosensitivity that can predict tumour response to radiation to facilitate individualised decision-making, dosing and treatment planning. Over the last few decades, the use of high throughput molecular biology technologies has led to an explosion of newly discovered cancer biomarkers. Gene expression signatures are now used routinely in clinic to aid decision-making regarding adjuvant systemic therapy. They have great potential as radiotherapy biomarkers. A previous systematic review published in 2015 reported only five studies of signatures evaluated for their ability to predict radiotherapy benefits in clinical cohorts. This updated systematic review encompasses the expanded number of studies reported in the last decade. An additional 27 studies were identified. In total, 22 distinct signatures were recognised (5 pre-2015, 17 post-2015). Seventeen signatures were 'radiosensitivity' signatures and five were breast cancer prognostic signatures aiming to identify patients at an increased risk of local recurrence and therefore were more likely to benefit from adjuvant radiation. Most signatures (15/22) had not progressed beyond the discovery phase of development, with no suitable validated clinical-grade assay for application. Very few signatures (4/17 'radiosensitivity' signatures) had undergone any laboratory-based biological validation of their ability to predict tumour radiosensitivity. No signatures have been assessed prospectively in a phase III biomarker-led trial to date and none are recommended for routine use in clinical guidelines. A phase III prospective evaluation is ongoing for two breast cancer prognostic signatures. The most promising radiosensitivity signature remains the radiosensitivity index (RSI), which is used to calculate a genomic adjusted radiation dose (GARD). There is an ongoing phase II prospective biomarker-led study of RSI/GARD in triple negative breast cancer. The results of these trials are eagerly anticipated over the coming years. Future work in this area should focus on (1) robust biological validation; (2) building biobanks alongside large radiotherapy randomised controlled trials with dose variance (to demonstrate an interaction between radiosensitivity signature and dose); (3) a validation of clinical-grade cost-effective assays that are deliverable within current healthcare infrastructure; and (4) an integration with biomarkers of other determinants of radiation response.

Personalized Radiation Therapy for Breast Cancer

Breast cancer is diagnosed in nearly 3 million people worldwide. Radiation therapy is an integral component of disease management for patients with breast cancer, and is used after breast-conserving surgery or a mastectomy to reduce the risk of a local recurrence. The following review describes the methods used to personalize radiation therapy by optimizing patient selection, using advanced treatment techniques to lessen the radiation dose to normal organs, and using hypofractionation in order to shorten the duration of radiation treatment.

Omission of radiation therapy post breast conserving surgery

Radiation therapy (RT) after breast conserving surgery decreases the risks of local recurrence and breast cancer mortality in the multidisciplinary management of patients with breast cancer. However, breast cancer is a heterogeneous disease, and the absolute benefit of post-operative RT in individual patients varies substantially. Clinical trials aiming to identify patients with low-risk early breast cancer in whom post-operative RT may be safely omitted, based on conventional clinical-pathologic variables alone, have not provided sufficiently tailored information on local recurrence risk assessment to guide treatment decisions. The majority of patients with early breast cancer continue to be routinely treated with RT after breast conserving surgery. This approach may represent over-treatment for a substantial proportion of the patients. The clinical impact of genomic signatures on local therapy decisions for early breast cancer has been remarkably modest due to the lack of high-level evidence supporting their clinical validity for assessment of the risk of local recurrence. Efforts to personalise breast cancer care must be supported by high level evidence to enable balanced, informed treatment decisions. These considerations underpin the importance of ongoing biomarker-directed clinical trials to generate the high-level evidence necessary for setting the future standard of care in personalised local therapy for patients with early breast cancer.

Locoregional Breast Cancer Recurrence in the European Organisation for Research and Treatment of Cancer 10041/BIG 03-04 MINDACT Trial: Analysis of Risk Factors Including the 70-Gene Signature

PURPOSE

A number of studies are currently investigating de-escalation of radiation therapy in patients with a low risk of in-breast relapses on the basis of clinicopathologic factors and molecular tests. We evaluated whether 70-gene risk score is associated with risk of locoregional recurrence (LRR) and estimated 8-year cumulative incidences for LRR in patients with early-stage breast cancer treated with breast conservation.

METHODS

In this exploratory substudy of European Organisation for Research and Treatment of Cancer 10041/BIG 03-04 MINDACT trial, we evaluated women with a known clinical and genomic 70-gene risk score test result and who had breast-conserving surgery (BCS). The primary end point was LRR at 8 years, estimated by cumulative incidences. Distant metastasis and death were considered competing risks.

RESULTS

Among 6,693 enrolled patients, 5,470 (81.7%) underwent BCS, of whom 98% received radiotherapy. At 8-year follow-up, 189 patients experienced a LRR, resulting in an 8-year cumulative incidence of 3.2% (95% CI, 2.7 to 3.7). In patients with a low-risk 70-gene signature, the 8-year LRR incidence was 2.7% (95% CI, 2.1 to 3.3). In univariable analysis, adjusted for chemotherapy, five of 12 variables were associated with LRR, including the 70-gene signature. In multivariable modeling, adjuvant endocrine therapy and to a lesser extent tumor size and grade remained significantly associated with LRR.

CONCLUSION

This exploratory analysis of the MINDACT trial estimated an 8-year low LRR rate of 3.2% after BCS. The 70-gene signature was not independently predictive of LRR perhaps because of the low number of events observed and currently cannot be used in clinical decision making regarding LRR. The overall low number of events does provide an opportunity to design trials toward de-escalation of local therapy.

Update on the Use of Molecular Subtyping in Breast Cancer

The standard of care for invasive cancers of the breast has been and continues to be to evaluate them for breast prognostic markers: estrogen receptor, progesterone receptor, and human epidermal growth factor receptor 2 by immunohistochemistry. Over 2 decades ago, a study was the first to report on the molecular subtypes of breast cancer. Four main subtypes were reported. Since then there have been some changes in the molecular subtype classification, but overall many studies have shown that this subtyping has clinical prognostic and predictive value. More recently, molecular assays have been developed and studies have shown similar clinical prognostic and predictive value. We reviewed the literature for studies evaluating the clinical significance of all 3 of these methods of evaluation and the follow-up findings of that review are presented below.

Individualising radiation therapy decisions in breast cancer patients based on tumour infiltrating lymphocytes and genomic biomarkers

Radiation therapy (RT) has long been fundamental for the curative treatment of breast cancer. While substantial progress has been made in the anatomical and technological precision of RT delivery, and some approaches to de-escalate or omit RT based on clinicopathologic features have been successful, there remain substantial opportunities to refine individualised RT based on tumour biology. A major area of clinical and research interest is to ascertain the individualised risk of loco-regional recurrence to direct treatment decisions regarding escalation and de-escalation of RT. Patient-tailored treatment with RT is considerably lagging behind compared with the massive progress made in the field of personalised medicine that currently mainly applies to decisions on the use of systemic therapy or targeted agents. Herein we review select literature surrounding the use of tumour genomic biomarkers and biomarkers of the immune system, including tumour infiltrating lymphocytes (TILs), within the management of breast cancer, specifically as they relate to progress in moving toward analytically validated and clinically tested biomarkers utilized in RT.

Whole Breast Irradiation in Comparison to Endocrine Therapy in Early Stage Breast Cancer-A Direct and Network Meta-Analysis of Published Randomized Trials

BACKGROUND

Multiple randomized trials have established adjuvant endocrine therapy (ET) and whole breast irradiation (WBI) as the standard approach after breast-conserving surgery (BCS) in early-stage breast cancer. The omission of WBI has been studied in multiple trials and resulted in reduced local control with maintained survival rates and has therefore been adapted as a treatment option in selected patients in several guidelines. Omitting ET instead of WBI might also be a valuable option as both treatments have distinctly different side effect profiles. However, the clinical outcomes of BCS + ET vs. BCS + WBI have not been formally analyzed.

METHODS

We performed a systematic literature review searching for randomized trials comparing BCS + ET vs. BCS + WBI in low-risk breast cancer patients with publication dates after 2000. We excluded trials using any form of chemotherapy, regional nodal radiation and mastectomy. The meta-analysis was performed using a two-step process. First, we extracted all available published event rates and the effect sizes for overall and breast-cancer-specific survival (OS, BCSS), local (LR) and regional recurrence, disease-free survival, distant metastases-free interval, contralateral breast cancer, second cancer other than breast cancer and mastectomy-free interval as investigated endpoints and compared them in a network meta-analysis. Second, the published individual patient data from the Early Breast Cancer Trialists' Collaborative Group (EBCTCG) publications were used to allow a comparison of OS and BCSS.

RESULTS

We identified three studies, including a direct comparison of BCS + ET vs. BCS + WBI (n = 1059) and nine studies randomizing overall 7207 patients additionally to BCS only and BCS + WBI + ET resulting in a four-arm comparison. In the network analysis, LR was significantly lower in the BCS + WBI group in comparison with the BCS + ET group (HR = 0.62; CI-95%: 0.42-0.92; p = 0.019). We did not find any differences in OS (HR = 0.93; CI-95%: 0.53-1.62; p = 0.785) and BCSS (OR = 1.04; CI-95%: 0.45-2.41; p = 0.928). Further, we found a lower distant metastasis-free interval, a higher rate of contralateral breast cancer and a reduced mastectomy-free interval in the BCS + WBI-arm. Using the EBCTCG data, OS and BCSS were not significantly different between BCS + ET and BCS + WBI after 10 years (OS: OR = 0.85; CI-95%: 0.59-1.22; p = 0.369) (BCSS: OR = 0.72; CI-95%: 0.38-1.36; p = 0.305).

CONCLUSION

Evidence from direct and indirect comparison suggests that BCS + WBI might be an equivalent de-escalation strategy to BCS + ET in low-risk breast cancer. Adverse events and quality of life measures have to be further compared between these approaches.

The Prognostic and Predictive Value of Genomic Assays in Guiding Adjuvant Breast Radiation Therapy

Many patients with non-metastatic breast cancer benefit from adjuvant radiation therapy after lumpectomy or mastectomy on the basis of many randomized trials. However, there are many patients that have such low risks of recurrence after surgery that de-intensification of therapy by either reducing the treatment volume or omitting radiation altogether may be appropriate options. On the other hand, dose intensification may be necessary for more aggressive breast cancers. Until recently, these treatment decisions were based solely on clinicopathologic factors. Here, we review the current literature on the role of genomic assays as prognostic and/or predictive biomarkers to help guide adjuvant radiation therapy decision-making.

IMNI PRECISION trial protocol: a phase II, open-label, non-inferior randomized controlled trial of tailoring omission of internal mammary node irradiation for early-stage breast cancer

BACKGROUND

Since the publication of MA-20 and EORTC-22922 trials, chest wall (CW)/ whole breast (WB) irradiation + comprehensive regional nodal irradiation (RNI) with internal mammary node irradiation (IMNI) has been the standard adjuvant treatment for early-stage breast cancer (BC). However, one size does not fit all BC, and the risk of recurrence significantly varies among this patient population. In addition, whether all BC patients presented with one to three positive lymph nodes (pN1) could benefit from IMNI remains controversial. Thus, the optimal adjuvant RNI volume for early-stage BC with T1-2N1 remains undetermined.

METHODS

The IMNI PRECISION trial is a single institute, open-labeled, non-inferior, randomized controlled trial. A total of 214 clinically "high risk" BC patients which is characterized as having at least two of the five clinically adverse factors (age ≤ 40, three positive LN, T2 stage, grade 3 and Ki-67 index ≥ 14%), but genomic score "low risk" (the genomic score ≤ 44) N1 breast cancers are randomly assigned to omitting IMNI group (experimental group) or with IMNI (control group) with a 1:1 ratio. The primary endpoint of this trial is event-free survival, and secondary endpoints include overall survival and locoregional recurrence-free survival.

DISCUSSION

The IMNI PRECISION design allows promising clinical-genomic model to stratify the individualized risk of developing recurrence and guides the optimal RNI treatment for early-stage (pT1-2N1) BC patients. We anticipate that our results would provide high-level evidence to tailor IMNI according to individualized recurrence risk of BC.

TRIAL REGISTRATION

ClinicalTrials.gov Identifier NCT04517266 . Date of registration: August 18, 2020. Status: Recruiting.

Clinical Utility of Genomic Assay in Node-Positive Early-Stage Breast Cancer

Breast cancer (BC) is the most common malignancy among women in Canada. Adjuvant treatment in early BC can reduce the risk of BC recurrence. Historically, the decision for adjuvant chemotherapy for early BC was made only based on clinical and tumour characteristics. In recent years, there has been an effort toward developing genomic assays as a predictive and prognostic tool to improve precision in estimating disease recurrence, sensitivity to systemic treatment and ultimately with clinical utility for guidance regarding adjuvant systemic treatment(s). There are various commercial genomic tests available for early-stage ER+/HER-2 negative BC. This paper will review the Oncotype DX 21-gene Recurrence Score (RS), MammaPrint, EndoPredict, Prosigna®, and Breast Cancer Index (BCI) genomic assays. We will also focus on these genomic assays’ clinical application and utility in node-positive early-stage BC based on the most recent evidence and guidance recommendations.

Therapeutic Implications for Intrinsic Phenotype Classification of Metastatic Castration-Resistant Prostate Cancer

PURPOSE

To determine whether metastatic castration-resistant prostate cancers (mCRPC) partition into molecular phenotypes corresponding to intrinsic differentiation states and ascertain whether these subtypes exhibit specific druggable features and associate with treatment outcomes.

EXPERIMENTAL DESIGN

We used RNAseq, digital spatial profiling, and histological assessments from metastatic biopsies and patient-derived xenografts to segregate mCRPCs into subtypes defined by the PAM50 breast cancer classification algorithm. Subtype associations with treatment responses in preclinical models and patients were determined.

RESULTS

Using the PAM50 algorithm, we partitioned 270 mCRPC tumors into LumA (42%), LumB (24%), and Basal (34%) subtypes with classification largely driven by proliferation rates and androgen receptor (AR) activity. Most neuroendocrine tumors classified as Basal. Pathways enriched in the LumA subtype include TGFß and NOTCH signaling. LumB subtype tumors were notable for elevated MYC activity. Basal subtype tumors exhibited elevated IL6-STAT3 signaling and features of adult stem cell states. In patients where multiple tumors were evaluated, the majority had concordant PAM50 subtype determination, though a subset exhibited marked inter- and intratumor heterogeneity, including divergent classifications between primary and metastatic sites. In preclinical models, LumA subtype tumors were highly responsive to androgen deprivation and docetaxel chemotherapy whereas Basal tumors were largely resistant. In clinical cohorts patients with Basal subtype tumors demonstrated a shorter time on treatment with AR signaling inhibitors and docetaxel relative to patients with luminal subtypes.

CONCLUSIONS

Subtyping of mCRPC based on cell differentiation states has potential clinical utility for identifying patients with divergent expression of treatment targets and responses to systemic therapy.

Tumor-infiltrating lymphocytes predict improved overall survival after post-mastectomy radiotherapy: a study of the randomized DBCG82bc cohort

BACKGROUND

The predictive value of tumor-infiltrating lymphocytes (TILs) on the benefit from radiotherapy (RT) remains unclear. Our aim was to investigate the association between TILs and post-mastectomy RT (PMRT) regarding the risk of recurrence and survival in a randomized cohort.

MATERIAL AND METHODS

Stromal TILs were histologically estimated in 1011 tumors from high-risk breast cancer (BC) patients from the DBCG82bc trial. Patients were diagnosed between 1982 and 90, treated with total mastectomy and partial axillary lymph node dissection, randomized to ± PMRT followed by adjuvant systemic treatment. A competing risk model, Kaplan-Meier analysis and multivariate Cox regression analysis were used for correlating TILs and clinical outcome.

RESULTS

106 of 1011 patients (10.5%) showed high TILs using a 30% cut-off. In multivariate regression analysis, a high level of TILs was an independent factor associated with lower risk of distant metastasis (DM) and improved overall survival (OS), but without association with loco-regional control. High TILs were associated with a significantly greater OS after PMRT at 20 years compared to low TILs (8% improvement for low TILs (23% to 31%) vs. 22% for high TILs (26% to 48%), interaction-test: p = 0.028). The association between TILs and PMRT was more pronounced among estrogen-receptor negative (ER-) tumors, and patients having ER-/low TILs tumors showed no OS benefit from PMRT at 20 years (-4% improvement for low TILs (28% to 24%) vs. 23% for high TILs (20% to 43%). A similar trend in the association between high TILs and reduced risk of DM after PMRT was seen.

CONCLUSION

High TILs predict improved OS from PMRT in BC patients, and the association appeared especially strong for ER- tumors. A trend in the association between high TILs and reduced risk of DM after PMRT was seen. These findings may indicate that RT triggers an immune response inducing a systemic effect outside the treatment field.

Advancement of prognostic models in breast cancer: a narrative review

Objective

To provide a reference for clinical work and guide the decision-making of healthcare providers and end-users, we systematically reviewed the development, validation and classification of classical prognostic models for breast cancer.

Background

Patients suffering from breast cancer have different prognosis for its high heterogeneity. Accurate prognosis prediction and risk stratification for breast cancer are crucial for individualized treatment. There is a lack of systematic summary of breast cancer prognostic models.

Methods

We conducted a PubMed search with keywords "breast neoplasm", "prognostic model", "recurrence" and "metastasis", and screened the retrieved publications at three levels: title, abstract and full text. We identified the articles presented the development and/or validation of models based on clinicopathological factors, genomics, and machine learning (ML) methods to predict survival and/or benefits of adjuvant therapy in female breast cancer patients.

Conclusions

Combining prognostic-related variables with long-term clinical outcomes, researchers have developed a series of prognostic models based on clinicopathological parameters, genomic assays, and medical figures. The discrimination, calibration, overall performance, and clinical usefulness were validated by internal and/or external verifications. Clinicopathological models integrated the clinical parameters, including tumor size, histological grade, lymph node status, hormone receptor status to provide prognostic information for patients and doctors. Gene-expression assays deeply revealed the molecular heterogeneity of breast cancer, some of which have been cited by AJCC and National Comprehensive Cancer Network (NCCN) guidelines. In addition, the models based on the ML methods provided more detailed information for prognosis prediction by increasing the data dimension. Combined models incorporating clinical variables and genomics information are still required to be developed as the focus of further researches.

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

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

Best Practices for Spatial Profiling for Breast Cancer Research with the GeoMx® Digital Spatial Profiler

Breast cancer is a heterogenous disease with variability in tumor cells and in the surrounding tumor microenvironment (TME). Understanding the molecular diversity in breast cancer is critical for improving prediction of therapeutic response and prognostication. High-plex spatial profiling of tumors enables characterization of heterogeneity in the breast TME, which can holistically illuminate the biology of tumor growth, dissemination and, ultimately, response to therapy. The GeoMx Digital Spatial Profiler (DSP) enables researchers to spatially resolve and quantify proteins and RNA transcripts from tissue sections. The platform is compatible with both formalin-fixed paraffin-embedded and frozen tissues. RNA profiling was developed at the whole transcriptome level for human and mouse samples and protein profiling of 100-plex for human samples. Tissue can be optically segmented for analysis of regions of interest or cell populations to study biology-directed tissue characterization. The GeoMx Breast Cancer Consortium (GBCC) is composed of breast cancer researchers who are developing innovative approaches for spatial profiling to accelerate biomarker discovery. Here, the GBCC presents best practices for GeoMx profiling to promote the collection of high-quality data, optimization of data analysis and integration of datasets to advance collaboration and meta-analyses. Although the capabilities of the platform are presented in the context of breast cancer research, they can be generalized to a variety of other tumor types that are characterized by high heterogeneity.

Reducing bias and increasing surgical knowledge: multicentre prospective breast cancer studies

In the March issue of BJS several hot topics within the breast surgery field are highlighted in beautifully planned and executed prospective multicentre trials. BJS encourages the surgical communities in most fields to move towards prospective collaborative and multicentre studies, thereby increasing both power and generalizability as well as reducing the risk of bias.