Molecular Signatures of Radiation Response in Breast Cancer: Towards Personalized Decision-Making in Radiation Treatment

Integrating Tumor-Intrinsic and Immunologic Factors to Identify Immunogenic Breast Cancers from a Low-Risk Cohort: Results from the Randomized SweBCG91RT Trial

PURPOSE

The local immune infiltrate's influence on tumor progression may be closely linked to tumor-intrinsic factors. The study aimed to investigate whether integrating immunologic and tumor-intrinsic factors can identify patients from a low-risk cohort who may be candidates for radiotherapy (RT) de-escalation.

EXPERIMENTAL DESIGN

The SweBCG91RT trial included 1,178 patients with stage I to IIA breast cancer, randomized to breast-conserving surgery with or without adjuvant RT, and followed for a median of 15.2 years. We trained two models designed to capture immunologic activity and immunomodulatory tumor-intrinsic qualities, respectively. We then analyzed if combining these two variables could further stratify tumors, allowing for identifying a subgroup where RT de-escalation is feasible, despite clinical indicators of a high risk of ipsilateral breast tumor recurrence (IBTR).

RESULTS

The prognostic effect of the immunologic model could be predicted by the tumor-intrinsic model (Pinteraction = 0.01). By integrating measurements of the immunologic- and tumor-intrinsic models, patients who benefited from an active immune infiltrate could be identified. These patients benefited from standard RT (HR, 0.28; 95% CI, 0.09-0.85; P = 0.025) and had a 5.4% 10-year incidence of IBTR after irradiation despite high-risk genomic indicators and a low frequency of systemic therapy. In contrast, high-risk tumors without an immune infiltrate had a high 10-year incidence of IBTR despite RT treatment (19.5%; 95% CI, 12.2-30.3).

CONCLUSIONS

Integrating tumor-intrinsic and immunologic factors may identify immunogenic tumors in early-stage breast cancer populations dominated by ER-positive tumors. Patients who benefit from an activated immune infiltrate may be candidates for RT de-escalation.

Predictive biomarkers for personalized medicine in breast cancer

Breast cancer is one of the most frequent malignancies among women worldwide. Based on clinical and molecular features of breast tumors, patients are treated with chemotherapy, hormonal therapy and/or radiotherapy and more recently with immunotherapy or targeted therapy. These different therapeutic options have markedly improved patient outcomes. However, further improvement is needed to fight against resistance to treatment. In the rapidly growing area of research for personalized medicine, predictive biomarkers - which predict patient response to therapy - are essential tools to select the patients who are most likely to benefit from the treatment, with the aim to give the right therapy to the right patient and avoid unnecessary overtreatment. The search for predictive biomarkers is an active field of research that includes genomic, proteomic and/or machine learning approaches. In this review, we describe current strategies and innovative tools to identify, evaluate and validate new biomarkers. We also summarize current predictive biomarkers in breast cancer and discuss companion biomarkers of targeted therapy in the context of precision medicine.

Radiotherapy of Breast Cancer-Professional Guideline 1st Central-Eastern European Professional Consensus Statement on Breast Cancer

The international radiotherapy (RT) expert panel has revised and updated the RT guidelines that were accepted in 2020 at the 4th Hungarian Breast Cancer Consensus Conference, based on new scientific evidence. Radiotherapy after breast-conserving surgery (BCS) is indicated in ductal carcinoma in situ (stage 0), as RT decreases the risk of local recurrence (LR) by 50-60%. In early stage (stage I-II) invasive breast cancer RT remains a standard treatment following BCS. However, in elderly (≥70 years) patients with stage I, hormone receptor-positive tumour, hormonal therapy without RT can be considered. Hypofractionated whole breast irradiation (WBI) and for selected cases accelerated partial breast irradiation are validated treatment alternatives to conventional WBI administered for 5 weeks. Following mastectomy, RT significantly decreases the risk of LR and improves overall survival of patients who have 1 to 3 or ≥4 positive axillary lymph nodes. In selected cases of patients with 1 to 2 positive sentinel lymph nodes axillary dissection can be substituted with axillary RT. After neoadjuvant systemic treatment (NST) followed by BCS, WBI is mandatory, while after NST followed by mastectomy, locoregional RT should be given in cases of initial stage III-IV and ypN1 axillary status.

Estrogen receptor inhibition mediates radiosensitization of ER-positive breast cancer models

Endocrine therapy (ET) is an effective first-line therapy for women with estrogen receptor-positive (ER + ) breast cancers. While both ionizing radiation (RT) and ET are used for the treatment of women with ER+ breast cancer, the most effective sequencing of therapy and the effect of ET on tumor radiosensitization remains unclear. Here we sought to understand the effects of inhibiting estrogen receptor (ER) signaling in combination with RT in multiple preclinical ER+ breast cancer models. Clonogenic survival assays were performed using variable pre- and post-treatment conditions to assess radiosensitization with estradiol, estrogen deprivation, tamoxifen, fulvestrant, or AZD9496 in ER+ breast cancer cell lines. Estrogen stimulation was radioprotective (radiation enhancement ratios [rER]: 0.51–0.82). Conversely, when given one hour prior to RT, ER inhibition or estrogen depletion radiosensitized ER+ MCF-7 and T47D cells (tamoxifen rER: 1.50–1.60, fulvestrant rER: 1.76–2.81, AZD9496 rER: 1.33–1.48, estrogen depletion rER: 1.47–1.51). Combination treatment resulted in an increase in double-strand DNA (dsDNA) breaks as a result of inhibition of non-homologous end joining-mediated dsDNA break repair with no effect on homologous recombination. Treatment with tamoxifen or fulvestrant in combination with RT also increased the number of senescent cells but did not affect apoptosis or cell cycle distribution. Using an MCF-7 xenograft model, concurrent treatment with tamoxifen and RT was synergistic and resulted in a significant decrease in tumor volume and a delay in time to tumor doubling without significant toxicity. These findings provide preclinical evidence that concurrent treatment with ET and RT may be an effective radiosensitization strategy.

3D Breast Tumor Models for Radiobiology Applications

Breast cancer is a leading cause of cancer-associated death in women. The clinical management of breast cancers is normally carried out using a combination of chemotherapy, surgery and radiation therapy. The majority of research investigating breast cancer therapy until now has mainly utilized two-dimensional (2D) in vitro cultures or murine models of disease. However, there has been significant uptake of three-dimensional (3D) in vitro models by cancer researchers over the past decade, highlighting a complimentary model for studies of radiotherapy, especially in conjunction with chemotherapy. In this review, we underline the effects of radiation therapy on normal and malignant breast cells and tissues, and explore the emerging opportunities that pre-clinical 3D models offer in improving our understanding of this treatment modality.

A six-gene-based signature for breast cancer radiotherapy sensitivity estimation

Breast cancer (BRCA) represents the most common malignancy among women worldwide with high mortality. Radiotherapy is a prevalent therapeutic for BRCA that with heterogeneous effectiveness among patients. Here, we proposed to develop a gene expression-based signature for BRCA radiotherapy sensitivity estimation. Gene expression profiles of BRCA samples from the Cancer Genome Atlas (TCGA) and International Cancer Genome Consortium (ICGC) were obtained and used as training and independent testing dataset, respectively. Differential expression genes (DEGs) in BRCA samples compared with their paracancerous samples in the training set were identified by using the edgeR Bioconductor package. Univariate Cox regression analysis and LASSO Cox regression method were applied to screen optimal genes for constructing a radiotherapy sensitivity estimation signature. Nomogram combining independent prognostic factors was used to predict 1-, 3-, and 5-year OS of radiation-treated BRCA patients. Relative proportions of tumor infiltrating immune cells (TIICs) calculated by CIBERSORT and mRNA levels of key immune checkpoint receptors was adopted to explore the relation between the signature and tumor immune response. As a result, 603 DEGs were obtained in BRCA tumor samples, six of which were retained and used to construct the radiotherapy sensitivity prediction model. The signature was proved to be robust in both training and testing sets. In addition, the signature was closely related to the immune microenvironment of BRCA in the context of TIICs and immune checkpoint receptors’ mRNA levels. In conclusion, the present study obtained a radiotherapy sensitivity estimation signature for BRCA, which should shed new light in clinical and experimental research.

Personalization in Modern Radiation Oncology: Methods, Results and Pitfalls. Personalized Interventions and Breast Cancer

Breast cancer, the most frequent malignancy in women worldwide, is a heterogeneous group of diseases, characterized by distinct molecular aberrations. In precision medicine, radiation oncology for breast cancer aims at tailoring treatment according to tumor biology and each patient’s clinical features and genetics. Although systemic therapies are personalized according to molecular sub-type [i.e. endocrine therapy for receptor-positive disease and anti-human epidermal growth factor receptor 2 (HER2) therapy for HER2-positive disease] and multi-gene assays, personalized radiation therapy has yet to be adopted in the clinical setting. Currently, attempts are being made to identify prognostic and/or predictive factors, biomarkers, signatures that could lead to personalized treatment in order to select appropriate patients who might, or might not, benefit from radiation therapy or whose radiation therapy might be escalated or de-escalated in dosages and volumes. This overview focuses on what has been achieved to date in personalized post-operative radiation therapy and individual patient radiosensitivity assessments by means of tumor sub-types and genetics.

Precision Medicine and the Role of Biomarkers of Radiotherapy Response in Breast Cancer

Radiotherapy remains an important treatment modality in nearly two thirds of all cancers, including the primary curative or palliative treatment of breast cancer. Unfortunately, largely due to tumor heterogeneity, tumor radiotherapy response rates can vary significantly, even between patients diagnosed with the same tumor type. Although in recent years significant technological advances have been made in the way radiation can be precisely delivered to tumors, it is proving more difficult to personalize radiotherapy regimens based on cancer biology. Biomarkers that provide prognostic or predictive information regarding a tumor's intrinsic radiosensitivity or its response to treatment could prove valuable in helping to personalize radiation dosing, enabling clinicians to make decisions between different treatment options whilst avoiding radiation-induced toxicity in patients unlikely to gain therapeutic benefit. Studies have investigated numerous ways in which both patient and tumor radiosensitivities can be assessed. Tumor molecular profiling has been used to develop radiosensitivity gene signatures, while the assessment of specific intracellular or secreted proteins, including circulating tumor cells, exosomes and DNA, has been performed to identify prognostic or predictive biomarkers of radiation response. Finally, the investigation of biomarkers related to radiation-induced toxicity could provide another means by which radiotherapy could become personalized. In this review, we discuss studies that have used these methods to identify or develop prognostic/predictive signatures of radiosensitivity, and how such assays could be used in the future as a means of providing personalized radiotherapy.

Proton Therapy and Src Family Kinase Inhibitor Combined Treatments on U87 Human Glioblastoma Multiforme Cell Line

Glioblastoma Multiforme (GBM) is the most common of malignant gliomas in adults with an exiguous life expectancy. Standard treatments are not curative and the resistance to both chemotherapy and conventional radiotherapy (RT) plans is the main cause of GBM care failures. Proton therapy (PT) shows a ballistic precision and a higher dose conformity than conventional RT. In this study we investigated the radiosensitive effects of a new targeted compound, SRC inhibitor, named Si306, in combination with PT on the U87 glioblastoma cell line. Clonogenic survival assay, dose modifying factor calculation and linear-quadratic model were performed to evaluate radiosensitizing effects mediated by combination of the Si306 with PT. Gene expression profiling by microarray was also conducted after PT treatments alone or combined, to identify gene signatures as biomarkers of response to treatments. Our results indicate that the Si306 compound exhibits a radiosensitizing action on the U87 cells causing a synergic cytotoxic effect with PT. In addition, microarray data confirm the SRC role as the main Si306 target and highlights new genes modulated by the combined action of Si306 and PT. We suggest, the Si306 as a new candidate to treat GBM in combination with PT, overcoming resistance to conventional treatments.

Proton-irradiated breast cells: molecular points of view

Breast cancer (BC) is the most common cancer in women, highly heterogeneous at both the clinical and molecular level. Radiation therapy (RT) represents an efficient modality to treat localized tumor in BC care, although the choice of a unique treatment plan for all BC patients, including RT, may not be the best option. Technological advances in RT are evolving with the use of charged particle beams (i.e. protons) which, due to a more localized delivery of the radiation dose, reduce the dose administered to the heart compared with conventional RT. However, few data regarding proton-induced molecular changes are currently available. The aim of this study was to investigate and describe the production of immunological molecules and gene expression profiles induced by proton irradiation. We performed Luminex assay and cDNA microarray analyses to study the biological processes activated following irradiation with proton beams, both in the non-tumorigenic MCF10A cell line and in two tumorigenic BC cell lines, MCF7 and MDA-MB-231. The immunological signatures were dose dependent in MCF10A and MCF7 cell lines, whereas MDA-MB-231 cells show a strong pro-inflammatory profile regardless of the dose delivered. Clonogenic assay revealed different surviving fractions according to the breast cell lines analyzed. We found the involvement of genes related to cell response to proton irradiation and reported specific cell line- and dose-dependent gene signatures, able to drive cell fate after radiation exposure. Our data could represent a useful tool to better understand the molecular mechanisms elicited by proton irradiation and to predict treatment outcome.