Aspirin use is associated with improvement in distant metastases outcome in patients with residual disease after neoadjuvant chemotherapy.

PURPOSE: Aspirin (ASA) use has been correlated with improved outcomes in high-risk patients at risk for distant metastases. Breast cancer (BC) patients with residual disease, particularly nodal disease (ypN +) after neoadjuvant chemotherapy (NAC), are high-risk patients portending worse outcomes. We hypothesized that ASA use can reduce distant metastases and improve outcomes in these patients. METHODS: Patients at our institutions from 2005 to 2018, with BC who did not achieve complete response (pCR) after NAC were reviewed (IRB protocol STU- 052012-019). Data, including evidence of ASA use, and clinico-pathologic parameters were analyzed. Survival outcomes were obtained (Kaplan Meier analysis) and univariate (UVA) and multivariable (MVA) Cox proportional hazards regression analyses were performed. RESULTS: 637 did not achieve pCR (ypN+ = 422). 138 were ASA users. Median follow-up for the control and ASA group were 3.8 (IQR 2.2-6.3) and 3.8 (IQR 2.5-6.4) years, respectively. Majority were stage II/III. 387 were hormone receptor positive, 191 HER2 +, and 157 triple negative. On UVA, ASA use, PR status, pathologic and clinical stage showed significance for DMFS, and disease-free survival (DFS). On MVA, ASA use associated with improved 5-year DFS (p = .01, 87.0% vs 79.6%, adjusted HR = 0.48) and improved 5-year DMFS (p = .04, 92.8% vs 89.2%, adjusted HR = 0.57). In the ypN + patients, ASA use associated with improved 5-year DMFS (p = .008, 85.7% vs 70.7%, adjusted HR = 0.43) and DFS (p = .02, 86.8% vs 74.3%, adjusted HR = 0.48). CONCLUSION: For non-responders, particularly ypN + patients, ASA use associated with improved outcome. These hypotheses-generating results suggest for development of prospective clinical trials of augmented ASA use in selected very high-risk BC patients.

On the feasibility of improved target coverage without compromising organs at risk using online adaptive stereotactic partial breast irradiation (A-SPBI).

PURPOSE: Describe an early-adopting institution's experience with online adaptive radiation for stereotactic partial breast irradiation. METHODS AND MATERIALS: Retrospective review of 22 women treated between May 2021 and March 2022 with adaptive stereotactic partial breast irradiation. A total of 106 of 110 fractions were evaluated for dosimetric changes in target coverage and organ-at-risk (OAR) dose. Patient set up with stereotactic wooden frame and adapted per fraction. Treatment and planning times were collected prospectively by radiation therapists. RESULTS: Scheduled PTV30 Gy was <95% in 72.1% and <90% in 38.5% of fractions, and both PTV and CTV coverage were improved significantly after adaption, and 83.7% of fractions were delivered as adapted per physician choice. There was no difference in OAR coverage. Average adaptive treatment planning took 15 min and average time-on-couch was 34.4 min. CONCLUSIONS: Adaptive stereotactic breast irradiation resulted in improved target coverage with equivalent dosing to OARs in an efficient and tolerated treatment time. Improved target coverage allowed for decreased PTV margins compared to prior trial protocols that may improve acute and late toxicities.

Feasibility and efficacy of active breathing coordinator assisted deep inspiration breath hold technique for treatment of locally advanced breast cancer.

BACKGROUND: Active breathing coordinator (ABC)-assisted deep inspiration breath hold (DIBH) is an important organ sparing radiation therapy (RT) technique for left-sided breast cancer patients. Patients with advanced breast cancer undergoing chest wall and regional nodal irradiation often require a field matching technique. While field matching has been demonstrated to be safe and effective in free breathing patients, its safety and accuracy in DIBH/ABC use has not been previously reported. PURPOSE: To report the accuracy, feasibility, and safety of field matching with ABC/DIBH for patients receiving breast/chest wall irradiation with nodal irradiation using a three-field technique. METHODS: From December 2012 to May 2018, breast cancer patients undergoing ABC/DIBH-based RT at a single institution were reviewed. For each fraction, the amount of overlap/gap between the supraclavicular and the tangential field were measured and recorded. Patient characteristics, including acute and delayed skin toxicities, were analyzed. RESULTS: A total of 202 patients utilized ABC/DIBH and 4973 fractions had gap/overlap measurements available for analysis. The average gap/overlap measured at junction was 0.28 mm ± 0.99 mm. A total of 72% of fractions had no measurable gap/overlap (0 mm), while 5.6% had an overlap and 22.7% a gap. There was no significant trend for worsening or improvement of gap/overlap measurements with increasing fraction number per patient. OSLD measurements were compared to the planned dose. The median dose 1 cm above the junction was 106% ± 7% of planned dose (range 94%-116%). One centimeter below the junction, the median dose was 114% ± 11% of planned dose (range 95%-131%). At the junction, the median dose was 106% ± 16.3% of planned dose (range 86%-131%). Acute skin toxicity was similar to historically reported values (grade 3, 5.4%, grade 4, 0%). CONCLUSION: ABC-assisted DIBH is a safe and technically feasible method of delivering RT in the setting of complex matching field technique for breast and regional nodal treatments.

Estrogen receptor coregulator binding modulator (ERX-11) enhances the activity of CDK4/6 inhibitors against estrogen receptor-positive breast cancers.

BACKGROUND: CDK4/6 inhibitors in combination with endocrine therapy (AE/AI/SERDs) are approved for the treatment of ER+ advanced breast cancer (BCa). However, not all patients benefit from CDK4/6 inhibitors therapy. We previously reported a novel therapeutic agent, ERX-11, that binds to the estrogen receptor (ER) and modulates ER-coregulator interactions. Here, we tested if the combination of ERX-11 with agents approved for ER+ BCa would be more potent. METHODS: We tested the effect of combination therapy using BCa cell line models, including those that have acquired resistance to tamoxifen, letrozole, or CDK4/6 inhibitors or have been engineered to express mutant forms of the ER. In vitro activity was tested using Cell Titer-Glo, MTT, and apoptosis assays. Mechanistic studies were conducted using western blot, reporter gene assays, RT-qPCR, and mass spectrometry approaches. Xenograft, patient-derived explants (PDEs), and xenograft-derived explants (XDE) were used for preclinical evaluation and toxicity. RESULTS: ERX-11 inhibited the proliferation of therapy-resistant BCa cells in a dose-dependent manner, including ribociclib resistance. The combination of ERX-11 and CDK4/6 inhibitor was synergistic in decreasing the proliferation of both endocrine therapy-sensitive and endocrine therapy-resistant BCa cells, in vitro, in xenograft models in vivo, xenograft-derived explants ex vivo, and in primary patient-derived explants ex vivo. Importantly, the combination caused xenograft tumor regression in vivo. Unbiased global mass spectrometry studies demonstrated profound decreases in proliferation markers with combination therapy and indicated global proteomic changes in E2F1, ER, and ER coregulators. Mechanistically, the combination of ERX-11 and CDK4/6 inhibitor decreased the interaction between ER and its coregulators, as evidenced by immunoprecipitation followed by mass spectrometry studies. Biochemical studies confirmed that the combination therapy significantly altered the expression of proteins involved in E2F1 and ER signaling, and this is primarily driven by a transcriptional shift, as noted in gene expression studies. CONCLUSIONS: Our results suggest that ERX-11 inhibited the proliferation of BCa cells resistant to both endocrine therapy and CDK4/6 inhibitors in a dose-dependent manner and that the combination of ERX-11 with a CDK4/6 inhibitor may represent a viable therapeutic approach.

Estrogen receptor mutations and their role in breast cancer progression.

Endocrine therapy is the mainstay of treatment in estrogen receptor-positive breast cancers and significantly reduces disease recurrence and breast cancer-related mortality. However, acquired resistance to therapy has been noted in nearly one-third of women treated with tamoxifen and other endocrine therapies. Mutations in the estrogen receptor have long been speculated to play a role in endocrine therapy resistance but have been rarely detected. However, recent studies utilizing next-generation sequencing on estrogen receptor-positive, metastatic clinical samples have revealed that recurrent ESR1 mutations are far more frequent than previously thought and may play an important role in acquired endocrine therapy resistance. Here we review recent advances in detection and characterization of ESR1 mutations in advanced, endocrine therapy-resistant breast cancers.

Master Breast Radiation Planning: Simple Guide for Radiation Oncology Residents.

This article focuses on various aspects of breast radiation treatment planning, from simulation to field design. It covers the most common techniques including tangents, mono isocentric, dual isocentric, electron-photon match, and VMAT. This can serve as a guide for radiation oncology residents and medical students to advance their understanding of key aspects of breast radiation treatment and planning processes.

Multimodal radiotherapy dose prediction using a multi-task deep learning model.

BACKGROUND: In radiation therapy (RT), accelerated partial breast irradiation (APBI) has emerged as an increasingly preferred treatment modality over conventional whole breast irradiation due to its targeted dose delivery and shorter course of treatment. APBI can be delivered through various modalities including Cobalt-60-based systems and linear accelerators with C-arm, O-ring, or robotic arm design. Each modality possesses distinct features, such as beam energy or the degrees of freedom in treatment planning, which influence their respective dose distributions. These modality-specific considerations emphasize the need for a quantitative approach in determining the optimal dose delivery modality on a patient-specific basis. However, manually generating treatment plans for each modality across every patient is time-consuming and clinically impractical. PURPOSE: We aim to develop an efficient and personalized approach for determining the optimal RT modality for APBI by training predictive models using two different deep learning-based convolutional neural networks. The baseline network performs a single-task (ST), predicting dose for a single modality. Our proposed multi-task (MT) network, which is capable of leveraging shared information among different tasks, can concurrently predict dose distributions for various RT modalities. Utilizing patient-specific input data, such as a patient's computed tomography (CT) scan and treatment protocol dosimetric goals, the MT model predicts patient-specific dose distributions across all trained modalities. These dose distributions provide patients and clinicians quantitative insights, facilitating informed and personalized modality comparison prior to treatment planning. METHODS: The dataset, comprising 28 APBI patients and their 92 treatment plans, was partitioned into training, validation, and test subsets. Eight patients were dedicated to the test subset, leaving 68 treatment plans across 20 patients to divide between the training and validation subsets. ST models were trained for each modality, and one MT model was trained to predict doses for all modalities simultaneously. Model performance was evaluated across the test dataset in terms of Mean Absolute Percent Error (MAPE). We conducted statistical analysis of model performance using the two-tailed Wilcoxon signed-rank test. RESULTS: Training times for five ST models ranged from 255 to 430 min per modality, totaling 1925 min, while the MT model required 2384 min. MT model prediction required an average of 1.82 s per patient, compared to ST model predictions at 0.93 s per modality. The MT model yielded MAPE of 1.1033 ± 0.3627% as opposed to the collective MAPE of 1.2386 ± 0.3872% from ST models, and the differences were statistically significant (p = 0.0003, 95% confidence interval = [-0.0865, -0.0712]). CONCLUSION: Our study highlights the potential benefits of a MT learning framework in predicting RT dose distributions across various modalities without notable compromises. This MT architecture approach offers several advantages, such as flexibility, scalability, and streamlined model management, making it an appealing solution for clinical deployment. With such a MT model, patients can make more informed treatment decisions, physicians gain more quantitative insight for pre-treatment decision-making, and clinics can better optimize resource allocation. With our proposed goal array and MT framework, we aim to expand this work to a site-agnostic dose prediction model, enhancing its generalizability and applicability.

A deep learning approach for automatic delineation of clinical target volume in stereotactic partial breast irradiation (S-PBI).

Accurate and efficient delineation of the clinical target volume (CTV) is of utmost significance in post-operative breast cancer radiotherapy. However, CTV delineation is challenging as the exact extent of microscopic disease encompassed by CTV is not visualizable in radiological images and remains uncertain. We proposed to mimic physicians' contouring practice for CTV segmentation in stereotactic partial breast irradiation (S-PBI) where CTV is derived from tumor bed volume (TBV) via a margin expansion followed by correcting the extensions for anatomical barriers of tumor invasion (e.g. skin, chest wall). We proposed a deep-learning model, where CT images and the corresponding TBV masks formed a multi-channel input for a 3D U-Net based architecture. The design guided the model to encode the location-related image features and directed the network to focus on TBV to initiate CTV segmentation. Gradient weighted class activation map (Grad-CAM) visualizations of the model predictions revealed that the extension rules and geometric/anatomical boundaries were learnt during model training to assist the network to limit the expansion to a certain distance from the chest wall and the skin. We retrospectively collected 175 prone CT images from 35 post-operative breast cancer patients who received 5-fraction partial breast irradiation regimen on GammaPod. The 35 patients were randomly split into training (25), validation (5) and test (5) sets. Our model achieved mean (standard deviation) of 0.94 (±0.02), 2.46 (±0.5) mm, and 0.53 (±0.14) mm for Dice similarity coefficient, 95th percentile Hausdorff distance, and average symmetric surface distance respectively on the test set. The results are promising for improving the efficiency and accuracy of CTV delineation during on-line treatment planning procedure.

On the possibility of estimating myocardial fiber architecture from cardiac strains.

The myocardium is composed of a complex network of contractile myofibers that are organized in such a way as to produce efficient contraction and relaxation of the heart. The myofiber architecture in the myocardium is a key determinant of cardiac motion and the global or organ-level function of the heart. Reports of architectural remodeling in cardiac diseases, such as pulmonary hypertension and myocardial infarction, potentially contributing to cardiac dysfunction call for the inclusion of an architectural marker for an improved assessment of cardiac function. However, the in-vivo quantification of three-dimensional myo-architecture has proven challenging. In this work, we examine the sensitivity of cardiac strains to varying myofiber orientation using a multiscale finite-element model of the LV. Additionally, we present an inverse modeling approach to predict the myocardium fiber structure from cardiac strains. Our results indicate a strong correlation between fiber orientation and LV kinematics, corroborating that the fiber structure is a principal determinant of LV contractile behavior. Our inverse model was capable of accurately predicting the myocardial fiber range and regional fiber angles from strain measures. A concrete understanding of the link between LV myofiber structure and motion, and the development of non-invasive and feasible means of characterizing the myocardium architecture is expected to lead to advanced LV functional metrics and improved prognostic assessment of structural heart disease.

Aspirin Use Is Associated With Improved Outcomes in Inflammatory Breast Cancer Patients.

PURPOSE: Inflammatory breast cancer (IBC) is the most aggressive form of breast cancer and has a high propensity for distant metastases. Our previous data suggested that aspirin (acetylsalicylic acid, ASA) use may be associated with reduced risk of distant metastases in aggressive breast cancer; however, there are no reported studies on the potential benefit of ASA use in patients with IBC. METHODS: Data from patients with non-metastatic IBC treated between 2000-2017 at two institutions, were reviewed. Overall survival (OS), disease-free survival (DFS), and distant metastasis-free survival (DMFS) were performed using Kaplan-Meier analysis. Univariate and multivariable logistic regression models were used to identify significant associated factors. RESULTS: Of 59 patients meeting the criteria for analysis and available for review, 14 ASA users were identified. ASA users demonstrated increased OS (p = 0.03) and DMFS (p = 0.02), with 5-year OS and DMFS of 92% (p = 0.01) and 85% (p = 0.01) compared to 51% and 43%, respectively, for non-ASA users. In univariate analysis, pT stage, pN stage, and ASA use were significantly correlated (p < 0.05) with OS and DFS. On multivariable analysis, ASA use (hazard ratio [HR], 0.11; 95% confidence interval [CI], 0.01-0.8) and lymph node stage (HR, 5.9; 95% CI, 1.4-25.9) remained significant for OS and DFS ASA use (HR, 0.13; 95% CI, 0.03-0.56) and lymph node stage (HR, 5.6; 95% CI, 1.9-16.4). CONCLUSION: ASA use during remission was associated with significantly improved OS and DMFS in patients with IBC. These results suggest that ASA may provide survival benefits to patients with IBC. Prospective clinical trials of ASA use in patients with high-risk IBC in remission should be considered.

Towards precision radiation oncology: endocrine therapy response as a biomarker for personalization of breast radiotherapy.

Targeted therapies, such as endocrine therapies (ET), can exert selective pressure on cancer cells and promote adaptations that confer treatment resistance. In this study, we show that ET resistance in breast cancer drives radiation resistance through reprogramming of DNA repair pathways. We also show that pharmacological bromodomain and extraterminal domain inhibition reverses pathological DNA repair reprogramming in ET-resistant breast tumors and overcomes resistance to radiation therapy.

A How-To Compendium for GammaPod Treatments, Clinical Workflow, and Clinical Program at an Early Adopting Institution.

In 2019, our institution became the second in the world to go live with GammaPod (Xcision Medical Systems, LLC, Columbia, MD), a device dedicated for stereotactic radiation therapy of breast cancer, with breast immobilization, real-time imaging, and highly-conformal dosimetry. At our institution, GammaPod is used for 5-fraction adjuvant partial breast irradiation, single-fraction tumor cavity boost before whole-breast irradiation, single-fraction preoperative radiation, and (in poor surgical candidates), single-fraction definitive radiation. Here, we describe our workflow, observed procedure step times, and homegrown techniques for improved efficiency in our institutional experience of 93 patients treated between 2019 and 2021.

Targeting radioresistance and replication fork stability in prostate cancer.

The bromodomain and extraterminal (BET) family of chromatin reader proteins bind to acetylated histones and regulate gene expression. The development of BET inhibitors (BETi) has expanded our knowledge of BET protein function beyond transcriptional regulation and has ushered several prostate cancer (PCa) clinical trials. However, BETi as a single agent is not associated with antitumor activity in patients with castration-resistant prostate cancer (CRPC). We hypothesized novel combinatorial strategies are likely to enhance the efficacy of BETi. By using PCa patient-derived explants and xenograft models, we show that BETi treatment enhanced the efficacy of radiation therapy (RT) and overcame radioresistance. Mechanistically, BETi potentiated the activity of RT by blocking DNA repair. We also report a synergistic relationship between BETi and topoisomerase I (TOP1) inhibitors (TOP1i). We show that the BETi OTX015 synergized with the new class of synthetic noncamptothecin TOP1i, LMP400 (indotecan), to block tumor growth in aggressive CRPC xenograft models. Mechanistically, BETi potentiated the antitumor activity of TOP1i by disrupting replication fork stability. Longitudinal analysis of patient tumors indicated that TOP1 transcript abundance increased as patients progressed from hormone-sensitive prostate cancer to CRPC. TOP1 was highly expressed in metastatic CRPC, and its expression correlated with the expression of BET family genes. These studies open new avenues for the rational combinatorial treatment of aggressive PCa.

Preliminary Results of Multi-Institutional Phase 1 Dose Escalation Trial Using Single-Fraction Stereotactic Partial Breast Irradiation for Early Stage Breast Cancer.

PURPOSE: We report on our early experience of our prospective multicenter phase 1 dose- escalation study of single-fraction stereotactic partial breast irradiation (S-PBI) for early stage breast cancer after partial mastectomy using a robotic stereotactic radiation system. METHODS AND MATERIALS: Thirty women with in situ or invasive breast cancer stage 0, I, or II with tumor size <3 cm treated with lumpectomy were enrolled in this phase 1 single-fraction S-PBI dose-escalation trial. Women received either 22.5, 26.5, or 30 Gy in a single fraction using a robotic stereotactic radiation system. The primary outcome was to reach tumoricidal dose of 30 Gy in a single fraction to the lumpectomy cavity without exceeding the maximum tolerated dose. Secondary outcomes were to determine dose-limiting toxicity and cosmesis. Tertiary goals were ipsilateral breast recurrence rate, distant disease-free interval, recurrence-free survival, and overall survival. RESULTS: From June 2016 to January 2021, 11, 8, and 10 patients were treated to doses of 22.5, 26.5, or 30 Gy in a single fraction, respectively, with median follow-up being 47.9, 25.1, and 16.2 months. No patients experienced acute (<90 days) grade 3 or higher treatment-related toxicity, and maximum tolerated dose was not reached. There were 2 delayed grade 3 toxicities. Four patients (13.8%) developed fat necrosis across all 3 cohorts, which compares favorably with results from other PBI trials. No dose cohort had a statistically significant cosmetic detriment from baseline to 12 months or 24 months follow-up by patient- or physician-reported global cosmetic scores. There were no reports of disease recurrence. CONCLUSIONS: This phase 1 trial demonstrates that S-PBI can be used to safely escalate dose to 30 Gy in a single fraction with low toxicity and without detriment in cosmesis relative to baseline.

Metabolic diversity within breast cancer brain-tropic cells determines metastatic fitness.

HER2+ breast cancer patients are presented with either synchronous (S-BM), latent (Lat), or metachronous (M-BM) brain metastases. However, the basis for disparate metastatic fitness among disseminated tumor cells of similar oncotype within a distal organ remains unknown. Here, employing brain metastatic models, we show that metabolic diversity and plasticity within brain-tropic cells determine metastatic fitness. Lactate secreted by aggressive metastatic cells or lactate supplementation to mice bearing Lat cells limits innate immunosurveillance and triggers overt metastasis. Attenuating lactate metabolism in S-BM impedes metastasis, while M-BM adapt and survive as residual disease. In contrast to S-BM, Lat and M-BM survive in equilibrium with innate immunosurveillance, oxidize glutamine, and maintain cellular redox homeostasis through the anionic amino acid transporter xCT. Moreover, xCT expression is significantly higher in matched M-BM brain metastatic samples compared to primary tumors from HER2+ breast cancer patients. Inhibiting xCT function attenuates residual disease and recurrence in these preclinical models.

Targeting ESR1 mutation-induced transcriptional addiction in breast cancer with BET inhibition.

Acquired mutations in the ligand-binding domain (LBD) of the gene encoding estrogen receptor α (ESR1) are common mechanisms of endocrine therapy resistance in patients with metastatic ER+ breast cancer. The ESR1 Y537S mutation, in particular, is associated with development of resistance to most endocrine therapies used to treat breast cancer. Employing a high-throughput screen of nearly 1,200 Federal Drug Administration-approved (FDA-approved) drugs, we show that OTX015, a bromodomain and extraterminal domain (BET) inhibitor, is one of the top suppressors of ESR1 mutant cell growth. OTX015 was more efficacious than fulvestrant, a selective ER degrader, in inhibiting ESR1 mutant xenograft growth. When combined with abemaciclib, a CDK4/6 inhibitor, OTX015 induced more potent tumor regression than current standard-of-care treatment of abemaciclib + fulvestrant. OTX015 has preferential activity against Y537S mutant breast cancer cells and blocks their clonal selection in competition studies with WT cells. Thus, BET inhibition has the potential to both prevent and overcome ESR1 mutant-induced endocrine therapy resistance in breast cancer.

LncRNA HOTAIR regulates glucose transporter Glut1 expression and glucose uptake in macrophages during inflammation.

Inflammation plays central roles in the immune response. Inflammatory response normally requires higher energy and therefore is associated with glucose metabolism. Our recent study demonstrates that lncRNA HOTAIR plays key roles in NF-kB activation, cytokine expression, and inflammation. Here, we investigated if HOTAIR plays any role in the regulation of glucose metabolism in immune cells during inflammation. Our results demonstrate that LPS-induced inflammation induces the expression of glucose transporter isoform 1 (Glut1) which controls the glucose uptake in macrophages. LPS-induced Glut1 expression is regulated via NF-kB activation. Importantly, siRNA-mediated knockdown of HOTAIR suppressed the LPS-induced expression of Glut1 suggesting key roles of HOTAIR in LPS-induced Glut1 expression in macrophage. HOTAIR induces NF-kB activation, which in turn increases Glut1 expression in response to LPS. We also found that HOTAIR regulates glucose uptake in macrophages during LPS-induced inflammation and its knockdown decreases LPS-induced increased glucose uptake. HOTAIR also regulates other upstream regulators of glucose metabolism such as PTEN and HIF1α, suggesting its multimodal functions in glucose metabolism. Overall, our study demonstrated that lncRNA HOTAIR plays key roles in LPS-induced Glut1 expression and glucose uptake by activating NF-kB and hence HOTAIR regulates metabolic programming in immune cells potentially to meet the energy needs during the immune response.

Saliency-guided deep learning network for automatic tumor bed volume delineation in post-operative breast irradiation.

Efficient, reliable and reproducible target volume delineation is a key step in the effective planning of breast radiotherapy. However, post-operative breast target delineation is challenging as the contrast between the tumor bed volume (TBV) and normal breast tissue is relatively low in CT images. In this study, we propose to mimic the marker-guidance procedure in manual target delineation. We developed a saliency-based deep learning segmentation (SDL-Seg) algorithm for accurate TBV segmentation in post-operative breast irradiation. The SDL-Seg algorithm incorporates saliency information in the form of markers' location cues into a U-Net model. The design forces the model to encode the location-related features, which underscores regions with high saliency levels and suppresses low saliency regions. The saliency maps were generated by identifying markers on CT images. Markers' location were then converted to probability maps using a distance transformation coupled with a Gaussian filter. Subsequently, the CT images and the corresponding saliency maps formed a multi-channel input for the SDL-Seg network. Our in-house dataset was comprised of 145 prone CT images from 29 post-operative breast cancer patients, who received 5-fraction partial breast irradiation (PBI) regimen on GammaPod. The 29 patients were randomly split into training (19), validation (5) and test (5) sets. The performance of the proposed method was compared against basic U-Net. Our model achieved mean (standard deviation) of 76.4(±2.7) %, 6.76(±1.83) mm, and 1.9(±0.66) mm for Dice similarity coefficient, 95 percentile Hausdorff distance, and average symmetric surface distance respectively on the test set with computation time of below 11 seconds per one CT volume. SDL-Seg showed superior performance relative to basic U-Net for all the evaluation metrics while preserving low computation cost. The findings demonstrate that SDL-Seg is a promising approach for improving the efficiency and accuracy of the on-line treatment planning procedure of PBI, such as GammaPod based PBI.

Cosmetic Outcomes of a Phase 1 Dose Escalation Study of 5-Fraction Stereotactic Partial Breast Irradiation for Early Stage Breast Cancer.

PURPOSE: Our purpose was to evaluate cosmetic changes after 5-fraction adjuvant stereotactic partial breast irradiation (S-PBI). METHODS AND MATERIALS: Seventy-five women with in situ or invasive breast cancer stage 0, I, or II, with tumor size ≤3 cm, were enrolled after lumpectomy in a phase 1 dose escalation trial of S-PBI into cohorts receiving 30, 32.5, 35, 37.5, or 40 Gy in 5 fractions. Before S-PBI, 3 to 4 gold fiducial markers were placed in the lumpectomy cavity for tracking with the Synchrony respiratory tracking system. S-PBI was delivered with a CyberKnife robotic radiosurgery system. Patients and physicians evaluated global cosmesis using the Harvard Breast Cosmesis Scale. Eight independent panelists evaluated digital photography for global cosmesis and 10 subdomains at baseline and follow-up. McNemar tests were used to evaluate change in cosmesis, graded as excellent/good or fair/poor, from baseline to year 3. Wilcoxon signed rank tests were used to evaluate change in subdomains. Cohen's kappa (κ) statistic was used to estimate interobserver agreement (IOA) between raters, and Fleiss' κ was used to estimate IOA between panelists. RESULTS: Median cosmetic follow-up was 5, 5, 5, 4, and 3 years for the 30, 32.5, 35, 37.5, and 40 Gy cohorts. Most patients reported excellent/good cosmesis at both baseline (86.3%) and year 3 (89.8%). No dose cohort had significantly worsened cosmesis by year 3 on McNemar analysis. No cosmetic subdomain had significant worsening by year 3. IOA was fair for patient-physician (κ = 0.300, P < .001), patient-panel (κ = 0.295, P < .001), physician-panel (κ = 0.256, P < .001), and individual panelists (Fleiss κ = 0.327, P < .001). CONCLUSIONS: Dose escalation of S-PBI from 30 to 40 Gy in 5 fractions for early stage breast cancer was not associated with a detectable change in cosmesis by year 3. S-PBI is a promising modality for treatment of early stage breast cancer.

HOXA5 Expression Is Elevated in Breast Cancer and Is Transcriptionally Regulated by Estradiol.

HOXA5 is a homeobox-containing gene associated with the development of the lung, gastrointestinal tract, and vertebrae. Here, we investigate potential roles and the gene regulatory mechanism in HOXA5 in breast cancer cells. Our studies demonstrate that HOXA5 expression is elevated in breast cancer tissues and in estrogen receptor (ER)-positive breast cancer cells. HOXA5 expression is critical for breast cancer cell viability. Biochemical studies show that estradiol (E2) regulates HOXA5 gene expression in cultured breast cancer cells in vitro. HOXA5 expression is also upregulated in vivo in the mammary tissues of ovariectomized female rats. E2-induced HOXA5 expression is coordinated by ERs. Knockdown of either ERα or ERß downregulated E2-induced HOXA5 expression. Additionally, ER co-regulators, including CBP/p300 (histone acetylases) and MLL-histone methylases (MLL2, MLL3), histone acetylation-, and H3K4 trimethylation levels are enriched at the HOXA5 promoter in present E2. In summary, our studies demonstrate that HOXA5 is overexpressed in breast cancer and is transcriptionally regulated via estradiol in breast cancer cells.