A spatially resolved single-cell genomic atlas of the adult human breast.

The adult human breast is comprised of an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue1-3. Although most previous studies have focused on the breast epithelial system4-6, many of the non-epithelial cell types remain understudied. Here we constructed the comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics study profiled 714,331 cells from 126 women, and 117,346 nuclei from 20 women, identifying 12 major cell types and 58 biological cell states. These data reveal abundant perivascular, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Spatial mapping using four different technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide a reference of the adult normal breast tissue for studying mammary biology and diseases such as breast cancer.

Prediction of breast cancer molecular subtypes on DCE-MRI using convolutional neural network with transfer learning between two centers.

OBJECTIVES: To apply deep learning algorithms using a conventional convolutional neural network (CNN) and a recurrent CNN to differentiate three breast cancer molecular subtypes on MRI. METHODS: A total of 244 patients were analyzed, 99 in training dataset scanned at 1.5 T and 83 in testing-1 and 62 in testing-2 scanned at 3 T. Patients were classified into 3 subtypes based on hormonal receptor (HR) and HER2 receptor: (HR+/HER2-), HER2+, and triple negative (TN). Only images acquired in the DCE sequence were used in the analysis. The smallest bounding box covering tumor ROI was used as the input for deep learning to develop the model in the training dataset, by using a conventional CNN and the convolutional long short-term memory (CLSTM). Then, transfer learning was applied to re-tune the model using testing-1(2) and evaluated in testing-2(1). RESULTS: In the training dataset, the mean accuracy evaluated using tenfold cross-validation was higher by using CLSTM (0.91) than by using CNN (0.79). When the developed model was applied to the independent testing datasets, the accuracy was 0.4-0.5. With transfer learning by re-tuning parameters in testing-1, the mean accuracy reached 0.91 by CNN and 0.83 by CLSTM, and improved accuracy in testing-2 from 0.47 to 0.78 by CNN and from 0.39 to 0.74 by CLSTM. Overall, transfer learning could improve the classification accuracy by greater than 30%. CONCLUSIONS: The recurrent network using CLSTM could track changes in signal intensity during DCE acquisition, and achieved a higher accuracy compared with conventional CNN during training. For datasets acquired using different settings, transfer learning can be applied to re-tune the model and improve accuracy. KEY POINTS: • Deep learning can be applied to differentiate breast cancer molecular subtypes. • The recurrent neural network using CLSTM could track the change of signal intensity in DCE images, and achieved a higher accuracy compared with conventional CNN during training. • For datasets acquired using different scanners with different imaging protocols, transfer learning provided an efficient method to re-tune the classification model and improve accuracy.

Development of U-Net Breast Density Segmentation Method for Fat-Sat MR Images Using Transfer Learning Based on Non-Fat-Sat Model.

To develop a U-net deep learning method for breast tissue segmentation on fat-sat T1-weighted (T1W) MRI using transfer learning (TL) from a model developed for non-fat-sat images. The training dataset (N = 126) was imaged on a 1.5 T MR scanner, and the independent testing dataset (N = 40) was imaged on a 3 T scanner, both using fat-sat T1W pulse sequence. Pre-contrast images acquired in the dynamic-contrast-enhanced (DCE) MRI sequence were used for analysis. All patients had unilateral cancer, and the segmentation was performed using the contralateral normal breast. The ground truth of breast and fibroglandular tissue (FGT) segmentation was generated using a template-based segmentation method with a clustering algorithm. The deep learning segmentation was performed using U-net models trained with and without TL, by using initial values of trainable parameters taken from the previous model for non-fat-sat images. The ground truth of each case was used to evaluate the segmentation performance of the U-net models by calculating the dice similarity coefficient (DSC) and the overall accuracy based on all pixels. Pearson's correlation was used to evaluate the correlation of breast volume and FGT volume between the U-net prediction output and the ground truth. In the training dataset, the evaluation was performed using tenfold cross-validation, and the mean DSC with and without TL was 0.97 vs. 0.95 for breast and 0.86 vs. 0.80 for FGT. When the final model developed with and without TL from the training dataset was applied to the testing dataset, the mean DSC was 0.89 vs. 0.83 for breast and 0.81 vs. 0.81 for FGT, respectively. Application of TL not only improved the DSC, but also decreased the required training case number. Lastly, there was a high correlation (R2 > 0.90) for both the training and testing datasets between the U-net prediction output and ground truth for breast volume and FGT volume. U-net can be applied to perform breast tissue segmentation on fat-sat images, and TL is an efficient strategy to develop a specific model for each different dataset.

Full dose neoadjuvant FOLFIRINOX is associated with prolonged survival in patients with locally advanced pancreatic adenocarcinoma.

BACKGROUND: The efficacy of FOLFIRINOX for metastatic pancreatic cancer has led to its use in patients with earlier stages of disease. This study retrospectively analyzed a cohort of patients with locally-advanced pancreatic cancer (LAPC) treated with FOLFIRINOX. METHODS: Between 2008 and 2013, 51 treatment-naïve patients with LAPC at a single institution received first-line FOLFIRINOX with neoadjuvant intent, at the full dose as described in the PRODIGE 4/ACCORD 11 study. Combined chemoradiation was administered for those who remained unresectable after maximum response to chemotherapy. The primary outcome measure was overall survival (OS), and secondary outcomes were progression-free survival (PFS) and margin-negative (R0) resection rate, and toxicity profile. RESULTS: A total of 429 cycles of FOLFIRINOX were given with a median of 8 cycles (range 2-29) per patient; 66% of cycles were full dose. After chemotherapy, 27 (53%) received chemoradiation. The median OS was 35.4 months (95% CI 25.8-45). Ten (4 borderline resectable and 6 unresectable) patients had successful R0 resections; those who had R0 resections had a significantly longer survival than those who did not (3-year OS rate 67% versus 21%, log rank p = 0.042). Increasing number of full-dose cycles was significantly associated with increased survival. The toxicity profile was similar to previous reports of this regimen. CONCLUSIONS: FOLFIRINOX is feasible as neoadjuvant therapy for LAPC. Although the R0 resection rate was only 20%, the median OS of almost 3 years appears promising. Dose intensity and duration were associated with increased survival in this study, arguing against dose attenuated versions of this regimen.

Multiorgan Failure From Nivolumab and Ipilimumab: A Case Report and Literature Review.

Immune checkpoint inhibitors (ICIs) as standard of care have revolutionized the treatment of patients with metastatic melanoma. The combination of nivolumab and ipilimumab improves treatment efficacy and prolongs survival compared to monotherapy alone. However, combination therapy is also associated with an increased incidence of adverse events. We report an uncommon yet important case of multi-organ failure in a patient following a single dose of nivolumab plus ipilimumab. A 60-year-old male with a history of ulcerative colitis in remission and metastatic melanoma was admitted on February 25, 2021, for presumed sepsis, after presenting with neutropenic fever. His brain metastases were previously resected on January 14, 2021, and he was started on dexamethasone 4 mg BID for six weeks. On February 11, 2021, he received one dose of nivolumab plus ipilimumab, per the CheckMate-067 protocol. He presented 14 days later with fever, diarrhea, pancytopenia, renal failure, drug-induced hepatitis, and myocarditis. The infectious workup was negative. His neutropenia responded to growth factors. He was diagnosed with interstitial nephritis due to immunotherapy and treated with corticosteroids. His symptoms resolved with concomitant improvement of his renal, hepatic, and cardiac function. He was discharged home in a stable condition. Although these specific immune-related adverse events (irAEs) are uncommon and rarely occur simultaneously, ICIs can trigger non-specific immune system activation, resulting in widespread inflammatory effects. Since irAEs can lead to multi-organ failure, as evidenced in this case, early recognition and institution of high-dose steroids are critical to preventing rapid deterioration. Given that ICI therapy is the standard of care for several cancers and is often studied in clinical trials, increased education on irAE toxicity and updated algorithms on the management of febrile cancer patients are warranted.

Goserelin-Induced Chemical Burn: A Case Report and Review of the Literature.

A chemical burn resulting from luteinizing hormone-releasing hormone agonists (LHRHa) is a rare adverse effect that has not been well-documented in prior literature. In this case report, we report a partial-thickness burn that developed following a single subcutaneous injection of goserelin. To our knowledge, this is the first description of goserelin-induced chemical burn in the literature. The importance of early identification and treatment of LHRHa-associated cutaneous reactions must be highlighted to ensure optimal oncologic management and patient comfort.

Deep Learning-based Automatic Diagnosis of Breast Cancer on MRI Using Mask R-CNN for Detection Followed by ResNet50 for Classification.

RATIONALE AND OBJECTIVES: Diagnosis of breast cancer on MRI requires, first, the identification of suspicious lesions; second, the characterization to give a diagnostic impression. We implemented Mask Reginal-Convolutional Neural Network (R-CNN) to detect abnormal lesions, followed by ResNet50 to estimate the malignancy probability. MATERIALS AND METHODS: Two datasets were used. The first set had 176 cases, 103 cancer, and 73 benign. The second set had 84 cases, 53 cancer, and 31 benign. For detection, the pre-contrast image and the subtraction images of left and right breasts were used as inputs, so the symmetry could be considered. The detected suspicious area was characterized by ResNet50, using three DCE parametric maps as inputs. The results obtained using slice-based analyses were combined to give a lesion-based diagnosis. RESULTS: In the first dataset, 101 of 103 cancers were detected by Mask R-CNN as suspicious, and 99 of 101 were correctly classified by ResNet50 as cancer, with a sensitivity of 99/103 = 96%. 48 of 73 benign lesions and 131 normal areas were identified as suspicious. Following classification by ResNet50, only 16 benign and 16 normal areas remained as malignant. The second dataset was used for independent testing. The sensitivity was 43/53 = 81%. Of the total of 121 identified non-cancerous lesions, only 6 of 31 benign lesions and 22 normal tissues were classified as malignant. CONCLUSION: ResNet50 could eliminate approximately 80% of false positives detected by Mask R-CNN. Combining Mask R-CNN and ResNet50 has the potential to develop a fully-automatic computer-aided diagnostic system for breast cancer on MRI.

Electroacupuncture for the management of symptom clusters in cancer patients and survivors (EAST).

BACKGROUND: Neuropsychiatric symptoms, comprising cognitive impairment, fatigue, insomnia, depression, and anxiety, are prevalent and may co-occur during and after chemotherapy treatment for cancer. Electroacupuncture (EA), which involves mild electrical stimulation with acupuncture, holds great potential in addressing the management of individual symptoms. However, there is a lack of studies evaluating if EA can manage concurrent neuropsychiatric symptoms in cancer (i.e., symptom cluster). Hence, we designed a trial to evaluate the efficacy, safety, and feasibility of administering EA as an intervention to mitigate neuropsychiatric symptom clusters amongst cancer patients and survivors. METHODS: The EAST study is a randomized, sham-controlled, patient- and assessor-blinded clinical trial. Sixty-four cancer patients and survivors with complaints of one or more neuropsychiatric symptom(s) in the seven days prior to enrollment are recruited from the University of California Irvine (UCI) and Children's Hospital of Orange County (CHOC). Individuals with needle phobia, metastases, bleeding disorders, electronic implants, epilepsy, exposure to acupuncture in the three months prior to enrollment, and who are breastfeeding, pregnant, or planning to get pregnant during the duration of the study will be excluded. Screening for metal fragments and claustrophobia are performed prior to the optional neuroimaging procedures. Recruited patients will be randomized (1:1) in random blocks of four or six to receive either ten weekly verum EA (treatment arm, vEA) or weekly sham EA (control arm, sEA) treatment visits with a follow-up appointment four to twelve weeks after their last treatment visit. The treatment arm will receive EA at 13 acupuncture points (acupoints) chosen for their therapeutic effects, while the control arm receives minimal EA at 7 non-disease-related acupoints. Questionnaires and cognitive assessments are administered, and blood drawn to assess changes in symptom clusters and biomarkers, respectively. CONCLUSION: The EAST study can provide insight into the efficacy of EA, an integrative medicine modality, in the management of cancer symptom clusters in routine clinical practice. TRIAL REGISTRATION: This trial is registered with clinicaltrials.gov NCT05283577.

Preneoplastic stromal cells promote BRCA1-mediated breast tumorigenesis.

Women with germline BRCA1 mutations (BRCA1+/mut) have increased risk for hereditary breast cancer. Cancer initiation in BRCA1+/mut is associated with premalignant changes in breast epithelium; however, the role of the epithelium-associated stromal niche during BRCA1-driven tumor initiation remains unclear. Here we show that the premalignant stromal niche promotes epithelial proliferation and mutant BRCA1-driven tumorigenesis in trans. Using single-cell RNA sequencing analysis of human preneoplastic BRCA1+/mut and noncarrier breast tissues, we show distinct changes in epithelial homeostasis including increased proliferation and expansion of basal-luminal intermediate progenitor cells. Additionally, BRCA1+/mut stromal cells show increased expression of pro-proliferative paracrine signals. In particular, we identify pre-cancer-associated fibroblasts (pre-CAFs) that produce protumorigenic factors including matrix metalloproteinase 3 (MMP3), which promotes BRCA1-driven tumorigenesis in vivo. Together, our findings demonstrate that precancerous stroma in BRCA1+/mut may elevate breast cancer risk through the promotion of epithelial proliferation and an accumulation of luminal progenitor cells with altered differentiation.

A spatially resolved single cell genomic atlas of the adult human breast.

The adult human breast comprises an intricate network of epithelial ducts and lobules that are embedded in connective and adipose tissue. While previous studies have mainly focused on the breast epithelial system, many of the non-epithelial cell types remain understudied. Here, we constructed a comprehensive Human Breast Cell Atlas (HBCA) at single-cell and spatial resolution. Our single-cell transcriptomics data profiled 535,941 cells from 62 women, and 120,024 nuclei from 20 women, identifying 11 major cell types and 53 cell states. These data revealed abundant pericyte, endothelial and immune cell populations, and highly diverse luminal epithelial cell states. Our spatial mapping using three technologies revealed an unexpectedly rich ecosystem of tissue-resident immune cells in the ducts and lobules, as well as distinct molecular differences between ductal and lobular regions. Collectively, these data provide an unprecedented reference of adult normal breast tissue for studying mammary biology and disease states such as breast cancer.

Longitudinal evaluation of dehydroepiandrosterone (DHEA), its sulfated form and estradiol with cancer-related cognitive impairment in early-stage breast cancer patients receiving chemotherapy.

The purpose of this study is to elucidate how patient-reported cognitive symptoms manifest from variations in hormone levels or precursors such as dehydroepiandrosterone (DHEA) and its sulfated form [collectively termed as DHEA(S)] and to investigate their association in breast cancer survivors. Levels of estradiol and DHEA(S) were compared between early-stage breast cancer patients with and without cancer-related cognitive impairment (CRCI) during adjuvant chemotherapy. Data were analyzed from 242 patients (mean age ± SD = 50.8 ± 9.2 years) who had completed FACT-Cog v.3.0, blood draws and questionnaires. Regression model was used to fit the magnitude of change in each respective biomarker levels against overall cognitive impairment status while adjusting for clinically important covariates. There was reduction in mean plasma levels of estradiol and DHEAS during and towards the end of chemotherapy (p-values < 0.001). Compared to non-impaired patients, smaller magnitude of decline was observed in DHEA(S) levels in patients reporting CRCI, with significant association between decline in DHEAS levels and acute onset of CRCI at 6 weeks from baseline (adjusted ß of 0.40, p-value of 0.02). In contrast, patients reporting CRCI showed greater magnitude of decline in estradiol compared to non-impaired patients, although this was not found to be statistically significant. There was an association between magnitude of change in biomarker levels with self-reported CRCI which suggests that the hormonal pathway related to DHEAS may be implicated in acute CRCI for breast cancer survivors. Our findings help to improve biological understanding of the pathway from which DHEAS may correlate with cognitive dysfunction and its impact on cancer survivors.

Automatic Detection and Segmentation of Breast Cancer on MRI Using Mask R-CNN Trained on Non-Fat-Sat Images and Tested on Fat-Sat Images.

RATIONALE AND OBJECTIVES: Computer-aided methods have been widely applied to diagnose lesions on breast magnetic resonance imaging (MRI). The first step was to identify abnormal areas. A deep learning Mask Regional Convolutional Neural Network (R-CNN) was implemented to search the entire set of images and detect suspicious lesions. MATERIALS AND METHODS: Two DCE-MRI datasets were used, 241 patients acquired using non-fat-sat sequence for training, and 98 patients acquired using fat-sat sequence for testing. All patients have confirmed unilateral mass cancers. The tumor was segmented using fuzzy c-means clustering algorithm to serve as the ground truth. Mask R-CNN was implemented with ResNet-101 as the backbone. The neural network output the bounding boxes and the segmented tumor for evaluation using the Dice Similarity Coefficient (DSC). The detection performance, and the trade-off between sensitivity and specificity, was analyzed using free response receiver operating characteristic. RESULTS: When the precontrast and subtraction image of both breasts were used as input, the false positive from the heart and normal parenchymal enhancements could be minimized. The training set had 1469 positive slices (containing lesion) and 9135 negative slices. In 10-fold cross-validation, the mean accuracy = 0.86 and DSC = 0.82. The testing dataset had 1568 positive and 7264 negative slices, with accuracy = 0.75 and DSC = 0.79. When the obtained per-slice results were combined, 240 of 241 (99.5%) lesions in the training and 98 of 98 (100%) lesions in the testing datasets were identified. CONCLUSION: Deep learning using Mask R-CNN provided a feasible method to search breast MRI, localize, and segment lesions. This may be integrated with other artificial intelligence algorithms to develop a fully automatic breast MRI diagnostic system.

Rapid isolation of circulating cancer associated fibroblasts by acoustic microstreaming for assessing metastatic propensity of breast cancer patients.

We demonstrate a label free and high-throughput microbubble-based acoustic microstreaming technique to isolate rare circulating cells such as circulating cancer associated fibroblasts (cCAFs) in addition to circulating tumor cells (CTCs) and immune cells (i.e. leukocytes) from clinically diagnosed patients with a capture efficiency of 94% while preserving cell functional integrity within 8 minutes. The microfluidic device is self-pumping and was optimized to increase flow rate and achieve near perfect capturing of rare cells enabled by having a trapping capacity above the acoustic vortex saturation concentration threshold. Our approach enables rapid isolation of CTCs, cCAFs and their associated clusters from blood samples of cancer patients at different stages. By examining the combined role of cCAFs and CTCs in early cancer onset and metastasis progression, the device accurately diagnoses both cancer and the metastatic propensity of breast cancer patients. This was confirmed by flow cytometry where we observed that metastatic breast cancer blood samples had significantly higher percentage of exhausted CD8+ T cells expressing programmed cell death protein 1 (PD1), higher number of CD4+ T regulatory cells and T helper cells. We show for the first time that our lateral cavity acoustic transducers (LCATs)-based approach can thus be developed into a metastatic propensity assay for clinical usage by elucidating cancer immunological responses and the complex relationships between CTCs and its companion tumor microenvironment.

Diagnosis of Breast Cancer Using Radiomics Models Built Based on Dynamic Contrast Enhanced MRI Combined With Mammography.

OBJECTIVE: To build radiomics models using features extracted from DCE-MRI and mammography for diagnosis of breast cancer. MATERIALS AND METHODS: 266 patients receiving MRI and mammography, who had well-enhanced lesions on MRI and histologically confirmed diagnosis were analyzed. Training dataset had 146 malignant and 56 benign, and testing dataset had 48 malignant and 18 benign lesions. Fuzzy-C-means clustering algorithm was used to segment the enhanced lesion on subtraction MRI maps. Two radiologists manually outlined the corresponding lesion on mammography by consensus, with the guidance of MRI maximum intensity projection. Features were extracted using PyRadiomics from three DCE-MRI parametric maps, and from the lesion and a 2-cm bandshell margin on mammography. The support vector machine (SVM) was applied for feature selection and model building, using 5 datasets: DCE-MRI, mammography lesion-ROI, mammography margin-ROI, mammography lesion+margin, and all combined. RESULTS: In the training dataset evaluated using 10-fold cross-validation, the diagnostic accuracy of the individual model was 83.2% for DCE-MRI, 75.7% for mammography lesion, 64.4% for mammography margin, and 77.2% for lesion+margin. When all features were combined, the accuracy was improved to 89.6%. By adding mammography features to MRI, the specificity was significantly improved from 69.6% (39/56) to 82.1% (46/56), p<0.01. When the developed models were applied to the independent testing dataset, the accuracy was 78.8% for DCE-MRI and 83.3% for combined MRI+Mammography. CONCLUSION: The radiomics model built from the combined MRI and mammography has the potential to provide a machine learning-based diagnostic tool and decrease the false positive diagnosis of contrast-enhanced benign lesions on MRI.

BI-RADS Reading of Non-Mass Lesions on DCE-MRI and Differential Diagnosis Performed by Radiomics and Deep Learning.

BACKGROUND: A wide variety of benign and malignant processes can manifest as non-mass enhancement (NME) in breast MRI. Compared to mass lesions, there are no distinct features that can be used for differential diagnosis. The purpose is to use the BI-RADS descriptors and models developed using radiomics and deep learning to distinguish benign from malignant NME lesions. MATERIALS AND METHODS: A total of 150 patients with 104 malignant and 46 benign NME were analyzed. Three radiologists performed reading for morphological distribution and internal enhancement using the 5th BI-RADS lexicon. For each case, the 3D tumor mask was generated using Fuzzy-C-Means segmentation. Three DCE parametric maps related to wash-in, maximum, and wash-out were generated, and PyRadiomics was applied to extract features. The radiomics model was built using five machine learning algorithms. ResNet50 was implemented using three parametric maps as input. Approximately 70% of earlier cases were used for training, and 30% of later cases were held out for testing. RESULTS: The diagnostic BI-RADS in the original MRI report showed that 104/104 malignant and 36/46 benign lesions had a BI-RADS score of 4A-5. For category reading, the kappa coefficient was 0.83 for morphological distribution (excellent) and 0.52 for internal enhancement (moderate). Segmental and Regional distribution were the most prominent for the malignant group, and focal distribution for the benign group. Eight radiomics features were selected by support vector machine (SVM). Among the five machine learning algorithms, SVM yielded the highest accuracy of 80.4% in training and 77.5% in testing datasets. ResNet50 had a better diagnostic performance, 91.5% in training and 83.3% in testing datasets. CONCLUSION: Diagnosis of NME was challenging, and the BI-RADS scores and descriptors showed a substantial overlap. Radiomics and deep learning may provide a useful CAD tool to aid in diagnosis.

Somatic BRCA2 Mutation-Positive Concurrent Accessory Male Breast Cancer (BC) and Non-Small Cell Lung Cancer (NSCLC): Excellent Efficacy of Palbociclib, Fulvestrant and Leuprolide in Platinum-Exposed and Endocrine-Refractory BC Associated with Cyclin D1 and FGFR1 Amplification and of Carboplatin, Paclitaxel and Radiation in NSCLC.

Accessory male breast cancer (BC) is a rare entity and is associated with poor outcome. We report a 76-year-old patient who was diagnosed with concurrent accessory breast and primary lung cancer, both were positive for somatic BRCA-2 (E1593D) mutation. He received concurrent radiation and platinum-based chemotherapy for lung cancer with good response, but breast cancer progressed in about 8 months, and further progressed after single agent anastrozole in 10 months. Next Generation Sequencing (NGS) of breast cancer was also positive for CCND1 (Cyclin D1) and FGFR1 amplifications. Despite a poor molecular profile of breast cancer, and progression following platinum-based chemotherapy and anastrozole, he was successfully treated with the Cyclin-dependent kinase (CKD) 4/6 inhibitor palbociclib, estrogen-receptor down-regulator fulvestrant and luteinizing hormone-releasing hormone (LHRH) agonist leuprolide with the duration of response of 21 months which has exceeded duration of response to prior treatments. This case is of interest given FDA expanded the approval of palbociclib in combination with AI or fulvestrant for male patients with HR-positive, HER2-negative metastatic breast cancer in Apr. 2019 based on real-world data from electronic health records.

Treatment of Sinonasal Undifferentiated Carcinoma with TPF and Concurrent Chemo-Radiation: Case Report and Literature Review.

Sinonasal undifferentiated carcinoma (SNUC) is a rare, poorly differentiated and aggressive malignancy of the nasal cavity and paranasal sinuses first reported by Frierson et al. in 1986 with less than 300 known cases reported since then. Due to the rarity and aggressive nature of the disease, there is a lack of consensus regarding optimal management in these patients. Treatment decisions have mostly been guided by a small number of cases series and can vary widely between institutions. In this unique case presentation, we review a case of sinonasal undifferentiated carcinoma in a young Hispanic male reviewing the literature on a rare disease, in order to elucidate effective treatment options for improved future outcomes. Based off of literature review and prior case series, the multiple modality approach should result in the best possible outcome for this rare and aggressive disease. In this specific case of a young Hispanic male with Stage IVB SNUC, we proceeded with Neo-adjuvant TPF (Docetaxel, cisplatin and fluorouracil) with effective results, followed by Cisplatin and concurrent radiation once the patient had interval progression, and was deemed unresectable. Given the rarity and complexity of this disease, a prospective randomized controlled study should eventually be pursued to properly determine the most effective mode and combination of therapies. At this time treatment can only be based on reported case series and a small number of retrospective studies, and therefore it is important to continue to evaluate different institutions' methods of treatment.

Benign Metastasizing Leiomyoma to the Lung and Spine: A Case Report and Literature Review.

Benign metastasizing leiomyomas (BML) represent a rare phenomenon consisting of the extra-uterine spread of smooth muscle cells with similar histological, immunological, and molecular patterns to those of benign uterine leiomyomas. They are considered benign based off their low mitotic activity, lack of anaplasia or necrosis, and limited vascularization. This condition represents an interesting diagnostic and treatment challenge based on their rarity and indolent nature. Our case represents a unique finding of BML in the thoracic spine in a postmenopausal woman many years after hysterectomy and partial oophorectomy. There are currently no standard guidelines for treatment of BML, given the rare nature of this condition, with most patients treated with a combination of surgical resection and radiotherapy, followed by hormonal treatment and radiological surveillance serving as the primary backbone of current management plans. Given that these patients present a unique clinical challenge in terms of diagnosis and management, it is important to delineate and further examine these rare entities.

Orbital Metastases from Breast Cancer with BRCA2 Mutation: A Case Report and Literature Review.

Breast cancer is the second leading cause of cancer-related deaths in women in the United States. Of these women, 5-10% have an inherited form of breast cancer with a mutation in a major gene, such as the breast cancer susceptibility genes 1 or 2 (BRCA1 or BRCA2). Triple negative (the most common subtype of BRCA1-associated breast cancers) and Her2-positive breast cancer patients have more frequently been observed to develop central nervous system (CNS) metastases compared to other molecular subtypes of breast cancers. However, it remains an open question if BRCA2-associated breast cancers also have a higher propensity to develop CNS metastases. Here we report a rare case of recurrent BRCA2-associated breast cancer which manifested as orbital metastases. At the time of this publication, this is one of the first cases of BRCA2-associated breast cancer to present with orbital metastases. In this article, we discuss the diagnostic challenges and review the literature regarding this rare presentation.

Radiation-Associated Angiosarcoma of the Breast: A Case Report and Literature Review.

In the last couple of decades, breast conservation therapy, which utilizes a combination of surgery, radiotherapy, and endocrine or chemotherapy, has become the standard of care for treating early-stage breast cancer. This practice has been greatly beneficial in the improvement of the patient's quality of life but has also led to the increased use of radiotherapy and associated soft-tissue sarcomas, with angiosarcoma being the most common malignancy. Radiation-associated angiosarcoma (RAS) of the breast is a rare phenomenon, which has been reported to occur in approximately 0.9 out of 1,000 cases, with a reported onset as late as 23 years following radiotherapy. Here we report 2 cases of RAS that occurred within 6 and 13 years following radiotherapy of their primary breast lesion. We discuss the diagnostic and therapeutic challenges regarding this disease and review the current literature. This case report serves as cautionary lessons on the importance of considering RAS of the breast in the differential diagnosis during evaluation for recurrent breast neoplasms. Ongoing clinical trials using combinations of vascular endothelial growth factor inhibitors and chemotherapy may provide future avenues of treatment for this difficult-to-treat disease.