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Autologous cell therapy depends on T lymphocyte expansion efficiency and is hindered by suboptimal interactions between T cell receptors (TCR) and peptide-MHC molecules. Various artificial antigen presenting cell systems that enhance these interactions are often labor-intensive, fabrication costly, highly variable, and potentially unscalable toward clinical setting. Here, 3D centrifugation-enabled priming of T cell immune-synapse junctions is performed to generate tight T cell–Dynabead aggregates at a rate 200-fold faster than that of conventional 24-h bulk shaking. Furthermore, by forming T cell–Dynabead aggregates in the starting culture, two- to sixfold greater T cell expansion is achieved over conventional T cell expansion for cancer patient-derived primary T cells while limiting over-activation. Creating 3D T cell–Dynabead aggregates as the “booster” material enables highly efficient polyclonal T cell expansion without the need for complex surface modification of artificial antigen-presenting cells (APCs). This method can be modularly adapted to existing T cell expansion processes for various applications, including adoptive cell therapies (ACTs).

Rationale and objectivesDiagnosis 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 methodsTwo 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.ResultsIn 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.ConclusionResNet50 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.

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.

BackgroundNeuropsychiatric 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.MethodsThe 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.ConclusionThe 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 registrationThis trial is registered with clinicaltrials.gov NCT05283577.

Rationale and objectivesComputer-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 methodsTwo 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.ResultsWhen 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.ConclusionDeep 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.

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

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.

ObjectivesTo apply deep learning algorithms using a conventional convolutional neural network (CNN) and a recurrent CNN to differentiate three breast cancer molecular subtypes on MRI.MethodsA 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).ResultsIn 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%.ConclusionsThe 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.

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.

ObjectiveTo build radiomics models using features extracted from DCE-MRI and mammography for diagnosis of breast cancer.Materials and methods266 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.ResultsIn 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

BackgroundA 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 methodsA 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.ResultsThe 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.ConclusionDiagnosis 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.

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.

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 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.

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.

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.

Metastasis is facilitated by cancer-associated fibroblasts (CAF) in the tumor microenvironment through mechanisms yet to be elucidated. In this study, we used a size-based microfilter technology developed by our group to examine whether circulating CAF identified by FAP and α-SMA coexpression (cCAF) could be distinguished in the peripheral blood of patients with metastatic breast cancer. In a pilot study of patients with breast cancer, we detected the presence of cCAFs in 30/34 (88%) patients with metastatic disease (MET group) and in 3/13 (23%) patients with localized breast cancer (LOC group) with long-term disease-free survival. No cCAFs as defined were detected in healthy donors. Further, both cCAF and circulating tumor cells (CTC) were significantly greater in the MET group compared with the LOC group. Thus, the presence of cCAF was associated with clinical metastasis, suggesting that cCAF may complement CTC as a clinically relevant biomarker in metastatic breast cancer. [ABSTRACT FROM AUTHOR]