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Background the problem in early diagnosis of sporadic cancer is understanding the individual’s risk to develop disease. In response to this need, global scientific research is focusing on developing predictive models based on non-invasive screening tests. A tentative solution to the problem may be a cancer screening blood-based test able to discover those cell requirements triggering subclinical and clinical onset latency, at the stage when the cell disorder, i.e. atypical epithelial hyperplasia, is still in a subclinical stage of proliferative dysregulation. Methods a well-established procedure to identify proliferating circulating tumor cells was deployed to measure the cell proliferation of circulating non-haematological cells which may suggest tumor pathology. Moreover, the data collected were processed by a supervised machine learning model to make the prediction. Results the developed test combining circulating non-haematological cell proliferation data and artificial intelligence shows 98.8% of accuracy, 100% sensitivity, and 95% specificity. Conclusion this proof of concept study demonstrates that integration of innovative non invasive methods and predictive-models can be decisive in assessing the health status of an individual, and achieve cutting-edge results in cancer prevention and management.

Accurate determination of estrogen receptor (ER) and progesterone receptor (PR) status in breast carcinoma is essential. Preanalytic variation may contribute to discordant results. Recently, American Society of Clinical Oncology (ASCO)/College of American Pathologists (CAP) made recommendations to normalize fixation for breast biomarkers. To evaluate this, a 4-cm invasive lobular carcinoma was processed according to ASCO/CAP guidelines. The remainder was stored fresh at 4°C for 4 days and cut into biopsy-sized pieces. Each was fixed in 10% formalin, Pen-Fix (Richard-Allan Scientific, Kalamazoo, MI), Bouin solution, Sakura Molecular Fixative (Sakura Tissue-Tek Xpress, Torrance, CA), zinc formalin, or 15% formaldehyde for times ranging between 1 and 168 hours. Immunohistochemical studies for ER and PR were performed and interpreted. After 4 days at 4°C, all samples showed no degradation or ER/PR staining differences, except 2 Bouin-fixed samples, in comparison with the patient's sample processed according to ASCO/CAP guidelines. In our study, the preanalytic variables of fixative type, fixation time, and 4 days of ischemic time did not affect immunohistochemical accuracy for ER/PR.

: Unlike traditional biopsy, liquid biopsy, which is a largely non-invasive diagnostic and monitoring tool, can be performed more frequently to better track tumors and mutations over time and to validate the efficiency of a cancer treatment. Circulating tumor cells (CTCs) are considered promising liquid biopsy biomarkers; however, their use in clinical settings is limited by high costs and a low throughput of standard platforms for CTC enumeration and analysis. In this study, we used a label-free, high-throughput method for CTC isolation directly from whole blood of patients using a standalone, clinical setting-friendly platform. : A CTC-based liquid biopsy approach was used to examine the efficacy of therapy and emergent drug resistance via longitudinal monitoring of CTC counts, DNA mutations, and single-cell-level gene expression in a prospective cohort of 40 patients with epidermal growth factor receptor (EGFR)-mutant non-small cell lung cancer. : The change ratio of the CTC counts was associated with tumor response, detected by CT scan, while the baseline CTC counts did not show association with progression-free survival or overall survival. We achieved a 100% concordance rate for the detection of mutation, including emergence of T790M, between tumor tissue and CTCs. More importantly, our data revealed the importance of the analysis of the epithelial/mesenchymal signature of individual pretreatment CTCs to predict drug responsiveness in patients. : The fluid-assisted separation technology disc platform enables serial monitoring of CTC counts, DNA mutations, as well as unbiased molecular characterization of individual CTCs associated with tumor progression during targeted therapy.

Cellular therapies offer a promising therapeutic strategy for the highly malignant brain tumor, glioblastoma (GBM). However, their clinical translation is limited by the lack of effective target identification and stringent testing in pre-clinical models that replicate standard treatment in GBM patients. In this study, we show the detection of cell surface death receptor (DR) target on CD146-enriched circulating tumor cells (CTC) captured from the blood of mice bearing GBM and patients diagnosed with GBM. Next, we developed allogeneic “off-the-shelf” clinical-grade bifunctional mesenchymal stem cells (MSC Bif ) expressing DR-targeted ligand and a safety kill switch. We show that biodegradable hydrogel encapsulated MSC Bif (EnMSC Bif ) has a profound therapeutic efficacy in mice bearing patient-derived invasive, primary and recurrent GBM tumors following surgical resection. Activation of the kill switch enhances the efficacy of MSC Bif and results in their elimination post-tumor treatment which can be tracked by positron emission tomography (PET) imaging. This study establishes a foundation towards a clinical trial of EnMSC Bif in primary and recurrent GBM patients. The use of cell-based therapies may be helpful in eradicating cancer cells. Here, the authors generate bifunctional mesenchymal stem cells and demonstrate their therapeutic efficacy in glioblastoma patient-derived models.

Accurate determination of HER2/neu status in breast carcinoma is essential. Alteration of preanalytic variables is known to affect HER2/neu results. American Society of Clinical Oncology (ASCO) and the College of American Pathologists (CAP) issued guidelines to standardize fixation for increased HER2/neu accuracy. We studied the effects of changing preanalytic variables on HER2/neu immunohistochemical and fluorescence in situ hybridization (FISH) results in a known HER2/neu+ invasive carcinoma. The clinical specimen was processed according to ASCO/CAP guidelines, with remaining tumor stored fresh without any fixatives for 4 days at 4°C and cut into core biopsy-sized pieces. Each was fixed in 10% formalin, 15% formalin, Pen-Fix (Richard-Allan Scientific, Kalamazoo, MI), Bouin solution, Sakura molecular fixative (Sakura Tissue-Tek Xpress, Torrance, CA), or zinc formalin for 0 to 168 hours. Immunohistochemical studies and FISH were performed. Compared with the clinical specimen, the samples showed no tumor degradation or marked difference by immunohistochemical studies, except the 1-hour 10% formalin and Bouin samples, or FISH, except the Bouin-fixed samples. Our study demonstrates that HER2/neu results remain accurate beyond ASCO/CAP-recommended preanalytic variables, with the exception of Bouin solution for FISH analysis.

Background Circulating tumors cells (CTCs) are considered an early step towards metastasis and have been linked to poor prognosis in several types of cancer. CTCs in squamous cell carcinoma of the head and neck (SCCHN) have an unclear role. Methods In this prospective study, patients with locally advanced or metastatic SCCHN had CTC counts assessed before starting systemic treatment using the CellSearch System. Select cases also had sequential CTC evaluation. Presence of CTCs was correlated with patient characteristics and outcomes. Results Forty‐eight patients enrolled, and 36 had evaluable clinical data and baseline CTC counts. Twenty‐five patients had locally advanced disease (LAD) and 11 had metastatic disease. ≥1 CTCs were detected in six patients with LAD (24%) and four with metastatic disease (36%). On univariate analysis, smoking was associated with CTCs. Conclusion CTCs are not associated with prognosis in patients with LAD and metastatic disease; however, they are present in this patient population, and ≥1 CTCs is associated with a history of smoking. Level of evidence 1b; individual prospective cohort study. Circulating tumors cells (CTCs) were detected in 6/25 patients with locoregionally advanced disease (24%) and 4/11 with metastatic squamous cell carcinoma of the head and neck (36%). On univariate analysis, smoking was associated with CTCs.

HER-2 / neu status is critical for the therapy for breast carcinoma. Fluorescent in situ hybridization for gene amplification and immunohistochemical stains for protein expression are widely used methods to detect HER-2 / neu status. Multiple studies have shown fluorescent in situ hybridization and immunohistochemical stain results to have high concordance rates. To our knowledge, a comparison between fluorescent in situ hybridization results for core needle biopsy and the subsequent excisional biopsy specimens has not yet been studied. We retrospectively evaluated the fluorescence in situ hybridization and immunohistochemical results in both the breast core needle and the excisional biopsy of 125 patients with invasive breast carcinoma from 2002 to 2005. There was complete concordance with respect to both immunohistochemical and fluorescence in situ hybridization results for core needle biopsy and excisional biopsy specimens in 87% of the patients evaluated. Comparison of fluorescent in situ hybridization results of the 129 core needle biopsies to the 131 excisional biopsies of all 125 patients showed a concordance rate of 92%. The immunohistochemical stain results of the same core needle and excisional biopsies showed a concordance rate of 98%. Comparison of the immunohistochemical stain results with the fluorescent in situ hybridization results for all 260 cases examined showed 95% concordance. On the basis of our study, we observed that repeating HER-2 / neu testing by immunohistochemical stain and/or fluorescent in situ hybridization methods on excisional biopsy is not unreasonable, in particular in cases of intratumoral heterogeneity, indeterminate/borderline HER-2 / neu results and after neoadjuvant chemotherapy.

Whole-exome sequencing of circulating tumor cells enables accurate and powered calling of somatic point mutations. Comprehensive analyses of cancer genomes promise to inform prognoses and precise cancer treatments. A major barrier, however, is inaccessibility of metastatic tissue. A potential solution is to characterize circulating tumor cells (CTCs), but this requires overcoming the challenges of isolating rare cells and sequencing low-input material. Here we report an integrated process to isolate, qualify and sequence whole exomes of CTCs with high fidelity using a census-based sequencing strategy. Power calculations suggest that mapping of >99.995% of the standard exome is possible in CTCs. We validated our process in two patients with prostate cancer, including one for whom we sequenced CTCs, a lymph node metastasis and nine cores of the primary tumor. Fifty-one of 73 CTC mutations (70%) were present in matched tissue. Moreover, we identified 10 early trunk and 56 metastatic trunk mutations in the non-CTC tumor samples and found 90% and 73% of these mutations, respectively, in CTC exomes. This study establishes a foundation for CTC genomics in the clinic.

Histopathological diagnoses of tumors from tissue biopsy after hematoxylin and eosin (H&E) dye staining is the criterion standard for oncological care, but H&E staining requires trained operators, dyes and reagents, and precious tissue samples that cannot be reused. To use deep learning algorithms to develop models that perform accurate computational H&E staining of native nonstained prostate core biopsy images and to develop methods for interpretation of H&E staining deep learning models and analysis of computationally stained images by computer vision and clinical approaches. This cross-sectional study used hundreds of thousands of native nonstained RGB (red, green, and blue channel) whole slide image (WSI) patches of prostate core tissue biopsies obtained from excess tissue material from prostate core biopsies performed in the course of routine clinical care between January 7, 2014, and January 7, 2017, at Brigham and Women's Hospital, Boston, Massachusetts. Biopsies were registered with their H&E-stained versions. Conditional generative adversarial neural networks (cGANs) that automate conversion of native nonstained RGB WSI to computational H&E-stained images were then trained. Deidentified whole slide images of prostate core biopsy and medical record data were transferred to Massachusetts Institute of Technology, Cambridge, for computational research. Results were shared with physicians for clinical evaluations. Data were analyzed from July 2018 to February 2019. Methods for detailed computer vision image analytics, visualization of trained cGAN model outputs, and clinical evaluation of virtually stained images were developed. The main outcome was interpretable deep learning models and computational H&E-stained images that achieved high performance in these metrics. Among 38 patients who provided samples, single core biopsy images were extracted from each whole slide, resulting in 102 individual nonstained and H&E dye-stained image pairs that were compared with matched computationally stained and unstained images. Calculations showed high similarities between computationally and H&E dye-stained images, with a mean (SD) structural similarity index (SSIM) of 0.902 (0.026), Pearson correlation coefficient (PCC) of 0.962 (0.096), and peak signal to noise ratio (PSNR) of 22.821 (1.232) dB. A second cGAN performed accurate computational destaining of H&E-stained images back to their native nonstained form, with a mean (SD) SSIM of 0.900 (0.030), PCC of 0.963 (0.011), and PSNR of 25.646 (1.943) dB compared with native nonstained images. A single blind prospective study computed approximately 95% pixel-by-pixel overlap among prostate tumor annotations provided by 5 board certified pathologists on computationally stained images, compared with those on H&E dye-stained images. This study also used the first visualization and explanation of neural network kernel activation maps during H&E staining and destaining of RGB images by cGANs. High similarities between kernel activation maps of computationally and H&E-stained images (mean-squared errors <0.0005) provide additional mathematical and mechanistic validation of the staining system. These findings suggest that computational H&E staining of native unlabeled RGB images of prostate core biopsy could reproduce Gleason grade tumor signatures that were easily assessed and validated by clinicians. Methods for benchmarking, visualization, and clinical validation of deep learning models and virtually H&E-stained images communicated in this study have wide applications in clinical informatics and oncology research. Clinical researchers may use these systems for early indications of possible abnormalities in native nonstained tissue biopsies prior to histopathological workflows.