RNA-Seq of Circulating Tumor Cells in Stage II-III Breast Cancer.

BACKGROUND: We characterized the whole transcriptome of circulating tumor cells (CTCs) in stage II-III breast cancer to evaluate correlations with primary tumor biology. METHODS: CTCs were isolated from peripheral blood (PB) via immunomagnetic enrichment followed by fluorescence-activated cell sorting (IE/FACS). CTCs, PB, and fresh tumors were profiled using RNA-seq. Formalin-fixed, paraffin-embedded (FFPE) tumors were subjected to RNA-seq and NanoString PAM50 assays with risk of recurrence (ROR) scores. RESULTS: CTCs were detected in 29/33 (88%) patients. We selected 21 cases to attempt RNA-seq (median number of CTCs = 9). Sixteen CTC samples yielded results that passed quality-control metrics, and these samples had a median of 4,311,255 uniquely mapped reads (less than PB or tumors). Intrinsic subtype predicted by comparing estrogen receptor (ER), progesterone receptor (PR), and human epidermal growth factor receptor 2 (HER2) versus PAM50 for FFPE tumors was 85% concordant. However, CTC RNA-seq subtype assessed by the PAM50 classification genes was highly discordant, both with the subtype predicted by ER/PR/HER2 and by PAM50 tumors. Two patients died of metastatic disease, both of whom had high ROR scores and high CTC counts. We identified significant genes, canonical pathways, upstream regulators, and molecular interaction networks comparing CTCs by various clinical factors. We also identified a 75-gene signature with highest expression in CTCs and tumors taken together that was prognostic in The Cancer Genome Atlas and Molecular Taxonomy of Breast Cancer International Consortium datasets. CONCLUSION: It is feasible to use RNA-seq of CTCs in non-metastatic patients to discover novel tumor biology characteristics.

Expression profiling of circulating tumor cells in metastatic breast cancer.

Circulating tumor cells (CTCs) are prognostic in all stages of breast cancer. However, since they are extremely rare, little is known about the molecular nature of these cells. We report a novel strategy for the isolation and expression profiling of pure populations of CTCs derived from peripheral blood. We developed a method to isolate CTCs based on immunomagnetic capture followed by fluorescence-activated cell sorting (IE/FACS). After assay validation using the BT474 cell line spiked into blood samples in vitro, RNA from CTCs isolated from the blood of five metastatic breast cancer (MBC) patients was linearly amplified and subjected to gene expression profiling via cDNA microarrays. We isolated a range of 9-993 captured CTCs from five MBC patients' blood and profiled their RNA in comparison to a diverse panel of primary breast tumors (n = 55). Unsupervised hierarchical clustering revealed that CTC profiles clustered with more aggressive subtypes of primary breast tumors and were readily distinguishable from peripheral blood (PB) and normal epithelium. Differential expression analysis revealed CTCs to have downregulated apoptosis, and they were distinguishable from PB by the relative absence of immune-related signals. As expected, CTCs from MBC had significantly higher risk of recurrence scores than primary tumors (p = 0.0073). This study demonstrates that it is feasible to isolate CTCs from PB with high purity through IE/FACS and profile them via gene expression analysis. Our approach may inform the discovery of therapeutic predictors and be useful for real-time identification of emerging resistance mechanisms in MBC patients.

Characterization of a novel radiation-induced sarcoma cell line.

BACKGROUND: Radiation-induced sarcoma (RIS) is a potential complication of cancer treatment. No widely available cell line models exist to facilitate studies of RIS. METHODS: We derived a spontaneously immortalized primary human cell line, UACC-SARC1, from a RIS. RESULTS: Short tandem repeat (STR) profiling of UACC-SARC1 was virtually identical to its parental tumor. Immunohistochemistry (IHC) analysis of the tumor and immunocytochemistry (ICC) analysis of UACC-SARC1 revealed shared expression of vimentin, osteonectin, CD68, Ki67 and PTEN but tumor-restricted expression of the histiocyte markers α1-antitrypsin and α1-antichymotrypsin. Karyotyping of the tumor demonstrated aneuploidy. Comparative genomic hybridization (CGH) provided direct genetic comparison between the tumor and UACC-SARC1. Sequencing of 740 mutation hotspots revealed no mutations in UACC-SARC1 nor in the tumor. NOD/SCID gamma mouse xenografts demonstrated tumor formation and metastasis. Clonogenicity assays demonstrated that 90% of single cells produced viable colonies. NOD/SCID gamma mice produced useful patient-derived xenografts for orthotopic or metastatic models. CONCLUSION: Our novel RIS strain constitutes a useful tool for pre-clinical studies of this rare, aggressive disease. UACC-SARC1 is an aneuploid cell line with complex genomics lacking common oncogenes or tumor suppressor genes as drivers of its biology. The UACC-SARC1 cell line will enable further studies of the drivers of RIS.

Prediction of High-Risk Donors for Kidney Discard and Nonrecovery Using Structured Donor Characteristics and Unstructured Donor Narratives.

Importance: Despite the unmet need, many deceased-donor kidneys are discarded or not recovered. Inefficient allocation and prolonged ischemia time are contributing factors, and early detection of high-risk donors may reduce organ loss. Objective: To evaluate the feasibility of machine learning (ML) and natural language processing (NLP) classification of donors with kidneys that are used vs not used for organ transplant. Design, Setting, and Participants: This retrospective cohort study used donor information (structured donor characteristics and unstructured donor narratives) from the United Network for Organ Sharing (UNOS). All donor offers to a single transplant center between January 2015 and December 2020 were used to train and validate ML models to predict donors who had at least 1 kidney transplanted (at our center or another center). The donor data from 2021 were used to test each model. Exposures: Donor information was provided by UNOS to the transplant centers with potential transplant candidates. Each center evaluated the donor and decided within an allotted time whether to accept the kidney for organ transplant. Main Outcomes and Measures: Outcome metrics of the test cohort included area under the receiver operating characteristic curve (AUROC), F1 score, accuracy, precision, and recall of each ML classifier. Feature importance and Shapley additive explanation (SHAP) summaries were assessed for model explainability. Results: The training/validation cohort included 9555 donors (median [IQR] age, 50 [36-58] years; 5571 male [58.3%]), and the test cohort included 2481 donors (median [IQR] age, 52 [40-59] years; 1496 male [60.3%]). Only 20% to 30% of potential donors had at least 1 kidney transplanted. The ML model with a single variable (Kidney Donor Profile Index) showed an AUROC of 0.69, F1 score of 0.42, and accuracy of 0.64. Multivariable ML models based on basic a priori structured donor data showed similar metrics (logistic regression: AUROC = 0.70; F1 score = 0.42; accuracy = 0.62; random forest classifier: AUROC = 0.69; F1 score = 0.42; accuracy = 0.64). The classic NLP model (bag-of-words model) showed its best metrics (AUROC = 0.60; F1 score = 0.35; accuracy = 0.59) by the logistic regression classifier. The advanced Bidirectional Encoder Representations From Transformers model showed comparable metrics (AUROC = 0.62; F1 score = 0.39; accuracy = 0.69) only after appending basic donor information. Feature importance and SHAP detected the variables (and words) that affected the models most. Conclusions and Relevance: Results of this cohort study suggest that models using ML can be applied to predict donors with high-risk kidneys not used for organ transplant, but the models still need further elaboration. The use of unstructured data is likely to expand the possibilities; further exploration of new approaches will be necessary to develop models with better predictive metrics.

Effectiveness of Postoperative Single-shot and Continuous Transverse Abdominis Plane Block Compared to Conventional Analgesia in Hand-assisted Laparoscopic Live-donor Nephrectomy.

Background: Few studies have evaluated the efficacy of transverse abdominis plane (TAP) block in patients undergoing hand-assisted laparoscopic live-donor nephrectomy (HALN). We aimed to evaluate the analgesic effectiveness of TAP block as part of a multimodal pain management regimen in patients undergoing HALN. Methods: We retrospectively reviewed the medical records of living kidney donors at our center between June 2016 and February 2020. HALNs were performed via a transperitoneal approach through a suprapubic incision. Additional laparoscopic ports were used in the upper midabdomen. In consenting donors, TAP block was performed postoperatively under ultrasound guidance with either a single-shot or continuous infusion of long-acting local anesthetic (0.2%-0.5% ropivacaine). All the patients received postoperative around-the-clock ketorolac and acetaminophen. Results: Overall, 72 donors received the block (block group, 38 single-shot, 34 continuous), whereas 86 donors did not receive the block (control group). Baseline characteristics were comparable between the groups except for body weight (control: 71.8 ±â€…13.3 versus block: 77.8 ±â€…17.3 kg; P = 0.01) and intraoperative opioid dose (32.1 ±â€…9.6 versus 26.6 ±â€…10.7 morphine milligram equivalents; P < 0.001). After adjusting for baseline differences, postoperative opioid requirements were similar between the groups. When the baseline pain scale was adjusted for, there was no difference in the overall pain scale scores between the groups (P = 0.242). Subgroup analyses comparing single-shot or continuous TAP versus control did not show any differences. Conclusions: With the caveat of the retrospective nature of the study, the adjunctive effect of TAP block after transabdominal HALN was limited when other multimodal analgesia was used.

Long-term Survival of Grafts From Small and Very Small Pediatric Donors in Women vs Men With End-stage Kidney Disease.

This cohort study compares graft survival of kidneys from small and very small pediatric donors in women vs men with end-stage kidney disease.

EpCAM based capture detects and recovers circulating tumor cells from all subtypes of breast cancer except claudin-low.

PURPOSE: The potential utility of circulating tumor cells (CTCs) as liquid biopsies is of great interest. We hypothesized that CTC capture using EpCAM based gating is feasible for most breast cancer subtypes. RESULTS: Cancer cells could be recovered from all intrinsic subtypes of breast cancer with IE/FACS, however, claudin-low cell lines showed very low capture rates compared to the four other groups (p = 0.03). IE/FACS detection of CTC mimic cells was time sensitive, emphasizing controlling for pre-analytic variables in CTC studies. Median fluorescent intensity for flow cytometry and RNA flow cell type characterization were highly correlated, predicting for CTC isolation across molecular subtypes. RNA-Seq of IE/FACS sorted single cell equivalents showed high correlation compared to bulk cell lines, and distinct gene expression signatures compared to PB. MATERIALS AND METHODS: Ten cell lines representing all major subtypes of breast cancer were spiked (as CTC mimics) into and recovered from peripheral blood (PB) using immunomagnetic enrichment followed by fluorescence-activated cell sorting (IE/FACS). Flow cytometry and RNA flow were used to quantify the expression of multiple breast cancer related markers of interest. Two different RNA-Seq technologies were used to analyze global gene expression of recovered sorted cells compared to bulk cell lines and PB. CONCLUSIONS: EpCAM based IE/FACS detected and captured a portion of spiked cells from each of the 10 cell lines representing all breast cancer subtypes, including basal-like but not claudin-low cancers. The assay allows for the isolation of high quality RNA suitable for accurate RNA-Seq of heterogeneous rare cell populations.