Search

STUDY QUESTION Are there differences in Mediator Complex Subunit 12 mutations (MED12) mutation, transcriptomics, and protein expression in uterine myometrium and leiomyomas of Black and White women? SUMMARY ANSWER RNA sequencing, tissue microarray, and immunohistochemistry data revealed that Black and White women have significant differences in their myometrium and leiomyoma profiles. WHAT IS KNOWN ALREADY Black women develop uterine leiomyoma earlier than White women, and are more likely to be anemic, have multiple tumors, undergo hysterectomy at an earlier age, have a higher uterine weight, and report very severe pelvic pain. STUDY DESIGN, SIZE, DURATION Uterine tissues were collected from premenopausal women undergoing hysterectomy or myomectomy at Northwestern University Prentice Women’s Hospital (Chicago, IL) from 2010 to 2021. Tissues were collected from a total of 309 women, including from 136 Black women, 135 White women, and 38 women from other racial groups. A total of 529 uterine leiomyomas (290 from Black women, 184 from White women, and 55 from women of other racial groups) were subjected to molecular analysis. Leiomyoma and matched myometrium from a total of 118 cases including 60 Black women and 58 White women, were used for tissue microarrays, along with 34 samples of myometrium without leiomyoma from White women. PARTICIPANTS/MATERIALS, SETTING, METHODS Tissues from the above patient cohorts were analyzed by tissue microarray, immunohistochemistry, RNA sequencing, and mutation analysis. MAIN RESULTS AND THE ROLE OF CHANCE The results indicated that leiomyoma from Black women have a higher rate of MED12 mutations (79.0%) than those from White women (68.5%) (*P ≤ 0.05). RNA-sequencing analysis in myometrium revealed differentially expressed genes (270 upregulated, 374 downregulated) dependent on race, wherein reactive oxygen species, hypoxia, and oxidative phosphorylation pathways were positively correlated with samples derived from Black patients. The levels of proteins associated with oxidative DNA damage and repair, 8-hydroxyguanosine (8-OHdG), 8-oxoguanine glycosylase (OGG1), heme oxygenase-1 (HO-1), and kelch-like ECH-associated protein 1 (KEAP1), were higher in leiomyoma and matched myometrium, particularly those from Black patients, compared to the control myometrium (with leiomyoma) (***P ≤ 0.001). LARGE SCALE DATA The datasets are available in the NCBI (The BioProject number: PRJNA859428). LIMITATIONS, REASONS FOR CAUTION Myometrium without leiomyoma derived from White patients was used as a control in the tissue microarray analysis, as myometrium without leiomyoma from Black patients was not accessible in large numbers. The RNA sequencing was performed on myometrium tissue with leiomyoma present from 10 White and 10 Black women. However, one sample from a Black woman yielded low-quality RNA-sequencing data and was excluded from further analysis. WIDER IMPLICATIONS OF THE FINDINGS Women with symptomatic leiomyomas have a considerable loss in their quality of life. This study provides information on underlying genetic and molecular defects that may be necessary for future therapeutics targeted at leiomyomas. STUDY FUNDING/COMPETING INTEREST(S) This work was supported by grants from NCI (R01CA254367) and NICHD (P01HD057877). The authors declare no conflict of interest.

Statistical analysis of apoptosis assay.

The list of antibodies used during this study.

ML264 treatment decreases KLF5 expression levels in DLD-1-derived xenografts.

Krüppel-like factor 5 (KLF5), a member of the SP/KLF family of zinc finger transcription factors, is overexpressed in human colorectal cancer specimens, and this overabundance is associated with aggressive cancer development and progression. We demonstrated that mice haploinsufficient for Klf5 had reduced intestinal tumor burden in the background of germline mutation in Apc, a gatekeeper of intestinal tumorigenesis. Based on a high-throughput screening strategy, we developed ML264, a small-molecule compound that inhibits KLF5, and showed that it inhibits growth of colorectal cancer in vitro and in vivo. Through optimization efforts based on the structure of ML264, we have now identified a new lead compound, SR18662. We find that treatment with SR18662 significantly reduces growth and proliferation of colorectal cancer cells as compared with treatment with vehicle control, ML264, or SR15006 (a less optimized analogue from SAR efforts leading to SR18662). SR18662 showed improved efficacy in reducing the viability of multiple colorectal cancer cell lines. Flow cytometry analysis following SR18662 treatment showed an increase in cells captured in either S or G2–M phases of the cell cycle and a significant increase in the number of apoptotic cells, the latter a unique property compared with ML264 or SR15006. SR18662 treatment also reduces the expression of cyclins and components of the MAPK and WNT signaling pathways. Importantly, we observed a significant dose-dependent inhibition of xenograft growth in mice following SR18662 treatment that exceeded the effect of ML264 at equivalent doses. These findings support further development of SR18662 and its analogues for colorectal cancer therapy.

Supplementary Figures Legends

Supplementary Figure S1. Synthesis steps of SR15006 and SR18662. Supplementary Figure S2. SR18662 changes cell cycle profiles of colorectal cancer cell lines. Supplementary Figure S3. SR18662 increases apoptosis of colorectal cancer cell lines. Supplementary Figures S4 - S6. SR18662 inhibits activity of MAPK, WNT/b-catenin signaling pathways and decreases the levels of cyclins. Supplementary Figure S7. The effect of SR18662 on the mice weight during xenograft studies.

Supplemental Figure 5. Ki67 Change was not associated with drug concentration in Tumor samples. Tumor samples were available for quantitation from 22 women.

Supplemental Figure 2. Ki67 Change relative to treatment duration. There was no significant correlation between change in Ki67 and treatment duration, in either TPA or placebo arms.

Supplemental Figure 4. Plots showing changes in serum estradiol (left panel) and serum progesterone (right panel) in three subgroups: placebo, TPA-treated Ki67 responders and TPA-treated Ki67-nonresponders.

Supplemental Figure 3. Validation of RNA seq results of responders by Nanostring nCounter gene expression assay

Purpose. Wnt signaling regulates the fine balance between stemness and differentiation. Here, the role of Wnt signaling to maintain the balance between estrogen-induced proliferation and progesterone-induced differentiation during the menstrual cycle, as well as during the induction of hyperplasia and carcinogenesis of the endometrium, was investigated.Experimental Design: Endometrial gene expression profiles from estradiol (E2) and E2 + medroxyprogesterone acetate–treated postmenopausal patients were combined with profiles obtained during the menstrual cycle (PubMed; GEO DataSets). Ishikawa cells were transfected with progesterone receptors and Wnt inhibitors dickkopf homologue 1 (DKK1) and forkhead box O1 (FOXO1), measuring Wnt activation. Expression of DKK1 and FOXO1 was inhibited by use of sequence-specific short hairpins. Furthermore, patient samples (hormone-treated endometria, hyperplasia, and endometrial cancer) were stained for Wnt activation using nuclear β-catenin and CD44.Results: In vivo, targets and components of the Wnt signaling pathway (among them DKK1 and FOXO1) are regulated by E2 and progesterone. In Wnt-activated Ishikawa cells, progesterone inhibits Wnt signaling by induction of DKK1 and FOXO1. Furthermore, using siRNA-mediated knockdown of both DKK1 and FOXO1, progesterone inhibition of Wnt signaling was partly circumvented. Subsequently, immunohistochemical analysis of the Wnt target gene CD44 showed that progesterone acted as an inhibitor of Wnt signaling in hyperplasia and in well-differentiated endometrial cancer.Conclusion: Progesterone induction of DKK1 and FOXO1 results in inhibition of Wnt signaling in the human endometrium. This Wnt inhibitory effect of progesterone is likely to play a rate-limiting role in the maintenance of endometrial homeostasis and, on its loss, in tumor onset and progression toward malignancy. (Clin Cancer Res 2009;15(18):5784–93)

Supplemental table 1. Differential gene expression and pathway enrichment analysis in TPA responders (excel file) -uploading separately.

Supplemental table 3. Nanostring nCounter Differential gene expression (excel file) -uploading separately.

Purpose:Selective progesterone receptor modulators (SPRMs) show preclinical activity against hormone-sensitive breast cancer, but have not been tested in patients with early, treatment-naïve tumors.Patients and Methods:In a double-blind presurgical window trial of oral telapristone acetate (TPA) 12 mg daily versus placebo, 70 patients with early-stage breast cancer were randomized 1:1 (stratified by menopause) and treated for 2 to 10 weeks. The primary endpoint was change in Ki67 between diagnostic biopsy and surgical specimens. Gene expression pre- and posttherapy was assessed using RNA-sequencing and gene set enrichment analysis was performed to determine pathways enriched in response to TPA and placebo treatments.Results:Among 61 evaluable women (29 placebo and 32 telapristone acetate), 91% of tumors were ER/PR positive. The mean Ki67 declined by 5.5% in all women treated with telapristone acetate (P = 0.003), and by 4.2% in all women treated with placebo (P = 0.04). After menopausal stratification, the Ki67 decline remained significant in 22 telapristone acetate–treated premenopausal women (P = 0.03). Differential gene expression analysis showed no significant modulation overall. However, in a subset of tumors that demonstrated ≥30% relative reduction in Ki67 in the telapristone acetate group, genes related to cell-cycle progression, and those in the HER2 amplicon were significantly downregulated. In contrast, no significantly enriched pathways were identified in the placebo group.Conclusions:Patients treated with telapristone acetate whose Ki67 decreased by ≥30% demonstrated a selective antiproliferative signal, with a potentially important effect on HER2 amplicon genes. Evaluation of SPRMs in a neoadjuvant trial is merited, with attention to predictors of response to SPRM therapy, and inclusion of pre- and postmenopausal women.

Supplementary Data from Progesterone Inhibition of Wnt/β-Catenin Signaling in Normal Endometrium and Endometrial Cancer

Supplemental Materials Supplemental Table 4. Changes in serum sex hormone concentrations according to the treatments. Supplemental Table 5. Summary of BESS Quality of Life Assessment by symptom clusters according to the treatments Supplemental Table 6. Summary of Adverse Effects.

Supplemental Figure 1. Validation of RNA seq results of responders by Nanostring nCounter gene expression assay.

Supplementary Figure Legend from Transcription Factor KLF11 Integrates Progesterone Receptor Signaling and Proliferation in Uterine Leiomyoma Cells

Supplementary Table 1 from Transcription Factor KLF11 Integrates Progesterone Receptor Signaling and Proliferation in Uterine Leiomyoma Cells

Supplementary Figure 1 from Transcription Factor KLF11 Integrates Progesterone Receptor Signaling and Proliferation in Uterine Leiomyoma Cells

Supplementary Table 2 from Transcription Factor KLF11 Integrates Progesterone Receptor Signaling and Proliferation in Uterine Leiomyoma Cells

This study investigated the effects of adding visual aesthetics to a music performance. Two modes of visual performance are explored: multimedia and performance cues. Multimedia and performer cues serve different purposes in expressing music in a performance and allowing the audience to interpret the music. Multimedia refers to the use of more than one medium of expression or communication in performance, while performance cues refer to the implementation of gestures and facial expressions to guide the direction of the music. In this research study, two experiments were performed with two separate samples of 24 subjects. These experiments observe the two modes' effects on engagement, understanding, and overall enjoyment of the performance by their audience. Participants were required to listen to the audio performance of an excerpt of music and listen to the same excerpt with the visuals as well. The subjects answered a series of questions rating the listening and watching experience. The results overall showed that having the visual experience while listening to a musical performance enhances the subjects engagement, enjoyment and understanding of the music. A wilcoxin ranked sum test was performed on the two different parts of this experiment and the p values were 0.0486 and 0.00148, which showed that there was enough statistical evidence in the experiment that showed engagement levels were higher when both audio and visual modes were provided to the audience.

Each month during a woman's reproductive years, the endometrium undergoes vast changes to prepare for a potential pregnancy. Diseases of the endometrium arise for numerous reasons, many of which remain unknown. These endometrial diseases, including endometriosis, adenomyosis, endometrial cancer and Asherman syndrome, affect many women, with an overall lack of efficient or permanent treatment solutions. The challenge lies in understanding the complexity of the endometrium and the extensive changes, orchestrated by ovarian hormones, that occur in multiple cell types over the period of the menstrual cycle. Appropriate model systems that closely mimic the architecture and function of the endometrium and its diseases are needed. The emergence of organoid technology using human cells is enabling a revolution in modelling the endometrium in vitro. The goal of this Review is to provide a focused reference for new models to study the diseases of the endometrium. We provide perspectives on the power of new and emerging models, from organoids to microfluidics, which have opened up a new frontier for studying endometrial diseases.

Background Laboratory parameters and the associated clinical outcomes have been an area of focus in COVID-19 research globally. Purpose We performed a scoping review to synthesize laboratory values described in the literature and their associations with mortality and disease severity. Methods We identified all primary studies involving laboratory values with clinical outcomes as a primary endpoint by performing data searches in various systematic review databases until 10th August, 2020. Two reviewers independently reviewed all abstracts (13,568 articles) and full text (1126 articles) data. A total of 529 studies involving 165,020 patients from 28 different countries were included. Investigation of the number of studies and patients from a geographical perspective showed that the majority of published literature from January–March 2020 to April–June 2020 was from Asia, though there was a temporal shift in published studies to Europe and the Americas. For each laboratory value, the proportion of studies that noted a statistically significant (p

NANOG engages with tumour initiation and metastasis by regulating the epithelial-mesenchymal transition (EMT) in epithelial ovarian cancer (EOC). However, its role in association with pAMPKα, and its clinical significance in EOC have not been elucidated even though AMPK is known to degrade NANOG in various human cancers. Hence, we investigated the role of pAMPKα and its association with NANOG as potential prognostic biomarkers in EOC. Both NANOG and pAMPKα expression were significantly overexpressed in EOCs comparing nonadjacent normal epithelial tissues, benign tissues, and borderline tumours. NANOG overexpression was significantly associated with poor disease-free survival (DFS) and overall survival (OS), whereas pAMPKα overexpression was associated with good DFS and OS. Importantly, multivariate analysis revealed that the combination of high NANOG and low pAMPKα expression was a poor independent prognostic factor for DFS and was associated with platinum resistance. In ovarian cancer cell lines, siRNA-mediated NANOG knockdown diminished migration and invasion properties by regulating the EMT process via the AMPK/mTOR signalling pathway. Furthermore, treatment with AMPK activator suppressed expression of stemness factors such as NANOG, Oct4 and Sox2. Collectively, these findings established that the combination of high NANOG and low pAMPKα expression was associated with EOC progression and platinum resistance, suggesting a potential prognostic biomarker for clinical management in EOC patients.

Background The radial approach has been gaining more widespread use by neurointerventionalists fueled by data from the cardiology literature showing better safety and overall reduced morbidity. Objective To present our institution's experience with the radial approach for neuroendovascular interventions in 614 consecutive patients who underwent a cumulative of 760 procedures. Methods A retrospective analysis was performed and identified neuroendovascular procedures performed via the upper extremity vasculature access site. Results Amongst 760 procedures, 34.2% (260) were therapeutic, and 65.7% (500) were nontherapeutic angiograms. Access sites were 71.5% (544) via a conventional radial artery, 27.8% (211) via a distal radial artery, 0.5% (4) via an ulnar artery, and 0.1% (1) via the brachial artery. Most of the procedures (96.9%) were performed via the right-sided (737), 2.9% (22) via the left-sided, and 0.1% (1) via a bilateral approach. Major access site complications occurred at a rate of 0.9% (7). The rate of transfemoral conversion was 4.7% (36). There was a statistically higher incidence of transfemoral conversion when repeat procedures were performed using the same access site. Also, there was no significant difference between nontherapeutic procedures performed using the right and left radial access, and conventional versus distal radial access. Procedural metrics improved after completion of 14 procedures, indicating a learning curve that should be surpassed by operators to reach optimal outcomes. Conclusion Radial artery catheterization is a safe and effective means of carrying out a wide range of neuroendovascular procedures associated with excellent clinical outcomes and an overall low rate of periprocedural complications.

Patients with metastatic breast cancer (MBC) can develop malignant effusions (MEs) when excess fluid and tumor cells accumulate in the pleural or peritoneal space. MEs are associated with significant morbidity and mortality. Tropism of tumor cells in these spaces is poorly understood and tumor cells residing in these fluids are difficult to study with traditional methods due to low abundance. To overcome this limitation and understand the pathobiology of MEs in MBCs, we applied a label-free method to isolate tumor cells based on morphology using an artificial intelligence (AI) model This method enabled isolation of live cells for downstream molecular analysis and characterization of morphological heterogeneity. We collected ME samples from 9 MBC patients with cytology-confirmed MEs, including 3 triple negative and 6 hormone receptor-positive/HER2-negative breast cancers. ME cells were imaged and isolated using a Deepcell AI model trained on a separate cohort of MEs to isolate and enrich tumor cells based on deep learning representations of morphological features. Quantitative morphological features were then extracted from cell images to perform cluster analysis and visualized by dimensionality reduction projections. In parallel, flow cytometry (FC) was used to quantify % EpCAM+/Claudin4+ tumor cells in ME samples. Copy number (CN) analysis was performed before and after enrichment with as few as 200 cells from each sample. Tumor cell fraction (TCF) estimates were calculated from the AI classifier, FC, and CN analyses. CN profiling of malignant cells sorted by the AI classifier revealed genomic alterations common in breast cancer in 7 of 9 patients. We observed increased amplitude of genomic alterations in tumor-enriched vs. unenriched samples, indicating successful tumor cell enrichment in 14 out of 16 samples. We observed a significant correlation in TCF between the AI classifier and FC with a Spearman test (rho=0.568, p0.008). In some cases, the AI classifier reported higher TCF than FC. This could indicate that the AI model detected tumor cells missed by antibody labeling or false positives from morphologically similar cell types, such as macrophages and mesothelial cells. Remarkably, analysis of serial ME samples from the same patient revealed morphological shifts in cell populations which were consistent with changes in TCF by CN analysis. We demonstrate the feasibility of label-free identification and isolation of tumor cells in MEs using an AI classifier. CN profiling of isolated tumor cells verified enrichment from low initial frequencies. Future work will include expression analysis of ME tumor and immune cells, and analysis of organoids derived from MEs. Combining molecular and morphological profiling of MEs will enable new insights into their pathobiology and inform targeted treatment strategies for patients with this condition. Citation Format: Mark J. Magbanua, Laura Huppert, Janifer Cruz, Jackson Goudreau, Shaopu Zhou, Keyi Yin, Pavel Stejskal, Ryan Carelli, Julie Kim, Linda Hsie, Maggie Wang, Zhouyang Lian, Michael Phelan, Manisha Ray, Chassidy Johnson, Amy L. Delson, Ron Balassanian, Jennifer Rosenbluth, Laura van't Veer, Michelle Melisko, Hope S. Rugo, Mahyar Salek, Maddison Masaeli, Hani Goodarzi. Artificial intelligence-assisted morphology-based detection and enrichment of malignant effusion tumor cells as a method for molecular profiling [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 2 (Clinical Trials and Late-Breaking Research); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(8_Suppl):Abstract nr LB170.

Studying tumor heterogeneity at single cell resolution is crucial for elucidating tumor progression and treatment response. Classically, tumor cells are characterized with a limited set of biomarkers that cannot cover the full extent of the tumor cell heterogeneity. The advent of single cell genomics has enabled scientists to study heterogeneity at high resolution, but gene expression signatures may not translate to readily available biomarkers for functional studies. Multi-dimensional morphology analysis has the potential for higher resolution than cell surface biomarkers while also reducing cell manipulations. The Deepcell platform enables multi-dimensional morphology analysis of unlabeled single cells using artificial intelligence (AI), imaging, and microfluidics, allowing for higher resolution of population heterogeneity beyond protein expression markers. Using the Deepcell platform, we trained an AI model to identify and sort for malignant cells from non-small cell lung cancer cells (NSCLC) tumor biopsies based on morphology. scRNA-seq, CNV, and targeted mutation analysis verified enrichment of carcinoma cells. Sorted cells had gene expression signatures indicative of tumor cells (e.g. EpCAM expression), increased genomic alterations by CNV analysis, and increased allele frequency of P53 and KRAS mutations relative to pre-sorted samples. Overall, the data indicate brightfield images of cells can be used to detect macro-level changes in cell morphology resulting from the molecular events involved in tumorigenesis. In addition, we induced chemotherapeutic drug resistance of lung cancer cell lines in vitro, imaged the resulting cultures on the Deepcell platform, and used the bright-field cell images to generate multi-dimensional morphological embeddings that can be visualized by UMAP. The resulting UMAPs showed distinct clusters of cells for both the parent and resistant cell lines, indicating that there are morphological differences associated with drug resistance. We developed a random forest classifier to identify the top differential morphological features between parental and resistant cell lines. These top features can distinguish between the populations with high accuracy (87%). Finally, we performed multi-dimensional morphology analysis to compare lung adenocarcinoma (LA) and squamous cell carcinoma (SCC) cell lines. The classifier predicted the correct cell type with 75% accuracy, suggesting that differences between these two carcinomas are reflected in their morphology. Together, our data suggest that AI-detected morphological differences between cell populations may show a biologically significant link between morphology and phenotype. As such, multi-dimensional morphology analysis will bolster the understanding of complex disease states and tumor microenvironment, particularly in patient derived biopsies. Citation Format: Andreja Jovic, Kiran Saini, Ryan Carelli, Tiffine Pham, Christian Corona, Jeanette Mei, Michael Phelan, Stephane C. Boutet, Kevn Jacobs, Julie Kim, Manisha Ray, Chassidy Johnson, Nianzhen Li, Mahyar Salek, Maddison Masaeli, Matt Barnes, Cyril Ramathal. Multi-dimensional morphology analysis enables identification and label-free enrichment of heterogeneous tumor cell populations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 2392.

Melanomas are the deadliest skin cancers, in part due to cellular plasticity and heterogeneity within the tumors. These characteristics have made a deeper understanding of melanomas challenging. Classically, melanoma cells are characterized with a limited set of protein biomarkers. Gene expression signatures and mutational analysis (e.g., BRAF and NRAS genotyping) can provide a more detailed view of heterogeneity but may not translate to readily available biomarkers for functional studies. The Deepcell platform enables multi-dimensional morphology analysis and enrichment of unlabeled single cells using artificial intelligence (AI), advanced imaging, and microfluidics, enabling high resolution profiling of population heterogeneity. Label-free multi-dimensional morphology analysis may have higher resolution than a limited set of protein biomarkers, minimizes perturbation to the transcriptome, and reduces cell handling steps. We used patient-derived cell lines and dissociated biopsy samples to train a Deepcell AI model to identify and sort for melanoma cells based on morphology alone. The model was tested on metastatic melanoma biopsies, with identification and enrichment of melanoma cells verified by various downstream assays, including scRNASeq. In addition to melanoma populations, the AI model classified various cells of the microenvironment, such as stromal cells and immune subtypes, based on morphology only. To further characterize tumor heterogeneity, we imaged >25 patient-derived melanoma cell lines representing melanocytic, mesenchymal, and intermediate phenotypic states on the Deepcell platform. Morphology analysis of these images revealed distinct clusters of cells for each phenotype, indicating that there are morphological differences associated with each state. We developed a random forest (RF) classifier to identify the top differential morphological features between the different cell lines, thereby providing a label-free means of phenotyping melanoma samples. The morphology analysis of the cell lines uncovered significant variability in pigmentation; an RF classifier distinguished between pigmented vs non-pigmented cells with >90% accuracy. Pigmentation is a hallmark of melanoma cells, and it has been associated with the melanocytic phenotype and differential drug response in vitro. However, there is not currently a robust method to profile and study pigmentation in live cells. We further investigated this observation by correlating morphological profiles, molecular, and functional information with the level of cell pigmentation. The Deepcell platform presents a new method for sorting and characterizing cellular heterogeneity using morphology, including pigmentation status. As such, multi-dimensional morphology analysis will bolster the understanding of complex melanoma states and tumor microenvironment, particularly in patient derived biopsies. Citation Format: Evelyn Lattmann, Aizhan Tastanova, Andreja Jovic, Kiran Saini, Tiffine Pham, Christian Corona, Jeanette Mei, Michael Phelan, Stephane C. Boutet, Ryan Carelli, Kevin B. Jacobs, Julie Kim, Manisha Ray, Chassidy Johnson, Nianzhen Li, Mahyar Salek, Maddison Masaeli, Mitchell P. Levesque. High dimensional morphology analysis reveals new insights in melanoma cell heterogeneity [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 5926.

The novel nature of the SARS-CoV-2 virus inherently creates a paucity of reliable and validated data. Implementing evidence-based and data-driven protocols have been exceedingly difficult. As new information is released and integrated into the complex system, the health care delivery workflow must adapt. Incorporating changes on a frequent, if not daily basis, has led to confusion, frustration and loss of confidence among clinicians across the nation. This report illustrates the negative impact that false-positive COVID-19 results can have on the health delivery workforce and the emotional implications that false-positive results cast on health care providers.

Background/Aim: Cryptochrome 1 (CRY1), a core circadian gene, modulates circadian rhythm and carcinogenesis. Here, we investigated the role of CRY1 and its correlation with NANOG, a stem cell transcription factor, in cervical cancer. Materials and Methods: Immunohistochemistry with tissue microarray was performed to evaluate CRY1 and NANOG expression in cervical cancer tissues, and their functional roles were assessed in cervical cancer cell lines. Results: CRY1 or NANOG was significantly over-expressed in cervical cancer tissues. Notably, combined over-expression of CRY1 and NANOG was correlated with a significantly poor OS and DFS and showed a stronger predictive value for chemoradiation response than each single protein. Furthermore, siCRY1 induced apoptosis, decreased NANOG expression, suppressed STAT3 signalling, and activated p53 signalling in cervical cancer cell lines. Conclusion: CRY1 and NANOG over-expression serves as a strong predictive biomarker for prognosis and chemoradiation response, and may be a new therapeutic target in patients with cervical cancer.