Investigation of Acute Hypoxia Effects on 3D model of Renal Cell Carcinoma

Partnership for Careers in Cancer Science and Medicine, Faculty Diversity, Equity & Inclusion Department of Genitourinary Medical Oncology Department of Genomic Medicine, Department of Genitourinary Medical Oncologyhttps://openworks.mdanderson.org/sumexp22/1050/thumbnail.jp

Tracking plasticity-dependent cell cycle effects

https://openworks.mdanderson.org/sumexp21/1143/thumbnail.jp

KRAS-Dependency in Pancreatic Ductal Adenocarcinoma: Mechanisms of Escaping in Resistance to KRAS Inhibitors and Perspectives of Therapy

Pancreatic ductal adenocarcinoma (PDAC) is still one of the deadliest cancers in oncology because of its increasing incidence and poor survival rate. More than 90% of PDAC patients are KRAS mutated (KRASmu), with KRASG12D and KRASG12V being the most common mutations. Despite this critical role, its characteristics have made direct targeting of the RAS protein extremely difficult. KRAS regulates development, cell growth, epigenetically dysregulated differentiation, and survival in PDAC through activation of key downstream pathways, such as MAPK-ERK and PI3K-AKT-mammalian target of rapamycin (mTOR) signaling, in a KRAS-dependent manner. KRASmu induces the occurrence of acinar-to-ductal metaplasia (ADM) and pancreatic intraepithelial neoplasia (PanIN) and leads to an immunosuppressive tumor microenvironment (TME). In this context, the oncogenic mutation of KRAS induces an epigenetic program that leads to the initiation of PDAC. Several studies have identified multiple direct and indirect inhibitors of KRAS signaling. Therefore, KRAS dependency is so essential in KRASmu PDAC that cancer cells have secured several compensatory escape mechanisms to counteract the efficacy of KRAS inhibitors, such as activation of MEK/ERK signaling or YAP1 upregulation. This review will provide insights into KRAS dependency in PDAC and analyze recent data on inhibitors of KRAS signaling, focusing on how cancer cells establish compensatory escape mechanisms

Intraoperative complications and early implant failure after transcrestal sinus floor elevation with residual bone height ≤5 mm: A retrospective multicenter study.

OBJECTIVE Clinical indications for maxillary sinus floor elevation with transcrestal techniques have increased in recent years even in sites with minimal residual bone height (RBH). Nevertheless, limited information is currently available on incidence of intraoperative complications and early implant failure in these cases. MATERIAL AND METHODS This retrospective multicenter study was performed on anonymized clinical and radiographic records of patients who underwent transcrestal sinus floor elevation in seven clinical centers. Influence of different factors related to patient, and sinus anatomy and surgical technique on the incidence of intraoperative complications and early implant failure rate after transcrestal sinus lift were investigated. RESULTS A total of 430 patients treated with transcrestal sinus floor elevation for single-implant insertion in sites with RBH ≤5 mm were included in the final analysis. After 1 year of loading, 418 implants of 430 were satisfactorily in function. Early implant failure was recorded in 12 cases (2.8%); results were significantly associated with the presence of large sinus cavities and with the occurrence of membrane perforation. The following adverse events were recorded: membrane perforation (7.2%), acute sinusitis (0.9%), implant displacement into the sinus cavity (0.7%), oro-antral fistula (0.2%), and benign paroxysmal positional vertigo (0.5% of osteotome cases). A strong direct correlation between sinus membrane perforation and bucco-palatal sinus width (p = .000) was demonstrated. CONCLUSIONS Early implant failure after transcrestal sinus elevation showed significant direct correlation with bucco-palatal maxillary sinus width and the presence of membrane perforation. Sinus membrane perforation was strongly associated with bucco-palatal sinus width (extremely low perforation rate in narrow and much higher incidence in wide sinuses)

Convergent evolutionary trajectories uncover metastatic drivers in renal cancer

View full abstracthttps://openworks.mdanderson.org/leading-edge/1060/thumbnail.jp

An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar

Background and objectives: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent.Methods: DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) mod-els based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo-and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality.Results: The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (> 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets.Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker ( <1 min versus 7 +/- 3 min), and required minimal user interaction. Conclusions: Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report.(c) 2022 Elsevier B.V. All rights reserved

An accurate and time-efficient deep learning-based system for automated segmentation and reporting of cardiac magnetic resonance-detected ischemic scar

BACKGROUND AND OBJECTIVES: Myocardial infarction scar (MIS) assessment by cardiac magnetic resonance provides prognostic information and guides patients' clinical management. However, MIS segmentation is time-consuming and not performed routinely. This study presents a deep-learning-based computational workflow for the segmentation of left ventricular (LV) MIS, for the first time performed on state-of-the-art dark-blood late gadolinium enhancement (DB-LGE) images, and the computation of MIS transmurality and extent. METHODS: DB-LGE short-axis images of consecutive patients with myocardial infarction were acquired at 1.5T in two centres between Jan 1, 2019, and June 1, 2021. Two convolutional neural network (CNN) models based on the U-Net architecture were trained to sequentially segment the LV and MIS, by processing an incoming series of DB-LGE images. A 5-fold cross-validation was performed to assess the performance of the models. Model outputs were compared respectively with manual (LV endo- and epicardial border) and semi-automated (MIS, 4-Standard Deviation technique) ground truth to assess the accuracy of the segmentation. An automated post-processing and reporting tool was developed, computing MIS extent (expressed as relative infarcted mass) and transmurality. RESULTS: The dataset included 1355 DB-LGE short-axis images from 144 patients (MIS in 942 images). High performance (> 0.85) as measured by the Intersection over Union metric was obtained for both the LV and MIS segmentations on the training sets. The performance for both LV and MIS segmentations was 0.83 on the test sets. Compared to the 4-Standard Deviation segmentation technique, our system was five times quicker (<1 min versus 7 ± 3 min), and required minimal user interaction. CONCLUSIONS: Our solution successfully addresses different issues related to automatic MIS segmentation, including accuracy, time-effectiveness, and the automatic generation of a clinical report

DPY30 loss leads to DNA re-replication and immunoediting in pancreatic ductal adenocarcinoma

View full abstracthttps://openworks.mdanderson.org/leading-edge/1036/thumbnail.jp

Neddylation Inhibition Sensitises Renal Medullary Carcinoma Tumours to Platinum Chemotherapy

BACKGROUND: Renal medullary carcinoma (RMC) is a highly aggressive cancer in need of new therapeutic strategies. The neddylation pathway can protect cells from DNA damage induced by the platinum-based chemotherapy used in RMC. We investigated if neddylation inhibition with pevonedistat will synergistically enhance antitumour effects of platinum-based chemotherapy in RMC. METHODS: We evaluated the IC50 concentrations of the neddylation‐activating enzyme inhibitor pevonedistat in vitro in RMC cell lines. Bliss synergy scores were calculated using growth inhibition assays following treatment with varying concentrations of pevonedistat and carboplatin. Protein expression was assessed by western blot and immunofluorescence assays. The efficacy of pevonedistat alone or in combination with platinum‐based chemotherapy was evaluated in vivo in platinum‐naïve and platinum‐experienced patient‐derived xenograft (PDX) models of RMC. RESULTS: The RMC cell lines demonstrated IC50 concentrations of pevonedistat below the maximum tolerated dose in humans. When combined with carboplatin, pevonedistat demonstrated a significant in vitro synergistic effect. Treatment with carboplatin alone increased nuclear ERCC1 levels used to repair the interstrand crosslinks induced by platinum salts. Conversely, the addition of pevonedistat to carboplatin led to p53 upregulation resulting in FANCD2 suppression and reduced nuclear ERCC1 levels. The addition of pevonedistat to platinum‐based chemotherapy significantly inhibited tumour growth in both platinum‐naïve and platinum‐experienced PDX models of RMC (p \u3c .01). CONCLUSIONS: Our results suggest that pevonedistat synergises with carboplatin to inhibit RMC cell and tumour growth through inhibition of DNA damage repair. These findings support the development of a clinical trial combining pevonedistat with platinum-based chemotherapy for RMC

SMARCB1 regulates the hypoxic stress response in sickle cell trait during the pathogenesis of renal medullary carcinoma.

View full abstracthttps://openworks.mdanderson.org/leading-edge/1058/thumbnail.jp