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Cone-beam computed tomography (CBCT) is widely used for daily anatomy monitoring and can be a potential source to support adaptation. However, low image quality and artifacts limit CBCT's clinical utility. Peristalsis and air bubbles can cause severe artifacts in pelvic CBCT. We have observed that severe air bubble-induced Feldkamp artifacts in the rectum may contribute to low automatic segmentation accuracy.In this study, air bubbles within the rectum were extracted and automatic rectum segmentation performance was measured in Dice similarity coefficient (DSC). A Gaussian mixture model (GMM) was used to characterize their correlation, and an expectation-maximization (EM) approach was used to solve the corresponding parameter estimation and decouple the impact from air bubbles versus other image attributes based on cluster memberships. Postprostatectomy patient data with high variability in air bubble size and shape were used in this study to reveal the regression relationship.GMM identified two distinct correlative relations between the air-bubble severity in the rectum and the rectum prediction DSC: one showed strong negative dependency of segmentation performance on air bubble presence, and the other one had mild-to-moderate dependency that suggested another group of contributing factors influencing rectum segmentation, such as the inconsistent presence of fiducial seeds and shape extremes.The presence of severe air bubbles contributes semilinearly to performance degradation in automatic rectum segmentation. A good correction mechanism may boost the accuracy and consistency of pelvic segmentation.

Cone-beam computed tomography (CBCT) is a widely accessible low-dose imaging approach compatible with on-table patient anatomy observation for radiotherapy. However, its use in comprehensive anatomy monitoring is hindered by low contrast and low signal-to-noise ratio and a large presence of artifacts, resulting in difficulty in identifying organ and structure boundaries either manually or automatically. In this study, we propose and develop an ensemble deep-learning model to segment post-prostatectomy organs automatically.We utilize the ensemble logic in various modules during the segmentation process to alleviate the impact of low image quality of CBCT. Specifically, (1) semantic attention was obtained from an ensemble 2.5D You-only-look-once detector to consistently define regions of interest, (2) multiple view-specific two-stream 2.5D segmentation networks were developed, using auxiliary high-quality CT data to aid CBCT segmentation, and (3) a novel tensor-regularized ensemble scheme was proposed to aggregate the estimates from multiple views and regularize the spatial integrity of the final segmentation.A cross-validation study achieved Dice similarity coefficient and mean surface distance of 0.779The proposed ensemble scheme manages to enhance the geometric integrity and robustness of the contours derived from CBCT with light network components. The tensor regularization approach generates organ results conforming to anatomy and physiology, without compromising typical quantitative performance in Dice similarity coefficient and mean surface distance, to support further clinical interpretation and decision making.

The functional impact of most genomic alterations found in cancer, alone or in combination, remains largely unknown. Here we integrate tumor barcoding, CRISPR/Cas9-mediated genome editing and ultra-deep barcode sequencing to interrogate pairwise combinations of tumor suppressor alterations in autochthonous mouse models of human lung adenocarcinoma. We map the tumor suppressive effects of 31 common lung adenocarcinoma genotypes and identify a landscape of context dependence and differential effect strengths.

Chromosome stability models are usually qualitative models derived from molecular-genetic mechanisms for DNA repair, DNA synthesis, and cell division. While qualitative models are informative, they are also challenging to reformulate as precise quantitative models. In this report we explore how (A) laboratory experiments, (B) quantitative simulation, and (C) seriation algorithms can inform models of chromosome stability. Laboratory experiments were used to identify 19 genes that when over-expressed cause chromosome instability in the yeast Saccharomyces cerevisiae To better understand the molecular mechanisms by which these genes act, we explored their genetic interactions with 18 deletion mutations known to cause chromosome instability. Quantitative simulations based on a mathematical model of the cell cycle were used to predict the consequences of several genetic interactions. These simulations lead us to suspect that the chromosome instability genes cause cell-cycle perturbations. Cell-cycle involvement was confirmed using a seriation algorithm, which was used to analyze the genetic interaction matrix to reveal an underlying cyclical pattern. The seriation algorithm searched over 10(14) possible arrangements of rows and columns to find one optimal arrangement, which correctly reflects events during cell cycle phases. To conclude, we illustrate how the molecular mechanisms behind these cell cycle events are consistent with established molecular interaction maps.

Catalase (CAT) decomposes hydrogen peroxide that is toxic to the body. In this study, simple and sensitive detector has been developed for observing catalase activity using liquid crystal droplet system. Microscale LC droplet patterns are formed by spreading aldehyde-doped nematic liquid crystal on pre-treated glass slides. When hydrogen peroxide is added, aldehyde is oxidized and amphiphiles are formed. Dodecanoates cause the pattern to transit from bright to dark as they self-assemble to form a carboxyalte monolayer at the interface. When a drop of pre-incubated CAT and hydrogen peroxide mixture is placed onto the pattern, bright fan-shape is observed. This planar optical appearance indicates that catalase has decomposed hydrogen peroxide. Compared to the detectors that have been previously developed, this system is more sensitive with detection limit of 1fM. This research suggests further studies to be on LC droplet patterning to develop highly sensitive and methodologically simple sensors for various chemicals.

e19059 Background: Single agent checkpoint inhibitors (CPI) in NHLs have resulted in modest successes. Exceptions include RT, PCNS and testicular lymphomas, where higher activity is seen. As a single agent, CPI response rates range from 10-40% in relapsed refractory (RR) DLBCL, with few CRs and short response durations. In a cohort of RR DLBCL patients at our institution, we sought to identify clinical features that defined responders and non-responders. Methods: Between 9/2016 and 7/2018, 13 pts with DLBCL/RT, treated with a CPI, either on trial or as off-label therapy, with at minimum 1 infusion/cycle were included. Pathology specimens confirming DLBCL/RT were reviewed at FCCC. All pts had measurable disease by CT or PET/CT prior to CPI and had an evaluable response. Cell of origin was determined by Hans IHC. Results: Almost half (6/13) of pts achieved a response to CPI. Notably all responders had either concurrent or pre-treatment XRT. All 3 RT patients responded to CPI and continued to allo transplant. P3 and P6 both developed GVHD post allo, resulting in a demise in P3. No GC subtype pts responded nor had prior/ concurrent XRT with CPI. In 2 pts responses are ongoing, > 1 yr, and 1 RT pt remains in CR. Conclusions: XRT, prior or concurrent with CPI , was associated with durable responses in RR DLBCL. Patients with bulky ( > 7cm), rapidly progressive disease (8/13 cases) may require a 'debulking' strategy for CPI efficacy. An abscopal effect achieved with XRT/CPI combinations, may be impactful RR NHL. Analysis of PDL1/2 and MHC I/II, with other biomarkers, are underway. These clinical results warrant validation in a larger cohort, therefore a prospectively designed study is planned for 2019 in RR DLBCL/RT. [Table: see text]

A healthy cell must maintain chromosome integrity during each phase of the cell cycle. Yet during the cell cycle, defects in DNA repair, DNA synthesis, and chromosome transmission can cause chromosome instability. Here, we build a cyclical model for chromosome maintenance by assaying the genetic interactions of pairs of genes that each normally functions to maintain chromosomes. First, we confirm that 19 genes affect chromosome maintenance by overexpressing the respective genes using an assay for chromosome instability. We then introduce plasmids containing these 19 genes into 18 deletion mutant strains each of which is known to cause chromosome maintenance defect. By conditionally overexpressing each of the 19 genes in each of the 18 deletion strains, we observe 34 cases of synthetic dosage lethality, 58 of synthetic dosage sickness, and 26 of suppression (out of 349 combinations tested). Ordinarily, given a large matrix of genetic interactions, it is computationally intractable (and NP hard) to compute all permutations of the arrangements of the rows and columns, which is necessary for finding an optimum arragement of entries for a given pattern of functional significance. Here, using our small yet dense matrix we rearrange the matrix into an optimal cycle. By optimally reordering the matrix representation of these genetic interactions using integer linear programming, we find that the optimal order of the functions of the genes approximates the temporal order of processes during the yeast cell cycle. To further validate this theoretical approach, we computed in silico genetic interactions derived from a predetermined cyclical model and confirmed that the predicted cycle was consistent. These results suggest that matrices from other genetic experiments could be computationally rearranged using this method to reveal an underlying cycle that is biological meaningful.

Despite the high prevalence and poor outcome of patients with metastatic lung cancer the mechanisms of tumour progression and metastasis remain largely uncharacterized. Here we modelled human lung adenocarcinoma, which frequently harbours activating point mutations in KRAS and inactivation of the p53 pathway, using conditional alleles in mice. Lentiviral-mediated somatic activation of oncogenic Kras and deletion of p53 in the lung epithelial cells of Kras[superscript LSL-G12D/+];p53[superscript flox/flox] mice initiates lung adenocarcinoma development4. Although tumours are initiated synchronously by defined genetic alterations, only a subset becomes malignant, indicating that disease progression requires additional alterations. Identification of the lentiviral integration sites allowed us to distinguish metastatic from non-metastatic tumours and determine the gene expression alterations that distinguish these tumour types. Cross-species analysis identified the NK2-related homeobox transcription factor Nkx2-1 (also called Ttf-1 or Titf1) as a candidate suppressor of malignant progression. In this mouse model, Nkx2-1 negativity is pathognomonic of high-grade poorly differentiated tumours. Gain- and loss-of-function experiments in cells derived from metastatic and non-metastatic tumours demonstrated that Nkx2-1 controls tumour differentiation and limitsmetastatic potential in vivo. Interrogation of Nkx2-1-regulated genes, analysis of tumours at defined developmental stages, and functional complementation experiments indicate that Nkx2-1 constrains tumours in part by repressing the embryonically restricted chromatin regulator Hmga2. Whereas focal amplification of NKX2-1 in a fraction of human lung adenocarcinomas has focused attention on its oncogenic function, our data specifically link Nkx2-1 downregulation to loss of differentiation, enhanced tumour seeding ability and increased metastatic proclivity. Thus, the oncogenic and suppressive functions ofNkx2-1 in the sametumour, National Institutes of Health (U.S.) (grant U01-CA84306 ), National Institutes of Health (U.S.) (grant K99-CA151968), Howard Hughes Medical Institute, Ludwig Center for Molecular Oncology, National Cancer Institute (U.S.) (Cancer Center Support (core) grant P30-CA14051)