Interactions between ploidy and resource availability shape clonal interference at initiation and recurrence of glioblastoma

Glioblastoma (GBM) is the most aggressive form of primary brain tumor. Complete surgical resection of GBM is almost impossible due to the infiltrative nature of the cancer. While no evidence for recent selection events have been found after diagnosis, the selective forces that govern gliomagenesis are strong, shaping the tumor's cell composition during the initial progression to malignancy with late consequences for invasiveness and therapy response. We present a mathematical model that simulates the growth and invasion of a glioma, given its ploidy level and the nature of its brain tissue micro-environment (TME), and use it to make inferences about GBM initiation and response to standard-of-care treatment. We approximate the spatial distribution of resource access in the TME through integration of in-silico modelling, multi-omics data and image analysis of primary and recurrent GBM. In the pre-malignant setting, our in-silico results suggest that low ploidy cancer cells are more resistant to starvation-induced cell death. In the malignant setting, between first and second surgery, simulated tumors with different ploidy compositions progressed at different rates. Whether higher ploidy predicted fast recurrence, however, depended on the TME. Historical data supports this dependence on TME resources, as shown by a significant correlation between the median glucose uptake rates in human tissues and the median ploidy of cancer types that arise in the respective tissues (Spearman r = -0.70; P = 0.026). Taken together our findings suggest that availability of metabolic substrates in the TME drives different cell fate decisions for cancer cells with different ploidy and shapes GBM disease initiation and relapse characteristics.

Virtual alignment of pathology image series for multi-gigapixel whole slide images

Interest in spatial omics is on the rise, but generation of highly multiplexed images remains challenging, due to cost, expertise, methodical constraints, and access to technology. An alternative approach is to register collections of whole slide images (WSI), generating spatially aligned datasets. WSI registration is a two-part problem, the first being the alignment itself and the second the application of transformations to huge multi-gigapixel images. To address both challenges, we developed Virtual Alignment of pathoLogy Image Series (VALIS), software which enables generation of highly multiplexed images by aligning any number of brightfield and/or immunofluorescent WSI, the results of which can be saved in the ome.tiff format. Benchmarking using publicly available datasets indicates VALIS provides state-of-the-art accuracy in WSI registration and 3D reconstruction. Leveraging existing open-source software tools, VALIS is written in Python, providing a free, fast, scalable, robust, and easy-to-use pipeline for registering multi-gigapixel WSI, facilitating downstream spatial analyses.

Addressing persistent challenges in digital image analysis of cancerous tissues

The National Cancer Institute (NCI) supports many research programs and consortia, many of which use imaging as a major modality for characterizing cancerous tissue. A trans-consortia Image Analysis Working Group (IAWG) was established in 2019 with a mission to disseminate imaging-related work and foster collaborations. In 2022, the IAWG held a virtual hackathon focused on addressing challenges of analyzing high dimensional datasets from fixed cancerous tissues. Standard image processing techniques have automated feature extraction, but the next generation of imaging data requires more advanced methods to fully utilize the available information. In this perspective, we discuss current limitations of the automated analysis of multiplexed tissue images, the first steps toward deeper understanding of these limitations, what possible solutions have been developed, any new or refined approaches that were developed during the Image Analysis Hackathon 2022, and where further effort is required. The outstanding problems addressed in the hackathon fell into three main themes: 1) challenges to cell type classification and assessment, 2) translation and visual representation of spatial aspects of high dimensional data, and 3) scaling digital image analyses to large (multi-TB) datasets. We describe the rationale for each specific challenge and the progress made toward addressing it during the hackathon. We also suggest areas that would benefit from more focus and offer insight into broader challenges that the community will need to address as new technologies are developed and integrated into the broad range of image-based modalities and analytical resources already in use within the cancer research community.

PhysiCOOL: A generalized framework for model Calibration and Optimization Of modeLing projects

In silico models of biological systems are usually very complex and rely on a large number of parameters describing physical and biological properties that require validation. As such, parameter space exploration is an essential component of computational model development to fully characterize and validate simulation results. Experimental data may also be used to constrain parameter space (or enable model calibration) to enhance the biological relevance of model parameters. One widely used computational platform in the mathematical biology community is PhysiCell, which provides a standardized approach to agent-based models of biological phenomena at different time and spatial scales. Nonetheless, one limitation of PhysiCell is the lack of a generalized approach for parameter space exploration and calibration that can be run without high-performance computing access. Here, we present PhysiCOOL, an open-source Python library tailored to create standardized calibration and optimization routines for PhysiCell models.

Surgical challenges of giant parathyroid adenomas weighing 10 g or more

PURPOSE

An average parathyroid adenoma (PA) weighs < 1 g. This study aimed to characterise giant PAs ≥ 10 g (GPAs) to facilitate surgical management of primary hyperparathyroidism (PHPT).

METHODS

All patients with a GPA confirmed on histology were recruited from the Monash University Endocrine Surgery Unit database. Clinical and demographic data were collected and compared to a group of non-GPA patients.

RESULTS

A total of 14 GPAs were identified between 2007 and 2018 out of 863 patients (1.6%) with a single PA excised for PHPT. The GPA patients were compared to a control group of 849 non-GPA patients in the same period with similar mean age (62 ± 16 vs 63 ± 14, P = 0.66) and gender distribution (64% vs 75% female, P = 0.35). Pre-operative calcium (Ca) and parathyroid hormone (PTH) levels were significantly higher in GPA patients (P < 0.001). A higher percentage of GPA patients (79%) had concordant localisation studies (ultrasound and sestamibi) than control patients (59%), (P = 0.13), but they were significantly less likely to undergo MIP (55% vs 82%, P = 0.02). The median GPA weighed 12.5 g (IQR 10.5-24.3). Median serum Ca normalised by day 1 post-operatively, while PTH remained elevated. Both serum Ca and PTH levels were in the normal range at 3 months. All GPA lesions were benign on histopathology.

CONCLUSION

GPAs are rare and display severe clinical and biochemical abnormalities. Despite their large size, concordant pre-operative imaging was not always achieved, and a few patients were suitable for MIP.

Efficiency of Ferritin bio-nanomaterial in reducing the pollutants level of water in the underground corridors of metro rail using GIS

The underground developments are likely to deteriorate the water quality, which causes damage to the structure. The pollutant levels largely affect the aquifer properties and alter the characteristics of the water quality. Ferritin nanoparticle usage proves to be an effective technology for reducing the pollutant level of the salts, which are likely to affect the underground structure. The observation wells are selected around the underground Metro Rail Corridor, and the secondary observation wells are selected around the corridors. Ferritin is a common iron storage protein as a powder used in the selected wells identified in the path of underground metro rail corridors. Water sampling was done to assess the water quality in the laboratory. The water quality index plots for the two phases (1995-2008) and (2009-2014) using GIS explains the water quality scenario before and after the Ferritin treatment. The Ferritin treatment in water was very effective in reducing the pollutants level of Fluoride and sulphate salts which is likely to bring damage to the structure.

Developing tools for analyzing and viewing multiplexed images

Dr. Prabhakaran and Dr Gatenbee are research scientists in Anderson's lab and have developed Mistic, a publicly available tool that simultaneously views multiplexed images and assists in gaining biological and clinical insights into patients' data. They discuss the role of mathematical modeling in translational cancer research and clinical decision making and describe how mathematical modeling fits into the data science definition.

Mistic: An open-source multiplexed image t-SNE viewer

Understanding the complex ecology of a tumor tissue and the spatiotemporal relationships between its cellular and microenvironment components is becoming a key component of translational research, especially in immuno-oncology. The generation and analysis of multiplexed images from patient samples is of paramount importance to facilitate this understanding. Here, we present Mistic, an open-source multiplexed image t-SNE viewer that enables the simultaneous viewing of multiple 2D images rendered using multiple layout options to provide an overall visual preview of the entire dataset. In particular, the positions of the images can be t-SNE or UMAP coordinates. This grouped view of all images allows an exploratory understanding of the specific expression pattern of a given biomarker or collection of biomarkers across all images, helps to identify images expressing a particular phenotype, and can help select images for subsequent downstream analysis. Currently, there is no freely available tool to generate such image t-SNEs.

Sparcle: assigning transcripts to cells in multiplexed images

Motivation

Imaging-based spatial transcriptomics has the power to reveal patterns of single-cell gene expression by detecting mRNA transcripts as individually resolved spots in multiplexed images. However, molecular quantification has been severely limited by the computational challenges of segmenting poorly outlined, overlapping cells and of overcoming technical noise; the majority of transcripts are routinely discarded because they fall outside the segmentation boundaries. This lost information leads to less accurate gene count matrices and weakens downstream analyses, such as cell type or gene program identification.

Results

Here, we present Sparcle, a probabilistic model that reassigns transcripts to cells based on gene covariation patterns and incorporates spatial features such as distance to nucleus. We demonstrate its utility on both multiplexed error-robust fluorescence in situ hybridization, single-molecule FISH data, probabilistic cell typing in situ sequencing, spatially resolved transcript amplicon readout mapping and MERFISH from Vizgen. Sparcle improves transcript assignment, providing more realistic per-cell quantification of each gene, better delineation of cell boundaries and improved cluster assignments. Critically, our approach does not require an accurate segmentation and is agnostic to technological platform.

Availability and implementation

The code is available at: https://github.com/sandhya212/Sparcle_for_spot_reassignments.

Contact

sandhya.prabhakaran@moffitt.org.

Supplementary information

Supplementary data are available at Bioinformatics Advances online.

Abstract 5037: Distinct tumor-immune ecologies in NSCLC patients predict progression and define a clinical biomarker of therapy response

Rationale: Examination of multiplexed images of tissues has recently emerged as a routine clinical procedure for cancer diagnosis and prognosis. The simultaneous detection of numerous biomarkers enables the interpretation of cellular states and the characterization of tumor-immune interactions in situ and at the single-cell level. However, image processing and the subsequent interpretive and predictive tools for multiplexed image data remain limited.

Methods: We developed a computational multiplexed-image analysis pipeline using cell-segmentation and quadrat-based approaches to analyze the spatial and temporal features of multiplexed non-small cell lung cancer (NSCLC) images, and predict disease progression and identify clinical biomarkers. Images were obtained from nine patients with advanced/metastatic NSCLC who were treated with the oral HDAC inhibitor vorinostat combined with the PD-1 inhibitor pembrolizumab. Images were collected from all patients both pre- and on-treatment (during the third week).

Results: Both cell-segmentation and quadrat-based approaches confirm that different spatial neighborhoods exist that distinguish progressors (PD) from non-progressors (SD): PD patients have distinct ecologies with higher colocalization of PanCK+PD-1+FoxP3 indicating an immunosuppressive environment, whereas SD patients have a higher colocalization of PanCK+PD-L1 along with T cells suggesting immunoactive tumor regions. These can be considered as potential biomarker candidates for predicting tumor progression. Further, from the single-cell analysis, we note there is a higher abundance of immune cells across the tumor border in PD patients than SD patients. Using the quadrat approach for species distribution modeling, we were able to predict treatment response with 91.4 percent accuracy given each patient’s spatial distribution of cell types from pre-treatment images. Further, we can generate risk maps for each image to identify tumor areas indicating higher probabilities of progression during treatment.

Conclusions: We leveraged both single-cell and quadrat-resolution analysis of multiplexed imaging data and identified fundamentally distinct spatial ecologies between PD and SD patients. The ecology in PD patients appears to be primed for immune resistance even before treatment. This ecological diversity between SD and PD patients acts as a biomarker that enables accurate disease progression prediction.

Citation Format: Sandhya Prabhakaran, Chandler Gatenbee, Mark Robertson-Tessi, Amer A. Beg, Jhanelle Gray, Scott Antonia, Robert A. Gatenby, Alexander R. Anderson. Distinct tumor-immune ecologies in NSCLC patients predict progression and define a clinical biomarker of therapy response [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2022; 2022 Apr 8-13. Philadelphia (PA): AACR; Cancer Res 2022;82(12_Suppl):Abstract nr 5037.

Abstract B020: Distinct spatiotemporal tumor-immune ecologies enable disease prediction in NSCLC patients

Purpose of study: Non-small cell lung cancer (NSCLC) is the most common and fatal of cancers. In this work, we examine multiplexed images of NSCLC tumors to investigate both the spatial and spatiotemporal eco-evolutionary interactions between the tumor and its microenvironment, to better understand NSCLC tumor progression and therapy response. Methods: We developed a scalable, computational image analysis pipeline using cell-segmentation and quadrat-based approaches to analyze the spatial and temporal features of high-dimensional multiplexed NSCLC images. Multiplexed images enable the spatial readouts of numerous biomarkers per tissue sample and allow the interpretation of cellular states and the characterization of tumor-immune interactions across tissue ensembles. We also implement statistical approaches for ecological niche modelling combined with machine learning and deep learning models to predict disease progression and identify clinical imaging biomarkers. Data: Images were obtained from two 9-patient cohorts having advanced/metastatic NSCLC who were treated with the oral HDAC inhibitor vorinostat combined with the PD-1 inhibitor pembrolizumab. The first cohort had 4 progressors (PD) and 5 with stable disease (SD). The second cohort had 3 patients each in the PD, SD and partial response (PR) categories. Images were collected from all patients both pre- and on-treatment (during the third week). Results: Using our computational framework based on cell segments and quadrats, we confirm that different spatial neighborhoods exist that distinguish PD from SD, and that these spatial ecologies aid disease progression: PD patients have distinct ecologies with higher colocalization of PanCK+PD-1+FoxP3 indicating an immunosuppressive environment, whereas SD patients have a higher colocalization of PanCK+PD-L1 along with T cells, suggesting immunoactive tumor regions. These can be considered as potential biomarker candidates for predicting tumor progression. In an additional experiment where we include PR samples in our analyses, these distinct spatial neighborhoods are reinforced amongst PD, SD and PR patient groups corroborating the existence of spatiotemporal patterns. Further, we were able to predict treatment response with &gt;91% accuracy given each patient’s spatial distribution of cell types from pre-treatment images. Using these predictions, we can generate risk maps at the patient level to identify areas of the tumor that are indicators of a higher probability of progression during treatment. Conclusions: We leveraged both single-cell and quadrat-resolution analysis of multiplexed imaging data and identified fundamentally distinct spatial ecologies between PD and SD patients. The ecology in PD patients appears to be primed for immune resistance even before treatment. This ecological diversity between SD and PD patients acts as a biomarker that enables accurate disease progression prediction. Our disease progression predictions can be used in conjunction with standard PD-L1 status to further strengthen personalized treatment strategies.

Citation Format: Sandhya Prabhakaran, Chandler Gatenbee, Mark Robertson-Tessi, Amer A. Beg, Jhanelle Gray, Scott Antonia, Robert A. Gatenby, Alexander R.A. Anderson. Distinct spatiotemporal tumor-immune ecologies enable disease prediction in NSCLC patients [abstract]. In: Proceedings of the AACR Special Conference on the Evolutionary Dynamics in Carcinogenesis and Response to Therapy; 2022 Mar 14-17. Philadelphia (PA): AACR; Cancer Res 2022;82(10 Suppl):Abstract nr B020.

Ovarian cancer immunogenicity is governed by a narrow subset of progenitor tissue-resident memory T cells

Despite repeated associations between T cell infiltration and outcome, human ovarian cancer remains poorly responsive to immunotherapy. We report that the hallmarks of tumor recognition in ovarian cancer-infiltrating T cells are primarily restricted to tissue-resident memory (TRM) cells. Single-cell RNA/TCR/ATAC sequencing of 83,454 CD3+CD8+CD103+CD69+ TRM cells and immunohistochemistry of 122 high-grade serous ovarian cancers shows that only progenitor (TCF1low) tissue-resident T cells (TRMstem cells), but not recirculating TCF1+ T cells, predict ovarian cancer outcome. TRMstem cells arise from transitional recirculating T cells, which depends on antigen affinity/persistence, resulting in oligoclonal, trogocytic, effector lymphocytes that eventually become exhausted. Therefore, ovarian cancer is indeed an immunogenic disease, but that depends on ∼13% of CD8+ tumor-infiltrating T cells (∼3% of CD8+ clonotypes), which are primed against high-affinity antigens and maintain waves of effector TRM-like cells. Our results define the signature of relevant tumor-reactive T cells in human ovarian cancer, which could be applicable to other tumors with unideal mutational burden.

Spontaneous class-switched antibody responses at endometrial cancer tumor bed drives superior patients’ outcome

The role of humoral responses in endometrial cancer remains insufficiently investigated. Using a cohort of 107 patients with different histological subtypes of endometrial carcinoma, we report that concomitant accumulation of T, B and plasma cells at tumor beds predicts better survival. However, only B cell markers predict survival specifically in high-grade endometrioid type and serous tumors. Accordingly, immune protection is associated with class-switched IgA and, to a lesser extent, IgG. Notably, expression of polymeric immunoglobulin receptor (pIgR) by tumor cells and its occupancy by IgA are superior predictors of outcome, and correlate with defects in methyl mismatch repair. Mechanistically, pIgR-dependent, antigen-independent IgA occupancy drives inflammatory pathways associated with IFN and TNF signaling in tumor cells, along with apoptotic and ER stress pathways, while thwarting DNA repair mechanisms. Therefore, coordinated humoral and cellular immune responses, characterized by IgA:pIgR interactions in tumor cells, determine the progression of human endometrial cancer, and therefore the potential for effective immunotherapies.

Supported by grants from NIH (R01CA157664, R01CA124515, R01CA178687 and R01CA211913), and from Cancer Center Support Grant (CCSG) CA076292

Ovarian cancer immunogenicity is governed by a narrow subset of progenitor tissue-resident memory T-cells

Despite repeated associations between T-cell infiltration and patient outcome, human ovarian cancer remains poorly responsive to immunotherapy. We report that hallmarks of tumor recognition in ovarian cancer-infiltrating T-cells are primarily restricted to tissue-resident memory (TRM) cells. In mouse models we found that TRM T-cells were better than the re-circulating counterpart at controlling tumor growth. Single-cell RNA/TCR/ATAC sequencing of 83,454 CD3+CD8+CD103+CD69+ TRM cells and 24,175 CD3+CD8+CD103− re-circulating TILs showed that progenitor (TCF1low) tissue-resident memory T-cells (TRMstem cells) arise from transitional recirculating T-cells, which depends on antigen affinity/persistence, resulting in oligoclonal, trogocytic, effector lymphocytes. This effector population develops into proliferative lymphocytes that eventually become exhausted TRMs. Immunohistochemistry of 122 high-grade serous ovarian cancer tissues showed that only TRMstem cells, but not re-circulating TCF1+ T-cells, predict ovarian cancer outcome. Therefore, ovarian cancer is indeed an immunogenic disease that depends on ~13% of CD8+ tumor-infiltrating T-cells (~3% of CD8+ clonotypes), which are primed against high-affinity antigens and maintain waves of effector TRM cells.

Support for Shared Resources was provided by Cancer Center Support Grant (CCSG) CA076292 to H. Lee Moffitt Cancer Center and by CCSG CA010815 to The Wistar Institute. This study was supported by grants from NIH (R01CA157664, R01CA124515, R01CA178687, R01CA211913 and U01CA232758 to JRCG; R01CA184185 and RO1CA262121 to PCR.)

Association between imaging response and survival following neoadjuvant chemotherapy in patients with resectable colorectal liver metastases: A cohort study

BACKGROUND

The association between the imaging response (structural or metabolic) to neoadjuvant chemotherapy (neoCT) before colorectal liver metastasis (CRLM) and survival is unclear.

METHOD

A total of 201 patients underwent their first CRLM resection. A total of 94 (47%) patients were treated with neoCT. A multivariable, Cox proportional hazard regression analysis was performed to compare overall survival (OS) and progression-free survival (PFS) between response groups.

RESULTS

Multivariable regression analysis of the CT/MRI (n = 94) group showed no difference in survival (OS and PFS) in patients who had stable disease/partial response (SD/PR) or complete response (CR) versus patients who had progressive disease (PD) (OS: HR, 0.36 (95% CI: 0.11-1.19) p = .094, HR, 0.78 (95% CI: 0.13-4.50) p = .780, respectively), (PFS: HR, 0.70 (95% CI: 0.36-1.35) p = .284, HR, 0.51 (0.18-1.45) p = .203, respectively). In the FDG-PET group (n = 60) there was no difference in the hazard of death for patients with SD/PR or CR versus patients with PD for OS or PFS except for the PFS in the small CR subgroup (OS: HR, 0.75 (95% CI: 0.11-4.88) p = .759, HR, 1.21 (95% CI: 0.15-9.43) p = .857), (PFS: HR, 0.34% (95% CI: 0.09-1.22), p = .097, HR, 0.17 (95% CI: 0.04-0.62) p = .008, respectively).

CONCLUSION

There was no convincing evidence of association between imaging response to neoCT and survival following CRLM resection.

Characteristics of a Diverse Cohort of Stroke Patients with SARS-CoV-2 and Outcome by Sex

•

COVID-19 disease is associated with stroke

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All strokes subtypes are seen in association with COVID-19, with ischemic stroke being most prevalent

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The most common etiology for ischemic stroke in SARS-CoV2 infection is cryptogenic

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Sex plays an important role in stroke outcomes in patients with COVID-19 disease

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Males have higher rates of ICU admission, in-hospital complications and more likely to have worse outcome at hospital discharge compare with females

Background and Purpose

Severe Acute Respiratory Syndrome Coronavirus 2 (SARS-CoV-2) infection is associated with stroke. The role of sex on stroke outcome has not been investigated. To objective of this paper is to describe the characteristics of a diverse cohort of acute stroke patients with COVID-19 disease and determine the role of sex on outcome.

Methods

This is a retrospective study of patients with acute stroke and SARS-CoV-2 infection admitted between March 15 to May 15, 2020 to one of the six participating comprehensive stroke centers. Baseline characteristics, stroke subtype, workup, treatment and outcome are presented as total number and percentage or median and interquartile range. Outcome at discharge was determined by the modified Rankin Scale Score (mRS). Variables and outcomes were compared for males and females using univariate and multivariate analysis.

Results

The study included 83 patients, 47% of which were Black, 28% Hispanics/Latinos, and 16% whites. Median age was 64 years. Approximately 89% had at least one preexisting vascular risk factor (VRF). The most common complications were respiratory failure (59%) and septic shock (34%). Compared with females, a higher proportion of males experienced severe SARS-CoV-2 symptoms requiring ICU hospitalization (73% vs. 49%; p = 0.04). When divided by stroke subtype, there were 77% ischemic, 19% intracerebral hemorrhage and 3% subarachnoid hemorrhage. The most common ischemic stroke etiologies were cryptogenic (39%) and cardioembolic (27%). Compared with females, males had higher mortality (38% vs. 13%; p = 0.02) and were less likely to be discharged home (12% vs. 33%; p = 0.04). After adjustment for age, race/ethnicity, and number of VRFs, mRS was higher in males than in females (OR = 1.47, 95% CI = 1.03–2.09).

Conclusion

In this cohort of SARS-CoV-2 stroke patients, most had clinical evidence of coronavirus infection on admission and preexisting VRFs. Severe in-hospital complications and worse outcomes after ischemic strokes were higher in males, than females.

Is Laparoscopic Roux-en-Y Gastric Bypass Still the Gold Standard Procedure for Indians? Mid- to Long-Term Outcomes from a Tertiary Care Center

PURPOSE

Laparoscopic Roux-en-Y gastric bypass (RYGB) is the oldest and most widely performed bariatric surgery worldwide. There is, however, a scarcity of mid- to long-term data of RYGB, especially from the Indian subcontinent.

MATERIALS AND METHODS

The study was a single-center, retrospective analysis from patients who underwent RYGB between January 2009 and November 2014 from a tertiary care center in India. Percent of total weight loss (%TWL) was taken as the primary outcome of the study. Secondary outcomes included type 2 diabetes mellitus (T2DM) remission, comorbidity resolution, revisional surgeries, and complications related to RYGB at 1 year, at 3 years, and during the long term, following surgery. Postoperative visits took place at 1 and 3 years, while the long-term outcome was at median 8.3 years (range 5.4-11.2 years), with a follow-up of 92.4% (488/528), 80.5% (424/527) and 69.5% (363/522), respectively.

RESULTS

Out of 528 patients studied, 56% were females. The mean body mass index (BMI) was 40.6 ± 6.9 kg/m². The %TWL in the long-term follow-up was 21.8 ± 11.3%. T2DM remission rates at 1 year, at 3 years, and during the long term were 84.5%, 70.0%, and 60.0%, respectively. Preoperative HBA1c (p = 0.002) and insulin usage (p = 0.016) had a significant predictive effect on T2DM remission. Gastroesophageal reflux disease (GERD) improved significantly (p < 0.001). Early (< 30 days) and late (> 30 days) complications were observed in 2.3% and 4.3% of the patients, respectively.

CONCLUSION

Weight loss during mid to long-term follow-up was maintained in the majority of the patients after RYGB. However, a small proportion had significant weight regain in the long term. T2DM, GERD, and other comorbidities were well improved after RYGB.

Diffusion Properties of Normal-Appearing White Matter Microstructure and Severity of Motor Impairment in Acute Ischemic Stroke

BACKGROUND AND PURPOSE

The effect of white matter hyperintensities as measured by FLAIR MR imaging on functional impairment and recovery after ischemic stroke has been investigated thoroughly. However, there has been growing interest in investigating normal-appearing white matter microstructural integrity following ischemic stroke onset with techniques such as DTI.

MATERIALS AND METHODS

Fifty-two patients with acute ischemic stroke and 36 without stroke were evaluated with a DTI and FLAIR imaging protocol and clinically assessed for the severity of motor impairment using the Motricity Index within 72 hours of suspected symptom onset.

RESULTS

There were widespread decreases in fractional anisotropy and increases in mean diffusivity and radial diffusivity for the acute stroke group compared with the nonstroke group. There was a significant positive association between fractional anisotropy and motor function and a significant negative association between mean diffusivity/radial diffusivity and motor function. The normal-appearing white matter ROIs that were most sensitive to the Motricity Index were the anterior/posterior limb of the internal capsule in the infarcted hemisphere and the splenium of the corpus callosum, external capsule, posterior limb/retrolenticular part of the internal capsule, superior longitudinal fasciculus, and cingulum (hippocampus) of the intrahemisphere/contralateral hemisphere.

CONCLUSIONS

The microstructural integrity of normal-appearing white matter is a significant parameter to identify neural differences not only between those individuals with and without acute ischemic stroke but also correlated with the severity of acute motor impairment.

Role of Preoperative Duplex Ultrasonography to Predict Functional Maturation of Wrist Radiocephalic Arteriovenous Fistula: A Study on Indian Population

Radiocephalic arteriovenous fistula (RCAVF) is the first choice for native arteriovenous fistula (AVF). Preoperative vessel assessment with ultrasonography (USG) has been reported to enhance the outcome of native AVF, but data regarding its predictive value for functional maturation of RCAVF are scanty. We aimed to determine the role of preoperative duplex USG (DUS) for prediction of functional maturity of radiocephalic fistula in the wrist. The data from 173 patients were analyzed prospectively. The estimated duplex variable included size, patency, and continuity of cephalic vein and size, peak systolic velocity, and wall calcifications in radial artery at the wrist. The subjects underwent RCAVF creation and were reviewed 6-8 weeks post procedure for adequacy of maturation. Doppler variables between successful and failed maturation groups were compared. Successful functional fistula maturation was noted in 138 (80.9%) patients. Values of radial artery diameter, cephalic vein diameter, and peak systolic velocity were >2 mm, 2.2 mm, and 32.8 cm/s, respectively, for successful maturation of RCAVF in more than 90% of cases. Vascular calcifications were detected preoperatively in 15 diabetic patients and 9 (60%) of them had fistula failure. Preoperative DUS can provide a good prediction on functional maturation of RCAVF. Vascular calcifications were associated with high risk of maturation failure in diabetics.

Abstract P4-09-07: Validation of 12-gene chemokine signature as a predictor of treatment response in breast cancer

Background: We had previously derived a unique 12-chemokine gene expression score (CS) from a metagene grouping with high enrichment for immune-and inflammation-related genes. A review of selected Stage I - III breast cancer patients showed that higher CS were associated with high-grade tumors and aggressive subtypes and in the HER2 positive group, correlated with increased recurrence free survival that trended towards significance. We tested the predictive capability of this CS for pathological complete response (pCR) in an external dataset. We used the Neratinib and Veliparib arms of the I-SPY2 TRIAL dataset with their respective controls for this analysis.

Methods: Gene expression signature probes (CCL2, CCL3, CCL4, CCL5, CCL8, CCL18, CCL19, CCL21, CXCL9, CXCL10, CXCL11 and CXCL13) were extracted from existing Agilent custom 44k microarray from the I-SPY2 TRIAL dataset. The arrays contain 40,793 probe sets representing ˜25,000 unique genes. The expression data for the 246 distinct solid tumors were normalized using IRON and expression data for the 12- chemokine genes were extracted for principal component analysis (PCA). The first principal component (PC1, explaining ˜57%) was calculated using R package. The median CS of 0.79 was used as the cut-off with any score above this defined as high and scores at or below the median were classified as low. The Chi-Square test or Fisher's exact test was used to test pCR vs CS within each treatment arm [table 1]. Cochran-Mantel-Haenszel test was performed to test the pCR for the pooled control and treatment arms between CS high and low groups adjusting for hormone receptors (HR), HER2 and Mammaprint status. Breslow-Day test was performed to test treatment difference in the odds ratios for the CS and response.

Results: There were 56 patients in the paclitaxel arm (A), 115 in the Paclitaxel+Neratinib arm (B), 22 patients on the Paclitaxel + Trastuzumab arm (C) and 72 on the Paclitaxel + Veliparib + Carboplatin arm (D). In all treatment arms, high CS were associated with higher pCR rates with significant association found in treatment arms A and D (38.5% vs 6.7% and 47.5 vs. 25% respectively)[table 1]. Analysis of pooled data for all arms adjusting for HR, HER2 and Mammaprint status showed statistically significant association between CS and pCR (P &lt; 0.05). There were no significant differences in the odds ratios for the CS and pCR.

Conclusion: The 12 gene CS predicted for treatment response even after adjusting for the treatment with no differences noted in the odds ratio for CS and pCR. The 12 gene CS can be readily obtained from I-SPY2 TRIAL microarrays to characterize tumors immunologically and possibly predict response to novel therapies. Continued investigation of the CS in other I-SPY2 TRIAL treatment arms is warranted.

Table: 1 Comparison of treatment arms with gene scores and treatment responseArm (N)12 gene scorepCR N(%)Incomplete Response N(%)P valueA. Paclitaxel (56)High10 (38.5)16 (61.5)0.007 Low2 (6.7)28 (93.3) B. Paclitaxel+Neratinib (115)High23 (41.8)32 (58.2)0.24 Low18 (30.0)42 (70.0) C. Paclitaxel + Trastuzumab (22)High4 (36.4)7 (63.6)0.31 Low1 (9.1)10 (90.9) D. Paclitaxel + Veliparib + Carboplatin (72)High19 (47.5)21 (52.5)0.05 Low8 (25.0)24 (75.0) 

Citation Format: Prabhakaran S, Cheng C-H, Boulware D, Ma Z, Mulé JJ, Soliman H. Validation of 12-gene chemokine signature as a predictor of treatment response in breast cancer [abstract]. In: Proceedings of the 2017 San Antonio Breast Cancer Symposium; 2017 Dec 5-9; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2018;78(4 Suppl):Abstract nr P4-09-07.

Bayesian Inference for Single-cell Clustering and Imputing

Single-cell RNA-seq gives access to gene expression measurements for thousands of cells, allowing discovery and characterization of cell types. However, the data is noise-prone due to experimental errors and cell type-specific biases. Current computational approaches for analyzing single-cell data involve a global normalization step which introduces incorrect biases and spurious noise and does not resolve missing data (dropouts). This can lead to misleading conclusions in downstream analyses. Moreover, a single normalization removes important cell type-specific information. We propose a data-driven model, BISCUIT, that iteratively normalizes and clusters cells, thereby separating noise from interesting biological signals. BISCUIT is a Bayesian probabilistic model that learns cell-specific parameters to intelligently drive normalization. This approach displays superior performance to global normalization followed by clustering in both synthetic and real single-cell data compared with previous methods, and allows easy interpretation and recovery of the underlying structure and cell types.

In Vivo Assessment of the Impact of Regional Intracranial Atherosclerotic Lesions on Brain Arterial 3D Hemodynamics

BACKGROUND AND PURPOSE

Intracranial atherosclerosis induces hemodynamic disturbance, which is not well-characterized, particularly in cerebral flow redistribution. We aimed to characterize the impact of regional stenotic lesions on intracranial hemodynamics by using 4D flow MR imaging.

MATERIALS AND METHODS

4D flow MR imaging was performed in 22 symptomatic patients (mean age, 68.4 ± 14.2 years) with intracranial stenosis (ICA, n = 7; MCA, n = 9; basilar artery, n = 6) and 10 age-appropriate healthy volunteers (mean age, 60.7 ± 8.1 years). 3D blood flow patterns were visualized by using time-integrated pathlines. Blood flow and peak velocity asymmetry indices were compared between patients and healthy volunteers in 4 prespecified arteries: ICAs, MCAs, and anterior/posterior cerebral arteries.

RESULTS

3D blood flow pathlines demonstrated flow redistribution across cerebral arteries in patients with unilateral intracranial stenosis. For patients with ICA stenosis compared with healthy volunteers, significantly lower flow and peak velocities were identified in the ipsilateral ICA (P = .001 and P = .001) and MCA (P < .001 and P = .001), but higher flow, in the ipsilateral PCA (P < .001). For patients with MCA stenosis, significantly lower flow and peak velocities were observed in the ipsilateral ICA (P = .009 and P = .045) and MCA (P < .001 and P = .005), but significantly higher flow was found in the ipsilateral posterior cerebral artery (P = .014) and anterior cerebral artery (P = .006). The asymmetry indices were not significantly different between patients with basilar artery stenosis and the healthy volunteers.

CONCLUSIONS

Regional intracranial atherosclerotic lesions not only alter distal arterial flow but also significantly affect ipsilateral collateral arterial hemodynamics.

Neurointerventional management of carotid webs associated with recurrent and acute cerebral ischemic syndromes

BACKGROUND

A carotid web can be defined as an endoluminal shelf-like projection often noted at the origin of the internal carotid artery (ICA) just beyond the bifurcation. Diagnosis of a carotid web as an underlying cause of recurrent ischemic stroke is infrequent and easily misdiagnosed as an atheromatous plaque. Surgery has traditionally been used to resect symptomatic lesions while there is no enough evidence supporting medical therapy as the sole management. To our knowledge there is only one report about carotid artery stenting (CAS) as a definite management of carotid web and no previous reports of acute large-vessel occlusions undergoing mechanical thrombectomy in the setting of carotid web as the etiology.

CASE REPORT

We report two cases: The first presented with recurrent ischemic stroke in the same arterial territory and the other with an emergent left middle cerebral artery (MCA) occlusion that underwent endovascular mechanical thrombectomy in which initial computed tomographic angiograms (CTA) suggested carotid web etiologies. Following confirmation with digital subtraction angiography (DSA), both patients ultimately underwent endovascular carotid stenting instead of surgical resection for definitive carotid web treatment.

CONCLUSIONS

Carotid webs are a rare cause of ischemic stroke in young and middle-aged adults that can readily be identified by CTA. Endovascular management may include emergent mechanical thrombectomy for large-vessel thromboembolic complications, and for definitive treatment with carotid stenting across the carotid web as an alternative to surgical resection and medical management for secondary stroke prevention.

Abstract WMP8: Results of Trevo Acute Ischemic Stroke Thrombectomy Registry: Predictors of Clinical Outcome

Background and Purpose: Recent randomized clinical trial (RCTs) demonstrated efficacy of mechanical thrombectomy using stent-retrievers in acute ischemic stroke (AIS) patients. The main purpose of TRevo ACute Ischemic StroKe (TRACK) stent-retriever thrombectomy multicenter registry is to demonstrate safety and efficacy in real life clinical practice.

Methods: The investigator-initiated TRACK multicenter registry recruited 24 sites in north America to submit demographic, clinical, site-adjudicated angiographic, and outcome data on consecutive AIS patients treated with Trevo stent-retriever device as the first treatment option. Standard clinical safety (symptomatic intracranial hemorrhage (sICH), and mortality) and efficacy (revascularization and disability) outcomes and predictors of clinical outcome were analyzed.

Results: 624 patients were enrolled in the TRACK registry. Median age was 68 years (range 16-94, 118 (18.1%) >80), male gender was 51.4%, and 67.7% were white. The median National Institutes of Health Stroke Severity Scale (NIHSS) was 17 (IQR 13-22). Transfer cases were 50.6% with IV-rtPA use in 318 cases (51.3%). Median onset to groin puncture (OTG) time was 283 min (IQR 198.5-443), and groin puncture to revascularization was 66 min (IQR 37.5-103). Anterior circulation occlusion was 86.2% (MCA/M2 in 55.2% followed by ICA in 15.9% and M2 in 12.7%). Use of GA was in 389 cases (62.3%), number of passes were ≤ 3 in 92% of the cases (1: 45.2%, 2:28%, and 3:18.7%), 291 (46.7%) had BGC use. Rescue use was seen in 21.7%. Revascularization of ≥ TIMI 2 was 81.8% and ≥ TICI 2b was 70%. The primary outcome of mRS of ≥ 2 was 48.3% in the full cohort, and 50.6% in TREVO-2 like group. sICH and mortality were 7.2%, and 20.1% in the full cohort vs 6.9% and 17.5% in the TREVO-2 like group, respectively. The independent predictors of clinical outcome were lower baseline NIHSS, younger age, use of BGC, successful recanalization, and no general anesthesia (GA).

Conclusions: The real life clinical practice Trevo registry demonstrated good clinical outcome and high rate of recanalization. Younger age, lower baseline NIHSS, use of balloon guide catheter, successful recanalization, and avoiding endotrachaeal GA independent predictors of good clinical outcome.

Dirichlet Process Mixture Model for Correcting Technical Variation in Single-Cell Gene Expression Data

We introduce an iterative normalization and clustering method for single-cell gene expression data. The emerging technology of single-cell RNA-seq gives access to gene expression measurements for thousands of cells, allowing discovery and characterization of cell types. However, the data is confounded by technical variation emanating from experimental errors and cell type-specific biases. Current approaches perform a global normalization prior to analyzing biological signals, which does not resolve missing data or variation dependent on latent cell types. Our model is formulated as a hierarchical Bayesian mixture model with cell-specific scalings that aid the iterative normalization and clustering of cells, teasing apart technical variation from biological signals. We demonstrate that this approach is superior to global normalization followed by clustering. We show identifiability and weak convergence guarantees of our method and present a scalable Gibbs inference algorithm. This method improves cluster inference in both synthetic and real single-cell data compared with previous methods, and allows easy interpretation and recovery of the underlying structure and cell types.

Full-length haplotype reconstruction to infer the structure of heterogeneous virus populations

Next-generation sequencing (NGS) technologies enable new insights into the diversity of virus populations within their hosts. Diversity estimation is currently restricted to single-nucleotide variants or to local fragments of no more than a few hundred nucleotides defined by the length of sequence reads. To study complex heterogeneous virus populations comprehensively, novel methods are required that allow for complete reconstruction of the individual viral haplotypes. Here, we show that assembly of whole viral genomes of ∼8600 nucleotides length is feasible from mixtures of heterogeneous HIV-1 strains derived from defined combinations of cloned virus strains and from clinical samples of an HIV-1 superinfected individual. Haplotype reconstruction was achieved using optimized experimental protocols and computational methods for amplification, sequencing and assembly. We comparatively assessed the performance of the three NGS platforms 454 Life Sciences/Roche, Illumina and Pacific Biosciences for this task. Our results prove and delineate the feasibility of NGS-based full-length viral haplotype reconstruction and provide new tools for studying evolution and pathogenesis of viruses.

An improved Pearson's correlation proximity-based hierarchical clustering for mining biological association between genes

Microarray gene expression datasets has concerned great awareness among molecular biologist, statisticians, and computer scientists. Data mining that extracts the hidden and usual information from datasets fails to identify the most significant biological associations between genes. A search made with heuristic for standard biological process measures only the gene expression level, threshold, and response time. Heuristic search identifies and mines the best biological solution, but the association process was not efficiently addressed. To monitor higher rate of expression levels between genes, a hierarchical clustering model was proposed, where the biological association between genes is measured simultaneously using proximity measure of improved Pearson's correlation (PCPHC). Additionally, the Seed Augment algorithm adopts average linkage methods on rows and columns in order to expand a seed PCPHC model into a maximal global PCPHC (GL-PCPHC) model and to identify association between the clusters. Moreover, a GL-PCPHC applies pattern growing method to mine the PCPHC patterns. Compared to existing gene expression analysis, the PCPHC model achieves better performance. Experimental evaluations are conducted for GL-PCPHC model with standard benchmark gene expression datasets extracted from UCI repository and GenBank database in terms of execution time, size of pattern, significance level, biological association efficiency, and pattern quality.

Perfusion-based selection for endovascular reperfusion therapy in anterior circulation acute ischemic stroke

BACKGROUND AND PURPOSE

Controversy exists about the role of perfusion imaging in patient selection for endovascular reperfusion therapy in acute ischemic stroke. We hypothesized that perfusion imaging versus noncontrast CT- based selection would be associated with improved functional outcomes at 3 months.

MATERIALS AND METHODS

We reviewed consecutive patients with anterior circulation strokes treated with endovascular reperfusion therapy within 8 hours and with baseline NIHSS score of ≥8. Baseline clinical data, selection mode (perfusion versus NCCT), angiographic data, complications, and modified Rankin Scale score at 3 months were collected. Using multivariable logistic regression, we assessed whether the mode of selection for endovascular reperfusion therapy (perfusion-based versus NCCT-based) was independently associated with good outcome.

RESULTS

Two-hundred fourteen patients (mean age, 67.2 years; median NIHSS score, 18; MCA occlusion 74% and ICA occlusion 26%) were included. Perfusion imaging was used in 76 (35.5%) patients (39 CT and 37 MR imaging). Perfusion imaging-selected patients were more likely to have good outcomes compared with NCCT-selected patients (55.3 versus 33.3%, P = .002); perfusion selection by CT was associated with similar outcomes as that by MR imaging (CTP, 56.; MR perfusion, 54.1%; P = .836). In multivariable analysis, CT or MR perfusion imaging selection remained strongly associated with good outcome (adjusted OR, 2.34; 95% CI, 1.22-4.47), independent of baseline severity and reperfusion.

CONCLUSIONS

In this multicenter study, patients with acute ischemic stroke who underwent perfusion imaging were more than 2-fold more likely to have good outcomes following endovascular reperfusion therapy. Randomized studies should compare perfusion imaging with NCCT imaging for patient selection for endovascular reperfusion therapy.

HIV Haplotype Inference Using a Propagating Dirichlet Process Mixture Model

This paper presents a new computational technique for the identification of HIV haplotypes. HIV tends to generate many potentially drug-resistant mutants within the HIV-infected patient and being able to identify these different mutants is important for efficient drug administration. With the view of identifying the mutants, we aim at analyzing short deep sequencing data called reads. From a statistical perspective, the analysis of such data can be regarded as a nonstandard clustering problem due to missing pairwise similarity measures between non-overlapping reads. To overcome this problem we propagate a Dirichlet Process Mixture Model by sequentially updating the prior information from successive local analyses. The model is verified using both simulated and real sequencing data.