bluuebunny/crossref_metadata_2025 huggingface dataset:
Does histological assessment accurately distinguish separate primary lung adenocarcinomas from intrapulmonary metastases? A study of paired resected lung nodules in 32 patients using a routine next-generation sequencing panel for driver mutations
Authors: Bruehl Frido K , Doxtader Erika E , Cheng Yu-Wei , Farkas Daniel H , Farver Carol , Mukhopadhyay Sanjay
Published: March 2021

Aim

Various approaches have been reported for distinguishing separate primary lung adenocarcinomas from intrapulmonary metastases in patients with two lung nodules. The aim of this study was to determine whether histological assessment is reliable and accurate in distinguishing separate primary lung adenocarcinomas from intrapulmonary metastases using routine molecular findings as an adjunct.

Methods

We studied resected tumour pairs from 32 patients with lung adenocarcinomas in different lobes. In 15 of 32 tumour pairs, next-generation sequencing (NGS) for common driver mutations was performed on both nodules. The remainder of tumour pairs underwent limited NGS, orEGFRgenotyping. Tumour pairs with different drivers (or one driver/one wild-type) were classified as molecularly unrelated, while those with identical low-frequency drivers were classified as related. Three pathologists independently and blinded to the molecular results categorised tumour pairs as related or unrelated based on histological assessment.

Results

Of 32 pairs, 15 were classified as related by histological assessment, and 17 as unrelated. Of 15 classified as related by histology, 6 were classified as related by molecular analysis, 4 were unrelated and 5 were indeterminate. Of 17 classified as unrelated by histology, 14 were classified as unrelated by molecular analysis, none was related and 3 were indeterminate. Histological assessment of relatedness was inaccurate in 4/32 (12.5%) tumour pairs.

Conclusions

A small but significant subset of two-nodule adenocarcinoma pairs is inaccurately judged as related by histological assessment, and can be proven to be unrelated by molecular analysis (driver gene mutations), leading to significant downstaging.
Socioeconomic inequalities in physical and cognitive functioning: cross-sectional evidence from 37 cohorts across 28 countries in the ATHLOS project
Authors: Stefler Denes , Prina Matthew , Wu Yu-Tzu , Sánchez-Niubò Albert , Lu Wentian , Haro Josep Maria , Marmot Michael , Bobak Martin
Published: March 2021

Background

Physical and cognitive functioning in older age follows a socioeconomic gradient but it is unclear whether the strength of the association differs between populations. Using harmonised data from an international collaboration of cohort studies, we assessed socioeconomic inequalities in physical and cognitive functioning and explored if the extent of inequalities varied across countries based on their economic strength or wealth distribution.

Methods

Harmonised data from 37 population-based cohorts in 28 countries were used, with an overall sample size of 126 765. Socioeconomic position of participants was indicated by education and household income. Physical functioning was assessed by self-reported mobility and activities of daily living; and cognitive functioning by memory and verbal fluency tests. Relative (RII) and slope (SII) index of inequality were calculated in each cohort, and their association with the source country’s Gross Domestic Product (GDP) and Gini-index was assessed with correlation and cross-level interaction in multilevel models.

Results

RII and SII values indicated consistently higher risk of low physical and cognitive functioning in participants with lower education or income across cohorts. Regarding RII, there were weak but statistically significant correlations and interactions with GDP and Gini-index, suggesting larger inequalities in countries with lower Gini-index and higher GDP. For SII, no such correlations were observed.

Conclusion

This study confirms that socioeconomic inequalities in physical and cognitive functioning exist across different social contexts but the magnitude of these inequalities varies. Relative inequalities appear to be larger in higher-income countries but it remains to be seen whether such observation can be replicated.
Increased cardiorespiratory stress during submaximal cycling after ketone monoester ingestion in endurance-trained adults
Authors: McCarthy Devin G. , Bostad William , Powley Fiona J. , Little Jonathan P. , Richards Douglas L. , Gibala Martin J.
Published: March 2021

There is growing interest in the effect of exogenous ketone body supplementation on exercise responses and performance. The limited studies to date have yielded equivocal data, likely due in part to differences in dosing strategy, increase in blood ketones, and participant training status. Using a randomized, double-blind, counterbalanced design, we examined the effect of ingesting a ketone monoester (KE) supplement (600 mg/kg body mass) or flavour-matched placebo in endurance-trained adults (n = 10 males, n = 9 females; V̇O2peak = 57 ± 8 mL/kg/min). Participants performed a 30-min cycling bout at ventilatory threshold intensity (71 ± 3% V̇O2peak), followed 15 min later by a 3 kJ/kg body mass time-trial. KE versus placebo ingestion increased plasma β-hydroxybutyrate concentration before exercise (3.9 ± 1.0 vs 0.2 ± 0.3 mM, p < 0.0001, dz = 3.4), ventilation (77 ± 17 vs 71 ± 15 L/min, p < 0.0001, dz = 1.3) and heart rate (155 ± 11 vs 150 ± 11 beats/min, p < 0.001, dz = 1.2) during exercise, and rating of perceived exertion at the end of exercise (15.4 ± 1.6 vs 14.5 ± 1.2, p < 0.01, dz = 0.85). Plasma β-hydroxybutyrate concentration remained higher after KE vs placebo ingestion before the time-trial (3.5 ± 1.0 vs 0.3 ± 0.2 mM, p < 0.0001, dz = 3.1), but performance was not different (KE: 16:25 ± 2:50 vs placebo: 16:06 ± 2:40 min:s, p = 0.20; dz = 0.31). We conclude that acute ingestion of a relatively large KE bolus dose increased markers of cardiorespiratory stress during submaximal exercise in endurance-trained participants.

Novelty: Limited studies have yielded equivocal data regarding exercise responses after acute ketone body supplementation. Using a randomized, double-blind, placebo-controlled, counterbalanced design, we found that ingestion of a large bolus dose of a commercial ketone monoester supplement increased markers of cardiorespiratory stress during cycling at ventilatory threshold intensity in endurance-trained adults.
Stability analysis and modified projective synchronization of fractional-order hyperchaotic dynamical systems using nonlinear controllers
Authors: Ray S. Saha
Published: March 2021

This paper deals with the modified projective synchronization between two fractional-order four-dimensional (4D) hyperchaotic systems. Based on the Lyapunov stability theory, a new controller for the synchronization of two fractional-order hyperchaotic systems is developed. The stability analysis of the error dynamics system is performed by the fractional-order Lyapunov direct method alongwith Routh–Hurwitz stability criterion. Numerical simulations are presented to demonstrate the validity and verify the effectiveness of the proposed synchronization scheme.
Improved Ring Radius Transform-Based Reconstruction for Video Character Recognition
Authors: Huang Zhiheng , Shivakumara Palaiahnakote , Lu Tong , Pal Umapada , Blumenstein Michael , Chetty Bhaarat , Kumar G. Hemantha
Published: March 2021

Character shape reconstruction in video is challenging due to low contrast, complex backgrounds and arbitrary orientation of characters. This work proposes an Improved Ring Radius Transform (IRRT) for reconstructing impaired characters through medial axis prediction. At first, the technique proposes a novel idea based on the Tangent Vector (TV) concept that identifies each actual pair of end pixels caused by gaps in impaired character components. Next, the actual direction to predict medial axis pixels using IRRT for each pair of end pixels is proposed with a new normal vector concept. The process of prediction repeats iteratively to find all the medial axis pixels for every gap in question. Further, medial axis pixels with their radii are used to reconstruct the shapes of impaired characters. The proposed technique is tested on benchmark datasets consisting of video, natural scenes, objects and multi-lingual data to demonstrate that it reconstructs shapes well, even for heterogeneous data. Comparative studies with different binarization and character recognition methods show that the proposed technique is effective, useful and outperforms existing methods.
NEW ANALYTICAL APPROACH FOR NONLINEAR FRACTAL K(p,q) MODEL
Authors: WANG KANG-LE , WEI CHUN-FU
Published: March 2021

In this work, the nonlinear fractal [Formula: see text] model is defined via the fractal derivative. The variational principle of the fractal model is constructed by using the fractal semi-inverse method, and it is of great significance to study the structure and properties of its soliton solution. Based on the established variational principle, we successfully proposed a new method to solve the fractal model. The efficiency and accuracy of the proposed approach are illustrated by an example.
A NEW PERSPECTIVE ON THE STUDY OF THE FRACTAL COUPLED BOUSSINESQ–BURGER EQUATION IN SHALLOW WATER
Authors: WANG KANG-JIA , WANG GUO-DONG , ZHU HONG-WEI
Published: March 2021

The well-known coupled Boussinesq–Burger equation can be used to describe the flow of the shallow water of the harbor. But when the boundary is nonsmooth, it becomes powerless. So, the fractal calculus is needed to be applied to it and the fractal coupled Boussinesq–Burger equation (FCBBe) is presented for the first time in this paper. By using the semiinverse method, we have successfully established the fractal variational formulation of the FCBBe, which can not only provide the conservation laws in an energy form in the fractal space but also reveal the possible solution structures of the equation. Then a novel variational approach based on He’s variational method and the two-scale transform are used to seek its periodic wave solutions. The main advantage of variational approach is that it can reduce the order of differential equation and make the equation more simple. Finally, the numerical results have been shown through graphs to discuss the effect of different fractal orders on the wave motion. The obtained results in this work are expected to shed a bright light on the study of fractal nonlinear partial differential equations in the fractal space.
A NONLOCAL LOW-RANK REGULARIZATION METHOD FOR FRACTAL IMAGE CODING
Authors: ZOU YURU , HU HUAXUAN , LU JIAN , LIU XIAOXIA , JIANG QINGTANG , SONG GUOHUI
Published: March 2021

Fractal coding has been widely used as an image compression technique in many image processing problems in the past few decades. On the other hand side, most of the natural images have the characteristic of nonlocal self-similarity that motivates low-rank representations of them. We would employ both the fractal image coding and the nonlocal self-similarity priors to achieve image compression in image denoising problems. Specifically, we propose a new image denoising model consisting of three terms: a patch-based nonlocal low-rank prior, a data-fidelity term describing the closeness of the underlying image to the given noisy image, and a quadratic term measuring the closeness of the underlying image to a fractal image. Numerical results demonstrate the superior performance of the proposed model in terms of peak-signal-to-noise ratio, structural similarity index and mean absolute error.
ON THE NONLINEAR GENERALIZED LANGEVIN EQUATION INVOLVING ψ-CAPUTO FRACTIONAL DERIVATIVES
Authors: DIEN NGUYEN MINH , TRONG DANG DUC
Published: March 2021

This paper considers the generalized Langevin equation involving [Formula: see text]-Caputo fractional derivatives in a Banach space. The fractional derivative is generalized from the Caputo derivative ([Formula: see text]), the Caputo–Katugampola ([Formula: see text], the Hadamard derivative ([Formula: see text]). We investigate the existence of mild solutions [Formula: see text] of the problem, in which the source function is assumed to satisfy some weakly singular conditions. Before proceeding to the main results, we transform the problem into an integral equation. Based on the obtained integral equation, the main results are proved via the nonlinear Leray–Schauder alternatives and Banach fixed point theorems. To prove this end, a new generalized weakly Gronwall-type inequality is established. Further, we prove that the mild solution of the problem is dependent continuously on the inputs: initial data, fractional orders and the friction constant. As a consequence, we deduce that the solution [Formula: see text] of the equation involving the Caputo–Katugampola derivative tends to the solution [Formula: see text] of the equation involving the Hadamard derivative as [Formula: see text].
MULTIFRACTAL BEHAVIOR OF CRYPTOCURRENCIES BEFORE AND DURING COVID-19
Authors: SHAO YING-HUI , XU HAN , LIU YING-LIN , XU HAI-CHUAN
Published: March 2021

Based on high-frequency data, we study the difference in cryptocurrency market before and during the COVID-19. We analyze the multifractality of three major cryptocurrencies via the multifractal detrended fluctuation analysis (MFDFA). To investigate the source of multifractality, we construct shuffled, surrogated and truncate data. The results show that market efficiency of cryptocurrency has decreased during COVID-19. The cryptocurrency multifractal characteristics mainly come from non-Gaussian distribution. Additionally, the components of multifractal nature have changed during the pandemic. The results provide evidence for the impact of COVID-19 on cryptocurrency market.
Buckling and Postbuckling of Plates Made of FG-GPL-Reinforced Porous Nanocomposite with Various Shapes and Boundary Conditions
Authors: Ansari R. , Hassani R. , Gholami R. , Rouhi H.
Published: March 2021

Within the framework of a variational mixed formation and higher-order shear deformation theory (HSDT), a numerical approach is developed in this research to investigate the buckling and post buckling behaviors of variously-shaped plates made of functionally graded graphene platelet-reinforced composites (FG-GPLRCs) taking the effect of porosity into account. By the proposed approach, which can be named as VDQ-FEM, thick and moderately thick plate-type structures with different shapes (e.g. rectangular, skew, or quadrilateral) with arbitrary-shaped cutout (e.g. circular or rectangular) can be studied. Various types for porosity distribution scheme and GPL dispersion pattern including uniform and different functionally graded patterns are considered along the thickness of plate. In the computation of material properties, the closed-cell Gaussian Random field scheme and Halpin–Tsai micromechanical model are utilized. One of the key novelties of proposed approach is developing an efficient way according to the mixed formulation to accommodate the continuity of first-order derivatives on the common boundaries of elements for the used HSDT model. Several numerical examples are given to analyze the influences of porosity coefficient/distribution pattern, GPL weight fraction/dispersion pattern, cutout and boundary conditions on the buckling and postbuckling characteristics of FG-GPLR porous composite plates.
Hybrid Technique of the Branch-Cut and the Quality-Guided for Insar Phase Unwrapping
Authors: Bentahar Tarek , Bentahar Atef , Saidi Riad , Mayache Hichem , Ferroudji Karim
Published: March 2021

Phase unwrapping is a key step for interferometric synthetic aperture radar imaging. It is widely used for earth mapping and surface change detection. Several residue-immune phase unwrapping algorithms have been proposed; among them, we find branch-cut and quality-guided in the path-following category. Branch-cut methods are usually faster than the quality-guided techniques; however, the accuracy of their unwrapped phase images is lower. In this paper, a hybrid model which combines both algorithms is proposed in order to establish a satisfactory compromise between processing time and accuracy. In order to verify the usefulness of the proposed hybridization, it is tested on simulated and real inSAR data. The obtained results are compared with the two methods under several relevant metrics.
Martingale solutions to a stochastic smectic-A liquid crystal model with multiplicative noise of jump type
Authors: Tachim Medjo Theodore , Zhao Caidi
Published: March 2021

In this paper, we are interested in proving the existence of a weak martingale solution of the stochastic smectic-A liquid crystal system driven by a pure jump noise in both 2D and 3D bounded domains. We prove the existence of a global weak martingale solution under some non-Lipschitz assumptions on the coefficients. The construction of the solution is based on a Faedo–Galerkin approximation, a compactness method and the Skorokhod representation theorem. In the two-dimensional case, we prove the pathwise uniqueness of the weak solution, which implies the existence of a unique probabilistic strong solution.
Form of the periodic solutions of some systems of higher order difference equations
Authors: Göcen Melih , Cebeci Adem
Published: March 2021

In this paper, we investigate the periodic nature of the solutions of some systems of rational difference equations where the initial values are arbitrary real numbers such that the denominator is always nonzero. Furthermore, some numerical examples are presented to verify our theoretical results.
Chapter 4 Large-scale structure of the study area
Authors: Scharf Andreas , Mattern Frank , Al-Wardi Mohammed , Frijia Gianluca , Moraetis Daniel , Pracejus Bernhard , Bauer Wilfried , Callegari Ivan
Published: March 2021

The Southeastern Oman Mountains are dominated by two major culminations: the Jabal Akhdar and Saih Hatat domes, surrounded by allochthonous and/or neo-autochthonous rocks. In the cores of both domes, folded autochthonous and par-autochthonous pre-Permian metasedimentary rocks are exposed, subjacent to the ‘Hercynian’ Unconformity. Above the unconformity are Permo--Mesozoic shelfal sedimentary rocks, characterized by carbonates. These sedimentary rocks were openly folded. The open folds are large-scale elongate structures that define the shapes of both domes. The main elongation direction is NW--SE. Doming is syn- to post-obductional. Most margins of the domes are marked by major post-obductional, extensional faults. Reactivated basement faults along the eastern margin of the Jabal Akhdar Dome may be responsible for the straight NNE-striking eastern margin which is perpendicular to the main elongation direction of the domes. The deep structure of both domes is poorly known. However, the Moho depth below the centre of the Jabal Akhdar Dome is at 50 km. We present a geological map of both domes, depicting the main faults and folds, and schematic cross-sections, parallel and perpendicular to the Oman Mountains.
A Statistical Approach to Model the H‐Index Based on the Total Number of Citations and the Duration from the Publishing of the First Article
Authors: Mahmoudi Mohammad Reza , Rahmati Marzieh , Mansor Zulkefli , Mosavi Amirhosein , Band Shahab S.
Published: March 2021

The productivity of researchers and the impact of the work they do are a preoccupation of universities, research funding agencies, and sometimes even researchers themselves. The h‐index (h) is the most popular of different metrics to measure these activities. This research deals with presenting a practical approach to model the h‐index based on the total number of citations (NC) and the duration from the publishing of the first article (D1). To determine the effect of every factor (NC and D1) on h, we applied a set of simple nonlinear regression. The results indicated that both NC and D1 had a significant effect on h (p < 0.001). The determination of coefficient for these equations to estimate the h‐index was 93.4% and 39.8%, respectively, which verified that the model based on NC had a better fit. Then, to record the simultaneous effects of NC and D1 on h, multiple nonlinear regression was applied. The results indicated that NC and D1 had a significant effect on h (p < 0.001). Also, the determination of coefficient for this equation to estimate h was 93.6%. Finally, to model and estimate the h‐index, as a function of NC and D1, multiple nonlinear quartile regression was used. The goodness of the fitted model was also assessed.
Recent Advances of Hepatitis B Detection towards Paper-Based Analytical Devices
Authors: Tyas Aulia A. , Raeni Septi F. , Sakti Setyawan P. , Sabarudin Akhmad
Published: March 2021

Hepatitis B virus (HBV) still remains a major global public health problem. One-half to one-third of the total HBV infected people died due to late detection of HBV. Serological antigen and viral HBV detections can help in the diagnosis, referral, and treatment of HBV. Available methods for HBV detection mostly used bulky instruments. Miniaturization of devices for HBV detection has been started by narrowing down the size of the devices. Several methods have also been proposed to increase the selectivity and sensitivity of the miniaturized methods, such as sandwich recognition of the biomarkers and the use of nano- to micro-sized materials. This review presents recent HBV detections in the last two decades from laboratory-based instruments towards microfluidic paper-based analytical devices (µPADs) for point-of-care testing (POCT) purposes. Early and routine analysis to detect HBV as early as possible could be achieved by POCT, especially for areas with limited access to a central laboratory and/or medical facilities.
Bloodstream Infection Etiology among Children and Adults
Authors: Kolesnichenko Svetlana I. , Lavrinenko Alyona V. , Akhmaltdinova Lyudmila L.
Published: March 2021

Every year in the world, sepsis occurs in 31.5 million people, and the number of deaths reaches 5.3 million per year. There are not enough studies that describe etiological structure of sepsis pathogens in different groups of population of the Republic of Kazakhstan. In this study, we have investigated difference of local sepsis etiology and antibiotic susceptibility among children and adults. A total 200 blood samples were examined using the standard and express method of identification of bloodstream pathogens. The determination of antimicrobial sensitivity was carried out by the disc-diffusion method according to CLSI guidelines. Overall, 23/90 (25.5%) positive blood cultures were isolated from adult patients and 43/110 (39%) from pediatric patients. It was found that children are statistically more often affected with bacterial bloodstream infection than adults (

p
<
0.05

). The Gram-positive bacteria are the leading cause of sepsis in both groups: S. epidermidis (35.5%) in pediatric patients and S. aureus (21.7%) in adults. However, statistical significance was detected in pediatric patients (

p
<
0.05

). The number of resistant strains of S. epidermidis (MRSE) in the group of children was 66.7%, while in adults, all S. epidermidis was resistant to azithromycin and cefoxitin (MRSE). S. aureus strains from adult patients and children had a similar picture of antibiotic patterns. The proportion of MRSA in pediatric patients was 16, 6%, and in adult patients, 20%. Enterobacterales (39%) were the second cause of sepsis in adult patients. 62.5% of Enterobacterales strains isolated from adults were phenotypically identified as ESBL, while in pediatric patients, 25% of ESBL producers were isolated. We have noted the resistance to antibiotics that are prescribed according to protocols of treatment of the Republic of Kazakhstan in the strains isolated from the patient’s blood.
A Simulation Method Based on the Candidate Operation Strategies for the Combined Cooling, Heat, and Power (CCHP) System
Authors: Zhu Feng , Fu Jing-Qi , Zou Bin , Si Wen-Rong
Published: March 2021

With the change of the distributed Combined cooling, heat, and power (CCHP) system operation environment, such as the operation under the power market, CCHP needs to adopt the operation strategy based on cost minimization instead of the traditional following thermal load strategy (FTL). However, the current CCHP system simulation adopts FTL or FEL or a combination of the two; whether these strategies have the least cost has not been confirmed. In this paper, all kinds of operation strategies of the CCHP with two cooling methods are investigated according to the KKT conditions (Karush–Kuhn–Tucker conditions) of the optimal operation model. Among all the feasible operation strategies, most of them do not meet the KKT condition, and only 43 strategies may be the optimal ones that constitute candidate set. The conditions of the optimal operation strategy in the candidate set are obtained, and it is easy to calculate, so the optimal operation strategy can be easily selected from the candidate set. Some previously unnoticed strategies, for example, TEWM, PE-FPG, FTL-R, and FEL-L, may become optimal. A rule-based CCHP simulation method is established that selects the strategy with minimal cost as the optimal strategy from the candidate strategy set. The presented method has small time consumption and can simulate the optimal operation. The case study verified the characteristics of the proposed simulation method.
Abstract IA010: How opposing roles of interferon and pattern recognition receptor signaling in the TME influence immunotherapy
Authors: Minn Andy J.
Published: March 2021

Interferon (IFN) signaling pathways are broadly active across multiple cancer types. These pathways are typically associated with pathogen infection, but their roles in cancer are less understood. Emerging evidence indicates that IFN signaling has complex and even dichotomous roles in both cancer and host-pathogen interactions. On the one hand, IFN is often essential for immune activation and for promoting response to cancer immunotherapies. On the other hand, prolonged IFN signaling that accompanies persistent disease can lead to immunotherapy resistance and immunosuppression. This latter role represents a feedback inhibition mechanism that can also be observed in chronic pathogen infections to limit immune-mediated pathology. Thus, how these opposing effects of IFN signaling are controlled and what factors dictate one function over the other are important questions. In this presentation, insight into how opposing functions of IFN and pattern recognition receptor signaling impact cancer immunotherapy will be discussed. This will include evidence for what factors might favor one function of IFN over the other. Finally, work will be presented that illustrates how key interferon-stimulated genes (ISGs) expressed by cancer cells can control whether an immune stimulatory or suppressive tumor immune microenvironment develops.

Citation Format: Andy J. Minn. How opposing roles of interferon and pattern recognition receptor signaling in the TME influence immunotherapy [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr IA010.
Abstract LT008: Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy
Authors: Bachireddy Pavan , Azizi Elham , Burdziak Cassandra , Nguyen Vinhkhang , Ennis Christina , Choo Zi- Ning , Li Shuqiang , Livak Kenneth , Neuberg Donna , Soiffer Robert , Ritz Jerome , Alyea Edwin , Pe'er Dana , Wu Catherine
Published: March 2021

Immune therapies have transformed the cancer therapeutic landscape but fail to benefit most patients. To elucidate the underlying mechanisms by which T cells mediate elimination of leukemia, we generated a high-resolution map of longitudinal T cell dynamics within the same tumor microenvironment (TME; bone marrow) during response or resistance to donor lymphocyte infusion (DLI), a widely used immunotherapy for relapsed leukemia. We analyzed 87,939 bone marrow-derived single T cell transcriptomes, along with chromatin accessibility and single T cell receptor clonality profiles, by developing novel machine learning tools for integrating longitudinal and multimodal data. We found that pre-treatment enrichment and post-treatment rapid, durable expansion of ‘terminal’ (TEX) and ‘precursor’ (TPEX) exhausted subsets, respectively, defined DLI response. In contrast to the common, shared pathways marking DLI response, a heterogeneous pattern of T cell dysfunction marked DLI resistance. Unexpectedly, TPEX cells that expanded in responders did not arise from the infusion product but instead from both pre-existing and novel clonotypes recruited to the TME. Further, we introduce a Bayesian method, Symphony, to define the T cell regulatory circuitry and master regulators underlying TEX and TPEX subsets that may be broadly relevant to other exhaustion antagonists across cancers. In conclusion, our data implicate the hierarchy of both TEX and TPEX subsets for immunotherapeutic responses in leukemia, extending the scope of their relevance beyond checkpoint blockade to adoptive cellular therapy. Moreover, our results provocatively suggest that immunologic ‘help’ from DLI, rather than direct transfer of anti-leukemic T cells, drove leukemic remission. Finally, we provide a general analysis paradigm for exploiting temporal single-cell genomic profiling for deep understanding of how immune therapies differentially shape the evolutionary trajectories of the TME in accordance with clinical outcome.

Citation Format: Pavan Bachireddy, Elham Azizi, Cassandra Burdziak, Vinhkhang Nguyen, Christina Ennis, Zi- Ning Choo, Shuqiang Li, Kenneth Livak, Donna Neuberg, Robert Soiffer, Jerome Ritz, Edwin Alyea, Dana Pe'er, Catherine Wu. Mapping the evolution of T cell states during response and resistance to adoptive cellular therapy [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr LT008.
Abstract LT021: Targeting AXL favors an anti-tumorigenic tumor microenvironment that enhances immunotherapy responses by decreasing HIF-1a levels in cancer cells
Authors: Goyette Marie-Anne , Gratton Jean-Philippe , Côté Jean-François
Published: March 2021

Breast cancer-related deaths are predominantly associated to complications arising from metastases. Hypoxia is an important biological process that fuels metastasis and contributes to deregulate the tumor microenvironment (TME) of solid tumors. Hypoxia also promotes resistance to therapies by a plethora of mechanisms including enhancing tumoral angiogenesis, metabolic reprogramming, epithelial-to-mesenchymal transition (EMT) and immune evasion. The expression of the receptor tyrosine kinase AXL, a hypoxia-induced gene, is associated with poor clinical prognosis and metastasis in various cancers, including breast cancer. Here, we hypothesize that genetic or pharmacological interference of AXL may alter hypoxic responses and associated modulation of the TME offering an ideal setting for immunotherapy. In a HER2 mouse model of breast cancer (MMTV-NIC), we found that the mammary gland epithelial cells specific deletion of AXL led to a reduction of lung metastases and improved the TME by reducing the hypoxic response in tumor cells. When breast cancer cells were subjected to hypoxic conditions, we found that AXL inhibition reduced the levels of the hypoxia transcription factor HIF-1a which resulted in an altered hypoxic response. Specifically, AXL deletion led to a reduction of hypoxia-induced EMT and invasion, and a reduction of the secretion of key cytokines for macrophages recruitment and polarization. We demonstrate in vivo that AXL pharmacological inhibition in the MMTV-NIC breast cancer model generated an anti-inflammatory TME that enhanced an anti-PD-1 immune checkpoint blockade response and decreased the metastatic burden. Collectively, these results suggest that targeting AXL could be a powerful approach to improve immunotherapy response by generating an anti-tumoral microenvironment therefore limiting the metastatic burden of breast cancer.

Citation Format: Marie-Anne Goyette, Jean-Philippe Gratton, Jean-François Côté. Targeting AXL favors an anti-tumorigenic tumor microenvironment that enhances immunotherapy responses by decreasing HIF-1a levels in cancer cells [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr LT021.
Abstract PR002: STAT3 in cancer-associated fibroblasts promotes an immunosuppressive tumor microenvironment in PDAC
Authors: Lefler Julia E. , MarElia Katie , Ostrowski Michael C.
Published: March 2021

One of the defining characteristics of pancreatic ductal adenocarcinoma (PDAC) is the formation of a dense stroma comprised of cancer associated fibroblasts (CAFs) and immune cell populations. This stroma is immunosuppressive and can act as a physical barrier against common therapeutic treatments. Attempts to therapeutically target the PDAC stroma have yielded contradictory results, suggesting both tumor promoting and tumor limiting roles for CAFs. These studies emphasize the need to understand important transsignaling pathways between CAFs, tumor cells, and the immune microenvironment. IL-6 is a pleiotropic cytokine involved in several physiological functions and its increased expression is strongly associated with poor survival rates in PDAC patients. STAT3 is a major downstream target of IL-6, and its aberrant activation has been implicated in PDAC tumor progression and immune evasion. IL-6 expression and the IL-6/STAT3 signaling axis in PDAC has been characterized in epithelial tumor cells, however its stromal-specific function on PDAC has yet to be elucidated. We hypothesized that the STAT3 signaling axis in pancreatic CAFs contributes to the immunosuppressive and fibrotic phenotype seen with disease progression. Employing CreLoxP technology, the fibroblast specific protein-1 (Fsp-Cre) transgene was used to conditionally delete STAT3 in fibroblasts in the PdxFlp; KrasG12D; p53frt/frt (KPF) PDAC mouse model developed by our lab. Deletion of STAT3 in fibroblasts significantly increased the survival in a cohort of KPF mice compared to those with intact STAT3. Loss of fibroblast STAT3 also resulted in a significant decrease in ECM deposition in the pancreas. In preliminary investigations into the immune microenvironment, we found an increase in overall T cell infiltration and decrease in regulatory T cell populations in the STAT3 deleted cohort. Further, STAT3 deletion resulted in an increase in the ratio of M1 to M2 macrophages within the PDAC stroma, suggesting CAF STAT3 signaling is important to maintaining an immune microenvironment conducive to tumor progression. Ultimately, these preliminary results suggest STAT3 signaling in PDAC CAFs contributes to an overall survival disadvantage, an immunosuppressive tumor microenvironment, and an increase in ECM deposition.

Citation Format: Julia E. Lefler, Katie MarElia, Michael C. Ostrowski. STAT3 in cancer-associated fibroblasts promotes an immunosuppressive tumor microenvironment in PDAC [abstract]. In: Proceedings of the AACR Virtual Special Conference on the Evolving Tumor Microenvironment in Cancer Progression: Mechanisms and Emerging Therapeutic Opportunities; in association with the Tumor Microenvironment (TME) Working Group; 2021 Jan 11-12. Philadelphia (PA): AACR; Cancer Res 2021;81(5 Suppl):Abstract nr PR002.
Abstract IA-03: Unsupervised resolution of intra- and inter-tumoral heterogeneity using deep learning
Authors: Diamandis Phedias
Published: March 2021

Applications of deep learning to histopathology has demonstrated expert-level performance, but approaches have been largely concentrated on supervised classification tasks requiring context-specific training and deployment. We demonstrate that deep neural networks, previously trained to recognize diverse histomorphologies in one environment can effectively extrapolate their learned representations to resolve clinically relevant intra- and inter-patient tissue pattern differences in other cancer types without explicit instruction or additional optimization. Moreover, this unsupervised approach highlighted numerous human misclassifications, suggesting that repositioning of existing histology-educated networks could provide scalable approaches for quality assurance and discovery of unappreciated subgroups of disease.

Citation Format: Phedias Diamandis. Unsupervised resolution of intra- and inter-tumoral heterogeneity using deep learning [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-03.
Abstract IA-13: Machine learning for improved management of patients with pancreatic cysts
Authors: Karchin Rachel
Published: March 2021

Invasive pancreatic cancer is a deadly disease, with the lowest five year survival rate among all cancers. Cysts in the pancreas are relatively common, but while some have malignant potential, many are entirely benign, while surgical resection of the pancreas has relatively high morbidity and mortality. Clinical management of patients with pancreatic cysts must balance these risks. We developed a supervised machine learning approach to integrate clinical, imaging and molecular data, as part of a large retrospective international study of almost 900 patients with pancreatic cysts. Using the Multivariate Optimization of Combinatorial Alterations (MOCA) algorithm, patients were classified into those who required immediate surgery, those who required routine monitoring, and those who could be discharged without future surveillance, using a stepwise approach. The resulting diagnostic test, called CompCyst, was more accurate than current patient management which relies on imaging and clinical criteria. More than half of the patients sent to surgery were found to have unnecessary resection of their cysts, which could have been avoided by application of the CompCyst test. Our results demonstrate how machine learning and molecular features can be applied to improve pre-surgical diagnostic testing in the clinic.

Citation Format: Rachel Karchin. Machine learning for improved management of patients with pancreatic cysts [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-13.
Abstract IA-17: Plumbing the depths of the non-coding cancer genome
Authors: Stein Lincoln D.
Published: March 2021

Cancer is driven by genomic mutations that alter common pathways that regulate cell growth and interaction with the microenvironment. More than 20,000 cancer genomes have been sequenced over the past decade, revealing multiple insights into the causes, evolution and consequences of cancer. However, almost everything we know about the cancer genome has been obtained using exome sequencing, which surveys the 2% of the genome that encodes protein coding genes. The 98% of the genome that is ignored by exome sequencing is the "dark matter" of the cancer genome and was until recently largely unexplored. In this talk, I will discuss highlights from the recently-published Pan-Cancer of Whole Genomes project (https://www.nature.com/articles/s41586-020-1969-6), in which an international team of researchers performed a comprehensive analysis of over 2,600 whole cancer genomes and matched normals, revealing new insights into origins, mechanisms and evolution of tumours. I'll also talk about how machine-learning techniques contributed to the Pan-Cancer project, including the development of an "electronic pathologist" that uses deep learning to accurately identify the tissue origins of tumours based on whole genome sequencing data.

Citation Format: Lincoln D. Stein. Plumbing the depths of the non-coding cancer genome [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr IA-17.
Abstract PO-006: Artificial intelligence aided interpretation of ALK fluorescent in situ hybridization for lung cancer: An algorithm development based on 10-year-annotated quality control files in central laboratory
Authors: Kim Tae-Jung , Kang Ho Chul , Lee Yonggeun , Seo Seokman , Kim Donghwan , Cho Sungjin
Published: March 2021

Molecular testing became the most important part of lung cancer diagnostics. Among them, an interpretation of fluorescent in situ hybridization (FISH) is challenging for the routine clinical samples with various amounts and quality. In this study, we trained a variant of DenseNet, a convolutional neural network model with the highest performance in image classification and suitable for quality assessment. Train dataset is composed of the arbitrarily classified dataset made and assessed by a professional pathologist during routine molecular test using whole-slide tissue images and molecular profiles including DNA density and the signal intensity of FISH obtained from 15,000 tests from lung cancer biopsies and resections, which were sent to the central laboratory for molecular studies from 45 hospitals nationwide. The performance of our platform is tested and compared to the quality assessment dataset made from each a general pathologist and a technician. The performance of our method for classifying DNA concentration and the signal intensity of FISH is comparable to that of a general pathologist and technicians. we are under improving its average area under the curve (AUC) over 0.85. This result suggests that a deep learning model based on a convolutional neural network can assist pathologists and pathology technician in rigorous assessing the quality of tissue morphology and signal of FISH for tissue samples with various molecular quality before or during the molecular test and can be applied to quality control of lung cancer molecular testing in a central laboratory.

Citation Format: Tae-Jung Kim, Ho Chul Kang, Yonggeun Lee, Seokman Seo, Donghwan Kim, Sungjin Cho. Artificial intelligence aided interpretation of ALK fluorescent in situ hybridization for lung cancer: An algorithm development based on 10-year-annotated quality control files in central laboratory [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-006.
Abstract PO-036: A sophisticated bioinformatics framework for integrative study of radiomics and genomics profiles of tumors
Authors: Sukhadia Shrey S. , Nagaraj Shivashankar H. , Gevaert Olivier , Arumugam Sivakumaran Theru , Tyagi Aayush , Mukherjee Pritam , Prathosh A.P.
Published: March 2021

The potential for radiomics to support oncology decision-making has grown substantially in recent years, as these scanning techniques have been found to offer unique information regarding the tumor phenotype and microenvironment that is distinct from that provided by genomic or proteomic assays. Radiomic and genomic (or proteomic) data can be correlated with one another, thereby facilitating radiogenomic efforts. Radiogenomically-informed biopsies have the potential to yield better pathological outcomes and can aid in the planning of more appropriate treatment strategies for cancer patients. However, the field lacks a unified software platform wherein radiomic and genomics/proteomic data could be brought together to conduct a variety of correlational analyses and build robust artificial intelligence models that would aid the prediction of genomic/proteomic profiles of tumors from their radiological images. We have built such a comprehensive platform that could be utilized by scientists and clinicians globally to conduct radiogenomic studies for a variety of cancer types, and further validate and deploy it in clinics to aid effective monitoring, diagnosis, and treatment of cancer patients.

Citation Format: Shrey S. Sukhadia, Shivashankar H. Nagaraj, Olivier Gevaert, Sivakumaran Theru Arumugam, Aayush Tyagi, Pritam Mukherjee, A.P. Prathosh. A sophisticated bioinformatics framework for integrative study of radiomics and genomics profiles of tumors [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-036.
Abstract PO-077: Image clustering of brain tumor patients using a deep neural network
Authors: Hosseini Hadi , Cheng Cheng , Glass John , Reddick Gene , Gajjar Amar , Lu Zhaohua
Published: March 2021

Purpose: The clustering of pediatric patients based on their unlabeled brain magnetic resonance (MR) images reveal the latent features and group structures. It is an unbiased approach to finding image-based markers and risk-stratification for neurocognitive functions. Analyzing large sets of MR images is time-intensive and challenging due to the MR images' high dimensionality and complexity. The extraction of low-dimension informative feature space instead of high-dimension MR image is an effective and fast solution to cluster images accurately. We aimed at developing a feature extraction method using deep learning. Our long-term research goal is to develop a feature extraction model using deep learning to automatically and accurately identify brain regions in association with neurocognitive outcomes. Methods: We used 124 registered fractional anisotropy (FA)-mapped of diffusion tensor (DT) images of pediatric medulloblastoma patients obtained after tumor surgery and before radiation from the SJMB03 clinical trial. First, we developed a 3D convolutional auto-encoder model based on a deep neural network for the voxel-wise analysis of 3D images to produce low-dimensional features, which can reconstruct input images accurately. Then, we applied the density-based clustering model, named OPTICS (Ordering Points To Identify the Clustering Structure), to the features. To train two models, we used a soft and dynamic algorithm to simultaneously combine two loss functions. Distance measure and CAHL (Cluster Assignment Loss Hardening) are used to measure feature learning and clustering performance. Results: Our experiment demonstrates that the proposed models yield results comparable in quality to manual clustering. The model trained regarding using a sufficient dataset and reducing dynamic loss function. Our model can cluster patients precisely and extract representative hidden features of the images, which is much more time- and labor-efficient compared to manual clustering by experts. Conclusions: In this study, we focused on a new feature extraction method and clustering of brain images. Because of the deep neural networks' unique advantages in image processing, we considered a 3D convolutional auto-encoder for the feature extraction part and OPTICS algorithm for the clustering part of the model. This method is highly useful in neuroimaging and neurocognitive research.

Citation Format: Hadi Hosseini, Cheng Cheng, John Glass, Gene Reddick, Amar Gajjar, Zhaohua Lu. Image clustering of brain tumor patients using a deep neural network [abstract]. In: Proceedings of the AACR Virtual Special Conference on Artificial Intelligence, Diagnosis, and Imaging; 2021 Jan 13-14. Philadelphia (PA): AACR; Clin Cancer Res 2021;27(5_Suppl):Abstract nr PO-077.