bluuebunny/crossref_metadata_2025 huggingface dataset:
Abstract LB366: KRASG12C inhibition enhances immunogenicity in KRASG12C-mutant colorectal cancer
Authors: Patel Parasvi S. , Tian Jun , Cruz Ferran Fece de la , Curtis Jacquelyn , Panda Apekshya , Oka Tomonori , Demehri Shadmehr , Sade-Feldman Moshe , Koptez Scott , Corcoran Ryan B.
Published: April 2024

RAS mutations are among the most prevalent oncogenic mutations, found in approximately 20% of human cancers. KRAS, the predominantly mutated isoform of RAS, is mutated in 40% of colorectal cancer (CRC) and leads to constitutive activation of the MAPK pathway and other downstream effectors. Despite recent FDA approval for KRASG12C selective inhibitor in non-small cell lung cancer, the response rate in CRC is below 20%, posing a challenge for treatment of these tumors.

Several studies have implicated oncogenic KRAS signaling in creating an immunosuppressive tumor microenvironment; therefore, we hypothesized that KRAS inhibitors can enhance anti-tumor immunity, providing a rationale for their combination with immune checkpoint blockade. Using a syngeneic KRASG12C CRC mouse model with knockout of APC, TP53, and SMAD4, we examined tumor cell-intrinsic mechanism of cooperativity between KRAS inhibition and immune response. We found that combining KRASG12C inhibition with anti-PD1 produced a more substantial and sustained reduction in tumor volume compared with KRAS or PD-1 inhibition alone. KRASG12C inhibition led to a significant increase in CD3+CD8+ T cell infiltration compared with vehicle control. Single-cell RNA-sequencing corroborated our observations and revealed increased T cell infiltration in KRASG12C tumors following KRASG12C inhibition. Moreover, these T cells upregulated genes involved in interferon gamma production and T cell activation. Importantly, we observed a significant reduction in the Ras pathway genes, specifically in the tumor cell population, along with enrichment of genes involved in abnormal antigen presentation and MHC Class I peptide loading complex. Mechanistically, we found that KRASG12C inhibition activates the innate immune response, which subsequently induce Type I interferons that activate the expression of T cell-recruiting interferon stimulated genes like CXCL9, CXCL10, IFIT1, IFIT2, and IFIT3. Our data presents a connection between KRAS inhibition and anti-tumor immunity, providing insights into novel combination strategies for KRASG12C colorectal cancers.

Citation Format: Parasvi S. Patel, Jun Tian, Ferran Fece de la Cruz, Jacquelyn Curtis, Apekshya Panda, Tomonori Oka, Shadmehr Demehri, Moshe Sade-Feldman, Scott Koptez, Ryan B. Corcoran. KRASG12C inhibition enhances immunogenicity in KRASG12C-mutant colorectal cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB366.
Abstract LB377: The combination of epigallocatechin gallate (EGCG) and resveratrol prevents 4-NQO-induced oral carcinogenesis in mice
Authors: Adeluola Adeoluwa A. , Raji Lukmon , Sigdel Saroj , Anisuzzaman ASM , Amin A.R.M. Ruhul
Published: April 2024

Head and neck cancer (HNC), commonly known as oral cancer, is a devastating disease and the 6th most common cancer worldwide. Most HNC patients are diagnosed with advanced-stage disease for which the 5-year survival is below 50%, stressing the need for chemoprevention. Recently, we have reported that the combination of resveratrol and EGCG induces synergistic apoptosis and inhibits xenografted HNC growth by inhibiting the AKT-mTOR pathway. This study investigated the chemopreventive efficacy of resveratrol, EGCG, and their combination using the 4NQO-induced oral carcinogenesis model. C57BL/6 mice were exposed to 4-NQO (50 μg/ml) via drinking water for 10 weeks, followed by treatment with vehicle (50% sweetened condensed milk), resveratrol (30 mg/kg), EGCG (30 mg/kg) and their combination for 8 weeks, 5 days/week. The mice were sacrificed on week 24, and the number of visible and microscopic lesions was counted. Resveratrol alone and in combination with EGCG significantly inhibited the number of visible lesions. In contrast, the number of microscopic lesions and lesion area was significantly inhibited only in the combination group. The expression of Ki-67 was also significantly inhibited in resveratrol and combination groups when compared with untreated control. Furthermore, RNASeq and qPCR analysis using an HNC cell line identified GDF15, ATF3, p21, p27, and Bim as significantly upregulated genes, with GDF15 being the most upregulated one. Expression of GDF15 and ATF3 proteins was confirmed by Western blotting. Taken together, our data strongly demonstrate the chemopreventive potential of the combination of EGCG and resveratrol and pave the way for further clinical developments. (Supported by NIH Grants R15DE032063 and P20GM103434)

Citation Format: Adeoluwa A. Adeluola, Lukmon Raji, Saroj Sigdel, ASM Anisuzzaman, A.R.M. Ruhul Amin. The combination of epigallocatechin gallate (EGCG) and resveratrol prevents 4-NQO-induced oral carcinogenesis in mice [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB377.
Abstract LB389: Highly potent and selective RET inhibitor with robust anti-tumor activities against wild-type and acquired drug-resistant mutations
Authors: Xing Li , Zhu Wei , Wang Jingbing
Published: April 2024

RET gene mutations and fusions are the driving events for the generation and deterioration of many cancer types. The first generation RET inhibitors, selpercatinib (Loxo-292) and pralsetinib (Blu-667), demonstrated clinically acquired resistance (e.g., solvent frontier mutations G810X, double mutations V804X-G810X etc.). Novel treatment that overcomes multiple resistance mutations is needed to improve patient outcomes. Leveraging our in-house AI technology platform, we have discovered the next generation RET inhibitors that overcome multiple resistance mutations including solvent front, gatekeeper and various double mutations. We employ the synergistic advantages of domain expertise and modern technology to accelerate early stage drug discovery. On our deep learning (DL) centric AI platform, extensive scientific computing supports large scale virtual screening, generative design, drug property prediction and multi-objective lead optimization. Only the optimized candidate compounds are passed along for synthesis and testing to save time and cost. In cell proliferation assays, our compound effectively inhibited KIF5B-RET Ba/F3 WT, V804M, G810S, G810R, V804M-G810S with IC50 values of 0.4 nM, 1.3 nM, 1.1 nM, 3.4 nM, 6.3 nM, respectively, demonstrating significant improvement over selpercatinib in the range of several hundred fold in activities. Importantly, the compound is highly selective against VEGFR2, an off-target responsible for cardiovascular adverse effects in the approved RET inhibitors. The superior cellular potencies translated consistently into animal models. In Ba/F3 KIF5B-RET-G810R xenograft model, treatment of 25 mg/kg bid resulted in >90% TGI with a100% survival rate. The compound showed favorable pharmacokinetic properties, lack of drug-drug interaction (DDI), and devoid of hERG liability (IC50 > 10uM). Preclinical safety evaluations in rats exhibited good tolerance margin. We have discovered the second generation compound that potently inhibits a diverse range of RET alterations. The compound exhibits preferable pharmaceutical profiles and is advancing toward IND application.

Citation Format: Li Xing, Wei Zhu, Jingbing Wang. Highly potent and selective RET inhibitor with robust anti-tumor activities against wild-type and acquired drug-resistant mutations [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB389.
Abstract LB395: NetraAI-driven discovery of novel biomarkers in MSI-high colon cancer for precision immunotherapy
Authors: Qorri Bessi , Tsay Mike J. , Leonchyk Paul , Alphs Larry , Pani Luca , Geraci Joseph
Published: April 2024

By leveraging NetraAI, a novel machine learning (ML) approach, we identify potential biomarkers in microsatellite instability-high (MSI-H) colon cancer. MSI, marked by DNA mismatch repair (MMR) defects characterizes 5-20% of colorectal cancers (CRCs). MSI-H tumors harbor higher mutational burdens, produce neoantigens, and enhance immune recognition and responsiveness to immunotherapy. The innovative Sub-Insight Learning approach of NetraAI, allows us to overcome challenges associated with smaller data sets not reflecting the totality of the disease they represent. This approach utilizes a set of validated mathematical methods to identify sub-insights about patients even from small data sets. This allows the system to decompose the data sets into high and low confidence patient subpopulations, enhancing predictive model accuracy and reducing overfitting. Further, the system explains what variables are driving the etiology defining the subpopulations of patients. Utilizing a small data set consisting of 141 RNA expression profiles of CRC samples (E-GEOD-41258) with MSI-H and MSI-low colon cancer, we were able to derive several unique insights into patients’ molecular profiles. NetraAI analysis identified several unique variables, including CATSPERB, FUT8, PLLP, DUSP4, and MLPH that may play significant roles in MSI-H colon cancer pathology. These markers hold potential for personalizing clinical trials as well as potential therapeutic targets. Furthermore, protein-protein interaction (PPI) networks revealed co-expression and co-localizations that exist amongst these genes, suggesting a complex interplay among these genes, particularly in the context of spermatogenesis. The application of NetraAI and its Sub-Insight Learning paradigm has revealed novel insights into the molecular profile of MSI-H colon cancer. These findings pave the way for more targeted and effective precision immunotherapy strategies, demonstrating the power of AI in advancing the understanding and treatment of complex cancers.

Citation Format: Bessi Qorri, Mike J. Tsay, Paul Leonchyk, Larry Alphs, Luca Pani, Joseph Geraci. NetraAI-driven discovery of novel biomarkers in MSI-high colon cancer for precision immunotherapy [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB395.
Abstract LB421: Entinostat alters cell cycle in ovarian cancer cells
Authors: Gupta Vijayalaxmi G. , Liu Heng , Zhang Rugang , Onken Michael , Wilson Andrew , Yull Fiona , Crispens Marta , Mullen Mary , Khabele Dineo
Published: April 2024

Introduction: We have reported that Class I/II HDACi sensitize PARPi resistant tumors, creating a possibility to expand the use of PARPi beyond homologous recombination deficient tumors. However, the underlying mechanism is still unknown. Here, we report leads obtained from differential gene expression in ovarian cancer cells treated with entinostat and olaparib.

Methods: OVCAR3 cells were treated with 0.5 µM entinostat and 10 µM olaparib (alone and combined) for 6h, followed by RNA Seq. Cell cycle was analyzed by flow cytometry in SKOV-3 cells treated with olaparib and entinostat (alone and combined) for 24h, 48h and 72h.

Results: RNA-Seq analysis showed reduced expression of genes that regulate G2/M checkpoint progression with both entinostat and olaparib. This correlated with flow cytometry results in SKOV-3 cells, where we observed accumulation of G2/M phase cells when treated with entinostat and olaparib. At 24h post treatment, olaparib and entinostat treated cells had higher population of cells in G2/M phase compared to control (18.5% and 16.7% respectively compared to 14.7% in control). Olaparib combined with entinostat further increased G2/M population to 21.7%. At 48h olaparib treated cells had 17.4% cells in G2/M compared to 14% in control. Entinostat treated cells had 18% G2/M cells. Combination treated cells had 19.7% G2/M cells. At 72h, olaparib and control had similar number of G2/M cells (9.2% and 9.7% respectively). Entinostat treated cells had 12% G2/M cells and combination treated cells had 13.6% cells at G2/M cells. This indicates that entinostat and olaparib disrupt progression of cells from G2/M phase, potentially leading to cell death before they can enter mitosis/cell division. Cell cycle analysis in OVCAR3 cells is in progress.

Conclusion: Entinostat and olaparib cause G2/M cell cycle arrest in ovarian cancer cells. Entinostat maintains G2/M cell cycle arrest longer than olaparib, potentially extending the window of efficacy of the combination therapy. Validation of G2/M markers and other gene targets/pathways is in progress.

Citation Format: Vijayalaxmi G. Gupta, Heng Liu, Rugang Zhang, Michael Onken, Andrew Wilson, Fiona Yull, Marta Crispens, Mary Mullen, Dineo Khabele. Entinostat alters cell cycle in ovarian cancer cells [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB421.
Abstract LB433: Targeting of ABI1 via DNA-based PROTACs
Authors: Lin Kevin M. , Li Xiang , Kotula Leszek
Published: April 2024

Breast cancer is the most commonly diagnosed non-cutaneous cancer in American women. Although current therapies reduce initial tumor burden, patients often relapse with aggressive tumors which can metastasize. Abelson Interactor 1 (ABI1) is an adaptor protein which is traditionally seen as a WAVE complex component- regulating actin polymerization, cell migration and adhesion. In this role, ABI1 serves as a driver of breast cancer metastasis. Recent studies have discovered that ABI1 contains a homeobox homology region (HHR) which is able to bind DNA and regulate transcription. In order to specifically target and degrade ABI1, we designed a novel PROteolysis-TArgeting Chimera (PROTAC) system using DNA as the bait for ABI1. We hypothesized that by linking ABI1 DNA targets to the ubiquitin system, we would be able to effectively target ABI1 for degradation. Lenalidomide, an E3 ubiquitin ligase ligand, was attached to an oligonucleotide via click chemistry. Cells were then treated with PROTAC systems via direct addition to media or via lipofection reagents, and levels of ABI1 were detected using western blotting. Using this DNA-based PROTAC system in cell-culture models of both breast and ABI1-elevated prostate cancer suggested that the system is able to target and degrade ABI1. Usage of lipofection reagents were able to improve drug delivery and effectiveness. Further research is needed to identify off-target effects and effectiveness in vivo models of disease to reduce metastasis. In conclusion, PROTACs targeting ABI1 may prove to be an effective and novel method to reduce metastasis and improve outcomes for cancer treatment.

Citation Format: Kevin M. Lin, Xiang Li, Leszek Kotula. Targeting of ABI1 via DNA-based PROTACs [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB433.
Abstract LB451: Targeting oncogenic transcription and signaling crosstalk fueling drug resistance in lung cancer
Authors: Chatterjee Nilanjana , Olivas Victor , Wu Wei , Tang Tracy , Powell Ben , Bivona Trever
Published: April 2024

Lung cancer is the leading cause of all cancer-related mortality worldwide. The RAS-RAF-MEK-ERK (RAS-MAPK) signaling pathway is critical for maintaining cell survival and proliferation. Somatic mutations in the RAS or RAF genes are associated with frequent hyper-activation of RAS-MAPK pathway in non-small cell lung cancer or NSCLC. While direct inhibitors of KRAS G12C are emerging with promising efficacy, resistance to these agents as well as to inhibitors of RAF and MEK remains an obstacle to long-term patient survival. Therefore, it is essential to understand how drug resistance emerges in lung cancer and to identify bypass survival pathways or compensatory mechanisms that limit the response to RAS-MAPK targeted therapies in lung cancer. Emerging literature indicates a critical role of bromodomain and extra-terminal domain (BET) family of bromodomain proteins (BRD4, BRD3, BRD2) in various human malignancies and provides the rational for targeting BET proteins as a strategy for the development of new anticancer drugs. Using combinatorial drug screens in a panel of NSCLC cell lines, we found that BET proteins act in parallel to the RAS-MAPK pathway to promote drug resistance in lung cancer. Combination of BET and RAS-MAPK inhibitors caused pronounced cell death and potent tumor regression/stasis in several KRAS or BRAF mutant models of NSCLC with synergistic apoptosis induction and downregulation of MYC, a transcriptional target of BET proteins. However, concurrent loss of the tumor suppressor STK11 (aka LKB1) in NSCLC cells induced activation of the Hippo pathway effector and transcriptional co-activator, YAP and conferred resistance to the BET and RAS-MAPK inhibitors combination in NSCLC cells and tumors.These findings suggest an unprecedented functional interplay between BET chromatin regulators, Hippo-YAP and RAS-MAPK signaling in lung cancer and therapy resistance. Further, this study identified STK11 as a modifier of sensitivity to the BET and RAS-MAPK inhibitors combination in NSCLC cells and tumors.

Citation Format: Nilanjana Chatterjee, Victor Olivas, Wei Wu, Tracy Tang, Ben Powell, Trever Bivona. Targeting oncogenic transcription and signaling crosstalk fueling drug resistance in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr LB451.
Abstract ND10: NST-628 is a novel, potent, fully brain-penetrant MAPK pathway molecular glue that inhibits RAS- and RAF-driven cancers
Authors: Ryan Meagan B. , Quade Bradley , Schenk Natasha , Fang Zhong , Zingg Marshall , Cohen Steven E. , Li Chun , Swalm Brooke M. , Ozen Aysegul , Ye Chaoyang , Ritorto Maria Stella , Huang Xin , Dar Arvin C. , Han Yongxin , Hale Michael , Hagel Margit , Hoeflich Klaus P.
Published: April 2024

Alterations in the RAS-MAPK signaling cascade are common across multiple solid tumor types and are drivers for many cancers. Apart from approved mutation-selective inhibitors for BRAF Class I and KRAS G12C mutations, other mutations of RAS and RAF are not directly addressable by currently approved inhibitors and there is a need for therapeutic strategies having superior efficacy, durability and tolerability for the MAPK pathway. NST-628 is a potent pan-RAF-MEK molecular glue that prevents phosphorylation and activation of MEK by RAF. In this way, NST-628 overcomes the limitations of traditional RAS-MAPK inhibitors and drives deep, durable inhibition of the RAS-MAPK pathway. Cellular, biochemical, and structural analysis of RAF-MEK complexes show that NST-628 engages all isoforms of RAF and prevents the formation of BRAF-CRAF and BRAF-ARAF heterodimers, a differentiated mechanism from all current RAF-directed inhibitors. With its potent and durable inhibition of the RAF-MEK signaling complex, NST-628 demonstrates broad efficacy in cellular and patient-derived, in vivo tumor models harboring KRAS, NRAS, BRAF class II/III, and NF1 mutations. In addition, high intrinsic permeability across the blood-brain-barrier results in NST-628-mediated intracranial anti-tumor activity in orthotopic tumor models. Furthermore, NST-628 pharmacokinetic and metabolic profiles have been optimized for daily dosing in the clinic to maximize therapeutic index. Given its differentiated molecular mechanism, best-in-class drug-like properties, and superior safety profile in IND-enabling GLP studies, NST-628 is positioned to make an impact clinically to address unmet patient need. First-in-human trials of NST-628 have been initiated in early 2024

Citation Format: Meagan B. Ryan, Bradley Quade, Natasha Schenk, Zhong Fang, Marshall Zingg, Steven E. Cohen, Chun Li, Brooke M. Swalm, Aysegul Ozen, Chaoyang Ye, Maria Stella Ritorto, Xin Huang, Arvin C. Dar, Yongxin Han, Michael Hale, Margit Hagel, Klaus P. Hoeflich. NST-628 is a novel, potent, fully brain-penetrant MAPK pathway molecular glue that inhibits RAS- and RAF-driven cancers [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr ND10.
Abstract SY07-03: Cell competition shapes metastatic latency and relapse
Authors: Malladi Srinivas
Published: April 2024

Cell competition, a fitness-sensing process, is essential for tissue homeostasis. Using cancer metastatic latency models, we show that cell competition results in the displacement of latent metastatic (Lat-M) cells from the primary tumor. Lat-M cells resist anoikis and survive as residual metastatic disease. A remodeled extracellular matrix facilitates Lat-M cell displacement and survival in circulation. Disrupting cell competition dynamics by depleting secreted protein and rich in cysteine (SPARC) reduced displacement from orthotopic tumors and attenuated metastases. In contrast, depletion of SPARC after extravasation in lung-resident Lat-M cells increased metastatic outgrowth. Furthermore, multiregional transcriptomic analyses of matched primary tumors and metachronous metastases from patients with kidney cancer identified tumor subclones with Lat-M traits. Kidney cancer enriched for these Lat-M traits had a rapid onset of metachronous metastases and significantly reduced disease-free survival. Thus, an unexpected consequence of cell competition is the displacement of cells with Lat-M potential, thereby shaping metastatic latency and relapse. Overall, we demonstrate that cell competition within the primary tumor results in the displacement of Lat-M cells. We are investigating the impact of altering cell competition dynamics on metastatic incidence that may guide strategies to limit metastatic recurrences.

Citation Format: Srinivas Malladi. Cell competition shapes metastatic latency and relapse [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY07-03.
Abstract SY31-03: Cancer takes a nerve: Challenges and opportunities in cancer neuroscience
Authors: Amit Moran
Published: April 2024

Neuronal reprogramming in cancer represents a groundbreaking frontier that intricately links the nervous system's plasticity to tumor progression and response to treatment. This process involves the aberrant activation or suppression of neuronal signals that can significantly influence cancer cell behavior, including proliferation, migration, and resistance to therapies. The concept of repurposing drugs originally designed to target the nervous system for use in clinical oncology offers a promising therapeutic strategy, potentially revolutionizing cancer treatment. By intercepting neuronal pathways that cancer cells exploit for their growth and survival, these drugs could modulate the tumor microenvironment, enhance the efficacy of conventional therapies, and potentially reverse drug resistance mechanisms. Moreover, targeting neuronal signaling in cancer could lead to improvements in patients' functional outcomes, mitigating cancer-related neurological deficits and improving quality of life. Importantly, this approach may also have profound implications for survival outcomes, as modulating the neuro-cancer interface could slow tumor progression and reduce metastatic spread. The repurposing of neuroactive drugs for cancer treatment thus opens a new avenue for personalized medicine, where the intricacies of neuronal reprogramming are leveraged to tailor therapy, improve response rates, and ultimately, enhance patient survival and well-being.

Citation Format: Moran Amit. Cancer takes a nerve: Challenges and opportunities in cancer neuroscience [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY31-03.
Abstract SY35-02: Spatial mapping of mitochondrial networks in lung cancer
Authors: Shackelford David B.
Published: April 2024

Mitochondria are critical to the governance of metabolism and bioenergetics in cancer cells. The mitochondria form highly organized networks, in which their outer and inner membrane structures define their bioenergetic capacity. However, in vivo studies delineating the relationship between the structural organization of mitochondrial networks and their bioenergetic activity have been limited. Here we present an in vivo structural and functional profiling of mitochondrial networks and bioenergetic phenotypes in non-small cell lung cancer (NSCLC) using an integrated platform comprised of positron emission tomography (PET) imaging, respirometry and 3-dimensional scanning block-face electron microscopy (3D SBEM). Interestingly, the diverse bioenergetic phenotypes and metabolic dependencies we identified in NSCLC tumors align with distinct structural organization of mitochondrial networks present. Further, we discovered that mitochondrial networks are organized into distinct compartments within tumor cells. In tumors with high rates of oxidative phosphorylation (OXPHOSHI) and fatty acid oxidation we identified peri-droplet mitochondrial networks wherein mitochondria contact and surround lipid droplets. In contrast, we discovered that in tumors with low rates of OXPHOS (OXPHOSLO), high glucose flux regulated i) peri-nuclear localization of mitochondria, ii) structural remodeling of cristae and iii) mitochondrial respiratory capacity. Our findings suggest that in NSCLC, mitochondrial networks are compartmentalized into distinct subpopulations that govern the bioenergetic capacity of tumors. Structure-and-function studies defining the relationship between mitochondrial architecture and metabolic dependencies may hold promise as strategies to profile metabolic liabilities unique to lung cancer subtypes. We anticipate that coupling PET imaging with 3D SBEM will have dynamic applications beyond that of lung cancer and enrich our understanding of how mitochondrial bioenergetics impact human disease.

Citation Format: David B. Shackelford. Spatial mapping of mitochondrial networks in lung cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY35-02.
Abstract SY41-02: Experimental methods for high throughput discovery and validation of tumor antigens
Authors: Heath James R.
Published: April 2024

The discovery and validation of tumor associated T-cell antigens, which can include neoantigens, viral antigens, cancer testis antigens, and others, is fundamental to the design of both cancer vaccines and engineered cell cancer immunotherapies. However, identifying tumor specific antigens that are highly presented within tumors, and identifying and validating T cell receptors (TCRs) that are specific to those antigens, remains a non-trivial exercise. Even identifying and validating a single antigen-specific T cell clonotype for advancement into a clinic trial remains a daunting task. In this talk, I will discuss a number of new experimental and computational tools that are designed to accelerate this process. The experimental toolsets include reagents and strategies that allow for the capture and analysis of T cells specific to hundreds to thousands of tumor-associated Class I and Class II antigens simultaneously, with as many as 100 patient bloods analyzed in a single experiment. Single cell analysis of the resulting data yields a deep characterization of the captured T cells, including T cell receptor α/β genes, antigen-MHC specificity, T cell phenotype, and donor identification. This type of analysis, in turn, provides large data bases that can fuel new computational strategies that can be harnessed (at a still early stage) to predict which TCRα/β genes associate with which putative antigen-MHCs, or to extract the biochemical and biophysical characteristics of TCR-antigen pairings that influence T cell phenotype. Thus, this presentation will span both new experimental and new computational toolsets.

Citation Format: James R. Heath. Experimental methods for high throughput discovery and validation of tumor antigens [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2024; Part 2 (Late-Breaking, Clinical Trial, and Invited Abstracts); 2024 Apr 5-10; San Diego, CA. Philadelphia (PA): AACR; Cancer Res 2024;84(7_Suppl):Abstract nr SY41-02.
User authentication using gait and enhanced attribute-based encryption: a case of smart home
Authors: Pin Lim Wei , Singh Manmeet Mahinderjit
Published: April 2024

With the increasing popularity of the internet of things (IoT) application such as smart home, more data is being collected, and subsequently, concerns about preserving the privacy and confidentiality of these data are growing. When intruders attack and get control of smart home devices, privacy is compromised. Attribute-based encryption (ABE) is a new technique proposed to solve the data privacy issue in smart homes. However, ABE involves high computational cost, and the length of its ciphertext/private key increases linearly with the number of attributes, thus limiting the usage of ABE. This study proposes an enhanced ABE that utilises gait profile. By combining lesser number of attributes and generating a profiling attribute that utilises gait, the proposed technique solves two issues: computational cost and one-to-one encryption. Based on experiment conducted, computational time has been reduced by 55.27% with nine static attributes and one profile attribute. Thus, enhanced ABE is better in terms of computational time.
A discernment of round-robin vs SD-WAN load-balancing performance for campus area network
Authors: Gamilla Anazel P. , Tolentino Anjela C. , Payongayong Reina T.
Published: April 2024

Efficient load balancing is crucial for optimizing network performance and ensuring seamless connectivity in modern campus area networks (CANs). With the proliferation of data-intensive applications and the increasing reliance on cloud-based services, organizations are seeking effective load-balancing solutions to distribute network traffic evenly across available resources. The continuous improvement of devices, tools, and techniques to cater a large amount of network traffic, started to be employed on different campuses. Understanding the best approach to maximize the utilization of the network resources is crucial in order to stabilize and maintain the network. The study aims to discern the round-robin and software defined-wide area network (SD-WAN) techniques based on defined metrics and conducted with a predefined payload for commonly used application conditions. The analysis shows that SD-WAN delivers a much superior performance than round-robin based on the criteria. The local area network (LAN) test shows difference between the two types of technology for the three given metrics. The WAN test shows that the round-robin has higher packet loss, latency, and jitter than the SD-WAN technology. While round-robin may suffice for small-scale deployments with relatively homogeneous traffic patterns, SD-WAN offers more sophisticated capabilities for larger CANs with diverse application workloads and distributed locations.
Power management system for a hybrid energy storage electric vehicle using fuzzy logic controller
Authors: Eragamreddy Gouthami , Gopiya Naik Sevyanaik
Published: April 2024

This research work introduces a power management system for a hybrid energy storage system (PMHESS) configuration for urban electric vehicles utilizing a fuzzy logic controller (FLC). Consequently, the configuration includes two DC/DC interleaved converters that establish a connection between the battery and ultra-capacitors (UCs), thereby ensuring a substantial power capacity. The FLC is adaptable and sturdy, considering information from sources other than the vehicle, such as other vehicles or road infrastructure. The study explores incorporating road topography into the control structure to improve hybrid storage performance. Simulation results show that combining lithium batteries with UCs improves the energy source’s performance and reliability. The power management algorithm reduces sudden demands on the battery by considering the slope of the ground. The work proposes energy storage integration for electric vehicles, exploring its benefits through simulations. Overall, the proposed hybrid energy storage system (HESS) demonstrates high efficiency and power for urban electric vehicles. The results are validated using MATLAB/Simulink.
Kernel rootkit detection multi class on deep learning techniques
Authors: Srinivasan Suresh Kumar , Thalavaipillai SudalaiMuthu
Published: April 2024

The harmful code application known as a rootkit is designed to be loaded and run directly from the operating system's (OSs') Kernel. Rootkits deployed in the Kernel, called Kernel-mode rootkits, can alter the OS. The intention behind these Kernel changes is to conceal the hack. Detecting a Kernel rootkit in a target machine is found to be quite challenging. Numerous techniques can be employed to modify the Kernel of a system. Kernel rootkits also create hidden access for attacks, enabling unauthorized entry to be gained by attackers on the machine. The ultimate consequence is that essential computer data can be modified, personal information can be gathered, and hackers can observe behavior. Synthetic neural networks support artificial intelligence, a branch of deep learning that models the human brain and operates on large datasets. This study proposed the Kernel rootkit detection multi-class deep learning techniques (KRDMCDLT). Deep learning algorithms are utilized to recognize the Kernel rootkit from a batch of data by selecting essential properties for learning tracking models. Thus, by identifying the OS malware, trojan assaults can be stopped before they can access infected data. This Kernel rootkit detection was tested in a Google Cloud Platform (GCP) computing system.
IoT-based fertigation system for agriculture
Authors: Idris Fakrulradzi , Latiff Anas Abdul , Buntat Muhammad Amirul , Lecthmanan Yogeswaran , Berahim Zulkarami
Published: April 2024

Fertigation system has been widely used by farmers to automate some processes of crops productions. A conventional system requires workers to prepare a fertilizer mixture, before transferring it into a main storage tank to be mixed with water. Then, electrical conductivity (EC) of the mixture will be measured. The existing fertigation system still relies heavily on workers and is manually operated and prone to human error. Therefore, internet of things (IoT) based fertigation system has been developed to deliver the fertilizer mixture with consistent EC value automatically to the plants. The main system controller is designed using ESP32 development module. The operation of the system can be monitored using an IoT dashboard and farmers can also control the system remotely. Alert will be given to the farmers if the condition of the system or plant does not meet the predefined settings. The values of EC together with temperature and humidity sensors are recorded for further analysis. A testbed is set up to provide fertigation to 120 polybags eggplants. Using the proposed fertigation system, the eggplants have been harvested earlier, therefore reducing the fertilizer usage. The cost of this IoT based fertigation system is lower compared to existing commercial products.
An extreme gradient boost based classification and regression tree for network intrusion detection in IoT
Authors: Chalichalamala Silpa , Govindan Niranjana , Kasarapu Ramani
Published: April 2024

Nowadays, modern technology includes various devices, networks, and apps from the internet of things (IoT), which consist of both positive and negative impacts on social, economic, and industrial effects. To address these issues, IoT applications and networks require lightweight, quick, and adaptable security solutions. In this sense, solutions based on artificial intelligence and big data analytics can yield positive outcomes in the realm of cyber security. This study presents a method called extreme gradient boost (XGBoost) based classification and regression tree to identify network intrusions in the IoT. This model is ideally suited for application in IoT networks with restricted resource availability because of its distributed structure and builtin higher generalization capabilities. This approach is thoroughly tested using botnet internet of things (BoT-IoT) new-generation IoT security datasets. All trials are conducted in a range of different settings, and a number of performance indicators are used to evaluate the effectiveness of the proposed method. The suggested study's findings provide recommendations and insights for situations involving binary classes and numerous classes. The suggested XGBoost model achieved 99.53% of accuracy in attack detection and 99.51% in precision for binary class and multiclass classifications, respectively.
A deep learning-based system for accurate diagnosis of pelvic bone tumors
Authors: Shouman Mona , Rahouma Kamel Hussein , Hamed Hesham Fathy Aly
Published: April 2024

Bone image analysis and categorizing bone cancers have both seen advancements thanks to deep learning (DL), more notably convolution neural networks (CNN). This study suggests a brand-new CNN-based methodology for categorizing pelvic bone tumors specifically. This work aims to create a pelvic bone computed tomography (CT) image categorization system based on deep learning. The proposed technique uses a convolutional neural network (CNN) architecture to automatically extract information from the CT images and classify them into distinct categories of tumors. A total of 178 3D CT pictures was discovered and added retroactively. DenseNet created the image-based model with Adam optimizer and cross entropy loss. The suggested system's accuracy is assessed using a variety of performance indicators, including sensitivity, specificity, and F1-score. As demonstrated by the experiment findings, the suggested deep learning based classification system has a high degree of accuracy (94%), making it useful for the diagnosis and treatment of pelvic bone tumors. Our promising results might hasten the use of DL-assisted CT diagnosis for pelvic bone tumors in the future.
Holistic personas to increase the novice developer productivity
Authors: Kusuma Wahyu Andhyka , Jantan Azrul Hazri , Admodisastro Novia Indriaty , Norowi Noris
Published: April 2024

A deeper understanding and integration with system users' thoughts and emotional experiences are required for user-engaged development. User experience (UX) journey integrates user requirements and problem-solving approaches. The integration of data-driven techniques and user-centric approaches in software development is investigated in this study. It focuses on using the Markov chain model to predict developer productivity based on data gathered while creating personas across three projects. Organizations can gain valuable insights into user needs and requirements by conducting purposeful activities such as strength, weaknesses, opportunities, and threats (SWOT) analysis, competitor analysis, hypothesis formulation, identification of behavioral variables, mapping interviews, and defining characteristics and objectives. The model has predictive capabilities that allow for more informed decision-making, more efficient resource allocation, and better project planning. The goal of the activity and the model ensure the development of software products that effectively meet the needs of users, resulting in a higher success rate for software development initiatives. This study emphasizes the importance of integrating quantitative and qualitative analysis to drive successful software development projects and increase productivity while meeting user needs. According to the findings of the research conducted from the three projects completed, the proposed methods have similarities, and predictions using the Markov chain can determine the success of novice developers.
The model of decision support system using hybrid method and actual weighting for the study program ranking
Authors: Amin M. Miftakul , Dwitayanti Yevi
Published: April 2024

Nowadays the good or bad study program can be seen from the accreditation rank that it obtains from the institution of college accreditation. However, it is frequently found at college that there are some study programs that have the same accreditation. This encourages the college to do another approach which can do this study program ranking from a different point of view. This research developed a model of decision support system to do ranking towards 25 study programs existed in the environment of Sriwijaya State Polytechnic. Hybrid method employed the combination of analytical hierarchy process (AHP) and simple additive weighting (SAW) to do the ranking. Actual weighting model was used in the calculation based on the fact obtained in each study program, and in line with the criteria which had been determined. As many as 7 relevant criteria and 21 sub criteria were used in this model. The results of this research showed that the model which had been developed can give recommendation in the form of study program ranking with actual condition based on the data attached to each study program.
The role of chat generative pre-trained transformer in facilitating decision-making and the e-learning process in higher education
Authors: Al-Moghrabi Khaldun G. , Al-Ghonmein Ali M.
Published: April 2024

Digital technology and artificial intelligence technologies have been progressing rapidly, thus giving rise to intelligent chatbots such as chat generative pre-trained transformer (ChatGPT). These chatbots make searching for information more efficient and provide higher education institutions with assistance in decision-making. The goal of this research is to explore the capabilities of ChatGPT technology and its role in enhancing the e-learning process. Moreover, it seeks to determine whether ChatGPT can provide useful suggestions to improve the decision-making process in higher education. ChatGPT is effective in an e-learning environment for the following reasons: it facilitates personalized learning experiences, offers real-time support, and enhances decision-making by leveraging natural language processing capabilities. As suggested by the findings, ChatGPT has significant potential in higher education, as demonstrated by its ability to improve interactive participation, educational strategies, and educational outcomes. This study highlights the importance of incorporating ChatGPT into higher education settings to improve e-learning and decision-making.
Compact dual-band antenna design for sub-6 GHz 5G application
Authors: Kadu Mahesh , Pawase Ramesh , Chitte Pankaj , Ubale Vilas S.
Published: April 2024

A design of a compact dual-band antenna for 5G application is presented in this research article. The dual-band operation includes the 3.6 GHz and 5.4 GHz frequency bands of the sub-6 GHz frequency band for 5G technology. The proposed antenna offers a compact design with satisfactory antenna performance parameters. Moreover, the dual-band antenna showcases the independent tuning ability for both frequency bands. The prototype of the dual-band antenna is manufactured and when tested for various antenna performance parameters shows a good agreement between the simulated and measured results. The proposed dual-band antenna has compact dimensions along with a peak gain of 2.2 dB and antenna efficiency of more than 90%.The antenna performance parameters are also compared with various dual-band antenna designs from the literature. The proposed dual-band antenna offers a compact design with satisfactory performance parameters and outperforms its counterparts.
Adaptive radio propagation model for maximizing performance efficiency in smart city disaster management application
Authors: Mangasuli Sushant , Kaluti Mahesh
Published: April 2024

Climate change poses several environmental threats like floods to urban environment; thus, effective and reliable communication of emergency information is needed during massive breakdown of network infrastructure. This paper presents a mobile adhoc network (MANETs) based effective information such as calls, image, and videos communication system that is compatible with current 3GPP and 5G communication network. Here in maintaining connectivity the information is communicated between different MANET nodes in a multi-hop manner. However, designing radio propagation is challenging considering higher local emergency request congestion at different terrain with varying speed of users. The current radio propagation model is designed without considering the effect of line-of-sight between communicating device and are not adaptive to different environment considering urban disaster management environment. This paper develops an adaptive radio propagation (ARP) model namely expressway, city and semiurban. Then, in reducing congestion and improving network performance efficiency the work introduced an adaptive medium access control (AMAC) protocol. The MAC incorporates a dynamic network controller (DNC) to optimize the contention window size in dynamic manner according to current traffic demands. The AMAC protocol achieves much improved throughput with lesser packet loss in comparison with existing MAC (EMAC) model considering different radio propagation model introduced in this work.
Real-time indoor tracking for augmented reality using computer vision technique
Authors: Shewail Ashraf Saad , H. Zayed Hala , A. M. Elsayed Neven
Published: April 2024

In recent times, there has been an increase in the stability and integration of augmented reality (AR) technology in everyday applications. AR relies on tracking techniques to capture the characteristics of the surrounding environment. Tracking falls into two categories: outdoor and indoor. While outdoor tracking predominantly relies on the global positioning system (GPS), it is performance indoors is hindered by imprecise GPS signals. Indoor tracking offers a solution for navigating complex indoor environments. This paper introduces an indoor tracking system that combines smartphone sensor data and computer vision using the oriented features from accelerated and segments test and rotated binary robust independent elementary features (ORB) algorithm for feature extraction, along with brute force match (BFM) and k-nearest neighbor (KNN) for matching. This approach outperforms previous systems, offering efficient navigation without relying on pre-existing maps. The system uses the A* algorithm to find the shortest path and cloud computing for data storage. Experimental results demonstrate an impressive 99% average accuracy within a 7-10 cm error range, even in scenarios with varying distances. Moreover, all users successfully reached their destinations during the experiments. This innovative model presents a promising advancement in indoor tracking, enhancing the accuracy and effectiveness of navigation in complex indoor spaces