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Targeted therapy for oncogenic genetic alterations has changed the treatment paradigm of advanced non-small cell lung cancer (NSCLC). Mutations in the BRAF gene are detected in approximately 4% of patients and result in hyper-activation of the MAPK pathway, leading to uncontrolled cellular proliferation. Inhibition of BRAF and its downstream effector MEK constitutes a therapeutic strategy for a subset of patients with NSCLC and is associated with clinical benefit. Unfortunately, the majority of patients will develop disease progression within 1 year. Preclinical and clinical evidence suggests that resistance mechanisms involve the restoration of MAPK signaling which becomes inhibition-independent due to upstream or downstream alterations, and the activation of bypass pathways, such as the PI3/AKT/mTOR pathway. Future research should be directed to deciphering the mechanisms of cancer cells' oncogenic dependence, understanding the tissue-specific mechanisms of BRAF-mutant tumors, and optimizing treatment strategies after progression on BRAF and MEK inhibition.

Introduction: Cancer diagnosis using cell-free DNA (cfDNA) has the potential to improve treatment and survival but has several technical limitations. Methods: Here, we used neomers, short DNA sequences (13-17bp in length) that are largely absent from the healthy human genome, but appear in the tumor genome due to somatic mutations, to detect cancer at early stages from cfDNA. cfDNA was extracted from 1mL of plasma, and utilized from Whole Genome Sequencing at 5X coverage. Results: First, we analyzed over 2,500 cancer whole-genome sequences (WGS), to show that a neomer-based classifier can distinguish twenty-one different tumor types with higher accuracy than state-of-the-art methods. Refinement of this classifier via manually selected neomer sets, identified additional cancer features with greater precision. We next generated cfDNA WGS data from >600 patients and non-cancerous controls across four different types of cancers (prostate, lung, ovarian, colorectal), and demonstrated that neomers can precisely identify cancer from a limited amount of plasma for all four cancer types, with exampled sensitivities of 93.73% and 73.47% for lung and ovarian cancers, respectively. Additionally, using both luciferase and massively parallel reporter assays (MPRAs), we show that our neomer-driven approach can detect cancer-associated non-coding mutations that affect gene regulatory activity. Conclusions: Our results show that neomers provide a novel, sensitive, specific and potentially broadly applicable cancer detection tool. This approach has several advantages over existing strategies for liquid biopsies: 1) it is based on standard next-generation sequencing which is already widely available in the clinic; 2) it requires low amounts of starting material and is completely tumor agnostic; 3) it searches the entire genome and is not restricted to known driver mutations. 4) it allows the detection of early-stage disease. Citation Format: Ofer Yizhar Barnea, Ilias Georgakopoulos-Soares, Nadav Ahituv, Jocelyn Chapman, Martin Hemberg, Ioannis Mouratidis, Konstantinos Syrigos, Nikolaos Syrigos, Ioannis Vathiotis, Mayank Mahajan, Emmanouil Panagiotou, Andriani Charpidou, Mark Kvale, Candace S. Chan, Ryder Easterlin. Leveraging sequences missing from the human genome to detect cancer [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2023; Part 1 (Regular and Invited Abstracts); 2023 Apr 14-19; Orlando, FL. Philadelphia (PA): AACR; Cancer Res 2023;83(7_Suppl):Abstract nr 991.

Cancer diagnosis using cell-free DNA (cfDNA) has potential to improve treatment and survival but has several technical limitations. Here, we show that tumor-associated mutations create neomers, DNA sequences 13-17 nucleotides in length that are predominantly absent from genomes of healthy individuals, that can accurately detect cancer, including early stages, and distinguish subtypes and features. Using a neomer-based classifier, we show that we can distinguish twenty-one different tumor-types with higher accuracy than state-of-the-art methods. Refinement of this classifier using a handcrafted set of kmers identified additional cancer features with greater precision. Generation and analysis of 451 cfDNA whole-genome sequences demonstrates that neomers can precisely detect lung and ovarian cancer with an area under the curve (AUC) of 0.93 and 0.89, respectively. In particular, for early stages, we show that neomers can detect lung cancer with an AUC of 0.94 and ovarian cancer, which lacks an early detection test, with an AUC of 0.93. Finally, testing over 9,000 sequences with either promoter or massively parallel reporter assays, we show that neomers can identify cancer-associated mutations that alter regulatory activity. Combined, our results identify a novel, sensitive, specific and simple diagnostic tool that can also identify novel cancer-associated mutations in gene regulatory elements.

Cluster of differentiation 24 (CD24) is a small, highly glycosylated cell adhesion protein that is normally expressed by immune as well as epithelial, neural, and muscle cells. Tumor CD24 expression has been linked with alterations in several oncogenic signaling pathways. In addition, the CD24/Siglec-10 interaction has been implicated in tumor immune evasion, inhibiting macrophage-mediated phagocytosis as well as natural killer (NK) cell cytotoxicity. CD24 blockade has shown promising results in preclinical studies. Although there are limited data on efficacy, monoclonal antibodies against CD24 have demonstrated clinical safety and tolerability in two clinical trials. Other treatment modalities evaluated in the preclinical setting include antibody–drug conjugates and chimeric antigen receptor (CAR) T cell therapy. In this review, we summarize current evidence and future perspectives on CD24 as a potential target for cancer immunotherapy.

Generating Digital Elevation Models (DEM) from satellite imagery or other data sources constitutes an essential tool for a plethora of applications and disciplines, ranging from 3D flight planning and simulation, autonomous driving and satellite navigation, such as GPS, to modeling water flow, precision farming and forestry. The task of extracting this 3D geometry from a given surface hitherto requires a combination of appropriately collected corresponding samples and/or specialized equipment, as inferring the elevation from single image data is out of reach for contemporary approaches. On the other hand, Artificial Intelligence (AI) and Machine Learning (ML) algorithms have experienced unprecedented growth in recent years as they can extrapolate rules in a data-driven manner and retrieve convoluted, nonlinear one-to-one mappings, such as an approximate mapping from satellite imagery to DEMs. Therefore, we propose an end-to-end Deep Learning (DL) approach to construct this mapping and to generate an absolute or relative point cloud estimation of a DEM given a single RGB satellite (Sentinel-2 imagery in this work) or drone image. The model has been readily extended to incorporate available information from the non-visible electromagnetic spectrum. Unlike existing methods, we only exploit one image for the production of the elevation data, rendering our approach less restrictive and constrained, but suboptimal compared to them at the same time. Moreover, recent advances in software and hardware allow us to make the inference and the generation extremely fast, even on moderate hardware. We deploy Conditional Generative Adversarial networks (CGAN), which are the state-of-the-art approach to image-to-image translation. We expect our work to serve as a springboard for further development in this field and to foster the integration of such methods in the process of generating, updating and analyzing DEMs.