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Cytokines are a broad category of intercellular signaling proteins that are critical for intercellular communication in human health and disease. However, systematic profiling of cytokine signaling activities has remained challenging due to the short half-lives of cytokines, and the pleiotropic functions and redundancy of cytokine activities within specific cellular contexts.

The use of tumor transcriptomics for precision oncology has made significant advances, mainly by identifying cancer driver genes or actionable mutations for treatment with targeted therapies.  However, this strategy misses out on broader genetic interactions that could reveal additional biologically testable biomarkers for therapy response prediction and inform the selection of more effective drugs for targeted treatment.

The development of precision medicine approaches for DLBCL (Diffuse Large B Cell Lymphoma) is complicated by its genetic, phenotypic and clinical heterogeneity. Current classification methods do not fully explain the observed differences in clinical outcomes to current chemotherapy and targeted therapy. Therefore, better analytical methods to classify and predict DLBCL patients’ treatment response are needed.

The tumor microenvironment (TME) is a complex mixture of cell types whose interactions affect tumor growth and clinical outcome. Recent studies using fluorescence-activated cell sorting (FACS) and single-cell RNA sequencing (RNAseq) to elucidate tissue composition and cell-cell interactions in the TME led to improved biomarkers of patient response and new treatment opportunities. However, the use of FACS is limited to simultaneously measuring the expression of a few protein markers, whereas the use of single-cell RNAseq has been limited due to cost and scarcity of fresh tumor biopsies.

Topoisomerase enzymes play an important role in cancer progression by controlling changes in DNA structure through catalyzing the breaking and rejoining of the phosphodiester backbone of DNA strands during the normal cell cycle. Therefore, topoisomerases are important targets for cancer chemotherapy. Many topoisomerase 1 (TOP1) inhibitors such as camptothecin, rinotecan, and topotecan are widely used anti-cancer agents that work by stabilizing the TOP1-DNA cleavage complex.

Antiretroviral therapy (ART) has changed the prognosis of HIV-1 infection to a chronic illness that, in most cases, can be managed or controlled. Integrase strand transfer inhibitors (INSTIs) and reverse transcription inhibitors are essential components of ART drug cocktails. In compliant individuals, ART has been found to block viral replication completely. Additionally, blocking viral replication can prevent the emergence of drug resistance.

The Corona virus disease, 2019 (COVID-19) pandemic is a worldwide public health crisis with over 153 million confirmed cases and 3.2 million deaths as of April 2021. COVID-19 is caused by a novel coronavirus called SARS-CoV-2. SARS-COV-2 infects hosts via its spike (S) protein, which has two portions, S1 that binds the cell and S2 that is involved in viral entry via fusion with the cell membrane. There are several vaccines available for COVID-19 patients that directly target SARS-CoV-2 by systemic immunization.

Mutation of amino acid 61of the neuroblastoma rat sarcoma viral oncogene homologue (NRAS) is a known driver of oncogenesis in melanoma. Glutamine (Q) to lysine (K) mutation at this position of NRAS is prevalent in approximately 10% of all melanoma cases and associated with aggressive tumors and low patient survival. Therefore, Q61K mutated NRAS is an important candidate for targeted therapies, including cellular immunotherapy. 

Tumor cell lines are important tools for the study of cancer. However, most tumor cell lines available today do not mimic physiological tumor development, progression, and host immune responses. Autochthonous tumors include spontaneously occurring tumors and chemical, viral, or physical carcinogen-induced tumors. They are considered to model human tumors more closely than transplanted tumors. Autochthonous tumors can be generated de novo in a model organism of interest and are thought to resemble physiological human tumor conditions.

Computer and imaging technologies led to the development of digital pathology and the capture and storage of pathological specimens as digitally formatted images. The use of artificial intelligence (AI) in digital pathology, such as in three-dimensional (3D) reconstruction, requires analyses of high volumes of data. This resulted in increased demands for processing and acquisition of digital images of pathology samples. Increased usage cannot be met by the time-consuming, manual, and laborious methods currently used.