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Human papillomavirus (HPV) is a group of human viruses known to cause various malignancies. Of the group, HPV-16 is the most prevalent strain – an estimated 90% of adults have been exposed. HPV-16 is also the strain most commonly associated with malignancy, causing the vast majority of cervical, anal, vaginal, vulvar, and penile cancers. Currently, HPV-positive malignancies non-responsive to surgery or radiation are incurable and poorly palliated by existing systemic therapies. Thus, an alternative therapeutic approach for HPV-positive malignancies is needed. 

Polo-like kinase 1 (Plk1), a member of the Polo-like kinase family, plays a critical role in regulating mitosis and cell cycle progression. Aberrant expression of Plk1 has been observed in a variety of human cancers, and it is known to be associated with tumorigenesis as well as poor prognosis in cancer patients. Unlike normal cells, some cancer cells are dependent on augmented Plk1 levels to remain viable and are killed when Plk1 function is attenuated.

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 results 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.

The treatment of cancer using immunotherapies has garnered substantial attention and excitement considering the clinical benefits observed in patient populations previously refractory to treatment. Tumor infiltrating T cells can significantly impact cancer progression and immunotherapy response; however, immunosuppressive tumor microenvironments can impede antitumor T cell induction, trafficking, and local activity. Thus, personalized immunotherapy approaches have shown limited efficacy against most solid tumors.

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.

Immune checkpoint inhibitors (ICIs) vastly improved the outcome of various advanced cancers; however, many are less likely to respond to single-agent ICI. Tumors with low T-cell infiltration are "immunologically cold" and less likely to respond to single-agent ICI therapy. This diminished response is presumably due to the lack of neoantigens necessary to activate an adaptive immune response. On the other hand, an "immunologically hot" tumor with high T-cell infiltration has an active anti-tumor immune response following ICI treatment.

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.

Bioluminescence imaging with luciferin-luciferase pairs is a well-established technique for tracking cells and other biological features in animal models. Bioluminescent is a chemical process which does not require an external input for excitation. Bioluminescent imaging is often limited to monitoring single processes in vivo due to the lack of distinguishable probes. Additionally, existing probes typically operate with light in the visible range, which is highly scattered and exhibits poor tissue penetration. 

Cancer immunotherapy approaches, such as adoptive cell transfer (ACT), proved effective against many cancer types. Yet, post-treatment analyses of ACT have suggested that efficacy may be enhanced by increasing the percentage of neoantigen-reactive T cells in the infused product. Neoantigens are new proteins that form on cancer cells when certain mutations occur in tumor DNA. Current techniques for identifying neoantigen-specific TCRs in T cell expression are labor-intensive, time-consuming and technically challenging.

Ovarian cancer is one of the most common and lethal types of gynecological malignancies worldwide, accounting for approximately 295,000 new cases and 185,000 deaths annually. The high lethality rate is due to multiple reasons, including recurrence and the resistance of recurrent tumors to chemotherapy. Cell line models are crucial for preclinical cancer studies, to identify mechanisms of disease, to study drug resistance, and to screen for candidate therapeutics.