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. 

Proviral Integration for the Moloney murine leukemia virus (PIM) kinases are overexpressed in many solid cancers – including prostate, breast, colon, endometrial, gastric and pancreatic. High of PIM1 expression is predictive of poor survival in multiple cancer types. While several selective pan-PIM inhibitors were developed and tested in clinical trials, all ultimately increased PIM1-3 protein levels and developed intrinsic resistance. 

Tyrosyl-DNA phosphodiesterase 2 (TDP2) is an enzyme that playings a critical role in repairing nucleic acid lesions, namely by repairing trapped DNA cleavage complexes. TDP2 repairs topoisomerase (TOP2)-mediated DNA damage induced by chemotherapeutic agents and removes endogenous TOP2-DNA cleavage complexes. Further, TDP2 deficiency potentiates the antiproliferative activity of TOP2 inhibitors. This suggest that combination therapies consisting of TDP2 and TOP2 inhibitors have a synergistic effect on tumor tissues.

The high mortality rate from ovarian cancers can be attributed to late-stage diagnosis and lack of effective treatment. Despite enormous effort to develop better targeted therapies, platinum-based chemotherapy still remains the standard of care for ovarian cancer patients, and resistance occurs at a high rate. One of the rate limiting factors for translation of new drug discoveries into clinical treatments has been the lack of suitable preclinical cancer models with high predictive value.