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The development of more targeted means of treating cancer is vital. One option for a targeted treatment is the creation of a vaccine that induces an immune response only against cancer cells. In this sense, vaccination involves the introduction of a peptide into a patient that causes the formation of antibodies or T cells that recognize the peptide. If the peptide is from a protein found selectively on/in cancer cells, those antibodies or T cells can trigger the death of those cancer cells without harming non-cancer cells. This can result in fewer side effects for the patient.

Tumors can develop unique genetic mutations which are specific to an individual patient. Some of these mutations are immunogenic; giving rise to autologous T cells which are tumor-reactive. Once isolated and sequenced, these neoantigen-specific TCRs can form the basis of effective adoptive cell therapy cancer treatment regimens; however, current methods of isolation are inefficient. Moreover, the process is technically challenging due to TCR sequence diversity and the need to correctly pair the a and b chain of each receptor.

Tumor invasion and metastasis are the primary drivers of cancer-related mortality. Therapies that have an ability to specifically target invasive and/or metastatic cells are anticipated to have a significant impact in the clinical management of advanced cancers.

This invention describes a general way to trigger the release of a bioactive small molecule from a targeting antibody. The key “trigger” is a fluorescent linker that is chemically disassembled upon irradiation with light in the near-IR range (~800 nm).

Immunotherapy is a promising method of treating cancer that leverages the immune system to promote tumor rejection. However, certain somatic mutations in cancer cells confer resistance to T cell-mediated cytolysis. To improve the effectiveness of immunotherapies for cancer, there exists a need to prospectively identify patients who are most likely to respond to such therapies.

The invention relates to novel lipid-based nanoparticles (liposomes) for use in targeted, on demand and on site drug delivery. The particles include a wall surrounding a cavity, wherein the wall is comprised of:

1. A lipid bilayer comprising 1,2-bis(tricosa-10,12-diynoyl)-sn-glycero-3-phosphocholine (DC_8,9PC), dipalmitoylphosphatidylcholine (DPPC), and 1,2-distearoyl-sn-glycero-3-

phosphoethanolamine-N-[methoxy(polyethylene glycol)-2000] (DSPE-PEG2000), and

Tissue obtained for both clinical and research purposes is routinely frozen, commonly in Optimal Cutting Temperature (OCT), an embedding media, for eventual downstream analysis, commonly including sectioning on a cryostat. Though OCT is the standard compound used for freezing, there is no standard freezing protocol. Thus, current methods of handling, labeling, and storing OCT-embedded tissue vary widely, and specimens are often damaged or degraded due to undesirable temperature fluctuations during handling and freezing.

Mesothelin is a cell surface protein that is highly expressed in aggressive cancers such as malignant mesothelioma, ovarian cancer, pancreatic cancer, lung cancer, breast cancer, cholangiocarcinoma, bile duct carcinoma and gastric cancer. As a result, mesothelin is an excellent candidate for tumor targeted immunotherapeutics. However, the antibodies against mesothelin that are available for clinical trials are of murine origin. These antibodies have the potential to elicit immune responses in patients, which may adversely affect the ability to provide patients with repeated doses.

Adoptive cell transfer (ACT) is a promising immunotherapeutic approach for cancer treatment. During ACT, if a patient is subjected to lymphodepletion prior to cell transfer, there is an observed improvement in a patient’s response to treatment. However, lymphodepletion is associated with detrimental effects, including severe immune dysfunction that persists after treatment.

Accurate automated organ and disease feature segmentation is a challenge for medical imaging analysis. The pancreas, for example, is a small, soft, organ with low uniformity of shape and volume between patients. Because of the lack of uniform image patterns, there are few features that can be used to aid in automated identification of anatomy and boundaries. Segmentation of high variability features is uniquely difficult for a computer to perform.