Categories
Oncology

How AI Is Revealing New Targets for Cancer Drugs

Introduction

Targeted drug therapy has proven to be a highly advantageous approach to cancer treatments, presenting high efficiency and low patient drug resistance. 

Yet, there are drawbacks to the use of targeted drug therapy for cancer, including a lack of identified druggable genomic targets that extend across the patient population. Artificial intelligence algorithms can help researchers better understand carcinogenesis and identify new cancer targets. 

Artificial Intelligence (AI) is a field combining computer science and extensive data sets to perceive, understand, and solve problems.

Two major branches of AI applied biologically are machine learning-based and network-based. Machine learning is an application of AI wherein a computer can learn without direct instruction through mathematical models and pattern recognition. Network-based AI sorts and compares data, providing and compensating different perspectives. 

The Food and Drug Administration (FDA) and the international community have presented an increased interest in trustworthy and ethical AI adoption and innovation.

Applications

The development of multiomics technology is a factor that has bolstered the process of identifying novel anti-cancer targets. Multiomics is an approach to biological analysis that consists of forming data sets out of “omics”, which in the case of cancer are epigenetics, genomics, proteomics, and metabolomics. AI can analyze these data sets to investigate for anti-cancer targets. 

BioRender image by You Et al. Artificial intelligence in cancer target identification and drug discovery. https://doi.org/10.1038/s41392-022-00994-0

Epigenetics

Unlike the changes in the nucleotide sequence characteristic of genetic mutation, epigenetics is similar to an “on-and-off” switch for the expression of specific genetic attributes without altering the genome. Cancer can be caused by both a genetic mutation or an epigenetic signal gone awry. 

A major problem facing epigenetics cancer research is finding specific gene patterns that predict which patients will respond to a cancer treatment. The study of the reversal of epigenetic modifications through AI can give insight into how exactly healthy cells become cancerous and which genetic marker within a patient will respond to cancer-treating drugs.

Genomics

The study of genomics involves the mapping of an entire genome including its structure, function, and evolution, through genome-wide assays such as sequencing. A network-based AI is capable of finding similarities in specific genetic sequences and patterns in their phenotypic expression and interactions. Cancerous biomarkers can be identified through genomic data sets, identifying which genes medical professionals should consider oncogenes of interest.

Proteomics

Proteomics refers to proteins and their interactions within the body. Protein-protein interaction (PPI) research classifies a certain type of protein as “indispensable” and associates them as a major site of disease-causing mutations and drug targeting. In a 2017 study, Ravindran et al. found by analyzing the human PPI data from cancer patients that there are 56 indispensable genes in nine cancers, 46 of which were associated with getting cancer for the first time. This protein interaction data can be harnessed through AI to reveal novel cancer-associated interactions and their potential drug targets. 

Metabolomics

Metabolomics is the study of metabolites: the substrates, intermediates, and products of the human metabolic pathway. A hallmark of cancer cells is that they work to alter these metabolites in order to support their rapid growth, providing energy to their biosynthetic pathways and changing their redox balance. Cancer can be detected through biological AI network analysis due to the presence of certain biomarker metabolites within biofluids, cells, and tissues of the body.

Conclusion

All aforementioned “omics” data can be presented for review in the form of multiomics integration analysis. Varied and interconnected data in a network format allows researchers to study carcinogenesis and drug targeting from an overarching perspective in a multifaceted group of patients. 

While treatment of cancer remains a formidable challenge, rapid technological advances in data collection and analysis through the use of artificial intelligence combined with robust information exchange may prove to be increasingly beneficial to the way cancer drugs are created and tested, leading to potential a safer and healthier world.

References
  • Breakthroughs Staff. (2017, December 12). Treating Cancer by Using Epigenetics, the ‘Software’ of Our Genes | Pfizer. Pfizer News. https://www.pfizer.com/news/articles/treating-cancer-using-epigenetics-%E2%80%98software%E2%80%99-our-genes
  • Guenthoer, J., Lilly, M., Starr, T. N., Dadonaite, B., Lovendahl, K. N., Croft, J. T., Stoddard, C. I., Chohan, V., Ding, S., Ruiz, F., Kopp, M. S., Finzi, A., Bloom, J. D., Chu, H. Y., Lee, K. K., & Overbaugh, J. (2023). Identification of broad, potent antibodies to functionally constrained regions of SARS-CoV-2 spike following a breakthrough infection. Proceedings of the National Academy of Sciences, 120(23), e2220948120. https://doi.org/10.1073/pnas.2220948120
  • Huang, S., Wang, Z., & Zhao, L. (2021). The Crucial Roles of Intermediate Metabolites in Cancer. Cancer Management and Research, 13, 6291–6307. https://doi.org/10.2147/CMAR.S321433
  • Ravindran, V., V., S., & Bagler, G. (2017). Identification of critical regulatory genes in cancer signaling network using controllability analysis. Physica A: Statistical Mechanics and Its Applications, 474, 134–143. https://doi.org/10.1016/j.physa.2017.01.059
  • U.S. Food and Drug Administration. (2022). Using Artificial Intelligence & Machine Learning in the Development of Drug & Biological Products. https://www.fda.gov/media/167973/download
  • You, Y., Lai, X., Pan, Y., Zheng, H., Vera, J., Liu, S., Deng, S., & Zhang, L. (2022). Artificial intelligence in cancer target identification and drug discovery. Signal Transduction and Targeted Therapy, 7(1), Article 1. https://doi.org/10.1038/s41392-022-00994-0
Categories
Immunotherapy Oncology

Mass-Producible Specialized T Cells Exhibit High Cancer-Killing Efficacy, Minimized Complications

UCLA researchers have shown in preclinical studies that their mass-producible engineered invariant natural killer T (iNKT) cells demonstrate promising antitumor efficacy and low immunogenicity (unwanted immune response) compared to current cell-based immunotherapy for cancer treatment.

Invariant natural killer T (iNKT) cells are specialized T cells that are notable for their speedy response to danger signals and activation of macrophages (white blood cells that destroy cancer cells, microbes, cellular debris, and foreign substances).

Dr. Lili Yang’s UCLA lab generated iNKT cells by engineering hematopoietic stem cells (HSCs, precursors to all types of blood cells). These cells are allogeneic—they are not genetically specific to patients. Normally, in the realm of cell-based immunotherapy, this would be expected to cause an immune response in the form of graft-versus-host disease (GvHD), a condition in which donor stem cells attack the recipient. The study mentioned that such immunogenicity can also decrease efficacy of therapeutic cells. Therefore, allogeneic cells have not been widely used for T-cell-based therapies, with most therapies using autologous (from the patient) cells instead.

Generally, autologous T-cell therapy requires a patient’s T cells to be extracted from blood, sent to a lab, engineered to find and kill cancer cells, then returned intravenously to the patient—all costing hundreds of thousands of dollars.

Unexpectedly, when tested on mice, the Yang Engineering Immunity Lab’s allogeneic HSC-iNKT cells did not cause the negative effects associated with allogeneic cells. The researchers found that while other types of allogeneic T cells killed mice by GvHD after 2 months of cell transfer, the mice that received HSC-iNKT cells sustained long-term survival.

Figures showing (G) experimental design, (H) mouse tumor imaging, (I) quantification of tumor size based on imaging, (J) survival curves of mice over 4 months following tumor challenge.

Following irradiation of mice, those without cell therapy (labeled as vehicle) died of tumors within 45 days. Those treated with allogeneic BCAR-T cells were tumor-free but died of GvHD. Only those treated with allogeneic HSC-iNKT were tumor-free and survived long term.

This important development means that cell-based cancer therapies would no longer have to rely only on autologous cells extracted from each individual patient. Instead, with the advent of the Yang lab’s one-size-fits-all allogeneic solution, therapeutic cells could be mass-produced and given to any patient, significantly bringing down treatment costs.

The reason why allogeneic HSC-iNKT cells do not cause GvHD is currently unknown to researchers.

Graphs showing tumor load of irradiated mice over time. The Yang lab’s HSC-iNKT cells are shown to have decreased tumor load to near-zero levels (p < 0.001), a more significant decrease than was shown by the other tested therapy (PBMC-NK).
Frozen and fresh allogeneic HSC-iNKT cells were shown to kill more live lung cancer cells (H292-FG) than PBMC-NK cell therapy.

The study also showed that both frozen and fresh allogeneic HSC-iNKT cells killed live leukemia, melanoma, lung cancer, prostate cancer, and multiple myeloma cells in vitro. Compared to PBMC-NK cells, the Yang lab’s cells displayed greater tumor-killing efficacy. Importantly, allogeneic HSC-iNKT cells were also found to remain functional following freezing and thawing, which is crucial for their viability as a widespread, mass-produced treatment.

Factors that support allogeneic HSC-iNKT cells’ prospects as a future widespread cancer therapy include remaining functional following freezing and thawing, high tumor-killing efficacy, and mass-producible by virtue of low immunogenicity.

Dr. Yang told the UCLA Newsroom that one peripheral blood donation could yield 300,000 doses. The researchers are now focused on streamlining manufacturing processes, hoping to better enable mass-production, potentially bringing it to clinical and commercial development more quickly. The Newsroom noted that clinical trials have not yet occurred—this therapy has yet to be tested in humans or evaluated by the FDA. The UCLA Technology Development Group has filed a patent application for this method.

This article is based on the following sources

UCLA scientists make strides toward an ‘off-the-shelf’ immune cell therapy for cancer. (2021, November 16). UCLA Newsroom. https://newsroom.ucla.edu/releases/off-the-shelf-immune-cell-therapy-for-cancer
– Yang, L., et al. (2021, November 16). Development of allogeneic HSC-engineered iNKT cells for off-the-shelf cancer immunotherapy. Cell Reports Medicine. https://doi.org/10.1016/j.xcrm.2021.100449

Categories
Oncology

Researchers Create Synthetic Microenvironment for Pancreatic Ductal Adenocarcinoma Organoids

When PDA (pancreatic ductal adenocarcinoma) cancer cells form in the body, the extracellular matrix is remodeled to create an immunosuppressive environment which is rigid and poorly perfused. Through the normalization of these matrices, therapeutic treatments can be administered more effectively which makes the replication and synthesis of such models instrumental to the development of the efficacies of remedial treatments. 

As a result, a team from the Cancer Research UK Manchester Institute has developed a synthetic hydrogel-based model for pancreatic organoids that aims to replicate the extracellular microenvironments of both normal and pancreatic cancer cells in vitro.  

To formulate a working prototype, they analyzed 10 normal and 12 tumorous pancreatic samples using liquid chromatography with tandem mass spectrometry and identified 83 proteins that were relevant to the structural function of the matrix. Through the comparison of the matricellular proteins involved in cellular adhesion, they found that the proteins fibronectin (FN), versican, and laminin-332 were upregulated in cancerous cells. Additionally, they found that the proteins laminin 521 and types 1 & 4 collagen were abundant in both normal and cancerous cells, highlighting the potential importance of matrisomal components in PDA development. 

To form the gel, researchers used an eight-arm PEG-based hydrogel system through a network of peptides to help mimic the environment in which PDA organelles could develop. They found that contrary to traditional assays that were supported through FN-mimicking peptides, the use of collagen-mimicking peptides helped to increase the variety of murine pancreatic cancer organoids (mPCOs) that were supported by the hydrogel system. This change has allowed for the increased the growth efficiency of the organoids and supports the efficacy of their microenvironment.

Researchers also found that their models were able to support stromal co-cultures as they were able to replicate phenotypes of elongated, mesenchymal-structured fibroblasts that were similar to what they would find in vivo. The morphology data of tested species were consistent with  myCAF, iCAF and apCAF (cancer-associated fibroblasts) subsets and illustrated that the environments they developed were able to successfully mirror those of live specimens. These results support the idea that stromal cells in the synthetic setting are able to display relevant phenotypes that are consistent with in vivo models.

The University of Manchester-led study found that their new scaffold models were not only able to replicate the stiffness range of both cancerous and normal tissue but were also able to facilitate the growth of associated organoids and induced stromal samples. The researchers hope that their research will help other scientists replicate essential cell-ECM interactions as well as grow cultures of epithelial and stromal cells to help facilitate growth of organoids to better understand the mechanisms behind PDA and its development.

This article is based on the following sources and clinical studies.

– Below, C.R., Kelly, J., Brown, A. et al. (2021, September 13). A microenvironment-inspired synthetic three-dimensional model for pancreatic ductal adenocarcinoma organoids. https://doi.org/10.1038/s41563-021-01085-1
– Tayao, M. (2016, March 16). Loss of BAP1 Expression Is Very Rare in Pancreatic Ductal Adenocarcinoma. https://dx.doi.org/10.1371%2Fjournal.pone.0150338