Category: Cancer

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Cancer Cell Therapies Infectious Diseases Precision Medicine

Genetically Engineered B Cells and Their Implications on Disease Treatment

Zachary Kazaz—McMaster Integrated Science Program, Specialization in Biology 2025

Recent advancements in genome editing have facilitated major developments in both existing and new gene therapies (Hirakawa et al., 2020). The culmination of these findings became most evident on July 5th 2018, when Dr. Carl June and Professor Michel Sadelain published a seminal review article in the New England Journal of Medicine, reporting the clinical success of chimeric antigen receptor (CAR) T cell therapy against certain haematological cancers; the therapy subsequently received FDA approval for lymphoma and leukaemia treatment (June & Sadelain, 2018). T cells were modified with artificial receptors that associated the recognition of target antigens with the signalling and immunological responses of the T cell (Ellis et al., 2021). Furthermore, genome editing was successfully used to remove immune checkpoints limiting T cell functionality and improving receptor efficacy (Ellis et al., 2021). The successes of CAR T cell therapies have extended the applications of immune cell reprogramming to a population less investigated thus far — the B cell.

Adaptive immune responses are facilitated by white blood cells, or lymphocytes. There are two classes of adaptive immune response: humoral (antibody responses) and cell-mediated responses, primarily enabled by two types of lymphocytes: B-cells and T-cells, respectively (Alberts et al., 2002). Humoral adaptive immunity produces antigen-specific antibodies via B-cells that identify pathogens by binding to their expressed antigens, stimulating cell-mediated immunity by signalling T-cells, cytokines, macrophages and chemical mediators which attack and neutralize the pathogen (Johnson et al., 2018). Thus, B-cells regulate the entire adaptive immune system via general and specific mechanisms, facilitated by cytokine secretion and antigen presentation respectively (Rogers & Cannon, 2021). As a result, B-cells provide a means of modifying holistic immune responses which is a more effective immunotherapy than the sole modification of cell-mediated immunity found in current immunotherapies (e.g., T-cells).

Following the first, primary, response to a specific antigen, the responding, naive, B-cells will proliferate into a colony, differentiate into effector B-cells which produce antibodies, and following infection, will form memory B-cells which encode for and maintain the antigen-specific antibody of the pathogen encountered (Akkaya et al., 2019). This enables more rapid secondary active immune response mobilization if the same antigen exposure occurs subsequently, and can provide lifelong immune surveillance (Akkaya et al., 2019). As a result, humoral immunity facilitated by B-cells is the most capable mechanism of providing prolonged immune surveillance (Rogers & Cannon, 2021).

B-cells possess many unique characteristics that make them a promising focal point for genetic engineering in immunotherapy settings. B-cells can be easily isolated in abundance from peripheral blood, then subsequently activated, grown, and matured in culture ex-vivo (Liebig et al., 2009). It is possible to engineer these isolated B-cells to express a particular gene, mature them into effector B-cells, which produce large amounts of antibodies, and add them back to the host as a remarkably effective form of gene therapy enabling long-term immunity to disease, as seen in Figure 1 (Johnson et al., 2018). In particular, gene editing can be used to alter antigen specificity with a predetermined antibody by means that permit access to all functions of the B-cell during every stage of its life cycle (Rogers & Cannon, 2021).

Figure 1. Diagram displaying the procedure of B-cell immunotherapy in patients using host donor cells (Traxinger, 2019).

By altering the variations of modified antigen-specificity, the response of B-cells to target antigens, their subsequent production of antibodies, expansion, and formation of long-term immunity, we can introduce and evolve long-term antibody specificity to reprogram B-cells that will continue to adapt to their associated pathogens (Rogers & Cannon, 2021). This is particularly relevant, as a majority of current literature on B-cell engineering focuses on producing HIV-specific B-cells (Rogers & Cannon, 2021). The prolonged antibody expression of B-cell therapy can be used to suppress chronic viral infections such as HIV. More importantly, highly mutagenic viruses, such as HIV, are capable of evading antigen-specific antibody responses, and edited B-cells can adapt to these changes, enabling B-cells to keep pace with viral mutations (Ouyang et al., 2017). This is not possible with current therapies that use fixed antigen-specificities.

The broad array of B-cell functions open many potential immune cell gene therapies. For example, the ability of antibody production in effector B-cells allows them to be used as a long-term source for the production of therapeutic proteins in-vivo, such as bnAbs and factor IX to combat hepatitis C virus and HIV (Kuhlmann et al., 2018). Also, it is possible to create these “cellular factories” using stem cells or editing B-cells ex-vivo which can be altered with a variety of methods such as CRISPR/CAS9 and viral vectors which transport the modified gene to loci of interest (Luo et al., 2020). Moreover, naive B-cells can be modified to present antigens and suppress or prevent immune responses to particular antigens, which can be used to combat autoimmune diseases (Scott, 2011).

Interestingly, B-cell engineering offers potential as an improved means of vaccination. In May of 2019, Howell et al. published findings in Scientific Immunology showing that B-cells edited to express antibodies countering respiratory syncytial virus (RSV) produced highly effective and long-lasting protection from RSV infection in mice (Moffett et al., 2019). Such findings convey that sterilizing immunity to pathogens can be accomplished in a more effective manner than current vaccination methods are able to induce or sustain.

Though great strides have been made, moving forwards, many further steps are needed for B-cell therapies to become viable in the future. Up to now, studies have used mice, however, larger animal models more related to human immunology are required to gain more practical insights. Further, studies must be conducted long-term to assess the durability of antibody production and memory in edited B-cells (Rogers & Cannon, 2021). Additionally, novel methods of delivering B-cell therapies to patients must be developed to make B-cells commercially viable and accessible on a broad scale. Engineered B-cells should be manufactured en masse from universally matching batches of unrelated donor cells (i.e. allogeneic) instead of an exclusive therapy manfactured from a single sample of patient-related donor cells (i.e. autologous). Current studies propose using B-cells from induced pluripotent stem cells, which is currently being investigated for use in CAR T cells (French et al., 2015).

Today, genetically engineered B-cells present a great capacity as a highly effective immunotherapy for particular autoimmune disorders, cancers, chronic infectious diseases, and pathogens. Further experimentation is required to progress B-cell gene therapy to human trials, however, in the near future, we are likely to see B-cells accompany CAR T-cells as a novel immune cell therapy. In the far future, the broad and holistic applications of engineered B-cells may become the basis of future immunotherapies, vaccines, and disease treatments.

References

  1. Akkaya M, Kwak K, Pierce SK. B cell memory: Building two walls of protection against pathogens. Nature Reviews Immunology [Internet]. 2019Dec13 [cited 2022Nov29];20(4):229–38. Available from: https://www.nature.com/articles/s41577-019-0244-2
  2. Alberts B, Johnson A, Lewis J, Raff M, Roberts K, Walter P. Chapter 24: The Adaptive Immune System. In: Molecular Biology of the Cell [Internet]. 4th ed. New York, NY: Garland Science; 2002 [cited 2022Nov28]. Available from: https://www.ncbi.nlm.nih.gov/books/NBK21070/
  3. Ellis GI, Sheppard NC, Riley JL. Genetic engineering of T cells for immunotherapy. Nature Reviews Genetics. 2021Feb18;22(7):427–47. French A, Yang C-T, Taylor S, Watt SM, Carpenter L. Human induced pluripotent stem cell-derived B lymphocytes express SIGM and can be generated via a hemogenic endothelium intermediate. Stem Cells and Development [Internet]. 2015May [cited 2022Dec8];24(9):1082–95. Available from: https://www.liebertpub.com/doi/10.1089/scd.2014.0318
  4. Hirakawa MP, Krishnakumar R, Timlin JA, Carney JP, Butler KS. Gene editing and CRISPR in the clinic: Current and future perspectives. Bioscience Reports [Internet]. 2020Apr9 [cited 2022Nov26];40(4). Available from: https://portlandpress.com/bioscirep/article/40/4/BSR20200127/222452/Gene-editing-and-C RISPR-in-the-clinic-current-and
  5. Johnson MJ, Laoharawee K, Lahr WS, Webber BR, Moriarity BS. Engineering of primary human B cells with CRISPR/Cas9 targeted nuclease. Scientific Reports [Internet]. 2018Aug14 [cited 2022Nov29];8(1). Available from: https://www.nature.com/articles/s41598-018-30358-0
  6. June CH, Sadelain M. Chimeric antigen receptor therapy. New England Journal of Medicine. 2018Jul5;379(1):64–73. Kuhlmann A-S, Haworth KG, Barber-Axthelm IM, Ironside C, Giese MA, Peterson CW, et al.
  7. Long-term persistence of Anti-HIV broadly neutralizing antibody-secreting hematopoietic cells in humanized mice. Molecular Therapy [Internet]. 2018Sep17 [cited 2022Dec6];27(1):164–77. Available from: https://www.sciencedirect.com/science/article/pii/S1525001618304581
  8. Liebig TM, Fiedler A, Zoghi S, Shimabukuro-Vornhagen A, von Bergwelt-Baildon MS. Generation of human CD40-activated B cells. Journal of Visualized Experiments [Internet]. 2009Oct16 [cited 2022Dec4];(32). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC3164064/
  9. Luo B, Zhan Y, Luo M, Dong H, Liu J, Lin Y, et al. Engineering of α-PD-1 antibody-expressing long-lived plasma cells by CRISPR/Cas9-mediated targeted gene integration. Cell Death and Disease [Internet]. 2020Nov12 [cited 2022Dec6];11(973). Available from: https://www.nature.com/articles/s41419-020-03187-1
  10. Moffett HF, Harms CK, Fitzpatrick KS, Tooley MR, Boonyaratanakornkit J, Taylor JJ. B cells engineered to express pathogen-specific antibodies protect against infection. Science Immunology [Internet]. 2019May17 [cited 2022Dec4];4(35). Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6913193/
  11. Ouyang Y, Yin Q, Li W, Li Z, Kong D, Wu Y, et al. Escape from humoral immunity is associated with treatment failure in HIV-1-infected patients receiving long-term antiretroviral therapy. Scientific Reports [Internet]. 2017Jul24 [cited 2022Dec4];7(1). Available from: https://www.nature.com/articles/s41598-017-05594-5
  12. Rogers GL, Cannon PM. Genome edited B cells: A new frontier in immune cell therapies. Molecular Therapy [Internet]. 2021Nov3 [cited 2022Nov28];29(11):3192–204. Available from: https://www.sciencedirect.com/science/article/pii/S1525001621004743
  13. Scott DW. Gene Therapy for Immunologic Tolerance: Using Bone Marrow-Derived Cells to Treat Autoimmunity and Hemophilia. Current Stem Cell Research & Therapy [Internet]. 2011Mar1 [cited 2022Dec4];6(1):38–43. Available from: https://www.ingentaconnect.com/content/ben/cscr/2011/00000006/00000001/art00006
  14. Traxinger B. Engineering B cells to bypass vaccines [Internet]. Fred Hutch Cancer Center. Taylor Lab, Vaccine and Infectious Disease Division; 2019 [cited 2022Dec14]. Available from: https://www.fredhutch.org/en/news/spotlight/2019/07/moffett_vidd_sciimmuno.html
Categories
Cancer Precision Medicine

Revolutionizing Cancer Detection: The Potential of Blood Tests

Jessie Xu—McMaster Life Sciences 2025

Cancer is a leading cause of death globally, accounting for nearly 10 million deaths in 2020, or nearly one in six deaths1. Cancer results from the uncontrollable division of abnormal cells. The most common cancer are breast, lung, colon and rectum, and prostate cancer1. While the survival rate varies depending upon the types of cancer, the chance of survival is significantly greater when cancer is diagnosed earlier. In general, the rate of survival of cancer is 91% when diagnosed at an early stage, and 26% when diagnosed at a later stage7. However, recently scientists have proposed the use of blood tests to diagnose various types of cancer. Blood testing is quick and non-invasive, relatively inexpensive, and readily available. Currently available cancer blood tests include Complete blood count (CBC), CancerSEEK Test, Galleri multicancer early detection (MCED) test and PanSeer Test. 

Complete blood count (CBC)

SOURCE: Verywell Health

CBC is a common blood test used to detect a variety of disorders. The results of the CBC can be used to direct one’s diagnosis towards a particular disease2. Moreover, this test can be used to detect Blood Cancers, such as leukemia and lymphoma3.

CancerSEEK Test

SOURCE: Dr. Lal PathLabs Blog

The CancerSEEK Test detects cell-free DNA, cfDNA, and identifies eight biomarkers released by Tumour cells4. This blood test can detect the problems in the early stage of tumor5. Tumors are detected by mutations in genomic positions, such as substitutions, insertions and deletions4. So far, eight types of cancer can be detected with over 99% accuracy, including ovarian, liver, stomach, pancreatic, esophageal, colorectal, breast, and lung4.

Galleri multicancer early detection (MCED) test

SOURCE: Johns Hopkins Medicine Newsroom

The Galleri MCED is a novel, high-performance genomic technology that can detect signals from Cancerous cells at the early stage6. The Galleri MCED test aims to identify cfDNA circulating in the blood, and specifically recognized DNA methylation. The test result can indicate the specific type of cancer, and identify in which organ cancerous cells are present4. There are 12 types of cancer at the early stage that can be detected with 93% accuracy, including anorectal, colorectal, esophageal, gastric, head and neck, hormone receptor-positive breast, liver, lung, ovarian, and pancreatic cancers, in addition to multiple myeloma and lymphoid neoplasms4.

PanSeer Test

SOURCE: Galleri for HCPS

The PanSeer test uses a similar approach to the Galleri MCED test. This test was developed by the Taizhou Longitudinal Study that compared blood samples of individuals with and without cancer. In this study, they compared approximately 400 blood samples with cancer and approximately 400 blood samples without cancer. After that, they record physical measurements and questionnaires about the cancer occurrence, collecting affectional plasma and tissue samples at 3-year intervals7. The test detected patterns of DNA methylation, and 95% of asymptomatic individuals were diagnosed with cancer by using standard detection methods4. However, this test is unable to determine the exact location of the cancer4.

Overall, although blood tests can be used to detect cancer at the early stage which increases the rate of survival, not all types of cancer can be detected. In addition, some confounding factors, such as personal lifestyle, environmental pollution, and individual genetic composition, would make it difficult to develop standardized blood tests for all individuals7.

References

1. Cancer [Internet]. World Health Organization. World Health Organization; 2022 [cited 2023Jan2]. Available from: https://www.who.int/news-room/fact-sheets/detail/cancer 

2. Complete blood count (CBC): Medlineplus medical test [Internet]. MedlinePlus. U.S. National Library of Medicine; 2022 [cited 2023Jan1]. Available from: https://medlineplus.gov/lab-tests/complete-blood-count-cbc/ 

3. Understanding your complete blood count (CBC) tests [Internet]. Cancer.Net. 2019 [cited 2023Jan1]. Available from: https://www.cancer.net/navigating-cancer-care/diagnosing-cancer/reports-and-results/understanding-your-complete-blood-count-cbc-tests 

4. Tomasz BM. Novel blood-based early cancer detection: Diagnostics in development [Internet]. AJMC. MJH Life Sciences; 2020 [cited 2023Jan1]. Available from: https://www.ajmc.com/view/novel-blood-based-early-cancer-detection-diagnostics-in-development 

5. Kuppuraj G. Cancerseek: A single blood test for early detection of eight cancer types – prescouter – custom intelligence from a global network of experts [Internet]. PreScouter. 2018 [cited 2023Jan2]. Available from: https://www.prescouter.com/2018/02/cancerseek-blood-test-detection-eight-cancer-types/ 

6. Hackshaw A, Clarke CA, Hartman A-R. New Genomic Technologies for multi-cancer early detection: Rethinking the scope of cancer screening [Internet]. Cancer Cell. Cell Press; 2022 [cited 2023Jan2]. Available from: https://www.sciencedirect.com/science/article/abs/pii/S1535610822000149?casa_token=fK-gzYIoCT0AAAAA%3AdLp8Q9xAoK6SAr2mWfC73FQKJIR_HXhPW61rkmvnfyqkXoQhyNKkQ-6mSfv5vy3BOYsRV1g_NTKT 

7. Chen X, Gole J, Gore A, He Q, Lu M, Min J, et al. Non-invasive early detection of cancer four years before conventional diagnosis using a blood test [Internet]. Nature News. Nature Publishing Group; 2020 [cited 2023Jan2]. Available from: https://www.nature.com/articles/s41467-020-17316-z 

Categories
AI Cancer Precision Medicine

Revolutionizing Cancer Care: The Power of Artificial Intelligence

Karina Bhargava—McMaster Kinesiology 2026

Cancer refers to any one of a large number of diseases characterized by the development of abnormal cells that divide uncontrollably and have the ability to infiltrate and destroy normal body tissue as well as spread throughout your body.1 It is the second leading cause of death in the world.1 Cancer is caused by mutations to the DNA within cells. These mutations can instruct a healthy cell to allow rapid growth, fail to stop uncontrolled growth or make mistakes when repairing DNA errors.1

Cancer is a highly adaptable disease which causes it to endure the constantly changing microenvironments that its cells encounter2. Cancer cells have a certain degree of adaptive immune resistance, a process in which the cells change their phenotype in response to cytotoxic or proinflammatory immune response9. This response is triggered by the recognition of cancer cells by T cells, leading to the production of immune-activating cytokines9. Cancer cells then hijack mechanisms developed to limit inflammatory and immune responses and protect themselves from the T cell attack9. Because of this process, cancer continues to thwart patients, researchers, and clinicians despite significant progress in understanding its biological underpinnings.3

SOURCE: Future Processing Better Future

As more is learned about the disease itself, more can be learned about how tools can be useful in treatment plans. Currently, artificial intelligence is used in the detection of cancer as it effectively analyzes complex data from many modalities, including clinical text, genomic, metabolomic, and radiomic data (the extraction of mineable data from medical imaging)6. An example of artificial intelligence in cancer diagnosis is imaging tests, which allow your doctor to examine your bones and internal organs in a non-invasive way7. This may include a computerized tomography (CT) scan, bone scan, magnetic resonance imaging (MRI), positron emission tomography (PET) scan, ultrasound and X-ray7.

SOURCE: Stanford Medicine

Artificial intelligence is also used for cancer treatment through something called “precision medicine.”  Precision medicine uses specific information about a person’s tumor to help make a diagnosis, plan treatment, and/ or evaluate the effectiveness of treatment5. It involves testing DNA from a patient’s tumor to identify the mutations or other genetic changes that drive their cancer8. Doctors can select a treatment plan that is best suited for that specific patient. Because no two cancers are identical, precision medicine is important as each patient   has a unique combination of genetic changes2.

Overall, artificial intelligence provides a gateway to push the boundary of cancer treatment. Currently, it is used most in the detection of cancers through CT scans, bone scans, and PET scans, among others. However, as artificial intelligence is adopted into clinical oncology, its potential to redefine cancer treatment is becoming evident.

References

1. Cancer [Internet]. Mayo Clinic. Mayo Foundation for Medical Education and Research; 2021 [cited 2022Nov13]. Available from: https://www.mayoclinic.org/diseases-conditions/cancer/symptoms-causes/syc-20370588

2. Nguyen LTS, Jacob MA, Parajon E, Robinson DN. Cancer as a biophysical disease: Targeting the mechanical-adaptability program [Internet]. Biophysical journal. U.S. National Library of Medicine; 2022 [cited 2022Nov13]. Available from: https://pubmed.ncbi.nlm.nih.gov/35505610/#:~:text=Instead%2C%20cancer%20is%20highly%20adaptable,the%20vascular%20system%20and%20body

3. Linda WB, Hosney A, Schabath MB, Giger ML, Birkbak N, et al. Artificial Intelligence in cancer imaging … – wiley online library [Internet]. American Cancer Society. John Wiley & Sons; 2019 [cited 2022Nov13]. Available from: https://acsjournals.onlinelibrary.wiley.com/doi/full/10.3322/caac.21552

4. Shreve JT, Khanani SA, Haddad TC. Artificial Intelligence in oncology: Current capabilities, future opportunities, and ethical considerations [Internet]. American Society of Clinical Oncology Educational Book. American Society of Clinical Oncology; 2022 [cited 2022Nov13]. Available from: https://ascopubs.org/doi/full/10.1200/EDBK_350652#:~:text=The%20application%20of%20AI%20in,system%20capacity%20and%20allocating%20resources

5. NCI Dictionary of Cancer terms [Internet]. National Cancer Institute. [cited 2022Nov13]. Available from: https://www.cancer.gov/publications/dictionaries/cancer-terms/def/precision-medicine

6. Hunter B, Hindocha S, Lee RW. The role of artificial intelligence in early cancer diagnosis [Internet]. National Library of Medicine. U.S. National Library of Medicine; 2022 [cited 2022Nov15]. Available from: https://www.ncbi.nlm.nih.gov/pmc/articles/PMC8946688/

7. Cancer [Internet]. Mayo Clinic. Mayo Foundation for Medical Education and Research; 2021 [cited 2022Nov15]. Available from: https://www.mayoclinic.org/diseases-conditions/cancer/diagnosis-treatment/drc-20370594

8. Precision cancer medicine: 5 things you should know [Internet]. Brigham Health Hub. Brigham and Women’s Hospital; 2020 [cited 2022Nov15]. Available from: https://brighamhealthhub.org/precision-cancer-medicine-five-things-you-should-know/#:~:text=In%20cancer%2C%20precision%20medicine%20involves,mutations%20in%20the%20tumor%20DNA

9. Ribas A. Adaptive immune resistance: How cancer protects from immune attack [Internet]. National Library of Medicine. U.S. National Library of Medicine; 2015 [cited 2022Nov25]. Available from: https://pubmed.ncbi.nlm.nih.gov/26272491/

10. Kornakiewicz A. Automation of Cancer Diagnostics and Treatment Using Artificial Intelligence [Internet]. Future Processing Better Future. Graylight Imaging; 2017 [cited 2023Jan3]. Available from: https://better.future-processing.com/knowledge/automation-of-cancer-diagnostics-and-treatment-using-artificial-intelligence

11. Types of Magnetic Resonance Imaging Exams [Internet]. Stanford Medicine. Stanford Health Care; 2022 [cited 2023Jan3]. Available from: https://stanfordhealthcare.org/medical-tests/m/mri/types.html