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Peripheral blood analysis is key to developing effective cancer immunotherapies

Research in the field of cancer immunotherapy increasingly demonstrates the value of studying circulating immune cells in peripheral blood to identify new therapeutic targets to enhance tumor-killing immune responses. Here we highlight a handful of impactful publications in this field and provide a strategy for implementing peripheral immune profiling in your research and drug development pipeline.

Fresh T cells from Blood Replenish Exhausted T Cells in Tumor to Sustain a Tumor-Killing Response

If tumor killing by immune cells happens at the tumor site, what is the role of the peripheral immune response in the effectiveness of immunotherapy treatments?  Wu et al. address this important question in a Nature 2020 article.1  Cancer cells hide from immune cells using checkpoint proteins, such as PD-L1, that keep immune cell responses “in check.”  Anti-PD-L1 monoclonal antibodies and others prevent cancer cells from doing this; they form a class of drugs known as immune checkpoint inhibitors. 

The researchers used deep single-cell sequencing of RNA and T cell receptors to analyze the characteristics of different populations of T cells and T cell receptors in tumors, normal tissue next to the tumor (normal adjacent tissue), and peripheral blood.  Using these methods, the study authors were able to identify the location of targeted clonal expansion (replication of identical cells, called clones) of effector-like T cells, the specific subset of T cells that have direct anti-tumor activity.  Targeted clonal expansion within peripheral blood predicted local clonal expansion both within the tumor and in normal adjacent tissue at the same time, which was  associated with the drug working better.  

Data both from this study and others indicate that non-exhausted T cells (T cells that are fully functional and active) and T cell clones that travel from the periphery to the tumor play a central role in patients’ ability to fight off tumors after receiving immune checkpoint inhibitors.  Thus, identifying and analyzing tumor-killing T cells in peripheral blood samples is not only convenient but critical to drug discovery.  Therapeutic breakthroughs may be missed if peripheral blood analysis is ignored.

Reinforcements Needed: Effective Therapy Requires Immune Cells from Outside the Tumor

Teiko Bio’s cofounder Matt Spitzer and colleagues have published multiple studies demonstrating the importance of the peripheral immune response in cancer biology.  In these studies, the research was carried out using mass cytometry, an advanced technique that expands on flow cytometry technology by using metal-tagged antibodies.  Given the distinct characteristics of the metals used, mass cytometry has the capacity to detect more than 40 cellular markers simultaneously.

Prior to the publication by Wu et al., work by Spitzer et al. provided evidence that the peripheral immune system is the source of a prolonged and sustained tumor-killing response in their 2017 Cell paper.2  Using mass cytometry, the study authors investigated immune activity in a mouse model of triple-negative breast cancer following treatment with either a successful or unsuccessful immunotherapy.  

The investigators found evidence of immune activity throughout the mouse’s body, not just in the tumor microenvironment.  During the period of tumor rejection, eight days after giving a successful therapy, only peripheral immune cells, such as those from draining lymph nodes, spleen, peripheral blood, or bone marrow, exhibited a higher level of activity and replication (production of new cells).  Furthermore, a specific subset of peripheral CD4+ T cells arose that provided immunity against new tumors, and their numbers grew in mice who responded to therapy. This peripheral immune activation was specific to successful therapy, as the peripheral immune cells of mice given an ineffective therapy showed very few immune differences when compared to untreated mice.

Overall, these findings suggest that a coordinated response engaging the entire immune system throughout the body is necessary to mount a persistent and effective tumor-killing response during treatment; simply activating immune cells within the tumor site is not enough.  Since peripheral blood samples provide information on the systemic immune response critical for developing effective immunotherapies, they should not be overlooked. 

Cancer’s Impact on the Immune System Goes Beyond Tumor-Killing

A 2020 study published in Nature Medicine by Allen et al. explored how cancer alters systemic immune response to infection.4  Eight mouse strains bioengineered to develop tumors across five tissue types were investigated.  In all mice studied, cancer development caused marked changes in systemic immunity; similar findings were demonstrated using gene expression data of breast cancer patients from the Norwegian Women and Cancer Study.  In the mice, the immune cell changes in peripheral tissues were distinct from those in the tumor microenvironment.  

The presence of tumor in the mouse models weakened the immune response to infectious disease.  This manifested in many ways, including decreased T cell activity to fight off infections from viruses or bacteria.  Investigators discovered that T cell activity could be restored by supercharging the response of another type of immune cell, called the antigen-presenting cell.  When tumors were surgically removed, the healthy baseline functioning of the entire immune system was reestablished.  Antibody blockade of specific immune signaling molecules (IL-1 and G-CSF) alone prevented many of the negative tumor-induced changes to overall immune function.  This shows the importance of these signaling molecules to how the immune system dynamically changes and adapts to its environment.  Results from this study provide convincing evidence that dynamic restructuring of the entire immune system, both in terms of composition and function, occurs in response to cancer.

Peripheral Immune Profiling in Practice

How the immune system interacts with different types of cancer is now being studied for its potential as a predictive biomarker of response to treatment.   Evidence for specific immune cell types guiding cancer progression, both positively or negatively, continues to build.  A 2021 study by Shen et al. published in Science Translational Medicine found that certain immune protein identifiers (one of which is called lymphocyte-activation gene 3 or LAG for short) on specific T cells (CD8+ T cells) predicted which patients would respond to immune checkpoint inhibitors. The researchers used flow cytometry to evaluate many different immune cell markers and the results were confirmed in two different patient data sets, one with melanoma (188 patients) and another with urothelial cancer.  Both melanoma and urothelial cancer patients that had low levels of the LAG immune biomarker on their CD8+ T cells were much more likely to respond to treatment.

A 2020 review paper published in Nature Reviews Cancer by Bruni, Angell, and Galon provides an outline of the primary immune factors that lead to tumors growing or shrinking.6  The importance of how the immune cell interacts with tumors can be seen in how checkpoint inhibitors work.  If a robust adaptive anti-tumor immune response does not already exist before treatment, checkpoint inhibitors will be ineffective.  There are many different cancer types, each having its own distinctive interactions with the immune system with many moving parts.  Given this complexity, the authors of the review suggest that being able to predict how cancer patients respond to treatment can be improved by evaluating multiple immune parameters instead of relying on just one.

What to Look For When You Don’t Know What You’re Looking For

An article published in Cell Reports in 2019 explored the power of mass cytometry to overcome investigator bias and ensure detection of unexpected cellular activities in clinical trials.3  These unforeseen activities might provide the key to a therapeutic breakthrough even for trials with limited sample sizes.  The study authors created a standardized, comprehensive reference panel of 33 antibodies that covers all major immune cells subsets in the innate and adaptive immune system, while also quantitatively measuring markers of activation and immune checkpoint molecules in one assay.  

The mass cytometry panel was able to categorize at least 98% of peripheral immune cells, with each cell population expressing four or more antigens.  The strength of the technique was demonstrated in its ability to produce consistent results across two research centers and different sample types, including peripheral blood samples, metastatic lymph node samples, and tumor biopsies.  The investigators were able to characterize disease-associated immune signatures following bone marrow transplantation in leukemia patients who developed graft-versus-host disease compared to those who did not using their standardized 33 antibody reference panel. This published work formed the initial foundation for Teiko’s TokuProfile base panel, which was later optimized to add up to 12 additional open channels for customization. 

In summary, a growing body of evidence suggests that not only can studying immune system changes in peripheral blood in clinical trial cancer patients result in important insights , but peripheral immune responses are key to finding immunotherapy breakthroughs. High-dimensional techniques such as mass cytometry, with its ability to gather data on the entire immune landscape at once, are leading the charge and helping researchers find clinically relevant biomarkers today.

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References

  1. Wu TD, Madireddi S, de Almeida PE, Banchereau R, Chen YJ, Chitre AS, Chiang EY, Iftikhar H, O’Gorman WE, Au-Yeung A, Takahashi C. Peripheral T cell expansion predicts tumour infiltration and clinical response. Nature. 2020 Mar;579(7798):274-8.
  2. Spitzer MH, Carmi Y, Reticker-Flynn NE, Kwek SS, Madhireddy D, Martins MM, Gherardini PF, Prestwood TR, Chabon J, Bendall SC, Fong L. Systemic immunity is required for effective cancer immunotherapy. Cell. 2017 Jan 26;168(3):487-502.
  3. Hartmann FJ, Babdor J, Gherardini PF, Amir ED, Jones K, Sahaf B, Marquez DM, Krutzik P, O’Donnell E, Sigal N, Maecker HT, Meyer E, Spitzer MH, Bendall SC. Comprehensive Immune Monitoring of Clinical Trials to Advance Human Immunotherapy. Cell Rep. 2019 Jul 16;28(3):819-831.e4. doi: 10.1016/j.celrep.2019.06.049. PMID: 31315057; PMCID: PMC6656694.
  4. Allen BM, Hiam KJ, Burnett CE, Venida A, DeBarge R, Tenvooren I, Marquez DM, Cho NW, Carmi Y, Spitzer MH. Systemic dysfunction and plasticity of the immune macroenvironment in cancer models. Nature medicine. 2020 Jul;26(7):1125-34.
  5. Shen R, Postow MA, Adamow M, Arora A, Hannum M, Maher C, Wong P, Curran MA, Hollmann TJ, Jia L, Al-Ahmadie H. LAG-3 expression on peripheral blood cells identifies patients with poorer outcomes after immune checkpoint blockade. Science translational medicine. 2021 Aug 25;13(608):eabf5107.
  6. Bruni D, Angell HK, Galon J. The immune contexture and Immunoscore in cancer prognosis and therapeutic efficacy. Nature Reviews Cancer. 2020 Nov;20(11):662-80.

Five things to look for in an immune profiling partner

Being able to leverage the strengths and skill sets of a strategic partner can mean the difference between finding a therapeutic breakthrough in your research pipeline or not. Make the most of your time and resources by choosing an immune profiling partner who can advance your oncology, autoimmune, or infectious disease pipelines. Here are five things to look for when evaluating an immune profiling partner.

Immunology Expertise

It may seem obvious, but producing high-quality, interpretable data requires knowledge, skill, and experience.  Be sure your immune profiling partner has enough experience to perform quality work in a timely manner and can troubleshoot challenges that arise.  

It is also important to look at the scientific leadership of the immune profiling organization.  Their team should be experienced and confident enough in the “language” of their technical knowledge base to creatively expand and grow its potential versus someone who only conventionally applies what has already been done before.  Ultimately, the proof is in the pudding.  The scientific leadership of the organization should have a trail of high-impact publications in established peer-reviewed journals and demonstrate innovative ways of understanding and exploiting our immune system to treat disease.

Another consideration is the technology being used to complete the immune profiling.  The technique should make use of latest advances in science but at the same time must be properly vetted through the scientific process to ensure consistency and accuracy over time.  

For example, mass cytometry or cytometry time of flight (CyTOF) is a technique that employs antibodies coupled to isotopically pure metals to detect and measure more than forty cellular features with single-cell resolution.  It builds on the technology of flow cytometry to maximize resolution and parameterization (the number of markers that can be detected in a single cell). At the same time, the technique has been tried and tested and widely validated through rigorous research since becoming commercially available in 2009.  Powerful informatics techniques to make use of all the information mass cytometry data has to offer have been developed over the years by leaders in the field. 

Batch Effects Correction

Batch effects arise from non-biological factors that influence data results, masking the relevant biological data necessary to move the research project forward. By using a barcoding set methodology, separate samples can be grouped together and analyzed in one tube, minimizing the variation between sets, or batches, that can develop from uncontrollable technical fluctuations.1  Such technical variability can arise from slight changes in the calibration of equipment or in the implementation of experimental procedures, such as staining, that are unavoidable when each batch is analyzed.2  

Equipment calibration fluctuations are detected and eliminated via normalization algorithms that are calculated based on the variation of standardized calibration control beads between batches, which would otherwise be identical.2  Likewise, the effects of slight differences in experimental technique, such as staining, are detected and eliminated using anchor, or reference, control samples.1,2 Technical and biological replicates applied to each batch are used as anchor samples.  A single donor, cell line, or tissue sample could be used as an anchor sample depending on the details of the study and its objectives.1  By means of computer analysis, transformation factors are then calculated to normalize anchor sample variation between batches.2  

Make sure your immune profiling company employs both instrument calibration controls and biological anchor samples to guarantee that data is normalized appropriately.  Doing so ensures that biologically relevant information can be clearly detected and data between batches is comparable.

Quality Control

Use of anchor, or reference, samples, calibration beads, and normalization algorithms are part of the broader picture of quality control.  Quality control must be implemented for all components of an experiment including the instrument, reagents, methods, and samples.3  Instruments should be checked daily to make sure they are functioning properly within specified parameters, using calibration beads and diagnostic checks.  Reagents must be tested for reliable performance, and methods must be consistent and standardized. Excellent quality control demands expert-level technical proficiency and problem-solving skills that only come from advanced training and experience.

Quality control extends to the handling and processing of your biological samples as well.  Biological specimen samples are precious, and it is critical that your immune profiling partner make every effort to ensure that they are handled properly to generate reliable data you can be confident in.   This includes quality control measures at every stage of sample processing, including on arrival, following fixation, and during data acquisition.

Regarding the handling of samples, disease stage and treatment can affect blood and PBMC (peripheral blood mononuclear cell) viability and quality.  Thus, a good immune profiling partner is one who has experience processing many different types of biological specimens (e.g. whole blood, PBMCs, dissociated tumor or tissue samples, tumor-infiltrating lymphocytes, and bone marrow aspirate) from a variety of sources (human, mouse, non-human primates) collected using a variety of techniques (fixed and frozen).

Bioinformatics Pipeline

The complexity and volume of data from high dimensional single-cell analysis requires increasingly sophisticated computing technology.  Many important informatics algorithms have been developed throughout the 2010s.  

One of these is CLARA (Clustering LARge Applications) clustering for large cytometry datasets which allows the data to cluster into groups and can identify unique subsets that may be missed by conventional cytometry gating strategies. This can be paired with an algorithm like Cluster Identification, Characterization, and Regression (Citrus).7  This program enables detection of cellular characteristics associated with targeted outcomes, such as patient survival to identify statistically significant and biologically relevant biomarkers. 

Another algorithm is called Single-cell analysis by fixed force- and landmark-directed (Scaffold) maps.7  It employs a technique known as force-directed graphs, where the amount of likeness between cells functions as an attractive force while groupings are repelled by a repulsive force. The power of this bioinformatics technique is its ability to provide a 2-dimensional reference map of the immune system with static landmarks that can be used for easy comparison between samples or conditions. Changes to the system can then be better interpreted in their proper context and evaluated in comparison to other research study outcomes.

You’ll want an immune profiling partner that is familiar with a diversity of bioinformatics pipelines and may have even been a part of their development and validation. Computational approaches continue to evolve and a partner with a strong foundation who is continuing to evaluate modern approaches will ensure confidence and longevity of your analysis.

Turnaround Time

Time is precious and an important part of goal setting.  When you assess turnaround time, it is important to look for a company that will provide end-to-end support at every step of the process from data acquisition through delivery and interpretation of results.  Most contract research organizations (CROs) will cover the data acquisition, but leave the first mile and last mile up to you and your busy team. Look for a company that will work hand-in-glove with your existing scientific team to devise a detailed outline of the exact project scope, deadlines, and deliverables necessary to achieve your goals.  

Leveraging the skill and experience of the right immune profiling partner to scope your project could save you a great deal of time and expense in the long run by avoiding errors that would stall your project or force your team to go back to the drawing board.  Many CROs will leave you with raw data and excel spreadsheets.  Look for a partner that provides data analysis and interpretation to save your company critical time and resources that could be better spent elsewhere.

Where to go for help

Whether you’re in the final stages of evaluating immune profiling partners or just beginning your search, our team of Senior Scientists is available to help answer any questions in your consideration process.

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References

  1. Schuyler RP, Jackson C, Garcia-Perez JE, Baxter RM, Ogolla S, Rochford R, Ghosh D, Rudra P, Hsieh EW. Minimizing batch effects in mass cytometry data. Frontiers in Immunology. 2019 Oct 15;10:2367.
  2. Rybakowska P, Alarcón-Riquelme ME, Marañón C. Key steps and methods in the experimental design and data analysis of highly multi-parametric flow and mass cytometry. Computational and Structural Biotechnology Journal. 2020 Jan 1;18:874-86.
  3. FlowMetric. Quality Control and Flow Cytometry: What is it and why do it? [Internet]. www.flowmetric.com. [cited 2022 Feb 16]. Available from: https://www.flowmetric.com/cytometry-blog/quality-control-and-flow-cytometry-what-is-it-and-why-do-it
  4. ‌Bagwell CB, Inokuma M, Hunsberger B, Herbert D, Bray C, Hill B, Stelzer G, Li S, Kollipara A, Ornatsky O, Baranov V. Automated data cleanup for mass cytometry. Cytometry Part A. 2020 Feb;97(2):184-98.
  5. Casado J, Lehtonen O, Rantanen V, Kaipio K, Pasquini L, Häkkinen A, Petrucci E, Hynninen J, Hietanen S, Carpén O, Biffoni M. Agile workflow for interactive analysis of mass cytometry data. Bioinformatics. 2021 May 1;37(9):1263-8.
  6. Abdelaal T, Höllt T, van Unen V, Lelieveldt BP, Koning F, Reinders MJ, Mahfouz A. CyTOFmerge: integrating mass cytometry data across multiple panels. Bioinformatics. 2019 Oct 15;35(20):4063-71.
  7. Spitzer MH, Nolan GP. Mass cytometry: single cells, many features. Cell. 2016 May 5;165(4):780-91.