Name: Predictive Immune Modeling of Solid Tumors (Video) | JoVE Journal
Uploaded: 2020-02-25T12:00:00.000+00:00
Duration: 8 min 50 s
Description: Learn RNA-based immune profiling for solid tumor biomarker discovery using targeted capture techniques. This method enables sensitive measurement of immune contexture to assess therapy responses and disease progression.

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5 minutes

The authors wish to acknowledge TriStar Technology Group for providing the biological specimens for the representative results, as well as the entire molecular, analysis, product, and commercial teams at Cofactor Genomics for their technical expertise and support.

<ol><li>Brambilla, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Prognostic+Effect+of+Tumor+Lymphocytic+Infiltration+in+Resectable+Non-Small-Cell+Lung+Cancer%5BTitle%5D)+AND+%22Journal+of+Clinical+Oncology%22%5BJournal%5D)">Prognostic Effect of Tumor Lymphocytic Infiltration in Resectable Non-Small-Cell Lung Cancer.</a> Journal of Clinical Oncology. 34 (11), 1223-1230 (2016).</li>
<li>Iacono, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Tumour-infiltrating+lymphocytes%2C+programmed+death+ligand+1+and+cyclooxygenase-2+expression+in+skin+melanoma+of+elderly+patients%3A+clinicopathological+correlations%5BTitle%5D)+AND+%22Melanoma+Research%22%5BJournal%5D)">Tumour-infiltrating lymphocytes, programmed death ligand 1 and cyclooxygenase-2 expression in skin melanoma of elderly patients: clinicopathological correlations.</a> Melanoma Research. 28 (6), 547-554 (2018).</li>
<li>Fridman, W. H., Zitvogel, L., Sautes-Fridman, C., Kroemer, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+immune+contexture+in+cancer+prognosis+and+treatment%5BTitle%5D)+AND+%22Nature+Reviews+Clinical+Oncology%22%5BJournal%5D)">The immune contexture in cancer prognosis and treatment.</a> Nature Reviews Clinical Oncology. 14 (12), 717-734 (2017).</li>
<li>Sierant, M. C., Choi, J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Single-Cell+Sequencing+in+Cancer%3A+Recent+Applications+to+Immunogenomics+and+Multi-omics+Tools%5BTitle%5D)+AND+%22Genomics+Inform%22%5BJournal%5D)">Single-Cell Sequencing in Cancer: Recent Applications to Immunogenomics and Multi-omics Tools.</a> Genomics Inform. 16, (2018).</li>
<li>Klauschen, F., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Scoring+of+tumor-infiltrating+lymphocytes%3A+From+visual+estimation+to+machine+learning%5BTitle%5D)+AND+%22Seminars+in+Cancer+Biology%22%5BJournal%5D)">Scoring of tumor-infiltrating lymphocytes: From visual estimation to machine learning.</a> Seminars in Cancer Biology. 52 (Pt 2), 151-157 (2018).</li>
<li>Danaher, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Gene+expression+markers+of+Tumor+Infiltrating+Leukocytes%5BTitle%5D)+AND+%22Journal+for+ImmunoTherapy+of+Cancer%22%5BJournal%5D)">Gene expression markers of Tumor Infiltrating Leukocytes.</a> Journal for ImmunoTherapy of Cancer. 5, 18(2017).</li>
<li>Aran, D., Hu, Z., Butte, A. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((xCell%3A+digitally+portraying+the+tissue+cellular+heterogeneity+landscape%5BTitle%5D)+AND+%22Genome+Biology%22%5BJournal%5D)">xCell: digitally portraying the tissue cellular heterogeneity landscape.</a> Genome Biology. 18 (1), 220(2017).</li>
<li>Newman, A. M., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Robust+enumeration+of+cell+subsets+from+tissue+expression+profiles%5BTitle%5D)+AND+%22Nature+Methods%22%5BJournal%5D)">Robust enumeration of cell subsets from tissue expression profiles.</a> Nature Methods. 12 (5), 453-457 (2015).</li>
<li>Becht, E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Estimating+the+population+abundance+of+tissue-infiltrating+immune+and+stromal+cell+populations+using+gene+expression%5BTitle%5D)+AND+%22Genome+Biology%22%5BJournal%5D)">Estimating the population abundance of tissue-infiltrating immune and stromal cell populations using gene expression.</a> Genome Biology. 17 (1), 218(2016).</li>
<li>Newman, A. M., Gentles, A. J., Liu, C. L., Diehn, M., Alizadeh, A. A. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Data+normalization+considerations+for+digital+tumor+dissection%5BTitle%5D)+AND+%22Genome+Biology%22%5BJournal%5D)">Data normalization considerations for digital tumor dissection.</a> Genome Biology. 18 (1), 128(2017).</li>
<li>Chen, S. H., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((A+gene+profiling+deconvolution+approach+to+estimating+immune+cell+composition+from+complex+tissues%5BTitle%5D)+AND+%22BMC+Bioinformatics%22%5BJournal%5D)">A gene profiling deconvolution approach to estimating immune cell composition from complex tissues.</a> BMC Bioinformatics. 19 (Suppl 4), 154(2018).</li>
<li>Yoshihara, K., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Inferring+tumour+purity+and+stromal+and+immune+cell+admixture+from+expression+data%5BTitle%5D)+AND+%22Nature+Communications%22%5BJournal%5D)">Inferring tumour purity and stromal and immune cell admixture from expression data.</a> Nature Communications. 4, 2612(2013).</li>
<li>Foley, J. W., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Gene-expression+profiling+of+single+cells+from+archival+tissue+with+laser-capture+microdissection+and+Smart-3SEQ%5BTitle%5D)+AND+%22Genome+Research%22%5BJournal%5D)">Gene-expression profiling of single cells from archival tissue with laser-capture microdissection and Smart-3SEQ.</a> Genome Research. , (2019).</li>
<li>Civita, P., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Laser+Capture+Microdissection+and+RNA-Seq+Analysis%3A+High+Sensitivity+Approaches+to+Explain+Histopathological+Heterogeneity+in+Human+Glioblastoma+FFPE+Archived+Tissues%5BTitle%5D)+AND+%22Front+Oncol%22%5BJournal%5D)">Laser Capture Microdissection and RNA-Seq Analysis: High Sensitivity Approaches to Explain Histopathological Heterogeneity in Human Glioblastoma FFPE Archived Tissues.</a> Front Oncol. 9, 482(2019).</li>
<li>PD-L1 in cancer: ESMO Biomarker Factsheet | OncologyPRO. , OncologyPRO. https://oncologypro.esmo.org/Education-Library/Factsheets-on-Biomarkers/PD-L1-in-Cancer (2019).</li>
<li>Haslam, A., Prasad, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Estimation+of+the+Percentage+of+US+Patients+With+Cancer+Who+Are+Eligible+for+and+Respond+to+Checkpoint+Inhibitor+Immunotherapy+Drugs%5BTitle%5D)+AND+%22JAMA+Network+Open%22%5BJournal%5D)">Estimation of the Percentage of US Patients With Cancer Who Are Eligible for and Respond to Checkpoint Inhibitor Immunotherapy Drugs.</a> JAMA Network Open. 2 (5), e192535(2019).</li>
<li>Maecker, H. T., McCoy, J. P., Nussenblatt, R. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((Standardizing+immunophenotyping+for+the+Human+Immunology+Project%5BTitle%5D)+AND+%22Nature+Reviews+Immunology%22%5BJournal%5D)">Standardizing immunophenotyping for the Human Immunology Project.</a> Nature Reviews Immunology. 12 (3), 191-200 (2012).</li>
<li>Schillebeeckx, I., et al. Analytical Performance of an Immunoprofiling Assay Based on RNA Models. Association for Molecular Pathology 2019 Annual Meeting. Journal of Molecular Diagnostics. 21, Baltimore, MD. (2019).</li>
<li>Uryvaev, A., Passhak, M., Hershkovits, D., Sabo, E., Bar-Sela, G. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+role+of+tumor-infiltrating+lymphocytes+%28TILs%29+as+a+predictive+biomarker+of+response+to+anti-PD1+therapy+in+patients+with+metastatic+non-small+cell+lung+cancer+or+metastatic+melanoma%5BTitle%5D)+AND+%22Medical+Oncology%22%5BJournal%5D)">The role of tumor-infiltrating lymphocytes (TILs) as a predictive biomarker of response to anti-PD1 therapy in patients with metastatic non-small cell lung cancer or metastatic melanoma.</a> Medical Oncology. 35 (3), 25(2018).</li>
<li>Wang, K., Shen, T., Siegal, G. P., Wei, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/pubmed?term=((The+CD4%2FCD8+ratio+of+tumor-infiltrating+lymphocytes+at+the+tumor-host+interface+has+prognostic+value+in+triple-negative+breast+cancer%5BTitle%5D)+AND+%22Human+Pathology%22%5BJournal%5D)">The CD4/CD8 ratio of tumor-infiltrating lymphocytes at the tumor-host interface has prognostic value in triple-negative breast cancer.</a> Human Pathology. 69, 110-117 (2017).</li>
<li>Carney, W. P., Bhagat, M., LaFranzo, N. Multidimensional gene expression models for characterizing response and metastasis in solid tumor samples [abstract]. American Association for Cancer Research Annual Meeting. Cancer Research. 79 (13 Suppl), Atlanta, GA. (2019).</li>
</ol>

All authors are employed by Cofactor Genomics, Inc., the company that developed and produces the ImmunoPrism reagent kit and informatics tools used in this article. The ImmunoPrism Assay is for Research Use Only, and is not for use in diagnostic procedures.

The protocol requires 20 ng intact or 40 ng highly degraded (FFPE) RNA. The RNA sample should be free of DNA, salts (e.g., Mg2+, or guanidinium salts), divalent cation chelating agents (e.g., EDTA, EGTA, citrate), or organics (e.g., phenol and ethanol). It is not recommended to proceed with RNA samples that have a DV200 &#60;20%. Use of the in-kit control RNA is strongly recommended as these controls provide a means to evaluate performance throughout the entire protocol, from library preparation to analysis.
The protocol is designed to be performed using 0.2 mL PCR strip tubes. If preferred, the protocol can also be performed using the wells in a 96-well PCR plate. Simply use the wells of a 96-well PCR plate in place of all references to PCR tubes or strip tubes. Use PCR plates with clear wells only, as it is critical to visually confirm complete resuspension of beads during bead purifications and wash steps.
Throughout the protocol, keep reagents frozen or on ice unless otherwise specified. Do not use reagents until they are completely thawed. Be sure to thoroughly mix all reagents before use.
Keep enzymes at -20 &#176;C until ready to use and return to -20 &#176;C promptly after use. Use only molecular-grade nuclease-free water; it is not recommended to use DEPC-treated water. When pipetting to mix, gently aspirate and dispense at least 50% of the total volume until the solutions are well mixed. Pipette mix all master mixes containing enzymes. Using vortex to mix the enzymes could lead to denaturation and compromise their performance. During bead purifications, use freshly made 80% ethanol solutions from molecular grade ethanol. Using ethanol solutions that are not fresh may result in lower yields. Avoid over drying the beads, as this can reduce elution efficiency (beads look cracked if over dried).
As described in Step 10, unique index primers are added to each reaction. Based on the sequences of these indices, for low-level multiplexing, certain index combinations are optimal. The sequences of these indices are required for demultiplexing the data post-sequencing. The sequences and recommended multiplexing combinations are provided in Supplemental Table 3. In this same step, it is important to note that the number of recommended PCR cycles varies depending on the quality of RNA used, and, some optimization may be required to prevent PCR over-amplification. For the ImmunoPrism Intact Control RNA and other high-quality RNA, start optimization with 10 PCR cycles. For the ImmunoPrism FFPE Control RNA and other highly degraded/FFPE RNA, start optimization with 15 PCR cycles. Producing a test library using RNA representative of the material to be analyzed in order to optimize PCR cycles is recommended. The minimum number of PCR cycles that consistently yield sufficient pre-capture library yields (&#62;200 ng) should be used. A secondary peak around 1000 bp on the Bioanalyzer trace is indicative of over-amplification (Figure 4). Over-amplification should be minimized, but the presence of a small secondary peak will not interfere with assay results.
To minimize sample loss and avoid switching tubes, Step 13 may be performed in PCR tubes, strip tubes, or a 96-well PCR plate instead of 1.5 mL microtubes, if your vacuum concentrator allows. The rotor can be removed on many concentrators. This enables the strip tubes or plates to fit in the vacuum. The vacuum concentration can then be run using the aqueous desiccation setting with no centrifugation. Consult the manual for your vacuum concentrator for instructions. If the samples are dried down in strip tubes or a 96-well plate, the hybridization step can be performed in the same vessel.
During Step 17, be sure to vortex every 10-12 min to increase the bead capture efficiency. Carefully hold the caps of the warm strip tubes when mixing to prevent tubes from opening.
The washes described in Step 18 are critical to avoid high nonspecific contamination and must be followed closely. Be sure to completely resuspend the beads at each wash, completely remove the wash buffers, and during the Wash Buffer 2 wash, transfer the samples to a fresh strip tube (Step 18.6.5). Ensure that the streptavidin beads are completely resuspended and remain in suspension during the entire incubation. Splashing on the tube caps will not negatively impact the capture. During the room temperature washes, a microplate vortex mixer may be used to vortex the samples for the entirety of the two-minute incubation period for easier resuspension. Do not let the streptavidin beads dry out. If needed, extend incubations in the buffers to avoid drying the beads. If using more than one strip tube, work with one strip tube at a time for each wash while the other strip tubes sit in the thermocycler. This can help avoid over drying the beads or rushing, resulting in poor resuspension or other sub-optimal techniques. For first time users, it is not recommended to process more than 8 library reactions at a time.
Current immune profiling techniques deliver a continuum of information - from thousands of data points that require significant interpretation (RNA sequencing) to an individual, discrete data point (single-plex IHC). The protocol described here represents an approach that is somewhere in the middle, with a focused scope enabling high sensitivity, but capturing only a subset of clinically relevant transcriptomic data. Due to the nature of bulk RNA extraction, this protocol does not provide information about the spatial relationships between immune cells and the tumor microenvironment, however, results may be complemented with imaging technologies to add this information. There are a myriad of applications for the data generated by this protocol, as there is much to be learned about biology of cancer as a disease, and the therapies being developed to treat it. As shown in the representative results, the individual immune report is useful for understanding how a patient&#39;s immune profile may change in response to events such as disease progression or treatment. While the results presented here provide some example use cases, other applications including investigating the mechanism of action of a therapy and identifying putative biomarkers of clinical outcomes such as progression free and overall survival are also practical. When using this protocol for biomarker discovery applications, it is important to practice good study design to ensure homogenous populations are analyzed, sufficient samples are included for statistical power, and sources of bias are considered. Due to the focused, streamlined nature of the assay, it is feasible to imagine a path towards clinical validation and downstream application of these biomarkers once discovered.

Quantification of tumor-infiltrating lymphocytes (TILs) and other immune-related molecules in formalin-fixed and paraffin embedded (FFPE) solid tumor human tissue samples has demonstrated value in clinical research1,2,3. Common techniques such as flow cytometry and single-cell ribonucleic acid (RNA) sequencing are useful for fresh tissue and blood4, but are unsuitable for analysis of FFPE materials due to the inability to create viable cell suspensions. Current methods that have been used to quantify these cells in FFPE tissue suffer from major challenges. Immunohistochemistry (IHC) and other similar imaging workflows require specific antibodies to detect cell-surface proteins, which can be difficult to standardize across laboratories to enable reproducible quantification5. Platforms such as the nCounter system rely on the expression of single genes to define key immune cells6, limiting sensitivity and specificity of detection. More generic RNA sequencing methods, coupled with standalone software tools, are available but require significant optimization and validation prior to use7,8,9,10,11,12. Recent advances in combining laser capture microdissection (LCM) with RNA sequencing for FFPE tissue has shown promise; however, a more high-throughput, turnkey solution is required for translational studies aimed at identifying robust biomarkers13,14. Methods to generate multidimensional biomarkers, such as Predictive Immune Modeling, that define patient cohorts including therapy responders, cancer subtypes, or survival outcomes with high predictive accuracy and statistical significance are becoming increasingly important in the age of precision medicine and immunotherapy15,16.
To address this need, an immune profiling assay was developed to enable sensitive and specific quantification of immune cells in solid tumor FFPE tissue using standardized RNA-sequencing reagents and cloud-based informatics. In addition to accommodating degraded RNA from FFPE tissue, the protocol is able to accommodate RNA derived from limiting tissue samples such as core needle biopsies, needle aspirates, and micro- or macro-dissected tissue. RNA data from each sample is compared to a database of gene expression models of immune cells, called immune Health Expression Models, to quantify immune cells as a percentage of total cells present in the sample. Briefly, these models were built using machine-learning methods to identify unique multigenic expression patterns from whole-transcriptome data generated from purified immune cell populations (isolated using canonical cell-surface markers)17,18. The multidimensional Health Expression Models underlying the technology enables the assay to quantify each immune cell as a percent of the total cells present in the heterogenous mixture. This enables the researcher to generate inter- and intra-sample immune cell comparisons, which have been shown to have clinical value19,20. Other applications include quantification of immune response pre- and post-treatment, as described in the representative results. The assay reports on multiple features of immune contexture of the tumor and tumor microenvironment including the absolute percentages of eight immune cell types (derived from gene expression models): CD4+ T cells, CD8+ T cells, CD56+ Natural Killer cells, CD19+ B cells, CD14+ monocytes, Tregs, M1 macrophages, and M2 macrophages. In addition, the assay reports the expression (in transcripts per million, or TPM) of ten immune escape genes: PD-1, PD-L1, CTLA4, OX40, TIM-3, BTLA, ICOS, CD47, IDO1, and ARG1.
The reagent kit is used to make high quality libraries ready for sequencing on an Illumina platform following a hybrid capture-based library preparation method, as shown in Figure 1. If a researcher does not have an Illumina sequencing platform in their laboratory, they may submit their samples to a core laboratory for sequencing. Once generated, sequencing data is uploaded to the Prism Portal for automated analysis, and a comprehensive, quantitative profile for each individual sample, in the form of the Immune Report (Figure 2A), is returned to the user. Users may also define sample groupings in the Prism Portal to generate a Biomarker Report (Figure 2B), highlighting statistically significant biomarkers that distinguish two patient cohorts. Importantly, the data generated by the reagent kit is for research use only and may not be used for diagnostic purposes.
<img alt="RNA sequencing workflow; cDNA synthesis, PCR, hybridization; flowchart process diagram." class="xfigimg" src="/files/ftp_upload/60645/60645fig1a.jpg" data-altupdatedat="1788250015000" data-alt="Figure 1"> 
Figure 1: Overview of Workflow. In this protocol, RNA is first converted to cDNA. Sequencing adaptors are ligated, and adaptor-ligated cDNA is amplified and barcoded by PCR to create a pre-capture library. Biotinylated probes are then hybridized to specific cDNA targets which are then captured using streptavidin beads. Unbound, non-targeted cDNA is removed by washing. A final PCR enrichment yields a post-capture library ready for sequencing. *Total RNA must be from human samples; may be intact or degraded (FFPE) RNA. <a href="https://www.jove.com/files/ftp_upload/60645/60645fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="ImmunoPrism profiling, immune cell types chart, predictive accuracy graph, biomarker analysis." class="xfigimg" src="/files/ftp_upload/60645/60645fig2.jpg" data-altupdatedat="1788250015000" data-alt="Figure 2"> 
Figure 2: Representative Immune Reports. The workflow generates two reports, an individual immune report (A) for each sample processed, and a biomarker report (B) for defined patient cohorts. <a href="https://www.jove.com/files/ftp_upload/60645/60645fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The protocol requires approximately 16 h of preparation time (from total RNA to libraries ready for sequencing); however, there are a number of optional stopping points, as noted in the protocol. The assay makes use of the rich, dynamic nature of transcriptomics to move beyond legacy single-analyte biomarkers to multidimensional gene expression models, thereby enabling comprehensive biological characterization of tissue samples with standardized reagents and easy-to-use software tools. It empowers researchers to utilize a contemporary technology in their own laboratory, by leveraging machine-learning and a database of Health Expression Models to derive more accurate, quantitative immune profiles of precious clinical samples, and discover multidimensional RNA biomarkers with full statistical analysis.

<table><tbody><tr><td>0.2 mL PCR 8 tube strip</td><td>USA Scientific</td><td>1402-2700</td><td>USA Scientific 0.2 mL PCR 8-tube strip</td></tr><tr><td>200 Proof Ethanol</td><td>MilliporeSigma</td><td>EX0276-1</td><td>Prepare 80% by mixing with nuclease-free water on the day of the experiment</td></tr><tr><td>96-well thermal cyclers</td><td>BioRad</td><td>1861096</td><td></td></tr><tr><td>Solid-phase Reversible Immobilization (SPRI) Beads</td><td>Beckman-Coulter</td><td>A63882</td><td>Agencourt AMPure XP &amp;ndash; PCR Purification beads</td></tr><tr><td>Digital electrophoresis chips and kit</td><td>Agilent Technologies</td><td>5067-4626</td><td>Agilent High Sensitivity DNA chips and kit</td></tr><tr><td>Digital electrophoresis system</td><td>Agilent Technologies</td><td>G2939AA</td><td>Agilent 2100 Electrophoresis Bioanalyzer</td></tr><tr><td>Streptavidin Beads</td><td>ThermoFisher Scientific</td><td>65306</td><td>Dynabeads M-270 Streptavidin</td></tr><tr><td>ImmunoPrism Kit &amp;ndash; 24 reaction</td><td>Cofactor Genomics</td><td>CFGK-302</td><td>Cofactor ImmunoPrism Immune Profiling Kit &amp;ndash; 24 reactions</td></tr><tr><td>Human Cot-1 DNA</td><td>ThermoFisher Scientific</td><td>15279011</td><td>Invitrogen brand</td></tr><tr><td>Magnetic separation rack</td><td>Alpaqua/Invitrogen</td><td>A001322/12331D</td><td>96-well Magnetic Ring Stand</td></tr><tr><td>Microcentrifuge</td><td>Eppendorf</td><td>22620701</td><td></td></tr><tr><td>Microcentrifuge tubes</td><td>USA Scientific</td><td>1415-2600</td><td>USA Scientific 1.5 mL low-adhesion microcentrifuge tube</td></tr><tr><td>NextSeq550</td><td>Illumina</td><td>SY-415-1002</td><td>Any Illumina sequencer may be used for this protocol</td></tr><tr><td>Nuclease-free water</td><td>ThermoFisher Scientific</td><td>AM9937</td><td></td></tr><tr><td>Prism Extraction Kit</td><td>Cofactor Genomics</td><td>CFGK-401</td><td>Cofactor Prism FFPE Extraction Kit &amp;ndash; 24 samples</td></tr><tr><td>Purified RNA</td><td>-</td><td>-</td><td>Purified from human tissue samples</td></tr><tr><td>Fluorometer</td><td>ThermoFisher Scientific</td><td>Q33226</td><td>Qubit 4 System</td></tr><tr><td>Fluorometric Assay Tubes</td><td>Axygen</td><td>PCR-05-C</td><td>0.5mL Thin Wall PCR Tubes with Flat Caps</td></tr><tr><td>High Sensitivity Fluorometric Reagent Kit</td><td>Life Technologies</td><td>Q32854</td><td>Qubit dsDNA HS Assay Kit</td></tr><tr><td>Vacuum concentrator</td><td>Eppendorf</td><td>22820001</td><td>VacufugePlus</td></tr><tr><td>Vortex mixer</td><td>VWR</td><td>10153-838</td><td></td></tr><tr><td>Water bath or heating block</td><td>VWR/USA Scientific</td><td>NA/2510-1102</td><td>VWR water bath/USA Scientific heating block</td></tr></tbody></table>

predictive immune modeling of solid tumors

Predictive Immune Modeling of Solid Tumors

Immunotherapies show promise in the treatment of oncology patients, but complex heterogeneity of the tumor microenvironment makes predicting treatment response challenging. The ability to resolve the relative populations of immune cells present in and around the tumor tissue has been shown to be clinically-relevant to understanding response, but is limited by traditional techniques such as flow cytometry and immunohistochemistry (IHC), due the large amount of tissue required, lack of accurate cell type markers, and many technical and logistical hurdles. One assay (e.g., the ImmunoPrism Immune Profiling Assay) overcomes these challenges by accommodating both small amounts of RNA and highly degraded RNA, common features of RNA extracted from clinically archived solid tumor tissue. The assay is accessed via a reagent kit and cloud-based informatics that provides an end-to-end quantitative, high-throughput immuno-profiling solution for Illumina sequencing platforms. Researchers start with as few as two sections of formalin-fixed paraffin-embedded (FFPE) tissue or 20-40 ng of total RNA (depending on sample quality), and the protocol generates an immune profile report quantifying eight immune cell types and ten immune escape genes, capturing a complete view of the tumor microenvironment. No additional bioinformatic analysis is required to make use of the resulting data. With the appropriate sample cohorts, the protocol may also be used to identify statistically significant biomarkers within a patient population of interest.

There are a number of checkpoints throughout the protocol that enable a user to evaluate the quality and quantity of generated materials. Following Step 12 described in the protocol, an electropherogram is generated as shown in Figure 3, representative of a typical pre-capture library for an intact RNA sample (RIN = 7.8).
<img alt="Electrophoresis analysis graph, DNA separation, fluorescence units vs. base pairs, peak data." class="xfigimg" src="/files/ftp_upload/60645/60645fig3.jpg" data-altupdatedat="1788250015000" data-alt="Figure 3"> 
Figure 3: Typical Pre-capture Library Bioanalyzer trace for an intact RNA sample. Pre-capture libraries appear as a broad peak around 250-400 base pairs (bp) in size. <a href="https://www.jove.com/files/ftp_upload/60645/60645fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Care should be taken to avoid overamplification, as indicated by the second peak around 1,000 bp shown in Figure 4, a representative electropherogram of a pre-capture library generated from an FFPE RNA sample (DV200 = 46). If this peak is small relative to the main peak (around 250-400 base pairs (bp), as shown), it will not interfere with downstream steps or analysis. If the second peak is large relative to the 250-400 bp peak, the pre-capture library can be remade with fewer PCR cycles in order to reduce overamplification.
<img alt="Electropherogram showing DNA fragment analysis, peak intensity vs. fragment size, bp scale." class="xfigimg" src="/files/ftp_upload/60645/60645fig4.jpg" data-altupdatedat="1788250015000" data-alt="Figure 4"> 
Figure 4: Typical Pre-capture Library Bioanalyzer trace for an FFPE RNA sample. The second peak around 1,000 bp is indicative of over-amplification. If this peak is small relative to the main peak around 250-400 bp (as shown), it will not interfere with downstream steps or analysis. If the second peak is large relative to the 250-400 bp peak, the pre-capture library can be remade with fewer PCR cycles in order to reduce over-amplification. <a href="https://www.jove.com/files/ftp_upload/60645/60645fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
As described in Step 12.1.3, the presence of adaptor dimers should be evaluated to determine if additional cleanup is necessary. The electropherograms shown in Figure 5 are representative of unacceptable (Figure 5A, DV200 = 33) and acceptable (Figure 5B, DV200 = 46) levels of adaptor dimer, appearing as the sharp peak around 128 bp.
<img alt="Spectroscopy graph comparison depicting absorption peaks; analysis of chemical composition." class="xfigimg" src="/files/ftp_upload/60645/60645fig5.jpg" data-altupdatedat="1788250015000" data-alt="Figure 5"> 
Figure 5: Pre-capture library Bioanalyzer traces. The adaptor dimer shows up as a sharp peak around 128 bp. (A) Excessive adaptor dimers are present in this electropherogram. (B) Acceptable adaptor dimer levels are depicted in this trace. Both traces show evidence of mild over-amplification, but this should not interfere with the ImmunoPrism Assay. <a href="https://www.jove.com/files/ftp_upload/60645/60645fig5alarge.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
At the completion of the protocol, prior to sequencing, the final libraries are again evaluated using digital electrophoresis. Libraries made from FFPE RNA tend to have a smaller average size distribution than libraries made from intact RNA. For intact RNA samples, the resulting trace should look similar to Figure 6 (RIN = 9.5). For degraded or FFPE RNA, the resulting trace should look similar to Figure 7 (DV200 = 36).
<img alt="Electropherogram; capillary electrophoresis result; DNA fragment analysis; peak intensity chart." class="xfigimg" src="/files/ftp_upload/60645/60645fig6.jpg" data-altupdatedat="1788250015000" data-alt="Figure 6"> 
Figure 6: Typical Final Library Bioanalyzer trace for an intact RNA sample. Final libraries appear as a broad peak around 250-400 base pairs (bp) in size. <a href="https://www.jove.com/files/ftp_upload/60645/60645fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Electrophoresis results; peak analysis graph; DNA fragment separation; FU vs bp." class="xfigimg" src="/files/ftp_upload/60645/60645fig7.jpg" data-altupdatedat="1788250015000" data-alt="Figure 7"> 
Figure 7: Typical Final Library Bioanalyzer trace for an FFPE RNA sample. Libraries made from FFPE RNA tend to have a smaller average size distribution than libraries made from intact RNA. <a href="https://www.jove.com/files/ftp_upload/60645/60645fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
As described, the results generated with this protocol may be applied in two key ways, as shown in Figure 8.
<img alt="Individual and multidimensional immune profiling; RNA sequencing; diagram; biomarker discovery." class="xfigimg" src="/files/ftp_upload/60645/60645fig8.jpg" data-altupdatedat="1788250015000" data-alt="Figure 8"> 
Figure 8: Two use cases of the protocol. The results generated by this immune profiling assay are applied in two key translational applications. (A) The first use case starts from human solid tumor tissue (including FFPE archives) and generates an individual immune profile for the sample. (B) Once generated for a cohort of human samples, the data is combined using the Prism Portal to generate a multidimensional biomarker and corresponding Biomarker Report. <a href="https://www.jove.com/files/ftp_upload/60645/60645fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
To demonstrate each of these use cases, representative data from a small translational study is included21. The samples used in this study are a set of specimens from 7 patients diagnosed and treated for non-small cell lung cancer (NSCLC). The samples are patient-matched solid tumor tissue from pre and post treatment biopsies. First, individual samples were analyzed to generate an immune profile, such as the example report shown in Figure 9.
<img alt="ImmunoPrism report chart and result; immune profile analysis; CD4+ T cells, CD8+ T cells, B cells." class="xfigimg" src="/files/ftp_upload/60645/60645fig9.jpg" data-altupdatedat="1788250015000" data-alt="Figure 9"> 
Figure 9: Example individual immune report for a NSCLC sample. The Prism Portal pipeline generates a graphical report for each sample processed, with a representative report generated for a NSCLC solid tumor sample shown here. (A) The front side of the report graphically depicts the breakdown of immune cells present in the RNA sample extracted from the FFPE tissue. (B) The reverse side of the report includes a table of immune cells (in absolute percentages) and escape gene expression (in transcripts per million, or TPM), as well as a statement of performance for the assay. <a href="https://www.jove.com/files/ftp_upload/60645/60645fig9alarge.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
The immune profiles pre- and post-treatment may be used to understand how a therapy (chemotherapy or radiation, in this study) has modified the tumor microenvironment. An example is shown in Figure 10, where the changes in percentage for each immune cell and total immune content are shown pre- and post chemotherapy, for a single patient.
<img alt="Immune cell percentage changes pre/post experiment; line graph; CD4+, CD8+ T cells; immunology study." class="xfigimg" src="/files/ftp_upload/60645/60645fig10.jpg" data-altupdatedat="1788250015000" data-alt="Figure 10"> 
Figure 10: Example Pre and Post Treatment Results. Individual immune cell and total immune content data generated from pre- and post-treatment samples from a single NSCLC patient are shown. In this example, the patient received a chemotherapy regimen as treatment. <a href="https://www.jove.com/files/ftp_upload/60645/60645fig10large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Patients may be grouped by criteria such as clinical outcomes or phenotypes for comparison. For example, in Figure 11, the samples in the NSCLC study were compared according to time to disease progression following treatment. A subset of the patients showed disease recurrence in &gt;18 months, and another subset progressed faster, in ≤18 months. The median delta value (difference between pre- and post-treatment values) are compared for each sample to identify putative biomarkers of disease progression.
<img alt="Immune cell percentage bar chart comparing age groups; CD4, CD8 T Cells, B Cells, monocytes." class="xfigimg" src="/files/ftp_upload/60645/60645fig11.jpg" data-altupdatedat="1788250015000" data-alt="Figure 11"> 
Figure 11: Example Clinical Outcome Comparison. Quantitative changes between the immune cell percentages in matched pre and post-treatment NSCLC samples were calculated and reported as the "delta" value. Those highlighted in yellow show clear signal changes between the survival status. Blue bars represent median delta values for &gt;18 months until disease progression, orange bars represent median delta values for ≤18 months until disease progression. <a href="https://www.jove.com/files/ftp_upload/60645/60645fig11large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Finally, similar sample groupings may be used to look specifically at pre-treatment samples to identify predictive biomarkers by using the Prism Portal to generate a Biomarker Report. Shown in Figure 12, the same clinical phenotype (disease progression) as described above defines the sample groupings. In this example, two immune escape genes were identified as statistically significant differentiators of the sample groupings (CD47 and OX40, shown in the lower panel of Figure 12A). In this example, because the individual gene biomarkers are robust with clear statistical significance, the multidimensional biomarker does not add significant predictive value (ImmunoPrism, as labeled in the top right bar chart of Figure 12B). The full table of data, including results for all 18 analytes for the assay, is summarized on the reverse side of the report, including statistical analysis and a brief methods summary.
<img alt="ImmunoPrism biomarker report highlighting predictive accuracy with analyte performance data chart." class="xfigimg" src="/files/ftp_upload/60645/60645fig12.jpg" data-altupdatedat="1788250015000" data-alt="Figure 12"> 
Figure 12: Example Biomarker Report for NSCLC samples. The Biomarker Discovery pipeline delivers a visual report of individual biomarkers, and a machine-learning multidimensional biomarker, with detailed statistics. (A) For this study, the pipeline identified two individual biomarkers (CD47 and OX40) as statistically-significant for defining disease progression with a threshold of 18 months. (B) Details on the method and full results are included on the reverse side of the report. <a href="https://www.jove.com/files/ftp_upload/60645/60645fig12alarge.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Table 1: Reagent Kit Materials. A list of materials provided in the ImmunoPrism Kit are listed, along with the part numbers that referenced in the manufacturer's protocol. All other equipment and materials required are listed in the Table of Materials. Visit <a href="https://cofactorgenomics.com/product/immunoprism-kit/" target="_blank">https://cofactorgenomics.com/product/immunoprism-kit/</a> for Safety Data Sheets (SDS). <a href="https://www.jove.com/files/ftp_upload/60645/supplemental_table1.xlsx" target="_blank">Please click here to view this file (Right click to download).</a>
Supplemental Table 2: Thermal Cycler Programs. The recommended cycler programs referenced throughout the protocol are summarized for ease of programming. <a href="https://www.jove.com/files/ftp_upload/60645/supplemental_table2.xlsx" target="_blank">Please click here to view this file (Right click to download).</a>
Supplemental Table 3: Sequencing Index Guide. The index primers provided in the reagent kit are listed; a unique primer is added to each reaction for post-sequencing demultiplexing. Recommended low-level multiplexing combinations are also provided. <a href="https://www.jove.com/files/ftp_upload/60645/supplemental_table3.xlsx" target="_blank">Please click here to view this file (Right click to download).</a>

The use of an RNA-based approach to determine quantitative immune profiles of solid tumor tissues and leverage clinical cohorts for immune-oncology biomarker discovery is described through a molecular and informatics protocols.

Immunotherapies show promise in the treatment of oncology patients, but complex heterogeneity of the tumor microenvironment makes predicting treatment ...

predictive-immune-modeling-of-solid-tumors

Research

JoVE Journal

Immunology and Infection

The generation of an orthotopic rat model of pleural malignant mesothelioma by implantation of II-45 mesothelioma cells into the pleural cavity of immune competent rats is presented. A flow cytometric method to analyze seven immune cell subsets in these animals from a 25 µl blood sample is also described.

Orthotopic Implantation and Peripheral Immune Cell Monitoring in the II-45 Syngeneic Rat Mesothelioma Model

Studying tumor microenvironment may identify prognostic or predictive biomarkers of clinical response to immunotherapy. Presented here, is an innovative method based on in situ fluorescence multispectral imaging to analyze and count automatically various subpopulations of CD8+ T cells. This reproducible and reliable technique is suitable for large cohort analyses.

Multiplexed Immunofluorescence Analysis and Quantification of Intratumoral PD-1+ Tim-3+ CD8+ T Cells

The experimental procedure on the immunophenotyping of murine orthotopic PDAC homografts aims at profiling the tumor immuno-microenvironment. Tumors are orthotopically implanted via surgery. Tumors of 200–600 mm3 in size were harvested and dissociated to prepare single-cell suspensions, followed by multi-immune marker FACS analysis using different fluorescently-labeled antibodies.

Immunophenotyping of Orthotopic Homograft (Syngeneic) of Murine Primary KPC Pancreatic Ductal Adenocarcinoma by Flow Cytometry

In the age of immunotherapy and single-cell genomic profiling, cancer biology requires novel in vitro and computational tools for investigating the tumor-immune interface in a proper spatiotemporal context. We describe protocols to exploit tumor-immune microfluidic co-cultures in 2D and 3D settings, compatible with dynamic, multiparametric monitoring of cellular functions.

Microfluidic Co-Culture Models for Dissecting the Immune Response in in vitro Tumor Microenvironments

The protocol presents a cancer immunotherapy model using cell-based tumor vaccination with Flt3L-expressing B16-F10 melanoma. This protocol demonstrates the procedures, including preparation of cultured tumor cells, tumor implantation, cell irradiation, measurement of tumor growth, isolation of intratumoral immune cells, and flow cytometry analysis.

Experimental Melanoma Immunotherapy Model Using Tumor Vaccination with a Hematopoietic Cytokine

This protocol outlines the generation of human immune system (HIS) mice for immuno-oncology studies. Instructions and considerations in the use of this model for testing human immunotherapeutics on human tumors implanted in this model are presented with an emphasis on characterizing the response of the human immune system to the tumor.

Testing Cancer Immunotherapeutics in a Humanized Mouse Model Bearing Human Tumors

This protocol describes in detail how immune cell characterization of the tumor microenvironment using multiplex immunohistochemistry is carried out.

Multiplex Immunohistochemical Analysis of the Spatial Immune Cell Landscape of the Tumor Microenvironment

Here, we present a method to identify compounds that modulate the ADCC mechanism, an important cancer cell-killing mechanism of antitumor antibodies. The cytotoxic effect of NK cells is measured in breast cancer cell spheroids in the presence of Trastuzumab. Image analysis identifies live and dead killer and target cells in spheroids.

Quantifying Antibody-Dependent Cellular Cytotoxicity in a Tumor Spheroid Model: Application for Drug Discovery

This article will detail the protocol for measuring calpain activity in fixed and living cells using flow cytometry.

Measuring Calpain Activity in Fixed and Living Cells by Flow Cytometry

Here we describe a method for the isolation of hepatic stellate cells from mouse liver. For stellate cell purification, mouse livers are digested in situ and in vitro by pronase-collagenase treatment prior to density gradient centrifugation. This technique yields highly pure hepatic stellate cells.

Seven Steps to Stellate Cells

Learn RNA-based immune profiling for solid tumor biomarker discovery using targeted capture techniques. This method enables sensitive measurement of immune contexture to assess therapy responses and disease progression.

Predictive Immune Modeling of Solid Tumors

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