Immune Profiling and Its Role in Cancer

1. Definition and Concept

"There is an increasing awareness that the tumor microenvironment (stromal cells, immune cells, and extracellular matrix) plays an important role in breast cancer initiation, progression, and dissemination."

• Robbins & Cotran Pathologic Basis of Disease

2. Immunological Basis - The Tumor Microenvironment (TME)

A. Anti-tumor (Immunogenic/"Hot") TME

B. Pro-tumor (Immunosuppressive/"Cold") TME

Cellular and Molecular Immunology (Abbas, Lichtman & Pillai) describes how M2 macrophages and Tregs create the immunosuppressive microenvironment that works against the adaptive immune response.

Fig 1. Tumor-antagonizing vs tumor-promoting immune cells in the TME

3. Methods / Technologies Used in Immune Profiling

A. Histological / Tissue-Based Methods

• Immunohistochemistry (IHC): Detects CD3, CD8, CD45RO, FOXP3, PD-L1 on fixed tissue sections. Most widely used in clinical pathology.
• Multiplex IHC / Immunofluorescence: Simultaneously detects 5-10 markers on a single tissue slide; allows spatial characterization.
• Tumor-Infiltrating Lymphocyte (TIL) scoring: H&E-based or IHC-based assessment; standardized scoring criteria used in breast cancer (TILs Working Group).

B. Flow Cytometry and Mass Cytometry

• Flow Cytometry: Multi-parameter analysis of surface/intracellular markers on single cells from blood, bone marrow, or tumor disaggregates. Standard for hematologic malignancy immunophenotyping (ALL, CLL, AML, lymphomas).
• Mass Cytometry (CyTOF): Uses metal-isotope-tagged antibodies; allows simultaneous detection of 40+ markers per cell. High-dimensional immune profiling without spectral overlap.

C. Genomic and Transcriptomic Methods

• Next-Generation Sequencing (NGS): Detects tumor mutational burden (TMB), microsatellite instability (MSI), neoantigen load - all key predictors of immunotherapy response.
• Single-cell RNA Sequencing (scRNA-seq): Maps transcriptional states of individual immune cells within tumors; identifies rare subpopulations (e.g., exhausted CD8+ T cells, Treg subsets).
• Spatial Transcriptomics: Combines gene expression with spatial tissue architecture - maps "who is where" in the tumor.
• Bulk RNA-seq + Deconvolution (CIBERSORT, TIMER): Computationally infers immune cell composition from bulk tumor RNA data; used in large database studies (TCGA).

D. Proteomics

• Mass Spectrometry-Based Proteomics: Profiles immune-related proteins in tumor lysates and serum.
• Cytokine multiplex assays (Luminex): Simultaneous quantification of multiple cytokines (IL-2, IFN-γ, TNF-α, IL-10, TGF-β) in plasma or supernatants.

E. Liquid Biopsy

• Circulating Tumor Cells (CTCs) + immune cell co-analysis: Characterizes immune-tumor cell interactions in peripheral blood.
• Cell-free DNA (cfDNA): TMB and MSI assessment without tissue biopsy.

4. Key Biomarkers in Immune Profiling

Harrison's Principles of Internal Medicine 22E (2025): "Although PD-L1 has been identified as a biomarker that can predict response to immune checkpoint inhibitors, responses are observed in patients who do not appear to express the biomarker" - highlighting the need for multi-parameter profiling.

5. The Immunoscore - A Landmark Immune Profiling Tool

• Tumors are scored based on CD45RO+ memory T cells and CD8+ CTLs quantified at the center and invasive margins of resected tumors.
• Score ranges: Immunoscore 0 (low density, both regions) to Immunoscore 4 (high density, both regions).
• A low Immunoscore predicts higher risk of relapse, metastases, and death within 5 years.
• In multiple studies, the Immunoscore demonstrated greater prognostic value than TNM staging (the gold standard histological classification).

Cellular and Molecular Immunology (Abbas): "In some studies, the immune score was found to have greater prognostic value than the histologic evaluation of the tumor."

6. Immune Evasion Mechanisms (What Immune Profiling Detects)

1. Reduced antigen presentation: Downregulation/loss of MHC class I molecules - makes tumor invisible to CTLs but susceptible to NK cells.
2. Expression of immune checkpoints: PD-L1 upregulation on tumor cells inhibits PD-1+ T cells; CTLA-4 engagement on Tregs dampens APC function.
3. Immunosuppressive cytokine secretion: TGF-β, IL-10, VEGF suppress effector immune responses.
4. Recruitment of suppressive cells: TAMs, MDSCs, Tregs are actively recruited to the TME.
5. IDO pathway activation: Converts tryptophan to kynurenine, creating a local metabolic immune desert.
6. T-cell exhaustion: Chronic antigen stimulation induces expression of multiple co-inhibitory receptors (PD-1, TIM-3, LAG-3, TIGIT, TOX transcription factor) on CD8+ T cells, rendering them dysfunctional.
7. Neoantigen loss: Immune pressure selects for tumor clones with reduced or mutated neoantigens (immune editing).

Harrison's 22E: "Immune pressure within the tumor microenvironment can select for tumor cells that present few or mutated neoantigens and thus escape ongoing antitumor immunity."

Fig 2. Immune control vs immune evasion in the ovarian cancer TME - illustrating how M2 TAMs, Tregs, MDSCs, and CAFs create an immunosuppressive environment

7. Clinical Applications of Immune Profiling

A. Diagnosis and Classification

• Hematologic malignancies: Flow cytometry-based immunophenotyping is the gold standard for classifying ALL (B-cell vs T-cell), AML (FAB subtype), CLL, NHL subtypes, plasma cell disorders.
• Example: B-ALL - CD19+, CD22+, TdT+; T-ALL - CD3+, CD5+, CD7+; CLL - CD5+, CD19+, CD23+, dim CD20.

B. Prognosis

• TIL scoring in breast cancer: High TIL count, especially in triple-negative (TNBC) and HER2+ subtypes, correlates with complete pathologic response to neoadjuvant therapy and improved survival.
• Immunoscore in CRC: As above.
• NSCLC: CD8+ TIL infiltration predicts better outcomes with checkpoint inhibitors.
• Molecular subtypes (CRC): CMS1 ("MSI Immune" subtype) is highly hypermutated, immune-enriched, and responds best to immunotherapy.

C. Therapy Selection (Predictive Biomarkers)

• PD-L1 IHC (TPS ≥ 50% for pembrolizumab monotherapy in NSCLC first-line).
• TMB-High (≥ 10 mutations/Mb): Tissue-agnostic FDA approval for pembrolizumab.
• MSI-H/dMMR: Tissue-agnostic approval for pembrolizumab; Lynch syndrome screening.
• EBV+, viral status: Predicts checkpoint inhibitor response in gastric cancer and NPC.
• Pre-therapy immunophenotyping before CAR-T cell therapy (e.g., CD19 expression must be confirmed on B-ALL blast cells).

D. Monitoring Therapy Response

• Serial blood-based immune profiling tracks:
  • CD4:CD8 ratio changes during immunotherapy
  • Expansion of activated CD8+ T cell clones (favorable)
  • Development of immune-related adverse event (irAE) signatures
  • Minimal residual disease (MRD) by flow cytometry in leukemia post-therapy

E. Clinical Trial Design

• Immune profiling enables patient stratification for immunotherapy trials (e.g., "cold" vs "hot" tumors for combination vs monotherapy strategies).
• Identification of resistance mechanisms (e.g., reduced TIL infiltration after checkpoint inhibitor failure) guides second-line strategy.

8. Checkpoint Inhibition - The Clinical Culmination of Immune Profiling

• Anti-CTLA-4: Blocks Treg-mediated suppression at lymph nodes; broadens TCR repertoire for tumor neoantigens.
• Anti-PD-1/PD-L1: Reverses T-cell exhaustion in the TME; restores cytolytic function of tumor-infiltrating CD8+ T cells.

Harrison's 22E: "Optimal PD-1 antibody-mediated checkpoint inhibition is seen when infiltrating CD8 T cells are present in the tumor microenvironment and reversal of the exhausted T-cell state can occur in situ."

9. Emerging and Current Updates (2025-2026)

A. Multi-omic / Single-Cell Profiling

• scRNA-seq + spatial transcriptomics integration is now standard in translational oncology. 12 conserved "tumor immune archetypes" have been identified across 12 tumor types from 364 surgical specimens, enabling pan-cancer immune classification.
• CRISPR-based functional screens (e.g., Manguso Laboratory, MGH 2025-26) have identified novel immunotherapy targets including PTPN2 (tyrosine phosphatase) and ADAR1 (dsRNA-editing enzyme) as key mediators of immunotherapy sensitivity and resistance.

B. Next-Generation Checkpoint Inhibitors

• LAG-3 inhibitors (relatlimab + nivolumab - FDA approved for melanoma as "RELATIVITY-047" regimen).
• TIGIT inhibitors (vibostolimab, tiragolumab) - in phase III trials.
• TIM-3 inhibitors in combination trials.
• TOX transcription factor inhibition - emerging strategy to reverse T-cell exhaustion synergistically with checkpoint blockade.

C. Combination Strategies Based on Immune Profile

• Immune profiling-guided combinations: checkpoint inhibitors + targeted therapy (BRAF inhibitors + anti-PD-1 in melanoma), + antibody-drug conjugates, + CAR-T cells.
• "Cold" to "Hot" TME conversion: Strategies using radiation, oncolytic viruses, innate immune agonists (STING agonists) to remodel immunosuppressive TMEs - patient selection guided by immune profiling.

D. Resistance Mechanisms (Identified by Immune Profiling)

• Reduced TIL infiltration post-ICB
• Increased MDSC/Treg populations
• Loss of MHC-I expression
• SERPINB9 (PI-9) upregulation blocking granzyme B
• STK11/KEAP1 mutations in NSCLC (creates immunosuppressive TME, predicts poor response to PD-1 inhibitors)

E. CAR-T Cell Therapy and Immune Profiling

• Pre-infusion immune profiling of the recipient predicts CAR-T engraftment and efficacy.
• Profiling identifies optimal T cell phenotypes for manufacturing (stem cell memory T cells > central memory > effector memory).
• Post-infusion monitoring tracks CAR-T persistence and cytokine release syndrome (CRS) - characterized by IL-6, IFN-γ, ferritin elevation.

[Prostate cancer CAR-T therapy systematic review, BMC Cancer 2026 (PMID: 41826891)] confirms expanding CAR-T applications require careful immune profiling for patient selection.

10. Molecular Classification Linked to Immune Profiling

Colorectal Cancer (CRC) Consensus Molecular Subtypes (CMS)

Pancreatic Adenocarcinoma (PDAC)

• Immunogenic subtype: Rare but more likely to respond to immunotherapy
• Squamous subtype: TP53/KDM6A mutations; poor prognosis
• COMPASS trial validated pre-therapy genomic profiling to predict FOLFIRINOX response

11. Summary Table for Quick Revision

12. Key References

• Cellular and Molecular Immunology, 10th Ed. (Abbas, Lichtman & Pillai) - Chapter 18: Immunity to Tumors
• Robbins & Cotran Pathologic Basis of Disease, 10th Ed. (Kumar, Abbas, Aster) - Chapter on Neoplasia; Breast Cancer TME
• Harrison's Principles of Internal Medicine, 22nd Ed. (2025, McGraw Hill) - Chapters on Checkpoint Inhibition Therapy, Evasion of the Immune System by Cancers, NSCLC Management
• Yamada's Textbook of Gastroenterology, 7th Ed. - Tumor Immune Microenvironment in GI cancers; CRC molecular subtypes
• Harper's Illustrated Biochemistry, 32nd Ed. - Tumor Microenvironment and exosome-mediated signaling
• Goodman & Gilman's Pharmacological Basis of Therapeutics - Ipilimumab, PD-1/PD-L1 pharmacology
• Springer Nature, Medical Oncology (2025): Immune profiling in oncology: bridging the gap between technology and treatment - Current state of the art review
• StatPearls (NCBI): Immunophenotyping - Clinical Significance - Diagnostic applications

• The Immunoscore is a favorite exam topic - know it uses CD3/CD8 markers in CRC and has better prognostic value than TNM in some studies.
• PD-L1, TMB, MSI-H are the three tissue-agnostic/standard biomarkers tested before checkpoint inhibitors - memorize which thresholds trigger which drugs.
• MSI-H/dMMR = pembrolizumab (tissue-agnostic) - connects immune profiling to Lynch syndrome.
• The key message of immune evasion is multiple simultaneous mechanisms - no single biomarker is perfect, hence the need for multi-parameter immune profiling.
• "Cold" vs "Hot" tumor terminology is increasingly used clinically - know what determines each category.