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REVIEW 4 major objections 6 minor 139 references

The paper claims that a global sensitivity analysis of a five-population ODE model identifies the CAF-driven boost to tumor proliferation and M2-macrophage-driven immune suppression as the most influential regulators of TNBC tumor burden ov

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2026-08-03 09:46 UTC pith:D6PSWIHD

load-bearing objection A clearly-built but under-validated ODE sensitivity framework; the top target ranking is real but partly a consequence of arbitrary threshold values. the 4 major comments →

arxiv 2601.12455 v2 pith:D6PSWIHD submitted 2026-01-18 q-bio.QM

Identifying Therapeutic Targets for Triple-Negative Breast Cancer using a Novel Mathematical Model of the Tumor Microenvironment

Kyle Adams , Julia Bruner , Salma Ameziane , Ashley Brown , Mohammed Gbadamosi , Helen Moore This is my paper
classification q-bio.QM MSC 92C5092C4234A34
keywords triple-negative breast cancertumor microenvironmentmathematical modelordinary differential equationsglobal sensitivity analysisSobol indicescancer-associated fibroblastsM2 macrophages
verification ladder T0 review T1 audit T2 compute T3 formal T4 reserved

The pith

A machine-rendered reading of the paper's core claim, the machinery that carries it, and where it could break.

This paper tries to establish that a simple five-population ordinary differential equation model of the triple-negative breast cancer tumor microenvironment, when subjected to a global variance-based sensitivity analysis, reveals a small set of parameters that dominate tumor burden over the 168-day treatment-relevant window. The authors argue that, after the expected tumor-intrinsic parameters, the most influential pathways are those by which cancer-associated fibroblasts and M2 macrophages boost tumor growth and suppress cytotoxic T-cell activity. If true, the model gives a quantitative, literature-grounded tool for prioritizing combination-therapy hypotheses in a cancer with few targeted options. The paper is careful to present the findings as hypothesis-generating rather than predictions of therapeutic efficacy.

Core claim

The authors construct a system of five coupled ODEs for M2 macrophages, cancer-associated fibroblasts, TNBC tumor cells, activated cytotoxic T lymphocytes, and regulatory T cells, with each interaction term bounded by Michaelis-Menten kinetics. They then run a Sobol global sensitivity analysis over 39 parameters, taking the computed tumor volume at day 168 as the quantity of interest. Their central claim is that the resulting ranking identifies a small subset of parameters that exert a disproportionate influence on tumor burden, and that the most-influential parameters correspond to CAF-mediated and M2-macrophage-mediated support of tumor growth — mechanisms consistent with emerging therapeu

What carries the argument

The central machinery is the five-equation ODE model together with the Sobol variance-based global sensitivity analysis. Michaelis-Menten terms keep all boosting and inhibiting interactions bounded for biological plausibility, and the Sobol total-order indices rank each parameter's combined direct and interaction-driven contribution to the day-168 tumor volume. The histogram and pairwise-projection analyses are then used to show that varying only the top five or six parameters reproduces most of the variability generated by varying all thirty-nine, and to visualize interaction effects between the leading parameters.

Load-bearing premise

The sensitivity ranking is meaningful only if the nominal parameter values and their uncertainty ranges represent human Stage II TNBC; many values are estimated, taken from other cancers or species, or set by the rule that beta equals the corresponding initial cell count times four.

What would settle it

Re-estimate every beta parameter from TNBC-specific measurements (replacing the 'initial value times four' rule) and re-run the Sobol analysis; if the top-six ranking changes, the paper's central ordering is an artifact of its estimation rule. Alternatively, measure the CAF fold-boost (alpha_g) in human TNBC co-cultures: the sensitivity analysis samples only 0.5 to 1.5 times the nominal value of 1.63, so a measured value near 1 would demote CAF support from its current rank.

Watch this falsifier — get emailed when new claim-graph text bears on it.

If this is right

  • The top influencers are the TNBC proliferation rate and tumor carrying capacity, so any therapy that slows intrinsic growth should still be the primary lever.
  • CAF-driven tumor proliferation and M2-macrophage-driven tumor proliferation are the most influential microenvironment parameters, indicating stromal support as a high-priority target.
  • The pairwise analysis shows that a fast-proliferating tumor can still be kept small by a weak CAF boost, suggesting CAF-secreted factors as a combination-therapy component.
  • Parameters should be read as net effects of several biological mechanisms, so the ranking points to pathway-level intervention points rather than specific molecules.
  • The framework supports future in silico testing of combination regimens, including optimal-control approaches.

Where Pith is reading between the lines

These are editorial extensions of the paper, not claims the author makes directly.

  • A natural extension is to re-estimate every beta parameter from TNBC-specific measurements rather than the 'initial value times four' rule, and then check whether the top-six ranking persists.
  • Because the quantity of interest is day-168 tumor volume, the ranking favours pathways that act on early growth; a long-term or recurrence endpoint may shift priorities toward immune-regulatory parameters.
  • The aggregation of M1 macrophages and CD4+ T cells into implicit effects means the 'M2' and 'CAF' pathways may summarize several distinct molecular mechanisms, so the hits should be resolved at the level of specific factors (e.g., Chi3L1, TGF-beta, CXCL12) before clinical planning.
  • The same sensitivity-analysis protocol could be applied to other aggressive cancers with similar stromal-immune landscapes to see whether CAF/M2 dominance is a general phenomenon or specific to TNBC.

Editorial analysis

A structured set of objections, weighed in public.

Desk editor's note, referee report, simulated authors' rebuttal, and a circularity audit.

Referee Report

4 major / 6 minor

Summary. The paper develops a five-population ODE model of the TNBC tumor microenvironment (M2 macrophages, CAFs, tumor cells, CTLs, and Tregs) and performs a global Sobol sensitivity analysis to identify parameters that most influence tumor volume at day 168. The authors report that the top-ranked parameters correspond to CAF- and M2-related pathways and interpret this as supporting the relevance of stroma- and immune-mediated tumor support as therapeutic targets. The model is intended as a hypothesis-generation tool; treatment simulations are not included.

Significance. If the sensitivity ranking were robust to parameter uncertainty, the paper would provide a useful quantitative framework for prioritizing combination-therapy hypotheses in TNBC. The Sobol computation itself is well powered (175,000 base samples, 7,175,000 model evaluations) and follows standard Saltelli estimators. The authors are transparent about many parameter estimates being drawn from non-TNBC settings or marked as 'estimated.' However, the central claim that CAF/M2 pathways disproportionately control tumor burden is not yet supported, because the ranking is heavily influenced by an arbitrary beta-parameter rule and the model is not calibrated or validated. The strongest contribution is the model structure and the transparent parameter table; the weakest part is the leap from sensitivity indices to therapeutic-target conclusions without robustness checks or external validation.

major comments (4)
  1. [Parameter Calculations / Beta Parameters; Table 3] All beta parameters are set as 4 times the corresponding initial population, e.g., beta_g = 4 x 722 = 2888 mm^3 (row 17). This arbitrary rule fixes every Michaelis-Menten term at 20% saturation at baseline. With uniform +/-50% ranges, the Sobol indices for these betas are essentially guaranteed to be non-negligible. In particular, beta_g (#3) and beta_f (#15) are not biologically measured; their high ranking is an artifact of the chosen multiplier. The authors should test robustness by varying the multiplier (e.g., 2, 6, 10) or by using literature-based saturation estimates, and show whether the ranking of CAF/M2 parameters persists.
  2. [Model and Justifications / Simulations; Eq. (3)] The model is never calibrated or validated against observed TNBC tumor dynamics. The discussion claims the sensitivity analysis identifies parameters that 'exert a disproportionate influence on tumor burden,' but the nominal parameters and ranges are not grounded in TNBC-specific longitudinal data. Many parameters come from melanoma, lung cancer, LCMV-infected mice, or chick-embryo fibroblasts. Without calibration or a predictive check against independent data, the sensitivity ranking cannot support therapeutic-target conclusions. At minimum, the authors should perform a model-to-data comparison (e.g., patient tumor growth trajectories or xenograft data) or clearly reframe the claims as purely structural sensitivity of an unvalidated model.
  3. [Discussion; Figure 4; Eq. (3)] The interpretation that the top parameters correspond to 'CAF and M2 macrophage-mediated immune suppression' is not supported by the model. In Eq. (3), alpha_g and alpha_f directly multiply the logistic tumor growth term as boosts to proliferation, not immune-suppression pathways. The top-ranked parameters include the intrinsic proliferation rate and carrying capacity, followed by alpha_g, beta_g, and alpha_f, which are direct proliferative modulators. The sensitivity analysis does not separate immune suppression from direct stromal/macrophage support; the Discussion's phrasing overstates what the model identifies.
  4. [Simulations and Sensitivity Analysis; uniform distributions] The choice of uniform +/-50% ranges for all parameters is not justified, and the sensitivity indices are known to depend on the input distributions. The paper does not report any robustness analysis over alternative distribution shapes or widths. For parameters with strong biological constraints (e.g., death rates, thresholds with units inconsistent in Table 3), uniform ranges may place significant mass in implausible regions. The authors should either justify the ranges with data or include a distributional sensitivity check to demonstrate that the top-ranking parameters are stable.
minor comments (6)
  1. [Figure 1 caption] The caption says 'Each mechanism is labeled and discussed in detail in section blank.' The cross-reference is missing; please fill in the appropriate section.
  2. [Table 3, rows 7 and 20] The units for the CTL-related threshold parameters (beta_h and beta_t) are listed as 'cells/mm^3*day'; they should be 'cells/mm^3' to match the Michaelis-Menten form used in the equations.
  3. [Initial Value Calculations / Tregs] The text describes converting a percentage of tumor volume to a number of Tregs, then states 'We adjusted this value to 10.9e3 based on our clinical authors’ input.' The preceding computed number is 8.91e7 cells/mm^3, so the adjustment is a large reduction. Please clarify the reasoning and the exact final value used in Table 2.
  4. [Figure 6 caption] The caption says the blue curve is produced by 'freezing the top 5' most-influential parameters, while the text earlier says the top six parameters are used for the orange curve. Please make the statement consistent.
  5. [Model and Justifications / Equations (1)–(5)] The equations are not explicitly displayed in the text; the descriptions refer to pathways (a, b, c, etc.) but the ODE system itself is not written out in the manuscript. For reproducibility, the full equations should be included in the main text or in a clearly numbered appendix.
  6. [General] Several 'estimated' parameters (e.g., s_M2 = 10,000 cells/mm^3/day, d_m = 0.000998/day) are presented without a derivation or justification beyond 'clinical expertise.' A supplementary table with a brief rationale or a sensitivity analysis excluding these parameters would help readers judge their influence.

Circularity Check

2 steps flagged

CAF/M2 'disproportionate influence' is partly forced by the arbitrary beta=4x-initial-value convention and by fitting alpha from studies that already demonstrate CAF/M2-driven growth.

specific steps
  1. self definitional [Parameter Calculations (Beta Parameters); Table 3 rows 2, 4, 7, 15, 17, 20, 22, 24, 26, 30, 32, 36, 38]
    "We systematically estimated beta parameters by taking the corresponding cell population’s initial value and multiplying it by 4."

    The beta thresholds are not measured or calibrated; they are constructed as 4 times the corresponding initial population. For example, beta_g = 4 x 722 mm^3 = 2888 mm^3 and beta_f = 4 x 21500 cells/mm^3 = 86000 cells/mm^3. This places every initial population at exactly 20% of the Michaelis-Menten saturation point. Uniform +/-50% variation around these constructed thresholds then guarantees non-negligible Sobol indices for beta. The paper's third-ranked parameter, beta_g, is therefore influential by construction: changing the arbitrary multiplier would change the ranking. Calling this an identified biological driver of tumor burden reduces to a modeling convention, not an independent finding.

  2. fitted input called prediction [Alpha Parameters (alpha_g, alpha_f) and Discussion]
    "Takai et al. studied CAFs in human TNBC and how they promote tumor growth... we estimate the tumor volume with CAFs ... to be 2100 mm^3, and estimate the tumor volume without CAFs ... to be 800 mm^3 ... Thus alpha_g = 2.625 - 1 = 1.625. ... Tu et al. studied how M2 macrophages contribute to breast cancer cell proliferation ... Thus alpha_f = 1.318 - 1 = 0.318. ... the most-influential parameters correspond to pathways involving CAF and M2 macrophage-mediated immune suppression."

    The alpha parameters are computed as fold increases in tumor growth measured in experiments that already establish CAF-driven and M2-driven tumor promotion. The global sensitivity analysis then outputs these same parameters as the most influential. This is not a prediction; it is a recapitulation of the input data through the model structure. The Discussion's claim that the sensitivity analysis 'reveals' CAF and M2 pathways as influential presents fitted inputs as an independent result. The circularity is partial because the Sobol ranking also depends on model structure and ranges, but the central claim is substantially encoded in the parameter-fitting choices.

full rationale

The paper is not globally circular: the ODE model is self-contained, the Sobol analysis is a standard variance decomposition, and there is no load-bearing self-citation chain or imported uniqueness theorem. However, the central claim that CAF/M2 pathways 'disproportionately influence' tumor burden is partially forced by two construction choices. First, all beta thresholds are defined as 4 times initial population values, which sets the operating point for every Michaelis-Menten term and makes the beta thresholds sensitive by construction; beta_g is the third-ranked parameter even though it is a purely conventional threshold. Second, the top alpha parameters are fitted from, and numerically encode, experiments demonstrating CAF/M2 promotion of tumor growth, so the sensitivity ranking re-identifies the fitted inputs rather than deriving new biological signal. The Discussion also labels alpha_g and alpha_f as 'immune suppression' when they are direct proliferation boosts, an interpretive leap that is not supported by the model. These issues warrant a moderate circularity score: parts of the headline result reduce to the parameterization convention, but the modeling and sensitivity-analysis workflow itself is legitimate and not self-referential.

Axiom & Free-Parameter Ledger

17 free parameters · 7 axioms · 0 invented entities

The model contains 39 parameters, almost all of which are literature-derived or hand-estimated. The most load-bearing are the alpha interaction strengths and the half-saturation thresholds, many labeled 'estimated.' The beta-parameter rule (4× initial value) is a pure modeling convention. No new physical entities are introduced; the 'M1 via M2 loss' proxy is a modeling abstraction rather than an invented biological object.

free parameters (17)
  • M2 source rate s_M2 = 10000 cells/mm^3/day
    Estimated upward from a 4T1 migration-chamber study by orders of magnitude; no direct TNBC data.
  • Treg recruitment rate s_Treg = 583.33 cells/mm^3/day
    Computed from a murine lung-cancer labeling study with assumptions about mouse lung volume and tumor fraction.
  • CAF boost on TNBC proliferation α_g = 1.63
    Derived from a TNBC xenograft tumor-volume comparison (Takai et al.); directly enters the tumor-growth equation.
  • M2 boost on TNBC proliferation α_f = 0.318
    Derived from M2-conditioned-medium proliferation data (Tu et al.); directly enters the tumor-growth equation.
  • CTL kill effect α_t = 29
    Estimated from a melanoma CTL-injection study (Khazen et al.); used in the tumor death term.
  • M2 suppression of CTL activity α_o = 0.758
    Estimated from a VISTA-inhibitor melanoma study; used to scale CTL killing.
  • Tumor suppression of CTL activity α_r = 0.822
    Inferred from a granzyme-percentage proxy combining healthy donors and a TNBC macrophage study.
  • Treg suppression of CTL cytotoxicity α_w = 0.545
    Estimated from a murine colon-carcinoma Treg co-culture study.
  • M1-mediated CTL proliferation boost α_n = 7
    Estimated from PI3Kγ-inhibitor data in a murine breast-cancer model.
  • Treg inhibition of CTL proliferation α_y = 0.946
    Estimated from in vitro IL-2 measurements in healthy murine cells.
  • M2 boost to Treg recruitment α_b = 0.471
    Estimated from a JNK-active TNBC cluster study using pJNK-high vs pJNK-low Treg fractions.
  • Tumor boost to Treg recruitment α_p = 1.063
    Estimated from Treg percentages in malignant versus normal TNBC tissue.
  • Half-saturation threshold parameters K_* = various: 8360, 2890, 536000, 86000, 2890, 536000, 86000, 8360, 43600, 17200000, 43600, 86000, 8360
    Marked 'estimated' in Table 3; chosen without direct experimental fits, affecting when boosts and inhibitions saturate.
  • Beta parameters (all interaction β's) = 4 × initial cell population
    Defined by the rule 'taking the corresponding cell population's initial value and multiplying it by 4'; no biological derivation is given.
  • M2-to-M1 repolarization rate e = 0.005 1/day
    Chosen as 'reasonable' after computing an upper bound of 0.2128 from a treatment study; the value itself is a hand-picked constant.
  • CAF differentiation rate d_m = 0.000998 1/day
    Labeled 'estimated' in Table 3 with the assumption that CAF differentiation is slower than tumor proliferation.
  • Carrying capacities K_CAF, K_B, K_CTL = 65450 mm^3, 262000 mm^3, 223000 cells/mm^3
    Estimated from a 10-cm tumor volume and from CAF/CTL fractions; K_CAF is set to 50% of K_B by choice.
axioms (7)
  • domain assumption Five ODE populations with Michaelis-Menten bounded interactions capture the key TNBC TME dynamics as 'net effects'.
    The authors aggregate multiple biological mechanisms into single pathways to avoid identifiability problems; the central sensitivity analysis depends on this reduction being faithful enough.
  • domain assumption Parameters estimated from non-TNBC or non-human systems are transferable to human Stage II TNBC.
    Table 3 uses melanoma, lung cancer, LCMV-infected mouse, chick-embryo fibroblast, and healthy-human data; the sensitivity ranking inherits this transferability assumption.
  • domain assumption Initial values represent an average Stage II TNBC tumor composition.
    Based on 10 TNBC samples of unreported stage, including two previously treated patients, plus geometric assumptions and clinical adjustment; the authors acknowledge heterogeneity.
  • ad hoc to paper Uniform ±50% parameter ranges are appropriate for global sensitivity analysis.
    No empirical uncertainty estimates are available; ranges are chosen by the authors, with inhibitory alphas capped at 1 to avoid sign reversal.
  • ad hoc to paper Beta parameters can be set as 4 times the initial population value.
    This rule appears in the Beta Parameters section with no experimental justification; it directly sets the scale of many interaction terms.
  • domain assumption Tregs are continuously recruited from a source outside the model.
    The Treg equation includes recruitment terms from an external pool, following the labeling-study conclusion that Tregs are not self-renewing in the TME.
  • standard math Sobol variance-based sensitivity indices correctly estimate parameter influence for this ODE system.
    Sobol decomposition is a standard global sensitivity method; the main caveat is that the input distributions are themselves chosen ad hoc.

pith-pipeline@v1.3.0-alltime-deepseek · 40426 in / 13388 out tokens · 141412 ms · 2026-08-03T09:46:03.584037+00:00 · methodology

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Triple-negative breast cancer (TNBC) is an aggressive disease with high mortality and limited treatment options, due to its lack of receptors that have targeted therapies available. The tumor microenvironment (TME) plays a critical role in TNBC progression and therapeutic resistance. In this work, we developed a novel mathematical model to describe key cellular interactions within the TNBC TME, informed by current literature and expert input. Our model consists of a system of ordinary differential equations representing five interacting cell populations: M2 macrophages, cancer-associated fibroblasts, TNBC tumor cells, cytotoxic T lymphocytes, and regulatory T cells. We performed global sensitivity analysis to determine which model parameters most strongly influence tumor burden over a clinically-relevant treatment timeframe. The pathways associated with the most-influential parameters correspond to biological mechanisms that are consistent with known and emerging therapeutic strategies in TNBC, including stromal-mediated tumor support. These results highlight key regulatory interactions within the TNBC TME and provide a quantitative framework for hypothesis generation and future investigation of combination treatment strategies.

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Reference graph

Works this paper leans on

139 extracted references · 108 canonical work pages

  1. [2]

    American Cancer Society

    Triple-negative Breast Cancer | Details, Diagnosis, and Signs. American Cancer Society. March 1, 2023. Accessed July 11, 2024. https://www.cancer.org/cancer/types/breast-cancer/about/types-of-breast-cancer/triple-negative.html

    2023

  2. [3]

    Breast Cancer Statistics, 2022

    Giaquinto AN, Sung H, Miller KD, et al. Breast Cancer Statistics, 2022. CA Cancer J Clin . 2022;72(6):524-541. doi:10.3322/caac.21754

    work page doi:10.3322/caac.21754 2022

  3. [4]

    The Characteristics of Tumor Microenvironment in Triple Negative Breast Cancer

    Fan Y, He S. The Characteristics of Tumor Microenvironment in Triple Negative Breast Cancer. Cancer Manag Res . 2022;14:1-17. doi:10.2147/CMAR.S316700

    work page doi:10.2147/cmar.s316700 2022

  4. [5]

    Crosstalk between triple negative breast cancer and microenvironment

    Smrekar K, Belyakov A, Jin K. Crosstalk between triple negative breast cancer and microenvironment. Oncotarget . 2023;14:284-293. doi:10.18632/oncotarget.28397

    work page doi:10.18632/oncotarget.28397 2023

  5. [6]

    Mathematical model optimized for prediction and health care planning for COVID-19

    Garrido JM, Martínez-Rodríguez D, Rodríguez-Serrano F, et al. Mathematical model optimized for prediction and health care planning for COVID-19. Med Intensiva . 2022;46(5):248-258. doi:10.1016/j.medine.2022.02.020

    work page doi:10.1016/j.medine.2022.02.020 2022

  6. [7]

    Optimizing the future: how mathematical models inform treatment schedules for cancer

    Mathur D, Barnett E, Scher HI, Xavier JB. Optimizing the future: how mathematical models inform treatment schedules for cancer. Trends Cancer . 2022;8(6):506-516. doi:10.1016/j.trecan.2022.02.005

    work page doi:10.1016/j.trecan.2022.02.005 2022

  7. [8]

    Determining Optimal Combination Regimens for Patients with Multiple Myeloma

    Aghaee M, Ledzewicz U, Robbins M, Bezman N, Cho HJ, Moore H. Determining Optimal Combination Regimens for Patients with Multiple Myeloma. arXiv . Preprint posted online November 16, 2022. Accessed June 23, 2024. http://arxiv.org/abs/2211.09280

    Pith/arXiv arXiv 2022

  8. [9]

    A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors

    Yin A, Moes DJAR, van Hasselt JGC, Swen JJ, Guchelaar H. A Review of Mathematical Models for Tumor Dynamics and Treatment Resistance Evolution of Solid Tumors. CPT Pharmacomet Syst Pharmacol . 2019;8(10):720-737. doi:10.1002/psp4.12450

    work page doi:10.1002/psp4.12450 2019

  9. [10]

    The clinical promise of immunotherapy in triple-negative breast cancer

    Vikas P, Borcherding N, Zhang W. The clinical promise of immunotherapy in triple-negative breast cancer. Cancer Manag Res . 2018;10:6823-6833. doi:10.2147/CMAR.S185176

    work page doi:10.2147/cmar.s185176 2018

  10. [11]

    Modeling of Mouse Experiments Suggests that Optimal Anti-Hormonal Treatment for Breast Cancer is Diet-Dependent

    Akman T, Arendt LM, Geisler J, Kristensen VN, Frigessi A, Köhn-Luque A. Modeling of Mouse Experiments Suggests that Optimal Anti-Hormonal Treatment for Breast Cancer is Diet-Dependent. Bull Math Biol . 2024;86(4):42. doi:10.1007/s11538-023-01253-1

    work page doi:10.1007/s11538-023-01253-1 2024

  11. [12]

    Computational Model Predicts Patient Outcomes in Luminal B Breast Cancer Treated with Endocrine Therapy and CDK4/6 Inhibition

    Schmiester L, Brasó-Maristany F, González-Farré B, et al. Computational Model Predicts Patient Outcomes in Luminal B Breast Cancer Treated with Endocrine Therapy and CDK4/6 Inhibition. Clin Cancer Res Off J Am Assoc Cancer Res . 2024;30(17):3779-3787. doi:10.1158/1078-0432.CCR-24-0244

    work page doi:10.1158/1078-0432.ccr-24-0244 2024

  12. [13]

    Global Dynamics of a Breast Cancer Competition Model

    Abernathy K, Abernathy Z, Baxter A, Stevens M. Global Dynamics of a Breast Cancer Competition Model. Differ Equ Dyn Syst . 2020;28(4):791-805. doi:10.1007/s12591-017-0346-x 29

    work page doi:10.1007/s12591-017-0346-x 2020

  13. [14]

    A Cancer Subpopulation Competition Model Reveals Optimal Levels of Immune Response that Minimize Tumor Size

    Sukpol W, Laomettachit T, Tangthanawatsakul A. A Cancer Subpopulation Competition Model Reveals Optimal Levels of Immune Response that Minimize Tumor Size. J Comput Biol . 2024;31(11):1179-1194. doi:10.1089/cmb.2024.0618

    arXiv 2024

  14. [15]

    A fractional mathematical model of breast cancer competition model

    Solís-Pérez JE, Gómez-Aguilar JF, Atangana A. A fractional mathematical model of breast cancer competition model. Chaos Solitons Fractals . 2019;127:38-54. doi:10.1016/j.chaos.2019.06.027

    work page doi:10.1016/j.chaos.2019.06.027 2019

  15. [16]

    Fractional mathematical modeling of breast cancer stages with true data from Saudi Arabia

    Chavada A, Pathak N, Raval R. Fractional mathematical modeling of breast cancer stages with true data from Saudi Arabia. Results Control Optim . 2024;15:100431. doi:10.1016/j.rico.2024.100431

    arXiv 2024

  16. [17]

    Fractional and stochastic modeling of breast cancer progression with real data validation

    Aldwoah K, Louati H, Eljaneid N, Aljaaidi T, Alqarni F, Elsayed A. Fractional and stochastic modeling of breast cancer progression with real data validation. PLOS ONE . 2025;20(1):e0313676. doi:10.1371/journal.pone.0313676

    work page doi:10.1371/journal.pone.0313676 2025

  17. [18]

    Mathematical modelling of trastuzumab-induced immune response in an in vivo murine model of HER2+ breast cancer

    Jarrett AM, Bloom MJ, Godfrey W, et al. Mathematical modelling of trastuzumab-induced immune response in an in vivo murine model of HER2+ breast cancer. Math Med Biol . 2019;36(3):381-410. doi:10.1093/imammb/dqy014

    work page doi:10.1093/imammb/dqy014 2019

  18. [19]

    A Mathematical Model of Breast Cancer Treatment with CMF and Doxorubicin

    Roe-Dale R, Isaacson D, Kupferschmid M. A Mathematical Model of Breast Cancer Treatment with CMF and Doxorubicin. Bull Math Biol . Published online June 8, 2010:585-608. doi:10.1007/s11538-010-9549-9

    work page doi:10.1007/s11538-010-9549-9 2010

  19. [20]

    Mathematical modeling of tumor growth and treatment: Triple negative breast cancer

    Wei HC. Mathematical modeling of tumor growth and treatment: Triple negative breast cancer. Math Comput Simul . 2023;204:645-659. doi:10.1016/j.matcom.2022.09.005

    work page doi:10.1016/j.matcom.2022.09.005 2023

  20. [21]

    Mathematical modeling of ER-positive breast cancer treatment with AZD9496 and palbociclib

    Wei HC. Mathematical modeling of ER-positive breast cancer treatment with AZD9496 and palbociclib. AIMS Math . 2020;5(4):3446-3455. doi:10.3934/math.2020223

    work page doi:10.3934/math.2020223 2020

  21. [22]

    Asymptotic analysis of mathematical model describing a new treatment of breast cancer using AZD9496 and palbociclib

    Nave O. Asymptotic analysis of mathematical model describing a new treatment of breast cancer using AZD9496 and palbociclib. Front Oncol . 2025;14. doi:10.3389/fonc.2024.1482223

    arXiv 2025

  22. [23]

    Dormant Tumor Cell Vaccination: A Mathematical Model of Immunological Dormancy in Triple-Negative Breast Cancer

    Mehdizadeh R, Shariatpanahi SP, Goliaei B, Peyvandi S, Rüegg C. Dormant Tumor Cell Vaccination: A Mathematical Model of Immunological Dormancy in Triple-Negative Breast Cancer. Cancers . 2021;13(2):245. doi:10.3390/cancers13020245

    work page doi:10.3390/cancers13020245 2021

  23. [24]

    A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration

    Mohammad Mirzaei N, Su S, Sofia D, et al. A Mathematical Model of Breast Tumor Progression Based on Immune Infiltration. J Pers Med . 2021;11(10):1031. doi:10.3390/jpm11101031

    work page doi:10.3390/jpm11101031 2021

  24. [25]

    Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model

    Mirzaei NM, Changizi N, Asadpoure A, et al. Investigating key cell types and molecules dynamics in PyMT mice model of breast cancer through a mathematical model. PLOS Comput Biol . 2022;18(3):e1009953. doi:10.1371/journal.pcbi.1009953

    work page doi:10.1371/journal.pcbi.1009953 2022

  25. [26]

    A transcriptome-informed QSP model of metastatic triple-negative breast cancer identifies predictive biomarkers for PD-1 inhibition

    Arulraj T, Wang H, Emens LA, Santa-Maria CA, Popel AS. A transcriptome-informed QSP model of metastatic triple-negative breast cancer identifies predictive biomarkers for PD-1 inhibition. Sci Adv . 9(26):eadg0289. doi:10.1126/sciadv.adg0289

    work page doi:10.1126/sciadv.adg0289

  26. [27]

    Experimentally-driven mathematical model to understand the effects of matrix deprivation in breast cancer metastasis

    Maiti S, Rangarajan A, Kareenhalli V. Experimentally-driven mathematical model to understand the effects of matrix deprivation in breast cancer metastasis. NPJ Syst Biol Appl . 2024;10:132. doi:10.1038/s41540-024-00443-4 30

    work page doi:10.1038/s41540-024-00443-4 2024

  27. [28]

    Mathematical optimization of breast cancer treatments: analyzing control strategies for hormone therapy, ketogenic diet and immunotherapy

    Akil H, Fatmi NI, Hattaf K. Mathematical optimization of breast cancer treatments: analyzing control strategies for hormone therapy, ketogenic diet and immunotherapy. J Math Comput Sci . 2025;15(0):Article ID 9. doi:10.28919/10.28919/jmcs/9420

    work page doi:10.28919/10.28919/jmcs/9420 2025

  28. [29]

    Anderson, Mark A.J

    Enderling H, Alexander R.A. Anderson, Mark A.J. Chaplain, Alastair J. Munro, Jayant S. Vaidya. Mathematical modelling of radiotherapy strategies for early breast cancer. J Theor Biol . 2006;241(1):158-171. doi:https://doi.org/10.1016/j.jtbi.2005.11.015

    work page doi:10.1016/j.jtbi.2005.11.015 2006

  29. [30]

    Mathematical model of macrophage-facilitated breast cancer cells invasion

    Knútsdóttir H, Pálsson E, Edelstein-Keshet L. Mathematical model of macrophage-facilitated breast cancer cells invasion. J Theor Biol . 2014;357:184-199. doi:10.1016/j.jtbi.2014.04.031

    work page doi:10.1016/j.jtbi.2014.04.031 2014

  30. [31]

    Personalizing neoadjuvant chemotherapy regimens for triple-negative breast cancer using a biology-based digital twin

    Christenson C, Wu C, Hormuth DA, et al. Personalizing neoadjuvant chemotherapy regimens for triple-negative breast cancer using a biology-based digital twin. NPJ Syst Biol Appl . 2025;11:53. doi:10.1038/s41540-025-00531-z

    work page doi:10.1038/s41540-025-00531-z 2025

  31. [32]

    Fast model calibration for predicting the response of breast cancer to chemotherapy using proper orthogonal decomposition

    Christenson C, Wu C, Hormuth DA, et al. Fast model calibration for predicting the response of breast cancer to chemotherapy using proper orthogonal decomposition. J Comput Sci . 2024;82:102400. doi:10.1016/j.jocs.2024.102400

    arXiv 2024

  32. [33]

    Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model

    Weis JA, Miga MI, Arlinghaus LR, et al. Predicting the Response of Breast Cancer to Neoadjuvant Therapy Using a Mechanically Coupled Reaction-Diffusion Model. Cancer Res . 2015;75(22):4697-4707. doi:10.1158/0008-5472.CAN-14-2945

    work page doi:10.1158/0008-5472.can-14-2945 2015

  33. [34]

    A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin

    Miniere HJM, Lima EABF, Lorenzo G, et al. A mathematical model for predicting the spatiotemporal response of breast cancer cells treated with doxorubicin. Cancer Biol Ther . 25(1):2321769. doi:10.1080/15384047.2024.2321769

    arXiv 2024

  34. [35]

    An agent-based model of triple-negative breast cancer: the interplay between chemokine receptor CCR5 expression, cancer stem cells, and hypoxia

    Norton KA, Wallace T, Pandey NB, Popel AS. An agent-based model of triple-negative breast cancer: the interplay between chemokine receptor CCR5 expression, cancer stem cells, and hypoxia. BMC Syst Biol . 2017;11(1):68. doi:10.1186/s12918-017-0445-x

    work page doi:10.1186/s12918-017-0445-x 2017

  35. [37]

    Triple-negative breast cancer influences a mixed M1/M2 macrophage phenotype associated with tumor aggressiveness

    Pe KCS, Saetung R, Yodsurang V, et al. Triple-negative breast cancer influences a mixed M1/M2 macrophage phenotype associated with tumor aggressiveness. PLoS ONE . 2022;17(8):e0273044. doi:10.1371/journal.pone.0273044

    work page doi:10.1371/journal.pone.0273044 2022

  36. [38]

    The Interaction between Macrophages and Triple-negative Breast Cancer Cells Induces ROS-Mediated Interleukin 1α Expression to Enhance Tumorigenesis and Metastasis

    Hao M, Huang B, Wu R, Peng Z, Luo KQ. The Interaction between Macrophages and Triple-negative Breast Cancer Cells Induces ROS-Mediated Interleukin 1α Expression to Enhance Tumorigenesis and Metastasis. Adv Sci . 2023;10(29):2302857. doi:10.1002/advs.202302857

    work page doi:10.1002/advs.202302857 2023

  37. [39]

    High infiltration of tumor-associated macrophages in triple-negative breast cancer is associated with a higher risk of distant metastasis

    Yuan ZY, Luo RZ, Peng RJ, Wang SS, Xue C. High infiltration of tumor-associated macrophages in triple-negative breast cancer is associated with a higher risk of distant metastasis. OncoTargets Ther . 2014;7:1475-1480. doi:10.2147/OTT.S61838

    work page doi:10.2147/ott.s61838 2014

  38. [40]

    M1/M2 macrophages and their overlaps – myth or reality? Clin Sci Lond Engl 1979

    Strizova Z, Benesova I, Bartolini R, et al. M1/M2 macrophages and their overlaps – myth or reality? Clin Sci Lond Engl 1979 . 2023;137(15):1067-1093. doi:10.1042/CS20220531 31

    work page doi:10.1042/cs20220531 1979

  39. [41]

    Targeting M2-like tumor-associated macrophages is a potential therapeutic approach to overcome antitumor drug resistance

    Wang S, Wang J, Chen Z, et al. Targeting M2-like tumor-associated macrophages is a potential therapeutic approach to overcome antitumor drug resistance. Npj Precis Oncol . 2024;8(1):1-19. doi:10.1038/s41698-024-00522-z

    work page doi:10.1038/s41698-024-00522-z 2024

  40. [42]

    Subverted macrophages in the triple-negative breast cancer ecosystem

    Shang L, Zhong Y, Yao Y, et al. Subverted macrophages in the triple-negative breast cancer ecosystem. Biomed Pharmacother . 2023;166:115414. doi:10.1016/j.biopha.2023.115414

    arXiv 2023

  41. [43]

    Fibroblasts drive an immunosuppressive and growth-promoting microenvironment in breast cancer via secretion of Chitinase 3-like 1

    Cohen N, Shani O, Raz Y, et al. Fibroblasts drive an immunosuppressive and growth-promoting microenvironment in breast cancer via secretion of Chitinase 3-like 1. Oncogene . 2017;36(31):4457-4468. doi:10.1038/onc.2017.65

    work page doi:10.1038/onc.2017.65 2017

  42. [44]

    Cancer-associated fibroblasts induce epithelial–mesenchymal transition of breast cancer cells through paracrine TGF- β signalling

    Yu Y, Xiao CH, Tan LD, Wang QS, Li XQ, Feng YM. Cancer-associated fibroblasts induce epithelial–mesenchymal transition of breast cancer cells through paracrine TGF- β signalling. Br J Cancer . 2014;110(3):724-732. doi:10.1038/bjc.2013.768

    work page doi:10.1038/bjc.2013.768 2014

  43. [45]

    Cancer-associated fibroblasts promote hepatocellular carcinoma metastasis through chemokine-activated hedgehog and TGF- β pathways

    Liu J, Chen S, Wang W, et al. Cancer-associated fibroblasts promote hepatocellular carcinoma metastasis through chemokine-activated hedgehog and TGF- β pathways. Cancer Lett . 2016;379(1):49-59. doi:10.1016/j.canlet.2016.05.022

    work page doi:10.1016/j.canlet.2016.05.022 2016

  44. [46]

    Tumor-associated macrophages in breast cancer: distinct subsets, distinct functions

    Laoui D, Movahedi K, Van Overmeire E, et al. Tumor-associated macrophages in breast cancer: distinct subsets, distinct functions. Int J Dev Biol . 2011;55(7-8-9):861-867. doi:10.1387/ijdb.113371dl

    work page doi:10.1387/ijdb.113371dl 2011

  45. [47]

    TGF- β induces M2-like macrophage polarization via SNAIL-mediated suppression of a pro-inflammatory phenotype

    Zhang F, Wang H, Wang X, et al. TGF- β induces M2-like macrophage polarization via SNAIL-mediated suppression of a pro-inflammatory phenotype. Oncotarget . 2016;7(32):52294-52306. doi:10.18632/oncotarget.10561

    work page doi:10.18632/oncotarget.10561 2016

  46. [48]

    G-CSF regulates macrophage phenotype and associates with poor overall survival in human triple-negative breast cancer

    Hollmén M, Karaman S, Schwager S, et al. G-CSF regulates macrophage phenotype and associates with poor overall survival in human triple-negative breast cancer. OncoImmunology . 2016;5(3):e1115177. doi:10.1080/2162402X.2015.1115177

    arXiv 2016

  47. [49]

    Triple negative breast cancer: Key role of Tumor-Associated Macrophages in regulating the activity of anti-PD-1/PD-L1 agents

    Santoni M, Romagnoli E, Saladino T, et al. Triple negative breast cancer: Key role of Tumor-Associated Macrophages in regulating the activity of anti-PD-1/PD-L1 agents. Biochim Biophys Acta Rev Cancer . 2018;1869(1):78-84. doi:10.1016/j.bbcan.2017.10.007

    work page doi:10.1016/j.bbcan.2017.10.007 2018

  48. [50]

    B7 family checkpoint regulators in immune regulation and disease

    Ceeraz S, Nowak EC, Noelle RJ. B7 family checkpoint regulators in immune regulation and disease. Trends Immunol . 2013;34(11):556-563. doi:10.1016/j.it.2013.07.003

    work page doi:10.1016/j.it.2013.07.003 2013

  49. [51]

    Activated monocytes in peritumoral stroma of hepatocellular carcinoma foster immune privilege and disease progression through PD-L1

    Kuang DM, Zhao Q, Peng C, et al. Activated monocytes in peritumoral stroma of hepatocellular carcinoma foster immune privilege and disease progression through PD-L1. J Exp Med . 2009;206(6):1327-1337. doi:10.1084/jem.20082173

    work page doi:10.1084/jem.20082173 2009

  50. [52]

    Transforming growth factor- β and the immune response: Implications for anticancer therapy

    Wrzesinski SH, Wan YY, Flavell RA. Transforming growth factor- β and the immune response: Implications for anticancer therapy. Clin Cancer Res . 2007;13(18):5262-5270. doi:10.1158/1078-0432.CCR-07-1157

    work page doi:10.1158/1078-0432.ccr-07-1157 2007

  51. [53]

    The role of tumor-associated macrophages in tumor immune evasion

    Huang R, Kang T, Chen S. The role of tumor-associated macrophages in tumor immune evasion. J Cancer Res Clin Oncol . 2024;150(5):238. doi:10.1007/s00432-024-05777-4 32

    work page doi:10.1007/s00432-024-05777-4 2024

  52. [54]

    PGRN inhibits CD8+T cell recruitment and promotes breast cancer progression by up-regulating ICAM-1 on TAM

    Zhou T, Qian H, Zhang D, et al. PGRN inhibits CD8+T cell recruitment and promotes breast cancer progression by up-regulating ICAM-1 on TAM. Cancer Immunol Immunother . 2024;73(5):76. doi:10.1007/s00262-024-03655-z

    work page doi:10.1007/s00262-024-03655-z 2024

  53. [55]

    Effects of L-Arginine on the Proliferation of T Lymphocyte Subpopulations

    Ochoa JB, Strange J, Kearney P, Gellin G, Endean E, Fitzpatrick E. Effects of L-Arginine on the Proliferation of T Lymphocyte Subpopulations. J Parenter Enter Nutr . 2001;25(1):23-29. doi:10.1177/014860710102500123

    work page doi:10.1177/014860710102500123 2001

  54. [56]

    l-Arginine Consumption by Macrophages Modulates the Expression of CD3 ζ Chain in T Lymphocytes1

    Rodriguez PC, Zea AH, DeSalvo J, et al. l-Arginine Consumption by Macrophages Modulates the Expression of CD3 ζ Chain in T Lymphocytes1. J Immunol . 2003;171(3):1232-1239. doi:10.4049/jimmunol.171.3.1232

    work page doi:10.4049/jimmunol.171.3.1232 2003

  55. [57]

    Nanodrugs mediate TAMs-related arginine metabolism interference to boost photodynamic immunotherapy

    Chen Y, Shu X, Guo JY, et al. Nanodrugs mediate TAMs-related arginine metabolism interference to boost photodynamic immunotherapy. J Controlled Release . 2024;367:248-264. doi:10.1016/j.jconrel.2024.01.045

    work page doi:10.1016/j.jconrel.2024.01.045 2024

  56. [58]

    Regulation of lymphokine-activated killer activity and pore-forming protein gene expression in human peripheral blood CD8+ T lymphocytes

    Smyth MJ, Strobl SL, Young HA, Ortaldo JR, Ochoa AC. Regulation of lymphokine-activated killer activity and pore-forming protein gene expression in human peripheral blood CD8+ T lymphocytes. Inhibition by transforming growth factor-beta. J Immunol . 1991;146(10):3289-3297. doi:10.4049/jimmunol.146.10.3289

    work page doi:10.4049/jimmunol.146.10.3289 1991

  57. [59]

    Mechanisms Underlying the Induction of Regulatory T cells and Its Relevance in the Adaptive Immune Response in Parasitic Infections

    Adalid-Peralta L, Fragoso G, Fleury A, Sciutto E. Mechanisms Underlying the Induction of Regulatory T cells and Its Relevance in the Adaptive Immune Response in Parasitic Infections. Int J Biol Sci . 2011;7(9):1412-1426

    2011

  58. [60]

    Specific recruitment of regulatory T cells in ovarian carcinoma fosters immune privilege and predicts reduced survival

    Curiel TJ, Coukos G, Zou L, et al. Specific recruitment of regulatory T cells in ovarian carcinoma fosters immune privilege and predicts reduced survival. Nat Med . 2004;10(9):942-949. doi:10.1038/nm1093

    work page doi:10.1038/nm1093 2004

  59. [61]

    Macrophage Repolarization as a Therapeutic Strategy for Osteosarcoma

    Anand N, Peh KH, Kolesar JM. Macrophage Repolarization as a Therapeutic Strategy for Osteosarcoma. Int J Mol Sci . 2023;24(3):2858. doi:10.3390/ijms24032858

    work page doi:10.3390/ijms24032858 2023

  60. [62]

    Matairesinol repolarizes M2 macrophages to M1 phenotype to induce apoptosis in triple-negative breast cancer cells

    Chaudhary A, Patil P, Raina P, Kaul-Ghanekar R. Matairesinol repolarizes M2 macrophages to M1 phenotype to induce apoptosis in triple-negative breast cancer cells. Immunopharmacol Immunotoxicol . 2025;47(1):8-22. doi:10.1080/08923973.2024.2425028

    arXiv 2025

  61. [63]

    Triple-Negative Breast Cancer: A Review of Conventional and Advanced Therapeutic Strategies

    Medina MA, Oza G, Sharma A, et al. Triple-Negative Breast Cancer: A Review of Conventional and Advanced Therapeutic Strategies. Int J Environ Res Public Health . 2020;17(6):2078. doi:10.3390/ijerph17062078

    work page doi:10.3390/ijerph17062078 2020

  62. [64]

    Cancer-associated fibroblasts: from basic science to anticancer therapy

    Yang D, Liu J, Qian H, Zhuang Q. Cancer-associated fibroblasts: from basic science to anticancer therapy. Exp Mol Med . 2023;55(7):1322-1332. doi:10.1038/s12276-023-01013-0

    work page doi:10.1038/s12276-023-01013-0 2023

  63. [65]

    Cancer ‐ associated fibroblasts in breast cancer: Challenges and opportunities

    Hu D, Li Z, Zheng B, et al. Cancer ‐ associated fibroblasts in breast cancer: Challenges and opportunities. Cancer Commun . 2022;42(5):401-434. doi:10.1002/cac2.12291

    work page doi:10.1002/cac2.12291 2022

  64. [66]

    Targeting the cancer-associated fibroblasts as a treatment in triple-negative breast cancer

    Takai K, Le A, Weaver VM, Werb Z. Targeting the cancer-associated fibroblasts as a treatment in triple-negative breast cancer. Oncotarget . 2016;7(50):82889-82901. doi:10.18632/oncotarget.12658 33

    work page doi:10.18632/oncotarget.12658 2016

  65. [67]

    Stromal Fibroblasts Present in Invasive Human Breast Carcinomas Promote Tumor Growth and Angiogenesis through Elevated SDF-1/CXCL12 Secretion

    Orimo A, Gupta PB, Sgroi DC, et al. Stromal Fibroblasts Present in Invasive Human Breast Carcinomas Promote Tumor Growth and Angiogenesis through Elevated SDF-1/CXCL12 Secretion. Cell . 2005;121(3):335-348. doi:10.1016/j.cell.2005.02.034

    work page doi:10.1016/j.cell.2005.02.034 2005

  66. [68]

    Characterization of cancer-associated fibroblasts (CAFs) and development of a CAF-based risk model for triple-negative breast cancer

    Wang G, Zhang H, Shen X, Jin W, Wang X, Zhou Z. Characterization of cancer-associated fibroblasts (CAFs) and development of a CAF-based risk model for triple-negative breast cancer. Cancer Cell Int . 2023;23(1):294. doi:10.1186/s12935-023-03152-w

    work page doi:10.1186/s12935-023-03152-w 2023

  67. [69]

    Identification of Cancer-Associated Fibroblast Subtype of Triple-Negative Breast Cancer

    Wang M, Feng R, Chen Z, et al. Identification of Cancer-Associated Fibroblast Subtype of Triple-Negative Breast Cancer. J Oncol . 2022;2022:6452636. doi:10.1155/2022/6452636

    work page doi:10.1155/2022/6452636 2022

  68. [70]

    Cancer-associated fibroblasts: The chief architect in the tumor microenvironment

    Sarkar M, Nguyen T, Gundre E, et al. Cancer-associated fibroblasts: The chief architect in the tumor microenvironment. Front Cell Dev Biol . 2023;11:1089068. doi:10.3389/fcell.2023.1089068

    arXiv 2023

  69. [71]

    Cancer-Associated Fibroblasts Correlate with Tumor-Associated Macrophages Infiltration and Lymphatic Metastasis in Triple Negative Breast Cancer Patients

    Zhou J, Wang XH, Zhao YX, et al. Cancer-Associated Fibroblasts Correlate with Tumor-Associated Macrophages Infiltration and Lymphatic Metastasis in Triple Negative Breast Cancer Patients. J Cancer . 2018;9(24):4635-4641. doi:10.7150/jca.28583

    work page doi:10.7150/jca.28583 2018

  70. [72]

    Precision targeted cancer-associated fibroblast nano-regulator enhanced chemo-immunotherapy for triple-negative breast cancer

    Xian P, Zou L, Zhang J, et al. Precision targeted cancer-associated fibroblast nano-regulator enhanced chemo-immunotherapy for triple-negative breast cancer. Biomaterials . 2026;326:123679. doi:10.1016/j.biomaterials.2025.123679

    arXiv 2026

  71. [73]

    PD-1 status in CD8+ T cells associates with survival and anti-PD-1 therapeutic outcomes in head and neck cancer

    Kansy BA, Concha-Benavente F, Srivastava RM, et al. PD-1 status in CD8+ T cells associates with survival and anti-PD-1 therapeutic outcomes in head and neck cancer. Cancer Res . 2017;77(22):6353-6364. doi:10.1158/0008-5472.CAN-16-3167

    work page doi:10.1158/0008-5472.can-16-3167 2017

  72. [74]

    Immunotherapy for triple-negative breast cancer: Existing challenges and exciting prospects

    Jia H, Truica CI, Wang B, et al. Immunotherapy for triple-negative breast cancer: Existing challenges and exciting prospects. Drug Resist Updat . 2017;32:1-15. doi:10.1016/j.drup.2017.07.002

    work page doi:10.1016/j.drup.2017.07.002 2017

  73. [75]

    How Do Cytotoxic Lymphocytes Kill Cancer Cells? Clin Cancer Res

    Martínez-Lostao L, Anel A, Pardo J. How Do Cytotoxic Lymphocytes Kill Cancer Cells? Clin Cancer Res . 2015;21(22):5047-5056. doi:10.1158/1078-0432.CCR-15-0685

    work page doi:10.1158/1078-0432.ccr-15-0685 2015

  74. [76]

    PD-L1 expression correlates with tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy in breast cancer

    Wimberly H, Brown JR, Schalper K, et al. PD-L1 expression correlates with tumor-infiltrating lymphocytes and response to neoadjuvant chemotherapy in breast cancer. Cancer Immunol Res . 2015;3(4):326-332. doi:10.1158/2326-6066.CIR-14-0133

    work page doi:10.1158/2326-6066.cir-14-0133 2015

  75. [77]

    Macrophage Differentiation and Polarization Regulate the Release of the Immune Checkpoint Protein V-Domain Ig Suppressor of T Cell Activation

    Noubissi Nzeteu GA, Schlichtner S, David S, et al. Macrophage Differentiation and Polarization Regulate the Release of the Immune Checkpoint Protein V-Domain Ig Suppressor of T Cell Activation. Front Immunol . 2022;13:837097. doi:10.3389/fimmu.2022.837097

    arXiv 2022

  76. [78]

    Engagement of the ICOS pathway markedly enhances efficacy of CTLA-4 blockade in cancer immunotherapy

    Fan X, Quezada SA, Sepulveda MA, Sharma P, Allison JP. Engagement of the ICOS pathway markedly enhances efficacy of CTLA-4 blockade in cancer immunotherapy. J Exp Med . 2014;211(4):715-725. doi:10.1084/jem.20130590

    work page doi:10.1084/jem.20130590 2014

  77. [79]

    ICOS costimulation in combination with CTLA-4 blockade remodels tumor-associated macrophages toward an antitumor phenotype

    Sharma N, Fan X, Atolagbe OT, et al. ICOS costimulation in combination with CTLA-4 blockade remodels tumor-associated macrophages toward an antitumor phenotype. J Exp Med . 2024;221(4):e20231263. doi:10.1084/jem.20231263

    work page doi:10.1084/jem.20231263 2024

  78. [80]

    Rapid Turnover of Effector–Memory CD4+ T Cells in Healthy Humans

    Macallan DC, Wallace D, Zhang Y, et al. Rapid Turnover of Effector–Memory CD4+ T Cells in Healthy Humans. J Exp Med . 2004;200(2):255-260. doi:10.1084/jem.20040341 34

    work page doi:10.1084/jem.20040341 2004

  79. [82]

    Regulatory T cells: Their role in triple-negative breast cancer progression and metastasis

    Malla RR, Vasudevaraju P, Vempati RK, Rakshmitha M, Merchant N, Nagaraju GP. Regulatory T cells: Their role in triple-negative breast cancer progression and metastasis. Cancer . 2022;128(6):1171-1183. doi:10.1002/cncr.34084

    work page doi:10.1002/cncr.34084 2022

  80. [83]

    Accumulation of Regulatory T Cells in Triple Negative Breast Cancer Can Boost Immune Disruption

    Zahran AM, El-Badawy O, Kamel LM, Rayan A, Rezk K, Abdel-Rahim MH. Accumulation of Regulatory T Cells in Triple Negative Breast Cancer Can Boost Immune Disruption. Cancer Manag Res . 2021;13:6019-6029. doi:10.2147/CMAR.S285128

    work page doi:10.2147/cmar.s285128 2021

Showing first 80 references.