Immunological tumor heterogeneity and diagnostic profiling for advanced and immune therapies

Huss, Ralf; Schmid, Christoph; Manesse, Maël; Thagaard, Jeppe; Maerkl, Bruno

doi:10.1002/acg2.113

Cited by 4 publications

(9 citation statements)

References 60 publications

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“…However, the TEAL model predicts, for adaptive threats in an excluded pro-tumor IME, the emergence of an unstable state, resulting in either accrual or depletion of TAAs in a manner that depends on the current TAA abundance. This splitting behavior into ‘hot’ and ‘cold’ tumors offers insight into the microenvironmental features generating spatial immunogenic diversity within solid tumors and is consistent with prior observations ( Huss et al, 2021 ; Jia et al, 2022 ; Meiller et al, 2021 ; Lakatos et al, 2020 ). This argues that TAA-depleted tumors share in common the tendency for their evasion strategies to incur less antigenic penalties.…”

Section: Discussionsupporting

confidence: 88%

Optimal cancer evasion in a dynamic immune microenvironment generates diverse post-escape tumor antigenicity profiles

George

Levine

2023

eLife

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The failure of cancer treatments, including immunotherapy, continues to be a major obstacle in preventing durable remission. This failure often results from tumor evolution, both genotypic and phenotypic, away from sensitive cell states. Here, we propose a mathematical framework for studying the dynamics of adaptive immune evasion that tracks the number of tumor-associated antigens available for immune targeting. We solve for the unique optimal cancer evasion strategy using stochastic dynamic programming and demonstrate that this policy results in increased cancer evasion rates compared to a passive, fixed strategy. Our foundational model relates the likelihood and temporal dynamics of cancer evasion to features of the immune microenvironment, where tumor immunogenicity reflects a balance between cancer adaptation and host recognition. In contrast with a passive strategy, optimally adaptive evaders navigating varying selective environments result in substantially heterogeneous post-escape tumor antigenicity, giving rise to immunogenically hot and cold tumors.

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Section: Discussionsupporting

confidence: 88%

Optimal cancer evasion in a dynamic immune microenvironment generates diverse post-escape tumor antigenicity profiles

George

Levine

2023

eLife

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“…However the TEAL model predicts, for adaptive threats in an excluded pro-tumor IME, the emergence of an unstable state resulting in either accrual or depletion of TAAs in a manner that depends on the current TAA abundance. This splitting behavior into 'hot' and 'cold' tumors offers insight into the microenvironmental features generating spatial immunogenic diversity within solid tumors and is consistent with prior observations (Huss et al, 2021;Jia et al, 2022;Meiller et al, 2021;Lakatos et al, 2020). This argues that TAA-depleted tumors share in common the tendency for their evasion strategies to incur less antigenic penalties.…”

Section: Discussionsupporting

confidence: 88%

Optimal Cancer Evasion in a Dynamic Immune Microenvironment

George

Levine

2022

Preprint

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The failure of cancer treatments, including immunotherapy, continues to be a major obstacle in preventing durable remission. This failure often results from tumor evolution, both genotypic and phenotypic, away from sensitive cell states. Here, we propose a mathematical framework for studying the dynamics of adaptive immune evasion that tracks the number of tumor-associated antigens available for immune targeting. We solve for the unique optimal cancer evasion strategy using stochastic dynamic programming and demonstrate that this policy results in increased cancer evasion rates when compared to a passive, fixed strategy. Our foundational model relates the likelihood and temporal dynamics of cancer evasion to features of the immune microenvironment, where tumor immunogenicity reflects a balance between cancer adaptation and host recognition. In contrast with a passive strategy, optimally adaptive evaders navigating varying selective environments result in substantially heterogeneous post-escape tumor antigenicity, giving rise to immunogenically hot and cold tumors.

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“…Especially for advanced therapeutics (cell-and gene therapy) it is the only path towards a statement on the prognosis and possible predictions for the most effective treatment. 54 A significant number of relevant biomarkers including proteins and genetic alterations have already been identified which guide therapeutic strategies and decisions in many tumor entities. 55 Galon et al 56 demonstrated that the combination of two spatially resolved immune cell markers in different cancer tissue compartments show a better predictive value than each single marker alone.…”

Section: Analysis Of Immune and Tumor Heterogeneitymentioning

confidence: 99%

“…Both and in particular their spatial co-existence have a significant prognostic value and need be included in therapeutic considerations. [103][104][105][106] • Multiplex immunohistochemistry 23,24,49,51 • Whole sllide imaging 5 • Image analysis 27,43 • PD-L1 quantification 72,94,95,99 • Large date extraction 22 • Multidimensional data analysis 31,48,49 • Mutation detection and analysis 6,89,107 • Spatial transcriptomics 41 • Drug development 36 Digitale Biomarker • Disease diagnosis 2 • (Tumor) Immune infiltrates 58,73 • Prognostic biomarker 30,105,106 • Personalized medicine and response prediction 23,42,47 • Cell-and Gene therapy 54,68 • IO/non-OI combination therapies 55,64,70,77 • Immune escape prediction 63,64 • Metastases assessment 39,74,98 F I G U R 1 Simplified scheme the proposed path from a regular diagnostic pathology workflow to delivering a precision oncology approach through the AI-supported application of digital biomarker that allow the best possible resp...…”

Section: Artificial Intelligencementioning

confidence: 99%

Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology

2023

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Background: The currently available immunotherapies already changed the strategy how many cancers are treated from first to last line. Understanding even the most complex heterogeneity in tumor tissue and mapping the spatial cartography of the tumor immunity allows the best and optimized selection of immune modulating agents to (re-)activate the patient's immune system and direct it against the individual cancer in the most effective way.Recent Findings: Primary cancer and metastases maintain a high degree of plasticity to escape any immune surveillance and continue to evolve depending on many intrinsic and extrinsic factors In the field of immune-oncology (IO) immune modulating agents are recognized as practice changing therapeutic modalities. Recent studies have shown that an optimal and lasting efficacy of IO therapeutics depends on the understanding of the spatial communication network and functional context of immune and cancer cells within the tumor microenvironment. Artificial intelligence (AI) provides an insight into the immune-cancer-network through the visualization of very complex tumor and immune interactions in cancer tissue specimens and allows the computer-assisted development and clinical validation of such digital biomarker. Conclusions:The successful implementation of AI-supported digital biomarker solutions guides the clinical selection of effective immune therapeutics based on the retrieval and visualization of spatial and contextual information from cancer tissue images and standardized data. As such, computational pathology (CP) turns into "precision pathology" delivering individual therapy response prediction. Precision Pathology does not only include digital and computational solutions but also high levels of standardized processes in the routine histopathology workflow and the use of mathematical tools to support clinical and diagnostic decisions as the basic principle of a "precision oncology".

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Immunological tumor heterogeneity and diagnostic profiling for advanced and immune therapies

Cited by 4 publications

References 60 publications

Optimal cancer evasion in a dynamic immune microenvironment generates diverse post-escape tumor antigenicity profiles

Optimal cancer evasion in a dynamic immune microenvironment generates diverse post-escape tumor antigenicity profiles

Optimal Cancer Evasion in a Dynamic Immune Microenvironment

Artificial intelligence and digital biomarker in precision pathology guiding immune therapy selection and precision oncology

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