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Файл:Ординатура / Хирургия / Библиотека им академика М.И. Перельмана / Книга_5563_Библиотеки_им_академика_М_И_Перельмана.pdf
136 The Dynamics of Tumor Microenvironment and Therapeutic Targets
FIGURE 7.1 AI algorithm for drug repurposing: (a) feedforward neural network (FNN)
model [1], (b) convolutional neural networks (CNN) model, (c) recurrent neural networks
(RNN) model.
Source: Adapted from Selvaraj et al., 2021, under CCBY 4.0 license
complex patterns from large datasets (Figure 7.1). Deep learning models can pre-
dict drug–target interactions, identify synergistic drug combinations, and optimize
repurposing strategies, signicantly accelerating the drug repurposing process. The
integration of comprehensive databases and powerful articial intelligence tech-
niques has transformed drug repurposing into a data-driven and efcient approach
for expanding the therapeutic landscape. By leveraging these tools, researchers can
accelerate the discovery and development of new treatments, addressing unmet med-
ical needs and improving patient outcomes. As drug repurposing continues to gain
traction, the role of databases and articial intelligence will only grow in impor-
tance, paving the way for a more efcient and effective drug discovery process. In
conclusion, drug repurposing offers a hopeful approach to expedite the identication
and advancement of novel therapeutic treatments.
7.9 A RTIFICIAL INTELLIGENCE AND MACHINE
LEARNING IN TME ANALYSIS AND DETECTION
The complex, heterogeneous nature of the TME presents signicant challenges for
traditional analytical approaches. AI and ML technologies have emerged as powerful
137Tumor Microenvironment and Cancer Growth
tools for analyzing, characterizing, and detecting features of the TME, offering
unprecedented insights and clinical applications:
7.9.1 digital pathology aNd Computer visioN
a. Multiplex tissue analysis: Deep learning algorithms can simultaneously
analyze multiple immunohistochemistry or immunouorescence markers
in tissue sections, quantifying cell types, spatial relationships, and archi-
tectural patterns within the TME.
b. Cell segmentation and classication: CNNs achieve expert-level accu-
racy in distinguishing cancer cells from diverse stromal cell populations,
enabling precise quantication of cellular composition.
c. Spatial analytics: Graph-based neural networks analyze cell–cell interac-
tions and spatial organization, revealing neighborhood structures and inter-
action patterns that predict disease progression and treatment response.
d. Tumor–stroma ratio assessment: Automated algorithms quantify the pro-
portion of tumor versus stromal components, an emerging prognostic indi-
cator in multiple cancer types.
7.9.2 multi-omiCs iNtegratioN
a. Single-cell data analysis: ML techniques, including dimensionality reduc-
tion, clustering, and trajectory inference, extract meaningful patterns from
high-dimensional single-cell RNA sequencing data, revealing cellular het-
erogeneity within the TME.
b. Spatial transcriptomics interpretation: AI methods correlate spatial gene
expression patterns with histological features, linking molecular states to
tissue architecture.
c. Multi-modal data fusion: Advanced algorithms integrate complementary
data types (genomics, transcriptomics, proteomics, metabolomics, imag-
ing) to build comprehensive models of TME function.
d. Network analysis: Graph-based algorithms reconstruct signaling networks
and intercellular communication patterns within the TME from multi-
omics data.
7.9.3 prediCtive modeliNg of tme features
a. Radiomics and radiogenomics: ML models extract quantitative features
from medical imaging (CT, MRI, PET) that correlate with molecular TME
characteristics, enabling non-invasive TME assessment.
b. Hypoxia prediction: Neural networks detect image-based signatures of
tumor hypoxia, a critical determinant of treatment response.
c. Immune inltration estimation: Computational approaches predict the
extent and composition of immune inltration from routine imaging or
limited molecular data.
138 The Dynamics of Tumor Microenvironment and Therapeutic Targets
d. Vasculature analysis: Deep learning methods characterize tumor vascular
patterns from contrast-enhanced imaging, providing insights into perfu-
sion, drug delivery, and angiogenic activity.
7.9.4 CliNiCal appliCatioNs of ai/ml for the tme
Response prediction: ML models incorporating TME features outperform conven-
tional methods in predicting response to immunotherapy, chemotherapy, and radia-
tion therapy.
a. Patient stratication: Unsupervised clustering based on TME character-
istics identies patient subgroups with distinct prognoses and treatment
sensitivities.
b. Monitoring dynamics: Longitudinal analysis with AI techniques tracks
TME evolution during treatment, detecting resistance mechanisms, and
adaptive responses.
c. Drug discovery: AI-powered virtual screening identies compounds tar-
geting specic TME components, while network pharmacology approaches
predict effects on the TME ecosystem.
d. Clinical decision support: Integrative models synthesize TME data to
guide personalized treatment selection, dosing, and sequencing.
7.9.5 emergiNg froNtiers iN ai/ml for tme aNalysis
a. Digital twins: Computational models simulate individual patient TMEs,
allowing virtual testing of treatment strategies and combinations.
b. Federated learning: Privacy-preserving techniques enable model training
across institutions without data sharing, accelerating development of robust
TME analysis tools.
c. Explainable AI: Interpretable ML approaches not only predict outcomes
but reveal mechanisms underlying TME-driven disease processes.
d. Real-time analysis: Integration of AI with emerging technologies like
liquid biopsy and intraoperative imaging enables dynamic assessment of
TME status.
e. Synthetic data generation: Generative models create realistic TME
representations, augmenting limited datasets and exploring hypothetical
scenarios.
While the potential of AI/ML approaches in TME analysis is vast, several chal-
lenges must be addressed to ensure effective application. Standardized protocols for
data acquisition and preprocessing are critical to ensure consistency and reliabil-
ity across research studies and clinical settings. Equally important is the need for
validation across diverse patient populations and technical platforms to establish the
generalizability of AI models. Moreover, integrating domain knowledge with data-
driven discoveries can bridge gaps in understanding and further rene AI applica-
tions. Finally, addressing biases in training data is essential to prevent perpetuation
139Tumor Microenvironment and Cancer Growth
of existing healthcare disparities, ensuring that advancements in AI/ML benet all
patient groups equitably.
7.10 BALANCING MODEL COMPLEXITY WITH
INTERPRETABILITY FOR CLINICAL IMPLEMENTATION
The convergence of AI/ML technologies with our expanding understanding of the
TME holds transformative potential for cancer diagnosis, prognosis, and treatment.
As these methodologies mature from research tools to clinical decision support sys-
tems, they will enable more precise characterization of the TME and more effective
targeting of this critical determinant of cancer behavior.
The TME presents both challenges and opportunities in cancer therapy. Its com-
plexity facilitates tumor growth and therapeutic resistance, yet its various compo-
nents provide numerous potential targets for intervention. Future strategies will
likely integrate TME-directed therapies with conventional treatments in temporally
and spatially optimized sequences, increasingly guided by AI-powered analysis and
prediction. Recently, single-cell technologies revealing unprecedented heterogeneity
within stromal populations is gaining extensive attention.
7.11 SPATIAL TRANSCRIPTOMICS AND PROTEOMICS MAPPING
THE GEOGRAPHIC ORGANIZATION OF THE TME
The TME is inuenced by various processes critical to cancer research and therapies.
• Microbiome impacts on the TME in specic cancers: The communities of
microorganisms in and around tumors can affect cancer development, immune
responses, and treatment outcomes, offering potential targets for therapy.
• Systemic effects of the TME: The TME can inuence distant organs and
the immune system through signaling pathways, impacting cancer progres-
sion and metastasis.
• Biomimetic in vitro models: Lab-created models that mimic the TME offer
a controlled environment for testing therapies and understanding tumor
behavior.
• AI/ML integration: AI and ML are being used to analyze TME data, pro-
viding insights into cancer processes and optimizing treatments.
Understanding the TME’s role in cancer requires insights from molecular interac-
tions to systemic effects. Computational approaches can help shorten the timeline for
analysis and developing strategies that target the entire ecosystem supporting cancer
growth and spread.
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Index
A
AI/ML, 138, 139
ANG2, 99
angiogenesis, 1, 4, 5, 6, 7, 8, 9, 10
angiopoietin-2, 86, 103, 131
antigen presentation, 26, 28 – 29, 37, 84, 98
apoptosis, 11, 14, 35, 54, 85, 97, 101, 120,
121 – 122, 131
articial intelligence, 121, 125, 133, 135, 136
B
breast cancer, 10, 11, 49, 51, 53 – 56, 58,
123 – 124
broblast, 9, 11, 12, 48, 49, 50, 51, 55, 63, 83,
86, 102, 133, 53, 57, 61, 63, 81, 91, 102,
117, 129, 130
stromal cells, 9, 10, 12, 14, 25, 31, 48, 81
tumor microenvironment, 1, 3, 4, 12, 25, 27,
29, 38, 57, 58, 60, 63, 81, 87, 91, 103,
115, 129
C
CAFs, 50 – 60, 61 – 63, 102, 133
cancer, 1, 3, 5, 6, 9, 10, 11, 12, 14, 25, 26, 28, 30,
32, 33, 37, 38, 49, 59 – 60, 62, 81 – 83, 85,
91, 94, 96 – 98, 102
therapy, 14, 33, 53, 86, 87, 99, 102, 118,
121 – 122, 139
cancer stem cell, 50, 120
CAR-T, 26, 30, 36, 103, 119
CD4+ T cell, 4, 28, 37, 60, 94
CD4+ T helper, 4, 37
CD40, 34, 94
chemokines, 81, 85, 86, 91, 93 – 94, 95, 115,
118 – 119, 130, 131
circulating tumor DNA, 14, 103
colony stimulating factor 1 receptor, 93
colorectal cancer, 53, 99 – 102, 124
CXCL12/CXCR4, 58, 62
CXCR2 inhibitor, 95
cytokine, 1, 4, 14, 25 – 26, 31 – 32, 35, 37, 52 – 55
D
dendritic cell, 95, 96
drug repurposing, 115 – 117, 120 – 123, 125,
133, 135
E
endovascular endothelial growth factor A, 86, 103
epithelial to mesenchymal transition (EMT), 9, 51,
32, 36, 51, 55, 101, 131
extracellular vesicles (EVs), 1, 13, 91, 103
F
FGF, 57, 86, 99, 130
FMS-related tyrosine kinase 3, 95
focal adhesion kinase, 101
FoxP3+, 4
G
glioma cells, 61, 62, 103
G-protein coupled receptor, 86
I
ICAM-1, 87
IL-17A, 4
IL-17F, 4
IL-21, 4
IL-22, 4
immune checkpoint inhibitors, 26 – 27, 32, 34, 61,
87, 96, 98, 102 – 103
immunomodulation, 57, 131 – 132
inammation, 4 – 5, 7 – 9, 32, 54, 92, 115, 116, 124
interferon-gamma (IFN-γ), 4
interleukin-2, 4
interstitial uid pressure, 14, 92, 100, 132
J
JNK signalling, 53
L
lung cancer, 6, 51, 59, 85, 95, 96, 98, 123, 124
CD8+ T cells, 4, 11, 26, 28, 36, 84 – 85, 95,
96 – 98, 100
CD10, 54, 102
broblast-activating protein, 9, 51
glioblastoma, 10, 34, 36, 60, 61, 62, 93, 99,
100, 103
regulatory T cells, 4, 5, 7, 32, 37, 59, 92, 131
TASCs, 9, 10
tumor-associated neutrophils, 7, 36
143
144
M
machine learning, 125, 133, 134, 135,
136 – 137
mesenchymal stem cells, 32, 50, 117
N
NF-κB signaling, 55, 132
non-small-cell lung cancer, 51, 98, 123 – 124
O
oncology, 1, 14, 122, 125
P
pancreatic neuroendocrine tumors, 123
phagocytosis, 27 – 28, 34, 84, 93
podoplanin, 52, 56
POSTN, 52, 53
pro-angiogenic factors, 7, 131, 58, 84, 86
prostaglandin E2 (PGE2)/cyclooxygenase 2
(COX-2) axis, 36, 58
R
regulator of G protein, 100
Index
S
solid tumors, 3, 52, 61, 93, 98, 102, 118
STAT3 phosphorylation, 53
T
TAM-derived proteases, 131
Tenascin C, 9
TGFβ, 8, 10, 101
TH17, 4
TIME (tumour immune microenvironment),
25, 26, 27, 31, 32, 33, 36, 38, 49
T lymphocytes, 4, 100
tumor-associated macrophages, 5,6, 10, 92 – 95
tumor-associated stromal cells, 9
tumor-associated T cells, 94
tumor endothelial cells, 82, 85
tyrosine kinase inhibitors, 36, 56, 59, 93, 99
V
vascular endothelial cells, 9, 28, 53 – 56, 117
VEGFR, 54, 83, 86, 99, 100
W
Wnt/β-catenin, 52, 63