Machine-Learning–Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance

Laurinavičius, Arvydas; Rasmusson, Allan G.; Plancoulaine, Benoît; Shribak, Michael; Levenson, Richard M.

doi:10.1016/j.ajpath.2021.04.008

Cited by 8 publications

(9 citation statements)

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“…In particular, robust biomarkers are in demand for HER2-positive disease to improve selection of patients for current and emerging therapies of HER2-positive metastatic BC ( 5 ) as well as for prediction of resistance for anti-HER2 therapies, recurrence ( 6 , 7 ), and particular consequences of the disease ( 8 ). Novel approaches based on pathology image analytics and machine learning methods open new perspectives for predictive modeling and clinical decision support ( 9 , 10 ). Importantly, both molecular and image-based biomarkers can be explored and validated using The Cancer Genome Atlas (TCGA) Data Portal ( 11 ).…”

Section: Introductionmentioning

confidence: 99%

Intratumoral Heterogeneity and Immune Response Indicators to Predict Overall Survival in a Retrospective Study of HER2-Borderline (IHC 2+) Breast Cancer Patients

et al. 2021

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Breast cancer (BC) categorized as human epidermal growth factor receptor 2 (HER2) borderline [2+ by immunohistochemistry (IHC 2+)] presents challenges for the testing, frequently obscured by intratumoral heterogeneity (ITH). This leads to difficulties in therapy decisions. We aimed to establish prognostic models of overall survival (OS) of these patients, which take into account spatial aspects of ITH and tumor microenvironment by using hexagonal tiling analytics of digital image analysis (DIA). In particular, we assessed the prognostic value of Immunogradient indicators at the tumor–stroma interface zone (IZ) as a feature of antitumor immune response. Surgical excision samples stained for estrogen receptor (ER), progesterone receptor (PR), Ki67, HER2, and CD8 from 275 patients with HER2 IHC 2+ invasive ductal BC were used in the study. DIA outputs were subsampled by HexT for ITH quantification and tumor microenvironment extraction for Immunogradient indicators. Multiple Cox regression revealed HER2 membrane completeness (HER2 MC) (HR: 0.18, p = 0.0007), its spatial entropy (HR: 0.37, p = 0.0341), and ER contrast (HR: 0.21, p = 0.0449) as independent predictors of better OS, with worse OS predicted by pT status (HR: 6.04, p = 0.0014) in the HER2 non-amplified patients. In the HER2-amplified patients, HER2 MC contrast (HR: 0.35, p = 0.0367) and CEP17 copy number (HR: 0.19, p = 0.0035) were independent predictors of better OS along with worse OS predicted by pN status (HR: 4.75, p = 0.0018). In the non-amplified tumors, three Immunogradient indicators provided the independent prognostic value: CD8 density in the tumor aspect of the IZ and CD8 center of mass were associated with better OS (HR: 0.23, p = 0.0079 and 0.14, p = 0.0014, respectively), and CD8 density variance along the tumor edge predicted worse OS (HR: 9.45, p = 0.0002). Combining these three computational indicators of the CD8 cell spatial distribution within the tumor microenvironment augmented prognostic stratification of the patients. In the HER2-amplified group, CD8 cell density in the tumor aspect of the IZ was the only independent immune response feature to predict better OS (HR: 0.22, p = 0.0047). In conclusion, we present novel prognostic models, based on computational ITH and Immunogradient indicators of the IHC biomarkers, in HER2 IHC 2+ BC patients.

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Section: Introductionmentioning

confidence: 99%

Intratumoral Heterogeneity and Immune Response Indicators to Predict Overall Survival in a Retrospective Study of HER2-Borderline (IHC 2+) Breast Cancer Patients

et al. 2021

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“…Their trained model was able to carry out spatially‐resolved inferences of gene expression relying only on computational analysis of phenotype 79 . These are only a selection of many recent studies that clearly demonstrate the successful application of deep learning to improve our understanding of heterogeneity in cancer 74,77–89 …”

Section: Artificial Intelligence As An Aide In Pathologymentioning

confidence: 99%

Standardizing analysis of intra‐tumoral heterogeneity with computational pathology

Paliwal

Faust

Alshoumer

et al. 2023

Genes Chromosomes & Cancer

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Many malignant cancers like glioblastoma are highly adaptive diseases that dynamically change their regional biology to survive and thrive under diverse microenvironmental and therapeutic pressures. While the concept of intra‐tumoral heterogeneity has become a major paradigm in cancer research and care, systematic approaches to assess and document bio‐variation in cancer are still in their infancy. Here we discuss existing approaches and challenges to documenting intra‐tumoral heterogeneity and emerging computational approaches that leverage artificial intelligence to begin to overcome these limitations. We propose how these emerging techniques can be coupled with a diversity of molecular tools to address intra‐tumoral heterogeneity more systematically in research and in practice, especially across larger specimens and longitudinal analyses. Systematic documentation and characterization of heterogeneity across entire tumor specimens and their longitudinal evolution has the potential to improve our understanding and treatment of cancer.

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“…Last advances in artificial intelligence and machine learning models allowed us to better predict cell subtypes and infer their proportions in tumours and TME based on their inherent multi-omics characteristics. These approaches have the potential to integrate both molecular and histopathological imaging data to refine tumour heterogeneity in the spatial context and go beyond what can be distinguished by routine microscopy observations [90,91]. However, due to their recent development, they still lack standardisation and need further evaluation prior to their implementation in a clinical setting [91].…”

Section: Ith Is Associated With Poorer Clinical Outcomesmentioning

confidence: 99%

“…These approaches have the potential to integrate both molecular and histopathological imaging data to refine tumour heterogeneity in the spatial context and go beyond what can be distinguished by routine microscopy observations [90,91]. However, due to their recent development, they still lack standardisation and need further evaluation prior to their implementation in a clinical setting [91].…”

Section: Ith Is Associated With Poorer Clinical Outcomesmentioning

confidence: 99%

Deciphering Tumour Heterogeneity: From Tissue to Liquid Biopsy

Gilson

Merlin

Harlé

2022

Cancers

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Human solid malignancies harbour a heterogeneous set of cells with distinct genotypes and phenotypes. This heterogeneity is installed at multiple levels. A biological diversity is commonly observed between tumours from different patients (inter-tumour heterogeneity) and cannot be fully captured by the current consensus molecular classifications for specific cancers. To extend the complexity in cancer, there are substantial differences from cell to cell within an individual tumour (intra-tumour heterogeneity, ITH) and the features of cancer cells evolve in space and time. Currently, treatment-decision making usually relies on the molecular characteristics of a limited tumour tissue sample at the time of diagnosis or disease progression but does not take into account the complexity of the bulk tumours and their constant evolution over time. In this review, we explore the extent of tumour heterogeneity with an emphasis on ITH and report the mechanisms that promote and sustain this diversity in cancers. We summarise the clinical strikes of ITH in the management of patients with cancer. Finally, we discuss the current material and technological approaches that are relevant to adequately appreciate ITH.

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Machine-Learning–Based Evaluation of Intratumoral Heterogeneity and Tumor-Stroma Interface for Clinical Guidance

Abstract: Supported by the European Social Fund grant 09.3.3-LMT-K-712. Disclosures: None declared. This article is part of a mini-review series on the applications of artificial intelligence and deep learning in advancing research and diagnosis in pathology.

Cited by 8 publications

References 50 publications

Intratumoral Heterogeneity and Immune Response Indicators to Predict Overall Survival in a Retrospective Study of HER2-Borderline (IHC 2+) Breast Cancer Patients

Intratumoral Heterogeneity and Immune Response Indicators to Predict Overall Survival in a Retrospective Study of HER2-Borderline (IHC 2+) Breast Cancer Patients

Standardizing analysis of intra‐tumoral heterogeneity with computational pathology

Deciphering Tumour Heterogeneity: From Tissue to Liquid Biopsy

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