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

Radziuviene, Gedmante; Rasmusson, Allan G.; Augulis, Renaldas; Grineviciute, Ruta Barbora; Zilenaite, Dovile; Laurinavičienė, Aida; Ostapenko, Valerijus; Laurinavičius, Arvydas

doi:10.3389/fonc.2021.774088

Cited by 9 publications

(8 citation statements)

References 116 publications

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“…Previous studies (38,39,43,44) As in previous studies including Ki67, PR, and HER2 IHC (38, 43, 44), we found a non-linear association between the rate of Ki67 and its entropy: high ITH values are detected only in the middle of the range of the biomarker expression rate, while lower ITH is characteristic at the low and high ends of the range (Fig. 4).…”

Section: Discussionsupporting

confidence: 80%

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Intratumoral Heterogeneity of Ki67 Proliferation Index Outperforms Conventional Prognostic Factors in Hormone Receptor-Positive Breast Cancer

Zilenaite-Petrulaitiene,

Rasmusson,

Besusparis

et al. 2023

Preprint

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Background Immunohistochemistry (IHC) for ER, PR, HER2, and Ki67 is used in breast cancer (BC) pathology to assess tumor properties and predict patient outcomes and therapy responses. Visual scoring of the IHC biomarkers by pathologists, apart from reproducibility issues, does not sufficiently account for the intratumoral heterogeneity (ITH), often a subvisual feature within the tumor tissue. It has been reported that the ITH indicators of IHC biomarker expression can provide independent prognostic value. In this study, we applied digital image analysis (DIA) and computational pathology methods to investigate the prognostic value of IHC ITH indicators in hormone receptor-positive (HR-positive) BC patients.Methods Whole slide images of surgically excised specimens stained for ER, PR, Ki67, and HER2 from 178 patients with a diagnosis of HR-positive invasive ductal carcinoma were used in the study. Digital tumor tissue segmentation and detection of biomarker-positive and negative cells were performed. The DIA-generated data were systematically subsampled by a hexagonal grid to compute Haralick’s texture indicators for ER, PR, Ki67, and HER2. Univariate and multivariable Cox regression analyses were performed to assess the prognostic significance of the IHC and ITH indicators in the context of clinicopathologic variables, including conventional assessment of the IHC results provided by pathologists.Results In multivariable analysis, the ITH of Ki67-positive cells, measured by Haralick’s texture entropy, emerged as an independent prognostic factor associated with worse overall survival (hazard ratio = 11.40, p-value = 0.021). Remarkably, the entropy representing the spatial disarrangement of tumor proliferation outperformed the proliferation rate per se established either by pathology reports or DIA. None of the clinicopathologic variables were selected as independent prognostic features in our dataset.Conclusions These results add to the evidence from previous studies that ITH of IHC biomarkers, in particular, ITH of Ki67 proliferation index, exceeds the informative value of Ki67% per se (both visual and digital) in HR-positive BC. The study further demonstrates the benefits of high-capacity DIA-generated data for quantifying the essentially subvisual ITH properties.

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Section: Discussionsupporting

confidence: 80%

“…AshD) was calculated for the intratumoral distributions of ER, PR, Ki67, and HER2 positive cells across the grid as described in(38,39,43,44). SAS Institute Inc., Cary, North Carolina, USA) and R (version 4.1.0) were used for statistical analyses and to generate plots.…”

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confidence: 99%

Intratumoral Heterogeneity of Ki67 Proliferation Index Outperforms Conventional Prognostic Factors in Hormone Receptor-Positive Breast Cancer

Zilenaite-Petrulaitiene,

Rasmusson,

Besusparis

et al. 2023

Preprint

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“…Intratumoral staining heterogeneity is well known, with three patterns (mosaic, scattered, and clustered) being described 49 . Intratumoral heterogeneity is acknowledged as a source of disconcordance within HER2 IHC scoring 50–52 and has been investigated as a separate negative prognostic marker 53–57 . Incomplete fixation has been recognised as one cause of immunoreactivity heterogeneity 54 .…”

Section: Discussionmentioning

confidence: 99%

“…49 Intratumoral heterogeneity is acknowledged as a source of disconcordance within HER2 IHC scoring [50][51][52] and has been investigated as a separate negative prognostic marker. [53][54][55][56][57] Incomplete fixation has been recognised as one cause of immunoreactivity heterogeneity. 54 The pitfall of HER2 heterogeneity in the evaluation of HER2-low can be accounted for in the development of the software.…”

Section: Discussionmentioning

confidence: 99%

Digital image analysis and assisted reading of theHER2score display reduced concordance: pitfalls in the categorisation ofHER2‐low breast cancer

Sode

Thagaard²,

Eriksen

et al. 2023

Histopathology

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Digital image analysis and assisted reading of the HER2 score display reduced concordance: pitfalls in the categorisation of HER2-low breast cancer Aims: Digital image analysis (DIA) is used increasingly as an assisting tool to evaluate biomarkers, including human epidermal growth factor receptor 2 (HER2) in invasive breast cancer (BC). DIA can assist pathologists in HER2 evaluation by presenting quantitative information about the HER2 staining in APP assisted reading (AR). Concurrently, the HER2-low category (HER2-1+/2+ without HER2 gene amplification) has gained prominence due to newly developed antibody-drug conjugates. However, major inter-and intraobserver variability have been observed for the entity. The present quality assurance study investigated the concordance between DIA and AR in clinical use, especially concerning the HER2-low category. Methods and results: HER2 immunohistochemistry (IHC) in 761 tumours from 727 patients was evaluated in tissue microarray (TMA) cores by DIA (Visiopharm HER2-CONNECT) and AR. Overall concordance between HER2-scores were 73% (n = 552, weighted-j: 0.66), and 88% (n = 669, weighted-j: 0.70), when combining HER2-0/1+. A total of 205 scores were discordant by one category, while four were discordant by two categories. A heterogeneous HER2 pattern was relatively common in the discordant cases and a pitfall in the categorisation of HER2-low BC. AR more commonly reassigned a lower HER2 score (from HER2-1+ to HER2-0) within the HER2-low subgroup (n = 624) compared with DIA. Conclusion: DIA and AR display moderate agreement with heterogeneous and aberrant staining, representing a source of discordance and a pitfall in the evaluation of HER2.

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“…Currently, changes in therapy are made according to imaging size-based response assessment, RECIST 1.1 (response evaluation criteria in solid tumors), and on follow-up CT examinations [16,17]. Interpretations of most malignant tumors with heterogenous growth and response patterns remains an issue of clinical decision, especially because of the limitations of RECIST 1.1 [18][19][20][21]. Intra-tumoral heterogeneity and tumor infiltration are newer imaging features that can be spatially assessed on imaging using quantitative measures of image signal heterogeneity and may improve prognostication compared with size assessment alone [22,23].…”

Section: Introductionmentioning

confidence: 99%

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma

Tabari

Cox

D'Amore

et al. 2023

Cancers

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Pretreatment LDH is a standard prognostic biomarker for advanced melanoma and is associated with response to ICI. We assessed the role of machine learning-based radiomics in predicting responses to ICI and in complementing LDH for prognostication of metastatic melanoma. From 2008–2022, 79 patients with 168 metastatic hepatic lesions were identified. All patients had arterial phase CT images 1-month prior to initiation of ICI. Response to ICI was assessed on follow-up CT at 3 months using RECIST criteria. A machine learning algorithm was developed using radiomics. Maximum relevance minimum redundancy (mRMR) was used to select features. ROC analysis and logistic regression analyses evaluated performance. Shapley additive explanations were used to identify the variables that are the most important in predicting a response. mRMR selection revealed 15 features that are associated with a response to ICI. The machine learning model combining both radiomics features and pretreatment LDH resulted in better performance for response prediction compared to models that included radiomics or LDH alone (AUC of 0.89 (95% CI: [0.76–0.99]) vs. 0.81 (95% CI: [0.65–0.94]) and 0.81 (95% CI: [0.72–0.91]), respectively). Using SHAP analysis, LDH and two GLSZM were the most predictive of the outcome. Pre-treatment CT radiomic features performed equally well to serum LDH in predicting treatment response.

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

Cited by 9 publications

References 116 publications

Intratumoral Heterogeneity of Ki67 Proliferation Index Outperforms Conventional Prognostic Factors in Hormone Receptor-Positive Breast Cancer

Intratumoral Heterogeneity of Ki67 Proliferation Index Outperforms Conventional Prognostic Factors in Hormone Receptor-Positive Breast Cancer

Digital image analysis and assisted reading of theHER2score display reduced concordance: pitfalls in the categorisation ofHER2‐low breast cancer

Machine Learning Improves the Prediction of Responses to Immune Checkpoint Inhibitors in Metastatic Melanoma

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