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Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer.

ABSTRACT: Prognostic markers currently utilized in clinical practice for estrogen receptor-positive (ER+) and lymph node-negative (LN-) invasive breast cancer (IBC) patients include the Nottingham grading system and Oncotype Dx (ODx). However, these biomarkers are not always optimal and remain subject to inter-/intra-observer variability and high cost. In this study, we evaluated the association between computationally derived image features from H&E images and disease-free survival (DFS) in ER+ and LN- IBC. H&E images from a total of n = 321 patients with ER+ and LN- IBC from three cohorts were employed for this study (Training set: D1 (n = 116), Validation sets: D2 (n = 121) and D3 (n = 84)). A total of 343 features relating to nuclear morphology, mitotic activity, and tubule formation were computationally extracted from each slide image. A Cox regression model (IbRiS) was trained to identify significant predictors of DFS and predict a high/low-risk category using D1 and was validated on independent testing sets D2 and D3 as well as within each ODx risk category. IbRiS was significantly prognostic of DFS with a hazard ratio (HR) of 2.33 (95% confidence interval (95% CI) = 1.02-5.32, p = 0.045) on D2 and a HR of 2.94 (95% CI = 1.18-7.35, p = 0.0208) on D3. In addition, IbRiS yielded significant risk stratification within high ODx risk categories (D1 + D2: HR = 10.35, 95% CI = 1.20-89.18, p = 0.0106; D1: p = 0.0238; D2: p = 0.0389), potentially providing more granular risk stratification than offered by ODx alone.

SUBMITTER: Chen Y

PROVIDER: S-EPMC10192429 | biostudies-literature | 2023 May

REPOSITORIES: biostudies-literature

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Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer.

Chen Yuli Y Li Haojia H Janowczyk Andrew A Toro Paula P Corredor Germán G Whitney Jon J Lu Cheng C Koyuncu Can F CF Mokhtari Mojgan M Buzzy Christina C Ganesan Shridar S Feldman Michael D MD Fu Pingfu P Corbin Haley H Harbhajanka Aparna A Gilmore Hannah H Goldstein Lori J LJ Davidson Nancy E NE Desai Sangeeta S Parmar Vani V Madabhushi Anant A

NPJ breast cancer 20230517 1

Prognostic markers currently utilized in clinical practice for estrogen receptor-positive (ER+) and lymph node-negative (LN-) invasive breast cancer (IBC) patients include the Nottingham grading system and Oncotype Dx (ODx). However, these biomarkers are not always optimal and remain subject to inter-/intra-observer variability and high cost. In this study, we evaluated the association between computationally derived image features from H&E images and disease-free survival (DFS) in ER+ and LN- I ...[more]

PMID: 37198173

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Project description:BackgroundThe 21-gene recurrence score (RS) (Oncotype DX®; Genomic Health, Redwood City, CA) partitions hormone receptor positive, node negative breast cancers into three risk groups for recurrence. The Anne Arundel Medical Center (AAMC) model has previously been shown to accurately predict RS risk categories using standard pathology data. A pathologic-genomic (P-G) algorithm then is presented using the AAMC model and reserving the RS assay only for AAMC intermediate-risk patients.Patients and methodsA survival analysis was done using a prospectively collected institutional database of newly diagnosed invasive breast cancers that underwent RS assay testing from February 2005 to May 2015. Patients were assigned to risk categories based on the AAMC model. Using Kaplan-Meier methods, 5-year distant recurrence rates (DRR) were evaluated within each risk group and compared between AAMC and RS-defined risk groups. Five-year DRR were calculated for the P-G algorithm and compared with DRR for RS risk groups and the AAMC model's risk groups.ResultsA total of 1268 cases were included. Five-year DRR were similar between the AAMC low-risk group (2.7%, n = 322) and the RS < 18 low-risk group (3.4%, n = 703), as well as between the AAMC high-risk group (22.8%, n = 230) and the RS > 30 high-risk group (23.0%, n = 141). Using the P-G algorithm, more patients were categorized as either low or high risk and the distant metastasis rate was 3.3% for the low-risk group (n = 739) and 24.2% for the high-risk group (n = 272). Using the P-G algorithm, 44% (552/1268) of patients would have avoided RS testing.ConclusionsAAMC model is capable of predicting 5-year recurrences in high- and low-risk groups similar to RS. Further, using the P-G algorithm, reserving RS for AAMC intermediate cases, results in larger low- and high-risk groups with similar prognostic accuracy. Thus, the P-G algorithm reliably identifies a significant portion of patients unlikely to benefit from RS assay and with improved ability to categorize risk.

Project description:BackgroundGene-expression companion diagnostic tests, such as the Oncotype DX test, assess the risk of early stage Estrogen receptor (ER) positive (+) breast cancers, and guide clinicians in the decision of whether or not to use chemotherapy. However, these tests are typically expensive, time consuming, and tissue-destructive.MethodsIn this paper, we evaluate the ability of computer-extracted nuclear morphology features from routine hematoxylin and eosin (H&E) stained images of 178 early stage ER+ breast cancer patients to predict corresponding risk categories derived using the Oncotype DX test. A total of 216 features corresponding to the nuclear shape and architecture categories from each of the pathologic images were extracted and four feature selection schemes: Ranksum, Principal Component Analysis with Variable Importance on Projection (PCA-VIP), Maximum-Relevance, Minimum Redundancy Mutual Information Difference (MRMR MID), and Maximum-Relevance, Minimum Redundancy - Mutual Information Quotient (MRMR MIQ), were employed to identify the most discriminating features. These features were employed to train 4 machine learning classifiers: Random Forest, Neural Network, Support Vector Machine, and Linear Discriminant Analysis, via 3-fold cross validation.ResultsThe four sets of risk categories, and the top Area Under the receiver operating characteristic Curve (AUC) machine classifier performances were: 1) Low ODx and Low mBR grade vs. High ODx and High mBR grade (Low-Low vs. High-High) (AUC = 0.83), 2) Low ODx vs. High ODx (AUC = 0.72), 3) Low ODx vs. Intermediate and High ODx (AUC = 0.58), and 4) Low and Intermediate ODx vs. High ODx (AUC = 0.65). Trained models were tested independent validation set of 53 cases which comprised of Low and High ODx risk, and demonstrated per-patient accuracies ranging from 75 to 86%.ConclusionOur results suggest that computerized image analysis of digitized H&E pathology images of early stage ER+ breast cancer might be able predict the corresponding Oncotype DX risk categories.

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Project description:Each of the pathological stages (I-IIIa) of surgically resected non-small-cell lung cancer has hidden biological heterogeneity, manifested as heterogeneous outcomes within each stage. Thus, the finding of robust and precise molecular classifiers with which to assess individual patient risk is an unmet medical need. Here, we identified and validated the clinical utility of a new prognostic signature based on three proteins (BRCA1, QKI, and SLC2A1) to stratify early-stage lung adenocarcinoma patients according to their risk of recurrence or death. Patients were staged according to the new International Association for the Study of Lung Cancer (IASLC) staging criteria (8th edition, 2018). A test cohort (n = 239) was used to assess the value of this new prognostic index (PI) based on the three proteins. The prognostic signature was developed by Cox regression with the use of stringent statistical criteria (TRIPOD: Transparent reporting of a multivariable prediction model for individual prognosis or diagnosis). The model resulted in a highly significant predictor of 5-year outcome for disease-free survival (p < 0.001) and overall survival (p < 0.001). The prognostic ability of the model was externally validated in an independent multi-institutional cohort of patients (n = 114, p = 0.021). We also demonstrated that this molecular classifier adds relevant information to the gold standard TNM-based pathological staging, with a highly significant improvement of the likelihood ratio. We subsequently developed a combined PI including both the molecular and the pathological data that improved the risk stratification in both cohorts (p ≤ 0.001). Moreover, the signature may help to select stage I-IIA patients who might benefit from adjuvant chemotherapy. In summary, this protein-based signature accurately identifies those patients with a high risk of recurrence and death, and adds further prognostic information to the TNM-based clinical staging, even when the new IASLC 8th edition staging criteria are applied. More importantly, it may be a valuable tool for selecting patients for adjuvant therapy. Copyright © 2018 Pathological Society of Great Britain and Ireland. Published by John Wiley & Sons, Ltd.

Project description:BackgroundInvasive disease-free survival (IDFS) rates are excellent in patients with breast cancer (BC) with hormone receptor-positive (HR+), human epidermal growth factor receptor 2-negative (HER2-), axillary lymph node-negative (LN-) tumors with a 21-gene expression assay recurrence score (RS) of 0 to 10. However, to the authors' knowledge, the outcomes among patients with an RS of 11 to 25 who are treated with endocrine therapy alone are unknown.MethodsIn this retrospective single-institution study, the authors described the characteristics of patients with HR+, HER2-, LN- BC who underwent a 21-gene expression assay. In addition, among those individuals diagnosed between 2005 and 2011, we measured IDFS, recurrence-free survival, distant recurrence-free survival, and overall survival rates, focusing on patients with an RS of 11 to 25 by receipt of chemotherapy. The Kaplan-Meier method was used to estimate survival rates and multivariable Cox proportional hazards models were used to calculate hazard ratios and 95% confidence intervals (95% CIs).ResultsAmong 1424 patients, the RS distribution was 0 to 10 in 297 patients (21%), 11 to 25 in 894 patients (63%), and >25 in 233 patients (16%); of these, 1.7%, 15%, and 73.4% of patients, respectively, received chemotherapy. With a median follow-up of 58 months, those patients with an RS of 11 to 25 had an IDFS rate at 5 years of 92.6% (95% CI, 89.6%-94.7%), which was comparable between those who received chemotherapy and those who did not. The hazard ratios of the effect of chemotherapy were 1.64 for IDFS (95% CI, 0.73-3.71), 1.46 for recurrence-free survival (95% CI, 0.41-5.23), 1.25 for distant recurrence-free survival (95% CI, 0.32-4.92), and 2.19 for overall survival (95% CI, 0.44-11.0).ConclusionsThe results of the current study demonstrate similar outcomes with or without chemotherapy in patients with HR+, HER2-, LN- BC who have an RS of 11 to 25, but a benefit from chemotherapy in this group cannot be ruled out. Cancer 2017;123:2422-31. © 2017 American Cancer Society.

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Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer.

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Computational pathology improves risk stratification of a multi-gene assay for early stage ER+ breast cancer.

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