Development and validation of an AI-enabled digital breast cancer assay to predict early-stage breast cancer recurrence within 6 years

U.S. payer budget impact of using an AI-augmented cancer risk discrimination digital histopathology platform to identify high-risk of recurrence in women with early-stage invasive breast cancer

Aims: Use of gene expression signatures to predict adjuvant chemotherapy benefit in women with early-stage breast cancer is increasing. However, high cost, limited access, and eligibility for these tests results in the adoption of less precise assessment approaches. This study evaluates the cost impact of PreciseDx Breast (PDxBr), an AI-augmented histopathology platform that assesses the 6-year risk of recurrence in early-stage invasive breast cancer patients to help improve informed use of adjuvant chemotherapy.Materials and Methods: A decision-tree Markov model was developed to compare the costs of treatment guided by standard of care (SOC) risk assessment (i.e., clinical diagnostic workup with or without Oncotype DX) versus PDxBr with SOC in a hypothetical cohort of U.S. women with early-stage invasive breast cancer. A commercial payer perspective compares costs of testing, adjuvant therapy, recurrence, adverse events, surveillance, and end-of-life care.Results: PDxBr use in prognostic evaluation resulted in savings of $4 million (M) in year one compared to current SOC in 1M females members. Over 6-years, savings increased to $12.5M. The per-treated patient costs in year one amounted to $19.5 thousand (K) for SOC and $16.9K for PDxBr.Limitations: For simplicity, recurrence was not specified. We performed scenario analyses to account for variations in rates for local, regional, and distant recurrence. Second, a recurrent patient incurs the total cost of treated recurrence in the first year and goes back to remission or death. Third, CDK4/6i treatment is only incorporated in the recurrence costs but not in the first line of treatment for early-stage breast cancer due to limited data.Conclusions: Sensitivity analyses demonstrated robust overall savings to changes in all variables in the model. The use of PDxBr to assess breast cancer recurrence risk has the potential to fill gaps in care and reduce costs when gene expression signatures are not available.

The yin and yang of chromosomal instability in prostate cancer

Metastatic prostate cancer remains an incurable lethal disease. Studies indicate that prostate cancer accumulates genomic changes during disease progression and displays the highest levels of chromosomal instability (CIN) across all types of metastatic tumours. CIN, which refers to ongoing chromosomal DNA gain or loss during mitosis, and derived aneuploidy, are known to be associated with increased tumour heterogeneity, metastasis and therapy resistance in many tumour types. Paradoxically, high CIN levels are also proposed to be detrimental to tumour cell survival, suggesting that cancer cells must develop adaptive mechanisms to ensure their survival. In the context of prostate cancer, studies indicate that CIN has a key role in disease progression and might also offer a therapeutic vulnerability that can be pharmacologically targeted. Thus, a comprehensive evaluation of the causes and consequences of CIN in prostate cancer, its contribution to aggressive advanced disease and a better understanding of the acquired CIN tolerance mechanisms can translate into new tumour classifications, biomarker development and therapeutic strategies.

Artificial intelligence in digital histopathology for predicting patient prognosis and treatment efficacy in breast cancer

INTRODUCTION

Histological images contain phenotypic information predictive of patient outcomes. Due to the heavy workload of pathologists, the time-consuming nature of quantitatively assessing histological features, and human eye limitations to recognize spatial patterns, manually extracting prognostic information in routine pathological workflows remains challenging. Digital pathology has facilitated the mining and quantification of these features utilizing whole-slide image (WSI) scanners and artificial intelligence (AI) algorithms. AI algorithms to identify image-based biomarkers from the tumor microenvironment (TME) have the potential to revolutionize the field of oncology, reducing delays between diagnosis and prognosis determination, allowing for rapid stratification of patients and prescription of optimal treatment regimes, thereby improving patient outcomes.

AREAS COVERED

In this review, the authors discuss how AI algorithms and digital pathology can predict breast cancer patient prognosis and treatment outcomes using image-based biomarkers, along with the challenges of adopting this technology in clinical settings.

EXPERT OPINION

The integration of AI and digital pathology presents significant potential for analyzing the TME and its diagnostic, prognostic, and predictive value in breast cancer patients. Widespread clinical adoption of AI faces ethical, regulatory, and technical challenges, although prospective trials may offer reassurance and promote uptake, ultimately improving patient outcomes by reducing diagnosis-to-prognosis delivery delays.

Analytical Validation of the PreciseDx Digital Prognostic Breast Cancer Test in Early-Stage Breast Cancer

BACKGROUND

PreciseDx Breast (PDxBr) is a digital test that predicts early-stage breast cancer recurrence within 6-years of diagnosis.

MATERIALS AND METHODS

Using hematoxylin and eosin-stained whole slide images of invasive breast cancer (IBC) and artificial intelligence-enabled morphology feature array, microanatomic features are generated. Morphometric attributes in combination with patient's age, tumor size, stage, and lymph node status predict disease free survival using a proprietary algorithm. Here, analytical validation of the automated annotation process and extracted histologic digital features of the PDxBr test, including impact of methodologic variability on the composite risk score is presented. Studies of precision, repeatability, reproducibility and interference were performed on morphology feature array-derived features. The final risk score was assessed over 20-days with 2-operators, 2-runs/day, and 2-replicates across 8-patients, allowing for calculation of within-run repeatability, between-run and within-laboratory reproducibility.

RESULTS

Analytical validation of features derived from whole slide images demonstrated a high degree of precision for tumor segmentation (0.98, 0.98), lymphocyte detection (0.91, 0.93), and mitotic figures (0.85, 0.84). Correlation of variation of the assay risk score for both reproducibility and repeatability were less than 2%, and interference from variation in hematoxylin and eosin staining or tumor thickness was not observed demonstrating assay robustness across standard histopathology preparations.

CONCLUSION

In summary, the analytical validation of the digital IBC risk assessment test demonstrated a strong performance across all features in the model and complimented the clinical validation of the assay previously shown to accurately predict recurrence within 6-years in early-stage invasive breast cancer patients.

Automated Prognosis Marker Assessment in Breast Cancers Using BLEACH&STAIN Multiplexed Immunohistochemistry

Prognostic markers in routine clinical management of breast cancer are often assessed using RNA-based multi-gene panels that depend on fluctuating tumor purity. Multiplex fluorescence immunohistochemistry (mfIHC) holds the potential for an improved risk assessment. To enable automated prognosis marker detection (i.e., progesterone receptor [PR], estrogen receptor [ER], androgen receptor [AR], GATA3, TROP2, HER2, PD-L1, Ki67, TOP2A), a framework for automated breast cancer identification was developed and validated involving thirteen different artificial intelligence analysis steps and an algorithm for cell distance analysis using 11+1-marker-BLEACH&STAIN-mfIHC staining in 1404 invasive breast cancers of no special type (NST). The framework for automated breast cancer detection discriminated normal glands from malignant glands with an accuracy of 98.4%. This approach identified that five (PR, ER, AR, GATA3, PD-L1) of nine biomarkers were associated with prolonged overall survival (p ≤ 0.0095 each) and two of these (PR, AR) were found to be independent risk factors in multivariate analysis (p ≤ 0.0151 each). The combined assessment of PR-ER-AR-GATA3-PD-L1 as a five-marker prognosis score showed strong prognostic relevance (p < 0.0001) and was an independent risk factor in multivariate analysis (p = 0.0034). Automated breast cancer detection in combination with an artificial intelligence-based analysis of mfIHC enables a rapid and reliable analysis of multiple prognostic parameters. The strict limitation of the analysis to malignant cells excludes the impact of fluctuating tumor purity on assay precision.

Improving the efficacy of anti-EGFR drugs in GBM: Where we are going?

The therapies targeting mutations of driver genes in cancer have advanced into clinical trials for a variety of tumors. In glioblastoma (GBM), epidermal growth factor receptor (EGFR) is the most commonly mutated oncogene, and targeting EGFR has been widely investigated as a promising direction. However, the results of EGFR pathway inhibitors have not been satisfactory. Limited blood-brain barrier (BBB) permeability, drug resistance, and pathway compensation mechanisms contribute to the failure of anti-EGFR therapies. This review summarizes recent research advances in EGFR-targeted therapy for GBM and provides insight into the reasons for the unsatisfactory results of EGFR-targeted therapy. By combining the results of preclinical studies with those of clinical trials, we discuss that improved drug penetration across the BBB, the use of multi-target combinations, and the development of peptidomimetic drugs under the premise of precision medicine may be promising strategies to overcome drug resistance in GBM.

Construction and validation of a nomogram for predicting the prognosis of patients with lymph node-positive invasive micropapillary carcinoma of the breast: based on SEER database and external validation cohort

Background

Invasive micropapillary carcinoma (IMPC) of the breast is a rare subtype of breast cancer with high incidence of aggressive clinical behavior, lymph node metastasis (LNM) and poor prognosis. In the present study, using the Surveillance, Epidemiology, and End Results (SEER) database, we analyzed the clinicopathological characteristics and prognostic factors of IMPC with LNM, and constructed a prognostic nomogram.

Methods

We retrospectively analyzed data for 487 breast IMPC patients with LNM in the SEER database from January 2010 to December 2015, and randomly divided these patients into a training cohort (70%) and an internal validation cohort (30%) for the construction and internal validation of the nomogram, respectively. In addition, 248 patients diagnosed with IMPC and LNM at the Fourth Hospital of Hebei Medical University from January 2010 to December 2019 were collected as an external validation cohort. Lasso regression, along with Cox regression, was used to screen risk factors. Further more, the discrimination, calibration, and clinical utility of the nomogram were assessed based on the consistency index (C-index), time-dependent receiver operating characteristic (ROC), calibration curve, and decision curve analysis (DCA).

Results

In summary, we identified six variables including molecular subtype of breast cancer, first malignant primary indicator, tumor grade, AJCC stage, radiotherapy and chemotherapy were independent prognostic factors in predicting the prognosis of IMPC patients with LNM (P < 0.05). Based on these factors, a nomogram was constructed for predicting 3- and 5-year overall survival (OS) of patients. The nomogram achieved a C-index of 0.789 (95%CI: 0.759-0.819) in the training cohort, 0.775 (95%CI: 0.731-0.819) in the internal validation cohort, and 0.788 (95%CI: 0.756-0.820) in the external validation cohort. According to the calculated patient risk score, the patients were divided into a high-risk group and a low-risk group, which showed a significant difference in the survival prognosis of the two groups (P<0.0001). The time-dependent ROC curves, calibration curves and DCA curves proved the superiority of the nomogram.

Conclusions

We have successfully constructed a nomogram that could predict 3- and 5-year OS of IMPC patients with LNM and may assist clinicians in decision-making and personalized treatment planning.