A Multiparameter Molecular Classifier to Predict Response to Neoadjuvant Lapatinib plus Trastuzumab without Chemotherapy in HER2+ Breast Cancer

iGenSig-Rx: an integral genomic signature based white-box tool for modeling cancer therapeutic responses using multi-omics data

Multi-omics sequencing is poised to revolutionize clinical care in the coming decade. However, there is a lack of effective and interpretable genome-wide modeling methods for the rational selection of patients for personalized interventions. To address this, we present iGenSig-Rx, an integral genomic signature-based approach, as a transparent tool for modeling therapeutic response using clinical trial datasets. This method adeptly addresses challenges related to cross-dataset modeling by capitalizing on high-dimensional redundant genomic features, analogous to reinforcing building pillars with redundant steel rods. Moreover, it integrates adaptive penalization of feature redundancy on a per-sample basis to prevent score flattening and mitigate overfitting. We then developed a purpose-built R package to implement this method for modeling clinical trial datasets. When applied to genomic datasets for HER2 targeted therapies, iGenSig-Rx model demonstrates consistent and reliable predictive power across four independent clinical trials. More importantly, the iGenSig-Rx model offers the level of transparency much needed for clinical application, allowing for clear explanations as to how the predictions are produced, how the features contribute to the prediction, and what are the key underlying pathways. We anticipate that iGenSig-Rx, as an interpretable class of multi-omics modeling methods, will find broad applications in big-data based precision oncology. The R package is available: https://github.com/wangxlab/iGenSig-Rx .

Optimal [18F]FDG PET/CT Cutoff for Pathologic Complete Response in HER2-Positive Early Breast Cancer Patients Treated with Neoadjuvant Trastuzumab and Pertuzumab in the PHERGain Trial

The PHERGain trial investigated the potential of metabolic imaging to identify candidates for chemotherapy deescalation in human epidermal growth factor receptor 2 (HER2)-positive, invasive, operable breast cancer with at least 1 breast lesion evaluable by [18F]FDG PET/CT. [18F]FDG PET/CT responders were defined as patients with an SUVmax reduction (ΔSUVmax) of at least 40% in all of their target lesions after 2 cycles of trastuzumab and pertuzumab (HP) (with or without endocrine therapy). In total, 227 of 285 patients (80%) included in the HP arm showed a predefined metabolic response and received a total of 8 cycles of HP (with or without endocrine therapy). Pathologic complete response (pCR), defined as ypT0/isN0, was achieved in 37.9% of the patients. Here, we describe the secondary preplanned analysis of the best cutoff of ΔSUVmax for pCR prediction. Methods: Receiver-operating-characteristic analysis was applied to look for the most appropriate ΔSUVmax cutoff in HER2-positive early breast cancer patients treated exclusively with neoadjuvant HP (with or without endocrine therapy). Results: The ΔSUVmax capability of predicting pCR in terms of the area under the receiver-operating-characteristic curve was 72.1% (95% CI, 65.1-79.2%). The optimal ΔSUVmax cutoff was found to be 77.0%, with a 51.2% sensitivity and a 78.7% specificity. With this cutoff, 74 of 285 patients (26%) would be classified as metabolic responders, increasing the pCR rate from 37.9% (cutoff ≥ 40%) to 59.5% (44/74 patients) (P < 0.01). With this optimized cutoff, 44 of 285 patients (15.4%) would avoid chemotherapy in either the neoadjuvant or the adjuvant setting compared with 86 of 285 patients (30.2%) using the original cutoff (P < 0.001). Conclusion: In the PHERGain trial, an increased SUVmax cutoff (≥77%) after 2 cycles of exclusive HP (with or without endocrine therapy) achieves a pCR in the range of the control arm with chemotherapy plus HP (59.5% vs. 57.7%, respectively), further identifying a subgroup of patients with HER2-addicted tumors. However, the original cutoff (≥40%) maximizes the number of patients who could avoid chemotherapy.

Treatment of residual disease following neoadjuvant therapy in breast cancer

Substantial advances have been made in the systemic treatment of breast cancer with residual disease following neoadjuvant therapy. We reviewed recent and ongoing studies informing the standard clinical management of residual disease by subtype: HER2+, TNBC, and HR+/HER2-, as well as strategies for BRCA+ disease. We conclude with a discussion of ongoing clinical trials and current controversies regarding the treatment of residual disease in breast cancer.