HER-2 (Human epidermal growth factor receptor 2)

P1/2, N=315, Active, not recruiting, Dizal Pharmaceuticals | Trial completion date: Jul 2025 --> Dec 2025

18F-FDG PET-CT before and after the first cycle of neoadjuvant treatment does not appear to be an effective tool to predict pCR in patients with HER2+ BC.

This trial has been approved by the Institutional Review Board (IRB) and began patient enrollment in January 2024. The trial will provide insights into the safety and effectiveness of a novel combinatorial therapy for BCBM.

Early clinical data demonstrated activity of BLU-222, a potent and selective CDK2 inhibitor, both as monotherapy (CCNE1 amplified) and in combination with ribociclib and fulvestrant in patients with HR+/HER2- breast cancer. These findings provide evidence that CDK2i combined with CDK4/6i can address multiple known mechanisms of resistance to CDK4/6i, enhancing antitumor responses in preclinical breast cancer models.

In the two cases we reported, first-line palbociclib therapy shows adequate and timely responses for premenopausal HR+/HER2- metastatic breast cancer patients. Although not widely utilized, frontline therapy with palbociclib combined with endocrine treatments may be a choice for HR+/HER2- metastatic breast cancer patients experiencing severe visceral metastasis.

Despite unchanged guideline recommendations, fewer patients started ET over time. This trend, and regional variation, suggests that a more reticent approach by physicians to initiating ET for HR+/HER2- breast cancer may be contributing to it.

P1, N=16, Completed, Providence Health & Services | Active, not recruiting --> Completed

While challenges remain in distinguishing Luminal B2, the use of BT imaging and AI demonstrates promise for refining molecular subtype assessment and improving clinical decision-making.Clinical Relevance-This study demonstrates that AI-driven analysis of digital breast tomosynthesis can capture tumor and microenvironment characteristics, providing valuable insights into disease staging and progression. By leveraging comprehensive imaging data rather than limited biopsy samples, this approach has the potential to improve non-invasive molecular subtyping, aiding personalized treatment decisions while reducing reliance on invasive procedures.

Extensive experiments on the BCI dataset demonstrate that our approach outperforms existing single-model methods, achieving superior Peak Signal-to-Noise Ratio (PSNR) and Structural Similarity Index (SSIM) metrics. All of our code is available at: https://github.com/arshamhaq/BCI-fusion.Clinical RelevanceImproving the quality of synthetic IHC images can potentially reduce costs and streamline the diagnostic process, ultimately benefiting patient outcomes.

Our HI-MAE model opens new avenues for future research, facilitating the incorporation of IHC information into other classification tasks, particularly for precise biomarker predictions.Clinical relevance- By incorporating IHC images into the pre-training process, we enable the prediction of HER2 status directly from routine H&E-stained tissue sections under the guidance of IHC information, thereby eliminating the need for IHC images during the testing phase. This approach significantly reduces the staining costs associated with IHC.

To further expand its functionality, CaWSCP was engineered with a SpyTag-SpyCatcher system, enabling site-specific conjugation with HER2-specific affibodies for precise molecular targeting. Experimental findings demonstrate CaWSCP's biocompatibility, photoconversion capabilities, and potential for targeted imaging, making it a promising platform for advanced biomedical applications.Clinical Relevance- The engineered CaWSCP contrast agent, with its targeting capability via the SpyTag-SpyCatcher system, improves PA imaging for non-invasive detection and monitoring of cancer and vascular disorders by enhancing resolution and minimizing background interference.

Overall, the features extracted from baseline data and follow-up data or after the first course of NAC, combined with information of breast cancer subtype, offer strong predictive value for pCR in follow-up patients.Clinical Relevance-By providing a more accurate assessment of treatment response after the first course of NAC, this approach empowers clinicians to make artificial intelligence-driven decisions, customize therapy plans for individual patients, and avoid ineffective treatments. Consequently, this strategy could improve patient outcomes and optimize therapeutic efficacy.