Protein signaling and drug target activation signatures to guide therapy prioritization: Therapeutic resistance and sensitivity in the I-SPY 2 Trial

Gallagher, Rosa I.; Wulfkuhle, Julia; Wolf, Denise M.; Brown-Swigart, Lamorna; Yau, Christina; O’Grady, Nicholas; Basu, Amrita; Lu, Ruixiao; Campbell, Michael J.; Magbanua, Mark J.; Coppé, Jean-Philippe; Asare, Smita M.; Sit, Laura; Matthews, Jeffrey B.; Perlmutter, Jane; Hylton, Nola; Liu, Minetta C.; Symmans, W. Fraser; Rugo, Hope S.; Isaacs, Claudine; DeMichele, Angela M.; Yee, Douglas; Pohlmann, Paula R.; Hirst, Gillian L.; Esserman, Laura J.; van ‘t Veer, Laura J.; Petricoin, Emanuel F.

doi:10.1016/j.xcrm.2023.101312

Cited by 5 publications

(1 citation statement)

References 70 publications

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“…This includes multimodal approaches that involve biomarkers beyond ctDNA and imaging features. For example, the I-SPY2 trial is testing whether gene expression- [49] and protein-based [50] tumor biomarkers can improve the prediction of pathCR to guide treatment selection. Wolf and colleagues in I-SPY2 have created alternative breast cancer subtypes by incorporating these molecular biomarkers into the standard breast cancer subtyping and showed improved performance in predicting treatment response [49].…”

Section: Future Directionsmentioning

confidence: 99%

Integrating Imaging and Circulating Tumor DNA Features for Predicting Patient Outcomes

Magbanua,

Li,

van ’t Veer

2024

Cancers

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Biomarkers for evaluating tumor response to therapy and estimating the risk of disease relapse represent tremendous areas of clinical need. To evaluate treatment efficacy, tumor response is routinely assessed using different imaging modalities like positron emission tomography/computed tomography or magnetic resonance imaging. More recently, the development of circulating tumor DNA detection assays has provided a minimally invasive approach to evaluate tumor response and prognosis through a blood test (liquid biopsy). Integrating imaging- and circulating tumor DNA-based biomarkers may lead to improvements in the prediction of patient outcomes. For this mini-review, we searched the scientific literature to find original articles that combined quantitative imaging and circulating tumor DNA biomarkers to build prediction models. Seven studies reported building prognostic models to predict distant recurrence-free, progression-free, or overall survival. Three discussed building models to predict treatment response using tumor volume, pathologic complete response, or objective response as endpoints. The limited number of articles and the modest cohort sizes reported in these studies attest to the infancy of this field of study. Nonetheless, these studies demonstrate the feasibility of developing multivariable response-predictive and prognostic models using regression and machine learning approaches. Larger studies are warranted to facilitate the building of highly accurate response-predictive and prognostic models that are generalizable to other datasets and clinical settings.

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Section: Future Directionsmentioning

confidence: 99%

Integrating Imaging and Circulating Tumor DNA Features for Predicting Patient Outcomes

Magbanua,

Li,

van ’t Veer

2024

Cancers

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Protein biomarkers for subtyping breast cancer and implications for future research: a 2024 update

Mueller,

Davis,

Espina

2024

Expert Review of Proteomics

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Integrating Functional Protein Drug Target Data into a Precision Oncology Molecular Tumor Board

Hunt,

Randall,

Mansukhani

et al. 2024

Preprint

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Collaborative review of molecular profiling data by multidisciplinary molecular tumor boards (MTB) is increasingly important for improving patient management and outcomes, though currently relies nearly exclusively on nucleic acid next-generation sequencing (NGS) and limited panels of immunohistochemistry-based protein abundance data. We examined the feasibility of incorporating real-time laser microdissection (LMD) enrichment of tumor epithelium and commercial CLIA-based reverse phase protein array (RPPA) protein drug target expression/activation profiling into our cancer center MTB to complement standard clinical NGS-based profiling. The LMD-RPPA workflow was performed within a therapeutically permissive timeframe with a median dwell time of nine days, during which specimens were processed outside of standard clinical workflows. The RPPA-generated data supported additional and/or alternative therapeutic considerations for 54% of profiled patients following review by the MTB. These findings suggest that integrating proteomic/phosphoproteomic and NGS-based genomic data creates opportunities to further personalize clinical decision-making for precision oncology.

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Protein signaling and drug target activation signatures to guide therapy prioritization: Therapeutic resistance and sensitivity in the I-SPY 2 Trial

Cited by 5 publications

References 70 publications

Integrating Imaging and Circulating Tumor DNA Features for Predicting Patient Outcomes

Integrating Imaging and Circulating Tumor DNA Features for Predicting Patient Outcomes

Protein biomarkers for subtyping breast cancer and implications for future research: a 2024 update

Integrating Functional Protein Drug Target Data into a Precision Oncology Molecular Tumor Board

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