Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies

Wolf, Denise M.; Yau, Christina; Wulfkuhle, Julia; Brown-Swigart, Lamorna; Gallagher, Isela R.; Lee, Pei Rong Evelyn; Zhu, Zelos; Magbanua, Mark Jesus M.; Sayaman, Rosalyn W.; O'Grady, Nicholas; Basu, Amrita; Delson, Amy L.; Coppé, Jean Philippe; Lu, Ruixiao; Braun, Jerome; Asare, Smita; Sit, Laura; Matthews, Jeffrey B.; Perlmutter, Jane; Hylton, Nola M.; Liu, Minetta C.; Pohlmann, Paula Raffin; Symmans, W. Fraser; Rugo, Hope S.; Isaacs, Claudine; DeMichele, Angela; Yee, Douglas; Berry, Donald A.; Pusztai, Lajos; Petricoin, Emanuel F.; Hirst, Gillian; Esserman, Laura J.; Veer, Laura J. van ’t

doi:10.1016/j.ccell.2022.05.005

Cited by 163 publications

(167 citation statements)

References 47 publications

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“…A recent study identified the third-generation subtype classification of breast cancer patients based on treatment responses 42 . In the present study, we used c-Jun downstream signals to further classify the subtypes of luminal breast cancer and their clinical prognoses.…”

Section: Discussionmentioning

confidence: 99%

Targeting c-Jun is a potential therapy of luminal breast cancer bone metastasis

Han

Futakuchi

Nakamichi

et al. 2022

Preprint

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Luminal breast cancer has the highest bone metastasis frequency among all breast cancer subtypes, but its metastatic mechanism has not been elucidated because of the lack of appropriate metastatic cell lines. The study aim was to characterize high-osteolytic bone metastatic MCF7-BM cell lines and extract c-Jun, a novel bone metastasis marker. We found that c-Jun was upregulated in MCF7-BM cells, and its deficiency was associated with suppression of the cell migration, transformation, and stemness of BM cells. In vivo, c-Jun-deficient MCF7-TAM67 cells exhibited weaker bone metastatic ability. Additionally, c-Jun overexpression in MCF7-BM cells led to a tumor-migration promotion cycle in the bone microenvironment possibly by enhancing calcium-induced migration and releasing the osteoclast activator BMP5. Inhibition of c-Jun by JNK-IN-8, a JNK inhibitor, effectively reduced tumorigenesis activities and bone metastatic tumors. Our results indicate the potential benefits of a therapy that targets c-Jun to prevent or minimize luminal breast cancer bone metastasis.

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Section: Discussionmentioning

confidence: 99%

Targeting c-Jun is a potential therapy of luminal breast cancer bone metastasis

Han

Futakuchi

Nakamichi

et al. 2022

Preprint

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“…Pretreatment DWI-derived ADC maps and manual tumor segmentations were retrieved for radiomics model development. Pathologic complete response (pCR), which is the binary outcome assessing the absence of invasive disease in breast and lymph nodes at the time of surgery [17], was used as the prediction endpoint. We adopted the same train-test split as the BMMR2 challenge with 60% (n=117) randomly chosen as the training set and the remaining 40% (n=74) set as the testing set.…”

Section: A Patient Datamentioning

confidence: 99%

Comparing Effectiveness of Image Perturbation and Test-retest Imaging Towards Establishment of Reliable Radiomic Models

Jiang¹

2022

Preprint

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<p>Image perturbation is the promising technique to assess radiomic feature repeatability without test-retest imaging. However, whether it can achieve the same effect on model reliability enhancement as test-retest imaging is unknown. This study aimed to compare radiomic model reliability based on repeatable features determined by image perturbation and test-retest imaging. A 191-patient public breast cancer dataset with 71 test-retest scans was used with pre-determined 117 training and 74 testing samples. We collected apparent diffusion coefficient images and the manually segmented tumor structures for radiomic feature extraction and pathological complete response record for model prediction. Random translations, rotations, and contour randomizations were performed on the training images, and intra-class correlation coefficient (ICC) was used to quantify feature repeatability. After removing volume correlated features, multiple ICC thresholds were applied for repeatable feature filtering, and separate logistic-regression models were developed using 5 most relevant and independent features. We evaluated model reliability in both generalizability and robustness, which were quantified by training and testing area under the receiver operating characteristic curve (AUC) and prediction ICC under perturbation and test-retest. Higher testing performance was found at higher ICC thresholds, but it dropped significantly at ICC=0.95 for the test-retest model. Similar optimal reliability can be achieved with testing AUC = 0.76-0.77 and prediction ICC>0.9 at the ICC threshold of 0.9. It is recommended to include feature repeatability analysis using image perturbation in any radiomic study when test-retest is not feasible, but care should be taken when deciding the optimal feature repeatability criteria.</p>

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“…All the data employed in the study has been obtained from publicly available databases 24,25,[40][41][42][43][44][45][46][47] . All the accession numbers are available in Supplementary Table 1.…”

Section: Supplementary Figure Legendsmentioning

confidence: 99%

“…These agnostic approaches generate robust classifiers from diverse and complex data types, such as imaging, clinical, histology, and molecular profiling 22,23 . Here, we employed machine learning to construct and evaluate gene expression-based signatures that efficiently predict pCR to ICI plus chemotherapy in patients with primary TNBC treated in the phase II/III I-SPY2 clinical trial 24,25 . Thus, we constructed and tested a TNBC ICI response (TNBC-ICI) predictive classifier that involved 37 genes.…”

Section: Introductionmentioning

confidence: 99%

Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer

Ensenyat-Mendez

Orozco²,

Llinàs‐Arias³

et al. 2022

Preprint

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Background: Immune checkpoint inhibitors (ICI) improve clinical outcomes in triple-negative breast cancer (TNBC) patients. However, a subset of patients does not respond to treatment. Biomarkers that show ICI predictive potential in other solid tumors, such as levels of PD-L1 and the tumor mutational burden, among others, show a modest predictive performance in patients with TNBC. Methods: We built machine learning models based on pre-ICI treatment gene expression profiles, to construct gene expression classifiers to identify primary TNBC ICI-responder patients. This study involved 132 ICI-naïve and 721 specimens treated with ICI plus chemotherapy, including TNBC tumors, HR+/HER2- breast tumors, and other solid non-breast tumors. Results: A 37-gene TNBC ICI predictive (TNBC-ICI) classifier showed a high performance in predicting pathological complete response (pCR) to ICI plus chemotherapy on an independent TNBC validation cohort (AUC=0.86). The TNBC-ICI classifier showed better performance than other previously established molecular signatures, including PD-1 (PDCD1) and PD-L1 (CD274) gene expression (AUC=0.67). Integrating molecular signatures did not improve the efficiency of the classifier (AUC=0.75). TNBC-ICI displayed a modest accuracy in predicting ICI response in two different cohorts of HR+/HER2- breast cancer patients (AUC: 0.72-0.76). Evaluation of six cohorts of patients with non-breast solid tumors treated with ICI plus chemotherapy showed overall poor performance (median AUC=0.67). Conclusion: TNBC-ICI predicts pCR to ICI plus chemotherapy in patients with primary TNBC. The study provides a guide to implementing the TNBC-ICI classifier in clinical studies. Further validations will consolidate a novel predictive panel to improve the treatment decision-making for patients with TNBC.

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Redefining breast cancer subtypes to guide treatment prioritization and maximize response: Predictive biomarkers across 10 cancer therapies

Cited by 163 publications

References 47 publications

Targeting c-Jun is a potential therapy of luminal breast cancer bone metastasis

Targeting c-Jun is a potential therapy of luminal breast cancer bone metastasis

Comparing Effectiveness of Image Perturbation and Test-retest Imaging Towards Establishment of Reliable Radiomic Models

Construction and validation of a gene expression classifier to predict immunotherapy response in primary triple-negative breast cancer

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