Using biological constraints to improve prediction in precision oncology

Omar, Mohamed; Dinalankara, Wikum; Mulder, Lotte; Coady, Tendai; Zanettini, Claudio; Imada, Eddie Luidy; Younes, Laurent; Geman, Donald; Marchionni, Luigi

doi:10.1016/j.isci.2023.106108

Cited by 3 publications

(4 citation statements)

References 87 publications

(110 reference statements)

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“…To accomplish this, we used gene expression profiles from 369 pre-NACT TNBC samples, of which 223 samples were used for training and the remainder for testing. We leveraged our group's recent demonstration of the importance of incorporating biological constraints in predictive classifiers [9,42] and used this approach to identify a subset of features predictive of NACT response. Specifically, we first designed a biological mechanism capturing the Notch signaling network by pairing genes known to be upregulated with those downregulated by Notch signaling.…”

Section: Discussionmentioning

confidence: 99%

“…Specifically, we first designed a biological mechanism capturing the Notch signaling network by pairing genes known to be upregulated with those downregulated by Notch signaling. This mechanism was subsequently used as a biological constraint [9] to train a k-top scoring pairs (k-TSPs) model [16,17] to select the maximum number of "Notch signaling" gene pairs that could distinguish patients who had RD from those with pCR in the training data. These pairs were then ranked using a regularized random forest (RRF) model trained on 100 bootstraps of the training data to select the most informative features.…”

Section: Discussionmentioning

confidence: 99%

“…Studies focusing on TNBC often have small sample sizes or propose signatures that are challenging to implement in clinical practice owing to the large number of genes involved. Furthermore, these studies typically use agnostic approaches for developing gene signatures, resulting in less robustness compared to classifiers built upon biologically consistent constraints [9].…”

Section: Introductionmentioning

confidence: 99%

See 2 more Smart Citations

Notch-based gene signature for predicting the response to neoadjuvant chemotherapy in triple-negative breast cancer

Omar,

Nuzzo,

Ravera

et al. 2023

J Transl Med

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Background While the efficacy of neoadjuvant chemotherapy (NACT) in treating triple-negative breast cancer (TNBC) is generally accepted, not all patients derive benefit from this preoperative treatment. Presently, there are no validated biomarkers to predict the NACT response, and previous attempts to develop predictive classifiers based on gene expression data have not demonstrated clinical utility. However, predictive models incorporating biological constraints have shown increased robustness and improved performance compared to agnostic classifiers. Methods We used the preoperative transcriptomic profiles from 298 patients with TNBC to train and test a rank-based classifier, k-top scoring pairs, to predict whether the patient will have pathological complete response (pCR) or residual disease (RD) following NACT. To reduce overfitting and enhance the signature’s interpretability, we constrained the training process to genes involved in the Notch signaling pathway. Subsequently, we evaluated the signature performance on two independent cohorts with 75 and 71 patients. Finally, we assessed the prognostic value of the signature by examining its association with relapse-free survival (RFS) using Kaplan‒Meier (KM) survival estimates and a multivariate Cox proportional hazards model. Results The final signature consists of five gene pairs, whose relative ordering can be predictive of the NACT response. The signature has a robust performance at predicting pCR in TNBC patients with an area under the ROC curve (AUC) of 0.76 and 0.85 in the first and second testing cohorts, respectively, outperforming other gene signatures developed for the same purpose. Additionally, the signature was significantly associated with RFS in an independent TNBC patient cohort even after adjusting for T stage, patient age at the time of diagnosis, type of breast surgery, and menopausal status. Conclusion We introduce a robust gene signature to predict pathological complete response (pCR) in patients with TNBC. This signature applies easily interpretable, rank-based decision rules to genes regulated by the Notch signaling pathway, a known determinant in breast cancer chemoresistance. The robust predictive and prognostic performance of the signature make it a strong candidate for clinical implementation, aiding in the stratification of TNBC patients undergoing NACT.

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Section: Discussionmentioning

confidence: 99%

Section: Discussionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

Notch-based gene signature for predicting the response to neoadjuvant chemotherapy in triple-negative breast cancer

Omar,

Nuzzo,

Ravera

et al. 2023

J Transl Med

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“…Each pair consists of two genes, one is up-and another is downregulated in the PRN mesenchyme. This mechanism was then used as a priori biological constraint during the training of the k-TSPs algorithm 85 , and the resulting signature was evaluated on the indepedent testing set.…”

Section: Development Of the Prn Signature To Predict Metastasis In Pr...mentioning

confidence: 99%

Distinct mesenchymal cell states mediate prostate cancer progression

Pakula

Omar

Carelli

et al. 2023

Preprint

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Alterations in tumor stroma influence prostate cancer progression and metastatic potential. However, the molecular underpinnings of this stromal-epithelial crosstalk are largely unknown. Here, we compare mesenchymal cells from four genetically engineered mouse models (GEMMs) of prostate cancer representing different stages of the disease to their wild-type (WT) counterparts by single-cell RNA sequencing (scRNA-seq) and, ultimately, to human tumors with comparable genotypes. We identified 8 transcriptionally and functionally distinct stromal populations responsible for common and GEMM-specific transcriptional programs. We show that stromal responses are conserved in mouse models and human prostate cancers with the same genomic alterations. We noted striking similarities between the transcriptional profiles of the stroma of murine models of advanced disease and those of of human prostate cancer bone metastases. These profiles were then used to build a robust gene signature that can predict metastatic progression in prostate cancer patients with localized disease and is also associated with progression-free survival independent of Gleason score. Taken together, this offers new evidence that stromal microenvironment mediates prostate cancer progression, further identifying tissue-based biomarkers and potential therapeutic targets of aggressive and metastatic disease.

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Distinct mesenchymal cell states mediate prostate cancer progression

Pakula,

Omar,

Carelli

et al. 2024

Nat Commun

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In the complex tumor microenvironment (TME), mesenchymal cells are key players, yet their specific roles in prostate cancer (PCa) progression remain to be fully deciphered. This study employs single-cell RNA sequencing to delineate molecular changes in tumor stroma that influence PCa progression and metastasis. Analyzing mesenchymal cells from four genetically engineered mouse models (GEMMs) and correlating these findings with human tumors, we identify eight stromal cell populations with distinct transcriptional identities consistent across both species. Notably, stromal signatures in advanced mouse disease reflect those in human bone metastases, highlighting periostin’s role in invasion and differentiation. From these insights, we derive a gene signature that predicts metastatic progression in localized disease beyond traditional Gleason scores. Our results illuminate the critical influence of stromal dynamics on PCa progression, suggesting new prognostic tools and therapeutic targets.

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Using biological constraints to improve prediction in precision oncology

Cited by 3 publications

References 87 publications

Notch-based gene signature for predicting the response to neoadjuvant chemotherapy in triple-negative breast cancer

Notch-based gene signature for predicting the response to neoadjuvant chemotherapy in triple-negative breast cancer

Distinct mesenchymal cell states mediate prostate cancer progression

Distinct mesenchymal cell states mediate prostate cancer progression

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