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Multiomics-Driven Drug-Cell Interaction Network for Chemotherapy Sensitivity Prediction in Metabolically Defined
Jingyuan Zhang1,2, Xuejun Sun2,3
1Department of Breast Surgery, Shannxi Provincial Cancer Hospital, Xi'an, Shaanxi, China.
Abstract:
Triple-negative breast cancer (TNBC) is associated with a poor prognosis due to insufficient molecular subtyping precision and limited actionable targets. Although metabolic reprogramming underlies TNBC chemotherapy resistance, establishing metabolic subtyping systems and investigating drug sensitivity across distinct metabolic subgroups could provide novel therapeutic avenues for breast cancer management. GSVA (Gene Set Variation Analysis) analysis of metabolic pathways reveals significant differences in TNBC (Triple-Negative Breast Cancer) patients. TNBC patients are classified into four metabolic subtypes through consensus clustering, based on their GSVA values of metabolic pathways. These subtypes are: MS_1, characterised by increased lipogenic activity; MS_2, characterised by increased carbohydrate and nucleotide metabolism; MS_3, a metabolism-active subtype with activation of all types of metabolism; and MS_4, characterised by suppressed metabolic activity across all types of metabolism. We next propose a novel method called MODIN (Multiomics-Driven Drug-Cell Interaction Network), which embeds multi-omics gene information (mRNA expression, copy number variation and DNA methylation) and drug SMILES data into a latent space, and then employs a multi-head attention-based interaction module to accurately predict the LN_IC50 values of 621 drugs in TNBC. Based on MODIN, noteworthy disparities in drug sensitivity emerge between the patient cohorts categorised as MS_2 and MS_3. MS_3 patients show a significantly higher sensitivity to chemotherapy regimens, especially for doxorubicin and docetaxel, while the MS_2 cohort displays marked resistance to these drugs. Our study reveals the metabolic heterogeneity of TNBC, and TNBC patients with increased carbohydrate and nucleotide metabolism exhibit the poorest prognoses and greater resistance to doxorubicin and docetaxel.
Insights
Triple-negative breast cancer (TNBC) exhibits metabolic heterogeneity, with distinct subtypes showing varied drug responses. Identifying these metabolic subtypes is crucial for improving chemotherapy effectiveness and patient outcomes in TNBC treatment.
Area of Science:
- Oncology
- Metabolomics
- Bioinformatics
Background:
- Triple-negative breast cancer (TNBC) presents a significant clinical challenge due to poor prognosis, limited molecular subtyping, and few targeted therapies.
- Metabolic reprogramming is a key factor in TNBC chemotherapy resistance, highlighting the need for metabolic subtyping to guide treatment strategies.
Purpose of the Study:
- To classify TNBC patients into distinct metabolic subtypes based on gene set variation analysis (GSVA) of metabolic pathways.
- To develop and apply a novel multi-omics driven drug-cell interaction network (MODIN) for predicting drug sensitivity across TNBC metabolic subtypes.
- To investigate the differential drug sensitivity profiles of TNBC metabolic subtypes, particularly concerning common chemotherapy regimens.
Main Methods:
- Gene Set Variation Analysis (GSVA) was employed to analyze metabolic pathway activity in TNBC patient data.
- Consensus clustering was used to categorize TNBC patients into four distinct metabolic subtypes (MS_1, MS_2, MS_3, MS_4).
- The MODIN (Multiomics-Driven Drug-Cell Interaction Network) method integrated multi-omics data and drug SMILES to predict drug sensitivity (LN_IC50 values) for 621 drugs.
Main Results:
- Four TNBC metabolic subtypes were identified: MS_1 (lipogenic), MS_2 (carbohydrate/nucleotide metabolism), MS_3 (highly metabolic), and MS_4 (suppressed metabolism).
- Significant disparities in drug sensitivity were observed between subtypes MS_2 and MS_3.
- MS_3 patients demonstrated higher sensitivity to doxorubicin and docetaxel, whereas MS_2 patients exhibited resistance to these agents.
Conclusions:
- TNBC is metabolically heterogeneous, with distinct subtypes associated with different prognoses and drug sensitivities.
- The MS_2 subtype, characterized by increased carbohydrate and nucleotide metabolism, is linked to poorer prognoses and resistance to standard chemotherapy.
- Metabolic subtyping and predictive models like MODIN offer promising avenues for personalized therapeutic strategies in TNBC management.
