AIMC Topic: Drug Resistance, Neoplasm

Clear Filters Showing 21 to 30 of 112 articles

Deep learning-driven drug response prediction and mechanistic insights in cancer genomics.

Scientific reports
In the field of cancer therapy, the diversity and heterogeneity of cancer genomes in clinical patients complicate and challenge the effective use of non-targeted drugs, as these drugs often fail to address specific genetic events. Recent advancements...

Advances in molecular pathology and therapy of non-small cell lung cancer.

Signal transduction and targeted therapy
Over the past two decades, non-small cell lung cancer (NSCLC) has witnessed encouraging advancements in basic and clinical research. However, substantial unmet needs remain for patients worldwide, as drug resistance persists as an inevitable reality....

Integrated muti-omics data and machine learning reveal CD151 as a key biomarker inducing chemoresistance in metabolic syndrome-related early-onset left-sided colorectal cancer.

Functional & integrative genomics
Emerging evidence has suggested a potential pathological association between early-onset left-sided colorectal cancer (EOLCC) and metabolic syndrome (MetS). However, the underlying genetic and molecular mechanisms remain insufficiently elucidated. Th...

Acquired resistance in cancer: towards targeted therapeutic strategies.

Nature reviews. Cancer
Development of acquired therapeutic resistance limits the efficacy of cancer treatments and accounts for therapeutic failure in most patients. How resistance arises, varies across cancer types and differs depending on therapeutic modalities is incomp...

Neddylation status determines the therapeutic sensitivity of tyrosine kinase inhibitors in chronic myeloid leukemia.

Scientific reports
BCR::ABL1-targeting tyrosine kinase inhibitors (TKIs) dominate the treatment of chronic myeloid leukemia (CML) over the past decades. In this study, we reported an unexpected role of neddylation inhibitors in desensitizing the therapeutic efficacy of...

A robust multiplex-DIA workflow profiles protein turnover regulations associated with cisplatin resistance and aneuploidy.

Nature communications
Quantifying protein turnover is fundamental to understanding cellular processes and advancing drug discovery. Multiplex-DIA mass spectrometry (MS), combined with dynamic SILAC labeling (pulse-SILAC, or pSILAC) reliably measures protein turnover and d...

Emerging artificial intelligence-driven precision therapies in tumor drug resistance: recent advances, opportunities, and challenges.

Molecular cancer
Drug resistance is one of the main reasons for cancer treatment failure, leading to a rapid recurrence/disease progression of the cancer. Recently, artificial intelligence (AI) has empowered physicians to use its powerful data processing and pattern ...

Circumventing glioblastoma resistance to temozolomide through optimal drug combinations designed by systems pharmacology and machine learning.

British journal of pharmacology
BACKGROUND AND PURPOSE: Glioblastoma (GBM), the most frequent and aggressive brain tumour in adults, is associated with a dismal prognostic despite intensive treatment involving surgery, radiotherapy and temozolomide (TMZ)-based chemotherapy. The ini...

Machine learning based intratumor heterogeneity related signature for prognosis and drug sensitivity in breast cancer.

Scientific reports
Intratumor heterogeneity (ITH) is involved in tumor evolution and drug resistance. Drug sensitivity shows discrepancy in different breast cancer (BRCA) patients due to ITH. The genes mediating ITH in BRCA and their role in predicting prognosis and dr...

Unveiling the power of Treg.Sig: a novel machine-learning derived signature for predicting ICI response in melanoma.

Frontiers in immunology
BACKGROUND: Although immune checkpoint inhibitor (ICI) represents a significant breakthrough in cancer immunotherapy, only a few patients benefit from it. Given the critical role of Treg cells in ICI treatment resistance, we explored a Treg-associate...