Advances in Precision Medicine Approaches for Colorectal Cancer: From Molecular Profiling to Targeted Therapies

ACS Pharmacology & Translational Science, Journal Year: 2024, Volume and Issue: 7(4), P. 967 - 990

Published: March 19, 2024

Precision medicine is transforming colorectal cancer treatment through the integration of advanced technologies and biomarkers, enhancing personalized effective disease management. Identification key driver mutations molecular profiling have deepened our comprehension genetic alterations in cancer, facilitating targeted therapy immunotherapy selection. Biomarkers such as microsatellite instability (MSI) DNA mismatch repair deficiency (dMMR) guide decisions, opening avenues for immunotherapy. Emerging liquid biopsies, artificial intelligence, machine learning promise to revolutionize early detection, monitoring, selection precision medicine. Despite these advancements, ethical regulatory challenges, including equitable access data privacy, emphasize importance responsible implementation. The dynamic nature with its tumor heterogeneity clonal evolution, underscores necessity adaptive strategies. future lies potential enhance patient care, clinical outcomes, understanding this intricate disease, marked by ongoing evolution field. current reviews focus on providing in-depth knowledge various diverse approaches utilized against at both biochemical levels.

Language: Английский

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Sequencing of high-frequency mutated genes in breast cancer (BRCA) and associated-functions analysis

International Journal of Clinical and Experimental Pathology, Journal Year: 2025, Volume and Issue: 18(2), P. 46 - 62

Published: Jan. 1, 2025

Mutations or aberrant expression of genes in an organism tend not to be completely random and this cumulative effect predisposes the development malignant tumours. This study aims reveal possible high frequency mutated genes, then investigate their role development, prognosis, signalling pathway function drug resistance breast cancer. The cancer (BRCA) clinical samples were identified detected by high-throughput sequencing. High-frequency mutant counted. Gene profiles relationship with prognosis analysed throughout TCGA database. qRT-PCR was used analyse mRNA levels six high-frequency BRCA tissues cell lines. IHC protein tissues. linear interaction, single-cell layer clustering status influence immune infiltration degree among these bioinformatics analysis. STITCH cMAP datasets for gene interaction networks, association enrichment drug-transcriptome analyses. effects trastuzumab on proliferative capacity cells, as well determined CCK8 assay. that statistically found have mutations recruited present sequencing analysis included TP53, PIK3CA, NF1, TBX3, BRCA1 BRCA2. correlation further demonstrated using database: trend similar TCGA. showed BRCA2 higher tumor than normal samples, opposite a observed expressions displayed same IHC. Other results include 1) single resulted significant few overlapping regions; 2) different degrees infiltration; 3) between each other; 4) network had partially molecules; 5) PI3K key BRCA. Finally, proliferation ability confirmed optimal concentration its genes.

Language: Английский

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Diagnostic Utility of a 90-Gene Expression Assay (Canhelp-Origin) for Patients with Metastatic Cancer with an Unclear or Unknown Diagnosis

Molecular Diagnosis & Therapy, Journal Year: 2024, Volume and Issue: unknown

Published: Sept. 27, 2024

Language: Английский

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Application of Transcriptome-Based Gene Set Featurization for Machine Learning Model to Predict the Origin of Metastatic Cancer

Current Issues in Molecular Biology, Journal Year: 2024, Volume and Issue: 46(7), P. 7291 - 7302

Published: July 9, 2024

Identifying the primary site of origin metastatic cancer is vital for guiding treatment decisions, especially patients with unknown (CUP). Despite advanced diagnostic techniques, CUP remains difficult to pinpoint and responsible a considerable number cancer-related fatalities. Understanding its crucial effective management potentially improving patient outcomes. This study introduces machine learning framework, ONCOfind-AI, that leverages transcriptome-based gene set features enhance accuracy predicting cancers. We demonstrate potential facilitate integration RNA sequencing microarray data by using scores characterization transcriptome profiles generated from different platforms. Integrating platforms resulted in improved models origins. validated our method external clinical samples collected through Kangbuk Samsung Medical Center Gene Expression Omnibus. The validation results top-1 ranging 0.80 0.86, top-2 0.90. highlights incorporating biological knowledge curated sets can help merge expression platforms, thereby enhancing compatibility needed develop more prediction models.

Language: Английский

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Predicting tumour origin with cytology-based deep learning: hype or hope?

Nature Reviews Clinical Oncology, Journal Year: 2024, Volume and Issue: 21(9), P. 641 - 642

Published: May 21, 2024

Language: Английский

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Explainable Machine Learning Models Using Robust Cancer Biomarkers Identification from Paired Differential Gene Expression

International Journal of Molecular Sciences, Journal Year: 2024, Volume and Issue: 25(22), P. 12419 - 12419

Published: Nov. 19, 2024

In oncology, there is a critical need for robust biomarkers that can be easily translated into the clinic. We introduce novel approach using paired differential gene expression analysis biological feature selection in machine learning models, enhancing robustness and interpretability while accounting patient variability. This method compares primary tumor tissue with same patient's healthy tissue, improving by eliminating individual-specific artifacts. A focus on carcinoma was selected due to its prevalence availability of data; we aim identify involved general progression, including less-researched types. Our findings identified 27 pivotal genes distinguish between even unseen Additionally, panel could precisely tissue-of-origin eight types used discovery phase. Notably, proof concept, model accurately origin metastatic samples despite limited sample availability. Functional annotation reveals these genes' involvement cancer hallmarks, detecting subtle variations across propose as reference discovering biomarkers.

Language: Английский

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Application of Transcriptome–Based Gene Set Featurization for Machine Learning Model to Predict the Origin of Metastatic Cancer

Published: May 29, 2024

Identifying the primary site of origin metastatic cancer is vital for guiding treatment decisions, especially patients with unknown (CUP). Despite advanced diagnostic techniques, CUP remains difficult to pinpoint and responsible a considerable number cancer-related fatalities. Understanding its crucial effective management potentially improving patient outcomes. This study introduces machine learning framework ONCOfind-AI that leverages transcriptome-based gene set features enhance accuracy predicting cancers. By ensuring compatibility between RNA-sequencing micro-array data, we were able construct more comprehensive training dataset. Integrating data from different platforms improved our models origins. Our method was validated using external clinical samples collected through Kangbuk Samsung Medical Center Gene Expression Omnibus. The validation results demonstrated top-1 ranging 0.80 0.86, top-2 0.90. highlights incorporating biological knowledge curated sets can merge expression platforms, enhancing needed prediction models.

Language: Английский

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Poorly differentiated squamous cell carcinoma of unknown primary location a case report of perineal presentation

International Journal of Surgery Case Reports, Journal Year: 2024, Volume and Issue: 122, P. 110169 - 110169

Published: Aug. 13, 2024

Language: Английский

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Cancer of Unknown Primary Site: A New Era of Practice-Changing Approaches to Diagnosis, Staging, and Precision Therapy

JCO oncology advances., Journal Year: 2024, Volume and Issue: 1

Published: Nov. 1, 2024

The enigmatic syndrome of metastatic cancer unknown primary (CUP) site has frustrated physicians and patients for decades. There been debate whether CUP is a single biologically distinct or constellation many different cancers with clinically undetectable anatomic sites. For the past 40 years, diagnosis specific type most was indeterminate, fit were usually treated as same empiric chemotherapy (EC) regimens poor overall results. aggregate data from autopsies, clinical observations, specialized standard pathology, molecular testing, several trials support multitude occult invasive tumors requiring site-specific therapies (SSTs). In improved genomic testing used, addition molecular-guided (MGTs) immunotherapy (IO) shown to be superior advanced cancers. Two older randomized prospective conducted before advent IO MGT failed show better outcome molecularly diagnosed who received SST (tailored regimens) versus EC, although more responsive tumor types appeared benefit. recently reported documented relevance comprehensive profiling. administration precision guided by characterization revealed significantly outcomes compared EC. management undergoing rapid change including presumed tumors, TNM staging selected patients, profiling, an expanded role highlighting emergent new era practice changing standards care.

Language: Английский

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Reevaluating feature importances in machine learning models for schizophrenia and bipolar disorder: The need for true associations

Brain Behavior and Immunity, Journal Year: 2024, Volume and Issue: 124, P. 123 - 124

Published: Nov. 29, 2024

Language: Английский

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