Biomedical subjects

Efsevia Vakiani

Publications and source records attributed to Efsevia Vakiani.

3 recordsLinked to original sources

PubMed2026

Risk of Relapse and Efficacy of Adjuvant Chemotherapy in Localized Appendiceal Adenocarcinoma.

IMPORTANCE: Relapse risk and benefit of adjuvant chemotherapy after resection of appendiceal adenocarcinoma (AA) are uncertain. OBJECTIVE: To identify clinicopathologic and genomic factors associated with relapse and assess efficacy of adjuvant chemotherapy in localized AA. DESIGN, SETTING, AND PARTICIPANTS: This retrospective cohort study (January 2000 through February 2024; median follow-up, 62.6 months) used Kaplan-Meier and Cox proportional hazards modeling. It took place at the University of Texas MD (UT MD) Anderson Cancer Center with validation from Memorial Sloan Kettering Cancer Center (MSKCC). Participants included a complete localized cohort of 439 patients with stage I to III AA from UT MD Anderson, of whom 202 underwent surgery at UT MD Anderson and also included a validation cohort of 128 patients with stage II AA from MSKCC. EXPOSURES: Surgical resection with or without adjuvant chemotherapy. MAIN OUTCOMES AND MEASURES: Rate of recurrence, recurrence-free survival (RFS), and overall survival (OS). RESULTS: There were 439 patients with localized AA (median age, 56.5 [IQR, 22.2-83.7] years; 50% female and 50% male) managed at MD Anderson between January 2000 and February 2024. Of 202 MDA surgical patients, 19 (9.4%) had a relapse including 9 (6%) patients with stage II and 8 (19.5%) of patients stage III disease. Five-year OS was 95.7% without vs 77.2% with relapse (hazard ratio [HR], 5.50; 95% CI, 3.07-9.83; P < .001). Relative to goblet cell tumors, mucinous (HR, 5.60; 95% CI, 2.1-15; P < .001) and enteric-type (HR, 6.60; 95% CI, 2.9-15; P < .001) histologies were independently associated with relapse, as was pathologic T4 (HR, 3.30; 95% CI, 1.9-5.7; P < .001). Importantly, poor differentiation, perforation, lymphovascular invasion, and perineural invasion, known risk factors in colorectal cancer, were not significantly associated with relapse. For the complete localized cohort, adjuvant chemotherapy was not associated with improved RFS (univariate HR, 2.06; 95% CI, 1.36-3.13; P = .001 and multivariable HR, 0.98; 95% CI, 0.43-2.28; P = .90) or OS (univariate HR, 1.80; 95% CI, 1.0-3.2; P = .04 and multivariable HR, 0.71; 95% CI, 0.24-2.1; P = .53). TP53 mutation in goblet cell tumors (HR, 6.93; 95% CI, 1.50-31.00; P = .01) and GNAS mutation in nongoblet tumors (HR, 17.0; 95% CI, 3.09-93.3; P = .001) were associated with greater risk of relapse. CONCLUSIONS AND RELEVANCE: These results demonstrate that relapse after resection of localized AA is uncommon. Molecular profiling and histopathologic subtype refine risk. Adjuvant chemotherapy were not associated with benefit.

Journal Article↗

PubMed2026

Integrated clinicogenomic analysis reveals the evolution and metastatic tropisms of advanced colorectal cancer.

We performed an integrated clinical and genomic analysis of over 7,000 consecutively sequenced colorectal cancer (CRC) samples to comprehensively characterize genetic drivers and metastatic tropisms of CRC. We find that genomic evolutionary changes, such as clonal mutations and oncogenic mutant allelic imbalance, selectively enhance the impact of recurrent oncogenic alterations. We identify the relative timing of organ-specific metastasis, showing sequential metastatic progression in microsatellite stable CRC with brain and adrenal metastases as late events; metastatic sites that cluster together, such as lung, bone, and brain metastases; and genomic events that enhance or decrease risk for each metastatic site, with WNT pathway activation as overall protective while RAS pathway activation increased risk for spread to all metastatic sites. Our data suggest that despite the heterogeneity in CRC, genomic evolution increases the impact of recurrent alterations, and integrating information about tumor primary location and genomics can be used to predict organ-specific metastasis risk.

Humans↗

PubMed2024

Deep-Learning Model for Tumor-Type Prediction Using Targeted Clinical Genomic Sequencing Data.

UNLABELLED: Tumor type guides clinical treatment decisions in cancer, but histology-based diagnosis remains challenging. Genomic alterations are highly diagnostic of tumor type, and tumor-type classifiers trained on genomic features have been explored, but the most accurate methods are not clinically feasible, relying on features derived from whole-genome sequencing (WGS), or predicting across limited cancer types. We use genomic features from a data set of 39,787 solid tumors sequenced using a clinically targeted cancer gene panel to develop Genome-Derived-Diagnosis Ensemble (GDD-ENS): a hyperparameter ensemble for classifying tumor type using deep neural networks. GDD-ENS achieves 93% accuracy for high-confidence predictions across 38 cancer types, rivaling the performance of WGS-based methods. GDD-ENS can also guide diagnoses of rare type and cancers of unknown primary and incorporate patient-specific clinical information for improved predictions. Overall, integrating GDD-ENS into prospective clinical sequencing workflows could provide clinically relevant tumor-type predictions to guide treatment decisions in real time. SIGNIFICANCE: We describe a highly accurate tumor-type prediction model, designed specifically for clinical implementation. Our model relies only on widely used cancer gene panel sequencing data, predicts across 38 distinct cancer types, and supports integration of patient-specific nongenomic information for enhanced decision support in challenging diagnostic situations. See related commentary by Garg, p. 906. This article is featured in Selected Articles from This Issue, p. 897.

Humans↗