Evidence map›Paper›PMID 41648744›Full record

ArticleESMO gastrointestinal oncology2024

Integration of genomic aberrations to predict clinical outcomes for patients with gastroesophageal adenocarcinoma receiving neoadjuvant chemotherapy.

E C Smyth, D Watson, M P Castro, B Nutzinger, S Kapoor, S Rajagopalan, C Cheah, P R Nair, A Alam, G Devonshire and 12 more

Abstract read
PubMed Full text at PMC Publisher
In one paragraph

Article in ESMO gastrointestinal oncology, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

22 authors.

E C SmythCambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
D WatsonCellworks Group, Inc., South San Francisco, USA.
M P CastroCellworks Group, Inc., South San Francisco, USA.
B NutzingerEarly Cancer Institute, Department of Oncology, University of Cambridge, Cambridge.
S KapoorCellworks Group, Inc., South San Francisco, USA.
S RajagopalanCellworks Group, Inc., South San Francisco, USA.
C CheahEarly Cancer Institute, Department of Oncology, University of Cambridge, Cambridge.
P R NairCellworks Group, Inc., South San Francisco, USA.
A AlamCellworks Group, Inc., South San Francisco, USA.
G DevonshireCRUK Cambridge Institute, University of Cambridge, Cambridge, UK.
N GrehanCambridge University Hospitals NHS Foundation Trust, Cambridge, UK.
R P SuseelaCellworks Group, Inc., South San Francisco, USA.
A TyagiCellworks Group, Inc., South San Francisco, USA.
A K AgrawalCellworks Group, Inc., South San Francisco, USA.
M SaubanCellworks Group, Inc., South San Francisco, USA.
A PampanaCellworks Group, Inc., South San Francisco, USA.
A GhoshCellworks Group, Inc., South San Francisco, USA.
Y UllalCellworks Group, Inc., South San Francisco, USA.
Y NarvekarCellworks Group, Inc., South San Francisco, USA.
M D MacphersonCellworks Group, Inc., South San Francisco, USA.
J A WingroveCellworks Group, Inc., South San Francisco, USA.
R C FitzgeraldEarly Cancer Institute, Department of Oncology, University of Cambridge, Cambridge.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Background: Esophageal cancer [esophagogastric adenocarcinoma (OGA)] shows heterogeneity at the molecular level, leading to lower efficacy rates and highlighting the need for personalized treatment strategies. We have developed a computational model that uses both mechanistic and statistical approaches to integrate a patient's genomic aberrations, revealing signaling pathway dysregulation and variable drug response. The model output, Therapy Response Index (TRI), has been used to predict therapeutic outcomes in this study. Design: TRI's ability to predict patient outcomes was retrospectively evaluated in a prospectively collected cohort of patients with operable OGA from the UK Oesophageal Cancer Clinical and Molecular Stratification (OCCAMS) consortium receiving neoadjuvant chemotherapy. Stratified random sampling was used to split the data into training and validation subsets. Multivariate Cox proportional hazard and proportional odds models were used to predict survival and pathological response as a function of TRI and clinical thresholds compared with clinical factors. Results: A total of 270 patients with OGA were selected who had 50× whole genome sequencing carried out on tissue derived from either biopsy or resection. Patients were treated with chemotherapy drugs or regimens according to UK clinical guidelines. The association of TRI with overall survival (OS) was significant above and beyond standard clinical factors ( Conclusions: TRI was predictive of OS and DFS beyond clinical factors. These positive results suggest the potential utility of personalized biosimulation-informed therapy selection and that further assessment in prospective clinical studies is warranted.

Indexed as

biomarkerbiosimulationesophageal cancergastric cancergenomicsprecision medicine

Identifiers

PMID41648744
PMCPMC12836727

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.