Evidence map›Paper›PMID 37404765›Full record

ArticleFrontiers in oncology2023

Genomic landscape of clinically advanced

Prashanth Ashok Kumar, Serenella Serinelli, Daniel J Zaccarini, Richard Huang, Natalie Danziger, Tyler Janovitz, Alina Basnet, Abirami Sivapiragasam, Stephen Graziano, Jeffrey S Ross

Open access · goldAbstract read
In one paragraph

Article in Frontiers in oncology, 2023. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 9 papers, 1 of them a synthesis that pooled it.

0numbers the graph read from it
0cells of the map it votes in
9citing papers in PubMed, 1 pooled it
2.1field-weighted citation impact, top 12% of its field
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

9 citing papers in PubMed, 1 synthesis or guideline pooled it, 9 citations in OpenAlex.

  1. Pooled it
  2. Review
  3. Observational
  4. Article
  5. Article
  6. Article
  7. Frontiers in oncology · 2025
    Article
  8. Characteristics ofTherapeutic advances in medical oncology · 2025
    Article
  9. Review
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

10 authors at 3 institutions in 1 country.

Prashanth Ashok KumarUpstate Cancer Center, Upstate Medical University, Syracuse, NY, United States.
Serenella SerinelliDepartment of Pathology, Upstate Medical University, Syracuse, NY, United States.
Daniel J ZaccariniDepartment of Pathology, Upstate Medical University, Syracuse, NY, United States.
Richard HuangFoundation Medicine, Cambridge, MA, United States.
Natalie DanzigerFoundation Medicine, Cambridge, MA, United States.
Tyler JanovitzFoundation Medicine, Cambridge, MA, United States.
Alina BasnetUpstate Cancer Center, Upstate Medical University, Syracuse, NY, United States.
Abirami SivapiragasamUpstate Cancer Center, Upstate Medical University, Syracuse, NY, United States.
Stephen GrazianoUpstate Cancer Center, Upstate Medical University, Syracuse, NY, United States.
Jeffrey S RossDepartment of Pathology, Upstate Medical University, Syracuse, NY, United States.
SUNY Upstate Medical University · USFoundation Medicine (United States)Emergency Medicine Foundation · US

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

Introduction: KRAS mutation is a common occurrence in Pancreatic Ductal Adenocarcinoma (PDA) and is a driver mutation for disease development and progression. KRAS wild-type PDA may constitute a distinct molecular and clinical subtype. We used the Foundation one data to analyze the difference in Genomic Alterations (GAs) that occur in KRAS mutated and wild-type PDA. Methods: Comprehensive genomic profiling (CGP) data, tumor mutational burden (TMB), microsatellite instability (MSI) and PD-L1 by Immunohistochemistry (IHC) were analyzed. Results and discussion: Our cohort had 9444 cases of advanced PDA. 8723 (92.37%) patients had KRAS mutation. 721 (7.63%) patients were KRAS wild-type. Among potentially targetable mutations, GAs more common in KRAS wild-type included ERBB2 (mutated vs wild-type: 1.7% vs 6.8%, p <0.0001), BRAF (mutated vs wild-type: 0.5% vs 17.9%, p <0.0001), PIK3CA (mutated vs wild-type: 2.3% vs 6.5%, p <0.001), FGFR2 (mutated vs wild-type: 0.1% vs 4.4%, p <0.0001), ATM (mutated vs wild-type: 3.6% vs 6.8%, p <0.0001). On analyzing untargetable GAs, the KRAS mutated group had a significantly higher percentage of TP53 (mutated vs wild-type: 80.2% vs 47.6%, p <0.0001), CDKN2A (mutated vs wild-type: 56.2% vs 34.4%, p <0.0001), CDKN2B (mutated vs wild-type: 28.9% vs 23%, p =0.007), SMAD4 (mutated vs wild-type: 26.8% vs 15.7%, p <0.0001) and MTAP (mutated vs wild-type: 21.7% vs 18%, p =0.02). ARID1A (mutated vs wild-type: 7.7% vs 13.6%, p <0.0001 and RB1(mutated vs wild-type: 2% vs 4%, p =0.01) were more prevalent in the wild-type subgroup. Mean TMB was higher in the KRAS wild-type subgroup (mutated vs wild-type: 2.3 vs 3.6, p <0.0001). High TMB, defined as TMB > 10 mut/mB (mutated vs wild-type: 1% vs 6.3%, p <0.0001) and very-high TMB, defined as TMB >20 mut/mB (mutated vs wild-type: 0.5% vs 2.4%, p <0.0001) favored the wild-type. PD-L1 high expression was similar between the 2 groups (mutated vs wild-type: 5.7% vs 6%,). GA associated with immune checkpoint inhibitors (ICPIs) response including PBRM1 (mutated vs wild-type: 0.7% vs 3.2%, p <0.0001) and MDM2 (mutated vs wild-type: 1.3% vs 4.4%, p <0.0001) were more likely to be seen in KRAS wild-type PDA.

Indexed as

genomic alterationsKRAS mutationKRAS wild-type pancreatic cancerpancreatic ductal adenocarcinomatargeted therapy

Identifiers

PMID37404765
PMCPMC10315669
OpenAlexW4381165337

What Socratic holds

Textmetadata
LicenceCC BY
Read underepoch 390

Registered trials

None linked

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 Socratic graph.