Evidence map›Paper›PMID 28231819›Full record

ArticleGenome medicine2017

Genomic analysis of 63,220 tumors reveals insights into tumor uniqueness and targeted cancer immunotherapy strategies.

Ryan J Hartmaier, Jehad Charo, David Fabrizio, Michael E Goldberg, Lee A Albacker, William Pao, Juliann Chmielecki

Open access · goldAbstract read
In one paragraph

Article in Genome medicine, 2017. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 34 papers.

0numbers the graph read from it
0cells of the map it votes in
34citing papers in PubMed
3.7field-weighted citation impact, top 6% 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

34 citing papers in PubMed, 58 citations in OpenAlex.

  1. Trial
  2. PI3Kα Inhibitor and Degrader Inavolisib Can Co-opt FGFR2 to Enhance Responses in Patients with PIK3CA-Mutated Solid Tumors and in Preclinical Models.Clinical cancer research : an official journal of the American Association for Cancer Research · 2026
    Article
  3. Review
  4. Computational Methods for Cancer Neoantigen Prediction.Methods in molecular biology (Clifton, N.J.) · 2026
    Article
  5. Article
  6. Article
  7. Current Landscape of Therapeutic Cancer Vaccines.Methods in molecular biology (Clifton, N.J.) · 2025
    Review
  8. Article
  9. Review
  10. Article
  11. Review
  12. Review
  13. Review
  14. Article
  15. Review
  16. AnFrontiers in immunology · 2022
    Article
  17. Article
  18. Review
  19. Population mutation properties of tumor evolution.Medical oncology (Northwood, London, England) · 2020
    Article
  20. Article
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

7 authors at 2 institutions in 1 country.

Ryan J HartmaierFoundation Medicine, Inc, 150 2nd St, Cambridge, MA, 02141, USA. rhartmaier@foundationmedicine.com.
Jehad CharoRoche Pharma Research & Early Development, Basel, Switzerland.
David FabrizioFoundation Medicine, Inc, 150 2nd St, Cambridge, MA, 02141, USA.
Michael E GoldbergFoundation Medicine, Inc, 150 2nd St, Cambridge, MA, 02141, USA.
Lee A AlbackerFoundation Medicine, Inc, 150 2nd St, Cambridge, MA, 02141, USA.
William PaoRoche Pharma Research & Early Development, Basel, Switzerland.
Juliann ChmieleckiFoundation Medicine, Inc, 150 2nd St, Cambridge, MA, 02141, USA.
Foundation Medicine (United States)Roche (Switzerland) · CH

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

Abstract

backgroundThe integration of genomics with immunotherapy has potential value for cancer vaccine development. Given the clinical successes of immune checkpoint modulators, interest in cancer vaccines as therapeutic options has been revived. Current data suggest that each tumor contains a unique set of mutations (mutanome), thus requiring the creation of individualized cancer vaccines. However, rigorous analysis of non-individualized cancer immunotherapy approaches across multiple cancer types and in the context of known driver alterations has yet to be reported. We therefore set out to determine the feasibility of a generalizable cancer vaccine strategy based on targeting multiple neoantigens in an HLA-A/B subtype-directed manner.

methodsA cancer gene-focused, hybrid capture-based genomic analysis was performed on 63,220 unique tumors. Neoantigens were predicted using a combined peptide processing and MHC-I binding prediction tool (IEDB) for all recurrent (>10 tumors) missense alterations and non-frameshift indels for the two most common HLA-A/B subtypes in North American/European populations.

resultsDespite being overwhelmingly unique overall, many mutanomes (~45%) contain at least one mutation from a set of ten mutations chosen to maximize the number of unique tumors. This held true for tumors driven by KRAS G12C (n = 1799), PIK3CA E545K (n = 1713), or EGFR L858R (n = 478) alterations, which define distinct sample subsets. We therefore hypothesized that sets of carefully selected mutations/neoantigens may allow the development of broadly applicable semi-universal cancer vaccines. To test the feasibility of such an approach, antigen processing and MHC-I binding prediction was applied for HLA subtypes A*01:01/B*08:01 and A*02:01/B*44:02. In tumors with a specific HLA type, 0.7 and 2.5% harbored at least one of a set of ten neoantigens predicted to bind to each subtype, respectively. In comparison, KRAS G12C-driven tumors produced similar results (0.8 and 2.6% for each HLA subtype, respectively), indicating that neoantigen targets still remain highly diverse even within the context of major driver mutations.

conclusionsThis "best case scenario" analysis of a large tumor set across multiple cancer types and in the context of driver alterations reveals that semi-universal, HLA-specific cancer vaccine strategies will be relevant to only a small subset of the general population. Similar analysis of whole exome/genome sequencing, although not currently feasible at scale in a clinical setting, will likely uncover further diversity.

Indexed as

GenomicsMutationPrecision MedicineCancer VaccinesExomeHLA AntigensHumansImmunotherapyNeoplasm ProteinsNeoplasmsSequence Analysis, DNACancer VaccinesHLA AntigensNeoplasm ProteinsCancer vaccinesGenomic profilingNeoantigensPoly-epitope

Identifiers

PMID28231819
PMCPMC5324279
OpenAlexW2588307902

What OpenQuestion holds

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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 OpenQuestion graph.