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Role of intratumoral NK cells in triple-negative breast cancer in the FinXX trial and Mayo Clinic cohort.

2020· article· en· W3030219555 on OpenAlexaff
Saranya Chumsri, Jodi M. Carter, Yaohua Ma, Douglas Hinerfeld, Heather Ann Brauer, Sarah Warren, Torsten O. Nielsen, Karama Asleh, Heikki Joensuu, Edith A. Perez, Roberto A. Leon‐Ferre, David W. Hillman, Judy C. Boughey, Minetta C. Liu, James N. Ingle, Krishna R. Kalari, Fergus J. Couch, Keith L. Knutson, Matthew P. Goetz, E. Aubrey Thompson

Bibliographic record

VenueJournal of Clinical Oncology · 2020
Typearticle
Languageen
FieldMedicine
TopicCancer Immunotherapy and Biomarkers
Canadian institutionsUniversity of British Columbia
FundersNational Institutes of HealthU.S. Department of Defense
KeywordsMedicineTriple-negative breast cancerBreast cancerOncologyCohortTumor-infiltrating lymphocytesInternal medicineProportional hazards modelStromal cellGene signatureAdjuvantImmune systemPathologyCancerImmunologyImmunotherapyGene expressionGene

Abstract

fetched live from OpenAlex

510 Background: Several studies have established the critical role of preexisting immune response in triple negative breast cancer (TNBC). Most studies evaluated the tumor infiltrating lymphocytes in stroma. However, limited data are available with regards to the importance of specific subtypes and spatial distribution of these immune infiltrates. Methods: NanoString IO360 gene expression analysis and Digital Spatial Profiling (DSP) were used. DSP was used to quantify 39 immune-related proteins in stromal and tumor-enriched segments from 44 TNBC samples from the FinXX trial (NCT00114816) and 335 samples from the Mayo Clinic (MC) cohort of centrally reviewed TNBC (Leon-Ferre BCRT 2018). In FinXX trial, 22 patients with recurrence and 22 patients without recurrence were included. In MC cohort, 217/335 patients received adjuvant chemotherapy while 118 patients had surgery only without adjuvant chemotherapy. Regions were segmented based on pancytokeratin staining. The general linear model was used for statistical analysis of differential expression with recurrence free survival (RFS) as a categorical variable (recur yes or no). Kaplan-Meier estimates and Cox regression models were also used for analysis. Results: In the FinXX trial, using global gene expression analysis with IO360, there was no signature significantly associated with RFS. However, using DSP, high protein expression of CD56 in the tumor-enriched segments was associated with significant improvement in RFS (HR 0.26, 95%CI 0.09-0.78, p 0.01). Nevertheless, CD56 expression in the stroma (HR 0.66, 95%CI 0.29-1.53, p 0.33) and all segments (HR 0.53, 95%CI 0.23-1.25, p 0.14) was not significantly associated with improved outcome. We further validated these findings in the MC TNBC cohort where intratumoral CD56 expression was associated with a significant improvement in RFS (HR 0.23, p 0.002) but not stromal CD56 (p 0.79). Interestingly, when evaluating the MC TNBC cohort according to receipt of chemotherapy, intratumoral CD56 was associated with improved outcome only in patients who received chemotherapy (p 0.02 vs. 0.07). In both cohorts, higher expressions of intratumoral PD-L1, HLA-DR, and CD8 were associated with improved outcome. Conclusions: Using an in-depth analysis with spatially defined context, we identify that intratumoral CD56-positive NK cells are associated with improved outcome in TNBC. Our study highlights the potential role of NK cells in TNBC and future implications for biomarkers and therapeutic targets.Support: W81XWH-15-1-0292, P50CA116201-9, P50CA015083. Clinical trial information: NCT00114816 .

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame machine prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.

metaresearch head score (Codex)0.001
metaresearch head score (Gemma)0.001
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.002
Threshold uncertainty score0.007

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0010.001
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0010.001
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0010.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.076
GPT teacher head0.436
Teacher spread0.360 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

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Citations2
Published2020
Admission routes1
Has abstractyes

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