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Abstract LB-129: Identifying tumor subpopulations and the functional consequences of intratumor heterogeneity using single-cell profiling of breast cancer patient-derived xenografts

2015· article· en· W1788100462 on OpenAlexaff
Paul Savage, Sadiq M.I. Saleh, Ernesto Iacucci, Timothe Revil, Yu-Chang Wang, Nicholas Bertos, Anie Monast, Hong Zhao, Margarita Souleimanova, Keith E. Szulwach, Chandana Batchu, Atilla Ömeroğlu, Morag Park, Ioannis Ragoussis

Bibliographic record

VenueCancer Research · 2015
Typearticle
Languageen
FieldBiochemistry, Genetics and Molecular Biology
TopicCancer Genomics and Diagnostics
Canadian institutionsMcGill University
Fundersnot available
KeywordsBiologyBreast cancerExomeCancer researchExome sequencingPopulationCancerGeneGeneticsPhenotypeMedicine

Abstract

fetched live from OpenAlex

Abstract Human breast tumors have been shown to exhibit extensive inter- and intra-tumor heterogeneity. While recent advances in genomic technologies have allowed us to deconvolute this heterogeneity, few studies have addressed the functional consequences of diversity within tumor populations. Here, we identified an index case for which we have derived a patient-derived xenograft (PDX) as a renewable tissue source to identify subpopulations and perform functional assays. On pathology, the tumor was an invasive ductal carcinoma which was hormone receptor-negative, HER2-positive (IHC 2+, FISH average HER2/CEP17 2.4), though the FISH signal was noted to be heterogeneous. On gene expression profiling of bulk samples, the primary tumor and PDX were classified as basal-like. We performed single cell RNA and exome sequencing of the PDX to identify population structure. Using a single sample predictor of breast cancer subtype, we have identified single basal-like, HER2-enriched and normal-like cells co-existing within the PDX tumor. Genes differentially expressed between these subpopulations are involved in proliferation and differentiation. Functional studies distinguishing these subpopulations are ongoing. Microfluidic whole genome amplification followed by whole exome capture of 81 single cells showed high and homogeneous target enrichment with >75% of reads mapping uniquely on target. Variant calling using GATK and Samtools revealed founder mutations in key genes as BRCA1 and TP53, as well as subclonal mutations that are being investigated further. Loss of heterozygocity was observed in 16 TCGA cancer driver genes and novel mutations in 7 cancer driver genes. These findings may be important in understanding the functional consequences of intra-tumor heterogeneity with respect to clinically important phenotypes such as invasion, metastasis and drug-resistance. Citation Format: Paul Savage, Sadiq M. Saleh, Ernesto Iacucci, Timothe Revil, Yu-Chang Wang, Nicholas Bertos, Anie Monast, Hong Zhao, Margarita Souleimanova, Keith Szulwach, Chandana Batchu, Atilla Omeroglu, Morag Park, Ioannis Ragoussis. Identifying tumor subpopulations and the functional consequences of intratumor heterogeneity using single-cell profiling of breast cancer patient-derived xenografts. [abstract]. In: Proceedings of the 106th Annual Meeting of the American Association for Cancer Research; 2015 Apr 18-22; Philadelphia, PA. Philadelphia (PA): AACR; Cancer Res 2015;75(15 Suppl):Abstract nr LB-129. doi:10.1158/1538-7445.AM2015-LB-129

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)0.000
metaresearch head score (Gemma)0.000
Version: metacan-v3-hybrid-931329e0061cValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Bench or experimental · Consensus signal: Bench or experimental
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.001
Threshold uncertainty score0.003

Distilled classifier scores by category (both heads)

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
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 head0.136
GPT teacher head0.369
Teacher spread0.233 · 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 designBench or experimental
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".

Quick stats

Citations0
Published2015
Admission routes1
Has abstractyes

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