title: Statistical measures of transcriptional diversity capture genomic heterogeneity of cancer.
abstract: BACKGROUND
  Molecular heterogeneity of tumors suggests the presence of multiple different subclones that may limit response to targeted therapies and contribute to acquisition of drug resistance, but its quantification has remained challenging.
  
  
  RESULTS
  We performed simulations to evaluate statistical measures that best capture the molecular diversity within a group of tumors for either continuous (gene expression) or discrete (mutations, copy number alterations) molecular data. Dispersion based metrics in the principal component space best captured the underlying heterogeneity. To demonstrate utility of these measures, we characterized the diversity in transcriptional and genomic profiles of different breast tumor subtypes, and showed that basal-like or triple-negative breast cancers (TNBC) are significantly more heterogeneous molecularly than other subtypes. Our analysis also suggests that transcriptional diversity is a global characteristic of the tumors observed across the majority of molecular pathways. Among basal-like tumors, those that were resistant to multi-agent chemotherapy showed greater transcriptional diversity compared to chemotherapy-sensitive tumors, suggesting that potentially multiple mechanisms may be contributing to chemotherapy resistance.
  
  
  CONCLUSIONS
  We proposed and validated measures of transcriptional and genomic diversity that can quantify the molecular diversity of tumors. We applied the new measures to genomic data from breast tumors and demonstrated that basal-like breast cancers are significantly more diverse than other breast cancers. The observation that chemo-resistant tumors are significantly more diverse molecularly than chemosensitive tumors implies that multiple resistance mechanisms may be active, thus limiting the sensitivity and accuracy of predictive markers of chemotherapy response.
authors.forename: Tingting
authors.initials: T
authors.lastname: Jiang
authors.forename: Weiwei
authors.initials: W
authors.lastname: Shi
authors.forename: René
authors.initials: R
authors.lastname: Natowicz
authors.forename: Sophia N
authors.initials: SN
authors.lastname: Ononye
authors.forename: Vikram B
authors.initials: VB
authors.lastname: Wali
authors.forename: Yuval
authors.initials: Y
authors.lastname: Kluger
authors.forename: Lajos
authors.initials: L
authors.lastname: Pusztai
authors.affiliation: Department of Internal Medicine, Yale School of Medicine, Yale Cancer Center, New Haven, Connecticut, USA. christos.hatzis@yale.edu.
authors.forename: Christos
authors.initials: C
authors.lastname: Hatzis
mesh_terms: D000970:Antineoplastic Agents / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000188:drug therapy / Q000235:genetics* / Q000473:pathology; D056915:DNA Copy Number Variations; D030541:Databases, Genetic / Q000706:statistics & numerical data; D019008:Drug Resistance, Neoplasm / Q000235:genetics; D005260:Female; D015972:Gene Expression Regulation, Neoplastic / Q000187:drug effects / Q000235:genetics; D052138:Genes, Neoplasm; D006801:Humans; D009363:Neoplasm Proteins / Q000235:genetics / Q000378:metabolism; D020411:Oligonucleotide Array Sequence Analysis
chemical_list: D000970:Antineoplastic Agents; D009363:Neoplasm Proteins