Breast tumor subgroups reveal diverse clinical prognostic power.
Predicting the outcome of cancer therapies using molecular features and clinical observations is a key goal of cancer biology, which has been addressed comprehensively using whole patient datasets without considering the effect of tumor heterogeneity. We hypothesized that molecular features and clinical observations have different prognostic abilities for different cancer subtypes, and made a systematic study using both clinical observations and gene expression data. This analysis revealed that (1) gene expression profiles and clinical features show different prognostic power for the five breast cancer subtypes; (2) gene expression data of the normal-like subgroup contains more valuable prognostic information and survival associated contexts than the other subtypes, and the patient survival time of the normal-like subtype is more predictable based on the gene expression profiles; and (3) the prognostic power of many previously reported breast cancer gene signatures increased in the normal-like subtype and reduced in the other subtypes compared with that in the whole sample set.
National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
Zhaoqi
Z
Liu
National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
Xiang-Sun
XS
Zhang
National Center for Mathematics and Interdisciplinary Sciences, Academy of Mathematics and Systems Science, Chinese Academy of Sciences, Beijing 100190, China.
Shihua
S
Zhang
D000903:Antibiotics, Antineoplastic / Q000627:therapeutic use; D061067:Antibodies, Monoclonal, Humanized / Q000627:therapeutic use; D000972:Antineoplastic Agents, Phytogenic / Q000627:therapeutic use; D001943:Breast Neoplasms / Q000145:classification* / Q000188:drug therapy / Q000235:genetics / Q000401:mortality*; D004317:Doxorubicin / Q000627:therapeutic use; D019008:Drug Resistance, Neoplasm; D005260:Female; D020869:Gene Expression Profiling; D015972:Gene Expression Regulation, Neoplastic; D006801:Humans; D020411:Oligonucleotide Array Sequence Analysis; D017239:Paclitaxel / Q000627:therapeutic use; D011379:Prognosis; D018719:Erb-b2 Receptor Tyrosine Kinases / Q000378:metabolism; D011960:Receptors, Estrogen / Q000378:metabolism; D011980:Receptors, Progesterone / Q000378:metabolism; D000068878:Trastuzumab
D000903:Antibiotics, Antineoplastic; D061067:Antibodies, Monoclonal, Humanized; D000972:Antineoplastic Agents, Phytogenic; D004317:Doxorubicin; D017239:Paclitaxel; D018719:Erb-b2 Receptor Tyrosine Kinases; D011960:Receptors, Estrogen; D011980:Receptors, Progesterone; D000068878:Trastuzumab; C508053:ERBB2 protein, human