Cancer stem cells from human breast tumors are involved in spontaneous metastases in orthotopic mouse models.

To examine the role of breast cancer stem cells (BCSCs) in metastasis, we generated human-in-mouse breast cancer orthotopic models using patient tumor specimens, labeled with optical reporter fusion genes. These models recapitulate human cancer features not captured with previous models, including spontaneous metastasis in particular, and provide a useful platform for studies of breast tumor initiation and progression. With noninvasive imaging approaches, as few as 10 cells of stably labeled BCSCs could be tracked in vivo, enabling studies of early tumor growth and spontaneous metastasis. These advances in BCSC imaging revealed that CD44(+) cells from both primary tumors and lung metastases are highly enriched for tumor-initiating cells. Our metastatic cancer models, combined with noninvasive imaging techniques, constitute an integrated approach that could be applied to dissect the molecular mechanisms underlying the dissemination of metastatic CSCs (MCSCs) and to explore therapeutic strategies targeting MCSCs in general or to evaluate individual patient tumor cells and predict response to therapy.

Single-cell dissection of transcriptional heterogeneity in human colon tumors.

Cancer is often viewed as a caricature of normal developmental processes, but the extent to which its cellular heterogeneity truly recapitulates multilineage differentiation processes of normal tissues remains unknown. Here we implement single-cell PCR gene-expression analysis to dissect the cellular composition of primary human normal colon and colon cancer epithelia. We show that human colon cancer tissues contain distinct cell populations whose transcriptional identities mirror those of the different cellular lineages of normal colon. By creating monoclonal tumor xenografts from injection of a single (n = 1) cell, we demonstrate that the transcriptional diversity of cancer tissues is largely explained by in vivo multilineage differentiation and not only by clonal genetic heterogeneity. Finally, we show that the different gene-expression programs linked to multilineage differentiation are strongly associated with patient survival. We develop two-gene classifier systems (KRT20 versus CA1, MS4A12, CD177, SLC26A3) that predict clinical outcomes with hazard ratios superior to those of pathological grade and comparable to those of microarray-derived multigene expression signatures.