Search

Graphical abstract, Genetic heterogeneity within a tumor arises by clonal evolution, and patients with highly heterogeneous tumors are more likely to be resistant to therapy and have reduced survival. Clonal evolution also occurs when a subset of cells leave the primary tumor to form metastases, which leads to reduced genetic heterogeneity at the metastatic site. Although this process has been observed in human cancer, experimental models which recapitulate this process are lacking. Patient-derived tumor xenografts (PDX) have been shown to recapitulate the patient’s original tumor’s intra-tumor genetic heterogeneity, as well as its genomics and response to treatment, but whether they can be used to model clonal evolution in the metastatic process is currently unknown. Here, we address this question by following genetic changes in two breast cancer PDX models during metastasis. First, we discovered that mouse stroma can be a confounding factor in assessing intra-tumor heterogeneity by whole exome sequencing, thus we developed a new bioinformatic approach to correct for this. Finally, in a spontaneous, but not experimental (tail-vein) metastasis model we observed a loss of heterogeneity in PDX metastases compared to their orthotopic “primary” tumors, confirming that PDX models can faithfully mimic the clonal evolution process undergone in human patients during metastatic spreading.

Background Immune therapies have had a major impact in multiple myeloma (MM). Responses induced in relapsed and/or refractory MM (RRMM) patients (pts) are unprecedented; however the key challenge is their durability. Most immunotherapeutic approaches rely on the fitness of pts’ T cells but exposure to multiple therapies may alter immune cell numbers and function. Here we analyzed and compared the transcriptome and phenotype of T cells from healthy donors (HD), newly diagnosed MM (NDMM) and RRMM pts. Methods Peripheral Blood Mononuclear Cells (PBMCs) from age-matched HD, NDMM and RRMM pts were collected before the start of a new line of treatment. Single cell RNA sequencing (scRNAseq) was performed and proportion of cell types were inferred using a reference PBMC dataset with Seurat. Cell type proportions were also estimated with CyTOF and conventional flow cytometry Results PBMCs from 10 HDs (median age 69, range 53-79), 20 NDMM pts (median age 71, range 54-88), 20 RRMM pts (median age 71, range 54-80) were analyzed. RRMM pts were exposed to 1 (n=8), 2 (n=6) or 3 (n=6) lines of treatment. All RRMM pts were exposed to a proteasome inhibitor, 75% were exposed to an immunomodulatory drug, and 65% received autologous stem cell transplantation. 48 out of 50 samples were processed by scRNAseq, 45 by CyTOF and 33 by flow cytometry. NDMM pts demonstrated a trend towards lower frequency of CD4 T cells compared to HD. This decreased number was accompanied by a decrease in the expression of genes involved in mitochondrial oxidative phosphorylation and an increase in expression of immediate early response genes (e.g. FOS, JUN, JUNB). In the CD8 compartment, CD8 naive cells were lower in NDMM compared to HD. No differences were found on CD8 central memory, effector memory and/or terminal effector populations In RRMM, the proportion of naive CD4 cells was significantly lower compared to NDMM (p Conclusion T cell phenotypes in MM pts are altered, especially after treatment. These findings may provide the rationale for the investigation of immunotherapy in NDMM and less heavily pretreated MM. Ongoing functional studies will demonstrate whether these phenotypic and transcriptomic changes lead to less fit T cells with consequent impact on effectiveness of T cell directed therapies.

Elucidation of the mutational landscape of human cancer has progressed rapidly and been accompanied by the development of therapeutics targeting mutant oncogenes. However, a comprehensive mapping of cancer dependencies has lagged behind and the discovery of therapeutic targets for counteracting tumor suppressor gene loss is needed. To identify vulnerabilities relevant to specific cancer subtypes, we conducted a large-scale RNAi screen in which viability effects of mRNA knockdown were assessed for 7,837 genes using an average of 20 shRNAs per gene in 398 cancer cell lines. We describe findings of this screen, outlining the classes of cancer dependency genes and their relationships to genetic, expression, and lineage features. In addition, we describe robust gene-interaction networks recapitulating both protein complexes and functional cooperation among complexes and pathways. This dataset along with a web portal is provided to the community to assist in the discovery and translation of new therapeutic approaches for cancer.