Sonia Marsilio

A novel adoptive transfer model of chronic lymphocytic leukemia suggests a key role for T lymphocytes in the disease.

Chronic lymphocytic leukemia (CLL) is an incurable adult disease of unknown etiology. Understanding the biology of CLL cells, particularly cell maturation and growth in vivo, has been impeded by lack of a reproducible adoptive transfer model. We report a simple, reproducible system in which primary CLL cells proliferate in nonobese diabetes/severe combined immunodeficiency/γc(null) mice under the influence of activated CLL-derived T lymphocytes. By co-transferring autologous T lymphocytes, activated in vivo by alloantigens, the survival and growth of primary CFSE-labeled CLL cells in vivo is achieved and quantified. Using this approach, we have identified key roles for CD4(+) T cells in CLL expansion, a direct link between CD38 expression by leukemic B cells and their activation, and support for CLL cells preferentially proliferating in secondary lymphoid tissues. The model should simplify analyzing kinetics of CLL cells in vivo, deciphering involvement of nonleukemic elements and nongenetic factors promoting CLL cell growth, identifying and characterizing potential leukemic stem cells, and permitting preclinical studies of novel therapeutics. Because autologous activated T lymphocytes are 2-edged swords, generating unwanted graph-versus-host and possibly autologous antitumor reactions, the model may also facilitate analyses of T-cell populations involved in immune surveillance relevant to hematopoietic transplantation and tumor cytoxicity.

Intraclonal complexity in chronic lymphocytic leukemia: fractions enriched in recently born/divided and older/quiescent cells.

The failure of chemotherapeutic regimens to eradicate cancers often results from the outgrowth of minor subclones with more dangerous genomic abnormalities or with self-renewing capacity. To explore such intratumor complexities in B-cell chronic lymphocytic leukemia (CLL), we measured B-cell kinetics in vivo by quantifying deuterium (2H)-labeled cells as an indicator of a cell that had divided. Separating CLL clones on the basis of reciprocal densities of chemokine (C-X-C motif) receptor 4 (CXCR4) and cluster designation 5 (CD5) revealed that the CXCR4dimCD5bright (proliferative) fraction contained more 2H-labeled DNA and hence divided cells than the CXCR4brightCD5dim (resting) fraction. This enrichment was confirmed by the relative expression of two cell cycle-associated molecules in the same fractions, Ki-67 and minichromosome maintenance protein 6 (MCM6). Comparisons of global gene expression between the CXCR4dimCD5bright and CXCR4brightCD5dim fractions indicated higher levels of pro-proliferation and antiapoptotic genes and genes involved in oxidative injury in the proliferative fraction. An extended immunophenotype was also defined, providing a wider range of surface molecules characteristic of each fraction. These intraclonal analyses suggest a model of CLL cell biology in which the leukemic clone contains a spectrum of cells from the proliferative fraction, enriched in recently divided robust cells that are lymphoid tissue emigrants, to the resting fraction enriched in older, less vital cells that need to immigrate to lymphoid tissue or die. The model also suggests several targets preferentially expressed in the two populations amenable for therapeutic attack. Finally, the study lays the groundwork for future analyses that might provide a more robust understanding of the development and clonal evolution of this currently incurable disease.

Somatic CLL mutations occur at multiple distinct hematopoietic maturation stages: Documentation and cautionary note regarding cell fraction purity

Somatic CLL mutations occur at multiple distinct hematopoietic maturation stages: documentation and cautionary note regarding cell fraction purity

On Statistical Modeling of Sequencing Noise in High Depth Data to Assess Tumor Evolution

One cause of cancer mortality is tumor evolution to therapy-resistant disease. First line therapy often targets the dominant clone, and drug resistance can emerge from preexisting clones that gain fitness through therapy-induced natural selection. Such mutations may be identified using targeted sequencing assays by analysis of noise in high-depth data. Here, we develop a comprehensive, unbiased model for sequencing error background. We find that noise in sufficiently deep DNA sequencing data can be approximated by aggregating negative binomial distributions. Mutations with frequencies above noise may have prognostic value. We evaluate our model with simulated exponentially expanded populations as well as data from cell line and patient sample dilution experiments, demonstrating its utility in prognosticating tumor progression. Our results may have the potential to identify significant mutations that can cause recurrence. These results are relevant in the pretreatment clinical setting to determine appropriate therapy and prepare for potential recurrence pretreatment.

Highly sensitive detection of small variants in multi-sample ultra-deep tumor sequencing

One of the main causes of cancer mortality is tumor evolution to therapy-resistant disease. Drug resistance may emerge from the rise of ancestral clones that gain fitness through therapy-induced natural selection. Previously, it was shown that the presence of drug-resistant sub-clones at diagnosis or prior to therapy could be a strong predictor of poor survival, disease transformation, and refractoriness, with direct implications for disease management. Although such prognostic mutations are most commonly identified using amplicon-based or hybrid-capture deep sequencing in a clinical setting, their sensitive detection relies on the accurate analysis of background noise, specifically sequencing errors that arise from prior polymerase chain reaction cycles. In this work, we provide a comprehensive, unbiased model that precisely describes this background noise and show that it can be approximated by aggregating negative binomial (NB) distributions, using tumor-only data. We evaluate our model and its NB approximation with simulated exponentially expanded populations, as well as ultra-deep sequencing data from cell line and patient sample dilution experiments. Our method goes beyond estimating fixed detection thresholds for all variants, having the power to assess mutation-specific sensitivities that allow identification of 1-2 mutated alleles out of 10,000 wild-type. This facilitates the design of precise treatment strategies and contributes significantly to combatting drug resistance and increasing positive outcomes.One cause of cancer mortality is tumor evolution to therapy-resistant disease. First line therapy often targets the dominant clone, and drug resistance can emerges from preexisting clones that gain fitness through therapy-induced natural selection. Such mutations may be identified using targeted sequencing assays by analysis of noise in high-depth data. Here, we develop a comprehensive, unbiased model for sequencing error background. We find that noise in sufficiently deep DNA sequencing data can be approximated by aggregating negative binomial distributions. Mutations with frequencies above noise may have prognostic value. We evaluate our model with simulated exponentially expanded populations as well as data from cell line and patient sample dilution experiments, demonstrating its utility in prognosticating tumor progression. Our results may have the potential to identify significant mutations that can cause recurrence. These results are relevant in the pretreatment clinical setting to determine appropriate therapy and prepare for potential recurrence pretreatment.