Impact of rhG-CSF on Clinical Efficacy and Immune Cell Subsets after Initial Induction Chemotherapy in AML.

BACKGROUND: The impact of recombinant human granulocyte colony-stimulating factor (rhG-CSF) in acute myeloid leukemia (AML) is still controversial. The purpose of this study is to explore the impact of rhG-CSF administration on clinical efficacy and immune cell subsets after initial induction chemotherapy in AML. METHODS: The clinical efficacy and immune cell subsets were compared in the newly diagnosed patients with AML according to whether rhG-CSF was used after initial induction chemotherapy. Next, rhG-CSF stimulation experi-ments on leukemia cell lines and primary leukemia blasts were performed in vitro. RESULTS: There was no statistical difference between control group and rhG-CSF therapy group in complete remission rate and relapse free survival. The duration of agranulocytosis was significantly shortened in rhG-CSF therapy group compared with control group. The percentage of circulating monocytic myeloid-derived suppressor cells (M-MDSCs) and regulatory T cells (Tregs) were significantly increased after the administration of rhG-CSF. Furthermore, it was found that rhG-CSF did not promote the proliferation of leukemia cell lines and primary leukemia blasts, but increased the proportion of M-MDSCs and Tregs in vitro. CONCLUSIONS: Administration of rhG-CSF after initial induction therapy of AML does not affect the clinical remission and relapse rate, but reduces the duration of agranulocytosis and increases the proportion of M-MDSCs and Tregs.

An Evolutionary Approach for Identifying Driver Mutations in Colorectal Cancer.

The traditional view of cancer as a genetic disease that can successfully be treated with drugs targeting mutant onco-proteins has motivated whole-genome sequencing efforts in many human cancer types. However, only a subset of mutations found within the genomic landscape of cancer is likely to provide a fitness advantage to the cell. Distinguishing such "driver" mutations from innocuous "passenger" events is critical for prioritizing the validation of candidate mutations in disease-relevant models. We design a novel statistical index, called the Hitchhiking Index, which reflects the probability that any observed candidate gene is a passenger alteration, given the frequency of alterations in a cross-sectional cancer sample set, and apply it to a mutational data set in colorectal cancer. Our methodology is based upon a population dynamics model of mutation accumulation and selection in colorectal tissue prior to cancer initiation as well as during tumorigenesis. This methodology can be used to aid in the prioritization of candidate mutations for functional validation and contributes to the process of drug discovery.

Stochastic Evolution of Pancreatic Cancer Metastases During Logistic Clonal Expansion.

Despite recent progress in diagnostic and multimodal treatment approaches, most cancer deaths are still caused by metastatic spread and the subsequent growth of tumor cells in sites distant from the primary organ. So far, few quantitative studies are available that allow for the estimation of metastatic parameters and the evaluation of alternative treatment strategies. Most computational studies have focused on situations in which the tumor cell population expands exponentially over time; however, tumors may eventually be subject to resource and space limitations so that their growth patterns deviate from exponential growth to adhere to density-dependent growth models. In this study, we developed a stochastic evolutionary model of cancer progression that considers alterations in metastasis-related genes and intercellular growth competition leading to density effects described by logistic growth. Using this stochastic model, we derived analytical approximations for the time between the initiation of tumorigenesis and diagnosis, the expected number of metastatic sites, the total number of metastatic cells, the size of the primary tumor, and survival. Furthermore, we investigated the effects of drug administration and surgical resection on these quantities and predicted outcomes for different treatment regimens. Parameter values used in the analysis were estimated from data obtained from a pancreatic cancer rapid autopsy program. Our theoretical approach allows for flexible modeling of metastatic progression dynamics.

Computational modeling of pancreatic cancer patients receiving FOLFIRINOX and gemcitabine-based therapies identifies optimum intervention strategies.

Pancreatic ductal adenocarcinoma (PDAC) exhibits a variety of phenotypes with regard to disease progression and treatment response. This variability complicates clinical decision-making despite the improvement of survival due to the recent introduction of FOLFIRINOX (FFX) and nab-paclitaxel. Questions remain as to the timing and sequence of therapies and the role of radiotherapy for unresectable PDAC. Here we developed a computational analysis platform to investigate the dynamics of growth, metastasis and treatment response to FFX, gemcitabine (GEM), and GEM+nab-paclitaxel. Our approach was informed using data of 1,089 patients treated at the Massachusetts General Hospital and validated using an independent cohort from Osaka Medical College. Our framework establishes a logistic growth pattern of PDAC and defines the Local Advancement Index (LAI), which determines the eventual primary tumor size and predicts the number of metastases. We found that a smaller LAI leads to a larger metastatic burden. Furthermore, our analyses ascertain that i) radiotherapy after induction chemotherapy improves survival in cases receiving induction FFX or with larger LAI, ii) neoadjuvant chemotherapy improves survival in cases with resectable PDAC, and iii) temporary cessations of chemotherapies do not impact overall survival, which supports the feasibility of treatment holidays for patients with FFX-associated adverse effects. Our findings inform clinical decision-making for PDAC patients and allow for the rational design of clinical strategies using FFX, GEM, GEM+nab-paclitaxel, neoadjuvant chemotherapy, and radiation.

Nuclear topology modulates the mutational landscapes of cancer genomes.

Nuclear organization of genomic DNA affects processes of DNA damage and repair, yet its effects on mutational landscapes in cancer genomes remain unclear. Here we analyzed genome-wide somatic mutations from 366 samples of six cancer types. We found that lamina-associated regions, which are typically localized at the nuclear periphery, displayed higher somatic mutation frequencies than did the interlamina regions at the nuclear core. This effect was observed even after adjustment for features such as GC percentage, chromatin, and replication timing. Furthermore, mutational signatures differed between the nuclear core and periphery, thus indicating differences in the patterns of DNA-damage or DNA-repair processes. For instance, smoking and UV-related signatures, as well as substitutions at certain motifs, were more enriched in the nuclear periphery. Thus, the nuclear architecture may influence mutational landscapes in cancer genomes beyond the previously described effects of chromatin structure and replication timing.

mTOR and HDAC Inhibitors Converge on the TXNIP/Thioredoxin Pathway to Cause Catastrophic Oxidative Stress and Regression of RAS-Driven Tumors.

Although agents that inhibit specific oncogenic kinases have been successful in a subset of cancers, there are currently few treatment options for malignancies that lack a targetable oncogenic driver. Nevertheless, during tumor evolution cancers engage a variety of protective pathways, which may provide alternative actionable dependencies. Here, we identify a promising combination therapy that kills NF1-mutant tumors by triggering catastrophic oxidative stress. Specifically, we show that mTOR and HDAC inhibitors kill aggressive nervous system malignancies and shrink tumors in vivo by converging on the TXNIP/thioredoxin antioxidant pathway, through cooperative effects on chromatin and transcription. Accordingly, TXNIP triggers cell death by inhibiting thioredoxin and activating apoptosis signal-regulating kinase 1 (ASK1). Moreover, this drug combination also kills NF1-mutant and KRAS-mutant non-small cell lung cancers. Together, these studies identify a promising therapeutic combination for several currently untreatable malignancies and reveal a protective nodal point of convergence between these important epigenetic and oncogenic enzymes.Significance: There are no effective therapies for NF1- or RAS-mutant cancers. We show that combined mTOR/HDAC inhibitors kill these RAS-driven tumors by causing catastrophic oxidative stress. This study identifies a promising therapeutic combination and demonstrates that selective enhancement of oxidative stress may be more broadly exploited for developing cancer therapies. Cancer Discov; 7(12); 1450-63. ©2017 AACR.This article is highlighted in the In This Issue feature, p. 1355.

Immune Escape in Breast Cancer During In Situ to Invasive Carcinoma Transition.

To investigate immune escape during breast tumor progression, we analyzed the composition of leukocytes in normal breast tissues, ductal carcinoma in situ (DCIS), and invasive ductal carcinomas (IDC). We found significant tissue and tumor subtype-specific differences in multiple cell types including T cells and neutrophils. Gene expression profiling of CD45+CD3+ T cells demonstrated a decrease in CD8+ signatures in IDCs. Immunofluorescence analysis showed fewer activated GZMB+CD8+ T cells in IDC than in DCIS, including in matched DCIS and recurrent IDC. T-cell receptor clonotype diversity was significantly higher in DCIS than in IDCs. Immune checkpoint protein TIGIT-expressing T cells were more frequent in DCIS, whereas high PD-L1 expression and amplification of CD274 (encoding PD-L1) was only detected in triple-negative IDCs. Coamplification of a 17q12 chemokine cluster with ERBB2 subdivided HER2+ breast tumors into immunologically and clinically distinct subtypes. Our results show coevolution of cancer cells and the immune microenvironment during tumor progression.Significance: The design of effective cancer immunotherapies requires the understanding of mechanisms underlying immune escape during tumor progression. Here we demonstrate a switch to a less active tumor immune environment during the in situ to invasive breast carcinoma transition, and identify immune regulators and genomic alterations that shape tumor evolution. Cancer Discov; 7(10); 1098-115. ©2017 AACR.See related commentary by Speiser and Verdeil, p. 1062This article is highlighted in the In This Issue feature, p. 1047.

Probabilistic Modeling of Reprogramming to Induced Pluripotent Stem Cells.

Reprogramming of somatic cells to induced pluripotent stem cells (iPSCs) is typically an inefficient and asynchronous process. A variety of technological efforts have been made to accelerate and/or synchronize this process. To define a unified framework to study and compare the dynamics of reprogramming under different conditions, we developed an in silico analysis platform based on mathematical modeling. Our approach takes into account the variability in experimental results stemming from probabilistic growth and death of cells and potentially heterogeneous reprogramming rates. We suggest that reprogramming driven by the Yamanaka factors alone is a more heterogeneous process, possibly due to cell-specific reprogramming rates, which could be homogenized by the addition of additional factors. We validated our approach using publicly available reprogramming datasets, including data on early reprogramming dynamics as well as cell count data, and thus we demonstrated the general utility and predictive power of our methodology for investigating reprogramming and other cell fate change systems.

The mathematics of cancer: integrating quantitative models.

Mathematical modelling approaches have become increasingly abundant in cancer research. The complexity of cancer is well suited to quantitative approaches as it provides challenges and opportunities for new developments. In turn, mathematical modelling contributes to cancer research by helping to elucidate mechanisms and by providing quantitative predictions that can be validated. The recent expansion of quantitative models addresses many questions regarding tumour initiation, progression and metastases as well as intra-tumour heterogeneity, treatment responses and resistance. Mathematical models can complement experimental and clinical studies, but also challenge current paradigms, redefine our understanding of mechanisms driving tumorigenesis and shape future research in cancer biology.

Dose-Dependent Mutation Rates Determine Optimum Erlotinib Dosing Strategies for EGFR Mutant Non-Small Cell Lung Cancer Patients.

BACKGROUND: The advent of targeted therapy for cancer treatment has brought about a paradigm shift in the clinical management of human malignancies. Agents such as erlotinib used for EGFR-mutant non-small cell lung cancer or imatinib for chronic myeloid leukemia, for instance, lead to rapid tumor responses. Unfortunately, however, resistance often emerges and renders these agents ineffective after a variable amount of time. The FDA-approved dosing schedules for these drugs were not designed to optimally prevent the emergence of resistance. To this end, we have previously utilized evolutionary mathematical modeling of treatment responses to elucidate the dosing schedules best able to prevent or delay the onset of resistance. Here we expand on our approaches by taking into account dose-dependent mutation rates at which resistant cells emerge. The relationship between the serum drug concentration and the rate at which resistance mutations arise can lead to non-intuitive results about the best dose administration strategies to prevent or delay the emergence of resistance. METHODS: We used mathematical modeling, available clinical trial data, and different considerations of the relationship between mutation rate and drug concentration to predict the effectiveness of different dosing strategies. RESULTS: We designed several distinct measures to interrogate the effects of different treatment dosing strategies and found that a low-dose continuous strategy coupled with high-dose pulses leads to the maximal delay until clinically observable resistance. Furthermore, the response to treatment is robust against different assumptions of the mutation rate as a function of drug concentration. CONCLUSIONS: For new and existing targeted drugs, our methodology can be employed to compare the effectiveness of different dose administration schedules and investigate the influence of changing mutation rates on outcomes.

Olanzapine-divalproex combination versus divalproex monotherapy in the treatment of bipolar mixed episodes: a double-blind, placebo-controlled study.

OBJECTIVE: This 6-week, randomized, double-blind, placebo-controlled trial used simultaneous depression and mania criteria to compare a single mood stabilizer, divalproex, with and without adjunctive olanzapine in patients with bipolar I disorder experiencing acute mixed episodes. METHOD: Two hundred two adults, aged 18 to 60 years, who met DSM-IV-TR criteria for bipolar disorder with a current mixed episode and had been taking divalproex for >or=14 days at levels of 75 to 125 microg/mL with inadequate efficacy (21-item Hamilton Depression Rating Scale [HDRS-21] and Young Mania Rating Scale [YMRS] scores >or=16) were randomly assigned to olanzapine 5 to 20 mg/d versus placebo augmentation. HDRS-21, YMRS, Clinical Global Impressions for Bipolar Disorder (CGI-BP), hospitalizations, concomitant medications, and adverse events were assessed. Comparisons included changes in both HDRS-21 and YMRS (primary outcome measure), time to partial response and time to response, CGI-BP improvement, hospitalizations, and safety (secondary outcome measures). The study was conducted from December 2006 to February 2008. RESULTS: Mean (SD) baseline HDRS-21 and YMRS scores were 22.2 (4.5) and 20.9 (4.4), respectively, with 59% female and 51% white subjects. Mean +/- SE score changes from baseline across the 6-week treatment period for adjunctive olanzapine (n = 100) versus adjunctive placebo (n = 101) arms, respectively, were -9.37 +/- 0.55 versus -7.69 +/- 0.54, P = .022, on the HDRS-21 and -10.15 +/- 0.44 versus -7.68 +/- 0.44 P < .001, on the YMRS. Mean +/- SE score changes from baseline to last observation carried forward for CGI-BP measures were -1.34 +/- 0.11 for adjunctive olanzapine versus -1.06 +/- 0.11 for adjunctive placebo, P = .056. Time to partial response (>or=25% HDRS-21 and YMRS decreases, median 7 versus 14 days) and time to response (>or=50% HDRS-21 and YMRS decreases, median 25 versus 49 days) were significantly shorter with adjunctive olanzapine. Increases in weight (total and >or=7%) and fasting blood glucose were significantly greater with adjunctive olanzapine. CONCLUSION: Adjunctive olanzapine yielded greater and earlier reduction of manic and depressive symptoms in mixed-episode patients with inadequate response to at least 2 weeks of divalproex. TRIAL REGISTRATION: clinicaltrials.gov Identifier: NCT00402324.