GENE-04. THE ONCOGENIC FUNCTIONS OF YAP1-GENE FUSIONS CAN BE INHIBITED BY DISRUPTION OF YAP1-TEAD INTERACTION

Supratentorial ependymoma can be sub-stratified into clinically relevant subtypes characterized by distinct molecular features. The subtype defined by high YAP1 activity harbored two distinct YAP1 gene fusions, YAP1-MAMLD1 and YAP1-FAM118B. In addition, YAP1 gene fusions have been detected in several other cancer types, including Epithelioid Hemangioendothelioma and Endocervical Adenocarcinoma. YAP1 is a key transcriptional co-activator and proto-oncogene that is negatively regulated by the Hippo pathway. Here, we show that both YAP1-MAMLD1 and YAP1-FAM118B, as well as additional YAP1 fusion genes found in other cancer types, are potent oncogenic drivers that cause tumor formation in the brain and the hindlimb in mice upon overexpression by somatic cell gene transfer. Using different in vitro assays, including Luciferase, RNA-, and ChIP Seq, we show that both the N-terminal YAP1 part and the C-terminal fusion partners exert activity. We can show that the YAP1 activity still relies on the binding to TEAD transcription factors, whereas the C terminal activity does not. Furthermore, the different fusion proteins have become independent from negative Hippo pathway signaling by constitutive nuclear localization and protection from degradation. In addition, by introducing point mutations and truncations to block the YAP1 and the MAMLD1 function we can show that the activity of both halves contributes to the oncogenic function of YAP1-MAMLD1. Using in vitro and in vivo assays we can show that pharmacological and genetic ablation of YAP-TEAD interaction diminishes the oncogenic potential of the fusions, indicating that this might be a potential therapeutic approach for these tumors in the future.

Computational Model of Progression to Multiple Myeloma Identifies Optimum Screening Strategies

Purpose

Recent advances have uncovered therapeutic interventions that might reduce the risk of progression of premalignant diagnoses, such as monoclonal gammopathy of undetermined significance (MGUS) to multiple myeloma (MM). It remains unclear how to best screen populations at risk and how to evaluate the ability of these interventions to reduce disease prevalence and mortality at the population level. To address these questions, we developed a computational modeling framework.

Materials and Methods

We used individual-based computational modeling of MGUS incidence and progression across a population of diverse individuals to determine best screening strategies in terms of screening start, intervals, and risk-group specificity. Inputs were life tables, MGUS incidence, and baseline MM survival. We measured MM-specific mortality and MM prevalence after MGUS detection from simulations and mathematic modeling predictions.

Results

Our framework is applicable to a wide spectrum of screening and intervention scenarios, including variation of the baseline MGUS to MM progression rate and evolving MGUS, in which progression increases over time. Given the currently available point estimate of progression risk reduction to 61% risk, starting screening at age 55 years and performing follow-up screening every 6 years reduced total MM prevalence by 19%. The same reduction could be achieved with starting screening at age 65 years and performing follow-up screening every 2 years. A 40% progression risk reduction per patient with MGUS per year would reduce MM-specific mortality by 40%. Specifically, screening onset age and screening frequency can change disease prevalence, and progression risk reduction changes both prevalence and disease-specific mortality. Screening would generally be favorable in high-risk individuals.

Conclusion

Screening efforts should focus on specifically identified groups with high lifetime risk of MGUS, for which screening benefits can be significant. Screening low-risk individuals with MGUS would require improved preventions.

DIFFpop: a stochastic computational approach to simulate differentiation hierarchies with single cell barcoding

SUMMARY

DIFFpop is an R package designed to simulate cellular differentiation hierarchies using either exponentially-expanding or fixed population sizes. The software includes functionalities to simulate clonal evolution due to the emergence of driver mutations under the infinite-allele assumption as well as options for simulation and analysis of single cell barcoding and labeling data. The software uses the Gillespie Stochastic Simulation Algorithm and a modification of expanding or fixed-size stochastic process models expanded to a large number of cell types and scenarios.

AVAILABILITY AND IMPLEMENTATION

DIFFpop is available as an R-package along with vignettes on Github (https://github.com/ferlicjl/diffpop).

SUPPLEMENTARY INFORMATION

Supplementary data are available at Bioinformatics online.

Subclonal cooperation drives metastasis by modulating local and systemic immune microenvironments

Most human tumours are heterogeneous, composed of cellular clones with different properties present at variable frequencies. Highly heterogeneous tumours have poor clinical outcomes, yet the underlying mechanism remains poorly understood. Here, we show that minor subclones of breast cancer cells expressing IL11 and FIGF (VEGFD) cooperate to promote metastatic progression and generate polyclonal metastases composed of driver and neutral subclones. Expression profiling of the epithelial and stromal compartments of monoclonal and polyclonal primary and metastatic lesions revealed that this cooperation is indirect, mediated through the local and systemic microenvironments. We identified neutrophils as a leukocyte population stimulated by the IL11-expressing minor subclone and showed that the depletion of neutrophils prevents metastatic outgrowth. Single-cell RNA-seq of CD45+ cell populations from primary tumours, blood and lungs demonstrated that IL11 acts on bone-marrow-derived mesenchymal stromal cells, which induce pro-tumorigenic and pro-metastatic neutrophils. Our results indicate key roles for non-cell-autonomous drivers and minor subclones in metastasis.

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.

The Warburg effect: persistence of stem-cell metabolism in cancers as a failure of differentiation

Background

Two recent observations regarding the Warburg effect are that (i) the metabolism of stem cells is constitutive (aerobic) glycolysis while normal cellular differentiation involves a transition to oxidative phosphorylation and (ii) the degree of glucose uptake of a malignancy as imaged by 18F-fluorodeoxyglucose positron emission tomography (FDG-PET) is associated with histologic measures of tumor differentiation. Combining these observations, we hypothesized that the high levels of glucose uptake observed in poorly differentiated cancers may reflect persistence of the glycolytic metabolism of stem cells in malignant cells that fail to fully differentiate.

Patients and methods

Tumor glucose uptake was measured by FDG-PET in 552 patients with histologically diverse cancers. We used normal mixture modeling to explore FDG-PET standardized uptake value (SUV) distributions and tested for associations between glucose uptake and histological differentiation, risk of lymph node metastasis, and survival. Using RNA-seq data, we carried out pathway and transcription factor analyses to compare tumors with high and low levels of glucose uptake.

Results

We found that well-differentiated tumors had low FDG uptake, while moderately and poorly differentiated tumors had higher uptake. The distribution of SUV for each histology was bimodal, with a low peak around SUV 2-5 and a high peak at SUV 8-14. The cancers in the two modes were clinically distinct in terms of the risk of nodal metastases and death. Carbohydrate metabolism and the pentose-related pathway were elevated in the poorly differentiated/high SUV clusters. Embryonic stem cell-related signatures were activated in poorly differentiated/high SUV clusters.

Conclusions

Our findings support the hypothesis that the biological basis for the Warburg effect is a persistence of stem cell metabolism (i.e. aerobic glycolysis) in cancers as a failure to transition from glycolysis-utilizing undifferentiated cells to oxidative phosphorylation-utilizing differentiated cells. We found that cancers cluster along the differentiation pathway into two groups, utilizing either glycolysis or oxidative phosphorylation. Our results have implications for multiple areas of clinical oncology.

Hidden heterogeneity and circadian-controlled cell fate inferred from single cell lineages

The origin of lineage correlations among single cells and the extent of heterogeneity in their intermitotic times (IMT) and apoptosis times (AT) remain incompletely understood. Here we developed single cell lineage-tracking experiments and computational algorithms to uncover correlations and heterogeneity in the IMT and AT of a colon cancer cell line before and during cisplatin treatment. These correlations could not be explained using simple protein production/degradation models. Sister cell fates were similar regardless of whether they divided before or after cisplatin administration and did not arise from proximity-related factors, suggesting fate determination early in a cell's lifetime. Based on these findings, we developed a theoretical model explaining how the observed correlation structure can arise from oscillatory mechanisms underlying cell fate control. Our model recapitulated the data only with very specific oscillation periods that fit measured circadian rhythms, thereby suggesting an important role of the circadian clock in controlling cellular fates.

KDM5 Histone Demethylase Activity Links Cellular Transcriptomic Heterogeneity to Therapeutic Resistance

Members of the KDM5 histone H3 lysine 4 demethylase family are associated with therapeutic resistance, including endocrine resistance in breast cancer, but the underlying mechanism is poorly defined. Here we show that genetic deletion of KDM5A/B or inhibition of KDM5 activity increases sensitivity to anti-estrogens by modulating estrogen receptor (ER) signaling and by decreasing cellular transcriptomic heterogeneity. Higher KDM5B expression levels are associated with higher transcriptomic heterogeneity and poor prognosis in ER⁺ breast tumors. Single-cell RNA sequencing, cellular barcoding, and mathematical modeling demonstrate that endocrine resistance is due to selection for pre-existing genetically distinct cells, while KDM5 inhibitor resistance is acquired. Our findings highlight the importance of cellular phenotypic heterogeneity in therapeutic resistance and identify KDM5A/B as key regulators of this process.

Fitness variation in isogenic populations leads to a novel evolutionary mechanism for crossing fitness valleys

Individuals in a population often have different fitnesses even when they have identical genotypes, but the effect of this variation on the evolution of a population through complicated fitness landscapes is unknown. Here, we investigate how populations with non-genetic fitness variation cross fitness valleys, common barriers to adaptation in rugged fitness landscapes in which a population must pass through a deleterious intermediate to arrive at a final advantageous stage. We develop a stochastic computational model describing the dynamics of an asexually reproducing population crossing a fitness valley, in which individuals of the same evolutionary stage can have variable fitnesses. We find that fitness variation that persists over multiple generations increases the rate of valley crossing through a novel evolutionary mechanism different from previously characterized mechanisms such as stochastic tunneling. By reducing the strength of selection against deleterious intermediates, persistent fitness variation allows for faster adaptation through rugged fitness landscapes.

Unravelling subclonal heterogeneity and aggressive disease states in TNBC through single-cell RNA-seq

Triple-negative breast cancer (TNBC) is an aggressive subtype characterized by extensive intratumoral heterogeneity. To investigate the underlying biology, we conducted single-cell RNA-sequencing (scRNA-seq) of >1500 cells from six primary TNBC. Here, we show that intercellular heterogeneity of gene expression programs within each tumor is variable and largely correlates with clonality of inferred genomic copy number changes, suggesting that genotype drives the gene expression phenotype of individual subpopulations. Clustering of gene expression profiles identified distinct subgroups of malignant cells shared by multiple tumors, including a single subpopulation associated with multiple signatures of treatment resistance and metastasis, and characterized functionally by activation of glycosphingolipid metabolism and associated innate immunity pathways. A novel signature defining this subpopulation predicts long-term outcomes for TNBC patients in a large cohort. Collectively, this analysis reveals the functional heterogeneity and its association with genomic evolution in TNBC, and uncovers unanticipated biological principles dictating poor outcomes in this disease.

Abstract A15: Immune-related changes in breast cancer tumor evolution

Immunotherapy is a highly promising therapeutic option in metastatic disease albeit only in a subset of patients possibly due to heterogeneity in the mechanisms by which tumors escape immune surveillance. Immune cells shape tumor evolution directly (e.g., anti-tumor immune response) and indirectly (e.g., changing the microenvironment) by selecting for cancer cells with certain properties. We hypothesized that the in situ (DCIS) to invasive ductal carcinoma (IDC) transition is a critical tumor progression step for immune escape in breast cancer that defines subsequent tumor evolution. In DCIS, cancer cells are physically separated from the stroma by the basement membrane and myoepithelial cell layer, and tumor-infiltrating leukocytes are rarely detected in direct contact with cancer cells. In contrast, in IDC, cancer cells and leukocytes are intermingled, thus, only cancer cells that can survive in this environment will play a role in disease progression. To dissect mechanisms of immune escape in breast cancer, we first analyzed the composition of leukocytes in normal breast tissues, DCIS, and IDC by polychromatic FACS. We found that DCIS and IDC contained significantly higher numbers of leukocytes, compared to normal breast, whereas in normal tissues more leukocytes were in the stromal than in the epithelial fraction. We also observed significant differences in the relative frequencies of several CD45+ cell types including increased neutrophils and decreased CD8+/CD4+ T cell ratios in tumors compared to normal stroma. Next, we analyzed the gene expression profiles of CD45+CD3+ T cells and found gene set enrichment of cytotoxic cells in DCIS including CD8+ T cells and NKT cells when compared to IDC. Conversely, we found enrichment for gene sets corresponding to regulatory T cells in IDC when compared to DCIS. Overall this suggested that DCIS had a more activated immune environment and IDC a more suppressed immune environment. We further explored this result by immunofluorescence (IF) and found fewer activated GZMB+CD8+ T cells in IDC than in DCIS, including a set of matched DCIS and locally recurrent IDC tissues. We also found that the TCR clonotype was more diverse in DCIS than in normal breast and IDCs. Interestingly we detected a few relatively frequent clones that were shared among different DCIS, one of which was previously shown to recognize a protein from the Epstein-Bar virus. To elucidate mechanisms of immune evasion in IDC, we performed IF analysis of immune checkpoint proteins PD-L1 and TIGIT and found significant differences between DCIS and IDC. TIGIT-expressing T cells were more slightly frequent in DCIS than in IDC. PD-L1 expression was higher in the epithelial cancer cells in triple negative IDC compared to DCIS, and amplification of CD274 (encoding PD-L1) was only detected in triple negative IDCs. Given the close proximity of ERBB2 (encoding HER2) to a cluster of genes encoding several chemokines, we analyzed the HER2+ samples from the TCGA. We found that co-amplification of 17q12 chemokine cluster (CC) with ERBB2 was enriched in HER+ER+ luminal-like tumors but not in the HER2+ER breast tumors. We also found higher expression of both T cell activation and inhibition-related genes in tumors that lack CC gain. Also by assessing tumor samples by multicolor FISH and IF, we determined that there is an inverse correlation between CC amplification and activation of CD8+ T cells. Overall our results show co-evolution of cancer cells and the immune microenvironment during tumor progression.

Citation Format: Carlos R. Gil del Alcazar, SungJin Huh, Muhammad B. Ekram, Anne Trinh, Lin L. Liu, Francisco Beca, Zi Xiaoyuan, Misuk Kwak, Helga Bergholtz, Ying Su, Lina Ding, Lina Ding, Hege G. Russnes, Andrea L. Richardson, Kirsten Babski, Elizabeth Min Hui Kim, Charles H. McDonnell, III, Jon Wagner, Ron Rowberry, Gordon Freeman, Deborah Dillon, Therese Sorlie, Lisa M. Coussens, Judy E. Garber, Rong Fan, Kristie Bobolis, Joon Jeong, So Yeon Park, Franziska Michor, Kornelia Polyak. Immune-related changes in breast cancer tumor evolution [abstract]. In: Proceedings of the AACR Special Conference on Tumor Immunology and Immunotherapy; 2017 Oct 1-4; Boston, MA. Philadelphia (PA): AACR; Cancer Immunol Res 2018;6(9 Suppl):Abstract nr A15.

A PDGFRα-driven mouse model of glioblastoma reveals a stathmin1-mediated mechanism of sensitivity to vinblastine

Glioblastoma multiforme (GBM) is an aggressive primary brain cancer that includes focal amplification of PDGFRα and for which there are no effective therapies. Herein, we report the development of a genetically engineered mouse model of GBM based on autocrine, chronic stimulation of overexpressed PDGFRα, and the analysis of GBM signaling pathways using proteomics. We discover the tubulin-binding protein Stathmin1 (STMN1) as a PDGFRα phospho-regulated target, and that this mis-regulation confers sensitivity to vinblastine (VB) cytotoxicity. Treatment of PDGFRα-positive mouse and a patient-derived xenograft (PDX) GBMs with VB in mice prolongs survival and is dependent on STMN1. Our work reveals a previously unconsidered link between PDGFRα activity and STMN1, and highlight an STMN1-dependent cytotoxic effect of VB in GBM.

Abstract A21: Characterization of the immune environment in the in situ to invasive breast carcinoma transition

Reactivation of immune responses against cancer cells—immunotherapy—is one of the few cancer therapies that can successfully eliminate even metastatic disease in a relatively nontoxic manner. However, its success has been limited to a subset of patients. For example, in breast cancer only ~20% of triple-negative breast cancer (TNBC) patients benefit from anti-PDL1 therapy. One reason for this limited success can be that different tumors evade the immune system via different mechanisms, which suggests that they may respond to different types of immunotherapies. Epithelial cancer cells in ductal carcinoma in situ (DCIS) are physically separated from the tumor-infiltrating leukocytes by the myoepithelial cell layer and the basement membrane, whereas in invasive ductal carcinoma (IDC), the epithelial cancer cells are intermingled with leukocytes. Therefore, we hypothesize that the DCIS to IDC transition is a key step in tumor progression as cancer cells are under different selection pressures, and only those that can evade the immune system can continue tumor progression, hence shaping subsequent tumor evolution. To dissect the role of leukocytes in the DCIS to IDC transition, we began by analyzing the composition and molecular profiles of leukocytes, with special emphasis on T cells, in normal breast tissues, DCIS, and IDC. We found that the relative frequency of leukocytes increases during tumor progression but the CD8/CD4 T cell ratio decreases. In addition, the gene expression profile of CD45+CD3+ T cells is different in DCIS compared to those isolated from normal breast tissue and IDCs. We found that gene set signatures corresponding to CD8+ T cells and NKT cells were enriched over regulatory T-cell signatures in DCIS compared to IDC. This result suggested that DCIS had a more activated immune environment compared to IDC. We further examined T-cell activation by immunofluorescence (IF) analysis and found a higher percentage of activated GZMB+CD8+ T cells in DCIS compared to IDC including a set of matched DCIS and locally recurrent IDC. We also found that the TCR clonotype was more diverse in DCIS than in IDCs. Interestingly, we detected a few relatively frequent clones that were shared among different DCIS patients, one of which was previously shown to recognize a protein from the Epstein-Bar virus. In order to dissect mechanisms of immune evasion in IDC, we analyzed immune checkpoint genes and proteins by FISH and IF. We found that TIGIT+ T cells were slightly more frequent in DCIS than in IDC. In triple-negative IDC, there was high expression of PD-L1 in epithelial cells and in 3/10 cases amplification of CD274 (encoding PD-L1), whereas DCIS had lower expression of PD-L1 and no amplification of CD274. To further elucidate mechanisms of immune evasion, we explored the significance of a cluster of genes encoding several chemokines that are located in close proximity of ERBB2 (encoding HER2). When analyzing the HER2+ samples from the TCGA, we found that coamplification of the 17q12 chemokine cluster (CC) with ERBB2 was enriched in HER2+ER+ luminal-like tumors, whereas there was either no gain or loss of the cluster in the HER2+ER breast tumors. Interestingly, we found higher expression of both T-cell activation and exhaustion-related genes in tumors that lack CC gain. Moreover, when assessing a cohort of HER2+ samples by multicolor FISH and IF, we found an inverse correlation between CC amplification and activation of CD8+ T cells. There was no correlation between CC amplification and recruitment of macrophages or myeloid-derived suppressor cells. Overall our results show coevolution of cancer cells and the immune microenvironment during tumor progression.

Citation Format: Carlos R. Gil del Alcazar, SungJin Huh, Muhammad B. Ekram, Anne Trinh, Lin L. Liu, Francisco Beca, Zi Xiaoyuan, Misuk Kwak, Helga Bergholtz, Ying Su, Lina Ding, Hege G. Russnes, Andrea L. Richardson, Kirsten Babski, Elizabeth Min Hui Kim, Charles H. McDonnell, III, Jon Wagner, Ron Rowberry, Gordon J. Freeman, Deborah Dillon, Therese Sorlie, Lisa M. Coussens, Judy E. Garber, Rong Fan, Kristie Bobolis, D. Craig Allred, Joon Jeong, So Yeon Park, Franziska Michor, Kornelia Polyak. Characterization of the immune environment in the in situ to invasive breast carcinoma transition [abstract]. In: Proceedings of the AACR Special Conference: Advances in Breast Cancer Research; 2017 Oct 7-10; Hollywood, CA. Philadelphia (PA): AACR; Mol Cancer Res 2018;16(8_Suppl):Abstract nr A21.

Abstract PR02: Minor clones can drive polyclonal metastasis by affecting immune microenvironment

Most breast tumors display a high degree of intratumor heterogeneity, with many distinct subpopulations of cancer cells present. Elevated diversity within a tumor increases the chance for cellular adaptation, as individual clones may react differently to changes in the tumor microenvironment. Thus, treatment of heterogeneous tumors my lead to selection of a resistant clone, its expansion, and tumor progression. However, the fitness of cancer cells depends not only on their intrinsic properties, but could also be affected through interactions between different subpopulations. These interactions could be the reason for maintenance of minor clones along the major population. Therefore, intratumor heterogeneity may have functional relevance in tumor progression and colonization of metastatic sites.

To emulate clonal interactions, we used the previously developed polyclonal breast cancer model of MDA-MB-468 cell line expressing soluble factors, IL-11 and FIGF. The IL-11 and FIGF clones, when present as minor population, support the growth of other clones in vivo. Moreover, polyclonal tumors with minor driving clone population are highly metastatic. Thus, we hypothesized that clonal interactions could not only drive tumor growth, but could also play an important role in metastasis.

We have found that polyclonal tumors lead to polyclonal metastases, composed of mixture of neutral and driver clones. To investigate the mechanisms of clonal interactions driving polyclonal metastasis, we performed RNA profiling of subpopulations and stroma from polyclonal tumors. Our results suggest that this cooperation is indirect and that driver clones promote metastasis by altering the tumor microenvironment. We have also found that minor driver clones affect the immune cells within primary tumor, circulating blood, and metastatic site. These systemic changes significantly influence the metastatic progression. We are currently testing whether a treatment targeting this indirect clonal interaction mechanism could prevent polyclonal metastatic spread.

Our study shows that the interaction between minor clones and other cancer cells could drive tumor growth and metastasis. Moreover, our results suggest that clonal cooperation in metastatic progression may be indirect and involve modulation of immune microenvironment of the primary tumor and distant organs.

This abstract is also being presented as Poster B03.

Citation Format: Michalina Janiszewska, Doris P. Tabassum, Andriy Marusyk, Muhammad B. Ekram, Zafira Castaño, Natalie L. Kingston, Yuanbo Qin, Tyler Laszewski, Minsuk Kwak, Kimiyo Nakamura, Rong Fan, Franziska Michor, Sandra S. McAllister, Kornelia Polyak. Minor clones can drive polyclonal metastasis by affecting immune microenvironment [abstract]. In: Proceedings of the AACR Special Conference: Advances in Breast Cancer Research; 2017 Oct 7-10; Hollywood, CA. Philadelphia (PA): AACR; Mol Cancer Res 2018;16(8_Suppl):Abstract nr PR02.

SIApopr: a computational method to simulate evolutionary branching trees for analysis of tumor clonal evolution

Summary

SIApopr (Simulating Infinite-Allele populations) is an R package to simulate time-homogeneous and inhomogeneous stochastic branching processes under a very flexible set of assumptions using the speed of C ++. The software simulates clonal evolution with the emergence of driver and passenger mutations under the infinite-allele assumption. The software is an application of the Gillespie Stochastic Simulation Algorithm expanded to a large number of cell types and scenarios, with the intention of allowing users to easily modify existing models or create their own.

Availability and Implementation

SIApopr is available as an R library on Github ( https://github.com/olliemcdonald/siapopr ).

Supplementary information

Supplementary data are available at Bioinformatics online.

Contact

michor@jimmy.harvard.edu.

Increased HOXA5 expression provides a selective advantage for gain of whole chromosome 7 in IDH wild-type glioblastoma

Cimino et al. developed an unbiased bioinformatics approach that identified homeobox A5 (HOXA5) as a gene whose expression correlated with gain of chromosome 7 and a more aggressive phenotype of the resulting glioma. HOXA5 overexpression promoted cellular proliferation and potentiated radioresistance.

Glioblastoma is the most frequently occurring and invariably fatal primary brain tumor in adults. The vast majority of glioblastomas is characterized by chromosomal copy number alterations, including gain of whole chromosome 7 and loss of whole chromosome 10. Gain of whole chromosome 7 is an early event in gliomagenesis that occurs in proneural-like precursor cells, which give rise to all isocitrate dehydrogenase (IDH) wild-type glioblastoma transcriptional subtypes. Platelet-derived growth factor A (PDGFA) is one gene on chromosome 7 known to drive gliomagenesis, but, given its location near the end of 7p, there are likely several other genes located along chromosome 7 that select for its increased whole-chromosome copy number within glioblastoma cells. To identify other potential genes that could select for gain of whole chromosome 7, we developed an unbiased bioinformatics approach that identified homeobox A5 (HOXA5) as a gene whose expression correlated with gain of chromosome 7 and a more aggressive phenotype of the resulting glioma. High expression of HOXA5 in glioblastoma was associated with a proneural gene expression pattern and decreased overall survival in both human proneural and PDGF-driven mouse glioblastoma. Furthermore, HOXA5 overexpression promoted cellular proliferation and potentiated radioresistance. We also found enrichment of HOXA5 expression in recurrent human and mouse glioblastoma at first recurrence after radiotherapy. Overall, this study implicates HOXA5 as a chromosome 7-associated gene-level locus that promotes selection for gain of whole chromosome 7 and an aggressive phenotype in glioblastoma.

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.

Bisulfite-independent analysis of CpG island methylation enables genome-scale stratification of single cells

Conventional DNA bisulfite sequencing has been extended to single cell level, but the coverage consistency is insufficient for parallel comparison. Here we report a novel method for genome-wide CpG island (CGI) methylation sequencing for single cells (scCGI-seq), combining methylation-sensitive restriction enzyme digestion and multiple displacement amplification for selective detection of methylated CGIs. We applied this method to analyzing single cells from two types of hematopoietic cells, K562 and GM12878 and small populations of fibroblasts and induced pluripotent stem cells. The method detected 21 798 CGIs (76% of all CGIs) per cell, and the number of CGIs consistently detected from all 16 profiled single cells was 20 864 (72.7%), with 12 961 promoters covered. This coverage represents a substantial improvement over results obtained using single cell reduced representation bisulfite sequencing, with a 66-fold increase in the fraction of consistently profiled CGIs across individual cells. Single cells of the same type were more similar to each other than to other types, but also displayed epigenetic heterogeneity. The method was further validated by comparing the CpG methylation pattern, methylation profile of CGIs/promoters and repeat regions and 41 classes of known regulatory markers to the ENCODE data. Although not every minor methylation differences between cells are detectable, scCGI-seq provides a solid tool for unsupervised stratification of a heterogeneous cell population.

Estimating mono- and bi-phasic regression parameters using a mixture piecewise linear Bayesian hierarchical model

The dynamics of tumor burden, secreted proteins or other biomarkers over time, is often used to evaluate the effectiveness of therapy and to predict outcomes for patients. Many methods have been proposed to investigate longitudinal trends to better characterize patients and to understand disease progression. However, most approaches assume a homogeneous patient population and a uniform response trajectory over time and across patients. Here, we present a mixture piecewise linear Bayesian hierarchical model, which takes into account both population heterogeneity and nonlinear relationships between biomarkers and time. Simulation results show that our method was able to classify subjects according to their patterns of treatment response with greater than 80% accuracy in the three scenarios tested. We then applied our model to a large randomized controlled phase III clinical trial of multiple myeloma patients. Analysis results suggest that the longitudinal tumor burden trajectories in multiple myeloma patients are heterogeneous and nonlinear, even among patients assigned to the same treatment cohort. In addition, between cohorts, there are distinct differences in terms of the regression parameters and the distributions among categories in the mixture. Those results imply that longitudinal data from clinical trials may harbor unobserved subgroups and nonlinear relationships; accounting for both may be important for analyzing longitudinal data.

Abstract LB-280: The landscape of somatic microsatellite indels across cancer: detection and identification of driver events

Microsatellites (MSs) are tracts of variable-length repeats of short DNA motifs that are abundant in the human genome and exhibit high rates of mutations in the form of insertions or deletions of the repeated motif (MS indels). Despite their prevalence, the contribution of somatic MS indels to cancer is largely unexplored due to difficulties in detecting them and assessing their significance. Here, we present a comprehensive analysis of MS indels across 20 tumor types. We characterize the overall MS indel landscape and detect genes with candidate driver MS indel events. We present two novel tools: MSMuTect for accurate detection of somatic MS indels and MSMutSig for identifying candidate cancer genes containing events at higher frequency than expected by chance. We observe high variability of the frequency of MS indels across tumors and demonstrate that the number and pattern of MS indels can accurately distinguish microsatellite stable (MSS) tumors from tumors with microsatellite instability (MSI). Applying MSMutSig across 6,788 tumors from 20 different tumor types identified 7 genes with significant MS indel hotspots: ACVR2A, RNF43, DOCK3, MSH3, ESRP1, PRDM2 and JAK1. In the four genes that have been previously implicated in cancer (ACVR2A, RNF43, JAK1 and MSH3), we identified previously unreported MS indels events. Three of the genes with significant loci - DOCK3, PRDM2 and ESRP1- had not been previously listed as cancer genes. MS indels in DOCK3, a negative regulator of the WNT pathway, were mutually exclusive with mutations in CTNNB1. MS indels in ESRP1, an RNA processing gene, correlated with alternative splicing of FGFR2, an event associated with the epithelial-to-mesenchymal transition. Overall, our comprehensive analysis of somatic MS indels across cancer highlights their importance, particularly in

MSI tumors, significantly contributes to the ongoing global efforts to detect cancer genes, and may improve classification of patients into clinically-relevant subgroups.

Citation Format: Yosef E. Maruvka, Kent W. Mouw, Rosa Karlic, Rasanna Parasuraman, Atanas Kamburov, Paz Polak, Nicholas J. Haradhvala, Julian M. Hess, Esther Rheinbay, Yehuda Brody, Lior Z. Braunstein, Alan D’Andrea, Michael S. Lawrence, Adam Bass, Andre Bernards, Franziska Michor, Gad Getz. The landscape of somatic microsatellite indels across cancer: detection and identification of driver events [abstract]. In: Proceedings of the American Association for Cancer Research Annual Meeting 2017; 2017 Apr 1-5; Washington, DC. Philadelphia (PA): AACR; Cancer Res 2017;77(13 Suppl):Abstract nr LB-280. doi:10.1158/1538-7445.AM2017-LB-280

Pharmacokinetics and Drug Interactions Determine Optimum Combination Strategies in Computational Models of Cancer Evolution

The identification of optimal drug administration schedules to battle the emergence of resistance is a major challenge in cancer research. The existence of a multitude of resistance mechanisms necessitates administering drugs in combination, significantly complicating the endeavor of predicting the evolutionary dynamics of cancers and optimal intervention strategies. A thorough understanding of the important determinants of cancer evolution under combination therapies is therefore crucial for correctly predicting treatment outcomes. Here we developed the first computational strategy to explore pharmacokinetic and drug interaction effects in evolutionary models of cancer progression, a crucial step towards making clinically relevant predictions. We found that incorporating these phenomena into our multiscale stochastic modeling framework significantly changes the optimum drug administration schedules identified, often predicting nonintuitive strategies for combination therapies. We applied our approach to an ongoing phase Ib clinical trial (TATTON) administering AZD9291 and selumetinib to EGFR-mutant lung cancer patients. Our results suggest that the schedules used in the three trial arms have almost identical efficacies, but slight modifications in the dosing frequencies of the two drugs can significantly increase tumor cell eradication. Interestingly, we also predict that drug concentrations lower than the MTD are as efficacious, suggesting that lowering the total amount of drug administered could lower toxicities while not compromising on the effectiveness of the drugs. Our approach highlights the fact that quantitative knowledge of pharmacokinetic, drug interaction, and evolutionary processes is essential for identifying best intervention strategies. Our method is applicable to diverse cancer and treatment types and allows for a rational design of clinical trials. Cancer Res; 77(14); 3908-21. ©2017 AACR.

Mathematical Modeling Links Pregnancy-Associated Changes and Breast Cancer Risk

Recent debate has concentrated on the contribution of bad luck to cancer development. The tight correlation between the number of tissue-specific stem cell divisions and cancer risk of the same tissue suggests that bad luck has an important role to play in tumor development, but the full extent of this contribution remains an open question. Improved understanding of the interplay between extrinsic and intrinsic factors at the molecular level is one promising route to identifying the limits on extrinsic control of tumor initiation, which is highly relevant to cancer prevention. Here, we use a simple mathematical model to show that recent data on the variation in numbers of breast epithelial cells with progenitor features due to pregnancy are sufficient to explain the known protective effect of full-term pregnancy in early adulthood for estrogen receptor-positive (ER⁺) breast cancer later in life. Our work provides a mechanism for this previously ill-understood effect and illuminates the complex influence of extrinsic factors at the molecular level in breast cancer. These findings represent an important contribution to the ongoing research into the role of bad luck in human tumorigenesis. Cancer Res; 77(11); 2800-9. ©2017 AACR.

Distance in cancer gene expression from stem cells predicts patient survival

The degree of histologic cellular differentiation of a cancer has been associated with prognosis but is subjectively assessed. We hypothesized that information about tumor differentiation of individual cancers could be derived objectively from cancer gene expression data, and would allow creation of a cancer phylogenetic framework that would correlate with clinical, histologic and molecular characteristics of the cancers, as well as predict prognosis. Here we utilized mRNA expression data from 4,413 patient samples with 7 diverse cancer histologies to explore the utility of ordering samples by their distance in gene expression from that of stem cells. A differentiation baseline was obtained by including expression data of human embryonic stem cells (hESC) and human mesenchymal stem cells (hMSC) for solid tumors, and of hESC and CD34+ cells for liquid tumors. We found that the correlation distance (the degree of similarity) between the gene expression profile of a tumor sample and that of stem cells orients cancers in a clinically coherent fashion. For all histologies analyzed (including carcinomas, sarcomas, and hematologic malignancies), patients with cancers with gene expression patterns most similar to that of stem cells had poorer overall survival. We also found that the genes in all undifferentiated cancers of diverse histologies that were most differentially expressed were associated with up-regulation of specific oncogenes and down-regulation of specific tumor suppressor genes. Thus, a stem cell-oriented phylogeny of cancers allows for the derivation of a novel cancer gene expression signature found in all undifferentiated forms of diverse cancer histologies, that is competitive in predicting overall survival in cancer patients compared to previously published prediction models, and is coherent in that gene expression was associated with up-regulation of specific oncogenes and down-regulation of specific tumor suppressor genes associated with regulation of the multicellular state.

Phase 1 study of twice weekly pulse dose and daily low-dose erlotinib as initial treatment for patients with EGFR-mutant lung cancers

Background

Patients with EGFR-mutant lung cancers treated with EGFR tyrosine kinase inhibitors (TKIs) develop clinical resistance, most commonly with acquisition of EGFR T790M. Evolutionary modeling suggests that a schedule of twice weekly pulse and daily low-dose erlotinib may delay emergence of EGFR T790M. Pulse dose erlotinib has superior central nervous system (CNS) penetration and may result in superior CNS disease control.

Methods

We evaluated toxicity, pharmacokinetics, and efficacy of twice weekly pulse and daily low-dose erlotinib. We assessed six escalating pulse doses of erlotinib.

Results

We enrolled 34 patients; 11 patients (32%) had brain metastases at study entry. We observed 3 dose-limiting toxicities in dose escalation: transaminitis, mucositis, and rash. The MTD was erlotinib 1200 mg days 1-2 and 50 mg days 3-7 weekly. The most frequent toxicities (any grade) were rash, diarrhea, nausea, fatigue, and mucositis. 1 complete and 24 partial responses were observed (74%, 95% CI 60-84%). Median progression-free survival was 9.9 months (95% CI 5.8-15.4 months). No patient had progression of an untreated CNS metastasis or developed a new CNS lesion while on study (0%, 95% CI 0-13%). Of the 18 patients with biopsies at progression, EGFR T790M was identified in 78% (95% CI 54-91%).

Conclusion

This is the first clinical implementation of an anti-cancer TKI regimen combining pulse and daily low-dose administration. This evolutionary modeling-based dosing schedule was well-tolerated but did not improve progression-free survival or prevent emergence of EGFR T790M, likely due to insufficient peak serum concentrations of erlotinib. This dosing schedule prevented progression of untreated or any new central nervous system metastases in all patients.

Proceedings of the third international molecular pathological epidemiology (MPE) meeting

Molecular pathological epidemiology (MPE) is a transdisciplinary and relatively new scientific discipline that integrates theory, methods, and resources from epidemiology, pathology, biostatistics, bioinformatics, and computational biology. The underlying objective of MPE research is to better understand the etiology and progression of complex and heterogeneous human diseases with the goal of informing prevention and treatment efforts in population health and clinical medicine. Although MPE research has been commonly applied to investigating breast, lung, and colorectal cancers, its methodology can be used to study most diseases. Recent successes in MPE studies include: (1) the development of new statistical methods to address etiologic heterogeneity; (2) the enhancement of causal inference; (3) the identification of previously unknown exposure-subtype disease associations; and (4) better understanding of the role of lifestyle/behavioral factors on modifying prognosis according to disease subtype. Central challenges to MPE include the relative lack of transdisciplinary experts, educational programs, and forums to discuss issues related to the advancement of the field. To address these challenges, highlight recent successes in the field, and identify new opportunities, a series of MPE meetings have been held at the Dana-Farber Cancer Institute in Boston, MA. Herein, we share the proceedings of the Third International MPE Meeting, held in May 2016 and attended by 150 scientists from 17 countries. Special topics included integration of MPE with immunology and health disparity research. This meeting series will continue to provide an impetus to foster further transdisciplinary integration of divergent scientific fields.

Clonal Hematopoiesis Associated With Adverse Outcomes After Autologous Stem-Cell Transplantation for Lymphoma

Purpose Clonal hematopoiesis of indeterminate potential (CHIP) is an age-related condition characterized by somatic mutations in the blood of otherwise healthy adults. We hypothesized that in patients undergoing autologous stem-cell transplantation (ASCT) for lymphoma, CHIP at the time of ASCT would be associated with an increased risk of myelodysplastic syndrome and acute myeloid leukemia, collectively termed therapy-related myeloid neoplasm (TMN), and other adverse outcomes. Methods We performed whole-exome sequencing on pre- and post-ASCT samples from 12 patients who developed TMN after autologous transplantation for Hodgkin lymphoma or non-Hodgkin lymphoma and targeted sequencing on cryopreserved aliquots of autologous stem-cell products from 401 patients who underwent ASCT for non-Hodgkin lymphoma between 2003 and 2010. We assessed the effect of CHIP at the time of ASCT on subsequent outcomes, including TMN, cause-specific mortality, and overall survival. Results For six of 12 patients in the exome sequencing cohort, mutations found in the TMN specimen were also detectable in the pre-ASCT specimen. In the targeted sequencing cohort, 120 patients (29.9%) had CHIP at the time of ASCT, which was associated with an increased rate of TMN (10-year cumulative incidence, 14.1% v 4.3% for those with and without CHIP, respectively; P = .002). Patients with CHIP had significantly inferior overall survival compared with those without CHIP (10-year overall survival, 30.4% v 60.9%, respectively; P < .001), including increased risk of death from TMN and cardiovascular disease. Conclusion In patients undergoing ASCT for lymphoma, CHIP at the time of transplantation is associated with inferior survival and increased risk of TMN.

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.

A computational strategy to adjust for copy number in tumor Hi-C data

MOTIVATION

The Hi-C technology was designed to decode the three-dimensional conformation of the genome. Despite progress towards more and more accurate contact maps, several systematic biases have been demonstrated to affect the resulting data matrix. Here we report a new source of bias that can arise in tumor Hi-C data, which is related to the copy number of genomic DNA. To address this bias, we designed a chromosome-adjusted iterative correction method called caICB. Our caICB correction method leads to significant improvements when compared with the original iterative correction in terms of eliminating copy number bias.

AVAILABILITY AND IMPLEMENTATION

The method is available at https://bitbucket.org/mthjwu/hicapp CONTACT: michor@jimmy.harvard.eduSupplementary information: Supplementary data are available at Bioinformatics online.

Mathematical modeling of erythrocyte chimerism informs genetic intervention strategies for sickle cell disease

Recent advances in gene therapy and genome-engineering technologies offer the opportunity to correct sickle cell disease (SCD), a heritable disorder caused by a point mutation in the β-globin gene. The developmental switch from fetal γ-globin to adult β-globin is governed in part by the transcription factor (TF) BCL11A. This TF has been proposed as a therapeutic target for reactivation of γ-globin and concomitant reduction of β-sickle globin. In this and other approaches, genetic alteration of a portion of the hematopoietic stem cell (HSC) compartment leads to a mixture of sickling and corrected red blood cells (RBCs) in periphery. To reverse the sickling phenotype, a certain proportion of corrected RBCs is necessary; the degree of HSC alteration required to achieve a desired fraction of corrected RBCs remains unknown. To address this issue, we developed a mathematical model describing aging and survival of sickle-susceptible and normal RBCs; the former can have a selective survival advantage leading to their overrepresentation. We identified the level of bone marrow chimerism required for successful stem cell-based gene therapies in SCD. Our findings were further informed using an experimental mouse model, where we transplanted mixtures of Berkeley SCD and normal murine bone marrow cells to establish chimeric grafts in murine hosts. Our integrative theoretical and experimental approach identifies the target frequency of HSC alterations required for effective treatment of sickling syndromes in humans. Our work replaces episodic observations of such target frequencies with a mathematical modeling framework that covers a large and continuous spectrum of chimerism conditions. Am. J. Hematol. 91:931-937, 2016. © 2016 Wiley Periodicals, Inc.

Punctuated copy number evolution and clonal stasis in triple-negative breast cancer

Aneuploidy is a hallmark of breast cancer; however, our knowledge of how these complex genomic rearrangements evolve during tumorigenesis is limited. In this study we developed a highly multiplexed single-nucleus-sequencing method to investigate copy number evolution in triple-negative breast cancer patients. We sequenced 1000 single cells from 12 patients and identified 1–3 major clonal subpopulations in each tumor that shared a common evolutionary lineage. We also identified a minor subpopulation of non-clonal cells that were classified as: 1) metastable, 2) pseudo-diploid, or 3) chromazemic. Phylogenetic analysis and mathematical modeling suggest that these data are unlikely to be explained by the gradual accumulation of copy number events over time. In contrast, our data challenge the paradigm of gradual evolution, showing that the majority of copy number aberrations are acquired at the earliest stages of tumor evolution, in short punctuated bursts, followed by stable clonal expansions that form the tumor mass.

ABCG2 regulates self-renewal and stem cell marker expression but not tumorigenicity or radiation resistance of glioma cells

Glioma cells with stem cell traits are thought to be responsible for tumor maintenance and therapeutic failure. Such cells can be enriched based on their inherent drug efflux capability mediated by the ABC transporter ABCG2 using the side population assay, and their characteristics include increased self-renewal, high stem cell marker expression and high tumorigenic capacity in vivo. Here, we show that ABCG2 can actively drive expression of stem cell markers and self-renewal in glioma cells. Stem cell markers and self-renewal was enriched in cells with high ABCG2 activity, and could be specifically inhibited by pharmacological and genetic ABCG2 inhibition. Importantly, despite regulating these key characteristics of stem-like tumor cells, ABCG2 activity did not affect radiation resistance or tumorigenicity in vivo. ABCG2 effects were Notch-independent and mediated by diverse mechanisms including the transcription factor Mef. Our data demonstrate that characteristics of tumor stem cells are separable, and highlight ABCG2 as a potential driver of glioma stemness.

Improving Cancer Treatment via Mathematical Modeling: Surmounting the Challenges Is Worth the Effort

Drug delivery schedules are key factors in the efficacy of cancer therapies, and mathematical modeling of population dynamics and treatment responses can be applied to identify better drug administration regimes as well as provide mechanistic insights. To capitalize on the promise of this approach, the cancer field must meet the challenges of moving this type of work into clinics.

Myeloma Cell Dynamics in Response to Treatment Supports a Model of Hierarchical Differentiation and Clonal Evolution

PURPOSE

Since the pioneering work of Salmon and Durie, quantitative measures of tumor burden in multiple myeloma have been used to make clinical predictions and model tumor growth. However, such quantitative analyses have not yet been performed on large datasets from trials using modern chemotherapy regimens.

EXPERIMENTAL DESIGN

We analyzed a large set of tumor response data from three randomized controlled trials of bortezomib-based chemotherapy regimens (total sample size n = 1,469 patients) to establish and validate a novel mathematical model of multiple myeloma cell dynamics.

RESULTS

Treatment dynamics in newly diagnosed patients were most consistent with a model postulating two tumor cell subpopulations, "progenitor cells" and "differentiated cells." Differential treatment responses were observed with significant tumoricidal effects on differentiated cells and less clear effects on progenitor cells. We validated this model using a second trial of newly diagnosed patients and a third trial of refractory patients. When applying our model to data of relapsed patients, we found that a hybrid model incorporating both a differentiation hierarchy and clonal evolution best explains the response patterns.

CONCLUSIONS

The clinical data, together with mathematical modeling, suggest that bortezomib-based therapy exerts a selection pressure on myeloma cells that can shape the disease phenotype, thereby generating further inter-patient variability. This model may be a useful tool for improving our understanding of disease biology and the response to chemotherapy regimens. Clin Cancer Res; 22(16); 4206-14. ©2016 AACR.

Abstract PR05: The effect of chemotherapy on HER2+ breast cancer heterogeneity measured by STAR-FISH: Detection of PIK3CA mutation and HER2 amplification at single-cell level in situ

Current therapies in HER2-positive breast cancer are effective in only a subset of cases and part of the resistance is attributed to single nucleotide mutation H1047R in PIK3CA. Conventional PIK3CA mutation detection methods require isolation of DNA from the tumor bulk, which requires relatively large amount of tissue and may not detect mutations in rare cancer cells.

We developed a novel method, Specific-To-Allele PCR-FISH (STAR-FISH), which allows for in situ detection of point mutation and gene amplification at single cell level. The assay consists of in situ PCR steps with mutation specific primers, followed by hybridization of a fluorescently labeled DNA probe homologous to PCR primer overhangs and probes for genomic regions of interest. The STAR-FISH signals present in intact formalin-fixed paraffin embedded (FFPE) samples are imaged and quantified in each individual nucleus within a tissue, with false discovery rate at 0.001, which facilitates identification of sub-populations of cells with different genetic makeup. The method was validated against FACS, immunofluorescence, droplet digital PCR, and MassArray; high correlation of the results was observed (R2=0.901 -0.9037, p&lt;0.001).

We applied STAR-FISH for PIK3CA hot-spot mutation and HER2 amplification to FFPE samples of HER2 positive breast tumors from 22 patients. For each case a chemotherapy naïve core needle biopsy and a post-neoadjuvant chemotherapy sample upon tumor resection were collected. STAR-FISH analysis was performed on 3-5 regions of each sample, to account for intratumor heterogeneity. Long-term patient survival data after adjuvant treatment, mostly with trastuzumab, were available for all the patients.

High-sensitivity of STAR-FISH allowed us to detect rare single cells carrying PIK3CA mutation in most of the pre-treatment samples. After adjuvant chemotherapy the frequency of these cells was significantly increased. Since the STAR-FISH signals are quantified in each individual nucleus, subpopulations of cells with PIK3CA mutation or HER2 amplification or both features can be distinguished. Based on frequencies of cells within each of these subpopulations we calculated Shannon diversity index for each pre- and post-chemotherapy sample. The index was significantly increased after treatment. However, only topologic and not overall changes in diversity predicted poor long-term survival of the patients.

In addition to analyzing the frequency of cells with PIK3CA mutation, HER2 amplification or both changes, STAR-FISH also assesses the spatial distribution of genetically distinct subtypes. We have found that cells with PIK3CA mutation, irrespective of their HER2 status, are much more dispersed within tumors after neaodjuvant chemotherapy, whereas cells with HER2 amplification and wild-type PIK3CA cluster together. These results suggest that PIK3CA mutant cells are more migratory and invasive, in agreement with prior studies of cell lines and animal models.

STAR-FISH provides a unique view into genetic intratumor heterogeneity since thousands of cells within different regions of a single tumor biopsy can be analyzed within their tissue environment. Application of this novel in situ method allowed us to detect rare cells with PIK3CA mutation, pre-existing in the majority of treatment-naïve tumors and increasing in frequency after neoadjuvant chemotherapy. Moreover, STAR-FISH data revealed the correlation of chemotherapy-induced changes in intratumor heterogeneity with long-term survival of HER2+ breast cancer patients and support the significance of tumor diversity in situ analyses.

Citation Format: Michalina Janiszewska, Lin Liu, Vanessa Almendro, Yanan Kuang, Cloud Paweletz, Britta Weigelt, Rita A. Sakr, Tari A. King, Sarat Chandarlapaty, Jorge S. Reis-Filho, Ariella B. Hanker, Carlos L. Arteaga, Park So Yeon, Franziska Michor, Kornelia Polyak. The effect of chemotherapy on HER2+ breast cancer heterogeneity measured by STAR-FISH: Detection of PIK3CA mutation and HER2 amplification at single-cell level in situ. [abstract]. In: Proceedings of the AACR Special Conference on Advances in Breast Cancer Research; Oct 17-20, 2015; Bellevue, WA. Philadelphia (PA): AACR; Mol Cancer Res 2016;14(2_Suppl):Abstract nr PR05.

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.

Abstract A21: Proteomic profiling to elucidate intratumoral heterogeneity and cancer evolution in lung cancer

Tumors often display a high degree of intratumoral heterogeneity as manifested by dynamic changes in gene expression, protein expression, and on gross examination of histology, among many other features. Clinically, this underlying heterogeneity can drive tumor evolution and progression towards a more aggressive neoplastic state and a worse prognosis for patients; therefore, identifying the diverse composition of a tumor for early risk stratification is of critical importance. To elucidate intratumoral heterogeneity and intracellular hierarchy in a novel manner, we first conducted a low-cost quantitative proteomics analysis using MALDI-TOF mass spectrometry on over 1900 samples from different histological regions of individual tumors from 35 lung cancer patients, as well as from 3 mesenchymal stem cell samples. The histologies identified were acinar, basal cells, bronchial epithelium, lepidic, complex gland, micropapillary, near tumor normal, normal alveolar, papillary, papillary lepidic, papillary mucinous, and solid. Patient-specific information including survival status, sex, age, smoking status, SUV by FDG-PET scan, tumor size, EGFR, KRAS, and ERCC1 mutation status, among other variables was obtained. We then compared the proteomes derived from each tumor to the stem cell proteomes, and using computational strategies, mapped the distance of each histological sample from the mesenchymal stem cell state; using clustering techniques, we organized the major histological subtypes into a phylogenetic tree from stem cells to normal lung. We hypothesized that by applying and improving upon map of tumor evolution based on the distance of each individual histological sample from a stem cell state. Apart from liquid tumors, there have thus far been limited studies on the prognostic significance of different subclones in solid tumors, and therefore we treated each histological sample as a subclone within each patient. We also aimed to identify survival-associated subclones and prognostic molecular signatures across combinations of subclones. Identifying these subclones may provide insight into malignant micrometastases to other organs. Using co-expression network analysis, we further pinpointed distinctive significantly dysregulated co-regulatory protein networks within each histological subtype. Based on these networks, we sought to identify important hub proteins within each histology. Ultimately, using proteomic profiling in solid tumors can be a novel approach in functionally characterizing intratumoral heterogeneity, and may allow for a more robust analysis of the diverse molecular expression of single tumor samples. Our results may help inform the field of targeted broad-scale proteomics profiling for therapeutic use.

Citation Format: Charlotte Lee, Hua-Jun Wu, Andre L. Moreira, Erin H. Seeley, Callee Walsh, Robert J. Downey, Franziska Michor. Proteomic profiling to elucidate intratumoral heterogeneity and cancer evolution in lung cancer. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A21.

Abstract A82: Prognostic relevance and genomic profile of circulating tumor cells in multiple myeloma

Introduction: Genomic sequencing of tumor cells obtained from the bone marrow (BM) of patients with multiple myeloma (MM) has demonstrated significant clonal heterogeneity. However, it could be envisioned that such clonal diversity may be even higher since the pattern of BM infiltration in MM is typically patchy. In addition, BM biopsies cannot be repeated multiple times during the course of therapy, indicating a need for less invasive methods to genomically characterize MM patients. In this study, we aimed to determine the overall applicability of performing genomic characterization of MM patients non-invasively using circulating tumor cells (CTC).

Methods: We performed CTC enumeration using multi-parameter flow cytometry (MFC) in 50 newly-diagnosed patients with symptomatic MM who were prospectively enrolled on the Spanish clinical trial PETHEMA/GEM2010MAS65 as well as 64 patients with MM with relapsed disease or in remission/on maintenance therapy seen at the Dana-Farber Cancer Institute. For sequencing studies, we obtained 8 samples of newly-diagnosed untreated patients. We sequenced BM clonal PCs and CTCs up to 200x, and germline cells up to 50x. Whole genome amplification (WGA) was performed for CTCs, and two independent libraries were sequenced up to 100x for each duplicate. Only single nucleotide variants (SNVs) shared in both parallel WGA libraries were used.

Results: Using sensitive MFC, we showed that CTCs were detectable in 40/50 (80%) newly-diagnosed MM patients, and in 71/130 (55%) of multiple sequential samples from patients with relapsed disease or in remission/on maintenance. Nineteen of the 40 newly-diagnosed cases displaying PB CTCs had relapsed (median TTP of 31 months); by contrast, only 1 of the 10 patients with undetectable CTCs has relapsed (median TTP not reached; P = .08). Afterward, increasing CTC counts in sequential PB samples from patients with relapsed disease or in remission/on maintenance therapy were associated with poor overall survival (P = .01), indicating that both the absolute numbers of CTCs and trend of CTC are predictive of outcome in MM.

After demonstrating that CTCs can be readily detected in the majority of MM patients, we then determined the mutational profile of CTCs and compared it to that of patient-paired BM clonal PCs. We identified a median of 223 and 118 SNVs in BM clonal PCs and CTCs, respectively. The concordance of somatic variants found in matched BM clonal PCs and CTCs was of 79%. Noteworthy, upon investigating specific mutations implicated in MM (eg. KRAS, NRAS, BRAF) a total of 18 non-synonymous SNVs (NS-SNVs) in 13 genes were identified in our cohort, and most of these NS-SNVs were simultaneously detected in matched BM clonal PCs and CTCs. That notwithstanding, we also identified several unique mutations present in CTC or BM clonal PCs; of those, up to 39 NS-SNV were identified as CTC specific, and 6 NS-SNVs in 4 genes (CR1, DPY19L2, TMPRSS13, HBG1) were detected in multiple patient samples. A significant concordance for the pattern of copy number variations (CNVs) between matched BM and PB tumor cells was also observed.

Conclusion: This study defines a new role for CTCs in the prognostic and molecular profiling of MM patients, and provides the rational for an integrated flow-molecular algorithm to detect CTCs in PB and identify candidate patients for noninvasive genomic characterization to predict outcomes.

Citation Format: Yuji Mishima, Bruno Paiva, Jiantao Shi, Mira Massoud, Salomon Manier, Adriana Perilla-Glen, Yosra Aljawai, Satoshi Takagi, Daisy Huynh, Daisy Huynh, Aldo Roccaro, Antonio Sacco, Diego Alignani, Maria-Victoria Mateos, Joan Blade, Juan-Jose Lahuerta, Paul Richardson, Jacob Laubach, Robert Schlossman, Kenneth Anderson, Nikhil Munshi, Felipe Prosper, Jesus San Miguel, Franziska Michor, Irene M. Ghobrial. Prognostic relevance and genomic profile of circulating tumor cells in multiple myeloma. [abstract]. In: Proceedings of the AACR-NCI-EORTC International Conference: Molecular Targets and Cancer Therapeutics; 2015 Nov 5-9; Boston, MA. Philadelphia (PA): AACR; Mol Cancer Ther 2015;14(12 Suppl 2):Abstract nr A82.

Abstract IA05: Evolution of glioblastoma subtypes

To understand the relationships between the non-GCIMP glioblastoma (GBM) subgroups, we performed mathematical modeling to predict the temporal sequence of driver events during tumorigenesis. The most common order of evolutionary events is 1) chromosome (chr) 7 gain and chr10 loss, followed by 2) CDKN2A loss and/or TP53 mutation, and 3) alterations canonical for specific subtypes. We then developed a computational methodology to identify drivers of broad copy number changes, identifying PDGFA (chr7) and PTEN (chr10) as driving initial nondisjunction events. These predictions were validated using mouse modeling, showing that PDGFA is sufficient to induce proneural-like gliomas and that additional NF1 loss converts proneural to the mesenchymal subtype. We further performed integrated genomic and epigenetic profiling of patient-derived cell lines to identify further genes driving subtype transformation as well as alterations in the three-dimensional architecture of the GBM genome during subtype changes. Our computational methodologies can be applied to study the evolutionary dynamics of other cancer types.

Abstract PR09: Extracellular stiffness cues drive spatial reorganization of the genome to globally constrain RNA abundance

The stiffness of the extracellular matrix (ECM) drives mechanosignaling that regulates tissue development and malignancy. We previously showed that a stiff ECM disrupts tissue organization and enhances malignant progression by inducing cell invasion and migration. However, the specific transcriptional and molecular events in which mechanotransduction directs these phenotypes are not well understood. To clarify this process, we used a combination of genome-scale approaches to monitor changes in gene expression and protein abundance as a function of acinar morphogenesis and tissue homeostasis in three dimensional extracellular matrix hydrogels with tunable stiffness. Elevated ECM stiffness perturbed tissue homeostasis and reverted the transcriptional phenotype of differentiated mammary acini to resemble that observed in rapidly proliferating nonpolarized mammary cell aggregates. These findings suggest that tissue tension induces cellular changes that directly reflect higher-order tissue organizational states. We found that these changes involve the spatial rearrangement of peripheral chromatin, and that the expression levels of multiple histone deacetylases increase in organized tissues concurrently with elevated nuclear heterochromatin content, an effect that is abrogated in rigid ECM conditions. We support these observations by mapping mechanoresponsive peripheral heterochromatin elements via ChIPseq, enabling us to directly identify dynamic regions containing genes whose transcriptional activity is responsive to mechanical cues. Finally, using a combination of genomic, imaging, and molecular biology techniques we demonstrated that ECM compliance and tissue organization significantly influences global RNA abundance. Notably, this model presents formidable conceptual and practical challenges for the interpretation of genomic data. Collectively, this work indicates that tissue organization is critically dependent on the cellular mechanical environment, which qualitatively and quantitatively shapes the epigenetic and transcriptional landscape by mechanisms that have not yet been elucidated.

Note: This abstract was not presented at the conference.

Citation Format: Russell Bainer, Yoshihiro Yui, Shannon Mumenthaler, Parag Mallick, Lin Liu, Hua-Jun Wu, Ondrej Podlaha, Franziska Michor, Jan Liphardt, Jonathan Licht, Valerie Weaver. Extracellular stiffness cues drive spatial reorganization of the genome to globally constrain RNA abundance. [abstract]. In: Proceedings of the AACR Special Conference on Computational and Systems Biology of Cancer; Feb 8-11 2015; San Francisco, CA. Philadelphia (PA): AACR; Cancer Res 2015;75(22 Suppl 2):Abstract nr PR09.

In situ single-cell analysis identifies heterogeneity for PIK3CA mutation and HER2 amplification in HER2-positive breast cancer

Detection of minor, genetically distinct subpopulations within tumors is a key challenge in cancer genomics. Here we report STAR-FISH (specific-to-allele PCR-FISH), a novel method for the combined detection of single-nucleotide and copy number alterations in single cells in intact archived tissues. Using this method, we assessed the clinical impact of changes in the frequency and topology of PIK3CA mutation and HER2 (ERBB2) amplification within HER2-positive breast cancer during neoadjuvant therapy. We found that these two genetic events are not always present in the same cells. Chemotherapy selects for PIK3CA-mutant cells, a minor subpopulation in nearly all treatment-naive samples, and modulates genetic diversity within tumors. Treatment-associated changes in the spatial distribution of cellular genetic diversity correlated with poor long-term outcome following adjuvant therapy with trastuzumab. Our findings support the use of in situ single cell-based methods in cancer genomics and imply that chemotherapy before HER2-targeted therapy may promote treatment resistance.

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.

Evolutionary dynamic models have been intensively studied to elucidate the process of tumorigenesis. One key aspect of studying tumorigenesis is to distinguish the “driver” mutations providing a fitness advantage to cancer cells against neutral “passenger” or “hitchhiking” mutations. Many statistical models to address this question have been developed. Evolutionary models, however, add another layer of complexity by taking into account the process of mutation accumulation and selection within the tissue. Here we present a novel approach combining both statistical and evolutionary thinking to identify driver mutations in cancer genomes using cross-sectional mutation data. Our method considers the process of mutation accumulation and selection before and during colorectal cancer initiation. This work demonstrates the importance of using evolutionary population dynamic models to study driver events of tumorigenesis.