Franziska Michor

Dynamics of chronic myeloid leukaemia

The clinical success of the ABL tyrosine kinase inhibitor imatinib in chronic myeloid leukaemia (CML) serves as a model for molecularly targeted therapy of cancer, but at least two critical questions remain. Can imatinib eradicate leukaemic stem cells? What are the dynamics of relapse due to imatinib resistance, which is caused by mutations in the ABL kinase domain? The precise understanding of how imatinib exerts its therapeutic effect in CML and the ability to measure disease burden by quantitative polymerase chain reaction provide an opportunity to develop a mathematical approach. We find that a four-compartment model, based on the known biology of haematopoietic differentiation, can explain the kinetics of the molecular response to imatinib in a 169-patient data set. Successful therapy leads to a biphasic exponential decline of leukaemic cells. The first slope of 0.05 per day represents the turnover rate of differentiated leukaemic cells, while the second slope of 0.008 per day represents the turnover rate of leukaemic progenitors. The model suggests that imatinib is a potent inhibitor of the production of differentiated leukaemic cells, but does not deplete leukaemic stem cells. We calculate the probability of developing imatinib resistance mutations and estimate the time until detection of resistance. Our model provides the first quantitative insights into the in vivo kinetics of a human cancer.

Clonal Evolution in Breast Cancer Revealed by Single Nucleus Genome Sequencing

Sequencing studies of breast tumour cohorts have identified many prevalent mutations, but provide limited insight into the genomic diversity within tumours. Here we developed a whole-genome and exome single cell sequencing approach called nuc-seq that uses G2/M nuclei to achieve 91% mean coverage breadth. We applied this method to sequence single normal and tumour nuclei from an oestrogen-receptor-positive (ER+) breast cancer and a triple-negative ductal carcinoma. In parallel, we performed single nuclei copy number profiling. Our data show that aneuploid rearrangements occurred early in tumour evolution and remained highly stable as the tumour masses clonally expanded. In contrast, point mutations evolved gradually, generating extensive clonal diversity. Using targeted single-molecule sequencing, many of the diverse mutations were shown to occur at low frequencies (<10%) in the tumour mass. Using mathematical modelling we found that the triple-negative tumour cells had an increased mutation rate (13.3×), whereas the ER+ tumour cells did not. These findings have important implications for the diagnosis, therapeutic treatment and evolution of chemoresistance in breast cancer.

Optimization of dosing for EGFR-mutant non-small cell lung cancer with evolutionary cancer modeling.

Predictive models of EGFR-mutant tumor behavior point to alternative drug dosing strategies to prevent and treat acquired resistance. Harnessing Evolution to Improve Lung Cancer Therapy Like any organism under severe evolutionary pressure, a few select members of a cancer cell population acquire molecular changes that strengthen the clan’s chances of survival. Therapeutic drugs exert a powerful selective force on characteristically compliant cancer cells, as the common recurrence of drug-resistant cancers testifies. To learn how to better fight the potent forces of evolution, Chmielecki et al. examined the behavior of non–small cell lung cancer (NSCLC) before and after the cells acquire resistance to targeted therapy, which inevitably they do. The growth characteristics of these cells were consistent with patient tumor behavior, enabling construction of a mathematical model that predicted alternative therapeutic strategies to delay the development of drug-resistant cancer cells. The authors made paired isogenic cell lines that were sensitive and resistant to afatinib and erlotinib—drugs used to treat NSCLC that are directed against the epidermal growth factor receptor (EGFR) tyrosine kinase, which is activated in a subset of NSCLCs. To the authors’ surprise, the drug-resistant cells grew more slowly than their sensitive counterparts, and resistance was not maintained in the absence of selection. Multiple clinical observations corroborated these findings. For example, patients with resistant tumors showed a slow course of disease progression, and patients with acquired resistance have re-responded to tyrosine kinase inhibitor (TKI) therapy after a drug holiday. The authors then constructed an evolutionary mathematical model of tumor behavior based on the differential growth rates of TKI-sensitive and TKI-resistant cells in heterogeneous tumor cell populations. Understanding the growth dynamics of how tumors behave allowed the authors to calculate what would happen under different treatment regimes. Their models predicted that continuous administration of a low-dose EGFR TKI combined with high-dose pulses of an EGFR TKI should delay the onset of resistance. Subsequent cellular studies bore out this prediction. Modeling also indicated the wisdom of prolonging treatment with the EGFR TKI after the development of resistance to prevent fast overgrowth by the sensitive cells, a result also born out in vitro and in vivo. Ultimate proof will have to come from patients. Clinical trials based on these alternative dosing strategies will be the true test of the utility of evolutionary mathematical modeling in designing cancer treatments. If they prove beneficial, individual models based on the characteristics of diverse cancer cell types could offer clues for designing optimal treatment strategies. Non–small cell lung cancers (NSCLCs) that harbor mutations within the epidermal growth factor receptor (EGFR) gene are sensitive to the tyrosine kinase inhibitors (TKIs) gefitinib and erlotinib. Unfortunately, all patients treated with these drugs will acquire resistance, most commonly as a result of a secondary mutation within EGFR (T790M). Because both drugs were developed to target wild-type EGFR, we hypothesized that current dosing schedules were not optimized for mutant EGFR or to prevent resistance. To investigate this further, we developed isogenic TKI-sensitive and TKI-resistant pairs of cell lines that mimic the behavior of human tumors. We determined that the drug-sensitive and drug-resistant EGFR-mutant cells exhibited differential growth kinetics, with the drug-resistant cells showing slower growth. We incorporated these data into evolutionary mathematical cancer models with constraints derived from clinical data sets. This modeling predicted alternative therapeutic strategies that could prolong the clinical benefit of TKIs against EGFR-mutant NSCLCs by delaying the development of resistance.

Dynamics of cancer progression

Evolutionary concepts such as mutation and selection can be best described when formulated as mathematical equations. Cancer arises as a consequence of somatic evolution. Therefore, a mathematical approach can be used to understand the process of cancer initiation and progression. But what are the fundamental principles that govern the dynamics of activating oncogenes and inactivating tumour-suppressor genes in populations of reproducing cells? Also, how does a quantitative theory of somatic mutation and selection help us to evaluate the role of genetic instability?

A novel tumour-suppressor function for the Notch pathway in myeloid leukaemia

Notch signalling is a central regulator of differentiation in a variety of organisms and tissue types. Its activity is controlled by the multi-subunit γ-secretase (γSE) complex. Although Notch signalling can play both oncogenic and tumour-suppressor roles in solid tumours, in the haematopoietic system it is exclusively oncogenic, notably in T-cell acute lymphoblastic leukaemia, a disease characterized by Notch1-activating mutations. Here we identify novel somatic-inactivating Notch pathway mutations in a fraction of patients with chronic myelomonocytic leukaemia (CMML). Inactivation of Notch signalling in mouse haematopoietic stem cells (HSCs) results in an aberrant accumulation of granulocyte/monocyte progenitors (GMPs), extramedullary haematopoieisis and the induction of CMML-like disease. Transcriptome analysis revealed that Notch signalling regulates an extensive myelomonocytic-specific gene signature, through the direct suppression of gene transcription by the Notch target Hes1. Our studies identify a novel role for Notch signalling during early haematopoietic stem cell differentiation and suggest that the Notch pathway can play both tumour-promoting and -suppressive roles within the same tissue.

Epidermal Growth Factor Receptor Tyrosine Kinase Inhibitor–Resistant Disease

PURPOSE EGFR-mutant lung cancer was first described as a new clinical entity in 2004. Here, we present an update on new controversies and conclusions regarding the disease. METHODS This article reviews the clinical implications of EGFR mutations in lung cancer with a focus on epidermal growth factor receptor tyrosine kinase inhibitor resistance. RESULTS The discovery of EGFR mutations has altered the ways in which we consider and treat non-small-cell lung cancer (NSCLC). Patients whose metastatic tumors harbor EGFR mutations are expected to live longer than 2 years, more than double the previous survival rates for lung cancer. CONCLUSION The information presented in this review can guide practitioners and help them inform their patients about EGFR mutations and their impact on the treatment of NSCLC. Efforts should now concentrate on making EGFR-mutant lung cancer a chronic rather than fatal disease.

Cellular and genetic diversity in the progression of in situ human breast carcinomas to an invasive phenotype

Intratumor genetic heterogeneity is a key mechanism underlying tumor progression and therapeutic resistance. The prevailing model for explaining intratumor diversity, the clonal evolution model, has recently been challenged by proponents of the cancer stem cell hypothesis. To investigate this issue, we performed combined analyses of markers associated with cellular differentiation states and genotypic alterations in human breast carcinomas and evaluated diversity with ecological and evolutionary methods. Our analyses showed a high degree of genetic heterogeneity both within and between distinct tumor cell populations that were defined based on markers of cellular phenotypes including stem cell-like characteristics. In several tumors, stem cell-like and more-differentiated cancer cell populations were genetically distinct, leading us to question the validity of a simple differentiation hierarchy-based cancer stem cell model. The degree of diversity correlated with clinically relevant breast tumor subtypes and in some tumors was markedly different between the in situ and invasive cell populations. We also found that diversity measures were associated with clinical variables. Our findings highlight the importance of genetic diversity in intratumor heterogeneity and the value of analyzing tumors as distinct populations of cancer cells to more effectively plan treatments.

Non-cell-autonomous driving of tumour growth supports sub-clonal heterogeneity

Cancers arise through a process of somatic evolution that can result in substantial sub-clonal heterogeneity within tumours. The mechanisms responsible for the coexistence of distinct sub-clones and the biological consequences of this coexistence remain poorly understood. Here we used a mouse xenograft model to investigate the impact of sub-clonal heterogeneity on tumour phenotypes and the competitive expansion of individual clones. We found that tumour growth can be driven by a minor cell subpopulation, which enhances the proliferation of all cells within a tumour by overcoming environmental constraints and yet can be outcompeted by faster proliferating competitors, resulting in tumour collapse. We developed a mathematical modelling framework to identify the rules underlying the generation of intra-tumour clonal heterogeneity. We found that non-cell-autonomous driving of tumour growth, together with clonal interference, stabilizes sub-clonal heterogeneity, thereby enabling inter-clonal interactions that can lead to new phenotypic traits.

Determinants of Human Immunodeficiency Virus Type 1 Escape from the Primary CD8 Cytotoxic T Lymphocyte Response

CD8+ cytotoxic T lymphocytes (CTLs) play an important role in containment of virus replication in primary human immunodeficiency virus (HIV) infection. HIVs ability to mutate to escape from CTL pressure is increasingly recognized; but comprehensive studies of escape from the CD8 T cell response in primary HIV infection are currently lacking. Here, we have fully characterized the primary CTL response to autologous virus Env, Gag, and Tat proteins in three patients, and investigated the extent, kinetics, and mechanisms of viral escape from epitope-specific components of the response. In all three individuals, we observed variation beginning within weeks of infection at epitope-containing sites in the viral quasispecies, which conferred escape by mechanisms including altered peptide presentation/recognition and altered antigen processing. The number of epitope-containing regions exhibiting evidence of early CTL escape ranged from 1 out of 21 in a subject who controlled viral replication effectively to 5 out of 7 in a subject who did not. Evaluation of the extent and kinetics of HIV-1 escape from >40 different epitope-specific CD8 T cell responses enabled analysis of factors determining escape and suggested that escape is restricted by costs to intrinsic viral fitness and by broad, codominant distribution of CTL-mediated pressure on viral replication.

Computational modeling of pancreatic cancer reveals kinetics of metastasis suggesting optimum treatment strategies

Pancreatic cancer is a leading cause of cancer-related death, largely due to metastatic dissemination. We investigated pancreatic cancer progression by utilizing a mathematical framework of metastasis formation together with comprehensive data of 228 patients, 101 of whom had autopsies. We found that pancreatic cancer growth is initially exponential. After estimating the rates of pancreatic cancer growth and dissemination, we determined that patients likely harbor metastases at diagnosis and predicted the number and size distribution of metastases as well as patient survival. These findings were validated in an independent database. Finally, we analyzed the effects of different treatment modalities, finding that therapies that efficiently reduce the growth rate of cells earlier in the course of treatment appear to be superior to upfront tumor resection. These predictions can be validated in the clinic. Our interdisciplinary approach provides insights into the dynamics of pancreatic cancer metastasis and identifies optimum therapeutic interventions.