Publications by authors named "Franziska Michor"

161 Publications

Impact of HER2 heterogeneity on treatment response of early-stage HER2-positive breast cancer: phase II neoadjuvant clinical trial of T-DM1 combined with pertuzumab.

Cancer Discov 2021 May 3. Epub 2021 May 3.

Medical Oncology, Dana-Farber Cancer Institute

Intratumor heterogeneity is postulated to cause therapeutic resistance. To prospectively assess the impact of HER2 (ERBB2) heterogeneity on response to HER2-targeted therapy, we treated 164 patients with centrally confirmed HER2-positive early-stage breast cancer with neoadjuvant trastuzumab emtansine plus pertuzumab. HER2 heterogeneity was assessed on pretreatment biopsies from 2 locations of each tumor. HER2 heterogeneity, defined as an area with ERBB2 amplification in > 5% but < 50% of tumor cells, or a HER2-negative area by FISH, was detected in 10% (16/157) of evaluable cases. The pathologic complete response rate was 55% in the non-heterogenous subgroup and 0% in the heterogenous group (p<0.0001, adjusted for hormone receptor status). Single cell ERBB2 FISH analysis of cellular heterogeneity identified the fraction of ERBB2 non-amplified cells as a driver of therapeutic resistance. These data suggest HER2 heterogeneity is associated with resistance to HER2-targeted therapy and should be considered in efforts to optimize treatment strategies.
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http://dx.doi.org/10.1158/2159-8290.CD-20-1557DOI Listing
May 2021

The impact of tumor epithelial and microenvironmental heterogeneity on treatment responses in HER2-positive breast cancer.

JCI Insight 2021 Apr 22. Epub 2021 Apr 22.

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, United States of America.

Despite the availability of multiple HER2-targeted treatments, therapeutic resistance in HER2+ breast cancer remains a clinical challenge. Intratumor heterogeneity for HER2 and resistance-conferring mutations (e.g., PIK3CA) have been investigated in response and resistance to HER2-targeting agents, while the role of divergent cellular phenotypes and tumor epithelial-stromal cell interactions is less well understood. Here, we assessed the effect of intratumor cellular genetic heterogeneity for ERBB2 copy number and PIK3CA mutation on different types of neoadjuvant HER2-targeting therapies and clinical outcome in HER2+ breast cancer. We found that the frequency of cells lacking HER2 was a better predictor of response to HER2-targeted treatment than intratumor heterogeneity. We also compared the efficacy of different therapies in the same tumor using patient-derived xenograft models of heterogeneous HER2+ breast cancer and single cell approaches. Stromal determinants were better predictors of response than tumor epithelial cells, and we identified alveolar epithelial and fibroblastic reticular cells as well as Lyve1+ macrophages as putative drivers of therapeutic resistance. Our results demonstrate that both pre-existing and acquired resistance to HER2-targeting agents involves multiple mechanisms including the tumor microenvironment. Furthermore, our data also suggest that intratumor heterogeneity for HER2 should be incorporated into treatment design.
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http://dx.doi.org/10.1172/jci.insight.147617DOI Listing
April 2021

Computational modelling of perivascular-niche dynamics for the optimization of treatment schedules for glioblastoma.

Nat Biomed Eng 2021 04 16;5(4):346-359. Epub 2021 Apr 16.

Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.

Glioblastoma stem-like cells dynamically transition between a chemoradiation-resistant state and a chemoradiation-sensitive state. However, physical barriers in the tumour microenvironment restrict the delivery of chemotherapy to tumour compartments that are distant from blood vessels. Here, we show that a massively parallel computational model of the spatiotemporal dynamics of the perivascular niche that incorporates glioblastoma stem-like cells and differentiated tumour cells as well as relevant tissue-level phenomena can be used to optimize the administration schedules of concurrent radiation and temozolomide-the standard-of-care treatment for glioblastoma. In mice with platelet-derived growth factor (PDGF)-driven glioblastoma, the model-optimized treatment schedule increased the survival of the animals. For standard radiation fractionation in patients, the model predicts that chemotherapy may be optimally administered about one hour before radiation treatment. Computational models of the spatiotemporal dynamics of the tumour microenvironment could be used to predict tumour responses to a broader range of treatments and to optimize treatment regimens.
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http://dx.doi.org/10.1038/s41551-021-00710-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8054983PMC
April 2021

Breast tumours maintain a reservoir of subclonal diversity during expansion.

Nature 2021 Apr 24;592(7853):302-308. Epub 2021 Mar 24.

Department of Genetics, The University of Texas MD Anderson Cancer Center, Houston, TX, USA.

Our knowledge of copy number evolution during the expansion of primary breast tumours is limited. Here, to investigate this process, we developed a single-cell, single-molecule DNA-sequencing method and performed copy number analysis of 16,178 single cells from 8 human triple-negative breast cancers and 4 cell lines. The results show that breast tumours and cell lines comprise a large milieu of subclones (7-22) that are organized into a few (3-5) major superclones. Evolutionary analysis suggests that after clonal TP53 mutations, multiple loss-of-heterozygosity events and genome doubling, there was a period of transient genomic instability followed by ongoing copy number evolution during the primary tumour expansion. By subcloning single daughter cells in culture, we show that tumour cells rediversify their genomes and do not retain isogenic properties. These data show that triple-negative breast cancers continue to evolve chromosome aberrations and maintain a reservoir of subclonal diversity during primary tumour growth.
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http://dx.doi.org/10.1038/s41586-021-03357-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8049101PMC
April 2021

A Quantitative Paradigm for Decision-Making in Precision Oncology.

Trends Cancer 2021 Apr 23;7(4):293-300. Epub 2021 Feb 23.

Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA; Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, MA, USA; Department of Stem Cell and Regenerative Biology, Harvard University, Cambridge, MA, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA, USA; The Broad Institute of Harvard and MIT, Cambridge, MA, USA; The Ludwig Center at Harvard, Boston, MA, USA.

The complexity and variability of cancer progression necessitate a quantitative paradigm for therapeutic decision-making that is dynamic, personalized, and capable of identifying optimal treatment strategies for individual patients under substantial uncertainty. Here, we discuss the core components and challenges of such an approach and highlight the need for comprehensive longitudinal clinical and molecular data integration in its development. We describe the complementary and varied roles of mathematical modeling and machine learning in constructing dynamic optimal cancer treatment strategies and highlight the potential of reinforcement learning approaches in this endeavor.
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http://dx.doi.org/10.1016/j.trecan.2021.01.006DOI Listing
April 2021

Reconstructing the Lineage Histories and Differentiation Trajectories of Individual Cancer Cells in Myeloproliferative Neoplasms.

Cell Stem Cell 2021 Mar 22;28(3):514-523.e9. Epub 2021 Feb 22.

Department of Systems Biology, Harvard Medical School, Boston, MA 02115, USA; Department of Data Science, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Broad Institute of MIT and Harvard, Cambridge, MA 02142, USA. Electronic address:

Some cancers originate from a single mutation event in a single cell. Blood cancers known as myeloproliferative neoplasms (MPNs) are thought to originate when a driver mutation is acquired by a hematopoietic stem cell (HSC). However, when the mutation first occurs in individuals and how it affects the behavior of HSCs in their native context is not known. Here we quantified the effect of the JAK2-V617F mutation on the self-renewal and differentiation dynamics of HSCs in treatment-naive individuals with MPNs and reconstructed lineage histories of individual HSCs using somatic mutation patterns. We found that JAK2-V617F mutations occurred in a single HSC several decades before MPN diagnosis-at age 9 ± 2 years in a 34-year-old individual and at age 19 ± 3 years in a 63-year-old individual-and found that mutant HSCs have a selective advantage in both individuals. These results highlight the potential of harnessing somatic mutations to reconstruct cancer lineages.
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http://dx.doi.org/10.1016/j.stem.2021.02.001DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7939520PMC
March 2021

Imaging dynamic mTORC1 pathway activity in vivo reveals marked shifts that support time-specific inhibitor therapy in AML.

Nat Commun 2021 01 11;12(1):245. Epub 2021 Jan 11.

Center for Regenerative Medicine, Massachusetts General Hospital, Boston, MA, 02114, USA.

Acute myeloid leukemia (AML) is a high remission, high relapse fatal blood cancer. Although mTORC1 is a master regulator of cell proliferation and survival, its inhibitors have not performed well as AML treatments. To uncover the dynamics of mTORC1 activity in vivo, fluorescent probes are developed to track single cell proliferation, apoptosis and mTORC1 activity of AML cells in the bone marrow of live animals and to quantify these activities in the context of microanatomical localization and intra-tumoral heterogeneity. When chemotherapy drugs commonly used clinically are given to mice with AML, apoptosis is rapid, diffuse and not preferentially restricted to anatomic sites. Dynamic measurement of mTORC1 activity indicated a decline in mTORC1 activity with AML progression. However, at the time of maximal chemotherapy response, mTORC1 signaling is high and positively correlated with a leukemia stemness transcriptional profile. Cell barcoding reveals the induction of mTORC1 activity rather than selection of mTORC1 high cells and timed inhibition of mTORC1 improved the killing of AML cells. These data define the real-time dynamics of AML and the mTORC1 pathway in association with AML growth, response to and relapse after chemotherapy. They provide guidance for timed intervention with pathway-specific inhibitors.
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http://dx.doi.org/10.1038/s41467-020-20491-8DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7801403PMC
January 2021

Genomic epidemiology of superspreading events in Austria reveals mutational dynamics and transmission properties of SARS-CoV-2.

Sci Transl Med 2020 12 23;12(573). Epub 2020 Nov 23.

Department of Theoretical Chemistry, University of Vienna, 1090 Vienna, Austria.

Superspreading events shaped the coronavirus disease 2019 (COVID-19) pandemic, and their rapid identification and containment are essential for disease control. Here, we provide a national-scale analysis of severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2) superspreading during the first wave of infections in Austria, a country that played a major role in initial virus transmissions in Europe. Capitalizing on Austria's well-developed epidemiological surveillance system, we identified major SARS-CoV-2 clusters during the first wave of infections and performed deep whole-genome sequencing of more than 500 virus samples. Phylogenetic-epidemiological analysis enabled the reconstruction of superspreading events and charts a map of tourism-related viral spread originating from Austria in spring 2020. Moreover, we exploited epidemiologically well-defined clusters to quantify SARS-CoV-2 mutational dynamics, including the observation of low-frequency mutations that progressed to fixation within the infection chain. Time-resolved virus sequencing unveiled viral mutation dynamics within individuals with COVID-19, and epidemiologically validated infector-infectee pairs enabled us to determine an average transmission bottleneck size of 10 SARS-CoV-2 particles. In conclusion, this study illustrates the power of combining epidemiological analysis with deep viral genome sequencing to unravel the spread of SARS-CoV-2 and to gain fundamental insights into mutational dynamics and transmission properties.
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http://dx.doi.org/10.1126/scitranslmed.abe2555DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7857414PMC
December 2020

Progression signature underlies clonal evolution and dissemination of multiple myeloma.

Blood 2021 Apr;137(17):2360-2372

Department of Medical Oncology, Dana-Farber Cancer Institute, Harvard Medical School, Boston, MA.

Clonal evolution drives tumor progression, dissemination, and relapse in multiple myeloma (MM), with most patients dying of relapsed disease. This multistage process requires tumor cells to enter the circulation, extravasate, and colonize distant bone marrow (BM) sites. Here, we developed a fluorescent or DNA-barcode clone-tracking system on MM PrEDiCT (progression through evolution and dissemination of clonal tumor cells) xenograft mouse model to study clonal behavior within the BM microenvironment. We showed that only the few clones that successfully adapt to the BM microenvironment can enter the circulation and colonize distant BM sites. RNA sequencing of primary and distant-site MM tumor cells revealed a progression signature sequentially activated along human MM progression and significantly associated with overall survival when evaluated against patient data sets. A total of 28 genes were then computationally predicted to be master regulators (MRs) of MM progression. HMGA1 and PA2G4 were validated in vivo using CRISPR-Cas9 in the PrEDiCT model and were shown to be significantly depleted in distant BM sites, indicating their role in MM progression and dissemination. Loss of HMGA1 and PA2G4 also compromised the proliferation, migration, and adhesion abilities of MM cells in vitro. Overall, our model successfully recapitulates key characteristics of human MM disease progression and identified potential new therapeutic targets for MM.
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http://dx.doi.org/10.1182/blood.2020005885DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC8085483PMC
April 2021

Circadian clock effects on cellular proliferation: Insights from theory and experiments.

Curr Opin Cell Biol 2020 Dec 7;67:17-26. Epub 2020 Aug 7.

Department of Data Science, Dana-Farber Cancer Institute, Department of Biostatistics, Harvard T.H. Chan School of Public Health, Boston, MA, USA; Department of Stem Cell and Regenerative Biology Biology, Harvard University, Cambridge, MA, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Ludwig Center at Harvard, Boston, MA, USA; The Broad Institute of Harvard and MIT, Cambridge, MA, USA.

Oscillations of the cellular circadian clock have emerged as an important regulator of many physiological processes, both in health and in disease. One such process, cellular proliferation, is being increasingly recognized to be affected by the circadian clock. Here, we review how a combination of experimental and theoretical work has furthered our understanding of the way circadian clocks couple to the cell cycle and play a role in tissue homeostasis and cancer. Finally, we discuss recently introduced methods for modeling coupling of clocks based on techniques from survival analysis and machine learning and highlight their potential importance for future studies.
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http://dx.doi.org/10.1016/j.ceb.2020.07.003DOI Listing
December 2020

Pharmacokinetic Profiles Determine Optimal Combination Treatment Schedules in Computational Models of Drug Resistance.

Cancer Res 2020 08 19;80(16):3372-3382. Epub 2020 Jun 19.

Department of Data Sciences, Dana-Farber Cancer Institute, and Department of Biostatistics, Harvard T. H. Chan School of Public Health, Boston, Massachusetts.

Identification of optimal schedules for combination drug administration relies on accurately estimating the correct pharmacokinetics, pharmacodynamics, and drug interaction effects. Misspecification of pharmacokinetics can lead to wrongly predicted timing or order of treatments, leading to schedules recommended on the basis of incorrect assumptions about absorption and elimination of a drug and its effect on tumor growth. Here, we developed a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data. The software can be used to compare prespecified schedules on the basis of the number of resistant cells where drug interactions and pharmacokinetic curves can be estimated from user-provided data or models. We applied our approach to publicly available data of treatment with different tyrosine kinase inhibitors of BT-20 triple-negative breast cancer cells and of treatment with erlotinib of PC-9 non-small cell lung cancer cells. Our approach is publicly available in the form of an R package called ACESO (https://github.com/Michorlab/aceso) and can be used to investigate optimum dosing for any combination treatment. SIGNIFICANCE: These findings introduce a computational modeling platform and software package for combination treatment strategies with flexible pharmacokinetic profiles and multidrug interaction curves that are estimated from data.
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http://dx.doi.org/10.1158/0008-5472.CAN-20-0056DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7442591PMC
August 2020

Accelerating massively parallel hemodynamic models of coarctation of the aorta using neural networks.

Sci Rep 2020 06 11;10(1):9508. Epub 2020 Jun 11.

Department of Biomedical Engineering, Duke University, Durham, NC, USA.

Comorbidities such as anemia or hypertension and physiological factors related to exertion can influence a patient's hemodynamics and increase the severity of many cardiovascular diseases. Observing and quantifying associations between these factors and hemodynamics can be difficult due to the multitude of co-existing conditions and blood flow parameters in real patient data. Machine learning-driven, physics-based simulations provide a means to understand how potentially correlated conditions may affect a particular patient. Here, we use a combination of machine learning and massively parallel computing to predict the effects of physiological factors on hemodynamics in patients with coarctation of the aorta. We first validated blood flow simulations against in vitro measurements in 3D-printed phantoms representing the patient's vasculature. We then investigated the effects of varying the degree of stenosis, blood flow rate, and viscosity on two diagnostic metrics - pressure gradient across the stenosis (ΔP) and wall shear stress (WSS) - by performing the largest simulation study to date of coarctation of the aorta (over 70 million compute hours). Using machine learning models trained on data from the simulations and validated on two independent datasets, we developed a framework to identify the minimal training set required to build a predictive model on a per-patient basis. We then used this model to accurately predict ΔP (mean absolute error within 1.18 mmHg) and WSS (mean absolute error within 0.99 Pa) for patients with this disease.
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http://dx.doi.org/10.1038/s41598-020-66225-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7289812PMC
June 2020

ESTIpop: a computational tool to simulate and estimate parameters for continuous-time Markov branching processes.

Bioinformatics 2020 08;36(15):4372-4373

Department of Data Sciences, Center for Cancer Evolution, Dana-Farber Cancer Institute.

Summary: ESTIpop is an R package designed to simulate and estimate parameters for continuous-time Markov branching processes with constant or time-dependent rates, a common model for asexually reproducing cell populations. Analytical approaches to parameter estimation quickly become intractable in complex branching processes. In ESTIpop, parameter estimation is based on a likelihood function with respect to a time series of cell counts, approximated by the Central Limit Theorem for multitype branching processes. Additionally, simulation in ESTIpop via approximation can be performed many times faster than exact simulation methods with similar results.

Availability And Implementation: ESTIpop is available as an R package on Github (https://github.com/michorlab/estipop).

Supplementary Information: Supplementary data are available at Bioinformatics online.
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http://dx.doi.org/10.1093/bioinformatics/btaa526DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7520045PMC
August 2020

Synthetic Lethal and Resistance Interactions with BET Bromodomain Inhibitors in Triple-Negative Breast Cancer.

Mol Cell 2020 06 15;78(6):1096-1113.e8. Epub 2020 May 15.

Department of Cancer Immunology and Virology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Microbiology and Immunobiology, Harvard Medical School, Boston, MA 02115, USA.

BET bromodomain inhibitors (BBDIs) are candidate therapeutic agents for triple-negative breast cancer (TNBC) and other cancer types, but inherent and acquired resistance to BBDIs limits their potential clinical use. Using CRISPR and small-molecule inhibitor screens combined with comprehensive molecular profiling of BBDI response and resistance, we identified synthetic lethal interactions with BBDIs and genes that, when deleted, confer resistance. We observed synergy with regulators of cell cycle progression, YAP, AXL, and SRC signaling, and chemotherapeutic agents. We also uncovered functional similarities and differences among BRD2, BRD4, and BRD7. Although deletion of BRD2 enhances sensitivity to BBDIs, BRD7 loss leads to gain of TEAD-YAP chromatin binding and luminal features associated with BBDI resistance. Single-cell RNA-seq, ATAC-seq, and cellular barcoding analysis of BBDI responses in sensitive and resistant cell lines highlight significant heterogeneity among samples and demonstrate that BBDI resistance can be pre-existing or acquired.
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http://dx.doi.org/10.1016/j.molcel.2020.04.027DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7306005PMC
June 2020

Acquired resistance to combined BET and CDK4/6 inhibition in triple-negative breast cancer.

Nat Commun 2020 05 11;11(1):2350. Epub 2020 May 11.

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, 02215, USA.

BET inhibitors are promising therapeutic agents for the treatment of triple-negative breast cancer (TNBC), but the rapid emergence of resistance necessitates investigation of combination therapies and their effects on tumor evolution. Here, we show that palbociclib, a CDK4/6 inhibitor, and paclitaxel, a microtubule inhibitor, synergize with the BET inhibitor JQ1 in TNBC lines. High-complexity DNA barcoding and mathematical modeling indicate a high rate of de novo acquired resistance to these drugs relative to pre-existing resistance. We demonstrate that the combination of JQ1 and palbociclib induces cell division errors, which can increase the chance of developing aneuploidy. Characterizing acquired resistance to combination treatment at a single cell level shows heterogeneous mechanisms including activation of G1-S and senescence pathways. Our results establish a rationale for further investigation of combined BET and CDK4/6 inhibition in TNBC and suggest novel mechanisms of action for these drugs and new vulnerabilities in cells after emergence of resistance.
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http://dx.doi.org/10.1038/s41467-020-16170-3DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7214447PMC
May 2020

Drug Sensitivity and Allele Specificity of First-Line Osimertinib Resistance Mutations.

Cancer Res 2020 05 19;80(10):2017-2030. Epub 2020 Mar 19.

Department of Pathology, Yale School of Medicine, New Haven, Connecticut.

Osimertinib, a mutant-specific third-generation EGFR tyrosine kinase inhibitor, is emerging as the preferred first-line therapy for -mutant lung cancer, yet resistance inevitably develops in patients. We modeled acquired resistance to osimertinib in transgenic mouse models of -induced lung adenocarcinoma and found that it is mediated largely through secondary mutations in -either C797S or L718V/Q. Analysis of circulating free DNA data from patients revealed that L718Q/V mutations almost always occur in the context of an L858R driver mutation. Therapeutic testing in mice revealed that both erlotinib and afatinib caused regression of osimertinib-resistant C797S-containing tumors, whereas only afatinib was effective on L718Q mutant tumors. Combination first-line osimertinib plus erlotinib treatment prevented the emergence of secondary mutations in . These findings highlight how knowledge of the specific characteristics of resistance mutations is important for determining potential subsequent treatment approaches and suggest strategies to overcome or prevent osimertinib resistance . SIGNIFICANCE: This study provides insight into the biological and molecular properties of osimertinib resistance mutations and evaluates therapeutic strategies to overcome resistance. GRAPHICAL ABSTRACT: http://cancerres.aacrjournals.org/content/canres/80/10/2017/F1.large.jpg.
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http://dx.doi.org/10.1158/0008-5472.CAN-19-3819DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7392201PMC
May 2020

Ascl2-Dependent Cell Dedifferentiation Drives Regeneration of Ablated Intestinal Stem Cells.

Cell Stem Cell 2020 03 20;26(3):377-390.e6. Epub 2020 Feb 20.

Department of Medical Oncology and Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Departments of Medicine, Brigham & Women's Hospital and Harvard Medical School, Boston, MA 02215, USA; Harvard Stem Cell Institute, Cambridge, MA 02138, USA. Electronic address:

Ablation of LGR5 intestinal stem cells (ISCs) is associated with rapid restoration of the ISC compartment. Different intestinal crypt populations dedifferentiate to provide new ISCs, but the transcriptional and signaling trajectories that guide this process are unclear, and a large body of work suggests that quiescent "reserve" ISCs contribute to regeneration. By timing the interval between LGR5 lineage tracing and lethal injury, we show that ISC regeneration is explained nearly completely by dedifferentiation, with contributions from absorptive and secretory progenitors. The ISC-restricted transcription factor ASCL2 confers measurable competitive advantage to resting ISCs and is essential to restore the ISC compartment. Regenerating cells re-express Ascl2 days before Lgr5, and single-cell RNA sequencing (scRNA-seq) analyses reveal transcriptional paths underlying dedifferentiation. ASCL2 target genes include the interleukin-11 (IL-11) receptor Il11ra1, and recombinant IL-11 enhances crypt cell regenerative potential. These findings reveal cell dedifferentiation as the principal means for ISC restoration and highlight an ASCL2-regulated signal that enables this adaptive response.
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http://dx.doi.org/10.1016/j.stem.2019.12.011DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC7147146PMC
March 2020

Subclonal cooperation drives metastasis by modulating local and systemic immune microenvironments.

Nat Cell Biol 2019 07 1;21(7):879-888. Epub 2019 Jul 1.

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA, USA.

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.
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http://dx.doi.org/10.1038/s41556-019-0346-xDOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6609451PMC
July 2019

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

PLoS One 2019 26;14(4):e0215409. Epub 2019 Apr 26.

Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, United States of America.

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.
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http://journals.plos.org/plosone/article?id=10.1371/journal.pone.0215409PLOS
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6485645PMC
February 2020

Stochastic Evolution of Pancreatic Cancer Metastases During Logistic Clonal Expansion.

JCO Clin Cancer Inform 2019 03;3:1-11

Dana-Farber Cancer Institute, Boston, MA.

Despite recent progress in diagnostic and multimodal treatment approaches, most cancer deaths are still caused by metastatic spread and the subsequent growth of tumor cells in sites distant from the primary organ. So far, few quantitative studies are available that allow for the estimation of metastatic parameters and the evaluation of alternative treatment strategies. Most computational studies have focused on situations in which the tumor cell population expands exponentially over time; however, tumors may eventually be subject to resource and space limitations so that their growth patterns deviate from exponential growth to adhere to density-dependent growth models. In this study, we developed a stochastic evolutionary model of cancer progression that considers alterations in metastasis-related genes and intercellular growth competition leading to density effects described by logistic growth. Using this stochastic model, we derived analytical approximations for the time between the initiation of tumorigenesis and diagnosis, the expected number of metastatic sites, the total number of metastatic cells, the size of the primary tumor, and survival. Furthermore, we investigated the effects of drug administration and surgical resection on these quantities and predicted outcomes for different treatment regimens. Parameter values used in the analysis were estimated from data obtained from a pancreatic cancer rapid autopsy program. Our theoretical approach allows for flexible modeling of metastatic progression dynamics.
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http://dx.doi.org/10.1200/CCI.18.00079DOI Listing
March 2019

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

Bioinformatics 2019 10;35(19):3849-3851

Department of Data Science, Dana-Farber Cancer Institute, Boston, MA, USA.

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.
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http://dx.doi.org/10.1093/bioinformatics/btz074DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6761956PMC
October 2019

Computational Model of Progression to Multiple Myeloma Identifies Optimum Screening Strategies.

JCO Clin Cancer Inform 2018 12;2:1-12

Philipp M. Altrock, Moffitt Cancer Center and Research Institute; Morsani College of Medicine, University of South Florida, Tampa, FL; Jeremy Ferlic and Franziska Michor, Dana-Farber Cancer Institute and Harvard University; Harvard T.H. Chan School of Public Health, Boston; Franziska Michor, Center for Cancer Evolution, Dana-Farber Cancer Institute, and The Ludwig Center at Harvard, Boston; Broad Institute of Harvard and Massachusetts Institute of Technology, Cambridge, MA; Tobias Galla, University of Manchester, Manchester, United Kingdom; and Michael H. Tomasson, Roy J. and Lucille A. Carver College of Medicine, University of Iowa, Iowa City, IA.

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.
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http://dx.doi.org/10.1200/CCI.17.00131DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6873949PMC
December 2018

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

Nat Commun 2018 12 18;9(1):5372. Epub 2018 Dec 18.

Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, 02215, MA, USA.

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.
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http://dx.doi.org/10.1038/s41467-018-07788-5DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6299096PMC
December 2018

KDM5 Histone Demethylase Activity Links Cellular Transcriptomic Heterogeneity to Therapeutic Resistance.

Cancer Cell 2018 12 21;34(6):939-953.e9. Epub 2018 Nov 21.

Department of Medical Oncology, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Department of Medicine, Harvard Medical School, Boston, MA 02115, USA; Center for Cancer Evolution, Dana-Farber Cancer Institute, Boston, MA 02215, USA; The Eli and Edythe L Broad Institute of MIT and Harvard, Cambridge, MA 02139, USA; Center for Functional Cancer Epigenetics, Dana-Farber Cancer Institute, Boston, MA 02215, USA; Ludwig Center at Harvard, Boston, MA 02215, USA. Electronic address:

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.
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http://dx.doi.org/10.1016/j.ccell.2018.10.014DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6310147PMC
December 2018

Currently available bulk sequencing data do not necessarily support a model of neutral tumor evolution.

Nat Genet 2018 12;50(12):1620-1623

Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, USA.

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http://dx.doi.org/10.1038/s41588-018-0217-6DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6467055PMC
December 2018

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

Commun Biol 2018 26;1:151. Epub 2018 Sep 26.

Department of Biostatistics and Computational Biology, Dana-Farber Cancer Institute, Boston, MA, 02115, USA.

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.
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http://dx.doi.org/10.1038/s42003-018-0160-1DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6158234PMC
September 2018

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

Nat Commun 2018 09 4;9(1):3588. Epub 2018 Sep 4.

Center for Cancer Research, Massachusetts General Hospital and Harvard Medical School, Boston, MA, 02114, USA.

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.
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http://dx.doi.org/10.1038/s41467-018-06052-0DOI Listing
http://www.ncbi.nlm.nih.gov/pmc/articles/PMC6123496PMC
September 2018