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"Cancer evolution"
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Adaptive oncogenesis : a new understanding of how cancer evolves inside us
\"Popular understanding holds that genetic changes create cancer. James DeGregori uses evolutionary principles to propose a new way of thinking about cancer's occurrence. Cancer is as much a disease of evolution as it is of mutation, one in which mutated cells outcompete healthy cells in the ecosystem of the body's tissues. His theory ties cancer's progression, or lack thereof, to evolved strategies to maximize reproductive success. Through natural selection, humans evolved genetic programs to maintain bodily health for as long as necessary to increase the odds of passing on our genes--but not much longer. These mechanisms engender a tissue environment that favors normal stem cells over precancerous ones. Healthy tissues thwart cancer cells' ability to outcompete their precancerous rivals. But as our tissues age or accumulate damage from exposures such as smoking, normal stem cells find themselves less optimized to their ecosystem. Cancer-causing mutations can now help cells adapt to these altered tissue environments, and thus outcompete normal cells. Just as changes in a species' habitat favor the evolution of new species, changes in tissue environments favor the growth of cancerous cells. DeGregori's perspective goes far in explaining who gets cancer, when it appears, and why. While we cannot avoid mutations, it may be possible to sustain our tissues' natural and effective system of defense, even in the face of aging or harmful exposures. For those interested in learning how cancers arise within the human body, the insights in Cancer: Evolution Within Us offer a compelling perspective\"-- Provided by publisher.
Book
Secondary resistance to anti-EGFR therapy by transcriptional reprogramming in patient-derived colorectal cancer models
Background
The development of secondary resistance (SR) in metastatic colorectal cancer (mCRC) treated with anti-epidermal growth factor receptor (anti-EGFR) antibodies is not fully understood at the molecular level. Here we tested in vivo selection of anti-EGFR SR tumors in CRC patient-derived xenograft (PDX) models as a strategy for a molecular dissection of SR mechanisms.
Methods
We analyzed 21
KRAS, NRAS, BRAF,
and
PI3K
wildtype CRC patient-derived xenograft (PDX) models for their anti-EGFR sensitivity. Furthermore, 31 anti-EGFR SR tumors were generated via chronic in vivo treatment with cetuximab. A multi-omics approach was employed to address molecular primary and secondary resistance mechanisms. Gene set enrichment analyses were used to uncover SR pathways. Targeted therapy of SR PDX models was applied to validate selected SR pathways.
Results
In vivo anti-EGFR SR could be established with high efficiency. Chronic anti-EGFR treatment of CRC PDX tumors induced parallel evolution of multiple resistant lesions with independent molecular SR mechanisms. Mutations in driver genes explained SR development in a subgroup of CRC PDX models, only. Transcriptional reprogramming inducing anti-EGFR SR was discovered as a common mechanism in CRC PDX models frequently leading to RAS signaling pathway activation. We identified cAMP and STAT3 signaling activation, as well as paracrine and autocrine signaling via growth factors as novel anti-EGFR secondary resistance mechanisms. Secondary resistant xenograft tumors could successfully be treated by addressing identified transcriptional changes by tailored targeted therapies.
Conclusions
Our study demonstrates that SR PDX tumors provide a unique platform to study molecular SR mechanisms and allow testing of multiple treatments for efficient targeting of SR mechanisms, not possible in the patient. Importantly, it suggests that the development of anti-EGFR tolerant cells via transcriptional reprogramming as a cause of anti-EGFR SR in CRC is likely more prevalent than previously anticipated. It emphasizes the need for analyses of SR tumor tissues at a multi-omics level for a comprehensive molecular understanding of anti-EGFR SR in CRC.
Journal Article
How Should Cancer Models Be Constructed?
Choosing and optimizing treatment strategies for cancer requires capturing its complex dynamics sufficiently well for understanding but without being overwhelmed. Mathematical models are essential to achieve this understanding, and we discuss the challenge of choosing the right level of complexity to address the full range of tumor complexity from growth, the generation of tumor heterogeneity, and interactions within tumors and with treatments and the tumor microenvironment. We discuss the differences between conceptual and descriptive models, and compare the use of predator-prey models, evolutionary game theory, and dynamic precision medicine approaches in the face of uncertainty about mechanisms and parameter values. Although there is of course no one-size-fits-all approach, we conclude that broad and flexible thinking about cancer, based on combined modeling approaches, will play a key role in finding creative and improved treatments.
Journal Article
Exploiting Cancer’s Tactics to Make Cancer a Manageable Chronic Disease
The history of modern oncology started around eighty years ago with the introduction of cytotoxic agents such as nitrogen mustard into the clinic, followed by multi-agent chemotherapy protocols. Early success in radiation therapy in Hodgkin lymphoma gave birth to the introduction of radiation therapy into different cancer treatment protocols. Along with better understanding of cancer biology, we developed drugs targeting cancer-related cellular and genetic aberrancies. Discovery of the crucial role of vasculature in maintenance, survival, and growth of a tumor opened the way to the development of anti-angiogenic agents. A better understanding of T-cell regulatory pathways advanced immunotherapy. Awareness of stem-like cancer cells and their role in cancer metastasis and local recurrence led to the development of drugs targeting them. At the same time, sequential and rapidly accelerating advances in imaging and surgical technology have markedly increased our ability to safely remove ≥90% of tumor cells. While we have advanced our ability to kill cells from multiple directions, we have still failed to stop most types of cancer from recurring. Here we analyze the tactics employed in cancer evolution; namely, chromosomal instability (CIN), intra-tumoral heterogeneity (ITH), and cancer-specific metabolism. These tactics govern the resistance to current cancer therapeutics. It is time to focus on maximally delaying the time to recurrence, with drugs that target these fundamental tactics of cancer evolution. Understanding the control of CIN and the optimal state of ITH as the most important tactics in cancer evolution could facilitate the development of improved cancer therapeutic strategies designed to transform cancer into a manageable chronic disease.
Journal Article
Assessing intratumor heterogeneity and tracking longitudinal and spatial clonal evolutionary history by next-generation sequencing
Cancer is a disease driven by evolutionary selection on somatic genetic and epigenetic alterations. Here, we propose Canopy, a method for inferring the evolutionary phylogeny of a tumor using both somatic copy number alterations and single-nucleotide alterations from one or more samples derived from a single patient. Canopy is applied to bulk sequencing datasets of both longitudinal and spatial experimental designs and to a transplantable metastasis model derived from human cancer cell line MDA-MB-231. Canopy successfully identifies cell populations and infers phylogenies that are in concordance with existing knowledge and ground truth. Through simulations, we explore the effects of key parameters on deconvolution accuracy and compare against existing methods. Canopy is an open-source R package available at https://cran.r-project.org/web/packages/Canopy/.
Journal Article
Evolution-based mathematical models significantly prolong response to abiraterone in metastatic castrate-resistant prostate cancer and identify strategies to further improve outcomes
Abiraterone acetate is an effective treatment for metastatic castrate-resistant prostate cancer (mCRPC), but evolution of resistance inevitably leads to progression. We present a pilot study in which abiraterone dosing is guided by evolution-informed mathematical models to delay onset of resistance.
In the study cohort, abiraterone was stopped when PSA was <50% of pretreatment value and resumed when PSA returned to baseline. Results are compared to a contemporaneous cohort who had >50% PSA decline after initial abiraterone administration and met trial eligibility requirements but chose standard of care (SOC) dosing.
17 subjects were enrolled in the adaptive therapy group and 16 in the SOC group. All SOC subjects have progressed, but four patients in the study cohort remain stably cycling (range 53-70 months). The study cohort had significantly improved median time to progression (TTP; 33.5 months; p<0.001) and median overall survival (OS; 58.5 months; hazard ratio, 0.41, 95% confidence interval (CI), 0.20-0.83, p<0.001) compared to 14.3 and 31.3 months in the SOC cohort. On average, study subjects received no abiraterone during 46% of time on trial. Longitudinal trial data demonstrated the competition coefficient ratio (
) of sensitive and resistant populations, a critical factor in intratumoral evolution, was two- to threefold higher than pre-trial estimates. Computer simulations of intratumoral evolutionary dynamics in the four long-term survivors found that, due to the larger value for
cycled therapy significantly decreased the resistant population. Simulations in subjects who progressed predicted further increases in OS could be achieved with prompt abiraterone withdrawal after achieving 50% PSA reduction.
Incorporation of evolution-based mathematical models into abiraterone monotherapy for mCRPC significantly increases TTP and OS. Computer simulations with updated parameters from longitudinal trial data can estimate intratumoral evolutionary dynamics in each subject and identify strategies to improve outcomes.
Moffitt internal grants and NIH/NCI U54CA143970-05 (Physical Science Oncology Network).
Journal Article
A survey of open questions in adaptive therapy: bridging mathematics and clinical translation
Adaptive therapy is a dynamic cancer treatment protocol that updates (or \"adapts\") treatment decisions in anticipation of evolving tumor dynamics. This broad term encompasses many possible dynamic treatment protocols of patient-specific dose modulation or dose timing. Adaptive therapy maintains high levels of tumor burden to benefit from the competitive suppression of treatment-sensitive subpopulations on treatment-resistant subpopulations. This evolution-based approach to cancer treatment has been integrated into several ongoing or planned clinical trials, including treatment of metastatic castrate resistant prostate cancer, ovarian cancer, and BRAF-mutant melanoma. In the previous few decades, experimental and clinical investigation of adaptive therapy has progressed synergistically with mathematical and computational modeling. In this work, we discuss 11 open questions in cancer adaptive therapy mathematical modeling. The questions are split into three sections: 1) the necessary components of mathematical models of adaptive therapy 2) design and validation of dosing protocols, and 3) challenges and opportunities in clinical translation.
Journal Article
Single-cell transcriptome and antigen-immunoglobin analysis reveals the diversity of B cells in non-small cell lung cancer
Background
Malignant transformation and progression of cancer are driven by the co-evolution of cancer cells and their dysregulated tumor microenvironment (TME). Recent studies on immunotherapy demonstrate the efficacy in reverting the anti-tumoral function of T cells, highlighting the therapeutic potential in targeting certain cell types in TME. However, the functions of other immune cell types remain largely unexplored.
Results
We conduct a single-cell RNA-seq analysis of cells isolated from tumor tissue samples of non-small cell lung cancer (NSCLC) patients, and identify subtypes of tumor-infiltrated B cells and their diverse functions in the progression of NSCLC. Flow cytometry and immunohistochemistry experiments on two independent cohorts confirm the co-existence of the two major subtypes of B cells, namely the naïve-like and plasma-like B cells. The naïve-like B cells are decreased in advanced NSCLC, and their lower level is associated with poor prognosis. Co-culture of isolated naïve-like B cells from NSCLC patients with two lung cancer cell lines demonstrate that the naïve-like B cells suppress the growth of lung cancer cells by secreting four factors negatively regulating the cell growth. We also demonstrate that the plasma-like B cells inhibit cancer cell growth in the early stage of NSCLC, but promote cell growth in the advanced stage of NSCLC. The roles of the plasma-like B cell produced immunoglobulins, and their interacting proteins in the progression of NSCLC are further validated by proteomics data.
Conclusion
Our analysis reveals versatile functions of tumor-infiltrating B cells and their potential clinical implications in NSCLC.
Journal Article
OncoNEM: inferring tumor evolution from single-cell sequencing data
Single-cell sequencing promises a high-resolution view of genetic heterogeneity and clonal evolution in cancer. However, methods to infer tumor evolution from single-cell sequencing data lag behind methods developed for bulk-sequencing data. Here, we present OncoNEM, a probabilistic method for inferring intra-tumor evolutionary lineage trees from somatic single nucleotide variants of single cells. OncoNEM identifies homogeneous cellular subpopulations and infers their genotypes as well as a tree describing their evolutionary relationships. In simulation studies, we assess OncoNEM’s robustness and benchmark its performance against competing methods. Finally, we show its applicability in case studies of muscle-invasive bladder cancer and essential thrombocythemia.
Journal Article