Novel evolutionary dynamics of small populations in breast cancer adjuvant and neoadjuvant therapy

Onco-Breastomics: An Eco-Evo-Devo Holistic Approach

Known as a diverse collection of neoplastic diseases, breast cancer (BC) can be hyperbolically characterized as a dynamic pseudo-organ, a living organism able to build a complex, open, hierarchically organized, self-sustainable, and self-renewable tumor system, a population, a species, a local community, a biocenosis, or an evolving dynamical ecosystem (i.e., immune or metabolic ecosystem) that emphasizes both developmental continuity and spatio-temporal change. Moreover, a cancer cell community, also known as an oncobiota, has been described as non-sexually reproducing species, as well as a migratory or invasive species that expresses intelligent behavior, or an endangered or parasite species that fights to survive, to optimize its features inside the host's ecosystem, or that is able to exploit or to disrupt its host circadian cycle for improving the own proliferation and spreading. BC tumorigenesis has also been compared with the early embryo and placenta development that may suggest new strategies for research and therapy. Furthermore, BC has also been characterized as an environmental disease or as an ecological disorder. Many mechanisms of cancer progression have been explained by principles of ecology, developmental biology, and evolutionary paradigms. Many authors have discussed ecological, developmental, and evolutionary strategies for more successful anti-cancer therapies, or for understanding the ecological, developmental, and evolutionary bases of BC exploitable vulnerabilities. Herein, we used the integrated framework of three well known ecological theories: the Bronfenbrenner's theory of human development, the Vannote's River Continuum Concept (RCC), and the Ecological Evolutionary Developmental Biology (Eco-Evo-Devo) theory, to explain and understand several eco-evo-devo-based principles that govern BC progression. Multi-omics fields, taken together as onco-breastomics, offer better opportunities to integrate, analyze, and interpret large amounts of complex heterogeneous data, such as various and big-omics data obtained by multiple investigative modalities, for understanding the eco-evo-devo-based principles that drive BC progression and treatment. These integrative eco-evo-devo theories can help clinicians better diagnose and treat BC, for example, by using non-invasive biomarkers in liquid-biopsies that have emerged from integrated omics-based data that accurately reflect the biomolecular landscape of the primary tumor in order to avoid mutilating preventive surgery, like bilateral mastectomy. From the perspective of preventive, personalized, and participatory medicine, these hypotheses may help patients to think about this disease as a process governed by natural rules, to understand the possible causes of the disease, and to gain control on their own health.

Metabolic Imaging as a Tool to Characterize Chemoresistance and Guide Therapy in Triple-Negative Breast Cancer (TNBC)

After an initial response to chemotherapy, tumor relapse is frequent. This event is reflective of both the spatiotemporal heterogeneities of the tumor microenvironment as well as the evolutionary propensity of cancer cell populations to adapt to variable conditions. Because the cause of this adaptation could be genetic or epigenetic, studying phenotypic properties such as tumor metabolism is useful as it reflects molecular, cellular, and tissue-level dynamics. In triple-negative breast cancer (TNBC), the characteristic metabolic phenotype is a highly fermentative state. However, during treatment, the spatial and temporal dynamics of the metabolic landscape are highly unstable, with surviving populations taking on a variety of metabolic states. Thus, longitudinally imaging tumor metabolism provides a promising approach to inform therapeutic strategies, and to monitor treatment responses to understand and mitigate recurrence. Here we summarize some examples of the metabolic plasticity reported in TNBC following chemotherapy and review the current metabolic imaging techniques available in monitoring chemotherapy responses clinically and preclinically. The ensemble of imaging technologies we describe has distinct attributes that make them uniquely suited for a particular length scale, biological model, and/or features that can be captured. We focus on TNBC to highlight the potential of each of these technological advances in understanding evolution-based therapeutic resistance.

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) integrating the appropriate components into mathematical models (2) design and validation of dosing protocols, and (3) challenges and opportunities in clinical translation.

The Genetic Evolution of Metastasis

Cancer is an evolutionary process that is characterized by the emergence of multiple genetically distinct populations or clones within the primary tumor. Intratumor heterogeneity provides a substrate for the selection of adaptive clones, such as those that lead to metastasis. Comparative molecular studies of primary tumors and metastases have identified distinct genomic features associated with the development of metastases. In this review, we discuss how these insights could inform clinical decision-making and uncover rational antimetastasis treatment strategies.

Is There One Key Step in the Metastatic Cascade?

Simple Summary

To successfully metastasize, cancer cells must complete a sequence of obligatory steps called the metastatic cascade. To model the metastatic cascade, we used the framework of the Drake equation, initially created to describe the emergence of intelligent life in the Milky way, using a similar logic of a sequence of obligatory steps. Then within this framework, we used simulations on breast cancer to investigate the contribution of each step to the metastatic cascade. We show that the half-life of circulating tumor cells is one of the most important parameters in the cascade, suggesting that therapies reducing the survival of those cells in the vascular system could significantly reduce the risk of metastasis.

Abstract

The majority of cancer-related deaths are the result of metastases (i.e., dissemination and establishment of tumor cells at distant sites from the origin), which develop through a multi-step process classically termed the metastatic cascade. The respective contributions of each step to the metastatic process are well described but are also currently not completely understood. Is there, for example, a critical phase that disproportionately affects the probability of the development of metastases in individual patients? Here, we address this question using a modified Drake equation, initially formulated by the astrophysicist Frank Drake to estimate the probability of the emergence of intelligent civilizations in the Milky Way. Using simulations based on realistic parameter values obtained from the literature for breast cancer, we examine, under the linear progression hypothesis, the contribution of each component of the metastatic cascade. Simulations demonstrate that the most critical parameter governing the formation of clinical metastases is the survival duration of circulating tumor cells (CTCs).

Serial circulating tumor DNA identification associated with the efficacy and prognosis of neoadjuvant chemotherapy in breast cancer

BACKGROUND

Circulating tumor DNA (ctDNA) provides a promising noninvasive alternative to evaluate the efficacy of neoadjuvant chemotherapy (NCT) in breast cancer.

METHODS

Herein, we collected 63 tissue (aspiration biopsies and resected tissues) and 206 blood samples (baseline, during chemotherapy (Chemo), after chemotherapy (Post-Chemo), after operation (Post-Op), during follow-up) from 32 patients, and preformed targeted deep sequencing with a customed 1021-gene panel.

RESULTS

As the results, TP53 (43.8%) and PIK3CA (40.6%) were the most common mutant genes in the primary tumors. At least one tumor-derived mutation was detected in the following number of blood samples: 21, baseline; 3, Chemo; 9, Post-Chemo; and 5, Post-Op. Four patients with pathologic complete response had no tissue mutation in Chemo and Post-Chemo blood. Compared to patients with mutation-positive Chemo or Post-Chemo blood, the counterparts showed a superior primary tumor decrease (median, 86.5% versus 54.6%) and lymph involvement (median, 1 versus 3.5). All five patients with mutation-positive Post-Op developed distant metastases during follow-up, and the sensitivity of detecting clinically relapsed patients was 71.4% (5/7). The median DFS was 9.8 months for patients with mutation-positive Post-Op but not reached for the others (HR 23.53; 95% CI, 1.904-290.9; p < 0.0001).

CONCLUSIONS

Our study shows that sequential monitoring of blood ctDNA was an effective method for evaluating NCT efficacy and patient recurrence. Integrating ctDNA profiling into the management of LABC patients might improve clinical outcome.

TRIAL REGISTRATION

This prospective study recruited LABC patients at Peking Union Medical College Hospital (ClinicalTrials.gov Identifier: NCT02797652).

Treatment-induced evolutionary dynamics in nonmetastatic locally advanced rectal adenocarcinoma

Multi-modal treatment of non-metastatic locally advanced rectal adenocarcinoma (LARC) includes chemotherapy, radiation, and life-altering surgery. Although highly effective for local cancer control, metastatic failure remains significant and drives rectal cancer-related mortality. A consistent observation of this tri-modality treatment paradigm is that histologic response of the primary tumor to neoadjuvant treatment(s), which varies across patients, predicts overall oncologic outcome. In this chapter, we will examine this treatment response heterogeneity in the context of evolutionary dynamics. We hypothesize that improved understanding of eco-evolutionary pressures rendering small cancer cell populations vulnerable to extinction may influence treatment strategies and improve patient outcomes. Applying effective treatment(s) to cancer populations causes a "race to extinction." We explore principles of eco-evolutionary extinction in the context of these small cancer cell populations, evaluating how treatment(s) aim to eradicate the cancer populations to ultimately result in cure. In this chapter, we provide an evolutionary rationale for limiting continuous treatment(s) with the same agent or combination of agents to avoid selection of resistant cancer subpopulation phenotypes, allowing "evolutionary rescue." We draw upon evidence from nature demonstrating species extinction rarely occurring as a single event phenomenon, but rather a series of events in the slide to extinction. We posit that eradicating small cancer populations, similar to small populations in natural extinctions, will usually require a sequence of different external perturbations that produce negative, synergistic dynamics termed the "extinction vortex." By exploiting these unique extinction vulnerabilities of small cancer populations, the optimal therapeutic sequences may be informed by evolution-informed strategies for patients with LARC.