A complexity-informed perspective on why cancer heterogeneity may reflect not only tumor biology, but differences in host regulatory architecture

Most cancer models still center the lesion: what mutation, what carcinogen, what pathway, what exposure. Those questions matter. But they do not fully explain why similar exposures, similar molecular findings, or even similar tumors can lead to very different trajectories across people. The Evolutionary-Stress Framework (ESF) offers a way to widen that picture by asking not only what changed in the tumor, but what kind of regulatory system the tumor emerged within. This move builds on a broader cancer literature that already recognizes cancer as involving multiple interacting capabilities, enabling conditions, and microenvironmental dependencies rather than a single isolated lesion process (Hanahan & Weinberg, 2000, 2011; Hanahan, 2022).

From an ESF perspective, one of the biggest problems in cancer thinking is that we often use the word cancer as if it names one kind of problem. But in practice, cancer questions span several very different domains. Some questions are straightforward and protocolized. Some require expert decomposition and classification. Some involve dynamic tumor-host coupling across time. And some are true crisis states in which immediate stabilization matters more than explanation. Much of the confusion in cancer discourse comes from collapsing all of these levels into one explanatory frame.

Most cancer models still center the lesion: what mutation, what carcinogen, what pathway, what exposure. Those questions matter. But they do not fully explain why similar exposures, similar molecular findings, or even similar tumors can lead to very different trajectories across people. The Evolutionary-Stress Framework (ESF) offers a way to widen that picture by asking not only what changed in the tumor, but what kind of regulatory system the tumor emerged within. This move builds on a broader cancer literature that already recognizes cancer as involving multiple interacting capabilities, enabling conditions, and microenvironmental dependencies rather than a single isolated lesion process (Hanahan & Weinberg, 2000, 2011; Hanahan, 2022).

From an ESF perspective, one of the biggest problems in cancer thinking is that we often use the word cancer as if it names one kind of problem. But in practice, cancer questions span several very different domains. Some questions are straightforward and protocolized. Some require expert decomposition and classification. Some involve dynamic tumor-host coupling across time. And some are true crisis states in which immediate stabilization matters more than explanation. Much of the confusion in cancer discourse comes from collapsing all of these levels into one explanatory frame.

Note: ESF does not claim that stress causes cancer, and it does not replace tumor genomics or standard oncology. It offers a systems-level framework for understanding why similar tumor features can unfold differently across differently organized host systems.

Figure 1. Cancer is not one kind of problem. This conceptual map distinguishes cancer questions that are clear, complicated, complex, and chaotic. The point is not to replace standard oncology, but to clarify which kinds of reasoning fit which kinds of cancer problems.

A note on the figures: These are conceptual framework diagrams, intended to organize cancer questions across levels and domains — not to present a finalized clinical model or substitute for established oncology evidence. The four-domain distinction in The four-domain distinction in Figure 1 adapts Snowden’s Cynefin framework, a sense-making model from complexity science.

This distinction matters because linear medicine is often entirely appropriate within its proper domain. If there is a localized lesion that should be removed, a validated screening pathway to follow, or a defined deficiency or complication to treat, protocolized cause-action reasoning is exactly what we want. The same is true for the complicated domain, where expert pathology, tumor staging, histology, genomics, and treatment matching decompose the tumor into clinically meaningful features. ESF does not challenge the value of these approaches. It challenges the habit of assuming that they exhaust the problem.

Where ESF becomes especially useful is in the complex domain: cancer as a tumor-host adaptive ecosystem. Here the central issue is no longer only what mutation is present, but how tumor biology, tissue ecology, immune behavior, endocrine-metabolic coupling, inflammation, repair capacity, and life-course stress history interact. This resonates with work on tumor microenvironments, immune contexture, and cancer as an ecological and evolutionary process rather than a purely cell-autonomous event (Quail & Joyce, 2013; Hinshaw & Shevde, 2019; Fridman et al., 2017; Gillies et al., 2012).

In most biomedical discussions, heterogeneity is treated as a matter of degree: more or less inflammation, more or less insulin resistance, more or less cortisol, more or less immune suppression. Architectural heterogeneity asks a different kind of question. It asks how the regulatory system is organized. Which loops amplify? Which loops buffer? Where are the bottlenecks? Which state transitions become likely under prolonged load? Two patients may both show “high inflammation,” but the broader architecture producing and containing that inflammation may be very different. Those are not only different amounts. They may reflect different system organizations. This distinction is not identical to existing oncology frameworks, but it is compatible with growing recognition that non-genetic heterogeneity, plasticity, and host context shape tumor behavior and treatment response (Brock et al., 2009; Marusyk et al., 2012; Boumahdi & de Sauvage, 2020).

Figure 2. Cancer questions are nested across levels.
Cancer can be studied as cellular evolution, tissue ecology, host regulation, and life-course stress history. ESF argues that confusion often arises when these different levels are collapsed into one explanatory frame.

This nested view helps clarify why cancer often seems both highly specific and frustratingly nonspecific at the same time. At the cellular level, cancer clearly involves mutation, clonal selection, apoptosis escape, genomic instability, phenotypic plasticity, and metabolic rewiring (Hanahan & Weinberg, 2011; Greaves & Maley, 2012; Pavlova & Thompson, 2016). At the tissue level, it also involves hypoxia, stromal remodeling, inflammatory niches, angiogenesis, and changing patterns of immune-cell infiltration (Quail & Joyce, 2013; Vaupel & Mayer, 2007; Semenza, 2012). At the host level, those local processes sit within broader patterns of endocrine, autonomic, metabolic, and immune regulation, including neuroendocrine influences on the tumor microenvironment (Cole et al., 2015). Across the life-course, those host systems have already been shaped by developmental timing, chronic adversity, sleep disruption, infection, nutrition, environmental exposures, treatment history, and cumulative repair burden.

This nesting is not only an organizing exercise. It is the reason two people with similar tumors can face different recurrence risks, different treatment responses, and different symptom burdens — because those outcomes depend on the host architecture the tumor is embedded in, not on the lesion alone.

This is why broad cancer risk factors so often appear messy or nonspecific. Obesity, chronic inflammation, disrupted circadian rhythms, trauma burden, endocrine disruption, infection, sedentary life, malnutrition, and pollution do not all “cause cancer” in the same simple way. From an ESF perspective, they matter because they place load on shared regulatory architecture. They reshape the stress–energy–repair landscape in which tissues operate. That does not make cancer reducible to stress. It means that stress history helps shape the regulatory terrain in which malignant processes either remain contained, become stabilized, or escape control. This claim is most consistent with literature on inflammation, allostasis, host regulation, and biobehavioral influences on tumor biology, not with any simple “stress causes cancer” narrative (Coussens & Werb, 2002; Grivennikov et al., 2010; McEwen & Wingfield, 2003; Cole et al., 2015).

A useful conceptual move here is to think of cancer progression as involving self-reinforcing system states. Tumor cells, tissue ecology, and host responses do not remain independent. Inflammation, hypoxia, immune evasion, angiogenesis, and metabolic rewiring can begin to stabilize one another over time. In that sense, cancer may be better understood not only as abnormal cells growing, but as a disturbed multicellular ecology in which tumor and host become dynamically coupled (Quail & Joyce, 2013; Schreiber et al., 2011; Faubert et al., 2020).

Figure 3. Cancer as tumor-host attractor dynamics.
This ESF-style diagram illustrates cancer progression as movement through a changing regulatory landscape. It is offered as a conceptual model for thinking about dynamic coupling between tumor biology, tissue ecology, and host state.

The language of attractors is helpful here, as long as it is understood as a conceptual tool rather than a finalized clinical model. In this framing, coordinated tissue regulation is not “perfect health,” but a relatively stable zone in which growth, apoptosis, immune surveillance, repair, and resource allocation remain sufficiently coordinated. Prolonged load can shift the system into more perturbed terrain, where inflammation, hypoxia, endocrine disruption, immune exhaustion, and metabolic strain make maladaptive states easier to stabilize. Once tumor and host responses begin reinforcing one another, cancer can become a more deeply entrenched system state rather than a purely localized defect. This idea is compatible with cancer ecology, tumor evolution, and non-genetic plasticity frameworks (Anderson et al., 2006; Huang, 2011; Greaves & Maley, 2012; Turajlic & Swanton, 2016).

This framing also clarifies why crisis states should not be confused with the whole cancer process. Acute treatment toxicity, sepsis, organ failure, airway or bleeding crises, severe cachexia, or end-stage destabilization belong to a different domain: one in which the first task is stabilization, not full-system explanation. ESF helps separate these domains so that people do not mistake emergency regulation for the whole architecture of cancer, or mistake conceptual complexity for a reason to abandon urgent action.

The practical implication is not that oncology should abandon genetics, pathology, surgery, chemotherapy, targeted therapy, or immunotherapy. It is that precision oncology may be incomplete if it stops at tumor genomics alone. Host architecture matters. Differences in immune-metabolic coupling, recovery dynamics, endocrine regulation, autonomic tone, developmental history, and available regulatory bandwidth may all help shape symptom burden, treatment response, recurrence risk, and long-term trajectory. In this sense, cancer heterogeneity may reflect not only tumor heterogeneity, but host-architecture heterogeneity as well. This is broadly aligned with systems medicine and network medicine arguments that disease cannot always be understood solely from isolated components (Ahn et al., 2006; Barabási et al., 2011; Hasin et al., 2017).

That is the specific contribution ESF hopes to make. It does not replace established oncology. It offers a complexity-informed framework for organizing observations that linear models often leave fragmented: why similar lesions can produce different outcomes, why risk factors are broad and convergent, and why organism-level regulation may be as important to cancer trajectories as tumor-level variation. Seen this way, cancer is not only a disease of bad cells. It is also a breakdown in multicellular coordination occurring within a heterogeneous living system.

At the Center for Adaptive Stress, our work is grounded in the idea that many of today’s most difficult health problems cannot be adequately understood through single-cause models alone. Cancer is one of the clearest examples. ESF is an effort to build better conceptual scaffolding: not to dismiss standard medicine, but to help clarify where it is strongest, where it becomes incomplete, and how a more coherent systems language might help medicine better understand variation, vulnerability, recovery, and repair.

---

Note: This perspective does not replace established oncology; it builds on converging work in cancer hallmarks, tumor microenvironment research, cancer immunology, evolutionary oncology, and systems medicine to argue that cancer trajectories may reflect not only tumor heterogeneity, but host-architecture heterogeneity as well.

The underlying components of this argument are well supported across oncology, tumor microenvironment research, immunology, metabolism, and systems biology. What is more novel—and therefore more open to debate—is the ESF synthesis: the proposal that cancer trajectories are shaped not only by tumor features, but by differences in host regulatory architecture across the life course.

References (working bibliography)

Ahn, A. C., Tewari, M., Poon, C.-S., & Phillips, R. S. (2006). The limits of reductionism in medicine: Could systems biology offer an alternative? PLoS Medicine, 3(6), e208. https://pubmed.ncbi.nlm.nih.gov/16681415/

Anderson, A. R. A., Weaver, A. M., Cummings, P. T., & Quaranta, V. (2006). Tumor morphology and phenotypic evolution driven by selective pressure from the microenvironment. Cell, 127(5), 905–915. https://pubmed.ncbi.nlm.nih.gov/17129778/

Barabási, A.-L., Gulbahce, N., & Loscalzo, J. (2011). Network medicine: A network-based approach to human disease. Nature Reviews Genetics, 12(1), 56–68. https://pubmed.ncbi.nlm.nih.gov/21164525/

Beatty, G. L., & Gladney, W. L. (2015). Immune escape mechanisms as a guide for cancer immunotherapy. Clinical Cancer Research, 21(4), 687–692. https://pubmed.ncbi.nlm.nih.gov/25501578/

Binnewies, M., Roberts, E. W., Kersten, K., Chan, V., Fearon, D. F., Merad, M., … Krummel, M. F. (2018). Understanding the tumor immune microenvironment (TIME) for effective therapy. Nature Medicine, 24(5), 541–550.https://pubmed.ncbi.nlm.nih.gov/29686425/

Brock, A., Chang, H., & Huang, S. (2009). Non-genetic heterogeneity—A mutation-independent driving force for the somatic evolution of tumours. Nature Reviews Genetics, 10(5), 336–342.

Boumahdi, S., & de Sauvage, F. J. (2020). The great escape: Tumour cell plasticity in resistance to targeted therapy. Nature Reviews Drug Discovery, 19(1), 39–56.

Chen, D. S., & Mellman, I. (2013). Oncology meets immunology: The cancer-immunity cycle. Immunity, 39(1), 1–10.

Cole, S. W., Nagaraja, A. S., Lutgendorf, S. K., Green, P. A., & Sood, A. K. (2015). Sympathetic nervous system regulation of the tumour microenvironment. Nature Reviews Cancer, 15(9), 563–572.

Coussens, L. M., & Werb, Z. (2002). Inflammation and cancer. Nature, 420(6917), 860–867. https://pubmed.ncbi.nlm.nih.gov/12490959/

Faubert, B., Solmonson, A., & DeBerardinis, R. J. (2020). Metabolic reprogramming and cancer progression. Science, 368(6487), eaaw5473.

Fridman, W. H., Zitvogel, L., Sautès-Fridman, C., & Kroemer, G. (2017). The immune contexture in cancer prognosis and treatment. Nature Reviews Clinical Oncology, 14(12), 717–734.

Gatenby, R. A., & Gillies, R. J. (2004). Why do cancers have high aerobic glycolysis? Nature Reviews Cancer, 4(11), 891–899.

Gillies, R. J., Verduzco, D., & Gatenby, R. A. (2012). Evolutionary dynamics of carcinogenesis and why targeted therapy does not work. Nature Reviews Cancer, 12(7), 487–493.

Greaves, M., & Maley, C. C. (2012). Clonal evolution in cancer. Nature, 481(7381), 306–313. https://pubmed.ncbi.nlm.nih.gov/22258609/

Grivennikov, S. I., Greten, F. R., & Karin, M. (2010). Immunity, inflammation, and cancer. Cell, 140(6), 883–899.

Hanahan, D. (2022). Hallmarks of cancer: New dimensions. Cancer Discovery, 12(1), 31–46.

Hanahan, D., & Weinberg, R. A. (2000). The hallmarks of cancer. Cell, 100(1), 57–70.

Hanahan, D., & Weinberg, R. A. (2011). Hallmarks of cancer: The next generation. Cell, 144(5), 646–674.

Hasin, Y., Seldin, M., & Lusis, A. (2017). Multi-omics approaches to disease. Genome Biology, 18, 83.

Hinshaw, D. C., & Shevde, L. A. (2019). The tumor microenvironment innately modulates cancer progression. Cancer Research, 79(18), 4557–4566.

Hood, L., & Flores, M. (2012). A personal view on systems medicine and the emergence of proactive P4 medicine. New Biotechnology, 29(6), 613–624.

Huang, S. (2011). Tumor progression: Chance and necessity in Darwinian and non-Darwinian somatic evolution. Progress in Biophysics and Molecular Biology, 106(2), 215–220.

Loscalzo, J., & Barabási, A.-L. (2011). Systems biology and the future of medicine. Wiley Interdisciplinary Reviews: Systems Biology and Medicine, 3(6), 619–627.

Marusyk, A., Almendro, V., & Polyak, K. (2012). Intra-tumour heterogeneity: A looking glass for cancer? Nature Reviews Cancer, 12(5), 323–334.

McEwen, B. S., & Wingfield, J. C. (2003). The concept of allostasis in biology and biomedicine. Hormones and Behavior, 43(1), 2–15.

Pavlova, N. N., & Thompson, C. B. (2016). The emerging hallmarks of cancer metabolism. Cell Metabolism, 23(1), 27–47.

Picard, M., McEwen, B. S., Epel, E. S., & Sandi, C. (2018). An energetic view of stress: Focus on mitochondria. Frontiers in Neuroendocrinology, 49, 72–85.

Quail, D. F., & Joyce, J. A. (2013). Microenvironmental regulation of tumor progression and metastasis. Nature Medicine, 19(11), 1423–1437.

Schreiber, R. D., Old, L. J., & Smyth, M. J. (2011). Cancer immunoediting: Integrating immunity’s roles in cancer suppression and promotion. Science, 331(6024), 1565–1570.

Semenza, G. L. (2012). Hypoxia-inducible factors in physiology and medicine. Cell, 148(3), 399–408.

Turajlic, S., & Swanton, C. (2016). Metastasis as an evolutionary process. Science, 352(6282), 169–175.

Vaupel, P., & Mayer, A. (2007). Hypoxia in cancer: Significance and impact on clinical outcome. Cancer and Metastasis Reviews, 26(2), 225–239.

FURTHER READING:

Aktipis, C. A., & Nesse, R. M. (2013). Evolutionary foundations for cancer biology. Evolutionary Applications, 6(1), 144–159.

Basanta, D., & Anderson, A. R. A. (2013). Exploiting ecological principles to better understand cancer progression and treatment. Interface Focus, 3(4), 20130020.

Ashley, E. A. (2016). Towards precision medicine. Nature Reviews Genetics, 17(9), 507–522.