An integrative model of influences on cancer prevention, detection, and treatment

Corresponding Author:

Fatimah Jackson
Biology and W Montague Cobb Research Laboratory, College of Arts and Sciences, Howard University, USA
E-mail: fatimah.jackson@howard.edu

Abstract

Our scientific and clinical understanding of cancer is changing. Advances in technology coupled with new discoveries in cancer biology are forcing a broadened perspective and with that, recognition of a more inclusive model of cancer’s influencing factors. We present a multidimensional dynamic model of the interactions of genomic, epigenomic, and environmental factors that can provide research pathways for future studies. These are predicted to stimulate the development of comprehensive cancer algorithms and the evolution of universal precision medicine. The current inadequacy of human genomic databases on African-descended human groups and their cancer tumor genomics are discussed and the efforts at Howard University to ameliorate these deficiencies through the development of comprehensive genomic databases. The importance of systematically addressing environmental catalysts for cancer including bioactive dietary factors, the body’s commensal microbiota, and the role of immunotherapy in cancer control are discussed. The need for population-and cancer-specific biomarkers to be identified and algorithms developed is highlighted and the role for large-scale studies of African-descended groups emphasized.

Keywords

Gene-environment interactions in cancer, epigenome, big data, African Americans, cancer tumor biology

Introduction

New discoveries in cancer genetics and epigenetics have opened novel vistas for understanding cancer biology and behavior. This has generated tremendous opportunities for new and more comprehensive approaches to the diagnosis and treatment of different types of cancer. The overall rate of death from cancer in the US dropped a total of 27% from 1991 to 2016, according to recently published data [1]. Death rates declined for lung, breast, prostate, and colorectal cancers, but they increased for cancers related to obesity, such as uterine, pancreatic and liver. This shift may reflect advances in screening and diagnosis, improvements in therapeutics and supportive care, and reductions in exposure to the environmental catalysts associated with lung, breast, prostate, and colorectal cancers.

Cancer survivorship has increased from 3.6 million in 1975 to 15.5 million in 2016 and it is estimated that by 2040 there will be about 26 million cancer survivors, the majority of whom will be over 60 years of age [2]. Treatments in oncology are moving at an accelerated rate towards precision medicine. The key now is to make this trajectory available for all and to decrease the frequency of cancers related to obesity through efficacious and sustainable preventative measures.

This latter goal (modulating environmental factors) is surprisingly less arduous than the former (developing precision medicine for diverse populations). Universal precision medicine will remain elusive until we develop the reference databases for a broad crosssection of humanity. Right now, the bulk of the databases are limited to populations of North Atlantic European ancestry. This imbalance significantly limits the development of precision medicine for non-Europeans, particularly for those of African descent. For these millions, the elusiveness of access to precision/personalized medicine is compounded by the very high levels of unchartered genetic variability among individuals of African ancestry. Much of this variability is unstudied and what is known, is often inadequately referenced. We, therefore, have fragmented profiles of human genetic variability within the US and even more so worldwide. We know even less about their epigenomic diversity and have poorly characterized their tumor genetics. The environmental contexts within which these genetic and epigenetic factors interact are largely unexplored. What is needed is an integrated model for cancer that combines data from genetics and genomics, epigenomics, and environmental sources in identifying the most salient components of cancer’s initiation and persistence. These interrelationships are depicted in FIGURE 1. Each interactive segment of FIGURE 1(a-f) is a critical contributing aspect of carcinogenesis for further scientific and clinical investigations. As such, each provides a suggested pathway of future investigation figure.

Figure 1. A model of the interaction of environmental, epigenomic, and genomic factors in cancer. Advances in cancer treatment and therapy will need to take into account the synergistic influences of abiotic and biotic environmental factors on tumor and human genomes and epigenomes, the cross-talk between tumor genomes and host genomes, between the epigenome and tumor genomes, and the influence of the host epigenome on human gene expression patterns. These complex and ongoing interactions require an integrated bioinformatics approach to cancer treatment and therapy and make the case for broad based knowledge of each component of the model.

The least explored part of this interactive model has been a comprehensive reference database for peoples of African descent that can provide the essential insights for understanding the genetic contributions to the increased cancer susceptibilities in African Americans. At Howard University we have looked into historic cancer in Washington DC [3] and initiated three unique genomic databases (in collaboration with Helix and National Geographic) through the 1000 Transatlantic African Diaspora Genomic Database Project, the 10,000 Continental African Genomic Database Project, and the 1000 Red Sea African Diaspora Genome Project. These three databases are designed to capture much of the initial “missing” human genetic variation in these understudied populations. To this is needed a comprehensive assessment of the African and African American cancer tumor genomics including the Tumor Mutational Burden (TMB) in patient populations. Multiple clinical trial results support TMB’s potential in identifying patients most likely to benefit from immunotherapy across a wide range of tumor types [4]. We suspect that many of the cancer tumor genomic profiles in African descended patients may vary compared to those observed in European-descended patients simply because the background environmental and epigenomic factors may differ in cancer onset, progression, and establishment. Tumor genomics has been demonstrated to play a major role in the pattern of carcinogenesis.

Genetic and molecular studies of different types of human cancers have revealed an interaction between genome and epigenome in the process of carcinogenesis. Epigenetics can affect gene expression without affecting the sequence of the DNA and it does so through different mechanisms including alterations in DNA methylation, histone modification, and nucleosome positioning, and miRNA mediated targeting of various genes without affecting the sequence of the DNA [5]. Genetic alterations of the epigenome (FIGURE 1c) can cause cancer just as mutations imposed be epigenetic processes (FIGURE 1f). Genetic and epigenetic alterations intertwine and affect each other in a way that changes in epigenetic processes may lead to genetic mutations and on the other hand genetic mutations can cause an alteration in epigenome (FIGURE 1b,c,f). This interaction between the genome and the epigenome offers possibilities to develop new therapies.

The environment has long been recognized as an influencing factor in cancer development (FIGURE 1a). The most famous environmental factor known to cause cancer is tobacco which accounts for about 20%-30% of cancer cases. Other environmental factors such as infectious agents, diet, ultraviolet radiation, and alcohol are well-known carcinogens, but a number of environmental anti-carcinogens are also under study. Altering the diet can alter the epigenetic state of genes (FIGURE 1d), thereby modulating cancer risk (FIGURE 1f) [6]. Researchers around the world are actively studying how nutrients and nonnutritive phytochemicals such as capsaicin in colon cancer and β-catenin in breast cancer impact molecular carcinogenesis with the goal of identifying chemopreventative agents that positively impact target genes (FIGURE 1e) [7,8].

The commensal microbiota of the body is another environmental factor that we are exposed to throughout our life and due to its interaction with the immune system and metabolic capacity has a significant effect on human biology. Increasing numbers of studies are showing that our microbiota composition is an important factor affecting cancer development (FIGURE 1a), and response to therapy. Moreover, the microbiota composition of the human body, especially that of the GI tract affects some side effects of chemotherapy. As an example, studies have shown that increased diversity of intestinal microbiome is predictive of decreased mortality in patient with hematopoietic malignancies who undergo allogeneic hematopoietic stem cell transplant [9] These effects of the commensal microbiota of the body are a great subject for research and are potential targets for developing novel therapies for cancer or as adjunctive medications to chemotherapy and immunotherapy to increase their effect or to reduce their side effects because of the interactive relationships depicted in FIGURE 1.

Another area that has gained significant interest in recent years is immunotherapy. Immunotherapy and targeted therapy are hoped to revolutionize cancer care, following the model pathway e b. Immune checkpoint inhibitors are new therapies that have shown promising response and improved survival in some patients with cancers such as lung, head and neck, and melanoma. These medications modulate a patient’s own immune system to fight cancer cells. One such medication is Pembrolizumab, a humanized monoclonal antibody against programmed death 1 (PD- 1). In a randomized trial of pembrolizumab compared to platinum-based chemotherapy on 305 patients with advanced non-small-cell lung cancer with PD-L1 expression on at least 50% of tumor cells, pembrolizumab was associated with significantly longer progression-free and overall survival and with fewer adverse events.

A major challenge towards personalized/ precision medicine is to identify which medication works best for which patient. To achieve this goal, we need biomarkers that will show us in each patient which medication or combination of medications is most efficacious. One genomic biomarker with significant potential impact on immunotherapy is Tumor Mutational Burden (TMB). Tumor mutational burden measures the number of mutations within a tumor genome. It has been shown that tumors with high TMB have better response to immunotherapy (FIGURE 1a) and also better survival. TMB has potential use in all types of cancer and it is already being used in lung cancer. The beneficial effects of high TMB are particularly relevant to PD-1/PD-L1 blockade. Studies of pembrolizumab in non-small cell lung cancer has shown that higher TMB in tumors correlated with better response rate and progression-free survival [10]. Although TMB has mostly been studied in cancers such as melanoma and lung cancer, studies have shown that it can potentially predict outcome in other tumor histologies as well. Gut microbiome composition is also significant, considering its interaction with the immune system (FIGURE 1e). It is being studied as another biomarker affecting response to Immune checkpoint inhibitors (e.g., studies in mouse models Sivan et al; Vétizou et al) Therefore, it has been hypothesized that individualized differences in commensal microbiota may be a factor responsible for variations in clinical response to cancer treatment [11,12].

The usefulness of our model is best tested in its application to understanding the role of specific genes to cancer tumor genomics. One gene that could be considered is that of Sirtuin 1 (SIRT 1), silent mating type information regulation 2 homolog. This enzyme deacetylates proteins that contribute to cellular regulation (e.g., reaction to stressors, longevity) and in humans, it may function as an intracellular regulatory protein with mono-ADP-ribosyltransferase activity. Indeed, the sirtuin family has emerged as important regulators of diverse physiological and pathological events, including life-span extension, neurodegeneration, age-related disorders, obesity, heart disease, inflammation, and cancer. Of the 7 mammalian members of the sirtuin family, the function of SIRT1 has been evaluated most extensively. SIRT1 can deacetylate histones (FIGURE 1c) and a number of nonhistone substrates, which are involved in multiple signaling pathways [13] of which tumor suppression is just one (FIGURE 1b). In fact, SIRT1 may act as either a tumor suppressor or tumor promoter depending on its targets in specific signaling pathways or in specific cancers. SIRT 1 expression is influenced by resveratrol (and a range of other dietary-derived plant polyphenols) appear to activate sirtuin 1 against non-modified peptide substrates (FIGURE 1a and e).

A clear limitation to making meaningful insights into cancer genomics, however, remains the lack of inclusion of diverse populations for sequencing, genotyping and expression analyses [14]. In addition, the lack of understanding about the biological implications of a structured and admixed population history continues to haunt biomedical science research with the likely erroneous assumption that African Americans are genomically uniform population despite their ancestry being primarily a composite of African peoples. This paucity of ethnic inclusion in phase III trials is in spite of the disproportionate impact that breast cancer and prostate cancer play in the health of African American populations [3,15]. Little population genomic thought has been invested for example in how the cancer phenotype of Henrietta Lacks, an African American woman whose aggressive cervical cancer has been immortalized in cell lines, has advanced science. Clearly, the ethnic configuration of most cancer trials tilts the field toward ascertainment bias in favor of European descendant populations. But cancer variants are being largely tested in cells immortalized with African descendant genomic backgrounds. The long term effects of this are still unknown but may impact the pleiotropy interactions in cancer [16-19]. A solution to this problem is both the increased inclusion of African descendant populations in clinical trials such that they study populations reflect the American incidence of disease and the creation of comprehensive databases that reflect African American descendant communities. Without these two initiatives, it will be challenging to close the health disparities gap in cancers with multifactorial etiologies.

Despite all the discoveries and progressions in the understanding of cancer biology, the realization of precision/personalized medicine seems to be years away for African descended populations. Although many biomarkers have been introduced and some are being actually used in practice, still they are not enough. Many more biomarkers need to be identified and algorithms developed to use these biomarkers as guidance to select optimal therapies. This information could come from big data analyses of large scale population studies of diverse and underrepresented groups, with an emphasis on peoples of African descent. This is precisely the rationale for the big data projects we have initiated at Howard University. With these databases in place and subjected to bioinformatics analyses, the promise of more universal precision/personalized medicine for different types of cancer and for all peoples becomes more feasible.

Author’s Contributions

N.A. contributed the clinical insights and cancer epidemiological trends. L.J. contributed information on the bioinformatics. R.J contributed dietary phytochemical information. F.J. was invited by the editor-in-chief to submit a commentary article. F.J. designed the model and identified the interactive components. The authors collaborated on synthesizing data and refining the argument for an integrated model for cancer’s influences. The authors declare no financial interest the data and model discussed in this commentary.

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