Investigations into the Role of Aging in Carcinogenesis:
Focus on Tumor Progression

Once considered a cell-based process, progression of existing cancers is now appreciated to involve elaborate tumor-host interactions, including, but not limited to, angiogenesis, matrix remodeling, and immune editing. Although the conduciveness of the host to tumor advancement is itself strongly dependent on host age, the direct role of age as a modifier of cancer progression remains unexplored. This is true despite age being among the strongest risk factors for cancer incidence. Epidemiological data show that from adolescence through middle age, cancer incidence increases with age, while during middle-age the incidence begins to decelerate and, surprisingly, it actually decreases at sufficiently advanced ages, for many tumor sites (Fig. 1). Several theories have been proposed to account for the decreased incidence observed at old age including: 1) Incidence data obtained may not be accurately collected in older patients; however, this notion is not supported by studies done on animal models which demonstrate the same pattern as observed in humans, 2) A widely accepted theory that no doubt contributes, is that there is heterogeneity among individuals with respect to intrinsic cancer susceptibility, also referred to as frailty. More susceptible individuals develop cancer and are removed from the statistical population leaving, on average, a population of less susceptible individuals at older ages, and 3) Age-dependent host influences that act to modify tumor promotion and/or progression, which now appear, at sufficiently old ages, to inhibit tumor progression and actually lower clinical incidence. At CCSB, our research reveals this reduced capacity of older hosts to support tumor advancement, which offers insight into an important phenomenon that limits tumor progression and incidence at advanced age.

Figure 1     Figure 1. Schematic of U.S. cancer incidence based on Surveillance, Epidemiology and End Results (SEER) data for cancers at all sites. The vertical axis is age-specific cancer incidence. The curve indicates, that among 100 individuals aged 70 who have not previously had cancer, about 2 will present with cancer during their 70th year. It is seen that from age ~10 to age ~85 incidence increases (i.e. the slope of the curve is positive); thereafter, as has been recently realized, there is for some sites actually a decrease (negative slope) in cancer incidence at these advanced ages. From age ~10 to age ~60 incidence accelerates (slope is increasing). Around 60yrs of age a maximum slope is reached and thereafter incidence is decelerating, characterized by decreasing slope. Formally, the slope is given by the first derivative of the incidence function; acceleration corresponds to a positive second derivative, and deceleration to a negative second derivative. [Beheshti et al, 2013]

Host Dependent Changes as a Function of Age Impact Tumor Progression

The literature associates a number of age-related changes with lower tumor progression. These include loss in host angiogenic potential altered immune response, and age-related cellular senescence. Although many of investigations have focused on decreased angiogenic factors that contribute to slower tumor progression at older ages, they often fail to consider the simultaneous impact of other systemic factors of the aged hosts on tumor progression. A more global age related perspective was recently discussed by López-Otín et al. [López-Otín et al, 2013], “hallmarks” of aging were identified, but little was discussed as to how these hallmarks would impact carcinogenesis other than acknowledging that aging and cancer can be considered as two conditions deriving from many of the same processes (i.e. increases in mutations and accumulating cellular damage). To reveal a more global appreciation of the impact of aged hosts on carcinogenesis, particularly the tumor progression phases, we at CCSB utilize a systems biology approach. For examining progression of tumors as a function of age, in vivo syngeneic tumor studies coupled with molecular and -omics analysis are undertaken and the data incorporated into multi-scale mathematical models.

Our investigations have shown modulation of tumor progression, and growth as a function of host age, is attributable to specific genetic and functional changes between the tumor/host that affect the ability of the host to support that growth. Global gene array analysis was performed on excised tumors from different host (mice) age groups injected with the same population of cancer cells. Differential gene expression observed for tumors from different aged mice is then directly attributable to differences in host age. Differential gene regulations among tumors from the various host ages were attributable to host interactions. Thus revealing crucial genetic regulations and shedding light on the host functional process impacting the tumor dynamics observed. Tumor inhibitory effects observed with age were attributed primarily to classes of genes, including those regulating metabolism, and transcriptional regulators modulating angiogenesis, and apoptosis. One key player revealed was Transforming growth factor, beta 1 (TGFβ1), which, as it turns out, is also found to be integral to a number of functional “hallmarks of aging” e,g, intercellular communication, stem cell exhaustion, telomere attrition, etc. Employing a systems biology approach, the tumor growth dynamics for cohorts of various aged mice were further linked to the underlying biology using a version of the mathematical model of Hahnfeldt et al. [Hahnfeldt et al, 1999] in which the quantiative construct describing the tumor carrying capacity of the host under angiogenic signaling is modified to allow for evaluation of the age-dependent variation in the “carrying capacity”, i.e., the tumor size that could potentially be supported by the current host microenvironmental state at a given age. Associating carrying capacity with angiogenesis state, individual fittings of the tumor growth model revealed substantial differences in the capacity to support tumor progression with host age, as quantified by parameter responsiveness to tumor-derived stimulatory and inhibitory signals. More generally, our studies reveal that aging itself appears to be a powerful orchestrator of global gene and tissue function to the specific end that the aging host presents resistance to the pathological changes characteristic of advancing cancer disease. The ability of age to exert control over such a panoply of tumor progression regulators, with the end result being suppression of disease progression, allows for the construction of such tumor/age models which offer insights into the modulation of a multitude of molecular processes that in aggregate offer a means to gain a more sweeping control of the cancer progression process that has heretofore defied control at the level of specific genes.

Effects of Age on Tumor Growth and Progression Following Proton Irradiation

Over the past decade, proton therapy has attracted considerable attention within the radiation oncology community. There are now about 40 proton centers dedicated to treatment of a wide range of cancers. Accepted advantages demonstrated for proton therapy, over conventional x-ray radiotherapy, include decreased dosing of normal tissue, with consequent decreased side effects, and improved targeting of treatment to tumors within close proximity of vital organs. Recently in pre-clinical models, proton irradiation has been shown to modulate several key processes critical in tumor advancement and progression, including angiogenesis and immunogenicity. In addition to clinical importance, proton irradiation also needs to be better understood when dealing with risk of space travel, since a large component of space radiation derives from protons. At CCSB we examine the role of interactions between cancer and host cells, and how tumors develop differently as a function of age and of irradiation. We have shown that tumor growth is modulated by proton irradiation, with increased inhibition and a significant radiation-altered molecular fingerprint evident in tumors grown in old hosts. Through global transcriptome analysis, TGFβ1 and TGFβ2 were determined to be key players that contributed to the tumor dynamics observed (Fig. 2). These findings point to old hosts exhibiting a reduced capacity to support tumor advancement, which can be further reduced by proton irradiation.
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Figure 2
Figure 2. Characterization of TGFβ1 and TGFβ2 in tumors from proton irradiated and unirradiated adolescent and old mice. The mRNA expression for A) TGFβ1 and for B) TGFβ2, was determined by real-time PCR (RTPCR). Fold changes for mRNA expression were determined for each group, compared to 0Gy adolescent mice. Significance is indicated by the p-values within the plots.   C) Gene network depiction of upstream regulators predicted to be either activated (orange) or inhibited (blue) in tumors from irradiated old mice versus from unirradiated old mice, determined by IPA software. Specific up-regulated (red) and down-regulated (green) genes from the experimental data set involved in determining the activation state of the upstream regulator are shown with direct (—solid lines) and indirect (- - dashed lines) relationships to the upstream regulators. The predicted relationships are color coded to indicate whether it leads to activation (orange) or inhibition (blue). Relationships that are inconsistent with the prediction (yellow) or have an undetermined effect (grey) are also shown. The darker the shade of green or red, the greater the fold change.   D) A schematic of the activation states of the upstream regulators illustrating the balance between the tumor promoters (text in yellow) and tumor suppressors (text in green) with a predicted activation (orange oval) or predicted inhibition (blue oval). [Beheshti et al, 2014]

The Effects of Age on HZE Radiation Response

Since astronaut age is a consideration for extended space missions, understanding the role of age in modulating spontaneous and HZE radiation-induced cancer risk is of considerable interest. Epidemiological studies show that after childhood spontaneous tumor incidence increases, but in late middle-age tumor incidence starts to decrease. The increase or decrease of tumor incidence does not automatically imply advancement of tumor progression. In fact, it has been shown that older aged hosts provide an environment which suppresses tumor growth rate and in some cases leads to tumor regression. To assess carcinogenic risk of extended space radiation exposure, it is important to understand the functional dependence of response to GCR with age. It is also of interest to identify the age window at which humans have the lowest risk for tumor progression when exposed to GCR.

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Figure 3
Figure 3. A murine protein-protein interaction (PPI) network for signaling proteins assessed from the regulation of gene expressions. For A) & B) the network contains 281 murine proteins linked by 551 unique interactions. Blue nodes represent proteins that are in the network but are not regulated by the 56Fe irradiation perturbation, red nodes are determined to be up-regulated, and green nodes are down-regulated. A) PPI network showing regulations of tumors growing in young 56Fe-irradiated mice as compared with those in young unirradiated mice. B) PPI network regulations in tumors growing in middle-age 56Fe-irradiated mice compared with those in middle-aged unirradiated mice. For (C) – (F) pathway analysis was done with Ingenuity Pathway Analysis (IPA) software. Network depicted contains central nodes from AKT1 and FASN with direct (— solid lines) and indirect (- - dashed lines) relations to these molecules. Log2 fold changes to the gene expression were used to obtain different shades of green for regulation levels for down-regulated genes, while different shades of red depict regulation levels for up-regulated genes. Grey genes exist in the network without a significant 2-fold change under the perturbation investigated. The darker the shade of green or red, the greater the fold change. [Beheshti et al, 2013.]

Comparing Age-Driven and Ionizing-Radiation-Driven CML (Chronic Myeloid Leukemia)

We considered SEER and atomic bomb survivor data to show that sex differences in CML age-specific incidence are primarily due to higher risks for males, as opposed to longer female latency periods (Fig. 4).

Figure 4     Figure 4. A) If CML log incidences for males and females are linear and parallel, a continuum of interpretations exists that includes: (1) males having shorter latencies between initiation and clinical CML than females but the same risks (i.e., males left of females) and (2) males having higher risks than females but no difference in latency (i.e., males above females). B) Interpretations of CML sex differences form a curve through two pure (single cause) forms (o): males with shorter latencies than females but the same risks (x-axis point) and males with higher risks than females but no difference in latency (y-axis point). Thin curve mechanisms indistinguishable by SEER data alone. Thick curve points consistent with fit to Japanese A-bomb survivor data. [Radivoyevitch et al, 2014.]


A strong body of radiation biology work that looks at impact of age on carcinogenesis processes has been published by researchers at CCSB (click on title to go to manuscript abstract):