Due to advances in early diagnosis and treatment, together with a population with an increasing proportion of older people, the number of cancer survivors in the US is expected to reach 22.1 million by 2030 (1, http://cancerstatisticscenter.cancer.org. Accessed Dec 29, 2023). Most cancer survivors face varying degrees of compromised quality of life (QoL) issues, resulting in reduced healthspan even for those for whom primary treatment was successful and have become cancer-free (2). QoL issues stem from either damage inflicted by the cancer itself, from toxic side effects of therapy (ies) or from a combination of both. While early cancer detection and treatment have improved in recent years, efforts to regain wellness and enhance healthspan in survivors has lagged behind (3). In both survivors living with cancer and in individuals who are cancer-free, optimal wellness goals should include ensuring maximal QoL while preventing cancer recurrence or halting cancer progression. Indeed, it may prove that such goals are well aligned as increasing overall health may help increase resilience to recurrence. As a case in point, in 2022, the American Cancer Society (ACS), after ten years, updated their dietary and exercise recommendations for survivors. At least for survivors of prostate, colorectal and breast cancers, exercise and optimal nutrition appears to be associated with a lower risk of disease relapse and mortality (4–7). Thus, there’s a pressing need to quantitatively transform these observations into personalized lifestyle recommendations for survivors with a view towards especially dealing with cancer complications.

Long-term and late effects are two common types of side effects (8). Long-term effects typically start during cancer treatment and linger on for weeks to months after termination of therapy (8). In contrast, late effects show up only after termination of cancer therapy (2, 9). For example, in some pediatric cancer survivors, later-effects of treatment have manifested several decades after treatment (3) and include cardiac problems.

Here, we briefly discuss some of the more common side-effects of standard cancer therapies before discussing scientific wellness strategies (10, 11) to ameliorating them, reducing risk or early detection of cancer recurrence in survivors.

Given the myriad issues detailed above for cancer survivors, and their growing numbers, better, and more global, solutions are needed than just attempting to treat individual symptoms. Many of these can be achieved by leveraging data-driven approaches to enhancing health. The feasibility and utility of gathering longitudinal, multi-tiered data from individuals has been demonstrated in recent studies (10, 55–57) and focused on the development of what we call scientific wellness (58).

In 2014, the first of these cohort studies, the Pioneer 100 Wellness Project (P100) was carried out at the Institute for Systems Biology. The P100 generated personal, dense, dynamic data clouds from a cohort of 108 individuals, sampled on a quarterly basis over 9 months (10). Each participant had their whole genome sequenced as well as longitudinal sampling at 3-month intervals of a large battery of over 100 clinical blood tests, metabolomics and proteomics from the blood, and microbiome from the stool. Additionally, Fitbits were used to track daily activity and tracking of goals and results was monitored and facilitated individually with a health coach. The authors identified thousands of molecular correlates across the various datatypes, including effects of genetics on the metabolome and proteome. Further, when these data were used to guide highly personalized behavioral coaching, participants improved their clinical biomarkers of health and wellness (10).

From 2015-2019, a similar ‘scientific wellness’ program was offered by Arivale (co-founded by L.H. and N.D.P) that generated longitudinal multi-omic datasets on approximately 5000 people who gave consent for their de-identified data to be used for research purposes. Similar to the P100 study, the program participants, on average, achieved sustained, significant improvements in clinical markers of cardiometabolic risk, inflammation, nutrition, and body mass index (BMI). Improvements in HbA1c levels matched those observed in other landmark clinical trials (59). By finding genetic markers associated with longitudinal changes in such clinical markers, the study also concluded that genetic predisposition impacts clinical responses to lifestyle change in distinct manners for individuals of high and low genetic risk (60).

Subsequent studies using these identified 766 multi-omic associations in the blood related to polygenic risk scores for 54 diseases and traits to provide clues about prevention strategies for prodromal disease (61), discovered early candidate protein biomarkers of cancer and cancer metastasis (62), proposed multi-omic models of biological age as a measure of wellness (55), identified microbiome-derived candidate biomarkers of cardiovascular disease (63) and revealed a key role for the microbiome in healthy aging (Wilmanski et al). Specifically, the Magis et al. study identified carcinoembryonic antigen-related cell adhesion molecule 5 (CAECAM 5), as a persistent, longitudinal outlier, showing up as early as 26 months before individuals were diagnosed with breast, lung or pancreatic cancer. CAECAM 5 is known to be overexpressed in the primary versions of these three cancers and also in metastatic disease. In this case, the scientific wellness data enabled the discovery that such biomarkers of cancer metastasis rose well before diagnosis. Additionally, the study found two additional persistent outliers – calcitonin-related polypeptide alpha (CALCA) and delta-like 1 homolog (DLK1), for metastatic pancreatic cancer. All three proteins would serve as candidate early biomarkers for metastatic disease.

Similar datasets in cancer survivors could provide critical insights to improving health in these populations as well. Actionable discoveries from the scientific wellness approach offer promise to shape choices of intervention for reducing toxic side-effects. This is especially relevant since radiation therapy, hormone therapy and chemotherapy are all known to accelerate biological age in patients (Jacob 64). Their success can be measured using a range of metrics, including objective and subjective measures of QoL, biological age estimates (physiology-based, mutli-omic) and established measures of cognition and wellness. In addition, in cancer survivors, longitudinal phenomic analyses will be useful in detecting reoccurrences early.

Another recent study (56) used integrative omics and data from wearable monitoring to demonstrate the usefulness of longitudinal molecular and physiological deep profiling in precision health. The study authors assayed the genome, transcriptome, proteome, metabolome, immunome and the microbiome, all paired with quarterly enhanced clinical tests, of 109 individuals up to 8 years. These individuals were at elevated risk for developing type II diabetes. Importantly, they were able to discover “molecular pathways associated with metabolic, cardiovascular and oncological pathophysiologies”. Additionally, they made 67 clinically actionable discoveries, developed an omics-based model for insulin resistance and fomented healthy dietary and exercise behaviors in the study participants.

The feasibility and stability of building longitudinal, personalized omics profiles of wellness was demonstrated by another recent study in which 100 healthy subjects not only showed the surprising stability of blood plasma protein profiles over time but also their significant association with blood chemistry (57). For example, the authors found a strong association between C- Reactive Protein (synthesized by the liver and a biomarker of inflammation) and Interleukin – 6 (an immune system cytokine related to inflammation). In agreement with other recent studies, the intra-individual baseline variability of omics profiles was low compared to the inter-individual ones.

Thus, longitudinal, high-throughput multi-omic data generation and analysis can be used to (1) define omics baselines of wellness, (2) define early wellness-to-disease and disease-to-wellness transitional omics signatures, (3) discover molecular pathways that underlie specific pathology, (4) find potential biomarkers of disease and (5) help guide and optimize personalized wellness coaching for individuals.

Cancer survivors stand to benefit from such a scientific wellness approach for several reasons as discussed below. Survivors make up a specific group with limited traits, which improves the ability to find meaningful information in a long-term study of large data sets, even with relatively small sample size.

First and foremost, the longitudinal, multi-tiered data collection will enable us to build an integrated baseline in this population. Deviations from this baseline can inform the discovery of novel diagnostic and prognostic biomarkers of various toxic side-effects of therapy discussed above and equally importantly, those of cancer recurrence. For example, data from survivors can be mined to potentially discover new temporal signatures (consisting of metabolites, peptides, proteins or microbiota) that correlate with known prognostic or diagnostic biomarkers (e.g. PSA and BCR ABL, respectively, for prostate and CML recurrence) (65, 66). The same approach can also be used in survivors to find novel, more effective biomarkers to monitor the effectiveness of maintenance therapy used in some cancer patients even after achieving complete remission.

Second, scientific wellness approaches can be a powerful tool to assess and eventually better tailor, diet and physical activity protocols for survivors with a goal of preventing cancer recurrence and accelerating their return to wellness. This is a corollary to the previously mentioned, new and revised guidelines from the NCI, with regard to diet and physical activity, for cancer survivors.

Third, the longitudinal multimodal data gathering, along with clinical records, will allow us to ask questions around correlative links between other comorbidities like obesity, diabetes or chronic inflammation and cancer recurrence. While the associations between conditions such as obesity and risk of cancer are known, data from our approach will provide a quantitative framework to better assess risks and make new biological discoveries.

Finally, the data clouds from survivors who go on to develop recurrences, could offer insights into tumor evolution from a new point of view (67) and yield new insights into transitions to a state of cancer recurrence. As was true with the other wellness studies, it appears that malignant lesions of the same cancer type, from the same patient are more alike than those from different patients (67).

A longitudinal, big data approach will provide a foundation for future studies in this area, and it will represent a first of its kind resource that can provide a foundation for future studies in this direction. This approach will generate virtual and dynamic clouds myriad data points that provide unique and personalized insights into the wellness and disease of each individual (see Figure 1 ). We are engaged in two such trials currently focused on cancer survivors: one is a collaboration between Mayo Clinic and Thorne HealthTech and the other is a collaboration between the Institute for Systems Biology and the Swedish Cancer Institute of Providence Health.

As a specific example, metabolomic data for each survivor along with relevant clinical lab data enables monitoring of integral components of metabolic syndrome. Using these dynamic data, a shift towards the metabolic state can be identified and may open up opportunities for early intervention. The data will also allow the effects of physical activity and other medical interventions on the various health metrics of the cancer survivor to be assessed. Other covariates from the data cloud can also be identified in the cohort of survivors, and these will open-up new avenues for intervention and exploration.

Another instance of the translational potential of this approach is the monitoring of pro- and anti-inflammatory cytokines in the blood of cancer survivors. Inflammation is thought to underlie many of the long-term and late-effects after cancer treatment including chemobrain, arthralgia, fatigue, and the metabolic syndrome (18, 25, 32, 43). Therefore, analyzing cytokine levels from each survivor across the cohort will enable discovery of prognostic and predictive biomarkers for these effects.

The longitudinal multi-omic protocol will complement existing, state-of-the-art tumor sequencing methods that provide predictive, prognostic and diagnostic value. Examples of these approaches include the Oncopanel by Eurofin and some other custom panels offered by some cancer centers and clinics in the US (Mayo clinic, MD Anderson and Memorial Sloan Kettering Cancer Center). The scientific wellness approach will bring additional value since it is longitudinal, multi-tiered (not just sequencing) and not tumor tissue specific. The combined value of enriching existing methods that offer a snapshot, with multi-parametric measurements spread over several time points can be evaluated in clinical trials.

Additionally, the dense, dynamic, personal data clouds will contain data from blood proteins which are emerging as potential biomarkers for organ function, especially the heart (45). Importantly, such biomarkers, taken together with physiological data from the same data clouds, will not only yield clues to discover underlying pathophysiological pathways, but will also identify time-points for early medical intervention. Finally, microbiome data from these data clouds can lead to discovery of new associations with disease, wellness, and ways to improve survivor health via the microbiome (68).

We anticipate that moving from carefully selected cohorts to a patient-centric personalized treatment plan will be a multi-step process, requiring carefully planning and evaluation all along the way. The number of studies and patients that would be required to arrive at a clinically translatable recommendation will depend on a variety of factors – robustness of biomarker validation, clinical trial outcomes and other regulatory considerations. The sample sizes provided by the initial exploratory studies provide clues on the effect sizes and can help estimation of the number of participants for future clinical trials.

In summary, robust molecular correlates do not exist for most of the long-term and late effects of cancer treatment; the identification of molecular signatures and biomarkers underlying such effects will enable early interventions, and subsequently to their better management in the clinic. Beyond cancer diagnosis and treatment that are rightly the primary focus, further work is needed to improve wellness and quality of life in the increasing number of cancer survivors, including reduction in their co-morbidities and the early detection of transitions back to a cancerous state. The multi-parameter data from the cancer survivor cohort will allow the generation of sophisticated and highly accurate predictive models for such toxic side-effects and for cancer recurrence. These models can catalyze the discovery of new genetic modifiers for many of the treatment effects, predict high-risk sub-populations, and will help pave a more certain and accelerated path back to wellness for cancer survivors.

The original contributions presented in the study are included in the article/supplementary material. Further inquiries can be directed to the corresponding author.

RH: Conceptualization, Writing – original draft. NP: Conceptualization, Supervision, Writing – review & editing. LH: Conceptualization, Writing – review & editing.