Optimal Timing for Cancer Screening and Adaptive Surveillance Using Mathematical Modeling

A war on many fronts: cross disciplinary approaches for novel cancer treatment strategies

Cancer is a disease characterized by uncontrolled cellular growth where cancer cells take advantage of surrounding cellular populations to obtain resources and promote invasion. Carcinomas are the most common type of cancer accounting for almost 90% of cancer cases. One of the major subtypes of carcinomas are adenocarcinomas, which originate from glandular cells that line certain internal organs. Cancers such as breast, prostate, lung, pancreas, colon, esophageal, kidney are often adenocarcinomas. Current treatment strategies include surgery, chemotherapy, radiation, targeted therapy, and more recently immunotherapy. However, patients with adenocarcinomas often develop resistance or recur after the first line of treatment. Understanding how networks of tumor cells interact with each other and the tumor microenvironment is crucial to avoid recurrence, resistance, and high-dose therapy toxicities. In this review, we explore how mathematical modeling tools from different disciplines can aid in the development of effective and personalized cancer treatment strategies. Here, we describe how concepts from the disciplines of ecology and evolution, economics, and control engineering have been applied to mathematically model cancer dynamics and enhance treatment strategies.

Evaluation of benefits and harms of adaptive screening schedules for lung cancer: A microsimulation study

BACKGROUND

Although lung cancer screening (LCS) has been proven effective in reducing lung cancer mortality, it is associated with some potential harms, such as false positives and invasive follow-up procedures. Determining the time to next screen based on individual risk could reduce harms while maintaining health gains. Here, we evaluate the benefits and harms of LCS strategies with adaptive schedules, and compare these with those from non-adaptive strategies.

METHODS

We extended the Lee and Zelen risk threshold method to select screening schedules based on individual's lung cancer risk and life expectancy (adaptive schedules). We compared the health benefits and harms of these adaptive schedules with regular (non-adaptive) schedules (annual, biennial and triennial) using a validated lung cancer microsimulation model. Outcomes include lung cancer deaths (LCD) averted, life years gained (LYG), discounted quality adjusted life years (QALYs) gained, and false positives per LCD averted. We also explored the impact of varying screening-related disutilities.

RESULTS

In comparison to standard regular screening recommendations, risk-dependent adaptive screening reduced screening harms while maintaining a similar level of health benefits. The net gains and the balance of benefits and harms from LCS with efficient adaptive schedules were improved compared to those from regular screening, especially when the screening-related disutilities are high.

CONCLUSIONS

Adaptive screening schedules can reduce the associated harms of screening while maintaining its associated lung cancer mortality reductions and years of life gained. Our study identifies individually tailored schedules that optimize the screening benefit/harm trade-offs.

Cancer's second genome: Microbial cancer diagnostics and redefining clonal evolution as a multispecies process: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution: Humans and their tumors are not aseptic, and the multispecies nature of cancer modulates clinical care and clonal evolution

The presence and role of microbes in human cancers has come full circle in the last century. Tumors are no longer considered aseptic, but implications for cancer biology and oncology remain underappreciated. Opportunities to identify and build translational diagnostics, prognostics, and therapeutics that exploit cancer's second genome-the metagenome-are manifold, but require careful consideration of microbial experimental idiosyncrasies that are distinct from host-centric methods. Furthermore, the discoveries of intracellular and intra-metastatic cancer bacteria necessitate fundamental changes in describing clonal evolution and selection, reflecting bidirectional interactions with non-human residents. Reconsidering cancer clonality as a multispecies process similarly holds key implications for understanding metastasis and prognosing therapeutic resistance while providing rational guidance for the next generation of bacterial cancer therapies. Guided by these new findings and challenges, this Review describes opportunities to exploit cancer's metagenome in oncology and proposes an evolutionary framework as a first step towards modeling multispecies cancer clonality. Also see the video abstract here: https://youtu.be/-WDtIRJYZSs.

An integrated framework for quantifying immune-tumour interactions in a 3D co-culture model

Investigational in vitro models that reflect the complexity of the interaction between the immune system and tumours are limited and difficult to establish. Herein, we present a platform to study the tumour-immune interaction using a co-culture between cancer spheroids and activated immune cells. An algorithm was developed for analysis of confocal images of the co-culture to evaluate the following quantitatively; immune cell infiltration, spheroid roundness and spheroid growth. As a proof of concept, the effect of the glucocorticoid stress hormone, cortisol was tested on 66CL4 co-culture model. Results were comparable to 66CL4 syngeneic in vivo mouse model undergoing psychological stress. Furthermore, administration of glucocorticoid receptor antagonists demonstrated the use of this model to determine the effect of treatments on the immune-tumour interplay. In conclusion, we provide a method of quantifying the interaction between the immune system and cancer, which can become a screening tool in immunotherapy design.

A mathematical model of ctDNA shedding predicts tumor detection size

Early cancer detection aims to find tumors before they progress to an incurable stage. To determine the potential of circulating tumor DNA (ctDNA) for cancer detection, we developed a mathematical model of tumor evolution and ctDNA shedding to predict the size at which tumors become detectable. From 176 patients with stage I to III lung cancer, we inferred that, on average, 0.014% of a tumor cell's DNA is shed into the bloodstream per cell death. For annual screening, the model predicts median detection sizes of 2.0 to 2.3 cm representing a ~40% decrease from the current median detection size of 3.5 cm. For informed monthly cancer relapse testing, the model predicts a median detection size of 0.83 cm and suggests that treatment failure can be detected 140 days earlier than with imaging-based approaches. This mechanistic framework can help accelerate clinical trials by precomputing the most promising cancer early detection strategies.