Estimating the frequency of indolent breast cancer in screening trials

Ductal Carcinoma In Situ

In breast cancer (BC) pathogenesis models, normal cells acquire somatic mutations and there is a stepwise progression from high-risk lesions and ductal carcinoma in situ to invasive cancer. The precancer biology of mammary tissue warrants better characterization to understand how different BC subtypes emerge. Primary methods for BC prevention or risk reduction include lifestyle changes, surgery, and chemoprevention. Surgical intervention for BC prevention involves risk-reducing prophylactic mastectomy, typically performed either synchronously with the treatment of a primary tumor or as a bilateral procedure in high-risk women. Chemoprevention with endocrine therapy carries adherence-limiting toxicity.

The Contrasting Seasonality Patterns of Some Cancer-Types and Herpes Zoster Can Be Explained by a Binary Classification of Immunological Reactions

A binary classification of the pathogenic immune reactions as anti-inflammatory high-Treg reactions or pro-inflammatory low-Treg reactions explains both the relatively low incidence rate of several types of cancer, and the relatively high incidence rate of herpes zoster cases diagnosed in the summer compared to cases diagnosed in the winter (in regions with temperate climate). This binary model also elucidates the longer survival of cancer patients diagnosed during the summer compared to these diagnosed in the winter. The three key elements of this explanation are: (a) the effect of sunlight on Treg production; (b) the evolvement of cancer from a low-Treg condition at early stage, to a high-Treg condition at advanced stage, and (c) the evolvement of herpes zoster from a high-Treg condition at pre-exudative stage to a low-Treg condition at acute exudative stage. A significant proportion of indolent tumors at the time of diagnosis (>20%) is a prerequisite for a beneficial effect of sunlight on cancer incidence rate and prognosis. This prerequisite restricts the beneficial effect of diagnosis during summer to certain types of cancer. Clinical implication: the prognosis of early stage tumors may be improved by a course of corticosteroid (or other immunosuppressant) treatment.

Coupé VM. A progressive three-state model to estimate time to cancer: a likelihood-based approach

BACKGROUND

To optimize colorectal cancer (CRC) screening and surveillance, information regarding the time-dependent risk of advanced adenomas (AA) to develop into CRC is crucial. However, since AA are removed after diagnosis, the time from AA to CRC cannot be observed in an ethically acceptable manner. We propose a statistical method to indirectly infer this time in a progressive three-state disease model using surveillance data.

METHODS

Sixteen models were specified, with and without covariates. Parameters of the parametric time-to-event distributions from the adenoma-free state (AF) to AA and from AA to CRC were estimated simultaneously, by maximizing the likelihood function. Model performance was assessed via simulation. The methodology was applied to a random sample of 878 individuals from a Norwegian adenoma cohort.

RESULTS

Estimates of the parameters of the time distributions are consistent and the 95% confidence intervals (CIs) have good coverage. For the Norwegian sample (AF: 78%, AA: 20%, CRC: 2%), a Weibull model for both transition times was selected as the final model based on information criteria. The mean time among those who have made the transition to CRC since AA onset within 50 years was estimated to be 4.80 years (95% CI: 0; 7.61). The 5-year and 10-year cumulative incidence of CRC from AA was 13.8% (95% CI: 7.8%;23.8%) and 15.4% (95% CI: 8.2%;34.0%), respectively.

CONCLUSIONS

The time-dependent risk from AA to CRC is crucial to explain differences in the outcomes of microsimulation models used for the optimization of CRC prevention. Our method allows for improving models by the inclusion of data-driven time distributions.

The future of early cancer detection

A proactive approach to detecting cancer at an early stage can make treatments more effective, with fewer side effects and improved long-term survival. However, as detection methods become increasingly sensitive, it can be difficult to distinguish inconsequential changes from lesions that will lead to life-threatening cancer. Progress relies on a detailed understanding of individualized risk, clear delineation of cancer development stages, a range of testing methods with optimal performance characteristics, and robust evaluation of the implications for individuals and society. In the future, advances in sensors, contrast agents, molecular methods, and artificial intelligence will help detect cancer-specific signals in real time. To reduce the burden of cancer on society, risk-based detection and prevention needs to be cost effective and widely accessible.