Excess risk estimation for matched cohort survival data

Cancer-related reductions in survival: extent and duration evaluated using a large cohort study of twins, 1943-2011

BACKGROUND

The time during which there is an increased risk of death for cancer survivors was evaluated in a large twin study, which allows for matching on shared components such as age, genes, and socioeconomic factors in childhood.

METHODS

By use of data from Danish registers, time to death from initial cancer was studied prospectively in twins in two different settings. The twins were diagnosed with at least one cancer in the period 1943-2011. Setting I included 5,680 same-sex twin pairs aged six and over, while Setting II included 3,218 twin individuals from age 70 and over. The study provides comparisons within twin pairs and across birth cohorts, age at diagnoses, and time at diagnosis.

RESULTS

In 2001-2011, the five-year mortality risk for a twin surviving cancer after the age of 70 was twofold that of the co-twin, regardless of sex and zygosity, and it was 1.5-fold if the twin survived the initial nine months. After five to six years, the mortality risk corresponded to that of the co-twin. In previous decades, the excess hazard risk was considerably higher for both older and younger cohorts. There were no indications of change in relative survival across old birth cohorts.

CONCLUSIONS

This large twin study suggested that for a cancer-treatment survivor diagnosed at age 70 or later, the additional mortality risk was largely absent five years later, by which time the survival relative to the co-twin was 60%.

IMPACT

Elevated mortality risk after cancer is offset after five-six years.

Risk of late health effects after soft-tissue sarcomas in childhood - a population-based cohort study within the Adult Life after Childhood Cancer in Scandinavia research programme

BACKGROUND

In the 1960s only 1/3 of children with soft-tissue sarcomas survived, however with improved treatments survival today has reached 70%. Given the previous poor survival and the rarity of soft-tissue sarcomas, the risk of somatic late effects in a large cohort of Nordic soft-tissue sarcoma survivors has not yet been assessed.

METHODS

In this population-based cohort study we identified 985 five-year soft-tissue sarcoma survivors in Nordic nationwide cancer registries and late effects in national hospital registries covering the period 1964-2012. Information on tumour site and radiotherapy was available for Danish and Finnish survivors (N = 531). Using disease-specific rates of first-time hospital contacts for somatic diseases in survivors and in 4,830 matched comparisons we calculated relative rates (RR) and rate differences (RD).

RESULTS

Survivors had a RR of 1.5 (95% CI 1.4-1.7) and an absolute RD of 23.5 (17.7-29.2) for a first hospital contact per 1,000 person-years. The highest risks in both relative and absolute terms were of endocrine disorders (RR = 2.5; RD = 7.6), and diseases of the nervous system (RR = 1.9; RD = 6.6), digestive organs (RR = 1.7; RD = 5.4) and urinary system (RR = 1.7; RD = 5.6). By tumour site, excess risk was lower after extremity tumours. Irradiated survivors had a 2.6 (1.2-5.9) times higher risk than non-irradiated.

CONCLUSIONS

Soft-tissue sarcoma survivors have an increased risk of somatic late effects in 5 out of 10 main diagnostic groups of diseases, and the risk remains increased up to 40 years after cancer diagnosis. Risks were slightly lower for those treated for tumours in the extremities, and radiotherapy increased the risk by more than two-fold.

Excess cumulative incidence estimation for matched cohort survival studies

We suggest a regression approach to estimate the excess cumulative incidence function (CIF) when matched data are available. In a competing risk setting, we define the excess risk as the difference between the CIF in the exposed group and the background CIF observed in the unexposed group. We show that the excess risk can be estimated through an extended binomial regression model that actively uses the matched structure of the data, avoiding further estimation of both the exposed and the unexposed CIFs. The method naturally deals with two time scales, age and time since exposure and simplifies how to deal with the left truncation on the age time-scale. The model makes it easy to predict individual excess risk scenarios and allows for a direct interpretation of the covariate effects on the cumulative incidence scale. After introducing the model and some theory to justify the approach, we show via simulations that our model works well in practice. We conclude by applying the excess risk model to data from the ALiCCS study to investigate the excess risk of late events in childhood cancer survivors.

A qualitative transcriptional signature to reclassify histological grade of ER-positive breast cancer patients

BACKGROUND

Histological grade (HG) is commonly adopted as a prognostic factor for ER-positive breast cancer patients. However, HG evaluation methods, such as the pathological Nottingham grading system, are highly subjective with only 50-85% inter-observer agreements. Specifically, the subjectivity in the pathological assignment of the intermediate grade (HG2) breast cancers, comprising of about half of breast cancer cases, results in uncertain disease outcomes prediction. Here, we developed a qualitative transcriptional signature, based on within-sample relative expression orderings (REOs) of gene pairs, to define HG1 and HG3 and reclassify pathologically-determined HG2 (denoted as pHG2) breast cancer patients.

RESULTS

From the gene pairs with significantly stable REOs in pathologically-determined HG1 (denoted as pHG1) samples and reversely stable REOs in pathologically-determined HG3 (denoted as pHG3) samples, concordantly identified from seven datasets, we extracted a signature which could determine the HG state of samples through evaluating whether the within-sample REOs match with the patterns of the pHG1 REOs or pHG3 REOs. A sample was classified into the HG3 group if at least a half of the REOs of the 10 gene pairs signature within this sample voted for HG3; otherwise, HG1. Using four datasets including samples of early stage (I-II) ER-positive breast cancer patients who accepted surgery only, we validated that this signature was able to reclassify pHG2 patients into HG1 and HG3 groups with significantly different survival time. For the original pHG1 and pHG3 patients, the signature could also more accurately and objectively stratify them into distinct prognostic groups. And the up-regulated and down down-regulated genes in HG1 compared with HG3 involved in cell proliferation and extracellular signal transduction pathways respectively. By comparing with existing signatures, 10-GPS was with prognostic significance and was more aligned with survival of patients especially for pHG2 samples.

CONCLUSIONS

The transcriptional qualitative signature can provide an objective assessment of HG states of ER-positive breast cancer patients, especially for reclassifying patients with pHG2, to assist decision making on clinical therapy.