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Liquid Biopsies

There has been a lot of excitement in the life insurance industry over the potential applications of liquid biopsies. This is a test on a blood sample which can detect circulating cancer DNA.

In a pair of editorials in the journal Cancer recent developments in the field are presented. Part 1 is of particular interest, as it discusses the potential use of liquid biopsy as a screening test in the general population. It was very gratifying to see the the principal researcher, Dr. Papadopoulos, was cautious in his approach despite the 99% specificity. As he pointed out, though 99% sounds good, it is not sufficiently high for general population screening, where the prevalence of cancer is low. It was also noted that the test is much better at detecting late-stage cancer than early-stage cancer. This may be a problem, since early detection is what is needed to drive down mortality rates.

This particular weakness may actually be a strength if the purpose is life insurance testing. One of the main goals of testing in the life insurance industry is to guard against possible anti-selection. Because even late-stange malignancies may not cause abberations in the standard laboratory panel, current testing regimens may not be able to detect malignancy in those who do not know or do not want to admit that they have cancer. The liquid biopsy, however, if it is sufficiently inexpensive, may fill this need. Certainly, more research is needed before any conclusions related to population or life insurance testing can be reached.

Part 2 of the series deals primarily with the possible change in the way that Pap tests are done. Some new research has shown that Pap tests done with a different collection brush combined with liquid biopsy technology can detect cervical, endometrial and even some ovarian cancers. This is exciting because ovarian cancer really has no good screening test and is the 4th leading cause of cancer deaths in women. Again, caution is needed because this is not the first time that a promising screening test for ovarian cancer has come around, only to be found to be useless later on (CA-125).

 

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Breast Cancer Survival in the SEER Data

SEER, a division of the National Cancer Institute, records and tracks cancer cases in 18 mostly urban areas across the country, spanning nearly 60% of the population. They have been doing this since 1973 and data is available for the asking.

Since my prior publication in the Journal of Insurance Medicine on the impact of micrometastases in breast cancer survival, I have been waiting for the SEER data to age enough to determine if immunohistochemically detected tumor cells in the lymph nodes (so-called “isolated tumor cells” or ITCs) actually impact prognosis.

Recently I looked into the data and was pleased to find that there were 1379 cases with ITCs among all stage I or II (AJCC 6th edition) with no nodal ‘macro’ metastases (N0), and no distant metastases (M0). There were another 22,731 who had been tested and were negative for ITCs. Additionally, from 2004 forward there were 36,530 who had not had the testing done.

I used Cox models to evaluate the possible risks of these ITCs. In each of then I used restricted cubic splines for age, and included sex and T stage as co-variates. The findings were pretty surprising. When the women who had not had testing were included, both positive and negative ITC tests were protective (HR 0.67 and 0.72 , respectively).

Since this could have been due to ‘informative missing’ – meaning the test was not done because of good prognosis or some other beneficial factor not related to the other co-variates, I tried another fit with only women who had the test done. This really did not change anything – the group with a positive test had a HR of 0.94 compared to the group with negative testing – an insignificant difference (p=0.68).

One obvious factor missing from this analysis is treatment. It is quite likely that the women with ITCs were treated more aggressively than their counterparts who had no testing. Nonetheless, the results here imply that, within the current milieu of testing and treatment, women with ITCs do just as well as women without them, and better than those who were never tested.

You can view my R-code  here and my SEER*Stat query here (on my Google drive site – you may not be able to navigate here if behind a firewall). If I can expand this out a bit more it may be the basis for a future JIM submission.

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New Publications

The latest issue of the Journal of Insurance Medicine posted today. It contains 2 articles that I authored, one as a contributor along with a great group of friends and colleagues on MIB’s Mortality Research and Analysis Committee (about breast cancer mortality), and another as the lone author about the Random Forest algorithm for survival data.

I’ll spoil the conclusion on that last one – when I used a Cox model and a RSF model on colon cancer survival data from SEER, they had very similar concordance error rates, which is kind of a vote for Cox in that circumstance since the hazard ratio output offers a readier quantification of the relative importance of the predictors.

I got the idea to do this while taking courses in the Coursera Data Analysis Signature track. We had to do a project with our own data and create a Shiny app to go with it. (A Shiny app is an interactive web page that can be created using R and R-studio). I chose to create a colon cancer survival calculator based on SEER data and using a Random Forest approach. You can try out my app here, but be patient, it takes a while to load the first time.