Characterization of tumor evolution by functional clonality and phylogenetics in hepatocellular carcinoma

Hepatocellular carcinoma (HCC) is a molecularly heterogeneous solid malignancy, and its fitness may be shaped by how its tumor cells evolve. However, ability to monitor tumor cell evolution is hampered by the presence of numerous passenger mutations that do not provide any biological consequences. Here we develop a strategy to determine the tumor clonality of three independent HCC cohorts of 524 patients with diverse etiologies and race/ethnicity by utilizing somatic mutations in cancer driver genes. We identify two main types of tumor evolution, i.e., linear, and non-linear models where non-linear type could be further divided into classes, which we call shallow branching and deep branching. We find that linear evolving HCC is less aggressive than other types. GTF2IRD2B mutations are enriched in HCC with linear evolution, while TP53 mutations are the most frequent genetic alterations in HCC with non-linear models. Furthermore, we observe significant B cell enrichment in linear trees compared to non-linear trees suggesting the need for further research to uncover potential variations in immune cell types within genomically determined phylogeny types. These results hint at the possibility that tumor cells and their microenvironment may collectively influence the tumor evolution process.

A better index for analysis of co-occurrence and similarity

Scientists often need to know whether pairs of entities tend to occur together or independently. Standard approaches to this issue use co-occurrence indices such as Jaccard, Sørensen-Dice, and Simpson. We show that these indices are sensitive to the prevalences of the entities they describe and that this invalidates their interpretability. We propose an index, α, that is insensitive to prevalences. Published datasets reanalyzed with both α and Jaccard's index (J) yield profoundly different biological inferences. For example, a published analysis using J contradicted predictions of the island biogeography theory finding that community stability increased with increasing physical isolation. Reanalysis of the same dataset with the estimator [Formula: see text] reversed that result and supported theoretical predictions. We found similarly marked effects in reanalyses of antibiotic cross-resistance and human disease biomarkers. Our index α is not merely an improvement; its use changes data interpretation in fundamental ways.

Relative extinction times for independent branching processes

The conditional probability that a Galton-Watson process with initial population j dies out before an independent Galton-Watson process initially i, is computed asymptotically as i or j gets large, given that both processes eventually die out. The results are interpreted qualitatively to discuss parameters of fitness for biological populations, especially with respect to the spread of mutant genes under evolutionary models.

Moderate- and large-deviation probabilities in actuarial risk theory

A general model for the actuarial risk-reserve process as a superposition of compound delayed-renewal processes is introduced and related to previous models which have been used in collective risk theory. It is observed that non-stationarity of the portfolio ‘age-structure' within this model can have a significant impact upon probabilities of ruin. When the portfolio size is constant and the policy age-distribution is stationary, the moderate- and large-deviation probabilities of ruin are bounded and calculated using the strong approximation results of Csörg et al. (1987a, b) and a large-deviation theorem of Groeneboom et al. (1979). One consequence is that for non-Poisson claim-arrivals, the large-deviation probabilities of ruin are noticeably affected by the decision to model many parallel policy lines in place of one line with correspondingly faster claim-arrivals.

Multivariate dependent renewal processes

A new class of reliability point-process models for dependent components is introduced. The dependence is expressed through a regression, following a form suggested by Cox (1972) for survival data analysis involving the current life-length of the components. After formulating the current-life process as a Markov process with stationary transitions and stating some general results on asymptotic behavior, we describe the stationary distributions in some bivariate examples. Finally, we discuss statistical inference for the new models, exhibiting and justifying full- and partial-likelihood methods for their analysis.

Principal components representation of the two-dimensional coronal tongue surface

This paper uses principal components (PC) analysis to represent coronal tongue contours for the 11 vowels of English in two consonant contexts (/s/, /l/), based upon five replicated measurements in three sessions for each of 6 subjects. Curves from multiple sessions and speakers were overlaid before analysis onto a common (x, y) coordinate system by extensive preprocessing of the curves including: extension (padding) or truncation within session, translation, and truncation to a common x range. Four PCs plus a mean level allow accurate representation of coronal tongue curves, but PC shapes depend strongly on the degree of padding or truncation. The PCs successfully reduced the dimensionality of the curves and reflected vowel height, consonant context, and physiological features.

Competing risks bias arising from an omitted risk factor

The authors describe a form of selection bias that may arise when a second disease selectively removes from the population persons susceptible to the primary disease of interest. Two examples of this bias are given: 1) a lack of association between an exposure and the primary disease may appear as an inverse association, and 2) a direct association between exposure and primary disease may be greatly attenuated. These examples of bias require the presence of an unknown risk factor in addition to the exposure of interest.

How dependent causes of death can make risk factors appear protective

It is shown, using the results of Slud and Rubinstein (1983, Biometrika 70, 643-649) in a specially constructed theoretical example, that competing latent failure times Ti and Ci and a two-level covariate Vi, if analyzed as though Ti and Ci are independent for each Vi level, can lead to exactly the wrong conclusion about the ordering of Pr(Ti greater than or equal to t[Vi = 1) and Pr(Ti greater than or equal to t[Vi = 0) for every t. This phenomenon can never be excluded on purely statistical grounds using such data and should be considered when interpreting data analyses involving competing risks.