Elsevier

Seminars in Oncology

Volume 41, Issue 2, April 2014, Pages 185-194
Seminars in Oncology

Macro- and Micro-environmental Factors in Clinical Hepatocellular Cancer

https://doi.org/10.1053/j.seminoncol.2014.03.001Get rights and content

We previously developed a network phenotyping strategy (NPS), a graph theory-based transformation of clinical practice data, for recognition of two primary subgroups of hepatocellular cancer (HCC), called S and L, which differed significantly in their tumor masses. In the current study, we have independently validated this result on 641 HCC patients from another continent. We identified the same HCC subgroups with mean tumor masses 9 cm x n (S) and 22 cm x n (L), P<10-14. The means of survival distribution (not available previously) for this new cohort were also significantly different (S was 12 months, L was 7 months, P<10-5). We characterized nine unique reference patterns of interactions between tumor and clinical environment factors, identifying four subtypes for S and five subtypes for L phenotypes, respectively. In L phenotype, all reference patterns were portal vein thrombosis (PVT)-positive, all platelet/alpha fetoprotein (AFP) levels were high, and all were chronic alcohol consumers. L had phenotype landmarks with worst survival. S phenotype interaction patterns were PVT-negative, with low platelet/AFP levels. We demonstrated that tumor–clinical environment interaction patterns explained how a given parameter level can have a different significance within a different overall context. Thus, baseline bilirubin is low in S1 and S4, but high in S2 and S3, yet all are S subtype patterns, with better prognosis than in L. Gender and age, representing macro-environmental factors, and bilirubin, prothrombin time, and AST levels representing micro-environmental factors, had a major impact on subtype characterization. Clinically important HCC phenotypes are therefore represented by complete parameter relationship patterns and cannot be replaced by individual parameter levels.

Section snippets

Methods

We approached the extraction of novel information from a standard set of baseline clinical parameter data at diagnosis, used in routine clinical practice clinic for HCC evaluation, in a way that allows us to better characterize HCC clinical heterogeneity. We have previously demonstrated that this can be done by application of graph theory tools. Mathematical graphs, when properly selected, can capture what at first sight are complicated relationship patterns, in an elegant and, most

HCC Heterogeneity: Identification of Two General HCC Phenotypes

We present the first independent validation of the previously published results obtained by NPS analysis of HCC screening data from another HCC cohort, which was not part of a screening program. With these independent clinical data, we therefore followed without any modification all of the previously used steps in their preparation for NPS transformation and analysis. The first step was the definition of the partitions in the k-partite graph. That included a data-driven approach that simplified

Discussion

It has been long recognized in HCC studies that unlike many other tumors, prognosis depends upon both tumor and micro-environment factors (liver inflammation), as well as macro-environmental factors such as age and gender. In order to discuss these combinations of various factors, a variety of approaches have been taken,23 such as multivariable regression, principal component analysis,24, 25, 26, 27, 28 or neural networks.27, 29, 30, 31, 32 Regression methods become too complicated for

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    Conflicts of interest: none.

    Grant support: ERZ-CZ LL1201 (CORES) to P.P. and NIH grant CA 82723 to B.I.C.

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