The prevailing view that the evolution of cells in a tumor is driven by Darwinian selection has never been rigorously tested. Because selection greatly affects the level of intratumor genetic diversity, it is important to assess whether intratumor evolution follows the Darwinian or the non-Darwinian mode of evolution. To provide the statistical power, many regions in a single tumor need to be sampled and analyzed much more extensively than has been attempted in previous intratumor studies. Here, from a hepatocellular carcinoma (HCC) tumor, we evaluated multiregional samples from the tumor, using either whole-exome sequencing (WES) (n = 23 samples) or genotyping (n = 286) under both the infinite-site and infinite-allele models of population genetics. In addition to the many single-nucleotide variations (SNVs) present in all samples, there were 35 “polymorphic” SNVs among samples. High genetic diversity was evident as the 23 WES samples defined 20 unique cell clones. With all 286 samples genotyped, clonal diversity agreed well with the non-Darwinian model with no evidence of positive Darwinian selection. Under the non-Darwinian model, MALL (the number of coding region mutations in the entire tumor) was estimated to be greater than 100 million in this tumor. DNA sequences reveal local diversities in small patches of cells and validate the estimation. In contrast, the genetic diversity under a Darwinian model would generally be orders of magnitude smaller. Because the level of genetic diversity will have implications on therapeutic resistance, non-Darwinian evolution should be heeded in cancer treatments even for microscopic tumors.

Acute leukemia characterized by chromosomal rearrangements requires additional molecular disruptions to develop into full-blown malignancy1,2, yet the cooperative mechanisms remain elusive. Using whole-genome sequencing of a pair of monozygotic twins discordant for MLL (also called KMT2A) gene-rearranged leukemia, we identified a transforming MLL-NRIP3 fusion gene3 and biallelic mutations in SETD2 (encoding a histone H3K36 methyltransferase)4. Moreover, loss-of-function point mutations in SETD2 were recurrent (6.2%) in 241 patients with acute leukemia and were associated with multiple major chromosomal aberrations. We observed a global loss of H3K36 trimethylation (H3K36me3) in leukemic blasts with mutations in SETD2. In the presence of a genetic lesion, downregulation of SETD2 contributed to both initiation and progression during leukemia development by promoting the self-renewal potential of leukemia stem cells. Therefore, our study provides compelling evidence for SETD2 as a new tumor suppressor. Disruption of the SETD2-H3K36me3 pathway is a distinct epigenetic mechanism for leukemia development.

We present the analysis of the evolution of tumors in a case of hepatocellular carcinoma. This case is particularly informative about cancer growth dynamics and the underlying driving mutations. We sampled nine different sections from three tumors and seven more sections from the adjacent nontumor tissues. Selected sections were subjected to exon as well as whole-genome sequencing. Putative somatic mutations were then individually validated across all 9 tumor and 7 nontumor sections. Among the mutations validated, 24 were amino acid changes; in addition, 22 large indels/copy number variants (>1 Mb) were detected. These somatic mutations define four evolutionary lineages among tumor cells. Separate evolution and expansion of these lineages were recent and rapid, each apparently having only one lineage-specific protein-coding mutation. Hence, by using a cell-population genetic definition, this approach identified three coding changes (CCNG1, P62, and an indel/fusion gene) as tumor driver mutations. These three mutations, affecting cell cycle control and apoptosis, are functionally distinct from mutations that accumulated earlier, many of which are involved in inflammation/immunity or cell anchoring. These distinct functions of mutations at different stages may reflect the genetic interactions underlying tumor growth.cell genealogy | cellular evolution | foreground mutation T umorigenesis is generally believed to be the consequence of mutation accumulation, including single nucleotide substitutions, structural variations, and epigenetic changes, in somatic cells (1). A typical cancer may have thousands of somatic mutations, of which 10-100 may be in coding regions (2-7). A central issue in cancer genomics is then the dynamics of tumor growth in relation to the accumulation of these mutations. Given any individual case of cancer, the questions are hence: (i) how many adaptive mutations drive the tumor growth; (ii) how strongly each mutation drives the growth; and (iii) what their molecular nature is vis-à-vis that of the background mutations. To answer these questions, we treat each tumor as a population of cells and apply population genetic principles to infer adaptive mutations (8).Cancer mutations are often divided into drivers and passengers (9). Driver mutations are those that contribute directly to tumorigenesis and their identification is crucial for understanding the molecular biology of cancers. An important issue is how driver mutations should be defined operationally. Candidate driver mutation in the literature often refers to coding changes in genes that are commonly mutated, for example, in multiple cases of hepatocellular carcinoma (HCC). Adaptive mutation proposed here is an alternative definition of candidate driver mutation, inferred from the dynamics of cell proliferation in its natural setting within a single patient.In this report, we analyze a case of HCC, the fifth most common cancer worldwide, by such an approach. We regard HCC as particularly favorable for identifying candidate driver mutatio...