Correspondence between evolution and development has been discussed for more than two centuries. Recent work reveals that phylogeny-ontogeny correlations are indeed present in developmental transcriptomes of eukaryotic clades with complex multicellularity. Nevertheless, it has been largely ignored that the pervasive presence of phylogeny-ontogeny correlations is a hallmark of development in eukaryotes. This perspective opens a possibility to look for similar parallelisms in biological settings where developmental logic and multicellular complexity are more obscure. For instance, it has been increasingly recognized that multicellular behaviour underlies biofilm formation in bacteria. However, it remains unclear whether bacterial biofilm growth shares some basic principles with development in complex eukaryotes. Here we show that the ontogeny of growing Bacillus subtilis biofilms recapitulates phylogeny at the expression level. Using time-resolved transcriptome and proteome profiles, we found that biofilm ontogeny correlates with the evolutionary measures, in a way that evolutionary younger and more diverged genes were increasingly expressed towards later timepoints of biofilm growth. Molecular and morphological signatures also revealed that biofilm growth is highly regulated and organized into discrete ontogenetic stages, analogous to those of eukaryotic embryos. Together, this suggests that biofilm formation in Bacillus is a bona fide developmental process comparable to organismal development in animals, plants and fungi. Given that most cells on Earth reside in the form of biofilms and that biofilms represent the oldest known fossils, we anticipate that the widely-adopted vision of the first life as a single-cell and free-living organism needs rethinking.
Background Cancer is a somatic evolutionary disease and adenocarcinomas of the stomach and gastroesophageal junction (GC) may serve as a two-dimensional model of cancer expansion, in which tumor subclones are not evenly mixed during tumor progression but rather spatially separated and diversified. We hypothesize that precision medicine efforts are compromised when clinical decisions are based on a single-sample analysis, which ignores the mechanisms of cancer evolution and resulting intratumoral heterogeneity. Using multiregional whole-exome sequencing, we investigated the effect of somatic evolution on intratumoral heterogeneity aiming to shed light on the evolutionary biology of GC. Methods The study comprised a prospective discovery cohort of 9 and a validation cohort of 463 GCs. Multiregional whole-exome sequencing was performed using samples form 45 primary tumors and 3 lymph node metastases (range 3–10 tumor samples/patient) of the discovery cohort. Results In total, the discovery cohort harbored 16,537 non-synonymous mutations. Intratumoral heterogeneity of somatic mutations and copy number variants were present in all tumors of the discovery cohort. Of the non-synonymous mutations, 53–91% were not present in each patient’s sample; 399 genes harbored 2–4 different non-synonymous mutations in the same patient; 175 genes showed copy number variations, the majority being heterogeneous, including CD274 (PD-L1). Multi-sample tree-based analyses provided evidence for branched evolution being most complex in a microsatellite instable GC. The analysis of the mode of evolution showed a high degree of heterogeneity in deviation from neutrality within each tumor. We found evidence of parallel evolution and evolutionary trajectories: different mutations of SMAD4 aligned with different subclones and were found only in TP53 mutant GCs. Conclusions Neutral and non-neutral somatic evolution shape the mutational landscape in GC along its lateral expansions. It leads to complex spatial intratumoral heterogeneity, where lymph node metastases may stem from different areas of the primary tumor, synchronously. Our findings may have profound effects on future patient management. They illustrate the risk of mis-interpreting tumor genetics based on single-sample analysis and open new avenues for an evolutionary classification of GC, i.e., the discovery of distinct evolutionary trajectories which can be utilized for precision medicine.
Background Modern cancer treatment strategies aim to target tumour specific genetic (or epigenetic) alterations. Treatment response improves if these alterations are clonal, i.e. present in all cancer cells within tumours. However, the identification of truly clonal alterations is impaired by the tremendous intra-tumour genetic heterogeneity and unavoidable sampling biases. Methods Here, we investigate the underlying causes of these spatial sampling biases and how the distribution and sizes of biopsies in sampling protocols can be optimised to minimize such biases. Results We find that in the ideal case, less than a handful of samples can be enough to infer truly clonal mutations. The frequency of the largest sub-clone at diagnosis is the main factor determining the accuracy of truncal mutation estimation in structured tumours. If the first sub-clone is dominating the tumour, higher spatial dispersion of samples and larger sample size can increase the accuracy of the estimation. In such an improved sampling scheme, fewer samples will enable the detection of truly clonal alterations with the same probability. Conclusions Taking spatial tumour structure into account will decrease the probability to misclassify a sub-clonal mutation as clonal and promises better informed treatment decisions.
34Correspondence between evolution and development has been discussed for more than 35 two centuries 1 . Recent work reveals that phylogeny-ontogeny correlations are indeed 36 present in developmental transcriptomes of eukaryotic clades with complex 37 multicellularity 2-10 . Nevertheless, it has been largely ignored that the pervasive presence 38 of phylogeny-ontogeny correlations is a hallmark of development in eukaryotes 6,10-12 . This 39 perspective opens a possibility to look for similar parallelisms in biological settings where 40 developmental logic and multicellular complexity are more obscure [13][14][15][16] . For instance, it 41 has been increasingly recognized that multicellular behaviour underlies biofilm formation 42 in bacteria 13,14,[17][18][19] . However, it remains unclear whether bacterial biofilm growth shares 43 some basic principles with development in complex eukaryotes [14][15][16]18,20 . Here we show that 44 the ontogeny of growing Bacillus subtilis biofilms recapitulates phylogeny at the 45 expression level. Using time-resolved transcriptome and proteome profiles, we found that 46 biofilm ontogeny correlates with the evolutionary measures, in a way that evolutionary 47 younger and more diverged genes were increasingly expressed towards later timepoints 48 of biofilm growth. Molecular and morphological signatures also revealed that biofilm 49 growth is highly regulated and organized into discrete ontogenetic stages, analogous to 50 those of eukaryotic embryos 11,21 . Together, this suggests that biofilm formation in Bacillus 51 is a bona fide developmental process comparable to organismal development in animals, 52 plants and fungi. Given that most cells on Earth reside in the form of biofilms 22 and that 53 biofilms represent the oldest known fossils 23 , we anticipate that the widely-adopted vision 54 of the first life as a single-cell and free-living organism needs rethinking. 55 56 Multicellular behaviour is wide-spread in bacteria and it was proposed that they should be 57 considered multicellular organisms 13 . However, this idea has not been generally adopted likely 58 3 due to the widespread laboratory use of domesticated bacterial models selected against 59 multicellular behaviours, the long tradition of viewing early diverging groups as simple, and 60 the lack of evidence for system-level commonalities between bacteria and multicellular 61 eukaryotes 15,16,18,24 . Recently developed phylo-transcriptomic tools for tracking evolutionary 62 signatures in animal development 2-4 were also successfully applied in the analysis of 63 developmental processes in plants and fungi 5,6,10 . Although development evolved 64 independently in these three major branches of eukaryotic diversity 12 , their ontogenies showed 65 similar phylogeny-ontogeny correlations indicating that possibly all eukaryotic developmental 66 programs have an evolutionary imprint. Transferability of the phylo-transcriptomic tools across 67 clades and likely universal patterns of phylogeny-ontogeny correlations in e...
Background Modern cancer treatment strategies aim to target tumour specific genetic (or epigenetic) alterations. Treatment response improves if these alterations are clonal, i.e. present in all cancer cells within tumours. However, the identification of truly clonal alterations is impaired by the tremendous intra-tumour genetic heterogeneity and unavoidable sampling biases. Methods Here, we investigate the underlying causes of these spatial sampling biases and how the distribution and sizes of biopsies in sampling protocols can be optimized to minimize such biases. Results We find that in the ideal case, less than a handful of samples can be enough to infer truly clonal mutations. The frequency of the largest sub-clone at diagnosis is the main factor determining the accuracy of truncal mutation estimation in structured tumours. If the first sub-clone is dominating the tumour, higher spatial dispersion of samples and larger sample size can increase the accuracy of the estimation. In such an improved sampling scheme, fewer samples will enable the detection of truly clonal alterations with the same probability. Conclusions Taking spatial tumour structure into account will decrease the probability to misclassify a sub-clonal mutation as clonal and promises better informed treatment decisions.
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