Genetic and Non-Genetic Mechanisms Underlying Cancer Evolution

Shlyakhtina, Yelyzaveta; Moran, Katherine L.; Portal, Maximiliano M

doi:10.3390/cancers13061380

Cited by 68 publications

(45 citation statements)

References 258 publications

(424 reference statements)

Supporting

Mentioning

Contrasting

Unclassified

Order By: Relevance

“…However, the same mutation can be selected for in some tissues and selected against in others (87). This selection most likely depends on the tissue-specific epigenetic profiles and microenvironments of the cancer-initiating stem or progenitor cells (88, 89). Thus, investigating the interplay between stem cell mutations, epigenetic profiles and microenvironments in various tissues appears to be a promising and exciting avenue for future research.…”

Section: Discussionmentioning

confidence: 99%

Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients

Belikov

Otnyukov

Vyatkin

et al. 2021

Preprint

View full text Add to dashboard Cite

Elucidating crucial driver genes is paramount for understanding the cancer origins and mechanisms of progression, as well as selecting targets for molecular therapy. Cancer genes are usually ranked by the frequency of mutation, which, however, does not necessarily reflect their driver strength. Here we hypothesize that driver strength is higher for genes that are preferentially mutated in patients with few driver mutations overall, because these few mutations should be strong enough to initiate cancer. We propose a formula to calculate the corresponding Driver Strength Index (DSI), as well as the Normalized Driver Strength Index (NDSI), the latter completely independent of the overall gene mutation frequency. We validate these indices using the largest database of human cancer mutations - TCGA PanCanAtlas, multiple established algorithms for cancer driver prediction (2020plus, CHASMplus, CompositeDriver, dNdScv, DriverNet, HotMAPS, IntOGen Plus, OncodriveCLUSTL, OncodriveFML) and four custom computational pipelines that integrate driver contributions from SNA, CNA and aneuploidy at the patient-level resolution. We demonstrate that NDSI provides substantially different rankings of genes as compared to DSI and frequency approach. For example, NDSI highlighted the importance of guanine nucleotide-binding protein subunits GNAQ, GNA11, GNAI1, GNAZ and GNB3, General Transcription Factor II family members GTF2I and GTF2F2, as well as fibroblast growth factor receptors FGFR2 and FGFR3. Intriguingly, NDSI prioritized CIC, FUBP1, IDH1 and IDH2 mutations, as well as 19q and 1p chromosome arm losses, that comprise characteristic molecular alterations of gliomas. KEGG analysis shows that top NDSI-ranked genes comprise PDGFRA-GRB2-SOS2-HRAS/NRAS-BRAF pathway, GNAQ/GNA11-HRAS/NRAS-BRAF pathway, GNB3-AKT1-IKBKG/GSK3B/CDKN1B pathway and TCEB1-VHL pathway. NDSI does not seem to correlate with the number of protein-protein interactions. We share our software to enable calculation of DSI and NDSI for outputs of any third-party driver prediction algorithms or their combinations.

show abstract

Section: Discussionmentioning

confidence: 99%

Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients

Belikov

Otnyukov

Vyatkin

et al. 2021

Preprint

View full text Add to dashboard Cite

show abstract

“…Although non-genetic resistance to chemotherapy/targetselective drugs appears to be pervasive in cancer cells, the molecular mechanisms that drive this resistance are yet incompletely understood (12,27). Non-genetic resistance can be acquired by environmental induction, i.e., drug-induced epigenetic reconfigurations can enhance cells' survival to the very therapeutic environment (28,29).…”

Section: The Problemmentioning

confidence: 99%

“…This wealth of possible paths to resistance is confusing and consequently not good news for the design of effective cancer therapies. On the positive side, several observations (27,34,35) indicate that mechanisms similar to those that drive therapeutic resistance in cancer cells may also drive adaptive phases during the poorly understood process of tumorigenesis. Thus, the insights gained in the context of therapeutic resistance may shine light on tumorigenesis and help reveal some general principles and/or deterministic dynamics (36), which can in turn contribute to the development of more effective cancer prevention.…”

Section: The Problemmentioning

confidence: 99%

Bridging Tumorigenesis and Therapy Resistance With a Non-Darwinian and Non-Lamarckian Mechanism of Adaptive Evolution

et al. 2021

View full text Add to dashboard Cite

Although neo-Darwinian (and less often Lamarckian) dynamics are regularly invoked to interpret cancer’s multifarious molecular profiles, they shine little light on how tumorigenesis unfolds and often fail to fully capture the frequency and breadth of resistance mechanisms. This uncertainty frames one of the most problematic gaps between science and practice in modern times. Here, we offer a theory of adaptive cancer evolution, which builds on a molecular mechanism that lies outside neo-Darwinian and Lamarckian schemes. This mechanism coherently integrates non-genetic and genetic changes, ecological and evolutionary time scales, and shifts the spotlight away from positive selection towards purifying selection, genetic drift, and the creative-disruptive power of environmental change. The surprisingly simple use-it or lose-it rationale of the proposed theory can help predict molecular dynamics during tumorigenesis. It also provides simple rules of thumb that should help improve therapeutic approaches in cancer.

show abstract

“…RNA sequencing studies from multiple tumor-types showed that individual tumors with similar genetic profile may exhibit transcriptional heterogeneity of tumors [2], that may be arising due to the presence of multiple phenotypic states along with diversity in interactions with tumor microenvironment [3][4][5]. Recent studies are uncovering the presence of extensive non-genetic heterogeneity at the phenotypic and functional levels within genetically identical sub-clones of cancer cells [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Spontaneous differentiation leads to emergence of hybrid cell states relate to poor prognosis in oral cancer

Vipparthi

Hari

Chakraborty

et al. 2021

Preprint

View full text Add to dashboard Cite

Cancer cells within individual tumors often exist in distinct phenotypic states contributing to intratumoral heterogeneity (ITH). However, studies on cell state dynamics among oral cancer cells are largely missing. Here, we have multiplexed phenotypic markers of putative oral-stem-like cancer cells (SLCCs) and characterized diversity among CD44-positive oral cancer cell subpopulations with respect to distinct expression of CD24 and aldehyde dehydrogenase (ALDH)-activity. Our in vitro experimental observations were explained by a Markov model where subpopulations followed two distinct patterns of spontaneous repopulation dynamics. Cells showed stochastic inter-conversions on ALDH-axis, harnessed by cancer cells to enrich ALDHHigh subpopulations in response to Cisplatin treatment. However, these cells followed a strict non-interconvertible transition of CD24Low to CD24High subpopulations, even in response to chemotherapy-induced stress. Using phenotype-specific RNAseq, we suggest the organization of subpopulations into hierarchical structure with possible maintenance of intermediate alternate states of stemness within the differentiating oral cancer cells. We also show that the population dynamics described here may influence tumor behaviour by increasing ITH in aggressive oral tumors. Overall, the described phenotypic subgroups not only reliably exhibited spontaneous or Cisplatin-driven cellular dynamics but also the distinct transcription states of oral cancer cells. Most importantly, our in vitro model system derived observations emphasized the prognostic power which may be translated for betterment of oral cancer patients.Graphical AbstractWe have characterized diversity among CD44-positive oral cancer cells lines with respect to distinct expression of CD24 and ALDH-activity. Cells showed stochastic inter-conversions on ALDH-axis but a strict non-interconvertible transition of CD24Low to CD24High phenotype, even in response to chemotherapy-induced stress. RNAseq study suggested the organization of subpopulations into hierarchical structure with possible maintenance of intermediate alternate states of stemness within the differentiating oral cancer cells. The described population dynamics may influence tumor behaviour by increasing intratumoral heterogeneity in aggressive oral tumors.

show abstract

Genetic and Non-Genetic Mechanisms Underlying Cancer Evolution

Cited by 68 publications

References 258 publications

Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients

Novel Driver Strength Index highlights important cancer genes in TCGA PanCanAtlas patients

Bridging Tumorigenesis and Therapy Resistance With a Non-Darwinian and Non-Lamarckian Mechanism of Adaptive Evolution

Spontaneous differentiation leads to emergence of hybrid cell states relate to poor prognosis in oral cancer

Contact Info

Product

Resources

About