Genomic Evolution Patterns

The striking Nature cover this week once again highlights the power of genome sequencing across time and space to give us new insights into the history of our own species. The last continent in the world to be settled by humans was the Americas. Beringia, a land bridge that replaced the Bering Strait during the last ice age, allowed the ancestors of Indigenous Americans to travel from northeast Asia into North America. Once there, they dispersed south, settling in and adapting to dramatically different environments as they did so. Tábita Hünemeier and colleagues report whole-genome sequencing data for Indigenous populations from 8 Latin American countries, representing 28 language families. They combine these data with genomes from ancient individuals and present-day populations to explore how patterns of genetic variation evolved. They reveal evidence for at least three separate dispersals into South America as well as long-term continuity and adaptation to diverse environments. This is reflected in our cover image - it illustrates the genomic diversity of Indigenous Americans through a stylized Indigenous headdress in which the colour variations represent genetic mixing within the populations, and the three upper feathers symbolize the major migration waves that shaped the peopling of South America. Cover image: Emiliano Bellini Kelly Krause https://lnkd.in/dyUkChKu

Today our paper was published in Nature Magazine. I’d like to start with a patient’s story: A bone marrow transplant patient's gut microbiome was devastated by chemotherapy and medications. Enterococcus faecium moved in to fill the void. And then, inside this one person's gut over just 26 days, a single piece of jumping DNA rewired metabolism. The bacterium became better at scavenging folate, which is exactly the nutrient that becomes scarce when the microbiome collapses. This was the ‘magic bullet’ E. faecium needed to dominate. This isn't an evolutionary story from millions of years ago. It happened in a patient we cared for at Stanford hospital, measurable because we now have the sequencing tools to read it. The jumping gene ISL3 has been proliferating in hospital-associated E. faecium for 30 years, quietly, and in several clinical lineages. And these elements are generating extensive structural variation: not random noise, but regulatory rewiring of genes that helps this bacterium not only survive but thrive in the environments modern medicine creates. I am very proud of co-first authors Matthew Grieshop and Aaron Behr, who led this work with rigor, patience and creativity. And I am thankful to members of the Stanford Department of Medicine BMT team and clinical microbiology team who made this work possible. Most importantly, I’m grateful to the patients, their families and the nurses who made our sample collections and this meaningful patient-driven science possible. This paper required combining global genomic surveys (~20,000 pathogen genomes), long-read sequencing of clinical isolates, and longitudinal metagenomic sequencing of patient stool, because short-read sequencing simply cannot resolve highly repetitive elements like ISL3 accurately. Measuring what actually matters required building the tools to see it. There are still many mysteries to solve: the mechanism driving ISL3 expansion, its long-term consequences, and how generalizable the folate scavenging story is. But we now know that IS elements are a contemporary evolutionary force in hospital pathogens, operating on clinical timescales, inside individual patients. The hospital is a habitat. E. faecium has been learning to live in it. Open access link to the paper in comments.

Cancer is evolution under pressure. Melanoma is one of its most aggressive expressions. All cancers begin with genetic disruption — activation of oncogenes, inactivation of tumor suppressors, loss of cell-cycle control. But melanoma adds another layer: extreme genomic instability driven largely by UV-induced DNA damage. From the general principles of cancer biology: • Oncogenic activation → In melanoma, often BRAF, NRAS, NF1 • Tumor suppressor loss → CDKN2A, PTEN, TP53 alterations • Sustained proliferation → MAPK pathway hyperactivation • Immune evasion → PD-L1 expression, T-cell exhaustion • Angiogenesis and invasion → Early metastatic competence Melanoma exemplifies how mutational burden shapes biology. Its high tumor mutational burden (TMB) makes it immunogenic — which partly explains the transformative impact of immune checkpoint inhibitors. Yet, the same genomic instability that makes melanoma visible to the immune system also drives heterogeneity and resistance: • MAPK reactivation after BRAF/MEK inhibition • Loss of antigen presentation • Adaptive resistance through microenvironment remodeling Melanoma is not simply a skin tumor. It is a dynamic ecosystem — tumor cells, immune cells, stroma, vasculature — constantly evolving. From early radial growth phase confined above the basement membrane to vertical growth phase with vascular access to distant metastasis — including brain tropism — melanoma reflects the full arc of cancer biology. The lesson is clear: Understanding signaling pathways (MAPK, PI3K/AKT), immune regulation, and clonal evolution is not theoretical — it determines whether we choose immunotherapy, targeted therapy, combinations, or neoadjuvant strategies. In melanoma, biology is destiny — unless we intercept it. #Melanoma #CancerBiology #MAPK #Immunotherapy #TumorEvolution #PrecisionOncology #TranslationalResearch

How do we use evolutionary principles to concretely map how drug resistance develops in ovarian cancer patients? New work from the lab, led by the amazing Marc Williams, published today in Springer Nature addresses this problem in ovarian cancer https://lnkd.in/ecJXRUGr. Typically, studying tumor evolution has been carried out through error prone evolutionary reconstructions from single snapshots in patients. This has limited our ability to study the problem of tracking evolution of drug resistance in ovarian cancer that almost inevitably emerges through treatments and over time in patients. To overcome these hurdles, we developed an approach called CloneSeq-SV based on single cell whole genome sequencing technology and longitudinal cell-free DNA tracking technology. Our goal was to map the growth patterns of individual clones within patients to decipher the properties of cancer cells that evade therapy. To develop our approach, we exploited clone-specific structural variants that frequently accrue in ovarian cancer genomes, using them as endogenous barcodes - clone-specific fingerprints - that could be measured and tracked in the blood of cancer patients. We arrived at three important insights: 1) significant clonal evolution occurs over the therapeutic timeline- even with standard front line chemotherapy- that prunes the complexity of clonal populations found at relapse; 2) drug resistant clones frequently harbored distinctive genomic properties such as clone-specific oncogene amplification, genome doubling and chromothripsis; 3) using single cell RNA sequencing from pre-treatment samples we mapped phenotypic states to individual clones and found that plausible drug-resistant phenotypes were already present at diagnosis. This is consistent with a model of therapy induced selective pressure acting on cancer cells that might be pre-adapted prior to any treatment. Finally, in one remarkable case example, CloneSeq-SV was able to define the evolutionary steps that led to a durable clinical response to a new targeted therapy. Might an evolution-aware approach be useful to optimize therapy for our patients more generally? Future studies would be needed to confirm this. I'm grateful for the continuing collaboration with the Gynecologic Disease Management Team under the leadership of Dr. Carol Aghajanian, MD and Dr. Nadeem Abu Rustum, Dr. Dennis Chi and #TeamOvary, the Gyn molecular lab led by Dr. Britta Weigelt, Neeman Mohibullah and her team at the Integrated Genomics Operation Core facility working with Andrew McPherson, and Claire Friedman Dmitriy Zamarin, MD PhD who were along the journey for many years in the SPECTRUM program. The work was generously funded by Break Through Cancer, the Halvorsen Center for Computational Oncology, Seidenberg Family Foundation and the Ovarian Cancer Research Alliance. We are also incredibly thankful to the patients and families who are our partners in research.

Breast cancer remains a major global health challenge1. Here, to comprehensively characterize its genomic landscape and the clinical significance of genomic characteristics, we analysed whole-genome sequences from 1,364 clinically annotated breast cancers, with transcriptome data available for most cases. Our study expands the repertoire of oncogenic alterations and identifies novel driver genes, recurrent gene fusions, structural variants and copy number alterations. Timing analyses on copy number alterations suggest that genomic instability emerges decades before tumour diagnosis, and offer insights into early initiation of tumorigenesis. Pattern-driven genomic features, including mutational signatures2, homologous recombination deficiency3, tumour mutational burden and tumour heterogeneity scores4, were associated with clinical outcomes, highlighting their potential utility as predictive biomarkers for clinical evaluation of treatments such as CDK4/6 and HER2 inhibitors, as well as adjuvant and neoadjuvant chemotherapy. These findings highlight the power of large-scale, clinically annotated whole-genome sequencing in advancing our understanding of how genomic alterations shape patient outcomes. Paper and research by @Ryul Kim and larger team at Inocras Inc.

The Structural History of Eukarya For decades, comparative genomics has relied primarily on sequence markers of single genes. Yet sequence diverges rapidly and variably between genes, often obscuring the deeper signals that encode evolutionary history. Today, we move beyond single genetic sequences. We are proud to introduce the Structural History of Eukarya (SHE): the first proteome-scale phylogeny constructed entirely from 3D protein structure. To build this new map of life, our team undertook an unprecedented computational effort: nearly 300 trillion structural alignments across 1,542 eukaryotic proteomes. By analysing evolution through global protein architecture rather than local sequence, we reveal patterns that remain invisible to traditional genomics. Key insights from SHE: 🐁 Data-driven model selection Beyond reconstructing evolutionary history, SHE is a practical resource. With valuable input from M. Madan Babu, we developed a search engine enabling researchers to quantitatively rank model organisms by their structural fidelity to specific human pathways, transforming model selection from intuition into data-driven science. 🧬 A bipartite evolutionary mode The eukaryotic cell is not a uniform molecular landscape. Instead, we resolve a deeply conserved Architectural Core that stabilises cellular organisation, supporting a highly plastic Operational Engine of metabolism and translation that drives adaptation. 🕊️ Intrinsic structural acceleration We identify lineage-specific bursts of structural innovation in groups such as Birds and Ants, demonstrating that structural evolutionary tempo can diverge markedly from simple genomic expansion. This project represents a major collaborative effort from my fantastic team Qiuzhen Li and Diandra Daumiller who did a fantastic job driving this work to completion at SciLifeLab and Stockholm University! The preprint is now available on bioRxiv, and the full dataset can be explored via our interactive server - try it to rank your model organism! 📄 BioRxiv preprint: https://lnkd.in/dvmSG-iF 🖥️ Interactive server: https://lnkd.in/dXapbNS7 Big thanks to Google DeepMind (AlphaFoldDB). Martin Steinegger (MMseqs, FoldComp, Foldseek). SciLifeLab (Web server) and M. Madan Babu (Model organism selection).

🚀 New Single-Cell Study Reveals Hidden Evolution of Pancreatic Cancer A groundbreaking study published in Nature Genetics used single-nucleus DNA sequencing on over 137,000 cells from 24 pancreatic cancer patients. The high-resolution data provides an unprecedented look at how this lethal cancer evolves and adapts, revealing critical insights that bulk sequencing methods have missed. This research has significant implications for understanding treatment resistance and developing more effective, personalized therapies. Key Findings: · Deeper Genetic Complexity: Single-cell analysis detected driver gene alterations in CDKN2A and SMAD4 at much higher frequencies (71% each) than previously reported by large-scale bulk sequencing projects like The Cancer Genome Atlas (~30%). · Copy Number Alterations Drive Diversity: While key driver mutations (like in KRAS and TP53) are often fixed early, most of the tumor's internal heterogeneity is sculpted by later, subclonal copy number changes, not new mutations. · Varied KRAS Dependence: In cancers with the classic KRAS mutation, tumors showed a likely varied dependence on this oncogene, which may explain the differential patient responses seen with KRAS-targeted therapies. · BRCA2 Inactivation Timing Shapes Destiny: In patients with inherited BRCA2 mutations, the study found that the timing and mechanism of losing the healthy copy of the gene can steer the cancer down different evolutionary paths. · A Late Push for Metastasis: The tumor's intrinsic response to TGF-β signaling is consistently inactivated, but this happens after the initial cancer forms and coincides with the critical shift to invasion and metastasis. Why It Matters: This work moves us beyond a static "list of mutations" to a dynamic understanding of how pancreatic cancer genomes change over space and time. By mapping these evolutionary trajectories at the single-cell level, we can better identify the true drivers of progression and metastasis, paving the way for interventions that target a cancer's specific evolutionary vulnerabilities. #PancreaticCancer #CancerResearch #SingleCell #Genomics #PrecisionMedicine #PDAC #Oncology #NatureGenetics #Biotech #Innovation Citation: Genomic evolution of pancreatic cancer at single-cell resolution. Nat Genet (2026). https://lnkd.in/g-FzGWWq

🧬 The Molecular Genesis of Malignant Transformation The Evolutionary Timeline of Carcinogenesis Cancer is generally considered a monoclonal disease process, meaning it often originates from a single genetically altered cell. The development of malignancy is typically slow and multi-step, frequently evolving over decades as cells gradually accumulate genetic and epigenetic alterations. This prolonged latent period allows successive generations of abnormal cells to progress through pre-malignant stages under the influence of environmental exposures, chronic inflammation, inherited susceptibility, and DNA damage from genotoxic agents such as tobacco smoke, radiation, or carcinogenic chemicals. ━━━━━━━━━━━━━━━ 🧬 Genetic Imbalance: Oncogenes vs Tumor Suppressor Genes Normal cellular growth depends on a balance between growth-promoting and growth-inhibiting genetic mechanisms. ⚡ Proto-oncogenes These are normal genes involved in cell growth and signaling. When mutated or overactivated, they become oncogenes that drive uncontrolled proliferation. Examples include: • RAS • MYC • HER2 🛑 Tumor Suppressor Genes These genes function as the cell’s protective “brakes” by regulating cell division, DNA repair, and apoptosis. Loss or inactivation of these genes contributes to malignant transformation. Important examples: • RB1 • TP53 (p53), often called the “guardian of the genome” According to the classic “two-hit hypothesis,” both functional copies of certain tumor suppressor genes may need to be lost before malignant progression occurs. ━━━━━━━━━━━━━━━ ⚠️ Genomic Instability and Malignant Transformation When DNA repair systems fail and damaged cells escape apoptosis, genomic instability increases. This “mutator phenotype” accelerates the accumulation of additional mutations necessary for cancer development. Over time, transformed cells may acquire the ability to: ✔ Proliferate autonomously ✔ Resist programmed cell death ✔ Induce angiogenesis ✔ Invade surrounding tissues ✔ Metastasize through blood or lymphatic pathways ━━━━━━━━━━━━━━━ 💡 Cancer development is therefore not a single event, but a progressive evolutionary process involving complex interactions between genetics, environment, and cellular adaptation. ⚠️ Disclaimer: This content is for educational purposes only and should not be considered medical advice, diagnosis, or treatment. #CancerBiology #MolecularBiology #Oncology #Genetics #Epigenetics #Carcinogenesis #MedicalEducation #CancerResearch

Strain tracking in complex microbiomes reveals per-species modes of evolution Microbial species in the human microbiome diversify into strains through single-nucleotide mutations and structural changes like insertions, deletions and recombination events. Tracking these strain-level changes is crucial for understanding microbiome dynamics and linking them to host health and disease. This recent research published in nature biotechnology (https://lnkd.in/etR-XbFC ), introduces SynTracker, a new tool that compares microbial strains using genome synteny - the order of sequence blocks in homologous genomic regions. This allows detection of structural changes missed by current SNP-based strain comparison methods. - SynTracker outperforms existing tools for tracking strains in metagenomic data, especially for phages, plasmids and low-abundance species. - Combining SynTracker with an SNP-based tool reveals species that evolve mainly through point mutations (hypermutators) vs structural changes (hyperrecombinators). - SynTracker can classify strains into phylogenetic groups using just 1-2% of the genome, showing synteny is a rich source of untapped genomic information. In summary, SynTracker could enable new insights into the ecology and evolution of microbial communities, including revealing species-specific modes of evolution, monitoring strain dynamics over time and space, and improving metagenome-assembled genome binning #microbiome #straintracking #cancer #PrecisionMedicine

Complex Rearrangements in ER+ and HER2+ Breast Tumors: A Paradigm Shift in Breast Cancer Understanding A recent Nature publication titled "Complex Rearrangements Fuel ER+ and HER2+ Breast Tumours" by Kathleen E. Houlahan et al. delves into the genomic intricacies of breast cancer, presenting a transformative perspective on its classification, progression, and treatment. Key Insights: Three Dominant Genomic Archetypes: > ER+ High-Risk Subgroup: Characterized by cyclic extrachromosomal DNA amplifications driven by ER-induced R-loops and APOBEC3B-editing, this subgroup shares genomic similarities with HER2+ tumors. >> HER2+ Tumors: Driven by focal amplifications, often involving ERBB2. >>> TNBC (Triple-Negative Breast Cancer): Marked by genome-wide instability and homologous repair deficiency (HRD)-like signatures. <> Evolutionary Patterns: These archetypes are established early in tumorigenesis, sculpting the tumor microenvironment (TME) and persisting into metastatic stages. ER+ high-risk tumors exhibit immune evasion and replication stress, presenting distinct therapeutic vulnerabilities. <>Therapeutic Implications: Replication stress pathways and ecDNA dynamics emerge as promising targets for intervention, especially in ER+ high-risk and HER2+ subgroups. TNBC's HRD-like profiles reinforce the role of PARP inhibitors, while a subset of ER+ high-risk tumors might also benefit from these therapies. <>Why This Matters: This study redefines breast cancer's genomic landscape, emphasizing the role of complex rearrangements in tumor progression and therapeutic resistance. By moving beyond traditional receptor-based classifications, it opens doors to more precise treatment strategies tailored to specific genomic archetypes. The implications for personalized medicine are profound, promising better outcomes for patients through targeted therapies that address the unique vulnerabilities of each subtype. 🔗 https://lnkd.in/gvtrdjrD #BreastCancerResearch #Genomics #OncologyInnovation #PrecisionMedicine