Evolving insights:how DNA repair pathways impact cancer evolution

Viewing cancer as a large,evolving population of heterogeneous cells is a common perspective.Because genomic instability is one of the fundamental features of cancer,this intrinsic tendency of genomic variation leads to striking intratumor heterogeneity and functions during the process of cancer formation,development,metastasis,and relapse.With the increased mutation rate and abundant diversity of the gene pool,this heterogeneity leads to cancer evolution,which is the major obstacle in the clinical treatment of cancer.Cells rely on the integrity of DNA repair machineries to maintain genomic stability,but these machineries often do not function properly in cancer cells.The deficiency of DNA repair could contribute to the generation of cancer genomic instability,and ultimately promote cancer evolution.With the rapid advance of new technologies,such as single-cell sequencing in recent years,we have the opportunity to better understand the specific processes and mechanisms of cancer evolution,and让s relationship with DNA repair.Here,we review recent findings on how DNA repair affects cancer evolution,and discuss how these mechanisms provide the basis for critical clinical challenges and therapeutic applications.

CELLO:a longitudinal data analysis toolbox untangling cancer evolution

The complex pattern of cancer evolution poses a huge challenge to precision oncology.Longitudinal sequencing of tumor samples allows us to monitor the dynamics of mutations that occurred during this clonal evolution process.Here,we present a versatile toolbox,namely CELLO(Cancer EvoLution for Longitudinal data),accompanied with a step-by-step tutorial,to exemplify how to profile,analyze and visualize the dynamic change of somatic mutational landscape using longitudinal genomic sequencing data.Moreover,we customize the hypermutation detection module in CELLO to adapt targeted-DNA and whole-transcriptome sequencing data,and verify the extensive applicability of CELLO in published longitudinal datasets from brain,bladder and breast cancers.The entire tutorial and reusable programs in MATLAB,R and docker versions are open access at https://github.com/WaiigLabHKUST/CELLO.

Lack of evolutionary convergence in multiple primary lung cancer suggests insufficient specificity of personalized therapy

Multiple primary lung cancer(MPLC)is an increasingly prevalent subtype of lung cancer.According to recent genomic studies,the different lesions of a single MPLC patient exhibit functional similarities that may reflect evolutionary convergence.We perform whole-exome sequencing for a unique cohort of MPLC patients with multiple samples from each lesion found.Using our own and other relevant public data,evolutionary tree reconstruction reveals that cancer driver gene mutations occurred at the early trunk,indicating evolutionary contingency rather than adaptive convergence.Additionally,tumors from the same MPLC patient are as genetically diverse as those from different patients,while within-tumor genetic heterogeneity is significantly lower.Furthermore,the aberrant molecular functions enriched in mutated genes for a sample show a strong overlap with other samples from the same tumor,but not with samples from other tumors or other patients.Overall,there is no evidence of adaptive convergence during the evolution of MPLC.Most importantly,the similar between-tumor diversity and between-patient diversity suggest that personalized therapies may not adequately account for the genetic diversity among different tumors in an MPLC patient.To fully exploit the strategic value of precision medicine,targeted therapies should be designed and delivered on a per-lesion basis.

Stem cells, immortality, and the evolution of metastatic properties in breast cancer: telomere maintenance mechanisms and metastatic evolution

Breast cancer is the most significant cause of cancer-related death in women around the world.The vast majority of breast cancer-associated mortality stems from metastasis,which remains an incurable disease state.Metastasis results from evolution of clones that possess the insidious properties required for dissemination and colonization of distant organs.These clonal populations are descended from breast cancer stem cells(CSCs),which are also responsible for their prolonged maintenance and continued evolution.Telomeres impose a lifespan on cells that can be extended when they are actively elongated,as occurs in CSCs.Thus,changes in telomere structure serve to promote the survival of CSCs and subsequent metastatic evolution.The selection of telomere maintenance mechanism(TMM)has important consequences not only for CSC survival and evolution,but also for their coordination of various signaling pathways that choreograph the metastatic cascade.Targeting the telomere maintenance machinery may therefore provide a boon to the treatment of metastatic breast cancer.Here we review the two major TMMs and the roles they play in the development of stem and metastatic breast cancer cells.We also highlight current and future approaches to targeting these mechanisms in clinical settings to alleviate metastatic breast cancers.

Tumor organoids to study gastroesophageal cancer: a primer

Gastroesophageal cancers are leading causes of cancer death.Our attempts at adopting molecularly based treatment approaches have been slow and ineffective even though we begin to identify specific targetable gene mutations and pathways.It is dear that we should no longer treat all gastroesophageal cancers as a homogeneous disease,which is what we do when we use nonspecific chemotherapy.However,we currently cannot monitor successful gene/pathway targeting,nor understand how/when tumors develop resistance,nor predict which patients will derive maximal benefit.To improve outcomes,we must precisely detail the heterogeneity of these tumors to then individualize cancer therapy as well as develop novel avenues to study and predict treatment effects in individual patients.To this end,patient-derived organoids,in which tumor cells from individual patients are grown in a Petri dish,are a new versatile system that allows for timely expandability,detailed molecular characterization,and genetic manipulation with the promise of enabling predictive assessment of treatment response.In this review,we will explore the development and basic techniques for organoid generation,and discuss the current and potential future applications of this exciting technology to study the basic science of carcinogenesis and to predict/guide cancer patient care in the clinics.

Population dynamics inside cancer biomass driven by repeated hypoxia-reoxygenation cycles

A computational analysis of genome-scale transcriptomic data collected on -1,700 tissue samples of three cancer types： breast carcinoma, colon adenocarcinoma and lung adenocarcinoma, revealed that each tissue consists of （at least） two major subpopulations of cancer cells with different capabilities to handle fluctuating Oz levels. The two populations have distinct genomic and transcriptomic characteristics, one accelerating its proliferation under hypoxic conditions and the other proliferating faster with higher O2 levels, referred to as the hypoxia and the reoxygenation subpopulations, respectively. The proportions of the two subpopulations within a cancer tissue change as the average 02 level changes. They both contribute to cancer development but in a complementary manner. The hypoxia subpopulation tends to have higher proliferation rates than the reoxygenation one as well as higher apoptosis rates; and it is largely responsible for the acidic environment that enables tissue invasion and provides protection against attacks from T-cells. In comparison, the reoxygenation subpopulation generates new extracellular matrices in support of further growth of the tumor and strengthens cell-cell adhesion to provide scaffolds to keep all the cells connected. This subpopulation also serves as the major source of growth factors for tissue growth. These data and observations strongly suggest that these two major subpopulations within each tumor work together in a conjugative relationship to allow the tumor to overcome stresses associated with the constantly changing Oz level due to repeated growth and angiogenesis. The analysis results not only reveal new insights about the population dynamics within a tumor but also have implications to our understanding of possible causes of different cancer phenotypes such as diffused versus more tightly connected tumor tissues.

Identification of Tumor Evolution Patterns by Means of Inductive Logic Programming

In considering key events of genomic disorders in the development and progression of cancer, the correlation between genomic instability and carcinogenesis is currently under investigation. In this work, we propose an inductive logic programming approach to the problem of modeling evolution patterns for breast cancer. Using this approach, it is possible to extract fingerprints of stages of the disease that can be used in order to develop and deliver the most adequate therapies to patients. Furthermore, such a model can help physicians and biologists in the elucidation of molecular dynamics underlying the aberrations-waterfall model behind carcinogenesis. By showing results obtained some hints about further approach to the hypotheses. on a real-world dataset, we try to give knowledge-driven validations of such

Cancer: An unknown territory;rethinking before going ahead

Cancer is a disease of altered signaling and metabolism,causing uncontrolled divi-sion and survival of transformed cells.A host of molecules,factors,and conditions have been designated as underlying causes for the inception and progression of the disease.An enormous amount of data is available,system-wide interaction networks of the genes and proteins are generated over the years and have now reached up to a level of saturation,where we need to shift our focus to the more advanced and comprehensive methods and approaches of data analysis and visualization.Even with the availability of enormous literature on this one of the most pressing pathological conditions,a successful cure of the disease seems to be obscure.New treatment plans,like immunotherapy and precision medicine,are being employed for different studies.Nevertheless,their actual benefits to the patients would be known only after the evaluation of clinical data over the next few years.Therefore,we need to look at few fundamental challenges that should be addressed in more depth before we could devise bet-ter,rigorous,and comprehensive treatment plans and may successfully reach a possible cure of the disease.This article aims at bringing attention towards some fundamental gaps in our approach towards the disease that leads to failure in devising successful therapeutics.