Overall Survival
Introduction
Section titled “Introduction”Background and Definition
Section titled “Background and Definition”Overall survival (OS) is a fundamental endpoint in medical research, particularly in oncology and studies of chronic diseases. It is typically defined as the time from a specific point, such as diagnosis or the start of a treatment, until the date of death from any cause, or the date of the last follow-up if the patient is still alive.[1]This comprehensive measure reflects the total lifespan of a patient following an intervention or diagnosis, making it a robust indicator of treatment efficacy and disease prognosis.
Biological Basis
Section titled “Biological Basis”Genetic variations play a significant role in influencing an individual’s overall survival. Genome-wide association studies (GWAS) and meta-analyses are frequently employed to identify single nucleotide polymorphisms (SNPs) associated with differences in survival outcomes.[1]These studies often utilize statistical models such as Kaplan–Meier curves, log-rank tests, and Cox proportional hazard models to assess the association between specific genotypes and survival time.[1] For instance, research has identified SNPs in genes like PDLIM3 (rs28520013 ) that are strongly associated with overall survival in specific patient populations, such as those with metastatic renal cell carcinoma treated with sunitinib.[2] Other genes, including CACNA2D3, ART1, DAB1, and LIMCH1, have also shown suggestive associations with OS.[2]These genetic markers can influence various biological pathways, affecting disease progression, treatment response, and ultimately, longevity.
Clinical Relevance
Section titled “Clinical Relevance”In clinical practice, overall survival is a critical metric for evaluating the effectiveness of therapies and for prognosticating patient outcomes. For example, in non-small cell lung cancer (NSCLC) patients receiving platinum-based chemotherapy, genetic variants have been found to be associated with decreased overall survival, highlighting the potential for personalized medicine approaches.[1] Identifying genetic markers that predict survival can help clinicians stratify patients into different risk groups, guide treatment selection, and provide more accurate prognoses. Studies have shown that combining genetic variables with clinical and epidemiological factors can improve the prediction of survival outcomes.[1]The significant difference in median overall survival observed between carriers and non-carriers of certain genetic variants, such asrs28520013 in PDLIM3, underscores the profound clinical implications of these genetic findings.[2]
Social Importance
Section titled “Social Importance”The study of overall survival extends beyond individual patient care to broader societal implications. Understanding the genetic determinants of survival contributes to our knowledge of human longevity and the biological processes of aging.[3]By identifying factors that influence how long individuals live, researchers can develop strategies to extend healthy lifespans and improve quality of life. For patients and their families, accurate information about overall survival, particularly when influenced by genetics, can help in making informed decisions about treatment pathways and life planning. Furthermore, research into genetic associations with survival can inform public health initiatives aimed at reducing mortality and improving health outcomes across populations.
Methodological and Statistical Challenges
Section titled “Methodological and Statistical Challenges”Genetic studies on complex traits like overall survival face inherent methodological and statistical constraints that can influence the robustness and interpretability of findings. For instance, meta-analyses often apply stringent exclusion criteria for single nucleotide polymorphisms (SNPs), removing those with unreliable standard errors, poor imputation quality, or very low minor allele frequencies (MAF).[4]While these filters enhance statistical reliability, they can inadvertently limit the discovery of genetic variants with smaller effects or those that are less common, potentially leading to an incomplete genetic architecture of overall survival.
A critical limitation is the challenge of replicating initial genetic associations, as the ultimate validation of findings necessitates confirmation in independent cohorts.[5] Low allele frequency, in particular, has been identified as a factor contributing to the failure of replication.[6]Without consistent replication across diverse studies, observed associations may represent false positives or be specific to the discovery population, thereby hindering the confident translation of genetic insights into a broader understanding of overall survival. Furthermore, assumptions underlying statistical models, such as the proportional hazards assumption in survival analysis, can impact results if violated, even with adjustments for specific covariates like age, sex, and ancestry principal components.[7] potentially leading to biased effect estimates.
Population Diversity and Generalizability
Section titled “Population Diversity and Generalizability”The generalizability of genetic findings to overall survival is significantly influenced by the demographic and ancestral composition of the study cohorts. Population stratification, where differences in allele frequencies between subgroups can lead to spurious associations, is a known confounder, and while methods like genomic control inflation factor (λ) correction are applied to mitigate this.[4] residual effects or uncaptured diversity can persist. This limitation implies that genetic associations identified predominantly within specific ancestral groups, such as studies focused on Japanese populations.[6] may not be directly transferable or hold the same clinical relevance in other global populations.
Consequently, the utility of identified genetic variants for predicting or influencing overall survival may vary considerably across different populations due to diverse genetic backgrounds, allele frequencies, and gene–environment interactions. A comprehensive understanding of overall survival and the robust application of genetic discoveries require extensive validation and replication in ethnically diverse cohorts. The focus on specific cohorts without broad representation can limit the global applicability of findings and contribute to health disparities if interventions are developed based on non-generalizable genetic insights.
Incomplete Understanding of Genetic and Environmental Factors
Section titled “Incomplete Understanding of Genetic and Environmental Factors”The genetic architecture underlying overall survival is complex, involving numerous genetic variants, many of which may exert subtle effects or interact in non-additive ways, contributing to the phenomenon of “missing heritability.” A significant challenge in genome-wide association studies (GWAS) is prioritizing genetic associations for follow-up, particularly given the potential for pleiotropy, where a single genetic variant may influence multiple biological traits.[5]This inherent complexity means that current genetic findings likely only explain a fraction of the heritable component of overall survival, leaving substantial biological pathways and genetic contributions yet to be discovered.
Moreover, environmental factors and intricate gene–environment interactions play a crucial, yet often unmeasured, role in modulating overall survival and its genetic underpinnings. While studies can identify robust links between genes and their protein products.[5]they frequently do not comprehensively account for the myriad of environmental confounders, lifestyle choices, or social determinants of health that significantly impact long-term survival outcomes. Therefore, the observed genetic effects may be influenced by unmeasured environmental exposures, leading to an incomplete understanding of the true causal genetic pathways and limiting the development of holistic interventions for improving overall survival.
Variants
Section titled “Variants”Genetic variations play a crucial role in determining an individual’s susceptibility to disease and their overall lifespan, influencing pathways from immune response to cellular metabolism. Variants in genes such asOSMR, IL17F, and PRB2 exemplify how diverse genetic elements can impact human health and longevity. The OSMRgene encodes the Oncostatin M Receptor, a key player in inflammation, immune regulation, and cellular growth, with variations potentially affecting chronic inflammatory conditions and cancer progression, thereby influencing overall survival. Similarly,IL17F(Interleukin 17F) is a cytokine central to the body’s immune and inflammatory responses, particularly in maintaining mucosal barriers; genetic differences here can alter immune function and disease susceptibility. Meanwhile,PRB2 (Proline-Rich Protein B2), associated with rs2900174 , is primarily known for its role in salivary gland function and oral health, and may indirectly affect systemic health and longevity through its influence on the oral microbiome and inflammation. Studies consistently highlight the importance of genetic factors in determining survival to advanced ages.[1], [3] Other variants impact fundamental cellular processes like adhesion, signaling, and metabolism, which are critical for maintaining health throughout life. For instance, a variant like rs913493 in PCDH9(Protocadherin 9) relates to a gene involved in cell-cell adhesion and neuronal development, suggesting potential implications for neurological health and age-related cognitive decline that affect overall survival. TheIQCM gene, with its associated rs17026425 variant, contributes to cell motility and cytoskeletal organization, processes vital for tissue repair and immune cell function. Furthermore, the SLC2A13 gene, linked to rs2242367 , encodes a glucose transporter essential for myo-inositol metabolism, a pathway involved in various cellular signaling events; variations here could influence metabolic disorders and neurodegenerative conditions. Lastly,CMTR2 (Cap Methyltransferase 2), associated with rs3096380 , plays a role in mRNA cap methylation, a fundamental process in gene expression that can broadly affect cellular resilience and longevity. Genetic investigations frequently explore such broad cellular functions in relation to survival outcomes.[1], [8]Non-coding RNAs and proteases also represent significant genetic contributors to longevity and disease. Variants such asrs76766811 found within the LINC03095 - LINC02860region highlight the role of long intergenic non-coding RNAs (lncRNAs) in regulating gene expression, thereby influencing cellular aging and disease susceptibility. Similarly, thers584368 variant located near MIR3166 and CTSC involves both a microRNA (MIR3166) and a lysosomal protease, CTSC (Cathepsin C), which is crucial for immune cell activation and inflammatory responses. Alterations in this region could impact immune system regulation, a key factor in combating infections and chronic diseases that affect survival. The rs720251 variant, associated with SALRNA2 - UACA, points to the importance of small nucleolar RNAs and UACA(Uveal Autoantigen with Coiled-Coil Domains and Ankyrin Repeats), a gene involved in cell cycle control and apoptosis. Such variations can influence fundamental cellular processes like cell division and programmed cell death, thereby impacting cancer risk and tissue maintenance, which are critical for overall survival.[1], [3]
Key Variants
Section titled “Key Variants”| RS ID | Gene | Related Traits |
|---|---|---|
| rs34675408 | OSMR | overall survival |
| rs913493 | PCDH9 | overall survival |
| rs3096380 | CMTR2 | overall survival |
| rs763780 | IL17F | response to gemcitabine overall survival |
| rs76766811 | LINC03095 - LINC02860 | disease free survival overall survival |
| rs17026425 | IQCM | overall survival |
| rs720251 | SALRNA2 - UACA | overall survival |
| rs584368 | MIR3166 - CTSC | overall survival |
| rs2900174 | PRB2 | response to gemcitabine overall survival |
| rs2242367 | SLC2A13 | overall survival |
Definition and Conceptual Framework
Section titled “Definition and Conceptual Framework”Overall survival is precisely defined as the duration an individual lives from a specified point in time, such as age at study entry, until the date of death from any cause.[8] This metric serves as a fundamental endpoint in medical and epidemiological research, reflecting the total lifespan observed within a study’s follow-up period. Conceptually, it represents the ultimate outcome of all health-related factors and interventions, providing a comprehensive measure of longevity. Operationally, it is measured as the time interval between a baseline assessment or study enrollment and the documented date when an individual’s life ends.
Ascertainment and Criteria
Section titled “Ascertainment and Criteria”The accurate ascertainment of overall survival relies on robust data collection and validation protocols to determine the precise date of death. In studies, this often involves a multi-pronged approach, including obtaining records from hospitals, searching obituaries in local newspapers, and utilizing national registries such as the National Death Index.[8] Death certificates are routinely acquired, and all pertinent medical records, including hospital and nursing home reports prior to death, as well as autopsy reports if performed, are requested.[8] In cases where information is insufficient to determine a cause of death, next of kin may be interviewed by senior investigators to gather additional details, ensuring a comprehensive assessment.[8] An endpoint panel, typically composed of multiple senior investigators, rigorously reviews all records relevant to the death to confirm the date and circumstances, establishing critical criteria.[8]
Related Terminology and Outcome Classifications
Section titled “Related Terminology and Outcome Classifications”In the context of research, “overall survival” is often used interchangeably with “survival time,” particularly when referring to the duration from a study’s start to the event of death.[8]While overall survival quantifies the duration of life, related classifications categorize thecauseof death, which provides crucial insights into factors impacting longevity. Causes of death are systematically classified, for instance, into categories such as coronary heart disease, stroke, other cardiovascular disease (CVD), cancer, other causes, or unknown cause.[8] Survival data are frequently analyzed using statistical methods like Cox proportional hazards models, which evaluate the relationship between various factors and the time to death.[8]These analyses often adjust for covariates that can influence overall survival, including birth cohort, education level, smoking status, obesity (defined as a body mass index ≥30 kg/m2), hypertension (blood pressure ≥140/90 mmHg or on antihypertensive treatment), elevated cholesterol (cholesterol > 239 mg/dL), and diabetes.[8]
Overall Survival
Section titled “Overall Survival”Overall survival (OS) is a critical endpoint in clinical research, defined generally as the time from a specific event, such as diagnosis or the start of treatment, until death from any cause or the date of last follow-up.[9] It can also be defined from the date of chemotherapy start, blood sampling, or age at study entry to death.[1]In some contexts, it may specifically refer to cancer-specific mortality.[10]The assessment of overall survival often involves multivariable Cox regression analyses, adjusting for various clinical and genetic factors to provide a comprehensive understanding of patient outcomes.[1]
Prognostic and Predictive Utility
Section titled “Prognostic and Predictive Utility”Overall survival serves as a fundamental prognostic indicator, offering crucial insights into the natural history of a disease and the long-term implications for patients. Studies frequently assess the association of various clinical and genetic factors with overall survival to predict outcomes and disease progression. For instance, specific genetic markers likers1878022 have been significantly associated with poorer overall survival in non-small cell lung cancer (NSCLC) patients receiving platinum-based chemotherapy.[1] Similarly, variants in genes such as DSCAM and PDLIM3have been shown to correlate with efficacy outcomes in metastatic renal cell carcinoma patients treated with sunitinib, highlighting their potential to predict treatment response.[2]The ability of markers like Cystatin C to predict failure of cancer immunotherapy further underscores the prognostic power of such indicators in anticipating therapeutic effectiveness.[10]Beyond genetic markers, established clinical factors like gender, disease stage, and microsatellite instability (MSI) status are also significantly associated with overall survival in conditions such as colorectal cancer, providing essential context for patient prognosis.[9] The integration of these variables into predictive models, often evaluated using receiver operating characteristic (ROC) curves and area under the curve (AUC) analyses, can enhance the specificity and sensitivity of predicting survival.[1]Such comprehensive prognostic assessments are vital for informing patient and clinician expectations regarding disease trajectory and the potential impact of interventions.
Risk Stratification and Personalized Therapeutic Approaches
Section titled “Risk Stratification and Personalized Therapeutic Approaches”The of overall survival is instrumental in risk stratification, enabling the identification of high-risk individuals and the development of personalized medicine approaches. By analyzing genetic markers and clinical variables, studies aim to refine risk models, allowing for more precise patient categorization. For example, the inclusion of genetic markers has been shown to statistically significantly improve the prediction of 1-year survival in NSCLC patients, suggesting their utility in identifying individuals who may benefit from altered or intensified treatment strategies.[1]This approach extends to conditions like metastatic renal cell carcinoma, where genetic variants can guide the selection of targeted therapies such as sunitinib.[2]Personalized medicine is further advanced by analyzing specific patient subgroups, such as those with different MSI statuses or tumor locations (e.g., colon versus rectal cancer), to understand how genetic associations with survival outcomes may vary.[9]The use of prognostic risk groups, like the Heng prognostic risk group for renal cell carcinoma, in conjunction with genetic models, facilitates tailored treatment decisions.[2] These detailed stratification efforts are crucial for optimizing treatment selection, developing targeted prevention strategies, and designing clinical trials that focus on populations most likely to benefit.
Influence of Comorbidities and Associated Clinical Factors
Section titled “Influence of Comorbidities and Associated Clinical Factors”Overall survival is significantly influenced by a complex interplay of comorbidities and other associated clinical factors, necessitating their careful consideration in predictive models. Studies routinely adjust for various covariates, including age, gender, clinical stage, and performance status, to accurately assess the independent impact of genetic markers or treatments on survival.[1]Beyond these, broader health conditions and lifestyle factors such as hypertension, type 2 diabetes, obesity, elevated cholesterol, smoking status, and education have been incorporated into survival analyses, recognizing their profound effect on overall lifespan and disease outcomes.[11]Furthermore, the presence of multiple discrete cancer diagnoses or specific cancer types must be accounted for to ensure accurate survival analysis, as these can confound the definition of time since diagnosis and impact mortality rates.[10] The integration of diverse clinical and epidemiological variables, along with genetic data, allows for a more comprehensive understanding of patient survival, moving beyond single-factor analyses to embrace the multifactorial nature of long-term outcomes and inform holistic patient care.[8]
Longitudinal Cohort Studies and Long-term Survival
Section titled “Longitudinal Cohort Studies and Long-term Survival”Large-scale longitudinal cohort studies are fundamental to understanding overall survival (OS) patterns across populations, providing insights into temporal trends and the multifactorial determinants of life expectancy. The Framingham Study, for instance, exemplifies a robust cohort design, where participants from its Offspring Cohort have been regularly examined approximately every four years since 1971.[12]This extended follow-up enables comprehensive tracking of health outcomes and the ascertainment of survival time from age at study entry to age at death.[8] The meticulous methodology for determining death in such studies involves cross-referencing multiple sources, including hospital records, local obituaries, the National Death Index, death certificates, nursing home records, autopsy reports, and interviews with next of kin, with an endpoint panel reviewing all pertinent information to classify the cause of death.[8] Such detailed data collection is crucial for accurate survival analyses and for identifying long-term epidemiological associations with OS.
Epidemiological and Demographic Correlates of Survival
Section titled “Epidemiological and Demographic Correlates of Survival”Epidemiological studies leveraging large cohorts frequently analyze how various demographic and health factors correlate with overall survival. Within the Framingham Study, Cox proportional hazards models are employed to assess survival time, with analyses often conducted in a sex-specific manner and adjusted for key variables such as birth cohort, educational attainment, current smoking status, obesity (defined as BMI ≥30 kg/m²), hypertension (blood pressure ≥140/90 mmHg or on antihypertensive treatment), elevated cholesterol, and diabetes.[8]These adjustments are vital for isolating the independent effects of specific factors on survival and for understanding the complex interplay of socioeconomic and health-related determinants that influence population-level mortality patterns. Furthermore, the identification and classification of specific causes of death, such as coronary heart disease, stroke, other cardiovascular disease, and cancer, provide granular insights into the prevalence and incidence patterns of diseases contributing to overall mortality within the studied population.[8]
Ancestry, Geographic Variation, and Genetic Influences on Survival
Section titled “Ancestry, Geographic Variation, and Genetic Influences on Survival”Population studies also investigate the role of ancestry and geographic variations in overall survival, particularly in the context of specific diseases. A genome-wide association study (GWAS) on colorectal cancer (CRC) patients from Newfoundland, for example, focused on identifying genetic markers associated with overall survival in a population with a unique genetic background.[9]This study involved 505 subjects and examined 729,737 single nucleotide polymorphisms (SNPs), defining OS as the time from diagnosis until death from any cause or last follow-up.[9] Methodologically, principal component analysis was utilized for population stratification, leading to the identification and exclusion of five population outliers to ensure the homogeneity and representativeness of the study sample.[9]Such research highlights how genetic predispositions can vary across different ethnic groups and geographic regions, impacting disease prognosis and overall survival, thereby underscoring the importance of considering population-specific effects in genetic epidemiology.
Pharmacogenetics and Overall Survival
Section titled “Pharmacogenetics and Overall Survival”Pharmacogenetics explores how an individual’s genetic makeup influences their response to drugs, impacting critical clinical outcomes such as overall survival. Genetic variations can affect drug metabolism, target interaction, and the ultimate efficacy or toxicity of a therapeutic regimen, making personalized treatment strategies crucial, particularly in severe conditions like cancer where survival is a primary endpoint. Genome-wide association studies (GWAS) have been instrumental in identifying genetic markers associated with differential survival outcomes in patients undergoing various treatments.
Genetic Variants Influencing Chemotherapy Efficacy and Overall Survival
Section titled “Genetic Variants Influencing Chemotherapy Efficacy and Overall Survival”Genetic variations in drug targets or related signaling pathways can profoundly influence the effectiveness of chemotherapy and, consequently, a patient’s overall survival. For instance, in non-small cell lung cancer (NSCLC) patients receiving platinum-based chemotherapy, a single-nucleotide polymorphism (SNP) identified asrs1878022 in the chemokine-like receptor 1 (CMKLR1) gene has been statistically significantly associated with poor overall survival.[1] This finding suggests that variants affecting the function or expression of CMKLR1may modulate the therapeutic response to platinum-based agents, directly impacting patient longevity. Similarly, in metastatic renal cell carcinoma, a genome-wide meta-analysis identified variants inDSCAM and PDLIM3that correlated with efficacy outcomes, including overall survival, in patients treated with sunitinib.[2] These genetic insights highlight how specific polymorphisms in genes involved in drug action pathways can predict patient response and survival duration.
Pharmacokinetic and Pharmacodynamic Modulators of Survival Outcomes
Section titled “Pharmacokinetic and Pharmacodynamic Modulators of Survival Outcomes”Beyond direct drug targets, variations in genes governing drug absorption, distribution, metabolism, and excretion (ADME), as well as pharmacodynamic effects, contribute to interindividual differences in overall survival. Genetic variants in drug transporters, such asABCG2 (also known as BCRP), can influence drug response by altering drug absorption and excretion.[13]While directly studied for its impact on allopurinol pharmacodynamics, such variations exemplify how altered drug exposure can lead to differing therapeutic efficacy or toxicity, which are critical factors influencing a patient’s overall survival. Furthermore, pharmacodynamic effects, such as those related to DNA repair pathway genes, are assumed to influence individual responses to chemotherapy and overall survival in NSCLC patients, highlighting how genetic background can alter the body’s interaction with a drug’s mechanism of action.[1] These genetic differences can lead to suboptimal drug concentrations, reduced therapeutic effect, or increased toxicity, collectively impacting survival.
Clinical Integration of Pharmacogenetic Insights for Improved Survival
Section titled “Clinical Integration of Pharmacogenetic Insights for Improved Survival”The elucidation of pharmacogenetic associations with overall survival holds significant promise for clinical implementation, enabling personalized therapeutic strategies. Pharmacogenetic findings can guide physicians in the selection of an optimal treatment regimen, potentially avoiding ineffective therapies or mitigating severe adverse reactions that could compromise survival.[1] Such insights support personalized prescribing practices where drug selection and dosage recommendations are tailored to a patient’s genetic profile. For example, studies have explored flexible dosage arms designed to optimize treatment response, indicating a move towards individualized dosing based on patient characteristics, which can include genetic predispositions, to enhance therapeutic benefits.[14]By integrating pharmacogenetic information, clinicians can potentially improve patient outcomes, leading to enhanced overall survival through more effective and safer treatments.
Frequently Asked Questions About Overall Survival
Section titled “Frequently Asked Questions About Overall Survival”These questions address the most important and specific aspects of overall survival based on current genetic research.
1. My family seems to live much longer than average. Is that just luck, or is something else going on?
Section titled “1. My family seems to live much longer than average. Is that just luck, or is something else going on?”It’s likely more than just luck; genetics play a significant role in influencing how long individuals live. Research shows that inherited genetic variations contribute to human longevity and the biological processes of aging. While lifestyle is important, your family’s shared genetic background can predispose you to a longer lifespan.
2. If I’m diagnosed with a serious illness, can my genes tell doctors how well I’ll respond to treatment?
Section titled “2. If I’m diagnosed with a serious illness, can my genes tell doctors how well I’ll respond to treatment?”Yes, your genetics can provide valuable insights into treatment response and overall survival. Identifying specific genetic markers can help doctors predict how effective a therapy might be for you, guiding treatment selection. For instance, variants in genes likePDLIM3 have been linked to different survival outcomes for patients on certain medications.
3. Why do some people with the same cancer diagnosis survive much longer than others, even with similar treatments?
Section titled “3. Why do some people with the same cancer diagnosis survive much longer than others, even with similar treatments?”Genetic differences are a major factor. Even with the same diagnosis and treatment, variations in your genes can influence how your disease progresses and how your body responds to therapy. These genetic markers affect various biological pathways, ultimately impacting your overall survival time compared to someone else.
4. Could a DNA test really predict how long I might live or what health challenges I’ll face?
Section titled “4. Could a DNA test really predict how long I might live or what health challenges I’ll face?”A DNA test can provide some insights into your genetic predispositions, but it won’t give you a definitive lifespan prediction. Genetic markers can identify if you’re in a higher or lower risk group for certain survival outcomes, especially in specific diseases. Combining this genetic information with your clinical history and lifestyle factors can offer a more personalized prognosis.
5. Does my ancestry or ethnic background affect my chances of surviving certain diseases?
Section titled “5. Does my ancestry or ethnic background affect my chances of surviving certain diseases?”Yes, your ancestry can influence your genetic risks and how broadly genetic findings apply to you. Differences in allele frequencies and genetic backgrounds across various populations mean that associations found in one ethnic group might not be the same in another. This highlights the importance of diverse research to understand survival across all populations.
6. Can a healthy lifestyle truly overcome any “bad” genes I might have for longevity or disease survival?
Section titled “6. Can a healthy lifestyle truly overcome any “bad” genes I might have for longevity or disease survival?”A healthy lifestyle can significantly mitigate the impact of certain genetic predispositions, but whether it can “overcome” them entirely depends on the specific genes and the magnitude of their effect. Combining healthy habits with your genetic profile can improve survival predictions and outcomes. While genetics are important, lifestyle choices definitely play a crucial role in your overall health and longevity.
7. Why do doctors care so much about “overall survival” when they’re testing new treatments?
Section titled “7. Why do doctors care so much about “overall survival” when they’re testing new treatments?”Overall survival is considered the most robust and comprehensive measure in medical research because it reflects a patient’s total lifespan after an intervention, from diagnosis until death from any cause. It’s a critical metric for evaluating how effective new therapies truly are. Genetic factors, for example, can significantly influence this outcome, determining how well a patient responds to a specific treatment and ultimately impacts their survival.
8. If I’m a carrier of a specific genetic variant, does that mean my survival outlook is definitely worse?
Section titled “8. If I’m a carrier of a specific genetic variant, does that mean my survival outlook is definitely worse?”Not necessarily “definitely worse,” but it might indicate an increased risk or a different prognosis. Research often finds significant differences in median overall survival between carriers and non-carriers of certain genetic variants. This information helps clinicians stratify patients into risk groups, but it’s one piece of the puzzle among many factors influencing your individual outcome.
9. Can knowing my genetic risks for survival help me make better decisions about my health or treatment plans?
Section titled “9. Can knowing my genetic risks for survival help me make better decisions about my health or treatment plans?”Absolutely. Understanding your genetic risks for survival can empower you to make more informed decisions about your health and treatment pathways. This personalized genetic information can help guide discussions with your doctor about specific therapies, lifestyle adjustments, and overall life planning, potentially leading to better outcomes.
10. My sibling and I have the same parents, but their health outlook seems better. Why the difference in our survival potential?
Section titled “10. My sibling and I have the same parents, but their health outlook seems better. Why the difference in our survival potential?”Even with the same parents, you and your sibling inherit different combinations of genetic variants, which can lead to variations in your individual health and survival potential. These unique genetic profiles can influence everything from disease susceptibility to how effectively your body responds to treatments, contributing to different long-term health outlooks.
This FAQ was automatically generated based on current genetic research and may be updated as new information becomes available.
Disclaimer: This information is for educational purposes only and should not be used as a substitute for professional medical advice. Always consult with a healthcare provider for personalized medical guidance.
References
Section titled “References”[1] Wu, X. et al. “Genome-wide association study of survival in non-small cell lung cancer patients receiving platinum-based chemotherapy.”Journal of the National Cancer Institute, vol. 103, no. 8, 2011, pp. 627-35.
[2] Diekstra, M. H. M. et al. “Genome-Wide Meta-Analysis Identifies Variants in DSCAM and PDLIM3That Correlate with Efficacy Outcomes in Metastatic Renal Cell Carcinoma Patients Treated with Sunitinib.”Cancers (Basel), vol. 14, no. 12, 2022, p. 2838.
[3] Newman, A. B., et al. “A meta-analysis of four genome-wide association studies of survival to age 90 years or older: the Cohorts for Heart and Aging Research in Genomic Epidemiology Consortium.”The Journals of Gerontology Series A, Biological Sciences and Medical Sciences, vol. 65, no. 5, 2010, pp. 478-85.
[4] Tang, W. “A genome-wide association study for venous thromboembolism: the extended cohorts for heart and aging research in genomic epidemiology (CHARGE) consortium.”Genet Epidemiol, 2013, PMID: 23650146.
[5] Benjamin, E. J. et al. “Genome-wide association with select biomarker traits in the Framingham Heart Study.” BMC Med Genet, 2007, PMID: 17903293.
[6] Ishigaki, K. et al. “Large-scale genome-wide association study in a Japanese population identifies novel susceptibility loci across different diseases.” Nat Genet, 2020, PMID: 32514122.
[7] Khurshid, S. et al. “Clinical and genetic associations of deep learning-derived cardiac magnetic resonance-based left ventricular mass.”Nat Commun, 2023, PMID: 36944631.
[8] Lunetta, K. L. et al. “Genetic correlates of longevity and selected age-related phenotypes: a genome-wide association study in the Framingham Study.” BMC Medical Genetics, vol. 8, no. 1, 2007, p. 64.
[9] Xu, W. et al. “A genome wide association study on Newfoundland colorectal cancer patients’ survival outcomes.”Biomarkers Research, vol. 3, no. 1, 2015, p. 10.
[10] Kleeman, S. O. et al. “Cystatin C is glucocorticoid responsive, directs recruitment of Trem2+ macrophages, and predicts failure of cancer immunotherapy.”Cell Genomics, vol. 3, no. 8, 2023, p. 100347.
[11] Bi, W. et al. “A Fast and Accurate Method for Genome-Wide Time-to-Event Data Analysis and Its Application to UK Biobank.” The American Journal of Human Genetics, vol. 107, no. 1, 2020, pp. 108-119.
[12] Murabito, Joanne M., et al. “A genome-wide association study of breast and prostate cancer in the NHLBI’s Framingham Heart Study.”BMC Medical Genetics, vol. 8, 2007.
[13] Wen CC. “Genome-wide association study identifies ABCG2 (BCRP) as an allopurinol transporter and a determinant of drug response.” Clin Pharmacol Ther. PMID: 25676789.
[14] Li Q. “Genome-wide association study of paliperidone efficacy.” Pharmacogenet Genomics. PMID: 27846195.