Genetic Ancestry Inference from Cancer-Derived Molecular Data across Genomic and Transcriptomic Platforms

Inference of genetic ancestry from a multi-gene cancer panel in Colombian women with cancer

INTRODUCTION

Cancer health disparities among racial and ethnic populations significantly burden health systems due to unequal access to early detection, treatment, and healthcare resources. These disparities lead to worse outcomes and increased costs from delayed diagnoses, advanced treatments, and prolonged care. Genetic differences can also influence cancer susceptibility and treatment response, thus analyzing genetic ancestry is essential for uncovering genetic factors that may contribute to these disparities. Utilizing data from clinical multigene cancer panels to infer genetic ancestry offers a valuable approach to understand population structure and the impact of individual ancestries in development of complex diseases.

AIM

To evaluate the accuracy of global ancestry inference using genetic markers from the TruSight™ Hereditary Cancer Panel, which was used to investigate hereditary cancer syndromes in a cohort of 116 female cancer patients at the Colombian National Cancer Institute. Additionally, to compare these results with genetic ancestry estimations from traditional genome-wide markers.

RESULTS

Our results demonstrate a strong correlation between global genetic ancestry inferred with markers captured from TruSightTM panel (4785 markers) and Whole Genome Sequencing (WGS, 8 million markers in admixed populations. The correlation values were 0.96 (p < 0.0001) for the Native American and European ancestry components, and 0.99 (p < 0.0001) for the African ancestry fraction. Genetic ancestry mean proportions in the Colombian cohort were 45.7%, 46.2%, and 8.11% for the European, the Native American, and the African components, respectively.

CONCLUSION

This study demonstrates the accuracy of ancestry inference from clinical panel data offering a promising approach for understanding cancer health disparities in admixed populations.

A consensus-based classification workflow to determine genetically inferred ancestry from comprehensive genomic profiling of patients with solid tumors

Disparities in cancer diagnosis, treatment, and outcomes based on self-identified race and ethnicity (SIRE) are well documented, yet these variables have historically been excluded from clinical research. Without SIRE, genetic ancestry can be inferred using single-nucleotide polymorphisms (SNPs) detected from tumor DNA using comprehensive genomic profiling (CGP). However, factors inherent to CGP of tumor DNA increase the difficulty of identifying ancestry-informative SNPs, and current workflows for inferring genetic ancestry from CGP need improvements in key areas of the ancestry inference process. This study used genomic data from 4274 diverse reference subjects and CGP data from 491 patients with solid tumors and SIRE to develop and validate a workflow to obtain accurate genetically inferred ancestry (GIA) from CGP sequencing results. We use consensus-based classification to derive confident ancestral inferences from an expanded reference dataset covering eight world populations (African, Admixed American, Central Asian/Siberian, European, East Asian, Middle Eastern, Oceania, South Asian). Our GIA calls were highly concordant with SIRE (95%) and aligned well with reference populations of inferred ancestries. Further, our workflow could expand on SIRE by (i) detecting the ancestry of patients that usually lack appropriate racial categories, (ii) determining what patients have mixed ancestry, and (iii) resolving ancestries of patients in heterogeneous racial categories and who had missing SIRE. Accurate GIA provides needed information to enable ancestry-aware biomarker research, ensure the inclusion of underrepresented groups in clinical research, and increase the diverse representation of patient populations eligible for precision medicine therapies and trials.

Incidental pathogenic germline alterations detected through liquid biopsy in patients with solid tumors: prevalence, clinical utility and implications

Liquid biopsy, a minimally invasive approach for detecting tumor biomarkers in blood, has emerged as a leading-edge technique in cancer precision medicine. New evidence has shown that liquid biopsies can incidentally detect pathogenic germline variants (PGVs) associated with cancer predisposition, including in patients with a cancer for which genetic testing is not recommended. The ability to detect these incidental PGV in cancer patients through liquid biopsy raises important questions regarding the management of this information and its clinical implications. This incidental identification of PGVs raises concerns about cancer predisposition and the potential impact on patient management, not only in terms of providing access to treatment based on the tumor molecular profiling, but also the management of revealing genetic predisposition in patients and families. Understanding how to interpret this information is essential to ensure proper decision-making and to optimize cancer treatment and prevention strategies. In this review we provide a comprehensive summary of current evidence of incidental PGVs in cancer predisposition genes identified by liquid biopsy in patients with cancer. We critically review the methodological considerations of liquid biopsy as a tool for germline diagnosis, clinical utility and potential implications for cancer prevention, treatment, and research.

Pancreatic Cancer Health Disparity: Pharmacologic Anthropology

Pancreatic cancer (PCa) remains a formidable global health challenge, with high mortality rates and limited treatment options. While advancements in pharmacology have led to improved outcomes for various cancers, PCa continues to exhibit significant health disparities, disproportionately affecting certain populations. This paper explores the intersection of pharmacology and anthropology in understanding the health disparities associated with PCa. By considering the socio-cultural, economic, and behavioral factors that influence the development, diagnosis, treatment, and outcomes of PCa, pharmacologic anthropology provides a comprehensive framework to address these disparities and improve patient care.

Outcomes in breast cancer-does ethnicity matter?

Ethnic or racial differences in breast cancer (BC) survival outcomes have been reported, but current data are largely restricted to comparisons between African Americans and non-Hispanic whites. Most analyses have traditionally been based on self-reported race which may not always be accurate, or are oversimplified in their classification. With increasing globalization, quantification of the genetic ancestry from genomic data may offer a solution to infer the complex makeup from admixture of races. Focusing on the larger and the latest studies, we will discuss recent findings on the differing host and tumor biology that may be driving these disparities, in addition to the extrinsic environmental or lifestyle factors. Socioeconomic disparities with lower cancer literacy may lead to late presentation, poorer adherence to treatment, and other lifestyle factors such as unhealthy diet, obesity, and inadequate physical activity. These hardships may also result in greater allostatic load, which is in turn associated with aggressive BC features in disadvantaged populations. Epigenetic reprogramming may mediate the effects of the environment or lifestyle factors on gene expression, with ensuing differences in BC characteristics and outcome. There is increasing evidence that germline genetics can influence somatic gene alterations or expression, as well as modulate the tumor or immune microenvironment. Although the precise mechanisms remain elusive, this may account for the varying distribution of different BC subtypes across ethnicities. These gaps in our knowledge highlight the need to interrogate the multiomics landscape of BC in diverse populations, ideally in large-scale collaborative settings with standardized methodology for the comparisons to be statistically robust. Together with improving BC awareness and access to good quality health care, a holistic approach with insights of the biological underpinnings is much needed to eradicate ethnic disparities in BC outcomes.

Inferring genetic ancestry from cancer sequencing data

Genetic ancestry is an important biological determinant of cancer health disparities that is not captured by self-identified race and ethnicity (SIRE). Belleau et al. recently developed a systematic computational approach to infer genetic ancestry from cancer-derived molecular data from different genomic and transcriptomic profiling assays, creating opportunities to interrogate population-scale data sets.

The PANcreatic Disease ReseArch (PANDoRA) consortium: Ten years' experience of association studies to understand the genetic architecture of pancreatic cancer

Pancreatic cancer has an incidence that almost matches its mortality. Only a small number of risk factors and 33 susceptibility loci have been identified. so Moreover, the relative rarity of pancreatic cancer poses significant hurdles for research aimed at increasing our knowledge of the genetic mechanisms contributing to the disease. Additionally, the inability to adequately power research questions prevents small monocentric studies from being successful. Several consortia have been established to pursue a better understanding of the genetic architecture of pancreatic cancers. The Pancreatic disease research (PANDoRA) consortium is the largest in Europe. PANDoRA is spread across 12 European countries, Brazil and Japan, bringing together 29 basic and clinical research groups. In the last ten years, PANDoRA has contributed to the discovery of 25 susceptibility loci, a feat that will be instrumental in stratifying the population by risk and optimizing preventive strategies.