Ancestral characterization of 1018 cancer cell lines highlights disparities and reveals gene expression and mutational differences

Variation to biology: optimizing functional analysis of cancer risk variants

Research conducted over the past 15+ years has identified hundreds of common germline genetic variants associated with cancer risk, but understanding the biological impact of these primarily non-protein coding variants has been challenging. The National Cancer Institute sought to better understand and address those challenges by requesting input from the scientific community via a survey and a 2-day virtual meeting, which focused on discussions among participants. Here, we discuss challenges identified through the survey as important to advancing functional analysis of common cancer risk variants: 1) When is a variant truly characterized; 2) Developing and standardizing databases and computational tools; 3) Optimization and implementation of high-throughput assays; 4) Use of model organisms for understanding variant function; 5) Diversity in data and assays; and 6) Creating and improving large multidisciplinary collaborations. We define these 6 challenges, describe how success in addressing them may look, propose potential solutions, and note issues that span all the challenges. Implementation of these ideas could help develop a framework for methodically analyzing common cancer risk variants to understand their function and make effective and efficient use of the wealth of existing genomic association data.

Genetic diversity among the present Japanese population: evidence from genotyping of human cell lines established in Japan

Japan is often assumed to have a highly homogeneous ethnic population, because it is an island country. This is evident in human cell lines collected from cell banks; however, these genotypes have not been thoroughly characterized. To examine the population genotypes of human cell lines established in Japan, we conducted SNP genotyping on 57 noncancerous cell lines and 43 lung cancer cell lines. Analysis of biogeographic ancestry revealed that 58 cell lines had non-admixed Japanese genotypes, 21 cell lines had an admixture of Japanese and East Asian genotypes, and the remaining 21 cell lines had East Asian genotypes. The proportion of non-admixed Japanese genotypes was similar between lung cancer and noncancerous cell lines, suggesting that patients in Japan may not exclusively have Japanese genotypes. This could influence the incidence of inherited diseases and should be taken into account in personalized medicine tailored to genetic background. The genetic makeup of the present-day Japanese population cannot be fully explained by the ancestral Jomon and Yayoi lineages. Instead, it is necessary to consider a certain level of genetic admixture between Japanese and neighboring Asian populations. Our study revealed genetic variation among human cell lines derived from Japanese individuals, reflecting the diversity present within the Japanese population.

Germline variation contributes to false negatives in CRISPR-based experiments with varying burden across ancestries

Reducing disparities is vital for equitable access to precision treatments in cancer. Socioenvironmental factors are a major driver of disparities, but differences in genetic variation likely also contribute. The impact of genetic ancestry on prioritization of cancer targets in drug discovery pipelines has not been systematically explored due to the absence of pre-clinical data at the appropriate scale. Here, we analyze data from 611 genome-scale CRISPR/Cas9 viability experiments in human cell line models to identify ancestry-associated genetic dependencies essential for cell survival. Surprisingly, we find that most putative associations between ancestry and dependency arise from artifacts related to germline variants. Our analysis suggests that for 1.2-2.5% of guides, germline variants in sgRNA targeting sequences reduce cutting by the CRISPR/Cas9 nuclease, disproportionately affecting cell models derived from individuals of recent African descent. We propose three approaches to mitigate this experimental bias, enabling the scientific community to address these disparities.

Tackling the lack of diversity in cancer research

Despite the clear benefit of studying biological samples from diverse genetic backgrounds and geographical locations, our current knowledge of disease is mostly derived from the study of European-descent individuals. In the cancer field, this is reflected in the poor representation of African and Amerindian/Latino samples in most large public data repositories. This lack of diversity is due to several reasons, but here we focus on (1) the lack of support for studies on non-European populations that are performed in low- and middle-income countries (LMICs), and (2) unequal partnerships between scientists in LMICs and those in high-income countries. We argue that expanding access to research funding, increasing the participation of underrepresented scientists in editorial boards and international conferences, facilitating the publication of studies conducted in these countries, and properly acknowledging LMIC researchers' contributions in publications and grant applications will promote equity for scientists working in LMICs. We envisage that this will translate to more impactful research in these countries, which will include more samples from diverse populations. For the cancer field, this will broaden our understanding of pathomechanisms and may help to improve the treatment of patients from all backgrounds.

Parabens Promote Protumorigenic Effects in Luminal Breast Cancer Cell Lines With Diverse Genetic Ancestry

Context

One in 8 women will develop breast cancer in their lifetime. Yet, the burden of disease is greater in Black women. Black women have a 40% higher mortality rate than White women, and a higher incidence of breast cancer at age 40 and younger. While the underlying cause of this disparity is multifactorial, exposure to endocrine disrupting chemicals (EDCs) in hair and other personal care products has been associated with an increased risk of breast cancer. Parabens are known EDCs that are commonly used as preservatives in hair and other personal care products, and Black women are disproportionately exposed to products containing parabens.

Objective

Studies have shown that parabens impact breast cancer cell proliferation, death, migration/invasion, and metabolism, as well as gene expression in vitro. However, these studies were conducted using cell lines of European ancestry; to date, no studies have utilized breast cancer cell lines of West African ancestry to examine the effects of parabens on breast cancer progression. Like breast cancer cell lines with European ancestry, we hypothesize that parabens promote protumorigenic effects in breast cancer cell lines of West African ancestry.

Methods

Luminal breast cancer cell lines with West African ancestry (HCC1500) and European ancestry (MCF-7) were treated with biologically relevant doses of methylparaben, propylparaben, and butylparaben.

Results

Following treatment, estrogen receptor target gene expression and cell viability were examined. We observed altered estrogen receptor target gene expression and cell viability that was paraben and cell line specific.

Conclusion

This study provides greater insight into the tumorigenic role of parabens in the progression of breast cancer in Black women.

SLC9A1 Binding mTOR Signaling Pathway-Derived Risk Score Predicting Survival and Immune in Clear Cell Renal Cell Carcinoma

Objective. Clear cell renal cell carcinoma (ccRCC) is one of the common renal cell carcinomas (RCC) with a high risk of recurrence. Considering that SLC9A1 is involved in various cellular physiological processes and probably mediates the course of mTOR signaling in tumors, this study constructed a risk model for SLC9A1 combined with mTOR signaling in ccRCC, aiming at better predicting the prognosis of patients. Methods. ccRCC expression matrices were downloaded from TCGA and ICGC databases to compare the expression of SLC9A1 in TCGA, and qRT-PCR was adopted to validate the SLC9A1 expression in different RCC cells and normal kidney cells. The CIBERSORT and ESTIMATE algorithms were used to assess samples for immunity. mTOR signaling-associated genes were downloaded from the KEGG website, and then the genes were adopted to screen genes associated with SLC9A1 expression and mTOR signaling pathway colleagues, based on which univariate COX regression and lasso regression Cox analyses were conducted to construct a ccRCC prognostic risk model. ROC curves and nomograms were used to assess the validity of the models. Results. ccRCC tumor samples showed lower SLC9A1 expression than normal samples, as also evidenced by qRT-PCR. The SLC9A1 expression was highly correlated with tumor immunity. Totally, 564 key genes associated with both SLC9A1 expression and mTOR signaling were screened out, and the risk model consisting of 11 gene signatures was constructed in ccRCC based on the 564 genes. Since patients at a high risk had poorer survival outcomes, the high-risk group presented poorer immunotherapy outcomes. Moreover, a higher clinical grade of patients suggested a higher risk score. The risk score can serve as one independent prognostic factor for the prognosis prediction of ccRCC patients. Conclusion. An extremely promising prognostic indicator for ccRCC based on SLCA9A1 and mTOR signaling has been constructed to provide reference for clinical treatment.

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

Genetic ancestry-oriented cancer research requires the ability to perform accurate and robust genetic ancestry inference from existing cancer-derived data, including whole-exome sequencing, transcriptome sequencing, and targeted gene panels, very often in the absence of matching cancer-free genomic data. Here we examined the feasibility and accuracy of computational inference of genetic ancestry relying exclusively on cancer-derived data. A data synthesis framework was developed to optimize and assess the performance of the ancestry inference for any given input cancer-derived molecular profile. In its core procedure, the ancestral background of the profiled patient is replaced with one of any number of individuals with known ancestry. The data synthesis framework is applicable to multiple profiling platforms, making it possible to assess the performance of inference specifically for a given molecular profile and separately for each continental-level ancestry; this ability extends to all ancestries, including those without statistically sufficient representation in the existing cancer data. The inference procedure was demonstrated to be accurate and robust in a wide range of sequencing depths. Testing of the approach in four representative cancer types and across three molecular profiling modalities showed that continental-level ancestry of patients can be inferred with high accuracy, as quantified by its agreement with the gold standard of deriving ancestry from matching cancer-free molecular data. This study demonstrates that vast amounts of existing cancer-derived molecular data are potentially amenable to ancestry-oriented studies of the disease without requiring matching cancer-free genomes or patient self-reported ancestry.

SIGNIFICANCE

The development of a computational approach that enables accurate and robust ancestry inference from cancer-derived molecular profiles without matching cancer-free data provides a valuable methodology for genetic ancestry-oriented cancer research.

How inclusive are cell lines in preclinical engineered cancer models?

Diverse factors contribute to significant and dire disparities in cancer risk and treatment outcomes. To address this, there was a call for inclusion of sex as a biological variable, which resulted in more instances of careful inclusion of sex in preclinical studies of cancer. Another variable in cancer treatment is genetic ancestry. Although this is considered explicitly in clinical research, it is considerably neglected in preclinical studies. Preclinical research can use several 3D in vitro model systems, such as spheroids/organoids, xenografts, or other bioengineered systems that combine biomaterials and cellular material. Ultimately, the cellular base for all of these in vitro model systems is derived from human cell lines or patient samples, to investigate mechanisms of cancer and screen novel therapeutics, all of which aim to maximize successful outcomes in clinical trials. This in itself offers an opportunity to potentiate effective treatments for many groups of people, when diverse variables like genetic ancestry are consciously included into study design. This Perspective highlights the need for conscious inclusion of genetic ancestry in preclinical cancer tissue engineering, especially when it pertains to determining therapeutic outcomes.

Summary: Genetic determinants, like ancestry, impact cancer risk and therapeutic outcomes. Hence, this is an important variable to consider at the very initial stages of biomedical research at the bench.

Diversifying preclinical research tools: expanding patient-derived models to address cancer health disparities

Cancer health disparities define a critical healthcare issue for racial/ethnic minorities in the USA. Key findings have led to cancer treatment improvements tailored to minority patients, but such successes have been rare. Here, we highlight how the use of patient-derived xenograft (PDX) and organoid models could resolve current blocks toward precision cancer health equity.

Peptide ancestry informative markers in uterine neoplasms from women of European, African, and Asian ancestry

Characterization of ancestry-linked peptide variants in disease-relevant patient tissues represents a foundational step to connect patient ancestry with disease pathogenesis. Nonsynonymous single-nucleotide polymorphisms encoding missense substitutions within tryptic peptides exhibiting high allele frequencies in European, African, and East Asian populations, termed peptide ancestry informative markers (pAIMs), were prioritized from 1000 genomes. In silico analysis identified that as few as 20 pAIMs can determine ancestry proportions similarly to >260K SNPs (R² = 0.99). Multiplexed proteomic analysis of >100 human endometrial cancer cell lines and uterine leiomyoma tissues combined resulted in the quantitation of 62 pAIMs that correlate with patient race and genotype-confirmed ancestry. Candidates include a D451E substitution in GC vitamin D-binding protein previously associated with altered vitamin D levels in African and European populations. pAIMs will support generalized proteoancestry assessment as well as efforts investigating the impact of ancestry on the human proteome and how this relates to the pathogenesis of uterine neoplasms.

HSD17B7 gene in self-renewal and oncogenicity of keratinocytes from Black versus White populations

Human populations of Black African ancestry have a relatively high risk of aggressive cancer types, including keratinocyte-derived squamous cell carcinomas (SCCs). We show that primary keratinocytes (HKCs) from Black African (Black) versus White Caucasian (White) individuals have on average higher oncogenic and self-renewal potential, which are inversely related to mitochondrial electron transfer chain activity and ATP and ROS production. HSD17B7 is the top-ranked differentially expressed gene in HKCs and Head/Neck SCCs from individuals of Black African versus Caucasian ancestries, with several ancestry-specific eQTLs linked to its expression. Mirroring the differences between Black and White HKCs, modulation of the gene, coding for an enzyme involved in sex steroid and cholesterol biosynthesis, determines HKC and SCC cell proliferation and oncogenicity as well as mitochondrial OXPHOS activity. Overall, the findings point to a targetable determinant of cancer susceptibility among different human populations, amenable to prevention and management of the disease.

Ancestry matters: Building inclusivity into preclinical study design

Human cell lines (CLs) are key assets for biomedicine but lack ancestral diversity. Here, we explore why genetic diversity among cell-based models is essential for making preclinical research more equitable and widely translatable. We lay out practical actions that can be taken to improve inclusivity in study design.

Extended transcriptome analysis reveals genome-wide lncRNA-mediated epigenetic dysregulation in colorectal cancer

It is estimated that the rate of epigenetic changes may be orders of magnitude higher than that of genetic changes and that purely epigenetic mechanisms may explain why cancers arise with few or no recurrent mutations. However, supporting evidence remains limited, partly due to the cost of experimentally studying genome-wide epigenetic dysregulation. Since genome modification enzymes are recruited by long noncoding RNAs (lncRNAs) to specific genomic sites, analyzing differentially expressed genes and differentially methylated regions (DMRs) at the DNA binding sites of differentially expressed lncRNAs is important for uncovering epigenetic dysregulation. We performed RNA-seq and MeDIP-seq on a set of colorectal cancer (CRC) and normal colon samples and developed an analysis pipeline for combined analyses of gene expression, DNA methylation, and lncRNA/DNA binding. The genes identified in our data and important for CRC agree with widely reported findings. We found that aberrantly transcribed noncoding transcripts may epigenetically dysregulate genes, that correlated gene expression is significantly determined by epigenetic dysregulation, that differentially expressed noncoding transcripts and their epigenetic targets form distinct modules in different cancer cells, and that many hub lncRNAs in these modules are primate-specific. These results suggest that lncRNA-mediated epigenetic dysregulation greatly determines aberrant gene expression and that epigenetic dysregulation is highly species-specific. The analysis pipeline can effectively unveil cancer- and cell-specific modules of epigenetic dysregulation, and such modules may provide novel clues for identifying diagnostic, therapeutic, and prognostic targets for epigenetic dysregulation.

Kasumi leukemia cell lines: characterization of tumor genomes with ethnic origin and scales of genomic alterations

Kasumi-1 has played an important role in an experimental model with t(8;21) translocation, which is a representative example of leukemia cell lines. However, previous studies using Kasumi-1 show discrepancies in the genome profile. The wide use of leukemia cell lines is limited to lines that are well-characterized. The use of additional cell lines extends research to various types of leukemia, and to further explore leukemia pathogenesis, which can be achieved by uncovering the fundamental features of each cell line with accurate data. In this study, ten Kasumi cell lines established in Japan, including five that were previously unknown, have been characterized by SNP microarray and targeted sequencing. SNP genotyping suggested that the genetic ancestry in four of the ten Kasumi cell lines was not classified as Japanese but covered several different east-Asian ethnicities, suggesting that patients in Japan are genetically diverse. TP53 mutations were detected in two cell lines with complex array profiles, indicating chromosomal instability (CIN). A quantitative assessment of tumor genomes at the chromosomal level was newly introduced to reveal total DNA sizes and Scales of Genomic Alterations (SGA) for each cell line. Kasumi-1 and 6 derived from relapsed phases demonstrated high levels of SGA, implying that the level of SGA would reflect on the tumor progression and could serve as an index of CIN. Our results extend the leukemia cellular resources with an additional five cell lines and provide reference genome data with ethnic identities for the ten Kasumi cell lines.