Delving Deeper: Advancing Cancer Research with Molecular Analysis

Delving deeper: Advancing cancer research with molecular analysis 

[Article 1] Harnessing the potential of cytogenetics for oncology

Cancer is a highly complex disease that is capable of afflicting almost every tissue in the human body.¹ As a result, scientists have described over 200 cancer types, where each type is normally identified by the primary site it developed or the tissue affected.² Oncologists broadly classify cancers into two major categories: solid and liquid tumors. 

For solid tumors, cancer cells undergoing excessive division produce at least one solid cellular mass within tissues or organs, such as the breast, ovaries, skin, lungs, or brain. In 2020, this tumor type accounted for approximately 90% of all cancer cases that clinicians diagnosed in the US.³ 

For liquid tumors, which are also known as hematological malignancies, cancer cells in the bone marrow and lymph nodes divide uncontrollably and circulate through the bloodstream or lymphatic system.⁴ However, this cancer type does not primarily form aggregates. Because liquid cancers represent only 10% of all cancer cases, most oncology research focuses on solid tumors. 

Key solid tumor types

Breast, ovarian, brain, and skin cancer are among the most prevalent or deadly solid tumors found worldwide. 

Breast cancer was the most common cancer diagnosed globally in 2020 and is a leading cause of cancer-associated death in women.^5,6 This cancer originates in the epithelial tissue of the breast, such as the cells lining the lactiferous ducts, and is thus considered a carcinoma.⁷

Ovarian cancer includes ovarian tumors of either epithelial or non-epithelial origin with over 95% of the subtypes being carcinomas.⁸ This disease has the highest mortality rate of any of the gynecological malignancies likely because patients present non-specific symptoms, which often impede diagnosis until the cancer reaches its advanced stages.    

Primary brain tumors are relatively rare cancers that oncologists frequently group into two types: glial or non-glial. Glial tumors, or gliomas, affect glial cells, whereas non-glial tumors originate from other tissues or cells in the brain.⁹ Gliomas are more prevalent in adults, while non-glial tumors are more common in children. Although they are rare, malignant brain tumors generally have a high mortality rate, which is dependent on a patient’s age and tumor type.¹⁰ 

In the US, skin cancer has the highest incidence rate of all cancer types and includes the subtypes melanoma, squamous cell carcinoma, and basal cell carcinoma.¹¹ Melanoma, which affects melanocytes that produce melanin, is uncommon compared to the other skin cancers but is very aggressive and known to metastasize to other organs, such as the liver or brain.¹²

Importance of genomically characterizing cancer

Genomic instability, which is the increased propensity of cells to acquire mutations during division, is a common characteristic of most cancers and scientists have determined that this feature drives cancer development, progression, and therapy resistance.¹³ Moreover, genomic instability also contributes to both intratumor and intertumor heterogeneity, which increases the difficulty of identifying the ideal treatment for patients with cancer.¹⁴ Consequently, clinicians use approaches, such as cytogenetic analysis, to search for potential diagnostic, prognostic, predictive, and therapeutic biomarkers. For instance, scientists developed BRAF kinase inhibitors to treat patients with melanoma after frequently finding mutations in the BRAF gene and thus, they now consider the gene a therapeutic biomarker for melanoma.¹⁵

History of cancer cytogenetics

Cytogenetics is a branch of genetics that explores the characteristics, structure, and behavior of chromosomes, as well as how chromosomal alterations occur.¹⁶ Scientists began investigating cancer cytogenetics over 60 years ago. In 1960, Peter Nowell, David Hungerford, and their colleagues discovered a shortened chromosome in patients with chronic myeloid leukemia (CML) using microscopy, which they named the Philadelphia chromosome.^17,18 Researchers consider this finding to be the first observed cancer-associated chromosomal aberration and oncologists still use this abnormality today to diagnose patients with CML. The development of chromosome banding techniques including Q-banding—a karyotyping approach where scientists treat chromosomes with quinacrine to produce fluorescent chromosome bands—facilitated further analysis of the Philadelphia chromosome.¹⁹ Using this method, Janet Rowley from the University of Chicago determined that a translocation event between chromosomes 9 and 22 produced this minute chromosome.^18,20 These findings highlighted the importance of cytogenetic analysis for cancer diagnosis and inspired scientists to develop new techniques for chromosomal abnormality detection.

References 

1.    Varmus H. The new era in cancer research. Science. 2006;312(5777):1162-1165.

2.    Tomczak K, et al. The Cancer Genome Atlas (TCGA): An immeasurable source of knowledge. Contemp Oncol. 2015;19(1A):A68-A77.

3.    Guha P, et al. Assessing the future of solid tumor immunotherapy. Biomedicines. 2022;10(3):655.

4.    Jiang Y, et al. Targeted drug delivery for the treatment of blood cancers. Molecules. 2022;27(4):1310.

5.    Sung H, et al. Global Cancer Statistics 2020: GLOBOCAN estimates of incidence and mortality worldwide for 36 cancers in 185 countries. CA Cancer J Clin. 2021;71(3):209-249.

6.    Menon G, et al. Breast cancer. In: StatPearls. StatPearls Publishing; 2024.

7.    Carbone A. Cancer classification at the crossroads. Cancers. 2020;12(4):980.

8.    Arora T, et al. Epithelial ovarian cancer. In: StatPearls. StatPearls Publishing; 2024.

9.    Mesfin FB, Al-Dhahir MA. Gliomas. In: StatPearls. StatPearls Publishing; 2024.

10.  Weller M, et al. Glioma. Nat Rev Dis Primer. 2015;1(1):1-18.

11.  Eddy K, et al. Decoding melanoma development and progression: Identification of therapeutic vulnerabilities. Front Oncol. 2021;10.

12.  Sundararajan S, et al. Metastatic melanoma. In: StatPearls. StatPearls Publishing; 2024.

13.  Negrini S, et al. Genomic instability-An evolving hallmark of cancer. Nat Rev Mol Cell Biol. 2010;11(3):220-228.

14.  Tellez-Gabriel M, et al. Tumour heterogeneity: The key advantages of single-cell analysis. Int J Mol Sci. 2016;17(12):2142.

15.  Kiniwa Y, et al. Usefulness of monitoring circulating tumor cells as a therapeutic biomarker in melanoma with BRAF mutation. BMC Cancer. 2021;21(1):287.

16.  Kannan TP, Zilfalil BA. Cytogenetics: Past, present and future. Malays J Med Sci MJMS. 2009;16(2):4-9.

17.  Nowell P, et al. A minute chromosome in human chronic granulocytic leukemia. Science. 1960;132:1497.

18.  Wan TSK. Cancer cytogenetics: Methodology revisited. Ann Lab Med. 2014;34(6):413-425.

19.  Ferguson-Smith MA. History and evolution of cytogenetics. Mol Cytogenet. 2015;8:19.

20.  Rowley JD. A new consistent chromosomal abnormality in chronic myelogenous leukaemia identified by quinacrine fluorescence and giemsa staining. Nature. 1973;243(5405):290-293.

[Article 2] Profiling cancer through innovative technologies

Scientists employ several technologies, such as karyotyping, fluorescent in situ hybridization (FISH), chromosomal microarrays, and next-generation sequencing (NGS), to genetically profile solid and liquid tumor samples, where both single nucleotide polymorphisms (SNPs) and copy number variations (CNVs) are common mutations observed in cancer.¹ SNPs are point mutations affecting one nucleotide, while CNVs are a type of chromosomal structural abnormality caused by duplications or deletions of particular DNA sequences. 

Early cytogenetic methods

Initially, scientists could only examine substantial morphological alterations to chromosomes through karyotyping, which is also known as conventional cytogenetics.² This technique employs a dye to produce a banding pattern on the chromosomes of dividing cells, which clinicians observe using a microscope and compare to the expected pattern. Although karyotyping allows scientists to detect large translocations, deletions, and inversions, the method lacks sensitivity, relies on the number of mitotic cells yielded, and requires substantial expertise to interpret the data.^3,4

To overcome these limitations, scientists developed FISH, a technique that uses fluorescently-labeled probes to detect specific DNA sequences in nondividing cells and determine their locations on the chromosomes using microscopy.⁵ This method allows clinicians to assess the cells for chromosomal abnormalities, such as CNVs, but is not sensitive enough to find SNPs and does not provide information about the entire genome.⁴ Additionally, scientists can only examine a small number of loci simultaneously and the analysis is time intensive.⁵ 

Later cytogenetic techniques

To improve the throughput of FISH analysis, scientists began genetically profiling tumor samples through chromosomal microarrays of which there are three major types: array-based comparative genomic hybridization (aCGH), SNP arrays, and molecular inversion probe (MIP) arrays. For aCGH, clinicians label genomic DNA (gDNA) extracted from a patient and reference sample with green or red fluorophores and add equal amounts of both samples to competitively hybridize with oligonucleotide probes spotted onto a solid surface.⁶ Differences in the expected 1:1 ratio of green to red fluorescence indicate a copy number change in the patient. Because most arrays contain hundreds of thousands of probes, each designed to target different areas of the chromosomes, aCGH allows scientists to detect aberrations across the entire genome.⁷ For SNP arrays, clinicians amplify a patient’s gDNA using PCR, fragment the products, and label them with fluorophores.⁶ They then add these DNA segments to an array with oligonucleotide probes designed to analyze specific SNPs in the genome. Changes to the expected fluorescence intensity reveal if the patient has an SNP mutation. Additionally, scientists have developed hybrid-SNP arrays, which contain both SNP and copy number probes to simultaneously detect SNPs and CNVs.⁷ For MIP arrays, clinicians use specialized probes called MIPs, which contain two domains that hybridize to specific regions of the gDNA and two different cleavage sites.⁸ After MIPs bind to the denatured gDNA to produce incomplete circles, they add free nucleotides to close the gaps and employ exonucleases, PCR, and restriction enzymes to generate linearized copies of the modified probe. Scientists allow these fragments to hybridize to an array containing copy number probes and sometimes SNP probes. 

More recently, clinicians have started using NGS to examine SNPs and CNVs in the entire genome without prior knowledge of the mutations.² Despite the many advantages of NGS, chromosomal microarrays have several benefits over this technique and the other methods. 

Advantages of microarrays

Microarray-based assays are accurate, cost-effective methods to detect both SNPs and chromosomal aberrations with simple data analysis and fast turn-around times. Additionally, unlike NGS, which requires fresh solid tumor samples, MIP assays can analyze low-quality, highly degraded sample types, such as formalin-fixed paraffin-embedded tumor samples, or limited amounts of DNA.⁸ Besides DNA, scientists can also use microarray-based assays for transcriptomic analysis of tumor samples. Overall, microarrays are a flexible, high-resolution technology that clinicians can employ to explore the genomic landscapes of various cancers.

References

1.    Sarhadi VK, Armengol G. Molecular biomarkers in cancer. Biomolecules. 2022;12(8):1021.

2.    Ribeiro IP, et al. Cytogenetics and cytogenomics evaluation in cancer. Int J Mol Sci. 2019;20(19):4711.

3.    Wan TSK. Cancer cytogenetics: Methodology revisited. Ann Lab Med. 2014;34(6):413-425.

4.    Granada I, et al. Cytogenetics in the genomic era. Best Pract Res Clin Haematol. 2020;33(3):101196.

5.    Kannan TP, Zilfalil BA. Cytogenetics: Past, present and future. Malays J Med Sci MJMS. 2009;16(2):4-9.

6.    Alsolami R, et al. Clinical application of targeted and genome-wide technologies: Can we predict treatment responses in chronic lymphocytic leukemia? Pers Med. 2013;10(4):361-376.

7.    Levy B, Burnside RD. Are all chromosome microarrays the same? What clinicians need to know. Prenat Diagn. 2019;39(3):157-164.

8.    Jung HS, et al. Utilization of the oncoscan microarray assay in cancer diagnostics. Appl Cancer Res. 2017;37(1):1.

[Infographic] Chromosomal microarrays: Unlocking cancer’s secrets

This infographic compares the workflows of the three major chromosomal microarray types used for cancer research: array-based comparative genomic hybridization, single nucleotide polymorphism arrays, and molecular inversion probe arrays. It visually depicts the main steps from DNA extraction to data analysis. 

Sample preparation

Tumor cells

DNA extraction

Test genomic DNA (gDNA)

Array-based comparative genomic hybridization¹

Test gDNA

Reference gDNA

Denaturation

Labeling with fluorophores

Competitive hybridization

Single nucleotide polymorphism arrays²

Test gDNA

Digestion

Adapter ligation

PCR amplification and complexity reduction

Fragmentation and fluorophore labeling

Hybridization

Molecular inversion probe (MIP) arrays³

Test gDNA

SNP

Region of interest

Add MIP linear probe

Denaturation and hybridization

Gap fill

Cleavage site 1

Exonuclease addition and cleavage at site 1

Cleavage site 2

PCR amplification and cleavage at site 2

Hybridization

Array

Oligonucleotide probe

References 

1.    Alsolami R, et al. Clinical application of targeted and genome-wide technologies: Can we predict treatment responses in chronic lymphocytic leukemia? Pers Med. 2013;10(4):361-376.

2.    Affymetrix. Genome-Wide Human SNP Array 6.0. Data Sheet. 2009.

1. Thermo Fisher Scientific. OncoScan CNV Assays for analysis of FFPE tumor samples. 2024.

[Article 3] Transcriptional microarrays: A key tool for tumor analysis

Yesim Gökmen-Polar, PhD

Associate Professor

Department of Pathology and Laboratory Medicine

Emory University School of Medicine

After completing her PhD in molecular biology and genetics, Yesim Gökmen-Polar applied her knowledge to unraveling cancer’s complexity. Although her work during her postdoctoral fellowship at the University of Texas Medical Branch focused on basic science, she collaborated with a surgeon to validate her results against patient tumor samples. Now an associate professor of pathology and laboratory medicine at Emory University School of Medicine, this experience has inspired her current research combining basic science and patient data to understand resistance and recurrence mechanisms in breast and thymic cancer. Gökmen-Polar hopes to translate this information into novel treatment options to improve the lives of people afflicted with the disease.

Why is molecular profiling of tumor samples considered crucial in modern oncology?

Cancer is not caused by alteration of one gene or one pathway. Through molecular profiling, researchers often identify mutations in multiple genes, and they must determine which genes are key drivers of tumor progression to develop new treatment strategies. Adding to this complexity, cancer is also a very dynamic disease. Although clinicians treat patients based on what they find in their initial tumor profiling, cancer finds new evasion mechanisms and requires continuous evaluation. 

How have you used microarray technology to examine cancer?

My team often employs transcriptional microarrays, which contain probes to detect both exons and splice junctions, alone or in combination with other approaches. We previously used these exon-junction assays to study the importance of an RNA binding protein, epithelial splicing regulatory protein 1 (ESRP1), to the recurrence of estrogen receptor-positive breast cancer.¹ After associating overexpression of this splicing factor with poor prognosis, my group performed a transcriptional microarray to examine the pathways affected in endocrine-resistant cancer cells following ESRP1 knockdown. We found that metabolic genes, such as phosphoglycerate dehydrogenase (PHGDH), showed decreased expression in these cells, suggesting that ESRP1 is important for the regulation of these metabolic pathways. 

In a follow-up paper, we validated our initial observations by performing an RNA-binding protein immunoprecipitation using ESRP1 antibody-coated beads and lysate from endocrine-resistant breast cancer cells to isolate mRNAs that bind to the protein.² We then employed exon-junction arrays to identify the transcripts and determined that PHGDH mRNA is a binding partner of ESRP1. Because this splicing factor is important for normal mechanisms as well, we cannot target ESRP1 directly. However, PHGDH is targetable and there are drugs against this metabolic enzyme available in the preclinical setting.

What are the advantages of using microarray technology over RNA sequencing (RNA-seq) for studying gene expression patterns?

With RNA-seq, certain factors can impede transcriptome profiling. If scientists do not use a sufficient sequencing depth, they may miss low abundance transcripts. This is not a problem with transcriptional microarrays because they are probe-based and not affected by RNA abundance. RNA sequencing is also more costly, especially if you have a large number of samples. As a molecular biologist, I like microarrays because they are more user-friendly. For RNA-seq, you need to involve a bioinformatician, but for transcriptional arrays, I can analyze the data myself.

How do you envision the future of molecular profiling in oncology? 

It is becoming more common for researchers to combine different omics methods, such as genomics, transcriptomics, proteomics, metabolomics, and epigenomics, to characterize tumors because multimodal analysis allows them to understand the big picture. For example, my team used a transcriptional microarray and mass spectrometry to show that ESRP1 knockdown affects both PHGDH mRNA and protein levels.²  

While these omics technologies will continue to be critical tools used by cancer researchers alone and in combination in the future, many of them use bulk molecules. This prevents scientists from determining which cells in the tumor microenvironment, such as cancer, stromal, or immune cells, are expressing these genes or producing these proteins. However, there are now spatial biology techniques including spatial transcriptomics and spatial metabolomics, which together with the existing omics technologies allow researchers to fully analyze tumor samples. A multimodal approach is the future of cancer research.

This interview has been condensed and edited for clarity.

References

1.    Gökmen?Polar Y, et al. Splicing factor ESRP1 controls ER?positive breast cancer by altering metabolic pathways. EMBO Rep. 2019;20(2):e46078.

2.    Gökmen-Polar Y, et al. The role of ESRP1 in the regulation of PHGDH in estrogen receptor–positive breast cancer. Lab Invest. 2023;103(3):100002.

Resources

[Panel 1] Unraveling the roles of copy number variants in tumorigenesis

Xiaolan Fang, PhD, FACMG

Associate Director, Cytogenomics and Molecular Pathology 

Henry Ford Health System

Learn how genomic analysis has provided valuable insights into the mechanisms of tumorigenesis in humans. Among the identified abnormalities, copy number variations (CNVs) have emerged as significant pathogenic variants and important biomarkers for early detection and follow-up.

The discussion focuses on deciphering molecular and cytogenetic mechanisms in representative types of solid tumors, utilizing Applied Biosystems™ Oncoscan CNV assays in a research setting.

Watch now

[Panel 2] Exploring the genetic landscape of solid tumors using whole-genome copy number analysis for research

Ravindra Kolhe, MD, PhD

Professor, Department of Pathology

Medical College of Georgia at Augusta University

Joanna Przybyl, PhD

Assistant Professor, Department of Surgery

McGill University Health Centre

Approximately 80% of all cancers are known to be affected by both somatic mutations and copy number changes. Furthermore, recent publications have shown that in certain types of cancers, copy number variations (CNVs) play a more important role than somatic mutations.

Delve into the growing need for more comprehensive profiling, including cytogenetic analysis of solid tumor samples, where Dr. Ravindra Kolhe (AUMC) discusses the use of chromosomal analysis in his melanoma research, and Dr. Joanna Przybyl (McGill) showcases her research of copy number changes in leiomyosarcoma (LMS).

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For research use only. Not for use in diagnostic procedures.

[Panel 3] Overcoming today’s lab challenges with CytoScan HD Accel

Debbie Black, PhD
Biological Sciences
 Senior Manager Research and Development
 Thermo Fisher Scientific

Learn about the latest innovation in chromosomal microarray analysis (CMA): the CytoScan HD Accel Array. In this on-demand webinar, Dr. Debbie Black discusses the substantial advancements it brings, such as:

• Two-day turnaround time
• Improved probe coverage
• A new reference model

This allows users to obtain more pertinent insights in less time compared to the currently dominant microarray in the market, boosting lab productivity by up to 100%.

Watch now

[Panel 4] Catch critical copy number changes

Explore the advanced capabilities of the Applied Biosystems OncoScan CNV and CNV Plus Assays. These tools offer precise identification of copy number changes and allelic imbalances in solid tumors. With comprehensive coverage, robust performance, and results in just three days, the OncoScan assays are a breakthrough in clinical cancer research and genomic analysis.

Read Now

For research use only. Not for use in diagnostic procedures.