A recent paper published in Nature Medicine (1) aimed to identify genes and genomic biomarkers which can better predict outcomes and personalized therapy for cancer patients by integrating genetic with clinical data. The large-scale study identified various clinically actionable genes and genomic biomarkers in 33 types of cancer that have a role in prognosis and survival, highlighting the importance of tumor molecular profiling in precision oncology and therapy.
Study overview
This study stems from the 100,000 Genomes Project, a UK Government project that aims to study the role of genes in health and disease by sequencing the whole genome of patients with cancer or rare diseases (2). Through this project, the researchers in the Cancer Program analyzed and evaluated the whole genome sequencing data of 13,880 solid tumors from 33 types of cancer, along with the clinical data, such as survival and treatment outcomes, of the corresponding cancer patients. Interestingly, numerous somatic genetic and germline mutations and genomic biomarkers which are associated with patient outcomes were identified in different tumors.
Summary of results
Firstly, different types of tumors were found to have one or more genetic mutation. For example, more than 90% of brain tumors, 74% of skin-cutaneous melanoma, and >50% of colon, lung adenocarcinoma, rectum adenocarcinoma, and head and neck squamous carcinoma tumors harbored more than one genetic mutation. Other tumors that revealed clinically relevant genetic mutations include breast, ovarian, sarcoma, bladder, kidney, stomach, and pancreatic, among others. These findings show the complex nature of cancer and highlight the significance of identifying the clinically actionable genetic mutations that can help in precision medicine.
Furthermore, this study revealed the genes that are frequently mutated in different types of cancer as well as the type of genetic mutations such as single nucleotide variation (SNV), insertions and deletions (INDEL), copy number alterations (CNA), and fusions, that are mostly found in the corresponding genes. For example, TP53 was identified to be one of the most frequently mutated genes, with SNV/INDEL being one of the most prevalent types of mutations observed. The types of cancer with a high frequency of SNV/INDEL mutation in the TP53 gene are uterine corpus endometrial serous carcinoma, ovarian high-grade serous carcinoma, lung squamous carcinoma, rectum adenocarcinoma, esophageal adenocarcinoma, and esophageal squamous cell carcinoma. PIK3CA was the second most common altered gene with SNV/INDEL mutations, and it was mostly found in uterine corpus endometrial carcinoma, ovarian endometrioid adenocarcinoma, breast invasive carcinoma, and colon carcinoma. Other genes that were also found to have SNV/INDELs are APC, KRAS, VHL, and IDH1 among others.
CNAs were also frequently observed in TP53, CDKN2A, MYC, CDKN2B, and PTEN genes, in almost all types of cancer. In addition, structural variations in genes such as FUS, EWSR1, and ALK, were found in some types of cancer such as sarcoma, lung adenocarcinoma, and esophageal adenocarcinoma.
Tumors with germline SNV/INDEL mutations were also identified. Ovarian high-grade serous carcinoma had the highest frequency of SNV/INDELs found in BRCA1/2 genes. Other genes also found with germline SNV/INDELs include MSH6, PALB2, and PMS2, and were associated with different types of cancer such as skin cutaneous melanoma, ovarian endometroid, colon, lung, liver cancer, and others.
This study also revealed the percentage of tumors with genomic signatures such as tumor mutational burden (TMB), homologous recombination deficiency (HRD), and mismatch repair mechanism (MMR)/POLE signatures. TMB was frequently found in skin cutaneous melanoma, uterine corpus endometrial, and lung cancer tumors, whereas the highest average TMB score was identified in skin and lung cancer tumors. HRD was frequently found in ovarian high-grade serous carcinoma, whereas MMR/POLE signatures were mostly found in uterine corpus endometrial, colon, and ovarian endometroid cancer tumors.
Finally, the genetic information of the tumor samples was also combined and assessed with the clinical data of the patients to identify which genes and genomic biomarkers can be clinically relevant and can affect the overall survival. Interestingly, a high TMB score was associated with better survival probability in patients with skin cutaneous carcinoma but not in lung cancer patients. Also, platinum-treated patients with HRD showed better survival probability than patients with no HRD. Additionally, 15 genes including BRAF, KRAS, TERT promoter, and TP53, affected the overall survival of cancer patients, with CDKN2A mutations having the most severe outcome due to the poor prognosis for some types of cancer.
Conclusion
Overall, this large-scale study is the first to combine whole genome sequencing data of cancer patients with solid tumors with their corresponding clinical data, and provide additional prognostic insights for patients with specific types of mutations. The authors reveal the importance of linking genomic with real world data and highlight the importance of genetic testing and tumor profiling of solid tumors. Understanding the impact of genes and genetic mutations in cancer development and therapy resistance can provide prognostic insights, and guide tailored therapy, leading to improved cancer outcomes.
Additional information
Whole Genome Sequencing
WGS analysis examines the entire genome of individuals, including coding and non-coding regions, increasing the chances of identifying any genetic mutation that could contribute to disease.
References
[1] Sosinsky, Alona, et al. “Insights for precision oncology from the integration of genomic and clinical data of 13,880 tumors from the 100,000 genomes cancer programme.” Nature Medicine, 1-11, 2024, https://doi.org/10.1038/s41591-023-02682-0.
[2] Genomics England. “100,000 Genomes Project.” Genomics England, 2022, https://”www.genomicsengland.co.uk/initiatives/100000-genomes-project. Accessed 15 Jan. 2024.
